Disconnect after scan_port

Remove comment
Refactor feetech _broadcast_ping
2025-06-04 17:12:30 +02:00 · 2025-06-04 16:59:44 +02:00 · 2025-06-04 16:41:33 +02:00 · 2025-06-04 14:54:29 +02:00 · 2025-06-04 14:27:57 +02:00 · 2025-06-04 13:16:41 +02:00
356 changed files with 14230 additions and 29981 deletions
--- a/.cache/calibration/aloha_default/left_follower.json
+++ b/.cache/calibration/aloha_default/left_follower.json
@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -2048
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2015,
-        3058,
-        3061,
-        1071,
-        1071,
-        2035,
-        2152,
-        2029,
-        2499
-    ],
-    "end_pos": [
-        -1008,
-        -1963,
-        -1966,
-        2141,
-        2143,
-        -971,
-        3043,
-        -1077,
-        3144
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}
--- a/.cache/calibration/aloha_default/left_leader.json
+++ b/.cache/calibration/aloha_default/left_leader.json
@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -1024
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2035,
-        3024,
-        3019,
-        979,
-        981,
-        1982,
-        2166,
-        2124,
-        1968
-    ],
-    "end_pos": [
-        -990,
-        -2017,
-        -2015,
-        2078,
-        2076,
-        -1030,
-        3117,
-        -1016,
-        2556
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}
--- a/.cache/calibration/aloha_default/right_follower.json
+++ b/.cache/calibration/aloha_default/right_follower.json
@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -2048
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2056,
-        2895,
-        2896,
-        1191,
-        1190,
-        2018,
-        2051,
-        2056,
-        2509
-    ],
-    "end_pos": [
-        -1040,
-        -2004,
-        -2006,
-        2126,
-        2127,
-        -1010,
-        3050,
-        -1117,
-        3143
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}
--- a/.cache/calibration/aloha_default/right_leader.json
+++ b/.cache/calibration/aloha_default/right_leader.json
@@ -1,68 +0,0 @@
-{
-    "homing_offset": [
-        2048,
-        3072,
-        3072,
-        -1024,
-        -1024,
-        2048,
-        -2048,
-        2048,
-        -2048
-    ],
-    "drive_mode": [
-        1,
-        1,
-        1,
-        0,
-        0,
-        1,
-        0,
-        1,
-        0
-    ],
-    "start_pos": [
-        2068,
-        3034,
-        3030,
-        1038,
-        1041,
-        1991,
-        1948,
-        2090,
-        1985
-    ],
-    "end_pos": [
-        -1025,
-        -2014,
-        -2015,
-        2058,
-        2060,
-        -955,
-        3091,
-        -940,
-        2576
-    ],
-    "calib_mode": [
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "DEGREE",
-        "LINEAR"
-    ],
-    "motor_names": [
-        "waist",
-        "shoulder",
-        "shoulder_shadow",
-        "elbow",
-        "elbow_shadow",
-        "forearm_roll",
-        "wrist_angle",
-        "wrist_rotate",
-        "gripper"
-    ]
-}
--- a/.gitattributes
+++ b/.gitattributes
@@ -11,10 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 *.memmap filter=lfs diff=lfs merge=lfs -text
 *.stl filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.json !text !filter !merge !diff
+tests/artifacts/cameras/*.png filter=lfs diff=lfs merge=lfs -text
+*.bag filter=lfs diff=lfs merge=lfs -text
--- a/.gitignore
+++ b/.gitignore
@@ -12,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

+# Dev scripts
+.dev
+
 # Logging
 logs
 tmp
@@ -91,10 +94,8 @@ coverage.xml
 .hypothesis/
 .pytest_cache/

-# Ignore .cache except calibration
+# Ignore .cache
 .cache/*
-!.cache/calibration/
-!.cache/calibration/**

 # Translations
 *.mo
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -269,9 +269,6 @@ Follow these steps to start contributing:
   the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
   it from PRs ready to be merged;
 4. Make sure existing tests pass;
-<!-- 5. Add high-coverage tests. No quality testing = no merge.
-
-See an example of a good PR here: https://github.com/huggingface/lerobot/pull/ -->

 ### Tests

--- a/docker/lerobot-cpu/Dockerfile
+++ b/docker/lerobot-cpu/Dockerfile
@@ -22,7 +22,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 COPY . /lerobot
 WORKDIR /lerobot
 RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]" \
+    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht]" \
        --extra-index-url https://download.pytorch.org/whl/cpu

 # Execute in bash shell rather than python
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -5,8 +5,22 @@
    title: Installation
  title: Get started
 - sections:
-  - local: assemble_so101
-    title: Assemble SO-101
  - local: getting_started_real_world_robot
    title: Getting Started with Real-World Robots
+  - local: cameras
+    title: Cameras
  title: "Tutorials"
+- sections:
+  - local: so101
+    title: SO-101
+  - local: so100
+    title: SO-100
+  - local: koch
+    title: Koch v1.1
+  - local: lekiwi
+    title: LeKiwi
+  title: "Robots"
+- sections:
+  - local: contributing
+    title: Contribute to LeRobot
+  title: "Contribute"
--- a/docs/source/assemble_so101.mdx
+++ b/docs/source/assemble_so101.mdx
@@ -1,348 +0,0 @@
-# Assemble SO-101
-
-In the steps below we explain how to assemble our flagship robot, the SO-101.
-
-## Source the parts
-
-Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts,
-and advice if it's your first time printing or if you don't own a 3D printer.
-
-Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
-
-## Install LeRobot
-
-To install LeRobot follow our [Installation Guide](./installation)
-
-## Configure motors
-
-To configure the motors designate one bus servo adapter and 6 motors for your leader arm, and similarly the other bus servo adapter and 6 motors for the follower arm. It's convenient to label them and write on each motor if it's for the follower `F` or for the leader `L` and it's ID from 1 to 6.
-
-You now should plug the 5V or 12V power supply to the motor bus. 5V for the STS3215 7.4V motors and 12V for the STS3215 12V motors. Note that the leader arm always uses the 7.4V motors, so watch out that you plug in the right power supply if you have 12V and 7.4V motors, otherwise you might burn your motors! Now, connect the motor bus to your computer via USB. Note that the USB doesn't provide any power, and both the power supply and USB have to be plugged in.
-
-### Find the USB ports associated to each arm
-
-To find the port for each bus servo adapter, run this script:
-```bash
-python lerobot/scripts/find_motors_bus_port.py
-```
-##### Example outputs of script
-
-<hfoptions id="example">
-<hfoption id="Mac">
-
-Example output leader arm's port: `/dev/tty.usbmodem575E0031751`
-
-```bash
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0031751
-Reconnect the usb cable.
-```
-
-Example output follower arm port: `/dev/tty.usbmodem575E0032081`
-
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the usb cable.
-```
-
-</hfoption>
-<hfoption id="Linux">
-
-On Linux, you might need to give access to the USB ports by running:
-```bash
-sudo chmod 666 /dev/ttyACM0
-sudo chmod 666 /dev/ttyACM1
-```
-
-Example output leader arm port: `/dev/ttyACM0`
-
-```bash
-Finding all available ports for the MotorBus.
-['/dev/ttyACM0', '/dev/ttyACM1']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this MotorsBus is /dev/ttyACM0
-Reconnect the usb cable.
-```
-
-Example output follower arm port: `/dev/ttyACM1`
-
-```
-Finding all available ports for the MotorBus.
-['/dev/ttyACM0', '/dev/ttyACM1']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/ttyACM1
-Reconnect the usb cable.
-```
-</hfoption>
-</hfoptions>
-
-#### Update config file
-
-Now that you have your ports, update the **port** default values of [`SO101RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py).
-You will find a class called `so101` where you can update the `port` values with your actual motor ports:
-```diff
-@RobotConfig.register_subclass("so101")
-@dataclass
-class So101RobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/so101"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-               port="/dev/tty.usbmodem58760431091",
-+               port="{ADD YOUR LEADER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-+                port="{ADD YOUR FOLLOWER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-```
-
-Here is a video of the process:
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-find-motorbus.mp4" type="video/mp4" />
-  </video>
- </div>
-
-## Step-by-Step Assembly Instructions
-
-The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader however uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in table below.
-
-| Leader-Arm Axis | Motor | Gear Ratio |
-|-----------------|:-------:|:----------:|
-| Base / Shoulder Yaw | 1 | 1 / 191 |
-| Shoulder Pitch      | 2 | 1 / 345 |
-| Elbow               | 3 | 1 / 191 |
-| Wrist Roll          | 4 | 1 / 147 |
-| Wrist Pitch         | 5 | 1 / 147 |
-| Gripper             | 6 | 1 / 147 |
-
-### Set motor IDs
-
-Plug your motor in one of the two ports of the motor bus and run this script to set its ID to 1. Replace the text after --port to the corresponding control board port.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 1
-```
-
-Then unplug your motor and plug the second motor and set its ID to 2.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 2
-```
-
-Redo this process for all your motors until ID 6. Do the same for the 6 motors of the leader arm, but make sure to change the power supply if you use motors with different voltage and make sure you give the right ID to the right motor according to the table above.
-
-Here is a video of the process:
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-configure-motor.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Clean Parts
-Remove all support material from the 3D-printed parts, the easiest way to do this is using a small screwdriver to get underneath the support material.
-
-### Joint 1
-
- Place the first motor into the base.
- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from bottom.
- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
- Install both motor horns, securing the top horn with a M3x6mm screw.
- Attach the shoulder part.
- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
- Add the shoulder motor holder.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 2
-
- Slide the second motor in from the top.
- Fasten the second motor with 4 M2x6mm screws.
- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
- Attach the upper arm with 4 M3x6mm screws on each side.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 3
-
- Insert motor 3 and fasten using 4 M2x6mm screws
- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 4
-
- Slide over motor holder 4.
- Slide in motor 4.
- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Joint 5
-
- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-### Gripper / Handle
-
-<hfoptions id="assembly">
-<hfoption id="Follower">
-
- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
- Attach the motor horns and again use a M3x6mm horn screw.
- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-</hfoption>
-<hfoption id="Leader">
-
- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
- Attach the handle to motor 5 using 1 M2x6mm screw.
- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
- Attach the follower trigger with 4 M3x6mm screws.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-</hfoption>
-</hfoptions>
-
-##### Wiring
-
- Attach the motor controller on the back.
- Then insert all wires, use the wire guides everywhere to make sure the wires don't unplug themselves and stay in place.
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Wiring_v2.mp4" type="video/mp4" />
-  </video>
-</div>
-
-## Calibrate
-
-Next, you'll need to calibrate your SO-101 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
-The calibration process is very important because it allows a neural network trained on one SO-101 robot to work on another.
-
-#### Manual calibration of follower arm
-
-You will need to move the follower arm to these positions sequentially, note that the rotated position is on the right side of the robot and you have to open the gripper fully.
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/follower_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
-
-Make sure both arms are connected and run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_follower"]'
-```
-
-#### Manual calibration of leader arm
-You will also need to move the leader arm to these positions sequentially:
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/leader_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
-
-Run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_leader"]'
-```
-
-Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
--- a/docs/source/cameras.mdx
+++ b/docs/source/cameras.mdx
@@ -0,0 +1,173 @@
+# Cameras
+
+LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
+
+### Finding your camera
+
+To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system.
+
+To find the camera indices of the cameras plugged into your system, run the following script:
+```bash
+python lerobot/find_cameras.py opencv # or realsense for Intel Realsense cameras
+```
+
+The output will look something like this if you have two cameras connected:
+```
+--- Detected Cameras ---
+Camera #0:
+  Name: OpenCV Camera @ 0
+  Type: OpenCV
+  Id: 0
+  Backend api: AVFOUNDATION
+  Default stream profile:
+    Format: 16.0
+    Width: 1920
+    Height: 1080
+    Fps: 15.0
+--------------------
+(more cameras ...)
+```
+
+> [!WARNING]
+> When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable.
+
+
+## Use Cameras
+
+Below are two examples, demonstrating how to work with the API.
+
+- **Asynchronous frame capture** using an OpenCV-based camera
+- **Color and depth capture** using an Intel RealSense camera
+
+
+<hfoptions id="shell_restart">
+<hfoption id="Open CV Camera">
+
+```python
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera
+from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
+
+# Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
+config = OpenCVCameraConfig(
+    index_or_path=0,
+    fps=15,
+    width=1920,
+    height=1080,
+    color_mode=ColorMode.RGB,
+    rotation=Cv2Rotation.NO_ROTATION
+)
+
+# Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default).
+camera = OpenCVCamera(config)
+camera.connect()
+
+# Read frames asynchronously in a loop via `async_read(timeout_ms)`
+try:
+    for i in range(10):
+        frame = camera.async_read(timeout_ms=200)
+        print(f"Async frame {i} shape:", frame.shape)
+finally:
+    camera.disconnect()
+```
+
+</hfoption>
+<hfoption id="Intel Realsense Camera">
+
+```python
+from lerobot.common.cameras.intel.configuration_realsense import RealSenseCameraConfig
+from lerobot.common.cameras.intel.camera_realsense import RealSenseCamera
+from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
+
+# Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
+config = RealSenseCameraConfig(
+    serial_number="233522074606",
+    fps=15,
+    width=640,
+    height=480,
+    color_mode=ColorMode.RGB,
+    use_depth=True,
+    rotation=Cv2Rotation.NO_ROTATION
+)
+
+# Instantiate and connect a `RealSenseCamera` with warm-up read (default).
+camera = RealSenseCamera(config)
+camera.connect()
+
+# Capture a color frame via `read()` and a depth map via `read_depth()`.
+try:
+    color_frame = camera.read()
+    depth_map = camera.read_depth()
+    print("Color frame shape:", color_frame.shape)
+    print("Depth map shape:", depth_map.shape)
+finally:
+    camera.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+
+## Use your phone
+<hfoptions id="use phone">
+<hfoption id="Mac">
+
+To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
+- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
+- Sign in both devices with the same Apple ID.
+- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
+
+For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
+
+Your iPhone should be detected automatically when running the camera setup script in the next section.
+
+</hfoption>
+<hfoption id="Linux">
+
+If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
+
+1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
+```python
+sudo apt install v4l2loopback-dkms v4l-utils
+```
+2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
+3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
+```python
+flatpak install flathub com.obsproject.Studio
+```
+4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
+```python
+flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
+```
+5. *Start OBS Studio*. Launch with:
+```python
+flatpak run com.obsproject.Studio
+```
+6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
+7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
+8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
+9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
+```python
+v4l2-ctl --list-devices
+```
+You should see an entry like:
+```
+VirtualCam (platform:v4l2loopback-000):
+/dev/video1
+```
+10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
+```python
+v4l2-ctl -d /dev/video1 --get-fmt-video
+```
+You should see an entry like:
+```
+>>> Format Video Capture:
+>>>	Width/Height      : 640/480
+>>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
+```
+
+Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
+
+If everything is set up correctly, you can proceed with the rest of the tutorial.
+
+</hfoption>
+</hfoptions>
--- a/docs/source/contributing.md
+++ b/docs/source/contributing.md
@@ -0,0 +1 @@
+../../CONTRIBUTING.md
--- a/docs/source/getting_started_real_world_robot.mdx
+++ b/docs/source/getting_started_real_world_robot.mdx
@@ -1,173 +1,149 @@
 # Getting Started with Real-World Robots

-This tutorial will explain you how to train a neural network to autonomously control a real robot.
+This tutorial will explain how to train a neural network to control a real robot autonomously.

 **You'll learn:**
 1. How to record and visualize your dataset.
 2. How to train a policy using your data and prepare it for evaluation.
 3. How to evaluate your policy and visualize the results.

-By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
+By following these steps, you'll be able to replicate tasks, such as picking up a Lego block and placing it in a bin with a high success rate, as shown in the video below.

-This tutorial is specifically made for the affordable [SO-101](https://github.com/TheRobotStudio/SO-ARM100) robot, but it contains additional information to be easily adapted to various types of robots like [Aloha bimanual robot](https://aloha-2.github.io) by changing some configurations. The SO-101 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
+<details>
+<summary><strong>Video: pickup lego block task</strong></summary>

-During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot_task.mp4" type="video/mp4" />
+  </video>
+</div>

-If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb) or don't hesitate to iterate with us on the tutorial by creating issues or pull requests.
+</details>

-## Setup and Calibrate
+This tutorial isn’t tied to a specific robot: we walk you through the commands and API snippets you can adapt for any supported platform.

-If you haven't yet setup and calibrate the SO-101 follow these steps:
-1. [Find ports and update config file](./assemble_so101#find-the-usb-ports-associated-to-each-arm)
-2. [Calibrate](./assemble_so101#calibrate)
+During data collection, you’ll use a “teloperation” device, such as a leader arm or keyboard to teleoperate the robot and record its motion trajectories.
+
+Once you’ve gathered enough trajectories, you’ll train a neural network to imitate these trajectories and deploy the trained model so your robot can perform the task autonomously.
+
+If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support.
+
+## Set up and Calibrate
+
+If you haven't yet set up and calibrated your robot and teleop device, please do so by following the robot-specific tutorial.

 ## Teleoperate

-Run this simple script to teleoperate your robot (it won't connect and display the cameras):
+In this example, we’ll demonstrate how to teleoperate the SO101 robot. For each command, we also provide a corresponding API example.
+
+Note that the `id` associated with a robot is used to store the calibration file. It's important to use the same `id` when teleoperating, recording, and evaluating when using the same setup.
+
+<hfoptions id="teleoperate_so101">
+<hfoption id="Command">
 ```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=teleoperate
+python -m lerobot.teleoperate \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.id=my_awesome_follower_arm \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_awesome_leader_arm
 ```
-
-The teleoperate command will automatically:
-1. Identify any missing calibrations and initiate the calibration procedure.
-2. Connect the robot and start teleoperation.
-
-## Setup Cameras
-
-To connect a camera you have three options:
-1. OpenCVCamera which allows us to use any camera: usb, realsense, laptop webcam
-2. iPhone camera with MacOS
-3. Phone camera on Linux
-
-### Use OpenCVCamera
-
-The [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py) class allows you to efficiently record frames from most cameras using the [`opencv2`](https://docs.opencv.org) library.  For more details on compatibility, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
-
-To instantiate an [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py), you need a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
-
-To find the camera indices, run the following utility script, which will save a few frames from each detected camera:
-```bash
-python lerobot/common/robot_devices/cameras/opencv.py \
-    --images-dir outputs/images_from_opencv_cameras
-```
-
-The output will look something like this if you have two cameras connected:
-```
-Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
-[...]
-Camera found at index 0
-Camera found at index 1
-[...]
-Connecting cameras
-OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
-OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
-Saving images to outputs/images_from_opencv_cameras
-Frame: 0000	Latency (ms): 39.52
-[...]
-Frame: 0046	Latency (ms): 40.07
-Images have been saved to outputs/images_from_opencv_cameras
-```
-
-Check the saved images in `outputs/images_from_opencv_cameras` to identify which camera index corresponds to which physical camera (e.g. `0` for `camera_00` or `1` for `camera_01`):
-```
-camera_00_frame_000000.png
-[...]
-camera_00_frame_000047.png
-camera_01_frame_000000.png
-[...]
-camera_01_frame_000047.png
-```
-
-Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
-
-Now that you have the camera indexes, you should specify the camera's in the config.
-
-### Use your phone
-<hfoptions id="use phone">
-<hfoption id="Mac">
-
-To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
- Sign in both devices with the same Apple ID.
- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
-
-For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
-
-Your iPhone should be detected automatically when running the camera setup script in the next section.
-
 </hfoption>
-<hfoption id="Linux">
+<hfoption id="API example">
+```python
+from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
+from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower

-If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
+robot_config = SO101FollowerConfig(
+    port="/dev/tty.usbmodem58760431541",
+    id="my_red_robot_arm",
+)

-1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
-```python
-sudo apt install v4l2loopback-dkms v4l-utils
-```
-2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
-3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
-```python
-flatpak install flathub com.obsproject.Studio
-```
-4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
-```python
-flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
-```
-5. *Start OBS Studio*. Launch with:
-```python
-flatpak run com.obsproject.Studio
-```
-6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
-7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
-8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
-```python
-v4l2-ctl --list-devices
-```
-You should see an entry like:
-```
-VirtualCam (platform:v4l2loopback-000):
-/dev/video1
-```
-10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
-```python
-v4l2-ctl -d /dev/video1 --get-fmt-video
-```
-You should see an entry like:
-```
->>> Format Video Capture:
->>>	Width/Height      : 640/480
->>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
-```
+teleop_config = SO101LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_blue_leader_arm",
+)

-Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
-
-If everything is set up correctly, you can proceed with the rest of the tutorial.
+robot = SO101Follower(robot_config)
+teleop_device = SO101Leader(teleop_config)
+robot.connect()
+teleop_device.connect()

+while True:
+    action = teleop_device.get_action()
+    robot.send_action(action)
+```
 </hfoption>
 </hfoptions>

+The teleoperate command will automatically:
+1. Identify any missing calibrations and initiate the calibration procedure.
+2. Connect the robot and teleop device and start teleoperation.
+
+## Cameras
+
+To add cameras to your setup, follow this [Guide](./cameras#setup-cameras).
+
 ## Teleoperate with cameras

-We can now teleoperate again while at the same time visualizing the cameras and joint positions with `rerun`.
+With `rerun`, you can teleoperate again while simultaneously visualizing the camera feeds and joint positions. In this example, we’re using the Koch arm.

+<hfoptions id="teleoperate_koch_camera">
+<hfoption id="Command">
 ```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=teleoperate
-  --control.display_data=true
+python -m lerobot.teleoperate \
+    --robot.type=koch_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.id=my_awesome_follower_arm \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
+    --teleop.type=koch_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_awesome_leader_arm \
+    --display_data=true
 ```
+</hfoption>
+<hfoption id="API example">
+```python
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader
+from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower
+
+camera_config = {
+    "front": OpenCVCameraConfig(index_or_path=0, width=1920, height=1080, fps=30)
+}
+
+robot_config = KochFollowerConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_red_robot_arm",
+    cameras=camera_config
+)
+
+teleop_config = KochLeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_blue_leader_arm",
+)
+
+robot = KochFollower(robot_config)
+teleop_device = KochLeader(teleop_config)
+robot.connect()
+teleop_device.connect()
+
+while True:
+    observation = robot.get_observation()
+    action = teleop_device.get_action()
+    robot.send_action(action)
+```
+</hfoption>
+</hfoptions>

 ## Record a dataset

-Once you're familiar with teleoperation, you can record your first dataset with SO-101.
+Once you're familiar with teleoperation, you can record your first dataset.

 We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).

-Add your token to the cli by running this command:
+Add your token to the CLI by running this command:
 ```bash
 huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
@@ -178,41 +154,24 @@ HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
 ```

-Now you can record a dataset, to record 2 episodes and upload your dataset to the hub execute this command:
+Now you can record a dataset. To record 2 episodes and upload your dataset to the hub, execute this command tailored to the SO101.
 ```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/so101_test \
-  --control.tags='["so101","tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
+python -m lerobot.record \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem585A0076841 \
+    --robot.id=my_awesome_follower_arm \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_awesome_leader_arm \
+    --display_data=true \
+    --dataset.repo_id=${HF_USER}/record-test \
+    --dataset.num_episodes=2 \
+    --dataset.single_task="Grab the black cube"
 ```

-You will see a lot of lines appearing like this one:
-```
-INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
-```
-
-| Field | Meaning |
-|:---|:---|
-| `2024-08-10 15:02:58` | Timestamp when `print` was called. |
-| `ol_robot.py:219` | Source file and line number of the `print` call (`lerobot/scripts/control_robot.py` at line `219`). |
-| `dt: 33.34 (30.0 Hz)` | Delta time (ms) between teleop steps (target: 30.0 Hz, `--fps 30`). Yellow if step is too slow. |
-| `dtRlead: 5.06 (197.5 Hz)` | Delta time (ms) for reading present position from the **leader arm**. |
-| `dtWfoll: 0.25 (3963.7 Hz)` | Delta time (ms) for writing goal position to the **follower arm** (asynchronous). |
-| `dtRfoll: 6.22 (160.7 Hz)` | Delta time (ms) for reading present position from the **follower arm**. |
-| `dtRlaptop: 32.57 (30.7 Hz)` | Delta time (ms) for capturing an image from the **laptop camera** (async thread). |
-| `dtRphone: 33.84 (29.5 Hz)` | Delta time (ms) for capturing an image from the **phone camera** (async thread). |
-
-
 #### Dataset upload
-Locally your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/so101_test`). At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
+Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
 ```bash
 echo https://huggingface.co/datasets/${HF_USER}/so101_test
 ```
@@ -224,33 +183,26 @@ You can look for other LeRobot datasets on the hub by searching for `LeRobot` [t

 The `record` function provides a suite of tools for capturing and managing data during robot operation:

-##### 1. Frame Capture and Video Encoding
- Frames from cameras are saved to disk during recording.
- At the end of each episode, frames are encoded into video files.
+##### 1. Data Storage
+- Data is stored using the `LeRobotDataset` format and is stored on disk during recording.
+- By default, the dataset is pushed to your Hugging Face page after recording.
+  - To disable uploading, use `--dataset.push_to_hub=False`.

-##### 2. Data Storage
- Data is stored using the `LeRobotDataset` format.
- By default, the dataset is pushed to your Hugging Face page.
-  - To disable uploading, use `--control.push_to_hub=false`.
-
-##### 3. Checkpointing and Resuming
+##### 2. Checkpointing and Resuming
 - Checkpoints are automatically created during recording.
 - If an issue occurs, you can resume by re-running the same command with `--control.resume=true`.
 - To start recording from scratch, **manually delete** the dataset directory.

-##### 4. Recording Parameters
+##### 3. Recording Parameters
 Set the flow of data recording using command-line arguments:
- `--control.warmup_time_s=10`
-  Number of seconds before starting data collection (default: **10 seconds**).
-  Allows devices to warm up and synchronize.
- `--control.episode_time_s=60`
+- `--dataset.episode_time_s=60`
  Duration of each data recording episode (default: **60 seconds**).
- `--control.reset_time_s=60`
+- `--dataset.reset_time_s=60`
  Duration for resetting the environment after each episode (default: **60 seconds**).
- `--control.num_episodes=50`
+- `--dataset.num_episodes=50`
  Total number of episodes to record (default: **50**).

-##### 5. Keyboard Controls During Recording
+##### 4. Keyboard Controls During Recording
 Control the data recording flow using keyboard shortcuts:
 - Press **Right Arrow (`→`)**: Early stop the current episode or reset time and move to the next.
 - Press **Left Arrow (`←`)**: Cancel the current episode and re-record it.
@@ -264,6 +216,8 @@ In the following sections, you’ll train your neural network. After achieving r

 Avoid adding too much variation too quickly, as it may hinder your results.

+If you want to dive deeper into this important topic, you can check out the [blog post](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset) we wrote on what makes a good dataset.
+

 #### Troubleshooting:
 - On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
@@ -275,30 +229,18 @@ If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you c
 echo ${HF_USER}/so101_test
 ```

-If you didn't upload with `--control.push_to_hub=false`, you can visualize it locally with (via a window in the browser `http://127.0.0.1:9090` with the visualization tool):
-```bash
-python lerobot/scripts/visualize_dataset_html.py \
-  --repo-id ${HF_USER}/so101_test \
-  --local-files-only 1
-```
-
-This will launch a local web server that looks like this:
-<div style="text-align:center;">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/visualize_dataset_html.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="100%"></img>
-</div>
-
 ## Replay an episode

-A useful feature is the `replay` function, which allows to replay on your robot any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
+A useful feature is the `replay` function, which allows you to replay any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.

 You can replay the first episode on your robot with:
 ```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/so101_test \
-  --control.episode=0
+python -m lerobot.replay \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.id=my_awesome_follower_arm \
+    --dataset.repo_id=${HF_USER}/record-test \
+    --dataset.episode=0 # choose the episode you want to replay
 ```

 Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
@@ -348,21 +290,20 @@ huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \

 ## Evaluate your policy

-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
 ```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/eval_act_so101_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_so101_test/checkpoints/last/pretrained_model
+python -m lerobot.record  \
+  --robot.type=so100_follower \
+  --robot.port=/dev/ttyACM1 \
+  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
+  --robot.id=my_awesome_follower_arm \
+  --teleop.type=so100_leader \
+  --teleop.port=/dev/ttyACM0 \
+  --teleop.id=my_awesome_leader_arm \
+  --display_data=false \
+  --dataset.repo_id=$HF_USER/eval_so100 \
+  --dataset.single_task="Put lego brick into the transparent box" \
+  --policy.path=${HF_USER}/my_policy
 ```

 As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
--- a/docs/source/installation.mdx
+++ b/docs/source/installation.mdx
@@ -2,6 +2,8 @@

 ## Install LeRobot

+Currently only available from source.
+
 Download our source code:
 ```bash
 git clone https://github.com/huggingface/lerobot.git
@@ -13,28 +15,6 @@ Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.
 conda create -y -n lerobot python=3.10
 ```

-Now restart the shell by running:
-<hfoptions id="shell_restart">
-<hfoption id="Windows">
-
-```bash
-source ~/.bashrc
-```
-</hfoption>
-<hfoption id="Mac">
-
-```bash
-source ~/.bash_profile
-```
-</hfoption>
-<hfoption id="zshell">
-
-```bash
-source ~/.zshrc
-```
-</hfoption>
-</hfoptions>
-
 Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
 ```bash
 conda activate lerobot
@@ -51,14 +31,14 @@ conda install ffmpeg -c conda-forge
 >  ```bash
 >  conda install ffmpeg=7.1.1 -c conda-forge
 >  ```
->  - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+>  - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.

 Install 🤗 LeRobot:
 ```bash
-cd lerobot && pip install -e ".[feetech]"
+pip install -e .
 ```

-## Troubleshooting
+### Troubleshooting
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
 To install these for linux run:
 ```bash
@@ -66,18 +46,24 @@ sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev
 ```
 For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)

-## Sim
-For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
- [aloha](https://github.com/huggingface/gym-aloha)
- [xarm](https://github.com/huggingface/gym-xarm)
- [pusht](https://github.com/huggingface/gym-pusht)
+## Optional dependencies

-For instance, to install 🤗 LeRobot with aloha and pusht, use:
+LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
+
+### Simulations
+Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), `xarm` ([gym-xarm](https://github.com/huggingface/gym-xarm)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
+Example:
 ```bash
-pip install -e ".[aloha, pusht]"
+pip install -e ".[aloha]" # or "[pusht]" for example
 ```

