added tdmpc2 to policy factory; shape fixes in tdmpc2

entire policy

.cache/calibration/aloha_default/left_follower.json

@@ -0,0 +1,68 @@
+{
+    "homing_offset": [
+        2048,
+        3072,
+        3072,
+        -1024,
+        -1024,
+        2048,
+        -2048,
+        2048,
+        -2048
+    ],
+    "drive_mode": [
+        1,
+        1,
+        1,
+        0,
+        0,
+        1,
+        0,
+        1,
+        0
+    ],
+    "start_pos": [
+        2015,
+        3058,
+        3061,
+        1071,
+        1071,
+        2035,
+        2152,
+        2029,
+        2499
+    ],
+    "end_pos": [
+        -1008,
+        -1963,
+        -1966,
+        2141,
+        2143,
+        -971,
+        3043,
+        -1077,
+        3144
+    ],
+    "calib_mode": [
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "LINEAR"
+    ],
+    "motor_names": [
+        "waist",
+        "shoulder",
+        "shoulder_shadow",
+        "elbow",
+        "elbow_shadow",
+        "forearm_roll",
+        "wrist_angle",
+        "wrist_rotate",
+        "gripper"
+    ]
+}

.cache/calibration/aloha_default/left_leader.json

@@ -0,0 +1,68 @@
+{
+    "homing_offset": [
+        2048,
+        3072,
+        3072,
+        -1024,
+        -1024,
+        2048,
+        -2048,
+        2048,
+        -1024
+    ],
+    "drive_mode": [
+        1,
+        1,
+        1,
+        0,
+        0,
+        1,
+        0,
+        1,
+        0
+    ],
+    "start_pos": [
+        2035,
+        3024,
+        3019,
+        979,
+        981,
+        1982,
+        2166,
+        2124,
+        1968
+    ],
+    "end_pos": [
+        -990,
+        -2017,
+        -2015,
+        2078,
+        2076,
+        -1030,
+        3117,
+        -1016,
+        2556
+    ],
+    "calib_mode": [
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "LINEAR"
+    ],
+    "motor_names": [
+        "waist",
+        "shoulder",
+        "shoulder_shadow",
+        "elbow",
+        "elbow_shadow",
+        "forearm_roll",
+        "wrist_angle",
+        "wrist_rotate",
+        "gripper"
+    ]
+}

.cache/calibration/aloha_default/right_follower.json

@@ -0,0 +1,68 @@
+{
+    "homing_offset": [
+        2048,
+        3072,
+        3072,
+        -1024,
+        -1024,
+        2048,
+        -2048,
+        2048,
+        -2048
+    ],
+    "drive_mode": [
+        1,
+        1,
+        1,
+        0,
+        0,
+        1,
+        0,
+        1,
+        0
+    ],
+    "start_pos": [
+        2056,
+        2895,
+        2896,
+        1191,
+        1190,
+        2018,
+        2051,
+        2056,
+        2509
+    ],
+    "end_pos": [
+        -1040,
+        -2004,
+        -2006,
+        2126,
+        2127,
+        -1010,
+        3050,
+        -1117,
+        3143
+    ],
+    "calib_mode": [
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "LINEAR"
+    ],
+    "motor_names": [
+        "waist",
+        "shoulder",
+        "shoulder_shadow",
+        "elbow",
+        "elbow_shadow",
+        "forearm_roll",
+        "wrist_angle",
+        "wrist_rotate",
+        "gripper"
+    ]
+}

.cache/calibration/aloha_default/right_leader.json

@@ -0,0 +1,68 @@
+{
+    "homing_offset": [
+        2048,
+        3072,
+        3072,
+        -1024,
+        -1024,
+        2048,
+        -2048,
+        2048,
+        -2048
+    ],
+    "drive_mode": [
+        1,
+        1,
+        1,
+        0,
+        0,
+        1,
+        0,
+        1,
+        0
+    ],
+    "start_pos": [
+        2068,
+        3034,
+        3030,
+        1038,
+        1041,
+        1991,
+        1948,
+        2090,
+        1985
+    ],
+    "end_pos": [
+        -1025,
+        -2014,
+        -2015,
+        2058,
+        2060,
+        -955,
+        3091,
+        -940,
+        2576
+    ],
+    "calib_mode": [
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "DEGREE",
+        "LINEAR"
+    ],
+    "motor_names": [
+        "waist",
+        "shoulder",
+        "shoulder_shadow",
+        "elbow",
+        "elbow_shadow",
+        "forearm_roll",
+        "wrist_angle",
+        "wrist_rotate",
+        "gripper"
+    ]
+}

.dockerignore

@@ -65,7 +65,6 @@ htmlcov/
 .nox/
 .coverage
 .coverage.*
-.cache
 nosetests.xml
 coverage.xml
 *.cover
@@ -73,6 +72,11 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 
+# Ignore .cache except calibration
+.cache/*
+!.cache/calibration/
+!.cache/calibration/**
+
 # Translations
 *.mo
 *.pot

.gitattributes

@@ -3,4 +3,4 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
-*.json filter=lfs diff=lfs merge=lfs -text
+*.json !text !filter !merge !diff

.github/workflows/test.yml

@@ -11,6 +11,7 @@ on:
       - ".github/**"
       - "poetry.lock"
       - "Makefile"
+      - ".cache/**"
   push:
     branches:
       - main
@@ -21,6 +22,7 @@ on:
       - ".github/**"
       - "poetry.lock"
       - "Makefile"
+      - ".cache/**"
 
 jobs:
   pytest:
@@ -35,13 +37,17 @@ jobs:
           lfs: true  # Ensure LFS files are pulled
 
       - name: Install apt dependencies
-        run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev ffmpeg
+      # portaudio19-dev is needed to install pyaudio
+        run: |
+          sudo apt-get update && \
+          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
 
       - name: Install poetry
         run: |
           pipx install poetry && poetry config virtualenvs.in-project true
           echo "${{ github.workspace }}/.venv/bin" >> $GITHUB_PATH
 
+      # TODO(rcadene, aliberts): python 3.12 seems to be used in the tests, not python 3.10
       - name: Set up Python 3.10
         uses: actions/setup-python@v5
         with:
@@ -60,7 +66,6 @@ jobs:
             -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
             && rm -rf tests/outputs outputs
 
-
   pytest-minimal:
     name: Pytest (minimal install)
     runs-on: ubuntu-latest
@@ -80,6 +85,7 @@ jobs:
           pipx install poetry && poetry config virtualenvs.in-project true
           echo "${{ github.workspace }}/.venv/bin" >> $GITHUB_PATH
 
+      # TODO(rcadene, aliberts): python 3.12 seems to be used in the tests, not python 3.10
       - name: Set up Python 3.10
         uses: actions/setup-python@v5
         with:
@@ -110,7 +116,10 @@ jobs:
           lfs: true  # Ensure LFS files are pulled
 
       - name: Install apt dependencies
-        run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
+      # portaudio19-dev is needed to install pyaudio
+        run: |
+          sudo apt-get update && \
+          sudo apt-get install -y libegl1-mesa-dev portaudio19-dev
 
       - name: Install poetry
         run: |

.gitignore

@@ -66,7 +66,6 @@ htmlcov/
 .nox/
 .coverage
 .coverage.*
-.cache
 nosetests.xml
 coverage.xml
 *.cover
@@ -74,6 +73,11 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 
+# Ignore .cache except calibration
+.cache/*
+!.cache/calibration/
+!.cache/calibration/**
+
 # Translations
 *.mo
 *.pot

CONTRIBUTING.md

@@ -20,7 +20,7 @@ Some of the ways you can contribute to 🤗 LeRobot:
 * Contributing to the examples or to the documentation.
 * Submitting issues related to bugs or desired new features.
 
-Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](remi.cadene@huggingface.co).
+Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co).
 
 If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
 

README.md

@@ -23,20 +23,21 @@
 </div>
 
 <h2 align="center">
-    <p><a href="https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md">Hot new tutorial: Getting started with real-world robots</a></p>
+    <p><a href="https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md">New robot in town: SO-100</a></p>
 </h2>
 
 <div 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%">
-    <p>We just dropped an in-depth tutorial on how to build your own robot!</p>
+    <img src="media/so100/leader_follower.webp?raw=true" alt="SO-100 leader and follower arms" title="SO-100 leader and follower arms" width="50%">
+    <p>We just added a new tutorial on how to build a more affordable robot, at the price of $110 per arm!</p>
     <p>Teach it new skills by showing it a few moves with just a laptop.</p>
     <p>Then watch your homemade robot act autonomously 🤯</p>
-    <p>For more info, see <a href="https://x.com/RemiCadene/status/1825455895561859185">our thread on X</a> or <a href="https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md">our tutorial page</a>.</p>
+    <p>Follow the link to the <a href="https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md">full tutorial for SO-100</a>.</p>
 </div>
 
+<br/>
 
 <h3 align="center">
-    <p>State-of-the-art AI for real-world robotics</p>
+    <p>LeRobot: State-of-the-art AI for real-world robotics</p>
 </h3>
 
 ---
@@ -54,9 +55,9 @@
 
 <table>
   <tr>
-    <td><img src="http://remicadene.com/assets/gif/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
-    <td><img src="http://remicadene.com/assets/gif/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
-    <td><img src="http://remicadene.com/assets/gif/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
+    <td><img src="media/gym/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
+    <td><img src="media/gym/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
+    <td><img src="media/gym/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
   </tr>
   <tr>
     <td align="center">ACT policy on ALOHA env</td>
@@ -143,7 +144,7 @@ wandb login
 
 ### Visualize datasets
 
-Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically download data from the Hugging Face hub.
+Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
 
 You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
 ```bash
@@ -266,13 +267,20 @@ checkpoints
 │   └── training_state.pth  # optimizer/scheduler/rng state and training step
 ```
 
+To resume training from a checkpoint, you can add these to the `train.py` python command:
+```bash
+    hydra.run.dir=your/original/experiment/dir resume=true
+```
+
+It will load the pretrained model, optimizer and scheduler states for training. For more information please see our tutorial on training resumption [here](https://github.com/huggingface/lerobot/blob/main/examples/5_resume_training.md).
+
 To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding:
 
 ```bash
     wandb.enable=true
 ```
 
-A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser:
+A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser. Please also check [here](https://github.com/huggingface/lerobot/blob/main/examples/4_train_policy_with_script.md#typical-logs-and-metrics) for the explanation of some commonly used metrics in logs.
 
 ![](media/wandb.png)
 

docker/lerobot-cpu/Dockerfile

@@ -22,7 +22,7 @@ RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
 COPY . /lerobot
 WORKDIR /lerobot
 RUN pip install --upgrade --no-cache-dir pip
-RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht, koch]" \
+RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]" \
     --extra-index-url https://download.pytorch.org/whl/cpu
 
 # Set EGL as the rendering backend for MuJoCo

docker/lerobot-gpu/Dockerfile

@@ -24,7 +24,7 @@ RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
 COPY . /lerobot
 WORKDIR /lerobot
 RUN pip install --upgrade --no-cache-dir pip
-RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht, koch]"
+RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]"
 
 # Set EGL as the rendering backend for MuJoCo
 ENV MUJOCO_GL="egl"

examples/10_use_so100.md

@@ -0,0 +1,280 @@
+This tutorial explains how to use [SO-100](https://github.com/TheRobotStudio/SO-ARM100) with LeRobot.
+
+## Source the parts
+
+Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with link to source the parts, as well as the instructions to 3D print the parts, and advices if it's your first time printing or if you don't own a 3D printer already.
+
+**Important**: Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
+
+## Install LeRobot
+
+On your computer:
+
+1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
+```bash
+mkdir -p ~/miniconda3
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
+bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
+rm ~/miniconda3/miniconda.sh
+~/miniconda3/bin/conda init bash
+```
+
+2. Restart shell or `source ~/.bashrc`
+
+3. Create and activate a fresh conda environment for lerobot
+```bash
+conda create -y -n lerobot python=3.10 && conda activate lerobot
+```
+
+4. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+5. Install LeRobot with dependencies for the feetech motors:
+```bash
+cd ~/lerobot && pip install -e ".[feetech]"
+```
+
+For Linux only (not Mac), install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+
+## Configure the motors
+
+Follow steps 1 of the [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I) which illustrates the use of our scripts below.
+
+**Find USB ports associated to your arms**
+To find the correct ports for each arm, run the utility script twice:
+```bash
+python lerobot/scripts/find_motors_bus_port.py
+```
+
+Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect leader arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
+Reconnect the usb cable.
+```
+
+Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect follower arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the usb cable.
+```
+
+Troubleshooting: On Linux, you might need to give access to the USB ports by running:
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+**Configure your motors**
+Plug your first motor and run this script to set its ID to 1. It will also set its present position to 2048, so expect your motor to rotate:
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 1
+```
+
+Note: These motors are currently limitated. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
+
+Then unplug your motor and plug the second motor and set its ID to 2.
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 2
+```
+
+Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.
+
+**Remove the gears of the 6 leader motors**
+Follow step 2 of the [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I). You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm.
+
+**Add motor horn to the motors**
+Follow step 3 of the [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I). For SO-100, you need to align the holes on the motor horn to the motor spline to be approximately 1:30, 4:30, 7:30 and 10:30.
+Try to avoid rotating the motor while doing so to keep position 2048 set during configuration. It is especially tricky for the leader motors as it is more sensible without the gears, but it's ok if it's a bit rotated.
+
+## Assemble the arms
+
+Follow step 4 of the [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I). The first arm should take a bit more than 1 hour to assemble, but once you get use to it, you can do it under 1 hour for the second arm.
+
+## Calibrate
+
+Next, you'll need to calibrate your SO-100 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one SO-100 robot to work on another.
+
+**Manual calibration of follower arm**
+/!\ Contrarily to step 6 of the [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I) which illustrates the auto calibration, we will actually do manual calibration of follower for now.
+
+You will need to move the follower arm to these positions sequentially:
+
+| 1. Zero position | 2. Rotated position | 3. Rest position |
+|---|---|---|
+| <img src="../media/so100/follower_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/so100/follower_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/so100/follower_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
+
+Make sure both arms are connected and run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py calibrate \
+    --robot-path lerobot/configs/robot/so100.yaml \
+    --robot-overrides '~cameras' --arms main_follower
+```
+
+**Manual calibration of leader arm**
+Follow step 6 of the [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
+
+| 1. Zero position | 2. Rotated position | 3. Rest position |
+|---|---|---|
+| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
+
+Run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py calibrate \
+    --robot-path lerobot/configs/robot/so100.yaml \
+    --robot-overrides '~cameras' --arms main_leader
+```
+
+## Teleoperate
+
+**Simple teleop**
+Then you are ready to teleoperate your robot! Run this simple script (it won't connect and display the cameras):
+```bash
+python lerobot/scripts/control_robot.py teleoperate \
+    --robot-path lerobot/configs/robot/so100.yaml \
+    --robot-overrides '~cameras' \
+    --display-cameras 0
+```
+
+
+**Teleop with displaying cameras**
+Follow [this guide to setup your cameras](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). Then you will be able to display the cameras on your computer while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.
+```bash
+python lerobot/scripts/control_robot.py teleoperate \
+    --robot-path lerobot/configs/robot/so100.yaml
+```
+
+## Record a dataset
+
+Once you're familiar with teleoperation, you can record your first dataset with SO-100.
+
+If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Store your Hugging Face repository name in a variable to run these commands:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Record 2 episodes and upload your dataset to the hub:
+```bash
+python lerobot/scripts/control_robot.py record \
+    --robot-path lerobot/configs/robot/so100.yaml \
+    --fps 30 \
+    --root data \
+    --repo-id ${HF_USER}/so100_test \
+    --tags so100 tutorial \
+    --warmup-time-s 5 \
+    --episode-time-s 40 \
+    --reset-time-s 10 \
+    --num-episodes 2 \
+    --push-to-hub 1
+```
+
+## Visualize a dataset
+
+If you uploaded your dataset to the hub with `--push-to-hub 1`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
+```bash
+echo ${HF_USER}/so100_test
+```
+
+If you didn't upload with `--push-to-hub 0`, you can also visualize it locally with:
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --root data \
+  --repo-id ${HF_USER}/so100_test
+```
+
+## Replay an episode
+
+Now try to replay the first episode on your robot:
+```bash
+DATA_DIR=data python lerobot/scripts/control_robot.py replay \
+    --robot-path lerobot/configs/robot/so100.yaml \
+    --fps 30 \
+    --root data \
+    --repo-id ${HF_USER}/so100_test \
+    --episode 0
+```
+
+## Train a policy
+
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+```bash
+DATA_DIR=data python lerobot/scripts/train.py \
+  dataset_repo_id=${HF_USER}/so100_test \
+  policy=act_so100_real \
+  env=so100_real \
+  hydra.run.dir=outputs/train/act_so100_test \
+  hydra.job.name=act_so100_test \
+  device=cuda \
+  wandb.enable=true
+```
+
+Let's explain it:
+1. We provided the dataset as argument with `dataset_repo_id=${HF_USER}/so100_test`.
+2. We provided the policy with `policy=act_so100_real`. This loads configurations from [`lerobot/configs/policy/act_so100_real.yaml`](../lerobot/configs/policy/act_so100_real.yaml). Importantly, this policy uses 2 cameras as input `laptop`, `phone`.
+3. We provided an environment as argument with `env=so100_real`. This loads configurations from [`lerobot/configs/env/so100_real.yaml`](../lerobot/configs/env/so100_real.yaml).
+4. We provided `device=cuda` since we are training on a Nvidia GPU, but you can also use `device=mps` if you are using a Mac with Apple silicon, or `device=cpu` otherwise.
+5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+6. We added `DATA_DIR=data` to access your dataset stored in your local `data` directory. If you dont provide `DATA_DIR`, your dataset will be downloaded from Hugging Face hub to your cache folder `$HOME/.cache/hugginface`. In future versions of `lerobot`, both directories will be in sync.
+
+Training should take several hours. You will find checkpoints in `outputs/train/act_so100_test/checkpoints`.
+
+## Evaluate your policy
+
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+```bash
+python lerobot/scripts/control_robot.py record \
+  --robot-path lerobot/configs/robot/so100.yaml \
+  --fps 30 \
+  --root data \
+  --repo-id ${HF_USER}/eval_act_so100_test \
+  --tags so100 tutorial eval \
+  --warmup-time-s 5 \
+  --episode-time-s 40 \
+  --reset-time-s 10 \
+  --num-episodes 10 \
+  -p outputs/train/act_so100_test/checkpoints/last/pretrained_model
+```
+
+As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+1. There is an additional `-p` argument which indicates the path to your policy checkpoint with  (e.g. `-p outputs/train/eval_so100_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `-p ${HF_USER}/act_so100_test`).
+2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `--repo-id ${HF_USER}/eval_act_so100_test`).
+
+## More
+
+Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth tutorial on controlling real robots with LeRobot.
+
+If you have any question or need help, please reach out on Discord in the channel [`#so100-arm`](https://discord.com/channels/1216765309076115607/1237741463832363039).

examples/11_use_moss.md

@@ -0,0 +1,280 @@
+This tutorial explains how to use [Moss v1](https://github.com/jess-moss/moss-robot-arms) with LeRobot.
+
+## Source the parts
+
+Follow this [README](https://github.com/jess-moss/moss-robot-arms). It contains the bill of materials, with link to source the parts, as well as the instructions to 3D print the parts, and advices if it's your first time printing or if you don't own a 3D printer already.
+
+**Important**: Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
+
+## Install LeRobot
+
+On your computer:
+
+1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
+```bash
+mkdir -p ~/miniconda3
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
+bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
+rm ~/miniconda3/miniconda.sh
+~/miniconda3/bin/conda init bash
+```
+
+2. Restart shell or `source ~/.bashrc`
+
+3. Create and activate a fresh conda environment for lerobot
+```bash
+conda create -y -n lerobot python=3.10 && conda activate lerobot
+```
+
+4. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+5. Install LeRobot with dependencies for the feetech motors:
+```bash
+cd ~/lerobot && pip install -e ".[feetech]"
+```
+
+For Linux only (not Mac), install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+
+## Configure the motors
+
+Follow steps 1 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the use of our scripts below.
+
+**Find USB ports associated to your arms**
+To find the correct ports for each arm, run the utility script twice:
+```bash
+python lerobot/scripts/find_motors_bus_port.py
+```
+
+Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect leader arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
+Reconnect the usb cable.
+```
+
+Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect follower arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the usb cable.
+```
+
+Troubleshooting: On Linux, you might need to give access to the USB ports by running:
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+**Configure your motors**
+Plug your first motor and run this script to set its ID to 1. It will also set its present position to 2048, so expect your motor to rotate:
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 1
+```
+
+Note: These motors are currently limitated. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
+
+Then unplug your motor and plug the second motor and set its ID to 2.
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 2
+```
+
+Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.
+
+**Remove the gears of the 6 leader motors**
+Follow step 2 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm.
+
+**Add motor horn to the motors**
+Follow step 3 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). For Moss v1, you need to align the holes on the motor horn to the motor spline to be approximately 3, 6, 9 and 12 o'clock.
+Try to avoid rotating the motor while doing so to keep position 2048 set during configuration. It is especially tricky for the leader motors as it is more sensible without the gears, but it's ok if it's a bit rotated.
+
+## Assemble the arms
+
+Follow step 4 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). The first arm should take a bit more than 1 hour to assemble, but once you get use to it, you can do it under 1 hour for the second arm.
+
+## Calibrate
+
+Next, you'll need to calibrate your Moss v1 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one Moss v1 robot to work on another.
+
+**Manual calibration of follower arm**
+/!\ Contrarily to step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the auto calibration, we will actually do manual calibration of follower for now.
+
+You will need to move the follower arm to these positions sequentially:
+
+| 1. Zero position | 2. Rotated position | 3. Rest position |
+|---|---|---|
+| <img src="../media/moss/follower_zero.webp?raw=true" alt="Moss v1 follower arm zero position" title="Moss v1 follower arm zero position" style="width:100%;"> | <img src="../media/moss/follower_rotated.webp?raw=true" alt="Moss v1 follower arm rotated position" title="Moss v1 follower arm rotated position" style="width:100%;"> | <img src="../media/moss/follower_rest.webp?raw=true" alt="Moss v1 follower arm rest position" title="Moss v1 follower arm rest position" style="width:100%;"> |
+
+Make sure both arms are connected and run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py calibrate \
+    --robot-path lerobot/configs/robot/moss.yaml \
+    --robot-overrides '~cameras' --arms main_follower
+```
+
+**Manual calibration of leader arm**
+Follow step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
+
+| 1. Zero position | 2. Rotated position | 3. Rest position |
+|---|---|---|
+| <img src="../media/moss/leader_zero.webp?raw=true" alt="Moss v1 leader arm zero position" title="Moss v1 leader arm zero position" style="width:100%;"> | <img src="../media/moss/leader_rotated.webp?raw=true" alt="Moss v1 leader arm rotated position" title="Moss v1 leader arm rotated position" style="width:100%;"> | <img src="../media/moss/leader_rest.webp?raw=true" alt="Moss v1 leader arm rest position" title="Moss v1 leader arm rest position" style="width:100%;"> |
+
+Run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py calibrate \
+    --robot-path lerobot/configs/robot/moss.yaml \
+    --robot-overrides '~cameras' --arms main_leader
+```
+
+## Teleoperate
+
+**Simple teleop**
+Then you are ready to teleoperate your robot! Run this simple script (it won't connect and display the cameras):
+```bash
+python lerobot/scripts/control_robot.py teleoperate \
+    --robot-path lerobot/configs/robot/moss.yaml \
+    --robot-overrides '~cameras' \
+    --display-cameras 0
+```
+
+
+**Teleop with displaying cameras**
+Follow [this guide to setup your cameras](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). Then you will be able to display the cameras on your computer while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.
+```bash
+python lerobot/scripts/control_robot.py teleoperate \
+    --robot-path lerobot/configs/robot/moss.yaml
+```
+
+## Record a dataset
+
+Once you're familiar with teleoperation, you can record your first dataset with Moss v1.
+
+If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Store your Hugging Face repository name in a variable to run these commands:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Record 2 episodes and upload your dataset to the hub:
+```bash
+python lerobot/scripts/control_robot.py record \
+    --robot-path lerobot/configs/robot/moss.yaml \
+    --fps 30 \
+    --root data \
+    --repo-id ${HF_USER}/moss_test \
+    --tags moss tutorial \
+    --warmup-time-s 5 \
+    --episode-time-s 40 \
+    --reset-time-s 10 \
+    --num-episodes 2 \
+    --push-to-hub 1
+```
+
+## Visualize a dataset
+
+If you uploaded your dataset to the hub with `--push-to-hub 1`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
+```bash
+echo ${HF_USER}/moss_test
+```
+
+If you didn't upload with `--push-to-hub 0`, you can also visualize it locally with:
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --root data \
+  --repo-id ${HF_USER}/moss_test
+```
+
+## Replay an episode
+
+Now try to replay the first episode on your robot:
+```bash
+DATA_DIR=data python lerobot/scripts/control_robot.py replay \
+    --robot-path lerobot/configs/robot/moss.yaml \
+    --fps 30 \
+    --root data \
+    --repo-id ${HF_USER}/moss_test \
+    --episode 0
+```
+
+## Train a policy
+
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+```bash
+DATA_DIR=data python lerobot/scripts/train.py \
+  dataset_repo_id=${HF_USER}/moss_test \
+  policy=act_moss_real \
+  env=moss_real \
+  hydra.run.dir=outputs/train/act_moss_test \
+  hydra.job.name=act_moss_test \
+  device=cuda \
+  wandb.enable=true
+```
+
+Let's explain it:
+1. We provided the dataset as argument with `dataset_repo_id=${HF_USER}/moss_test`.
+2. We provided the policy with `policy=act_moss_real`. This loads configurations from [`lerobot/configs/policy/act_moss_real.yaml`](../lerobot/configs/policy/act_moss_real.yaml). Importantly, this policy uses 2 cameras as input `laptop`, `phone`.
+3. We provided an environment as argument with `env=moss_real`. This loads configurations from [`lerobot/configs/env/moss_real.yaml`](../lerobot/configs/env/moss_real.yaml).
+4. We provided `device=cuda` since we are training on a Nvidia GPU, but you can also use `device=mps` if you are using a Mac with Apple silicon, or `device=cpu` otherwise.
+5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+6. We added `DATA_DIR=data` to access your dataset stored in your local `data` directory. If you dont provide `DATA_DIR`, your dataset will be downloaded from Hugging Face hub to your cache folder `$HOME/.cache/hugginface`. In future versions of `lerobot`, both directories will be in sync.
+
+Training should take several hours. You will find checkpoints in `outputs/train/act_moss_test/checkpoints`.
+
+## Evaluate your policy
+
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+```bash
+python lerobot/scripts/control_robot.py record \
+  --robot-path lerobot/configs/robot/moss.yaml \
+  --fps 30 \
+  --root data \
+  --repo-id ${HF_USER}/eval_act_moss_test \
+  --tags moss tutorial eval \
+  --warmup-time-s 5 \
+  --episode-time-s 40 \
+  --reset-time-s 10 \
+  --num-episodes 10 \
+  -p outputs/train/act_moss_test/checkpoints/last/pretrained_model
+```
+
+As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+1. There is an additional `-p` argument which indicates the path to your policy checkpoint with  (e.g. `-p outputs/train/eval_moss_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `-p ${HF_USER}/act_moss_test`).
+2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `--repo-id ${HF_USER}/eval_act_moss_test`).
+
+## More
+
+Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth tutorial on controlling real robots with LeRobot.
+
+If you have any question or need help, please reach out on Discord in the channel [`#moss-arm`](https://discord.com/channels/1216765309076115607/1275374638985252925).

