Disconnect after scan_port

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

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

docs/source/getting_started_real_world_robot.mdx

@@ -36,16 +36,18 @@ If you haven't yet set up and calibrated your robot and teleop device, please do
 
 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 -m lerobot.teleoperate \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=my_red_robot_arm \
+    --robot.id=my_awesome_follower_arm \
     --teleop.type=so101_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_blue_leader_arm
+    --teleop.id=my_awesome_leader_arm
 ```
 </hfoption>
 <hfoption id="API example">
@@ -93,11 +95,11 @@ With `rerun`, you can teleoperate again while simultaneously visualizing the cam
 python -m lerobot.teleoperate \
     --robot.type=koch_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=my_koch_robot \
+    --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_koch_teleop \
+    --teleop.id=my_awesome_leader_arm \
     --display_data=true
 ```
 </hfoption>
@@ -157,13 +159,13 @@ Now you can record a dataset. To record 2 episodes and upload your dataset to th
 python -m lerobot.record \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem585A0076841 \
-    --robot.id=my_red_robot_arm \
+    --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_blue_leader_arm \
+    --teleop.id=my_awesome_leader_arm \
     --display_data=true \
-    --dataset.repo_id=aliberts/record-test \
+    --dataset.repo_id=${HF_USER}/record-test \
     --dataset.num_episodes=2 \
     --dataset.single_task="Grab the black cube"
 ```
@@ -227,18 +229,6 @@ 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 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.
@@ -248,9 +238,9 @@ You can replay the first episode on your robot with:
 python -m lerobot.replay \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=black \
-    --dataset.repo_id=aliberts/record-test \
-    --dataset.episode=2
+    --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).
@@ -306,14 +296,14 @@ 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=blue_follower_arm \
+  --robot.id=my_awesome_follower_arm \
   --teleop.type=so100_leader \
   --teleop.port=/dev/ttyACM0 \
-  --teleop.id=red_leader_arm \
+  --teleop.id=my_awesome_leader_arm \
   --display_data=false \
-  --dataset.repo_id=$HF_USER/eval_lego_${EPOCHREALTIME/[^0-9]/} \
+  --dataset.repo_id=$HF_USER/eval_so100 \
   --dataset.single_task="Put lego brick into the transparent box" \
-  --policy.path=${HF_USER}/act_johns_arm
+  --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:

examples/5_control_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!

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()

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()

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()

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)

examples/robots/lekiwi_client_app.py

@@ -1,94 +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 logging
-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
-
-
-def main():
-    logging.info("Configuring Teleop Devices")
-    leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem58760433331")
-    leader_arm = SO100Leader(leader_arm_config)
-
-    keyboard_config = KeyboardTeleopConfig()
-    keyboard = KeyboardTeleop(keyboard_config)
-
-    logging.info("Configuring LeKiwi Client")
-    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
-    robot = LeKiwiClient(robot_config)
-
-    logging.info("Creating LeRobot Dataset")
-
-    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,
-    )
-
-    logging.info("Connecting Teleop Devices")
-    leader_arm.connect()
-    keyboard.connect()
-
-    logging.info("Connecting remote LeKiwi")
-    robot.connect()
-
-    if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-        logging.error("Failed to connect to all devices")
-        return
-
-    logging.info("Starting LeKiwi teleoperation")
-    i = 0
-    while i < NB_CYCLES_CLIENT_CONNECTION:
-        arm_action = leader_arm.get_action()
-        base_action = keyboard.get_action()
-        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"
-
-        logging.info("Saved a frame into the dataset")
-        dataset.add_frame(frame, task)
-        i += 1
-
-    logging.info("Disconnecting Teleop Devices and LeKiwi Client")
-    robot.disconnect()
-    leader_arm.disconnect()
-    keyboard.disconnect()
-
-    logging.info("Uploading dataset to the hub")
-    dataset.save_episode()
-    dataset.push_to_hub()
-
-    logging.info("Finished LeKiwi cleanly")
-
-
-if __name__ == "__main__":
-    main()

lerobot/__init__.py

@@ -168,12 +168,7 @@ available_datasets = sorted(
 )
 
 # lists all available policies from `lerobot/common/policies`
-available_policies = [
-    "act",
-    "diffusion",
-    "tdmpc",
-    "vqbet",
-]
+available_policies = ["act", "diffusion", "tdmpc", "vqbet"]
 
 # lists all available robots from `lerobot/common/robot_devices/robots`
 available_robots = [

lerobot/common/cameras/opencv/camera_opencv.py

@@ -124,10 +124,9 @@ class OpenCVCamera(Camera):
         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
-            else:
-                self.capture_width, self.capture_height = self.width, self.height
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}({self.index_or_path})"
@@ -206,12 +205,11 @@ class OpenCVCamera(Camera):
         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.width, self.height = default_width, default_height
-                self.capture_width, self.capture_height = default_width, default_height
         else:
             self._validate_width_and_height()
 

lerobot/common/cameras/realsense/camera_realsense.py

@@ -138,10 +138,9 @@ class RealSenseCamera(Camera):
         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
-            else:
-                self.capture_width, self.capture_height = self.width, self.height
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}({self.serial_number})"

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,
@@ -314,23 +315,9 @@ 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."
-        #         )
-
         # 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}'.")
+        _validate_feature_names(features)
 
         obj.tasks, obj.task_to_task_index = {}, {}
         obj.episodes_stats, obj.stats, obj.episodes = {}, {}, {}

lerobot/common/motors/configs.py

@@ -1,41 +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 MotorsBusConfig(draccus.ChoiceRegistry, abc.ABC):
-    @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

lerobot/common/motors/dynamixel/dynamixel.py

@@ -39,7 +39,6 @@ DEFAULT_BAUDRATE = 1_000_000
 DEFAULT_TIMEOUT_MS = 1000
 
 NORMALIZED_DATA = ["Goal_Position", "Present_Position"]
-CONVERT_UINT32_TO_INT32_REQUIRED = ["Goal_Position", "Present_Position"]
 
 logger = logging.getLogger(__name__)
 

lerobot/common/motors/feetech/feetech.py

@@ -154,7 +154,7 @@ class FeetechMotorsBus(MotorsBus):
             )
 
     def _assert_same_firmware(self) -> None:
-        firmware_versions = self._read_firmware_version(self.ids)
+        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:"
@@ -251,7 +251,6 @@ class FeetechMotorsBus(MotorsBus):
 
     def read_calibration(self) -> dict[str, MotorCalibration]:
         offsets, mins, maxes = {}, {}, {}
-        drive_modes = dict.fromkeys(self.motors, 0)
         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)
@@ -263,7 +262,7 @@ class FeetechMotorsBus(MotorsBus):
         for motor, m in self.motors.items():
             calibration[motor] = MotorCalibration(
                 id=m.id,
-                drive_mode=drive_modes[motor],
+                drive_mode=0,
                 homing_offset=offsets[motor],
                 range_min=mins[motor],
                 range_max=maxes[motor],
@@ -359,13 +358,10 @@ class FeetechMotorsBus(MotorsBus):
         self.port_handler.setPacketTimeoutMillis((wait_length * tx_time_per_byte) + (3.0 * scs.MAX_ID) + 16.0)
 
         rxpacket = []
-        while True:
+        while not self.port_handler.isPacketTimeout() and rx_length < wait_length:
             rxpacket += self.port_handler.readPort(wait_length - rx_length)
             rx_length = len(rxpacket)
 
-            if self.port_handler.isPacketTimeout():  # or rx_length >= wait_length
-                break
-
         self.port_handler.is_using = False
 
         if rx_length == 0:
@@ -434,13 +430,13 @@ class FeetechMotorsBus(MotorsBus):
                 *FIRMWARE_MAJOR_VERSION, id_, raise_on_error=raise_on_error
             )
             if not self._is_comm_success(comm) or self._is_error(error):
-                return
+                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):
-                return
+                continue
 
             firmware_versions[id_] = f"{firm_ver_major}.{firm_ver_minor}"
 
@@ -451,7 +447,7 @@ class FeetechMotorsBus(MotorsBus):
         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):
-                return
+                continue
 
             model_numbers[id_] = model_nb
 

lerobot/common/motors/motors_bus.py

@@ -38,8 +38,6 @@ from lerobot.common.utils.utils import enter_pressed, move_cursor_up
 NameOrID: TypeAlias = str | int
 Value: TypeAlias = int | float
 
-MAX_ID_RANGE = 252
-
 logger = logging.getLogger(__name__)
 
 
@@ -241,11 +239,11 @@ class MotorsBus(abc.ABC):
     )
     bus.connect()
 
-    position = bus.read("Present_Position", normalize=False)
+    position = bus.read("Present_Position", "my_motor", normalize=False)
 
     # Move from a few motor steps as an example
     few_steps = 30
-    bus.write("Goal_Position", position + few_steps, normalize=False)
+    bus.write("Goal_Position", "my_motor", position + few_steps, normalize=False)
 
     # When done, properly disconnect the port using
     bus.disconnect()
@@ -449,7 +447,7 @@ class MotorsBus(abc.ABC):
         except (FileNotFoundError, OSError, serial.SerialException) as e:
             raise ConnectionError(
                 f"\nCould not connect on port '{self.port}'. Make sure you are using the correct port."
-                "\nTry running `python lerobot/scripts/find_motors_bus_port.py`\n"
+                "\nTry running `python lerobot/find_port.py`\n"
             ) from e
 
     @abc.abstractmethod
@@ -499,6 +497,7 @@ class MotorsBus(abc.ABC):
                 tqdm.write(f"Motors found for {baudrate=}: {pformat(ids_models, indent=4)}")
                 baudrate_ids[baudrate] = list(ids_models)
 
+        bus.port_handler.closePort()
         return baudrate_ids
 
     def setup_motor(
@@ -582,8 +581,8 @@ class MotorsBus(abc.ABC):
 
         Args:
             motor (int): Same semantics as :pymeth:`disable_torque`. Defaults to `None`.
-            model (str): _description_
-            num_retry (int, optional): _description_. Defaults to 0.
+            num_retry (int, optional): Number of additional retry attempts on communication failure.
+                Defaults to 0.
         """
         pass
 
@@ -748,7 +747,9 @@ class MotorsBus(abc.ABC):
         start_positions = self.sync_read("Present_Position", motors, normalize=False)
         mins = start_positions.copy()
         maxes = start_positions.copy()
-        while True:
+
+        user_pressed_enter = False
+        while not user_pressed_enter:
             positions = self.sync_read("Present_Position", motors, normalize=False)
             mins = {motor: min(positions[motor], min_) for motor, min_ in mins.items()}
             maxes = {motor: max(positions[motor], max_) for motor, max_ in maxes.items()}
@@ -760,9 +761,9 @@ class MotorsBus(abc.ABC):
                     print(f"{motor:<15} | {mins[motor]:>6} | {positions[motor]:>6} | {maxes[motor]:>6}")
 
             if enter_pressed():
-                break
+                user_pressed_enter = True
 
-            if display_values:
+            if display_values and not user_pressed_enter:
                 # Move cursor up to overwrite the previous output
                 move_cursor_up(len(motors) + 3)
 

lerobot/common/motors/utils.py

@@ -1,56 +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 .configs import MotorsBusConfig
-from .motors_bus import MotorsBus
-
-
-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 .dynamixel import DynamixelMotorsBus
-
-            motors_buses[key] = DynamixelMotorsBus(cfg)
-
-        elif cfg.type == "feetech":
-            from lerobot.common.motors.feetech.feetech import FeetechMotorsBus
-
-            motors_buses[key] = FeetechMotorsBus(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 .configs import DynamixelMotorsBusConfig
-        from .dynamixel import DynamixelMotorsBus
-
-        config = DynamixelMotorsBusConfig(**kwargs)
-        return DynamixelMotorsBus(config)
-
-    elif motor_type == "feetech":
-        from feetech import FeetechMotorsBus
-
-        from .configs import FeetechMotorsBusConfig
-
-        config = FeetechMotorsBusConfig(**kwargs)
-        return FeetechMotorsBus(config)
-
-    else:
-        raise ValueError(f"The motor type '{motor_type}' is not valid.")

lerobot/common/policies/__init__.py

@@ -15,5 +15,6 @@
 from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .pi0.configuration_pi0 import PI0Config as PI0Config
+from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
 from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig
 from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig

lerobot/common/policies/factory.py

@@ -27,6 +27,7 @@ from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionC
 from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
 from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.configs.policies import PreTrainedConfig
@@ -59,6 +60,10 @@ def get_policy_class(name: str) -> PreTrainedPolicy:
         from lerobot.common.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
 
         return PI0FASTPolicy
+    elif name == "smolvla":
+        from lerobot.common.policies.smolvla.modeling_smolvla import SmolVLAPolicy
+
+        return SmolVLAPolicy
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -76,6 +81,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return PI0Config(**kwargs)
     elif policy_type == "pi0fast":
         return PI0FASTConfig(**kwargs)
+    elif policy_type == "smolvla":
+        return SmolVLAConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 

lerobot/common/policies/smolvla/configuration_smolvla.py

@@ -0,0 +1,154 @@
+# 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.optim.optimizers import AdamWConfig
+from lerobot.common.optim.schedulers import (