-## W&B
+### Motor Control
+For Koch v1.1 install the Dynamixel SDK, for SO100/SO101/Moss install the Feetech SDK.
+```bash
+pip install -e ".[feetech]" # or "[dynamixel]" for example
+```
+
+### Experiment Tracking
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
 ```bash
 wandb login
--- a/docs/source/koch.mdx
+++ b/docs/source/koch.mdx
@@ -0,0 +1 @@
+../../lerobot/common/robots/koch_follower/koch.mdx
--- a/docs/source/lekiwi.mdx
+++ b/docs/source/lekiwi.mdx
@@ -0,0 +1 @@
+../../lerobot/common/robots/lekiwi/lekiwi.mdx
--- a/docs/source/so100.mdx
+++ b/docs/source/so100.mdx
@@ -0,0 +1 @@
+../../lerobot/common/robots/so100_follower/so100.mdx
--- a/docs/source/so101.mdx
+++ b/docs/source/so101.mdx
@@ -0,0 +1 @@
+../../lerobot/common/robots/so101_follower/so101.mdx
--- a/examples/10_use_so100.md
+++ b/examples/10_use_so100.md
@@ -1,624 +0,0 @@
-# Using the [SO-100](https://github.com/TheRobotStudio/SO-ARM100) with LeRobot
-
-## Table of Contents
-
-  - [A. Source the parts](#a-source-the-parts)
-  - [B. Install LeRobot](#b-install-lerobot)
-  - [C. Configure the Motors](#c-configure-the-motors)
-  - [D. Step-by-Step Assembly Instructions](#d-step-by-step-assembly-instructions)
-  - [E. Calibrate](#e-calibrate)
-  - [F. Teleoperate](#f-teleoperate)
-  - [G. Record a dataset](#g-record-a-dataset)
-  - [H. Visualize a dataset](#h-visualize-a-dataset)
-  - [I. Replay an episode](#i-replay-an-episode)
-  - [J. Train a policy](#j-train-a-policy)
-  - [K. Evaluate your policy](#k-evaluate-your-policy)
-  - [L. More Information](#l-more-information)
-
-## A. Source the parts
-
-Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts,
-and advice if it's your first time printing or if you don't own a 3D printer.
-
-Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
-
-## B. Install LeRobot
-
-> [!TIP]
-> We use the Command Prompt (cmd) quite a lot. If you are not comfortable using the cmd or want to brush up using the command line you can have a look here: [Command line crash course](https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Command_line)
-
-On your computer:
-
-#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
-
-#### 2. Restart shell
-Copy paste in your shell: `source ~/.bashrc` or for Mac: `source ~/.bash_profile` or `source ~/.zshrc` if you're using zshell
-
-#### 3. Create and activate a fresh conda environment for lerobot
-
-<details>
-<summary><strong>Video install instructions</strong></summary>
-
-<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
-
-</details>
-
-```bash
-conda create -y -n lerobot python=3.10
-```
-
-Then activate your conda environment (do this each time you open a shell to use lerobot!):
-```bash
-conda activate lerobot
-```
-
-#### 4. Clone LeRobot:
-```bash
-git clone https://github.com/huggingface/lerobot.git ~/lerobot
-```
-
-#### 5. Install ffmpeg in your environment:
-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-#### 6. Install LeRobot with dependencies for the feetech motors:
-```bash
-cd ~/lerobot && pip install -e ".[feetech]"
-```
-
-Great :hugs:! You are now done installing LeRobot and we can begin assembling the SO100 arms :robot:.
-Every time you now want to use LeRobot you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
-
-## C. Configure the motors
-
-> [!NOTE]
-> Throughout this tutorial you will find videos on how to do the steps, the full video tutorial can be found here: [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I).
-
-### 1. Find the USB ports associated to each arm
-
-Designate one bus servo adapter and 6 motors for your leader arm, and similarly the other bus servo adapter and 6 motors for the follower arm. It's convenient to label them and write on each motor if it's for the follower `F` or for the leader `L` and it's ID from 1 to 6 (F1...F6 and L1...L6).
-
-#### a. Run the script to find port
-
-<details>
-<summary><strong>Video finding port</strong></summary>
-  <video src="https://github.com/user-attachments/assets/4a21a14d-2046-4805-93c4-ee97a30ba33f"></video>
-  <video src="https://github.com/user-attachments/assets/1cc3aecf-c16d-4ff9-aec7-8c175afbbce2"></video>
-</details>
-
-To find the port for each bus servo adapter, run the utility script:
-```bash
-python lerobot/scripts/find_motors_bus_port.py
-```
-
-#### b. Example outputs
-
-Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0031751
-Reconnect the usb cable.
-```
-Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the usb cable.
-```
-
-#### c. Troubleshooting
-On Linux, you might need to give access to the USB ports by running:
-```bash
-sudo chmod 666 /dev/ttyACM0
-sudo chmod 666 /dev/ttyACM1
-```
-
-#### d. Update config file
-
-IMPORTANTLY: Now that you have your ports, update the **port** default values of [`SO100RobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
-```diff
-@RobotConfig.register_subclass("so100")
-@dataclass
-class So100RobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/so100"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431091",
-+                port="{ADD YOUR LEADER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-+                port="{ADD YOUR FOLLOWER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-```
-
-### 2. Assembling the Base
-Let's begin with assembling the follower arm base
-
-#### a. Set IDs for all 12 motors
-
-<details>
-<summary><strong>Video configuring motor</strong></summary>
-  <video src="https://github.com/user-attachments/assets/ef9b3317-2e11-4858-b9d3-f0a02fb48ecf"></video>
-  <video src="https://github.com/user-attachments/assets/f36b5ed5-c803-4ebe-8947-b39278776a0d"></video>
-</details>
-
-Plug your first motor F1 and run this script to set its ID to 1. It will also set its present position to 2048, so expect your motor to rotate. Replace the text after --port to the corresponding follower control board port and run this command in cmd:
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 1
-```
-
-> [!NOTE]
-> These motors are currently limited. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
-
-Then unplug your motor and plug the second motor and set its ID to 2.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 2
-```
-
-Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.
-
-
-#### b. Remove the gears of the 6 leader motors
-
-<details>
-<summary><strong>Video removing gears</strong></summary>
-
-<video src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7"></video>
-
-</details>
-
-
-Follow the video for removing gears. You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm.
-
-## D. Step-by-Step Assembly Instructions
-
-**Step 1: Clean Parts**
- Remove all support material from the 3D-printed parts.
---
-
-### Additional Guidance
-
-<details>
-<summary><strong>Video assembling arms</strong></summary>
-
-<video src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca"></video>
-
-</details>
-
-**Note:**
-This video provides visual guidance for assembling the arms, but it doesn't specify when or how to do the wiring. Inserting the cables beforehand is much easier than doing it afterward. The first arm may take a bit more than 1 hour to assemble, but once you get used to it, you can assemble the second arm in under 1 hour.
-
---
-
-### First Motor
-
-**Step 2: Insert Wires**
- Insert two wires into the first motor.
-
-  <img src="../media/tutorial/img1.jpg" style="height:300px;">
-
-**Step 3: Install in Base**
- Place the first motor into the base.
-
-  <img src="../media/tutorial/img2.jpg" style="height:300px;">
-
-**Step 4: Secure Motor**
- Fasten the motor with 4 screws. Two from the bottom and two from top.
-
-**Step 5: Attach Motor Holder**
- Slide over the first motor holder and fasten it using two screws (one on each side).
-
-  <img src="../media/tutorial/img4.jpg" style="height:300px;">
-
-**Step 6: Attach Motor Horns**
- Install both motor horns, securing the top horn with a screw. Try not to move the motor position when attaching the motor horn, especially for the leader arms, where we removed the gears.
-
-  <img src="../media/tutorial/img5.jpg" style="height:300px;">
-<details>
-  <summary><strong>Video adding motor horn</strong></summary>
-  <video src="https://github.com/user-attachments/assets/ef3391a4-ad05-4100-b2bd-1699bf86c969"></video>
-</details>
-
-**Step 7: Attach Shoulder Part**
- Route one wire to the back of the robot and the other to the left or in photo towards you (see photo).
- Attach the shoulder part.
-
-  <img src="../media/tutorial/img6.jpg" style="height:300px;">
-
-**Step 8: Secure Shoulder**
- Tighten the shoulder part with 4 screws on top and 4 on the bottom
-*(access bottom holes by turning the shoulder).*
-
---
-
-### Second Motor Assembly
-
-**Step 9: Install Motor 2**
- Slide the second motor in from the top and link the wire from motor 1 to motor 2.
-
-  <img src="../media/tutorial/img8.jpg" style="height:300px;">
-
-**Step 10: Attach Shoulder Holder**
- Add the shoulder motor holder.
- Ensure the wire from motor 1 to motor 2 goes behind the holder while the other wire is routed upward (see photo).
- This part can be tight to assemble, you can use a workbench like the image or a similar setup to push the part around the motor.
-
-  <div style="display: flex;">
-    <img src="../media/tutorial/img9.jpg" style="height:250px;">
-    <img src="../media/tutorial/img10.jpg" style="height:250px;">
-    <img src="../media/tutorial/img12.jpg" style="height:250px;">
-  </div>
-
-**Step 11: Secure Motor 2**
- Fasten the second motor with 4 screws.
-
-**Step 12: Attach Motor Horn**
- Attach both motor horns to motor 2, again use the horn screw.
-
-**Step 13: Attach Base**
- Install the base attachment using 2 screws.
-
-  <img src="../media/tutorial/img11.jpg" style="height:300px;">
-
-**Step 14: Attach Upper Arm**
- Attach the upper arm with 4 screws on each side.
-
-  <img src="../media/tutorial/img13.jpg" style="height:300px;">
-
---
-
-### Third Motor Assembly
-
-**Step 15: Install Motor 3**
- Route the motor cable from motor 2 through the cable holder to motor 3, then secure motor 3 with 4 screws.
-
-**Step 16: Attach Motor Horn**
- Attach both motor horns to motor 3 and secure one again with a horn screw.
-
-  <img src="../media/tutorial/img14.jpg" style="height:300px;">
-
-**Step 17: Attach Forearm**
- Connect the forearm to motor 3 using 4 screws on each side.
-
-  <img src="../media/tutorial/img15.jpg" style="height:300px;">
-
---
-
-### Fourth Motor Assembly
-
-**Step 18: Install Motor 4**
- Slide in motor 4, attach the cable from motor 3, and secure the cable in its holder with a screw.
-
-  <div style="display: flex;">
-    <img src="../media/tutorial/img16.jpg" style="height:300px;">
-    <img src="../media/tutorial/img19.jpg" style="height:300px;">
-  </div>
-
-**Step 19: Attach Motor Holder 4**
- Install the fourth motor holder (a tight fit). Ensure one wire is routed upward and the wire from motor 3 is routed downward (see photo).
-
-  <img src="../media/tutorial/img17.jpg" style="height:300px;">
-
-**Step 20: Secure Motor 4 & Attach Horn**
- Fasten motor 4 with 4 screws and attach its motor horns, use for one a horn screw.
-
-  <img src="../media/tutorial/img18.jpg" style="height:300px;">
-
---
-
-### Wrist Assembly
-
-**Step 21: Install Motor 5**
- Insert motor 5 into the wrist holder and secure it with 2 front screws.
-
-  <img src="../media/tutorial/img20.jpg" style="height:300px;">
-
-**Step 22: Attach Wrist**
- Connect the wire from motor 4 to motor 5. And already insert the other wire for the gripper.
- Secure the wrist to motor 4 using 4 screws on both sides.
-
-  <img src="../media/tutorial/img22.jpg" style="height:300px;">
-
-**Step 23: Attach Wrist Horn**
- Install only one motor horn on the wrist motor and secure it with a horn screw.
-
-  <img src="../media/tutorial/img23.jpg" style="height:300px;">
-
---
-
-### Follower Configuration
-
-**Step 24: Attach Gripper**
- Attach the gripper to motor 5.
-
-  <img src="../media/tutorial/img24.jpg" style="height:300px;">
-
-**Step 25: Install Gripper Motor**
- Insert the gripper motor, connect the motor wire from motor 5 to motor 6, and secure it with 3 screws on each side.
-
-  <img src="../media/tutorial/img25.jpg" style="height:300px;">
-
-**Step 26: Attach Gripper Horn & Claw**
- Attach the motor horns and again use a horn screw.
- Install the gripper claw and secure it with 4 screws on both sides.
-
-  <img src="../media/tutorial/img26.jpg" style="height:300px;">
-
-**Step 27: Mount Controller**
- Attach the motor controller on the back.
-
-  <div style="display: flex;">
-    <img src="../media/tutorial/img27.jpg" style="height:300px;">
-    <img src="../media/tutorial/img28.jpg" style="height:300px;">
-  </div>
-
-*Assembly complete – proceed to Leader arm assembly.*
-
---
-
-### Leader Configuration
-
-For the leader configuration, perform **Steps 1–23**. Make sure that you removed the motor gears from the motors.
-
-**Step 24: Attach Leader Holder**
- Mount the leader holder onto the wrist and secure it with a screw.
-
-  <img src="../media/tutorial/img29.jpg" style="height:300px;">
-
-**Step 25: Attach Handle**
- Attach the handle to motor 5 using 4 screws.
-
-  <img src="../media/tutorial/img30.jpg" style="height:300px;">
-
-**Step 26: Install Gripper Motor**
- Insert the gripper motor, secure it with 3 screws on each side, attach a motor horn using a horn screw, and connect the motor wire.
-
-  <img src="../media/tutorial/img31.jpg" style="height:300px;">
-
-**Step 27: Attach Trigger**
- Attach the follower trigger with 4 screws.
-
-  <img src="../media/tutorial/img32.jpg" style="height:300px;">
-
-**Step 28: Mount Controller**
- Attach the motor controller on the back.
-
-  <div style="display: flex;">
-    <img src="../media/tutorial/img27.jpg" style="height:300px;">
-    <img src="../media/tutorial/img28.jpg" style="height:300px;">
-  </div>
-
-*Assembly complete – proceed to calibration.*
-
-
-## E. Calibrate
-
-Next, you'll need to calibrate your SO-100 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
-The calibration process is very important because it allows a neural network trained on one SO-100 robot to work on another.
-
-#### Manual calibration of follower arm
-
-You will need to move the follower arm to these positions sequentially, note that the rotated position is on the right side of the robot and you have to open the gripper fully.
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="../media/so101/follower_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="../media/so101/follower_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="../media/so101/follower_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="../media/so101/follower_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
-
-Make sure both arms are connected and run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_follower"]'
-```
-
-#### Manual calibration of leader arm
-You will also need to move the leader arm to these positions sequentially:
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="../media/so101/leader_middle.webp?raw=true" alt="SO-100 leader arm middle position" title="SO-100 leader arm middle position" style="width:100%;"> | <img src="../media/so101/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so101/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so101/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
-
-Run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_leader"]'
-```
-
-## F. Teleoperate
-
-**Simple teleop**
-Then you are ready to teleoperate your robot! Run this simple script (it won't connect and display the cameras):
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --robot.cameras='{}' \
-  --control.type=teleoperate
-```
-
-
-#### a. Teleop with displaying cameras
-Follow [this guide to setup your cameras](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). Then you will be able to display the cameras on your computer while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --control.type=teleoperate
-```
-
-## G. Record a dataset
-
-Once you're familiar with teleoperation, you can record your first dataset with SO-100.
-
-If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Store your Hugging Face repository name in a variable to run these commands:
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-
-Record 2 episodes and upload your dataset to the hub:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/so100_test \
-  --control.tags='["so100","tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
-```
-
-Note: You can resume recording by adding `--control.resume=true`.
-
-## H. Visualize a dataset
-
-If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
-```bash
-echo ${HF_USER}/so100_test
-```
-
-If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with (a window can be opened in the browser `http://127.0.0.1:9090` with the visualization tool):
-```bash
-python lerobot/scripts/visualize_dataset_html.py \
-  --repo-id ${HF_USER}/so100_test \
-  --local-files-only 1
-```
-
-## I. Replay an episode
-
-Now try to replay the first episode on your robot:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/so100_test \
-  --control.episode=0
-```
-
-## J. Train a policy
-
-To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python lerobot/scripts/train.py \
-  --dataset.repo_id=${HF_USER}/so100_test \
-  --policy.type=act \
-  --output_dir=outputs/train/act_so100_test \
-  --job_name=act_so100_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain it:
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so100_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-Training should take several hours. You will find checkpoints in `outputs/train/act_so100_test/checkpoints`.
-
-To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so100_test` policy:
-```bash
-python lerobot/scripts/train.py \
-  --config_path=outputs/train/act_so100_test/checkpoints/last/pretrained_model/train_config.json \
-  --resume=true
-```
-
-## K. Evaluate your policy
-
-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/eval_act_so100_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_so100_test/checkpoints/last/pretrained_model
-```
-
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_so100_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so100_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so100_test`).
-
-## L. More Information
-
-Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth tutorial on controlling real robots with LeRobot.
-
-> [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb) in the channel [`#so100-arm`](https://discord.com/channels/1216765309076115607/1237741463832363039).
--- a/examples/11_use_lekiwi.md
+++ b/examples/11_use_lekiwi.md
@@ -1,597 +0,0 @@
-# Using the [LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi) Robot with LeRobot
-
-## Table of Contents
-
-  - [A. Source the parts](#a-source-the-parts)
-  - [B. Install software Pi](#b-install-software-on-pi)
-  - [C. Setup LeRobot laptop/pc](#c-install-lerobot-on-laptop)
-  - [D. Assemble the arms](#d-assembly)
-  - [E. Calibrate](#e-calibration)
-  - [F. Teleoperate](#f-teleoperate)
-  - [G. Record a dataset](#g-record-a-dataset)
-  - [H. Visualize a dataset](#h-visualize-a-dataset)
-  - [I. Replay an episode](#i-replay-an-episode)
-  - [J. Train a policy](#j-train-a-policy)
-  - [K. Evaluate your policy](#k-evaluate-your-policy)
-
-> [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb) in the channel [`#mobile-so-100-arm`](https://discord.com/channels/1216765309076115607/1318390825528332371).
-
-## A. Source the parts
-
-Follow this [README](https://github.com/SIGRobotics-UIUC/LeKiwi). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts, and advice if it's your first time printing or if you don't own a 3D printer.
-
-Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
-
-### Wired version
-If you have the **wired** LeKiwi version you can skip the installation of the Raspberry Pi and setting up SSH. You can also run all commands directly on your PC for both the LeKiwi scripts and the leader arm scripts for teleoperating.
-
-## B. Install software on Pi
-Now we have to setup the remote PC that will run on the LeKiwi Robot. This is normally a Raspberry Pi, but can be any PC that can run on 5V and has enough usb ports (2 or more) for the cameras and motor control board.
-
-### Install OS
-For setting up the Raspberry Pi and its SD-card see: [Setup PI](https://www.raspberrypi.com/documentation/computers/getting-started.html). Here is explained how to download the [Imager](https://www.raspberrypi.com/software/) to install Raspberry Pi OS or Ubuntu.
-
-### Setup SSH
-After setting up your Pi, you should enable and setup [SSH](https://www.raspberrypi.com/news/coding-on-raspberry-pi-remotely-with-visual-studio-code/) (Secure Shell Protocol) so you can login into the Pi from your laptop without requiring a screen, keyboard and mouse in the Pi. A great tutorial on how to do this can be found [here](https://www.raspberrypi.com/documentation/computers/remote-access.html#ssh). Logging into your Pi can be done in your Command Prompt (cmd) or if you use VSCode you can use [this](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh) extension.
-
-### Install LeRobot
-
-On your Raspberry Pi:
-
-#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
-
-#### 2. Restart shell
-Copy paste in your shell: `source ~/.bashrc` or for Mac: `source ~/.bash_profile` or `source ~/.zshrc` if you're using zshell
-
-#### 3. Create and activate a fresh conda environment for lerobot
-
-<details>
-<summary><strong>Video install instructions</strong></summary>
-
-<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
-
-</details>
-
-```bash
-conda create -y -n lerobot python=3.10
-```
-
-Then activate your conda environment (do this each time you open a shell to use lerobot!):
-```bash
-conda activate lerobot
-```
-
-#### 4. Clone LeRobot:
-```bash
-git clone https://github.com/huggingface/lerobot.git ~/lerobot
-```
-
-#### 5. Install ffmpeg in your environment:
-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-#### 6. Install LeRobot with dependencies for the feetech motors:
-```bash
-cd ~/lerobot && pip install -e ".[feetech]"
-```
-
-## C. Install LeRobot on laptop
-If you already have install LeRobot on your laptop you can skip this step, otherwise please follow along as we do the same steps we did on the Pi.
-
-> [!TIP]
-> We use the Command Prompt (cmd) quite a lot. If you are not comfortable using the cmd or want to brush up using the command line you can have a look here: [Command line crash course](https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Command_line)
-
-On your computer:
-
-#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
-
-#### 2. Restart shell
-Copy paste in your shell: `source ~/.bashrc` or for Mac: `source ~/.bash_profile` or `source ~/.zshrc` if you're using zshell
-
-#### 3. Create and activate a fresh conda environment for lerobot
-
-<details>
-<summary><strong>Video install instructions</strong></summary>
-
-<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
-
-</details>
-
-```bash
-conda create -y -n lerobot python=3.10
-```
-
-Then activate your conda environment (do this each time you open a shell to use lerobot!):
-```bash
-conda activate lerobot
-```
-
-#### 4. Clone LeRobot:
-```bash
-git clone https://github.com/huggingface/lerobot.git ~/lerobot
-```
-
-#### 5. Install ffmpeg in your environment:
-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-#### 6. Install LeRobot with dependencies for the feetech motors:
-```bash
-cd ~/lerobot && pip install -e ".[feetech]"
-```
-
-Great :hugs:! You are now done installing LeRobot and we can begin assembling the SO100 arms and Mobile base :robot:.
-Every time you now want to use LeRobot you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
-
-# D. Assembly
-
-First we will assemble the two SO100 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base.
-
-## SO100 Arms
-### Configure motors
-The instructions for configuring the motors can be found [Here](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#c-configure-the-motors) in step C of the SO100 tutorial. Besides the ID's for the arm motors we also need to set the motor ID's for the mobile base. These need to be in a specific order to work. Below an image of the motor ID's and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ID's for the wheels are 7, 8 and 9.
-
-<img src="../media/lekiwi/motor_ids.webp?raw=true" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
-
-### Assemble arms
-[Assemble arms instruction](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#d-assemble-the-arms)
-
-## Mobile base (LeKiwi)
-[Assemble LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi)
-
-### Update config
-Both config files on the LeKiwi LeRobot and on the laptop should be the same. First we should find the Ip address of the Raspberry Pi of the mobile manipulator. This is the same Ip address used in SSH. We also need the usb port of the control board of the leader arm on the laptop and the port of the control board on LeKiwi. We can find these ports with the following script.
-
-#### a. Run the script to find port
-
-<details>
-<summary><strong>Video finding port</strong></summary>
-  <video src="https://github.com/user-attachments/assets/4a21a14d-2046-4805-93c4-ee97a30ba33f"></video>
-  <video src="https://github.com/user-attachments/assets/1cc3aecf-c16d-4ff9-aec7-8c175afbbce2"></video>
-</details>
-
-To find the port for each bus servo adapter, run the utility script:
-```bash
-python lerobot/scripts/find_motors_bus_port.py
-```
-
-#### b. Example outputs
-
-Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
-Reconnect the usb cable.
-```
-Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the usb cable.
-```
-
-#### c. Troubleshooting
-On Linux, you might need to give access to the USB ports by running:
-```bash
-sudo chmod 666 /dev/ttyACM0
-sudo chmod 666 /dev/ttyACM1
-```
-
-#### d. Update config file
-
-IMPORTANTLY: Now that you have your ports of leader and follower arm and ip address of the mobile-so100, update the **ip** in Network configuration, **port** in leader_arms and **port** in lekiwi. In the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py) file. Where you will find something like:
-```python
-@RobotConfig.register_subclass("lekiwi")
-@dataclass
-class LeKiwiRobotConfig(RobotConfig):
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    # Network Configuration
-    ip: str = "172.17.133.91"
-    port: int = 5555
-    video_port: int = 5556
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "mobile": OpenCVCameraConfig(camera_index="/dev/video0", fps=30, width=640, height=480),
-            "mobile2": OpenCVCameraConfig(camera_index="/dev/video2", fps=30, width=640, height=480),
-        }
-    )
-
-    calibration_dir: str = ".cache/calibration/lekiwi"
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0077581",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/ttyACM0",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                    "left_wheel": (7, "sts3215"),
-                    "back_wheel": (8, "sts3215"),
-                    "right_wheel": (9, "sts3215"),
-                },
-            ),
-        }
-    )
-
-    teleop_keys: dict[str, str] = field(
-        default_factory=lambda: {
-            # Movement
-            "forward": "w",
-            "backward": "s",
-            "left": "a",
-            "right": "d",
-            "rotate_left": "z",
-            "rotate_right": "x",
-            # Speed control
-            "speed_up": "r",
-            "speed_down": "f",
-            # quit teleop
-            "quit": "q",
-        }
-    )
-
-    mock: bool = False
-```
-
-## Wired version
-
-For the wired LeKiwi version your configured IP address should refer to your own laptop (127.0.0.1), because leader arm and LeKiwi are in this case connected to own laptop. Below and example configuration for this wired setup:
-```python
-@RobotConfig.register_subclass("lekiwi")
-@dataclass
-class LeKiwiRobotConfig(RobotConfig):
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    # Network Configuration
-    ip: str = "127.0.0.1"
-    port: int = 5555
-    video_port: int = 5556
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "front": OpenCVCameraConfig(
-                camera_index=0, fps=30, width=640, height=480, rotation=90
-            ),
-            "wrist": OpenCVCameraConfig(
-                camera_index=1, fps=30, width=640, height=480, rotation=180
-            ),
-        }
-    )
-
-    calibration_dir: str = ".cache/calibration/lekiwi"
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0077581",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431061",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                    "left_wheel": (7, "sts3215"),
-                    "back_wheel": (8, "sts3215"),
-                    "right_wheel": (9, "sts3215"),
-                },
-            ),
-        }
-    )
-
-    teleop_keys: dict[str, str] = field(
-        default_factory=lambda: {
-            # Movement
-            "forward": "w",
-            "backward": "s",
-            "left": "a",
-            "right": "d",
-            "rotate_left": "z",
-            "rotate_right": "x",
-            # Speed control
-            "speed_up": "r",
-            "speed_down": "f",
-            # quit teleop
-            "quit": "q",
-        }
-    )
-
-    mock: bool = False
-```
-
-# E. Calibration
-Now we have to calibrate the leader arm and the follower arm. The wheel motors don't have to be calibrated.
-
-
-### Calibrate follower arm (on mobile base)
-> [!IMPORTANT]
-> Contrarily to step 6 of the [assembly video](https://youtu.be/FioA2oeFZ5I?t=724) which illustrates the auto calibration, we will actually do manual calibration of follower for now.
-
-You will need to move the follower arm to these positions sequentially:
-
-| 1. Zero position                                                                                                                                                  | 2. Rotated position                                                                                                                                                        | 3. Rest position                                                                                                                                                  |
-| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| <img src="../media/lekiwi/mobile_calib_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
-
-Make sure the arm is connected to the Raspberry Pi and run this script (on the Raspberry Pi) to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_follower"]'
-```
-
-### Wired version
-If you have the **wired** LeKiwi version please run all commands including this calibration command on your laptop.
-
-### Calibrate leader arm
-Then to calibrate the leader arm (which is attached to the laptop/pc). You will need to move the leader arm to these positions sequentially:
-
-| 1. Zero position                                                                                                                                       | 2. Rotated position                                                                                                                                             | 3. Rest position                                                                                                                                       |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
-
-Run this script (on your laptop/pc) to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_leader"]'
-```
-
-# F. Teleoperate
-
-> [!TIP]
-> If you're using a Mac, you might need to give Terminal permission to access your keyboard. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
-
-To teleoperate SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --control.type=remote_robot
-```
-
-Then on your laptop, also run `conda activate lerobot` and this script:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --control.type=teleoperate \
-  --control.fps=30
-```
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`. For the `--control.type=remote_robot` you will also need to set `--control.viewer_ip` and `--control.viewer_port`
-
-You should see on your laptop something like this: ```[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.``` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:
-| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
-| ---------- | ------------------ | ---------------------- |
-| Fast       | 0.4                | 90                     |
-| Medium     | 0.25               | 60                     |
-| Slow       | 0.1                | 30                     |
-
-
-| Key | Action         |
-| --- | -------------- |
-| W   | Move forward   |
-| A   | Move left      |
-| S   | Move backward  |
-| D   | Move right     |
-| Z   | Turn left      |
-| X   | Turn right     |
-| R   | Increase speed |
-| F   | Decrease speed |
-
-> [!TIP]
->  If you use a different keyboard you can change the keys for each command in the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py).
-
-### Wired version
-If you have the **wired** LeKiwi version please run all commands including both these teleoperation commands on your laptop.
-
-## Troubleshoot communication
-
-If you are having trouble connecting to the Mobile SO100, follow these steps to diagnose and resolve the issue.
-
-### 1. Verify IP Address Configuration
-Make sure that the correct ip for the Pi is set in the configuration file. To check the Raspberry Pi's IP address, run (on the Pi command line):
-```bash
-hostname -I
-```
-
-### 2. Check if Pi is reachable from laptop/pc
-Try pinging the Raspberry Pi from your laptop:
-```bach
-ping <your_pi_ip_address>
-```
-
-If the ping fails:
- Ensure the Pi is powered on and connected to the same network.
- Check if SSH is enabled on the Pi.
-
-### 3. Try SSH connection
-If you can't SSH into the Pi, it might not be properly connected. Use:
-```bash
-ssh <your_pi_user_name>@<your_pi_ip_address>
-```
-If you get a connection error:
- Ensure SSH is enabled on the Pi by running:
-  ```bash
-  sudo raspi-config
-  ```
-  Then navigate to: **Interfacing Options -> SSH** and enable it.
-
-### 4. Same config file
-Make sure the configuration file on both your laptop/pc and the Raspberry Pi is the same.
-
-# G. Record a dataset
-Once you're familiar with teleoperation, you can record your first dataset with LeKiwi.
-
-To start the program on LeKiwi, SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --control.type=remote_robot
-```
-
-If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Store your Hugging Face repository name in a variable to run these commands:
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-On your laptop then run this command to record 2 episodes and upload your dataset to the hub:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/lekiwi_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
-```
-
-Note: You can resume recording by adding `--control.resume=true`.
-
-### Wired version
-If you have the **wired** LeKiwi version please run all commands including both these record dataset commands on your laptop.
-
-# H. Visualize a dataset
-
-If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
-```bash
-echo ${HF_USER}/lekiwi_test
-```
-
-If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with (a window can be opened in the browser `http://127.0.0.1:9090` with the visualization tool):
-```bash
-python lerobot/scripts/visualize_dataset_html.py \
-  --repo-id ${HF_USER}/lekiwi_test \
-  --local-files-only 1
-```
-
-# I. Replay an episode
-Now try to replay the first episode on your robot:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/lekiwi_test \
-  --control.episode=0
-```
-
-## J. Train a policy
-
-To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python lerobot/scripts/train.py \
-  --dataset.repo_id=${HF_USER}/lekiwi_test \
-  --policy.type=act \
-  --output_dir=outputs/train/act_lekiwi_test \
-  --job_name=act_lekiwi_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain it:
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/lekiwi_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-Training should take several hours. You will find checkpoints in `outputs/train/act_lekiwi_test/checkpoints`.
-
-## K. Evaluate your policy
-
-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=lekiwi \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Drive to the red block and pick it up" \
-  --control.repo_id=${HF_USER}/eval_act_lekiwi_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_lekiwi_test/checkpoints/last/pretrained_model
-```
-
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_lekiwi_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_lekiwi_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_lekiwi_test`).
--- a/examples/11_use_moss.md
+++ b/examples/11_use_moss.md
@@ -1,337 +0,0 @@
-This tutorial explains how to use [Moss v1](https://github.com/jess-moss/moss-robot-arms) with LeRobot.
-
-## Source the parts
-
-Follow this [README](https://github.com/jess-moss/moss-robot-arms). It contains the bill of materials with link to source the parts, as well as the instructions to 3D print the parts and advice if it's your first time printing or if you don't own a 3D printer already.
-
-**Important**: Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
-
-## Install LeRobot
-
-On your computer:
-
-1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
-```bash
-mkdir -p ~/miniconda3
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
-bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
-rm ~/miniconda3/miniconda.sh
-~/miniconda3/bin/conda init bash
-```
-
-2. Restart shell or `source ~/.bashrc`
-
-3. Create and activate a fresh conda environment for lerobot
-```bash
-conda create -y -n lerobot python=3.10 && conda activate lerobot
-```
-
-4. Clone LeRobot:
-```bash
-git clone https://github.com/huggingface/lerobot.git ~/lerobot
-```
-
-5. Install ffmpeg in your environment:
-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-6. Install LeRobot with dependencies for the feetech motors:
-```bash
-cd ~/lerobot && pip install -e ".[feetech]"
-```
-
-## Configure the motors
-
-Follow step 1 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the use of our scripts below.
-
-**Find USB ports associated to your arms**
-To find the correct ports for each arm, run the utility script twice:
-```bash
-python lerobot/scripts/find_motors_bus_port.py
-```
-
-Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
-Reconnect the usb cable.
-```
-
-Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the usb cable.
-```
-
-Troubleshooting: On Linux, you might need to give access to the USB ports by running:
-```bash
-sudo chmod 666 /dev/ttyACM0
-sudo chmod 666 /dev/ttyACM1
-```
-
-#### Update config file
-
-IMPORTANTLY: Now that you have your ports, update the **port** default values of [`MossRobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
-```python
-@RobotConfig.register_subclass("moss")
-@dataclass
-class MossRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/moss"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431091",  <-- UPDATE HERE
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",  <-- UPDATE HERE
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-```
-
-**Configure your motors**
-Plug your first motor and run this script to set its ID to 1. It will also set its present position to 2048, so expect your motor to rotate:
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 1
-```
-
-Note: These motors are currently limited. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
-
-Then unplug your motor and plug the second motor and set its ID to 2.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 2
-```
-
-Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.
-
-**Remove the gears of the 6 leader motors**
-Follow step 2 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm.
-
-**Add motor horn to the motors**
-Follow step 3 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). For Moss v1, you need to align the holes on the motor horn to the motor spline to be approximately 3, 6, 9 and 12 o'clock.
-Try to avoid rotating the motor while doing so to keep position 2048 set during configuration. It is especially tricky for the leader motors as it is more sensible without the gears, but it's ok if it's a bit rotated.
-
-## Assemble the arms
-
-Follow step 4 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). The first arm should take a bit more than 1 hour to assemble, but once you get used to it, you can do it under 1 hour for the second arm.
-
-## Calibrate
-
-Next, you'll need to calibrate your Moss v1 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one Moss v1 robot to work on another.
-
-**Manual calibration of follower arm**
-/!\ Contrarily to step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the auto calibration, we will actually do manual calibration of follower for now.
-
-You will need to move the follower arm to these positions sequentially:
-
-| 1. Zero position                                                                                                                                              | 2. Rotated position                                                                                                                                                    | 3. Rest position                                                                                                                                              |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| <img src="../media/moss/follower_zero.webp?raw=true" alt="Moss v1 follower arm zero position" title="Moss v1 follower arm zero position" style="width:100%;"> | <img src="../media/moss/follower_rotated.webp?raw=true" alt="Moss v1 follower arm rotated position" title="Moss v1 follower arm rotated position" style="width:100%;"> | <img src="../media/moss/follower_rest.webp?raw=true" alt="Moss v1 follower arm rest position" title="Moss v1 follower arm rest position" style="width:100%;"> |
-
-Make sure both arms are connected and run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=moss \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_follower"]'
-```
-
-**Manual calibration of leader arm**
-Follow step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
-
-| 1. Zero position                                                                                                                                        | 2. Rotated position                                                                                                                                              | 3. Rest position                                                                                                                                        |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| <img src="../media/moss/leader_zero.webp?raw=true" alt="Moss v1 leader arm zero position" title="Moss v1 leader arm zero position" style="width:100%;"> | <img src="../media/moss/leader_rotated.webp?raw=true" alt="Moss v1 leader arm rotated position" title="Moss v1 leader arm rotated position" style="width:100%;"> | <img src="../media/moss/leader_rest.webp?raw=true" alt="Moss v1 leader arm rest position" title="Moss v1 leader arm rest position" style="width:100%;"> |
-
-Run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=moss \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_leader"]'
-```
-
-## Teleoperate
-
-**Simple teleop**
-Then you are ready to teleoperate your robot! Run this simple script (it won't connect and display the cameras):
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=moss \
-  --robot.cameras='{}' \
-  --control.type=teleoperate
-```
-
-
-**Teleop with displaying cameras**
-Follow [this guide to setup your cameras](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). Then you will be able to display the cameras on your computer while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=moss \
-  --control.type=teleoperate
-```
-
-## Record a dataset
-
-Once you're familiar with teleoperation, you can record your first dataset with Moss v1.
-
-If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Store your Hugging Face repository name in a variable to run these commands:
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-
-Record 2 episodes and upload your dataset to the hub:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=moss \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/moss_test \
-  --control.tags='["moss","tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
-```
-
-Note: You can resume recording by adding `--control.resume=true`.
-
-## Visualize a dataset
-
-If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
-```bash
-echo ${HF_USER}/moss_test
-```
-
-If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with:
-```bash
-python lerobot/scripts/visualize_dataset_html.py \
-  --repo-id ${HF_USER}/moss_test \
-  --local-files-only 1
-```
-
-## Replay an episode
-
-Now try to replay the first episode on your robot:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=moss \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/moss_test \
-  --control.episode=0
-```
-
-## Train a policy
-
-To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python lerobot/scripts/train.py \
-  --dataset.repo_id=${HF_USER}/moss_test \
-  --policy.type=act \
-  --output_dir=outputs/train/act_moss_test \
-  --job_name=act_moss_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain it:
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/moss_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-Training should take several hours. You will find checkpoints in `outputs/train/act_moss_test/checkpoints`.
-
-## Evaluate your policy
-
-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=moss \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/eval_act_moss_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_moss_test/checkpoints/last/pretrained_model
-```
-
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_moss_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_moss_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_moss_test`).
-
-## More
-
-Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth tutorial on controlling real robots with LeRobot.
-
-If you have any question or need help, please reach out on Discord in the channel [`#moss-arm`](https://discord.com/channels/1216765309076115607/1275374638985252925).
--- a/examples/12_use_so101.md
+++ b/examples/12_use_so101.md
@@ -1,711 +0,0 @@
-# Assemble and use SO-101
-
-In the steps below we explain how to assemble and use our flagship robot, the SO-101 with LeRobot 🤗.
-
-## Source the parts
-
-Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts,
-and advice if it's your first time printing or if you don't own a 3D printer.
-
-Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
-
-## Install LeRobot
-
-> [!TIP]
-> We use the Command Prompt (cmd) quite a lot. If you are not comfortable using the cmd or want to brush up using the command line you can have a look here: [Command line crash course](https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Command_line)
-
-Download our source code:
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
-
-Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
-```bash
-conda create -y -n lerobot python=3.10
-```
-Now restart the shell by running:
-
-##### Windows:
-```bash
-`source ~/.bashrc`
-```
-
-##### Mac:
-```bash
-`source ~/.bash_profile`
-```
-
-##### zshell:
-```bash
-`source ~/.zshrc`
-```
-
-Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
-```bash
-conda activate lerobot
-```
-
-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-> [!NOTE]
-> This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->  - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->  ```bash
->  conda install ffmpeg=7.1.1 -c conda-forge
->  ```
->  - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
-
-Install 🤗 LeRobot:
-```bash
-cd lerobot && pip install -e ".[feetech]"
-```
-
-> [!NOTE]
-> If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run: `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
-
-
-## Configure motors
-
-To configure the motors designate one bus servo adapter and 6 motors for your leader arm, and similarly the other bus servo adapter and 6 motors for the follower arm. It's convenient to label them and write on each motor if it's for the follower `F` or for the leader `L` and it's ID from 1 to 6.
-
-You now should plug the 5V or 12V power supply to the motor bus. 5V for the STS3215 7.4V motors and 12V for the STS3215 12V motors. Note that the leader arm always uses the 7.4V motors, so watch out that you plug in the right power supply if you have 12V and 7.4V motors, otherwise you might burn your motors! Now, connect the motor bus to your computer via USB. Note that the USB doesn't provide any power, and both the power supply and USB have to be plugged in.
-
-### Find the USB ports associated to each arm
-
-To find the port for each bus servo adapter, run this script:
-```bash
-python lerobot/scripts/find_motors_bus_port.py
-```
-#### Example outputs of script
-
-##### Mac:
-Example output leader arm's port: `/dev/tty.usbmodem575E0031751`
-
-```bash
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0031751
-Reconnect the usb cable.
-```
-
-Example output follower arm port: `/dev/tty.usbmodem575E0032081`
-
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the usb cable.
-```
-
-##### Linux:
-On Linux, you might need to give access to the USB ports by running:
-```bash
-sudo chmod 666 /dev/ttyACM0
-sudo chmod 666 /dev/ttyACM1
-```
-
-Example output leader arm port: `/dev/ttyACM0`
-
-```bash
-Finding all available ports for the MotorBus.
-['/dev/ttyACM0', '/dev/ttyACM1']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this MotorsBus is /dev/ttyACM0
-Reconnect the usb cable.
-```
-
-Example output follower arm port: `/dev/ttyACM1`
-
-```
-Finding all available ports for the MotorBus.
-['/dev/ttyACM0', '/dev/ttyACM1']
-Remove the usb cable from your MotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/ttyACM1
-Reconnect the usb cable.
-```
-
-#### Update config file
-
-Now that you have your ports, update the **port** default values of [`SO101RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py).
-You will find a class called `so101` where you can update the `port` values with your actual motor ports:
-```diff
-@RobotConfig.register_subclass("so101")
-@dataclass
-class So101RobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/so101"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-               port="/dev/tty.usbmodem58760431091",
-+               port="{ADD YOUR LEADER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-+                port="{ADD YOUR FOLLOWER PORT}",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-```
-
-Here is a video of the process:
-
-<video controls width="640" src="https://github.com/user-attachments/assets/fc45d756-31bb-4a61-b973-a87d633d08a7" type="video/mp4"></video>
-
-### Set motor IDs
-
-Now we need to set the motor ID for each motor. Plug your motor in only one of the two ports of the motor bus and run this script to set its ID to 1. Replace the text after --port to the corresponding control board port.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 1
-```
-
-Then unplug your motor and plug the second motor and set its ID to 2.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand feetech \
-  --model sts3215 \
-  --baudrate 1000000 \
-  --ID 2
-```
-
-Redo this process for all your motors until ID 6. Do the same for the 6 motors of the leader arm, but make sure to change the power supply if you use motors with different voltage.
-
-Here is a video of the process:
-
-<video controls width="640" src="https://github.com/user-attachments/assets/b31c115f-e706-4dcd-b7f1-4535da62416d" type="video/mp4"></video>
-
-## Step-by-Step Assembly Instructions
-
-The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader however uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in table below.
-
-| Leader-Arm Axis | Motor | Gear Ratio |
-|-----------------|:-------:|:----------:|
-| Base / Shoulder Yaw | 1 | 1 / 191 |
-| Shoulder Pitch      | 2 | 1 / 345 |
-| Elbow               | 3 | 1 / 191 |
-| Wrist Roll          | 4 | 1 / 147 |
-| Wrist Pitch         | 5 | 1 / 147 |
-| Gripper             | 6 | 1 / 147 |
-
-
-### Clean Parts
-Remove all support material from the 3D-printed parts.
-
-### Joint 1
-
- Place the first motor into the base.
- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from bottom.
- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
- Install both motor horns, securing the top horn with a M3x6mm screw.
- Attach the shoulder part.
- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
- Add the shoulder motor holder.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/b0ee9dee-a2d0-445b-8489-02ebecb3d639" type="video/mp4"></video>
-
-### Joint 2
-
- Slide the second motor in from the top.
- Fasten the second motor with 4 M2x6mm screws.
- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
- Attach the upper arm with 4 M3x6mm screws on each side.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/32453dc2-5006-4140-9f56-f0d78eae5155" type="video/mp4"></video>
-
-### Joint 3
-
- Insert motor 3 and fasten using 4 M2x6mm screws
- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/7384b9a7-a946-440c-b292-91391bcc4d6b" type="video/mp4"></video>
-
-### Joint 4
-
- Slide over motor holder 4.
- Slide in motor 4.
- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/dca78ad0-7c36-4bdf-8162-c9ac42a1506f" type="video/mp4"></video>
-
-### Joint 5
-
- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/55f5d245-976d-49ff-8b4a-59843c441b12" type="video/mp4"></video>
-
-### Gripper / Handle
-
-#### Follower:
-
- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
- Attach the motor horns and again use a M3x6mm horn screw.
- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/6f766aa9-cfae-4388-89e7-0247f198c086" type="video/mp4"></video>
-
-#### Leader:
-
- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
- Attach the handle to motor 5 using 1 M2x6mm screw.
- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
- Attach the follower trigger with 4 M3x6mm screws.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/1308c93d-2ef1-4560-8e93-a3812568a202" type="video/mp4"></video>
-
-##### Wiring
-
- Attach the motor controller on the back.
- Then insert all wires, use the wire guides everywhere to make sure the wires don't unplug themselves and stay in place.
-
-<video controls width="640" src="https://github.com/user-attachments/assets/4c2cacfd-9276-4ee4-8bf2-ba2492667b78" type="video/mp4"></video>
-
-## Calibrate
-
-Next, you'll need to calibrate your SO-101 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
-The calibration process is very important because it allows a neural network trained on one SO-101 robot to work on another.
-
-#### Manual calibration of follower arm
-
-You will need to move the follower arm to these positions sequentially, note that the rotated position is on the right side of the robot and you have to open the gripper fully.
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="../media/so101/follower_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="../media/so101/follower_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="../media/so101/follower_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="../media/so101/follower_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
-
-Make sure both arms are connected and run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_follower"]'
-```
-
-#### Manual calibration of leader arm
-You will also need to move the leader arm to these positions sequentially:
-
-| 1. Middle position | 2. Zero position                                                                                                                                       | 3. Rotated position                                                                                                                                             | 4. Rest position                                                                                                                                       |
-| ------------ |------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| <img src="../media/so101/leader_middle.webp?raw=true" alt="SO-101 leader arm middle position" title="SO-101 leader arm middle position" style="width:100%;"> | <img src="../media/so101/leader_zero.webp?raw=true" alt="SO-101 leader arm zero position" title="SO-101 leader arm zero position" style="width:100%;"> | <img src="../media/so101/leader_rotated.webp?raw=true" alt="SO-101 leader arm rotated position" title="SO-101 leader arm rotated position" style="width:100%;"> | <img src="../media/so101/leader_rest.webp?raw=true" alt="SO-101 leader arm rest position" title="SO-101 leader arm rest position" style="width:100%;"> |
-
-Run this script to launch manual calibration:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=calibrate \
-  --control.arms='["main_leader"]'
-```
-## Control your robot
-
-Congrats 🎉, your robot is all set to learn a task on its own. Next we will explain to you how to train a neural network to autonomously control a real robot.
-
-**You'll learn to:**
-1. How to record and visualize your dataset.
-2. How to train a policy using your data and prepare it for evaluation.
-3. How to evaluate your policy and visualize the results.
-
-By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
-
-This tutorial is specifically made for the affordable [SO-101](https://github.com/TheRobotStudio/SO-ARM100) robot, but it contains additional information to be easily adapted to various types of robots like [Aloha bimanual robot](https://aloha-2.github.io) by changing some configurations. The SO-101 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
-
-During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
-
-If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb) or don't hesitate to iterate with us on the tutorial by creating issues or pull requests.
-
-## Teleoperate
-
-Run this simple script to teleoperate your robot (it won't connect and display the cameras):
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --robot.cameras='{}' \
-  --control.type=teleoperate
-```
-
-The teleoperate command will automatically:
-1. Identify any missing calibrations and initiate the calibration procedure.
-2. Connect the robot and start teleoperation.
-
-## Setup Cameras
-
-To connect a camera you have three options:
-1. OpenCVCamera which allows us to use any camera: usb, realsense, laptop webcam
-2. iPhone camera with MacOS
-3. Phone camera on Linux
-
-### Use OpenCVCamera
-
-The [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py) class allows you to efficiently record frames from most cameras using the [`opencv2`](https://docs.opencv.org) library.  For more details on compatibility, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
-
-To instantiate an [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py), you need a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
-
-To find the camera indices, run the following utility script, which will save a few frames from each detected camera:
-```bash
-python lerobot/common/robot_devices/cameras/opencv.py \
-    --images-dir outputs/images_from_opencv_cameras
-```
-
-The output will look something like this if you have two cameras connected:
-```
-Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
-[...]
-Camera found at index 0
-Camera found at index 1
-[...]
-Connecting cameras
-OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
-OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
-Saving images to outputs/images_from_opencv_cameras
-Frame: 0000 Latency (ms): 39.52
-[...]
-Frame: 0046 Latency (ms): 40.07
-Images have been saved to outputs/images_from_opencv_cameras
-```
-
-Check the saved images in `outputs/images_from_opencv_cameras` to identify which camera index corresponds to which physical camera (e.g. `0` for `camera_00` or `1` for `camera_01`):
-```
-camera_00_frame_000000.png
-[...]
-camera_00_frame_000047.png
-camera_01_frame_000000.png
-[...]
-camera_01_frame_000047.png
-```
-
-Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
-
-Now that you have the camera indexes, you should change them in the config. You can also change the fps, width or height of the camera.
-
-The camera config is defined per robot, can be found here [`RobotConfig`](https://github.com/huggingface/lerobot/blob/main/lerobot/common/robot_devices/robots/configs.py) and looks like this:
-```python
-cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "wrist": OpenCVCameraConfig(
-                camera_index=0,  <-- UPDATE HERE
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "base": OpenCVCameraConfig(
-                camera_index=1,   <-- UPDATE HERE
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-```
-
-### Use your phone
-#### Mac:
-
-To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
- Sign in both devices with the same Apple ID.
- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
-
-For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
-
-Your iPhone should be detected automatically when running the camera setup script in the next section.
-
-#### Linux:
-
-If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
-
-1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
-```python
-sudo apt install v4l2loopback-dkms v4l-utils
-```
-2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
-3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
-```python
-flatpak install flathub com.obsproject.Studio
-```
-4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
-```python
-flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
-```
-5. *Start OBS Studio*. Launch with:
-```python
-flatpak run com.obsproject.Studio
-```
-6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
-7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
-8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
-```python
-v4l2-ctl --list-devices
-```
-You should see an entry like:
-```
-VirtualCam (platform:v4l2loopback-000):
-/dev/video1
-```
-10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
-```python
-v4l2-ctl -d /dev/video1 --get-fmt-video
-```
-You should see an entry like:
-```
->>> Format Video Capture:
->>> Width/Height      : 640/480
->>> Pixel Format      : 'YUYV' (YUYV 4:2:2)
-```
-
-Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
-
-If everything is set up correctly, you can proceed with the rest of the tutorial.
-
-### Add wrist camera
-If you have an additional camera you can add a wrist camera to the SO101. There are already many premade wrist camera holders that you can find in the SO101 repo: [Wrist camera's](https://github.com/TheRobotStudio/SO-ARM100#wrist-cameras)
-
-## Teleoperate with cameras
-
-We can now teleoperate again while at the same time visualizing the cameras and joint positions with `rerun`.
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=teleoperate \
-  --control.display_data=true
-```
-
-## Record a dataset
-
-Once you're familiar with teleoperation, you can record your first dataset with SO-101.
-
-We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
-
-Add your token to the cli by running this command:
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Then store your Hugging Face repository name in a variable:
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-
-Now you can record a dataset, to record 2 episodes and upload your dataset to the hub execute this command:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/so101_test \
-  --control.tags='["so101","tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.display_data=true \
-  --control.push_to_hub=true
-```
-
-You will see a lot of lines appearing like this one:
-```
-INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
-```
-It contains:
- `2024-08-10 15:02:58` which is the date and time of the call to the print function,
- `ol_robot.py:219` which is the end of the file name and the line number where the print function is called  (`lerobot/scripts/control_robot.py` line `219`).
- `dt:33.34 (30.0hz)` which is the "delta time" or the number of milliseconds spent between the previous call to `robot.teleop_step(record_data=True)` and the current one, associated with the frequency (33.34 ms equals 30.0 Hz) ; note that we use `--fps 30` so we expect 30.0 Hz ; when a step takes more time, the line appears in yellow.
- `dtRlead: 5.06 (197.5hz)` which is the delta time of reading the present position of the leader arm.
- `dtWfoll: 0.25 (3963.7hz)` which is the delta time of writing the goal position on the follower arm ; writing is asynchronous so it takes less time than reading.
- `dtRfoll: 6.22 (160.7hz)` which is the delta time of reading the present position on the follower arm.
- `dtRlaptop:32.57 (30.7hz) ` which is the delta time of capturing an image from the laptop camera in the thread running asynchronously.
- `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchronously.
-
-#### Dataset upload
-Locally your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/so101_test`). At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
-```bash
-echo https://huggingface.co/datasets/${HF_USER}/so101_test
-```
-Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
-
-You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot).
-
-#### Record function
-
-The `record` function provides a suite of tools for capturing and managing data during robot operation:
-1. Set the flow of data recording using command line arguments:
-   - `--control.warmup_time_s=10` defines the number of seconds before starting data collection. It allows the robot devices to warmup and synchronize (10 seconds by default).
-   - `--control.episode_time_s=60` defines the number of seconds for data recording for each episode (60 seconds by default).
-   - `--control.reset_time_s=60` defines the number of seconds for resetting the environment after each episode (60 seconds by default).
-   - `--control.num_episodes=50` defines the number of episodes to record (50 by default).
-2. Control the flow during data recording using keyboard keys:
-   - Press right arrow `->` at any time during episode recording to early stop and go to resetting. Same during resetting, to early stop and to go to the next episode recording.
-   - Press left arrow `<-` at any time during episode recording or resetting to early stop, cancel the current episode, and re-record it.
-   - Press escape `ESC` at any time during episode recording to end the session early and go straight to video encoding and dataset uploading.
-3. Checkpoints are done set during recording, so if any issue occurs, you can resume recording by re-running the same command again with `--control.resume=true`. You will need to manually delete the dataset directory if you want to start recording from scratch.
-
-#### Tips for gathering data
-
-Once you're comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera's. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images.
-
-In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.
-
-Avoid adding too much variation too quickly, as it may hinder your results.
-
-#### Troubleshooting:
- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
-
-## Visualize a dataset
-
-If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
-```bash
-echo ${HF_USER}/so101_test
-```
-
-If you didn't upload with `--control.push_to_hub=false`, you can visualize it locally with (via a window in the browser `http://127.0.0.1:9090` with the visualization tool):
-```bash
-python lerobot/scripts/visualize_dataset_html.py \
-  --repo-id ${HF_USER}/so101_test \
-  --local-files-only 1
-```
-
-This will launch a local web server that looks like this:
-
-<div style="text-align:center;">
-  <img src="../media/tutorial/visualize_dataset_html.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="100%"></img>
-</div>
-
-## Replay an episode
-
-A useful feature is the `replay` function, which allows to replay on your robot any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
-
-You can replay the first episode on your robot with:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/so101_test \
-  --control.episode=0
-```
-
-Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
-
-## Train a policy
-
-To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python lerobot/scripts/train.py \
-  --dataset.repo_id=${HF_USER}/so101_test \
-  --policy.type=act \
-  --output_dir=outputs/train/act_so101_test \
-  --job_name=act_so101_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain the command:
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-Training should take several hours. You will find checkpoints in `outputs/train/act_so101_test/checkpoints`.
-
-To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:
-```bash
-python lerobot/scripts/train.py \
-  --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
-  --resume=true
-```
-
-#### Upload policy checkpoints
-
-Once training is done, upload the latest checkpoint with:
-```bash
-huggingface-cli upload ${HF_USER}/act_so101_test \
-  outputs/train/act_so101_test/checkpoints/last/pretrained_model
-```
-
-You can also upload intermediate checkpoints with:
-```bash
-CKPT=010000
-huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
-  outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
-```
-
-## Evaluate your policy
-
-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=so101 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/eval_act_so101_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_so101_test/checkpoints/last/pretrained_model
-```
-
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so101_test`).
--- a/examples/7_get_started_with_real_robot.md
+++ b/examples/7_get_started_with_real_robot.md
@@ -1,998 +0,0 @@
-# Getting Started with Real-World Robots
-
-This tutorial will guide you through the process of setting up and training a neural network to autonomously control a real robot.
-
-**What You'll Learn:**
-1. How to order and assemble your robot.
-2. How to connect, configure, and calibrate your robot.
-3. How to record and visualize your dataset.
-4. How to train a policy using your data and prepare it for evaluation.
-5. How to evaluate your policy and visualize the results.
-
-By following these steps, you'll be able to replicate tasks like picking up a Lego block and placing it in a bin with a high success rate, as demonstrated in [this video](https://x.com/RemiCadene/status/1814680760592572934).
-
-This tutorial is specifically made for the affordable [Koch v1.1](https://github.com/jess-moss/koch-v1-1) robot, but it contains additional information to be easily adapted to various types of robots like [Aloha bimanual robot](https://aloha-2.github.io) by changing some configurations. The Koch v1.1 consists of a leader arm and a follower arm, each with 6 motors. It can work with one or several cameras to record the scene, which serve as visual sensors for the robot.
-
-During the data collection phase, you will control the follower arm by moving the leader arm. This process is known as "teleoperation." This technique is used to collect robot trajectories. Afterward, you'll train a neural network to imitate these trajectories and deploy the network to enable your robot to operate autonomously.
-
-If you encounter any issues at any step of the tutorial, feel free to seek help on [Discord](https://discord.com/invite/s3KuuzsPFb) or don't hesitate to iterate with us on the tutorial by creating issues or pull requests. Thanks!
-
-## 1. Order and Assemble your Koch v1.1
-
-Follow the sourcing and assembling instructions provided on the [Koch v1.1 Github page](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below.
-
-<div style="text-align:center;">
-  <img src="../media/tutorial/koch_v1_1_leader_follower.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="50%">
-</div>
-
-For a visual walkthrough of the assembly process, you can refer to [this video tutorial](https://youtu.be/8nQIg9BwwTk).
-
-## 2. Configure motors, calibrate arms, teleoperate your Koch v1.1
-
-First, install the additional dependencies required for robots built with dynamixel motors like Koch v1.1 by running one of the following commands (make sure gcc is installed).
-
-Using `pip`:
-```bash
-pip install -e ".[dynamixel]"
-```
-
-Using `poetry`:
-```bash
-poetry sync --extras "dynamixel"
-```
-
-Using `uv`:
-```bash
-uv sync --extra "dynamixel"
-```
-
-You are now ready to plug the 5V power supply to the motor bus of the leader arm (the smaller one) since all its motors only require 5V.
-
-Then plug the 12V power supply to the motor bus of the follower arm. It has two motors that need 12V, and the rest will be powered with 5V through the voltage convertor.
-
-Finally, connect both arms to your computer via USB. Note that the USB doesn't provide any power, and both arms need to be plugged in with their associated power supply to be detected by your computer.
-
-Now you are ready to configure your motors for the first time, as detailed in the sections below. In the upcoming sections, you'll learn about our classes and functions by running some python code in an interactive session, or by copy-pasting it in a python file.
-
-If you have already configured your motors the first time, you can streamline the process by directly running the teleoperate script (which is detailed further in the tutorial):
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=koch \
-  --control.type=teleoperate
-```
-
-It will automatically:
-1. Identify any missing calibrations and initiate the calibration procedure.
-2. Connect the robot and start teleoperation.
-
-### a. Control your motors with DynamixelMotorsBus
-
-You can use the [`DynamixelMotorsBus`](../lerobot/common/robot_devices/motors/dynamixel.py) to communicate with the motors connected as a chain to the corresponding USB bus. This class leverages the Python [Dynamixel SDK](https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20) to facilitate reading from and writing to the motors.
-
-**First Configuration of your motors**
-
-You will need to unplug each motor in turn and run a command the identify the motor. The motor will save its own identification, so you only need to do this once. Start by unplugging all of the motors.
-
-Do the Leader arm first, as all of its motors are of the same type. Plug in your first motor on your leader arm and run this script to set its ID to 1.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand dynamixel \
-  --model xl330-m288 \
-  --baudrate 1000000 \
-  --ID 1
-```
-
-Then unplug your first motor and plug the second motor and set its ID to 2.
-```bash
-python lerobot/scripts/configure_motor.py \
-  --port /dev/tty.usbmodem58760432961 \
-  --brand dynamixel \
-  --model xl330-m288 \
-  --baudrate 1000000 \
-  --ID 2
-```
-
-Redo the process for all your motors until ID 6.
-
-The process for the follower arm is almost the same, but the follower arm has two types of motors. For the first two motors, make sure you set the model to `xl430-w250`. _Important: configuring follower motors requires plugging and unplugging power. Make sure you use the 5V power for the XL330s and the 12V power for the XL430s!_
-
-After all of your motors are configured properly, you're ready to plug them all together in a daisy-chain as shown in the original video.
-
-**Instantiate the DynamixelMotorsBus**
-
-To begin, create two instances of the  [`DynamixelMotorsBus`](../lerobot/common/robot_devices/motors/dynamixel.py), one for each arm, using their corresponding USB ports (e.g. `DynamixelMotorsBus(port="/dev/tty.usbmodem575E0031751"`).
-
-To find the correct ports for each arm, run the utility script twice:
-```bash
-python lerobot/scripts/find_motors_bus_port.py
-```
-
-Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
-
-[...Disconnect leader arm and press Enter...]
-
-The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
-Reconnect the usb cable.
-```
-
-Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
-
-[...Disconnect follower arm and press Enter...]
-
-The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the usb cable.
-```
-
-Troubleshooting: On Linux, you might need to give access to the USB ports by running this command with your ports:
-```bash
-sudo chmod 666 /dev/tty.usbmodem575E0032081
-sudo chmod 666 /dev/tty.usbmodem575E0031751
-```
-
-*Listing and Configuring Motors*
-
-Next, you'll need to list the motors for each arm, including their name, index, and model. Initially, each motor is assigned the factory default index `1`. Since each motor requires a unique index to function correctly when connected in a chain on a common bus, you'll need to assign different indices. It's recommended to use an ascending index order, starting from `1` (e.g., `1, 2, 3, 4, 5, 6`). These indices will be saved in the persistent memory of each motor during the first connection.
-
-To assign indices to the motors, run this code in an interactive Python session. Replace the `port` values with the ones you identified earlier:
-```python
-from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig
-from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus
-
-leader_config = DynamixelMotorsBusConfig(
-    port="/dev/tty.usbmodem575E0031751",
-    motors={
-        # name: (index, model)
-        "shoulder_pan": (1, "xl330-m077"),
-        "shoulder_lift": (2, "xl330-m077"),
-        "elbow_flex": (3, "xl330-m077"),
-        "wrist_flex": (4, "xl330-m077"),
-        "wrist_roll": (5, "xl330-m077"),
-        "gripper": (6, "xl330-m077"),
-    },
-)
-
-follower_config = DynamixelMotorsBusConfig(
-    port="/dev/tty.usbmodem575E0032081",
-    motors={
-        # name: (index, model)
-        "shoulder_pan": (1, "xl430-w250"),
-        "shoulder_lift": (2, "xl430-w250"),
-        "elbow_flex": (3, "xl330-m288"),
-        "wrist_flex": (4, "xl330-m288"),
-        "wrist_roll": (5, "xl330-m288"),
-        "gripper": (6, "xl330-m288"),
-    },
-)
-
-leader_arm = DynamixelMotorsBus(leader_config)
-follower_arm = DynamixelMotorsBus(follower_config)
-```
-
-IMPORTANTLY: Now that you have your ports, update [`KochRobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
-```python
-@RobotConfig.register_subclass("koch")
-@dataclass
-class KochRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/koch"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0085511", <-- UPDATE HERE
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl330-m077"],
-                    "shoulder_lift": [2, "xl330-m077"],
-                    "elbow_flex": [3, "xl330-m077"],
-                    "wrist_flex": [4, "xl330-m077"],
-                    "wrist_roll": [5, "xl330-m077"],
-                    "gripper": [6, "xl330-m077"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891", <-- UPDATE HERE
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl430-w250"],
-                    "shoulder_lift": [2, "xl430-w250"],
-                    "elbow_flex": [3, "xl330-m288"],
-                    "wrist_flex": [4, "xl330-m288"],
-                    "wrist_roll": [5, "xl330-m288"],
-                    "gripper": [6, "xl330-m288"],
-                },
-            ),
-        }
-    )
-```
-
-**Connect and Configure your Motors**
-
-Before you can start using your motors, you'll need to configure them to ensure proper communication. When you first connect the motors, the [`DynamixelMotorsBus`](../lerobot/common/robot_devices/motors/dynamixel.py) automatically detects any mismatch between the current motor indices (factory set to `1`) and the specified indices (e.g., `1, 2, 3, 4, 5, 6`). This triggers a configuration procedure that requires you to unplug the power cord and motors, then reconnect each motor sequentially, starting from the one closest to the bus.
-
-For a visual guide, refer to the [video tutorial of the configuration procedure](https://youtu.be/U78QQ9wCdpY).
-
-To connect and configure the leader arm, run the following code in the same Python interactive session as earlier in the tutorial:
-```python
-leader_arm.connect()
-```
-
-When you connect the leader arm for the first time, you might see an output similar to this:
-```
-Read failed due to communication error on port /dev/tty.usbmodem575E0032081 for group_key ID_shoulder_pan_shoulder_lift_elbow_flex_wrist_flex_wrist_roll_gripper: [TxRxResult] There is no status packet!
-
-/!\ A configuration issue has been detected with your motors:
-If this is the first time you are using these motors, press enter to configure your motors... but before verify that all the cables are connected the proper way. If you find an issue, before making a modification, kill the python process, unplug the power cord to not damage the motors, rewire correctly, then plug the power again and relaunch the script.
-
-Motor indices detected: {9600: [1]}
-
-1. Unplug the power cord
-2. Plug/unplug minimal number of cables to only have the first 1 motor(s) (['shoulder_pan']) connected.
-3. Re-plug the power cord
-Press Enter to continue...
-
-*Follow the procedure*
-
-Setting expected motor indices: [1, 2, 3, 4, 5, 6]
-```
-
-Once the leader arm is configured, repeat the process for the follower arm by running:
-```python
-follower_arm.connect()
-```
-
-Congratulations! Both arms are now properly configured and connected. You won't need to go through the configuration procedure again in the future.
-
-**Troubleshooting**:
-
-If the configuration process fails, you may need to do the configuration process via the Dynamixel Wizard.
-
-Known failure modes:
- Calling `arm.connect()` raises `OSError: No motor found, but one new motor expected. Verify power cord is plugged in and retry` on Ubuntu 22.
-
-Steps:
-1. Visit https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_wizard2/#connect-dynamixel.
-2. Follow the software installation instructions in section 3 of the web page.
-3. Launch the software.
-4. Configure the device scanning options in the menu under `Tools` > `Options` > `Scan`. Check only Protocol 2.0, select only the USB port identifier of interest, select all baudrates, set the ID range to `[0, 10]`. _While this step was not strictly necessary, it greatly speeds up scanning_.
-5. For each motor in turn:
-    - Disconnect the power to the driver board.
-    - Connect **only** the motor of interest to the driver board, making sure to disconnect it from any other motors.
-    - Reconnect the power to the driver board.
-    - From the software menu select `Device` > `Scan` and let the scan run. A device should appear.
-    - If the device has an asterisk (*) near it, it means the firmware is indeed outdated. From the software menu, select `Tools` > `Firmware Update`. Follow the prompts.
-    - The main panel should have table with various parameters of the device (refer to the web page, section 5). Select the row with `ID`, and then set the desired ID on the bottom right panel by selecting and clicking `Save`.
-    - Just like you did with the ID, also set the `Baud Rate` to 1 Mbps.
-6. Check everything has been done right:
-   - Rewire the arms in their final configuration and power both of them.
-   - Scan for devices. All 12 motors should appear.
-   - Select the motors one by one and move the arm. Check that the graphical indicator near the top right shows the movement.
-
-** There is a common issue with the Dynamixel XL430-W250 motors where the motors become undiscoverable after upgrading their firmware from Mac and Windows Dynamixel Wizard2 applications.  When this occurs, it is required to do a firmware recovery (Select `DYNAMIXEL Firmware Recovery` and follow the prompts).   There are two known workarounds to conduct this firmware reset:
-  1) Install the Dynamixel Wizard on a linux machine and complete the firmware recovery
-  2) Use the Dynamixel U2D2 in order to perform the reset with Windows or Mac.  This U2D2 can be purchased [here](https://www.robotis.us/u2d2/).
-  For either solution, open DYNAMIXEL Wizard 2.0 and select the appropriate port. You will likely be unable to see the motor in the GUI at this time. Select `Firmware Recovery`, carefully choose the correct model, and wait for the process to complete. Finally, re-scan to confirm the firmware recovery was successful.