examples/4_train_policy_with_script.md

@@ -170,6 +170,36 @@ python lerobot/scripts/train.py --config-dir outputs/train/my_experiment/checkpo
 
 Note that you may still use the regular syntax for config parameter overrides (eg: by adding `training.offline_steps=200000`).
 
+## Typical logs and metrics
+
+When you start the training process, you will first see your full configuration being printed in the terminal. You can check it to make sure that you config it correctly and your config is not overrided by other files. The final configuration will also be saved with the checkpoint.
+
+After that, you will see training log like this one:
+
+```
+INFO 2024-08-14 13:35:12 ts/train.py:192 step:0 smpl:64 ep:1 epch:0.00 loss:1.112 grdn:15.387 lr:2.0e-07 updt_s:1.738 data_s:4.774
+```
+
+or evaluation log like:
+
+```
+INFO 2024-08-14 13:38:45 ts/train.py:226 step:100 smpl:6K ep:52 epch:0.25 ∑rwrd:20.693 success:0.0% eval_s:120.266
+```
+
+These logs will also be saved in wandb if `wandb.enable` is set to `true`. Here are the meaning of some abbreviations:
+
+- `smpl`: number of samples seen during training.
+- `ep`: number of episodes seen during training. An episode contains multiple samples in a complete manipulation task.
+- `epch`: number of time all unique samples are seen (epoch).
+- `grdn`: gradient norm.
+- `∑rwrd`: compute the sum of rewards in every evaluation episode and then take an average of them.
+- `success`: average success rate of eval episodes. Reward and success are usually different except for the sparsing reward setting, where reward=1 only when the task is completed successfully.
+- `eval_s`: time to evaluate the policy in the environment, in second.
+- `updt_s`: time to update the network parameters, in second.
+- `data_s`: time to load a batch of data, in second.
+
+Some metrics are useful for initial performance profiling. For example, if you find the current GPU utilization is low via the `nvidia-smi` command and `data_s` sometimes is too high, you may need to modify batch size or number of dataloading workers to accelerate dataloading. We also recommend [pytorch profiler](https://github.com/huggingface/lerobot?tab=readme-ov-file#improve-your-code-with-profiling) for detailed performance probing.
+
 ---
 
 So far we've seen how to train Diffusion Policy for PushT and ACT for ALOHA. Now, what if we want to train ACT for PushT? Well, there are aspects of the ACT configuration that are specific to the ALOHA environments, and these happen to be incompatible with PushT. Therefore, trying to run the following will almost certainly raise an exception of sorts (eg: feature dimension mismatch):

examples/7_get_started_with_real_robot.md

@@ -11,7 +11,7 @@ This tutorial will guide you through the process of setting up and training a ne
 
 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).
 
-Although this tutorial is general and can be easily adapted to various types of robots by changing the configuration, it is specifically based on the [Koch v1.1](https://github.com/jess-moss/koch-v1-1), an affordable robot. 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.
+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.
 
@@ -29,16 +29,23 @@ For a visual walkthrough of the assembly process, you can refer to [this video t
 
 ## 2. Configure motors, calibrate arms, teleoperate your Koch v1.1
 
-First, install the additional dependencies required for Koch v1.1 by running one of the following commands.
+First, install the additional dependencies required for robots built with dynamixel motors like Koch v1.1 by running one of the following commands.
 
 Using `pip`:
 ```bash
-pip install -e ".[koch]"
+pip install -e ".[dynamixel]"
 ```
 
 Or using `poetry`:
 ```bash
-poetry install --sync --extras "koch"
+poetry install --sync --extras "dynamixel"
+```
+
+/!\ For Linux only, ffmpeg and opencv requires conda install for now. Run this exact sequence of commands:
+```bash
+conda install -c conda-forge ffmpeg
+pip uninstall opencv-python
+conda install -c conda-forge "opencv>=4.10.0"
 ```
 
 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.
@@ -71,12 +78,12 @@ To begin, create two instances of the  [`DynamixelMotorsBus`](../lerobot/common/
 
 To find the correct ports for each arm, run the utility script twice:
 ```bash
-python lerobot/common/robot_devices/motors/dynamixel.py
+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 DynamixelMotorsBus.
+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.
 
@@ -88,7 +95,7 @@ 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 DynamixelMotorsBus.
+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.
 
@@ -147,6 +154,7 @@ follower_arm = DynamixelMotorsBus(
 Next, update the port values in the YAML configuration file for the Koch robot at [`lerobot/configs/robot/koch.yaml`](../lerobot/configs/robot/koch.yaml) with the ports you've identified:
 ```yaml
 [...]
+robot_type: koch
 leader_arms:
   main:
     _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
@@ -174,6 +182,8 @@ follower_arms:
 [...]
 ```
 
+Don't forget to set `robot_type: aloha` if you follow this tutorial with [Aloha bimanual robot](aloha-2.github.io) instead of Koch v1.1
+
 This configuration file is used to instantiate your robot across all scripts. We'll cover how this works later on.
 
 **Connect and Configure your Motors**
@@ -298,32 +308,37 @@ Alternatively, you can unplug the power cord, which will automatically disable t
 
 */!\ 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 KochRobot
+### b. Teleoperate your Koch v1.1 with ManipulatorRobot
 
-**Instantiate the KochRobot**
+**Instantiate the ManipulatorRobot**
 
-Before you can teleoperate your robot, you need to instantiate the  [`KochRobot`](../lerobot/common/robot_devices/robots/koch.py) using the previously defined `leader_arm` and `follower_arm`.
+Before you can teleoperate your robot, you need to instantiate the  [`ManipulatorRobot`](../lerobot/common/robot_devices/robots/manipulator.py) using the previously defined `leader_arm` and `follower_arm`.
 
-For the Koch robot, we only have one leader, so we refer to it as `"main"` and define it as `leader_arms={"main": leader_arm}`. 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_arm, "right": right_leader_arm},`. Same thing for the follower arms.
+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_arm}`. 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_arm, "right": right_leader_arm},`. Same thing for the follower arms.
 
-You also need to provide a path to a calibration file, such as  `calibration_path=".cache/calibration/koch.pkl"`. More on this in the next section.
+You also need to provide a path to a calibration directory, such as  `calibration_dir=".cache/calibration/koch"`. More on this in the next section.
 
-Run the following code to instantiate your Koch robot:
+Run the following code to instantiate your manipulator robot:
 ```python
-from lerobot.common.robot_devices.robots.koch import KochRobot
+from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot
 
-robot = KochRobot(
+robot = ManipulatorRobot(
+    robot_type="koch",
     leader_arms={"main": leader_arm},
     follower_arms={"main": follower_arm},
-    calibration_path=".cache/calibration/koch.pkl",
+    calibration_dir=".cache/calibration/koch",
 )
 ```
 
-**Calibrate and Connect the KochRobot**
+The `robot_type="koch"` 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.
 
-Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one Koch robot to work on another.
+For the [Aloha bimanual robot](https://aloha-2.github.io), we would use `robot_type="aloha"` 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 in `.cache/calibration/aloha_default`. Assuming the motors have been properly assembled, no manual calibration step is expected. If you need to run manual calibration, simply update `calibration_dir` to `.cache/calibration/aloha`.
 
-When you connect your robot for the first time, the [`KochRobot`](../lerobot/common/robot_devices/robots/koch.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.
+**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:
 
@@ -354,27 +369,26 @@ The output will look like this:
 ```
 Connecting main follower arm
 Connecting main leader arm
-Missing calibration file '.cache/calibration/koch.pkl'. Starting calibration procedure.
-
-Running calibration of main follower...
 
+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'
 