+    CosineDecayWithWarmupSchedulerConfig,
+)
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+
+
+@PreTrainedConfig.register_subclass("smolvla")
+@dataclass
+class SmolVLAConfig(PreTrainedConfig):
+    # Input / output structure.
+    n_obs_steps: int = 1
+    chunk_size: int = 50
+    n_action_steps: int = 50
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "ACTION": NormalizationMode.MEAN_STD,
+        }
+    )
+
+    # Shorter state and action vectors will be padded
+    max_state_dim: int = 32
+    max_action_dim: int = 32
+
+    # Image preprocessing
+    resize_imgs_with_padding: tuple[int, int] = (512, 512)
+
+    # Add empty images. Used by smolvla_aloha_sim which adds the empty
+    # left and right wrist cameras in addition to the top camera.
+    empty_cameras: int = 0
+
+    # Converts the joint and gripper values from the standard Aloha space to
+    # the space used by the pi internal runtime which was used to train the base model.
+    adapt_to_pi_aloha: bool = False
+
+    # Converts joint dimensions to deltas with respect to the current state before passing to the model.
+    # Gripper dimensions will remain in absolute values.
+    use_delta_joint_actions_aloha: bool = False
+
+    # Tokenizer
+    tokenizer_max_length: int = 48
+
+    # Decoding
+    num_steps: int = 10
+
+    # Attention utils
+    use_cache: bool = True
+
+    # Finetuning settings
+    freeze_vision_encoder: bool = True
+    train_expert_only: bool = True
+    train_state_proj: bool = True
+
+    # Training presets
+    optimizer_lr: float = 1e-4
+    optimizer_betas: tuple[float, float] = (0.9, 0.95)
+    optimizer_eps: float = 1e-8
+    optimizer_weight_decay: float = 1e-10
+    optimizer_grad_clip_norm: float = 10
+
+    scheduler_warmup_steps: int = 1_000
+    scheduler_decay_steps: int = 30_000
+    scheduler_decay_lr: float = 2.5e-6
+
+    vlm_model_name: str = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"  # Select the VLM backbone.
+    load_vlm_weights: bool = False  # Set to True in case of training the expert from scratch. True when init from pretrained SmolVLA weights
+
+    add_image_special_tokens: bool = False  # Whether to use special image tokens around image features.
+
+    attention_mode: str = "cross_attn"
+
+    prefix_length: int = -1
+
+    pad_language_to: str = "longest"  # "max_length"
+
+    num_expert_layers: int = -1  # Less or equal to 0 is the default where the action expert has the same number of layers of VLM. Otherwise the expert have less layers.
+    num_vlm_layers: int = 16  # Number of layers used in the VLM (first num_vlm_layers layers)
+    self_attn_every_n_layers: int = 2  # Interleave SA layers each self_attn_every_n_layers
+    expert_width_multiplier: float = 0.75  # The action expert hidden size (wrt to the VLM)
+
+    min_period: float = 4e-3  # sensitivity range for the timestep used in sine-cosine positional encoding
+    max_period: float = 4.0
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        """Input validation (not exhaustive)."""
+        if self.n_action_steps > self.chunk_size:
+            raise ValueError(
+                f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
+                f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
+            )
+        if self.use_delta_joint_actions_aloha:
+            raise NotImplementedError(
+                "`use_delta_joint_actions_aloha` is used by smolvla for aloha real models. It is not ported yet in LeRobot."
+            )
+
+    def validate_features(self) -> None:
+        for i in range(self.empty_cameras):
+            key = f"observation.images.empty_camera_{i}"
+            empty_camera = PolicyFeature(
+                type=FeatureType.VISUAL,
+                shape=(3, 480, 640),
+            )
+            self.input_features[key] = empty_camera
+
+    def get_optimizer_preset(self) -> AdamWConfig:
+        return AdamWConfig(
+            lr=self.optimizer_lr,
+            betas=self.optimizer_betas,
+            eps=self.optimizer_eps,
+            weight_decay=self.optimizer_weight_decay,
+            grad_clip_norm=self.optimizer_grad_clip_norm,
+        )
+
+    def get_scheduler_preset(self):
+        return CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=self.optimizer_lr,
+            decay_lr=self.scheduler_decay_lr,
+            num_warmup_steps=self.scheduler_warmup_steps,
+            num_decay_steps=self.scheduler_decay_steps,
+        )
+
+    @property
+    def observation_delta_indices(self) -> list:
+        return [0]
+
+    @property
+    def action_delta_indices(self) -> list:
+        return list(range(self.chunk_size))
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None

lerobot/common/policies/smolvla/modeling_smolvla.py

@@ -0,0 +1,801 @@
+#!/usr/bin/env python
+
+# Copyright 2025 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.
+
+"""
+SmolVLA:
+
+[Paper](https://huggingface.co/papers/2506.01844)
+
+Designed by Hugging Face.
+
+Install smolvla extra dependencies:
+```bash
+pip install -e ".[smolvla]"
+```
+
+Example of finetuning the smolvla pretrained model (`smolvla_base`):
+```bash
+python lerobot/scripts/train.py \
+--policy.path=lerobot/smolvla_base \
+--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
+--batch_size=64 \
+--steps=200000
+```
+
+Example of finetuning a smolVLA. SmolVLA is composed of a pretrained VLM,
+and an action expert.
+```bash
+python lerobot/scripts/train.py \
+--policy.type=smolvla \
+--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
+--batch_size=64 \
+--steps=200000
+```
+
+Example of using the smolvla pretrained model outside LeRobot training framework:
+```python
+policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
+```
+
+"""
+
+import math
+from collections import deque
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from torch import Tensor, nn
+from transformers import AutoProcessor
+
+from lerobot.common.constants import ACTION, OBS_ROBOT
+from lerobot.common.policies.normalize import (
+    Normalize,
+    Unnormalize,
+)
+from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.policies.smolvla.configuration_smolvla import SmolVLAConfig
+from lerobot.common.policies.smolvla.smolvlm_with_expert import SmolVLMWithExpertModel
+from lerobot.common.policies.utils import (
+    populate_queues,
+)
+from lerobot.common.utils.utils import get_safe_dtype
+
+
+def create_sinusoidal_pos_embedding(
+    time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu"
+) -> Tensor:
+    """Computes sine-cosine positional embedding vectors for scalar positions."""
+    if dimension % 2 != 0:
+        raise ValueError(f"dimension ({dimension}) must be divisible by 2")
+
+    if time.ndim != 1:
+        raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.")
+
+    dtype = get_safe_dtype(torch.float64, device.type)
+    fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device)
+    period = min_period * (max_period / min_period) ** fraction
+
+    # Compute the outer product
+    scaling_factor = 1.0 / period * 2 * math.pi
+    sin_input = scaling_factor[None, :] * time[:, None]
+    pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1)
+    return pos_emb
+
+
+def sample_beta(alpha, beta, bsize, device):
+    gamma1 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / alpha)
+    gamma2 = torch.empty((bsize,), device=device).uniform_(0, 1).pow(1 / beta)
+    return gamma1 / (gamma1 + gamma2)
+
+
+def make_att_2d_masks(pad_masks, att_masks):
+    """Copied from big_vision.
+
+    Tokens can attend to valid inputs tokens which have a cumulative mask_ar
+    smaller or equal to theirs. This way `mask_ar` int[B, N] can be used to
+    setup several types of attention, for example:
+
+      [[1 1 1 1 1 1]]: pure causal attention.
+
+      [[0 0 0 1 1 1]]: prefix-lm attention. The first 3 tokens can attend between
+          themselves and the last 3 tokens have a causal attention. The first
+          entry could also be a 1 without changing behaviour.
+
+      [[1 0 1 0 1 0 0 1 0 0]]: causal attention between 4 blocks. Tokens of a
+          block can attend all previous blocks and all tokens on the same block.
+
+    Args:
+      input_mask: bool[B, N] true if its part of the input, false if padding.
+      mask_ar: int32[B, N] mask that's 1 where previous tokens cannot depend on
+        it and 0 where it shares the same attention mask as the previous token.
+    """
+    if att_masks.ndim != 2:
+        raise ValueError(att_masks.ndim)
+    if pad_masks.ndim != 2:
+        raise ValueError(pad_masks.ndim)
+
+    cumsum = torch.cumsum(att_masks, dim=1)
+    att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None]
+    pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None]
+    att_2d_masks = att_2d_masks & pad_2d_masks
+    return att_2d_masks
+
+
+def resize_with_pad(img, width, height, pad_value=-1):
+    # assume no-op when width height fits already
+    if img.ndim != 4:
+        raise ValueError(f"(b,c,h,w) expected, but {img.shape}")
+
+    cur_height, cur_width = img.shape[2:]
+
+    ratio = max(cur_width / width, cur_height / height)
+    resized_height = int(cur_height / ratio)
+    resized_width = int(cur_width / ratio)
+    resized_img = F.interpolate(
+        img, size=(resized_height, resized_width), mode="bilinear", align_corners=False
+    )
+
+    pad_height = max(0, int(height - resized_height))
+    pad_width = max(0, int(width - resized_width))
+
+    # pad on left and top of image
+    padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value)
+    return padded_img
+
+
+def pad_vector(vector, new_dim):
+    """Can be (batch_size x sequence_length x features_dimension)
+    or (batch_size x features_dimension)
+    """
+    if vector.shape[-1] == new_dim:
+        return vector
+    shape = list(vector.shape)
+    current_dim = shape[-1]
+    shape[-1] = new_dim
+    new_vector = torch.zeros(*shape, dtype=vector.dtype, device=vector.device)
+    new_vector[..., :current_dim] = vector
+    return new_vector
+
+
+def normalize(x, min_val, max_val):
+    return (x - min_val) / (max_val - min_val)
+
+
+def unnormalize(x, min_val, max_val):
+    return x * (max_val - min_val) + min_val
+
+
+def safe_arcsin(value):
+    # This ensures that the input stays within
+    # [−1,1] to avoid invalid values for arcsin
+    return torch.arcsin(torch.clamp(value, -1.0, 1.0))
+
+
+def aloha_gripper_to_angular(value):
+    # Aloha transforms the gripper positions into a linear space. The following code
+    # reverses this transformation to be consistent with smolvla which is pretrained in
+    # angular space.
+    #
+    # These values are coming from the Aloha code:
+    # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED
+    value = unnormalize(value, min_val=0.01844, max_val=0.05800)
+
+    # This is the inverse of the angular to linear transformation inside the Interbotix code.
+    def linear_to_radian(linear_position, arm_length, horn_radius):
+        value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position)
+        return safe_arcsin(value)
+
+    # The constants are taken from the Interbotix code.
+    value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022)
+
+    # Normalize to [0, 1].
+    # The values 0.4 and 1.5 were measured on an actual Trossen robot.
+    return normalize(value, min_val=0.4, max_val=1.5)
+
+
+def aloha_gripper_from_angular(value):
+    # Convert from the gripper position used by smolvla to the gripper position that is used by Aloha.
+    # Note that the units are still angular but the range is different.
+
+    # The values 0.4 and 1.5 were measured on an actual Trossen robot.
+    value = unnormalize(value, min_val=0.4, max_val=1.5)
+
+    # These values are coming from the Aloha code:
+    # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE
+    return normalize(value, min_val=-0.6213, max_val=1.4910)
+
+
+def aloha_gripper_from_angular_inv(value):
+    # Directly inverts the gripper_from_angular function.
+    value = unnormalize(value, min_val=-0.6213, max_val=1.4910)
+    return normalize(value, min_val=0.4, max_val=1.5)
+
+
+class SmolVLAPolicy(PreTrainedPolicy):
+    """Wrapper class around VLAFlowMatching model to train and run inference within LeRobot."""
+
+    config_class = SmolVLAConfig
+    name = "smolvla"
+
+    def __init__(
+        self,
+        config: SmolVLAConfig,
+        dataset_stats: dict[str, dict[str, Tensor]] | None = None,
+    ):
+        """
+        Args:
+            config: Policy configuration class instance or None, in which case the default instantiation of
+                    the configuration class is used.
+            dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected
+                that they will be passed with a call to `load_state_dict` before the policy is used.
+        """
+
+        super().__init__(config)
+        config.validate_features()
+        self.config = config
+        self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats)
+        self.normalize_targets = Normalize(
+            config.output_features, config.normalization_mapping, dataset_stats
+        )
+        self.unnormalize_outputs = Unnormalize(
+            config.output_features, config.normalization_mapping, dataset_stats
+        )
+
+        self.language_tokenizer = AutoProcessor.from_pretrained(self.config.vlm_model_name).tokenizer
+        self.model = VLAFlowMatching(config)
+        self.reset()
+
+    def reset(self):
+        """This should be called whenever the environment is reset."""
+        self._queues = {
+            ACTION: deque(maxlen=self.config.n_action_steps),
+        }
+
+    def get_optim_params(self) -> dict:
+        return self.parameters()
+
+    @torch.no_grad
+    def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
+        """Select a single action given environment observations.
+
+        This method wraps `select_actions` in order to return one action at a time for execution in the
+        environment. It works by managing the actions in a queue and only calling `select_actions` when the
+        queue is empty.
+        """
+        self.eval()
+
+        if self.config.adapt_to_pi_aloha:
+            batch[OBS_ROBOT] = self._pi_aloha_decode_state(batch[OBS_ROBOT])
+
+        batch = self.normalize_inputs(batch)
+
+        self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION])
+        # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by
+        # querying the policy.
+        if len(self._queues[ACTION]) == 0:
+            for k in batch:
+                if k in self._queues:
+                    batch[k] = torch.stack(list(self._queues[k]), dim=1)