-
-**Read and Write with DynamixelMotorsBus**
-
-To get familiar with how `DynamixelMotorsBus` communicates with the motors, you can start by reading data from them. Copy past this code in the same interactive python session:
-```python
-leader_pos = leader_arm.read("Present_Position")
-follower_pos = follower_arm.read("Present_Position")
-print(leader_pos)
-print(follower_pos)
-```
-
-Expected output might look like:
-```
-array([2054,  523, 3071, 1831, 3049, 2441], dtype=int32)
-array([2003, 1601,   56, 2152, 3101, 2283], dtype=int32)
-```
-
-Try moving the arms to various positions and observe how the values change.
-
-Now let's try to enable torque in the follower arm by copy pasting this code:
-```python
-from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
-
-follower_arm.write("Torque_Enable", TorqueMode.ENABLED.value)
-```
-
-With torque enabled, the follower arm will be locked in its current position. Do not attempt to manually move the arm while torque is enabled, as this could damage the motors.
-
-Now, to get more familiar with reading and writing, let's move the arm programmatically copy pasting the following example code:
-```python
-# Get the current position
-position = follower_arm.read("Present_Position")
-
-# Update first motor (shoulder_pan) position by +10 steps
-position[0] += 10
-follower_arm.write("Goal_Position", position)
-
-# Update all motors position by -30 steps
-position -= 30
-follower_arm.write("Goal_Position", position)
-
-# Update gripper by +30 steps
-position[-1] += 30
-follower_arm.write("Goal_Position", position[-1], "gripper")
-```
-
-When you're done playing, you can try to disable the torque, but make sure you hold your robot so that it doesn't fall:
-```python
-follower_arm.write("Torque_Enable", TorqueMode.DISABLED.value)
-```
-
-Finally, disconnect the arms:
-```python
-leader_arm.disconnect()
-follower_arm.disconnect()
-```
-
-Alternatively, you can unplug the power cord, which will automatically disable torque and disconnect the motors.
-
-*/!\ Warning*: These motors tend to overheat, especially under torque or if left plugged in for too long. Unplug after use.
-
-### b. Teleoperate your Koch v1.1 with ManipulatorRobot
-
-**Instantiate the ManipulatorRobot**
-
-Before you can teleoperate your robot, you need to instantiate the  [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py) using the previously defined `leader_config` and `follower_config`.
-
-For the Koch v1.1 robot, we only have one leader, so we refer to it as `"main"` and define it as `leader_arms={"main": leader_config}`. We do the same for the follower arm. For other robots (like the Aloha), which may have two pairs of leader and follower arms, you would define them like this: `leader_arms={"left": left_leader_config, "right": right_leader_config},`. Same thing for the follower arms.
-
-
-Run the following code to instantiate your manipulator robot:
-```python
-from lerobot.common.robot_devices.robots.configs import KochRobotConfig
-from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot
-
-robot_config = KochRobotConfig(
-    leader_arms={"main": leader_config},
-    follower_arms={"main": follower_config},
-    cameras={},  # We don't use any camera for now
-)
-robot = ManipulatorRobot(robot_config)
-```
-
-The `KochRobotConfig` is used to set the associated settings and calibration process. For instance, we activate the torque of the gripper of the leader Koch v1.1 arm and position it at a 40 degree angle to use it as a trigger.
-
-For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `AlohaRobotConfig` to set different settings such as a secondary ID for shadow joints (shoulder, elbow). Specific to Aloha, LeRobot comes with default calibration files stored in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected for Aloha.
-
-**Calibrate and Connect the ManipulatorRobot**
-
-Next, you'll need to calibrate your Koch robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one Koch robot to work on another.
-
-When you connect your robot for the first time, the [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py) will detect if the calibration file is missing and trigger the calibration procedure. During this process, you will be guided to move each arm to three different positions.
-
-Here are the positions you'll move the follower arm to:
-
-| 1. Zero position                                                                                                                                                  | 2. Rotated position                                                                                                                                                        | 3. Rest position                                                                                                                                                  |
-| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| <img src="../media/koch/follower_zero.webp?raw=true" alt="Koch v1.1 follower arm zero position" title="Koch v1.1 follower arm zero position" style="width:100%;"> | <img src="../media/koch/follower_rotated.webp?raw=true" alt="Koch v1.1 follower arm rotated position" title="Koch v1.1 follower arm rotated position" style="width:100%;"> | <img src="../media/koch/follower_rest.webp?raw=true" alt="Koch v1.1 follower arm rest position" title="Koch v1.1 follower arm rest position" style="width:100%;"> |
-
-And here are the corresponding positions for the leader arm:
-
-| 1. Zero position                                                                                                                                            | 2. Rotated position                                                                                                                                                  | 3. Rest position                                                                                                                                            |
-| ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| <img src="../media/koch/leader_zero.webp?raw=true" alt="Koch v1.1 leader arm zero position" title="Koch v1.1 leader arm zero position" style="width:100%;"> | <img src="../media/koch/leader_rotated.webp?raw=true" alt="Koch v1.1 leader arm rotated position" title="Koch v1.1 leader arm rotated position" style="width:100%;"> | <img src="../media/koch/leader_rest.webp?raw=true" alt="Koch v1.1 leader arm rest position" title="Koch v1.1 leader arm rest position" style="width:100%;"> |
-
-You can watch a [video tutorial of the calibration procedure](https://youtu.be/8drnU9uRY24) for more details.
-
-During calibration, we count the number of full 360-degree rotations your motors have made since they were first used. That's why we ask you to move to this arbitrary "zero" position. We don't actually "set" the zero position, so you don't need to be accurate. After calculating these "offsets" to shift the motor values around 0, we need to assess the rotation direction of each motor, which might differ. That's why we ask you to rotate all motors to roughly 90 degrees, to measure if the values changed negatively or positively.
-
-Finally, the rest position ensures that the follower and leader arms are roughly aligned after calibration, preventing sudden movements that could damage the motors when starting teleoperation.
-
-Importantly, once calibrated, all Koch robots will move to the same positions (e.g. zero and rotated position) when commanded.
-
-Run the following code to calibrate and connect your robot:
-```python
-robot.connect()
-```
-
-The output will look like this:
-```
-Connecting main follower arm
-Connecting main leader arm
-
-Missing calibration file '.cache/calibration/koch/main_follower.json'
-Running calibration of koch main follower...
-Move arm to zero position
-[...]
-Move arm to rotated position
-[...]
-Move arm to rest position
-[...]
-Calibration is done! Saving calibration file '.cache/calibration/koch/main_follower.json'
-
-Missing calibration file '.cache/calibration/koch/main_leader.json'
-Running calibration of koch main leader...
-Move arm to zero position
-[...]
-Move arm to rotated position
-[...]
-Move arm to rest position
-[...]
-Calibration is done! Saving calibration file '.cache/calibration/koch/main_leader.json'
-```
-
-*Verifying Calibration*
-
-Once calibration is complete, you can check the positions of the leader and follower arms to ensure they match. If the calibration was successful, the positions should be very similar.
-
-Run this code to get the positions in degrees:
-```python
-leader_pos = robot.leader_arms["main"].read("Present_Position")
-follower_pos = robot.follower_arms["main"].read("Present_Position")
-
-print(leader_pos)
-print(follower_pos)
-```
-
-Example output:
-```
-array([-0.43945312, 133.94531, 179.82422, -18.984375, -1.9335938, 34.541016], dtype=float32)
-array([-0.58723712, 131.72314, 174.98743, -16.872612, 0.786213, 35.271973], dtype=float32)
-```
-
-These values are in degrees, which makes them easier to interpret and debug. The zero position used during calibration should roughly correspond to 0 degrees for each motor, and the rotated position should roughly correspond to 90 degrees for each motor.
-
-**Teleoperate your Koch v1.1**
-
-You can easily teleoperate your robot by reading the positions from the leader arm and sending them as goal positions to the follower arm.
-
-To teleoperate your robot for 30 seconds at a frequency of approximately 200Hz, run the following code:
-```python
-import tqdm
-seconds = 30
-frequency = 200
-for _ in tqdm.tqdm(range(seconds*frequency)):
-    leader_pos = robot.leader_arms["main"].read("Present_Position")
-    robot.follower_arms["main"].write("Goal_Position", leader_pos)
-```
-
-*Using `teleop_step` for Teleoperation*
-
-Alternatively, you can teleoperate the robot using the `teleop_step` method from [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py).
-
-Run this code to teleoperate:
-```python
-for _ in tqdm.tqdm(range(seconds*frequency)):
-    robot.teleop_step()
-```
-
-*Recording data during Teleoperation*
-
-Teleoperation is particularly useful for recording data. You can use the `teleop_step(record_data=True)` to returns both the follower arm's position as `"observation.state"` and the leader arm's position as `"action"`. This function also converts the numpy arrays into PyTorch tensors. If you're working with a robot that has two leader and two follower arms (like the Aloha), the positions are concatenated.
-
-Run the following code to see how slowly moving the leader arm affects the observation and action:
-```python
-leader_pos = robot.leader_arms["main"].read("Present_Position")
-follower_pos = robot.follower_arms["main"].read("Present_Position")
-observation, action = robot.teleop_step(record_data=True)
-
-print(follower_pos)
-print(observation)
-print(leader_pos)
-print(action)
-```
-
-Expected output:
-```
-array([7.8223, 131.1328, 165.5859, -23.4668, -0.9668, 32.4316], dtype=float32)
-{'observation.state': tensor([7.8223, 131.1328, 165.5859, -23.4668, -0.9668, 32.4316])}
-array([3.4277, 134.1211, 179.8242, -18.5449, -1.5820, 34.7168], dtype=float32)
-{'action': tensor([3.4277, 134.1211, 179.8242, -18.5449, -1.5820, 34.7168])}
-```
-
-*Asynchronous Frame Recording*
-
-Additionally, `teleop_step` can asynchronously record frames from multiple cameras and include them in the observation dictionary as `"observation.images.CAMERA_NAME"`. This feature will be covered in more detail in the next section.
-
-*Disconnecting the Robot*
-
-When you're finished, make sure to disconnect your robot by running:
-```python
-robot.disconnect()
-```
-
-Alternatively, you can unplug the power cord, which will also disable torque.
-
-*/!\ Warning*: These motors tend to overheat, especially under torque or if left plugged in for too long. Unplug after use.
-
-### c. Add your cameras with OpenCVCamera
-
-**(Optional) Use your phone as camera on Linux**
-
-If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
-
-1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
-```python
-sudo apt install v4l2loopback-dkms v4l-utils
-```
-2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
-3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
-```python
-flatpak install flathub com.obsproject.Studio
-```
-4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
-```python
-flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
-```
-5. *Start OBS Studio*. Launch with:
-```python
-flatpak run com.obsproject.Studio
-```
-6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
-7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
-8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
-```python
-v4l2-ctl --list-devices
-```
-You should see an entry like:
-```
-VirtualCam (platform:v4l2loopback-000):
-/dev/video1
-```
-10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
-```python
-v4l2-ctl -d /dev/video1 --get-fmt-video
-```
-You should see an entry like:
-```
->>> Format Video Capture:
->>>	Width/Height      : 640/480
->>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
-```
-
-Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
-
-If everything is set up correctly, you can proceed with the rest of the tutorial.
-
-**(Optional) Use your iPhone as a camera on MacOS**
-
-To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
- Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
- Sign in both devices with the same Apple ID.
- Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
-
-For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
-
-Your iPhone should be detected automatically when running the camera setup script in the next section.
-
-**Instantiate an OpenCVCamera**
-
-The [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py) class allows you to efficiently record frames from most cameras using the [`opencv2`](https://docs.opencv.org) library.  For more details on compatibility, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
-
-To instantiate an [`OpenCVCamera`](../lerobot/common/robot_devices/cameras/opencv.py), you need a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
-
-To find the camera indices, run the following utility script, which will save a few frames from each detected camera:
-```bash
-python lerobot/common/robot_devices/cameras/opencv.py \
-    --images-dir outputs/images_from_opencv_cameras
-```
-
-The output will look something like this if you have two cameras connected:
-```
-Mac or Windows detected. Finding available camera indices through scanning all indices from 0 to 60
-[...]
-Camera found at index 0
-Camera found at index 1
-[...]
-Connecting cameras
-OpenCVCamera(0, fps=30.0, width=1920.0, height=1080.0, color_mode=rgb)
-OpenCVCamera(1, fps=24.0, width=1920.0, height=1080.0, color_mode=rgb)
-Saving images to outputs/images_from_opencv_cameras
-Frame: 0000	Latency (ms): 39.52
-[...]
-Frame: 0046	Latency (ms): 40.07
-Images have been saved to outputs/images_from_opencv_cameras
-```
-
-Check the saved images in `outputs/images_from_opencv_cameras` to identify which camera index corresponds to which physical camera (e.g. `0` for `camera_00` or `1` for `camera_01`):
-```
-camera_00_frame_000000.png
-[...]
-camera_00_frame_000047.png
-camera_01_frame_000000.png
-[...]
-camera_01_frame_000047.png
-```
-
-Note: Some cameras may take a few seconds to warm up, and the first frame might be black or green.
-
-Finally, run this code to instantiate and connect your camera:
-```python
-from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig
-from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
-
-config = OpenCVCameraConfig(camera_index=0)
-camera = OpenCVCamera(config)
-camera.connect()
-color_image = camera.read()
-
-print(color_image.shape)
-print(color_image.dtype)
-```
-
-Expected output for a laptop camera on MacBookPro:
-```
-(1080, 1920, 3)
-uint8
-```
-
-Or like this if you followed our tutorial to set a virtual camera:
-```
-(480, 640, 3)
-uint8
-```
-
-With certain camera, you can also specify additional parameters like frame rate, resolution, and color mode during instantiation. For instance:
-```python
-config = OpenCVCameraConfig(camera_index=0, fps=30, width=640, height=480)
-```
-
-If the provided arguments are not compatible with the camera, an exception will be raised.
-
-*Disconnecting the camera*
-
-When you're done using the camera, disconnect it by running:
-```python
-camera.disconnect()
-```
-
-**Instantiate your robot with cameras**
-
-Additionally, you can set up your robot to work with your cameras.
-
-Modify the following Python code with the appropriate camera names and configurations:
-```python
-robot = ManipulatorRobot(
-    KochRobotConfig(
-        leader_arms={"main": leader_arm},
-        follower_arms={"main": follower_arm},
-        calibration_dir=".cache/calibration/koch",
-        cameras={
-            "laptop": OpenCVCameraConfig(0, fps=30, width=640, height=480),
-            "phone": OpenCVCameraConfig(1, fps=30, width=640, height=480),
-        },
-    )
-)
-robot.connect()
-```
-
-As a result, `teleop_step(record_data=True` will return a frame for each camera following the pytorch "channel first" convention but we keep images in `uint8` with pixels in range [0,255] to easily save them.
-
-Modify this code with the names of your cameras and run it:
-```python
-observation, action = robot.teleop_step(record_data=True)
-print(observation["observation.images.laptop"].shape)
-print(observation["observation.images.phone"].shape)
-print(observation["observation.images.laptop"].min().item())
-print(observation["observation.images.laptop"].max().item())
-```
-
-The output should look like this:
-```
-torch.Size([3, 480, 640])
-torch.Size([3, 480, 640])
-0
-255
-```
-
-### d. Use `control_robot.py` and our `teleoperate` function
-
-Instead of manually running the python code in a terminal window, you can use [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) to instantiate your robot by providing the robot configurations via command line and control your robot with various modes as explained next.
-
-Try running this code to teleoperate your robot (if you dont have a camera, keep reading):
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=koch \
-  --control.type=teleoperate
-```
-
-You will see a lot of lines appearing like this one:
-```
-INFO 2024-08-10 11:15:03 ol_robot.py:209 dt: 5.12 (195.1hz) dtRlead: 4.93 (203.0hz) dtWfoll: 0.19 (5239.0hz)
-```
-
-It contains
- `2024-08-10 11:15:03` which is the date and time of the call to the print function.
- `ol_robot.py:209` which is the end of the file name and the line number where the print function is called  (`lerobot/scripts/control_robot.py` line `209`).
- `dt: 5.12 (195.1hz)` which is the "delta time" or the number of milliseconds spent between the previous call to `robot.teleop_step()` and the current one, associated with the frequency (5.12 ms equals 195.1 Hz) ; note that you can control the maximum frequency by adding fps as argument such as `--fps 30`.
- `dtRlead: 4.93 (203.0hz)` which is the number of milliseconds it took to read the position of the leader arm using `leader_arm.read("Present_Position")`.
- `dtWfoll: 0.22 (4446.9hz)` which is the number of milliseconds it took to set a new goal position for the follower arm using `follower_arm.write("Goal_position", leader_pos)` ; note that writing is done asynchronously so it takes less time than reading.
-
-Importantly: If you don't have any camera, you can remove them dynamically with this [draccus](https://github.com/dlwh/draccus) syntax `--robot.cameras='{}'`:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=koch \
-  --robot.cameras='{}' \
-  --control.type=teleoperate
-```
-
-We advise to create a new yaml file when the command becomes too long.
-
-## 3. Record your Dataset and Visualize it
-
-Using what you've learned previously, you can now easily record a dataset of states and actions for one episode. You can use `busy_wait` to control the speed of teleoperation and record at a fixed `fps` (frame per seconds).
-
-Try this code to record 30 seconds at 60 fps:
-```python
-import time
-from lerobot.scripts.control_robot import busy_wait
-
-record_time_s = 30
-fps = 60
-
-states = []
-actions = []
-for _ in range(record_time_s * fps):
-    start_time = time.perf_counter()
-    observation, action = robot.teleop_step(record_data=True)
-
-    states.append(observation["observation.state"])
-    actions.append(action["action"])
-
-    dt_s = time.perf_counter() - start_time
-    busy_wait(1 / fps - dt_s)
-
-# Note that observation and action are available in RAM, but
-# you could potentially store them on disk with pickle/hdf5 or
-# our optimized format `LeRobotDataset`. More on this next.
-```
-
-Importantly, many utilities are still missing. For instance, if you have cameras, you will need to save the images on disk to not go out of RAM, and to do so in threads to not slow down communication with your robot. Also, you will need to store your data in a format optimized for training and web sharing like [`LeRobotDataset`](../lerobot/common/datasets/lerobot_dataset.py). More on this in the next section.
-
-### a. Use the `record` function
-
-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) to achieve efficient data recording. It encompasses many recording utilities:
-1. Frames from cameras are saved on disk in threads, and encoded into videos at the end of each episode recording.
-2. Video streams from cameras are displayed in window so that you can verify them.
-3. Data is stored with [`LeRobotDataset`](../lerobot/common/datasets/lerobot_dataset.py) format which is pushed to your Hugging Face page (unless `--control.push_to_hub=false` is provided).
-4. Checkpoints are done during recording, so if any issue occurs, you can resume recording by re-running the same command again with `--control.resume=true`. You will need to manually delete the dataset directory if you want to start recording from scratch.
-5. Set the flow of data recording using command line arguments:
-   - `--control.warmup_time_s=10` defines the number of seconds before starting data collection. It allows the robot devices to warmup and synchronize (10 seconds by default).
-   - `--control.episode_time_s=60` defines the number of seconds for data recording for each episode (60 seconds by default).
-   - `--control.reset_time_s=60` defines the number of seconds for resetting the environment after each episode (60 seconds by default).
-   - `--control.num_episodes=50` defines the number of episodes to record (50 by default).
-6. Control the flow during data recording using keyboard keys:
-   - Press right arrow `->` at any time during episode recording to early stop and go to resetting. Same during resetting, to early stop and to go to the next episode recording.
-   - Press left arrow `<-` at any time during episode recording or resetting to early stop, cancel the current episode, and re-record it.
-   - Press escape `ESC` at any time during episode recording to end the session early and go straight to video encoding and dataset uploading.
-7. Similarly to `teleoperate`, you can also use the command line to override anything.
-
-Before trying `record`, if you want to push your dataset to the hub, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-Also, store your Hugging Face repository name in a variable (e.g. `cadene` or `lerobot`). For instance, run this to use your Hugging Face user name as repository:
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-If you don't want to push to hub, use `--control.push_to_hub=false`.
-
-Now run this to record 2 episodes:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=koch \
-  --control.type=record \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/koch_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
-```
-
-
-This will write your dataset locally to `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/koch_test`) and push it on the hub at `https://huggingface.co/datasets/{HF_USER}/{repo-id}`. Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
-
-You can look for other LeRobot datasets on the hub by searching for `LeRobot` tags: https://huggingface.co/datasets?other=LeRobot
-
-You will see a lot of lines appearing like this one:
-```
-INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
-```
-It contains:
- `2024-08-10 15:02:58` which is the date and time of the call to the print function,
- `ol_robot.py:219` which is the end of the file name and the line number where the print function is called  (`lerobot/scripts/control_robot.py` line `219`).
- `dt:33.34 (30.0hz)` which is the "delta time" or the number of milliseconds spent between the previous call to `robot.teleop_step(record_data=True)` and the current one, associated with the frequency (33.34 ms equals 30.0 Hz) ; note that we use `--fps 30` so we expect 30.0 Hz ; when a step takes more time, the line appears in yellow.
- `dtRlead: 5.06 (197.5hz)` which is the delta time of reading the present position of the leader arm.
- `dtWfoll: 0.25 (3963.7hz)` which is the delta time of writing the goal position on the follower arm ; writing is asynchronous so it takes less time than reading.
- `dtRfoll: 6.22 (160.7hz)` which is the delta time of reading the present position on the follower arm.
- `dtRlaptop:32.57 (30.7hz) ` which is the delta time of capturing an image from the laptop camera in the thread running asynchronously.
- `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchronously.
-
-Troubleshooting:
- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
-
-At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/koch_test) that you can obtain by running:
-```bash
-echo https://huggingface.co/datasets/${HF_USER}/koch_test
-```
-
-### b. Advice for recording dataset
-
-Once you're comfortable with data recording, it's time to create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings.
-
-In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.
-
-Avoid adding too much variation too quickly, as it may hinder your results.
-
-In the coming months, we plan to release a foundational model for robotics. We anticipate that fine-tuning this model will enhance generalization, reducing the need for strict consistency during data collection.
-
-### c. Visualize all episodes
-
-You can visualize your dataset by running:
-```bash
-python lerobot/scripts/visualize_dataset_html.py \
-  --repo-id ${HF_USER}/koch_test
-```
-
-Note: You might need to add `--local-files-only 1` if your dataset was not uploaded to hugging face hub.
-
-This will launch a local web server that looks like this:
-<div style="text-align:center;">
-  <img src="../media/tutorial/visualize_dataset_html.webp?raw=true" alt="Koch v1.1 leader and follower arms" title="Koch v1.1 leader and follower arms" width="100%">
-</div>
-
-### d. Replay episode on your robot with the `replay` function
-
-A useful feature of [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) is the `replay` function, which allows to replay on your robot any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
-
-To replay the first episode of the dataset you just recorded, run the following command:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=koch \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/koch_test \
-  --control.episode=0
-```
-
-Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
-
-## 4. Train a policy on your data
-
-### a. Use the `train` script
-
-To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python lerobot/scripts/train.py \
-  --dataset.repo_id=${HF_USER}/koch_test \
-  --policy.type=act \
-  --output_dir=outputs/train/act_koch_test \
-  --job_name=act_koch_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain it:
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/koch_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)
-
-### b. (Optional) Upload policy checkpoints to the hub
-
-Once training is done, upload the latest checkpoint with:
-```bash
-huggingface-cli upload ${HF_USER}/act_koch_test \
-  outputs/train/act_koch_test/checkpoints/last/pretrained_model
-```
-
-You can also upload intermediate checkpoints with:
-```bash
-CKPT=010000
-huggingface-cli upload ${HF_USER}/act_koch_test_${CKPT} \
-  outputs/train/act_koch_test/checkpoints/${CKPT}/pretrained_model
-```
-
-## 5. Evaluate your policy
-
-Now that you have a policy checkpoint, you can easily control your robot with it using methods from [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py) and the policy.
-
-Try this code for running inference for 60 seconds at 30 fps:
-```python
-from lerobot.common.policies.act.modeling_act import ACTPolicy
-
-inference_time_s = 60
-fps = 30
-device = "cuda"  # TODO: On Mac, use "mps" or "cpu"
-
-ckpt_path = "outputs/train/act_koch_test/checkpoints/last/pretrained_model"
-policy = ACTPolicy.from_pretrained(ckpt_path)
-policy.to(device)
-
-for _ in range(inference_time_s * fps):
-    start_time = time.perf_counter()
-
-    # Read the follower state and access the frames from the cameras
-    observation = robot.capture_observation()
-
-    # Convert to pytorch format: channel first and float32 in [0,1]
-    # with batch dimension
-    for name in observation:
-        if "image" in name:
-            observation[name] = observation[name].type(torch.float32) / 255
-            observation[name] = observation[name].permute(2, 0, 1).contiguous()
-        observation[name] = observation[name].unsqueeze(0)
-        observation[name] = observation[name].to(device)
-
-    # Compute the next action with the policy
-    # based on the current observation
-    action = policy.select_action(observation)
-    # Remove batch dimension
-    action = action.squeeze(0)
-    # Move to cpu, if not already the case
-    action = action.to("cpu")
-    # Order the robot to move
-    robot.send_action(action)
-
-    dt_s = time.perf_counter() - start_time
-    busy_wait(1 / fps - dt_s)
-```
-
-### a. Use our `record` function
-
-Ideally, when controlling your robot with your neural network, you would want to record evaluation episodes and to be able to visualize them later on, or even train on them like in Reinforcement Learning. This pretty much corresponds to recording a new dataset but with a neural network providing the actions instead of teleoperation.
-
-To this end, you can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=koch \
-  --control.type=record \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/eval_act_koch_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_koch_test/checkpoints/last/pretrained_model
-```
-
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_koch_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_koch_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_koch_test`).
-
-### b. Visualize evaluation afterwards
-
-You can then visualize your evaluation dataset by running the same command as before but with the new inference dataset as argument:
-```bash
-python lerobot/scripts/visualize_dataset.py \
-  --repo-id ${HF_USER}/eval_act_koch_test
-```
-
-## 6. Next step
-
-Join our [Discord](https://discord.com/invite/s3KuuzsPFb) to collaborate on data collection and help us train a fully open-source foundational models for robotics!
--- a/examples/lekiwi/evaluate.py
+++ b/examples/lekiwi/evaluate.py
@@ -0,0 +1,38 @@
+import torch
+
+from lerobot.common.policies.act.modeling_act import ACTPolicy
+from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.common.utils.control_utils import predict_action
+from lerobot.common.utils.utils import get_safe_torch_device
+
+NB_CYCLES_CLIENT_CONNECTION = 1000
+
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+robot = LeKiwiClient(robot_config)
+
+robot.connect()
+
+policy = ACTPolicy.from_pretrained("pepijn223/act_lekiwi_circle")
+policy.reset()
+
+print("Running inference")
+i = 0
+while i < NB_CYCLES_CLIENT_CONNECTION:
+    obs = robot.get_observation()
+
+    for key, value in obs.items():
+        if isinstance(value, torch.Tensor):
+            obs[key] = value.numpy()
+
+    action_values = predict_action(
+        obs, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
+    )
+    action = {
+        key: action_values[i].item() if isinstance(action_values[i], torch.Tensor) else action_values[i]
+        for i, key in enumerate(robot.action_features)
+    }
+    robot.send_action(action)
+    i += 1
+
+robot.disconnect()
--- a/examples/lekiwi/record.py
+++ b/examples/lekiwi/record.py
@@ -0,0 +1,67 @@
+import time
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.utils import hw_to_dataset_features
+from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.common.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
+from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+
+NB_CYCLES_CLIENT_CONNECTION = 250
+
+leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem58760431551")
+leader_arm = SO100Leader(leader_arm_config)
+
+keyboard_config = KeyboardTeleopConfig()
+keyboard = KeyboardTeleop(keyboard_config)
+
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+robot = LeKiwiClient(robot_config)
+
+action_features = hw_to_dataset_features(robot.action_features, "action")
+obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+dataset_features = {**action_features, **obs_features}
+
+dataset = LeRobotDataset.create(
+    repo_id="user/lekiwi" + str(int(time.time())),
+    fps=10,
+    features=dataset_features,
+    robot_type=robot.name,
+)
+
+leader_arm.connect()
+keyboard.connect()
+robot.connect()
+
+if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+    exit()
+
+print("Starting LeKiwi teleoperation")
+i = 0
+while i < NB_CYCLES_CLIENT_CONNECTION:
+    arm_action = leader_arm.get_action()
+    arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
+
+    keyboard_keys = keyboard.get_action()
+
+    base_action = robot._from_keyboard_to_base_action(keyboard_keys)
+
+    action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
+
+    action_sent = robot.send_action(action)
+    observation = robot.get_observation()
+
+    frame = {**action_sent, **observation}
+    task = "Dummy Example Task Dataset"
+
+    dataset.add_frame(frame, task)
+    i += 1
+
+print("Disconnecting Teleop Devices and LeKiwi Client")
+robot.disconnect()
+leader_arm.disconnect()
+keyboard.disconnect()
+
+print("Uploading dataset to the hub")
+dataset.save_episode()
+dataset.push_to_hub()
--- a/examples/lekiwi/replay.py
+++ b/examples/lekiwi/replay.py
@@ -0,0 +1,25 @@
+import time
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.common.utils.robot_utils import busy_wait
+
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+robot = LeKiwiClient(robot_config)
+
+dataset = LeRobotDataset("pepijn223/lekiwi1749025613", episodes=[0])
+
+robot.connect()
+
+print("Replaying episode…")
+for _, action_array in enumerate(dataset.hf_dataset["action"]):
+    t0 = time.perf_counter()
+
+    action = {name: float(action_array[i]) for i, name in enumerate(dataset.features["action"]["names"])}
+    robot.send_action(action)
+
+    busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+
+print("Disconnecting LeKiwi Client")
+robot.disconnect()
--- a/examples/lekiwi/teleoperate.py
+++ b/examples/lekiwi/teleoperate.py
@@ -0,0 +1,32 @@
+from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
+from lerobot.common.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig
+from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi")
+
+teleop__arm_config = SO100LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_awesome_leader_arm",
+)
+
+teleop_keyboard_config = KeyboardTeleopConfig(
+    id="my_laptop_keyboard",
+)
+
+robot = LeKiwiClient(robot_config)
+teleop_arm = SO100Leader(teleop__arm_config)
+telep_keyboard = KeyboardTeleop(teleop_keyboard_config)
+robot.connect()
+teleop_arm.connect()
+telep_keyboard.connect()
+
+while True:
+    observation = robot.get_observation()
+
+    arm_action = teleop_arm.get_action()
+    arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
+
+    keyboard_keys = telep_keyboard.get_action()
+    base_action = robot._from_keyboard_to_base_action(keyboard_keys)
+
+    robot.send_action(arm_action | base_action)
--- a/lerobot/init.py
+++ b/lerobot/init.py
@@ -177,7 +177,6 @@ available_robots = [
    "aloha",
    "so100",
    "so101",
-    "moss",
 ]

 # lists all available cameras from `lerobot/common/robot_devices/cameras`
--- a/lerobot/calibrate.py
+++ b/lerobot/calibrate.py
@@ -0,0 +1,84 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Helper to recalibrate your device (robot or teleoperator).
+
+Example:
+
+```shell
+python -m lerobot.calibrate \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=blue
+```
+"""
+
+import logging
+from dataclasses import asdict, dataclass
+from pprint import pformat
+
+import draccus
+
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.common.robots import (  # noqa: F401
+    Robot,
+    RobotConfig,
+    koch_follower,
+    lekiwi,
+    make_robot_from_config,
+    so100_follower,
+    so101_follower,
+)
+from lerobot.common.teleoperators import (  # noqa: F401
+    Teleoperator,
+    TeleoperatorConfig,
+    koch_leader,
+    make_teleoperator_from_config,
+    so100_leader,
+    so101_leader,
+)
+from lerobot.common.utils.utils import init_logging
+
+
+@dataclass
+class CalibrateConfig:
+    teleop: TeleoperatorConfig | None = None
+    robot: RobotConfig | None = None
+
+    def __post_init__(self):
+        if bool(self.teleop) == bool(self.robot):
+            raise ValueError("Choose either a teleop or a robot.")
+
+        self.device = self.robot if self.robot else self.teleop
+
+
+@draccus.wrap()
+def calibrate(cfg: CalibrateConfig):
+    init_logging()
+    logging.info(pformat(asdict(cfg)))
+
+    if isinstance(cfg.device, RobotConfig):
+        device = make_robot_from_config(cfg.device)
+    elif isinstance(cfg.device, TeleoperatorConfig):
+        device = make_teleoperator_from_config(cfg.device)
+
+    device.connect(calibrate=False)
+    device.calibrate()
+    device.disconnect()
+
+
+if __name__ == "__main__":
+    calibrate()
--- a/lerobot/common/cameras/init.py
+++ b/lerobot/common/cameras/init.py
@@ -0,0 +1,17 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .camera import Camera
+from .configs import CameraConfig, ColorMode, Cv2Rotation
+from .utils import make_cameras_from_configs
--- a/lerobot/common/cameras/camera.py
+++ b/lerobot/common/cameras/camera.py
@@ -0,0 +1,120 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+from typing import Any, Dict, List
+
+import numpy as np
+
+from .configs import CameraConfig, ColorMode
+
+
+class Camera(abc.ABC):
+    """Base class for camera implementations.
+
+    Defines a standard interface for camera operations across different backends.
+    Subclasses must implement all abstract methods.
+
+    Manages basic camera properties (FPS, resolution) and core operations:
+    - Connection/disconnection
+    - Frame capture (sync/async)
+
+    Attributes:
+        fps (int | None): Configured frames per second
+        width (int | None): Frame width in pixels
+        height (int | None): Frame height in pixels
+
+    Example:
+        class MyCamera(Camera):
+            def __init__(self, config): ...
+            @property
+            def is_connected(self) -> bool: ...
+            def connect(self, warmup=True): ...
+            # Plus other required methods
+    """
+
+    def __init__(self, config: CameraConfig):
+        """Initialize the camera with the given configuration.
+
+        Args:
+            config: Camera configuration containing FPS and resolution.
+        """
+        self.fps: int | None = config.fps
+        self.width: int | None = config.width
+        self.height: int | None = config.height
+
+    @property
+    @abc.abstractmethod
+    def is_connected(self) -> bool:
+        """Check if the camera is currently connected.
+
+        Returns:
+            bool: True if the camera is connected and ready to capture frames,
+                  False otherwise.
+        """
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def find_cameras() -> List[Dict[str, Any]]:
+        """Detects available cameras connected to the system.
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries,
+            where each dictionary contains information about a detected camera.
+        """
+        pass
+
+    @abc.abstractmethod
+    def connect(self, warmup: bool = True) -> None:
+        """Establish connection to the camera.
+
+        Args:
+            warmup: If True (default), captures a warmup frame before returning. Useful
+                   for cameras that require time to adjust capture settings.
+                   If False, skips the warmup frame.
+        """
+        pass
+
+    @abc.abstractmethod
+    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
+        """Capture and return a single frame from the camera.
+
+        Args:
+            color_mode: Desired color mode for the output frame. If None,
+                        uses the camera's default color mode.
+
+        Returns:
+            np.ndarray: Captured frame as a numpy array.
+        """
+        pass
+
+    @abc.abstractmethod
+    def async_read(self, timeout_ms: float = ...) -> np.ndarray:
+        """Asynchronously capture and return a single frame from the camera.
+
+        Args:
+            timeout_ms: Maximum time to wait for a frame in milliseconds.
+                        Defaults to implementation-specific timeout.
+
+        Returns:
+            np.ndarray: Captured frame as a numpy array.
+        """
+        pass
+
+    @abc.abstractmethod
+    def disconnect(self) -> None:
+        """Disconnect from the camera and release resources."""
+        pass
--- a/lerobot/common/robot_devices/motors/configs.py
+++ b/lerobot/common/robot_devices/motors/configs.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env python
+
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,37 +16,29 @@

 import abc
 from dataclasses import dataclass
+from enum import Enum

 import draccus


-@dataclass
-class MotorsBusConfig(draccus.ChoiceRegistry, abc.ABC):
+class ColorMode(str, Enum):
+    RGB = "rgb"
+    BGR = "bgr"
+
+
+class Cv2Rotation(int, Enum):
+    NO_ROTATION = 0
+    ROTATE_90 = 90
+    ROTATE_180 = 180
+    ROTATE_270 = -90
+
+
+@dataclass(kw_only=True)
+class CameraConfig(draccus.ChoiceRegistry, abc.ABC):
+    fps: int | None = None
+    width: int | None = None
+    height: int | None = None
+
    @property
    def type(self) -> str:
        return self.get_choice_name(self.__class__)
-
-
-@MotorsBusConfig.register_subclass("dynamixel")
-@dataclass
-class DynamixelMotorsBusConfig(MotorsBusConfig):
-    port: str
-    motors: dict[str, tuple[int, str]]
-    mock: bool = False
-
-
-@MotorsBusConfig.register_subclass("feetech")
-@dataclass
-class FeetechMotorsBusConfig(MotorsBusConfig):
-    port: str
-    motors: dict[str, tuple[int, str]]
-    mock: bool = False
-
-
-@MotorsBusConfig.register_subclass("realman")
-@dataclass
-class RealmanMotorsBusConfig(MotorsBusConfig):
-    ip: str
-    port: int
-    motors: dict[str, tuple[int, str]]
-    init_joint: dict[str, list]
--- a/lerobot/common/cameras/opencv/init.py
+++ b/lerobot/common/cameras/opencv/init.py
@@ -0,0 +1,16 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .camera_opencv import OpenCVCamera
+from .configuration_opencv import OpenCVCameraConfig
--- a/lerobot/common/cameras/opencv/camera_opencv.py
+++ b/lerobot/common/cameras/opencv/camera_opencv.py
@@ -0,0 +1,479 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Provides the OpenCVCamera class for capturing frames from cameras using OpenCV.
+"""
+
+import logging
+import math
+import platform
+import time
+from pathlib import Path
+from threading import Event, Lock, Thread
+from typing import Any, Dict, List
+
+import cv2
+import numpy as np
+
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+
+from ..camera import Camera
+from ..utils import get_cv2_backend, get_cv2_rotation
+from .configuration_opencv import ColorMode, OpenCVCameraConfig
+
+# NOTE(Steven): The maximum opencv device index depends on your operating system. For instance,
+# if you have 3 cameras, they should be associated to index 0, 1, and 2. This is the case
+# on MacOS. However, on Ubuntu, the indices are different like 6, 16, 23.
+# When you change the USB port or reboot the computer, the operating system might
+# treat the same cameras as new devices. Thus we select a higher bound to search indices.
+MAX_OPENCV_INDEX = 60
+
+logger = logging.getLogger(__name__)
+
+
+class OpenCVCamera(Camera):
+    """
+    Manages camera interactions using OpenCV for efficient frame recording.
+
+    This class provides a high-level interface to connect to, configure, and read
+    frames from cameras compatible with OpenCV's VideoCapture. It supports both
+    synchronous and asynchronous frame reading.
+
+    An OpenCVCamera instance requires a camera index (e.g., 0) or a device path
+    (e.g., '/dev/video0' on Linux). Camera indices can be unstable across reboots
+    or port changes, especially on Linux. Use the provided utility script to find
+    available camera indices or paths:
+    ```bash
+    python -m lerobot.find_cameras opencv
+    ```
+
+    The camera's default settings (FPS, resolution, color mode) are used unless
+    overridden in the configuration.
+
+    Example:
+        ```python
+        from lerobot.common.cameras.opencv import OpenCVCamera
+        from lerobot.common.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation
+
+        # Basic usage with camera index 0
+        config = OpenCVCameraConfig(index_or_path=0)
+        camera = OpenCVCamera(config)
+        camera.connect()
+
+        # Read 1 frame synchronously
+        color_image = camera.read()
+        print(color_image.shape)
+
+        # Read 1 frame asynchronously
+        async_image = camera.async_read()
+
+        # When done, properly disconnect the camera using
+        camera.disconnect()
+
+        # Example with custom settings
+        custom_config = OpenCVCameraConfig(
+            index_or_path='/dev/video0', # Or use an index
+            fps=30,
+            width=1280,
+            height=720,
+            color_mode=ColorMode.RGB,
+            rotation=Cv2Rotation.ROTATE_90
+        )
+        custom_camera = OpenCVCamera(custom_config)
+        # ... connect, read, disconnect ...
+        ```
+    """
+
+    def __init__(self, config: OpenCVCameraConfig):
+        """
+        Initializes the OpenCVCamera instance.
+
+        Args:
+            config: The configuration settings for the camera.
+        """
+        super().__init__(config)
+
+        self.config = config
+        self.index_or_path = config.index_or_path
+
+        self.fps = config.fps
+        self.color_mode = config.color_mode
+        self.warmup_s = config.warmup_s
+
+        self.videocapture: cv2.VideoCapture | None = None
+
+        self.thread: Thread | None = None
+        self.stop_event: Event | None = None
+        self.frame_lock: Lock = Lock()
+        self.latest_frame: np.ndarray | None = None
+        self.new_frame_event: Event = Event()
+
+        self.rotation: int | None = get_cv2_rotation(config.rotation)
+        self.backend: int = get_cv2_backend()
+
+        if self.height and self.width:
+            self.capture_width, self.capture_height = self.width, self.height
+            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+                self.capture_width, self.capture_height = self.height, self.width
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}({self.index_or_path})"
+
+    @property
+    def is_connected(self) -> bool:
+        """Checks if the camera is currently connected and opened."""
+        return isinstance(self.videocapture, cv2.VideoCapture) and self.videocapture.isOpened()
+
+    def connect(self, warmup: bool = True):
+        """
+        Connects to the OpenCV camera specified in the configuration.
+
+        Initializes the OpenCV VideoCapture object, sets desired camera properties
+        (FPS, width, height), and performs initial checks.
+
+        Raises:
+            DeviceAlreadyConnectedError: If the camera is already connected.
+            ConnectionError: If the specified camera index/path is not found or the camera is found but fails to open.
+            RuntimeError: If the camera opens but fails to apply requested FPS/resolution settings.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
+
+        # Use 1 thread for OpenCV operations to avoid potential conflicts or
+        # blocking in multi-threaded applications, especially during data collection.
+        cv2.setNumThreads(1)
+
+        self.videocapture = cv2.VideoCapture(self.index_or_path, self.backend)
+
+        if not self.videocapture.isOpened():
+            self.videocapture.release()
+            self.videocapture = None
+            raise ConnectionError(
+                f"Failed to open {self}."
+                f"Run `python -m lerobot.find_cameras opencv` to find available cameras."
+            )
+
+        self._configure_capture_settings()
+
+        if warmup:
+            start_time = time.time()
+            while time.time() - start_time < self.warmup_s:
+                self.read()
+                time.sleep(0.1)
+
+        logger.info(f"{self} connected.")
+
+    def _configure_capture_settings(self) -> None:
+        """
+        Applies the specified FPS, width, and height settings to the connected camera.
+
+        This method attempts to set the camera properties via OpenCV. It checks if
+        the camera successfully applied the settings and raises an error if not.
+
+        Args:
+            fps: The desired frames per second. If None, the setting is skipped.
+            width: The desired capture width. If None, the setting is skipped.
+            height: The desired capture height. If None, the setting is skipped.
+
+        Raises:
+            RuntimeError: If the camera fails to set any of the specified properties
+                          to the requested value.
+            DeviceNotConnectedError: If the camera is not connected when attempting
+                                     to configure settings.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.")
+
+        if self.fps is None:
+            self.fps = self.videocapture.get(cv2.CAP_PROP_FPS)
+        else:
+            self._validate_fps()
+
+        default_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH)))
+        default_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+
+        if self.width is None or self.height is None:
+            self.width, self.height = default_width, default_height
+            self.capture_width, self.capture_height = default_width, default_height
+            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+                self.width, self.height = default_height, default_width
+                self.capture_width, self.capture_height = default_width, default_height
+        else:
+            self._validate_width_and_height()
+
+    def _validate_fps(self) -> None:
+        """Validates and sets the camera's frames per second (FPS)."""
+
+        success = self.videocapture.set(cv2.CAP_PROP_FPS, float(self.fps))
+        actual_fps = self.videocapture.get(cv2.CAP_PROP_FPS)
+        # Use math.isclose for robust float comparison
+        if not success or not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
+            raise RuntimeError(f"{self} failed to set fps={self.fps} ({actual_fps=}).")
+
+    def _validate_width_and_height(self) -> None:
+        """Validates and sets the camera's frame capture width and height."""
+
+        success = self.videocapture.set(cv2.CAP_PROP_FRAME_WIDTH, float(self.capture_width))
+        actual_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH)))
+        if not success or self.capture_width != actual_width:
+            raise RuntimeError(f"{self} failed to set capture_width={self.capture_width} ({actual_width=}).")
+
+        success = self.videocapture.set(cv2.CAP_PROP_FRAME_HEIGHT, float(self.capture_height))
+        actual_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+        if not success or self.capture_height != actual_height:
+            raise RuntimeError(
+                f"{self} failed to set capture_height={self.capture_height} ({actual_height})."
+            )
+
+    @staticmethod
+    def find_cameras() -> List[Dict[str, Any]]:
+        """
+        Detects available OpenCV cameras connected to the system.
+
+        On Linux, it scans '/dev/video*' paths. On other systems (like macOS, Windows),
+        it checks indices from 0 up to `MAX_OPENCV_INDEX`.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries,
+            where each dictionary contains 'type', 'id' (port index or path),
+            and the default profile properties (width, height, fps, format).
+        """
+        found_cameras_info = []
+
+        if platform.system() == "Linux":
+            possible_paths = sorted(Path("/dev").glob("video*"), key=lambda p: p.name)
+            targets_to_scan = [str(p) for p in possible_paths]
+        else:
+            targets_to_scan = list(range(MAX_OPENCV_INDEX))
+
+        for target in targets_to_scan:
+            camera = cv2.VideoCapture(target)
+            if camera.isOpened():
+                default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
+                default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                default_fps = camera.get(cv2.CAP_PROP_FPS)
+                default_format = camera.get(cv2.CAP_PROP_FORMAT)
+                camera_info = {
+                    "name": f"OpenCV Camera @ {target}",
+                    "type": "OpenCV",
+                    "id": target,
+                    "backend_api": camera.getBackendName(),
+                    "default_stream_profile": {
+                        "format": default_format,
+                        "width": default_width,
+                        "height": default_height,
+                        "fps": default_fps,
+                    },
+                }
+
+                found_cameras_info.append(camera_info)
+                camera.release()
+
+        return found_cameras_info
+
+    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
+        """
+        Reads a single frame synchronously from the camera.
+
+        This is a blocking call. It waits for the next available frame from the
+        camera hardware via OpenCV.
+
+        Args:
+            color_mode (Optional[ColorMode]): If specified, overrides the default
+                color mode (`self.color_mode`) for this read operation (e.g.,
+                request RGB even if default is BGR).
+
+        Returns:
+            np.ndarray: The captured frame as a NumPy array in the format
+                       (height, width, channels), using the specified or default
+                       color mode and applying any configured rotation.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If reading the frame from the camera fails or if the
+                          received frame dimensions don't match expectations before rotation.
+            ValueError: If an invalid `color_mode` is requested.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        start_time = time.perf_counter()
+
+        ret, frame = self.videocapture.read()
+
+        if not ret or frame is None:
+            raise RuntimeError(f"{self} read failed (status={ret}).")
+
+        processed_frame = self._postprocess_image(frame, color_mode)
+
+        read_duration_ms = (time.perf_counter() - start_time) * 1e3
+        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+
+        return processed_frame
+
+    def _postprocess_image(self, image: np.ndarray, color_mode: ColorMode | None = None) -> np.ndarray:
+        """
+        Applies color conversion, dimension validation, and rotation to a raw frame.
+
+        Args:
+            image (np.ndarray): The raw image frame (expected BGR format from OpenCV).
+            color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None,
+                                             uses the instance's default `self.color_mode`.
+
+        Returns:
+            np.ndarray: The processed image frame.
+
+        Raises:
+            ValueError: If the requested `color_mode` is invalid.
+            RuntimeError: If the raw frame dimensions do not match the configured
+                          `width` and `height`.
+        """
+        requested_color_mode = self.color_mode if color_mode is None else color_mode
+
+        if requested_color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"Invalid color mode '{requested_color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+            )
+
+        h, w, c = image.shape
+
+        if h != self.capture_height or w != self.capture_width:
+            raise RuntimeError(
+                f"{self} frame width={w} or height={h} do not match configured width={self.capture_width} or height={self.capture_height}."
+            )
+
+        if c != 3:
+            raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).")
+
+        processed_image = image
+        if requested_color_mode == ColorMode.RGB:
+            processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+            processed_image = cv2.rotate(processed_image, self.rotation)
+
+        return processed_image
+
+    def _read_loop(self):
+        """
+        Internal loop run by the background thread for asynchronous reading.
+
+        On each iteration:
+        1. Reads a color frame
+        2. Stores result in latest_frame (thread-safe)
+        3. Sets new_frame_event to notify listeners
+
+        Stops on DeviceNotConnectedError, logs other errors and continues.
+        """
+        while not self.stop_event.is_set():
+            try:
+                color_image = self.read()
+
+                with self.frame_lock:
+                    self.latest_frame = color_image
+                self.new_frame_event.set()
+
+            except DeviceNotConnectedError:
+                break
+            except Exception as e:
+                logger.warning(f"Error reading frame in background thread for {self}: {e}")
+
+    def _start_read_thread(self) -> None:
+        """Starts or restarts the background read thread if it's not running."""
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=0.1)
+        if self.stop_event is not None:
+            self.stop_event.set()
+
+        self.stop_event = Event()
+        self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
+        self.thread.daemon = True
+        self.thread.start()
+
+    def _stop_read_thread(self) -> None:
+        """Signals the background read thread to stop and waits for it to join."""
+        if self.stop_event is not None:
+            self.stop_event.set()
+
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=2.0)
+
+        self.thread = None
+        self.stop_event = None
+
+    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+        """
+        Reads the latest available frame asynchronously.
+
+        This method retrieves the most recent frame captured by the background
+        read thread. It does not block waiting for the camera hardware directly,
+        but may wait up to timeout_ms for the background thread to provide a frame.
+
+        Args:
+            timeout_ms (float): Maximum time in milliseconds to wait for a frame
+                to become available. Defaults to 200ms (0.2 seconds).
+
+        Returns:
+            np.ndarray: The latest captured frame as a NumPy array in the format
+                       (height, width, channels), processed according to configuration.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            TimeoutError: If no frame becomes available within the specified timeout.
+            RuntimeError: If an unexpected error occurs.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        if self.thread is None or not self.thread.is_alive():
+            self._start_read_thread()
+
+        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+            thread_alive = self.thread is not None and self.thread.is_alive()
+            raise TimeoutError(
+                f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
+                f"Read thread alive: {thread_alive}."
+            )
+
+        with self.frame_lock:
+            frame = self.latest_frame
+            self.new_frame_event.clear()
+
+        if frame is None:
+            raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
+
+        return frame
+
+    def disconnect(self):
+        """
+        Disconnects from the camera and cleans up resources.
+
+        Stops the background read thread (if running) and releases the OpenCV
+        VideoCapture object.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is already disconnected.
+        """
+        if not self.is_connected and self.thread is None:
+            raise DeviceNotConnectedError(f"{self} not connected.")
+
+        if self.thread is not None:
+            self._stop_read_thread()
+
+        if self.videocapture is not None:
+            self.videocapture.release()
+            self.videocapture = None
+
+        logger.info(f"{self} disconnected.")
--- a/lerobot/common/cameras/opencv/configuration_opencv.py
+++ b/lerobot/common/cameras/opencv/configuration_opencv.py
@@ -0,0 +1,73 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from pathlib import Path
+
+from ..configs import CameraConfig, ColorMode, Cv2Rotation
+
+
+@CameraConfig.register_subclass("opencv")
+@dataclass
+class OpenCVCameraConfig(CameraConfig):
+    """Configuration class for OpenCV-based camera devices or video files.
+
+    This class provides configuration options for cameras accessed through OpenCV,
+    supporting both physical camera devices and video files. It includes settings
+    for resolution, frame rate, color mode, and image rotation.
+
+    Example configurations:
+    ```python
+    # Basic configurations
+    OpenCVCameraConfig(0, 30, 1280, 720)   # 1280x720 @ 30FPS
+    OpenCVCameraConfig(/dev/video4, 60, 640, 480)   # 640x480 @ 60FPS
+
+    # Advanced configurations
+    OpenCVCameraConfig(128422271347, 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)     # With 90° rotation
+    ```
+
+    Attributes:
+        index_or_path: Either an integer representing the camera device index,
+                      or a Path object pointing to a video file.
+        fps: Requested frames per second for the color stream.
+        width: Requested frame width in pixels for the color stream.
+        height: Requested frame height in pixels for the color stream.
+        color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
+        rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
+        warmup_s: Time reading frames before returning from connect (in seconds)
+
+    Note:
+        - Only 3-channel color output (RGB/BGR) is currently supported.
+    """
+
+    index_or_path: int | Path
+    color_mode: ColorMode = ColorMode.RGB
+    rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
+    warmup_s: int = 1
+
+    def __post_init__(self):
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
+
+        if self.rotation not in (
+            Cv2Rotation.NO_ROTATION,
+            Cv2Rotation.ROTATE_90,
+            Cv2Rotation.ROTATE_180,
+            Cv2Rotation.ROTATE_270,
+        ):
+            raise ValueError(
+                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
+            )
--- a/lerobot/common/cameras/realsense/init.py
+++ b/lerobot/common/cameras/realsense/init.py
@@ -0,0 +1,16 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .camera_realsense import RealSenseCamera
+from .configuration_realsense import RealSenseCameraConfig
--- a/lerobot/common/cameras/realsense/camera_realsense.py
+++ b/lerobot/common/cameras/realsense/camera_realsense.py
@@ -0,0 +1,556 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Provides the RealSenseCamera class for capturing frames from Intel RealSense cameras.
+"""
+
+import logging
+import time
+from threading import Event, Lock, Thread
+from typing import Any, Dict, List
+
+import cv2
+import numpy as np
+
+try:
+    import pyrealsense2 as rs
+except Exception as e:
+    logging.info(f"Could not import realsense: {e}")
+
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+
+from ..camera import Camera
+from ..configs import ColorMode
+from ..utils import get_cv2_rotation
+from .configuration_realsense import RealSenseCameraConfig
+
+logger = logging.getLogger(__name__)
+
+
+class RealSenseCamera(Camera):
+    """
+    Manages interactions with Intel RealSense cameras for frame and depth recording.
+
+    This class provides an interface similar to `OpenCVCamera` but tailored for
+    RealSense devices, leveraging the `pyrealsense2` library. It uses the camera's
+    unique serial number for identification, offering more stability than device
+    indices, especially on Linux. It also supports capturing depth maps alongside
+    color frames.
+
+    Use the provided utility script to find available camera indices and default profiles:
+    ```bash
+    python -m lerobot.find_cameras realsense
+    ```
+
+    A `RealSenseCamera` instance requires a configuration object specifying the
+    camera's serial number or a unique device name. If using the name, ensure only
+    one camera with that name is connected.
+
+    The camera's default settings (FPS, resolution, color mode) from the stream
+    profile are used unless overridden in the configuration.
+
+    Example:
+        ```python
+        from lerobot.common.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
+        from lerobot.common.cameras import ColorMode, Cv2Rotation
+
+        # Basic usage with serial number
+        config = RealSenseCameraConfig(serial_number_or_name="0123456789") # Replace with actual SN
+        camera = RealSenseCamera(config)
+        camera.connect()
+
+        # Read 1 frame synchronously
+        color_image = camera.read()
+        print(color_image.shape)
+
+        # Read 1 frame asynchronously
+        async_image = camera.async_read()
+
+        # When done, properly disconnect the camera using
+        camera.disconnect()
+
+        # Example with depth capture and custom settings
+        custom_config = RealSenseCameraConfig(
+            serial_number_or_name="0123456789", # Replace with actual SN
+            fps=30,
+            width=1280,
+            height=720,
+            color_mode=ColorMode.BGR, # Request BGR output
+            rotation=Cv2Rotation.NO_ROTATION,
+            use_depth=True
+        )
+        depth_camera = RealSenseCamera(custom_config)
+        depth_camera.connect()
+
+        # Read 1 depth frame
+        depth_map = depth_camera.read_depth()
+
+        # Example using a unique camera name
+        name_config = RealSenseCameraConfig(serial_number_or_name="Intel RealSense D435") # If unique
+        name_camera = RealSenseCamera(name_config)
+        # ... connect, read, disconnect ...
+        ```
+    """
+
+    def __init__(self, config: RealSenseCameraConfig):
+        """
+        Initializes the RealSenseCamera instance.
+
+        Args:
+            config: The configuration settings for the camera.
+        """
+
+        super().__init__(config)
+
+        self.config = config
+
+        if config.serial_number_or_name.isdigit():
+            self.serial_number = config.serial_number_or_name
+        else:
+            self.serial_number = self._find_serial_number_from_name(config.serial_number_or_name)
+
+        self.fps = config.fps
+        self.color_mode = config.color_mode
+        self.use_depth = config.use_depth
+        self.warmup_s = config.warmup_s
+
+        self.rs_pipeline: rs.pipeline | None = None
+        self.rs_profile: rs.pipeline_profile | None = None
+
+        self.thread: Thread | None = None
+        self.stop_event: Event | None = None
+        self.frame_lock: Lock = Lock()
+        self.latest_frame: np.ndarray | None = None
+        self.new_frame_event: Event = Event()
+
+        self.rotation: int | None = get_cv2_rotation(config.rotation)
+
+        if self.height and self.width:
+            self.capture_width, self.capture_height = self.width, self.height
+            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+                self.capture_width, self.capture_height = self.height, self.width
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}({self.serial_number})"
+
+    @property
+    def is_connected(self) -> bool:
+        """Checks if the camera pipeline is started and streams are active."""
+        return self.rs_pipeline is not None and self.rs_profile is not None
+
+    def connect(self, warmup: bool = True):
+        """
+        Connects to the RealSense camera specified in the configuration.
+
+        Initializes the RealSense pipeline, configures the required streams (color
+        and optionally depth), starts the pipeline, and validates the actual stream settings.
+
+        Raises:
+            DeviceAlreadyConnectedError: If the camera is already connected.
+            ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique).
+            ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all.
+            RuntimeError: If the pipeline starts but fails to apply requested settings.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
+
+        self.rs_pipeline = rs.pipeline()
+        rs_config = rs.config()
+        self._configure_rs_pipeline_config(rs_config)
+
+        try:
+            self.rs_profile = self.rs_pipeline.start(rs_config)
+        except RuntimeError as e:
+            self.rs_profile = None
+            self.rs_pipeline = None
+            raise ConnectionError(
+                f"Failed to open {self}."
+                "Run `python -m lerobot.find_cameras realsense` to find available cameras."
+            ) from e
+
+        self._configure_capture_settings()
+
+        if warmup:
+            time.sleep(
+                1
+            )  # NOTE(Steven): RS cameras need a bit of time to warm up before the first read. If we don't wait, the first read from the warmup will raise.
+            start_time = time.time()
+            while time.time() - start_time < self.warmup_s:
+                self.read()
+                time.sleep(0.1)
+
+        logger.info(f"{self} connected.")
+
+    @staticmethod
+    def find_cameras() -> List[Dict[str, Any]]:
+        """
+        Detects available Intel RealSense cameras connected to the system.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries,
+            where each dictionary contains 'type', 'id' (serial number), 'name',
+            firmware version, USB type, and other available specs, and the default profile properties (width, height, fps, format).
+
+        Raises:
+            OSError: If pyrealsense2 is not installed.
+            ImportError: If pyrealsense2 is not installed.
+        """
+        found_cameras_info = []
+        context = rs.context()
+        devices = context.query_devices()
+
+        for device in devices:
+            camera_info = {
+                "name": device.get_info(rs.camera_info.name),
+                "type": "RealSense",
+                "id": device.get_info(rs.camera_info.serial_number),
+                "firmware_version": device.get_info(rs.camera_info.firmware_version),
+                "usb_type_descriptor": device.get_info(rs.camera_info.usb_type_descriptor),
+                "physical_port": device.get_info(rs.camera_info.physical_port),
+                "product_id": device.get_info(rs.camera_info.product_id),
+                "product_line": device.get_info(rs.camera_info.product_line),
+            }
+
+            # Get stream profiles for each sensor
+            sensors = device.query_sensors()
+            for sensor in sensors:
+                profiles = sensor.get_stream_profiles()
+
+                for profile in profiles:
+                    if profile.is_video_stream_profile() and profile.is_default():
+                        vprofile = profile.as_video_stream_profile()
+                        stream_info = {
+                            "stream_type": vprofile.stream_name(),
+                            "format": vprofile.format().name,
+                            "width": vprofile.width(),
+                            "height": vprofile.height(),
+                            "fps": vprofile.fps(),
+                        }
+                        camera_info["default_stream_profile"] = stream_info
+
+            found_cameras_info.append(camera_info)
+
+        return found_cameras_info
+
+    def _find_serial_number_from_name(self, name: str) -> str:
+        """Finds the serial number for a given unique camera name."""
+        camera_infos = self.find_cameras()
+        found_devices = [cam for cam in camera_infos if str(cam["name"]) == name]
+
+        if not found_devices:
+            available_names = [cam["name"] for cam in camera_infos]
+            raise ValueError(
+                f"No RealSense camera found with name '{name}'. Available camera names: {available_names}"
+            )
+
+        if len(found_devices) > 1:
+            serial_numbers = [dev["serial_number"] for dev in found_devices]
+            raise ValueError(
+                f"Multiple RealSense cameras found with name '{name}'. "
+                f"Please use a unique serial number instead. Found SNs: {serial_numbers}"
+            )
+
+        serial_number = str(found_devices[0]["serial_number"])
+        return serial_number
+
+    def _configure_rs_pipeline_config(self, rs_config):
+        """Creates and configures the RealSense pipeline configuration object."""
+        rs.config.enable_device(rs_config, self.serial_number)
+
+        if self.width and self.height and self.fps:
+            rs_config.enable_stream(
+                rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
+            )
+            if self.use_depth:
+                rs_config.enable_stream(
+                    rs.stream.depth, self.capture_width, self.capture_height, rs.format.z16, self.fps
+                )
+        else:
+            rs_config.enable_stream(rs.stream.color)
+            if self.use_depth:
+                rs_config.enable_stream(rs.stream.depth)
+
+    def _configure_capture_settings(self) -> None:
+        """Sets fps, width, and height from device stream if not already configured.
+
+        Uses the color stream profile to update unset attributes. Handles rotation by
+        swapping width/height when needed. Original capture dimensions are always stored.
+
+        Raises:
+            DeviceNotConnectedError: If device is not connected.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Cannot validate settings for {self} as it is not connected.")
+
+        stream = self.rs_profile.get_stream(rs.stream.color).as_video_stream_profile()
+
+        if self.fps is None:
+            self.fps = stream.fps()
+
+        if self.width is None or self.height is None:
+            actual_width = int(round(stream.width()))
+            actual_height = int(round(stream.height()))
+            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+                self.width, self.height = actual_height, actual_width
+                self.capture_width, self.capture_height = actual_width, actual_height
+            else:
+                self.width, self.height = actual_width, actual_height
+                self.capture_width, self.capture_height = actual_width, actual_height
+
+    def read_depth(self, timeout_ms: int = 200) -> np.ndarray:
+        """
+        Reads a single frame (depth) synchronously from the camera.
+
+        This is a blocking call. It waits for a coherent set of frames (depth)
+        from the camera hardware via the RealSense pipeline.
+
+        Args:
+            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
+
+        Returns:
+            np.ndarray: The depth map as a NumPy array (height, width)
+                  of type `np.uint16` (raw depth values in millimeters) and rotation.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If reading frames from the pipeline fails or frames are invalid.
+        """
+
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+        if not self.use_depth:
+            raise RuntimeError(
+                f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}."
+            )
+
+        start_time = time.perf_counter()
+
+        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
+
+        if not ret or frame is None:
+            raise RuntimeError(f"{self} read_depth failed (status={ret}).")
+
+        depth_frame = frame.get_depth_frame()
+        depth_map = np.asanyarray(depth_frame.get_data())
+
+        depth_map_processed = self._postprocess_image(depth_map, depth_frame=True)
+
+        read_duration_ms = (time.perf_counter() - start_time) * 1e3
+        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+
+        return depth_map_processed
+
+    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> np.ndarray:
+        """
+        Reads a single frame (color) synchronously from the camera.
+
+        This is a blocking call. It waits for a coherent set of frames (color)
+        from the camera hardware via the RealSense pipeline.
+
+        Args:
+            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
+
+        Returns:
+            np.ndarray: The captured color frame as a NumPy array
+              (height, width, channels), processed according to `color_mode` and rotation.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If reading frames from the pipeline fails or frames are invalid.
+            ValueError: If an invalid `color_mode` is requested.
+        """
+
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        start_time = time.perf_counter()
+
+        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
+
+        if not ret or frame is None:
+            raise RuntimeError(f"{self} read failed (status={ret}).")
+
+        color_frame = frame.get_color_frame()
+        color_image_raw = np.asanyarray(color_frame.get_data())
+
+        color_image_processed = self._postprocess_image(color_image_raw, color_mode)
+
+        read_duration_ms = (time.perf_counter() - start_time) * 1e3
+        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+
+        return color_image_processed
+
+    def _postprocess_image(
+        self, image: np.ndarray, color_mode: ColorMode | None = None, depth_frame: bool = False
+    ) -> np.ndarray:
+        """
+        Applies color conversion, dimension validation, and rotation to a raw color frame.
+
+        Args:
+            image (np.ndarray): The raw image frame (expected RGB format from RealSense).
+            color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None,
+                                             uses the instance's default `self.color_mode`.
+
+        Returns:
+            np.ndarray: The processed image frame according to `self.color_mode` and `self.rotation`.
+
+        Raises:
+            ValueError: If the requested `color_mode` is invalid.
+            RuntimeError: If the raw frame dimensions do not match the configured
+                          `width` and `height`.
+        """
+
+        if color_mode and color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"Invalid requested color mode '{color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+            )
+
+        if depth_frame:
+            h, w = image.shape
+        else:
+            h, w, c = image.shape
+
+            if c != 3:
+                raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).")
+
+        if h != self.capture_height or w != self.capture_width:
+            raise RuntimeError(
+                f"{self} frame width={w} or height={h} do not match configured width={self.capture_width} or height={self.capture_height}."
+            )
+
+        processed_image = image
+        if self.color_mode == ColorMode.BGR:
+            processed_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+            processed_image = cv2.rotate(processed_image, self.rotation)
+
+        return processed_image
+
+    def _read_loop(self):
+        """
+        Internal loop run by the background thread for asynchronous reading.
+
+        On each iteration:
+        1. Reads a color frame with 500ms timeout
+        2. Stores result in latest_frame (thread-safe)
+        3. Sets new_frame_event to notify listeners
+
+        Stops on DeviceNotConnectedError, logs other errors and continues.
+        """
+        while not self.stop_event.is_set():
+            try:
+                color_image = self.read(timeout_ms=500)
+
+                with self.frame_lock:
+                    self.latest_frame = color_image
+                self.new_frame_event.set()
+
+            except DeviceNotConnectedError:
+                break
+            except Exception as e:
+                logger.warning(f"Error reading frame in background thread for {self}: {e}")
+
+    def _start_read_thread(self) -> None:
+        """Starts or restarts the background read thread if it's not running."""
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=0.1)
+        if self.stop_event is not None:
+            self.stop_event.set()
+
+        self.stop_event = Event()
+        self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
+        self.thread.daemon = True
+        self.thread.start()
+
+    def _stop_read_thread(self):
+        """Signals the background read thread to stop and waits for it to join."""
+        if self.stop_event is not None:
+            self.stop_event.set()
+
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=2.0)
+
+        self.thread = None
+        self.stop_event = None
+
+    # NOTE(Steven): Missing implementation for depth for now
+    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+        """
+        Reads the latest available frame data (color) asynchronously.
+
+        This method retrieves the most recent color frame captured by the background
+        read thread. It does not block waiting for the camera hardware directly,
+        but may wait up to timeout_ms for the background thread to provide a frame.
+
+        Args:
+            timeout_ms (float): Maximum time in milliseconds to wait for a frame
+                to become available. Defaults to 200ms (0.2 seconds).
+
+        Returns:
+            np.ndarray:
+            The latest captured frame data (color image), processed according to configuration.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            TimeoutError: If no frame data becomes available within the specified timeout.
+            RuntimeError: If the background thread died unexpectedly or another error occurs.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        if self.thread is None or not self.thread.is_alive():
+            self._start_read_thread()
+
+        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+            thread_alive = self.thread is not None and self.thread.is_alive()
+            raise TimeoutError(
+                f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
+                f"Read thread alive: {thread_alive}."
+            )
+
+        with self.frame_lock:
+            frame = self.latest_frame
+            self.new_frame_event.clear()
+
+        if frame is None:
+            raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
+
+        return frame
+
+    def disconnect(self):
+        """
+        Disconnects from the camera, stops the pipeline, and cleans up resources.
+
+        Stops the background read thread (if running) and stops the RealSense pipeline.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is already disconnected (pipeline not running).
+        """
+
+        if not self.is_connected and self.thread is None:
+            raise DeviceNotConnectedError(
+                f"Attempted to disconnect {self}, but it appears already disconnected."
+            )
+
+        if self.thread is not None:
+            self._stop_read_thread()
+
+        if self.rs_pipeline is not None:
+            self.rs_pipeline.stop()
+            self.rs_pipeline = None
+            self.rs_profile = None
+