-Running calibration of main leader...
-
+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.pkl'
+Calibration is done! Saving calibration file '.cache/calibration/koch/main_leader.json'
 ```
 
 *Verifying Calibration*
@@ -414,7 +428,7 @@ for _ in tqdm.tqdm(range(seconds*frequency)):
 
 *Using `teleop_step` for Teleoperation*
 
-Alternatively, you can teleoperate the robot using the `teleop_step` method from [`KochRobot`](../lerobot/common/robot_devices/robots/koch.py).
+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
@@ -607,10 +621,10 @@ Additionaly, 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 = KochRobot(
+robot = ManipulatorRobot(
     leader_arms={"main": leader_arm},
     follower_arms={"main": follower_arm},
-    calibration_path=".cache/calibration/koch.pkl",
+    calibration_dir=".cache/calibration/koch",
     cameras={
         "laptop": OpenCVCamera(0, fps=30, width=640, height=480),
         "phone": OpenCVCamera(1, fps=30, width=640, height=480),
@@ -752,7 +766,7 @@ Before trying `record`, if you want to push your dataset to the hub, make sure y
 ```bash
 huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
-Also, store your Hugging Face repositery name in a variable (e.g. `cadene` or `lerobot`). For instance, run this to use your Hugging Face user name as repositery:
+Also, store your Hugging Face repository name in a variable (e.g. `cadene` or `lerobot`). For instance, run this to use your Hugging Face user name as repository:
 ```bash
 HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
@@ -925,7 +939,7 @@ huggingface-cli upload ${HF_USER}/act_koch_test_${CKPT} \
 
 ## 5. Evaluate your policy
 
-Now that you have a policy checkpoint, you can easily control your robot with it using methods from [`KochRobot`](../lerobot/common/robot_devices/robots/koch.py) and the 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

examples/8_use_stretch.md

@@ -0,0 +1,158 @@
+This tutorial explains how to use [Stretch 3](https://hello-robot.com/stretch-3-product) with LeRobot.
+
+## Setup
+
+Familiarize yourself with Stretch by following its [tutorials](https://docs.hello-robot.com/0.3/getting_started/hello_robot/) (recommended).
+
+To use LeRobot on Stretch, 3 options are available:
+- [tethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup)
+- [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup)
+- ssh directly into Stretch (you will first need to install and configure openssh-server on stretch using one of the two above setups)
+
+
+## Install LeRobot
+
+On Stretch's CLI, follow these steps:
+
+1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
+```bash
+mkdir -p ~/miniconda3
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
+bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
+rm ~/miniconda3/miniconda.sh
+~/miniconda3/bin/conda init bash
+```
+
+2. Comment out these lines in `~/.profile` (this can mess up paths used by conda and ~/.local/bin should already be in your PATH)
+```
+# set PATH so it includes user's private bin if it exists
+if [ -d "$HOME/.local/bin" ] ; then
+    PATH="$HOME/.local/bin:$PATH"
+fi
+```
+
+3. Restart shell or `source ~/.bashrc`
+
+4. Create and activate a fresh conda environment for lerobot
+```bash
+conda create -y -n lerobot python=3.10 && conda activate lerobot
+```
+
+5. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+6. Install LeRobot with stretch dependencies:
+```bash
+cd ~/lerobot && pip install -e ".[stretch]"
+```
+
+> **Note:** If you get this message, you can ignore it: `ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.`
+
+For Linux only (not Mac), install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+
+7. Run a [system check](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#system-check) to make sure your robot is ready:
+```bash
+stretch_system_check.py
+```
+
+> **Note:** You may need to free the "robot process" after booting Stretch by running `stretch_free_robot_process.py`. For more info this Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#turning-off-gamepad-teleoperation).
+
+You should get something like this:
+```bash
+For use with S T R E T C H (R) from Hello Robot Inc.
+---------------------------------------------------------------------
+
+Model = Stretch 3
+Tool = DexWrist 3 w/ Gripper
+Serial Number = stretch-se3-3054
+
+---- Checking Hardware ----
+[Pass] Comms are ready
+[Pass] Actuators are ready
+[Warn] Sensors not ready (IMU AZ = -10.19 out of range -10.1 to -9.5)
+[Pass] Battery voltage is 13.6 V
+
+---- Checking Software ----
+[Pass] Ubuntu 22.04 is ready
+[Pass] All APT pkgs are setup correctly
+[Pass] Firmware is up-to-date
+[Pass] Python pkgs are up-to-date
+[Pass] ROS2 Humble is ready
+```
+
+## Teleoperate, record a dataset and run a policy
+
+**Calibrate (Optional)**
+Before operating Stretch, you need to [home](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#homing) it first. Be mindful about giving Stretch some space as this procedure will move the robot's arm and gripper. Now run this command:
+```bash
+python lerobot/scripts/control_robot.py calibrate \
+    --robot-path lerobot/configs/robot/stretch.yaml
+```
+This is equivalent to running `stretch_robot_home.py`
+
+> **Note:** If you run any of the LeRobot scripts below and Stretch is not poperly homed, it will automatically home/calibrate first.
+
+**Teleoperate**
+Before trying teleoperation, you need activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation).
+
+Now try out teleoperation (see above documentation to learn about the gamepad controls):
+```bash
+python lerobot/scripts/control_robot.py teleoperate \
+    --robot-path lerobot/configs/robot/stretch.yaml
+```
+This is essentially the same as running `stretch_gamepad_teleop.py`
+
+**Record a dataset**
+Once you're familiar with the gamepad controls and after a bit of practice, you can try to record your first dataset with Stretch.
+
+If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Store your Hugging Face repository name in a variable to run these commands:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Record one episode:
+```bash
+python lerobot/scripts/control_robot.py record \
+    --robot-path lerobot/configs/robot/stretch.yaml \
+    --fps 20 \
+    --root data \
+    --repo-id ${HF_USER}/stretch_test \
+    --tags stretch tutorial \
+    --warmup-time-s 3 \
+    --episode-time-s 40 \
+    --reset-time-s 10 \
+    --num-episodes 1 \
+    --push-to-hub 0
+```
+
+> **Note:** If you're using ssh to connect to Stretch and run this script, you won't be able to visualize its cameras feed (though they will still be recording). To see the cameras stream, use [tethered](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup) or [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup).
+
+**Replay an episode**
+Now try to replay this episode (make sure the robot's initial position is the same):
+```bash
+python lerobot/scripts/control_robot.py replay \
+    --robot-path lerobot/configs/robot/stretch.yaml \
+    --fps 20 \
+    --root data \
+    --repo-id ${HF_USER}/stretch_test \
+    --episode 0
+```
+
+Follow [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) to train a policy on your data and run inference on your robot. You will need to adapt the code for Stretch.
+
+> TODO(rcadene, aliberts): Add already setup environment and policy yaml configuration files
+
+If you need help, please reach out on Discord in the channel `#stretch3-mobile-arm`.

examples/9_use_aloha.md

@@ -0,0 +1,179 @@
+This tutorial explains how to use [Aloha and Aloha 2 stationary](https://www.trossenrobotics.com/aloha-stationary) with LeRobot.
+
+## Setup
+
+Follow the [documentation from Trossen Robotics](https://docs.trossenrobotics.com/aloha_docs/getting_started/stationary/hardware_setup.html) for setting up the hardware and plugging the 4 arms and 4 cameras to your computer.
+
+
+## Install LeRobot
+
+On your computer:
+
+1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
+```bash
+mkdir -p ~/miniconda3
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
+bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
+rm ~/miniconda3/miniconda.sh
+~/miniconda3/bin/conda init bash
+```
+
+2. Restart shell or `source ~/.bashrc`
+
+3. Create and activate a fresh conda environment for lerobot
+```bash
+conda create -y -n lerobot python=3.10 && conda activate lerobot
+```
+
+4. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+5. Install LeRobot with dependencies for the Aloha motors (dynamixel) and cameras (intelrealsense):
+```bash
+cd ~/lerobot && pip install -e ".[dynamixel, intelrealsense]"
+```
+
+For Linux only (not Mac), install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+
+## Teleoperate
+
+**/!\ FOR SAFETY, READ THIS /!\**
+Teleoperation consists in manually operating the leader arms to move the follower arms. Importantly:
+1. Make sure your leader arms are in the same position as the follower arms, so that the follower arms don't move too fast to match the leader arms,
+2. Our code assumes that your robot has been assembled following Trossen Robotics instructions. This allows us to skip calibration, as we use the pre-defined calibration files in `.cache/calibration/aloha_default`. If you replace a motor, make sure you follow the exact instructions from Trossen Robotics.
+
+By running the following code, you can start your first **SAFE** teleoperation:
+```bash
+python lerobot/scripts/control_robot.py teleoperate \
+    --robot-path lerobot/configs/robot/aloha.yaml \
+    --robot-overrides max_relative_target=5
+```
+
+By adding `--robot-overrides max_relative_target=5`, we override the default value for `max_relative_target` defined in `lerobot/configs/robot/aloha.yaml`. It is expected to be `5` to limit the magnitude of the movement for more safety, but the teloperation won't be smooth. When you feel confident, you can disable this limit by adding `--robot-overrides max_relative_target=null` to the command line:
+```bash
+python lerobot/scripts/control_robot.py teleoperate \
+    --robot-path lerobot/configs/robot/aloha.yaml \
+    --robot-overrides max_relative_target=null
+```
+
+## Record a dataset
+
+Once you're familiar with teleoperation, you can record your first dataset with Aloha.
+
+If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Store your Hugging Face repository name in a variable to run these commands:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Record 2 episodes and upload your dataset to the hub:
+```bash
+python lerobot/scripts/control_robot.py record \
+    --robot-path lerobot/configs/robot/aloha.yaml \
+    --robot-overrides max_relative_target=null \
+    --fps 30 \
+    --root data \
+    --repo-id ${HF_USER}/aloha_test \
+    --tags aloha tutorial \
+    --warmup-time-s 5 \
+    --episode-time-s 40 \
+    --reset-time-s 10 \
+    --num-episodes 2 \
+    --push-to-hub 1
+```
+
+## Visualize a dataset
+
+If you uploaded your dataset to the hub with `--push-to-hub 1`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
+```bash
+echo ${HF_USER}/aloha_test
+```
+
+If you didn't upload with `--push-to-hub 0`, you can also visualize it locally with:
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --root data \
+  --repo-id ${HF_USER}/aloha_test
+```
+
+## Replay an episode
+
+**/!\ FOR SAFETY, READ THIS /!\**
+Replay consists in automatically replaying the sequence of actions (i.e. goal positions for your motors) recorded in a given dataset episode. Make sure the current initial position of your robot is similar to the one in your episode, so that your follower arms don't move too fast to go to the first goal positions. For safety, you might want to add `--robot-overrides max_relative_target=5` to your command line as explained above.
+
+Now try to replay the first episode on your robot:
+```bash
+python lerobot/scripts/control_robot.py replay \
+    --robot-path lerobot/configs/robot/aloha.yaml \
+    --robot-overrides max_relative_target=null \
+    --fps 30 \
+    --root data \
+    --repo-id ${HF_USER}/aloha_test \
+    --episode 0
+```
+
+## Train a policy
+
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+```bash
+DATA_DIR=data python lerobot/scripts/train.py \
+  dataset_repo_id=${HF_USER}/aloha_test \
+  policy=act_aloha_real \
+  env=aloha_real \
+  hydra.run.dir=outputs/train/act_aloha_test \
+  hydra.job.name=act_aloha_test \
+  device=cuda \
+  wandb.enable=true
+```
+
+Let's explain it:
+1. We provided the dataset as argument with `dataset_repo_id=${HF_USER}/aloha_test`.
+2. We provided the policy with `policy=act_aloha_real`. This loads configurations from [`lerobot/configs/policy/act_aloha_real.yaml`](../lerobot/configs/policy/act_aloha_real.yaml). Importantly, this policy uses 4 cameras as input `cam_right_wrist`, `cam_left_wrist`, `cam_high`, and `cam_low`.
+3. We provided an environment as argument with `env=aloha_real`. This loads configurations from [`lerobot/configs/env/aloha_real.yaml`](../lerobot/configs/env/aloha_real.yaml). Note: this yaml defines 18 dimensions for the `state_dim` and `action_dim`, corresponding to 18 motors, not 14 motors as used in previous Aloha work. This is because, we include the `shoulder_shadow` and `elbow_shadow` motors for simplicity.
+4. We provided `device=cuda` since we are training on a Nvidia GPU.
+5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+6. We added `DATA_DIR=data` to access your dataset stored in your local `data` directory. If you dont provide `DATA_DIR`, your dataset will be downloaded from Hugging Face hub to your cache folder `$HOME/.cache/hugginface`. In future versions of `lerobot`, both directories will be in sync.
+
+Training should take several hours. You will find checkpoints in `outputs/train/act_aloha_test/checkpoints`.
+
+## Evaluate your policy
+
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+```bash
+python lerobot/scripts/control_robot.py record \
+  --robot-path lerobot/configs/robot/aloha.yaml \
+  --robot-overrides max_relative_target=null \
+  --fps 30 \
+  --root data \
+  --repo-id ${HF_USER}/eval_act_aloha_test \
+  --tags aloha tutorial eval \
+  --warmup-time-s 5 \
+  --episode-time-s 40 \
+  --reset-time-s 10 \
+  --num-episodes 10 \
+  --num-image-writer-processes 1 \
+  -p outputs/train/act_aloha_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 `-p` argument which indicates the path to your policy checkpoint with  (e.g. `-p outputs/train/eval_aloha_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `-p ${HF_USER}/act_aloha_test`).
+2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `--repo-id ${HF_USER}/eval_act_aloha_test`).
+3. We use `--num-image-writer-processes 1` instead of the default value (`0`). On our computer, using a dedicated process to write images from the 4 cameras on disk allows to reach constent 30 fps during inference. Feel free to explore different values for `--num-image-writer-processes`.
+
+## More
+
+Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth explaination.
+
+If you have any question or need help, please reach out on Discord in the channel `#aloha-arm`.

lerobot/__init__.py

@@ -27,6 +27,9 @@ Example:
         print(lerobot.available_real_world_datasets)
         print(lerobot.available_policies)
         print(lerobot.available_policies_per_env)
+        print(lerobot.available_robots)
+        print(lerobot.available_cameras)
+        print(lerobot.available_motors)
     ```
 
 When implementing a new dataset loadable with LeRobotDataset follow these steps:
@@ -129,13 +132,60 @@ available_real_world_datasets = [
     "lerobot/unitreeh1_rearrange_objects",
     "lerobot/unitreeh1_two_robot_greeting",
     "lerobot/unitreeh1_warehouse",
+    "lerobot/nyu_rot_dataset",
+    "lerobot/utokyo_saytap",
+    "lerobot/imperialcollege_sawyer_wrist_cam",
+    "lerobot/utokyo_xarm_bimanual",
+    "lerobot/tokyo_u_lsmo",
+    "lerobot/utokyo_pr2_opening_fridge",
+    "lerobot/cmu_franka_exploration_dataset",
+    "lerobot/cmu_stretch",
+    "lerobot/asu_table_top",
+    "lerobot/utokyo_pr2_tabletop_manipulation",
+    "lerobot/utokyo_xarm_pick_and_place",
+    "lerobot/ucsd_kitchen_dataset",
+    "lerobot/austin_buds_dataset",
+    "lerobot/dlr_sara_grid_clamp",
+    "lerobot/conq_hose_manipulation",
+    "lerobot/columbia_cairlab_pusht_real",
+    "lerobot/dlr_sara_pour",
+    "lerobot/dlr_edan_shared_control",
+    "lerobot/ucsd_pick_and_place_dataset",
+    "lerobot/berkeley_cable_routing",
+    "lerobot/nyu_franka_play_dataset",
+    "lerobot/austin_sirius_dataset",
+    "lerobot/cmu_play_fusion",
+    "lerobot/berkeley_gnm_sac_son",
+    "lerobot/nyu_door_opening_surprising_effectiveness",
+    "lerobot/berkeley_fanuc_manipulation",
+    "lerobot/jaco_play",
+    "lerobot/viola",
+    "lerobot/kaist_nonprehensile",
+    "lerobot/berkeley_mvp",
+    "lerobot/uiuc_d3field",
+    "lerobot/berkeley_gnm_recon",
+    "lerobot/austin_sailor_dataset",
+    "lerobot/utaustin_mutex",
+    "lerobot/roboturk",
+    "lerobot/stanford_hydra_dataset",
+    "lerobot/berkeley_autolab_ur5",
+    "lerobot/stanford_robocook",
+    "lerobot/toto",
+    "lerobot/fmb",
+    "lerobot/droid_100",
+    "lerobot/berkeley_rpt",
+    "lerobot/stanford_kuka_multimodal_dataset",
+    "lerobot/iamlab_cmu_pickup_insert",
+    "lerobot/taco_play",
+    "lerobot/berkeley_gnm_cory_hall",
+    "lerobot/usc_cloth_sim",
 ]
 
 available_datasets = list(
     itertools.chain(*available_datasets_per_env.values(), available_real_world_datasets)
 )
 
-# lists all available policies from `lerobot/common/policies` by their class attribute: `name`.
+# lists all available policies from `lerobot/common/policies`
 available_policies = [
     "act",
     "diffusion",
@@ -143,12 +193,35 @@ available_policies = [
     "vqbet",
 ]
 
+# lists all available robots from `lerobot/common/robot_devices/robots`
+available_robots = [
+    "koch",
+    "koch_bimanual",
+    "aloha",
+    "so100",
+    "moss",
+]
+
+# lists all available cameras from `lerobot/common/robot_devices/cameras`
+available_cameras = [
+    "opencv",
+    "intelrealsense",
+]
+
+# lists all available motors from `lerobot/common/robot_devices/motors`
+available_motors = [
+    "dynamixel",
+    "feetech",
+]
+
 # keys and values refer to yaml files
 available_policies_per_env = {
     "aloha": ["act"],
     "pusht": ["diffusion", "vqbet"],
     "xarm": ["tdmpc"],
-    "dora_aloha_real": ["act_real"],
+    "koch_real": ["act_koch_real"],
+    "aloha_real": ["act_aloha_real"],
+    "dora_aloha_real": ["act_aloha_real"],
 }
 
 env_task_pairs = [(env, task) for env, tasks in available_tasks_per_env.items() for task in tasks]

lerobot/common/datasets/compute_stats.py

@@ -40,6 +40,10 @@ def get_stats_einops_patterns(dataset, num_workers=0):
 
     stats_patterns = {}
     for key, feats_type in dataset.features.items():
+        # NOTE: skip language_instruction embedding in stats computation
+        if key == "language_instruction":
+            continue
+
         # sanity check that tensors are not float64
         assert batch[key].dtype != torch.float64
 
@@ -64,7 +68,7 @@ def get_stats_einops_patterns(dataset, num_workers=0):
     return stats_patterns
 
 
-def compute_stats(dataset, batch_size=32, num_workers=16, max_num_samples=None):
+def compute_stats(dataset, batch_size=8, num_workers=8, max_num_samples=None):
     """Compute mean/std and min/max statistics of all data keys in a LeRobotDataset."""
     if max_num_samples is None:
         max_num_samples = len(dataset)

lerobot/common/datasets/populate_dataset.py

@@ -0,0 +1,468 @@
+"""Functions to create an empty dataset, and populate it with frames."""
+# TODO(rcadene, aliberts): to adapt as class methods of next version of LeRobotDataset
+
+import concurrent
+import json
+import logging
+import multiprocessing
+import shutil
+from pathlib import Path
+
+import torch
+import tqdm
+from PIL import Image
+
+from lerobot.common.datasets.compute_stats import compute_stats
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
+from lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format import to_hf_dataset
+from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes, get_default_encoding
+from lerobot.common.datasets.utils import calculate_episode_data_index, create_branch
+from lerobot.common.datasets.video_utils import encode_video_frames
+from lerobot.common.utils.utils import log_say
+from lerobot.scripts.push_dataset_to_hub import (
+    push_dataset_card_to_hub,
+    push_meta_data_to_hub,
+    push_videos_to_hub,
+    save_meta_data,
+)
+
+########################################################################################
+# Asynchrounous saving of images on disk
+########################################################################################
+
+
+def safe_stop_image_writer(func):
+    # TODO(aliberts): Allow to pass custom exceptions
+    # (e.g. ThreadServiceExit, KeyboardInterrupt, SystemExit, UnpluggedError, DynamixelCommError)
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            image_writer = kwargs.get("dataset", {}).get("image_writer")
+            if image_writer is not None:
+                print("Waiting for image writer to terminate...")
+                stop_image_writer(image_writer, timeout=20)
+            raise e
+
+    return wrapper
+
+
+def save_image(img_tensor, key, frame_index, episode_index, videos_dir: str):
+    img = Image.fromarray(img_tensor.numpy())
+    path = Path(videos_dir) / f"{key}_episode_{episode_index:06d}" / f"frame_{frame_index:06d}.png"
+    path.parent.mkdir(parents=True, exist_ok=True)
+    img.save(str(path), quality=100)
+
+
+def loop_to_save_images_in_threads(image_queue, num_threads):
+    if num_threads < 1:
+        raise NotImplementedError(f"Only `num_threads>=1` is supported for now, but {num_threads=} given.")
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
+        futures = []
+        while True:
+            # Blocks until a frame is available
+            frame_data = image_queue.get()
+
+            # As usually done, exit loop when receiving None to stop the worker
+            if frame_data is None:
+                break
+
+            image, key, frame_index, episode_index, videos_dir = frame_data
+            futures.append(executor.submit(save_image, image, key, frame_index, episode_index, videos_dir))
+
+        # Before exiting function, wait for all threads to complete
+        with tqdm.tqdm(total=len(futures), desc="Writing images") as progress_bar:
+            concurrent.futures.wait(futures)
+            progress_bar.update(len(futures))
+
+
+def start_image_writer_processes(image_queue, num_processes, num_threads_per_process):
+    if num_processes < 1:
+        raise ValueError(f"Only `num_processes>=1` is supported, but {num_processes=} given.")
+
+    if num_threads_per_process < 1:
+        raise NotImplementedError(
+            "Only `num_threads_per_process>=1` is supported for now, but {num_threads_per_process=} given."
+        )
+
+    processes = []
+    for _ in range(num_processes):
+        process = multiprocessing.Process(
+            target=loop_to_save_images_in_threads,
+            args=(image_queue, num_threads_per_process),
+        )
+        process.start()
+        processes.append(process)
+    return processes
+
+
+def stop_processes(processes, queue, timeout):
+    # Send None to each process to signal them to stop
+    for _ in processes:
+        queue.put(None)
+
+    # Wait maximum 20 seconds for all processes to terminate
+    for process in processes:
+        process.join(timeout=timeout)
+
+    # If not terminated after 20 seconds, force termination
+    if process.is_alive():
+        process.terminate()
+
+    # Close the queue, no more items can be put in the queue
+    queue.close()
+
+    # Ensure all background queue threads have finished
+    queue.join_thread()
+
+
+def start_image_writer(num_processes, num_threads):
+    """This function abstract away the initialisation of processes or/and threads to
+    save images on disk asynchrounously, which is critical to control a robot and record data
+    at a high frame rate.
+
+    When `num_processes=0`, it returns a dictionary containing a threads pool of size `num_threads`.
+    When `num_processes>0`, it returns a dictionary containing a processes pool of size `num_processes`,
+    where each subprocess starts their own threads pool of size `num_threads`.
+
+    The optimal number of processes and threads depends on your computer capabilities.
+    We advise to use 4 threads per camera with 0 processes. If the fps is not stable, try to increase or lower
+    the number of threads. If it is still not stable, try to use 1 subprocess, or more.
+    """
+    image_writer = {}
+
+    if num_processes == 0:
+        futures = []
+        threads_pool = concurrent.futures.ThreadPoolExecutor(max_workers=num_threads)
+        image_writer["threads_pool"], image_writer["futures"] = threads_pool, futures
+    else:
+        # TODO(rcadene): When using num_processes>1, `multiprocessing.Manager().Queue()`
+        # might be better than `multiprocessing.Queue()`. Source: https://www.geeksforgeeks.org/python-multiprocessing-queue-vs-multiprocessing-manager-queue
+        image_queue = multiprocessing.Queue()
+        processes_pool = start_image_writer_processes(
+            image_queue, num_processes=num_processes, num_threads_per_process=num_threads
+        )
+        image_writer["processes_pool"], image_writer["image_queue"] = processes_pool, image_queue
+
+    return image_writer
+
+
+def async_save_image(image_writer, image, key, frame_index, episode_index, videos_dir):
+    """This function abstract away the saving of an image on disk asynchrounously. It uses a dictionary
+    called image writer which contains either a pool of processes or a pool of threads.
+    """
+    if "threads_pool" in image_writer:
+        threads_pool, futures = image_writer["threads_pool"], image_writer["futures"]
+        futures.append(threads_pool.submit(save_image, image, key, frame_index, episode_index, videos_dir))
+    else:
+        image_queue = image_writer["image_queue"]
+        image_queue.put((image, key, frame_index, episode_index, videos_dir))
+
+
+def stop_image_writer(image_writer, timeout):
+    if "threads_pool" in image_writer:
+        futures = image_writer["futures"]
+        # Before exiting function, wait for all threads to complete
+        with tqdm.tqdm(total=len(futures), desc="Writing images") as progress_bar:
+            concurrent.futures.wait(futures, timeout=timeout)
+            progress_bar.update(len(futures))
+    else:
+        processes_pool, image_queue = image_writer["processes_pool"], image_writer["image_queue"]
+        stop_processes(processes_pool, image_queue, timeout=timeout)
+
+
+########################################################################################
+# Functions to initialize, resume and populate a dataset
+########################################################################################
+
+
+def init_dataset(
+    repo_id,
+    root,
+    force_override,
+    fps,
+    video,
+    write_images,
+    num_image_writer_processes,
+    num_image_writer_threads,
+):
+    local_dir = Path(root) / repo_id
+    if local_dir.exists() and force_override:
+        shutil.rmtree(local_dir)