+            images, img_masks = self.prepare_images(batch)
+            state = self.prepare_state(batch)
+            lang_tokens, lang_masks = self.prepare_language(batch)
+
+            actions = self.model.sample_actions(
+                images, img_masks, lang_tokens, lang_masks, state, noise=noise
+            )
+            # Unpad actions
+            original_action_dim = self.config.action_feature.shape[0]
+            actions = actions[:, :, :original_action_dim]
+
+            actions = self.unnormalize_outputs({"action": actions})["action"]
+
+            if self.config.adapt_to_pi_aloha:
+                actions = self._pi_aloha_encode_actions(actions)
+
+            # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
+            # effectively has shape (n_action_steps, batch_size, *), hence the transpose.
+            self._queues[ACTION].extend(actions.transpose(0, 1)[: self.config.n_action_steps])
+        return self._queues[ACTION].popleft()
+
+    def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> 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[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION])
+        batch = self.normalize_inputs(batch)
+        batch = self.normalize_targets(batch)
+        images, img_masks = self.prepare_images(batch)
+        state = self.prepare_state(batch)
+        lang_tokens, lang_masks = self.prepare_language(batch)
+        actions = self.prepare_action(batch)
+        actions_is_pad = batch.get("actions_id_pad")
+        loss_dict = {}
+        losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time)
+        loss_dict["losses_after_forward"] = losses.clone()
+
+        if actions_is_pad is not None:
+            in_episode_bound = ~actions_is_pad
+            losses = losses * in_episode_bound.unsqueeze(-1)
+            loss_dict["losses_after_in_ep_bound"] = losses.clone()
+
+        # Remove padding
+        losses = losses[:, :, : self.config.max_action_dim]
+        loss_dict["losses_after_rm_padding"] = losses.clone()
+
+        # For backward pass
+        loss = losses.mean()
+        # For backward pass
+        loss_dict["loss"] = loss
+        return loss, loss_dict
+
+    def prepare_images(self, batch):
+        """Apply SmolVLA preprocessing to the images, like resizing to 224x224 and padding to keep aspect ratio, and
+        convert pixel range from [0.0, 1.0] to [-1.0, 1.0] as requested by SigLIP.
+        """
+        images = []
+        img_masks = []
+        present_img_keys = [key for key in self.config.image_features if key in batch]
+        missing_img_keys = [key for key in self.config.image_features if key not in batch]
+
+        if len(present_img_keys) == 0:
+            raise ValueError(
+                f"All image features are missing from the batch. At least one expected. (batch: {batch.keys()}) (image_features:{self.config.image_features})"
+            )
+        # Preprocess image features present in the batch
+        for key in present_img_keys:
+            img = batch[key][:, -1, :, :, :] if batch[key].ndim == 5 else batch[key]
+            if self.config.resize_imgs_with_padding is not None:
+                img = resize_with_pad(img, *self.config.resize_imgs_with_padding, pad_value=0)
+
+            # Normalize from range [0,1] to [-1,1] as expacted by siglip
+            img = img * 2.0 - 1.0
+
+            bsize = img.shape[0]
+            device = img.device
+            if f"{key}_padding_mask" in batch:
+                mask = batch[f"{key}_padding_mask"].bool()
+            else:
+                mask = torch.ones(bsize, dtype=torch.bool, device=device)
+            images.append(img)
+            img_masks.append(mask)
+
+        # Create image features not present in the batch
+        # as fully 0 padded images.
+        for num_empty_cameras in range(len(missing_img_keys)):
+            if num_empty_cameras >= self.config.empty_cameras:
+                break
+            img = torch.ones_like(img) * -1
+            mask = torch.zeros_like(mask)
+            images.append(img)
+            img_masks.append(mask)
+        return images, img_masks
+
+    def prepare_language(self, batch) -> tuple[Tensor, Tensor]:
+        """Tokenize the text input"""
+        device = batch[OBS_ROBOT].device
+        tasks = batch["task"]
+        if len(tasks) == 1:
+            tasks = [tasks[0] for _ in range(batch[OBS_ROBOT].shape[0])]
+
+        tasks = [task if task.endswith("\n") else f"{task}\n" for task in tasks]
+        tokenized_prompt = self.language_tokenizer.__call__(
+            tasks,
+            padding=self.config.pad_language_to,
+            padding_side="right",
+            max_length=self.config.tokenizer_max_length,
+            return_tensors="pt",
+        )
+        lang_tokens = tokenized_prompt["input_ids"].to(device=device)
+        lang_masks = tokenized_prompt["attention_mask"].to(device=device, dtype=torch.bool)
+
+        return lang_tokens, lang_masks
+
+    def _pi_aloha_decode_state(self, state):
+        # Flip the joints.
+        for motor_idx in [1, 2, 8, 9]:
+            state[:, motor_idx] *= -1
+        # Reverse the gripper transformation that is being applied by the Aloha runtime.
+        for motor_idx in [6, 13]:
+            state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx])
+        return state
+
+    def _pi_aloha_encode_actions(self, actions):
+        # Flip the joints.
+        for motor_idx in [1, 2, 8, 9]:
+            actions[:, :, motor_idx] *= -1
+        # Reverse the gripper transformation that is being applied by the Aloha runtime.
+        for motor_idx in [6, 13]:
+            actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx])
+        return actions
+
+    def _pi_aloha_encode_actions_inv(self, actions):
+        # Flip the joints again.
+        for motor_idx in [1, 2, 8, 9]:
+            actions[:, :, motor_idx] *= -1
+        # Reverse the gripper transformation that is being applied by the Aloha runtime.
+        for motor_idx in [6, 13]:
+            actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx])
+        return actions
+
+    def prepare_state(self, batch):
+        """Pad state"""
+        state = batch[OBS_ROBOT][:, -1, :] if batch[OBS_ROBOT].ndim > 2 else batch[OBS_ROBOT]
+        state = pad_vector(state, self.config.max_state_dim)
+        return state
+
+    def prepare_action(self, batch):
+        """Pad action"""
+        actions = pad_vector(batch[ACTION], self.config.max_action_dim)
+        return actions
+
+
+def pad_tensor(tensor, max_len, pad_value=0):
+    """
+    Efficiently pads a tensor along sequence dimension to match max_len.
+
+    Args:
+        tensor (torch.Tensor): Shape (B, L, ...) or (B, L).
+        max_len (int): Fixed sequence length.
+        pad_value (int/float): Value for padding.
+
+    Returns:
+        torch.Tensor: Shape (B, max_len, ...) or (B, max_len).
+    """
+    b, d = tensor.shape[:2]
+
+    # Create a padded tensor of max_len and copy the existing values
+    padded_tensor = torch.full(
+        (b, max_len, *tensor.shape[2:]), pad_value, dtype=tensor.dtype, device=tensor.device
+    )
+    padded_tensor[:, :d] = tensor  # Efficient in-place copy
+
+    return padded_tensor
+
+
+class VLAFlowMatching(nn.Module):
+    """
+    SmolVLA
+
+    [Paper]()
+
+    Designed by Hugging Face.
+    ┌──────────────────────────────┐
+    │                 actions      │
+    │                    ▲         │
+    │ ┌─────────┐      ┌─|────┐    │
+    │ |         │────► │      │    │
+    │ |         │ kv   │      │    │
+    │ |         │────► │Action│    │
+    │ |   VLM   │cache │Expert│    |
+    │ │         │────► |      │    │
+    │ │         │      │      │    │
+    │ └▲──▲───▲─┘      └───▲──┘    |
+    │  │  |   |            │       |
+    │  |  |   |          noise     │
+    │  │  │ state                  │
+    │  │ language tokens           │
+    │  image(s)                    │
+    └──────────────────────────────┘
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.vlm_with_expert = SmolVLMWithExpertModel(
+            model_id=self.config.vlm_model_name,
+            freeze_vision_encoder=self.config.freeze_vision_encoder,
+            train_expert_only=self.config.train_expert_only,
+            load_vlm_weights=self.config.load_vlm_weights,
+            attention_mode=self.config.attention_mode,
+            num_expert_layers=self.config.num_expert_layers,
+            num_vlm_layers=self.config.num_vlm_layers,
+            self_attn_every_n_layers=self.config.self_attn_every_n_layers,
+            expert_width_multiplier=self.config.expert_width_multiplier,
+        )
+        self.state_proj = nn.Linear(
+            self.config.max_state_dim, self.vlm_with_expert.config.text_config.hidden_size
+        )
+        self.action_in_proj = nn.Linear(self.config.max_action_dim, self.vlm_with_expert.expert_hidden_size)
+        self.action_out_proj = nn.Linear(self.vlm_with_expert.expert_hidden_size, self.config.max_action_dim)
+
+        self.action_time_mlp_in = nn.Linear(
+            self.vlm_with_expert.expert_hidden_size * 2, self.vlm_with_expert.expert_hidden_size
+        )
+        self.action_time_mlp_out = nn.Linear(
+            self.vlm_with_expert.expert_hidden_size, self.vlm_with_expert.expert_hidden_size
+        )
+
+        self.set_requires_grad()
+        self.fake_image_token = self.vlm_with_expert.processor.tokenizer.fake_image_token_id
+        self.global_image_token = self.vlm_with_expert.processor.tokenizer.global_image_token_id
+        self.global_image_start_token = torch.tensor(
+            [self.fake_image_token, self.global_image_token], dtype=torch.long
+        )
+
+        self.add_image_special_tokens = self.config.add_image_special_tokens
+        self.image_end_token = torch.tensor([self.fake_image_token], dtype=torch.long)
+        self.prefix_length = self.config.prefix_length
+
+    def set_requires_grad(self):
+        for params in self.state_proj.parameters():
+            params.requires_grad = self.config.train_state_proj
+
+    def sample_noise(self, shape, device):
+        noise = torch.normal(
+            mean=0.0,
+            std=1.0,
+            size=shape,
+            dtype=torch.float32,
+            device=device,
+        )
+        return noise
+
+    def sample_time(self, bsize, device):
+        time_beta = sample_beta(1.5, 1.0, bsize, device)
+        time = time_beta * 0.999 + 0.001
+        return time.to(dtype=torch.float32, device=device)
+
+    def embed_prefix(
+        self, images, img_masks, lang_tokens, lang_masks, state: torch.Tensor = None
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Embed images with SigLIP and language tokens with embedding layer to prepare
+        for SmolVLM transformer processing.
+        """
+        embs = []
+        pad_masks = []
+        att_masks = []
+        for _img_idx, (
+            img,
+            img_mask,
+        ) in enumerate(zip(images, img_masks, strict=False)):
+            if self.add_image_special_tokens:
+                image_start_token = (
+                    self.vlm_with_expert.embed_language_tokens(
+                        self.global_image_start_token.to(device=self.vlm_with_expert.vlm.device)
+                    )
+                    .unsqueeze(0)
+                    .expand(img.shape[0], -1, -1)
+                )
+                image_start_mask = torch.ones_like(
+                    image_start_token[:, :, 0], dtype=torch.bool, device=image_start_token.device
+                )
+                att_masks += [0] * (image_start_mask.shape[-1])
+                embs.append(image_start_token)
+                pad_masks.append(image_start_mask)
+
+            img_emb = self.vlm_with_expert.embed_image(img)
+            img_emb = img_emb
+
+            # Normalize image embeddings
+            img_emb_dim = img_emb.shape[-1]
+            img_emb = img_emb * torch.tensor(img_emb_dim**0.5, dtype=img_emb.dtype, device=img_emb.device)
+
+            bsize, num_img_embs = img_emb.shape[:2]
+            img_mask = img_mask[:, None].expand(bsize, num_img_embs)
+
+            embs.append(img_emb)
+            pad_masks.append(img_mask)
+
+            att_masks += [0] * (num_img_embs)
+            if self.add_image_special_tokens:
+                image_end_token = (
+                    self.vlm_with_expert.embed_language_tokens(
+                        self.image_end_token.to(device=self.vlm_with_expert.vlm.device)
+                    )
+                    .unsqueeze(0)
+                    .expand(img.shape[0], -1, -1)
+                )
+                image_end_mask = torch.ones_like(
+                    image_end_token[:, :, 0], dtype=torch.bool, device=image_end_token.device
+                )
+                embs.append(image_end_token)
+                pad_masks.append(image_end_mask)
+                att_masks += [0] * (image_end_mask.shape[1])
+        lang_emb = self.vlm_with_expert.embed_language_tokens(lang_tokens)
+        # Normalize language embeddings
+        lang_emb_dim = lang_emb.shape[-1]
+        lang_emb = lang_emb * math.sqrt(lang_emb_dim)
+
+        embs.append(lang_emb)
+        pad_masks.append(lang_masks)
+
+        num_lang_embs = lang_emb.shape[1]
+        att_masks += [0] * num_lang_embs
+
+        state_emb = self.state_proj(state)
+        state_emb = state_emb[:, None, :] if state_emb.ndim == 2 else state_emb
+        embs.append(state_emb)
+        bsize = state_emb.shape[0]
+        device = state_emb.device
+
+        states_seq_len = state_emb.shape[1]
+        state_mask = torch.ones(bsize, states_seq_len, dtype=torch.bool, device=device)
+        pad_masks.append(state_mask)
+
+        # Set attention masks so that image and language inputs do not attend to state or actions
+        att_masks += [1] * (states_seq_len)
+        embs = torch.cat(embs, dim=1)
+        pad_masks = torch.cat(pad_masks, dim=1)
+        att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device)
+        att_masks = att_masks[None, :]
+
+        seq_len = pad_masks.shape[1]
+        if seq_len < self.prefix_length:
+            embs = pad_tensor(embs, self.prefix_length, pad_value=0)
+            pad_masks = pad_tensor(pad_masks, self.prefix_length, pad_value=0)
+            att_masks = pad_tensor(att_masks, self.prefix_length, pad_value=0)
+
+        att_masks = att_masks.expand(bsize, -1)
+
+        return embs, pad_masks, att_masks
+
+    def embed_suffix(self, noisy_actions, timestep):
+        """Embed state, noisy_actions, timestep to prepare for Expert Gemma processing."""
+        embs = []
+        pad_masks = []