+        logger.info(f"{self} disconnected.")
--- a/lerobot/common/cameras/realsense/configuration_realsense.py
+++ b/lerobot/common/cameras/realsense/configuration_realsense.py
@@ -0,0 +1,82 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+from ..configs import CameraConfig, ColorMode, Cv2Rotation
+
+
+@CameraConfig.register_subclass("intelrealsense")
+@dataclass
+class RealSenseCameraConfig(CameraConfig):
+    """Configuration class for Intel RealSense cameras.
+
+    This class provides specialized configuration options for Intel RealSense cameras,
+    including support for depth sensing and device identification via serial number or name.
+
+    Example configurations for Intel RealSense D405:
+    ```python
+    # Basic configurations
+    RealSenseCameraConfig("0123456789", 30, 1280, 720)   # 1280x720 @ 30FPS
+    RealSenseCameraConfig("0123456789", 60, 640, 480)   # 640x480 @ 60FPS
+
+    # Advanced configurations
+    RealSenseCameraConfig("0123456789", 30, 640, 480, use_depth=True)  # With depth sensing
+    RealSenseCameraConfig("0123456789", 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)     # With 90° rotation
+    ```
+
+    Attributes:
+        fps: Requested frames per second for the color stream.
+        width: Requested frame width in pixels for the color stream.
+        height: Requested frame height in pixels for the color stream.
+        serial_number_or_name: Unique serial number or human-readable name to identify the camera.
+        color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
+        use_depth: Whether to enable depth stream. Defaults to False.
+        rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
+        warmup_s: Time reading frames before returning from connect (in seconds)
+
+    Note:
+        - Either name or serial_number must be specified.
+        - Depth stream configuration (if enabled) will use the same FPS as the color stream.
+        - The actual resolution and FPS may be adjusted by the camera to the nearest supported mode.
+        - For `fps`, `width` and `height`, either all of them need to be set, or none of them.
+    """
+
+    serial_number_or_name: str
+    color_mode: ColorMode = ColorMode.RGB
+    use_depth: bool = False
+    rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
+    warmup_s: int = 1
+
+    def __post_init__(self):
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
+
+        if self.rotation not in (
+            Cv2Rotation.NO_ROTATION,
+            Cv2Rotation.ROTATE_90,
+            Cv2Rotation.ROTATE_180,
+            Cv2Rotation.ROTATE_270,
+        ):
+            raise ValueError(
+                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
+            )
+
+        values = (self.fps, self.width, self.height)
+        if any(v is not None for v in values) and any(v is None for v in values):
+            raise ValueError(
+                "For `fps`, `width` and `height`, either all of them need to be set, or none of them."
+            )
--- a/lerobot/common/cameras/utils.py
+++ b/lerobot/common/cameras/utils.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import platform
+from pathlib import Path
+from typing import TypeAlias
+
+from .camera import Camera
+from .configs import CameraConfig, Cv2Rotation
+
+IndexOrPath: TypeAlias = int | Path
+
+
+def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[str, Camera]:
+    cameras = {}
+
+    for key, cfg in camera_configs.items():
+        if cfg.type == "opencv":
+            from .opencv import OpenCVCamera
+
+            cameras[key] = OpenCVCamera(cfg)
+
+        elif cfg.type == "intelrealsense":
+            from .realsense.camera_realsense import RealSenseCamera
+
+            cameras[key] = RealSenseCamera(cfg)
+        else:
+            raise ValueError(f"The motor type '{cfg.type}' is not valid.")
+
+    return cameras
+
+
+def get_cv2_rotation(rotation: Cv2Rotation) -> int | None:
+    import cv2
+
+    if rotation == Cv2Rotation.ROTATE_90:
+        return cv2.ROTATE_90_CLOCKWISE
+    elif rotation == Cv2Rotation.ROTATE_180:
+        return cv2.ROTATE_180
+    elif rotation == Cv2Rotation.ROTATE_270:
+        return cv2.ROTATE_90_COUNTERCLOCKWISE
+    else:
+        return None
+
+
+def get_cv2_backend() -> int:
+    import cv2
+
+    if platform.system() == "Windows":
+        return cv2.CAP_AVFOUNDATION
+    else:
+        return cv2.CAP_ANY
--- a/lerobot/common/constants.py
+++ b/lerobot/common/constants.py
@@ -17,12 +17,15 @@ from pathlib import Path

 from huggingface_hub.constants import HF_HOME

-OBS_ENV = "observation.environment_state"
-OBS_ROBOT = "observation.state"
+OBS_ENV_STATE = "observation.environment_state"
+OBS_STATE = "observation.state"
 OBS_IMAGE = "observation.image"
 OBS_IMAGES = "observation.images"
 ACTION = "action"

+ROBOTS = "robots"
+TELEOPERATORS = "teleoperators"
+
 # files & directories
 CHECKPOINTS_DIR = "checkpoints"
 LAST_CHECKPOINT_LINK = "last"
@@ -34,12 +37,16 @@ OPTIMIZER_STATE = "optimizer_state.safetensors"
 OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
 SCHEDULER_STATE = "scheduler_state.json"

-# cache dir
-default_cache_path = Path(HF_HOME) / "lerobot"
-HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser()
-
 if "LEROBOT_HOME" in os.environ:
    raise ValueError(
        f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n"
        "'LEROBOT_HOME' is deprecated, please use 'HF_LEROBOT_HOME' instead."
    )
+
+# cache dir
+default_cache_path = Path(HF_HOME) / "lerobot"
+HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser()
+
+# calibration dir
+default_calibration_path = HF_LEROBOT_HOME / "calibration"
+HF_LEROBOT_CALIBRATION = Path(os.getenv("HF_LEROBOT_CALIBRATION", default_calibration_path)).expanduser()
--- a/lerobot/common/datasets/lerobot_dataset.py
+++ b/lerobot/common/datasets/lerobot_dataset.py
@@ -38,6 +38,7 @@ from lerobot.common.datasets.utils import (
    DEFAULT_IMAGE_PATH,
    INFO_PATH,
    TASKS_PATH,
+    _validate_feature_names,
    append_jsonlines,
    backward_compatible_episodes_stats,
    check_delta_timestamps,
@@ -48,7 +49,6 @@ from lerobot.common.datasets.utils import (
    embed_images,
    get_delta_indices,
    get_episode_data_index,
-    get_features_from_robot,
    get_hf_features_from_features,
    get_safe_version,
    hf_transform_to_torch,
@@ -72,7 +72,6 @@ from lerobot.common.datasets.video_utils import (
    get_safe_default_codec,
    get_video_info,
 )
-from lerobot.common.robot_devices.robots.utils import Robot

 CODEBASE_VERSION = "v2.1"

@@ -304,10 +303,9 @@ class LeRobotDatasetMetadata:
        cls,
        repo_id: str,
        fps: int,
-        root: str | Path | None = None,
-        robot: Robot | None = None,
+        features: dict,
        robot_type: str | None = None,
-        features: dict | None = None,
+        root: str | Path | None = None,
        use_videos: bool = True,
    ) -> "LeRobotDatasetMetadata":
        """Creates metadata for a LeRobotDataset."""
@@ -317,33 +315,13 @@ class LeRobotDatasetMetadata:

        obj.root.mkdir(parents=True, exist_ok=False)

-        if robot is not None:
-            features = get_features_from_robot(robot, use_videos)
-            robot_type = robot.robot_type
-            if not all(cam.fps == fps for cam in robot.cameras.values()):
-                logging.warning(
-                    f"Some cameras in your {robot.robot_type} robot don't have an fps matching the fps of your dataset."
-                    "In this case, frames from lower fps cameras will be repeated to fill in the blanks."
-                )
-        elif features is None:
-            raise ValueError(
-                "Dataset features must either come from a Robot or explicitly passed upon creation."
-            )
-        else:
-            # TODO(aliberts, rcadene): implement sanity check for features
-            features = {**features, **DEFAULT_FEATURES}
-
-            # check if none of the features contains a "/" in their names,
-            # as this would break the dict flattening in the stats computation, which uses '/' as separator
-            for key in features:
-                if "/" in key:
-                    raise ValueError(f"Feature names should not contain '/'. Found '/' in feature '{key}'.")
-
-            features = {**features, **DEFAULT_FEATURES}
+        # TODO(aliberts, rcadene): implement sanity check for features
+        features = {**features, **DEFAULT_FEATURES}
+        _validate_feature_names(features)

        obj.tasks, obj.task_to_task_index = {}, {}
        obj.episodes_stats, obj.stats, obj.episodes = {}, {}, {}
-        obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, robot_type, features, use_videos)
+        obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, features, use_videos, robot_type)
        if len(obj.video_keys) > 0 and not use_videos:
            raise ValueError()
        write_json(obj.info, obj.root / INFO_PATH)
@@ -785,7 +763,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
        else:
            self.image_writer.save_image(image=image, fpath=fpath)

-    def add_frame(self, frame: dict) -> None:
+    def add_frame(self, frame: dict, task: str, timestamp: float | None = None) -> None:
        """
        This function only adds the frame to the episode_buffer. Apart from images — which are written in a
        temporary directory — nothing is written to disk. To save those frames, the 'save_episode()' method
@@ -803,17 +781,14 @@ class LeRobotDataset(torch.utils.data.Dataset):

        # Automatically add frame_index and timestamp to episode buffer
        frame_index = self.episode_buffer["size"]
-        timestamp = frame.pop("timestamp") if "timestamp" in frame else frame_index / self.fps
+        if timestamp is None:
+            timestamp = frame_index / self.fps
        self.episode_buffer["frame_index"].append(frame_index)
        self.episode_buffer["timestamp"].append(timestamp)
+        self.episode_buffer["task"].append(task)

        # Add frame features to episode_buffer
        for key in frame:
-            if key == "task":
-                # Note: we associate the task in natural language to its task index during `save_episode`
-                self.episode_buffer["task"].append(frame["task"])
-                continue
-
            if key not in self.features:
                raise ValueError(
                    f"An element of the frame is not in the features. '{key}' not in '{self.features.keys()}'."
@@ -989,10 +964,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
        cls,
        repo_id: str,
        fps: int,
+        features: dict,
        root: str | Path | None = None,
-        robot: Robot | None = None,
        robot_type: str | None = None,
-        features: dict | None = None,
        use_videos: bool = True,
        tolerance_s: float = 1e-4,
        image_writer_processes: int = 0,
@@ -1004,10 +978,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
        obj.meta = LeRobotDatasetMetadata.create(
            repo_id=repo_id,
            fps=fps,
-            root=root,
-            robot=robot,
            robot_type=robot_type,
            features=features,
+            root=root,
            use_videos=use_videos,
        )
        obj.repo_id = obj.meta.repo_id
--- a/lerobot/common/datasets/utils.py
+++ b/lerobot/common/datasets/utils.py
@@ -40,7 +40,7 @@ from lerobot.common.datasets.backward_compatibility import (
    BackwardCompatibilityError,
    ForwardCompatibilityError,
 )
-from lerobot.common.robot_devices.robots.utils import Robot
+from lerobot.common.robots import Robot
 from lerobot.common.utils.utils import is_valid_numpy_dtype_string
 from lerobot.configs.types import DictLike, FeatureType, PolicyFeature

@@ -387,6 +387,59 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
    return datasets.Features(hf_features)


+def _validate_feature_names(features: dict[str, dict]) -> None:
+    invalid_features = {name: ft for name, ft in features.items() if "/" in name}
+    if invalid_features:
+        raise ValueError(f"Feature names should not contain '/'. Found '/' in '{invalid_features}'.")
+
+
+def hw_to_dataset_features(
+    hw_features: dict[str, type | tuple], prefix: str, use_video: bool = True
+) -> dict[str, dict]:
+    features = {}
+    joint_fts = {key: ftype for key, ftype in hw_features.items() if ftype is float}
+    cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
+
+    if joint_fts and prefix == "action":
+        features[prefix] = {
+            "dtype": "float32",
+            "shape": (len(joint_fts),),
+            "names": list(joint_fts),
+        }
+
+    if joint_fts and prefix == "observation":
+        features[f"{prefix}.state"] = {
+            "dtype": "float32",
+            "shape": (len(joint_fts),),
+            "names": list(joint_fts),
+        }
+
+    for key, shape in cam_fts.items():
+        features[f"{prefix}.images.{key}"] = {
+            "dtype": "video" if use_video else "image",
+            "shape": shape,
+            "names": ["height", "width", "channels"],
+        }
+
+    _validate_feature_names(features)
+    return features
+
+
+def build_dataset_frame(
+    ds_features: dict[str, dict], values: dict[str, Any], prefix: str
+) -> dict[str, np.ndarray]:
+    frame = {}
+    for key, ft in ds_features.items():
+        if key in DEFAULT_FEATURES or not key.startswith(prefix):
+            continue
+        elif ft["dtype"] == "float32" and len(ft["shape"]) == 1:
+            frame[key] = np.array([values[name] for name in ft["names"]], dtype=np.float32)
+        elif ft["dtype"] in ["image", "video"]:
+            frame[key] = values[key.removeprefix(f"{prefix}.images.")]
+
+    return frame
+
+
 def get_features_from_robot(robot: Robot, use_videos: bool = True) -> dict:
    camera_ft = {}
    if robot.cameras:
@@ -415,7 +468,7 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
            type = FeatureType.ENV
        elif key.startswith("observation"):
            type = FeatureType.STATE
-        elif key == "action":
+        elif key.startswith("action"):
            type = FeatureType.ACTION
        else:
            continue
@@ -431,9 +484,9 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
 def create_empty_dataset_info(
    codebase_version: str,
    fps: int,
-    robot_type: str,
    features: dict,
    use_videos: bool,
+    robot_type: str | None = None,
 ) -> dict:
    return {
        "codebase_version": codebase_version,
@@ -699,16 +752,12 @@ class IterableNamespace(SimpleNamespace):


 def validate_frame(frame: dict, features: dict):
-    optional_features = {"timestamp"}
-    expected_features = (set(features) - set(DEFAULT_FEATURES.keys())) | {"task"}
-    actual_features = set(frame.keys())
+    expected_features = set(features) - set(DEFAULT_FEATURES)
+    actual_features = set(frame)

-    error_message = validate_features_presence(actual_features, expected_features, optional_features)
+    error_message = validate_features_presence(actual_features, expected_features)

-    if "task" in frame:
-        error_message += validate_feature_string("task", frame["task"])
-
-    common_features = actual_features & (expected_features | optional_features)
+    common_features = actual_features & expected_features
    for name in common_features - {"task"}:
        error_message += validate_feature_dtype_and_shape(name, features[name], frame[name])

@@ -716,12 +765,10 @@ def validate_frame(frame: dict, features: dict):
        raise ValueError(error_message)


-def validate_features_presence(
-    actual_features: set[str], expected_features: set[str], optional_features: set[str]
-):
+def validate_features_presence(actual_features: set[str], expected_features: set[str]):
    error_message = ""
    missing_features = expected_features - actual_features
-    extra_features = actual_features - (expected_features | optional_features)
+    extra_features = actual_features - expected_features

    if missing_features or extra_features:
        error_message += "Feature mismatch in `frame` dictionary:\n"
--- a/lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py
+++ b/lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py
@@ -27,7 +27,7 @@ from textwrap import dedent

 from lerobot import available_datasets
 from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset
-from lerobot.common.robot_devices.robots.configs import AlohaRobotConfig
+from lerobot.common.robots.aloha.configuration_aloha import AlohaRobotConfig

 LOCAL_DIR = Path("data/")

--- a/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py
+++ b/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py
@@ -141,8 +141,7 @@ from lerobot.common.datasets.video_utils import (
    get_image_pixel_channels,
    get_video_info,
 )
-from lerobot.common.robot_devices.robots.configs import RobotConfig
-from lerobot.common.robot_devices.robots.utils import make_robot_config
+from lerobot.common.robots import RobotConfig

 V16 = "v1.6"
 V20 = "v2.0"
@@ -598,6 +597,30 @@ def convert_dataset(
        create_branch(repo_id=repo_id, branch=V20, repo_type="dataset")


+def make_robot_config(robot_type: str, **kwargs) -> RobotConfig:
+    if robot_type == "aloha":
+        raise NotImplementedError  # TODO
+
+    elif robot_type == "koch_follower":
+        from lerobot.common.robots.koch_follower import KochFollowerConfig
+
+        return KochFollowerConfig(**kwargs)
+    elif robot_type == "so100_follower":
+        from lerobot.common.robots.so100_follower import SO100FollowerConfig
+
+        return SO100FollowerConfig(**kwargs)
+    elif robot_type == "stretch":
+        from lerobot.common.robots.stretch3 import Stretch3RobotConfig
+
+        return Stretch3RobotConfig(**kwargs)
+    elif robot_type == "lekiwi":
+        from lerobot.common.robots.lekiwi import LeKiwiConfig
+
+        return LeKiwiConfig(**kwargs)
+    else:
+        raise ValueError(f"Robot type '{robot_type}' is not available.")
+
+
 def main():
    parser = argparse.ArgumentParser()
    task_args = parser.add_mutually_exclusive_group(required=True)
--- a/lerobot/common/envs/configs.py
+++ b/lerobot/common/envs/configs.py
@@ -17,7 +17,7 @@ from dataclasses import dataclass, field

 import draccus

-from lerobot.common.constants import ACTION, OBS_ENV, OBS_IMAGE, OBS_IMAGES, OBS_ROBOT
+from lerobot.common.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 from lerobot.configs.types import FeatureType, PolicyFeature


@@ -32,7 +32,8 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
    def type(self) -> str:
        return self.get_choice_name(self.__class__)

-    @abc.abstractproperty
+    @property
+    @abc.abstractmethod
    def gym_kwargs(self) -> dict:
        raise NotImplementedError()

@@ -53,7 +54,7 @@ class AlohaEnv(EnvConfig):
    features_map: dict[str, str] = field(
        default_factory=lambda: {
            "action": ACTION,
-            "agent_pos": OBS_ROBOT,
+            "agent_pos": OBS_STATE,
            "top": f"{OBS_IMAGE}.top",
            "pixels/top": f"{OBS_IMAGES}.top",
        }
@@ -94,8 +95,8 @@ class PushtEnv(EnvConfig):
    features_map: dict[str, str] = field(
        default_factory=lambda: {
            "action": ACTION,
-            "agent_pos": OBS_ROBOT,
-            "environment_state": OBS_ENV,
+            "agent_pos": OBS_STATE,
+            "environment_state": OBS_ENV_STATE,
            "pixels": OBS_IMAGE,
        }
    )
@@ -136,7 +137,7 @@ class XarmEnv(EnvConfig):
    features_map: dict[str, str] = field(
        default_factory=lambda: {
            "action": ACTION,
-            "agent_pos": OBS_ROBOT,
+            "agent_pos": OBS_STATE,
            "pixels": OBS_IMAGE,
        }
    )
--- a/lerobot/common/errors.py
+++ b/lerobot/common/errors.py
@@ -0,0 +1,43 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+class DeviceNotConnectedError(ConnectionError):
+    """Exception raised when the device is not connected."""
+
+    def __init__(self, message="This device is not connected. Try calling `connect()` first."):
+        self.message = message
+        super().__init__(self.message)
+
+
+class DeviceAlreadyConnectedError(ConnectionError):
+    """Exception raised when the device is already connected."""
+
+    def __init__(
+        self,
+        message="This device is already connected. Try not calling `connect()` twice.",
+    ):
+        self.message = message
+        super().__init__(self.message)
+
+
+class InvalidActionError(ValueError):
+    """Exception raised when an action is already invalid."""
+
+    def __init__(
+        self,
+        message="The action is invalid. Check the value follows what it is expected from the action space.",
+    ):
+        self.message = message
+        super().__init__(self.message)
--- a/lerobot/common/motors/init.py
+++ b/lerobot/common/motors/init.py
@@ -0,0 +1 @@
+from .motors_bus import Motor, MotorCalibration, MotorNormMode, MotorsBus
--- a/lerobot/common/motors/dynamixel/init.py
+++ b/lerobot/common/motors/dynamixel/init.py
@@ -0,0 +1,2 @@
+from .dynamixel import DriveMode, DynamixelMotorsBus, OperatingMode, TorqueMode
+from .tables import *
--- a/lerobot/common/motors/dynamixel/dynamixel.py
+++ b/lerobot/common/motors/dynamixel/dynamixel.py
@@ -0,0 +1,263 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# TODO(aliberts): Should we implement FastSyncRead/Write?
+# https://github.com/ROBOTIS-GIT/DynamixelSDK/pull/643
+# https://github.com/ROBOTIS-GIT/DynamixelSDK/releases/tag/3.8.2
+# https://emanual.robotis.com/docs/en/dxl/protocol2/#fast-sync-read-0x8a
+# -> Need to check compatibility across models
+
+import logging
+from copy import deepcopy
+from enum import Enum
+
+from lerobot.common.utils.encoding_utils import decode_twos_complement, encode_twos_complement
+
+from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
+from .tables import (
+    AVAILABLE_BAUDRATES,
+    MODEL_BAUDRATE_TABLE,
+    MODEL_CONTROL_TABLE,
+    MODEL_ENCODING_TABLE,
+    MODEL_NUMBER_TABLE,
+    MODEL_RESOLUTION,
+)
+
+PROTOCOL_VERSION = 2.0
+DEFAULT_BAUDRATE = 1_000_000
+DEFAULT_TIMEOUT_MS = 1000
+
+NORMALIZED_DATA = ["Goal_Position", "Present_Position"]
+
+logger = logging.getLogger(__name__)
+
+
+class OperatingMode(Enum):
+    # DYNAMIXEL only controls current(torque) regardless of speed and position. This mode is ideal for a
+    # gripper or a system that only uses current(torque) control or a system that has additional
+    # velocity/position controllers.
+    CURRENT = 0
+
+    # This mode controls velocity. This mode is identical to the Wheel Mode(endless) from existing DYNAMIXEL.
+    # This mode is ideal for wheel-type robots.
+    VELOCITY = 1
+
+    # This mode controls position. This mode is identical to the Joint Mode from existing DYNAMIXEL. Operating
+    # position range is limited by the Max Position Limit(48) and the Min Position Limit(52). This mode is
+    # ideal for articulated robots that each joint rotates less than 360 degrees.
+    POSITION = 3
+
+    # This mode controls position. This mode is identical to the Multi-turn Position Control from existing
+    # DYNAMIXEL. 512 turns are supported(-256[rev] ~ 256[rev]). This mode is ideal for multi-turn wrists or
+    # conveyer systems or a system that requires an additional reduction gear. Note that Max Position
+    # Limit(48), Min Position Limit(52) are not used on Extended Position Control Mode.
+    EXTENDED_POSITION = 4
+
+    # This mode controls both position and current(torque). Up to 512 turns are supported (-256[rev] ~
+    # 256[rev]). This mode is ideal for a system that requires both position and current control such as
+    # articulated robots or grippers.
+    CURRENT_POSITION = 5
+
+    # This mode directly controls PWM output. (Voltage Control Mode)
+    PWM = 16
+
+
+class DriveMode(Enum):
+    NON_INVERTED = 0
+    INVERTED = 1
+
+
+class TorqueMode(Enum):
+    ENABLED = 1
+    DISABLED = 0
+
+
+def _split_into_byte_chunks(value: int, length: int) -> list[int]:
+    import dynamixel_sdk as dxl
+
+    if length == 1:
+        data = [value]
+    elif length == 2:
+        data = [dxl.DXL_LOBYTE(value), dxl.DXL_HIBYTE(value)]
+    elif length == 4:
+        data = [
+            dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)),
+            dxl.DXL_HIBYTE(dxl.DXL_LOWORD(value)),
+            dxl.DXL_LOBYTE(dxl.DXL_HIWORD(value)),
+            dxl.DXL_HIBYTE(dxl.DXL_HIWORD(value)),
+        ]
+    return data
+
+
+class DynamixelMotorsBus(MotorsBus):
+    """
+    The Dynamixel implementation for a MotorsBus. It relies on the python dynamixel sdk to communicate with
+    the motors. For more info, see the Dynamixel SDK Documentation:
+    https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20
+    """
+
+    apply_drive_mode = False
+    available_baudrates = deepcopy(AVAILABLE_BAUDRATES)
+    default_baudrate = DEFAULT_BAUDRATE
+    default_timeout = DEFAULT_TIMEOUT_MS
+    model_baudrate_table = deepcopy(MODEL_BAUDRATE_TABLE)
+    model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
+    model_encoding_table = deepcopy(MODEL_ENCODING_TABLE)
+    model_number_table = deepcopy(MODEL_NUMBER_TABLE)
+    model_resolution_table = deepcopy(MODEL_RESOLUTION)
+    normalized_data = deepcopy(NORMALIZED_DATA)
+
+    def __init__(
+        self,
+        port: str,
+        motors: dict[str, Motor],
+        calibration: dict[str, MotorCalibration] | None = None,
+    ):
+        super().__init__(port, motors, calibration)
+        import dynamixel_sdk as dxl
+
+        self.port_handler = dxl.PortHandler(self.port)
+        self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
+        self.sync_reader = dxl.GroupSyncRead(self.port_handler, self.packet_handler, 0, 0)
+        self.sync_writer = dxl.GroupSyncWrite(self.port_handler, self.packet_handler, 0, 0)
+        self._comm_success = dxl.COMM_SUCCESS
+        self._no_error = 0x00
+
+    def _assert_protocol_is_compatible(self, instruction_name: str) -> None:
+        pass
+
+    def _handshake(self) -> None:
+        self._assert_motors_exist()
+
+    def _find_single_motor(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]:
+        model = self.motors[motor].model
+        search_baudrates = (
+            [initial_baudrate] if initial_baudrate is not None else self.model_baudrate_table[model]
+        )
+
+        for baudrate in search_baudrates:
+            self.set_baudrate(baudrate)
+            id_model = self.broadcast_ping()
+            if id_model:
+                found_id, found_model = next(iter(id_model.items()))
+                expected_model_nb = self.model_number_table[model]
+                if found_model != expected_model_nb:
+                    raise RuntimeError(
+                        f"Found one motor on {baudrate=} with id={found_id} but it has a "
+                        f"model number '{found_model}' different than the one expected: '{expected_model_nb}'. "
+                        f"Make sure you are connected only connected to the '{motor}' motor (model '{model}')."
+                    )
+                return baudrate, found_id
+
+        raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.")
+
+    def configure_motors(self) -> None:
+        # By default, Dynamixel motors have a 500µs delay response time (corresponding to a value of 250 on
+        # the 'Return_Delay_Time' address). We ensure this is reduced to the minimum of 2µs (value of 0).
+        for motor in self.motors:
+            self.write("Return_Delay_Time", motor, 0)
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.calibration == self.read_calibration()
+
+    def read_calibration(self) -> dict[str, MotorCalibration]:
+        offsets = self.sync_read("Homing_Offset", normalize=False)
+        mins = self.sync_read("Min_Position_Limit", normalize=False)
+        maxes = self.sync_read("Max_Position_Limit", normalize=False)
+        drive_modes = self.sync_read("Drive_Mode", normalize=False)
+
+        calibration = {}
+        for motor, m in self.motors.items():
+            calibration[motor] = MotorCalibration(
+                id=m.id,
+                drive_mode=drive_modes[motor],
+                homing_offset=offsets[motor],
+                range_min=mins[motor],
+                range_max=maxes[motor],
+            )
+
+        return calibration
+
+    def write_calibration(self, calibration_dict: dict[str, MotorCalibration]) -> None:
+        for motor, calibration in calibration_dict.items():
+            self.write("Homing_Offset", motor, calibration.homing_offset)
+            self.write("Min_Position_Limit", motor, calibration.range_min)
+            self.write("Max_Position_Limit", motor, calibration.range_max)
+
+        self.calibration = calibration_dict
+
+    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+        for motor in self._get_motors_list(motors):
+            self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
+
+    def _disable_torque(self, motor_id: int, model: str, num_retry: int = 0) -> None:
+        addr, length = get_address(self.model_ctrl_table, model, "Torque_Enable")
+        self._write(addr, length, motor_id, TorqueMode.DISABLED.value, num_retry=num_retry)
+
+    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+        for motor in self._get_motors_list(motors):
+            self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
+
+    def _encode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]:
+        for id_ in ids_values:
+            model = self._id_to_model(id_)
+            encoding_table = self.model_encoding_table.get(model)
+            if encoding_table and data_name in encoding_table:
+                n_bytes = encoding_table[data_name]
+                ids_values[id_] = encode_twos_complement(ids_values[id_], n_bytes)
+
+        return ids_values
+
+    def _decode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]:
+        for id_ in ids_values:
+            model = self._id_to_model(id_)
+            encoding_table = self.model_encoding_table.get(model)
+            if encoding_table and data_name in encoding_table:
+                n_bytes = encoding_table[data_name]
+                ids_values[id_] = decode_twos_complement(ids_values[id_], n_bytes)
+
+        return ids_values
+
+    def _get_half_turn_homings(self, positions: dict[NameOrID, Value]) -> dict[NameOrID, Value]:
+        """
+        On Dynamixel Motors:
+        Present_Position = Actual_Position + Homing_Offset
+        """
+        half_turn_homings = {}
+        for motor, pos in positions.items():
+            model = self._get_motor_model(motor)
+            max_res = self.model_resolution_table[model] - 1
+            half_turn_homings[motor] = int(max_res / 2) - pos
+
+        return half_turn_homings
+
+    def _split_into_byte_chunks(self, value: int, length: int) -> list[int]:
+        return _split_into_byte_chunks(value, length)
+
+    def broadcast_ping(self, num_retry: int = 0, raise_on_error: bool = False) -> dict[int, int] | None:
+        for n_try in range(1 + num_retry):
+            data_list, comm = self.packet_handler.broadcastPing(self.port_handler)
+            if self._is_comm_success(comm):
+                break
+            logger.debug(f"Broadcast ping failed on port '{self.port}' ({n_try=})")
+            logger.debug(self.packet_handler.getTxRxResult(comm))
+
+        if not self._is_comm_success(comm):
+            if raise_on_error:
+                raise ConnectionError(self.packet_handler.getTxRxResult(comm))
+
+            return
+
+        return {id_: data[0] for id_, data in data_list.items()}
--- a/lerobot/common/motors/dynamixel/tables.py
+++ b/lerobot/common/motors/dynamixel/tables.py
@@ -0,0 +1,197 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# TODO(Steven): Consider doing the following:
+# from enum import Enum
+# class MyControlTableKey(Enum):
+#   ID = "ID"
+#   GOAL_SPEED = "Goal_Speed"
+#   ...
+#
+# MY_CONTROL_TABLE ={
+#   MyControlTableKey.ID.value: (5,1)
+#   MyControlTableKey.GOAL_SPEED.value: (46, 2)
+#   ...
+# }
+# This allows me do to:
+# bus.write(MyControlTableKey.GOAL_SPEED, ...)
+# Instead of:
+# bus.write("Goal_Speed", ...)
+# This is important for two reasons:
+# 1. The linter will tell me if I'm trying to use an invalid key, instead of me realizing when I get the RunTimeError
+# 2. We can change the value of the MyControlTableKey enums without impacting the client code
+
+
+# {data_name: (address, size_byte)}
+# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#control-table
+X_SERIES_CONTROL_TABLE = {
+    "Model_Number": (0, 2),
+    "Model_Information": (2, 4),
+    "Firmware_Version": (6, 1),
+    "ID": (7, 1),
+    "Baud_Rate": (8, 1),
+    "Return_Delay_Time": (9, 1),
+    "Drive_Mode": (10, 1),
+    "Operating_Mode": (11, 1),
+    "Secondary_ID": (12, 1),
+    "Protocol_Type": (13, 1),
+    "Homing_Offset": (20, 4),
+    "Moving_Threshold": (24, 4),
+    "Temperature_Limit": (31, 1),
+    "Max_Voltage_Limit": (32, 2),
+    "Min_Voltage_Limit": (34, 2),
+    "PWM_Limit": (36, 2),
+    "Current_Limit": (38, 2),
+    "Acceleration_Limit": (40, 4),
+    "Velocity_Limit": (44, 4),
+    "Max_Position_Limit": (48, 4),
+    "Min_Position_Limit": (52, 4),
+    "Shutdown": (63, 1),
+    "Torque_Enable": (64, 1),
+    "LED": (65, 1),
+    "Status_Return_Level": (68, 1),
+    "Registered_Instruction": (69, 1),
+    "Hardware_Error_Status": (70, 1),
+    "Velocity_I_Gain": (76, 2),
+    "Velocity_P_Gain": (78, 2),
+    "Position_D_Gain": (80, 2),
+    "Position_I_Gain": (82, 2),
+    "Position_P_Gain": (84, 2),
+    "Feedforward_2nd_Gain": (88, 2),
+    "Feedforward_1st_Gain": (90, 2),
+    "Bus_Watchdog": (98, 1),
+    "Goal_PWM": (100, 2),
+    "Goal_Current": (102, 2),
+    "Goal_Velocity": (104, 4),
+    "Profile_Acceleration": (108, 4),
+    "Profile_Velocity": (112, 4),
+    "Goal_Position": (116, 4),
+    "Realtime_Tick": (120, 2),
+    "Moving": (122, 1),
+    "Moving_Status": (123, 1),
+    "Present_PWM": (124, 2),
+    "Present_Current": (126, 2),
+    "Present_Velocity": (128, 4),
+    "Present_Position": (132, 4),
+    "Velocity_Trajectory": (136, 4),
+    "Position_Trajectory": (140, 4),
+    "Present_Input_Voltage": (144, 2),
+    "Present_Temperature": (146, 1),
+}
+
+# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#baud-rate8
+X_SERIES_BAUDRATE_TABLE = {
+    9_600: 0,
+    57_600: 1,
+    115_200: 2,
+    1_000_000: 3,
+    2_000_000: 4,
+    3_000_000: 5,
+    4_000_000: 6,
+}
+
+# {data_name: size_byte}
+X_SERIES_ENCODINGS_TABLE = {
+    "Homing_Offset": X_SERIES_CONTROL_TABLE["Homing_Offset"][1],
+    "Goal_PWM": X_SERIES_CONTROL_TABLE["Goal_PWM"][1],
+    "Goal_Current": X_SERIES_CONTROL_TABLE["Goal_Current"][1],
+    "Goal_Velocity": X_SERIES_CONTROL_TABLE["Goal_Velocity"][1],
+    "Present_PWM": X_SERIES_CONTROL_TABLE["Present_PWM"][1],
+    "Present_Current": X_SERIES_CONTROL_TABLE["Present_Current"][1],
+    "Present_Velocity": X_SERIES_CONTROL_TABLE["Present_Velocity"][1],
+}
+
+MODEL_ENCODING_TABLE = {
+    "x_series": X_SERIES_ENCODINGS_TABLE,
+    "xl330-m077": X_SERIES_ENCODINGS_TABLE,
+    "xl330-m288": X_SERIES_ENCODINGS_TABLE,
+    "xl430-w250": X_SERIES_ENCODINGS_TABLE,
+    "xm430-w350": X_SERIES_ENCODINGS_TABLE,
+    "xm540-w270": X_SERIES_ENCODINGS_TABLE,
+    "xc430-w150": X_SERIES_ENCODINGS_TABLE,
+}
+
+# {model: model_resolution}
+# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#specifications
+MODEL_RESOLUTION = {
+    "x_series": 4096,
+    "xl330-m077": 4096,
+    "xl330-m288": 4096,
+    "xl430-w250": 4096,
+    "xm430-w350": 4096,
+    "xm540-w270": 4096,
+    "xc430-w150": 4096,
+}
+
+# {model: model_number}
+# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#control-table-of-eeprom-area
+MODEL_NUMBER_TABLE = {
+    "xl330-m077": 1190,
+    "xl330-m288": 1200,
+    "xl430-w250": 1060,
+    "xm430-w350": 1020,
+    "xm540-w270": 1120,
+    "xc430-w150": 1070,
+}
+
+# {model: available_operating_modes}
+# https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#operating-mode11
+MODEL_OPERATING_MODES = {
+    "xl330-m077": [0, 1, 3, 4, 5, 16],
+    "xl330-m288": [0, 1, 3, 4, 5, 16],
+    "xl430-w250": [1, 3, 4, 16],
+    "xm430-w350": [0, 1, 3, 4, 5, 16],
+    "xm540-w270": [0, 1, 3, 4, 5, 16],
+    "xc430-w150": [1, 3, 4, 16],
+}
+
+MODEL_CONTROL_TABLE = {
+    "x_series": X_SERIES_CONTROL_TABLE,
+    "xl330-m077": X_SERIES_CONTROL_TABLE,
+    "xl330-m288": X_SERIES_CONTROL_TABLE,
+    "xl430-w250": X_SERIES_CONTROL_TABLE,
+    "xm430-w350": X_SERIES_CONTROL_TABLE,
+    "xm540-w270": X_SERIES_CONTROL_TABLE,
+    "xc430-w150": X_SERIES_CONTROL_TABLE,
+}
+
+MODEL_BAUDRATE_TABLE = {
+    "x_series": X_SERIES_BAUDRATE_TABLE,
+    "xl330-m077": X_SERIES_BAUDRATE_TABLE,
+    "xl330-m288": X_SERIES_BAUDRATE_TABLE,
+    "xl430-w250": X_SERIES_BAUDRATE_TABLE,
+    "xm430-w350": X_SERIES_BAUDRATE_TABLE,
+    "xm540-w270": X_SERIES_BAUDRATE_TABLE,
+    "xc430-w150": X_SERIES_BAUDRATE_TABLE,
+}
+
+AVAILABLE_BAUDRATES = [
+    9_600,
+    19_200,
+    38_400,
+    57_600,
+    115_200,
+    230_400,
+    460_800,
+    500_000,
+    576_000,
+    921_600,
+    1_000_000,
+    1_152_000,
+    2_000_000,
+    2_500_000,
+    3_000_000,
+    3_500_000,
+    4_000_000,
+]
--- a/lerobot/common/motors/feetech/init.py
+++ b/lerobot/common/motors/feetech/init.py
@@ -0,0 +1,2 @@
+from .feetech import DriveMode, FeetechMotorsBus, OperatingMode, TorqueMode
+from .tables import *
--- a/lerobot/common/motors/feetech/feetech.py
+++ b/lerobot/common/motors/feetech/feetech.py
@@ -0,0 +1,454 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+from copy import deepcopy
+from enum import Enum
+from pprint import pformat
+
+from lerobot.common.utils.encoding_utils import decode_sign_magnitude, encode_sign_magnitude
+
+from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
+from .tables import (
+    FIRMWARE_MAJOR_VERSION,
+    FIRMWARE_MINOR_VERSION,
+    MODEL_BAUDRATE_TABLE,
+    MODEL_CONTROL_TABLE,
+    MODEL_ENCODING_TABLE,
+    MODEL_NUMBER,
+    MODEL_NUMBER_TABLE,
+    MODEL_PROTOCOL,
+    MODEL_RESOLUTION,
+    SCAN_BAUDRATES,
+)
+
+DEFAULT_PROTOCOL_VERSION = 0
+DEFAULT_BAUDRATE = 1_000_000
+DEFAULT_TIMEOUT_MS = 1000
+
+NORMALIZED_DATA = ["Goal_Position", "Present_Position"]
+
+logger = logging.getLogger(__name__)
+
+
+class OperatingMode(Enum):
+    # position servo mode
+    POSITION = 0
+    # The motor is in constant speed mode, which is controlled by parameter 0x2e, and the highest bit 15 is
+    # the direction bit
+    VELOCITY = 1
+    # PWM open-loop speed regulation mode, with parameter 0x2c running time parameter control, bit11 as
+    # direction bit
+    PWM = 2
+    # In step servo mode, the number of step progress is represented by parameter 0x2a, and the highest bit 15
+    # is the direction bit
+    STEP = 3
+
+
+class DriveMode(Enum):
+    NON_INVERTED = 0
+    INVERTED = 1
+
+
+class TorqueMode(Enum):
+    ENABLED = 1
+    DISABLED = 0
+
+
+def _split_into_byte_chunks(value: int, length: int) -> list[int]:
+    import scservo_sdk as scs
+
+    if length == 1:
+        data = [value]
+    elif length == 2:
+        data = [scs.SCS_LOBYTE(value), scs.SCS_HIBYTE(value)]
+    elif length == 4:
+        data = [
+            scs.SCS_LOBYTE(scs.SCS_LOWORD(value)),
+            scs.SCS_HIBYTE(scs.SCS_LOWORD(value)),
+            scs.SCS_LOBYTE(scs.SCS_HIWORD(value)),
+            scs.SCS_HIBYTE(scs.SCS_HIWORD(value)),
+        ]
+    return data
+
+
+def patch_setPacketTimeout(self, packet_length):  # noqa: N802
+    """
+    HACK: This patches the PortHandler behavior to set the correct packet timeouts.
+
+    It fixes https://gitee.com/ftservo/SCServoSDK/issues/IBY2S6
+    The bug is fixed on the official Feetech SDK repo (https://gitee.com/ftservo/FTServo_Python)
+    but because that version is not published on PyPI, we rely on the (unofficial) on that is, which needs
+    patching.
+    """
+    self.packet_start_time = self.getCurrentTime()
+    self.packet_timeout = (self.tx_time_per_byte * packet_length) + (self.tx_time_per_byte * 3.0) + 50
+
+
+class FeetechMotorsBus(MotorsBus):
+    """
+    The FeetechMotorsBus class allows to efficiently read and write to the attached motors. It relies on the
+    python feetech sdk to communicate with the motors, which is itself based on the dynamixel sdk.
+    """
+
+    apply_drive_mode = True
+    available_baudrates = deepcopy(SCAN_BAUDRATES)
+    default_baudrate = DEFAULT_BAUDRATE
+    default_timeout = DEFAULT_TIMEOUT_MS
+    model_baudrate_table = deepcopy(MODEL_BAUDRATE_TABLE)
+    model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
+    model_encoding_table = deepcopy(MODEL_ENCODING_TABLE)
+    model_number_table = deepcopy(MODEL_NUMBER_TABLE)
+    model_resolution_table = deepcopy(MODEL_RESOLUTION)
+    normalized_data = deepcopy(NORMALIZED_DATA)
+
+    def __init__(
+        self,
+        port: str,
+        motors: dict[str, Motor],
+        calibration: dict[str, MotorCalibration] | None = None,
+        protocol_version: int = DEFAULT_PROTOCOL_VERSION,
+    ):
+        super().__init__(port, motors, calibration)
+        self.protocol_version = protocol_version
+        self._assert_same_protocol()
+        import scservo_sdk as scs
+
+        self.port_handler = scs.PortHandler(self.port)
+        # HACK: monkeypatch
+        self.port_handler.setPacketTimeout = patch_setPacketTimeout.__get__(
+            self.port_handler, scs.PortHandler
+        )
+        self.packet_handler = scs.PacketHandler(protocol_version)
+        self.sync_reader = scs.GroupSyncRead(self.port_handler, self.packet_handler, 0, 0)
+        self.sync_writer = scs.GroupSyncWrite(self.port_handler, self.packet_handler, 0, 0)
+        self._comm_success = scs.COMM_SUCCESS
+        self._no_error = 0x00
+
+        if any(MODEL_PROTOCOL[model] != self.protocol_version for model in self.models):
+            raise ValueError(f"Some motors are incompatible with protocol_version={self.protocol_version}")
+
+    def _assert_same_protocol(self) -> None:
+        if any(MODEL_PROTOCOL[model] != self.protocol_version for model in self.models):
+            raise RuntimeError("Some motors use an incompatible protocol.")
+
+    def _assert_protocol_is_compatible(self, instruction_name: str) -> None:
+        if instruction_name == "sync_read" and self.protocol_version == 1:
+            raise NotImplementedError(
+                "'Sync Read' is not available with Feetech motors using Protocol 1. Use 'Read' sequentially instead."
+            )
+        if instruction_name == "broadcast_ping" and self.protocol_version == 1:
+            raise NotImplementedError(
+                "'Broadcast Ping' is not available with Feetech motors using Protocol 1. Use 'Ping' sequentially instead."
+            )
+
+    def _assert_same_firmware(self) -> None:
+        firmware_versions = self._read_firmware_version(self.ids, raise_on_error=True)
+        if len(set(firmware_versions.values())) != 1:
+            raise RuntimeError(
+                "Some Motors use different firmware versions:"
+                f"\n{pformat(firmware_versions)}\n"
+                "Update their firmware first using Feetech's software. "
+                "Visit https://www.feetechrc.com/software."
+            )
+
+    def _handshake(self) -> None:
+        self._assert_motors_exist()
+        self._assert_same_firmware()
+
+    def _find_single_motor(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]:
+        if self.protocol_version == 0:
+            return self._find_single_motor_p0(motor, initial_baudrate)
+        else:
+            return self._find_single_motor_p1(motor, initial_baudrate)
+
+    def _find_single_motor_p0(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]:
+        model = self.motors[motor].model
+        search_baudrates = (
+            [initial_baudrate] if initial_baudrate is not None else self.model_baudrate_table[model]
+        )
+        expected_model_nb = self.model_number_table[model]
+
+        for baudrate in search_baudrates:
+            self.set_baudrate(baudrate)
+            id_model = self.broadcast_ping()
+            if id_model:
+                found_id, found_model = next(iter(id_model.items()))
+                if found_model != expected_model_nb:
+                    raise RuntimeError(
+                        f"Found one motor on {baudrate=} with id={found_id} but it has a "
+                        f"model number '{found_model}' different than the one expected: '{expected_model_nb}'. "
+                        f"Make sure you are connected only connected to the '{motor}' motor (model '{model}')."
+                    )
+                return baudrate, found_id
+
+        raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.")
+
+    def _find_single_motor_p1(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]:
+        import scservo_sdk as scs
+
+        model = self.motors[motor].model
+        search_baudrates = (
+            [initial_baudrate] if initial_baudrate is not None else self.model_baudrate_table[model]
+        )
+        expected_model_nb = self.model_number_table[model]
+
+        for baudrate in search_baudrates:
+            self.set_baudrate(baudrate)
+            for id_ in range(scs.MAX_ID + 1):
+                found_model = self.ping(id_)
+                if found_model is not None:
+                    if found_model != expected_model_nb:
+                        raise RuntimeError(
+                            f"Found one motor on {baudrate=} with id={id_} but it has a "
+                            f"model number '{found_model}' different than the one expected: '{expected_model_nb}'. "
+                            f"Make sure you are connected only connected to the '{motor}' motor (model '{model}')."
+                        )
+                    return baudrate, id_
+
+        raise RuntimeError(f"Motor '{motor}' (model '{model}') was not found. Make sure it is connected.")
+
+    def configure_motors(self) -> None:
+        for motor in self.motors:
+            # By default, Feetech motors have a 500µs delay response time (corresponding to a value of 250 on
+            # the 'Return_Delay_Time' address). We ensure this is reduced to the minimum of 2µs (value of 0).
+            self.write("Return_Delay_Time", motor, 0)
+            # Set 'Maximum_Acceleration' to 254 to speedup acceleration and deceleration of the motors.
+            # Note: this address is not in the official STS3215 Memory Table
+            self.write("Maximum_Acceleration", motor, 254)
+            self.write("Acceleration", motor, 254)
+
+    @property
+    def is_calibrated(self) -> bool:
+        motors_calibration = self.read_calibration()
+        if set(motors_calibration) != set(self.calibration):
+            return False
+
+        same_ranges = all(
+            self.calibration[motor].range_min == cal.range_min
+            and self.calibration[motor].range_max == cal.range_max
+            for motor, cal in motors_calibration.items()
+        )
+        if self.protocol_version == 1:
+            return same_ranges
+
+        same_offsets = all(
+            self.calibration[motor].homing_offset == cal.homing_offset
+            for motor, cal in motors_calibration.items()
+        )
+        return same_ranges and same_offsets
+
+    def read_calibration(self) -> dict[str, MotorCalibration]:
+        offsets, mins, maxes = {}, {}, {}
+        for motor in self.motors:
+            mins[motor] = self.read("Min_Position_Limit", motor, normalize=False)
+            maxes[motor] = self.read("Max_Position_Limit", motor, normalize=False)
+            offsets[motor] = (
+                self.read("Homing_Offset", motor, normalize=False) if self.protocol_version == 0 else 0
+            )
+
+        calibration = {}
+        for motor, m in self.motors.items():
+            calibration[motor] = MotorCalibration(
+                id=m.id,
+                drive_mode=0,
+                homing_offset=offsets[motor],
+                range_min=mins[motor],
+                range_max=maxes[motor],
+            )
+
+        return calibration
+
+    def write_calibration(self, calibration_dict: dict[str, MotorCalibration]) -> None:
+        for motor, calibration in calibration_dict.items():
+            if self.protocol_version == 0:
+                self.write("Homing_Offset", motor, calibration.homing_offset)
+            self.write("Min_Position_Limit", motor, calibration.range_min)
+            self.write("Max_Position_Limit", motor, calibration.range_max)
+
+        self.calibration = calibration_dict
+
+    def _get_half_turn_homings(self, positions: dict[NameOrID, Value]) -> dict[NameOrID, Value]:
+        """
+        On Feetech Motors:
+        Present_Position = Actual_Position - Homing_Offset
+        """
+        half_turn_homings = {}
+        for motor, pos in positions.items():
+            model = self._get_motor_model(motor)
+            max_res = self.model_resolution_table[model] - 1
+            half_turn_homings[motor] = pos - int(max_res / 2)
+
+        return half_turn_homings
+
+    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+        for motor in self._get_motors_list(motors):
+            self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
+            self.write("Lock", motor, 0, num_retry=num_retry)
+
+    def _disable_torque(self, motor_id: int, model: str, num_retry: int = 0) -> None:
+        addr, length = get_address(self.model_ctrl_table, model, "Torque_Enable")
+        self._write(addr, length, motor_id, TorqueMode.DISABLED.value, num_retry=num_retry)
+        addr, length = get_address(self.model_ctrl_table, model, "Lock")
+        self._write(addr, length, motor_id, 0, num_retry=num_retry)
+
+    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+        for motor in self._get_motors_list(motors):
+            self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
+            self.write("Lock", motor, 1, num_retry=num_retry)
+
+    def _encode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]:
+        for id_ in ids_values:
+            model = self._id_to_model(id_)
+            encoding_table = self.model_encoding_table.get(model)
+            if encoding_table and data_name in encoding_table:
+                sign_bit = encoding_table[data_name]
+                ids_values[id_] = encode_sign_magnitude(ids_values[id_], sign_bit)
+
+        return ids_values
+
+    def _decode_sign(self, data_name: str, ids_values: dict[int, int]) -> dict[int, int]:
+        for id_ in ids_values:
+            model = self._id_to_model(id_)
+            encoding_table = self.model_encoding_table.get(model)
+            if encoding_table and data_name in encoding_table:
+                sign_bit = encoding_table[data_name]
+                ids_values[id_] = decode_sign_magnitude(ids_values[id_], sign_bit)
+
+        return ids_values
+
+    def _split_into_byte_chunks(self, value: int, length: int) -> list[int]:
+        return _split_into_byte_chunks(value, length)
+
+    def _broadcast_ping(self) -> tuple[dict[int, int], int]:
+        import scservo_sdk as scs
+
+        data_list = {}
+
+        status_length = 6
+
+        rx_length = 0
+        wait_length = status_length * scs.MAX_ID
+
+        txpacket = [0] * 6
+
+        tx_time_per_byte = (1000.0 / self.port_handler.getBaudRate()) * 10.0
+
+        txpacket[scs.PKT_ID] = scs.BROADCAST_ID
+        txpacket[scs.PKT_LENGTH] = 2
+        txpacket[scs.PKT_INSTRUCTION] = scs.INST_PING
+
+        result = self.packet_handler.txPacket(self.port_handler, txpacket)
+        if result != scs.COMM_SUCCESS:
+            self.port_handler.is_using = False
+            return data_list, result
+
+        # set rx timeout
+        self.port_handler.setPacketTimeoutMillis((wait_length * tx_time_per_byte) + (3.0 * scs.MAX_ID) + 16.0)
+
+        rxpacket = []
+        while not self.port_handler.isPacketTimeout() and rx_length < wait_length:
+            rxpacket += self.port_handler.readPort(wait_length - rx_length)
+            rx_length = len(rxpacket)
+
+        self.port_handler.is_using = False
+
+        if rx_length == 0:
+            return data_list, scs.COMM_RX_TIMEOUT
+
+        while True:
+            if rx_length < status_length:
+                return data_list, scs.COMM_RX_CORRUPT
+
+            # find packet header
+            for idx in range(0, (rx_length - 1)):
+                if (rxpacket[idx] == 0xFF) and (rxpacket[idx + 1] == 0xFF):
+                    break
+
+            if idx == 0:  # found at the beginning of the packet
+                # calculate checksum
+                checksum = 0
+                for idx in range(2, status_length - 1):  # except header & checksum
+                    checksum += rxpacket[idx]
+
+                checksum = ~checksum & 0xFF
+                if rxpacket[status_length - 1] == checksum:
+                    result = scs.COMM_SUCCESS
+                    data_list[rxpacket[scs.PKT_ID]] = rxpacket[scs.PKT_ERROR]
+
+                    del rxpacket[0:status_length]
+                    rx_length = rx_length - status_length
+
+                    if rx_length == 0:
+                        return data_list, result
+                else:
+                    result = scs.COMM_RX_CORRUPT
+                    # remove header (0xFF 0xFF)
+                    del rxpacket[0:2]
+                    rx_length = rx_length - 2
+            else:
+                # remove unnecessary packets
+                del rxpacket[0:idx]
+                rx_length = rx_length - idx
+
+    def broadcast_ping(self, num_retry: int = 0, raise_on_error: bool = False) -> dict[int, int] | None:
+        self._assert_protocol_is_compatible("broadcast_ping")
+        for n_try in range(1 + num_retry):
+            ids_status, comm = self._broadcast_ping()
+            if self._is_comm_success(comm):
+                break
+            logger.debug(f"Broadcast ping failed on port '{self.port}' ({n_try=})")
+            logger.debug(self.packet_handler.getTxRxResult(comm))
+
+        if not self._is_comm_success(comm):
+            if raise_on_error:
+                raise ConnectionError(self.packet_handler.getTxRxResult(comm))
+            return
+
+        ids_errors = {id_: status for id_, status in ids_status.items() if self._is_error(status)}
+        if ids_errors:
+            display_dict = {id_: self.packet_handler.getRxPacketError(err) for id_, err in ids_errors.items()}
+            logger.error(f"Some motors found returned an error status:\n{pformat(display_dict, indent=4)}")
+
+        return self._read_model_number(list(ids_status), raise_on_error)
+
+    def _read_firmware_version(self, motor_ids: list[int], raise_on_error: bool = False) -> dict[int, str]:
+        firmware_versions = {}
+        for id_ in motor_ids:
+            firm_ver_major, comm, error = self._read(
+                *FIRMWARE_MAJOR_VERSION, id_, raise_on_error=raise_on_error
+            )
+            if not self._is_comm_success(comm) or self._is_error(error):
+                continue
+
+            firm_ver_minor, comm, error = self._read(
+                *FIRMWARE_MINOR_VERSION, id_, raise_on_error=raise_on_error
+            )
+            if not self._is_comm_success(comm) or self._is_error(error):
+                continue
+
+            firmware_versions[id_] = f"{firm_ver_major}.{firm_ver_minor}"
+
+        return firmware_versions
+
+    def _read_model_number(self, motor_ids: list[int], raise_on_error: bool = False) -> dict[int, int]:
+        model_numbers = {}
+        for id_ in motor_ids:
+            model_nb, comm, error = self._read(*MODEL_NUMBER, id_, raise_on_error=raise_on_error)
+            if not self._is_comm_success(comm) or self._is_error(error):
+                continue
+
+            model_numbers[id_] = model_nb
+
+        return model_numbers
--- a/lerobot/common/motors/feetech/tables.py
+++ b/lerobot/common/motors/feetech/tables.py
@@ -0,0 +1,252 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+FIRMWARE_MAJOR_VERSION = (0, 1)
+FIRMWARE_MINOR_VERSION = (1, 1)
+MODEL_NUMBER = (3, 2)
+
+# TODO(Steven): Consider doing the following:
+# from enum import Enum
+# class MyControlTableKey(Enum):
+#   ID = "ID"
+#   GOAL_SPEED = "Goal_Speed"
+#   ...
+#
+# MY_CONTROL_TABLE ={
+#   MyControlTableKey.ID.value: (5,1)
+#   MyControlTableKey.GOAL_SPEED.value: (46, 2)
+#   ...
+# }
+# This allows me do to:
+# bus.write(MyControlTableKey.GOAL_SPEED, ...)
+# Instead of:
+# bus.write("Goal_Speed", ...)
+# This is important for two reasons:
+# 1. The linter will tell me if I'm trying to use an invalid key, instead of me realizing when I get the RunTimeError
+# 2. We can change the value of the MyControlTableKey enums without impacting the client code
+
+# data_name: (address, size_byte)
+# http://doc.feetech.cn/#/prodinfodownload?srcType=FT-SMS-STS-emanual-229f4476422d4059abfb1cb0
+STS_SMS_SERIES_CONTROL_TABLE = {
+    # EPROM
+    "Firmware_Major_Version": FIRMWARE_MAJOR_VERSION,  # read-only
+    "Firmware_Minor_Version": FIRMWARE_MINOR_VERSION,  # read-only
+    "Model_Number": MODEL_NUMBER,  # read-only
+    "ID": (5, 1),
+    "Baud_Rate": (6, 1),
+    "Return_Delay_Time": (7, 1),
+    "Response_Status_Level": (8, 1),
+    "Min_Position_Limit": (9, 2),
+    "Max_Position_Limit": (11, 2),
+    "Max_Temperature_Limit": (13, 1),
+    "Max_Voltage_Limit": (14, 1),
+    "Min_Voltage_Limit": (15, 1),
+    "Max_Torque_Limit": (16, 2),
+    "Phase": (18, 1),
+    "Unloading_Condition": (19, 1),
+    "LED_Alarm_Condition": (20, 1),
+    "P_Coefficient": (21, 1),
+    "D_Coefficient": (22, 1),
+    "I_Coefficient": (23, 1),
+    "Minimum_Startup_Force": (24, 2),
+    "CW_Dead_Zone": (26, 1),
+    "CCW_Dead_Zone": (27, 1),
+    "Protection_Current": (28, 2),
+    "Angular_Resolution": (30, 1),
+    "Homing_Offset": (31, 2),
+    "Operating_Mode": (33, 1),
+    "Protective_Torque": (34, 1),
+    "Protection_Time": (35, 1),
+    "Overload_Torque": (36, 1),
+    "Velocity_closed_loop_P_proportional_coefficient": (37, 1),
+    "Over_Current_Protection_Time": (38, 1),
+    "Velocity_closed_loop_I_integral_coefficient": (39, 1),
+    # SRAM
+    "Torque_Enable": (40, 1),
+    "Acceleration": (41, 1),
+    "Goal_Position": (42, 2),
+    "Goal_Time": (44, 2),
+    "Goal_Velocity": (46, 2),
+    "Torque_Limit": (48, 2),
+    "Lock": (55, 1),
+    "Present_Position": (56, 2),  # read-only
+    "Present_Velocity": (58, 2),  # read-only
+    "Present_Load": (60, 2),  # read-only
+    "Present_Voltage": (62, 1),  # read-only
+    "Present_Temperature": (63, 1),  # read-only
+    "Status": (65, 1),  # read-only
+    "Moving": (66, 1),  # read-only
+    "Present_Current": (69, 2),  # read-only
+    "Goal_Position_2": (71, 2),  # read-only
+    # Factory
+    "Moving_Velocity": (80, 1),
+    "Moving_Velocity_Threshold": (80, 1),
+    "DTs": (81, 1),  # (ms)
+    "Velocity_Unit_factor": (82, 1),
+    "Hts": (83, 1),  # (ns) valid for firmware >= 2.54, other versions keep 0
+    "Maximum_Velocity_Limit": (84, 1),
+    "Maximum_Acceleration": (85, 1),
+    "Acceleration_Multiplier ": (86, 1),  # Acceleration multiplier in effect when acceleration is 0
+}
+
+# http://doc.feetech.cn/#/prodinfodownload?srcType=FT-SCSCL-emanual-cbcc8ab2e3384282a01d4bf3
+SCS_SERIES_CONTROL_TABLE = {
+    # EPROM
+    "Firmware_Major_Version": FIRMWARE_MAJOR_VERSION,  # read-only
+    "Firmware_Minor_Version": FIRMWARE_MINOR_VERSION,  # read-only
+    "Model_Number": MODEL_NUMBER,  # read-only
+    "ID": (5, 1),
+    "Baud_Rate": (6, 1),
+    "Return_Delay_Time": (7, 1),
+    "Response_Status_Level": (8, 1),
+    "Min_Position_Limit": (9, 2),
+    "Max_Position_Limit": (11, 2),
+    "Max_Temperature_Limit": (13, 1),
+    "Max_Voltage_Limit": (14, 1),
+    "Min_Voltage_Limit": (15, 1),
+    "Max_Torque_Limit": (16, 2),
+    "Phase": (18, 1),
+    "Unloading_Condition": (19, 1),
+    "LED_Alarm_Condition": (20, 1),
+    "P_Coefficient": (21, 1),
+    "D_Coefficient": (22, 1),
+    "I_Coefficient": (23, 1),
+    "Minimum_Startup_Force": (24, 2),
+    "CW_Dead_Zone": (26, 1),
+    "CCW_Dead_Zone": (27, 1),
+    "Protective_Torque": (37, 1),
+    "Protection_Time": (38, 1),
+    # SRAM
+    "Torque_Enable": (40, 1),
+    "Acceleration": (41, 1),
+    "Goal_Position": (42, 2),
+    "Running_Time": (44, 2),
+    "Goal_Velocity": (46, 2),
+    "Lock": (48, 1),
+    "Present_Position": (56, 2),  # read-only
+    "Present_Velocity": (58, 2),  # read-only
+    "Present_Load": (60, 2),  # read-only
+    "Present_Voltage": (62, 1),  # read-only
+    "Present_Temperature": (63, 1),  # read-only
+    "Sync_Write_Flag": (64, 1),  # read-only
+    "Status": (65, 1),  # read-only
+    "Moving": (66, 1),  # read-only
+    # Factory
+    "PWM_Maximum_Step": (78, 1),
+    "Moving_Velocity_Threshold*50": (79, 1),
+    "DTs": (80, 1),  # (ms)
+    "Minimum_Velocity_Limit*50": (81, 1),
+    "Maximum_Velocity_Limit*50": (82, 1),
+    "Acceleration_2": (83, 1),  # don't know what that is
+}
+
+STS_SMS_SERIES_BAUDRATE_TABLE = {
+    1_000_000: 0,
+    500_000: 1,
+    250_000: 2,
+    128_000: 3,
+    115_200: 4,
+    57_600: 5,
+    38_400: 6,
+    19_200: 7,
+}
+
+SCS_SERIES_BAUDRATE_TABLE = {
+    1_000_000: 0,
+    500_000: 1,
+    250_000: 2,
+    128_000: 3,
+    115_200: 4,
+    57_600: 5,
+    38_400: 6,
+    19_200: 7,
+}
+
+MODEL_CONTROL_TABLE = {
+    "sts_series": STS_SMS_SERIES_CONTROL_TABLE,
+    "scs_series": SCS_SERIES_CONTROL_TABLE,
+    "sms_series": STS_SMS_SERIES_CONTROL_TABLE,
+    "sts3215": STS_SMS_SERIES_CONTROL_TABLE,
+    "sts3250": STS_SMS_SERIES_CONTROL_TABLE,
+    "scs0009": SCS_SERIES_CONTROL_TABLE,
+    "sm8512bl": STS_SMS_SERIES_CONTROL_TABLE,
+}
+
+MODEL_RESOLUTION = {
+    "sts_series": 4096,
+    "sms_series": 4096,
+    "scs_series": 1024,
+    "sts3215": 4096,
+    "sts3250": 4096,
+    "sm8512bl": 65536,
+    "scs0009": 1024,
+}
+
+MODEL_BAUDRATE_TABLE = {
+    "sts_series": STS_SMS_SERIES_BAUDRATE_TABLE,
+    "sms_series": STS_SMS_SERIES_BAUDRATE_TABLE,
+    "scs_series": SCS_SERIES_BAUDRATE_TABLE,
+    "sm8512bl": STS_SMS_SERIES_BAUDRATE_TABLE,
+    "sts3215": STS_SMS_SERIES_BAUDRATE_TABLE,
+    "sts3250": STS_SMS_SERIES_BAUDRATE_TABLE,
+    "scs0009": SCS_SERIES_BAUDRATE_TABLE,
+}
+
+# Sign-Magnitude encoding bits
+STS_SMS_SERIES_ENCODINGS_TABLE = {
+    "Homing_Offset": 11,
+    "Goal_Velocity": 15,
+    "Present_Velocity": 15,
+}
+
+MODEL_ENCODING_TABLE = {
+    "sts_series": STS_SMS_SERIES_ENCODINGS_TABLE,
+    "sms_series": STS_SMS_SERIES_ENCODINGS_TABLE,
+    "scs_series": {},
+    "sts3215": STS_SMS_SERIES_ENCODINGS_TABLE,
+    "sts3250": STS_SMS_SERIES_ENCODINGS_TABLE,
+    "sm8512bl": STS_SMS_SERIES_ENCODINGS_TABLE,
+    "scs0009": {},
+}
+
+SCAN_BAUDRATES = [
+    4_800,
+    9_600,
+    14_400,
+    19_200,
+    38_400,
+    57_600,
+    115_200,
+    128_000,
+    250_000,
+    500_000,
+    1_000_000,
+]
+
+MODEL_NUMBER_TABLE = {
+    "sts3215": 777,
+    "sts3250": 2825,
+    "sm8512bl": 11272,
+    "scs0009": 1284,
+}
+
+MODEL_PROTOCOL = {
+    "sts_series": 0,
+    "sms_series": 0,
+    "scs_series": 1,
+    "sts3215": 0,
+    "sts3250": 0,
+    "sm8512bl": 0,
+    "scs0009": 1,
+}
--- a/lerobot/common/motors/motors_bus.py
+++ b/lerobot/common/motors/motors_bus.py
--- a/lerobot/common/policies/diffusion/modeling_diffusion.py
+++ b/lerobot/common/policies/diffusion/modeling_diffusion.py
@@ -33,7 +33,7 @@ from diffusers.schedulers.scheduling_ddim import DDIMScheduler
 from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
 from torch import Tensor, nn

-from lerobot.common.constants import OBS_ENV, OBS_ROBOT
+from lerobot.common.constants import OBS_ENV_STATE, OBS_STATE
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
@@ -238,8 +238,8 @@ class DiffusionModel(nn.Module):

    def _prepare_global_conditioning(self, batch: dict[str, Tensor]) -> Tensor:
        """Encode image features and concatenate them all together along with the state vector."""
-        batch_size, n_obs_steps = batch[OBS_ROBOT].shape[:2]
-        global_cond_feats = [batch[OBS_ROBOT]]
+        batch_size, n_obs_steps = batch[OBS_STATE].shape[:2]
+        global_cond_feats = [batch[OBS_STATE]]
        # Extract image features.
        if self.config.image_features:
            if self.config.use_separate_rgb_encoder_per_camera:
@@ -269,7 +269,7 @@ class DiffusionModel(nn.Module):
            global_cond_feats.append(img_features)

        if self.config.env_state_feature:
-            global_cond_feats.append(batch[OBS_ENV])
+            global_cond_feats.append(batch[OBS_ENV_STATE])

        # Concatenate features then flatten to (B, global_cond_dim).
        return torch.cat(global_cond_feats, dim=-1).flatten(start_dim=1)
--- a/lerobot/common/policies/pi0/modeling_pi0.py
+++ b/lerobot/common/policies/pi0/modeling_pi0.py
@@ -57,7 +57,7 @@ import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
 from transformers import AutoTokenizer

-from lerobot.common.constants import ACTION, OBS_ROBOT
+from lerobot.common.constants import ACTION, OBS_STATE
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.common.policies.pi0.paligemma_with_expert import (
@@ -271,7 +271,7 @@ class PI0Policy(PreTrainedPolicy):
        self.eval()

        if self.config.adapt_to_pi_aloha:
-            batch[OBS_ROBOT] = self._pi_aloha_decode_state(batch[OBS_ROBOT])
+            batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE])

        batch = self.normalize_inputs(batch)

@@ -303,7 +303,7 @@ class PI0Policy(PreTrainedPolicy):
    def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> tuple[Tensor, dict[str, Tensor]]:
        """Do a full training forward pass to compute the loss"""
        if self.config.adapt_to_pi_aloha:
-            batch[OBS_ROBOT] = self._pi_aloha_decode_state(batch[OBS_ROBOT])
+            batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE])
            batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION])

        batch = self.normalize_inputs(batch)
@@ -380,7 +380,7 @@ class PI0Policy(PreTrainedPolicy):

    def prepare_language(self, batch) -> tuple[Tensor, Tensor]:
        """Tokenize the text input"""
-        device = batch[OBS_ROBOT].device
+        device = batch[OBS_STATE].device
        tasks = batch["task"]

        # PaliGemma prompt has to end with a new line
@@ -427,7 +427,7 @@ class PI0Policy(PreTrainedPolicy):

    def prepare_state(self, batch):
        """Pad state"""
-        state = pad_vector(batch[OBS_ROBOT], self.config.max_state_dim)
+        state = pad_vector(batch[OBS_STATE], self.config.max_state_dim)
        return state

    def prepare_action(self, batch):
--- a/lerobot/common/policies/tdmpc/modeling_tdmpc.py
+++ b/lerobot/common/policies/tdmpc/modeling_tdmpc.py
@@ -35,7 +35,7 @@ import torch.nn as nn
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor

-from lerobot.common.constants import OBS_ENV, OBS_ROBOT
+from lerobot.common.constants import OBS_ENV_STATE, OBS_STATE
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
@@ -753,9 +753,9 @@ class TDMPCObservationEncoder(nn.Module):
                )
            )
        if self.config.env_state_feature:
-            feat.append(self.env_state_enc_layers(obs_dict[OBS_ENV]))
+            feat.append(self.env_state_enc_layers(obs_dict[OBS_ENV_STATE]))
        if self.config.robot_state_feature:
-            feat.append(self.state_enc_layers(obs_dict[OBS_ROBOT]))
+            feat.append(self.state_enc_layers(obs_dict[OBS_STATE]))
        return torch.stack(feat, dim=0).mean(0)


--- a/lerobot/common/robot_devices/cameras/configs.py
+++ b/lerobot/common/robot_devices/cameras/configs.py
@@ -1,114 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import abc
-from dataclasses import dataclass
-
-import draccus
-
-
-@dataclass
-class CameraConfig(draccus.ChoiceRegistry, abc.ABC):
-    @property
-    def type(self) -> str:
-        return self.get_choice_name(self.__class__)
-
-
-@CameraConfig.register_subclass("opencv")
-@dataclass
-class OpenCVCameraConfig(CameraConfig):
-    """
-    Example of tested options for Intel Real Sense D405:
-
-    ```python
-    OpenCVCameraConfig(0, 30, 640, 480)
-    OpenCVCameraConfig(0, 60, 640, 480)
-    OpenCVCameraConfig(0, 90, 640, 480)
-    OpenCVCameraConfig(0, 30, 1280, 720)
-    ```
-    """
-
-    camera_index: int
-    fps: int | None = None
-    width: int | None = None
-    height: int | None = None
-    color_mode: str = "rgb"
-    channels: int | None = None
-    rotation: int | None = None
-    mock: bool = False
-
-    def __post_init__(self):
-        if self.color_mode not in ["rgb", "bgr"]:
-            raise ValueError(
-                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
-            )
-
-        self.channels = 3
-
-        if self.rotation not in [-90, None, 90, 180]:
-            raise ValueError(f"`rotation` must be in [-90, None, 90, 180] (got {self.rotation})")
-
-
-@CameraConfig.register_subclass("intelrealsense")
-@dataclass
-class IntelRealSenseCameraConfig(CameraConfig):
-    """
-    Example of tested options for Intel Real Sense D405:
-
-    ```python
-    IntelRealSenseCameraConfig(128422271347, 30, 640, 480)
-    IntelRealSenseCameraConfig(128422271347, 60, 640, 480)
-    IntelRealSenseCameraConfig(128422271347, 90, 640, 480)
-    IntelRealSenseCameraConfig(128422271347, 30, 1280, 720)
-    IntelRealSenseCameraConfig(128422271347, 30, 640, 480, use_depth=True)
-    IntelRealSenseCameraConfig(128422271347, 30, 640, 480, rotation=90)
-    ```
-    """
-
-    name: str | None = None
-    serial_number: int | None = None
-    fps: int | None = None
-    width: int | None = None
-    height: int | None = None
-    color_mode: str = "rgb"
-    channels: int | None = None
-    use_depth: bool = False
-    force_hardware_reset: bool = True
-    rotation: int | None = None
-    mock: bool = False
-
-    def __post_init__(self):
-        # bool is stronger than is None, since it works with empty strings
-        if bool(self.name) and bool(self.serial_number):
-            raise ValueError(
-                f"One of them must be set: name or serial_number, but {self.name=} and {self.serial_number=} provided."
-            )
-
-        if self.color_mode not in ["rgb", "bgr"]:
-            raise ValueError(
-                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
-            )
-
-        self.channels = 3
-
-        at_least_one_is_not_none = self.fps is not None or self.width is not None or self.height is not None
-        at_least_one_is_none = self.fps is None or self.width is None or self.height is None
-        if at_least_one_is_not_none and at_least_one_is_none:
-            raise ValueError(
-                "For `fps`, `width` and `height`, either all of them need to be set, or none of them, "
-                f"but {self.fps=}, {self.width=}, {self.height=} were provided."
-            )
-
-        if self.rotation not in [-90, None, 90, 180]:
-            raise ValueError(f"`rotation` must be in [-90, None, 90, 180] (got {self.rotation})")
--- a/lerobot/common/robot_devices/cameras/intelrealsense.py
+++ b/lerobot/common/robot_devices/cameras/intelrealsense.py
@@ -1,538 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This file contains utilities for recording frames from Intel Realsense cameras.
-"""
-
-import argparse
-import concurrent.futures
-import logging
-import math
-import shutil
-import threading
-import time
-import traceback
-from collections import Counter
-from pathlib import Path
-from threading import Thread
-
-import numpy as np
-from PIL import Image
-
-from lerobot.common.robot_devices.cameras.configs import IntelRealSenseCameraConfig
-from lerobot.common.robot_devices.utils import (
-    RobotDeviceAlreadyConnectedError,
-    RobotDeviceNotConnectedError,
-    busy_wait,
-)
-from lerobot.common.utils.utils import capture_timestamp_utc
-
-SERIAL_NUMBER_INDEX = 1
-
-
-def find_cameras(raise_when_empty=True, mock=False) -> list[dict]:
-    """
-    Find the names and the serial numbers of the Intel RealSense cameras
-    connected to the computer.
-    """
-    if mock:
-        import tests.cameras.mock_pyrealsense2 as rs
-    else:
-        import pyrealsense2 as rs
-
-    cameras = []
-    for device in rs.context().query_devices():
-        serial_number = int(device.get_info(rs.camera_info(SERIAL_NUMBER_INDEX)))
-        name = device.get_info(rs.camera_info.name)
-        cameras.append(
-            {
-                "serial_number": serial_number,
-                "name": name,
-            }
-        )
-
-    if raise_when_empty and len(cameras) == 0:
-        raise OSError(
-            "Not a single camera was detected. Try re-plugging, or re-installing `librealsense` and its python wrapper `pyrealsense2`, or updating the firmware."
-        )
-
-    return cameras
-
-
-def save_image(img_array, serial_number, frame_index, images_dir):
-    try:
-        img = Image.fromarray(img_array)
-        path = images_dir / f"camera_{serial_number}_frame_{frame_index:06d}.png"
-        path.parent.mkdir(parents=True, exist_ok=True)
-        img.save(str(path), quality=100)
-        logging.info(f"Saved image: {path}")
-    except Exception as e:
-        logging.error(f"Failed to save image for camera {serial_number} frame {frame_index}: {e}")
-
-
-def save_images_from_cameras(
-    images_dir: Path,
-    serial_numbers: list[int] | None = None,
-    fps=None,
-    width=None,
-    height=None,
-    record_time_s=2,
-    mock=False,
-):
-    """
-    Initializes all the cameras and saves images to the directory. Useful to visually identify the camera
-    associated to a given serial number.
-    """
-    if serial_numbers is None or len(serial_numbers) == 0:
-        camera_infos = find_cameras(mock=mock)
-        serial_numbers = [cam["serial_number"] for cam in camera_infos]
-
-    if mock:
-        import tests.cameras.mock_cv2 as cv2
-    else:
-        import cv2
-
-    print("Connecting cameras")
-    cameras = []
-    for cam_sn in serial_numbers:
-        print(f"{cam_sn=}")
-        config = IntelRealSenseCameraConfig(
-            serial_number=cam_sn, fps=fps, width=width, height=height, mock=mock
-        )
-        camera = IntelRealSenseCamera(config)
-        camera.connect()
-        print(
-            f"IntelRealSenseCamera({camera.serial_number}, fps={camera.fps}, width={camera.capture_width}, height={camera.capture_height}, color_mode={camera.color_mode})"
-        )
-        cameras.append(camera)
-
-    images_dir = Path(images_dir)
-    if images_dir.exists():
-        shutil.rmtree(
-            images_dir,
-        )
-    images_dir.mkdir(parents=True, exist_ok=True)
-
-    print(f"Saving images to {images_dir}")
-    frame_index = 0
-    start_time = time.perf_counter()
-    try:
-        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
-            while True:
-                now = time.perf_counter()
-
-                for camera in cameras:
-                    # If we use async_read when fps is None, the loop will go full speed, and we will end up
-                    # saving the same images from the cameras multiple times until the RAM/disk is full.
-                    image = camera.read() if fps is None else camera.async_read()
-                    if image is None:
-                        print("No Frame")
-
-                    bgr_converted_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-
-                    executor.submit(
-                        save_image,
-                        bgr_converted_image,
-                        camera.serial_number,
-                        frame_index,
-                        images_dir,
-                    )
-
-                if fps is not None:
-                    dt_s = time.perf_counter() - now
-                    busy_wait(1 / fps - dt_s)
-
-                if time.perf_counter() - start_time > record_time_s:
-                    break
-
-                print(f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}")
-
-                frame_index += 1
-    finally:
-        print(f"Images have been saved to {images_dir}")
-        for camera in cameras:
-            camera.disconnect()
-
-
-class IntelRealSenseCamera:
-    """
-    The IntelRealSenseCamera class is similar to OpenCVCamera class but adds additional features for Intel Real Sense cameras:
-    - is instantiated with the serial number of the camera - won't randomly change as it can be the case of OpenCVCamera for Linux,
-    - can also be instantiated with the camera's name — if it's unique — using IntelRealSenseCamera.init_from_name(),
-    - depth map can be returned.
-
-    To find the camera indices of your cameras, you can run our utility script that will save a few frames for each camera:
-    ```bash
-    python lerobot/common/robot_devices/cameras/intelrealsense.py --images-dir outputs/images_from_intelrealsense_cameras
-    ```
-
-    When an IntelRealSenseCamera is instantiated, if no specific config is provided, the default fps, width, height and color_mode
-    of the given camera will be used.
-
-    Example of instantiating with a serial number:
-    ```python
-    from lerobot.common.robot_devices.cameras.configs import IntelRealSenseCameraConfig
-
-    config = IntelRealSenseCameraConfig(serial_number=128422271347)
-    camera = IntelRealSenseCamera(config)
-    camera.connect()
-    color_image = camera.read()
-    # when done using the camera, consider disconnecting
-    camera.disconnect()
-    ```
-
-    Example of instantiating with a name if it's unique:
-    ```
-    config = IntelRealSenseCameraConfig(name="Intel RealSense D405")
-    ```
-
-    Example of changing default fps, width, height and color_mode:
-    ```python
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, fps=30, width=1280, height=720)
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, fps=90, width=640, height=480)
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, fps=90, width=640, height=480, color_mode="bgr")
-    # Note: might error out upon `camera.connect()` if these settings are not compatible with the camera
-    ```
-
-    Example of returning depth:
-    ```python
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, use_depth=True)
-    camera = IntelRealSenseCamera(config)
-    camera.connect()
-    color_image, depth_map = camera.read()
-    ```
-    """
-
-    def __init__(
-        self,
-        config: IntelRealSenseCameraConfig,
-    ):
-        self.config = config
-        if config.name is not None:
-            self.serial_number = self.find_serial_number_from_name(config.name)
-        else:
-            self.serial_number = config.serial_number
-
-        # Store the raw (capture) resolution from the config.
-        self.capture_width = config.width
-        self.capture_height = config.height
-
-        # If rotated by ±90, swap width and height.
-        if config.rotation in [-90, 90]:
-            self.width = config.height
-            self.height = config.width
-        else:
-            self.width = config.width
-            self.height = config.height
-
-        self.fps = config.fps
-        self.channels = config.channels
-        self.color_mode = config.color_mode
-        self.use_depth = config.use_depth
-        self.force_hardware_reset = config.force_hardware_reset
-        self.mock = config.mock
-
-        self.camera = None
-        self.is_connected = False
-        self.thread = None
-        self.stop_event = None
-        self.color_image = None
-        self.depth_map = None
-        self.logs = {}
-
-        if self.mock:
-            import tests.cameras.mock_cv2 as cv2
-        else:
-            import cv2
-
-        self.rotation = None
-        if config.rotation == -90:
-            self.rotation = cv2.ROTATE_90_COUNTERCLOCKWISE
-        elif config.rotation == 90:
-            self.rotation = cv2.ROTATE_90_CLOCKWISE
-        elif config.rotation == 180:
-            self.rotation = cv2.ROTATE_180
-
-    def find_serial_number_from_name(self, name):
-        camera_infos = find_cameras()
-        camera_names = [cam["name"] for cam in camera_infos]
-        this_name_count = Counter(camera_names)[name]
-        if this_name_count > 1:
-            # TODO(aliberts): Test this with multiple identical cameras (Aloha)
-            raise ValueError(
-                f"Multiple {name} cameras have been detected. Please use their serial number to instantiate them."
-            )
-
-        name_to_serial_dict = {cam["name"]: cam["serial_number"] for cam in camera_infos}
-        cam_sn = name_to_serial_dict[name]
-
-        return cam_sn
-
-    def connect(self):
-        if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is already connected."
-            )
-
-        if self.mock:
-            import tests.cameras.mock_pyrealsense2 as rs
-        else:
-            import pyrealsense2 as rs
-
-        config = rs.config()
-        config.enable_device(str(self.serial_number))
-
-        if self.fps and self.capture_width and self.capture_height:
-            # TODO(rcadene): can we set rgb8 directly?
-            config.enable_stream(
-                rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
-            )
-        else:
-            config.enable_stream(rs.stream.color)
-
-        if self.use_depth:
-            if self.fps and self.capture_width and self.capture_height:
-                config.enable_stream(
-                    rs.stream.depth, self.capture_width, self.capture_height, rs.format.z16, self.fps
-                )
-            else:
-                config.enable_stream(rs.stream.depth)
-
-        self.camera = rs.pipeline()
-        try:
-            profile = self.camera.start(config)
-            is_camera_open = True
-        except RuntimeError:
-            is_camera_open = False
-            traceback.print_exc()
-
-        # If the camera doesn't work, display the camera indices corresponding to
-        # valid cameras.
-        if not is_camera_open:
-            # Verify that the provided `serial_number` is valid before printing the traceback
-            camera_infos = find_cameras()
-            serial_numbers = [cam["serial_number"] for cam in camera_infos]
-            if self.serial_number not in serial_numbers:
-                raise ValueError(
-                    f"`serial_number` is expected to be one of these available cameras {serial_numbers}, but {self.serial_number} is provided instead. "
-                    "To find the serial number you should use, run `python lerobot/common/robot_devices/cameras/intelrealsense.py`."
-                )
-
-            raise OSError(f"Can't access IntelRealSenseCamera({self.serial_number}).")
-
-        color_stream = profile.get_stream(rs.stream.color)
-        color_profile = color_stream.as_video_stream_profile()
-        actual_fps = color_profile.fps()
-        actual_width = color_profile.width()
-        actual_height = color_profile.height()
-
-        # Using `math.isclose` since actual fps can be a float (e.g. 29.9 instead of 30)
-        if self.fps is not None and not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
-            # Using `OSError` since it's a broad that encompasses issues related to device communication
-            raise OSError(
-                f"Can't set {self.fps=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_fps}."
-            )
-        if self.capture_width is not None and self.capture_width != actual_width:
-            raise OSError(
-                f"Can't set {self.capture_width=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_width}."
-            )
-        if self.capture_height is not None and self.capture_height != actual_height:
-            raise OSError(
-                f"Can't set {self.capture_height=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_height}."
-            )
-
-        self.fps = round(actual_fps)
-        self.capture_width = round(actual_width)
-        self.capture_height = round(actual_height)
-
-        self.is_connected = True
-
-    def read(self, temporary_color: str | None = None) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
-        """Read a frame from the camera returned in the format height x width x channels (e.g. 480 x 640 x 3)
-        of type `np.uint8`, contrarily to the pytorch format which is float channel first.
-
-        When `use_depth=True`, returns a tuple `(color_image, depth_map)` with a depth map in the format