+
+    episodes_dir = local_dir / "episodes"
+    episodes_dir.mkdir(parents=True, exist_ok=True)
+
+    videos_dir = local_dir / "videos"
+    videos_dir.mkdir(parents=True, exist_ok=True)
+
+    # Logic to resume data recording
+    rec_info_path = episodes_dir / "data_recording_info.json"
+    if rec_info_path.exists():
+        with open(rec_info_path) as f:
+            rec_info = json.load(f)
+        num_episodes = rec_info["last_episode_index"] + 1
+    else:
+        num_episodes = 0
+
+    dataset = {
+        "repo_id": repo_id,
+        "local_dir": local_dir,
+        "videos_dir": videos_dir,
+        "episodes_dir": episodes_dir,
+        "fps": fps,
+        "video": video,
+        "rec_info_path": rec_info_path,
+        "num_episodes": num_episodes,
+    }
+
+    if write_images:
+        # Initialize processes or/and threads dedicated to save images on disk asynchronously,
+        # which is critical to control a robot and record data at a high frame rate.
+        image_writer = start_image_writer(
+            num_processes=num_image_writer_processes,
+            num_threads=num_image_writer_threads,
+        )
+        dataset["image_writer"] = image_writer
+
+    return dataset
+
+
+def add_frame(dataset, observation, action):
+    if "current_episode" not in dataset:
+        # initialize episode dictionary
+        ep_dict = {}
+        for key in observation:
+            if key not in ep_dict:
+                ep_dict[key] = []
+        for key in action:
+            if key not in ep_dict:
+                ep_dict[key] = []
+
+        ep_dict["episode_index"] = []
+        ep_dict["frame_index"] = []
+        ep_dict["timestamp"] = []
+        ep_dict["next.done"] = []
+
+        dataset["current_episode"] = ep_dict
+        dataset["current_frame_index"] = 0
+
+    ep_dict = dataset["current_episode"]
+    episode_index = dataset["num_episodes"]
+    frame_index = dataset["current_frame_index"]
+    videos_dir = dataset["videos_dir"]
+    video = dataset["video"]
+    fps = dataset["fps"]
+
+    ep_dict["episode_index"].append(episode_index)
+    ep_dict["frame_index"].append(frame_index)
+    ep_dict["timestamp"].append(frame_index / fps)
+    ep_dict["next.done"].append(False)
+
+    img_keys = [key for key in observation if "image" in key]
+    non_img_keys = [key for key in observation if "image" not in key]
+
+    # Save all observed modalities except images
+    for key in non_img_keys:
+        ep_dict[key].append(observation[key])
+
+    # Save actions
+    for key in action:
+        ep_dict[key].append(action[key])
+
+    if "image_writer" not in dataset:
+        dataset["current_frame_index"] += 1
+        return
+
+    # Save images
+    image_writer = dataset["image_writer"]
+    for key in img_keys:
+        imgs_dir = videos_dir / f"{key}_episode_{episode_index:06d}"
+        async_save_image(
+            image_writer,
+            image=observation[key],
+            key=key,
+            frame_index=frame_index,
+            episode_index=episode_index,
+            videos_dir=str(videos_dir),
+        )
+
+        if video:
+            fname = f"{key}_episode_{episode_index:06d}.mp4"
+            frame_info = {"path": f"videos/{fname}", "timestamp": frame_index / fps}
+        else:
+            frame_info = str(imgs_dir / f"frame_{frame_index:06d}.png")
+
+        ep_dict[key].append(frame_info)
+
+    dataset["current_frame_index"] += 1
+
+
+def delete_current_episode(dataset):
+    del dataset["current_episode"]
+    del dataset["current_frame_index"]
+
+    # delete temporary images
+    episode_index = dataset["num_episodes"]
+    videos_dir = dataset["videos_dir"]
+    for tmp_imgs_dir in videos_dir.glob(f"*_episode_{episode_index:06d}"):
+        shutil.rmtree(tmp_imgs_dir)
+
+
+def save_current_episode(dataset):
+    episode_index = dataset["num_episodes"]
+    ep_dict = dataset["current_episode"]
+    episodes_dir = dataset["episodes_dir"]
+    rec_info_path = dataset["rec_info_path"]
+
+    ep_dict["next.done"][-1] = True
+
+    for key in ep_dict:
+        if "observation" in key and "image" not in key:
+            ep_dict[key] = torch.stack(ep_dict[key])
+
+    ep_dict["action"] = torch.stack(ep_dict["action"])
+    ep_dict["episode_index"] = torch.tensor(ep_dict["episode_index"])
+    ep_dict["frame_index"] = torch.tensor(ep_dict["frame_index"])
+    ep_dict["timestamp"] = torch.tensor(ep_dict["timestamp"])
+    ep_dict["next.done"] = torch.tensor(ep_dict["next.done"])
+
+    ep_path = episodes_dir / f"episode_{episode_index}.pth"
+    torch.save(ep_dict, ep_path)
+
+    rec_info = {
+        "last_episode_index": episode_index,
+    }
+    with open(rec_info_path, "w") as f:
+        json.dump(rec_info, f)
+
+    # force re-initialization of episode dictionnary during add_frame
+    del dataset["current_episode"]
+
+    dataset["num_episodes"] += 1
+
+
+def encode_videos(dataset, image_keys, play_sounds):
+    log_say("Encoding videos", play_sounds)
+
+    num_episodes = dataset["num_episodes"]
+    videos_dir = dataset["videos_dir"]
+    local_dir = dataset["local_dir"]
+    fps = dataset["fps"]
+
+    # Use ffmpeg to convert frames stored as png into mp4 videos
+    for episode_index in tqdm.tqdm(range(num_episodes)):
+        for key in image_keys:
+            # key = f"observation.images.{name}"
+            tmp_imgs_dir = videos_dir / f"{key}_episode_{episode_index:06d}"
+            fname = f"{key}_episode_{episode_index:06d}.mp4"
+            video_path = local_dir / "videos" / fname
+            if video_path.exists():
+                # Skip if video is already encoded. Could be the case when resuming data recording.
+                continue
+            # note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
+            # since video encoding with ffmpeg is already using multithreading.
+            encode_video_frames(tmp_imgs_dir, video_path, fps, overwrite=True)
+            shutil.rmtree(tmp_imgs_dir)
+
+
+def from_dataset_to_lerobot_dataset(dataset, play_sounds):
+    log_say("Consolidate episodes", play_sounds)
+
+    num_episodes = dataset["num_episodes"]
+    episodes_dir = dataset["episodes_dir"]
+    videos_dir = dataset["videos_dir"]
+    video = dataset["video"]
+    fps = dataset["fps"]
+    repo_id = dataset["repo_id"]
+
+    ep_dicts = []
+    for episode_index in tqdm.tqdm(range(num_episodes)):
+        ep_path = episodes_dir / f"episode_{episode_index}.pth"
+        ep_dict = torch.load(ep_path)
+        ep_dicts.append(ep_dict)
+    data_dict = concatenate_episodes(ep_dicts)
+
+    if video:
+        image_keys = [key for key in data_dict if "image" in key]
+        encode_videos(dataset, image_keys, play_sounds)
+
+    hf_dataset = to_hf_dataset(data_dict, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    if video:
+        info["encoding"] = get_default_encoding()
+
+    lerobot_dataset = LeRobotDataset.from_preloaded(
+        repo_id=repo_id,
+        hf_dataset=hf_dataset,
+        episode_data_index=episode_data_index,
+        info=info,
+        videos_dir=videos_dir,
+    )
+
+    return lerobot_dataset
+
+
+def save_lerobot_dataset_on_disk(lerobot_dataset):
+    hf_dataset = lerobot_dataset.hf_dataset
+    info = lerobot_dataset.info
+    stats = lerobot_dataset.stats
+    episode_data_index = lerobot_dataset.episode_data_index
+    local_dir = lerobot_dataset.videos_dir.parent
+    meta_data_dir = local_dir / "meta_data"
+
+    hf_dataset = hf_dataset.with_format(None)  # to remove transforms that cant be saved
+    hf_dataset.save_to_disk(str(local_dir / "train"))
+
+    save_meta_data(info, stats, episode_data_index, meta_data_dir)
+
+
+def push_lerobot_dataset_to_hub(lerobot_dataset, tags):
+    hf_dataset = lerobot_dataset.hf_dataset
+    local_dir = lerobot_dataset.videos_dir.parent
+    videos_dir = lerobot_dataset.videos_dir
+    repo_id = lerobot_dataset.repo_id
+    video = lerobot_dataset.video
+    meta_data_dir = local_dir / "meta_data"
+
+    if not (local_dir / "train").exists():
+        raise ValueError(
+            "You need to run `save_lerobot_dataset_on_disk(lerobot_dataset)` before pushing to the hub."
+        )
+
+    hf_dataset.push_to_hub(repo_id, revision="main")
+    push_meta_data_to_hub(repo_id, meta_data_dir, revision="main")
+    push_dataset_card_to_hub(repo_id, revision="main", tags=tags)
+    if video:
+        push_videos_to_hub(repo_id, videos_dir, revision="main")
+    create_branch(repo_id, repo_type="dataset", branch=CODEBASE_VERSION)
+
+
+def create_lerobot_dataset(dataset, run_compute_stats, push_to_hub, tags, play_sounds):
+    if "image_writer" in dataset:
+        logging.info("Waiting for image writer to terminate...")
+        image_writer = dataset["image_writer"]
+        stop_image_writer(image_writer, timeout=20)
+
+    lerobot_dataset = from_dataset_to_lerobot_dataset(dataset, play_sounds)
+
+    if run_compute_stats:
+        log_say("Computing dataset statistics", play_sounds)
+        lerobot_dataset.stats = compute_stats(lerobot_dataset)
+    else:
+        logging.info("Skipping computation of the dataset statistics")
+        lerobot_dataset.stats = {}
+
+    save_lerobot_dataset_on_disk(lerobot_dataset)
+
+    if push_to_hub:
+        push_lerobot_dataset_to_hub(lerobot_dataset, tags)
+
+    return lerobot_dataset

lerobot/common/datasets/push_dataset_to_hub/_download_raw.py

@@ -60,8 +60,8 @@ AVAILABLE_RAW_REPO_IDS = {
     "lerobot-raw/aloha_static_vinh_cup_left_raw": "aloha_hdf5",
     "lerobot-raw/aloha_static_vinh_cup_raw": "aloha_hdf5",
     "lerobot-raw/aloha_static_ziploc_slide_raw": "aloha_hdf5",
-    "lerobot-raw/pusht_raw": "pusht_zarr",
     "lerobot-raw/umi_cup_in_the_wild_raw": "umi_zarr",
+    "lerobot-raw/pusht_raw": "pusht_zarr",
     "lerobot-raw/unitreeh1_fold_clothes_raw": "aloha_hdf5",
     "lerobot-raw/unitreeh1_rearrange_objects_raw": "aloha_hdf5",
     "lerobot-raw/unitreeh1_two_robot_greeting_raw": "aloha_hdf5",
@@ -70,6 +70,74 @@ AVAILABLE_RAW_REPO_IDS = {
     "lerobot-raw/xarm_lift_medium_replay_raw": "xarm_pkl",
     "lerobot-raw/xarm_push_medium_raw": "xarm_pkl",
     "lerobot-raw/xarm_push_medium_replay_raw": "xarm_pkl",
+    "lerobot-raw/fractal20220817_data_raw": "openx_rlds.fractal20220817_data",
+    "lerobot-raw/kuka_raw": "openx_rlds.kuka",
+    "lerobot-raw/bridge_openx_raw": "openx_rlds.bridge_openx",
+    "lerobot-raw/taco_play_raw": "openx_rlds.taco_play",
+    "lerobot-raw/jaco_play_raw": "openx_rlds.jaco_play",
+    "lerobot-raw/berkeley_cable_routing_raw": "openx_rlds.berkeley_cable_routing",
+    "lerobot-raw/roboturk_raw": "openx_rlds.roboturk",
+    "lerobot-raw/nyu_door_opening_surprising_effectiveness_raw": "openx_rlds.nyu_door_opening_surprising_effectiveness",
+    "lerobot-raw/viola_raw": "openx_rlds.viola",
+    "lerobot-raw/berkeley_autolab_ur5_raw": "openx_rlds.berkeley_autolab_ur5",
+    "lerobot-raw/toto_raw": "openx_rlds.toto",
+    "lerobot-raw/language_table_raw": "openx_rlds.language_table",
+    "lerobot-raw/columbia_cairlab_pusht_real_raw": "openx_rlds.columbia_cairlab_pusht_real",
+    "lerobot-raw/stanford_kuka_multimodal_dataset_raw": "openx_rlds.stanford_kuka_multimodal_dataset",
+    "lerobot-raw/nyu_rot_dataset_raw": "openx_rlds.nyu_rot_dataset",
+    "lerobot-raw/io_ai_tech_raw": "openx_rlds.io_ai_tech",
+    "lerobot-raw/stanford_hydra_dataset_raw": "openx_rlds.stanford_hydra_dataset",
+    "lerobot-raw/austin_buds_dataset_raw": "openx_rlds.austin_buds_dataset",
+    "lerobot-raw/nyu_franka_play_dataset_raw": "openx_rlds.nyu_franka_play_dataset",
+    "lerobot-raw/maniskill_dataset_raw": "openx_rlds.maniskill_dataset",
+    "lerobot-raw/furniture_bench_dataset_raw": "openx_rlds.furniture_bench_dataset",
+    "lerobot-raw/cmu_franka_exploration_dataset_raw": "openx_rlds.cmu_franka_exploration_dataset",
+    "lerobot-raw/ucsd_kitchen_dataset_raw": "openx_rlds.ucsd_kitchen_dataset",
+    "lerobot-raw/ucsd_pick_and_place_dataset_raw": "openx_rlds.ucsd_pick_and_place_dataset",
+    "lerobot-raw/spoc_raw": "openx_rlds.spoc",
+    "lerobot-raw/austin_sailor_dataset_raw": "openx_rlds.austin_sailor_dataset",
+    "lerobot-raw/austin_sirius_dataset_raw": "openx_rlds.austin_sirius_dataset",
+    "lerobot-raw/bc_z_raw": "openx_rlds.bc_z",
+    "lerobot-raw/utokyo_pr2_opening_fridge_raw": "openx_rlds.utokyo_pr2_opening_fridge",
+    "lerobot-raw/utokyo_pr2_tabletop_manipulation_raw": "openx_rlds.utokyo_pr2_tabletop_manipulation",
+    "lerobot-raw/utokyo_xarm_pick_and_place_raw": "openx_rlds.utokyo_xarm_pick_and_place",
+    "lerobot-raw/utokyo_xarm_bimanual_raw": "openx_rlds.utokyo_xarm_bimanual",
+    "lerobot-raw/utokyo_saytap_raw": "openx_rlds.utokyo_saytap",
+    "lerobot-raw/robo_net_raw": "openx_rlds.robo_net",
+    "lerobot-raw/robo_set_raw": "openx_rlds.robo_set",
+    "lerobot-raw/berkeley_mvp_raw": "openx_rlds.berkeley_mvp",
+    "lerobot-raw/berkeley_rpt_raw": "openx_rlds.berkeley_rpt",
+    "lerobot-raw/kaist_nonprehensile_raw": "openx_rlds.kaist_nonprehensile",
+    "lerobot-raw/stanford_mask_vit_raw": "openx_rlds.stanford_mask_vit",
+    "lerobot-raw/tokyo_u_lsmo_raw": "openx_rlds.tokyo_u_lsmo",
+    "lerobot-raw/dlr_sara_pour_raw": "openx_rlds.dlr_sara_pour",
+    "lerobot-raw/dlr_sara_grid_clamp_raw": "openx_rlds.dlr_sara_grid_clamp",
+    "lerobot-raw/dlr_edan_shared_control_raw": "openx_rlds.dlr_edan_shared_control",
+    "lerobot-raw/asu_table_top_raw": "openx_rlds.asu_table_top",
+    "lerobot-raw/stanford_robocook_raw": "openx_rlds.stanford_robocook",
+    "lerobot-raw/imperialcollege_sawyer_wrist_cam_raw": "openx_rlds.imperialcollege_sawyer_wrist_cam",
+    "lerobot-raw/iamlab_cmu_pickup_insert_raw": "openx_rlds.iamlab_cmu_pickup_insert",
+    "lerobot-raw/uiuc_d3field_raw": "openx_rlds.uiuc_d3field",
+    "lerobot-raw/utaustin_mutex_raw": "openx_rlds.utaustin_mutex",
+    "lerobot-raw/berkeley_fanuc_manipulation_raw": "openx_rlds.berkeley_fanuc_manipulation",
+    "lerobot-raw/cmu_playing_with_food_raw": "openx_rlds.cmu_playing_with_food",
+    "lerobot-raw/cmu_play_fusion_raw": "openx_rlds.cmu_play_fusion",
+    "lerobot-raw/cmu_stretch_raw": "openx_rlds.cmu_stretch",
+    "lerobot-raw/berkeley_gnm_recon_raw": "openx_rlds.berkeley_gnm_recon",
+    "lerobot-raw/berkeley_gnm_cory_hall_raw": "openx_rlds.berkeley_gnm_cory_hall",
+    "lerobot-raw/berkeley_gnm_sac_son_raw": "openx_rlds.berkeley_gnm_sac_son",
+    "lerobot-raw/droid_raw": "openx_rlds.droid",
+    "lerobot-raw/droid_100_raw": "openx_rlds.droid100",
+    "lerobot-raw/fmb_raw": "openx_rlds.fmb",
+    "lerobot-raw/dobbe_raw": "openx_rlds.dobbe",
+    "lerobot-raw/usc_cloth_sim_raw": "openx_rlds.usc_cloth_sim",
+    "lerobot-raw/plex_robosuite_raw": "openx_rlds.plex_robosuite",
+    "lerobot-raw/conq_hose_manipulation_raw": "openx_rlds.conq_hose_manipulation",
+    "lerobot-raw/vima_raw": "openx_rlds.vima",
+    "lerobot-raw/robot_vqa_raw": "openx_rlds.robot_vqa",
+    "lerobot-raw/mimic_play_raw": "openx_rlds.mimic_play",
+    "lerobot-raw/tidybot_raw": "openx_rlds.tidybot",
+    "lerobot-raw/eth_agent_affordances_raw": "openx_rlds.eth_agent_affordances",
 }
 
 
@@ -110,7 +178,7 @@ def download_all_raw_datasets(data_dir: Path | None = None):
 def main():
     parser = argparse.ArgumentParser(
         description=f"""A script to download raw datasets from Hugging Face hub to a local directory. Here is a
-            non exhaustive list of available repositories to use in `--repo-id`: {AVAILABLE_RAW_REPO_IDS}""",
+            non exhaustive list of available repositories to use in `--repo-id`: {list(AVAILABLE_RAW_REPO_IDS.keys())}""",
     )
 
     parser.add_argument(

lerobot/common/datasets/push_dataset_to_hub/openx/configs.yaml

@@ -0,0 +1,639 @@
+OPENX_DATASET_CONFIGS:
+  fractal20220817_data:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - base_pose_tool_reached
+      - gripper_closed
+    fps: 3
+
+  kuka:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - clip_function_input/base_pose_tool_reached
+      - gripper_closed
+    fps: 10
+
+  bridge_openx:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - EEF_state
+      - gripper_state
+    fps: 5
+
+  taco_play:
+    image_obs_keys:
+      - rgb_static
+      - rgb_gripper
+    depth_obs_keys:
+      - depth_static
+      - depth_gripper
+    state_obs_keys:
+      - state_eef
+      - state_gripper
+    fps: 15
+
+  jaco_play:
+    image_obs_keys:
+      - image
+      - image_wrist
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state_eef
+      - state_gripper
+    fps: 10
+
+  berkeley_cable_routing:
+    image_obs_keys:
+      - image
+      - top_image
+      - wrist45_image
+      - wrist225_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - robot_state
+    fps: 10
+
+  roboturk:
+    image_obs_keys:
+      - front_rgb
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - null
+    fps: 10
+
+  nyu_door_opening_surprising_effectiveness:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - null
+    fps: 3
+
+  viola:
+    image_obs_keys:
+      - agentview_rgb
+      - eye_in_hand_rgb
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - joint_states
+      - gripper_states
+    fps: 20
+
+  berkeley_autolab_ur5:
+    image_obs_keys:
+      - image
+      - hand_image
+    depth_obs_keys:
+      - image_with_depth
+    state_obs_keys:
+      - state
+    fps: 5
+
+  toto:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 30
+
+  language_table:
+    image_obs_keys:
+      - rgb
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - effector_translation
+    fps: 10
+
+  columbia_cairlab_pusht_real:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - robot_state
+    fps: 10
+
+  stanford_kuka_multimodal_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - depth_image
+    state_obs_keys:
+      - ee_position
+      - ee_orientation
+    fps: 20
+
+  nyu_rot_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 3
+
+  io_ai_tech:
+    image_obs_keys:
+      - image
+      - image_fisheye
+      - image_left_side
+      - image_right_side
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 3
+
+  stanford_hydra_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 10
+
+  austin_buds_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 20
+
+  nyu_franka_play_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - image_additional_view
+    depth_obs_keys:
+      - depth
+      - depth_additional_view
+    state_obs_keys:
+      - eef_state
+    fps: 3
+
+  maniskill_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - depth
+      - wrist_depth
+    state_obs_keys:
+      - tcp_pose
+      - gripper_state
+    fps: 20
+
+  furniture_bench_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 10
+
+  cmu_franka_exploration_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - highres_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - null
+    fps: 10
+
+  ucsd_kitchen_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - joint_state
+    fps: 2
+
+  ucsd_pick_and_place_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 3
+
+  spoc:
+    image_obs_keys:
+      - image
+      - image_manipulation
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - null
+    fps: 3
+
+  austin_sailor_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 20
+
+  austin_sirius_dataset_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 20
+
+  bc_z:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - present/xyz
+      - present/axis_angle
+      - present/sensed_close
+    fps: 10
+
+  utokyo_pr2_opening_fridge_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 10
+
+  utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 10
+
+  utokyo_xarm_pick_and_place_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - image2
+      - hand_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - end_effector_pose
+    fps: 10
+
+  utokyo_xarm_bimanual_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - pose_r
+    fps: 10
+
+  robo_net:
+    image_obs_keys:
+      - image
+      - image1
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 1
+
+  robo_set:
+    image_obs_keys:
+      - image_left
+      - image_right
+      - image_wrist
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+      - state_velocity
+    fps: 5
+
+  berkeley_mvp_converted_externally_to_rlds:
+    image_obs_keys:
+      - hand_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - gripper
+      - pose
+      - joint_pos
+    fps: 5
+
+  berkeley_rpt_converted_externally_to_rlds:
+    image_obs_keys:
+      - hand_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - joint_pos
+      - gripper
+    fps: 30
+
+  kaist_nonprehensile_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 10
+
+  stanford_mask_vit_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+
+  tokyo_u_lsmo_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 10
+
+  dlr_sara_pour_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 10
+
+  dlr_sara_grid_clamp_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 10
+
+  dlr_edan_shared_control_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 5
+
+  asu_table_top_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 12.5
+
+  stanford_robocook_converted_externally_to_rlds:
+    image_obs_keys:
+      - image_1
+      - image_2
+    depth_obs_keys:
+      - depth_1
+      - depth_2
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 5
+
+  imperialcollege_sawyer_wrist_cam:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 10
+
+  iamlab_cmu_pickup_insert_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - joint_state
+      - gripper_state
+    fps: 20
+
+  uiuc_d3field:
+    image_obs_keys:
+      - image_1
+      - image_2
+    depth_obs_keys:
+      - depth_1
+      - depth_2
+    state_obs_keys:
+      - null
+    fps: 1
+
+  utaustin_mutex:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 20
+
+  berkeley_fanuc_manipulation:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - joint_state
+      - gripper_state
+    fps: 10
+
+  cmu_playing_with_food:
+    image_obs_keys:
+      - image
+      - finger_vision_1
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 10
+
+  cmu_play_fusion:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 5
+
+  cmu_stretch:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - eef_state
+      - gripper_state
+    fps: 10
+
+  berkeley_gnm_recon:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+      - position
+      - yaw
+    fps: 3
+
+  berkeley_gnm_cory_hall:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+      - position
+      - yaw
+    fps: 5
+
+  berkeley_gnm_sac_son:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+      - position
+      - yaw
+    fps: 10
+
+  droid:
+    image_obs_keys:
+      - exterior_image_1_left
+      - exterior_image_2_left
+      - wrist_image_left
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - proprio
+    fps: 15
+
+  droid_100:
+    image_obs_keys:
+      - exterior_image_1_left
+      - exterior_image_2_left
+      - wrist_image_left
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - proprio
+    fps: 15
+
+  fmb:
+    image_obs_keys:
+      - image_side_1
+      - image_side_2
+      - image_wrist_1
+      - image_wrist_2
+    depth_obs_keys:
+      - image_side_1_depth
+      - image_side_2_depth
+      - image_wrist_1_depth
+      - image_wrist_2_depth
+    state_obs_keys:
+      - proprio
+    fps: 10
+
+  dobbe:
+    image_obs_keys:
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - proprio
+    fps: 3.75
+
+  usc_cloth_sim_converted_externally_to_rlds:
+    image_obs_keys:
+      - image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - null
+    fps: 10
+
+  plex_robosuite:
+    image_obs_keys:
+      - image
+      - wrist_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 20
+
+  conq_hose_manipulation:
+    image_obs_keys:
+      - frontleft_fisheye_image
+      - frontright_fisheye_image
+      - hand_color_image
+    depth_obs_keys:
+      - null
+    state_obs_keys:
+      - state
+    fps: 30

lerobot/common/datasets/push_dataset_to_hub/openx/data_utils.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the Licens    e.
+# 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.
+"""
+NOTE(YL): Adapted from:
+    Octo: https://github.com/octo-models/octo/blob/main/octo/data/utils/data_utils.py
+
+data_utils.py
+
+Additional utils for data processing.
+"""
+
+from typing import Any, Dict, List
+
+import tensorflow as tf
+
+
+def binarize_gripper_actions(actions: tf.Tensor) -> tf.Tensor:
+    """
+    Converts gripper actions from continuous to binary values (0 and 1).
+
+    We exploit that fact that most of the time, the gripper is fully open (near 1.0) or fully closed (near 0.0). As it
+    transitions between the two, it sometimes passes through a few intermediate values. We relabel those intermediate
+    values based on the state that is reached _after_ those intermediate values.
+
+    In the edge case that the trajectory ends with an intermediate value, we give up on binarizing and relabel that
+    chunk of intermediate values as the last action in the trajectory.
+
+    The `scan_fn` implements the following logic:
+        new_actions = np.empty_like(actions)
+        carry = actions[-1]
+        for i in reversed(range(actions.shape[0])):
+            if in_between_mask[i]:
+                carry = carry
+            else:
+                carry = float(open_mask[i])
+            new_actions[i] = carry
+    """
+    open_mask, closed_mask = actions > 0.95, actions < 0.05
+    in_between_mask = tf.logical_not(tf.logical_or(open_mask, closed_mask))
+    is_open_float = tf.cast(open_mask, tf.float32)
+
+    def scan_fn(carry, i):
+        return tf.cond(in_between_mask[i], lambda: tf.cast(carry, tf.float32), lambda: is_open_float[i])
+
+    return tf.scan(scan_fn, tf.range(tf.shape(actions)[0]), actions[-1], reverse=True)
+
+
+def invert_gripper_actions(actions: tf.Tensor) -> tf.Tensor:
+    return 1 - actions
+
+
+def rel2abs_gripper_actions(actions: tf.Tensor) -> tf.Tensor:
+    """
+    Converts relative gripper actions (+1 for closing, -1 for opening) to absolute actions (0 = closed; 1 = open).
+
+    Assumes that the first relative gripper is not redundant (i.e. close when already closed)!
+    """
+    # Note =>> -1 for closing, 1 for opening, 0 for no change
+    opening_mask, closing_mask = actions < -0.1, actions > 0.1
+    thresholded_actions = tf.where(opening_mask, 1, tf.where(closing_mask, -1, 0))
+
+    def scan_fn(carry, i):
+        return tf.cond(thresholded_actions[i] == 0, lambda: carry, lambda: thresholded_actions[i])
+
+    # If no relative grasp, assumes open for whole trajectory
+    start = -1 * thresholded_actions[tf.argmax(thresholded_actions != 0, axis=0)]
+    start = tf.cond(start == 0, lambda: 1, lambda: start)
+
+    # Note =>> -1 for closed, 1 for open
+    new_actions = tf.scan(scan_fn, tf.range(tf.shape(actions)[0]), start)
+    new_actions = tf.cast(new_actions, tf.float32) / 2 + 0.5
+
+    return new_actions
+
+
+# === Bridge-V2 =>> Dataset-Specific Transform ===
+def relabel_bridge_actions(traj: Dict[str, Any]) -> Dict[str, Any]:
+    """Relabels actions to use reached proprioceptive state; discards last timestep (no-action)."""