+        att_masks = []
+
+        # Fuse timestep + action information using an MLP
+        action_emb = self.action_in_proj(noisy_actions)
+        device = action_emb.device
+        bsize = action_emb.shape[0]
+        dtype = action_emb.dtype
+        # Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1]
+        time_emb = create_sinusoidal_pos_embedding(
+            timestep,
+            self.vlm_with_expert.expert_hidden_size,
+            self.config.min_period,
+            self.config.max_period,
+            device=device,
+        )
+        time_emb = time_emb.type(dtype=dtype)
+
+        time_emb = time_emb[:, None, :].expand_as(action_emb)
+        action_time_emb = torch.cat([action_emb, time_emb], dim=2)
+
+        action_time_emb = self.action_time_mlp_in(action_time_emb)
+        action_time_emb = F.silu(action_time_emb)  # swish == silu
+        action_time_emb = self.action_time_mlp_out(action_time_emb)
+
+        # Add to input tokens
+        embs.append(action_time_emb)
+
+        bsize, action_time_dim = action_time_emb.shape[:2]
+        action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=device)
+        pad_masks.append(action_time_mask)
+
+        # Set attention masks so that image, language and state inputs do not attend to action tokens
+        att_masks += [1] * self.config.chunk_size
+        embs = torch.cat(embs, dim=1)
+        pad_masks = torch.cat(pad_masks, dim=1)
+        att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device)
+        att_masks = att_masks[None, :].expand(bsize, len(att_masks))
+        return embs, pad_masks, att_masks
+
+    def forward(
+        self, images, img_masks, lang_tokens, lang_masks, state, actions, noise=None, time=None
+    ) -> Tensor:
+        """Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)"""
+        if noise is None:
+            noise = self.sample_noise(actions.shape, actions.device)
+
+        if time is None:
+            time = self.sample_time(actions.shape[0], actions.device)
+
+        time_expanded = time[:, None, None]
+        x_t = time_expanded * noise + (1 - time_expanded) * actions
+        u_t = noise - actions
+        prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
+            images, img_masks, lang_tokens, lang_masks, state=state
+        )
+        suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(x_t, time)
+
+        pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1)
+        att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1)
+
+        att_2d_masks = make_att_2d_masks(pad_masks, att_masks)
+        position_ids = torch.cumsum(pad_masks, dim=1) - 1
+        (_, suffix_out), _ = self.vlm_with_expert.forward(
+            attention_mask=att_2d_masks,
+            position_ids=position_ids,
+            past_key_values=None,
+            inputs_embeds=[prefix_embs, suffix_embs],
+            use_cache=False,
+            fill_kv_cache=False,
+        )
+        suffix_out = suffix_out[:, -self.config.chunk_size :]
+        # Original openpi code, upcast attention output
+        suffix_out = suffix_out.to(dtype=torch.float32)
+        v_t = self.action_out_proj(suffix_out)
+        losses = F.mse_loss(u_t, v_t, reduction="none")
+        return losses
+
+    def sample_actions(self, images, img_masks, lang_tokens, lang_masks, state, noise=None) -> Tensor:
+        """Do a full inference forward and compute the action (batch_size x num_steps x num_motors)"""
+        bsize = state.shape[0]
+        device = state.device
+
+        if noise is None:
+            actions_shape = (bsize, self.config.chunk_size, self.config.max_action_dim)
+            noise = self.sample_noise(actions_shape, device)
+
+        prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
+            images, img_masks, lang_tokens, lang_masks, state=state
+        )
+        prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
+        prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
+        # Compute image and language key value cache
+        _, past_key_values = self.vlm_with_expert.forward(
+            attention_mask=prefix_att_2d_masks,
+            position_ids=prefix_position_ids,
+            past_key_values=None,
+            inputs_embeds=[prefix_embs, None],
+            use_cache=self.config.use_cache,
+            fill_kv_cache=True,
+        )
+        dt = -1.0 / self.config.num_steps
+        dt = torch.tensor(dt, dtype=torch.float32, device=device)
+
+        x_t = noise
+        time = torch.tensor(1.0, dtype=torch.float32, device=device)
+        while time >= -dt / 2:
+            expanded_time = time.expand(bsize)
+            v_t = self.denoise_step(
+                prefix_pad_masks,
+                past_key_values,
+                x_t,
+                expanded_time,
+            )
+            # Euler step
+            x_t += dt * v_t
+            time += dt
+        return x_t
+
+    def denoise_step(
+        self,
+        prefix_pad_masks,
+        past_key_values,
+        x_t,
+        timestep,
+    ):
+        """Apply one denoising step of the noise `x_t` at a given timestep."""
+        suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(x_t, timestep)
+
+        suffix_len = suffix_pad_masks.shape[1]
+        batch_size = prefix_pad_masks.shape[0]
+        prefix_len = prefix_pad_masks.shape[1]
+        prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len)
+
+        suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks)
+
+        full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2)
+        prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
+        position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
+
+        outputs_embeds, _ = self.vlm_with_expert.forward(
+            attention_mask=full_att_2d_masks,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=[None, suffix_embs],
+            use_cache=self.config.use_cache,
+            fill_kv_cache=False,
+        )
+        suffix_out = outputs_embeds[1]
+        suffix_out = suffix_out[:, -self.config.chunk_size :]
+        suffix_out = suffix_out.to(dtype=torch.float32)
+        v_t = self.action_out_proj(suffix_out)
+        return v_t

lerobot/common/policies/smolvla/smolvlm_with_expert.py

@@ -0,0 +1,550 @@
+# 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 copy
+from typing import List, Optional
+
+import torch
+from torch import nn
+from transformers import (
+    AutoConfig,
+    AutoModel,
+    AutoModelForImageTextToText,
+    AutoProcessor,
+    SmolVLMForConditionalGeneration,
+)
+
+
+def apply_rope(x, positions, max_wavelength=10_000):
+    """
+    Applies RoPE positions [B, L] to x [B, L, H, D].
+    """
+    d_half = x.shape[-1] // 2
+    device = x.device
+    dtype = x.dtype
+    x = x.to(torch.float32)
+
+    freq_exponents = (2.0 / x.shape[-1]) * torch.arange(d_half, dtype=torch.float32, device=device)
+    timescale = max_wavelength**freq_exponents
+    radians = positions[..., None].to(torch.float32) / timescale[None, None, :].to(torch.float32)
+
+    radians = radians[..., None, :]
+
+    sin = torch.sin(radians)  # .to(dtype=dtype)
+    cos = torch.cos(radians)  # .to(dtype=dtype)
+
+    x1, x2 = x.split(d_half, dim=-1)
+    res = torch.empty_like(x)
+    res[..., :d_half] = x1 * cos - x2 * sin
+    res[..., d_half:] = x2 * cos + x1 * sin
+
+    return res.to(dtype)
+
+
+def get_intermediate_size(hidden_dim, ffn_dim_multiplier=4, multiple_of=256):
+    hidden_dim = int(2 * hidden_dim / 3)
+    hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+    hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+    return hidden_dim
+
+
+class SmolVLMWithExpertModel(nn.Module):
+    def __init__(
+        self,
+        model_id: str = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
+        load_vlm_weights: bool = True,
+        train_expert_only: bool = True,
+        freeze_vision_encoder: bool = False,
+        attention_mode: str = "self_attn",
+        num_expert_layers: int = -1,
+        num_vlm_layers: int = -1,
+        self_attn_every_n_layers: int = -1,
+        expert_width_multiplier: float = 0.5,
+    ):
+        super().__init__()
+        if load_vlm_weights:
+            print(f"Loading  {model_id} weights ...")
+            self.vlm = AutoModelForImageTextToText.from_pretrained(
+                model_id,
+                device_map="auto",
+                torch_dtype="bfloat16",
+                low_cpu_mem_usage=True,
+            )
+            config = self.vlm.config
+        else:
+            config = AutoConfig.from_pretrained(model_id)
+            self.vlm = SmolVLMForConditionalGeneration(config=config)
+        self.processor = AutoProcessor.from_pretrained(model_id)
+        if num_vlm_layers > 0:
+            print(f"Reducing the number of VLM layers to {num_vlm_layers} ...")
+            self.get_vlm_model().text_model.layers = self.get_vlm_model().text_model.layers[:num_vlm_layers]
+        self.num_vlm_layers = len(self.get_vlm_model().text_model.layers)
+        self.config = config
+        # Smaller lm expert
+        lm_expert_config = copy.deepcopy(config.text_config)
+        hidden_size = lm_expert_config.hidden_size
+        lm_expert_config.hidden_size = int(hidden_size * expert_width_multiplier)  # hidden_size // 2
+        lm_expert_config.intermediate_size = get_intermediate_size(int(hidden_size * expert_width_multiplier))
+        lm_expert_config.num_hidden_layers = self.num_vlm_layers
+        if num_expert_layers > 0:
+            assert len(self.get_vlm_model().text_model.layers) % num_expert_layers == 0, (
+                f"Number of layers in the VLM {len(self.get_vlm_model().text_model.layers)} are not multiple of num_expert_layers {num_expert_layers}"
+            )
+            lm_expert_config.num_hidden_layers = num_expert_layers
+        self.lm_expert = AutoModel.from_config(lm_expert_config)
+
+        self.num_expert_layers = len(self.lm_expert.layers)
+        self.self_attn_every_n_layers = self_attn_every_n_layers
+        if "cross" in attention_mode:
+            # Reshape qkv projections to have the same input dimension as the vlm
+            for layer_idx in range(len(self.lm_expert.layers)):
+                if self.self_attn_every_n_layers > 0 and layer_idx % self.self_attn_every_n_layers == 0:
+                    continue
+                self.lm_expert.layers[layer_idx].self_attn.k_proj = nn.Linear(
+                    config.text_config.num_key_value_heads * config.text_config.head_dim,
+                    lm_expert_config.num_key_value_heads * lm_expert_config.head_dim,
+                    bias=lm_expert_config.attention_bias,
+                )
+                self.lm_expert.layers[layer_idx].self_attn.v_proj = nn.Linear(
+                    config.text_config.num_key_value_heads * config.text_config.head_dim,
+                    lm_expert_config.num_key_value_heads * lm_expert_config.head_dim,
+                    bias=lm_expert_config.attention_bias,
+                )
+        # Remove unused embed_tokens
+        self.lm_expert.embed_tokens = None
+
+        self.num_attention_heads = self.config.text_config.num_attention_heads
+        self.num_key_value_heads = self.config.text_config.num_key_value_heads
+
+        self.freeze_vision_encoder = freeze_vision_encoder
+        self.train_expert_only = train_expert_only
+        self.attention_mode = attention_mode
+        self.expert_hidden_size = lm_expert_config.hidden_size
+        self.set_requires_grad()
+
+    def get_vlm_model(self):
+        return self.vlm.model
+
+    def set_requires_grad(self):
+        if self.freeze_vision_encoder:
+            self.get_vlm_model().vision_model.eval()
+            for params in self.get_vlm_model().vision_model.parameters():
+                params.requires_grad = False
+        if self.train_expert_only:
+            self.vlm.eval()
+            for params in self.vlm.parameters():
+                params.requires_grad = False
+        else:
+            # To avoid unused params issue with distributed training
+            last_layers = [self.num_vlm_layers - 1]
+            if (
+                self.num_vlm_layers != self.num_expert_layers
+                and self.num_vlm_layers % self.num_expert_layers == 0
+            ):
+                last_layers.append(self.num_vlm_layers - 2)
+            frozen_layers = [
+                "lm_head",
+                "text_model.model.norm.weight",
+            ]
+            for layer in last_layers:
+                frozen_layers.append(f"text_model.model.layers.{layer}.")
+
+            for name, params in self.vlm.named_parameters():
+                if any(k in name for k in frozen_layers):
+                    params.requires_grad = False
+        # To avoid unused params issue with distributed training
+        for name, params in self.lm_expert.named_parameters():
+            if "lm_head" in name:
+                params.requires_grad = False
+
+    def train(self, mode: bool = True):
+        super().train(mode)
+
+        if self.freeze_vision_encoder:
+            self.get_vlm_model().vision_model.eval()
+
+        if self.train_expert_only:
+            self.vlm.eval()
+
+    def embed_image(self, image: torch.Tensor):
+        patch_attention_mask = None
+        # Get sequence from the vision encoder
+        image_hidden_states = (
+            self.get_vlm_model()
+            .vision_model(
+                pixel_values=image.to(dtype=self.get_vlm_model().vision_model.dtype),
+                patch_attention_mask=patch_attention_mask,
+            )
+            .last_hidden_state
+        )
+        # Modality projection & resampling
+        image_hidden_states = self.get_vlm_model().connector(image_hidden_states)
+        return image_hidden_states
+
+    def embed_language_tokens(self, tokens: torch.Tensor):
+        return self.get_vlm_model().text_model.get_input_embeddings()(tokens)
+
+    def forward_attn_layer(
+        self,
+        model_layers,
+        inputs_embeds,
+        layer_idx,
+        position_ids,
+        attention_mask,
+        batch_size,
+        head_dim,
+        use_cache: bool = True,
+        fill_kv_cache: bool = True,
+        past_key_values=None,
+    ) -> list[torch.Tensor]:
+        query_states = []
+        key_states = []
+        value_states = []
+        for i, hidden_states in enumerate(inputs_embeds):
+            layer = model_layers[i][layer_idx]
+            if hidden_states is None or layer is None:
+                continue
+            hidden_states = layer.input_layernorm(hidden_states)
+
+            input_shape = hidden_states.shape[:-1]