-        height x width (e.g. 480 x 640) of type np.uint16.
-
-        Note: Reading a frame is done every `camera.fps` times per second, and it is blocking.
-        If you are reading data from other sensors, we advise to use `camera.async_read()` which is non blocking version of `camera.read()`.
-        """
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
-            )
-
-        if self.mock:
-            import tests.cameras.mock_cv2 as cv2
-        else:
-            import cv2
-
-        start_time = time.perf_counter()
-
-        frame = self.camera.wait_for_frames(timeout_ms=5000)
-
-        color_frame = frame.get_color_frame()
-
-        if not color_frame:
-            raise OSError(f"Can't capture color image from IntelRealSenseCamera({self.serial_number}).")
-
-        color_image = np.asanyarray(color_frame.get_data())
-
-        requested_color_mode = self.color_mode if temporary_color is None else temporary_color
-        if requested_color_mode not in ["rgb", "bgr"]:
-            raise ValueError(
-                f"Expected color values are 'rgb' or 'bgr', but {requested_color_mode} is provided."
-            )
-
-        # IntelRealSense uses RGB format as default (red, green, blue).
-        if requested_color_mode == "bgr":
-            color_image = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)
-
-        h, w, _ = color_image.shape
-        if h != self.capture_height or w != self.capture_width:
-            raise OSError(
-                f"Can't capture color image with expected height and width ({self.height} x {self.width}). ({h} x {w}) returned instead."
-            )
-
-        if self.rotation is not None:
-            color_image = cv2.rotate(color_image, self.rotation)
-
-        # log the number of seconds it took to read the image
-        self.logs["delta_timestamp_s"] = time.perf_counter() - start_time
-
-        # log the utc time at which the image was received
-        self.logs["timestamp_utc"] = capture_timestamp_utc()
-
-        if self.use_depth:
-            depth_frame = frame.get_depth_frame()
-            if not depth_frame:
-                raise OSError(f"Can't capture depth image from IntelRealSenseCamera({self.serial_number}).")
-
-            depth_map = np.asanyarray(depth_frame.get_data())
-
-            h, w = depth_map.shape
-            if h != self.capture_height or w != self.capture_width:
-                raise OSError(
-                    f"Can't capture depth map with expected height and width ({self.height} x {self.width}). ({h} x {w}) returned instead."
-                )
-
-            if self.rotation is not None:
-                depth_map = cv2.rotate(depth_map, self.rotation)
-
-            return color_image, depth_map
-        else:
-            return color_image
-
-    def read_loop(self):
-        while not self.stop_event.is_set():
-            if self.use_depth:
-                self.color_image, self.depth_map = self.read()
-            else:
-                self.color_image = self.read()
-
-    def async_read(self):
-        """Access the latest color image"""
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
-            )
-
-        if self.thread is None:
-            self.stop_event = threading.Event()
-            self.thread = Thread(target=self.read_loop, args=())
-            self.thread.daemon = True
-            self.thread.start()
-
-        num_tries = 0
-        while self.color_image is None:
-            # TODO(rcadene, aliberts): intelrealsense has diverged compared to opencv over here
-            num_tries += 1
-            time.sleep(1 / self.fps)
-            if num_tries > self.fps and (self.thread.ident is None or not self.thread.is_alive()):
-                raise Exception(
-                    "The thread responsible for `self.async_read()` took too much time to start. There might be an issue. Verify that `self.thread.start()` has been called."
-                )
-
-        if self.use_depth:
-            return self.color_image, self.depth_map
-        else:
-            return self.color_image
-
-    def disconnect(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
-            )
-
-        if self.thread is not None and self.thread.is_alive():
-            # wait for the thread to finish
-            self.stop_event.set()
-            self.thread.join()
-            self.thread = None
-            self.stop_event = None
-
-        self.camera.stop()
-        self.camera = None
-
-        self.is_connected = False
-
-    def __del__(self):
-        if getattr(self, "is_connected", False):
-            self.disconnect()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Save a few frames using `IntelRealSenseCamera` for all cameras connected to the computer, or a selected subset."
-    )
-    parser.add_argument(
-        "--serial-numbers",
-        type=int,
-        nargs="*",
-        default=None,
-        help="List of serial numbers used to instantiate the `IntelRealSenseCamera`. If not provided, find and use all available camera indices.",
-    )
-    parser.add_argument(
-        "--fps",
-        type=int,
-        default=30,
-        help="Set the number of frames recorded per seconds for all cameras. If not provided, use the default fps of each camera.",
-    )
-    parser.add_argument(
-        "--width",
-        type=int,
-        default=640,
-        help="Set the width for all cameras. If not provided, use the default width of each camera.",
-    )
-    parser.add_argument(
-        "--height",
-        type=int,
-        default=480,
-        help="Set the height for all cameras. If not provided, use the default height of each camera.",
-    )
-    parser.add_argument(
-        "--images-dir",
-        type=Path,
-        default="outputs/images_from_intelrealsense_cameras",
-        help="Set directory to save a few frames for each camera.",
-    )
-    parser.add_argument(
-        "--record-time-s",
-        type=float,
-        default=2.0,
-        help="Set the number of seconds used to record the frames. By default, 2 seconds.",
-    )
-    args = parser.parse_args()
-    save_images_from_cameras(**vars(args))
--- a/lerobot/common/robot_devices/cameras/opencv.py
+++ b/lerobot/common/robot_devices/cameras/opencv.py
@@ -1,518 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This file contains utilities for recording frames from cameras. For more info look at `OpenCVCamera` docstring.
-"""
-
-import argparse
-import concurrent.futures
-import math
-import platform
-import shutil
-import threading
-import time
-from pathlib import Path
-from threading import Thread
-
-import numpy as np
-from PIL import Image
-
-from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig
-from lerobot.common.robot_devices.utils import (
-    RobotDeviceAlreadyConnectedError,
-    RobotDeviceNotConnectedError,
-    busy_wait,
-)
-from lerobot.common.utils.utils import capture_timestamp_utc
-
-# The maximum opencv device index depends on your operating system. For instance,
-# if you have 3 cameras, they should be associated to index 0, 1, and 2. This is the case
-# on MacOS. However, on Ubuntu, the indices are different like 6, 16, 23.
-# When you change the USB port or reboot the computer, the operating system might
-# treat the same cameras as new devices. Thus we select a higher bound to search indices.
-MAX_OPENCV_INDEX = 60
-
-
-def find_cameras(raise_when_empty=False, max_index_search_range=MAX_OPENCV_INDEX, mock=False) -> list[dict]:
-    cameras = []
-    if platform.system() == "Linux":
-        print("Linux detected. Finding available camera indices through scanning '/dev/video*' ports")
-        possible_ports = [str(port) for port in Path("/dev").glob("video*")]
-        ports = _find_cameras(possible_ports, mock=mock)
-        for port in ports:
-            cameras.append(
-                {
-                    "port": port,
-                    "index": int(port.removeprefix("/dev/video")),
-                }
-            )
-    else:
-        print(
-            "Mac or Windows detected. Finding available camera indices through "
-            f"scanning all indices from 0 to {MAX_OPENCV_INDEX}"
-        )
-        possible_indices = range(max_index_search_range)
-        indices = _find_cameras(possible_indices, mock=mock)
-        for index in indices:
-            cameras.append(
-                {
-                    "port": None,
-                    "index": index,
-                }
-            )
-
-    return cameras
-
-
-def _find_cameras(
-    possible_camera_ids: list[int | str], raise_when_empty=False, mock=False
-) -> list[int | str]:
-    if mock:
-        import tests.cameras.mock_cv2 as cv2
-    else:
-        import cv2
-
-    camera_ids = []
-    for camera_idx in possible_camera_ids:
-        camera = cv2.VideoCapture(camera_idx)
-        is_open = camera.isOpened()
-        camera.release()
-
-        if is_open:
-            print(f"Camera found at index {camera_idx}")
-            camera_ids.append(camera_idx)
-
-    if raise_when_empty and len(camera_ids) == 0:
-        raise OSError(
-            "Not a single camera was detected. Try re-plugging, or re-installing `opencv2`, "
-            "or your camera driver, or make sure your camera is compatible with opencv2."
-        )
-
-    return camera_ids
-
-
-def is_valid_unix_path(path: str) -> bool:
-    """Note: if 'path' points to a symlink, this will return True only if the target exists"""
-    p = Path(path)
-    return p.is_absolute() and p.exists()
-
-
-def get_camera_index_from_unix_port(port: Path) -> int:
-    return int(str(port.resolve()).removeprefix("/dev/video"))
-
-
-def save_image(img_array, camera_index, frame_index, images_dir):
-    img = Image.fromarray(img_array)
-    path = images_dir / f"camera_{camera_index:02d}_frame_{frame_index:06d}.png"
-    path.parent.mkdir(parents=True, exist_ok=True)
-    img.save(str(path), quality=100)
-
-
-def save_images_from_cameras(
-    images_dir: Path,
-    camera_ids: list | None = None,
-    fps=None,
-    width=None,
-    height=None,
-    record_time_s=2,
-    mock=False,
-):
-    """
-    Initializes all the cameras and saves images to the directory. Useful to visually identify the camera
-    associated to a given camera index.
-    """
-    if camera_ids is None or len(camera_ids) == 0:
-        camera_infos = find_cameras(mock=mock)
-        camera_ids = [cam["index"] for cam in camera_infos]
-
-    print("Connecting cameras")
-    cameras = []
-    for cam_idx in camera_ids:
-        config = OpenCVCameraConfig(camera_index=cam_idx, fps=fps, width=width, height=height, mock=mock)
-        camera = OpenCVCamera(config)
-        camera.connect()
-        print(
-            f"OpenCVCamera({camera.camera_index}, fps={camera.fps}, width={camera.capture_width}, "
-            f"height={camera.capture_height}, color_mode={camera.color_mode})"
-        )
-        cameras.append(camera)
-
-    images_dir = Path(images_dir)
-    if images_dir.exists():
-        shutil.rmtree(
-            images_dir,
-        )
-    images_dir.mkdir(parents=True, exist_ok=True)
-
-    print(f"Saving images to {images_dir}")
-    frame_index = 0
-    start_time = time.perf_counter()
-    with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
-        while True:
-            now = time.perf_counter()
-
-            for camera in cameras:
-                # If we use async_read when fps is None, the loop will go full speed, and we will endup
-                # saving the same images from the cameras multiple times until the RAM/disk is full.
-                image = camera.read() if fps is None else camera.async_read()
-
-                executor.submit(
-                    save_image,
-                    image,
-                    camera.camera_index,
-                    frame_index,
-                    images_dir,
-                )
-
-            if fps is not None:
-                dt_s = time.perf_counter() - now
-                busy_wait(1 / fps - dt_s)
-
-            print(f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}")
-
-            if time.perf_counter() - start_time > record_time_s:
-                break
-
-            frame_index += 1
-
-    print(f"Images have been saved to {images_dir}")
-
-
-class OpenCVCamera:
-    """
-    The OpenCVCamera class allows to efficiently record images from cameras. It relies on opencv2 to communicate
-    with the cameras. Most cameras are compatible. For more info, see the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
-
-    An OpenCVCamera instance requires a camera index (e.g. `OpenCVCamera(camera_index=0)`). When you only have one camera
-    like a webcam of a laptop, the camera index is expected to be 0, but it might also be very different, and the camera index
-    might change if you reboot your computer or re-plug your camera. This behavior depends on your operation system.
-
-    To find the camera indices of your cameras, you can run our utility script that will be save a few frames for each camera:
-    ```bash
-    python lerobot/common/robot_devices/cameras/opencv.py --images-dir outputs/images_from_opencv_cameras
-    ```
-
-    When an OpenCVCamera is instantiated, if no specific config is provided, the default fps, width, height and color_mode
-    of the given camera will be used.
-
-    Example of usage:
-    ```python
-    from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig
-
-    config = OpenCVCameraConfig(camera_index=0)
-    camera = OpenCVCamera(config)
-    camera.connect()
-    color_image = camera.read()
-    # when done using the camera, consider disconnecting
-    camera.disconnect()
-    ```
-
-    Example of changing default fps, width, height and color_mode:
-    ```python
-    config = OpenCVCameraConfig(camera_index=0, fps=30, width=1280, height=720)
-    config = OpenCVCameraConfig(camera_index=0, fps=90, width=640, height=480)
-    config = OpenCVCameraConfig(camera_index=0, fps=90, width=640, height=480, color_mode="bgr")
-    # Note: might error out open `camera.connect()` if these settings are not compatible with the camera
-    ```
-    """
-
-    def __init__(self, config: OpenCVCameraConfig):
-        self.config = config
-        self.camera_index = config.camera_index
-        self.port = None
-
-        # Linux uses ports for connecting to cameras
-        if platform.system() == "Linux":
-            if isinstance(self.camera_index, int):
-                self.port = Path(f"/dev/video{self.camera_index}")
-            elif isinstance(self.camera_index, str) and is_valid_unix_path(self.camera_index):
-                self.port = Path(self.camera_index)
-                # Retrieve the camera index from a potentially symlinked path
-                self.camera_index = get_camera_index_from_unix_port(self.port)
-            else:
-                raise ValueError(f"Please check the provided camera_index: {self.camera_index}")
-
-        # Store the raw (capture) resolution from the config.
-        self.capture_width = config.width
-        self.capture_height = config.height
-
-        # If rotated by ±90, swap width and height.
-        if config.rotation in [-90, 90]:
-            self.width = config.height
-            self.height = config.width
-        else:
-            self.width = config.width
-            self.height = config.height
-
-        self.fps = config.fps
-        self.channels = config.channels
-        self.color_mode = config.color_mode
-        self.mock = config.mock
-
-        self.camera = None
-        self.is_connected = False
-        self.thread = None
-        self.stop_event = None
-        self.color_image = None
-        self.logs = {}
-
-        if self.mock:
-            import tests.cameras.mock_cv2 as cv2
-        else:
-            import cv2
-
-        self.rotation = None
-        if config.rotation == -90:
-            self.rotation = cv2.ROTATE_90_COUNTERCLOCKWISE
-        elif config.rotation == 90:
-            self.rotation = cv2.ROTATE_90_CLOCKWISE
-        elif config.rotation == 180:
-            self.rotation = cv2.ROTATE_180
-
-    def connect(self):
-        if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(f"OpenCVCamera({self.camera_index}) is already connected.")
-
-        if self.mock:
-            import tests.cameras.mock_cv2 as cv2
-        else:
-            import cv2
-
-            # Use 1 thread to avoid blocking the main thread. Especially useful during data collection
-            # when other threads are used to save the images.
-            cv2.setNumThreads(1)
-
-        backend = (
-            cv2.CAP_V4L2
-            if platform.system() == "Linux"
-            else cv2.CAP_DSHOW
-            if platform.system() == "Windows"
-            else cv2.CAP_AVFOUNDATION
-            if platform.system() == "Darwin"
-            else cv2.CAP_ANY
-        )
-
-        camera_idx = f"/dev/video{self.camera_index}" if platform.system() == "Linux" else self.camera_index
-        # First create a temporary camera trying to access `camera_index`,
-        # and verify it is a valid camera by calling `isOpened`.
-        tmp_camera = cv2.VideoCapture(camera_idx, backend)
-        is_camera_open = tmp_camera.isOpened()
-        # Release camera to make it accessible for `find_camera_indices`
-        tmp_camera.release()
-        del tmp_camera
-
-        # If the camera doesn't work, display the camera indices corresponding to
-        # valid cameras.
-        if not is_camera_open:
-            # Verify that the provided `camera_index` is valid before printing the traceback
-            cameras_info = find_cameras()
-            available_cam_ids = [cam["index"] for cam in cameras_info]
-            if self.camera_index not in available_cam_ids:
-                raise ValueError(
-                    f"`camera_index` is expected to be one of these available cameras {available_cam_ids}, but {self.camera_index} is provided instead. "
-                    "To find the camera index you should use, run `python lerobot/common/robot_devices/cameras/opencv.py`."
-                )
-
-            raise OSError(f"Can't access OpenCVCamera({camera_idx}).")
-
-        # Secondly, create the camera that will be used downstream.
-        # Note: For some unknown reason, calling `isOpened` blocks the camera which then
-        # needs to be re-created.
-        self.camera = cv2.VideoCapture(camera_idx, backend)
-
-        if self.fps is not None:
-            self.camera.set(cv2.CAP_PROP_FPS, self.fps)
-        if self.capture_width is not None:
-            self.camera.set(cv2.CAP_PROP_FRAME_WIDTH, self.capture_width)
-        if self.capture_height is not None:
-            self.camera.set(cv2.CAP_PROP_FRAME_HEIGHT, self.capture_height)
-
-        actual_fps = self.camera.get(cv2.CAP_PROP_FPS)
-        actual_width = self.camera.get(cv2.CAP_PROP_FRAME_WIDTH)
-        actual_height = self.camera.get(cv2.CAP_PROP_FRAME_HEIGHT)
-
-        # Using `math.isclose` since actual fps can be a float (e.g. 29.9 instead of 30)
-        if self.fps is not None and not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
-            # Using `OSError` since it's a broad that encompasses issues related to device communication
-            raise OSError(
-                f"Can't set {self.fps=} for OpenCVCamera({self.camera_index}). Actual value is {actual_fps}."
-            )
-        if self.capture_width is not None and not math.isclose(
-            self.capture_width, actual_width, rel_tol=1e-3
-        ):
-            raise OSError(
-                f"Can't set {self.capture_width=} for OpenCVCamera({self.camera_index}). Actual value is {actual_width}."
-            )
-        if self.capture_height is not None and not math.isclose(
-            self.capture_height, actual_height, rel_tol=1e-3
-        ):
-            raise OSError(
-                f"Can't set {self.capture_height=} for OpenCVCamera({self.camera_index}). Actual value is {actual_height}."
-            )
-
-        self.fps = round(actual_fps)
-        self.capture_width = round(actual_width)
-        self.capture_height = round(actual_height)
-        self.is_connected = True
-
-    def read(self, temporary_color_mode: str | None = None) -> np.ndarray:
-        """Read a frame from the camera returned in the format (height, width, channels)
-        (e.g. 480 x 640 x 3), contrarily to the pytorch format which is channel first.
-
-        Note: Reading a frame is done every `camera.fps` times per second, and it is blocking.
-        If you are reading data from other sensors, we advise to use `camera.async_read()` which is non blocking version of `camera.read()`.
-        """
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"OpenCVCamera({self.camera_index}) is not connected. Try running `camera.connect()` first."
-            )
-
-        start_time = time.perf_counter()
-
-        ret, color_image = self.camera.read()
-
-        if not ret:
-            raise OSError(f"Can't capture color image from camera {self.camera_index}.")
-
-        requested_color_mode = self.color_mode if temporary_color_mode is None else temporary_color_mode
-
-        if requested_color_mode not in ["rgb", "bgr"]:
-            raise ValueError(
-                f"Expected color values are 'rgb' or 'bgr', but {requested_color_mode} is provided."
-            )
-
-        # OpenCV uses BGR format as default (blue, green, red) for all operations, including displaying images.
-        # However, Deep Learning framework such as LeRobot uses RGB format as default to train neural networks,
-        # so we convert the image color from BGR to RGB.
-        if requested_color_mode == "rgb":
-            if self.mock:
-                import tests.cameras.mock_cv2 as cv2
-            else:
-                import cv2
-
-            color_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB)
-
-        h, w, _ = color_image.shape
-        if h != self.capture_height or w != self.capture_width:
-            raise OSError(
-                f"Can't capture color image with expected height and width ({self.height} x {self.width}). ({h} x {w}) returned instead."
-            )
-
-        if self.rotation is not None:
-            color_image = cv2.rotate(color_image, self.rotation)
-
-        # log the number of seconds it took to read the image
-        self.logs["delta_timestamp_s"] = time.perf_counter() - start_time
-
-        # log the utc time at which the image was received
-        self.logs["timestamp_utc"] = capture_timestamp_utc()
-
-        self.color_image = color_image
-
-        return color_image
-
-    def read_loop(self):
-        while not self.stop_event.is_set():
-            try:
-                self.color_image = self.read()
-            except Exception as e:
-                print(f"Error reading in thread: {e}")
-
-    def async_read(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"OpenCVCamera({self.camera_index}) is not connected. Try running `camera.connect()` first."
-            )
-
-        if self.thread is None:
-            self.stop_event = threading.Event()
-            self.thread = Thread(target=self.read_loop, args=())
-            self.thread.daemon = True
-            self.thread.start()
-
-        num_tries = 0
-        while True:
-            if self.color_image is not None:
-                return self.color_image
-
-            time.sleep(1 / self.fps)
-            num_tries += 1
-            if num_tries > self.fps * 2:
-                raise TimeoutError("Timed out waiting for async_read() to start.")
-
-    def disconnect(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"OpenCVCamera({self.camera_index}) is not connected. Try running `camera.connect()` first."
-            )
-
-        if self.thread is not None:
-            self.stop_event.set()
-            self.thread.join()  # wait for the thread to finish
-            self.thread = None
-            self.stop_event = None
-
-        self.camera.release()
-        self.camera = None
-        self.is_connected = False
-
-    def __del__(self):
-        if getattr(self, "is_connected", False):
-            self.disconnect()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Save a few frames using `OpenCVCamera` for all cameras connected to the computer, or a selected subset."
-    )
-    parser.add_argument(
-        "--camera-ids",
-        type=int,
-        nargs="*",
-        default=None,
-        help="List of camera indices used to instantiate the `OpenCVCamera`. If not provided, find and use all available camera indices.",
-    )
-    parser.add_argument(
-        "--fps",
-        type=int,
-        default=None,
-        help="Set the number of frames recorded per seconds for all cameras. If not provided, use the default fps of each camera.",
-    )
-    parser.add_argument(
-        "--width",
-        type=int,
-        default=None,
-        help="Set the width for all cameras. If not provided, use the default width of each camera.",
-    )
-    parser.add_argument(
-        "--height",
-        type=int,
-        default=None,
-        help="Set the height for all cameras. If not provided, use the default height of each camera.",
-    )
-    parser.add_argument(
-        "--images-dir",
-        type=Path,
-        default="outputs/images_from_opencv_cameras",
-        help="Set directory to save a few frames for each camera.",
-    )
-    parser.add_argument(
-        "--record-time-s",
-        type=float,
-        default=4.0,
-        help="Set the number of seconds used to record the frames. By default, 2 seconds.",
-    )
-    args = parser.parse_args()
-    save_images_from_cameras(**vars(args))
--- a/lerobot/common/robot_devices/cameras/utils.py
+++ b/lerobot/common/robot_devices/cameras/utils.py
@@ -1,67 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Protocol
-
-import numpy as np
-
-from lerobot.common.robot_devices.cameras.configs import (
-    CameraConfig,
-    IntelRealSenseCameraConfig,
-    OpenCVCameraConfig,
-)
-
-
-# Defines a camera type
-class Camera(Protocol):
-    def connect(self): ...
-    def read(self, temporary_color: str | None = None) -> np.ndarray: ...
-    def async_read(self) -> np.ndarray: ...
-    def disconnect(self): ...
-
-
-def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> list[Camera]:
-    cameras = {}
-
-    for key, cfg in camera_configs.items():
-        if cfg.type == "opencv":
-            from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
-
-            cameras[key] = OpenCVCamera(cfg)
-
-        elif cfg.type == "intelrealsense":
-            from lerobot.common.robot_devices.cameras.intelrealsense import IntelRealSenseCamera
-
-            cameras[key] = IntelRealSenseCamera(cfg)
-        else:
-            raise ValueError(f"The camera type '{cfg.type}' is not valid.")
-
-    return cameras
-
-
-def make_camera(camera_type, **kwargs) -> Camera:
-    if camera_type == "opencv":
-        from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
-
-        config = OpenCVCameraConfig(**kwargs)
-        return OpenCVCamera(config)
-
-    elif camera_type == "intelrealsense":
-        from lerobot.common.robot_devices.cameras.intelrealsense import IntelRealSenseCamera
-
-        config = IntelRealSenseCameraConfig(**kwargs)
-        return IntelRealSenseCamera(config)
-
-    else:
-        raise ValueError(f"The camera type '{camera_type}' is not valid.")
--- a/lerobot/common/robot_devices/motors/dynamixel.py
+++ b/lerobot/common/robot_devices/motors/dynamixel.py
@@ -1,873 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import enum
-import logging
-import math
-import time
-import traceback
-from copy import deepcopy
-
-import numpy as np
-import tqdm
-
-from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
-from lerobot.common.utils.utils import capture_timestamp_utc
-
-PROTOCOL_VERSION = 2.0
-BAUDRATE = 1_000_000
-TIMEOUT_MS = 1000
-
-MAX_ID_RANGE = 252
-
-# The following bounds define the lower and upper joints range (after calibration).
-# For joints in degree (i.e. revolute joints), their nominal range is [-180, 180] degrees
-# which corresponds to a half rotation on the left and half rotation on the right.
-# Some joints might require higher range, so we allow up to [-270, 270] degrees until
-# an error is raised.
-LOWER_BOUND_DEGREE = -270
-UPPER_BOUND_DEGREE = 270
-# For joints in percentage (i.e. joints that move linearly like the prismatic joint of a gripper),
-# their nominal range is [0, 100] %. For instance, for Aloha gripper, 0% is fully
-# closed, and 100% is fully open. To account for slight calibration issue, we allow up to
-# [-10, 110] until an error is raised.
-LOWER_BOUND_LINEAR = -10
-UPPER_BOUND_LINEAR = 110
-
-HALF_TURN_DEGREE = 180
-
-# https://emanual.robotis.com/docs/en/dxl/x/xl330-m077
-# https://emanual.robotis.com/docs/en/dxl/x/xl330-m288
-# https://emanual.robotis.com/docs/en/dxl/x/xl430-w250
-# https://emanual.robotis.com/docs/en/dxl/x/xm430-w350
-# https://emanual.robotis.com/docs/en/dxl/x/xm540-w270
-# https://emanual.robotis.com/docs/en/dxl/x/xc430-w150
-
-# data_name: (address, size_byte)
-X_SERIES_CONTROL_TABLE = {
-    "Model_Number": (0, 2),
-    "Model_Information": (2, 4),
-    "Firmware_Version": (6, 1),
-    "ID": (7, 1),
-    "Baud_Rate": (8, 1),
-    "Return_Delay_Time": (9, 1),
-    "Drive_Mode": (10, 1),
-    "Operating_Mode": (11, 1),
-    "Secondary_ID": (12, 1),
-    "Protocol_Type": (13, 1),
-    "Homing_Offset": (20, 4),
-    "Moving_Threshold": (24, 4),
-    "Temperature_Limit": (31, 1),
-    "Max_Voltage_Limit": (32, 2),
-    "Min_Voltage_Limit": (34, 2),
-    "PWM_Limit": (36, 2),
-    "Current_Limit": (38, 2),
-    "Acceleration_Limit": (40, 4),
-    "Velocity_Limit": (44, 4),
-    "Max_Position_Limit": (48, 4),
-    "Min_Position_Limit": (52, 4),
-    "Shutdown": (63, 1),
-    "Torque_Enable": (64, 1),
-    "LED": (65, 1),
-    "Status_Return_Level": (68, 1),
-    "Registered_Instruction": (69, 1),
-    "Hardware_Error_Status": (70, 1),
-    "Velocity_I_Gain": (76, 2),
-    "Velocity_P_Gain": (78, 2),
-    "Position_D_Gain": (80, 2),
-    "Position_I_Gain": (82, 2),
-    "Position_P_Gain": (84, 2),
-    "Feedforward_2nd_Gain": (88, 2),
-    "Feedforward_1st_Gain": (90, 2),
-    "Bus_Watchdog": (98, 1),
-    "Goal_PWM": (100, 2),
-    "Goal_Current": (102, 2),
-    "Goal_Velocity": (104, 4),
-    "Profile_Acceleration": (108, 4),
-    "Profile_Velocity": (112, 4),
-    "Goal_Position": (116, 4),
-    "Realtime_Tick": (120, 2),
-    "Moving": (122, 1),
-    "Moving_Status": (123, 1),
-    "Present_PWM": (124, 2),
-    "Present_Current": (126, 2),
-    "Present_Velocity": (128, 4),
-    "Present_Position": (132, 4),
-    "Velocity_Trajectory": (136, 4),
-    "Position_Trajectory": (140, 4),
-    "Present_Input_Voltage": (144, 2),
-    "Present_Temperature": (146, 1),
-}
-
-X_SERIES_BAUDRATE_TABLE = {
-    0: 9_600,
-    1: 57_600,
-    2: 115_200,
-    3: 1_000_000,
-    4: 2_000_000,
-    5: 3_000_000,
-    6: 4_000_000,
-}
-
-CALIBRATION_REQUIRED = ["Goal_Position", "Present_Position"]
-CONVERT_UINT32_TO_INT32_REQUIRED = ["Goal_Position", "Present_Position"]
-
-MODEL_CONTROL_TABLE = {
-    "x_series": X_SERIES_CONTROL_TABLE,
-    "xl330-m077": X_SERIES_CONTROL_TABLE,
-    "xl330-m288": X_SERIES_CONTROL_TABLE,
-    "xl430-w250": X_SERIES_CONTROL_TABLE,
-    "xm430-w350": X_SERIES_CONTROL_TABLE,
-    "xm540-w270": X_SERIES_CONTROL_TABLE,
-    "xc430-w150": X_SERIES_CONTROL_TABLE,
-}
-
-MODEL_RESOLUTION = {
-    "x_series": 4096,
-    "xl330-m077": 4096,
-    "xl330-m288": 4096,
-    "xl430-w250": 4096,
-    "xm430-w350": 4096,
-    "xm540-w270": 4096,
-    "xc430-w150": 4096,
-}
-
-MODEL_BAUDRATE_TABLE = {
-    "x_series": X_SERIES_BAUDRATE_TABLE,
-    "xl330-m077": X_SERIES_BAUDRATE_TABLE,
-    "xl330-m288": X_SERIES_BAUDRATE_TABLE,
-    "xl430-w250": X_SERIES_BAUDRATE_TABLE,
-    "xm430-w350": X_SERIES_BAUDRATE_TABLE,
-    "xm540-w270": X_SERIES_BAUDRATE_TABLE,
-    "xc430-w150": X_SERIES_BAUDRATE_TABLE,
-}
-
-NUM_READ_RETRY = 10
-NUM_WRITE_RETRY = 10
-
-
-def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]) -> np.ndarray:
-    """This function converts the degree range to the step range for indicating motors rotation.
-    It assumes a motor achieves a full rotation by going from -180 degree position to +180.
-    The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
-    """
-    resolutions = [MODEL_RESOLUTION[model] for model in models]
-    steps = degrees / 180 * np.array(resolutions) / 2
-    steps = steps.astype(int)
-    return steps
-
-
-def convert_to_bytes(value, bytes, mock=False):
-    if mock:
-        return value
-
-    import dynamixel_sdk as dxl
-
-    # Note: No need to convert back into unsigned int, since this byte preprocessing
-    # already handles it for us.
-    if bytes == 1:
-        data = [
-            dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)),
-        ]
-    elif bytes == 2:
-        data = [
-            dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)),
-            dxl.DXL_HIBYTE(dxl.DXL_LOWORD(value)),
-        ]
-    elif bytes == 4:
-        data = [
-            dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)),
-            dxl.DXL_HIBYTE(dxl.DXL_LOWORD(value)),
-            dxl.DXL_LOBYTE(dxl.DXL_HIWORD(value)),
-            dxl.DXL_HIBYTE(dxl.DXL_HIWORD(value)),
-        ]
-    else:
-        raise NotImplementedError(
-            f"Value of the number of bytes to be sent is expected to be in [1, 2, 4], but "
-            f"{bytes} is provided instead."
-        )
-    return data
-
-
-def get_group_sync_key(data_name, motor_names):
-    group_key = f"{data_name}_" + "_".join(motor_names)
-    return group_key
-
-
-def get_result_name(fn_name, data_name, motor_names):
-    group_key = get_group_sync_key(data_name, motor_names)
-    rslt_name = f"{fn_name}_{group_key}"
-    return rslt_name
-
-
-def get_queue_name(fn_name, data_name, motor_names):
-    group_key = get_group_sync_key(data_name, motor_names)
-    queue_name = f"{fn_name}_{group_key}"
-    return queue_name
-
-
-def get_log_name(var_name, fn_name, data_name, motor_names):
-    group_key = get_group_sync_key(data_name, motor_names)
-    log_name = f"{var_name}_{fn_name}_{group_key}"
-    return log_name
-
-
-def assert_same_address(model_ctrl_table, motor_models, data_name):
-    all_addr = []
-    all_bytes = []
-    for model in motor_models:
-        addr, bytes = model_ctrl_table[model][data_name]
-        all_addr.append(addr)
-        all_bytes.append(bytes)
-
-    if len(set(all_addr)) != 1:
-        raise NotImplementedError(
-            f"At least two motor models use a different address for `data_name`='{data_name}' ({list(zip(motor_models, all_addr, strict=False))}). Contact a LeRobot maintainer."
-        )
-
-    if len(set(all_bytes)) != 1:
-        raise NotImplementedError(
-            f"At least two motor models use a different bytes representation for `data_name`='{data_name}' ({list(zip(motor_models, all_bytes, strict=False))}). Contact a LeRobot maintainer."
-        )
-
-
-class TorqueMode(enum.Enum):
-    ENABLED = 1
-    DISABLED = 0
-
-
-class DriveMode(enum.Enum):
-    NON_INVERTED = 0
-    INVERTED = 1
-
-
-class CalibrationMode(enum.Enum):
-    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
-    DEGREE = 0
-    # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100]
-    LINEAR = 1
-
-
-class JointOutOfRangeError(Exception):
-    def __init__(self, message="Joint is out of range"):
-        self.message = message
-        super().__init__(self.message)
-
-
-class DynamixelMotorsBus:
-    """
-    The DynamixelMotorsBus class allows to efficiently read and write to the attached motors. It relies on
-    the python dynamixel sdk to communicate with the motors. For more info, see the [Dynamixel SDK Documentation](https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20).
-
-    A DynamixelMotorsBus instance requires a port (e.g. `DynamixelMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
-    To find the port, you can run our utility script:
-    ```bash
-    python lerobot/scripts/find_motors_bus_port.py
-    >>> Finding all available ports for the MotorBus.
-    >>> ['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-    >>> Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
-    >>> The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751.
-    >>> Reconnect the usb cable.
-    ```
-
-    Example of usage for 1 motor connected to the bus:
-    ```python
-    motor_name = "gripper"
-    motor_index = 6
-    motor_model = "xl330-m288"
-
-    config = DynamixelMotorsBusConfig(
-        port="/dev/tty.usbmodem575E0031751",
-        motors={motor_name: (motor_index, motor_model)},
-    )
-    motors_bus = DynamixelMotorsBus(config)
-    motors_bus.connect()
-
-    position = motors_bus.read("Present_Position")
-
-    # move from a few motor steps as an example
-    few_steps = 30
-    motors_bus.write("Goal_Position", position + few_steps)
-
-    # when done, consider disconnecting
-    motors_bus.disconnect()
-    ```
-    """
-
-    def __init__(
-        self,
-        config: DynamixelMotorsBusConfig,
-    ):
-        self.port = config.port
-        self.motors = config.motors
-        self.mock = config.mock
-
-        self.model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
-        self.model_resolution = deepcopy(MODEL_RESOLUTION)
-
-        self.port_handler = None
-        self.packet_handler = None
-        self.calibration = None
-        self.is_connected = False
-        self.group_readers = {}
-        self.group_writers = {}
-        self.logs = {}
-
-    def connect(self):
-        if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
-                f"DynamixelMotorsBus({self.port}) is already connected. Do not call `motors_bus.connect()` twice."
-            )
-
-        if self.mock:
-            import tests.motors.mock_dynamixel_sdk as dxl
-        else:
-            import dynamixel_sdk as dxl
-
-        self.port_handler = dxl.PortHandler(self.port)
-        self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
-
-        try:
-            if not self.port_handler.openPort():
-                raise OSError(f"Failed to open port '{self.port}'.")
-        except Exception:
-            traceback.print_exc()
-            print(
-                "\nTry running `python lerobot/scripts/find_motors_bus_port.py` to make sure you are using the correct port.\n"
-            )
-            raise
-
-        # Allow to read and write
-        self.is_connected = True
-
-        self.port_handler.setPacketTimeoutMillis(TIMEOUT_MS)
-
-    def reconnect(self):
-        if self.mock:
-            import tests.motors.mock_dynamixel_sdk as dxl
-        else:
-            import dynamixel_sdk as dxl
-
-        self.port_handler = dxl.PortHandler(self.port)
-        self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
-
-        if not self.port_handler.openPort():
-            raise OSError(f"Failed to open port '{self.port}'.")
-
-        self.is_connected = True
-
-    def are_motors_configured(self):
-        # Only check the motor indices and not baudrate, since if the motor baudrates are incorrect,
-        # a ConnectionError will be raised anyway.
-        try:
-            return (self.motor_indices == self.read("ID")).all()
-        except ConnectionError as e:
-            print(e)
-            return False
-
-    def find_motor_indices(self, possible_ids=None, num_retry=2):
-        if possible_ids is None:
-            possible_ids = range(MAX_ID_RANGE)
-
-        indices = []
-        for idx in tqdm.tqdm(possible_ids):
-            try:
-                present_idx = self.read_with_motor_ids(self.motor_models, [idx], "ID", num_retry=num_retry)[0]
-            except ConnectionError:
-                continue
-
-            if idx != present_idx:
-                # sanity check
-                raise OSError(
-                    "Motor index used to communicate through the bus is not the same as the one present in the motor memory. The motor memory might be damaged."
-                )
-            indices.append(idx)
-
-        return indices
-
-    def set_bus_baudrate(self, baudrate):
-        present_bus_baudrate = self.port_handler.getBaudRate()
-        if present_bus_baudrate != baudrate:
-            print(f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}.")
-            self.port_handler.setBaudRate(baudrate)
-
-            if self.port_handler.getBaudRate() != baudrate:
-                raise OSError("Failed to write bus baud rate.")
-
-    @property
-    def motor_names(self) -> list[str]:
-        return list(self.motors.keys())
-
-    @property
-    def motor_models(self) -> list[str]:
-        return [model for _, model in self.motors.values()]
-
-    @property
-    def motor_indices(self) -> list[int]:
-        return [idx for idx, _ in self.motors.values()]
-
-    def set_calibration(self, calibration: dict[str, list]):
-        self.calibration = calibration
-
-    def apply_calibration_autocorrect(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """This function applies the calibration, automatically detects out of range errors for motors values and attempts to correct.
-
-        For more info, see docstring of `apply_calibration` and `autocorrect_calibration`.
-        """
-        try:
-            values = self.apply_calibration(values, motor_names)
-        except JointOutOfRangeError as e:
-            print(e)
-            self.autocorrect_calibration(values, motor_names)
-            values = self.apply_calibration(values, motor_names)
-        return values
-
-    def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
-        a "zero position" at 0 degree.
-
-        Note: We say "nominal degree range" since the motors can take values outside this range. For instance, 190 degrees, if the motor
-        rotate more than a half a turn from the zero position. However, most motors can't rotate more than 180 degrees and will stay in this range.
-
-        Joints values are original in [0, 2**32[ (unsigned int32). Each motor are expected to complete a full rotation
-        when given a goal position that is + or - their resolution. For instance, dynamixel xl330-m077 have a resolution of 4096, and
-        at any position in their original range, let's say the position 56734, they complete a full rotation clockwise by moving to 60830,
-        or anticlockwise by moving to 52638. The position in the original range is arbitrary and might change a lot between each motor.
-        To harmonize between motors of the same model, different robots, or even models of different brands, we propose to work
-        in the centered nominal degree range ]-180, 180[.
-        """
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
-        values = values.astype(np.float32)
-
-        for i, name in enumerate(motor_names):
-            calib_idx = self.calibration["motor_names"].index(name)
-            calib_mode = self.calibration["calib_mode"][calib_idx]
-
-            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
-
-                # Update direction of rotation of the motor to match between leader and follower.
-                # In fact, the motor of the leader for a given joint can be assembled in an
-                # opposite direction in term of rotation than the motor of the follower on the same joint.
-                if drive_mode:
-                    values[i] *= -1
-
-                # Convert from range [-2**31, 2**31] to
-                # nominal range [-resolution//2, resolution//2] (e.g. [-2048, 2048])
-                values[i] += homing_offset
-
-                # Convert from range [-resolution//2, resolution//2] to
-                # universal float32 centered degree range [-180, 180]
-                # (e.g. 2048 / (4096 // 2) * 180 = 180)
-                values[i] = values[i] / (resolution // 2) * HALF_TURN_DEGREE
-
-                if (values[i] < LOWER_BOUND_DEGREE) or (values[i] > UPPER_BOUND_DEGREE):
-                    raise JointOutOfRangeError(
-                        f"Wrong motor position range detected for {name}. "
-                        f"Expected to be in nominal range of [-{HALF_TURN_DEGREE}, {HALF_TURN_DEGREE}] degrees (a full rotation), "
-                        f"with a maximum range of [{LOWER_BOUND_DEGREE}, {UPPER_BOUND_DEGREE}] degrees to account for joints that can rotate a bit more, "
-                        f"but present value is {values[i]} degree. "
-                        "This might be due to a cable connection issue creating an artificial 360 degrees jump in motor values. "
-                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
-                    )
-
-            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                start_pos = self.calibration["start_pos"][calib_idx]
-                end_pos = self.calibration["end_pos"][calib_idx]
-
-                # Rescale the present position to a nominal range [0, 100] %,
-                # useful for joints with linear motions like Aloha gripper
-                values[i] = (values[i] - start_pos) / (end_pos - start_pos) * 100
-
-                if (values[i] < LOWER_BOUND_LINEAR) or (values[i] > UPPER_BOUND_LINEAR):
-                    raise JointOutOfRangeError(
-                        f"Wrong motor position range detected for {name}. "
-                        f"Expected to be in nominal range of [0, 100] % (a full linear translation), "
-                        f"with a maximum range of [{LOWER_BOUND_LINEAR}, {UPPER_BOUND_LINEAR}] % to account for some imprecision during calibration, "
-                        f"but present value is {values[i]} %. "
-                        "This might be due to a cable connection issue creating an artificial jump in motor values. "
-                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
-                    )
-
-        return values
-
-    def autocorrect_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """This function automatically detects issues with values of motors after calibration, and correct for these issues.
-
-        Some motors might have values outside of expected maximum bounds after calibration.
-        For instance, for a joint in degree, its value can be outside [-270, 270] degrees, which is totally unexpected given
-        a nominal range of [-180, 180] degrees, which represents half a turn to the left or right starting from zero position.
-
-        Known issues:
-        #1: Motor value randomly shifts of a full turn, caused by hardware/connection errors.
-        #2: Motor internal homing offset is shifted by a full turn, caused by using default calibration (e.g Aloha).
-        #3: motor internal homing offset is shifted by less or more than a full turn, caused by using default calibration
-            or by human error during manual calibration.
-
-        Issues #1 and #2 can be solved by shifting the calibration homing offset by a full turn.
-        Issue #3 will be visually detected by user and potentially captured by the safety feature `max_relative_target`,
-        that will slow down the motor, raise an error asking to recalibrate. Manual recalibrating will solve the issue.
-
-        Note: A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
-        """
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
-        values = values.astype(np.float32)
-
-        for i, name in enumerate(motor_names):
-            calib_idx = self.calibration["motor_names"].index(name)
-            calib_mode = self.calibration["calib_mode"][calib_idx]
-
-            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
-
-                # Update direction of rotation of the motor to match between leader and follower.
-                # In fact, the motor of the leader for a given joint can be assembled in an
-                # opposite direction in term of rotation than the motor of the follower on the same joint.
-                if drive_mode:
-                    values[i] *= -1
-
-                # Convert from initial range to range [-180, 180] degrees
-                calib_val = (values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
-                in_range = (calib_val > LOWER_BOUND_DEGREE) and (calib_val < UPPER_BOUND_DEGREE)
-
-                # Solve this inequality to find the factor to shift the range into [-180, 180] degrees
-                # values[i] = (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE
-                # - HALF_TURN_DEGREE <= (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE <= HALF_TURN_DEGREE
-                # (- (resolution // 2) - values[i] - homing_offset) / resolution <= factor <= ((resolution // 2) - values[i] - homing_offset) / resolution
-                low_factor = (-(resolution // 2) - values[i] - homing_offset) / resolution
-                upp_factor = ((resolution // 2) - values[i] - homing_offset) / resolution
-
-            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                start_pos = self.calibration["start_pos"][calib_idx]
-                end_pos = self.calibration["end_pos"][calib_idx]
-
-                # Convert from initial range to range [0, 100] in %
-                calib_val = (values[i] - start_pos) / (end_pos - start_pos) * 100
-                in_range = (calib_val > LOWER_BOUND_LINEAR) and (calib_val < UPPER_BOUND_LINEAR)
-
-                # Solve this inequality to find the factor to shift the range into [0, 100] %
-                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos + resolution * factor - start_pos - resolution * factor) * 100
-                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100
-                # 0 <= (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100 <= 100
-                # (start_pos - values[i]) / resolution <= factor <= (end_pos - values[i]) / resolution
-                low_factor = (start_pos - values[i]) / resolution
-                upp_factor = (end_pos - values[i]) / resolution
-
-            if not in_range:
-                # Get first integer between the two bounds
-                if low_factor < upp_factor:
-                    factor = math.ceil(low_factor)
-
-                    if factor > upp_factor:
-                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
-                else:
-                    factor = math.ceil(upp_factor)
-
-                    if factor > low_factor:
-                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
-
-                if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                    out_of_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
-                    in_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
-                elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                    out_of_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
-                    in_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
-
-                logging.warning(
-                    f"Auto-correct calibration of motor '{name}' by shifting value by {abs(factor)} full turns, "
-                    f"from '{out_of_range_str}' to '{in_range_str}'."
-                )
-
-                # A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
-                self.calibration["homing_offset"][calib_idx] += resolution * factor
-
-    def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """Inverse of `apply_calibration`."""
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        for i, name in enumerate(motor_names):
-            calib_idx = self.calibration["motor_names"].index(name)
-            calib_mode = self.calibration["calib_mode"][calib_idx]
-
-            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
-
-                # Convert from nominal 0-centered degree range [-180, 180] to
-                # 0-centered resolution range (e.g. [-2048, 2048] for resolution=4096)
-                values[i] = values[i] / HALF_TURN_DEGREE * (resolution // 2)
-
-                # Subtract the homing offsets to come back to actual motor range of values
-                # which can be arbitrary.
-                values[i] -= homing_offset
-
-                # Remove drive mode, which is the rotation direction of the motor, to come back to
-                # actual motor rotation direction which can be arbitrary.
-                if drive_mode:
-                    values[i] *= -1
-
-            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                start_pos = self.calibration["start_pos"][calib_idx]
-                end_pos = self.calibration["end_pos"][calib_idx]
-
-                # Convert from nominal lnear range of [0, 100] % to
-                # actual motor range of values which can be arbitrary.
-                values[i] = values[i] / 100 * (end_pos - start_pos) + start_pos
-
-        values = np.round(values).astype(np.int32)
-        return values
-
-    def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
-        if self.mock:
-            import tests.motors.mock_dynamixel_sdk as dxl
-        else:
-            import dynamixel_sdk as dxl
-
-        return_list = True
-        if not isinstance(motor_ids, list):
-            return_list = False
-            motor_ids = [motor_ids]
-
-        assert_same_address(self.model_ctrl_table, self.motor_models, data_name)
-        addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
-        group = dxl.GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
-        for idx in motor_ids:
-            group.addParam(idx)
-
-        for _ in range(num_retry):
-            comm = group.txRxPacket()
-            if comm == dxl.COMM_SUCCESS:
-                break
-
-        if comm != dxl.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Read failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-        values = []
-        for idx in motor_ids:
-            value = group.getData(idx, addr, bytes)
-            values.append(value)
-
-        if return_list:
-            return values
-        else:
-            return values[0]
-
-    def read(self, data_name, motor_names: str | list[str] | None = None):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
-            )
-
-        start_time = time.perf_counter()
-
-        if self.mock:
-            import tests.motors.mock_dynamixel_sdk as dxl
-        else:
-            import dynamixel_sdk as dxl
-
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        if isinstance(motor_names, str):
-            motor_names = [motor_names]
-
-        motor_ids = []
-        models = []
-        for name in motor_names:
-            motor_idx, model = self.motors[name]
-            motor_ids.append(motor_idx)
-            models.append(model)
-
-        assert_same_address(self.model_ctrl_table, models, data_name)
-        addr, bytes = self.model_ctrl_table[model][data_name]
-        group_key = get_group_sync_key(data_name, motor_names)
-
-        if data_name not in self.group_readers:
-            # create new group reader
-            self.group_readers[group_key] = dxl.GroupSyncRead(
-                self.port_handler, self.packet_handler, addr, bytes
-            )
-            for idx in motor_ids:
-                self.group_readers[group_key].addParam(idx)
-
-        for _ in range(NUM_READ_RETRY):
-            comm = self.group_readers[group_key].txRxPacket()
-            if comm == dxl.COMM_SUCCESS:
-                break
-
-        if comm != dxl.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Read failed due to communication error on port {self.port} for group_key {group_key}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-        values = []
-        for idx in motor_ids:
-            value = self.group_readers[group_key].getData(idx, addr, bytes)
-            values.append(value)
-
-        values = np.array(values)
-
-        # Convert to signed int to use range [-2048, 2048] for our motor positions.
-        if data_name in CONVERT_UINT32_TO_INT32_REQUIRED:
-            values = values.astype(np.int32)
-
-        if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
-            values = self.apply_calibration_autocorrect(values, motor_names)
-
-        # log the number of seconds it took to read the data from the motors
-        delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
-        self.logs[delta_ts_name] = time.perf_counter() - start_time
-
-        # log the utc time at which the data was received
-        ts_utc_name = get_log_name("timestamp_utc", "read", data_name, motor_names)
-        self.logs[ts_utc_name] = capture_timestamp_utc()
-
-        return values
-
-    def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
-        if self.mock:
-            import tests.motors.mock_dynamixel_sdk as dxl
-        else:
-            import dynamixel_sdk as dxl
-
-        if not isinstance(motor_ids, list):
-            motor_ids = [motor_ids]
-        if not isinstance(values, list):
-            values = [values]
-
-        assert_same_address(self.model_ctrl_table, motor_models, data_name)
-        addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
-        group = dxl.GroupSyncWrite(self.port_handler, self.packet_handler, addr, bytes)
-        for idx, value in zip(motor_ids, values, strict=True):
-            data = convert_to_bytes(value, bytes, self.mock)
-            group.addParam(idx, data)
-
-        for _ in range(num_retry):
-            comm = group.txPacket()
-            if comm == dxl.COMM_SUCCESS:
-                break
-
-        if comm != dxl.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Write failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-    def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
-            )
-
-        start_time = time.perf_counter()
-
-        if self.mock:
-            import tests.motors.mock_dynamixel_sdk as dxl
-        else:
-            import dynamixel_sdk as dxl
-
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        if isinstance(motor_names, str):
-            motor_names = [motor_names]
-
-        if isinstance(values, (int, float, np.integer)):
-            values = [int(values)] * len(motor_names)
-
-        values = np.array(values)
-
-        motor_ids = []
-        models = []
-        for name in motor_names:
-            motor_idx, model = self.motors[name]
-            motor_ids.append(motor_idx)
-            models.append(model)
-
-        if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
-            values = self.revert_calibration(values, motor_names)
-
-        values = values.tolist()
-
-        assert_same_address(self.model_ctrl_table, models, data_name)
-        addr, bytes = self.model_ctrl_table[model][data_name]
-        group_key = get_group_sync_key(data_name, motor_names)
-
-        init_group = data_name not in self.group_readers
-        if init_group:
-            self.group_writers[group_key] = dxl.GroupSyncWrite(
-                self.port_handler, self.packet_handler, addr, bytes
-            )
-
-        for idx, value in zip(motor_ids, values, strict=True):
-            data = convert_to_bytes(value, bytes, self.mock)
-            if init_group:
-                self.group_writers[group_key].addParam(idx, data)
-            else:
-                self.group_writers[group_key].changeParam(idx, data)
-
-        comm = self.group_writers[group_key].txPacket()
-        if comm != dxl.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Write failed due to communication error on port {self.port} for group_key {group_key}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-        # log the number of seconds it took to write the data to the motors
-        delta_ts_name = get_log_name("delta_timestamp_s", "write", data_name, motor_names)
-        self.logs[delta_ts_name] = time.perf_counter() - start_time
-
-        # TODO(rcadene): should we log the time before sending the write command?
-        # log the utc time when the write has been completed
-        ts_utc_name = get_log_name("timestamp_utc", "write", data_name, motor_names)
-        self.logs[ts_utc_name] = capture_timestamp_utc()
-
-    def disconnect(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"DynamixelMotorsBus({self.port}) is not connected. Try running `motors_bus.connect()` first."
-            )
-
-        if self.port_handler is not None:
-            self.port_handler.closePort()
-            self.port_handler = None
-
-        self.packet_handler = None
-        self.group_readers = {}
-        self.group_writers = {}
-        self.is_connected = False
-
-    def __del__(self):
-        if getattr(self, "is_connected", False):
-            self.disconnect()
--- a/lerobot/common/robot_devices/motors/feetech.py
+++ b/lerobot/common/robot_devices/motors/feetech.py
@@ -1,898 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import enum
-import logging
-import math
-import time
-import traceback
-from copy import deepcopy
-
-import numpy as np
-import tqdm
-
-from lerobot.common.robot_devices.motors.configs import FeetechMotorsBusConfig
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
-from lerobot.common.utils.utils import capture_timestamp_utc
-
-PROTOCOL_VERSION = 0
-BAUDRATE = 1_000_000
-TIMEOUT_MS = 1000
-
-MAX_ID_RANGE = 252
-
-# The following bounds define the lower and upper joints range (after calibration).
-# For joints in degree (i.e. revolute joints), their nominal range is [-180, 180] degrees
-# which corresponds to a half rotation on the left and half rotation on the right.
-# Some joints might require higher range, so we allow up to [-270, 270] degrees until
-# an error is raised.
-LOWER_BOUND_DEGREE = -270
-UPPER_BOUND_DEGREE = 270
-# For joints in percentage (i.e. joints that move linearly like the prismatic joint of a gripper),
-# their nominal range is [0, 100] %. For instance, for Aloha gripper, 0% is fully
-# closed, and 100% is fully open. To account for slight calibration issue, we allow up to
-# [-10, 110] until an error is raised.
-LOWER_BOUND_LINEAR = -10
-UPPER_BOUND_LINEAR = 110
-
-HALF_TURN_DEGREE = 180
-
-
-# See this link for STS3215 Memory Table:
-# https://docs.google.com/spreadsheets/d/1GVs7W1VS1PqdhA1nW-abeyAHhTUxKUdR/edit?usp=sharing&ouid=116566590112741600240&rtpof=true&sd=true
-# data_name: (address, size_byte)
-SCS_SERIES_CONTROL_TABLE = {
-    "Model": (3, 2),
-    "ID": (5, 1),
-    "Baud_Rate": (6, 1),
-    "Return_Delay": (7, 1),
-    "Response_Status_Level": (8, 1),
-    "Min_Angle_Limit": (9, 2),
-    "Max_Angle_Limit": (11, 2),
-    "Max_Temperature_Limit": (13, 1),
-    "Max_Voltage_Limit": (14, 1),
-    "Min_Voltage_Limit": (15, 1),
-    "Max_Torque_Limit": (16, 2),
-    "Phase": (18, 1),
-    "Unloading_Condition": (19, 1),
-    "LED_Alarm_Condition": (20, 1),
-    "P_Coefficient": (21, 1),
-    "D_Coefficient": (22, 1),
-    "I_Coefficient": (23, 1),
-    "Minimum_Startup_Force": (24, 2),
-    "CW_Dead_Zone": (26, 1),
-    "CCW_Dead_Zone": (27, 1),
-    "Protection_Current": (28, 2),
-    "Angular_Resolution": (30, 1),
-    "Offset": (31, 2),
-    "Mode": (33, 1),
-    "Protective_Torque": (34, 1),
-    "Protection_Time": (35, 1),
-    "Overload_Torque": (36, 1),
-    "Speed_closed_loop_P_proportional_coefficient": (37, 1),
-    "Over_Current_Protection_Time": (38, 1),
-    "Velocity_closed_loop_I_integral_coefficient": (39, 1),
-    "Torque_Enable": (40, 1),
-    "Acceleration": (41, 1),
-    "Goal_Position": (42, 2),
-    "Goal_Time": (44, 2),
-    "Goal_Speed": (46, 2),
-    "Torque_Limit": (48, 2),
-    "Lock": (55, 1),
-    "Present_Position": (56, 2),
-    "Present_Speed": (58, 2),
-    "Present_Load": (60, 2),
-    "Present_Voltage": (62, 1),
-    "Present_Temperature": (63, 1),
-    "Status": (65, 1),
-    "Moving": (66, 1),
-    "Present_Current": (69, 2),
-    # Not in the Memory Table
-    "Maximum_Acceleration": (85, 2),
-}
-
-SCS_SERIES_BAUDRATE_TABLE = {
-    0: 1_000_000,
-    1: 500_000,
-    2: 250_000,
-    3: 128_000,
-    4: 115_200,
-    5: 57_600,
-    6: 38_400,
-    7: 19_200,
-}
-
-CALIBRATION_REQUIRED = ["Goal_Position", "Present_Position"]
-CONVERT_UINT32_TO_INT32_REQUIRED = ["Goal_Position", "Present_Position"]
-
-
-MODEL_CONTROL_TABLE = {
-    "scs_series": SCS_SERIES_CONTROL_TABLE,
-    "sts3215": SCS_SERIES_CONTROL_TABLE,
-}
-
-MODEL_RESOLUTION = {
-    "scs_series": 4096,
-    "sts3215": 4096,
-}
-
-MODEL_BAUDRATE_TABLE = {
-    "scs_series": SCS_SERIES_BAUDRATE_TABLE,
-    "sts3215": SCS_SERIES_BAUDRATE_TABLE,
-}
-
-# High number of retries is needed for feetech compared to dynamixel motors.
-NUM_READ_RETRY = 20
-NUM_WRITE_RETRY = 20
-
-
-def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]) -> np.ndarray:
-    """This function converts the degree range to the step range for indicating motors rotation.
-    It assumes a motor achieves a full rotation by going from -180 degree position to +180.
-    The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
-    """
-    resolutions = [MODEL_RESOLUTION[model] for model in models]
-    steps = degrees / 180 * np.array(resolutions) / 2
-    steps = steps.astype(int)
-    return steps
-
-
-def convert_to_bytes(value, bytes, mock=False):
-    if mock:
-        return value
-
-    import scservo_sdk as scs
-
-    # Note: No need to convert back into unsigned int, since this byte preprocessing
-    # already handles it for us.
-    if bytes == 1:
-        data = [
-            scs.SCS_LOBYTE(scs.SCS_LOWORD(value)),
-        ]
-    elif bytes == 2:
-        data = [
-            scs.SCS_LOBYTE(scs.SCS_LOWORD(value)),
-            scs.SCS_HIBYTE(scs.SCS_LOWORD(value)),
-        ]
-    elif bytes == 4:
-        data = [
-            scs.SCS_LOBYTE(scs.SCS_LOWORD(value)),
-            scs.SCS_HIBYTE(scs.SCS_LOWORD(value)),
-            scs.SCS_LOBYTE(scs.SCS_HIWORD(value)),
-            scs.SCS_HIBYTE(scs.SCS_HIWORD(value)),
-        ]
-    else:
-        raise NotImplementedError(
-            f"Value of the number of bytes to be sent is expected to be in [1, 2, 4], but "
-            f"{bytes} is provided instead."
-        )
-    return data
-
-
-def get_group_sync_key(data_name, motor_names):
-    group_key = f"{data_name}_" + "_".join(motor_names)
-    return group_key
-
-
-def get_result_name(fn_name, data_name, motor_names):
-    group_key = get_group_sync_key(data_name, motor_names)
-    rslt_name = f"{fn_name}_{group_key}"
-    return rslt_name
-
-
-def get_queue_name(fn_name, data_name, motor_names):
-    group_key = get_group_sync_key(data_name, motor_names)
-    queue_name = f"{fn_name}_{group_key}"
-    return queue_name
-
-
-def get_log_name(var_name, fn_name, data_name, motor_names):
-    group_key = get_group_sync_key(data_name, motor_names)
-    log_name = f"{var_name}_{fn_name}_{group_key}"
-    return log_name
-
-
-def assert_same_address(model_ctrl_table, motor_models, data_name):
-    all_addr = []
-    all_bytes = []
-    for model in motor_models:
-        addr, bytes = model_ctrl_table[model][data_name]
-        all_addr.append(addr)
-        all_bytes.append(bytes)
-
-    if len(set(all_addr)) != 1:
-        raise NotImplementedError(
-            f"At least two motor models use a different address for `data_name`='{data_name}' ({list(zip(motor_models, all_addr, strict=False))}). Contact a LeRobot maintainer."
-        )
-
-    if len(set(all_bytes)) != 1:
-        raise NotImplementedError(
-            f"At least two motor models use a different bytes representation for `data_name`='{data_name}' ({list(zip(motor_models, all_bytes, strict=False))}). Contact a LeRobot maintainer."
-        )
-
-
-class TorqueMode(enum.Enum):
-    ENABLED = 1
-    DISABLED = 0
-
-
-class DriveMode(enum.Enum):
-    NON_INVERTED = 0
-    INVERTED = 1
-
-
-class CalibrationMode(enum.Enum):
-    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
-    DEGREE = 0
-    # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100]
-    LINEAR = 1
-
-
-class JointOutOfRangeError(Exception):
-    def __init__(self, message="Joint is out of range"):
-        self.message = message
-        super().__init__(self.message)
-
-
-class FeetechMotorsBus:
-    """
-    The FeetechMotorsBus class allows to efficiently read and write to the attached motors. It relies on
-    the python feetech sdk to communicate with the motors. For more info, see the [feetech SDK Documentation](https://emanual.robotis.com/docs/en/software/feetech/feetech_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20).
-
-    A FeetechMotorsBus instance requires a port (e.g. `FeetechMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
-    To find the port, you can run our utility script:
-    ```bash
-    python lerobot/scripts/find_motors_bus_port.py
-    >>> Finding all available ports for the MotorsBus.
-    >>> ['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-    >>> Remove the usb cable from your FeetechMotorsBus and press Enter when done.
-    >>> The port of this FeetechMotorsBus is /dev/tty.usbmodem575E0031751.
-    >>> Reconnect the usb cable.
-    ```
-
-    Example of usage for 1 motor connected to the bus:
-    ```python
-    motor_name = "gripper"
-    motor_index = 6
-    motor_model = "sts3215"
-
-    config = FeetechMotorsBusConfig(
-        port="/dev/tty.usbmodem575E0031751",
-        motors={motor_name: (motor_index, motor_model)},
-    )
-    motors_bus = FeetechMotorsBus(config)
-    motors_bus.connect()
-
-    position = motors_bus.read("Present_Position")
-
-    # move from a few motor steps as an example
-    few_steps = 30
-    motors_bus.write("Goal_Position", position + few_steps)
-
-    # when done, consider disconnecting
-    motors_bus.disconnect()
-    ```
-    """
-
-    def __init__(
-        self,
-        config: FeetechMotorsBusConfig,
-    ):
-        self.port = config.port
-        self.motors = config.motors
-        self.mock = config.mock
-
-        self.model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
-        self.model_resolution = deepcopy(MODEL_RESOLUTION)
-
-        self.port_handler = None
-        self.packet_handler = None
-        self.calibration = None
-        self.is_connected = False
-        self.group_readers = {}
-        self.group_writers = {}
-        self.logs = {}
-
-        self.track_positions = {}
-
-    def connect(self):
-        if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
-                f"FeetechMotorsBus({self.port}) is already connected. Do not call `motors_bus.connect()` twice."
-            )
-
-        if self.mock:
-            import tests.motors.mock_scservo_sdk as scs
-        else:
-            import scservo_sdk as scs
-
-        self.port_handler = scs.PortHandler(self.port)
-        self.packet_handler = scs.PacketHandler(PROTOCOL_VERSION)
-
-        try:
-            if not self.port_handler.openPort():
-                raise OSError(f"Failed to open port '{self.port}'.")
-        except Exception:
-            traceback.print_exc()
-            print(
-                "\nTry running `python lerobot/scripts/find_motors_bus_port.py` to make sure you are using the correct port.\n"
-            )
-            raise
-
-        # Allow to read and write
-        self.is_connected = True
-
-        self.port_handler.setPacketTimeoutMillis(TIMEOUT_MS)
-
-    def reconnect(self):
-        if self.mock:
-            import tests.motors.mock_scservo_sdk as scs
-        else:
-            import scservo_sdk as scs
-
-        self.port_handler = scs.PortHandler(self.port)
-        self.packet_handler = scs.PacketHandler(PROTOCOL_VERSION)
-
-        if not self.port_handler.openPort():
-            raise OSError(f"Failed to open port '{self.port}'.")
-
-        self.is_connected = True
-
-    def are_motors_configured(self):
-        # Only check the motor indices and not baudrate, since if the motor baudrates are incorrect,
-        # a ConnectionError will be raised anyway.
-        try:
-            return (self.motor_indices == self.read("ID")).all()
-        except ConnectionError as e:
-            print(e)
-            return False
-
-    def find_motor_indices(self, possible_ids=None, num_retry=2):
-        if possible_ids is None:
-            possible_ids = range(MAX_ID_RANGE)
-
-        indices = []
-        for idx in tqdm.tqdm(possible_ids):
-            try:
-                present_idx = self.read_with_motor_ids(self.motor_models, [idx], "ID", num_retry=num_retry)[0]
-            except ConnectionError:
-                continue
-
-            if idx != present_idx:
-                # sanity check
-                raise OSError(
-                    "Motor index used to communicate through the bus is not the same as the one present in the motor memory. The motor memory might be damaged."
-                )
-            indices.append(idx)
-
-        return indices
-
-    def set_bus_baudrate(self, baudrate):
-        present_bus_baudrate = self.port_handler.getBaudRate()
-        if present_bus_baudrate != baudrate:
-            print(f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}.")
-            self.port_handler.setBaudRate(baudrate)
-
-            if self.port_handler.getBaudRate() != baudrate:
-                raise OSError("Failed to write bus baud rate.")
-
-    @property
-    def motor_names(self) -> list[str]:
-        return list(self.motors.keys())
-
-    @property
-    def motor_models(self) -> list[str]:
-        return [model for _, model in self.motors.values()]
-
-    @property
-    def motor_indices(self) -> list[int]:
-        return [idx for idx, _ in self.motors.values()]
-
-    def set_calibration(self, calibration: dict[str, list]):
-        self.calibration = calibration
-
-    def apply_calibration_autocorrect(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """This function apply the calibration, automatically detects out of range errors for motors values and attempt to correct.
-
-        For more info, see docstring of `apply_calibration` and `autocorrect_calibration`.
-        """
-        try:
-            values = self.apply_calibration(values, motor_names)
-        except JointOutOfRangeError as e:
-            print(e)
-            self.autocorrect_calibration(values, motor_names)
-            values = self.apply_calibration(values, motor_names)
-        return values
-
-    def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
-        a "zero position" at 0 degree.
-
-        Note: We say "nominal degree range" since the motors can take values outside this range. For instance, 190 degrees, if the motor
-        rotate more than a half a turn from the zero position. However, most motors can't rotate more than 180 degrees and will stay in this range.
-
-        Joints values are original in [0, 2**32[ (unsigned int32). Each motor are expected to complete a full rotation
-        when given a goal position that is + or - their resolution. For instance, feetech xl330-m077 have a resolution of 4096, and
-        at any position in their original range, let's say the position 56734, they complete a full rotation clockwise by moving to 60830,
-        or anticlockwise by moving to 52638. The position in the original range is arbitrary and might change a lot between each motor.
-        To harmonize between motors of the same model, different robots, or even models of different brands, we propose to work
-        in the centered nominal degree range ]-180, 180[.
-        """
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
-        values = values.astype(np.float32)
-
-        for i, name in enumerate(motor_names):
-            calib_idx = self.calibration["motor_names"].index(name)
-            calib_mode = self.calibration["calib_mode"][calib_idx]
-
-            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
-
-                # Update direction of rotation of the motor to match between leader and follower.
-                # In fact, the motor of the leader for a given joint can be assembled in an
-                # opposite direction in term of rotation than the motor of the follower on the same joint.
-                if drive_mode:
-                    values[i] *= -1
-
-                # Convert from range [-2**31, 2**31[ to
-                # nominal range ]-resolution, resolution[ (e.g. ]-2048, 2048[)
-                values[i] += homing_offset
-
-                # Convert from range ]-resolution, resolution[ to
-                # universal float32 centered degree range ]-180, 180[
-                values[i] = values[i] / (resolution // 2) * HALF_TURN_DEGREE
-
-                if (values[i] < LOWER_BOUND_DEGREE) or (values[i] > UPPER_BOUND_DEGREE):
-                    raise JointOutOfRangeError(
-                        f"Wrong motor position range detected for {name}. "
-                        f"Expected to be in nominal range of [-{HALF_TURN_DEGREE}, {HALF_TURN_DEGREE}] degrees (a full rotation), "
-                        f"with a maximum range of [{LOWER_BOUND_DEGREE}, {UPPER_BOUND_DEGREE}] degrees to account for joints that can rotate a bit more, "
-                        f"but present value is {values[i]} degree. "
-                        "This might be due to a cable connection issue creating an artificial 360 degrees jump in motor values. "
-                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
-                    )
-
-            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                start_pos = self.calibration["start_pos"][calib_idx]
-                end_pos = self.calibration["end_pos"][calib_idx]
-
-                # Rescale the present position to a nominal range [0, 100] %,
-                # useful for joints with linear motions like Aloha gripper
-                values[i] = (values[i] - start_pos) / (end_pos - start_pos) * 100
-
-                if (values[i] < LOWER_BOUND_LINEAR) or (values[i] > UPPER_BOUND_LINEAR):
-                    raise JointOutOfRangeError(
-                        f"Wrong motor position range detected for {name}. "
-                        f"Expected to be in nominal range of [0, 100] % (a full linear translation), "
-                        f"with a maximum range of [{LOWER_BOUND_LINEAR}, {UPPER_BOUND_LINEAR}] % to account for some imprecision during calibration, "
-                        f"but present value is {values[i]} %. "
-                        "This might be due to a cable connection issue creating an artificial jump in motor values. "
-                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
-                    )
-
-        return values
-
-    def autocorrect_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """This function automatically detects issues with values of motors after calibration, and correct for these issues.
-
-        Some motors might have values outside of expected maximum bounds after calibration.
-        For instance, for a joint in degree, its value can be outside [-270, 270] degrees, which is totally unexpected given
-        a nominal range of [-180, 180] degrees, which represents half a turn to the left or right starting from zero position.
-
-        Known issues:
-        #1: Motor value randomly shifts of a full turn, caused by hardware/connection errors.
-        #2: Motor internal homing offset is shifted of a full turn, caused by using default calibration (e.g Aloha).
-        #3: motor internal homing offset is shifted of less or more than a full turn, caused by using default calibration
-            or by human error during manual calibration.
-
-        Issues #1 and #2 can be solved by shifting the calibration homing offset by a full turn.
-        Issue #3 will be visually detected by user and potentially captured by the safety feature `max_relative_target`,
-        that will slow down the motor, raise an error asking to recalibrate. Manual recalibrating will solve the issue.
-
-        Note: A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
-        """
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
-        values = values.astype(np.float32)
-
-        for i, name in enumerate(motor_names):
-            calib_idx = self.calibration["motor_names"].index(name)
-            calib_mode = self.calibration["calib_mode"][calib_idx]
-
-            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
-
-                if drive_mode:
-                    values[i] *= -1
-
-                # Convert from initial range to range [-180, 180] degrees
-                calib_val = (values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
-                in_range = (calib_val > LOWER_BOUND_DEGREE) and (calib_val < UPPER_BOUND_DEGREE)
-
-                # Solve this inequality to find the factor to shift the range into [-180, 180] degrees
-                # values[i] = (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE
-                # - HALF_TURN_DEGREE <= (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE <= HALF_TURN_DEGREE
-                # (- HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset) / resolution <= factor <= (HALF_TURN_DEGREE / 180 * (resolution // 2) - values[i] - homing_offset) / resolution
-                low_factor = (
-                    -HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
-                ) / resolution
-                upp_factor = (
-                    HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
-                ) / resolution
-
-            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                start_pos = self.calibration["start_pos"][calib_idx]
-                end_pos = self.calibration["end_pos"][calib_idx]
-
-                # Convert from initial range to range [0, 100] in %
-                calib_val = (values[i] - start_pos) / (end_pos - start_pos) * 100
-                in_range = (calib_val > LOWER_BOUND_LINEAR) and (calib_val < UPPER_BOUND_LINEAR)
-
-                # Solve this inequality to find the factor to shift the range into [0, 100] %
-                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos + resolution * factor - start_pos - resolution * factor) * 100
-                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100
-                # 0 <= (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100 <= 100
-                # (start_pos - values[i]) / resolution <= factor <= (end_pos - values[i]) / resolution
-                low_factor = (start_pos - values[i]) / resolution
-                upp_factor = (end_pos - values[i]) / resolution
-
-            if not in_range:
-                # Get first integer between the two bounds
-                if low_factor < upp_factor:
-                    factor = math.ceil(low_factor)
-
-                    if factor > upp_factor:
-                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
-                else:
-                    factor = math.ceil(upp_factor)
-
-                    if factor > low_factor:
-                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
-
-                if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                    out_of_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
-                    in_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
-                elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                    out_of_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
-                    in_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
-
-                logging.warning(
-                    f"Auto-correct calibration of motor '{name}' by shifting value by {abs(factor)} full turns, "
-                    f"from '{out_of_range_str}' to '{in_range_str}'."
-                )
-
-                # A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
-                self.calibration["homing_offset"][calib_idx] += resolution * factor
-
-    def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """Inverse of `apply_calibration`."""
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        for i, name in enumerate(motor_names):
-            calib_idx = self.calibration["motor_names"].index(name)
-            calib_mode = self.calibration["calib_mode"][calib_idx]
-
-            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
-
-                # Convert from nominal 0-centered degree range [-180, 180] to
-                # 0-centered resolution range (e.g. [-2048, 2048] for resolution=4096)