+    movement_actions = traj["observation"]["state"][1:, :6] - traj["observation"]["state"][:-1, :6]
+    traj_truncated = tf.nest.map_structure(lambda x: x[:-1], traj)
+    traj_truncated["action"] = tf.concat([movement_actions, traj["action"][:-1, -1:]], axis=1)
+
+    return traj_truncated
+
+
+# === RLDS Dataset Initialization Utilities ===
+def pprint_data_mixture(dataset_kwargs_list: List[Dict[str, Any]], dataset_weights: List[int]) -> None:
+    print("\n######################################################################################")
+    print(f"# Loading the following {len(dataset_kwargs_list)} datasets (incl. sampling weight):{'': >24} #")
+    for dataset_kwargs, weight in zip(dataset_kwargs_list, dataset_weights, strict=False):
+        pad = 80 - len(dataset_kwargs["name"])
+        print(f"# {dataset_kwargs['name']}: {weight:=>{pad}f} #")
+    print("######################################################################################\n")

lerobot/common/datasets/push_dataset_to_hub/openx/droid_utils.py

@@ -0,0 +1,200 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+NOTE(YL): Adapted from:
+    OpenVLA: https://github.com/openvla/openvla
+
+Episode transforms for DROID dataset.
+"""
+
+from typing import Any, Dict
+
+import tensorflow as tf
+import tensorflow_graphics.geometry.transformation as tfg
+
+
+def rmat_to_euler(rot_mat):
+    return tfg.euler.from_rotation_matrix(rot_mat)
+
+
+def euler_to_rmat(euler):
+    return tfg.rotation_matrix_3d.from_euler(euler)
+
+
+def invert_rmat(rot_mat):
+    return tfg.rotation_matrix_3d.inverse(rot_mat)
+
+
+def rotmat_to_rot6d(mat):
+    """
+    Converts rotation matrix to R6 rotation representation (first two rows in rotation matrix).
+    Args:
+        mat: rotation matrix
+
+    Returns: 6d vector (first two rows of rotation matrix)
+
+    """
+    r6 = mat[..., :2, :]
+    r6_0, r6_1 = r6[..., 0, :], r6[..., 1, :]
+    r6_flat = tf.concat([r6_0, r6_1], axis=-1)
+    return r6_flat
+
+
+def velocity_act_to_wrist_frame(velocity, wrist_in_robot_frame):
+    """
+    Translates velocity actions (translation + rotation) from base frame of the robot to wrist frame.
+    Args:
+        velocity: 6d velocity action (3 x translation, 3 x rotation)
+        wrist_in_robot_frame: 6d pose of the end-effector in robot base frame
+
+    Returns: 9d velocity action in robot wrist frame (3 x translation, 6 x rotation as R6)
+
+    """
+    r_frame = euler_to_rmat(wrist_in_robot_frame[:, 3:6])
+    r_frame_inv = invert_rmat(r_frame)
+
+    # world to wrist: dT_pi = R^-1 dT_rbt
+    vel_t = (r_frame_inv @ velocity[:, :3][..., None])[..., 0]
+
+    # world to wrist: dR_pi = R^-1 dR_rbt R
+    dr_ = euler_to_rmat(velocity[:, 3:6])
+    dr_ = r_frame_inv @ (dr_ @ r_frame)
+    dr_r6 = rotmat_to_rot6d(dr_)
+    return tf.concat([vel_t, dr_r6], axis=-1)
+
+
+def rand_swap_exterior_images(img1, img2):
+    """
+    Randomly swaps the two exterior images (for training with single exterior input).
+    """
+    return tf.cond(tf.random.uniform(shape=[]) > 0.5, lambda: (img1, img2), lambda: (img2, img1))
+
+
+def droid_baseact_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    DROID dataset transformation for actions expressed in *base* frame of the robot.
+    """
+    dt = trajectory["action_dict"]["cartesian_velocity"][:, :3]
+    dr_ = trajectory["action_dict"]["cartesian_velocity"][:, 3:6]
+
+    trajectory["action"] = tf.concat(
+        (
+            dt,
+            dr_,
+            1 - trajectory["action_dict"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["exterior_image_1_left"], trajectory["observation"]["exterior_image_2_left"] = (
+        rand_swap_exterior_images(
+            trajectory["observation"]["exterior_image_1_left"],
+            trajectory["observation"]["exterior_image_2_left"],
+        )
+    )
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["cartesian_position"],
+            trajectory["observation"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def droid_wristact_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    DROID dataset transformation for actions expressed in *wrist* frame of the robot.
+    """
+    wrist_act = velocity_act_to_wrist_frame(
+        trajectory["action_dict"]["cartesian_velocity"], trajectory["observation"]["cartesian_position"]
+    )
+    trajectory["action"] = tf.concat(
+        (
+            wrist_act,
+            trajectory["action_dict"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["exterior_image_1_left"], trajectory["observation"]["exterior_image_2_left"] = (
+        rand_swap_exterior_images(
+            trajectory["observation"]["exterior_image_1_left"],
+            trajectory["observation"]["exterior_image_2_left"],
+        )
+    )
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["cartesian_position"],
+            trajectory["observation"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def droid_finetuning_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    DROID dataset transformation for actions expressed in *base* frame of the robot.
+    """
+    dt = trajectory["action_dict"]["cartesian_velocity"][:, :3]
+    dr_ = trajectory["action_dict"]["cartesian_velocity"][:, 3:6]
+    trajectory["action"] = tf.concat(
+        (
+            dt,
+            dr_,
+            1 - trajectory["action_dict"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["cartesian_position"],
+            trajectory["observation"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def zero_action_filter(traj: Dict) -> bool:
+    """
+    Filters transitions whose actions are all-0 (only relative actions, no gripper action).
+    Note: this filter is applied *after* action normalization, so need to compare to "normalized 0".
+    """
+    droid_q01 = tf.convert_to_tensor(
+        [
+            -0.7776297926902771,
+            -0.5803514122962952,
+            -0.5795090794563293,
+            -0.6464047729969025,
+            -0.7041108310222626,
+            -0.8895104378461838,
+        ]
+    )
+    droid_q99 = tf.convert_to_tensor(
+        [
+            0.7597932070493698,
+            0.5726242214441299,
+            0.7351000607013702,
+            0.6705610305070877,
+            0.6464948207139969,
+            0.8897542208433151,
+        ]
+    )
+    droid_norm_0_act = (
+        2 * (tf.zeros_like(traj["action"][:, :6]) - droid_q01) / (droid_q99 - droid_q01 + 1e-8) - 1
+    )
+
+    return tf.reduce_any(tf.math.abs(traj["action"][:, :6] - droid_norm_0_act) > 1e-5)

lerobot/common/datasets/push_dataset_to_hub/openx/transforms.py

@@ -0,0 +1,859 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+NOTE(YL): Adapted from:
+    OpenVLA: https://github.com/openvla/openvla
+    Octo: https://github.com/octo-models/octo
+
+transforms.py
+
+Defines a registry of per-dataset standardization transforms for each dataset in Open-X Embodiment.
+
+Transforms adopt the following structure:
+    Input: Dictionary of *batched* features (i.e., has leading time dimension)
+    Output: Dictionary `step` =>> {
+        "observation": {
+            <image_keys, depth_image_keys>
+            State (in chosen state representation)
+        },
+        "action": Action (in chosen action representation),
+        "language_instruction": str
+    }
+"""
+
+from typing import Any, Dict
+
+import tensorflow as tf
+
+from lerobot.common.datasets.push_dataset_to_hub.openx.data_utils import (
+    binarize_gripper_actions,
+    invert_gripper_actions,
+    rel2abs_gripper_actions,
+    relabel_bridge_actions,
+)
+
+
+def droid_baseact_transform_fn():
+    from lerobot.common.datasets.push_dataset_to_hub.openx.droid_utils import droid_baseact_transform
+
+    return droid_baseact_transform
+
+
+def bridge_openx_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Applies to version of Bridge V2 in Open X-Embodiment mixture.
+
+    Note =>> In original Bridge V2 dataset, the first timestep has an all-zero action, so we remove it!
+    """
+    for key in trajectory:
+        if key == "traj_metadata":
+            continue
+        elif key in ["observation", "action"]:
+            for key2 in trajectory[key]:
+                trajectory[key][key2] = trajectory[key][key2][1:]
+        else:
+            trajectory[key] = trajectory[key][1:]
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            tf.cast(trajectory["action"]["open_gripper"][:, None], tf.float32),
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    trajectory = relabel_bridge_actions(trajectory)
+    trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def bridge_orig_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Applies to original version of Bridge V2 from the official project website.
+
+    Note =>> In original Bridge V2 dataset, the first timestep has an all-zero action, so we remove it!
+    """
+    for key in trajectory:
+        if key == "traj_metadata":
+            continue
+        elif key == "observation":
+            for key2 in trajectory[key]:
+                trajectory[key][key2] = trajectory[key][key2][1:]
+        else:
+            trajectory[key] = trajectory[key][1:]
+
+    trajectory["action"] = tf.concat(
+        [
+            trajectory["action"][:, :6],
+            binarize_gripper_actions(trajectory["action"][:, -1])[:, None],
+        ],
+        axis=1,
+    )
+    trajectory = relabel_bridge_actions(trajectory)
+    trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def ppgm_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        [
+            trajectory["action"][:, :6],
+            binarize_gripper_actions(trajectory["action"][:, -1])[:, None],
+        ],
+        axis=1,
+    )
+    trajectory["observation"]["EEF_state"] = trajectory["observation"]["cartesian_position"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["gripper_position"][:, -1:]
+    return trajectory
+
+
+def rt1_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def kuka_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    # decode compressed state
+    eef_value = tf.io.decode_compressed(
+        trajectory["observation"]["clip_function_input/base_pose_tool_reached"],
+        compression_type="ZLIB",
+    )
+    eef_value = tf.io.decode_raw(eef_value, tf.float32)
+    trajectory["observation"]["clip_function_input/base_pose_tool_reached"] = tf.reshape(eef_value, (-1, 7))
+    gripper_value = tf.io.decode_compressed(
+        trajectory["observation"]["gripper_closed"], compression_type="ZLIB"
+    )
+    gripper_value = tf.io.decode_raw(gripper_value, tf.float32)
+    trajectory["observation"]["gripper_closed"] = tf.reshape(gripper_value, (-1, 1))
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def taco_play_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state_eef"] = trajectory["observation"]["robot_obs"][:, :6]
+    trajectory["observation"]["state_gripper"] = trajectory["observation"]["robot_obs"][:, 7:8]
+    trajectory["action"] = trajectory["action"]["rel_actions_world"]
+
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            tf.clip_by_value(trajectory["action"][:, -1:], 0, 1),
+        ),
+        axis=-1,
+    )
+
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def jaco_play_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state_eef"] = trajectory["observation"]["end_effector_cartesian_pos"][:, :6]
+    trajectory["observation"]["state_gripper"] = trajectory["observation"]["end_effector_cartesian_pos"][
+        :, -1:
+    ]
+
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            tf.zeros_like(trajectory["action"]["world_vector"]),
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def berkeley_cable_routing_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            tf.zeros_like(trajectory["action"]["world_vector"][:, :1]),
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def roboturk_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert absolute gripper action, +1 = open, 0 = close
+    gripper_action = invert_gripper_actions(
+        tf.clip_by_value(trajectory["action"]["gripper_closedness_action"], 0, 1)
+    )
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action,
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    trajectory["language_embedding"] = trajectory["observation"]["natural_language_embedding"]
+    return trajectory
+
+
+def nyu_door_opening_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def viola_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, None]
+    gripper_action = tf.clip_by_value(gripper_action, 0, 1)
+    gripper_action = invert_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action,
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def berkeley_autolab_ur5_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["robot_state"][:, 6:14]
+
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def toto_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            tf.cast(trajectory["action"]["open_gripper"][:, None], tf.float32),
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def language_table_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # default to "open" gripper
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"]),
+            tf.ones_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+
+    # decode language instruction
+    instruction_bytes = trajectory["observation"]["instruction"]
+    instruction_encoded = tf.strings.unicode_encode(instruction_bytes, output_encoding="UTF-8")
+    # Remove trailing padding --> convert RaggedTensor to regular Tensor.
+    trajectory["language_instruction"] = tf.strings.split(instruction_encoded, "\x00")[:, :1].to_tensor()[
+        :, 0
+    ]
+    return trajectory
+
+
+def pusht_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            trajectory["action"]["gripper_closedness_action"][:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def stanford_kuka_multimodal_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["depth_image"] = trajectory["observation"]["depth_image"][..., 0]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tf.zeros_like(trajectory["action"][:, :3]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def nyu_rot_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][..., :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][..., -1:]
+    trajectory["action"] = trajectory["action"][..., :7]
+    return trajectory
+
+
+def stanford_hydra_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(trajectory["action"][:, -1:]),
+        ),
+        axis=-1,
+    )
+
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :3],
+            trajectory["observation"]["state"][:, 7:10],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -3:-2]
+    return trajectory
+
+
+def austin_buds_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, :8]
+    return trajectory
+
+
+def nyu_franka_play_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["depth"] = tf.cast(trajectory["observation"]["depth"][..., 0], tf.float32)
+    trajectory["observation"]["depth_additional_view"] = tf.cast(
+        trajectory["observation"]["depth_additional_view"][..., 0], tf.float32
+    )
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, -6:]
+
+    # clip gripper action, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, -8:-2],
+            tf.clip_by_value(trajectory["action"][:, -2:-1], 0, 1),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def maniskill_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][..., 7:8]
+    return trajectory
+
+
+def furniture_bench_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    import tensorflow_graphics.geometry.transformation as tft
+
+    trajectory["observation"]["state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :7],
+            trajectory["observation"]["state"][:, -1:],
+        ),
+        axis=-1,
+    )
+
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tft.euler.from_quaternion(trajectory["action"][:, 3:7]),
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def cmu_franka_exploration_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def ucsd_kitchen_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["joint_state"] = trajectory["observation"]["state"][:, :7]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def ucsd_pick_place_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tf.zeros_like(trajectory["action"][:, :3]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def austin_sailor_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def austin_sirius_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def bc_z_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["future/xyz_residual"][:, :3],
+            trajectory["action"]["future/axis_angle_residual"][:, :3],
+            invert_gripper_actions(tf.cast(trajectory["action"]["future/target_close"][:, :1], tf.float32)),
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def tokyo_pr2_opening_fridge_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def tokyo_pr2_tabletop_manipulation_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def utokyo_xarm_bimanual_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = trajectory["action"][..., -7:]
+    return trajectory
+
+
+def robo_net_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :4],
+            tf.zeros_like(trajectory["observation"]["state"][:, :2]),
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :4],
+            tf.zeros_like(trajectory["action"][:, :2]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def berkeley_mvp_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    trajectory["observation"]["state"] = tf.concat((
+        tf.cast(trajectory["observation"]["gripper"][:, None], tf.float32),
+                        trajectory["observation"]["pose"],
+                        trajectory["observation"]["joint_pos"],),
+                        axis=-1,)
+    """
+    trajectory["observation"]["gripper"] = tf.cast(trajectory["observation"]["gripper"][:, None], tf.float32)
+    return trajectory
+
+
+def berkeley_rpt_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["gripper"] = tf.cast(trajectory["observation"]["gripper"][:, None], tf.float32)
+    return trajectory
+
+
+def kaist_nonprehensible_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, -7:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            tf.zeros_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def stanford_mask_vit_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["end_effector_pose"][:, :4],
+            tf.zeros_like(trajectory["observation"]["end_effector_pose"][:, :2]),
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["end_effector_pose"][:, -1:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :4],
+            tf.zeros_like(trajectory["action"][:, :2]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def tokyo_lsmo_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def dlr_sara_grid_clamp_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, :6]
+    return trajectory
+
+
+def dlr_edan_shared_control_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(trajectory["action"][:, -1:]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def asu_table_top_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["ground_truth_states"]["EE"]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def robocook_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def imperial_wristcam_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def iamlab_pick_insert_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    import tensorflow_graphics.geometry.transformation as tft
+
+    trajectory["observation"]["joint_state"] = trajectory["observation"]["state"][:, :7]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, 7:8]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tft.euler.from_quaternion(trajectory["action"][:, 3:7]),
+            trajectory["action"][:, 7:8],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def uiuc_d3field_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def utaustin_mutex_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, :8]
+
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def berkeley_fanuc_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["joint_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, 6:7]
+
+    # dataset does not store gripper actions, so use gripper state info, invert so +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            invert_gripper_actions(trajectory["observation"]["gripper_state"]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def cmu_playing_with_food_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    import tensorflow_graphics.geometry.transformation as tft
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tft.euler.from_quaternion(trajectory["action"][:, 3:7]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def playfusion_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            trajectory["action"][:, -4:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def cmu_stretch_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :3],
+            tf.zeros_like(trajectory["observation"]["state"][:, :3]),
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def gnm_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = tf.concat(
+        (
+            trajectory["observation"]["position"],
+            tf.zeros_like(trajectory["observation"]["state"][:, :3]),
+            trajectory["observation"]["yaw"],
+        ),
+        axis=-1,
+    )
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def fmb_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # every input feature is batched, ie has leading batch dimension
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["eef_pose"],
+            trajectory["observation"]["state_gripper_pose"][..., None],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def dobbe_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # every input feature is batched, ie has leading batch dimension
+    trajectory["observation"]["proprio"] = trajectory["observation"]["state"]
+    return trajectory
+
+
+def robo_set_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # gripper action is in -1...1 --> clip to 0...1, flip
+    gripper_action = trajectory["action"][:, -1:]
+    gripper_action = invert_gripper_actions(tf.clip_by_value(gripper_action, 0, 1))
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :7],
+            gripper_action,
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def identity_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    return trajectory
+
+
+# === Registry ===
+OPENX_STANDARDIZATION_TRANSFORMS = {
+    "bridge_openx": bridge_openx_dataset_transform,
+    "bridge_orig": bridge_orig_dataset_transform,
+    "bridge_dataset": bridge_orig_dataset_transform,
+    "ppgm": ppgm_dataset_transform,
+    "ppgm_static": ppgm_dataset_transform,
+    "ppgm_wrist": ppgm_dataset_transform,
+    "fractal20220817_data": rt1_dataset_transform,
+    "kuka": kuka_dataset_transform,
+    "taco_play": taco_play_dataset_transform,
+    "jaco_play": jaco_play_dataset_transform,
+    "berkeley_cable_routing": berkeley_cable_routing_dataset_transform,
+    "roboturk": roboturk_dataset_transform,
+    "nyu_door_opening_surprising_effectiveness": nyu_door_opening_dataset_transform,
+    "viola": viola_dataset_transform,
+    "berkeley_autolab_ur5": berkeley_autolab_ur5_dataset_transform,
+    "toto": toto_dataset_transform,
+    "language_table": language_table_dataset_transform,
+    "columbia_cairlab_pusht_real": pusht_dataset_transform,
+    "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": stanford_kuka_multimodal_dataset_transform,
+    "nyu_rot_dataset_converted_externally_to_rlds": nyu_rot_dataset_transform,
+    "stanford_hydra_dataset_converted_externally_to_rlds": stanford_hydra_dataset_transform,
+    "austin_buds_dataset_converted_externally_to_rlds": austin_buds_dataset_transform,
+    "nyu_franka_play_dataset_converted_externally_to_rlds": nyu_franka_play_dataset_transform,
+    "maniskill_dataset_converted_externally_to_rlds": maniskill_dataset_transform,
+    "furniture_bench_dataset_converted_externally_to_rlds": furniture_bench_dataset_transform,
+    "cmu_franka_exploration_dataset_converted_externally_to_rlds": cmu_franka_exploration_dataset_transform,
+    "ucsd_kitchen_dataset_converted_externally_to_rlds": ucsd_kitchen_dataset_transform,
+    "ucsd_pick_and_place_dataset_converted_externally_to_rlds": ucsd_pick_place_dataset_transform,
+    "austin_sailor_dataset_converted_externally_to_rlds": austin_sailor_dataset_transform,
+    "austin_sirius_dataset_converted_externally_to_rlds": austin_sirius_dataset_transform,
+    "bc_z": bc_z_dataset_transform,
+    "utokyo_pr2_opening_fridge_converted_externally_to_rlds": tokyo_pr2_opening_fridge_dataset_transform,
+    "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": tokyo_pr2_tabletop_manipulation_dataset_transform,
+    "utokyo_xarm_pick_and_place_converted_externally_to_rlds": identity_transform,
+    "utokyo_xarm_bimanual_converted_externally_to_rlds": utokyo_xarm_bimanual_dataset_transform,
+    "robo_net": robo_net_dataset_transform,
+    "berkeley_mvp_converted_externally_to_rlds": berkeley_mvp_dataset_transform,
+    "berkeley_rpt_converted_externally_to_rlds": berkeley_rpt_dataset_transform,
+    "kaist_nonprehensile_converted_externally_to_rlds": kaist_nonprehensible_dataset_transform,
+    "stanford_mask_vit_converted_externally_to_rlds": stanford_mask_vit_dataset_transform,
+    "tokyo_u_lsmo_converted_externally_to_rlds": tokyo_lsmo_dataset_transform,
+    "dlr_sara_pour_converted_externally_to_rlds": identity_transform,
+    "dlr_sara_grid_clamp_converted_externally_to_rlds": dlr_sara_grid_clamp_dataset_transform,
+    "dlr_edan_shared_control_converted_externally_to_rlds": dlr_edan_shared_control_dataset_transform,
+    "asu_table_top_converted_externally_to_rlds": asu_table_top_dataset_transform,
+    "stanford_robocook_converted_externally_to_rlds": robocook_dataset_transform,
+    "imperialcollege_sawyer_wrist_cam": imperial_wristcam_dataset_transform,
+    "iamlab_cmu_pickup_insert_converted_externally_to_rlds": iamlab_pick_insert_dataset_transform,
+    "uiuc_d3field": uiuc_d3field_dataset_transform,
+    "utaustin_mutex": utaustin_mutex_dataset_transform,
+    "berkeley_fanuc_manipulation": berkeley_fanuc_dataset_transform,
+    "cmu_playing_with_food": cmu_playing_with_food_dataset_transform,
+    "cmu_play_fusion": playfusion_dataset_transform,
+    "cmu_stretch": cmu_stretch_dataset_transform,
+    "berkeley_gnm_recon": gnm_dataset_transform,
+    "berkeley_gnm_cory_hall": gnm_dataset_transform,
+    "berkeley_gnm_sac_son": gnm_dataset_transform,
+    "droid": droid_baseact_transform_fn(),
+    "droid_100": droid_baseact_transform_fn(),  # first 100 episodes of droid
+    "fmb": fmb_transform,
+    "dobbe": dobbe_dataset_transform,
+    "robo_set": robo_set_dataset_transform,
+    "usc_cloth_sim_converted_externally_to_rlds": identity_transform,
+    "plex_robosuite": identity_transform,
+    "conq_hose_manipulation": identity_transform,
+    "io_ai_tech": identity_transform,
+    "spoc": identity_transform,
+}

lerobot/common/datasets/push_dataset_to_hub/openx_rlds_format.py