+            hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
+
+            hidden_states = hidden_states.to(dtype=layer.self_attn.q_proj.weight.dtype)
+            query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape)
+            key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape)
+            value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape)
+
+            query_states.append(query_state)
+            key_states.append(key_state)
+            value_states.append(value_state)
+
+        # B,L,H,D with L sequence length, H number of heads, D head dim
+        # concatenate on the number of embeddings/tokens
+        query_states = torch.cat(query_states, dim=1)
+        key_states = torch.cat(key_states, dim=1)
+        value_states = torch.cat(value_states, dim=1)
+        seq_len = query_states.shape[1]
+        if seq_len < position_ids.shape[1]:
+            _position_ids = position_ids[:, :seq_len]
+            _attention_mask = attention_mask[:, :seq_len, :seq_len]
+        else:
+            _position_ids = position_ids
+            _attention_mask = attention_mask
+
+        attention_mask_ = _attention_mask
+        position_ids_ = _position_ids
+
+        query_states = apply_rope(query_states, position_ids_)
+        key_states = apply_rope(key_states, position_ids_)
+
+        if use_cache and past_key_values is None:
+            past_key_values = {}
+
+        if use_cache:
+            if fill_kv_cache:
+                past_key_values[layer_idx] = {
+                    "key_states": key_states,
+                    "value_states": value_states,
+                }
+            else:
+                # TODO here, some optimization can be done - similar to a `StaticCache` we can declare the `max_len` before.
+                # so we create an empty cache, with just one cuda malloc, and if (in autoregressive case) we reach
+                # the max len, then we (for instance) double the cache size. This implementation already exists
+                # in `transformers`. (molbap)
+                key_states = torch.cat([past_key_values[layer_idx]["key_states"], key_states], dim=1)
+                value_states = torch.cat([past_key_values[layer_idx]["value_states"], value_states], dim=1)
+
+        attention_interface = self.get_attention_interface()
+
+        att_output = attention_interface(
+            attention_mask_, batch_size, head_dim, query_states, key_states, value_states
+        )
+        return [att_output], past_key_values
+
+    def forward_cross_attn_layer(
+        self,
+        model_layers,
+        inputs_embeds,
+        layer_idx,
+        position_ids,
+        attention_mask,
+        batch_size,
+        head_dim,
+        use_cache: bool = True,
+        fill_kv_cache: bool = True,
+        past_key_values=None,
+    ) -> list[torch.Tensor]:
+        attention_interface = self.get_attention_interface()
+
+        att_outputs = []
+        assert len(inputs_embeds) == 2 or (use_cache and past_key_values is not None and not fill_kv_cache), (
+            f"Both len(inputs_embeds) == {len(inputs_embeds)} and past_key_values is {past_key_values}"
+        )
+
+        if len(inputs_embeds) == 2 and not past_key_values:
+            # Prefix attention
+            seq_len = inputs_embeds[0].shape[1]
+            position_id, expert_position_id = position_ids[:, :seq_len], position_ids[:, seq_len:]
+            prefix_attention_mask = attention_mask[:, :seq_len, :seq_len]
+
+            layer = model_layers[0][layer_idx]
+
+            hidden_states = layer.input_layernorm(inputs_embeds[0])
+
+            input_shape = hidden_states.shape[:-1]
+            hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
+
+            hidden_states = hidden_states.to(dtype=layer.self_attn.q_proj.weight.dtype)
+            query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape)
+            key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape)
+            value_states = layer.self_attn.v_proj(hidden_states).view(hidden_shape)
+
+            # B,L,H,D with L sequence length, H number of heads, D head dim
+            query_states = apply_rope(query_state, position_id)
+            key_states = apply_rope(key_state, position_id)
+
+            att_output = attention_interface(
+                prefix_attention_mask, batch_size, head_dim, query_states, key_states, value_states
+            )
+            att_outputs.append(att_output)
+        else:
+            expert_position_id = position_ids
+
+        if use_cache and past_key_values is None:
+            past_key_values = {}
+
+        if use_cache:
+            if fill_kv_cache:
+                past_key_values[layer_idx] = {
+                    "key_states": key_states,
+                    "value_states": value_states,
+                }
+            else:
+                # TODO here, some optimization can be done - similar to a `StaticCache` we can declare the `max_len` before.
+                # so we create an empty cache, with just one cuda malloc, and if (in autoregressive case) we reach
+                # the max len, then we (for instance) double the cache size. This implementation already exists
+                # in `transformers`. (molbap)
+                key_states = past_key_values[layer_idx]["key_states"]
+                value_states = past_key_values[layer_idx]["value_states"]
+
+        # Expert
+        expert_layer = model_layers[1][layer_idx]
+        if expert_layer is not None:
+            expert_hidden_states = expert_layer.input_layernorm(inputs_embeds[1])
+
+            expert_input_shape = expert_hidden_states.shape[:-1]
+            expert_hidden_shape = (*expert_input_shape, -1, expert_layer.self_attn.head_dim)
+
+            expert_hidden_states = expert_hidden_states.to(dtype=expert_layer.self_attn.q_proj.weight.dtype)
+            expert_query_state = expert_layer.self_attn.q_proj(expert_hidden_states).view(expert_hidden_shape)
+
+            _key_states = key_states.to(dtype=expert_layer.self_attn.k_proj.weight.dtype).view(
+                *key_states.shape[:2], -1
+            )
+            expert_key_states = expert_layer.self_attn.k_proj(_key_states).view(
+                *_key_states.shape[:-1], -1, expert_layer.self_attn.head_dim
+            )  # k_proj should have same dim as kv
+
+            _value_states = value_states.to(dtype=expert_layer.self_attn.v_proj.weight.dtype).view(
+                *value_states.shape[:2], -1
+            )
+            expert_value_states = expert_layer.self_attn.v_proj(_value_states).view(
+                *_value_states.shape[:-1], -1, expert_layer.self_attn.head_dim
+            )
+
+            expert_position_id = (
+                expert_position_id - torch.min(expert_position_id, dim=1, keepdim=True).values
+            )  # start from 0
+            expert_attention_mask = attention_mask[
+                :, -inputs_embeds[1].shape[1] :, : expert_key_states.shape[1] :
+            ]  # take into account kv
+
+            expert_query_states = apply_rope(expert_query_state, expert_position_id)
+
+            att_output = attention_interface(
+                expert_attention_mask,
+                batch_size,
+                head_dim,
+                expert_query_states,
+                expert_key_states,
+                expert_value_states,
+            )
+            att_outputs.append(att_output)
+        else:
+            att_outputs.append(None)
+
+        # att_output = att_output.to(dtype=models[i].dtype)
+        return att_outputs, past_key_values
+
+    def get_model_layers(self, models: list) -> list:
+        vlm_layers = []
+        expert_layers = []
+        multiple_of = self.num_vlm_layers // self.num_expert_layers
+        for i in range(self.num_vlm_layers):
+            if multiple_of > 0 and i > 0 and i % multiple_of != 0:
+                expert_layer = None
+            else:
+                expert_layer_index = i // multiple_of if multiple_of > 0 else i
+                expert_layer = models[1].layers[expert_layer_index]
+            vlm_layers.append(models[0].layers[i])
+            expert_layers.append(expert_layer)
+        return [vlm_layers, expert_layers]
+
+    def forward(
+        self,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: List[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        fill_kv_cache: Optional[bool] = None,
+    ):
+        models = [self.get_vlm_model().text_model, self.lm_expert]
+        model_layers = self.get_model_layers(models)
+        for hidden_states in inputs_embeds:
+            # TODO this is very inefficient
+            # dtype is always the same, batch size too (if > 1 len)
+            # device could be trickier in multi gpu edge cases but that's it
+            if hidden_states is None:
+                continue
+            batch_size = hidden_states.shape[0]
+
+        # RMSNorm
+        num_layers = self.num_vlm_layers
+        head_dim = self.vlm.config.text_config.head_dim
+        for layer_idx in range(num_layers):
+            if (
+                fill_kv_cache
+                or "cross" not in self.attention_mode
+                or (self.self_attn_every_n_layers > 0 and layer_idx % self.self_attn_every_n_layers == 0)
+            ):
+                att_outputs, past_key_values = self.forward_attn_layer(
+                    model_layers,
+                    inputs_embeds,
+                    layer_idx,
+                    position_ids,
+                    attention_mask,
+                    batch_size,
+                    head_dim,
+                    use_cache=use_cache,
+                    fill_kv_cache=fill_kv_cache,
+                    past_key_values=past_key_values,
+                )
+            else:
+                att_outputs, past_key_values = self.forward_cross_attn_layer(
+                    model_layers,
+                    inputs_embeds,
+                    layer_idx,
+                    position_ids,
+                    attention_mask,
+                    batch_size,
+                    head_dim,
+                    use_cache=use_cache,
+                    fill_kv_cache=fill_kv_cache,
+                    past_key_values=past_key_values,
+                )
+            outputs_embeds = []
+            start = 0
+            for i, hidden_states in enumerate(inputs_embeds):
+                layer = model_layers[i][layer_idx]
+                att_output = (
+                    att_outputs[i] if i < len(att_outputs) else att_outputs[0]
+                )  # in case of self_attn
+                if hidden_states is not None:
+                    if layer is None:
+                        outputs_embeds.append(hidden_states)
+                        continue
+                    end = start + hidden_states.shape[1]
+
+                    if att_output.dtype != layer.self_attn.o_proj.weight.dtype:
+                        att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
+                    att_out = att_output[:, start:end]
+                    out_emb = layer.self_attn.o_proj(att_out)
+
+                    out_emb += hidden_states
+                    after_first_residual = out_emb.clone()
+
+                    out_emb = layer.post_attention_layernorm(out_emb)
+                    out_emb = layer.mlp(out_emb)
+
+                    out_emb += after_first_residual
+
+                    outputs_embeds.append(out_emb)
+
+                    start = end if len(att_outputs) == 1 else 0
+                else:
+                    outputs_embeds.append(None)
+
+            inputs_embeds = outputs_embeds
+
+        # final norm
+        outputs_embeds = []
+        for i, hidden_states in enumerate(inputs_embeds):
+            if hidden_states is not None:
+                out_emb = models[i].norm(hidden_states)
+                outputs_embeds.append(out_emb)
+            else:
+                outputs_embeds.append(None)
+        return outputs_embeds, past_key_values
+
+    def get_attention_interface(self):
+        attention_interface = self.eager_attention_forward
+        return attention_interface
+
+    def eager_attention_forward(
+        self, attention_mask, batch_size, head_dim, query_states, key_states, value_states
+    ):
+        num_att_heads = self.num_attention_heads
+        num_key_value_heads = self.num_key_value_heads
+        num_key_value_groups = num_att_heads // num_key_value_heads
+
+        sequence_length = key_states.shape[1]
+
+        key_states = key_states[:, :, :, None, :].expand(
+            batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim
+        )
+        key_states = key_states.reshape(
+            batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim
+        )
+
+        value_states = value_states[:, :, :, None, :].expand(
+            batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim
+        )
+        value_states = value_states.reshape(
+            batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim
+        )
+
+        # Attention here is upcasted to float32 to match the original eager implementation.
+        query_states = query_states.to(dtype=torch.float32)
+        key_states = key_states.to(dtype=torch.float32)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+
+        att_weights = torch.matmul(query_states, key_states.transpose(2, 3))
+        att_weights *= head_dim**-0.5
+
+        att_weights = att_weights.to(dtype=torch.float32)
+        big_neg = torch.finfo(att_weights.dtype).min  # -2.3819763e38  # See gemma/modules.py
+        masked_att_weights = torch.where(attention_mask[:, None, :, :], att_weights, big_neg)
+        probs = nn.functional.softmax(masked_att_weights, dim=-1)
+        probs = probs.to(dtype=value_states.dtype)
+
+        att_output = torch.matmul(probs, value_states.permute(0, 2, 1, 3))
+
+        att_output = att_output.permute(0, 2, 1, 3)
+        # we use -1 because sequence length can change
+        att_output = att_output.reshape(batch_size, -1, num_key_value_heads * num_key_value_groups * head_dim)
+
+        return att_output

lerobot/common/robots/lekiwi/config_lekiwi.py

@@ -36,7 +36,7 @@ class LeKiwiConfig(RobotConfig):
         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_90
+                index_or_path="/dev/video2", fps=30, width=640, height=480, rotation=Cv2Rotation.ROTATE_180
             ),
         }
     )

lerobot/common/robots/lekiwi/lekiwi.mdx

@@ -88,33 +88,12 @@ The calibration process is very important because it allows a neural network tra
 ### 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:
-<hfoptions id="calibrate_follower">
-<hfoption id="Command">
 