-                values[i] = values[i] / HALF_TURN_DEGREE * (resolution // 2)
-
-                # Subtract the homing offsets to come back to actual motor range of values
-                # which can be arbitrary.
-                values[i] -= homing_offset
-
-                # Remove drive mode, which is the rotation direction of the motor, to come back to
-                # actual motor rotation direction which can be arbitrary.
-                if drive_mode:
-                    values[i] *= -1
-
-            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                start_pos = self.calibration["start_pos"][calib_idx]
-                end_pos = self.calibration["end_pos"][calib_idx]
-
-                # Convert from nominal lnear range of [0, 100] % to
-                # actual motor range of values which can be arbitrary.
-                values[i] = values[i] / 100 * (end_pos - start_pos) + start_pos
-
-        values = np.round(values).astype(np.int32)
-        return values
-
-    def avoid_rotation_reset(self, values, motor_names, data_name):
-        if data_name not in self.track_positions:
-            self.track_positions[data_name] = {
-                "prev": [None] * len(self.motor_names),
-                # Assume False at initialization
-                "below_zero": [False] * len(self.motor_names),
-                "above_max": [False] * len(self.motor_names),
-            }
-
-        track = self.track_positions[data_name]
-
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        for i, name in enumerate(motor_names):
-            idx = self.motor_names.index(name)
-
-            if track["prev"][idx] is None:
-                track["prev"][idx] = values[i]
-                continue
-
-            # Detect a full rotation occurred
-            if abs(track["prev"][idx] - values[i]) > 2048:
-                # Position went below 0 and got reset to 4095
-                if track["prev"][idx] < values[i]:
-                    # So we set negative value by adding a full rotation
-                    values[i] -= 4096
-
-                # Position went above 4095 and got reset to 0
-                elif track["prev"][idx] > values[i]:
-                    # So we add a full rotation
-                    values[i] += 4096
-
-            track["prev"][idx] = values[i]
-
-        return values
-
-    def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
-        if self.mock:
-            import tests.motors.mock_scservo_sdk as scs
-        else:
-            import scservo_sdk as scs
-
-        return_list = True
-        if not isinstance(motor_ids, list):
-            return_list = False
-            motor_ids = [motor_ids]
-
-        assert_same_address(self.model_ctrl_table, self.motor_models, data_name)
-        addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
-        group = scs.GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
-        for idx in motor_ids:
-            group.addParam(idx)
-
-        for _ in range(num_retry):
-            comm = group.txRxPacket()
-            if comm == scs.COMM_SUCCESS:
-                break
-
-        if comm != scs.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Read failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-        values = []
-        for idx in motor_ids:
-            value = group.getData(idx, addr, bytes)
-            values.append(value)
-
-        if return_list:
-            return values
-        else:
-            return values[0]
-
-    def read(self, data_name, motor_names: str | list[str] | None = None):
-        if self.mock:
-            import tests.motors.mock_scservo_sdk as scs
-        else:
-            import scservo_sdk as scs
-
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
-            )
-
-        start_time = time.perf_counter()
-
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        if isinstance(motor_names, str):
-            motor_names = [motor_names]
-
-        motor_ids = []
-        models = []
-        for name in motor_names:
-            motor_idx, model = self.motors[name]
-            motor_ids.append(motor_idx)
-            models.append(model)
-
-        assert_same_address(self.model_ctrl_table, models, data_name)
-        addr, bytes = self.model_ctrl_table[model][data_name]
-        group_key = get_group_sync_key(data_name, motor_names)
-
-        if data_name not in self.group_readers:
-            # Very Important to flush the buffer!
-            self.port_handler.ser.reset_output_buffer()
-            self.port_handler.ser.reset_input_buffer()
-
-            # create new group reader
-            self.group_readers[group_key] = scs.GroupSyncRead(
-                self.port_handler, self.packet_handler, addr, bytes
-            )
-            for idx in motor_ids:
-                self.group_readers[group_key].addParam(idx)
-
-        for _ in range(NUM_READ_RETRY):
-            comm = self.group_readers[group_key].txRxPacket()
-            if comm == scs.COMM_SUCCESS:
-                break
-
-        if comm != scs.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Read failed due to communication error on port {self.port} for group_key {group_key}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-        values = []
-        for idx in motor_ids:
-            value = self.group_readers[group_key].getData(idx, addr, bytes)
-            values.append(value)
-
-        values = np.array(values)
-
-        # Convert to signed int to use range [-2048, 2048] for our motor positions.
-        if data_name in CONVERT_UINT32_TO_INT32_REQUIRED:
-            values = values.astype(np.int32)
-
-        if data_name in CALIBRATION_REQUIRED:
-            values = self.avoid_rotation_reset(values, motor_names, data_name)
-
-        if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
-            values = self.apply_calibration_autocorrect(values, motor_names)
-
-        # log the number of seconds it took to read the data from the motors
-        delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
-        self.logs[delta_ts_name] = time.perf_counter() - start_time
-
-        # log the utc time at which the data was received
-        ts_utc_name = get_log_name("timestamp_utc", "read", data_name, motor_names)
-        self.logs[ts_utc_name] = capture_timestamp_utc()
-
-        return values
-
-    def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
-        if self.mock:
-            import tests.motors.mock_scservo_sdk as scs
-        else:
-            import scservo_sdk as scs
-
-        if not isinstance(motor_ids, list):
-            motor_ids = [motor_ids]
-        if not isinstance(values, list):
-            values = [values]
-
-        assert_same_address(self.model_ctrl_table, motor_models, data_name)
-        addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
-        group = scs.GroupSyncWrite(self.port_handler, self.packet_handler, addr, bytes)
-        for idx, value in zip(motor_ids, values, strict=True):
-            data = convert_to_bytes(value, bytes, self.mock)
-            group.addParam(idx, data)
-
-        for _ in range(num_retry):
-            comm = group.txPacket()
-            if comm == scs.COMM_SUCCESS:
-                break
-
-        if comm != scs.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Write failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-    def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
-            )
-
-        start_time = time.perf_counter()
-
-        if self.mock:
-            import tests.motors.mock_scservo_sdk as scs
-        else:
-            import scservo_sdk as scs
-
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        if isinstance(motor_names, str):
-            motor_names = [motor_names]
-
-        if isinstance(values, (int, float, np.integer)):
-            values = [int(values)] * len(motor_names)
-
-        values = np.array(values)
-
-        motor_ids = []
-        models = []
-        for name in motor_names:
-            motor_idx, model = self.motors[name]
-            motor_ids.append(motor_idx)
-            models.append(model)
-
-        if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
-            values = self.revert_calibration(values, motor_names)
-
-        values = values.tolist()
-
-        assert_same_address(self.model_ctrl_table, models, data_name)
-        addr, bytes = self.model_ctrl_table[model][data_name]
-        group_key = get_group_sync_key(data_name, motor_names)
-
-        init_group = data_name not in self.group_readers
-        if init_group:
-            self.group_writers[group_key] = scs.GroupSyncWrite(
-                self.port_handler, self.packet_handler, addr, bytes
-            )
-
-        for idx, value in zip(motor_ids, values, strict=True):
-            data = convert_to_bytes(value, bytes, self.mock)
-            if init_group:
-                self.group_writers[group_key].addParam(idx, data)
-            else:
-                self.group_writers[group_key].changeParam(idx, data)
-
-        comm = self.group_writers[group_key].txPacket()
-        if comm != scs.COMM_SUCCESS:
-            raise ConnectionError(
-                f"Write failed due to communication error on port {self.port} for group_key {group_key}: "
-                f"{self.packet_handler.getTxRxResult(comm)}"
-            )
-
-        # log the number of seconds it took to write the data to the motors
-        delta_ts_name = get_log_name("delta_timestamp_s", "write", data_name, motor_names)
-        self.logs[delta_ts_name] = time.perf_counter() - start_time
-
-        # TODO(rcadene): should we log the time before sending the write command?
-        # log the utc time when the write has been completed
-        ts_utc_name = get_log_name("timestamp_utc", "write", data_name, motor_names)
-        self.logs[ts_utc_name] = capture_timestamp_utc()
-
-    def disconnect(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"FeetechMotorsBus({self.port}) is not connected. Try running `motors_bus.connect()` first."
-            )
-
-        if self.port_handler is not None:
-            self.port_handler.closePort()
-            self.port_handler = None
-
-        self.packet_handler = None
-        self.group_readers = {}
-        self.group_writers = {}
-        self.is_connected = False
-
-    def __del__(self):
-        if getattr(self, "is_connected", False):
-            self.disconnect()
--- a/lerobot/common/robot_devices/motors/realman.py
+++ b/lerobot/common/robot_devices/motors/realman.py
@@ -1,150 +0,0 @@
-import time
-from typing import Dict
-from lerobot.common.robot_devices.motors.configs import RealmanMotorsBusConfig
-from Robotic_Arm.rm_robot_interface import *
-
-
-class RealmanMotorsBus:
-    """
-        对Realman SDK的二次封装
-    """
-    def __init__(self, 
-                 config: RealmanMotorsBusConfig):
-        self.rmarm = RoboticArm(rm_thread_mode_e.RM_TRIPLE_MODE_E)
-        self.handle = self.rmarm.rm_create_robot_arm(config.ip, config.port)
-        self.motors = config.motors
-        self.init_joint_position = config.init_joint['joint'] # [6 joints + 1 gripper]
-        self.safe_disable_position = config.init_joint['joint']
-        self.rmarm.rm_movej(self.init_joint_position[:-1], 5, 0, 0, 1)
-        time.sleep(3)
-        ret = self.rmarm.rm_get_current_arm_state()
-        self.init_pose = ret[1]['pose']
-
-    @property
-    def motor_names(self) -> list[str]:
-        return list(self.motors.keys())
-
-    @property
-    def motor_models(self) -> list[str]:
-        return [model for _, model in self.motors.values()]
-
-    @property
-    def motor_indices(self) -> list[int]:
-        return [idx for idx, _ in self.motors.values()]
-
-
-    def connect(self, enable=True) -> bool:
-        '''
-        使能机械臂并检测使能状态,尝试5s,如果使能超时则退出程序
-        '''
-        enable_flag = False
-        loop_flag = False
-        # 设置超时时间（秒）
-        timeout = 5
-        # 记录进入循环前的时间
-        start_time = time.time()
-        elapsed_time_flag = False
-        
-        while not loop_flag:
-            elapsed_time = time.time() - start_time
-            print("--------------------")
-            
-            if enable:
-                # 获取机械臂状态
-                ret = self.rmarm.rm_get_current_arm_state()
-                if ret[0] == 0:  # 成功获取状态
-                    enable_flag = True
-            else:
-                enable_flag = False
-                # 断开所有连接，销毁线程
-                RoboticArm.rm_destory()
-            print("使能状态:", enable_flag)
-            print("--------------------")
-            if(enable_flag == enable):
-                loop_flag = True
-                enable_flag = True
-            else: 
-                loop_flag = False
-                enable_flag = False
-            # 检查是否超过超时时间
-            if elapsed_time > timeout:
-                print("超时....")
-                elapsed_time_flag = True
-                enable_flag = True
-                break
-            time.sleep(1)
-
-        resp = enable_flag
-        print(f"Returning response: {resp}")
-        return resp
-    
-    def motor_names(self):
-        return
-
-    def set_calibration(self):
-        return
-    
-    def revert_calibration(self):
-        return
-
-    def apply_calibration(self):
-        """
-            移动到初始位置
-        """
-        self.write(target_joint=self.init_joint_position)
-
-    def write(self, target_joint:list):
-        # self.rmarm.rm_movej(target_joint[:-1], 50, 0, 0, 1)
-        self.rmarm.rm_movej_follow(target_joint[:-1])
-        self.rmarm.rm_set_gripper_position(target_joint[-1], block=False, timeout=2)
-    
-    def write_endpose(self, target_endpose: list, gripper: int):
-        self.rmarm.rm_movej_p(target_endpose, 50, 0, 0, 1)
-        self.rmarm.rm_set_gripper_position(gripper, block=False, timeout=2)
-
-    def write_joint_slow(self, target_joint: list):
-        self.rmarm.rm_movej(target_joint, 5, 0, 0, 0)
-
-    def write_joint_canfd(self, target_joint: list):
-        self.rmarm.rm_movej_canfd(target_joint, False)
-
-    def write_endpose_canfd(self, target_pose: list):
-        self.rmarm.rm_movep_canfd(target_pose, False)
-
-    def write_gripper(self, gripper: int):
-        self.rmarm.rm_set_gripper_position(gripper, False, 2)
-
-    def read(self) -> Dict:
-        """
-            - 机械臂关节消息,单位1度;[-1, 1]
-            - 机械臂夹爪消息,[-1, 1]
-        """
-        joint_msg = self.rmarm.rm_get_current_arm_state()[1]
-        joint_state = joint_msg['joint']
-
-        gripper_msg = self.rmarm.rm_get_gripper_state()[1]
-        gripper_state = gripper_msg['actpos']
-        
-        return {
-            "joint_1": joint_state[0]/180,
-            "joint_2": joint_state[1]/180,
-            "joint_3": joint_state[2]/180,
-            "joint_4": joint_state[3]/180,
-            "joint_5": joint_state[4]/180,
-            "joint_6": joint_state[5]/180,
-            "gripper": (gripper_state-500)/500
-        }
-    
-    def read_current_arm_joint_state(self):
-        return self.rmarm.rm_get_current_arm_state()[1]['joint']
-    
-    def read_current_arm_endpose_state(self):
-        return self.rmarm.rm_get_current_arm_state()[1]['pose']
-
-    def safe_disconnect(self):
-        """ 
-            Move to safe disconnect position
-        """
-        self.write(target_joint=self.safe_disable_position)
-        # 断开所有连接，销毁线程
-        RoboticArm.rm_destory()
--- a/lerobot/common/robot_devices/motors/utils.py
+++ b/lerobot/common/robot_devices/motors/utils.py
@@ -1,76 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Protocol
-
-from lerobot.common.robot_devices.motors.configs import (
-    DynamixelMotorsBusConfig,
-    FeetechMotorsBusConfig,
-    MotorsBusConfig,
-)
-
-
-class MotorsBus(Protocol):
-    def motor_names(self): ...
-    def set_calibration(self): ...
-    def apply_calibration(self): ...
-    def revert_calibration(self): ...
-    def read(self): ...
-    def write(self): ...
-
-
-def make_motors_buses_from_configs(motors_bus_configs: dict[str, MotorsBusConfig]) -> list[MotorsBus]:
-    motors_buses = {}
-
-    for key, cfg in motors_bus_configs.items():
-        if cfg.type == "dynamixel":
-            from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus
-
-            motors_buses[key] = DynamixelMotorsBus(cfg)
-
-        elif cfg.type == "feetech":
-            from lerobot.common.robot_devices.motors.feetech import FeetechMotorsBus
-
-            motors_buses[key] = FeetechMotorsBus(cfg)
-
-        elif cfg.type == "realman":
-            from lerobot.common.robot_devices.motors.realman import RealmanMotorsBus
-
-            motors_buses[key] = RealmanMotorsBus(cfg)
-
-        else:
-            raise ValueError(f"The motor type '{cfg.type}' is not valid.")
-
-    return motors_buses
-
-
-def make_motors_bus(motor_type: str, **kwargs) -> MotorsBus:
-    if motor_type == "dynamixel":
-        from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus
-
-        config = DynamixelMotorsBusConfig(**kwargs)
-        return DynamixelMotorsBus(config)
-
-    elif motor_type == "feetech":
-        from lerobot.common.robot_devices.motors.feetech import FeetechMotorsBus
-
-        config = FeetechMotorsBusConfig(**kwargs)
-        return FeetechMotorsBus(config)
-
-    else:
-        raise ValueError(f"The motor type '{motor_type}' is not valid.")
-
-
-def get_motor_names(arm: dict[str, MotorsBus]) -> list:
-    return [f"{arm}_{motor}" for arm, bus in arm.items() for motor in bus.motors]
--- a/lerobot/common/robot_devices/robots/configs.py
+++ b/lerobot/common/robot_devices/robots/configs.py
@@ -1,765 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import abc
-from dataclasses import dataclass, field
-from typing import Sequence
-
-import draccus
-
-from lerobot.common.robot_devices.cameras.configs import (
-    CameraConfig,
-    IntelRealSenseCameraConfig,
-    OpenCVCameraConfig,
-)
-from lerobot.common.robot_devices.motors.configs import (
-    DynamixelMotorsBusConfig,
-    FeetechMotorsBusConfig,
-    MotorsBusConfig,
-    RealmanMotorsBusConfig
-)
-
-
-@dataclass
-class RobotConfig(draccus.ChoiceRegistry, abc.ABC):
-    @property
-    def type(self) -> str:
-        return self.get_choice_name(self.__class__)
-
-
-# TODO(rcadene, aliberts): remove ManipulatorRobotConfig abstraction
-@dataclass
-class ManipulatorRobotConfig(RobotConfig):
-    leader_arms: dict[str, MotorsBusConfig] = field(default_factory=lambda: {})
-    follower_arms: dict[str, MotorsBusConfig] = field(default_factory=lambda: {})
-    cameras: dict[str, CameraConfig] = field(default_factory=lambda: {})
-
-    # Optionally limit the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length
-    # as the number of motors in your follower arms (assumes all follower arms have the same number of
-    # motors).
-    max_relative_target: list[float] | float | None = None
-
-    # Optionally set the leader arm in torque mode with the gripper motor set to this angle. This makes it
-    # possible to squeeze the gripper and have it spring back to an open position on its own. If None, the
-    # gripper is not put in torque mode.
-    gripper_open_degree: float | None = None
-
-    mock: bool = False
-
-    def __post_init__(self):
-        if self.mock:
-            for arm in self.leader_arms.values():
-                if not arm.mock:
-                    arm.mock = True
-            for arm in self.follower_arms.values():
-                if not arm.mock:
-                    arm.mock = True
-            for cam in self.cameras.values():
-                if not cam.mock:
-                    cam.mock = True
-
-        if self.max_relative_target is not None and isinstance(self.max_relative_target, Sequence):
-            for name in self.follower_arms:
-                if len(self.follower_arms[name].motors) != len(self.max_relative_target):
-                    raise ValueError(
-                        f"len(max_relative_target)={len(self.max_relative_target)} but the follower arm with name {name} has "
-                        f"{len(self.follower_arms[name].motors)} motors. Please make sure that the "
-                        f"`max_relative_target` list has as many parameters as there are motors per arm. "
-                        "Note: This feature does not yet work with robots where different follower arms have "
-                        "different numbers of motors."
-                    )
-
-
-@RobotConfig.register_subclass("aloha")
-@dataclass
-class AlohaRobotConfig(ManipulatorRobotConfig):
-    # Specific to Aloha, LeRobot comes with default calibration files. Assuming the motors have been
-    # properly assembled, no manual calibration step is expected. If you need to run manual calibration,
-    # simply update this path to ".cache/calibration/aloha"
-    calibration_dir: str = ".cache/calibration/aloha_default"
-
-    # /!\ FOR SAFETY, READ THIS /!\
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    # For Aloha, for every goal position request, motor rotations are capped at 5 degrees by default.
-    # When you feel more confident with teleoperation or running the policy, you can extend
-    # this safety limit and even removing it by setting it to `null`.
-    # Also, everything is expected to work safely out-of-the-box, but we highly advise to
-    # first try to teleoperate the grippers only (by commenting out the rest of the motors in this yaml),
-    # then to gradually add more motors (by uncommenting), until you can teleoperate both arms fully
-    max_relative_target: int | None = 5
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                # window_x
-                port="/dev/ttyDXL_leader_left",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm430-w350"],
-                    "shoulder": [2, "xm430-w350"],
-                    "shoulder_shadow": [3, "xm430-w350"],
-                    "elbow": [4, "xm430-w350"],
-                    "elbow_shadow": [5, "xm430-w350"],
-                    "forearm_roll": [6, "xm430-w350"],
-                    "wrist_angle": [7, "xm430-w350"],
-                    "wrist_rotate": [8, "xl430-w250"],
-                    "gripper": [9, "xc430-w150"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                # window_x
-                port="/dev/ttyDXL_leader_right",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm430-w350"],
-                    "shoulder": [2, "xm430-w350"],
-                    "shoulder_shadow": [3, "xm430-w350"],
-                    "elbow": [4, "xm430-w350"],
-                    "elbow_shadow": [5, "xm430-w350"],
-                    "forearm_roll": [6, "xm430-w350"],
-                    "wrist_angle": [7, "xm430-w350"],
-                    "wrist_rotate": [8, "xl430-w250"],
-                    "gripper": [9, "xc430-w150"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                port="/dev/ttyDXL_follower_left",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm540-w270"],
-                    "shoulder": [2, "xm540-w270"],
-                    "shoulder_shadow": [3, "xm540-w270"],
-                    "elbow": [4, "xm540-w270"],
-                    "elbow_shadow": [5, "xm540-w270"],
-                    "forearm_roll": [6, "xm540-w270"],
-                    "wrist_angle": [7, "xm540-w270"],
-                    "wrist_rotate": [8, "xm430-w350"],
-                    "gripper": [9, "xm430-w350"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                port="/dev/ttyDXL_follower_right",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm540-w270"],
-                    "shoulder": [2, "xm540-w270"],
-                    "shoulder_shadow": [3, "xm540-w270"],
-                    "elbow": [4, "xm540-w270"],
-                    "elbow_shadow": [5, "xm540-w270"],
-                    "forearm_roll": [6, "xm540-w270"],
-                    "wrist_angle": [7, "xm540-w270"],
-                    "wrist_rotate": [8, "xm430-w350"],
-                    "gripper": [9, "xm430-w350"],
-                },
-            ),
-        }
-    )
-
-    # Troubleshooting: If one of your IntelRealSense cameras freeze during
-    # data recording due to bandwidth limit, you might need to plug the camera
-    # on another USB hub or PCIe card.
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "cam_high": IntelRealSenseCameraConfig(
-                serial_number=128422271347,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "cam_low": IntelRealSenseCameraConfig(
-                serial_number=130322270656,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "cam_left_wrist": IntelRealSenseCameraConfig(
-                serial_number=218622272670,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "cam_right_wrist": IntelRealSenseCameraConfig(
-                serial_number=130322272300,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("koch")
-@dataclass
-class KochRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/koch"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0085511",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl330-m077"],
-                    "shoulder_lift": [2, "xl330-m077"],
-                    "elbow_flex": [3, "xl330-m077"],
-                    "wrist_flex": [4, "xl330-m077"],
-                    "wrist_roll": [5, "xl330-m077"],
-                    "gripper": [6, "xl330-m077"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl430-w250"],
-                    "shoulder_lift": [2, "xl430-w250"],
-                    "elbow_flex": [3, "xl330-m288"],
-                    "wrist_flex": [4, "xl330-m288"],
-                    "wrist_roll": [5, "xl330-m288"],
-                    "gripper": [6, "xl330-m288"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    # ~ Koch specific settings ~
-    # Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
-    # to squeeze the gripper and have it spring back to an open position on its own.
-    gripper_open_degree: float = 35.156
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("koch_bimanual")
-@dataclass
-class KochBimanualRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/koch_bimanual"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0085511",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl330-m077"],
-                    "shoulder_lift": [2, "xl330-m077"],
-                    "elbow_flex": [3, "xl330-m077"],
-                    "wrist_flex": [4, "xl330-m077"],
-                    "wrist_roll": [5, "xl330-m077"],
-                    "gripper": [6, "xl330-m077"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem575E0031751",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl330-m077"],
-                    "shoulder_lift": [2, "xl330-m077"],
-                    "elbow_flex": [3, "xl330-m077"],
-                    "wrist_flex": [4, "xl330-m077"],
-                    "wrist_roll": [5, "xl330-m077"],
-                    "gripper": [6, "xl330-m077"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl430-w250"],
-                    "shoulder_lift": [2, "xl430-w250"],
-                    "elbow_flex": [3, "xl330-m288"],
-                    "wrist_flex": [4, "xl330-m288"],
-                    "wrist_roll": [5, "xl330-m288"],
-                    "gripper": [6, "xl330-m288"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem575E0032081",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl430-w250"],
-                    "shoulder_lift": [2, "xl430-w250"],
-                    "elbow_flex": [3, "xl330-m288"],
-                    "wrist_flex": [4, "xl330-m288"],
-                    "wrist_roll": [5, "xl330-m288"],
-                    "gripper": [6, "xl330-m288"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    # ~ Koch specific settings ~
-    # Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
-    # to squeeze the gripper and have it spring back to an open position on its own.
-    gripper_open_degree: float = 35.156
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("moss")
-@dataclass
-class MossRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/moss"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431091",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("so101")
-@dataclass
-class So101RobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/so101"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431091",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("so100")
-@dataclass
-class So100RobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/so100"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431091",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("stretch")
-@dataclass
-class StretchRobotConfig(RobotConfig):
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "navigation": OpenCVCameraConfig(
-                camera_index="/dev/hello-nav-head-camera",
-                fps=10,
-                width=1280,
-                height=720,
-                rotation=-90,
-            ),
-            "head": IntelRealSenseCameraConfig(
-                name="Intel RealSense D435I",
-                fps=30,
-                width=640,
-                height=480,
-                rotation=90,
-            ),
-            "wrist": IntelRealSenseCameraConfig(
-                name="Intel RealSense D405",
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("lekiwi")
-@dataclass
-class LeKiwiRobotConfig(RobotConfig):
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    # Network Configuration
-    ip: str = "192.168.0.193"
-    port: int = 5555
-    video_port: int = 5556
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "front": OpenCVCameraConfig(
-                camera_index="/dev/video0", fps=30, width=640, height=480, rotation=90
-            ),
-            "wrist": OpenCVCameraConfig(
-                camera_index="/dev/video2", fps=30, width=640, height=480, rotation=180
-            ),
-        }
-    )
-
-    calibration_dir: str = ".cache/calibration/lekiwi"
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0077581",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/ttyACM0",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                    "left_wheel": (7, "sts3215"),
-                    "back_wheel": (8, "sts3215"),
-                    "right_wheel": (9, "sts3215"),
-                },
-            ),
-        }
-    )
-
-    teleop_keys: dict[str, str] = field(
-        default_factory=lambda: {
-            # Movement
-            "forward": "w",
-            "backward": "s",
-            "left": "a",
-            "right": "d",
-            "rotate_left": "z",
-            "rotate_right": "x",
-            # Speed control
-            "speed_up": "r",
-            "speed_down": "f",
-            # quit teleop
-            "quit": "q",
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("realman")
-@dataclass
-class RealmanRobotConfig(RobotConfig):
-    inference_time: bool = False
-    max_gripper: int = 990
-    min_gripper: int = 10
-    servo_config_file: str = "/home/maic/LYT/lerobot/lerobot/common/robot_devices/teleop/servo_arm.yaml"
-    
-
-    left_follower_arm: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": RealmanMotorsBusConfig(
-                ip = "192.168.3.18",
-                port = 8080,
-                motors={
-                    # name: (index, model)
-                    "joint_1": [1, "realman"],
-                    "joint_2": [2, "realman"],
-                    "joint_3": [3, "realman"],
-                    "joint_4": [4, "realman"],
-                    "joint_5": [5, "realman"],
-                    "joint_6": [6, "realman"],
-                    "gripper": [7, "realman"],
-                },
-                init_joint = {'joint': [-90, 90, 90, 90, 90, -90, 10]}
-            )
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            # "one": OpenCVCameraConfig(
-            #     camera_index=4,
-            #     fps=30,
-            #     width=640,
-            #     height=480,
-            # ),
-            "left": IntelRealSenseCameraConfig(
-                serial_number="153122077516",
-                fps=30,
-                width=640,
-                height=480,
-                use_depth=False
-            ),
-            # "right": IntelRealSenseCameraConfig(
-            #     serial_number="405622075165",
-            #     fps=30,
-            #     width=640,
-            #     height=480,
-            #     use_depth=False
-            # ),
-            "front": IntelRealSenseCameraConfig(
-                serial_number="145422072751",
-                fps=30,
-                width=640,
-                height=480,
-                use_depth=False
-            ),
-            "high": IntelRealSenseCameraConfig(
-                serial_number="145422072193",
-                fps=30,
-                width=640,
-                height=480,
-                use_depth=False
-            ),
-        }
-    )
-
-    # right_follower_arm: dict[str, MotorsBusConfig] = field(
-    #     default_factory=lambda: {
-    #         "main": RealmanMotorsBusConfig(
-    #             ip = "192.168.3.19",
-    #             port = 8080,
-    #             motors={
-    #                 # name: (index, model)
-    #                 "joint_1": [1, "realman"],
-    #                 "joint_2": [2, "realman"],
-    #                 "joint_3": [3, "realman"],
-    #                 "joint_4": [4, "realman"],
-    #                 "joint_5": [5, "realman"],
-    #                 "joint_6": [6, "realman"],
-    #                 "gripper": (7, "realman"),
-    #             },
-    #         )
-    #     }
-    # )
--- a/lerobot/common/robot_devices/robots/dynamixel_calibration.py
+++ b/lerobot/common/robot_devices/robots/dynamixel_calibration.py
@@ -1,144 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Logic to calibrate a robot arm built with dynamixel motors"""
-# TODO(rcadene, aliberts): move this logic into the robot code when refactoring
-
-import numpy as np
-
-from lerobot.common.robot_devices.motors.dynamixel import (
-    CalibrationMode,
-    TorqueMode,
-    convert_degrees_to_steps,
-)
-from lerobot.common.robot_devices.motors.utils import MotorsBus
-
-URL_TEMPLATE = (
-    "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
-)
-
-# The following positions are provided in nominal degree range ]-180, +180[
-# For more info on these constants, see comments in the code where they get used.
-ZERO_POSITION_DEGREE = 0
-ROTATED_POSITION_DEGREE = 90
-
-
-def assert_drive_mode(drive_mode):
-    # `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
-    if not np.all(np.isin(drive_mode, [0, 1])):
-        raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
-
-
-def apply_drive_mode(position, drive_mode):
-    assert_drive_mode(drive_mode)
-    # Convert `drive_mode` from [0, 1] with 0 indicates original rotation direction and 1 inverted,
-    # to [-1, 1] with 1 indicates original rotation direction and -1 inverted.
-    signed_drive_mode = -(drive_mode * 2 - 1)
-    position *= signed_drive_mode
-    return position
-
-
-def compute_nearest_rounded_position(position, models):
-    delta_turn = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, models)
-    nearest_pos = np.round(position.astype(float) / delta_turn) * delta_turn
-    return nearest_pos.astype(position.dtype)
-
-
-def run_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    """This function ensures that a neural network trained on data collected on a given robot
-    can work on another robot. For instance before calibration, setting a same goal position
-    for each motor of two different robots will get two very different positions. But after calibration,
-    the two robots will move to the same position.To this end, this function computes the homing offset
-    and the drive mode for each motor of a given robot.
-
-    Homing offset is used to shift the motor position to a ]-2048, +2048[ nominal range (when the motor uses 2048 steps
-    to complete a half a turn). This range is set around an arbitrary "zero position" corresponding to all motor positions
-    being 0. During the calibration process, you will need to manually move the robot to this "zero position".
-
-    Drive mode is used to invert the rotation direction of the motor. This is useful when some motors have been assembled
-    in the opposite orientation for some robots. During the calibration process, you will need to manually move the robot
-    to the "rotated position".
-
-    After calibration, the homing offsets and drive modes are stored in a cache.
-
-    Example of usage:
-    ```python
-    run_arm_calibration(arm, "koch", "left", "follower")
-    ```
-    """
-    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-        raise ValueError("To run calibration, the torque must be disabled on all motors.")
-
-    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
-
-    print("\nMove arm to zero position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero"))
-    input("Press Enter to continue...")
-
-    # We arbitrarily chose our zero target position to be a straight horizontal position with gripper upwards and closed.
-    # It is easy to identify and all motors are in a "quarter turn" position. Once calibration is done, this position will
-    # correspond to every motor angle being 0. If you set all 0 as Goal Position, the arm will move in this position.
-    zero_target_pos = convert_degrees_to_steps(ZERO_POSITION_DEGREE, arm.motor_models)
-
-    # Compute homing offset so that `present_position + homing_offset ~= target_position`.
-    zero_pos = arm.read("Present_Position")
-    zero_nearest_pos = compute_nearest_rounded_position(zero_pos, arm.motor_models)
-    homing_offset = zero_target_pos - zero_nearest_pos
-
-    # The rotated target position corresponds to a rotation of a quarter turn from the zero position.
-    # This allows to identify the rotation direction of each motor.
-    # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
-    # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
-    # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
-    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarily rotate clockwise from the point of view
-    # of the previous motor in the kinetic chain.
-    print("\nMove arm to rotated target position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
-    input("Press Enter to continue...")
-
-    rotated_target_pos = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
-
-    # Find drive mode by rotating each motor by a quarter of a turn.
-    # Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
-    rotated_pos = arm.read("Present_Position")
-    drive_mode = (rotated_pos < zero_pos).astype(np.int32)
-
-    # Re-compute homing offset to take into account drive mode
-    rotated_drived_pos = apply_drive_mode(rotated_pos, drive_mode)
-    rotated_nearest_pos = compute_nearest_rounded_position(rotated_drived_pos, arm.motor_models)
-    homing_offset = rotated_target_pos - rotated_nearest_pos
-
-    print("\nMove arm to rest position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest"))
-    input("Press Enter to continue...")
-    print()
-
-    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
-    calib_mode = [CalibrationMode.DEGREE.name] * len(arm.motor_names)
-
-    # TODO(rcadene): make type of joints (DEGREE or LINEAR) configurable from yaml?
-    if robot_type in ["aloha"] and "gripper" in arm.motor_names:
-        # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100]
-        calib_idx = arm.motor_names.index("gripper")
-        calib_mode[calib_idx] = CalibrationMode.LINEAR.name
-
-    calib_data = {
-        "homing_offset": homing_offset.tolist(),
-        "drive_mode": drive_mode.tolist(),
-        "start_pos": zero_pos.tolist(),
-        "end_pos": rotated_pos.tolist(),
-        "calib_mode": calib_mode,
-        "motor_names": arm.motor_names,
-    }
-    return calib_data
--- a/lerobot/common/robot_devices/robots/feetech_calibration.py
+++ b/lerobot/common/robot_devices/robots/feetech_calibration.py
@@ -1,506 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Logic to calibrate a robot arm built with feetech motors"""
-# TODO(rcadene, aliberts): move this logic into the robot code when refactoring
-
-import time
-
-import numpy as np
-
-from lerobot.common.robot_devices.motors.feetech import (
-    CalibrationMode,
-    TorqueMode,
-    convert_degrees_to_steps,
-)
-from lerobot.common.robot_devices.motors.utils import MotorsBus
-
-URL_TEMPLATE = (
-    "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
-)
-
-# The following positions are provided in nominal degree range ]-180, +180[
-# For more info on these constants, see comments in the code where they get used.
-ZERO_POSITION_DEGREE = 0
-ROTATED_POSITION_DEGREE = 90
-
-
-def reset_middle_positions(arm: MotorsBus):
-    input("Please move the robot to the new middle position for calibration, then press Enter...")
-    # Write 128 to Torque_Enable for all motors.
-    arm.write("Torque_Enable", 128)
-
-
-def assert_drive_mode(drive_mode):
-    # `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
-    if not np.all(np.isin(drive_mode, [0, 1])):
-        raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
-
-
-def apply_drive_mode(position, drive_mode):
-    assert_drive_mode(drive_mode)
-    # Convert `drive_mode` from [0, 1] with 0 indicates original rotation direction and 1 inverted,
-    # to [-1, 1] with 1 indicates original rotation direction and -1 inverted.
-    signed_drive_mode = -(drive_mode * 2 - 1)
-    position *= signed_drive_mode
-    return position
-
-
-def move_until_block(arm, motor_name, positive_direction=True, while_move_hook=None):
-    count = 0
-    while True:
-        present_pos = arm.read("Present_Position", motor_name)
-        if positive_direction:
-            # Move +100 steps every time. Lower the steps to lower the speed at which the arm moves.
-            arm.write("Goal_Position", present_pos + 100, motor_name)
-        else:
-            arm.write("Goal_Position", present_pos - 100, motor_name)
-
-        if while_move_hook is not None:
-            while_move_hook()
-
-        present_pos = arm.read("Present_Position", motor_name).item()
-        present_speed = arm.read("Present_Speed", motor_name).item()
-        present_current = arm.read("Present_Current", motor_name).item()
-        # present_load = arm.read("Present_Load", motor_name).item()
-        # present_voltage = arm.read("Present_Voltage", motor_name).item()
-        # present_temperature = arm.read("Present_Temperature", motor_name).item()
-
-        # print(f"{present_pos=}")
-        # print(f"{present_speed=}")
-        # print(f"{present_current=}")
-        # print(f"{present_load=}")
-        # print(f"{present_voltage=}")
-        # print(f"{present_temperature=}")
-
-        if present_speed == 0 and present_current > 40:
-            count += 1
-            if count > 100 or present_current > 300:
-                return present_pos
-        else:
-            count = 0
-
-
-def move_to_calibrate(
-    arm,
-    motor_name,
-    invert_drive_mode=False,
-    positive_first=True,
-    in_between_move_hook=None,
-    while_move_hook=None,
-):
-    initial_pos = arm.read("Present_Position", motor_name)
-
-    if positive_first:
-        p_present_pos = move_until_block(
-            arm, motor_name, positive_direction=True, while_move_hook=while_move_hook
-        )
-    else:
-        n_present_pos = move_until_block(
-            arm, motor_name, positive_direction=False, while_move_hook=while_move_hook
-        )
-
-    if in_between_move_hook is not None:
-        in_between_move_hook()
-
-    if positive_first:
-        n_present_pos = move_until_block(
-            arm, motor_name, positive_direction=False, while_move_hook=while_move_hook
-        )
-    else:
-        p_present_pos = move_until_block(
-            arm, motor_name, positive_direction=True, while_move_hook=while_move_hook
-        )
-
-    zero_pos = (n_present_pos + p_present_pos) / 2
-
-    calib_data = {
-        "initial_pos": initial_pos,
-        "homing_offset": zero_pos if invert_drive_mode else -zero_pos,
-        "invert_drive_mode": invert_drive_mode,
-        "drive_mode": -1 if invert_drive_mode else 0,
-        "zero_pos": zero_pos,
-        "start_pos": n_present_pos if invert_drive_mode else p_present_pos,
-        "end_pos": p_present_pos if invert_drive_mode else n_present_pos,
-    }
-    return calib_data
-
-
-def apply_offset(calib, offset):
-    calib["zero_pos"] += offset
-    if calib["drive_mode"]:
-        calib["homing_offset"] += offset
-    else:
-        calib["homing_offset"] -= offset
-    return calib
-
-
-def run_arm_auto_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    if robot_type == "so100":
-        return run_arm_auto_calibration_so100(arm, robot_type, arm_name, arm_type)
-    elif robot_type == "moss":
-        return run_arm_auto_calibration_moss(arm, robot_type, arm_name, arm_type)
-    else:
-        raise ValueError(robot_type)
-
-
-def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    """All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
-    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-        raise ValueError("To run calibration, the torque must be disabled on all motors.")
-
-    if not (robot_type == "so100" and arm_type == "follower"):
-        raise NotImplementedError("Auto calibration only supports the follower of so100 arms for now.")
-
-    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
-
-    print("\nMove arm to initial position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
-    input("Press Enter to continue...")
-
-    # Lower the acceleration of the motors (in [0,254])
-    initial_acceleration = arm.read("Acceleration")
-    arm.write("Lock", 0)
-    arm.write("Acceleration", 10)
-    time.sleep(1)
-
-    arm.write("Torque_Enable", TorqueMode.ENABLED.value)
-
-    print(f'{arm.read("Present_Position", "elbow_flex")=}')
-
-    calib = {}
-
-    init_wf_pos = arm.read("Present_Position", "wrist_flex")
-    init_sl_pos = arm.read("Present_Position", "shoulder_lift")
-    init_ef_pos = arm.read("Present_Position", "elbow_flex")
-    arm.write("Goal_Position", init_wf_pos - 800, "wrist_flex")
-    arm.write("Goal_Position", init_sl_pos + 150 + 1024, "shoulder_lift")
-    arm.write("Goal_Position", init_ef_pos - 2048, "elbow_flex")
-    time.sleep(2)
-
-    print("Calibrate shoulder_pan")
-    calib["shoulder_pan"] = move_to_calibrate(arm, "shoulder_pan")
-    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
-    time.sleep(1)
-
-    print("Calibrate gripper")
-    calib["gripper"] = move_to_calibrate(arm, "gripper", invert_drive_mode=True)
-    time.sleep(1)
-
-    print("Calibrate wrist_flex")
-    calib["wrist_flex"] = move_to_calibrate(arm, "wrist_flex")
-    calib["wrist_flex"] = apply_offset(calib["wrist_flex"], offset=80)
-
-    def in_between_move_hook():
-        nonlocal arm, calib
-        time.sleep(2)
-        ef_pos = arm.read("Present_Position", "elbow_flex")
-        sl_pos = arm.read("Present_Position", "shoulder_lift")
-        arm.write("Goal_Position", ef_pos + 1024, "elbow_flex")
-        arm.write("Goal_Position", sl_pos - 1024, "shoulder_lift")
-        time.sleep(2)
-
-    print("Calibrate elbow_flex")
-    calib["elbow_flex"] = move_to_calibrate(
-        arm, "elbow_flex", positive_first=False, in_between_move_hook=in_between_move_hook
-    )
-    calib["elbow_flex"] = apply_offset(calib["elbow_flex"], offset=80 - 1024)
-
-    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 1024 + 512, "elbow_flex")
-    time.sleep(1)
-
-    def in_between_move_hook():
-        nonlocal arm, calib
-        arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"], "elbow_flex")
-
-    print("Calibrate shoulder_lift")
-    calib["shoulder_lift"] = move_to_calibrate(
-        arm,
-        "shoulder_lift",
-        invert_drive_mode=True,
-        positive_first=False,
-        in_between_move_hook=in_between_move_hook,
-    )
-    # add an 30 steps as offset to align with body
-    calib["shoulder_lift"] = apply_offset(calib["shoulder_lift"], offset=1024 - 50)
-
-    def while_move_hook():
-        nonlocal arm, calib
-        positions = {
-            "shoulder_lift": round(calib["shoulder_lift"]["zero_pos"] - 1600),
-            "elbow_flex": round(calib["elbow_flex"]["zero_pos"] + 1700),
-            "wrist_flex": round(calib["wrist_flex"]["zero_pos"] + 800),
-            "gripper": round(calib["gripper"]["end_pos"]),
-        }
-        arm.write("Goal_Position", list(positions.values()), list(positions.keys()))
-
-    arm.write("Goal_Position", round(calib["shoulder_lift"]["zero_pos"] - 1600), "shoulder_lift")
-    time.sleep(2)
-    arm.write("Goal_Position", round(calib["elbow_flex"]["zero_pos"] + 1700), "elbow_flex")
-    time.sleep(2)
-    arm.write("Goal_Position", round(calib["wrist_flex"]["zero_pos"] + 800), "wrist_flex")
-    time.sleep(2)
-    arm.write("Goal_Position", round(calib["gripper"]["end_pos"]), "gripper")
-    time.sleep(2)
-
-    print("Calibrate wrist_roll")
-    calib["wrist_roll"] = move_to_calibrate(
-        arm, "wrist_roll", invert_drive_mode=True, positive_first=False, while_move_hook=while_move_hook
-    )
-
-    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["gripper"]["start_pos"], "gripper")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"], "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 2048, "elbow_flex")
-    arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] - 2048, "shoulder_lift")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
-    time.sleep(1)
-
-    calib_modes = []
-    for name in arm.motor_names:
-        if name == "gripper":
-            calib_modes.append(CalibrationMode.LINEAR.name)
-        else:
-            calib_modes.append(CalibrationMode.DEGREE.name)
-
-    calib_dict = {
-        "homing_offset": [calib[name]["homing_offset"] for name in arm.motor_names],
-        "drive_mode": [calib[name]["drive_mode"] for name in arm.motor_names],
-        "start_pos": [calib[name]["start_pos"] for name in arm.motor_names],
-        "end_pos": [calib[name]["end_pos"] for name in arm.motor_names],
-        "calib_mode": calib_modes,
-        "motor_names": arm.motor_names,
-    }
-
-    # Re-enable original accerlation
-    arm.write("Lock", 0)
-    arm.write("Acceleration", initial_acceleration)
-    time.sleep(1)
-
-    return calib_dict
-
-
-def run_arm_auto_calibration_moss(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    """All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
-    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-        raise ValueError("To run calibration, the torque must be disabled on all motors.")
-
-    if not (robot_type == "moss" and arm_type == "follower"):
-        raise NotImplementedError("Auto calibration only supports the follower of moss arms for now.")
-
-    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
-
-    print("\nMove arm to initial position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
-    input("Press Enter to continue...")
-
-    # Lower the acceleration of the motors (in [0,254])
-    initial_acceleration = arm.read("Acceleration")
-    arm.write("Lock", 0)
-    arm.write("Acceleration", 10)
-    time.sleep(1)
-
-    arm.write("Torque_Enable", TorqueMode.ENABLED.value)
-
-    sl_pos = arm.read("Present_Position", "shoulder_lift")
-    arm.write("Goal_Position", sl_pos - 1024 - 450, "shoulder_lift")
-    ef_pos = arm.read("Present_Position", "elbow_flex")
-    arm.write("Goal_Position", ef_pos + 1024 + 450, "elbow_flex")
-    time.sleep(2)
-
-    calib = {}
-
-    print("Calibrate shoulder_pan")
-    calib["shoulder_pan"] = move_to_calibrate(arm, "shoulder_pan")
-    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
-    time.sleep(1)
-
-    print("Calibrate gripper")
-    calib["gripper"] = move_to_calibrate(arm, "gripper", invert_drive_mode=True)
-    time.sleep(1)
-
-    print("Calibrate wrist_flex")
-    calib["wrist_flex"] = move_to_calibrate(arm, "wrist_flex", invert_drive_mode=True)
-    calib["wrist_flex"] = apply_offset(calib["wrist_flex"], offset=-210 + 1024)
-
-    wr_pos = arm.read("Present_Position", "wrist_roll")
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", wr_pos - 1024, "wrist_roll")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 2048, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["gripper"]["end_pos"], "gripper")
-    time.sleep(1)
-
-    print("Calibrate wrist_roll")
-    calib["wrist_roll"] = move_to_calibrate(arm, "wrist_roll", invert_drive_mode=True)
-    calib["wrist_roll"] = apply_offset(calib["wrist_roll"], offset=790)
-
-    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"] - 1024, "wrist_roll")
-    arm.write("Goal_Position", calib["gripper"]["start_pos"], "gripper")
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 2048, "wrist_flex")
-
-    def in_between_move_elbow_flex_hook():
-        nonlocal arm, calib
-        arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"], "wrist_flex")
-
-    print("Calibrate elbow_flex")
-    calib["elbow_flex"] = move_to_calibrate(
-        arm,
-        "elbow_flex",
-        invert_drive_mode=True,
-        in_between_move_hook=in_between_move_elbow_flex_hook,
-    )
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-
-    def in_between_move_shoulder_lift_hook():
-        nonlocal arm, calib
-        sl = arm.read("Present_Position", "shoulder_lift")
-        arm.write("Goal_Position", sl - 1500, "shoulder_lift")
-        time.sleep(1)
-        arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 1536, "elbow_flex")
-        time.sleep(1)
-        arm.write("Goal_Position", calib["wrist_flex"]["start_pos"], "wrist_flex")
-        time.sleep(1)
-
-    print("Calibrate shoulder_lift")
-    calib["shoulder_lift"] = move_to_calibrate(
-        arm, "shoulder_lift", in_between_move_hook=in_between_move_shoulder_lift_hook
-    )
-    calib["shoulder_lift"] = apply_offset(calib["shoulder_lift"], offset=-1024)
-
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] + 2048, "shoulder_lift")
-    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] - 1024 - 400, "elbow_flex")
-    time.sleep(2)
-
-    calib_modes = []
-    for name in arm.motor_names:
-        if name == "gripper":
-            calib_modes.append(CalibrationMode.LINEAR.name)
-        else:
-            calib_modes.append(CalibrationMode.DEGREE.name)
-
-    calib_dict = {
-        "homing_offset": [calib[name]["homing_offset"] for name in arm.motor_names],
-        "drive_mode": [calib[name]["drive_mode"] for name in arm.motor_names],
-        "start_pos": [calib[name]["start_pos"] for name in arm.motor_names],
-        "end_pos": [calib[name]["end_pos"] for name in arm.motor_names],
-        "calib_mode": calib_modes,
-        "motor_names": arm.motor_names,
-    }
-
-    # Re-enable original accerlation
-    arm.write("Lock", 0)
-    arm.write("Acceleration", initial_acceleration)
-    time.sleep(1)
-
-    return calib_dict
-
-
-def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    """This function ensures that a neural network trained on data collected on a given robot
-    can work on another robot. For instance before calibration, setting a same goal position
-    for each motor of two different robots will get two very different positions. But after calibration,
-    the two robots will move to the same position.To this end, this function computes the homing offset
-    and the drive mode for each motor of a given robot.
-
-    Homing offset is used to shift the motor position to a ]-2048, +2048[ nominal range (when the motor uses 2048 steps
-    to complete a half a turn). This range is set around an arbitrary "zero position" corresponding to all motor positions
-    being 0. During the calibration process, you will need to manually move the robot to this "zero position".
-
-    Drive mode is used to invert the rotation direction of the motor. This is useful when some motors have been assembled
-    in the opposite orientation for some robots. During the calibration process, you will need to manually move the robot
-    to the "rotated position".
-
-    After calibration, the homing offsets and drive modes are stored in a cache.
-
-    Example of usage:
-    ```python
-    run_arm_calibration(arm, "so100", "left", "follower")
-    ```
-    """
-    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-        raise ValueError("To run calibration, the torque must be disabled on all motors.")
-
-    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
-
-    reset_middle_positions(arm)
-
-    print("\nMove arm to zero position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero"))
-    input("Press Enter to continue...")
-
-    # We arbitrarily chose our zero target position to be a straight horizontal position with gripper upwards and closed.
-    # It is easy to identify and all motors are in a "quarter turn" position. Once calibration is done, this position will
-    # correspond to every motor angle being 0. If you set all 0 as Goal Position, the arm will move in this position.
-    zero_target_pos = convert_degrees_to_steps(ZERO_POSITION_DEGREE, arm.motor_models)
-
-    # Compute homing offset so that `present_position + homing_offset ~= target_position`.
-    zero_pos = arm.read("Present_Position")
-    homing_offset = zero_target_pos - zero_pos
-
-    # The rotated target position corresponds to a rotation of a quarter turn from the zero position.
-    # This allows to identify the rotation direction of each motor.
-    # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
-    # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
-    # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
-    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarily rotate clockwise from the point of view
-    # of the previous motor in the kinetic chain.
-    print("\nMove arm to rotated target position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
-    input("Press Enter to continue...")
-
-    rotated_target_pos = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
-
-    # Find drive mode by rotating each motor by a quarter of a turn.
-    # Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
-    rotated_pos = arm.read("Present_Position")
-    drive_mode = (rotated_pos < zero_pos).astype(np.int32)
-
-    # Re-compute homing offset to take into account drive mode
-    rotated_drived_pos = apply_drive_mode(rotated_pos, drive_mode)
-    homing_offset = rotated_target_pos - rotated_drived_pos
-
-    print("\nMove arm to rest position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest"))
-    input("Press Enter to continue...")
-    print()
-
-    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
-    calib_modes = []
-    for name in arm.motor_names:
-        if name == "gripper":
-            calib_modes.append(CalibrationMode.LINEAR.name)
-        else:
-            calib_modes.append(CalibrationMode.DEGREE.name)
-
-    calib_dict = {
-        "homing_offset": homing_offset.tolist(),
-        "drive_mode": drive_mode.tolist(),
-        "start_pos": zero_pos.tolist(),
-        "end_pos": rotated_pos.tolist(),
-        "calib_mode": calib_modes,
-        "motor_names": arm.motor_names,
-    }
-    return calib_dict
--- a/lerobot/common/robot_devices/robots/lekiwi_remote.py
+++ b/lerobot/common/robot_devices/robots/lekiwi_remote.py
@@ -1,224 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import base64
-import json
-import threading
-import time
-from pathlib import Path
-
-import cv2
-import zmq
-
-from lerobot.common.robot_devices.robots.mobile_manipulator import LeKiwi
-
-
-def setup_zmq_sockets(config):
-    context = zmq.Context()
-    cmd_socket = context.socket(zmq.PULL)
-    cmd_socket.setsockopt(zmq.CONFLATE, 1)
-    cmd_socket.bind(f"tcp://*:{config.port}")
-
-    video_socket = context.socket(zmq.PUSH)
-    video_socket.setsockopt(zmq.CONFLATE, 1)
-    video_socket.bind(f"tcp://*:{config.video_port}")
-
-    return context, cmd_socket, video_socket
-
-
-def run_camera_capture(cameras, images_lock, latest_images_dict, stop_event):
-    while not stop_event.is_set():
-        local_dict = {}
-        for name, cam in cameras.items():
-            frame = cam.async_read()
-            ret, buffer = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
-            if ret:
-                local_dict[name] = base64.b64encode(buffer).decode("utf-8")
-            else:
-                local_dict[name] = ""
-        with images_lock:
-            latest_images_dict.update(local_dict)
-        time.sleep(0.01)
-
-
-def calibrate_follower_arm(motors_bus, calib_dir_str):
-    """
-    Calibrates the follower arm. Attempts to load an existing calibration file;
-    if not found, runs manual calibration and saves the result.
-    """
-    calib_dir = Path(calib_dir_str)
-    calib_dir.mkdir(parents=True, exist_ok=True)
-    calib_file = calib_dir / "main_follower.json"
-    try:
-        from lerobot.common.robot_devices.robots.feetech_calibration import run_arm_manual_calibration
-    except ImportError:
-        print("[WARNING] Calibration function not available. Skipping calibration.")
-        return
-
-    if calib_file.exists():
-        with open(calib_file) as f:
-            calibration = json.load(f)
-        print(f"[INFO] Loaded calibration from {calib_file}")
-    else:
-        print("[INFO] Calibration file not found. Running manual calibration...")
-        calibration = run_arm_manual_calibration(motors_bus, "lekiwi", "follower_arm", "follower")
-        print(f"[INFO] Calibration complete. Saving to {calib_file}")
-        with open(calib_file, "w") as f:
-            json.dump(calibration, f)
-    try:
-        motors_bus.set_calibration(calibration)
-        print("[INFO] Applied calibration for follower arm.")
-    except Exception as e:
-        print(f"[WARNING] Could not apply calibration: {e}")
-
-
-def run_lekiwi(robot_config):
-    """
-    Runs the LeKiwi robot:
-      - Sets up cameras and connects them.
-      - Initializes the follower arm motors.
-      - Calibrates the follower arm if necessary.
-      - Creates ZeroMQ sockets for receiving commands and streaming observations.
-      - Processes incoming commands (arm and wheel commands) and sends back sensor and camera data.
-    """
-    # Import helper functions and classes
-    from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
-    from lerobot.common.robot_devices.motors.feetech import FeetechMotorsBus, TorqueMode
-
-    # Initialize cameras from the robot configuration.
-    cameras = make_cameras_from_configs(robot_config.cameras)
-    for cam in cameras.values():
-        cam.connect()
-
-    # Initialize the motors bus using the follower arm configuration.
-    motor_config = robot_config.follower_arms.get("main")
-    if motor_config is None:
-        print("[ERROR] Follower arm 'main' configuration not found.")
-        return
-    motors_bus = FeetechMotorsBus(motor_config)
-    motors_bus.connect()
-
-    # Calibrate the follower arm.
-    calibrate_follower_arm(motors_bus, robot_config.calibration_dir)
-
-    # Create the LeKiwi robot instance.
-    robot = LeKiwi(motors_bus)
-
-    # Define the expected arm motor IDs.
-    arm_motor_ids = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"]
-
-    # Disable torque for each arm motor.
-    for motor in arm_motor_ids:
-        motors_bus.write("Torque_Enable", TorqueMode.DISABLED.value, motor)
-
-    # Set up ZeroMQ sockets.
-    context, cmd_socket, video_socket = setup_zmq_sockets(robot_config)
-
-    # Start the camera capture thread.
-    latest_images_dict = {}
-    images_lock = threading.Lock()
-    stop_event = threading.Event()
-    cam_thread = threading.Thread(
-        target=run_camera_capture, args=(cameras, images_lock, latest_images_dict, stop_event), daemon=True
-    )
-    cam_thread.start()
-
-    last_cmd_time = time.time()
-    print("LeKiwi robot server started. Waiting for commands...")
-
-    try:
-        while True:
-            loop_start_time = time.time()
-
-            # Process incoming commands (non-blocking).
-            while True:
-                try:
-                    msg = cmd_socket.recv_string(zmq.NOBLOCK)
-                except zmq.Again:
-                    break
-                try:
-                    data = json.loads(msg)
-                    # Process arm position commands.
-                    if "arm_positions" in data:
-                        arm_positions = data["arm_positions"]
-                        if not isinstance(arm_positions, list):
-                            print(f"[ERROR] Invalid arm_positions: {arm_positions}")
-                        elif len(arm_positions) < len(arm_motor_ids):
-                            print(
-                                f"[WARNING] Received {len(arm_positions)} arm positions, expected {len(arm_motor_ids)}"
-                            )
-                        else:
-                            for motor, pos in zip(arm_motor_ids, arm_positions, strict=False):
-                                motors_bus.write("Goal_Position", pos, motor)
-                    # Process wheel (base) commands.
-                    if "raw_velocity" in data:
-                        raw_command = data["raw_velocity"]
-                        # Expect keys: "left_wheel", "back_wheel", "right_wheel".
-                        command_speeds = [
-                            int(raw_command.get("left_wheel", 0)),
-                            int(raw_command.get("back_wheel", 0)),
-                            int(raw_command.get("right_wheel", 0)),
-                        ]
-                        robot.set_velocity(command_speeds)
-                        last_cmd_time = time.time()
-                except Exception as e:
-                    print(f"[ERROR] Parsing message failed: {e}")
-
-            # Watchdog: stop the robot if no command is received for over 0.5 seconds.
-            now = time.time()
-            if now - last_cmd_time > 0.5:
-                robot.stop()
-                last_cmd_time = now
-
-            # Read current wheel speeds from the robot.
-            current_velocity = robot.read_velocity()
-
-            # Read the follower arm state from the motors bus.
-            follower_arm_state = []
-            for motor in arm_motor_ids:
-                try:
-                    pos = motors_bus.read("Present_Position", motor)
-                    # Convert the position to a float (or use as is if already numeric).
-                    follower_arm_state.append(float(pos) if not isinstance(pos, (int, float)) else pos)
-                except Exception as e:
-                    print(f"[ERROR] Reading motor {motor} failed: {e}")
-
-            # Get the latest camera images.
-            with images_lock:
-                images_dict_copy = dict(latest_images_dict)
-
-            # Build the observation dictionary.
-            observation = {
-                "images": images_dict_copy,
-                "present_speed": current_velocity,
-                "follower_arm_state": follower_arm_state,
-            }
-            # Send the observation over the video socket.
-            video_socket.send_string(json.dumps(observation))
-
-            # Ensure a short sleep to avoid overloading the CPU.
-            elapsed = time.time() - loop_start_time
-            time.sleep(
-                max(0.033 - elapsed, 0)
-            )  # If robot jitters increase the sleep and monitor cpu load with `top` in cmd
-    except KeyboardInterrupt:
-        print("Shutting down LeKiwi server.")
-    finally:
-        stop_event.set()
-        cam_thread.join()
-        robot.stop()
-        motors_bus.disconnect()
-        cmd_socket.close()
-        video_socket.close()
-        context.term()
--- a/lerobot/common/robot_devices/robots/manipulator.py
+++ b/lerobot/common/robot_devices/robots/manipulator.py
@@ -1,627 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Contains logic to instantiate a robot, read information from its motors and cameras,
-and send orders to its motors.
-"""
-# TODO(rcadene, aliberts): reorganize the codebase into one file per robot, with the associated
-# calibration procedure, to make it easy for people to add their own robot.
-
-import json
-import logging
-import time
-import warnings
-from pathlib import Path
-
-import numpy as np
-import torch
-
-from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
-from lerobot.common.robot_devices.motors.utils import MotorsBus, make_motors_buses_from_configs
-from lerobot.common.robot_devices.robots.configs import ManipulatorRobotConfig
-from lerobot.common.robot_devices.robots.utils import get_arm_id
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
-
-
-def ensure_safe_goal_position(
-    goal_pos: torch.Tensor, present_pos: torch.Tensor, max_relative_target: float | list[float]
-):
-    # Cap relative action target magnitude for safety.
-    diff = goal_pos - present_pos
-    max_relative_target = torch.tensor(max_relative_target)
-    safe_diff = torch.minimum(diff, max_relative_target)
-    safe_diff = torch.maximum(safe_diff, -max_relative_target)
-    safe_goal_pos = present_pos + safe_diff
-
-    if not torch.allclose(goal_pos, safe_goal_pos):
-        logging.warning(
-            "Relative goal position magnitude had to be clamped to be safe.\n"
-            f"  requested relative goal position target: {diff}\n"
-            f"    clamped relative goal position target: {safe_diff}"
-        )
-
-    return safe_goal_pos
-
-
-class ManipulatorRobot:
-    # TODO(rcadene): Implement force feedback
-    """This class allows to control any manipulator robot of various number of motors.
-
-    Non exhaustive list of robots:
-    - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow expansion, developed
-    by Alexander Koch from [Tau Robotics](https://tau-robotics.com)
-    - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss
-    - [Aloha](https://www.trossenrobotics.com/aloha-kits) developed by Trossen Robotics
-
-    Example of instantiation, a pre-defined robot config is required:
-    ```python
-    robot = ManipulatorRobot(KochRobotConfig())
-    ```
-
-    Example of overwriting motors during instantiation:
-    ```python
-    # Defines how to communicate with the motors of the leader and follower arms
-    leader_arms = {
-        "main": DynamixelMotorsBusConfig(
-            port="/dev/tty.usbmodem575E0031751",
-            motors={
-                # name: (index, model)
-                "shoulder_pan": (1, "xl330-m077"),
-                "shoulder_lift": (2, "xl330-m077"),
-                "elbow_flex": (3, "xl330-m077"),
-                "wrist_flex": (4, "xl330-m077"),
-                "wrist_roll": (5, "xl330-m077"),
-                "gripper": (6, "xl330-m077"),
-            },
-        ),
-    }
-    follower_arms = {
-        "main": DynamixelMotorsBusConfig(
-            port="/dev/tty.usbmodem575E0032081",
-            motors={
-                # name: (index, model)
-                "shoulder_pan": (1, "xl430-w250"),
-                "shoulder_lift": (2, "xl430-w250"),
-                "elbow_flex": (3, "xl330-m288"),
-                "wrist_flex": (4, "xl330-m288"),
-                "wrist_roll": (5, "xl330-m288"),
-                "gripper": (6, "xl330-m288"),
-            },
-        ),
-    }
-    robot_config = KochRobotConfig(leader_arms=leader_arms, follower_arms=follower_arms)
-    robot = ManipulatorRobot(robot_config)
-    ```
-
-    Example of overwriting cameras during instantiation:
-    ```python
-    # Defines how to communicate with 2 cameras connected to the computer.
-    # Here, the webcam of the laptop and the phone (connected in USB to the laptop)
-    # can be reached respectively using the camera indices 0 and 1. These indices can be
-    # arbitrary. See the documentation of `OpenCVCamera` to find your own camera indices.
-    cameras = {
-        "laptop": OpenCVCamera(camera_index=0, fps=30, width=640, height=480),
-        "phone": OpenCVCamera(camera_index=1, fps=30, width=640, height=480),
-    }
-    robot = ManipulatorRobot(KochRobotConfig(cameras=cameras))
-    ```
-
-    Once the robot is instantiated, connect motors buses and cameras if any (Required):
-    ```python
-    robot.connect()
-    ```
-
-    Example of highest frequency teleoperation, which doesn't require cameras:
-    ```python
-    while True:
-        robot.teleop_step()
-    ```
-
-    Example of highest frequency data collection from motors and cameras (if any):
-    ```python
-    while True:
-        observation, action = robot.teleop_step(record_data=True)
-    ```
-
-    Example of controlling the robot with a policy:
-    ```python
-    while True:
-        # Uses the follower arms and cameras to capture an observation
-        observation = robot.capture_observation()
-
-        # Assumes a policy has been instantiated
-        with torch.inference_mode():
-            action = policy.select_action(observation)
-
-        # Orders the robot to move
-        robot.send_action(action)
-    ```
-
-    Example of disconnecting which is not mandatory since we disconnect when the object is deleted:
-    ```python
-    robot.disconnect()
-    ```
-    """
-
-    def __init__(
-        self,
-        config: ManipulatorRobotConfig,
-    ):
-        self.config = config
-        self.robot_type = self.config.type
-        self.calibration_dir = Path(self.config.calibration_dir)
-        self.leader_arms = make_motors_buses_from_configs(self.config.leader_arms)
-        self.follower_arms = make_motors_buses_from_configs(self.config.follower_arms)
-        self.cameras = make_cameras_from_configs(self.config.cameras)
-        self.is_connected = False
-        self.logs = {}
-
-    def get_motor_names(self, arm: dict[str, MotorsBus]) -> list:
-        return [f"{arm}_{motor}" for arm, bus in arm.items() for motor in bus.motors]
-
-    @property
-    def camera_features(self) -> dict:
-        cam_ft = {}
-        for cam_key, cam in self.cameras.items():
-            key = f"observation.images.{cam_key}"
-            cam_ft[key] = {
-                "shape": (cam.height, cam.width, cam.channels),
-                "names": ["height", "width", "channels"],
-                "info": None,
-            }
-        return cam_ft
-
-    @property
-    def motor_features(self) -> dict:
-        action_names = self.get_motor_names(self.leader_arms)
-        state_names = self.get_motor_names(self.leader_arms)
-        return {
-            "action": {
-                "dtype": "float32",
-                "shape": (len(action_names),),
-                "names": action_names,
-            },
-            "observation.state": {
-                "dtype": "float32",
-                "shape": (len(state_names),),
-                "names": state_names,
-            },
-        }
-
-    @property
-    def features(self):
-        return {**self.motor_features, **self.camera_features}
-
-    @property
-    def has_camera(self):
-        return len(self.cameras) > 0
-
-    @property
-    def num_cameras(self):
-        return len(self.cameras)
-
-    @property
-    def available_arms(self):
-        available_arms = []
-        for name in self.follower_arms:
-            arm_id = get_arm_id(name, "follower")
-            available_arms.append(arm_id)
-        for name in self.leader_arms:
-            arm_id = get_arm_id(name, "leader")
-            available_arms.append(arm_id)
-        return available_arms
-
-    def connect(self):
-        if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
-                "ManipulatorRobot is already connected. Do not run `robot.connect()` twice."
-            )
-
-        if not self.leader_arms and not self.follower_arms and not self.cameras:
-            raise ValueError(
-                "ManipulatorRobot doesn't have any device to connect. See example of usage in docstring of the class."
-            )
-
-        # Connect the arms
-        for name in self.follower_arms:
-            print(f"Connecting {name} follower arm.")
-            self.follower_arms[name].connect()
-        for name in self.leader_arms:
-            print(f"Connecting {name} leader arm.")
-            self.leader_arms[name].connect()
-
-        if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
-            from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
-        elif self.robot_type in ["so100", "so101", "moss", "lekiwi"]:
-            from lerobot.common.robot_devices.motors.feetech import TorqueMode
-
-        # We assume that at connection time, arms are in a rest position, and torque can
-        # be safely disabled to run calibration and/or set robot preset configurations.
-        for name in self.follower_arms:
-            self.follower_arms[name].write("Torque_Enable", TorqueMode.DISABLED.value)
-        for name in self.leader_arms:
-            self.leader_arms[name].write("Torque_Enable", TorqueMode.DISABLED.value)
-
-        self.activate_calibration()
-
-        # Set robot preset (e.g. torque in leader gripper for Koch v1.1)
-        if self.robot_type in ["koch", "koch_bimanual"]:
-            self.set_koch_robot_preset()
-        elif self.robot_type == "aloha":
-            self.set_aloha_robot_preset()
-        elif self.robot_type in ["so100", "so101", "moss", "lekiwi"]:
-            self.set_so100_robot_preset()
-
-        # Enable torque on all motors of the follower arms
-        for name in self.follower_arms:
-            print(f"Activating torque on {name} follower arm.")
-            self.follower_arms[name].write("Torque_Enable", 1)
-
-        if self.config.gripper_open_degree is not None:
-            if self.robot_type not in ["koch", "koch_bimanual"]:
-                raise NotImplementedError(
-                    f"{self.robot_type} does not support position AND current control in the handle, which is require to set the gripper open."
-                )
-            # Set the leader arm in torque mode with the gripper motor set to an angle. This makes it possible
-            # to squeeze the gripper and have it spring back to an open position on its own.
-            for name in self.leader_arms:
-                self.leader_arms[name].write("Torque_Enable", 1, "gripper")
-                self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper")
-
-        # Check both arms can be read
-        for name in self.follower_arms:
-            self.follower_arms[name].read("Present_Position")
-        for name in self.leader_arms:
-            self.leader_arms[name].read("Present_Position")
-
-        # Connect the cameras
-        for name in self.cameras:
-            self.cameras[name].connect()
-
-        self.is_connected = True
-
-    def activate_calibration(self):
-        """After calibration all motors function in human interpretable ranges.
-        Rotations are expressed in degrees in nominal range of [-180, 180],
-        and linear motions (like gripper of Aloha) in nominal range of [0, 100].
-        """
-
-        def load_or_run_calibration_(name, arm, arm_type):
-            arm_id = get_arm_id(name, arm_type)
-            arm_calib_path = self.calibration_dir / f"{arm_id}.json"
-
-            if arm_calib_path.exists():
-                with open(arm_calib_path) as f:
-                    calibration = json.load(f)
-            else:
-                # TODO(rcadene): display a warning in __init__ if calibration file not available
-                print(f"Missing calibration file '{arm_calib_path}'")
-
-                if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
-                    from lerobot.common.robot_devices.robots.dynamixel_calibration import run_arm_calibration
-
-                    calibration = run_arm_calibration(arm, self.robot_type, name, arm_type)
-
-                elif self.robot_type in ["so100", "so101", "moss", "lekiwi"]:
-                    from lerobot.common.robot_devices.robots.feetech_calibration import (
-                        run_arm_manual_calibration,
-                    )
-
-                    calibration = run_arm_manual_calibration(arm, self.robot_type, name, arm_type)
-
-                print(f"Calibration is done! Saving calibration file '{arm_calib_path}'")
-                arm_calib_path.parent.mkdir(parents=True, exist_ok=True)
-                with open(arm_calib_path, "w") as f:
-                    json.dump(calibration, f)
-
-            return calibration
-
-        for name, arm in self.follower_arms.items():
-            calibration = load_or_run_calibration_(name, arm, "follower")
-            arm.set_calibration(calibration)
-        for name, arm in self.leader_arms.items():
-            calibration = load_or_run_calibration_(name, arm, "leader")
-            arm.set_calibration(calibration)
-
-    def set_koch_robot_preset(self):
-        def set_operating_mode_(arm):
-            from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
-
-            if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-                raise ValueError("To run set robot preset, the torque must be disabled on all motors.")
-
-            # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't
-            # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm,
-            # you could end up with a servo with a position 0 or 4095 at a crucial point See [
-            # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
-            all_motors_except_gripper = [name for name in arm.motor_names if name != "gripper"]
-            if len(all_motors_except_gripper) > 0:
-                # 4 corresponds to Extended Position on Koch motors
-                arm.write("Operating_Mode", 4, all_motors_except_gripper)
-
-            # Use 'position control current based' for gripper to be limited by the limit of the current.
-            # For the follower gripper, it means it can grasp an object without forcing too much even tho,
-            # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
-            # For the leader gripper, it means we can use it as a physical trigger, since we can force with our finger
-            # to make it move, and it will move back to its original target position when we release the force.
-            # 5 corresponds to Current Controlled Position on Koch gripper motors "xl330-m077, xl330-m288"
-            arm.write("Operating_Mode", 5, "gripper")
-
-        for name in self.follower_arms:
-            set_operating_mode_(self.follower_arms[name])
-
-            # Set better PID values to close the gap between recorded states and actions
-            # TODO(rcadene): Implement an automatic procedure to set optimal PID values for each motor
-            self.follower_arms[name].write("Position_P_Gain", 1500, "elbow_flex")
-            self.follower_arms[name].write("Position_I_Gain", 0, "elbow_flex")
-            self.follower_arms[name].write("Position_D_Gain", 600, "elbow_flex")
-
-        if self.config.gripper_open_degree is not None:
-            for name in self.leader_arms:
-                set_operating_mode_(self.leader_arms[name])
-
-                # Enable torque on the gripper of the leader arms, and move it to 45 degrees,
-                # so that we can use it as a trigger to close the gripper of the follower arms.
-                self.leader_arms[name].write("Torque_Enable", 1, "gripper")
-                self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper")
-
-    def set_aloha_robot_preset(self):
-        def set_shadow_(arm):
-            # Set secondary/shadow ID for shoulder and elbow. These joints have two motors.
-            # As a result, if only one of them is required to move to a certain position,
-            # the other will follow. This is to avoid breaking the motors.
-            if "shoulder_shadow" in arm.motor_names:
-                shoulder_idx = arm.read("ID", "shoulder")
-                arm.write("Secondary_ID", shoulder_idx, "shoulder_shadow")
-
-            if "elbow_shadow" in arm.motor_names:
-                elbow_idx = arm.read("ID", "elbow")
-                arm.write("Secondary_ID", elbow_idx, "elbow_shadow")
-
-        for name in self.follower_arms:
-            set_shadow_(self.follower_arms[name])
-
-        for name in self.leader_arms:
-            set_shadow_(self.leader_arms[name])