@@ -0,0 +1,359 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+For https://github.com/google-deepmind/open_x_embodiment (OPENX) datasets.
+
+Example:
+    python lerobot/scripts/push_dataset_to_hub.py \
+        --raw-dir /hdd/tensorflow_datasets/bridge_dataset/1.0.0/ \
+        --repo-id youliangtan/sampled_bridge_data_v2 \
+        --raw-format openx_rlds.bridge_orig \
+        --episodes 3 4 5 8 9
+
+Exact dataset fps defined in openx/config.py, obtained from:
+    https://docs.google.com/spreadsheets/d/1rPBD77tk60AEIGZrGSODwyyzs5FgCU9Uz3h-3_t2A9g/edit?gid=0#gid=0&range=R:R
+"""
+
+import shutil
+from pathlib import Path
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import torch
+import tqdm
+import yaml
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub.openx.transforms import OPENX_STANDARDIZATION_TRANSFORMS
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    concatenate_episodes,
+    get_default_encoding,
+    save_images_concurrently,
+)
+from lerobot.common.datasets.utils import (
+    calculate_episode_data_index,
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
+
+with open("lerobot/common/datasets/push_dataset_to_hub/openx/configs.yaml") as f:
+    _openx_list = yaml.safe_load(f)
+
+OPENX_DATASET_CONFIGS = _openx_list["OPENX_DATASET_CONFIGS"]
+
+np.set_printoptions(precision=2)
+
+
+def tf_to_torch(data):
+    return torch.from_numpy(data.numpy())
+
+
+def tf_img_convert(img):
+    if img.dtype == tf.string:
+        img = tf.io.decode_image(img, expand_animations=False, dtype=tf.uint8)
+    elif img.dtype != tf.uint8:
+        raise ValueError(f"Unsupported image dtype: found with dtype {img.dtype}")
+    return img.numpy()
+
+
+def _broadcast_metadata_rlds(i: tf.Tensor, traj: dict) -> dict:
+    """
+    In the RLDS format, each trajectory has some top-level metadata that is explicitly separated out, and a "steps"
+    entry. This function moves the "steps" entry to the top level, broadcasting any metadata to the length of the
+    trajectory. This function also adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`.
+
+    NOTE: adapted from DLimp library https://github.com/kvablack/dlimp/
+    """
+    steps = traj.pop("steps")
+
+    traj_len = tf.shape(tf.nest.flatten(steps)[0])[0]
+
+    # broadcast metadata to the length of the trajectory
+    metadata = tf.nest.map_structure(lambda x: tf.repeat(x, traj_len), traj)
+
+    # put steps back in
+    assert "traj_metadata" not in steps
+    traj = {**steps, "traj_metadata": metadata}
+
+    assert "_len" not in traj
+    assert "_traj_index" not in traj
+    assert "_frame_index" not in traj
+    traj["_len"] = tf.repeat(traj_len, traj_len)
+    traj["_traj_index"] = tf.repeat(i, traj_len)
+    traj["_frame_index"] = tf.range(traj_len)
+
+    return traj
+
+
+def load_from_raw(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int,
+    video: bool,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+    openx_dataset_name: str | None = None,
+):
+    """
+    Args:
+        raw_dir (Path): _description_
+        videos_dir (Path): _description_
+        fps (int): _description_
+        video (bool): _description_
+        episodes (list[int] | None, optional): _description_. Defaults to None.
+    """
+    ds_builder = tfds.builder_from_directory(str(raw_dir))
+    dataset = ds_builder.as_dataset(
+        split="all",
+        decoders={"steps": tfds.decode.SkipDecoding()},
+    )
+
+    dataset_info = ds_builder.info
+    print("dataset_info: ", dataset_info)
+
+    ds_length = len(dataset)
+    dataset = dataset.take(ds_length)
+    # "flatten" the dataset as such we can apply trajectory level map() easily
+    # each [obs][key] has a shape of (frame_size, ...)
+    dataset = dataset.enumerate().map(_broadcast_metadata_rlds)
+
+    # we will apply the standardization transform if the dataset_name is provided
+    # if the dataset name is not provided and the goal is to convert any rlds formatted dataset
+    # search for 'image' keys in the observations
+    if openx_dataset_name is not None:
+        print(" - applying standardization transform for dataset: ", openx_dataset_name)
+        assert openx_dataset_name in OPENX_STANDARDIZATION_TRANSFORMS
+        transform_fn = OPENX_STANDARDIZATION_TRANSFORMS[openx_dataset_name]
+        dataset = dataset.map(transform_fn)
+
+        image_keys = OPENX_DATASET_CONFIGS[openx_dataset_name]["image_obs_keys"]
+    else:
+        obs_keys = dataset_info.features["steps"]["observation"].keys()
+        image_keys = [key for key in obs_keys if "image" in key]
+
+    lang_key = "language_instruction" if "language_instruction" in dataset.element_spec else None
+
+    print(" - image_keys: ", image_keys)
+    print(" - lang_key: ", lang_key)
+
+    it = iter(dataset)
+
+    ep_dicts = []
+    # Init temp path to save ep_dicts in case of crash
+    tmp_ep_dicts_dir = videos_dir.parent.joinpath("ep_dicts")
+    tmp_ep_dicts_dir.mkdir(parents=True, exist_ok=True)
+
+    # check if ep_dicts have already been saved in /tmp
+    starting_ep_idx = 0
+    saved_ep_dicts = [ep.__str__() for ep in tmp_ep_dicts_dir.iterdir()]
+    if len(saved_ep_dicts) > 0:
+        saved_ep_dicts.sort()
+        # get last ep_idx number
+        starting_ep_idx = int(saved_ep_dicts[-1][-13:-3]) + 1
+        for i in range(starting_ep_idx):
+            episode = next(it)
+            ep_dicts.append(torch.load(saved_ep_dicts[i]))
+
+    # if we user specified episodes, skip the ones not in the list
+    if episodes is not None:
+        if ds_length == 0:
+            raise ValueError("No episodes found.")
+        # convert episodes index to sorted list
+        episodes = sorted(episodes)
+
+    for ep_idx in tqdm.tqdm(range(starting_ep_idx, ds_length)):
+        episode = next(it)
+
+        # if user specified episodes, skip the ones not in the list
+        if episodes is not None:
+            if len(episodes) == 0:
+                break
+            if ep_idx == episodes[0]:
+                # process this episode
+                print(" selecting episode idx: ", ep_idx)
+                episodes.pop(0)
+            else:
+                continue  # skip
+
+        num_frames = episode["action"].shape[0]
+
+        ###########################################################
+        # Handle the episodic data
+
+        # last step of demonstration is considered done
+        done = torch.zeros(num_frames, dtype=torch.bool)
+        done[-1] = True
+        ep_dict = {}
+        langs = []  # TODO: might be located in "observation"
+
+        image_array_dict = {key: [] for key in image_keys}
+
+        # We will create the state observation tensor by stacking the state
+        # obs keys defined in the openx/configs.py
+        if openx_dataset_name is not None:
+            state_obs_keys = OPENX_DATASET_CONFIGS[openx_dataset_name]["state_obs_keys"]
+            # stack the state observations, if is None, pad with zeros
+            states = []
+            for key in state_obs_keys:
+                if key in episode["observation"]:
+                    states.append(tf_to_torch(episode["observation"][key]))
+                else:
+                    states.append(torch.zeros(num_frames, 1))  # pad with zeros
+            states = torch.cat(states, dim=1)
+            # assert states.shape == (num_frames, 8), f"states shape: {states.shape}"
+        else:
+            states = tf_to_torch(episode["observation"]["state"])
+
+        actions = tf_to_torch(episode["action"])
+        rewards = tf_to_torch(episode["reward"]).float()
+
+        # If lang_key is present, convert the entire tensor at once
+        if lang_key is not None:
+            langs = [str(x) for x in episode[lang_key]]
+
+        for im_key in image_keys:
+            imgs = episode["observation"][im_key]
+            image_array_dict[im_key] = [tf_img_convert(img) for img in imgs]
+
+        # simple assertions
+        for item in [states, actions, rewards, done]:
+            assert len(item) == num_frames
+
+        ###########################################################
+
+        # loop through all cameras
+        for im_key in image_keys:
+            img_key = f"observation.images.{im_key}"
+            imgs_array = image_array_dict[im_key]
+            imgs_array = np.array(imgs_array)
+            if video:
+                # save png images in temporary directory
+                tmp_imgs_dir = videos_dir / "tmp_images"
+                save_images_concurrently(imgs_array, tmp_imgs_dir)
+
+                # encode images to a mp4 video
+                fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+                video_path = videos_dir / fname
+                encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
+
+                # clean temporary images directory
+                shutil.rmtree(tmp_imgs_dir)
+
+                # store the reference to the video frame
+                ep_dict[img_key] = [
+                    {"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
+                ]
+            else:
+                ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
+
+        if lang_key is not None:
+            ep_dict["language_instruction"] = langs
+
+        ep_dict["observation.state"] = states
+        ep_dict["action"] = actions
+        ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+        ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
+        ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+        ep_dict["next.reward"] = rewards
+        ep_dict["next.done"] = done
+
+        path_ep_dict = tmp_ep_dicts_dir.joinpath(
+            "ep_dict_" + "0" * (10 - len(str(ep_idx))) + str(ep_idx) + ".pt"
+        )
+        torch.save(ep_dict, path_ep_dict)
+
+        ep_dicts.append(ep_dict)
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video) -> Dataset:
+    features = {}
+
+    keys = [key for key in data_dict if "observation.images." in key]
+    for key in keys:
+        if video:
+            features[key] = VideoFrame()
+        else:
+            features[key] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    if "observation.velocity" in data_dict:
+        features["observation.velocity"] = Sequence(
+            length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    if "observation.effort" in data_dict:
+        features["observation.effort"] = Sequence(
+            length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    if "language_instruction" in data_dict:
+        features["language_instruction"] = Value(dtype="string", id=None)
+
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.reward"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+    openx_dataset_name: str | None = None,
+):
+    """This is a test impl for rlds conversion"""
+    if openx_dataset_name is None:
+        # set a default rlds frame rate if the dataset is not from openx
+        fps = 30
+    elif "fps" not in OPENX_DATASET_CONFIGS[openx_dataset_name]:
+        raise ValueError(
+            "fps for this dataset is not specified in openx/configs.py yet," "means it is not yet tested"
+        )
+    fps = OPENX_DATASET_CONFIGS[openx_dataset_name]["fps"]
+
+    data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding, openx_dataset_name)
+    hf_dataset = to_hf_dataset(data_dict, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    if video:
+        info["encoding"] = get_default_encoding()
+
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/utils.py

@@ -32,7 +32,7 @@ DATASET_CARD_TEMPLATE = """
 ---
 # Metadata will go there
 ---
-This dataset was created using [🤗 LeRobot](https://github.com/huggingface/lerobot).
+This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
 
 """
 
@@ -80,6 +80,11 @@ def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
         if isinstance(first_item, PILImage.Image):
             to_tensor = transforms.ToTensor()
             items_dict[key] = [to_tensor(img) for img in items_dict[key]]
+        elif isinstance(first_item, str):
+            # TODO (michel-aractingi): add str2embedding via language tokenizer
+            # For now we leave this part up to the user to choose how to address
+            # language conditioned tasks
+            pass
         elif isinstance(first_item, dict) and "path" in first_item and "timestamp" in first_item:
             # video frame will be processed downstream
             pass

lerobot/common/envs/utils.py

@@ -39,7 +39,7 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
 
             # sanity check that images are channel last
             _, h, w, c = img.shape
-            assert c < h and c < w, f"expect channel first images, but instead {img.shape}"
+            assert c < h and c < w, f"expect channel last images, but instead got {img.shape=}"
 
             # sanity check that images are uint8
             assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}"

lerobot/common/logger.py

@@ -189,7 +189,7 @@ class Logger:
             training_state["scheduler"] = scheduler.state_dict()
         torch.save(training_state, save_dir / self.training_state_file_name)
 
-    def save_checkpont(
+    def save_checkpoint(
         self,
         train_step: int,
         policy: Policy,

lerobot/common/policies/act/modeling_act.py

@@ -296,7 +296,7 @@ class ACT(nn.Module):
         self.use_images = any(k.startswith("observation.image") for k in config.input_shapes)
         self.use_env_state = "observation.environment_state" in config.input_shapes
         if self.config.use_vae:
-            self.vae_encoder = ACTEncoder(config)
+            self.vae_encoder = ACTEncoder(config, is_vae_encoder=True)
             self.vae_encoder_cls_embed = nn.Embedding(1, config.dim_model)
             # Projection layer for joint-space configuration to hidden dimension.
             if self.use_robot_state:
@@ -521,9 +521,11 @@ class ACT(nn.Module):
 class ACTEncoder(nn.Module):
     """Convenience module for running multiple encoder layers, maybe followed by normalization."""
 
-    def __init__(self, config: ACTConfig):
+    def __init__(self, config: ACTConfig, is_vae_encoder: bool = False):
         super().__init__()
-        self.layers = nn.ModuleList([ACTEncoderLayer(config) for _ in range(config.n_encoder_layers)])
+        self.is_vae_encoder = is_vae_encoder
+        num_layers = config.n_vae_encoder_layers if self.is_vae_encoder else config.n_encoder_layers
+        self.layers = nn.ModuleList([ACTEncoderLayer(config) for _ in range(num_layers)])
         self.norm = nn.LayerNorm(config.dim_model) if config.pre_norm else nn.Identity()
 
     def forward(

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -67,6 +67,7 @@ class DiffusionConfig:
         use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
             The group sizes are set to be about 16 (to be precise, feature_dim // 16).
         spatial_softmax_num_keypoints: Number of keypoints for SpatialSoftmax.
+        use_separate_rgb_encoders_per_camera: Whether to use a separate RGB encoder for each camera view.
         down_dims: Feature dimension for each stage of temporal downsampling in the diffusion modeling Unet.
             You may provide a variable number of dimensions, therefore also controlling the degree of
             downsampling.
@@ -130,6 +131,7 @@ class DiffusionConfig:
     pretrained_backbone_weights: str | None = None
     use_group_norm: bool = True
     spatial_softmax_num_keypoints: int = 32
+    use_separate_rgb_encoder_per_camera: bool = False
     # Unet.
     down_dims: tuple[int, ...] = (512, 1024, 2048)
     kernel_size: int = 5
@@ -196,3 +198,12 @@ class DiffusionConfig:
                 f"`noise_scheduler_type` must be one of {supported_noise_schedulers}. "
                 f"Got {self.noise_scheduler_type}."
             )
+
+        # Check that the horizon size and U-Net downsampling is compatible.
+        # U-Net downsamples by 2 with each stage.
+        downsampling_factor = 2 ** len(self.down_dims)
+        if self.horizon % downsampling_factor != 0:
+            raise ValueError(
+                "The horizon should be an integer multiple of the downsampling factor (which is determined "
+                f"by `len(down_dims)`). Got {self.horizon=} and {self.down_dims=}"
+            )

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -182,8 +182,13 @@ class DiffusionModel(nn.Module):
         self._use_env_state = False
         if num_images > 0:
             self._use_images = True
-            self.rgb_encoder = DiffusionRgbEncoder(config)
-            global_cond_dim += self.rgb_encoder.feature_dim * num_images
+            if self.config.use_separate_rgb_encoder_per_camera:
+                encoders = [DiffusionRgbEncoder(config) for _ in range(num_images)]
+                self.rgb_encoder = nn.ModuleList(encoders)
+                global_cond_dim += encoders[0].feature_dim * num_images
+            else:
+                self.rgb_encoder = DiffusionRgbEncoder(config)
+                global_cond_dim += self.rgb_encoder.feature_dim * num_images
         if "observation.environment_state" in config.input_shapes:
             self._use_env_state = True
             global_cond_dim += config.input_shapes["observation.environment_state"][0]
@@ -239,16 +244,32 @@ class DiffusionModel(nn.Module):
         """Encode image features and concatenate them all together along with the state vector."""
         batch_size, n_obs_steps = batch["observation.state"].shape[:2]
         global_cond_feats = [batch["observation.state"]]
-        # Extract image feature (first combine batch, sequence, and camera index dims).
+        # Extract image features.
         if self._use_images:
-            img_features = self.rgb_encoder(
-                einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...")
-            )
-            # Separate batch dim and sequence dim back out. The camera index dim gets absorbed into the
-            # feature dim (effectively concatenating the camera features).
-            img_features = einops.rearrange(
-                img_features, "(b s n) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
-            )
+            if self.config.use_separate_rgb_encoder_per_camera:
+                # Combine batch and sequence dims while rearranging to make the camera index dimension first.
+                images_per_camera = einops.rearrange(batch["observation.images"], "b s n ... -> n (b s) ...")
+                img_features_list = torch.cat(
+                    [
+                        encoder(images)
+                        for encoder, images in zip(self.rgb_encoder, images_per_camera, strict=True)
+                    ]
+                )
+                # Separate batch and sequence dims back out. The camera index dim gets absorbed into the
+                # feature dim (effectively concatenating the camera features).
+                img_features = einops.rearrange(
+                    img_features_list, "(n b s) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
+                )
+            else:
+                # Combine batch, sequence, and "which camera" dims before passing to shared encoder.
+                img_features = self.rgb_encoder(
+                    einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...")
+                )
+                # Separate batch dim and sequence dim back out. The camera index dim gets absorbed into the
+                # feature dim (effectively concatenating the camera features).
+                img_features = einops.rearrange(
+                    img_features, "(b s n) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
+                )
             global_cond_feats.append(img_features)
 
         if self._use_env_state:

lerobot/common/policies/factory.py

@@ -51,6 +51,13 @@ def get_policy_and_config_classes(name: str) -> tuple[Policy, object]:
         from lerobot.common.policies.tdmpc.modeling_tdmpc import TDMPCPolicy
 
         return TDMPCPolicy, TDMPCConfig
+
+    elif name == "tdmpc2":
+        from lerobot.common.policies.tdmpc2.configuration_tdmpc2 import TDMPC2Config
+        from lerobot.common.policies.tdmpc2.modeling_tdmpc2 import TDMPC2Policy
+
+        return TDMPC2Policy, TDMPC2Config
+
     elif name == "diffusion":
         from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
         from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy

lerobot/common/policies/tdmpc2/configuration_tdmpc2.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Nicklas Hansen, Xiaolong Wang, Hao Su,
+# and 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
+
+
+@dataclass
+class TDMPC2Config:
+    """Configuration class for TDMPC2Policy.
+
+    Defaults are configured for training with xarm_lift_medium_replay providing proprioceptive and single
+    camera observations.
+
+    The parameters you will most likely need to change are the ones which depend on the environment / sensors.
+    Those are: `input_shapes`, `output_shapes`, and perhaps `max_random_shift_ratio`.
+
+    Args:
+        n_action_repeats: The number of times to repeat the action returned by the planning. (hint: Google
+            action repeats in Q-learning or ask your favorite chatbot)
+        horizon: Horizon for model predictive control.
+        n_action_steps: Number of action steps to take from the plan given by model predictive control. This
+            is an alternative to using action repeats. If this is set to more than 1, then we require
+            `n_action_repeats == 1`, `use_mpc == True` and `n_action_steps <= horizon`. Note that this
+            approach of using multiple steps from the plan is not in the original implementation.
+        input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
+            the input data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
+            indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
+            include batch dimension or temporal dimension.
+        output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
+            the output data name, and the value is a list indicating the dimensions of the corresponding data.
+            For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
+            Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
+        input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
+            and the value specifies the normalization mode to apply. The two available modes are "mean_std"
+            which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a
+            [-1, 1] range. Note that here this defaults to None meaning inputs are not normalized. This is to
+            match the original implementation.
+        output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
+            original scale. Note that this is also used for normalizing the training targets. NOTE: Clipping
+            to [-1, +1] is used during MPPI/CEM. Therefore, it is recommended that you stick with "min_max"
+            normalization mode here.
+        image_encoder_hidden_dim: Number of channels for the convolutional layers used for image encoding.
+        state_encoder_hidden_dim: Hidden dimension for MLP used for state vector encoding.
+        latent_dim: Observation's latent embedding dimension.
+        q_ensemble_size: Number of Q function estimators to use in an ensemble for uncertainty estimation.
+        mlp_dim: Hidden dimension of MLPs used for modelling the dynamics encoder, reward function, policy
+            (π), Q ensemble, and V.
+        discount: Discount factor (γ) to use for the reinforcement learning formalism.
+        use_mpc: Whether to use model predictive control. The alternative is to just sample the policy model
+            (π) for each step.
+        cem_iterations: Number of iterations for the MPPI/CEM loop in MPC.
+        max_std: Maximum standard deviation for actions sampled from the gaussian PDF in CEM.
+        min_std: Minimum standard deviation for noise applied to actions sampled from the policy model (π).
+            Doubles up as the minimum standard deviation for actions sampled from the gaussian PDF in CEM.
+        n_gaussian_samples: Number of samples to draw from the gaussian distribution every CEM iteration. Must
+            be non-zero.
+        n_pi_samples: Number of samples to draw from the policy / world model rollout every CEM iteration. Can
+            be zero.
+        n_elites: The number of elite samples to use for updating the gaussian parameters every CEM iteration.
+        elite_weighting_temperature: The temperature to use for softmax weighting (by trajectory value) of the
+            elites, when updating the gaussian parameters for CEM.
+        max_random_shift_ratio: Maximum random shift (as a proportion of the image size) to apply to the
+            image(s) (in units of pixels) for training-time augmentation. If set to 0, no such augmentation
+            is applied. Note that the input images are assumed to be square for this augmentation.
+        reward_coeff: Loss weighting coefficient for the reward regression loss.
+        value_coeff: Loss weighting coefficient for both the state-action value (Q) TD loss, and the state
+            value (V) expectile regression loss.
+        consistency_coeff: Loss weighting coefficient for the consistency loss.
+        temporal_decay_coeff: Exponential decay coefficient for decaying the loss coefficient for future time-
+            steps. Hint: each loss computation involves `horizon` steps worth of actions starting from the
+            current time step.
+        target_model_momentum: Momentum (α) used for EMA updates of the target models. Updates are calculated
+            as ϕ ← αϕ + (1-α)θ where ϕ are the parameters of the target model and θ are the parameters of the
+            model being trained.
+    """
+
+    # Input / output structure.
+    n_action_repeats: int = 1
+    horizon: int = 3
+    n_action_steps: int = 1
+
+    input_shapes: dict[str, list[int]] = field(
+        default_factory=lambda: {
+            "observation.image": [3, 84, 84],
+            "observation.state": [4],
+        }
+    )
+    output_shapes: dict[str, list[int]] = field(
+        default_factory=lambda: {
+            "action": [4],
+        }
+    )
+
+    # Normalization / Unnormalization
+    input_normalization_modes: dict[str, str] | None = None
+    output_normalization_modes: dict[str, str] = field(
+        default_factory=lambda: {"action": "min_max"},
+    )
+
+    # Architecture / modeling.
+    # Neural networks.
+    image_encoder_hidden_dim: int = 32
+    state_encoder_hidden_dim: int = 256
+    latent_dim: int = 512
+    q_ensemble_size: int = 5
+    num_enc_layers: int = 2
+    mlp_dim: int = 512
+    # Reinforcement learning.
+    discount: float = 0.9
+    simnorm_dim: int = 8
+    dropout: float = 0.01
+
+    # actor
+    log_std_min: float = -10
+    log_std_max: float = 2
+
+    # critic
+    num_bins: int = 101
+    vmin: int = -10
+    vmax: int = +10
+
+    # Inference.
+    use_mpc: bool = True
+    cem_iterations: int = 6
+    max_std: float = 2.0
+    min_std: float = 0.05
+    n_gaussian_samples: int = 512