 ```bash
 python -m lerobot.calibrate \
     --robot.type=lekiwi \
-    --robot.port=/dev/ttyACM0 \ # <- The port of your robot
     --robot.id=my_awesome_kiwi # <- Give the robot a unique name
 ```
-</hfoption>
-<hfoption id="API example">
-
-```python
-from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-
-config = LeKiwiClientConfig(
-    remote_ip="192.168.0.23",
-    id="my_awesome_kiwi",
-)
-
-lekiwi = LeKiwiClient(config)
-lekiwi.connect(calibrate=False)
-lekiwi.calibrate()
-lekiwi.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).
 
@@ -161,60 +140,11 @@ To teleoperate, SSH into your Raspberry Pi, and run `conda activate lerobot` and
 python -m lerobot.common.robots.lekiwi.lekiwi_host
 ```
 
-Then on your laptop, also run `conda activate lerobot` and this command or API example:
-
-<hfoptions id="teleoperate_koch_camera">
-<hfoption id="Command">
+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 -m lerobot.teleoperate \
-    --robot.type=lekiwi \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.cameras="{}" \
-    --robot.id=my_lekiwi \
-    --teleop.type=so101_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_blue_leader_arm
+python examples/lekiwi/teleoperate.py
 ```
-</hfoption>
-<hfoption id="API example">
-
-```python
-from lerobot.common.teleoperators.keyboard.teleop_keyboard import KeyboardTeleopConfig, KeyboardTeleop
-from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
-from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-
-robot_config = LeKiwiClientConfig(
-    remote_ip="172.18.133.90",
-    id="my_red_lekiwi"
-)
-
-teleop__arm_config = SO100LeaderConfig(
-    port="/dev/tty.usbmodem58760431551",
-    id="my_blue_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()
-    action_arm = teleop_arm.get_action()
-    action_base = telep_keyboard.get_action()
-    robot.send_action(action_arm | action_base)
-```
-</hfoption>
-</hfoptions>
-
-> **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:
 
@@ -242,7 +172,69 @@ You should see on your laptop something like this: ```[INFO] Connected to remote
 ### Wired version
 If you have the **wired** LeKiwi version, please run all commands on your laptop.
 
-Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial (you can skip the teleoperation part): [Getting started with real-world robots](./getting_started_real_world_robot)
+## 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).

lerobot/common/robots/lekiwi/lekiwi_client.py

@@ -12,6 +12,8 @@
 # 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
@@ -321,23 +323,11 @@ class LeKiwiClient(Robot):
                 "ManipulatorRobot is not connected. You need to run `robot.connect()`."
             )
 
-        common_keys = [
-            key
-            for key in action
-            if key in (motor.replace("arm_", "") for motor, _ in self.action_features.items())
-        ]
-
-        arm_actions = {"arm_" + arm_motor: action[arm_motor] for arm_motor in common_keys}
-
-        keyboard_keys = np.array(list(set(action.keys()) - set(common_keys)))
-        base_actions = self._from_keyboard_to_base_action(keyboard_keys)
-        goal_pos = {**arm_actions, **base_actions}
-
-        self.zmq_cmd_socket.send_string(json.dumps(goal_pos))  # action is in motor space
+        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([goal_pos.get(k, 0.0) for k in self._state_order], dtype=np.float32)
-        return {"action.state": actions}
+        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"""

lerobot/common/robots/utils.py

@@ -34,9 +34,9 @@ def make_robot_from_config(config: RobotConfig) -> Robot:
 
         return SO101Follower(config)
     elif config.type == "lekiwi":
-        from .lekiwi import LeKiwiClient
+        from .lekiwi import LeKiwi
 
-        return LeKiwiClient(config)
+        return LeKiwi(config)
     elif config.type == "stretch3":
         from .stretch3 import Stretch3Robot
 
@@ -89,11 +89,3 @@ def ensure_safe_goal_position(
         )
 
     return safe_goal_positions
-
-
-# TODO(aliberts): Remove
-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}"

lerobot/common/utils/control_utils.py

@@ -98,7 +98,12 @@ def is_headless():
 
 
 def predict_action(
-    observation: dict[str, np.ndarray], policy: PreTrainedPolicy, device: torch.device, use_amp: bool
+    observation: dict[str, np.ndarray],
+    policy: PreTrainedPolicy,
+    device: torch.device,
+    use_amp: bool,
+    task: str | None = None,
+    robot_type: str | None = None,
 ):
     observation = copy(observation)
     with (
@@ -114,6 +119,9 @@ def predict_action(
             observation[name] = observation[name].unsqueeze(0)
             observation[name] = observation[name].to(device)
 
+        observation["task"] = task if task else ""
+        observation["robot_type"] = robot_type if robot_type else ""
+
         # Compute the next action with the policy
         # based on the current observation
         action = policy.select_action(observation)

lerobot/find_port.py

@@ -22,7 +22,7 @@ python -m lerobot.find_port
 ```
 """
 