-
-        for name in self.follower_arms:
-            # Set a velocity limit of 131 as advised by Trossen Robotics
-            self.follower_arms[name].write("Velocity_Limit", 131)
-
-            # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't
-            # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm,
-            # you could end up with a servo with a position 0 or 4095 at a crucial point See [
-            # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
-            all_motors_except_gripper = [
-                name for name in self.follower_arms[name].motor_names if name != "gripper"
-            ]
-            if len(all_motors_except_gripper) > 0:
-                # 4 corresponds to Extended Position on Aloha motors
-                self.follower_arms[name].write("Operating_Mode", 4, all_motors_except_gripper)
-
-            # Use 'position control current based' for follower gripper to be limited by the limit of the current.
-            # It can grasp an object without forcing too much even tho,
-            # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
-            # 5 corresponds to Current Controlled Position on Aloha gripper follower "xm430-w350"
-            self.follower_arms[name].write("Operating_Mode", 5, "gripper")
-
-            # Note: We can't enable torque on the leader gripper since "xc430-w150" doesn't have
-            # a Current Controlled Position mode.
-
-        if self.config.gripper_open_degree is not None:
-            warnings.warn(
-                f"`gripper_open_degree` is set to {self.config.gripper_open_degree}, but None is expected for Aloha instead",
-                stacklevel=1,
-            )
-
-    def set_so100_robot_preset(self):
-        for name in self.follower_arms:
-            # Mode=0 for Position Control
-            self.follower_arms[name].write("Mode", 0)
-            # Set P_Coefficient to lower value to avoid shakiness (Default is 32)
-            self.follower_arms[name].write("P_Coefficient", 16)
-            # Set I_Coefficient and D_Coefficient to default value 0 and 32
-            self.follower_arms[name].write("I_Coefficient", 0)
-            self.follower_arms[name].write("D_Coefficient", 32)
-            # Close the write lock so that Maximum_Acceleration gets written to EPROM address,
-            # which is mandatory for Maximum_Acceleration to take effect after rebooting.
-            self.follower_arms[name].write("Lock", 0)
-            # Set Maximum_Acceleration to 254 to speedup acceleration and deceleration of
-            # the motors. Note: this configuration is not in the official STS3215 Memory Table
-            self.follower_arms[name].write("Maximum_Acceleration", 254)
-            self.follower_arms[name].write("Acceleration", 254)
-
-    def teleop_step(
-        self, record_data=False
-    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
-            )
-
-        # Prepare to assign the position of the leader to the follower
-        leader_pos = {}
-        for name in self.leader_arms:
-            before_lread_t = time.perf_counter()
-            leader_pos[name] = self.leader_arms[name].read("Present_Position")
-            leader_pos[name] = torch.from_numpy(leader_pos[name])
-            self.logs[f"read_leader_{name}_pos_dt_s"] = time.perf_counter() - before_lread_t
-
-        # Send goal position to the follower
-        follower_goal_pos = {}
-        for name in self.follower_arms:
-            before_fwrite_t = time.perf_counter()
-            goal_pos = leader_pos[name]
-
-            # Cap goal position when too far away from present position.
-            # Slower fps expected due to reading from the follower.
-            if self.config.max_relative_target is not None:
-                present_pos = self.follower_arms[name].read("Present_Position")
-                present_pos = torch.from_numpy(present_pos)
-                goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
-
-            # Used when record_data=True
-            follower_goal_pos[name] = goal_pos
-
-            goal_pos = goal_pos.numpy().astype(np.float32)
-            self.follower_arms[name].write("Goal_Position", goal_pos)
-            self.logs[f"write_follower_{name}_goal_pos_dt_s"] = time.perf_counter() - before_fwrite_t
-
-        # Early exit when recording data is not requested
-        if not record_data:
-            return
-
-        # TODO(rcadene): Add velocity and other info
-        # Read follower position
-        follower_pos = {}
-        for name in self.follower_arms:
-            before_fread_t = time.perf_counter()
-            follower_pos[name] = self.follower_arms[name].read("Present_Position")
-            follower_pos[name] = torch.from_numpy(follower_pos[name])
-            self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
-
-        # Create state by concatenating follower current position
-        state = []
-        for name in self.follower_arms:
-            if name in follower_pos:
-                state.append(follower_pos[name])
-        state = torch.cat(state)
-
-        # Create action by concatenating follower goal position
-        action = []
-        for name in self.follower_arms:
-            if name in follower_goal_pos:
-                action.append(follower_goal_pos[name])
-        action = torch.cat(action)
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionaries
-        obs_dict, action_dict = {}, {}
-        obs_dict["observation.state"] = state
-        action_dict["action"] = action
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-
-        return obs_dict, action_dict
-
-    def capture_observation(self):
-        """The returned observations do not have a batch dimension."""
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
-            )
-
-        # Read follower position
-        follower_pos = {}
-        for name in self.follower_arms:
-            before_fread_t = time.perf_counter()
-            follower_pos[name] = self.follower_arms[name].read("Present_Position")
-            follower_pos[name] = torch.from_numpy(follower_pos[name])
-            self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
-
-        # Create state by concatenating follower current position
-        state = []
-        for name in self.follower_arms:
-            if name in follower_pos:
-                state.append(follower_pos[name])
-        state = torch.cat(state)
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionaries and format to pytorch
-        obs_dict = {}
-        obs_dict["observation.state"] = state
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-        return obs_dict
-
-    def send_action(self, action: torch.Tensor) -> torch.Tensor:
-        """Command the follower arms to move to a target joint configuration.
-
-        The relative action magnitude may be clipped depending on the configuration parameter
-        `max_relative_target`. In this case, the action sent differs from original action.
-        Thus, this function always returns the action actually sent.
-
-        Args:
-            action: tensor containing the concatenated goal positions for the follower arms.
-        """
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
-            )
-
-        from_idx = 0
-        to_idx = 0
-        action_sent = []
-        for name in self.follower_arms:
-            # Get goal position of each follower arm by splitting the action vector
-            to_idx += len(self.follower_arms[name].motor_names)
-            goal_pos = action[from_idx:to_idx]
-            from_idx = to_idx
-
-            # Cap goal position when too far away from present position.
-            # Slower fps expected due to reading from the follower.
-            if self.config.max_relative_target is not None:
-                present_pos = self.follower_arms[name].read("Present_Position")
-                present_pos = torch.from_numpy(present_pos)
-                goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
-
-            # Save tensor to concat and return
-            action_sent.append(goal_pos)
-
-            # Send goal position to each follower
-            goal_pos = goal_pos.numpy().astype(np.float32)
-            self.follower_arms[name].write("Goal_Position", goal_pos)
-
-        return torch.cat(action_sent)
-
-    def print_logs(self):
-        pass
-        # TODO(aliberts): move robot-specific logs logic here
-
-    def disconnect(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "ManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting."
-            )
-
-        for name in self.follower_arms:
-            self.follower_arms[name].disconnect()
-
-        for name in self.leader_arms:
-            self.leader_arms[name].disconnect()
-
-        for name in self.cameras:
-            self.cameras[name].disconnect()
-
-        self.is_connected = False
-
-    def __del__(self):
-        if getattr(self, "is_connected", False):
-            self.disconnect()
--- a/lerobot/common/robot_devices/robots/mobile_manipulator.py
+++ b/lerobot/common/robot_devices/robots/mobile_manipulator.py
@@ -1,703 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import base64
-import json
-import os
-import sys
-from pathlib import Path
-
-import cv2
-import numpy as np
-import torch
-import zmq
-
-from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
-from lerobot.common.robot_devices.motors.feetech import TorqueMode
-from lerobot.common.robot_devices.motors.utils import MotorsBus, make_motors_buses_from_configs
-from lerobot.common.robot_devices.robots.configs import LeKiwiRobotConfig
-from lerobot.common.robot_devices.robots.feetech_calibration import run_arm_manual_calibration
-from lerobot.common.robot_devices.robots.utils import get_arm_id
-from lerobot.common.robot_devices.utils import RobotDeviceNotConnectedError
-
-PYNPUT_AVAILABLE = True
-try:
-    # Only import if there's a valid X server or if we're not on a Pi
-    if ("DISPLAY" not in os.environ) and ("linux" in sys.platform):
-        print("No DISPLAY set. Skipping pynput import.")
-        raise ImportError("pynput blocked intentionally due to no display.")
-
-    from pynput import keyboard
-except ImportError:
-    keyboard = None
-    PYNPUT_AVAILABLE = False
-except Exception as e:
-    keyboard = None
-    PYNPUT_AVAILABLE = False
-    print(f"Could not import pynput: {e}")
-
-
-class MobileManipulator:
-    """
-    MobileManipulator is a class for connecting to and controlling a remote mobile manipulator robot.
-    The robot includes a three omniwheel mobile base and a remote follower arm.
-    The leader arm is connected locally (on the laptop) and its joint positions are recorded and then
-    forwarded to the remote follower arm (after applying a safety clamp).
-    In parallel, keyboard teleoperation is used to generate raw velocity commands for the wheels.
-    """
-
-    def __init__(self, config: LeKiwiRobotConfig):
-        """
-        Expected keys in config:
-          - ip, port, video_port for the remote connection.
-          - calibration_dir, leader_arms, follower_arms, max_relative_target, etc.
-        """
-        self.robot_type = config.type
-        self.config = config
-        self.remote_ip = config.ip
-        self.remote_port = config.port
-        self.remote_port_video = config.video_port
-        self.calibration_dir = Path(self.config.calibration_dir)
-        self.logs = {}
-
-        self.teleop_keys = self.config.teleop_keys
-
-        # For teleoperation, the leader arm (local) is used to record the desired arm pose.
-        self.leader_arms = make_motors_buses_from_configs(self.config.leader_arms)
-
-        self.follower_arms = make_motors_buses_from_configs(self.config.follower_arms)
-
-        self.cameras = make_cameras_from_configs(self.config.cameras)
-
-        self.is_connected = False
-
-        self.last_frames = {}
-        self.last_present_speed = {}
-        self.last_remote_arm_state = torch.zeros(6, dtype=torch.float32)
-
-        # Define three speed levels and a current index
-        self.speed_levels = [
-            {"xy": 0.1, "theta": 30},  # slow
-            {"xy": 0.2, "theta": 60},  # medium
-            {"xy": 0.3, "theta": 90},  # fast
-        ]
-        self.speed_index = 0  # Start at slow
-
-        # ZeroMQ context and sockets.
-        self.context = None
-        self.cmd_socket = None
-        self.video_socket = None
-
-        # Keyboard state for base teleoperation.
-        self.running = True
-        self.pressed_keys = {
-            "forward": False,
-            "backward": False,
-            "left": False,
-            "right": False,
-            "rotate_left": False,
-            "rotate_right": False,
-        }
-
-        if PYNPUT_AVAILABLE:
-            print("pynput is available - enabling local keyboard listener.")
-            self.listener = keyboard.Listener(
-                on_press=self.on_press,
-                on_release=self.on_release,
-            )
-            self.listener.start()
-        else:
-            print("pynput not available - skipping local keyboard listener.")
-            self.listener = None
-
-    def get_motor_names(self, arms: dict[str, MotorsBus]) -> list:
-        return [f"{arm}_{motor}" for arm, bus in arms.items() for motor in bus.motors]
-
-    @property
-    def camera_features(self) -> dict:
-        cam_ft = {}
-        for cam_key, cam in self.cameras.items():
-            key = f"observation.images.{cam_key}"
-            cam_ft[key] = {
-                "shape": (cam.height, cam.width, cam.channels),
-                "names": ["height", "width", "channels"],
-                "info": None,
-            }
-        return cam_ft
-
-    @property
-    def motor_features(self) -> dict:
-        follower_arm_names = [
-            "shoulder_pan",
-            "shoulder_lift",
-            "elbow_flex",
-            "wrist_flex",
-            "wrist_roll",
-            "gripper",
-        ]
-        observations = ["x_mm", "y_mm", "theta"]
-        combined_names = follower_arm_names + observations
-        return {
-            "action": {
-                "dtype": "float32",
-                "shape": (len(combined_names),),
-                "names": combined_names,
-            },
-            "observation.state": {
-                "dtype": "float32",
-                "shape": (len(combined_names),),
-                "names": combined_names,
-            },
-        }
-
-    @property
-    def features(self):
-        return {**self.motor_features, **self.camera_features}
-
-    @property
-    def has_camera(self):
-        return len(self.cameras) > 0
-
-    @property
-    def num_cameras(self):
-        return len(self.cameras)
-
-    @property
-    def available_arms(self):
-        available = []
-        for name in self.leader_arms:
-            available.append(get_arm_id(name, "leader"))
-        for name in self.follower_arms:
-            available.append(get_arm_id(name, "follower"))
-        return available
-
-    def on_press(self, key):
-        try:
-            # Movement
-            if key.char == self.teleop_keys["forward"]:
-                self.pressed_keys["forward"] = True
-            elif key.char == self.teleop_keys["backward"]:
-                self.pressed_keys["backward"] = True
-            elif key.char == self.teleop_keys["left"]:
-                self.pressed_keys["left"] = True
-            elif key.char == self.teleop_keys["right"]:
-                self.pressed_keys["right"] = True
-            elif key.char == self.teleop_keys["rotate_left"]:
-                self.pressed_keys["rotate_left"] = True
-            elif key.char == self.teleop_keys["rotate_right"]:
-                self.pressed_keys["rotate_right"] = True
-
-            # Quit teleoperation
-            elif key.char == self.teleop_keys["quit"]:
-                self.running = False
-                return False
-
-            # Speed control
-            elif key.char == self.teleop_keys["speed_up"]:
-                self.speed_index = min(self.speed_index + 1, 2)
-                print(f"Speed index increased to {self.speed_index}")
-            elif key.char == self.teleop_keys["speed_down"]:
-                self.speed_index = max(self.speed_index - 1, 0)
-                print(f"Speed index decreased to {self.speed_index}")
-
-        except AttributeError:
-            # e.g., if key is special like Key.esc
-            if key == keyboard.Key.esc:
-                self.running = False
-                return False
-
-    def on_release(self, key):
-        try:
-            if hasattr(key, "char"):
-                if key.char == self.teleop_keys["forward"]:
-                    self.pressed_keys["forward"] = False
-                elif key.char == self.teleop_keys["backward"]:
-                    self.pressed_keys["backward"] = False
-                elif key.char == self.teleop_keys["left"]:
-                    self.pressed_keys["left"] = False
-                elif key.char == self.teleop_keys["right"]:
-                    self.pressed_keys["right"] = False
-                elif key.char == self.teleop_keys["rotate_left"]:
-                    self.pressed_keys["rotate_left"] = False
-                elif key.char == self.teleop_keys["rotate_right"]:
-                    self.pressed_keys["rotate_right"] = False
-        except AttributeError:
-            pass
-
-    def connect(self):
-        if not self.leader_arms:
-            raise ValueError("MobileManipulator has no leader arm to connect.")
-        for name in self.leader_arms:
-            print(f"Connecting {name} leader arm.")
-            self.calibrate_leader()
-
-        # Set up ZeroMQ sockets to communicate with the remote mobile robot.
-        self.context = zmq.Context()
-        self.cmd_socket = self.context.socket(zmq.PUSH)
-        connection_string = f"tcp://{self.remote_ip}:{self.remote_port}"
-        self.cmd_socket.connect(connection_string)
-        self.cmd_socket.setsockopt(zmq.CONFLATE, 1)
-        self.video_socket = self.context.socket(zmq.PULL)
-        video_connection = f"tcp://{self.remote_ip}:{self.remote_port_video}"
-        self.video_socket.connect(video_connection)
-        self.video_socket.setsockopt(zmq.CONFLATE, 1)
-        print(
-            f"[INFO] Connected to remote robot at {connection_string} and video stream at {video_connection}."
-        )
-        self.is_connected = True
-
-    def load_or_run_calibration_(self, name, arm, arm_type):
-        arm_id = get_arm_id(name, arm_type)
-        arm_calib_path = self.calibration_dir / f"{arm_id}.json"
-
-        if arm_calib_path.exists():
-            with open(arm_calib_path) as f:
-                calibration = json.load(f)
-        else:
-            print(f"Missing calibration file '{arm_calib_path}'")
-            calibration = run_arm_manual_calibration(arm, self.robot_type, name, arm_type)
-            print(f"Calibration is done! Saving calibration file '{arm_calib_path}'")
-            arm_calib_path.parent.mkdir(parents=True, exist_ok=True)
-            with open(arm_calib_path, "w") as f:
-                json.dump(calibration, f)
-
-        return calibration
-
-    def calibrate_leader(self):
-        for name, arm in self.leader_arms.items():
-            # Connect the bus
-            arm.connect()
-
-            # Disable torque on all motors
-            for motor_id in arm.motors:
-                arm.write("Torque_Enable", TorqueMode.DISABLED.value, motor_id)
-
-            # Now run calibration
-            calibration = self.load_or_run_calibration_(name, arm, "leader")
-            arm.set_calibration(calibration)
-
-    def calibrate_follower(self):
-        for name, bus in self.follower_arms.items():
-            bus.connect()
-
-            # Disable torque on all motors
-            for motor_id in bus.motors:
-                bus.write("Torque_Enable", 0, motor_id)
-
-            # Then filter out wheels
-            arm_only_dict = {k: v for k, v in bus.motors.items() if not k.startswith("wheel_")}
-            if not arm_only_dict:
-                continue
-
-            original_motors = bus.motors
-            bus.motors = arm_only_dict
-
-            calibration = self.load_or_run_calibration_(name, bus, "follower")
-            bus.set_calibration(calibration)
-
-            bus.motors = original_motors
-
-    def _get_data(self):
-        """
-        Polls the video socket for up to 15 ms. If data arrives, decode only
-        the *latest* message, returning frames, speed, and arm state. If
-        nothing arrives for any field, use the last known values.
-        """
-        frames = {}
-        present_speed = {}
-        remote_arm_state_tensor = torch.zeros(6, dtype=torch.float32)
-
-        # Poll up to 15 ms
-        poller = zmq.Poller()
-        poller.register(self.video_socket, zmq.POLLIN)
-        socks = dict(poller.poll(15))
-        if self.video_socket not in socks or socks[self.video_socket] != zmq.POLLIN:
-            # No new data arrived → reuse ALL old data
-            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
-
-        # Drain all messages, keep only the last
-        last_msg = None
-        while True:
-            try:
-                obs_string = self.video_socket.recv_string(zmq.NOBLOCK)
-                last_msg = obs_string
-            except zmq.Again:
-                break
-
-        if not last_msg:
-            # No new message → also reuse old
-            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
-
-        # Decode only the final message
-        try:
-            observation = json.loads(last_msg)
-
-            images_dict = observation.get("images", {})
-            new_speed = observation.get("present_speed", {})
-            new_arm_state = observation.get("follower_arm_state", None)
-
-            # Convert images
-            for cam_name, image_b64 in images_dict.items():
-                if image_b64:
-                    jpg_data = base64.b64decode(image_b64)
-                    np_arr = np.frombuffer(jpg_data, dtype=np.uint8)
-                    frame_candidate = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
-                    if frame_candidate is not None:
-                        frames[cam_name] = frame_candidate
-
-            # If remote_arm_state is None and frames is None there is no message then use the previous message
-            if new_arm_state is not None and frames is not None:
-                self.last_frames = frames
-
-                remote_arm_state_tensor = torch.tensor(new_arm_state, dtype=torch.float32)
-                self.last_remote_arm_state = remote_arm_state_tensor
-
-                present_speed = new_speed
-                self.last_present_speed = new_speed
-            else:
-                frames = self.last_frames
-
-                remote_arm_state_tensor = self.last_remote_arm_state
-
-                present_speed = self.last_present_speed
-
-        except Exception as e:
-            print(f"[DEBUG] Error decoding video message: {e}")
-            # If decode fails, fall back to old data
-            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
-
-        return frames, present_speed, remote_arm_state_tensor
-
-    def _process_present_speed(self, present_speed: dict) -> torch.Tensor:
-        state_tensor = torch.zeros(3, dtype=torch.int32)
-        if present_speed:
-            decoded = {key: MobileManipulator.raw_to_degps(value) for key, value in present_speed.items()}
-            if "1" in decoded:
-                state_tensor[0] = decoded["1"]
-            if "2" in decoded:
-                state_tensor[1] = decoded["2"]
-            if "3" in decoded:
-                state_tensor[2] = decoded["3"]
-        return state_tensor
-
-    def teleop_step(
-        self, record_data: bool = False
-    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError("MobileManipulator is not connected. Run `connect()` first.")
-
-        speed_setting = self.speed_levels[self.speed_index]
-        xy_speed = speed_setting["xy"]  # e.g. 0.1, 0.25, or 0.4
-        theta_speed = speed_setting["theta"]  # e.g. 30, 60, or 90
-
-        # Prepare to assign the position of the leader to the follower
-        arm_positions = []
-        for name in self.leader_arms:
-            pos = self.leader_arms[name].read("Present_Position")
-            pos_tensor = torch.from_numpy(pos).float()
-            arm_positions.extend(pos_tensor.tolist())
-
-        y_cmd = 0.0  # m/s forward/backward
-        x_cmd = 0.0  # m/s lateral
-        theta_cmd = 0.0  # deg/s rotation
-        if self.pressed_keys["forward"]:
-            y_cmd += xy_speed
-        if self.pressed_keys["backward"]:
-            y_cmd -= xy_speed
-        if self.pressed_keys["left"]:
-            x_cmd += xy_speed
-        if self.pressed_keys["right"]:
-            x_cmd -= xy_speed
-        if self.pressed_keys["rotate_left"]:
-            theta_cmd += theta_speed
-        if self.pressed_keys["rotate_right"]:
-            theta_cmd -= theta_speed
-
-        wheel_commands = self.body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
-
-        message = {"raw_velocity": wheel_commands, "arm_positions": arm_positions}
-        self.cmd_socket.send_string(json.dumps(message))
-
-        if not record_data:
-            return
-
-        obs_dict = self.capture_observation()
-
-        arm_state_tensor = torch.tensor(arm_positions, dtype=torch.float32)
-
-        wheel_velocity_tuple = self.wheel_raw_to_body(wheel_commands)
-        wheel_velocity_mm = (
-            wheel_velocity_tuple[0] * 1000.0,
-            wheel_velocity_tuple[1] * 1000.0,
-            wheel_velocity_tuple[2],
-        )
-        wheel_tensor = torch.tensor(wheel_velocity_mm, dtype=torch.float32)
-        action_tensor = torch.cat([arm_state_tensor, wheel_tensor])
-        action_dict = {"action": action_tensor}
-
-        return obs_dict, action_dict
-
-    def capture_observation(self) -> dict:
-        """
-        Capture observations from the remote robot: current follower arm positions,
-        present wheel speeds (converted to body-frame velocities: x, y, theta),
-        and a camera frame.
-        """
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError("Not connected. Run `connect()` first.")
-
-        frames, present_speed, remote_arm_state_tensor = self._get_data()
-
-        body_state = self.wheel_raw_to_body(present_speed)
-
-        body_state_mm = (body_state[0] * 1000.0, body_state[1] * 1000.0, body_state[2])  # Convert x,y to mm/s
-        wheel_state_tensor = torch.tensor(body_state_mm, dtype=torch.float32)
-        combined_state_tensor = torch.cat((remote_arm_state_tensor, wheel_state_tensor), dim=0)
-
-        obs_dict = {"observation.state": combined_state_tensor}
-
-        # Loop over each configured camera
-        for cam_name, cam in self.cameras.items():
-            frame = frames.get(cam_name, None)
-            if frame is None:
-                # Create a black image using the camera's configured width, height, and channels
-                frame = np.zeros((cam.height, cam.width, cam.channels), dtype=np.uint8)
-            obs_dict[f"observation.images.{cam_name}"] = torch.from_numpy(frame)
-
-        return obs_dict
-
-    def send_action(self, action: torch.Tensor) -> torch.Tensor:
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError("Not connected. Run `connect()` first.")
-
-        # Ensure the action tensor has at least 9 elements:
-        #   - First 6: arm positions.
-        #   - Last 3: base commands.
-        if action.numel() < 9:
-            # Pad with zeros if there are not enough elements.
-            padded = torch.zeros(9, dtype=action.dtype)
-            padded[: action.numel()] = action
-            action = padded
-
-        # Extract arm and base actions.
-        arm_actions = action[:6].flatten()
-        base_actions = action[6:].flatten()
-
-        x_cmd_mm = base_actions[0].item()  # mm/s
-        y_cmd_mm = base_actions[1].item()  # mm/s
-        theta_cmd = base_actions[2].item()  # deg/s
-
-        # Convert mm/s to m/s for the kinematics calculations.
-        x_cmd = x_cmd_mm / 1000.0  # m/s
-        y_cmd = y_cmd_mm / 1000.0  # m/s
-
-        # Compute wheel commands from body commands.
-        wheel_commands = self.body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
-
-        arm_positions_list = arm_actions.tolist()
-
-        message = {"raw_velocity": wheel_commands, "arm_positions": arm_positions_list}
-        self.cmd_socket.send_string(json.dumps(message))
-
-        return action
-
-    def print_logs(self):
-        pass
-
-    def disconnect(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError("Not connected.")
-        if self.cmd_socket:
-            stop_cmd = {
-                "raw_velocity": {"left_wheel": 0, "back_wheel": 0, "right_wheel": 0},
-                "arm_positions": {},
-            }
-            self.cmd_socket.send_string(json.dumps(stop_cmd))
-            self.cmd_socket.close()
-        if self.video_socket:
-            self.video_socket.close()
-        if self.context:
-            self.context.term()
-        if PYNPUT_AVAILABLE:
-            self.listener.stop()
-        self.is_connected = False
-        print("[INFO] Disconnected from remote robot.")
-
-    def __del__(self):
-        if getattr(self, "is_connected", False):
-            self.disconnect()
-        if PYNPUT_AVAILABLE:
-            self.listener.stop()
-
-    @staticmethod
-    def degps_to_raw(degps: float) -> int:
-        steps_per_deg = 4096.0 / 360.0
-        speed_in_steps = abs(degps) * steps_per_deg
-        speed_int = int(round(speed_in_steps))
-        if speed_int > 0x7FFF:
-            speed_int = 0x7FFF
-        if degps < 0:
-            return speed_int | 0x8000
-        else:
-            return speed_int & 0x7FFF
-
-    @staticmethod
-    def raw_to_degps(raw_speed: int) -> float:
-        steps_per_deg = 4096.0 / 360.0
-        magnitude = raw_speed & 0x7FFF
-        degps = magnitude / steps_per_deg
-        if raw_speed & 0x8000:
-            degps = -degps
-        return degps
-
-    def body_to_wheel_raw(
-        self,
-        x_cmd: float,
-        y_cmd: float,
-        theta_cmd: float,
-        wheel_radius: float = 0.05,
-        base_radius: float = 0.125,
-        max_raw: int = 3000,
-    ) -> dict:
-        """
-        Convert desired body-frame velocities into wheel raw commands.
-
-        Parameters:
-          x_cmd      : Linear velocity in x (m/s).
-          y_cmd      : Linear velocity in y (m/s).
-          theta_cmd  : Rotational velocity (deg/s).
-          wheel_radius: Radius of each wheel (meters).
-          base_radius : Distance from the center of rotation to each wheel (meters).
-          max_raw    : Maximum allowed raw command (ticks) per wheel.
-
-        Returns:
-          A dictionary with wheel raw commands:
-             {"left_wheel": value, "back_wheel": value, "right_wheel": value}.
-
-        Notes:
-          - Internally, the method converts theta_cmd to rad/s for the kinematics.
-          - The raw command is computed from the wheels angular speed in deg/s
-            using degps_to_raw(). If any command exceeds max_raw, all commands
-            are scaled down proportionally.
-        """
-        # Convert rotational velocity from deg/s to rad/s.
-        theta_rad = theta_cmd * (np.pi / 180.0)
-        # Create the body velocity vector [x, y, theta_rad].
-        velocity_vector = np.array([x_cmd, y_cmd, theta_rad])
-
-        # Define the wheel mounting angles (defined from y axis cw)
-        angles = np.radians(np.array([300, 180, 60]))
-        # Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
-        # The third column (base_radius) accounts for the effect of rotation.
-        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
-
-        # Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s).
-        wheel_linear_speeds = m.dot(velocity_vector)
-        wheel_angular_speeds = wheel_linear_speeds / wheel_radius
-
-        # Convert wheel angular speeds from rad/s to deg/s.
-        wheel_degps = wheel_angular_speeds * (180.0 / np.pi)
-
-        # Scaling
-        steps_per_deg = 4096.0 / 360.0
-        raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps]
-        max_raw_computed = max(raw_floats)
-        if max_raw_computed > max_raw:
-            scale = max_raw / max_raw_computed
-            wheel_degps = wheel_degps * scale
-
-        # Convert each wheel’s angular speed (deg/s) to a raw integer.
-        wheel_raw = [MobileManipulator.degps_to_raw(deg) for deg in wheel_degps]
-
-        return {"left_wheel": wheel_raw[0], "back_wheel": wheel_raw[1], "right_wheel": wheel_raw[2]}
-
-    def wheel_raw_to_body(
-        self, wheel_raw: dict, wheel_radius: float = 0.05, base_radius: float = 0.125
-    ) -> tuple:
-        """
-        Convert wheel raw command feedback back into body-frame velocities.
-
-        Parameters:
-          wheel_raw   : Dictionary with raw wheel commands (keys: "left_wheel", "back_wheel", "right_wheel").
-          wheel_radius: Radius of each wheel (meters).
-          base_radius : Distance from the robot center to each wheel (meters).
-
-        Returns:
-          A tuple (x_cmd, y_cmd, theta_cmd) where:
-             x_cmd      : Linear velocity in x (m/s).
-             y_cmd      : Linear velocity in y (m/s).
-             theta_cmd  : Rotational velocity in deg/s.
-        """
-        # Extract the raw values in order.
-        raw_list = [
-            int(wheel_raw.get("left_wheel", 0)),
-            int(wheel_raw.get("back_wheel", 0)),
-            int(wheel_raw.get("right_wheel", 0)),
-        ]
-
-        # Convert each raw command back to an angular speed in deg/s.
-        wheel_degps = np.array([MobileManipulator.raw_to_degps(r) for r in raw_list])
-        # Convert from deg/s to rad/s.
-        wheel_radps = wheel_degps * (np.pi / 180.0)
-        # Compute each wheel’s linear speed (m/s) from its angular speed.
-        wheel_linear_speeds = wheel_radps * wheel_radius
-
-        # Define the wheel mounting angles (defined from y axis cw)
-        angles = np.radians(np.array([300, 180, 60]))
-        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
-
-        # Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
-        m_inv = np.linalg.inv(m)
-        velocity_vector = m_inv.dot(wheel_linear_speeds)
-        x_cmd, y_cmd, theta_rad = velocity_vector
-        theta_cmd = theta_rad * (180.0 / np.pi)
-        return (x_cmd, y_cmd, theta_cmd)
-
-
-class LeKiwi:
-    def __init__(self, motor_bus):
-        """
-        Initializes the LeKiwi with Feetech motors bus.
-        """
-        self.motor_bus = motor_bus
-        self.motor_ids = ["left_wheel", "back_wheel", "right_wheel"]
-
-        # Initialize motors in velocity mode.
-        self.motor_bus.write("Lock", 0)
-        self.motor_bus.write("Mode", [1, 1, 1], self.motor_ids)
-        self.motor_bus.write("Lock", 1)
-        print("Motors set to velocity mode.")
-
-    def read_velocity(self):
-        """
-        Reads the raw speeds for all wheels. Returns a dictionary with motor names:
-        """
-        raw_speeds = self.motor_bus.read("Present_Speed", self.motor_ids)
-        return {
-            "left_wheel": int(raw_speeds[0]),
-            "back_wheel": int(raw_speeds[1]),
-            "right_wheel": int(raw_speeds[2]),
-        }
-
-    def set_velocity(self, command_speeds):
-        """
-        Sends raw velocity commands (16-bit encoded values) directly to the motor bus.
-        The order of speeds must correspond to self.motor_ids.
-        """
-        self.motor_bus.write("Goal_Speed", command_speeds, self.motor_ids)
-
-    def stop(self):
-        """Stops the robot by setting all motor speeds to zero."""
-        self.motor_bus.write("Goal_Speed", [0, 0, 0], self.motor_ids)
-        print("Motors stopped.")
--- a/lerobot/common/robot_devices/robots/realman.py
+++ b/lerobot/common/robot_devices/robots/realman.py
@@ -1,292 +0,0 @@
-"""
-    Teleoperation Realman with a PS5 controller and 
-"""
-
-import time
-import torch
-import numpy as np
-from dataclasses import dataclass, field, replace
-from collections import deque
-from lerobot.common.robot_devices.teleop.gamepad import HybridController
-from lerobot.common.robot_devices.motors.utils import get_motor_names, make_motors_buses_from_configs
-from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
-from lerobot.common.robot_devices.robots.configs import RealmanRobotConfig
-
-
-class RealmanRobot:
-    def __init__(self, config: RealmanRobotConfig | None = None, **kwargs):
-        if config is None:
-            config = RealmanRobotConfig()
-        # Overwrite config arguments using kwargs
-        self.config = replace(config, **kwargs)
-        self.robot_type = self.config.type
-        self.inference_time = self.config.inference_time # if it is inference time
-        
-        # build cameras
-        self.cameras = make_cameras_from_configs(self.config.cameras)
-        
-        # build realman motors
-        self.piper_motors = make_motors_buses_from_configs(self.config.left_follower_arm)
-        self.arm = self.piper_motors['main']
-        
-        # build init teleop info
-        self.init_info = {
-            'init_joint': self.arm.init_joint_position,
-            'init_pose': self.arm.init_pose,
-            'max_gripper': config.max_gripper,
-            'min_gripper': config.min_gripper,
-            'servo_config_file': config.servo_config_file
-        }
-        
-        # build state-action cache
-        self.joint_queue = deque(maxlen=2)
-        self.last_endpose = self.arm.init_pose
-
-        # build gamepad teleop
-        if not self.inference_time:
-            self.teleop = HybridController(self.init_info)
-        else:
-            self.teleop = None
-        
-        self.logs = {}
-        self.is_connected = False
-
-    @property
-    def camera_features(self) -> dict:
-        cam_ft = {}
-        for cam_key, cam in self.cameras.items():
-            key = f"observation.images.{cam_key}"
-            cam_ft[key] = {
-                "shape": (cam.height, cam.width, cam.channels),
-                "names": ["height", "width", "channels"],
-                "info": None,
-            }
-        return cam_ft
-
-    
-    @property
-    def motor_features(self) -> dict:
-        action_names = get_motor_names(self.piper_motors)
-        state_names = get_motor_names(self.piper_motors)
-        return {
-            "action": {
-                "dtype": "float32",
-                "shape": (len(action_names),),
-                "names": action_names,
-            },
-            "observation.state": {
-                "dtype": "float32",
-                "shape": (len(state_names),),
-                "names": state_names,
-            },
-        }
-    
-    @property
-    def has_camera(self):
-        return len(self.cameras) > 0
-
-    @property
-    def num_cameras(self):
-        return len(self.cameras)
-
-
-    def connect(self) -> None:
-        """Connect RealmanArm and cameras"""
-        if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
-                "RealmanArm is already connected. Do not run `robot.connect()` twice."
-            )
-        
-        # connect RealmanArm
-        self.arm.connect(enable=True)
-        print("RealmanArm conneted")
-
-        # connect cameras
-        for name in self.cameras:
-            self.cameras[name].connect()
-            self.is_connected = self.is_connected and self.cameras[name].is_connected
-            print(f"camera {name} conneted")
-        
-        print("All connected")
-        self.is_connected = True
-        
-        self.run_calibration()
-
-
-    def disconnect(self) -> None:
-        """move to home position, disenable piper and cameras"""
-        # move piper to home position, disable
-        if not self.inference_time:
-            self.teleop.stop()
-
-        # disconnect piper
-        self.arm.safe_disconnect()
-        print("RealmanArm disable after 5 seconds")
-        time.sleep(5)
-        self.arm.connect(enable=False)
-
-        # disconnect cameras
-        if len(self.cameras) > 0:
-            for cam in self.cameras.values():
-                cam.disconnect()
-
-        self.is_connected = False
-
-
-    def run_calibration(self):
-        """move piper to the home position"""
-        if not self.is_connected:
-            raise ConnectionError()
-        
-        self.arm.apply_calibration()
-        if not self.inference_time:
-            self.teleop.reset()
-
-
-    def teleop_step(
-        self, record_data=False
-    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
-        if not self.is_connected:
-            raise ConnectionError()
-
-        if self.teleop is None and self.inference_time:
-            self.teleop = HybridController(self.init_info)
-
-        # read target pose state as 
-        before_read_t = time.perf_counter()
-        state = self.arm.read() # read current joint position from robot
-        action = self.teleop.get_action() # target joint position and pose end pos from gamepad
-        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
-
-        if action['control_mode'] == 'joint':
-            # 关节控制模式（主模式）
-            current_pose = self.arm.read_current_arm_endpose_state()
-            self.teleop.update_endpose_state(current_pose)
-
-            target_joints = action['joint_angles'][:-1]
-            self.arm.write_gripper(action['gripper'])
-            print(action['gripper'])
-            if action['master_controller_status']['infrared'] == 1:
-                if action['master_controller_status']['button'] == 1:
-                    self.arm.write_joint_canfd(target_joints)
-                else:
-                    self.arm.write_joint_slow(target_joints)
-                
-            # do action
-            before_write_t = time.perf_counter()
-            self.joint_queue.append(list(self.arm.read().values()))
-            self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
-
-        else:
-            target_pose = list(action['end_pose'])
-            # do action
-            before_write_t = time.perf_counter()
-            if self.last_endpose != target_pose:
-                self.arm.write_endpose_canfd(target_pose)
-                self.last_endpose = target_pose
-            self.arm.write_gripper(action['gripper'])
-            
-            target_joints = self.arm.read_current_arm_joint_state()
-            self.joint_queue.append(list(self.arm.read().values()))
-            self.teleop.update_joint_state(target_joints)
-            self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
-        
-        if not record_data:
-            return
-        
-        state = torch.as_tensor(list(self.joint_queue[0]), dtype=torch.float32)
-        action = torch.as_tensor(list(self.joint_queue[-1]), dtype=torch.float32)
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionnaries
-        obs_dict, action_dict = {}, {}
-        obs_dict["observation.state"] = state
-        action_dict["action"] = action
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-
-        return obs_dict, action_dict
-
-
-
-    def send_action(self, action: torch.Tensor) -> torch.Tensor:
-        """Write the predicted actions from policy to the motors"""
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "Piper is not connected. You need to run `robot.connect()`."
-            )
-
-        # send to motors, torch to list
-        target_joints = action.tolist()
-        len_joint = len(target_joints) - 1
-        target_joints = [target_joints[i]*180 for i in range(len_joint)] + [target_joints[-1]]
-        target_joints[-1] = int(target_joints[-1]*500 + 500)
-        self.arm.write(target_joints)
-
-        return action
-
-
-
-    def capture_observation(self) -> dict:
-        """capture current images and joint positions"""
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "Piper is not connected. You need to run `robot.connect()`."
-            )
-        
-        # read current joint positions
-        before_read_t = time.perf_counter()
-        state = self.arm.read()  # 6 joints + 1 gripper
-        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
-
-        state = torch.as_tensor(list(state.values()), dtype=torch.float32)
-
-        # read images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionnaries and format to pytorch
-        obs_dict = {}
-        obs_dict["observation.state"] = state
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-        return obs_dict
-    
-    def teleop_safety_stop(self):
-        """ move to home position after record one episode """
-        self.run_calibration()
-
-    
-    def __del__(self):
-        if self.is_connected:
-            self.disconnect()
-            if not self.inference_time:
-                self.teleop.stop()
-
-
-if __name__ == '__main__':
-    robot = RealmanRobot()
-    robot.connect()
-    # robot.run_calibration()
-    while True:
-        robot.teleop_step(record_data=True)
-    
-    robot.capture_observation()
-    dummy_action = torch.Tensor([-0.40586111280653214, 0.5522833506266276, 0.4998166826036241, -0.3539944542778863, -0.524433347913954, 0.9064999898274739, 0.482])
-    robot.send_action(dummy_action)
-    robot.disconnect()
-    print('ok')
--- a/lerobot/common/robot_devices/robots/stretch.py
+++ b/lerobot/common/robot_devices/robots/stretch.py
@@ -1,208 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import time
-from dataclasses import replace
-
-import torch
-from stretch_body.gamepad_teleop import GamePadTeleop
-from stretch_body.robot import Robot as StretchAPI
-from stretch_body.robot_params import RobotParams
-
-from lerobot.common.robot_devices.robots.configs import StretchRobotConfig
-
-
-class StretchRobot(StretchAPI):
-    """Wrapper of stretch_body.robot.Robot"""
-
-    def __init__(self, config: StretchRobotConfig | None = None, **kwargs):
-        super().__init__()
-        if config is None:
-            self.config = StretchRobotConfig(**kwargs)
-        else:
-            # Overwrite config arguments using kwargs
-            self.config = replace(config, **kwargs)
-
-        self.robot_type = self.config.type
-        self.cameras = self.config.cameras
-        self.is_connected = False
-        self.teleop = None
-        self.logs = {}
-
-        # TODO(aliberts): test this
-        RobotParams.set_logging_level("WARNING")
-        RobotParams.set_logging_formatter("brief_console_formatter")
-
-        self.state_keys = None
-        self.action_keys = None
-
-    def connect(self) -> None:
-        self.is_connected = self.startup()
-        if not self.is_connected:
-            print("Another process is already using Stretch. Try running 'stretch_free_robot_process.py'")
-            raise ConnectionError()
-
-        for name in self.cameras:
-            self.cameras[name].connect()
-            self.is_connected = self.is_connected and self.cameras[name].is_connected
-
-        if not self.is_connected:
-            print("Could not connect to the cameras, check that all cameras are plugged-in.")
-            raise ConnectionError()
-
-        self.run_calibration()
-
-    def run_calibration(self) -> None:
-        if not self.is_homed():
-            self.home()
-
-    def teleop_step(
-        self, record_data=False
-    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
-        # TODO(aliberts): return ndarrays instead of torch.Tensors
-        if not self.is_connected:
-            raise ConnectionError()
-
-        if self.teleop is None:
-            self.teleop = GamePadTeleop(robot_instance=False)
-            self.teleop.startup(robot=self)
-
-        before_read_t = time.perf_counter()
-        state = self.get_state()
-        action = self.teleop.gamepad_controller.get_state()
-        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
-
-        before_write_t = time.perf_counter()
-        self.teleop.do_motion(robot=self)
-        self.push_command()
-        self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
-
-        if self.state_keys is None:
-            self.state_keys = list(state)
-
-        if not record_data:
-            return
-
-        state = torch.as_tensor(list(state.values()))
-        action = torch.as_tensor(list(action.values()))
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionaries
-        obs_dict, action_dict = {}, {}
-        obs_dict["observation.state"] = state
-        action_dict["action"] = action
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-
-        return obs_dict, action_dict
-
-    def get_state(self) -> dict:
-        status = self.get_status()
-        return {
-            "head_pan.pos": status["head"]["head_pan"]["pos"],
-            "head_tilt.pos": status["head"]["head_tilt"]["pos"],
-            "lift.pos": status["lift"]["pos"],
-            "arm.pos": status["arm"]["pos"],
-            "wrist_pitch.pos": status["end_of_arm"]["wrist_pitch"]["pos"],
-            "wrist_roll.pos": status["end_of_arm"]["wrist_roll"]["pos"],
-            "wrist_yaw.pos": status["end_of_arm"]["wrist_yaw"]["pos"],
-            "gripper.pos": status["end_of_arm"]["stretch_gripper"]["pos"],
-            "base_x.vel": status["base"]["x_vel"],
-            "base_y.vel": status["base"]["y_vel"],
-            "base_theta.vel": status["base"]["theta_vel"],
-        }
-
-    def capture_observation(self) -> dict:
-        # TODO(aliberts): return ndarrays instead of torch.Tensors
-        before_read_t = time.perf_counter()
-        state = self.get_state()
-        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
-
-        if self.state_keys is None:
-            self.state_keys = list(state)
-
-        state = torch.as_tensor(list(state.values()))
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionaries
-        obs_dict = {}
-        obs_dict["observation.state"] = state
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-
-        return obs_dict
-
-    def send_action(self, action: torch.Tensor) -> torch.Tensor:
-        # TODO(aliberts): return ndarrays instead of torch.Tensors
-        if not self.is_connected:
-            raise ConnectionError()
-
-        if self.teleop is None:
-            self.teleop = GamePadTeleop(robot_instance=False)
-            self.teleop.startup(robot=self)
-
-        if self.action_keys is None:
-            dummy_action = self.teleop.gamepad_controller.get_state()
-            self.action_keys = list(dummy_action.keys())
-
-        action_dict = dict(zip(self.action_keys, action.tolist(), strict=True))
-
-        before_write_t = time.perf_counter()
-        self.teleop.do_motion(state=action_dict, robot=self)
-        self.push_command()
-        self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
-
-        # TODO(aliberts): return action_sent when motion is limited
-        return action
-
-    def print_logs(self) -> None:
-        pass
-        # TODO(aliberts): move robot-specific logs logic here
-
-    def teleop_safety_stop(self) -> None:
-        if self.teleop is not None:
-            self.teleop._safety_stop(robot=self)
-
-    def disconnect(self) -> None:
-        self.stop()
-        if self.teleop is not None:
-            self.teleop.gamepad_controller.stop()
-            self.teleop.stop()
-
-        if len(self.cameras) > 0:
-            for cam in self.cameras.values():
-                cam.disconnect()
-
-        self.is_connected = False
-
-    def __del__(self):
-        self.disconnect()
--- a/lerobot/common/robot_devices/robots/utils.py
+++ b/lerobot/common/robot_devices/robots/utils.py
@@ -1,99 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Protocol
-
-from lerobot.common.robot_devices.robots.configs import (
-    AlohaRobotConfig,
-    KochBimanualRobotConfig,
-    KochRobotConfig,
-    LeKiwiRobotConfig,
-    ManipulatorRobotConfig,
-    MossRobotConfig,
-    RobotConfig,
-    So100RobotConfig,
-    So101RobotConfig,
-    StretchRobotConfig,
-    RealmanRobotConfig
-)
-
-
-def get_arm_id(name, arm_type):
-    """Returns the string identifier of a robot arm. For instance, for a bimanual manipulator
-    like Aloha, it could be left_follower, right_follower, left_leader, or right_leader.
-    """
-    return f"{name}_{arm_type}"
-
-
-class Robot(Protocol):
-    # TODO(rcadene, aliberts): Add unit test checking the protocol is implemented in the corresponding classes
-    robot_type: str
-    features: dict
-
-    def connect(self): ...
-    def run_calibration(self): ...
-    def teleop_step(self, record_data=False): ...
-    def capture_observation(self): ...
-    def send_action(self, action): ...
-    def disconnect(self): ...
-
-
-def make_robot_config(robot_type: str, **kwargs) -> RobotConfig:
-    if robot_type == "aloha":
-        return AlohaRobotConfig(**kwargs)
-    elif robot_type == "koch":
-        return KochRobotConfig(**kwargs)
-    elif robot_type == "koch_bimanual":
-        return KochBimanualRobotConfig(**kwargs)
-    elif robot_type == "moss":
-        return MossRobotConfig(**kwargs)
-    elif robot_type == "so100":
-        return So100RobotConfig(**kwargs)
-    elif robot_type == "so101":
-        return So101RobotConfig(**kwargs)
-    elif robot_type == "stretch":
-        return StretchRobotConfig(**kwargs)
-    elif robot_type == "lekiwi":
-        return LeKiwiRobotConfig(**kwargs)
-    elif robot_type == 'realman':
-        return RealmanRobotConfig(**kwargs)
-
-    else:
-        raise ValueError(f"Robot type '{robot_type}' is not available.")
-
-
-def make_robot_from_config(config: RobotConfig):
-    if isinstance(config, ManipulatorRobotConfig):
-        from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot
-
-        return ManipulatorRobot(config)
-    elif isinstance(config, LeKiwiRobotConfig):
-        from lerobot.common.robot_devices.robots.mobile_manipulator import MobileManipulator
-
-        return MobileManipulator(config)
-    
-    elif isinstance(config, RealmanRobotConfig):
-        from lerobot.common.robot_devices.robots.realman import RealmanRobot
-
-        return RealmanRobot(config)
-
-    else:
-        from lerobot.common.robot_devices.robots.stretch import StretchRobot
-
-        return StretchRobot(config)
-
-
-def make_robot(robot_type: str, **kwargs) -> Robot:
-    config = make_robot_config(robot_type, **kwargs)
-    return make_robot_from_config(config)
--- a/lerobot/common/robot_devices/teleop/gamepad.py
+++ b/lerobot/common/robot_devices/teleop/gamepad.py
@@ -1,466 +0,0 @@
-import pygame
-import threading
-import time
-import serial
-import binascii
-import logging
-import yaml
-from typing import Dict
-from Robotic_Arm.rm_robot_interface import *
-
-
-
-class ServoArm:
-    def __init__(self, config_file="config.yaml"):
-        """初始化机械臂的串口连接并发送初始数据。
-
-        Args:
-            config_file (str): 配置文件的路径。
-        """
-        self.config = self._load_config(config_file)
-        self.port = self.config["port"]
-        self.baudrate = self.config["baudrate"]
-        self.joint_hex_data = self.config["joint_hex_data"]
-        self.control_hex_data = self.config["control_hex_data"]
-        self.arm_axis = self.config.get("arm_axis", 7)
-
-        try:
-            self.serial_conn = serial.Serial(self.port, self.baudrate, timeout=0)
-            self.bytes_to_send = binascii.unhexlify(self.joint_hex_data.replace(" ", ""))
-            self.serial_conn.write(self.bytes_to_send)
-            time.sleep(1)
-            self.connected = True
-            logging.info(f"串口连接成功: {self.port}")
-        except Exception as e:
-            logging.error(f"串口连接失败: {e}")
-            self.connected = False
-
-    def _load_config(self, config_file):
-        """加载配置文件。
-
-        Args:
-            config_file (str): 配置文件的路径。
-
-        Returns:
-            dict: 配置文件内容。
-        """
-        try:
-            with open(config_file, "r") as file:
-                config = yaml.safe_load(file)
-            return config
-        except Exception as e:
-            logging.error(f"配置文件加载失败: {e}")
-            # 返回默认配置
-            return {
-                "port": "/dev/ttyUSB0",
-                "baudrate": 460800,
-                "joint_hex_data": "55 AA 02 00 00 67",
-                "control_hex_data": "55 AA 08 00 00 B9",
-                "arm_axis": 6
-            }
-
-    def _bytes_to_signed_int(self, byte_data):
-        """将字节数据转换为有符号整数。
-
-        Args:
-            byte_data (bytes): 字节数据。
-
-        Returns:
-            int: 有符号整数。
-        """
-        return int.from_bytes(byte_data, byteorder="little", signed=True)
-
-    def _parse_joint_data(self, hex_received):
-        """解析接收到的十六进制数据并提取关节数据。
-
-        Args:
-            hex_received (str): 接收到的十六进制字符串数据。
-
-        Returns:
-            dict: 解析后的关节数据。
-        """
-        logging.debug(f"hex_received: {hex_received}")
-        joints = {}
-        for i in range(self.arm_axis):
-            start = 14 + i * 10
-            end = start + 8
-            joint_hex = hex_received[start:end]
-            joint_byte_data = bytearray.fromhex(joint_hex)
-            joint_value = self._bytes_to_signed_int(joint_byte_data) / 10000.0
-            joints[f"joint_{i+1}"] = joint_value
-        grasp_start = 14 + self.arm_axis*10
-        grasp_hex = hex_received[grasp_start:grasp_start+8]
-        grasp_byte_data = bytearray.fromhex(grasp_hex)
-        # 夹爪进行归一化处理
-        grasp_value = self._bytes_to_signed_int(grasp_byte_data)/1000
-
-        joints["grasp"] = grasp_value
-        return joints
-    
-    def _parse_controller_data(self, hex_received):
-        status = {
-            'infrared': 0,
-            'button': 0
-        }
-        if len(hex_received) == 18:
-            status['infrared'] = self._bytes_to_signed_int(bytearray.fromhex(hex_received[12:14]))
-            status['button'] = self._bytes_to_signed_int(bytearray.fromhex(hex_received[14:16]))
-            # print(infrared)
-        return status
-
-    def get_joint_actions(self):
-        """从串口读取数据并解析关节动作。
-
-        Returns:
-            dict: 包含关节数据的字典。
-        """
-        if not self.connected:
-            return {}
-        
-        try:
-            self.serial_conn.write(self.bytes_to_send)
-            time.sleep(0.02)
-            bytes_received = self.serial_conn.read(self.serial_conn.inWaiting())
-            if len(bytes_received) == 0:
-                return {}
-            
-            hex_received = binascii.hexlify(bytes_received).decode("utf-8").upper()
-            actions = self._parse_joint_data(hex_received)
-            return actions
-        except Exception as e:
-            logging.error(f"读取串口数据错误: {e}")
-            return {}
-
-    def get_controller_status(self):
-        bytes_to_send = binascii.unhexlify(self.control_hex_data.replace(" ", ""))
-        self.serial_conn.write(bytes_to_send)
-        time.sleep(0.02)
-        bytes_received = self.serial_conn.read(self.serial_conn.inWaiting())
-        hex_received = binascii.hexlify(bytes_received).decode("utf-8").upper()
-        # print("control status:", hex_received)
-        status = self._parse_controller_data(hex_received)
-        return status
-
-    def close(self):
-        """关闭串口连接"""
-        if self.connected and hasattr(self, 'serial_conn'):
-            self.serial_conn.close()
-            self.connected = False
-            logging.info("串口连接已关闭")
-
-
-class HybridController:
-    def __init__(self, init_info):
-        # 初始化pygame和手柄
-        pygame.init()
-        pygame.joystick.init()
-        
-        # 检查是否有连接的手柄
-        if pygame.joystick.get_count() == 0:
-            raise Exception("未检测到手柄")
-        
-        # 初始化手柄
-        self.joystick = pygame.joystick.Joystick(0)
-        self.joystick.init()
-        # 摇杆死区
-        self.deadzone = 0.15
-        # 控制模式: True为关节控制（主模式），False为末端控制
-        self.joint_control_mode = True
-        # 精细控制模式
-        self.fine_control_mode = False
-        
-        # 初始化末端姿态和关节角度
-        self.init_joint = init_info['init_joint']
-        self.init_pose = init_info.get('init_pose', [0]*6)
-        self.max_gripper = init_info['max_gripper']
-        self.min_gripper = init_info['min_gripper']
-        servo_config_file = init_info['servo_config_file']
-        self.joint = self.init_joint.copy()
-        self.pose = self.init_pose.copy()
-        self.pose_speeds = [0.0] * 6
-        self.joint_speeds = [0.0] * 6
-        self.tozero = False
-        
-        # 主臂关节状态
-        self.master_joint_actions = {}
-        self.master_controller_status = {}
-        self.use_master_arm = False
-        
-        # 末端位姿限制
-        self.pose_limits = [
-            (-0.800, 0.800),     # X (m)
-            (-0.800, 0.800),     # Y (m)
-            (-0.800, 0.800),     # Z (m)
-            (-3.14, 3.14),       # RX (rad)
-            (-3.14, 3.14),       # RY (rad)
-            (-3.14, 3.14)        # RZ (rad)
-        ]
-        
-        # 关节角度限制 (度)
-        self.joint_limits = [
-            (-180, 180),  # joint 1
-            (-180, 180),  # joint 2
-            (-180, 180),  # joint 3
-            (-180, 180),  # joint 4
-            (-180, 180),  # joint 5
-            (-180, 180)   # joint 6
-        ]
-        
-        # 控制参数
-        self.linear_step = 0.002      # 线性移动步长(m)
-        self.angular_step = 0.01      # 角度步长(rad)
-        
-        # 夹爪状态和速度
-        self.gripper_speed = 10
-        self.gripper = self.min_gripper
-        
-        # 初始化串口通信（主臂关节状态获取）
-        self.servo_arm = None
-        if servo_config_file:
-            try:
-                self.servo_arm = ServoArm(servo_config_file)
-                self.use_master_arm = True
-                logging.info("串口主臂连接成功，启用主从控制模式")
-            except Exception as e:
-                logging.error(f"串口主臂连接失败: {e}")
-                self.use_master_arm = False
-        
-        # 启动更新线程
-        self.running = True
-        self.thread = threading.Thread(target=self.update_controller)
-        self.thread.start()
-        
-        print("混合控制器已启动")
-        print("主控制模式: 关节控制")
-        if self.use_master_arm:
-            print("主从控制: 启用")
-        print("Back按钮: 切换控制模式(关节/末端)")
-        print("L3按钮: 切换精细控制模式")
-        print("Start按钮: 重置到初始位置")
-    
-    def _apply_nonlinear_mapping(self, value):
-        """应用非线性映射以提高控制精度"""
-        sign = 1 if value >= 0 else -1
-        return sign * (abs(value) ** 2)
-    
-    def _normalize_angle(self, angle):
-        """将角度归一化到[-π, π]范围内"""
-        import math
-        while angle > math.pi:
-            angle -= 2 * math.pi
-        while angle < -math.pi:
-            angle += 2 * math.pi
-        return angle
-    
-    def update_controller(self):
-        while self.running:
-            try:
-                pygame.event.pump()
-            except Exception as e:
-                print(f"控制器错误: {e}")
-                self.stop()
-                continue
-            
-            # 检查控制模式切换 (Back按钮)
-            if self.joystick.get_button(6):  # Back按钮
-                self.joint_control_mode = not self.joint_control_mode
-                mode_str = "关节控制" if self.joint_control_mode else "末端位姿控制"
-                print(f"切换到{mode_str}模式")
-                time.sleep(0.3)  # 防止多次触发
-            
-            # 检查精细控制模式切换 (L3按钮)
-            if self.joystick.get_button(10):  # L3按钮
-                self.fine_control_mode = not self.fine_control_mode
-                print(f"切换到{'精细' if self.fine_control_mode else '普通'}控制模式")
-                time.sleep(0.3)  # 防止多次触发
-            
-            # 检查重置按钮 (Start按钮)
-            if self.joystick.get_button(7):  # Start按钮
-                print("重置机械臂到初始位置...")
-                # print("init_joint", self.init_joint.copy())
-                self.tozero = True
-                self.joint = self.init_joint.copy()
-                self.pose = self.init_pose.copy()
-                self.pose_speeds = [0.0] * 6
-                self.joint_speeds = [0.0] * 6
-                self.gripper_speed = 10
-                self.gripper = self.min_gripper
-                print("机械臂已重置到初始位置")
-                time.sleep(0.3)  # 防止多次触发
-
-            # 从串口获取主臂关节状态
-            if self.servo_arm and self.servo_arm.connected:
-                try:
-                    self.master_joint_actions = self.servo_arm.get_joint_actions()
-                    self.master_controller_status = self.servo_arm.get_controller_status()
-                    if self.master_joint_actions:
-                        logging.debug(f"主臂关节状态: {self.master_joint_actions}")
-                    
-                except Exception as e:
-                    logging.error(f"获取主臂状态错误: {e}")
-                    self.master_joint_actions = {}
-            # print(self.master_joint_actions)
-
-            # 根据控制模式更新相应的控制逻辑
-            if self.joint_control_mode:
-                # 关节控制模式下，优先使用主臂数据，Xbox作为辅助
-                self.update_joint_control()
-            else:
-                # 末端控制模式，使用Xbox控制
-                self.update_end_pose()
-            time.sleep(0.02)
-            # print('gripper:', self.gripper)
-    
-    def update_joint_control(self):
-        """更新关节角度控制 - 优先使用主臂数据"""
-        if self.use_master_arm and self.master_joint_actions:
-            # 主从控制模式：直接使用主臂的关节角度
-            try:
-                # 将主臂关节角度映射到从臂
-                for i in range(6):  # 假设只有6个关节需要控制
-                    joint_key = f"joint_{i+1}"
-                    if joint_key in self.master_joint_actions:
-                        # 直接使用主臂的关节角度（已经是度数）
-                        self.joint[i] = self.master_joint_actions[joint_key]
-                        
-                        # 应用关节限制
-                        min_val, max_val = self.joint_limits[i]
-                        self.joint[i] = max(min_val, min(max_val, self.joint[i]))
-                
-                # print(self.joint)
-                logging.debug(f"主臂关节映射到从臂: {self.joint[:6]}")
-                
-            except Exception as e:
-                logging.error(f"主臂数据映射错误: {e}")
-        
-        # 如果有主臂夹爪数据，使用主臂夹爪状态
-        if self.use_master_arm and "grasp" in self.master_joint_actions:
-            self.gripper = self.master_joint_actions["grasp"] * 1000
-            self.joint[-1] = self.gripper
-    
-
-    def update_end_pose(self):
-        """更新末端位姿控制"""
-        # 根据控制模式调整步长
-        current_linear_step = self.linear_step * (0.1 if self.fine_control_mode else 1.0)
-        current_angular_step = self.angular_step * (0.1 if self.fine_control_mode else 1.0)
-        
-        # 方向键控制XY
-        hat = self.joystick.get_hat(0)
-        hat_up = hat[1] == 1     # Y+
-        hat_down = hat[1] == -1  # Y-
-        hat_left = hat[0] == -1  # X-
-        hat_right = hat[0] == 1  # X+
-        
-        # 右摇杆控制Z
-        right_y_raw = -self.joystick.get_axis(4)
-        # 左摇杆控制RZ
-        left_y_raw = -self.joystick.get_axis(1)
-        
-        # 应用死区
-        right_y = 0.0 if abs(right_y_raw) < self.deadzone else right_y_raw
-        left_y = 0.0 if abs(left_y_raw) < self.deadzone else left_y_raw
-
-        # 计算各轴速度
-        self.pose_speeds[1] = current_linear_step if hat_up else (-current_linear_step if hat_down else 0.0)  # Y
-        self.pose_speeds[0] = -current_linear_step if hat_left else (current_linear_step if hat_right else 0.0)  # X
-        
-        # 设置Z速度（右摇杆Y轴控制）
-        z_mapping = self._apply_nonlinear_mapping(right_y)
-        self.pose_speeds[2] = z_mapping * current_linear_step  # Z
-        
-        # L1/R1控制RX旋转
-        LB = self.joystick.get_button(4)  # RX-
-        RB = self.joystick.get_button(5)  # RX+
-        self.pose_speeds[3] = (-current_angular_step if LB else (current_angular_step if RB else 0.0))
-        
-        # △/□控制RY旋转
-        triangle = self.joystick.get_button(2)  # RY+
-        square = self.joystick.get_button(3)    # RY-
-        self.pose_speeds[4] = (current_angular_step if triangle else (-current_angular_step if square else 0.0))
-        
-        # 左摇杆Y轴控制RZ旋转
-        rz_mapping = self._apply_nonlinear_mapping(left_y)
-        self.pose_speeds[5] = rz_mapping * current_angular_step * 2  # RZ
-        
-        # 夹爪控制（圈/叉）
-        circle = self.joystick.get_button(1)  # 夹爪开
-        cross = self.joystick.get_button(0)   # 夹爪关
-        if circle:
-            self.gripper = min(self.max_gripper, self.gripper + self.gripper_speed)
-        elif cross:
-            self.gripper = max(self.min_gripper, self.gripper - self.gripper_speed)
-
-        # 更新末端位姿
-        for i in range(6):
-            self.pose[i] += self.pose_speeds[i]
-        
-        # 角度归一化处理
-        for i in range(3, 6):
-            self.pose[i] = self._normalize_angle(self.pose[i])
-
-    def update_joint_state(self, joint):
-        self.joint = joint
-        # self.tozero = False
-
-    def update_endpose_state(self, end_pose):
-        self.pose = end_pose
-        # self.tozero = False
-    
-    def update_tozero_state(self, tozero):
-        self.tozero = tozero
-
-            
-    def get_action(self) -> Dict:
-        """获取当前控制命令"""
-        return {
-            'control_mode': 'joint' if self.joint_control_mode else 'end_pose',
-            'use_master_arm': self.use_master_arm,
-            'master_joint_actions': self.master_joint_actions,
-            'master_controller_status': self.master_controller_status,
-            'end_pose': self.pose,
-            'joint_angles': self.joint,
-            'gripper': int(self.gripper),
-            'tozero': self.tozero
-        }
-    
-    def stop(self):
-        """停止控制器"""
-        self.running = False
-        if self.thread.is_alive():
-            self.thread.join()
-        if self.servo_arm:
-            self.servo_arm.close()
-        pygame.quit()
-        print("混合控制器已退出")
-    
-    def reset(self):
-        """重置到初始状态"""
-        self.joint = self.init_joint.copy()
-        self.pose = self.init_pose.copy()
-        self.pose_speeds = [0.0] * 6
-        self.joint_speeds = [0.0] * 6
-        self.gripper_speed = 10
-        self.gripper = self.min_gripper
-        print("已重置到初始状态")
-
-
-# 使用示例
-if __name__ == "__main__":
-    # 初始化睿尔曼机械臂
-    arm = RoboticArm(rm_thread_mode_e.RM_TRIPLE_MODE_E)
-    # 创建机械臂连接
-    handle = arm.rm_create_robot_arm("192.168.3.18", 8080)
-    print(f"机械臂连接ID: {handle.id}")
-    init_pose = arm.rm_get_current_arm_state()[1]['pose']
-
-    with open('/home/maic/LYT/lerobot/lerobot/common/robot_devices/teleop/realman_mix.yaml', "r") as file:
-        config = yaml.safe_load(file)
-    config['init_pose'] = init_pose
-    arm_controller = HybridController(config)
-    try:
-        while True:
-            print(arm_controller.get_action())
-            time.sleep(0.1)
-    except KeyboardInterrupt:
-        arm_controller.stop()
--- a/lerobot/common/robot_devices/teleop/realman_mix.yaml
+++ b/lerobot/common/robot_devices/teleop/realman_mix.yaml
@@ -1,4 +0,0 @@
-init_joint: [-90, 90, 90, -90, -90, 90]
-max_gripper: 990
-min_gripper: 10
-servo_config_file: "/home/maic/LYT/lerobot/lerobot/common/robot_devices/teleop/servo_arm.yaml"
--- a/lerobot/common/robot_devices/teleop/servo_arm.yaml
+++ b/lerobot/common/robot_devices/teleop/servo_arm.yaml
@@ -1,6 +0,0 @@
-port: /dev/ttyUSB0
-right_port: /dev/ttyUSB1
-baudrate: 460800
-joint_hex_data: "55 AA 02 00 00 67"
-control_hex_data: "55 AA 08 00 00 B9"
-arm_axis: 6
--- a/lerobot/common/robots/init.py
+++ b/lerobot/common/robots/init.py
@@ -0,0 +1,3 @@
+from .config import RobotConfig
+from .robot import Robot
+from .utils import make_robot_from_config
--- a/lerobot/common/robots/config.py
+++ b/lerobot/common/robots/config.py
@@ -0,0 +1,40 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+from dataclasses import dataclass
+from pathlib import Path
+
+import draccus
+
+
+@dataclass(kw_only=True)
+class RobotConfig(draccus.ChoiceRegistry, abc.ABC):
+    # Allows to distinguish between different robots of the same type
+    id: str | None = None
+    # Directory to store calibration file
+    calibration_dir: Path | None = None
+
+    def __post_init__(self):
+        if hasattr(self, "cameras") and self.cameras:
+            for _, config in self.cameras.items():
+                for attr in ["width", "height", "fps"]:
+                    if getattr(config, attr) is None:
+                        raise ValueError(
+                            f"Specifying '{attr}' is required for the camera to be used in a robot"
+                        )
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
--- a/lerobot/common/robots/koch_follower/init.py
+++ b/lerobot/common/robots/koch_follower/init.py
@@ -0,0 +1,2 @@
+from .config_koch_follower import KochFollowerConfig
+from .koch_follower import KochFollower
--- a/lerobot/common/robots/koch_follower/config_koch_follower.py
+++ b/lerobot/common/robots/koch_follower/config_koch_follower.py
@@ -0,0 +1,36 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.common.cameras import CameraConfig
+
+from ..config import RobotConfig
+
+
+@RobotConfig.register_subclass("koch_follower")
+@dataclass
+class KochFollowerConfig(RobotConfig):
+    # Port to connect to the arm
+    port: str
+
+    disable_torque_on_disconnect: bool = True
+
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    # cameras
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)
--- a/lerobot/common/robots/koch_follower/koch.mdx
+++ b/lerobot/common/robots/koch_follower/koch.mdx
@@ -0,0 +1,258 @@
+# Koch v1.1
+
+In the steps below, we explain how to assemble the Koch v1.1 robot.
+
+## Order and assemble the parts
+
+Follow the sourcing and assembling instructions provided in this [README](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below.
+
+For a visual walkthrough of the assembly process, you can refer to [this video tutorial](https://youtu.be/8nQIg9BwwTk).
+
+> [!WARNING]
+> Since the production of this video, we simplified the configuration phase. Because of this, two things differ from the instructions in that video:
+> - Don't plug in all the motor cables right away and wait to be instructed to do so in [Configure the motors](#configure-the-motors).
+> - Don't screw in the controller board (PCB) to the base right away and wait for being instructed to do so in [Configure the motors](#configure-the-motors).
+
+
+## Install LeRobot 🤗
+
+To install LeRobot follow, our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Dynamixel SDK:
+```bash
+pip install -e ".[dynamixel]"
+```
+
+## Configure the motors
+
+### 1. Find the USB ports associated with each arm
+
+To find the port for each bus servo adapter, run this script:
+```bash
+python lerobot/find_port.py
+```
+
+<hfoptions id="example">
+<hfoption id="Mac">
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the USB cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
+
+</hfoption>
+<hfoption id="Linux">
+
+On Linux, you might need to give access to the USB ports by running:
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/ttyACM0', '/dev/ttyACM1']
+Remove the usb cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/ttyACM1
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
+
+</hfoption>
+</hfoptions>
+
+### 2. Set the motors ids and baudrates
+
+Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
+
+To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
+
+If you are repurposing motors from another robot, you will probably also need to perform this step, as the ids and baudrate likely won't match.
+
+#### Follower
+
+Connect the usb cable from your computer and the 5V power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
+
+For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.setup_motors \
+    --robot.type=koch_follower \
+    --robot.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.robots.koch_follower import KochFollower, KochFollowerConfig
+
+config = KochFollowerConfig(
+    port="/dev/tty.usbmodem575E0031751",
+    id="my_awesome_follower_arm",
+)
+follower = KochFollower(config)
+follower.setup_motors()
+```
+</hfoption>
+</hfoptions>
+
+You should see the following instruction.
+```
+Connect the controller board to the 'gripper' motor only and press enter.
+```
+
+As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
+
+<details>
+<summary>Troubleshooting</summary>
+
+  If you get an error at that point, check your cables and make sure they are plugged in properly:
+  <ul>
+    <li>Power supply</li>
+    <li>USB cable between your computer and the controller board</li>
+    <li>The 3-pin cable from the controller board to the motor</li>
+  </ul>
+
+  If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
+</details>
+
+You should then see the following message:
+```
+'gripper' motor id set to 6
+```
+
+Followed by the next instruction:
+```
+Connect the controller board to the 'wrist_roll' motor only and press enter.
+```
+
+You can disconnect the 3-pin cable from the controller board but you can leave it connected to the gripper motor on the other end as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
+
+Repeat the operation for each motor as instructed.
+
+> [!TIP]
+> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
+
+When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
+
+#### Leader
+Do the same steps for the leader arm but modify the command or script accordingly.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.setup_motors \
+    --teleop.type=koch_leader \
+    --teleop.port=/dev/tty.usbmodem575E0031751 \  # <- paste here the port found at previous step
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.teleoperators.koch_leader import KochLeader, KochLeaderConfig
+
+config = KochLeaderConfig(
+    port="/dev/tty.usbmodem575E0031751",
+    id="my_awesome_leader_arm",
+)
+leader = KochLeader(config)
+leader.setup_motors()
+```
+</hfoption>
+</hfoptions>
+
+## Calibrate
+
+Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+#### Follower
+
+Run the following command or API example to calibrate the follower arm:
+
+<hfoptions id="calibrate_follower">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.calibrate \
+    --robot.type=koch_follower \
+    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --robot.id=my_awesome_follower_arm  # <- Give the robot a unique name
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower
+
+config = KochFollowerConfig(
+    port="/dev/tty.usbmodem585A0076891",
+    id="my_awesome_follower_arm",
+)
+
+follower = KochFollower(config)
+follower.connect(calibrate=False)
+follower.calibrate()
+follower.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+We unified the calibration method for most robots. Thus, the calibration steps for this Koch arm are the same as the steps for the SO100 and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
+
+#### Leader
+
+Do the same steps to calibrate the leader arm, run the following command or API example:
+
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.calibrate \
+    --teleop.type=koch_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm  # <- Give the robot a unique name
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader
+
+config = KochLeaderConfig(
+    port="/dev/tty.usbmodem575E0031751",
+    id="my_awesome_leader_arm",
+)
+
+leader = KochLeader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+> [!TIP]
+>  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
--- a/lerobot/common/robots/koch_follower/koch_follower.py
+++ b/lerobot/common/robots/koch_follower/koch_follower.py
@@ -0,0 +1,232 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from functools import cached_property
+from typing import Any
+
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.constants import OBS_STATE
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.common.motors.dynamixel import (
+    DynamixelMotorsBus,
+    OperatingMode,
+)
+
+from ..robot import Robot
+from ..utils import ensure_safe_goal_position
+from .config_koch_follower import KochFollowerConfig
+
+logger = logging.getLogger(__name__)
+
+
+class KochFollower(Robot):
+    """
+    - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow
+        expansion, developed by Alexander Koch from [Tau Robotics](https://tau-robotics.com)
+    - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss
+    """
+
+    config_class = KochFollowerConfig
+    name = "koch_follower"
+
+    def __init__(self, config: KochFollowerConfig):
+        super().__init__(config)
+        self.config = config
+        self.bus = DynamixelMotorsBus(
+            port=self.config.port,
+            motors={
+                "shoulder_pan": Motor(1, "xl430-w250", MotorNormMode.RANGE_M100_100),
+                "shoulder_lift": Motor(2, "xl430-w250", MotorNormMode.RANGE_M100_100),
+                "elbow_flex": Motor(3, "xl330-m288", MotorNormMode.RANGE_M100_100),
+                "wrist_flex": Motor(4, "xl330-m288", MotorNormMode.RANGE_M100_100),
+                "wrist_roll": Motor(5, "xl330-m288", MotorNormMode.RANGE_M100_100),
+                "gripper": Motor(6, "xl330-m288", MotorNormMode.RANGE_0_100),
+            },
+            calibration=self.calibration,
+        )
+        self.cameras = make_cameras_from_configs(config.cameras)
+
+    @property
+    def _motors_ft(self) -> dict[str, type]:
+        return {f"{motor}.pos": float for motor in self.bus.motors}
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._motors_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return self._motors_ft
+
+    @property
+    def is_connected(self) -> bool:
+        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+
+    def connect(self, calibrate: bool = True) -> None:
+        """
+        We assume that at connection time, arm is in a rest position,
+        and torque can be safely disabled to run calibration.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} already connected")
+
+        self.bus.connect()
+        if not self.is_calibrated and calibrate:
+            self.calibrate()
+
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.configure()
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.bus.is_calibrated
+
+    def calibrate(self) -> None:
+        logger.info(f"\nRunning calibration of {self}")
+        self.bus.disable_torque()
+        for motor in self.bus.motors:
+            self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value)
+
+        input(f"Move {self} to the middle of its range of motion and press ENTER....")
+        homing_offsets = self.bus.set_half_turn_homings()
+
+        full_turn_motors = ["shoulder_pan", "wrist_roll"]
+        unknown_range_motors = [motor for motor in self.bus.motors if motor not in full_turn_motors]
+        print(
+            f"Move all joints except {full_turn_motors} sequentially through their entire "
+            "ranges of motion.\nRecording positions. Press ENTER to stop..."
+        )
+        range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors)
+        for motor in full_turn_motors:
+            range_mins[motor] = 0
+            range_maxes[motor] = 4095
+
+        self.calibration = {}
+        for motor, m in self.bus.motors.items():
+            self.calibration[motor] = MotorCalibration(
+                id=m.id,
+                drive_mode=0,
+                homing_offset=homing_offsets[motor],
+                range_min=range_mins[motor],
+                range_max=range_maxes[motor],
+            )
+
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        logger.info(f"Calibration saved to {self.calibration_fpath}")
+
+    def configure(self) -> None:
+        with self.bus.torque_disabled():
+            self.bus.configure_motors()
+            # Use 'extended position mode' for all motors except gripper, because in joint mode the servos
+            # can't rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling
+            # the arm, you could end up with a servo with a position 0 or 4095 at a crucial point
+            for motor in self.bus.motors:
+                if motor != "gripper":
+                    self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value)
+
+            # Use 'position control current based' for gripper to be limited by the limit of the current. For