+    n_pi_samples: int = 24
+    n_elites: int = 64
+    elite_weighting_temperature: float = 0.5
+
+    # Training and loss computation.
+    max_random_shift_ratio: float = 0.0476
+    # Loss coefficients.
+    reward_coeff: float = 0.1
+    value_coeff: float = 0.1
+    consistency_coeff: float = 20.0
+    entropy_coef: float = 1e-4
+    temporal_decay_coeff: float = 0.5
+    # Target model. NOTE (michel_aractingi) this is equivelant to
+    # 1 - target_model_momentum of our TD-MPC1 implementation because
+    # of the use of `torch.lerp`
+    target_model_momentum: float = 0.01
+
+    def __post_init__(self):
+        """Input validation (not exhaustive)."""
+        # There should only be one image key.
+        image_keys = {k for k in self.input_shapes if k.startswith("observation.image")}
+        if len(image_keys) > 1:
+            raise ValueError(
+                f"{self.__class__.__name__} handles at most one image for now. Got image keys {image_keys}."
+            )
+        if len(image_keys) > 0:
+            image_key = next(iter(image_keys))
+            if self.input_shapes[image_key][-2] != self.input_shapes[image_key][-1]:
+                # TODO(alexander-soare): This limitation is solely because of code in the random shift
+                # augmentation. It should be able to be removed.
+                raise ValueError(
+                    f"Only square images are handled now. Got image shape {self.input_shapes[image_key]}."
+                )
+        if self.n_gaussian_samples <= 0:
+            raise ValueError(
+                f"The number of guassian samples for CEM should be non-zero. Got `{self.n_gaussian_samples=}`"
+            )
+        if self.output_normalization_modes != {"action": "min_max"}:
+            raise ValueError(
+                "TD-MPC assumes the action space dimensions to all be in [-1, 1]. Therefore it is strongly "
+                f"advised that you stick with the default. See {self.__class__.__name__} docstring for more "
+                "information."
+            )
+        if self.n_action_steps > 1:
+            if self.n_action_repeats != 1:
+                raise ValueError(
+                    "If `n_action_steps > 1`, `n_action_repeats` must be left to its default value of 1."
+                )
+            if not self.use_mpc:
+                raise ValueError("If `n_action_steps > 1`, `use_mpc` must be set to `True`.")
+            if self.n_action_steps > self.horizon:
+                raise ValueError("`n_action_steps` must be less than or equal to `horizon`.")

lerobot/common/policies/tdmpc2/modeling_tdmpc2.py

@@ -0,0 +1,834 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Nicklas Hansen and 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.
+"""Implementation of TD-MPC2: Scalable, Robust World Models for Continuous Control
+
+We refer to the main paper and codebase:
+    TD-MPC2 paper: (https://arxiv.org/abs/2310.16828)
+    TD-MPC2 code:  (https://github.com/nicklashansen/tdmpc2)
+"""
+
+# ruff: noqa: N806
+
+from collections import deque
+from copy import deepcopy
+from functools import partial
+from typing import Callable
+
+import einops
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F  # noqa: N812
+from huggingface_hub import PyTorchModelHubMixin
+from torch import Tensor
+
+from lerobot.common.policies.normalize import Normalize, Unnormalize
+from lerobot.common.policies.tdmpc2.configuration_tdmpc2 import TDMPC2Config
+from lerobot.common.policies.tdmpc2.tdmpc2_utils import (
+    NormedLinear,
+    SimNorm,
+    gaussian_logprob,
+    soft_cross_entropy,
+    squash,
+    two_hot_inv,
+)
+from lerobot.common.policies.utils import get_device_from_parameters, populate_queues
+
+
+class TDMPC2Policy(
+    nn.Module,
+    PyTorchModelHubMixin,
+    library_name="lerobot",
+    repo_url="https://github.com/huggingface/lerobot",
+    tags=["robotics", "tdmpc2"],
+):
+    """Implementation of TD-MPC2 learning + inference."""
+
+    name = "tdmpc2"
+
+    def __init__(
+        self, config: TDMPC2Config | None = None, 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__()
+
+        if config is None:
+            config = TDMPC2Config()
+        self.config = config
+        self.model = TDMPC2WorldModel(config)
+        # TODO (michel-aractingi) temp fix for gpu
+        self.model = self.model.to("cuda:0")
+
+        if config.input_normalization_modes is not None:
+            self.normalize_inputs = Normalize(
+                config.input_shapes, config.input_normalization_modes, dataset_stats
+            )
+        else:
+            self.normalize_inputs = nn.Identity()
+        self.normalize_targets = Normalize(
+            config.output_shapes, config.output_normalization_modes, dataset_stats
+        )
+        self.unnormalize_outputs = Unnormalize(
+            config.output_shapes, config.output_normalization_modes, dataset_stats
+        )
+
+        image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
+        # Note: This check is covered in the post-init of the config but have a sanity check just in case.
+        self._use_image = False
+        self._use_env_state = False
+        if len(image_keys) > 0:
+            assert len(image_keys) == 1
+            self._use_image = True
+            self.input_image_key = image_keys[0]
+        if "observation.environment_state" in config.input_shapes:
+            self._use_env_state = True
+
+        self.scale = RunningScale(self.config.target_model_momentum)
+        self.discount = (
+            self.config.discount
+        )  # TODO (michel-aractingi) downscale discount according to episode length
+
+        self.reset()
+
+    def reset(self):
+        """
+        Clear observation and action queues. Clear previous means for warm starting of MPPI/CEM. Should be
+        called on `env.reset()`
+        """
+        self._queues = {
+            "observation.state": deque(maxlen=1),
+            "action": deque(maxlen=max(self.config.n_action_steps, self.config.n_action_repeats)),
+        }
+        if self._use_image:
+            self._queues["observation.image"] = deque(maxlen=1)
+        if self._use_env_state:
+            self._queues["observation.environment_state"] = deque(maxlen=1)
+        # Previous mean obtained from the cross-entropy method (CEM) used during MPC. It is used to warm start
+        # CEM for the next step.
+        self._prev_mean: torch.Tensor | None = None
+
+    @torch.no_grad()
+    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
+        """Select a single action given environment observations."""
+        batch = self.normalize_inputs(batch)
+        if self._use_image:
+            batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
+            batch["observation.image"] = batch[self.input_image_key]
+
+        self._queues = populate_queues(self._queues, batch)
+
+        # When the action queue is depleted, populate it again by querying the policy.
+        if len(self._queues["action"]) == 0:
+            batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
+
+            # Remove the time dimensions as it is not handled yet.
+            for key in batch:
+                assert batch[key].shape[1] == 1
+                batch[key] = batch[key][:, 0]
+
+            # NOTE: Order of observations matters here.
+            encode_keys = []
+            if self._use_image:
+                encode_keys.append("observation.image")
+            if self._use_env_state:
+                encode_keys.append("observation.environment_state")
+            encode_keys.append("observation.state")
+            z = self.model.encode({k: batch[k] for k in encode_keys})
+            if self.config.use_mpc:  # noqa: SIM108
+                actions = self.plan(z)  # (horizon, batch, action_dim)
+            else:
+                # Plan with the policy (π) alone. This always returns one action so unsqueeze to get a
+                # sequence dimension like in the MPC branch.
+                actions = self.model.pi(z)[0].unsqueeze(0)
+
+            actions = torch.clamp(actions, -1, +1)
+
+            actions = self.unnormalize_outputs({"action": actions})["action"]
+
+            if self.config.n_action_repeats > 1:
+                for _ in range(self.config.n_action_repeats):
+                    self._queues["action"].append(actions[0])
+            else:
+                # Action queue is (n_action_steps, batch_size, action_dim), so we transpose the action.
+                self._queues["action"].extend(actions[: self.config.n_action_steps])
+
+        action = self._queues["action"].popleft()
+        return action
+
+    @torch.no_grad()
+    def plan(self, z: Tensor) -> Tensor:
+        """Plan sequence of actions using TD-MPC inference.
+
+        Args:
+            z: (batch, latent_dim,) tensor for the initial state.
+        Returns:
+            (horizon, batch, action_dim,) tensor for the planned trajectory of actions.
+        """
+        device = get_device_from_parameters(self)
+
+        batch_size = z.shape[0]
+
+        # Sample Nπ trajectories from the policy.
+        pi_actions = torch.empty(
+            self.config.horizon,
+            self.config.n_pi_samples,
+            batch_size,
+            self.config.output_shapes["action"][0],
+            device=device,
+        )
+        if self.config.n_pi_samples > 0:
+            _z = einops.repeat(z, "b d -> n b d", n=self.config.n_pi_samples)
+            for t in range(self.config.horizon):
+                # Note: Adding a small amount of noise here doesn't hurt during inference and may even be
+                # helpful for CEM.
+                pi_actions[t] = self.model.pi(_z)[0]
+                _z = self.model.latent_dynamics(_z, pi_actions[t])
+
+        # In the CEM loop we will need this for a call to estimate_value with the gaussian sampled
+        # trajectories.
+        z = einops.repeat(z, "b d -> n b d", n=self.config.n_gaussian_samples + self.config.n_pi_samples)
+
+        # Model Predictive Path Integral (MPPI) with the cross-entropy method (CEM) as the optimization
+        # algorithm.
+        # The initial mean and standard deviation for the cross-entropy method (CEM).
+        mean = torch.zeros(
+            self.config.horizon, batch_size, self.config.output_shapes["action"][0], device=device
+        )
+        # Maybe warm start CEM with the mean from the previous step.
+        if self._prev_mean is not None:
+            mean[:-1] = self._prev_mean[1:]
+        std = self.config.max_std * torch.ones_like(mean)
+
+        for _ in range(self.config.cem_iterations):
+            # Randomly sample action trajectories for the gaussian distribution.
+            std_normal_noise = torch.randn(
+                self.config.horizon,
+                self.config.n_gaussian_samples,
+                batch_size,
+                self.config.output_shapes["action"][0],
+                device=std.device,
+            )
+            gaussian_actions = torch.clamp(mean.unsqueeze(1) + std.unsqueeze(1) * std_normal_noise, -1, 1)
+
+            # Compute elite actions.
+            actions = torch.cat([gaussian_actions, pi_actions], dim=1)
+            value = self.estimate_value(z, actions).nan_to_num_(0).squeeze()
+            elite_idxs = torch.topk(value, self.config.n_elites, dim=0).indices  # (n_elites, batch)
+            elite_value = value.take_along_dim(elite_idxs, dim=0)  # (n_elites, batch)
+            # (horizon, n_elites, batch, action_dim)
+            elite_actions = actions.take_along_dim(einops.rearrange(elite_idxs, "n b -> 1 n b 1"), dim=1)
+
+            # Update gaussian PDF parameters to be the (weighted) mean and standard deviation of the elites.
+            max_value = elite_value.max(0, keepdim=True)[0]  # (1, batch)
+            # The weighting is a softmax over trajectory values. Note that this is not the same as the usage
+            # of Ω in eqn 4 of the TD-MPC paper. Instead it is the normalized version of it: s = Ω/ΣΩ. This
+            # makes the equations: μ = Σ(s⋅Γ), σ = Σ(s⋅(Γ-μ)²).
+            score = torch.exp(self.config.elite_weighting_temperature * (elite_value - max_value))
+            score /= score.sum(axis=0, keepdim=True)
+            # (horizon, batch, action_dim)
+            mean = torch.sum(einops.rearrange(score, "n b -> n b 1") * elite_actions, dim=1) / (
+                einops.rearrange(score.sum(0), "b -> 1 b 1") + 1e-9
+            )
+            std = torch.sqrt(
+                torch.sum(
+                    einops.rearrange(score, "n b -> n b 1")
+                    * (elite_actions - einops.rearrange(mean, "h b d -> h 1 b d")) ** 2,
+                    dim=1,
+                )
+                / (einops.rearrange(score.sum(0), "b -> 1 b 1") + 1e-9)
+            ).clamp_(self.config.min_std, self.config.max_std)
+
+        # Keep track of the mean for warm-starting subsequent steps.
+        self._prev_mean = mean
+
+        # Randomly select one of the elite actions from the last iteration of MPPI/CEM using the softmax
+        # scores from the last iteration.
+        actions = elite_actions[:, torch.multinomial(score.T, 1).squeeze(), torch.arange(batch_size)]
+        return actions
+
+    @torch.no_grad()
+    def estimate_value(self, z: Tensor, actions: Tensor):
+        """Estimates the value of a trajectory as per eqn 4 of the FOWM paper.
+
+        Args:
+            z: (batch, latent_dim) tensor of initial latent states.
+            actions: (horizon, batch, action_dim) tensor of action trajectories.
+        Returns:
+            (batch,) tensor of values.
+        """
+        # Initialize return and running discount factor.
+        G, running_discount = 0, 1
+        # Iterate over the actions in the trajectory to simulate the trajectory using the latent dynamics
+        # model. Keep track of return.
+        for t in range(actions.shape[0]):
+            # Estimate the next state (latent) and reward.
+            z, reward = self.model.latent_dynamics_and_reward(z, actions[t], discretize_reward=True)
+            # Update the return and running discount.
+            G += running_discount * reward
+            running_discount *= self.config.discount
+
+        # next_action = self.model.pi(z)[0]  # (batch, action_dim)
+        # terminal_values = self.model.Qs(z, next_action, return_type="avg")  # (ensemble, batch)
+
+        return G + running_discount * self.model.Qs(z, self.model.pi(z)[0], return_type="avg")
+
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor | float]:
+        """Run the batch through the model and compute the loss.
+
+        Returns a dictionary with loss as a tensor, and other information as native floats.
+        """
+        device = get_device_from_parameters(self)
+
+        batch = self.normalize_inputs(batch)
+        if self._use_image:
+            batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
+            batch["observation.image"] = batch[self.input_image_key]
+        batch = self.normalize_targets(batch)
+
+        info = {}
+
+        # (b, t) -> (t, b)
+        for key in batch:
+            if batch[key].ndim > 1:
+                batch[key] = batch[key].transpose(1, 0)
+
+        action = batch["action"]  # (t, b, action_dim)
+        reward = batch["next.reward"]  # (t, b)
+        observations = {k: v for k, v in batch.items() if k.startswith("observation.")}
+
+        # Apply random image augmentations.
+        if self._use_image and self.config.max_random_shift_ratio > 0:
+            observations["observation.image"] = flatten_forward_unflatten(
+                partial(random_shifts_aug, max_random_shift_ratio=self.config.max_random_shift_ratio),
+                observations["observation.image"],
+            )
+
+        # Get the current observation for predicting trajectories, and all future observations for use in
+        # the latent consistency loss and TD loss.
+        current_observation, next_observations = {}, {}
+        for k in observations:
+            current_observation[k] = observations[k][0]
+            next_observations[k] = observations[k][1:]
+        horizon, batch_size = next_observations[
+            "observation.image" if self._use_image else "observation.environment_state"
+        ].shape[:2]
+
+        # Run latent rollout using the latent dynamics model and policy model.
+        # Note this has shape `horizon+1` because there are `horizon` actions and a current `z`. Each action
+        # gives us a next `z`.
+        batch_size = batch["index"].shape[0]
+        z_preds = torch.empty(horizon + 1, batch_size, self.config.latent_dim, device=device)
+        z_preds[0] = self.model.encode(current_observation)
+        reward_preds = torch.empty(horizon, batch_size, self.config.num_bins, device=device)
+        for t in range(horizon):
+            z_preds[t + 1], reward_preds[t] = self.model.latent_dynamics_and_reward(z_preds[t], action[t])
+
+        # Compute Q value predictions based on the latent rollout.
+        q_preds_ensemble = self.model.Qs(
+            z_preds[:-1], action, return_type="all"
+        )  # (ensemble, horizon, batch)
+        info.update({"Q": q_preds_ensemble.mean().item()})
+
+        # Compute various targets with stopgrad.
+        with torch.no_grad():
+            # Latent state consistency targets for consistency loss.
+            z_targets = self.model.encode(next_observations)
+
+            # Compute the TD-target from a reward and the next observation
+            pi = self.model.pi(z_targets)[0]
+            td_targets = (
+                reward
+                + self.config.discount
+                * self.model.Qs(z_targets, pi, return_type="min", target=True).squeeze()
+            )
+
+        # Compute losses.
+        # Exponentially decay the loss weight with respect to the timestep. Steps that are more distant in the
+        # future have less impact on the loss. Note: unsqueeze will let us broadcast to (seq, batch).
+        temporal_loss_coeffs = torch.pow(
+            self.config.temporal_decay_coeff, torch.arange(horizon, device=device)
+        ).unsqueeze(-1)
+
+        # Compute consistency loss as MSE loss between latents predicted from the rollout and latents
+        # predicted from the (target model's) observation encoder.
+        consistency_loss = (
+            (
+                temporal_loss_coeffs
+                * F.mse_loss(z_preds[1:], z_targets, reduction="none").mean(dim=-1)
+                # `z_preds` depends on the current observation and the actions.
+                * ~batch["observation.state_is_pad"][0]
+                * ~batch["action_is_pad"]
+                # `z_targets` depends on the next observation.
+                * ~batch["observation.state_is_pad"][1:]
+            )
+            .sum(0)
+            .mean()
+        )
+        # Compute the reward loss as MSE loss between rewards predicted from the rollout and the dataset
+        # rewards.
+        reward_loss = (
+            (
+                temporal_loss_coeffs
+                * soft_cross_entropy(reward_preds, reward, self.config).mean(1)
+                * ~batch["next.reward_is_pad"]
+                * ~batch["observation.state_is_pad"][0]
+                * ~batch["action_is_pad"]
+            )
+            .sum(0)
+            .mean()
+        )
+
+        # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble.
+        ce_value_loss = 0.0
+        for i in range(self.config.q_ensemble_size):
+            ce_value_loss += soft_cross_entropy(q_preds_ensemble[i], td_targets, self.config).mean(1)
+
+        q_value_loss = (
+            (
+                temporal_loss_coeffs
+                * ce_value_loss
+                # `q_preds_ensemble` depends on the first observation and the actions.
+                * ~batch["observation.state_is_pad"][0]
+                * ~batch["action_is_pad"]
+                # q_targets depends on the reward and the next observations.
+                * ~batch["next.reward_is_pad"]
+                * ~batch["observation.state_is_pad"][1:]
+            )
+            .sum(0)
+            .mean()
+        )
+
+        # Calculate the advantage weighted regression loss for π as detailed in FOWM 3.1.
+        # We won't need these gradients again so detach.
+        z_preds = z_preds.detach()
+        action_preds, _, log_pis, _ = self.model.pi(z_preds[:-1])
+
+        with torch.no_grad():
+            # avoid unnessecary computation of the gradients during policy optimization
+            # TODO (michel-aractingi): the same logic should be extended when adding task embeddings
+            qs = self.model.Qs(z_preds[:-1], action_preds, return_type="avg")
+            self.scale.update(qs[0])
+            qs = self.scale(qs)
+
+        pi_loss = (
+            (self.config.entropy_coef * log_pis - qs).mean(dim=2)
+            * temporal_loss_coeffs
+            # `action_preds` depends on the first observation and the actions.
+            * ~batch["observation.state_is_pad"][0]
+            * ~batch["action_is_pad"]
+        ).mean()
+
+        loss = (
+            self.config.consistency_coeff * consistency_loss
+            + self.config.reward_coeff * reward_loss
+            + self.config.value_coeff * q_value_loss
+            + pi_loss
+        )
+
+        info.update(
+            {
+                "consistency_loss": consistency_loss.item(),
+                "reward_loss": reward_loss.item(),
+                "Q_value_loss": q_value_loss.item(),
+                "pi_loss": pi_loss.item(),
+                "loss": loss,
+                "sum_loss": loss.item() * self.config.horizon,
+                "pi_scale": float(self.scale.value),
+            }
+        )
+
+        # Undo (b, t) -> (t, b).
+        for key in batch:
+            if batch[key].ndim > 1:
+                batch[key] = batch[key].transpose(1, 0)
+
+        return info
+
+    def update(self):
+        """Update the target model's using polyak averaging."""
+        self.model.update_target_Q()
+
+
+class TDMPC2WorldModel(nn.Module):
+    """Latent dynamics model used in TD-MPC2."""
+
+    def __init__(self, config: TDMPC2Config):
+        super().__init__()
+        self.config = config
+
+        self._encoder = TDMPC2ObservationEncoder(config)
+
+        # Define latent dynamics head
+        self._dynamics = nn.Sequential(
+            NormedLinear(config.latent_dim + config.output_shapes["action"][0], config.mlp_dim),
+            NormedLinear(config.mlp_dim, config.mlp_dim),
+            NormedLinear(config.mlp_dim, config.latent_dim, act=SimNorm(config.simnorm_dim)),
+        )
+
+        # Define reward head
+        self._reward = nn.Sequential(
+            NormedLinear(config.latent_dim + config.output_shapes["action"][0], config.mlp_dim),
+            NormedLinear(config.mlp_dim, config.mlp_dim),
+            nn.Linear(config.mlp_dim, max(config.num_bins, 1)),
+        )
+
+        # Define policy head
+        self._pi = nn.Sequential(
+            NormedLinear(config.latent_dim, config.mlp_dim),
+            NormedLinear(config.mlp_dim, config.mlp_dim),
+            nn.Linear(config.mlp_dim, 2 * config.output_shapes["action"][0]),
+        )
+
+        # Define ensemble of Q functions
+        self._Qs = nn.ModuleList(
+            [
+                nn.Sequential(
+                    NormedLinear(
+                        config.latent_dim + config.output_shapes["action"][0],
+                        config.mlp_dim,
+                        dropout=config.dropout,
+                    ),
+                    NormedLinear(config.mlp_dim, config.mlp_dim),
+                    nn.Linear(config.mlp_dim, max(config.num_bins, 1)),
+                )
+                for _ in range(config.q_ensemble_size)
+            ]
+        )
+
+        self._init_weights()
+
+        self._target_Qs = deepcopy(self._Qs).requires_grad_(False)
+
+        self.log_std_min = torch.tensor(config.log_std_min)
+        self.log_std_dif = torch.tensor(config.log_std_max) - self.log_std_min
+
+        self.bins = torch.linspace(config.vmin, config.vmax, config.num_bins)
+        self.config.bin_size = (config.vmax - config.vmin) / (config.num_bins - 1)
+
+    def _init_weights(self):
+        """Initialize model weights.
+        Custom weight initializations proposed in TD-MPC2.
+
+        """
+
+        def _apply_fn(m):
+            if isinstance(m, nn.Linear):
+                nn.init.trunc_normal_(m.weight, std=0.02)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ParameterList):
+                for i, p in enumerate(m):
+                    if p.dim() == 3:  # Linear
+                        nn.init.trunc_normal_(p, std=0.02)  # Weight
+                        nn.init.constant_(m[i + 1], 0)  # Bias
+
+        self.apply(_apply_fn)
+
+        # initialize parameters of the
+        for m in [self._reward, *self._Qs]:
+            assert isinstance(
+                m[-1], nn.Linear
+            ), "Sanity check. The last linear layer needs 0 initialization on weights."
+            nn.init.zeros_(m[-1].weight)
+
+    def to(self, *args, **kwargs):
+        """
+        Overriding `to` method to also move additional tensors to device.
+        """
+        super().to(*args, **kwargs)
+        self.log_std_min = self.log_std_min.to(*args, **kwargs)
+        self.log_std_dif = self.log_std_dif.to(*args, **kwargs)
+        self.bins = self.bins.to(*args, **kwargs)
+        return self
+
+    def train(self, mode):
+        super().train(mode)
+        self._target_Qs.train(False)
+        return self
+
+    def encode(self, obs: dict[str, Tensor]) -> Tensor:
+        """Encodes an observation into its latent representation."""
+        return self._encoder(obs)
+
+    def latent_dynamics_and_reward(
+        self, z: Tensor, a: Tensor, discretize_reward: bool = False
+    ) -> tuple[Tensor, Tensor, bool]:
+        """Predict the next state's latent representation and the reward given a current latent and action.
+
+        Args:
+            z: (*, latent_dim) tensor for the current state's latent representation.
+            a: (*, action_dim) tensor for the action to be applied.
+        Returns:
+            A tuple containing:
+                - (*, latent_dim) tensor for the next state's latent representation.
+                - (*,) tensor for the estimated reward.
+        """
+        x = torch.cat([z, a], dim=-1)
+        reward = self._reward(x).squeeze(-1)
+        if discretize_reward:
+            reward = two_hot_inv(reward, self.bins)
+        return self._dynamics(x), reward
+
+    def latent_dynamics(self, z: Tensor, a: Tensor) -> Tensor:
+        """Predict the next state's latent representation given a current latent and action.
+
+        Args:
+            z: (*, latent_dim) tensor for the current state's latent representation.
+            a: (*, action_dim) tensor for the action to be applied.
+        Returns:
+            (*, latent_dim) tensor for the next state's latent representation.
+        """
+        x = torch.cat([z, a], dim=-1)
+        return self._dynamics(x)
+
+    def pi(self, z: Tensor) -> Tensor:
+        """Samples an action from the learned policy.
+