-import os
+import platform
 import time
 from pathlib import Path
 
@@ -30,7 +30,7 @@ from pathlib import Path
 def find_available_ports():
     from serial.tools import list_ports  # Part of pyserial library
 
-    if os.name == "nt":  # Windows
+    if platform.system() == "Windows":
         # List COM ports using pyserial
         ports = [port.device for port in list_ports.comports()]
     else:  # Linux/macOS

lerobot/record.py

@@ -92,7 +92,7 @@ class DatasetRecordConfig:
     single_task: str
     # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | Path | None = None
-    # Limit the frames per second. By default, uses the policy fps.
+    # Limit the frames per second.
     fps: int = 30
     # Number of seconds for data recording for each episode.
     episode_time_s: int | float = 60
@@ -159,15 +159,15 @@ class RecordConfig:
 def record_loop(
     robot: Robot,
     events: dict,
+    fps: int,
     dataset: LeRobotDataset | None = None,
     teleop: Teleoperator | None = None,
     policy: PreTrainedPolicy | None = None,
     control_time_s: int | None = None,
-    fps: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
 ):
-    if dataset is not None and fps is not None and dataset.fps != fps:
+    if dataset is not None and dataset.fps != fps:
         raise ValueError(f"The dataset fps should be equal to requested fps ({dataset.fps} != {fps}).")
 