+            # the follower gripper, it means it can grasp an object without forcing too much even tho, its
+            # goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
+            # For the leader gripper, it means we can use it as a physical trigger, since we can force with
+            # our finger to make it move, and it will move back to its original target position when we
+            # release the force.
+            self.bus.write("Operating_Mode", "gripper", OperatingMode.CURRENT_POSITION.value)
+
+            # Set better PID values to close the gap between recorded states and actions
+            # TODO(rcadene): Implement an automatic procedure to set optimal PID values for each motor
+            self.bus.write("Position_P_Gain", "elbow_flex", 1500)
+            self.bus.write("Position_I_Gain", "elbow_flex", 0)
+            self.bus.write("Position_D_Gain", "elbow_flex", 600)
+
+    def setup_motors(self) -> None:
+        for motor in reversed(self.bus.motors):
+            input(f"Connect the controller board to the '{motor}' motor only and press enter.")
+            self.bus.setup_motor(motor)
+            print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
+
+    def get_observation(self) -> dict[str, Any]:
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        obs_dict = {}
+
+        # Read arm position
+        start = time.perf_counter()
+        obs_dict[OBS_STATE] = self.bus.sync_read("Present_Position")
+        obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
+
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            start = time.perf_counter()
+            obs_dict[cam_key] = cam.async_read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+
+        return obs_dict
+
+    def send_action(self, action: dict[str, float]) -> dict[str, float]:
+        """Command arm to move to a target joint configuration.
+
+        The relative action magnitude may be clipped depending on the configuration parameter
+        `max_relative_target`. In this case, the action sent differs from original action.
+        Thus, this function always returns the action actually sent.
+
+        Args:
+            action (dict[str, float]): The goal positions for the motors.
+
+        Returns:
+            dict[str, float]: The action sent to the motors, potentially clipped.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
+
+        # Cap goal position when too far away from present position.
+        # /!\ Slower fps expected due to reading from the follower.
+        if self.config.max_relative_target is not None:
+            present_pos = self.bus.sync_read("Present_Position")
+            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
+            goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
+
+        # Send goal position to the arm
+        self.bus.sync_write("Goal_Position", goal_pos)
+        return {f"{motor}.pos": val for motor, val in goal_pos.items()}
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        self.bus.disconnect(self.config.disable_torque_on_disconnect)
+        for cam in self.cameras.values():
+            cam.disconnect()
+
+        logger.info(f"{self} disconnected.")
--- a/lerobot/common/robots/lekiwi/init.py
+++ b/lerobot/common/robots/lekiwi/init.py
@@ -0,0 +1,3 @@
+from .config_lekiwi import LeKiwiClientConfig, LeKiwiConfig
+from .lekiwi import LeKiwi
+from .lekiwi_client import LeKiwiClient
--- a/lerobot/common/robots/lekiwi/config_lekiwi.py
+++ b/lerobot/common/robots/lekiwi/config_lekiwi.py
@@ -0,0 +1,87 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.common.cameras.configs import CameraConfig, Cv2Rotation
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+
+from ..config import RobotConfig
+
+
+@RobotConfig.register_subclass("lekiwi")
+@dataclass
+class LeKiwiConfig(RobotConfig):
+    port = "/dev/ttyACM0"  # port to connect to the bus
+
+    disable_torque_on_disconnect: bool = True
+
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    cameras: dict[str, CameraConfig] = field(
+        default_factory=lambda: {
+            "front": OpenCVCameraConfig(index_or_path="/dev/video0", fps=30, width=640, height=480),
+            "wrist": OpenCVCameraConfig(
+                index_or_path="/dev/video2", fps=30, width=640, height=480, rotation=Cv2Rotation.ROTATE_180
+            ),
+        }
+    )
+
+
+@dataclass
+class LeKiwiHostConfig:
+    # Network Configuration
+    port_zmq_cmd: int = 5555
+    port_zmq_observations: int = 5556
+
+    # Duration of the application
+    connection_time_s: int = 30
+
+    # Watchdog: stop the robot if no command is received for over 0.5 seconds.
+    watchdog_timeout_ms: int = 500
+
+    # If robot jitters decrease the frequency and monitor cpu load with `top` in cmd
+    max_loop_freq_hz: int = 30
+
+
+@RobotConfig.register_subclass("lekiwi_client")
+@dataclass
+class LeKiwiClientConfig(RobotConfig):
+    # Network Configuration
+    remote_ip: str
+    port_zmq_cmd: int = 5555
+    port_zmq_observations: int = 5556
+
+    teleop_keys: dict[str, str] = field(
+        default_factory=lambda: {
+            # Movement
+            "forward": "w",
+            "backward": "s",
+            "left": "a",
+            "right": "d",
+            "rotate_left": "z",
+            "rotate_right": "x",
+            # Speed control
+            "speed_up": "r",
+            "speed_down": "f",
+            # quit teleop
+            "quit": "q",
+        }
+    )
+
+    polling_timeout_ms: int = 15
+    connect_timeout_s: int = 5
--- a/lerobot/common/robots/lekiwi/lekiwi.mdx
+++ b/lerobot/common/robots/lekiwi/lekiwi.mdx
@@ -0,0 +1,240 @@
+# LeKiwi
+
+In the steps below, we explain how to assemble the LeKiwi mobile robot.
+
+## Source the parts
+
+Follow this [README](https://github.com/SIGRobotics-UIUC/LeKiwi). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts.
+And advise if it's your first time printing or if you don't own a 3D printer.
+
+### Wired version
+If you have the **wired** LeKiwi version, you can skip the installation of the Raspberry Pi and setting up SSH. You can also run all commands directly on your PC for both the LeKiwi scripts and the leader arm scripts for teleoperating.
+
+## Install software on Pi
+Now we have to set up the remote PC that will run on the LeKiwi Robot. This is normally a Raspberry Pi, but can be any PC that can run on 5V and has enough usb ports (2 or more) for the cameras and motor control board.
+
+### Install OS
+For setting up the Raspberry Pi and its SD-card see: [Setup PI](https://www.raspberrypi.com/documentation/computers/getting-started.html). Here is explained how to download the [Imager](https://www.raspberrypi.com/software/) to install Raspberry Pi OS or Ubuntu.
+
+### Setup SSH
+After setting up your Pi, you should enable and set up [SSH](https://www.raspberrypi.com/news/coding-on-raspberry-pi-remotely-with-visual-studio-code/) (Secure Shell Protocol) so you can log in to the Pi from your laptop without requiring a screen, keyboard, and mouse on the Pi. A great tutorial on how to do this can be found [here](https://www.raspberrypi.com/documentation/computers/remote-access.html#ssh). Logging into your Pi can be done in your Command Prompt (cmd) or, if you use VSCode you can use [this](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh) extension.
+
+### Install LeRobot on Pi 🤗
+
+On your Raspberry Pi install LeRobot using our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Feetech sdk on your Pi:
+```bash
+pip install -e ".[feetech]"
+```
+
+## Install LeRobot locally
+If you already have installed LeRobot on your laptop/pc you can skip this step; otherwise, please follow along as we do the same steps we did on the Pi.
+
+Follow our [Installation Guide](./installation)
+
+Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:.
+Every time you now want to use LeRobot, you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
+
+# Step-by-Step Assembly Instructions
+
+First, we will assemble the two SO100/SO101 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base. The instructions for assembling can be found on these two pages:
+
+- [Assemble SO101](./so101#step-by-step-assembly-instructions)
+- [Assemble LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi/blob/main/Assembly.md)
+
+### Configure motors
+The instructions for configuring the motors can be found in the SO101 [docs](./so101#configure-the-motors). Besides the ids for the arm motors, we also need to set the motor ids for the mobile base. These need to be in a specific order to work. Below an image of the motor ids and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ids for the wheels are 7, 8 and 9.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/motor_ids.webp" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
+
+### Troubleshoot communication
+
+If you are having trouble connecting to the Mobile SO100, follow these steps to diagnose and resolve the issue.
+
+#### 1. Verify IP Address Configuration
+Make sure that the correct IP for the Pi is used in the commands or in your code. To check the Raspberry Pi's IP address, run (on the Pi command line):
+```bash
+hostname -I
+```
+
+#### 2. Check if Pi is reachable from laptop/pc
+Try pinging the Raspberry Pi from your laptop:
+```bach
+ping <your_pi_ip_address>
+```
+
+If the ping fails:
+- Ensure the Pi is powered on and connected to the same network.
+- Check if SSH is enabled on the Pi.
+
+#### 3. Try SSH connection
+If you can't SSH into the Pi, it might not be properly connected. Use:
+```bash
+ssh <your_pi_user_name>@<your_pi_ip_address>
+```
+If you get a connection error:
+- Ensure SSH is enabled on the Pi by running:
+  ```bash
+  sudo raspi-config
+  ```
+  Then navigate to: **Interfacing Options -> SSH** and enable it.
+
+### Calibration
+
+Now we have to calibrate the leader arm and the follower arm. The wheel motors don't have to be calibrated.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+### Calibrate follower arm (on mobile base)
+
+Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:
+
+```bash
+python -m lerobot.calibrate \
+    --robot.type=lekiwi \
+    --robot.id=my_awesome_kiwi # <- Give the robot a unique name
+```
+
+We unified the calibration method for most robots, thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
+
+### Wired version
+If you have the **wired** LeKiwi version, please run all commands on your laptop.
+
+### Calibrate leader arm
+Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the following command of API example on your laptop:
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.calibrate \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
+
+config = SO100LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_awesome_leader_arm",
+)
+
+leader = SO100Leader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+## Teleoperate LeKiwi
+
+> [!TIP]
+> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
+
+To teleoperate, SSH into your Raspberry Pi, and run `conda activate lerobot` and this command:
+```bash
+python -m lerobot.common.robots.lekiwi.lekiwi_host
+```
+
+Then on your laptop, also run `conda activate lerobot` and run the API example, make sure you set the correct `remote_ip` and `port`.
+
+```bash
+python examples/lekiwi/teleoperate.py
+```
+
+You should see on your laptop something like this: ```[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.``` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:
+
+| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
+| ---------- | ------------------ | ---------------------- |
+| Fast       | 0.4                | 90                     |
+| Medium     | 0.25               | 60                     |
+| Slow       | 0.1                | 30                     |
+
+
+| Key | Action         |
+| --- | -------------- |
+| W   | Move forward   |
+| A   | Move left      |
+| S   | Move backward  |
+| D   | Move right     |
+| Z   | Turn left      |
+| X   | Turn right     |
+| R   | Increase speed |
+| F   | Decrease speed |
+
+> [!TIP]
+>  If you use a different keyboard, you can change the keys for each command in the [`LeKiwiConfig`](../lerobot/common/robot_devices/robots/configs.py).
+
+### Wired version
+If you have the **wired** LeKiwi version, please run all commands on your laptop.
+
+## Record a dataset
+
+Once you're familiar with teleoperation, you can record your first dataset.
+
+We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
+
+Add your token to the CLI by running this command:
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Then store your Hugging Face repository name in a variable:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Now you can record a dataset. To record episodes and upload your dataset to the hub, execute this API example tailored for LeKiwi. Make sure to first adapt the `remote_ip`, `repo_id`, `port` and `task` in the script. If you would like to run the script for longer you can increase `NB_CYCLES_CLIENT_CONNECTION`.
+```bash
+python examples/lekiwi/record.py
+```
+
+#### Dataset upload
+Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
+```bash
+echo https://huggingface.co/datasets/${HF_USER}/so101_test
+```
+Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
+
+You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot).
+
+#### Tips for gathering data
+
+Once you're comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera's. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images.
+
+In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.
+
+Avoid adding too much variation too quickly, as it may hinder your results.
+
+If you want to dive deeper into this important topic, you can check out the [blog post](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset) we wrote on what makes a good dataset.
+
+#### Troubleshooting:
+- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
+
+
+## Replay an episode
+
+To replay an episode run the API example below, make sure to change `remote_ip`, `port`, LeRobotDatasetId and episode index.
+
+
+```bash
+python examples/lekiwi/replay.py
+```
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by the training part of this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+## Evaluate your policy
+
+To evaluate your policy run the `evaluate.py` API example, make sure to change `remote_ip`, `port`, model..
+
+```bash
+python examples/lekiwi/evaluate.py
+```
+
+> [!TIP]
+>  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
--- a/lerobot/common/robots/lekiwi/lekiwi.py
+++ b/lerobot/common/robots/lekiwi/lekiwi.py
@@ -0,0 +1,415 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from functools import cached_property
+from itertools import chain
+from typing import Any
+
+import numpy as np
+
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.constants import OBS_IMAGES, OBS_STATE
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.common.motors.feetech import (
+    FeetechMotorsBus,
+    OperatingMode,
+)
+
+from ..robot import Robot
+from ..utils import ensure_safe_goal_position
+from .config_lekiwi import LeKiwiConfig
+
+logger = logging.getLogger(__name__)
+
+
+class LeKiwi(Robot):
+    """
+    The robot includes a three omniwheel mobile base and a remote follower arm.
+    The leader arm is connected locally (on the laptop) and its joint positions are recorded and then
+    forwarded to the remote follower arm (after applying a safety clamp).
+    In parallel, keyboard teleoperation is used to generate raw velocity commands for the wheels.
+    """
+
+    config_class = LeKiwiConfig
+    name = "lekiwi"
+
+    def __init__(self, config: LeKiwiConfig):
+        super().__init__(config)
+        self.config = config
+        self.bus = FeetechMotorsBus(
+            port=self.config.port,
+            motors={
+                # arm
+                "arm_shoulder_pan": Motor(1, "sts3215", MotorNormMode.RANGE_M100_100),
+                "arm_shoulder_lift": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100),
+                "arm_elbow_flex": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100),
+                "arm_wrist_flex": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100),
+                "arm_wrist_roll": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100),
+                "arm_gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100),
+                # base
+                "base_left_wheel": Motor(7, "sts3215", MotorNormMode.RANGE_M100_100),
+                "base_right_wheel": Motor(8, "sts3215", MotorNormMode.RANGE_M100_100),
+                "base_back_wheel": Motor(9, "sts3215", MotorNormMode.RANGE_M100_100),
+            },
+            calibration=self.calibration,
+        )
+        self.arm_motors = [motor for motor in self.bus.motors if motor.startswith("arm")]
+        self.base_motors = [motor for motor in self.bus.motors if motor.startswith("base")]
+        self.cameras = make_cameras_from_configs(config.cameras)
+
+    @property
+    def _state_ft(self) -> dict[str, type]:
+        return dict.fromkeys(
+            (
+                "arm_shoulder_pan.pos",
+                "arm_shoulder_lift.pos",
+                "arm_elbow_flex.pos",
+                "arm_wrist_flex.pos",
+                "arm_wrist_roll.pos",
+                "arm_gripper.pos",
+                "x.vel",
+                "y.vel",
+                "theta.vel",
+            ),
+            float,
+        )
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._state_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return self._state_ft
+
+    @property
+    def is_connected(self) -> bool:
+        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+
+    def connect(self, calibrate: bool = True) -> None:
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} already connected")
+
+        self.bus.connect()
+        if not self.is_calibrated and calibrate:
+            self.calibrate()
+
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.configure()
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.bus.is_calibrated
+
+    def calibrate(self) -> None:
+        logger.info(f"\nRunning calibration of {self}")
+
+        motors = self.arm_motors + self.base_motors
+
+        self.bus.disable_torque(self.arm_motors)
+        for name in self.arm_motors:
+            self.bus.write("Operating_Mode", name, OperatingMode.POSITION.value)
+
+        input("Move robot to the middle of its range of motion and press ENTER....")
+        homing_offsets = self.bus.set_half_turn_homings(self.arm_motors)
+
+        homing_offsets.update(dict.fromkeys(self.base_motors, 0))
+
+        full_turn_motor = [
+            motor for motor in motors if any(keyword in motor for keyword in ["wheel", "wrist"])
+        ]
+        unknown_range_motors = [motor for motor in motors if motor not in full_turn_motor]
+
+        print(
+            f"Move all arm joints except '{full_turn_motor}' sequentially through their "
+            "entire ranges of motion.\nRecording positions. Press ENTER to stop..."
+        )
+        range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors)
+        for name in full_turn_motor:
+            range_mins[name] = 0
+            range_maxes[name] = 4095
+
+        self.calibration = {}
+        for name, motor in self.bus.motors.items():
+            self.calibration[name] = MotorCalibration(
+                id=motor.id,
+                drive_mode=0,
+                homing_offset=homing_offsets[name],
+                range_min=range_mins[name],
+                range_max=range_maxes[name],
+            )
+
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        print("Calibration saved to", self.calibration_fpath)
+
+    def configure(self):
+        # Set-up arm actuators (position mode)
+        # We assume that at connection time, arm is in a rest position,
+        # and torque can be safely disabled to run calibration.
+        self.bus.disable_torque()
+        self.bus.configure_motors()
+        for name in self.arm_motors:
+            self.bus.write("Operating_Mode", name, OperatingMode.POSITION.value)
+            # Set P_Coefficient to lower value to avoid shakiness (Default is 32)
+            self.bus.write("P_Coefficient", name, 16)
+            # Set I_Coefficient and D_Coefficient to default value 0 and 32
+            self.bus.write("I_Coefficient", name, 0)
+            self.bus.write("D_Coefficient", name, 32)
+
+        for name in self.base_motors:
+            self.bus.write("Operating_Mode", name, OperatingMode.VELOCITY.value)
+
+        self.bus.enable_torque()
+
+    def setup_motors(self) -> None:
+        for motor in chain(reversed(self.arm_motors), reversed(self.base_motors)):
+            input(f"Connect the controller board to the '{motor}' motor only and press enter.")
+            self.bus.setup_motor(motor)
+            print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
+
+    @staticmethod
+    def _degps_to_raw(degps: float) -> int:
+        steps_per_deg = 4096.0 / 360.0
+        speed_in_steps = degps * steps_per_deg
+        speed_int = int(round(speed_in_steps))
+        # Cap the value to fit within signed 16-bit range (-32768 to 32767)
+        if speed_int > 0x7FFF:
+            speed_int = 0x7FFF  # 32767 -> maximum positive value
+        elif speed_int < -0x8000:
+            speed_int = -0x8000  # -32768 -> minimum negative value
+        return speed_int
+
+    @staticmethod
+    def _raw_to_degps(raw_speed: int) -> float:
+        steps_per_deg = 4096.0 / 360.0
+        magnitude = raw_speed
+        degps = magnitude / steps_per_deg
+        return degps
+
+    def _body_to_wheel_raw(
+        self,
+        x: float,
+        y: float,
+        theta: float,
+        wheel_radius: float = 0.05,
+        base_radius: float = 0.125,
+        max_raw: int = 3000,
+    ) -> dict:
+        """
+        Convert desired body-frame velocities into wheel raw commands.
+
+        Parameters:
+          x_cmd      : Linear velocity in x (m/s).
+          y_cmd      : Linear velocity in y (m/s).
+          theta_cmd  : Rotational velocity (deg/s).
+          wheel_radius: Radius of each wheel (meters).
+          base_radius : Distance from the center of rotation to each wheel (meters).
+          max_raw    : Maximum allowed raw command (ticks) per wheel.
+
+        Returns:
+          A dictionary with wheel raw commands:
+             {"base_left_wheel": value, "base_back_wheel": value, "base_right_wheel": value}.
+
+        Notes:
+          - Internally, the method converts theta_cmd to rad/s for the kinematics.
+          - The raw command is computed from the wheels angular speed in deg/s
+            using _degps_to_raw(). If any command exceeds max_raw, all commands
+            are scaled down proportionally.
+        """
+        # Convert rotational velocity from deg/s to rad/s.
+        theta_rad = theta * (np.pi / 180.0)
+        # Create the body velocity vector [x, y, theta_rad].
+        velocity_vector = np.array([x, y, theta_rad])
+
+        # Define the wheel mounting angles with a -90° offset.
+        angles = np.radians(np.array([240, 120, 0]) - 90)
+        # Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
+        # The third column (base_radius) accounts for the effect of rotation.
+        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
+
+        # Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s).
+        wheel_linear_speeds = m.dot(velocity_vector)
+        wheel_angular_speeds = wheel_linear_speeds / wheel_radius
+
+        # Convert wheel angular speeds from rad/s to deg/s.
+        wheel_degps = wheel_angular_speeds * (180.0 / np.pi)
+
+        # Scaling
+        steps_per_deg = 4096.0 / 360.0
+        raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps]
+        max_raw_computed = max(raw_floats)
+        if max_raw_computed > max_raw:
+            scale = max_raw / max_raw_computed
+            wheel_degps = wheel_degps * scale
+
+        # Convert each wheel’s angular speed (deg/s) to a raw integer.
+        wheel_raw = [self._degps_to_raw(deg) for deg in wheel_degps]
+
+        return {
+            "base_left_wheel": wheel_raw[0],
+            "base_back_wheel": wheel_raw[1],
+            "base_right_wheel": wheel_raw[2],
+        }
+
+    def _wheel_raw_to_body(
+        self,
+        left_wheel_speed,
+        back_wheel_speed,
+        right_wheel_speed,
+        wheel_radius: float = 0.05,
+        base_radius: float = 0.125,
+    ) -> dict[str, Any]:
+        """
+        Convert wheel raw command feedback back into body-frame velocities.
+
+        Parameters:
+          wheel_raw   : Vector with raw wheel commands ("base_left_wheel", "base_back_wheel", "base_right_wheel").
+          wheel_radius: Radius of each wheel (meters).
+          base_radius : Distance from the robot center to each wheel (meters).
+
+        Returns:
+          A dict (x_cmd, y_cmd, theta_cmd) where:
+             OBS_STATE.x_cmd      : Linear velocity in x (m/s).
+             OBS_STATE.y_cmd      : Linear velocity in y (m/s).
+             OBS_STATE.theta_cmd  : Rotational velocity in deg/s.
+        """
+
+        # Convert each raw command back to an angular speed in deg/s.
+        wheel_degps = np.array(
+            [
+                self._raw_to_degps(left_wheel_speed),
+                self._raw_to_degps(back_wheel_speed),
+                self._raw_to_degps(right_wheel_speed),
+            ]
+        )
+
+        # Convert from deg/s to rad/s.
+        wheel_radps = wheel_degps * (np.pi / 180.0)
+        # Compute each wheel’s linear speed (m/s) from its angular speed.
+        wheel_linear_speeds = wheel_radps * wheel_radius
+
+        # Define the wheel mounting angles with a -90° offset.
+        angles = np.radians(np.array([240, 120, 0]) - 90)
+        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
+
+        # Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
+        m_inv = np.linalg.inv(m)
+        velocity_vector = m_inv.dot(wheel_linear_speeds)
+        x, y, theta_rad = velocity_vector
+        theta = theta_rad * (180.0 / np.pi)
+        return {
+            "x.vel": x,
+            "y.vel": y,
+            "theta.vel": theta,
+        }  # m/s and deg/s
+
+    def get_observation(self) -> dict[str, Any]:
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        # Read actuators position for arm and vel for base
+        start = time.perf_counter()
+        arm_pos = self.bus.sync_read("Present_Position", self.arm_motors)
+        base_wheel_vel = self.bus.sync_read("Present_Velocity", self.base_motors)
+
+        base_vel = self._wheel_raw_to_body(
+            base_wheel_vel["base_left_wheel"],
+            base_wheel_vel["base_back_wheel"],
+            base_wheel_vel["base_right_wheel"],
+        )
+
+        arm_state = {f"{k}.pos": v for k, v in arm_pos.items()}
+
+        flat_states = {**arm_state, **base_vel}
+
+        obs_dict = {f"{OBS_STATE}": flat_states}
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
+
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            start = time.perf_counter()
+            obs_dict[f"{OBS_IMAGES}.{cam_key}"] = cam.async_read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+
+        return obs_dict
+
+    def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
+        """Command lekiwi to move to a target joint configuration.
+
+        The relative action magnitude may be clipped depending on the configuration parameter
+        `max_relative_target`. In this case, the action sent differs from original action.
+        Thus, this function always returns the action actually sent.
+
+        Raises:
+            RobotDeviceNotConnectedError: if robot is not connected.
+
+        Returns:
+            np.ndarray: the action sent to the motors, potentially clipped.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        arm_goal_pos = {k: v for k, v in action.items() if k.endswith(".pos")}
+        base_goal_vel = {k: v for k, v in action.items() if k.endswith(".vel")}
+
+        base_wheel_goal_vel = self._body_to_wheel_raw(
+            base_goal_vel["x.vel"], base_goal_vel["y.vel"], base_goal_vel["theta.vel"]
+        )
+
+        # Cap goal position when too far away from present position.
+        # /!\ Slower fps expected due to reading from the follower.
+        if self.config.max_relative_target is not None:
+            present_pos = self.bus.sync_read("Present_Position", self.arm_motors)
+            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in arm_goal_pos.items()}
+            arm_safe_goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
+            arm_goal_pos = arm_safe_goal_pos
+
+        # Send goal position to the actuators
+        arm_goal_pos_raw = {k.replace(".pos", ""): v for k, v in arm_goal_pos.items()}
+        self.bus.sync_write("Goal_Position", arm_goal_pos_raw)
+        self.bus.sync_write("Goal_Velocity", base_wheel_goal_vel)
+
+        return {**arm_goal_pos, **base_goal_vel}
+
+    def stop_base(self):
+        self.bus.sync_write("Goal_Velocity", dict.fromkeys(self.base_motors, 0), num_retry=5)
+        logger.info("Base motors stopped")
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        self.stop_base()
+        self.bus.disconnect(self.config.disable_torque_on_disconnect)
+        for cam in self.cameras.values():
+            cam.disconnect()
+
+        logger.info(f"{self} disconnected.")
--- a/lerobot/common/robots/lekiwi/lekiwi_client.py
+++ b/lerobot/common/robots/lekiwi/lekiwi_client.py
@@ -0,0 +1,342 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# TODO(aliberts, Steven, Pepijn): use gRPC calls instead of zmq?
+
+import base64
+import json
+import logging
+from functools import cached_property
+from typing import Any, Dict, Optional, Tuple
+
+import cv2
+import numpy as np
+import torch
+import zmq
+
+from lerobot.common.constants import OBS_IMAGES, OBS_STATE
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+
+from ..robot import Robot
+from .config_lekiwi import LeKiwiClientConfig
+
+
+class LeKiwiClient(Robot):
+    config_class = LeKiwiClientConfig
+    name = "lekiwi_client"
+
+    def __init__(self, config: LeKiwiClientConfig):
+        super().__init__(config)
+        self.config = config
+        self.id = config.id
+        self.robot_type = config.type
+
+        self.remote_ip = config.remote_ip
+        self.port_zmq_cmd = config.port_zmq_cmd
+        self.port_zmq_observations = config.port_zmq_observations
+
+        self.teleop_keys = config.teleop_keys
+
+        self.polling_timeout_ms = config.polling_timeout_ms
+        self.connect_timeout_s = config.connect_timeout_s
+
+        self.zmq_context = None
+        self.zmq_cmd_socket = None
+        self.zmq_observation_socket = None
+
+        self.last_frames = {}
+
+        self.last_remote_state = {}
+
+        # Define three speed levels and a current index
+        self.speed_levels = [
+            {"xy": 0.1, "theta": 30},  # slow
+            {"xy": 0.2, "theta": 60},  # medium
+            {"xy": 0.3, "theta": 90},  # fast
+        ]
+        self.speed_index = 0  # Start at slow
+
+        self._is_connected = False
+        self.logs = {}
+
+    @cached_property
+    def _state_ft(self) -> dict[str, type]:
+        return dict.fromkeys(
+            (
+                "arm_shoulder_pan.pos",
+                "arm_shoulder_lift.pos",
+                "arm_elbow_flex.pos",
+                "arm_wrist_flex.pos",
+                "arm_wrist_roll.pos",
+                "arm_gripper.pos",
+                "x.vel",
+                "y.vel",
+                "theta.vel",
+            ),
+            float,
+        )
+
+    @cached_property
+    def _state_order(self) -> tuple[str, ...]:
+        return tuple(self._state_ft.keys())
+
+    @cached_property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            "front": (480, 640, 3),
+            "wrist": (640, 480, 3),
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._state_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return self._state_ft
+
+    @property
+    def is_connected(self) -> bool:
+        return self._is_connected
+
+    @property
+    def is_calibrated(self) -> bool:
+        pass
+
+    def connect(self) -> None:
+        """Establishes ZMQ sockets with the remote mobile robot"""
+
+        if self._is_connected:
+            raise DeviceAlreadyConnectedError(
+                "LeKiwi Daemon is already connected. Do not run `robot.connect()` twice."
+            )
+
+        self.zmq_context = zmq.Context()
+        self.zmq_cmd_socket = self.zmq_context.socket(zmq.PUSH)
+        zmq_cmd_locator = f"tcp://{self.remote_ip}:{self.port_zmq_cmd}"
+        self.zmq_cmd_socket.connect(zmq_cmd_locator)
+        self.zmq_cmd_socket.setsockopt(zmq.CONFLATE, 1)
+
+        self.zmq_observation_socket = self.zmq_context.socket(zmq.PULL)
+        zmq_observations_locator = f"tcp://{self.remote_ip}:{self.port_zmq_observations}"
+        self.zmq_observation_socket.connect(zmq_observations_locator)
+        self.zmq_observation_socket.setsockopt(zmq.CONFLATE, 1)
+
+        poller = zmq.Poller()
+        poller.register(self.zmq_observation_socket, zmq.POLLIN)
+        socks = dict(poller.poll(self.connect_timeout_s * 1000))
+        if self.zmq_observation_socket not in socks or socks[self.zmq_observation_socket] != zmq.POLLIN:
+            raise DeviceNotConnectedError("Timeout waiting for LeKiwi Host to connect expired.")
+
+        self._is_connected = True
+
+    def calibrate(self) -> None:
+        pass
+
+    def _poll_and_get_latest_message(self) -> Optional[str]:
+        """Polls the ZMQ socket for a limited time and returns the latest message string."""
+        poller = zmq.Poller()
+        poller.register(self.zmq_observation_socket, zmq.POLLIN)
+
+        try:
+            socks = dict(poller.poll(self.polling_timeout_ms))
+        except zmq.ZMQError as e:
+            logging.error(f"ZMQ polling error: {e}")
+            return None
+
+        if self.zmq_observation_socket not in socks:
+            logging.info("No new data available within timeout.")
+            return None
+
+        last_msg = None
+        while True:
+            try:
+                msg = self.zmq_observation_socket.recv_string(zmq.NOBLOCK)
+                last_msg = msg
+            except zmq.Again:
+                break
+
+        if last_msg is None:
+            logging.warning("Poller indicated data, but failed to retrieve message.")
+
+        return last_msg
+
+    def _parse_observation_json(self, obs_string: str) -> Optional[Dict[str, Any]]:
+        """Parses the JSON observation string."""
+        try:
+            return json.loads(obs_string)
+        except json.JSONDecodeError as e:
+            logging.error(f"Error decoding JSON observation: {e}")
+            return None
+
+    def _decode_image_from_b64(self, image_b64: str) -> Optional[np.ndarray]:
+        """Decodes a base64 encoded image string to an OpenCV image."""
+        if not image_b64:
+            return None
+        try:
+            jpg_data = base64.b64decode(image_b64)
+            np_arr = np.frombuffer(jpg_data, dtype=np.uint8)
+            frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+            if frame is None:
+                logging.warning("cv2.imdecode returned None for an image.")
+            return frame
+        except (TypeError, ValueError) as e:
+            logging.error(f"Error decoding base64 image data: {e}")
+            return None
+
+    def _remote_state_from_obs(
+        self, observation: Dict[str, Any]
+    ) -> Tuple[Dict[str, np.ndarray], Dict[str, Any]]:
+        """Extracts frames, and state from the parsed observation."""
+        flat_state = observation[OBS_STATE]
+
+        state_vec = np.array(
+            [flat_state.get(k, 0.0) for k in self._state_order],
+            dtype=np.float32,
+        )
+
+        # Decode images
+        image_observation = {k: v for k, v in observation.items() if k.startswith(OBS_IMAGES)}
+        current_frames: Dict[str, np.ndarray] = {}
+        for cam_name, image_b64 in image_observation.items():
+            frame = self._decode_image_from_b64(image_b64)
+            if frame is not None:
+                current_frames[cam_name] = frame
+
+        return current_frames, {"observation.state": state_vec}
+
+    def _get_data(self) -> Tuple[Dict[str, np.ndarray], Dict[str, Any], Dict[str, Any]]:
+        """
+        Polls the video socket for the latest observation data.
+
+        Attempts to retrieve and decode the latest message within a short timeout.
+        If successful, updates and returns the new frames, speed, and arm state.
+        If no new data arrives or decoding fails, returns the last known values.
+        """
+
+        # 1. Get the latest message string from the socket
+        latest_message_str = self._poll_and_get_latest_message()
+
+        # 2. If no message, return cached data
+        if latest_message_str is None:
+            return self.last_frames, self.last_remote_state
+
+        # 3. Parse the JSON message
+        observation = self._parse_observation_json(latest_message_str)
+
+        # 4. If JSON parsing failed, return cached data
+        if observation is None:
+            return self.last_frames, self.last_remote_state
+
+        # 5. Process the valid observation data
+        try:
+            new_frames, new_state = self._remote_state_from_obs(observation)
+        except Exception as e:
+            logging.error(f"Error processing observation data, serving last observation: {e}")
+            return self.last_frames, self.last_remote_state
+
+        self.last_frames = new_frames
+        self.last_remote_state = new_state
+
+        return new_frames, new_state
+
+    def get_observation(self) -> dict[str, Any]:
+        """
+        Capture observations from the remote robot: current follower arm positions,
+        present wheel speeds (converted to body-frame velocities: x, y, theta),
+        and a camera frame. Receives over ZMQ, translate to body-frame vel
+        """
+        if not self._is_connected:
+            raise DeviceNotConnectedError("LeKiwiClient is not connected. You need to run `robot.connect()`.")
+
+        frames, obs_dict = self._get_data()
+
+        # Loop over each configured camera
+        for cam_name, frame in frames.items():
+            if frame is None:
+                logging.warning("Frame is None")
+                frame = np.zeros((640, 480, 3), dtype=np.uint8)
+            obs_dict[cam_name] = torch.from_numpy(frame)
+
+        return obs_dict
+
+    def _from_keyboard_to_base_action(self, pressed_keys: np.ndarray):
+        # Speed control
+        if self.teleop_keys["speed_up"] in pressed_keys:
+            self.speed_index = min(self.speed_index + 1, 2)
+        if self.teleop_keys["speed_down"] in pressed_keys:
+            self.speed_index = max(self.speed_index - 1, 0)
+        speed_setting = self.speed_levels[self.speed_index]
+        xy_speed = speed_setting["xy"]  # e.g. 0.1, 0.25, or 0.4
+        theta_speed = speed_setting["theta"]  # e.g. 30, 60, or 90
+
+        x_cmd = 0.0  # m/s forward/backward
+        y_cmd = 0.0  # m/s lateral
+        theta_cmd = 0.0  # deg/s rotation
+
+        if self.teleop_keys["forward"] in pressed_keys:
+            x_cmd += xy_speed
+        if self.teleop_keys["backward"] in pressed_keys:
+            x_cmd -= xy_speed
+        if self.teleop_keys["left"] in pressed_keys:
+            y_cmd += xy_speed
+        if self.teleop_keys["right"] in pressed_keys:
+            y_cmd -= xy_speed
+        if self.teleop_keys["rotate_left"] in pressed_keys:
+            theta_cmd += theta_speed
+        if self.teleop_keys["rotate_right"] in pressed_keys:
+            theta_cmd -= theta_speed
+        return {
+            "x.vel": x_cmd,
+            "y.vel": y_cmd,
+            "theta.vel": theta_cmd,
+        }
+
+    def configure(self):
+        pass
+
+    def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
+        """Command lekiwi to move to a target joint configuration. Translates to motor space + sends over ZMQ
+
+        Args:
+            action (np.ndarray): array containing the goal positions for the motors.
+
+        Raises:
+            RobotDeviceNotConnectedError: if robot is not connected.
+
+        Returns:
+            np.ndarray: the action sent to the motors, potentially clipped.
+        """
+        if not self._is_connected:
+            raise DeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
+            )
+
+        self.zmq_cmd_socket.send_string(json.dumps(action))  # action is in motor space
+
+        # TODO(Steven): Remove the np conversion when it is possible to record a non-numpy array value
+        actions = np.array([action.get(k, 0.0) for k in self._state_order], dtype=np.float32)
+        return {"action": actions}
+
+    def disconnect(self):
+        """Cleans ZMQ comms"""
+
+        if not self._is_connected:
+            raise DeviceNotConnectedError(
+                "LeKiwi is not connected. You need to run `robot.connect()` before disconnecting."
+            )
+        self.zmq_observation_socket.close()
+        self.zmq_cmd_socket.close()
+        self.zmq_context.term()
+        self._is_connected = False
--- a/lerobot/common/robots/lekiwi/lekiwi_host.py
+++ b/lerobot/common/robots/lekiwi/lekiwi_host.py
@@ -0,0 +1,129 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import base64
+import json
+import logging
+import time
+
+import cv2
+import zmq
+
+from lerobot.common.constants import OBS_IMAGES
+
+from .config_lekiwi import LeKiwiConfig, LeKiwiHostConfig
+from .lekiwi import LeKiwi
+
+
+class LeKiwiHost:
+    def __init__(self, config: LeKiwiHostConfig):
+        self.zmq_context = zmq.Context()
+        self.zmq_cmd_socket = self.zmq_context.socket(zmq.PULL)
+        self.zmq_cmd_socket.setsockopt(zmq.CONFLATE, 1)
+        self.zmq_cmd_socket.bind(f"tcp://*:{config.port_zmq_cmd}")
+
+        self.zmq_observation_socket = self.zmq_context.socket(zmq.PUSH)
+        self.zmq_observation_socket.setsockopt(zmq.CONFLATE, 1)
+        self.zmq_observation_socket.bind(f"tcp://*:{config.port_zmq_observations}")
+
+        self.connection_time_s = config.connection_time_s
+        self.watchdog_timeout_ms = config.watchdog_timeout_ms
+        self.max_loop_freq_hz = config.max_loop_freq_hz
+
+    def disconnect(self):
+        self.zmq_observation_socket.close()
+        self.zmq_cmd_socket.close()
+        self.zmq_context.term()
+
+
+def main():
+    logging.info("Configuring LeKiwi")
+    robot_config = LeKiwiConfig()
+    robot = LeKiwi(robot_config)
+
+    logging.info("Connecting LeKiwi")
+    robot.connect()
+
+    logging.info("Starting HostAgent")
+    host_config = LeKiwiHostConfig()
+    host = LeKiwiHost(host_config)
+
+    last_cmd_time = time.time()
+    watchdog_active = False
+    logging.info("Waiting for commands...")
+    try:
+        # Business logic
+        start = time.perf_counter()
+        duration = 0
+        while duration < host.connection_time_s:
+            loop_start_time = time.time()
+            try:
+                msg = host.zmq_cmd_socket.recv_string(zmq.NOBLOCK)
+                data = dict(json.loads(msg))
+                _action_sent = robot.send_action(data)
+                last_cmd_time = time.time()
+                watchdog_active = False
+            except zmq.Again:
+                if not watchdog_active:
+                    logging.warning("No command available")
+            except Exception as e:
+                logging.error("Message fetching failed: %s", e)
+
+            now = time.time()
+            if (now - last_cmd_time > host.watchdog_timeout_ms / 1000) and not watchdog_active:
+                logging.warning(
+                    f"Command not received for more than {host.watchdog_timeout_ms} milliseconds. Stopping the base."
+                )
+                watchdog_active = True
+                robot.stop_base()
+
+            last_observation = robot.get_observation()
+
+            # Encode ndarrays to base64 strings
+            for cam_key, _ in robot.cameras.items():
+                ret, buffer = cv2.imencode(
+                    ".jpg", last_observation[f"{OBS_IMAGES}.{cam_key}"], [int(cv2.IMWRITE_JPEG_QUALITY), 90]
+                )
+                if ret:
+                    last_observation[f"{OBS_IMAGES}.{cam_key}"] = base64.b64encode(buffer).decode("utf-8")
+                else:
+                    last_observation[f"{OBS_IMAGES}.{cam_key}"] = ""
+
+            # Send the observation to the remote agent
+            try:
+                host.zmq_observation_socket.send_string(json.dumps(last_observation), flags=zmq.NOBLOCK)
+            except zmq.Again:
+                logging.info("Dropping observation, no client connected")
+
+            # Ensure a short sleep to avoid overloading the CPU.
+            elapsed = time.time() - loop_start_time
+
+            time.sleep(max(1 / host.max_loop_freq_hz - elapsed, 0))
+            duration = time.perf_counter() - start
+        print("Cycle time reached.")
+
+    except KeyboardInterrupt:
+        print("Keyboard interrupt received. Exiting...")
+    finally:
+        print("Shutting down Lekiwi Host.")
+        robot.disconnect()
+        host.disconnect()
+
+    logging.info("Finished LeKiwi cleanly")
+
+
+if __name__ == "__main__":
+    main()
--- a/lerobot/common/robots/robot.py
+++ b/lerobot/common/robots/robot.py
@@ -0,0 +1,109 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+from pathlib import Path
+from typing import Any
+
+import draccus
+
+from lerobot.common.constants import HF_LEROBOT_CALIBRATION, ROBOTS
+from lerobot.common.motors import MotorCalibration
+
+from .config import RobotConfig
+
+
+# TODO(aliberts): action/obs typing such as Generic[ObsType, ActType] similar to gym.Env ?
+# https://github.com/Farama-Foundation/Gymnasium/blob/3287c869f9a48d99454306b0d4b4ec537f0f35e3/gymnasium/core.py#L23
+class Robot(abc.ABC):
+    """The main LeRobot class for implementing robots."""
+
+    # Set these in ALL subclasses
+    config_class: RobotConfig
+    name: str
+
+    def __init__(self, config: RobotConfig):
+        self.robot_type = self.name
+        self.id = config.id
+        self.calibration_dir = (
+            config.calibration_dir if config.calibration_dir else HF_LEROBOT_CALIBRATION / ROBOTS / self.name
+        )
+        self.calibration_dir.mkdir(parents=True, exist_ok=True)
+        self.calibration_fpath = self.calibration_dir / f"{self.id}.json"
+        self.calibration: dict[str, MotorCalibration] = {}
+        if self.calibration_fpath.is_file():
+            self._load_calibration()
+
+    def __str__(self) -> str:
+        return f"{self.id} {self.__class__.__name__}"
+
+    # TODO(aliberts): create a proper Feature class for this that links with datasets
+    @property
+    @abc.abstractmethod
+    def observation_features(self) -> dict:
+        pass
+
+    @property
+    @abc.abstractmethod
+    def action_features(self) -> dict:
+        pass
+
+    @property
+    @abc.abstractmethod
+    def is_connected(self) -> bool:
+        pass
+
+    @abc.abstractmethod
+    def connect(self, calibrate: bool = True) -> None:
+        """Connects to the robot."""
+        pass
+
+    @property
+    @abc.abstractmethod
+    def is_calibrated(self) -> bool:
+        pass
+
+    @abc.abstractmethod
+    def calibrate(self) -> None:
+        """Calibrates the robot."""
+        pass
+
+    def _load_calibration(self, fpath: Path | None = None) -> None:
+        fpath = self.calibration_fpath if fpath is None else fpath
+        with open(fpath) as f, draccus.config_type("json"):
+            self.calibration = draccus.load(dict[str, MotorCalibration], f)
+
+    def _save_calibration(self, fpath: Path | None = None) -> None:
+        fpath = self.calibration_fpath if fpath is None else fpath
+        with open(fpath, "w") as f, draccus.config_type("json"):
+            draccus.dump(self.calibration, f, indent=4)
+
+    @abc.abstractmethod
+    def configure(self) -> None:
+        pass
+
+    @abc.abstractmethod
+    def get_observation(self) -> dict[str, Any]:
+        """Gets observation from the robot."""
+        pass
+
+    @abc.abstractmethod
+    def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
+        """Sends actions to the robot."""
+        pass
+
+    @abc.abstractmethod
+    def disconnect(self) -> None:
+        """Disconnects from the robot."""
+        pass
--- a/lerobot/common/robots/so100_follower/init.py
+++ b/lerobot/common/robots/so100_follower/init.py
@@ -0,0 +1,2 @@
+from .config_so100_follower import SO100FollowerConfig
+from .so100_follower import SO100Follower
--- a/lerobot/common/robots/so100_follower/config_so100_follower.py
+++ b/lerobot/common/robots/so100_follower/config_so100_follower.py
@@ -0,0 +1,36 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.common.cameras import CameraConfig
+
+from ..config import RobotConfig
+
+
+@RobotConfig.register_subclass("so100_follower")
+@dataclass
+class SO100FollowerConfig(RobotConfig):
+    # Port to connect to the arm
+    port: str
+
+    disable_torque_on_disconnect: bool = True
+
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    # cameras
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)
--- a/lerobot/common/robots/so100_follower/so100.mdx
+++ b/lerobot/common/robots/so100_follower/so100.mdx
@@ -0,0 +1,486 @@
+# SO-100
+
+In the steps below, we explain how to assemble the SO-100 robot.
+
+## Source the parts
+
+Follow this [README](https://github.com/TheRobotStudio/SO-ARM100/blob/main/SO100.md). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts. And advise if it's your first time printing or if you don't own a 3D printer.
+
+## Install LeRobot 🤗
+
+To install LeRobot, follow our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Feetech SDK:
+```bash
+pip install -e ".[feetech]"
+```
+
+## Step-by-Step Assembly Instructions
+
+## Remove the gears of the 6 leader motors
+
+<details>
+<summary><strong>Video removing gears</strong></summary>
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7" type="video/mp4" />
+  </video>
+</div>
+
+</details>
+
+Follow the video for removing gears. You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm.
+
+### Clean Parts
+Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
+
+### Additional Guidance
+
+<details>
+<summary><strong>Video assembling arms</strong></summary>
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca" type="video/mp4" />
+  </video>
+</div>
+
+</details>
+
+**Note:**
+This video provides visual guidance for assembling the arms, but it doesn't specify when or how to do the wiring. Inserting the cables beforehand is much easier than doing it afterward. The first arm may take a bit more than 1 hour to assemble, but once you get used to it, you can assemble the second arm in under 1 hour.
+
+---
+
+### First Motor
+
+**Step 2: Insert Wires**
+- Insert two wires into the first motor.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_1.webp" style="height:300px;"/>
+
+**Step 3: Install in Base**
+- Place the first motor into the base.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_2.webp" style="height:300px;"/>
+
+**Step 4: Secure Motor**
+- Fasten the motor with 4 screws. Two from the bottom and two from top.
+
+**Step 5: Attach Motor Holder**
+- Slide over the first motor holder and fasten it using two screws (one on each side).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_4.webp" style="height:300px;"/>
+
+**Step 6: Attach Motor Horns**
+- Install both motor horns, securing the top horn with a screw. Try not to move the motor position when attaching the motor horn, especially for the leader arms, where we removed the gears.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_5.webp" style="height:300px;"/>
+
+<details>
+  <summary><strong>Video adding motor horn</strong></summary>
+  <video src="https://github.com/user-attachments/assets/ef3391a4-ad05-4100-b2bd-1699bf86c969"></video>
+</details>
+
+**Step 7: Attach Shoulder Part**
+- Route one wire to the back of the robot and the other to the left or towards you (see photo).
+- Attach the shoulder part.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_6.webp" style="height:300px;"/>
+
+**Step 8: Secure Shoulder**
+- Tighten the shoulder part with 4 screws on top and 4 on the bottom
+*(access bottom holes by turning the shoulder).*
+
+---
+
+### Second Motor Assembly
+
+**Step 9: Install Motor 2**
+- Slide the second motor in from the top and link the wire from motor 1 to motor 2.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_8.webp" style="height:300px;"/>
+
+**Step 10: Attach Shoulder Holder**
+- Add the shoulder motor holder.
+- Ensure the wire from motor 1 to motor 2 goes behind the holder while the other wire is routed upward (see photo).
+- This part can be tight to assemble, you can use a workbench like the image or a similar setup to push the part around the motor.
+
+<div style="display: flex;">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_9.webp" style="height:250px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_10.webp" style="height:250px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_12.webp" style="height:250px;"/>
+</div>
+
+**Step 11: Secure Motor 2**
+- Fasten the second motor with 4 screws.
+
+**Step 12: Attach Motor Horn**
+- Attach both motor horns to motor 2, again use the horn screw.
+
+**Step 13: Attach Base**
+- Install the base attachment using 2 screws.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_11.webp" style="height:300px;">
+
+**Step 14: Attach Upper Arm**
+- Attach the upper arm with 4 screws on each side.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_13.webp" style="height:300px;">
+
+---
+
+### Third Motor Assembly
+
+**Step 15: Install Motor 3**
+- Route the motor cable from motor 2 through the cable holder to motor 3, then secure motor 3 with 4 screws.
+
+**Step 16: Attach Motor Horn**
+- Attach both motor horns to motor 3 and secure one again with a horn screw.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_14.webp" style="height:300px;"/>
+
+**Step 17: Attach Forearm**
+- Connect the forearm to motor 3 using 4 screws on each side.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_15.webp" style="height:300px;"/>
+
+---
+
+### Fourth Motor Assembly
+
+**Step 18: Install Motor 4**
+- Slide in motor 4, attach the cable from motor 3, and secure the cable in its holder with a screw.
+
+<div style="display: flex;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_16.webp" style="height:300px;"/>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_19.webp" style="height:300px;"/>
+</div>
+
+**Step 19: Attach Motor Holder 4**
+- Install the fourth motor holder (a tight fit). Ensure one wire is routed upward and the wire from motor 3 is routed downward (see photo).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_17.webp" style="height:300px;"/>
+
+**Step 20: Secure Motor 4 & Attach Horn**
+- Fasten motor 4 with 4 screws and attach its motor horns, use for one a horn screw.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_18.webp" style="height:300px;"/>
+
+---
+
+### Wrist Assembly
+
+**Step 21: Install Motor 5**
+- Insert motor 5 into the wrist holder and secure it with 2 front screws.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_20.webp" style="height:300px;"/>
+
+**Step 22: Attach Wrist**
+- Connect the wire from motor 4 to motor 5. And already insert the other wire for the gripper.
+- Secure the wrist to motor 4 using 4 screws on both sides.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_22.webp" style="height:300px;"/>
+
+**Step 23: Attach Wrist Horn**
+- Install only one motor horn on the wrist motor and secure it with a horn screw.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_23.webp" style="height:300px;"/>
+
+---
+
+### Follower Configuration
+
+**Step 24: Attach Gripper**
+- Attach the gripper to motor 5.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_24.webp" style="height:300px;"/>
+
+**Step 25: Install Gripper Motor**
+- Insert the gripper motor, connect the motor wire from motor 5 to motor 6, and secure it with 3 screws on each side.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_25.webp" style="height:300px;"/>
+
+**Step 26: Attach Gripper Horn & Claw**
+- Attach the motor horns and again use a horn screw.
+- Install the gripper claw and secure it with 4 screws on both sides.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_26.webp" style="height:300px;"/>
+
+**Step 27: Mount Controller**
+- Attach the motor controller to the back of the robot.
+
+<div style="display: flex;">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp" style="height:300px;"/>
+</div>
+
+*Assembly complete – proceed to Leader arm assembly.*
+
+---
+
+### Leader Configuration
+
+For the leader configuration, perform **Steps 1–23**. Make sure that you removed the motor gears from the motors.
+
+**Step 24: Attach Leader Holder**
+- Mount the leader holder onto the wrist and secure it with a screw.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_29.webp" style="height:300px;"/>
+
+**Step 25: Attach Handle**
+- Attach the handle to motor 5 using 4 screws.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_30.webp" style="height:300px;"/>
+
+**Step 26: Install Gripper Motor**
+- Insert the gripper motor, secure it with 3 screws on each side, attach a motor horn using a horn screw, and connect the motor wire.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_31.webp" style="height:300px;"/>
+
+**Step 27: Attach Trigger**
+- Attach the follower trigger with 4 screws.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_32.webp" style="height:300px;"/>
+
+**Step 28: Mount Controller**
+- Attach the motor controller to the back of the robot.
+
+<div style="display: flex;">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp" style="height:300px;"/>
+</div>
+
+## Configure the motors
+
+### 1. Find the USB ports associated with each arm
+
+To find the port for each bus servo adapter, run this script:
+```bash
+python lerobot/find_port.py
+```
+
+<hfoptions id="example">
+<hfoption id="Mac">
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the USB cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
+
+</hfoption>
+<hfoption id="Linux">
+
+On Linux, you might need to give access to the USB ports by running:
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/ttyACM0', '/dev/ttyACM1']
+Remove the usb cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/ttyACM1
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
+
+</hfoption>
+</hfoptions>
+
+### 2. Set the motors ids and baudrates
+
+Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
+
+To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
+
+If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match.
+
+#### Follower
+
+Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
+
+For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.setup_motors \
+    --robot.type=so100_follower \
+    --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.robots.so100_follower import SO100Follower, SO100FollowerConfig
+
+config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_follower_arm",
+)
+follower = SO100Follower(config)
+follower.setup_motors()
+```
+</hfoption>
+</hfoptions>
+
+You should see the following instruction
+```
+Connect the controller board to the 'gripper' motor only and press enter.
+```
+
+As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
+
+<details>
+<summary>Troubleshooting</summary>
+
+  If you get an error at that point, check your cables and make sure they are plugged in properly:
+  <ul>
+    <li>Power supply</li>
+    <li>USB cable between your computer and the controller board</li>
+    <li>The 3-pin cable from the controller board to the motor</li>
+  </ul>
+
+If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
+</details>
+
+You should then see the following message:
+```
+'gripper' motor id set to 6
+```
+
+Followed by the next instruction:
+```
+Connect the controller board to the 'wrist_roll' motor only and press enter.
+```
+
+You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
+
+Repeat the operation for each motor as instructed.
+
+> [!TIP]
+> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
+
+When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
+
+#### Leader
+Do the same steps for the leader arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+```bash
+python -m lerobot.setup_motors \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+
+config = SO100LeaderConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_leader_arm",
+)
+leader = SO100Leader(config)
+leader.setup_motors()
+```
+</hfoption>
+</hfoptions>
+
+## Calibrate
+
+Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+#### Follower
+
+Run the following command or API example to calibrate the follower arm:
+
+<hfoptions id="calibrate_follower">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.calibrate \
+    --robot.type=so100_follower \
+    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.robots.so100_follower import SO100FollowerConfig, SO100Follower
+
+config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem585A0076891",
+    id="my_awesome_follower_arm",
+)
+
+follower = SO100Follower(config)
+follower.connect(calibrate=False)
+follower.calibrate()
+follower.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+We unified the calibration method for most robots. Thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video)
+
+#### Leader
+
+Do the same steps to calibrate the leader arm, run the following command or API example:
+
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.calibrate \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
+
+config = SO100LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_awesome_leader_arm",
+)
+
+leader = SO100Leader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+> [!TIP]
+>  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
--- a/lerobot/common/robots/so100_follower/so100_follower.py
+++ b/lerobot/common/robots/so100_follower/so100_follower.py
@@ -0,0 +1,213 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from functools import cached_property
+from typing import Any
+
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.common.motors.feetech import (
+    FeetechMotorsBus,
+    OperatingMode,
+)
+
+from ..robot import Robot
+from ..utils import ensure_safe_goal_position
+from .config_so100_follower import SO100FollowerConfig
+
+logger = logging.getLogger(__name__)
+
+
+class SO100Follower(Robot):
+    """
+    [SO-100 Follower Arm](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
+    """
+
+    config_class = SO100FollowerConfig
+    name = "so100_follower"
+
+    def __init__(self, config: SO100FollowerConfig):
+        super().__init__(config)
+        self.config = config
+        self.bus = FeetechMotorsBus(
+            port=self.config.port,
+            motors={
+                "shoulder_pan": Motor(1, "sts3215", MotorNormMode.RANGE_M100_100),
+                "shoulder_lift": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100),
+                "elbow_flex": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100),
+                "wrist_flex": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100),
+                "wrist_roll": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100),
+                "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100),
+            },
+            calibration=self.calibration,
+        )
+        self.cameras = make_cameras_from_configs(config.cameras)
+
+    @property
+    def _motors_ft(self) -> dict[str, type]:
+        return {f"{motor}.pos": float for motor in self.bus.motors}
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._motors_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return self._motors_ft
+
+    @property
+    def is_connected(self) -> bool:
+        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+
+    def connect(self, calibrate: bool = True) -> None:
+        """
+        We assume that at connection time, arm is in a rest position,
+        and torque can be safely disabled to run calibration.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} already connected")
+
+        self.bus.connect()
+        if not self.is_calibrated and calibrate:
+            self.calibrate()
+
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.configure()
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.bus.is_calibrated
+
+    def calibrate(self) -> None:
+        logger.info(f"\nRunning calibration of {self}")
+        self.bus.disable_torque()
+        for motor in self.bus.motors:
+            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+
+        input(f"Move {self} to the middle of its range of motion and press ENTER....")
+        homing_offsets = self.bus.set_half_turn_homings()
+
+        full_turn_motor = "wrist_roll"
+        unknown_range_motors = [motor for motor in self.bus.motors if motor != full_turn_motor]
+        print(
+            f"Move all joints except '{full_turn_motor}' sequentially through their "
+            "entire ranges of motion.\nRecording positions. Press ENTER to stop..."
+        )
+        range_mins, range_maxes = self.bus.record_ranges_of_motion(unknown_range_motors)
+        range_mins[full_turn_motor] = 0
+        range_maxes[full_turn_motor] = 4095
+
+        self.calibration = {}
+        for motor, m in self.bus.motors.items():
+            self.calibration[motor] = MotorCalibration(
+                id=m.id,
+                drive_mode=0,
+                homing_offset=homing_offsets[motor],
+                range_min=range_mins[motor],
+                range_max=range_maxes[motor],
+            )
+
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        print("Calibration saved to", self.calibration_fpath)
+
+    def configure(self) -> None:
+        with self.bus.torque_disabled():
+            self.bus.configure_motors()
+            for motor in self.bus.motors:
+                self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+                # Set P_Coefficient to lower value to avoid shakiness (Default is 32)
+                self.bus.write("P_Coefficient", motor, 16)
+                # Set I_Coefficient and D_Coefficient to default value 0 and 32
+                self.bus.write("I_Coefficient", motor, 0)
+                self.bus.write("D_Coefficient", motor, 32)
+
+    def setup_motors(self) -> None:
+        for motor in reversed(self.bus.motors):
+            input(f"Connect the controller board to the '{motor}' motor only and press enter.")
+            self.bus.setup_motor(motor)
+            print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
+
+    def get_observation(self) -> dict[str, Any]:
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        # Read arm position
+        start = time.perf_counter()
+        obs_dict = self.bus.sync_read("Present_Position")
+        obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
+
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            start = time.perf_counter()
+            obs_dict[cam_key] = cam.async_read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+
+        return obs_dict
+
+    def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
+        """Command arm to move to a target joint configuration.
+
+        The relative action magnitude may be clipped depending on the configuration parameter
+        `max_relative_target`. In this case, the action sent differs from original action.
+        Thus, this function always returns the action actually sent.
+
+        Raises:
+            RobotDeviceNotConnectedError: if robot is not connected.
+
+        Returns:
+            the action sent to the motors, potentially clipped.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
+
+        # Cap goal position when too far away from present position.
+        # /!\ Slower fps expected due to reading from the follower.
+        if self.config.max_relative_target is not None:
+            present_pos = self.bus.sync_read("Present_Position")
+            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
+            goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
+
+        # Send goal position to the arm
+        self.bus.sync_write("Goal_Position", goal_pos)
+        return {f"{motor}.pos": val for motor, val in goal_pos.items()}
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        self.bus.disconnect(self.config.disable_torque_on_disconnect)
+        for cam in self.cameras.values():
+            cam.disconnect()
+
+        logger.info(f"{self} disconnected.")
--- a/lerobot/common/robots/so101_follower/init.py
+++ b/lerobot/common/robots/so101_follower/init.py
@@ -0,0 +1,2 @@
+from .config_so101_follower import SO101FollowerConfig
+from .so101_follower import SO101Follower
--- a/lerobot/common/robots/so101_follower/config_so101_follower.py
+++ b/lerobot/common/robots/so101_follower/config_so101_follower.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.common.cameras import CameraConfig
+
+from ..config import RobotConfig
+
+
+@RobotConfig.register_subclass("so101_follower")
+@dataclass
+class SO101FollowerConfig(RobotConfig):
+    # Port to connect to the arm
+    port: str
+
+    disable_torque_on_disconnect: bool = True
+
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    # cameras
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)
--- a/lerobot/common/robots/so101_follower/so101.mdx
+++ b/lerobot/common/robots/so101_follower/so101.mdx
@@ -0,0 +1,381 @@
+# SO-101
+
+In the steps below, we explain how to assemble our flagship robot, the SO-101.
+
+## Source the parts
+
+Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts.
+And advise if it's your first time printing or if you don't own a 3D printer.
+
+## Install LeRobot 🤗
+
+To install LeRobot, follow our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Feetech SDK:
+```bash
+pip install -e ".[feetech]"
+```
+
+## Step-by-Step Assembly Instructions
+
+The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader, however, uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in the table below.
+
+| Leader-Arm Axis | Motor | Gear Ratio |
+|-----------------|:-------:|:----------:|
+| Base / Shoulder Yaw | 1 | 1 / 191 |
+| Shoulder Pitch      | 2 | 1 / 345 |
+| Elbow               | 3 | 1 / 191 |
+| Wrist Roll          | 4 | 1 / 147 |
+| Wrist Pitch         | 5 | 1 / 147 |
+| Gripper             | 6 | 1 / 147 |
+
+### Clean Parts
+Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
+
+### Joint 1
+
+- Place the first motor into the base.
+- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from the bottom.
+- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
+- Install both motor horns, securing the top horn with a M3x6mm screw.
+- Attach the shoulder part.
+- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
+- Add the shoulder motor holder.
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+### Joint 2
+
+- Slide the second motor in from the top.
+- Fasten the second motor with 4 M2x6mm screws.
+- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
+- Attach the upper arm with 4 M3x6mm screws on each side.
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+### Joint 3
+
+- Insert motor 3 and fasten using 4 M2x6mm screws
+- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
+- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+### Joint 4
+
+- Slide over motor holder 4.
+- Slide in motor 4.
+- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+### Joint 5
+
+- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
+- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
+- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+### Gripper / Handle
+
+<hfoptions id="assembly">
+<hfoption id="Follower">
+
+- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
+- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
+- Attach the motor horns and again use a M3x6mm horn screw.
+- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+</hfoption>
+<hfoption id="Leader">
+
+- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
+- Attach the handle to motor 5 using 1 M2x6mm screw.
+- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
+- Attach the follower trigger with 4 M3x6mm screws.
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+</hfoption>
+</hfoptions>
+
+## Configure the motors
+
+### 1. Find the USB ports associated with each arm
+
+To find the port for each bus servo adapter, run this script:
+```bash
+python lerobot/find_port.py
+```
+
+<hfoptions id="example">
+<hfoption id="Mac">
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the USB cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
+
+</hfoption>
+<hfoption id="Linux">
+
+On Linux, you might need to give access to the USB ports by running:
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/ttyACM0', '/dev/ttyACM1']
+Remove the usb cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/ttyACM1
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
+
+</hfoption>
+</hfoptions>
+
+### 2. Set the motors ids and baudrates
+
+Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
+
+To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
+
+If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match.
+
+The video below shows the sequence of steps for setting the motor ids.
+
+##### Setup motors video
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/setup_motors_so101_2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+#### Follower
+
+Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.setup_motors \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.robots.so101_follower import SO101Follower, SO101FollowerConfig
+
+config = SO101FollowerConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_follower_arm",
+)
+follower = SO101Follower(config)
+follower.setup_motors()
+```
+</hfoption>
+</hfoptions>
+
+You should see the following instruction
+```bash
+Connect the controller board to the 'gripper' motor only and press enter.
+```
+
+As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
+
+<details>
+<summary>Troubleshooting</summary>
+
+  If you get an error at that point, check your cables and make sure they are plugged in properly:
+  <ul>
+    <li>Power supply</li>
+    <li>USB cable between your computer and the controller board</li>
+    <li>The 3-pin cable from the controller board to the motor</li>
+  </ul>
+
+  If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
+</details>
+
+You should then see the following message:
+```bash
+'gripper' motor id set to 6
+```
+
+Followed by the next instruction:
+```bash
+Connect the controller board to the 'wrist_roll' motor only and press enter.
+```
+
+You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
+
+Repeat the operation for each motor as instructed.
+
+> [!TIP]
+> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
+
+When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
+
+#### Leader
+Do the same steps for the leader arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.setup_motors \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.teleoperators.so101_leader import SO101Leader, SO101LeaderConfig
+
+config = SO101LeaderConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_leader_arm",
+)
+leader = SO101Leader(config)
+leader.setup_motors()
+```
+</hfoption>
+</hfoptions>
+
+## Calibrate
+
+Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+#### Follower
+
+Run the following command or API example to calibrate the follower arm:
+
+<hfoptions id="calibrate_follower">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.calibrate \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower
+
+config = SO101FollowerConfig(
+    port="/dev/tty.usbmodem585A0076891",
+    id="my_awesome_follower_arm",
+)
+
+follower = SO101Follower(config)
+follower.connect(calibrate=False)
+follower.calibrate()
+follower.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+The video below shows how to perform the calibration. First you need to move the robot to the position where all joints are in the middle of their ranges. Then after pressing enter you have to move each joint through its full range of motion.
+
+##### Calibration video
+
+<div class="video-container">
+  <video controls width="600">
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/calibrate_so101_2.mp4" type="video/mp4" />
+  </video>
+</div>
+
+#### Leader
+
+Do the same steps to calibrate the leader arm, run the following command or API example:
+
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+python -m lerobot.calibrate \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
+```
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
+
+config = SO101LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_awesome_leader_arm",
+)
+
+leader = SO101Leader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+</hfoption>
+</hfoptions>
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+> [!TIP]
+>  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
--- a/lerobot/common/robots/so101_follower/so101_follower.py
+++ b/lerobot/common/robots/so101_follower/so101_follower.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from functools import cached_property
+from typing import Any
+
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.common.motors.feetech import (
+    FeetechMotorsBus,
+    OperatingMode,
+)
+
+from ..robot import Robot
+from ..utils import ensure_safe_goal_position
+from .config_so101_follower import SO101FollowerConfig
+
+logger = logging.getLogger(__name__)
+
+
+class SO101Follower(Robot):
+    """
+    SO-101 Follower Arm designed by TheRobotStudio and Hugging Face.
+    """
+
+    config_class = SO101FollowerConfig
+    name = "so101_follower"
+
+    def __init__(self, config: SO101FollowerConfig):
+        super().__init__(config)
+        self.config = config
+        self.bus = FeetechMotorsBus(
+            port=self.config.port,
+            motors={
+                "shoulder_pan": Motor(1, "sts3215", MotorNormMode.RANGE_M100_100),
+                "shoulder_lift": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100),
+                "elbow_flex": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100),
+                "wrist_flex": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100),
+                "wrist_roll": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100),
+                "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100),
+            },
+            calibration=self.calibration,
+        )
+        self.cameras = make_cameras_from_configs(config.cameras)
+
+    @property
+    def _motors_ft(self) -> dict[str, type]:
+        return {f"{motor}.pos": float for motor in self.bus.motors}
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._motors_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return self._motors_ft
+
+    @property
+    def is_connected(self) -> bool:
+        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+
+    def connect(self, calibrate: bool = True) -> None:
+        """
+        We assume that at connection time, arm is in a rest position,
+        and torque can be safely disabled to run calibration.
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} already connected")
+
+        self.bus.connect()
+        if not self.is_calibrated and calibrate:
+            self.calibrate()
+
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.configure()
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.bus.is_calibrated
+
+    def calibrate(self) -> None:
+        logger.info(f"\nRunning calibration of {self}")
+        self.bus.disable_torque()
+        for motor in self.bus.motors:
+            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+
+        input(f"Move {self} to the middle of its range of motion and press ENTER....")
+        homing_offsets = self.bus.set_half_turn_homings()
+
+        print(
+            "Move all joints sequentially through their entire ranges "
+            "of motion.\nRecording positions. Press ENTER to stop..."
+        )
+        range_mins, range_maxes = self.bus.record_ranges_of_motion()
+
+        self.calibration = {}
+        for motor, m in self.bus.motors.items():
+            self.calibration[motor] = MotorCalibration(
+                id=m.id,
+                drive_mode=0,
+                homing_offset=homing_offsets[motor],
+                range_min=range_mins[motor],
+                range_max=range_maxes[motor],
+            )
+
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        print("Calibration saved to", self.calibration_fpath)
+
+    def configure(self) -> None:
+        with self.bus.torque_disabled():
+            self.bus.configure_motors()
+            for motor in self.bus.motors:
+                self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+                # Set P_Coefficient to lower value to avoid shakiness (Default is 32)
+                self.bus.write("P_Coefficient", motor, 16)
+                # Set I_Coefficient and D_Coefficient to default value 0 and 32
+                self.bus.write("I_Coefficient", motor, 0)
+                self.bus.write("D_Coefficient", motor, 32)
+
+    def setup_motors(self) -> None:
+        for motor in reversed(self.bus.motors):
+            input(f"Connect the controller board to the '{motor}' motor only and press enter.")
+            self.bus.setup_motor(motor)
+            print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
+
+    def get_observation(self) -> dict[str, Any]:
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        # Read arm position
+        start = time.perf_counter()
+        obs_dict = self.bus.sync_read("Present_Position")
+        obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
+
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            start = time.perf_counter()
+            obs_dict[cam_key] = cam.async_read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+
+        return obs_dict
+
+    def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
+        """Command arm to move to a target joint configuration.
+
+        The relative action magnitude may be clipped depending on the configuration parameter
+        `max_relative_target`. In this case, the action sent differs from original action.
+        Thus, this function always returns the action actually sent.
+
+        Raises:
+            RobotDeviceNotConnectedError: if robot is not connected.
+
+        Returns:
+            the action sent to the motors, potentially clipped.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
+
+        # Cap goal position when too far away from present position.
+        # /!\ Slower fps expected due to reading from the follower.
+        if self.config.max_relative_target is not None:
+            present_pos = self.bus.sync_read("Present_Position")
+            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
+            goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
+
+        # Send goal position to the arm
+        self.bus.sync_write("Goal_Position", goal_pos)
+        return {f"{motor}.pos": val for motor, val in goal_pos.items()}
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        self.bus.disconnect(self.config.disable_torque_on_disconnect)
+        for cam in self.cameras.values():
+            cam.disconnect()
+
+        logger.info(f"{self} disconnected.")
--- a/lerobot/common/robots/stretch3/README.md
+++ b/lerobot/common/robots/stretch3/README.md
--- a/lerobot/common/robots/stretch3/init.py
+++ b/lerobot/common/robots/stretch3/init.py
@@ -0,0 +1,2 @@
+from .configuration_stretch3 import Stretch3RobotConfig
+from .robot_stretch3 import Stretch3Robot
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				`../../lerobot/common/robots/koch_follower/koch.mdx`
				`@@ -0,0 +1 @@`
				`../../lerobot/common/robots/lekiwi/lekiwi.mdx`
				`@@ -0,0 +1 @@`
				`../../lerobot/common/robots/so100_follower/so100.mdx`
				`@@ -0,0 +1 @@`
				`../../lerobot/common/robots/so101_follower/so101.mdx`
				`@@ -0,0 +1 @@`
				`from .motors_bus import Motor, MotorCalibration, MotorNormMode, MotorsBus`