     # if policy is given it needs cleaning up
@@ -186,7 +186,12 @@ def record_loop(
 
         if policy is not None:
             action_values = predict_action(
-                observation_frame, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
+                observation_frame,
+                policy,
+                get_safe_torch_device(policy.config.device),
+                policy.config.use_amp,
+                task=single_task,
+                robot_type=robot.robot_type,
             )
             action = {key: action_values[i] for i, key in enumerate(robot.action_features)}
         else:
@@ -211,12 +216,8 @@ def record_loop(
                 if isinstance(val, float):
                     rr.log(f"action.{act}", rr.Scalar(val))
 
-        if fps is not None:
-            dt_s = time.perf_counter() - start_loop_t
-            busy_wait(1 / fps - dt_s)
-
         dt_s = time.perf_counter() - start_loop_t
-        # log_control_info(robot, dt_s, fps=fps)
+        busy_wait(1 / fps - dt_s)
 
         timestamp = time.perf_counter() - start_episode_t
         if events["exit_early"]:
@@ -243,10 +244,6 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
             cfg.dataset.repo_id,
             root=cfg.dataset.root,
         )
-        # for key, ft in dataset_features.items():
-        #     for property in ["dtype", "shape", "names"]:
-        #         if ft[property] != dataset.features[key][property]:
-        #             raise ValueError(ft)
 
         if hasattr(robot, "cameras") and len(robot.cameras) > 0:
             dataset.start_image_writer(
@@ -277,32 +274,16 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
 
     listener, events = init_keyboard_listener()
 
-    # Execute a few seconds without recording to:
-    # 1. teleoperate the robot to move it in starting position if no policy provided,
-    # 2. give times to the robot devices to connect and start synchronizing,
-    # 3. place the cameras windows on screen
-    # enable_teleoperation = policy is None
-    # log_say("Warmup record", cfg.play_sounds)
-    # record_loop(
-    #     robot=robot,
-    #     control_time_s=cfg.warmup_time_s,
-    #     display_data=cfg.display_data,
-    #     events=events,
-    #     fps=cfg.dataset.fps,
-    #     teleoperate=enable_teleoperation,
-    # )
-    # warmup_record(robot, events, enable_teleoperation, cfg.warmup_time_s, cfg.display_data, cfg.dataset.fps)
-
     for recorded_episodes in range(cfg.dataset.num_episodes):
         log_say(f"Recording episode {dataset.num_episodes}", cfg.play_sounds)
         record_loop(
             robot=robot,
             events=events,
+            fps=cfg.dataset.fps,
             teleop=teleop,
             policy=policy,
             dataset=dataset,
             control_time_s=cfg.dataset.episode_time_s,
-            fps=cfg.dataset.fps,
             single_task=cfg.dataset.single_task,
             display_data=cfg.display_data,
         )
@@ -316,9 +297,9 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
             record_loop(
                 robot=robot,
                 events=events,
+                fps=cfg.dataset.fps,
                 teleop=teleop,
                 control_time_s=cfg.dataset.reset_time_s,
-                fps=cfg.dataset.fps,
                 single_task=cfg.dataset.single_task,
                 display_data=cfg.display_data,
             )

lerobot/teleoperate.py

@@ -54,6 +54,7 @@ from lerobot.common.teleoperators import (
     TeleoperatorConfig,
     make_teleoperator_from_config,
 )
+from lerobot.common.utils.robot_utils import busy_wait
 from lerobot.common.utils.utils import init_logging, move_cursor_up
 from lerobot.common.utils.visualization_utils import _init_rerun
 
@@ -64,15 +65,15 @@ from .common.teleoperators import koch_leader, so100_leader, so101_leader  # noq
 class TeleoperateConfig:
     teleop: TeleoperatorConfig
     robot: RobotConfig
-    # Limit the maximum frames per second. By default, no limit.
-    fps: int | None = None
+    # Limit the maximum frames per second.
+    fps: int = 60
     teleop_time_s: float | None = None
     # Display all cameras on screen
     display_data: bool = False
 
 
 def teleop_loop(
-    teleop: Teleoperator, robot: Robot, display_data: bool = False, duration: float | None = None
+    teleop: Teleoperator, robot: Robot, fps: int, display_data: bool = False, duration: float | None = None
 ):
     display_len = max(len(key) for key in robot.action_features)
     start = time.perf_counter()
@@ -91,6 +92,9 @@ def teleop_loop(
                     rr.log(f"action_{act}", rr.Scalar(val))
 
         robot.send_action(action)
+        dt_s = time.perf_counter() - loop_start
+        busy_wait(1 / fps - dt_s)
+
         loop_s = time.perf_counter() - loop_start
 
         print("\n" + "-" * (display_len + 10))
@@ -119,7 +123,7 @@ def teleoperate(cfg: TeleoperateConfig):
     robot.connect()
 
     try:
-        teleop_loop(teleop, robot, display_data=cfg.display_data, duration=cfg.teleop_time_s)
+        teleop_loop(teleop, robot, cfg.fps, display_data=cfg.display_data, duration=cfg.teleop_time_s)
     except KeyboardInterrupt:
         pass
     finally:

pyproject.toml

@@ -89,6 +89,7 @@ intelrealsense = [
     "pyrealsense2-macosx>=2.54 ; sys_platform == 'darwin'",
 ]
 pi0 = ["transformers>=4.48.0"]
+smolvla = ["transformers>=4.50.3", "num2words>=0.5.14", "accelerate>=1.7.0"]
 pusht = ["gym-pusht>=0.1.5 ; python_version < '4.0'"]
 stretch = [
     "hello-robot-stretch-body>=0.7.27 ; python_version < '4.0' and sys_platform == 'linux'",

tests/cameras/test_opencv.py

@@ -141,9 +141,7 @@ def test_async_read_timeout():
 
     try:
         with pytest.raises(TimeoutError):
-            camera.async_read(
-                timeout_ms=0
-            )  # NOTE(Steven): This is flaky as sdometimes we actually get a frame
+            camera.async_read(timeout_ms=0)
     finally:
         if camera.is_connected:
             camera.disconnect()

tests/cameras/test_realsense.py

@@ -162,9 +162,7 @@ def test_async_read_timeout():
 
     try:
         with pytest.raises(TimeoutError):
-            camera.async_read(
-                timeout_ms=0
-            )  # NOTE(Steven): This is flaky as sdometimes we actually get a frame
+            camera.async_read(timeout_ms=0)
     finally:
         if camera.is_connected:
             camera.disconnect()

tests/conftest.py

@@ -19,8 +19,7 @@ import traceback
 import pytest
 from serial import SerialException
 
-from lerobot import available_cameras
-from tests.utils import DEVICE, make_camera
+from tests.utils import DEVICE
 
 # Import fixture modules as plugins
 pytest_plugins = [
@@ -63,11 +62,6 @@ def _check_component_availability(component_type, available_components, make_com
         return False
 
 
-@pytest.fixture
-def is_camera_available(camera_type):
-    return _check_component_availability(camera_type, available_cameras, make_camera)
-
-
 @pytest.fixture
 def patch_builtins_input(monkeypatch):
     def print_text(text=None):

tests/mocks/mock_dynamixel.py

@@ -48,7 +48,7 @@ DXL_CRC_TABLE = [
 
 class MockDynamixelPacketv2(abc.ABC):
     @classmethod
-    def build(cls, dxl_id: int, params: list[int], length: list[int], *args, **kwargs) -> bytes:
+    def build(cls, dxl_id: int, params: list[int], length: int, *args, **kwargs) -> bytes:
         packet = cls._build(dxl_id, params, length, *args, **kwargs)
         packet = cls._add_stuffing(packet)
         packet = cls._add_crc(packet)
@@ -281,7 +281,7 @@ class MockInstructionPacket(MockDynamixelPacketv2):
     @classmethod
     def sync_write(
         cls,
-        ids_values: dict[int],
+        ids_values: dict[int, int],
         start_address: int,
         data_length: int,
     ) -> bytes:

tests/mocks/mock_feetech.py

@@ -151,7 +151,7 @@ class MockInstructionPacket(MockFeetechPacket):
     @classmethod
     def sync_write(
         cls,
-        ids_values: dict[int],
+        ids_values: dict[int, int],
         start_address: int,
         data_length: int,
     ) -> bytes:
@@ -219,7 +219,7 @@ class MockStatusPacket(MockFeetechPacket):
 
         Args:
             scs_id (int): ID of the servo responding.
-            error (str, optional): Error to be returned. Defaults to "Success".
+            error (int, optional): Error to be returned. Defaults to 0 (success).
 
         Returns:
             bytes: The raw 'Ping' status packet ready to be sent through serial.

tests/test_available.py

@@ -45,12 +45,7 @@ def test_available_policies():
     This test verifies that the class attribute `name` for all policies is
     consistent with those listed in `lerobot/__init__.py`.
     """
-    policy_classes = [
-        ACTPolicy,
-        DiffusionPolicy,
-        TDMPCPolicy,
-        VQBeTPolicy,
-    ]
+    policy_classes = [ACTPolicy, DiffusionPolicy, TDMPCPolicy, VQBeTPolicy]
     policies = [pol_cls.name for pol_cls in policy_classes]
     assert set(policies) == set(lerobot.available_policies), policies
 

tests/utils.py

@@ -21,9 +21,6 @@ import pytest
 import torch
 
 from lerobot import available_cameras, available_motors, available_robots
-from lerobot.common.cameras import Camera
-from lerobot.common.motors.motors_bus import MotorsBus
-from lerobot.common.motors.utils import make_motors_bus as make_motors_bus_device
 from lerobot.common.utils.import_utils import is_package_available
 
 DEVICE = os.environ.get("LEROBOT_TEST_DEVICE", "cuda") if torch.cuda.is_available() else "cpu"
@@ -185,63 +182,3 @@ def require_package(package_name):
         return wrapper
 
     return decorator
-
-
-def require_camera(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        # Access the pytest request context to get the is_camera_available fixture
-        request = kwargs.get("request")
-        camera_type = kwargs.get("camera_type")
-        mock = kwargs.get("mock")
-
-        if request is None:
-            raise ValueError("The 'request' fixture must be an argument of the test function.")
-        if camera_type is None:
-            raise ValueError("The 'camera_type' must be an argument of the test function.")
-        if mock is None:
-            raise ValueError("The 'mock' variable must be an argument of the test function.")
-
-        if not mock and not request.getfixturevalue("is_camera_available"):
-            pytest.skip(f"A {camera_type} camera is not available.")
-
-        return func(*args, **kwargs)
-
-    return wrapper
-
-
-# TODO(rcadene, aliberts): remove this dark pattern that overrides
-def make_camera(camera_type: str, **kwargs) -> Camera:
-    if camera_type == "opencv":
-        camera_index = kwargs.pop("camera_index", OPENCV_CAMERA_INDEX)
-        kwargs["camera_index"] = camera_index
-        from lerobot.common.cameras.opencv import OpenCVCamera, OpenCVCameraConfig
-
-        config = OpenCVCameraConfig(**kwargs)
-        return OpenCVCamera(config)
-
-    elif camera_type == "intelrealsense":
-        serial_number = kwargs.pop("serial_number", INTELREALSENSE_SERIAL_NUMBER)
-        kwargs["serial_number"] = serial_number
-        from lerobot.common.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
-
-        config = RealSenseCameraConfig(**kwargs)
-        return RealSenseCamera(config)
-    else:
-        raise ValueError(f"The camera type '{camera_type}' is not valid.")
-
-
-# TODO(rcadene, aliberts): remove this dark pattern that overrides
-def make_motors_bus(motor_type: str, **kwargs) -> MotorsBus:
-    if motor_type == "dynamixel":
-        port = kwargs.pop("port", DYNAMIXEL_PORT)
-        motors = kwargs.pop("motors", DYNAMIXEL_MOTORS)
-        return make_motors_bus_device(motor_type, port=port, motors=motors, **kwargs)
-
-    elif motor_type == "feetech":
-        port = kwargs.pop("port", FEETECH_PORT)
-        motors = kwargs.pop("motors", FEETECH_MOTORS)
-        return make_motors_bus_device(motor_type, port=port, motors=motors, **kwargs)
-
-    else:
-        raise ValueError(f"The motor type '{motor_type}' is not valid.")