+        The policy can also have added (truncated) Gaussian noise injected for encouraging exploration when
+        generating rollouts for online training.
+
+        Args:
+            z: (*, latent_dim) tensor for the current state's latent representation.
+            std: The standard deviation of the injected noise.
+        Returns:
+            (*, action_dim) tensor for the sampled action.
+        """
+        mu, log_std = self._pi(z).chunk(2, dim=-1)
+        log_std = self.log_std_min + 0.5 * self.log_std_dif * (torch.tanh(log_std) + 1)
+        eps = torch.randn_like(mu)
+
+        log_pi = gaussian_logprob(eps, log_std)
+        pi = mu + eps * log_std.exp()
+        mu, pi, log_pi = squash(mu, pi, log_pi)
+
+        return pi, mu, log_pi, log_std
+
+    def Qs(self, z: Tensor, a: Tensor, return_type: str = "min", target=False) -> Tensor:  # noqa: N802
+        """Predict state-action value for all of the learned Q functions.
+
+        Args:
+            z: (*, latent_dim) tensor for the current state's latent representation.
+            a: (*, action_dim) tensor for the action to be applied.
+            return_type: either 'min' or 'all' otherwise the average is returned
+        Returns:
+            (q_ensemble, *) tensor for the value predictions of each learned Q function in the ensemble or the average or min
+        """
+        x = torch.cat([z, a], dim=-1)
+
+        if target:
+            out = torch.stack([q(x).squeeze(-1) for q in self._target_Qs], dim=0)
+        else:
+            out = torch.stack([q(x).squeeze(-1) for q in self._Qs], dim=0)
+
+        if return_type == "all":
+            return out
+
+        Q1, Q2 = out[np.random.choice(len(self._Qs), size=2, replace=False)]
+        Q1, Q2 = two_hot_inv(Q1, self.bins), two_hot_inv(Q2, self.bins)
+        return torch.min(Q1, Q2) if return_type == "min" else (Q1 + Q2) / 2
+
+    def update_target_Q(self):
+        """
+        Soft-update target Q-networks using Polyak averaging.
+        """
+        with torch.no_grad():
+            for p, p_target in zip(self._Qs.parameters(), self._target_Qs.parameters(), strict=False):
+                p_target.data.lerp_(p.data, self.config.target_model_momentum)
+
+
+class TDMPC2ObservationEncoder(nn.Module):
+    """Encode image and/or state vector observations."""
+
+    def __init__(self, config: TDMPC2Config):
+        """
+        Creates encoders for pixel and/or state modalities.
+        TODO(alexander-soare): The original work allows for multiple images by concatenating them along the
+            channel dimension. Re-implement this capability.
+        """
+        super().__init__()
+        self.config = config
+
+        # Define the observation encoder whether its pixels or states
+        encoder_dict = {}
+        for obs_key in config.input_shapes:
+            if "observation.image" in config.input_shapes:
+                encoder_module = nn.Sequential(
+                    nn.Conv2d(config.input_shapes[obs_key][0], config.image_encoder_hidden_dim, 7, stride=2),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 5, stride=2),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=2),
+                    nn.ReLU(inplace=True),
+                    nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=1),
+                )
+                dummy_batch = torch.zeros(1, *config.input_shapes[obs_key])
+                with torch.inference_mode():
+                    out_shape = encoder_module(dummy_batch).shape[1:]
+                encoder_module.extend(
+                    nn.Sequential(
+                        nn.Flatten(),
+                        NormedLinear(np.prod(out_shape), config.latent_dim, act=SimNorm(config.simnorm_dim)),
+                    )
+                )
+
+            elif (
+                "observation.state" in config.input_shapes
+                or "observation.environment_state" in config.input_shapes
+            ):
+                encoder_module = nn.ModuleList()
+                encoder_module.append(
+                    NormedLinear(config.input_shapes[obs_key][0], config.state_encoder_hidden_dim)
+                )
+                assert config.num_enc_layers > 0
+                for _ in range(config.num_enc_layers - 1):
+                    encoder_module.append(
+                        NormedLinear(config.state_encoder_hidden_dim, config.state_encoder_hidden_dim)
+                    )
+                encoder_module.append(
+                    NormedLinear(
+                        config.state_encoder_hidden_dim, config.latent_dim, act=SimNorm(config.simnorm_dim)
+                    )
+                )
+                encoder_module = nn.Sequential(*encoder_module)
+
+            else:
+                raise NotImplementedError(f"No corresponding encoder module for key {obs_key}.")
+
+            encoder_dict[obs_key.replace(".", "")] = encoder_module
+
+        self.encoder = nn.ModuleDict(encoder_dict)
+
+    def forward(self, obs_dict: dict[str, Tensor]) -> Tensor:
+        """Encode the image and/or state vector.
+
+        Each modality is encoded into a feature vector of size (latent_dim,) and then a uniform mean is taken
+        over all features.
+        """
+        feat = []
+        for obs_key in self.config.input_shapes:
+            if "observation.image" in obs_key:
+                feat.append(
+                    flatten_forward_unflatten(self.encoder[obs_key.replace(".", "")], obs_dict[obs_key])
+                )
+            else:
+                feat.append(self.encoder[obs_key.replace(".", "")](obs_dict[obs_key]))
+        return torch.stack(feat, dim=0).mean(0)
+
+
+def random_shifts_aug(x: Tensor, max_random_shift_ratio: float) -> Tensor:
+    """Randomly shifts images horizontally and vertically.
+
+    Adapted from https://github.com/facebookresearch/drqv2
+    """
+    b, _, h, w = x.size()
+    assert h == w, "non-square images not handled yet"
+    pad = int(round(max_random_shift_ratio * h))
+    x = F.pad(x, tuple([pad] * 4), "replicate")
+    eps = 1.0 / (h + 2 * pad)
+    arange = torch.linspace(
+        -1.0 + eps,
+        1.0 - eps,
+        h + 2 * pad,
+        device=x.device,
+        dtype=torch.float32,
+    )[:h]
+    arange = einops.repeat(arange, "w -> h w 1", h=h)
+    base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2)
+    base_grid = einops.repeat(base_grid, "h w c -> b h w c", b=b)
+    # A random shift in units of pixels and within the boundaries of the padding.
+    shift = torch.randint(
+        0,
+        2 * pad + 1,
+        size=(b, 1, 1, 2),
+        device=x.device,
+        dtype=torch.float32,
+    )
+    shift *= 2.0 / (h + 2 * pad)
+    grid = base_grid + shift
+    return F.grid_sample(x, grid, padding_mode="zeros", align_corners=False)
+
+
+def flatten_forward_unflatten(fn: Callable[[Tensor], Tensor], image_tensor: Tensor) -> Tensor:
+    """Helper to temporarily flatten extra dims at the start of the image tensor.
+
+    Args:
+        fn: Callable that the image tensor will be passed to. It should accept (B, C, H, W) and return
+            (B, *), where * is any number of dimensions.
+        image_tensor: An image tensor of shape (**, C, H, W), where ** is any number of dimensions, generally
+            different from *.
+    Returns:
+        A return value from the callable reshaped to (**, *).
+    """
+    if image_tensor.ndim == 4:
+        return fn(image_tensor)
+    start_dims = image_tensor.shape[:-3]
+    inp = torch.flatten(image_tensor, end_dim=-4)
+    flat_out = fn(inp)
+    return torch.reshape(flat_out, (*start_dims, *flat_out.shape[1:]))
+
+
+class RunningScale:
+    """Running trimmed scale estimator."""
+
+    def __init__(self, tau):
+        self.tau = tau
+        self._value = torch.ones(1, dtype=torch.float32, device=torch.device("cuda"))
+        self._percentiles = torch.tensor([5, 95], dtype=torch.float32, device=torch.device("cuda"))
+
+    def state_dict(self):
+        return dict(value=self._value, percentiles=self._percentiles)
+
+    def load_state_dict(self, state_dict):
+        self._value.data.copy_(state_dict["value"])
+        self._percentiles.data.copy_(state_dict["percentiles"])
+
+    @property
+    def value(self):
+        return self._value.cpu().item()
+
+    def _percentile(self, x):
+        x_dtype, x_shape = x.dtype, x.shape
+        x = x.view(x.shape[0], -1)
+        in_sorted, _ = torch.sort(x, dim=0)
+        positions = self._percentiles * (x.shape[0] - 1) / 100
+        floored = torch.floor(positions)
+        ceiled = floored + 1
+        ceiled[ceiled > x.shape[0] - 1] = x.shape[0] - 1
+        weight_ceiled = positions - floored
+        weight_floored = 1.0 - weight_ceiled
+        d0 = in_sorted[floored.long(), :] * weight_floored[:, None]
+        d1 = in_sorted[ceiled.long(), :] * weight_ceiled[:, None]
+        return (d0 + d1).view(-1, *x_shape[1:]).type(x_dtype)
+
+    def update(self, x):
+        percentiles = self._percentile(x.detach())
+        value = torch.clamp(percentiles[1] - percentiles[0], min=1.0)
+        self._value.data.lerp_(value, self.tau)
+
+    def __call__(self, x, update=False):
+        if update:
+            self.update(x)
+        return x * (1 / self.value)
+
+    def __repr__(self):
+        return f"RunningScale(S: {self.value})"

lerobot/common/policies/tdmpc2/tdmpc2_utils.py

@@ -0,0 +1,164 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from functorch import combine_state_for_ensemble
+
+
+class Ensemble(nn.Module):
+    """
+    Vectorized ensemble of modules.
+    """
+
+    def __init__(self, modules, **kwargs):
+        super().__init__()
+        modules = nn.ModuleList(modules)
+        fn, params, _ = combine_state_for_ensemble(modules)
+        self.vmap = torch.vmap(fn, in_dims=(0, 0, None), randomness="different", **kwargs)
+        self.params = nn.ParameterList([nn.Parameter(p) for p in params])
+        self._repr = str(modules)
+
+    def forward(self, *args, **kwargs):
+        return self.vmap([p for p in self.params], (), *args, **kwargs)
+
+    def __repr__(self):
+        return "Vectorized " + self._repr
+
+
+class SimNorm(nn.Module):
+    """
+    Simplicial normalization.
+    Adapted from https://arxiv.org/abs/2204.00616.
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        shp = x.shape
+        x = x.view(*shp[:-1], -1, self.dim)
+        x = F.softmax(x, dim=-1)
+        return x.view(*shp)
+
+    def __repr__(self):
+        return f"SimNorm(dim={self.dim})"
+
+
+class NormedLinear(nn.Linear):
+    """
+    Linear layer with LayerNorm, activation, and optionally dropout.
+    """
+
+    def __init__(self, *args, dropout=0.0, act=nn.Mish(inplace=True), **kwargs):
+        super().__init__(*args, **kwargs)
+        self.ln = nn.LayerNorm(self.out_features)
+        self.act = act
+        self.dropout = nn.Dropout(dropout, inplace=True) if dropout else None
+
+    def forward(self, x):
+        x = super().forward(x)
+        if self.dropout:
+            x = self.dropout(x)
+        return self.act(self.ln(x))
+
+    def __repr__(self):
+        repr_dropout = f", dropout={self.dropout.p}" if self.dropout else ""
+        return (
+            f"NormedLinear(in_features={self.in_features}, "
+            f"out_features={self.out_features}, "
+            f"bias={self.bias is not None}{repr_dropout}, "
+            f"act={self.act.__class__.__name__})"
+        )
+
+
+def soft_cross_entropy(pred, target, cfg):
+    """Computes the cross entropy loss between predictions and soft targets."""
+    pred = F.log_softmax(pred, dim=-1)
+    target = two_hot(target, cfg)
+    return -(target * pred).sum(-1, keepdim=True)
+
+
+@torch.jit.script
+def log_std(x, low, dif):
+    return low + 0.5 * dif * (torch.tanh(x) + 1)
+
+
+@torch.jit.script
+def _gaussian_residual(eps, log_std):
+    return -0.5 * eps.pow(2) - log_std
+
+
+@torch.jit.script
+def _gaussian_logprob(residual):
+    return residual - 0.5 * torch.log(2 * torch.pi)
+
+
+def gaussian_logprob(eps, log_std, size=None):
+    """Compute Gaussian log probability."""
+    residual = _gaussian_residual(eps, log_std).sum(-1, keepdim=True)
+    if size is None:
+        size = eps.size(-1)
+    return _gaussian_logprob(residual) * size
+
+
+@torch.jit.script
+def _squash(pi):
+    return torch.log(F.relu(1 - pi.pow(2)) + 1e-6)
+
+
+def squash(mu, pi, log_pi):
+    """Apply squashing function."""
+    mu = torch.tanh(mu)
+    pi = torch.tanh(pi)
+    log_pi -= _squash(pi).sum(-1, keepdim=True)
+    return mu, pi, log_pi
+
+
+@torch.jit.script
+def symlog(x):
+    """
+    Symmetric logarithmic function.
+    Adapted from https://github.com/danijar/dreamerv3.
+    """
+    return torch.sign(x) * torch.log(1 + torch.abs(x))
+
+
+@torch.jit.script
+def symexp(x):
+    """
+    Symmetric exponential function.
+    Adapted from https://github.com/danijar/dreamerv3.
+    """
+    return torch.sign(x) * (torch.exp(torch.abs(x)) - 1)
+
+
+def two_hot(x, cfg):
+    """Converts a batch of scalars to soft two-hot encoded targets for discrete regression."""
+
+    # x shape [horizon, num_features]
+    if cfg.num_bins == 0:
+        return x
+    elif cfg.num_bins == 1:
+        return symlog(x)
+    x = torch.clamp(symlog(x), cfg.vmin, cfg.vmax)
+    bin_idx = torch.floor((x - cfg.vmin) / cfg.bin_size).long()  # shape [num_features]
+    bin_offset = ((x - cfg.vmin) / cfg.bin_size - bin_idx.float()).unsqueeze(-1)  # shape [num_features , 1]
+    soft_two_hot = torch.zeros(
+        *x.shape, cfg.num_bins, device=x.device
+    )  # shape [horizon, num_features, num_bins]
+    soft_two_hot.scatter_(2, bin_idx.unsqueeze(-1), 1 - bin_offset)
+    soft_two_hot.scatter_(2, (bin_idx.unsqueeze(-1) + 1) % cfg.num_bins, bin_offset)
+    return soft_two_hot
+
+
+def two_hot_inv(x, bins):
+    """Converts a batch of soft two-hot encoded vectors to scalars."""
+    num_bins = bins.shape[0]
+    if num_bins == 0:
+        return x
+    elif num_bins == 1:
+        return symexp(x)
+
+    x = F.softmax(x, dim=-1)
+    x = torch.sum(x * bins, dim=-1, keepdim=True)
+    return symexp(x)

lerobot/common/policies/vqbet/modeling_vqbet.py

@@ -350,17 +350,22 @@ class VQBeTModel(nn.Module):
 
         # get action features (pass through GPT)
         features = self.policy(input_tokens)
-        # len(self.config.input_shapes) is the number of different observation modes. this line gets the index of action prompt tokens.
+        # len(self.config.input_shapes) is the number of different observation modes.
+        # this line gets the index of action prompt tokens.
         historical_act_pred_index = np.arange(0, n_obs_steps) * (len(self.config.input_shapes) + 1) + len(
             self.config.input_shapes
         )
 
         # only extract the output tokens at the position of action query:
-        # Behavior Transformer (BeT), and VQ-BeT are both sequence-to-sequence prediction models, mapping sequential observation to sequential action (please refer to section 2.2 in BeT paper https://arxiv.org/pdf/2206.11251).
-        # Thus, it predict historical action sequence, in addition to current and future actions (predicting future actions : optional).
-        features = torch.cat(
-            [features[:, historical_act_pred_index], features[:, -len_additional_action_token:]], dim=1
-        )
+        # Behavior Transformer (BeT), and VQ-BeT are both sequence-to-sequence prediction models,
+        # mapping sequential observation to sequential action (please refer to section 2.2 in BeT paper https://arxiv.org/pdf/2206.11251).
+        # Thus, it predicts a historical action sequence, in addition to current and future actions (predicting future actions : optional).
+        if len_additional_action_token > 0:
+            features = torch.cat(
+                [features[:, historical_act_pred_index], features[:, -len_additional_action_token:]], dim=1
+            )
+        else:
+            features = features[:, historical_act_pred_index]
         # pass through action head
         action_head_output = self.action_head(features)
         # if rollout, VQ-BeT don't calculate loss

lerobot/common/robot_devices/cameras/intelrealsense.py

@@ -0,0 +1,557 @@
+"""
+This file contains utilities for recording frames from Intel Realsense cameras.
+"""
+
+import argparse
+import concurrent.futures
+import logging
+import math
+import shutil
+import threading
+import time
+import traceback
+from collections import Counter
+from dataclasses import dataclass, replace
+from pathlib import Path
+from threading import Thread
+
+import numpy as np
+from PIL import Image
+
+from lerobot.common.robot_devices.utils import (
+    RobotDeviceAlreadyConnectedError,
+    RobotDeviceNotConnectedError,
+    busy_wait,
+)
+from lerobot.common.utils.utils import capture_timestamp_utc
+
+SERIAL_NUMBER_INDEX = 1
+
+
+def find_cameras(raise_when_empty=True, mock=False) -> list[dict]:
+    """
+    Find the names and the serial numbers of the Intel RealSense cameras
+    connected to the computer.
+    """
+    if mock:
+        import tests.mock_pyrealsense2 as rs
+    else:
+        import pyrealsense2 as rs
+
+    cameras = []
+    for device in rs.context().query_devices():
+        serial_number = int(device.get_info(rs.camera_info(SERIAL_NUMBER_INDEX)))
+        name = device.get_info(rs.camera_info.name)
+        cameras.append(
+            {
+                "serial_number": serial_number,
+                "name": name,
+            }
+        )
+
+    if raise_when_empty and len(cameras) == 0:
+        raise OSError(
+            "Not a single camera was detected. Try re-plugging, or re-installing `librealsense` and its python wrapper `pyrealsense2`, or updating the firmware."
+        )
+
+    return cameras
+
+
+def save_image(img_array, serial_number, frame_index, images_dir):
+    try:
+        img = Image.fromarray(img_array)
+        path = images_dir / f"camera_{serial_number}_frame_{frame_index:06d}.png"
+        path.parent.mkdir(parents=True, exist_ok=True)
+        img.save(str(path), quality=100)
+        logging.info(f"Saved image: {path}")
+    except Exception as e:
+        logging.error(f"Failed to save image for camera {serial_number} frame {frame_index}: {e}")
+
+
+def save_images_from_cameras(
+    images_dir: Path,
+    serial_numbers: list[int] | None = None,
+    fps=None,
+    width=None,
+    height=None,
+    record_time_s=2,
+    mock=False,
+):
+    """
+    Initializes all the cameras and saves images to the directory. Useful to visually identify the camera
+    associated to a given serial number.
+    """
+    if serial_numbers is None or len(serial_numbers) == 0:
+        camera_infos = find_cameras(mock=mock)
+        serial_numbers = [cam["serial_number"] for cam in camera_infos]
+
+    if mock:
+        import tests.mock_cv2 as cv2
+    else:
+        import cv2
+
+    print("Connecting cameras")
+    cameras = []
+    for cam_sn in serial_numbers:
+        print(f"{cam_sn=}")
+        camera = IntelRealSenseCamera(cam_sn, fps=fps, width=width, height=height, mock=mock)
+        camera.connect()
+        print(
+            f"IntelRealSenseCamera({camera.serial_number}, fps={camera.fps}, width={camera.width}, height={camera.height}, color_mode={camera.color_mode})"
+        )
+        cameras.append(camera)
+
+    images_dir = Path(images_dir)
+    if images_dir.exists():
+        shutil.rmtree(
+            images_dir,
+        )
+    images_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"Saving images to {images_dir}")
+    frame_index = 0
+    start_time = time.perf_counter()
+    try:
+        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+            while True:
+                now = time.perf_counter()
+
+                for camera in cameras:
+                    # If we use async_read when fps is None, the loop will go full speed, and we will end up
+                    # saving the same images from the cameras multiple times until the RAM/disk is full.
+                    image = camera.read() if fps is None else camera.async_read()
+                    if image is None:
+                        print("No Frame")
+
+                    bgr_converted_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+                    executor.submit(
+                        save_image,
+                        bgr_converted_image,
+                        camera.serial_number,
+                        frame_index,
+                        images_dir,
+                    )
+
+                if fps is not None:
+                    dt_s = time.perf_counter() - now
+                    busy_wait(1 / fps - dt_s)
+
+                if time.perf_counter() - start_time > record_time_s:
+                    break
+
+                print(f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}")
+
+                frame_index += 1
+    finally:
+        print(f"Images have been saved to {images_dir}")
+        for camera in cameras:
+            camera.disconnect()
+
+
+@dataclass
+class IntelRealSenseCameraConfig:
+    """
+    Example of tested options for Intel Real Sense D405:
+
+    ```python
+    IntelRealSenseCameraConfig(30, 640, 480)
+    IntelRealSenseCameraConfig(60, 640, 480)
+    IntelRealSenseCameraConfig(90, 640, 480)
+    IntelRealSenseCameraConfig(30, 1280, 720)
+    IntelRealSenseCameraConfig(30, 640, 480, use_depth=True)
+    IntelRealSenseCameraConfig(30, 640, 480, rotation=90)
+    ```
+    """
+
+    fps: int | None = None
+    width: int | None = None
+    height: int | None = None
+    color_mode: str = "rgb"
+    use_depth: bool = False
+    force_hardware_reset: bool = True
+    rotation: int | None = None
+    mock: bool = False
+
+    def __post_init__(self):
+        if self.color_mode not in ["rgb", "bgr"]:
+            raise ValueError(
+                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
+            )
+
+        at_least_one_is_not_none = self.fps is not None or self.width is not None or self.height is not None
+        at_least_one_is_none = self.fps is None or self.width is None or self.height is None
+        if at_least_one_is_not_none and at_least_one_is_none:
+            raise ValueError(
+                "For `fps`, `width` and `height`, either all of them need to be set, or none of them, "
+                f"but {self.fps=}, {self.width=}, {self.height=} were provided."
+            )
+
+        if self.rotation not in [-90, None, 90, 180]:
+            raise ValueError(f"`rotation` must be in [-90, None, 90, 180] (got {self.rotation})")
+
+
+class IntelRealSenseCamera:
+    """
+    The IntelRealSenseCamera class is similar to OpenCVCamera class but adds additional features for Intel Real Sense cameras:
+    - is instantiated with the serial number of the camera - won't randomly change as it can be the case of OpenCVCamera for Linux,
+    - can also be instantiated with the camera's name — if it's unique — using IntelRealSenseCamera.init_from_name(),
+    - depth map can be returned.
+
+    To find the camera indices of your cameras, you can run our utility script that will save a few frames for each camera:
+    ```bash
+    python lerobot/common/robot_devices/cameras/intelrealsense.py --images-dir outputs/images_from_intelrealsense_cameras
+    ```
+
+    When an IntelRealSenseCamera is instantiated, if no specific config is provided, the default fps, width, height and color_mode
+    of the given camera will be used.
+
+    Example of usage:
+    ```python
+    # Instantiate with its serial number
+    camera = IntelRealSenseCamera(128422271347)
+    # Or by its name if it's unique
+    camera = IntelRealSenseCamera.init_from_name("Intel RealSense D405")
+    camera.connect()
+    color_image = camera.read()
+    # when done using the camera, consider disconnecting
+    camera.disconnect()
+    ```
+
+    Example of changing default fps, width, height and color_mode:
+    ```python
+    camera = IntelRealSenseCamera(serial_number, fps=30, width=1280, height=720)
+    camera = connect()  # applies the settings, might error out if these settings are not compatible with the camera
+
+    camera = IntelRealSenseCamera(serial_number, fps=90, width=640, height=480)
+    camera = connect()
+
+    camera = IntelRealSenseCamera(serial_number, fps=90, width=640, height=480, color_mode="bgr")
+    camera = connect()
+    ```
+
+    Example of returning depth:
+    ```python
+    camera = IntelRealSenseCamera(serial_number, use_depth=True)
+    camera.connect()
+    color_image, depth_map = camera.read()
+    ```
+    """
+
+    def __init__(
+        self,
+        serial_number: int,
+        config: IntelRealSenseCameraConfig | None = None,
+        **kwargs,
+    ):
+        if config is None:
+            config = IntelRealSenseCameraConfig()
+
+        # Overwrite the config arguments using kwargs
+        config = replace(config, **kwargs)
+
+        self.serial_number = serial_number
+        self.fps = config.fps
+        self.width = config.width
+        self.height = config.height
+        self.color_mode = config.color_mode
+        self.use_depth = config.use_depth
+        self.force_hardware_reset = config.force_hardware_reset
+        self.mock = config.mock
+
+        self.camera = None
+        self.is_connected = False
+        self.thread = None
+        self.stop_event = None
+        self.color_image = None
+        self.depth_map = None
+        self.logs = {}
+
+        if self.mock:
+            import tests.mock_cv2 as cv2
+        else:
+            import cv2
+
+        # TODO(alibets): Do we keep original width/height or do we define them after rotation?
+        self.rotation = None
+        if config.rotation == -90:
+            self.rotation = cv2.ROTATE_90_COUNTERCLOCKWISE
+        elif config.rotation == 90:
+            self.rotation = cv2.ROTATE_90_CLOCKWISE
+        elif config.rotation == 180:
+            self.rotation = cv2.ROTATE_180
+
+    @classmethod
+    def init_from_name(cls, name: str, config: IntelRealSenseCameraConfig | None = None, **kwargs):
+        camera_infos = find_cameras()
+        camera_names = [cam["name"] for cam in camera_infos]
+        this_name_count = Counter(camera_names)[name]
+        if this_name_count > 1:
+            # TODO(aliberts): Test this with multiple identical cameras (Aloha)
+            raise ValueError(
+                f"Multiple {name} cameras have been detected. Please use their serial number to instantiate them."
+            )
+
+        name_to_serial_dict = {cam["name"]: cam["serial_number"] for cam in camera_infos}
+        cam_sn = name_to_serial_dict[name]
+
+        if config is None:
+            config = IntelRealSenseCameraConfig()
+
+        # Overwrite the config arguments using kwargs
+        config = replace(config, **kwargs)
+
+        return cls(serial_number=cam_sn, config=config, **kwargs)
+
+    def connect(self):
+        if self.is_connected:
+            raise RobotDeviceAlreadyConnectedError(
+                f"IntelRealSenseCamera({self.serial_number}) is already connected."
+            )
+
+        if self.mock:
+            import tests.mock_pyrealsense2 as rs
+        else:
+            import pyrealsense2 as rs
+
+        config = rs.config()
+        config.enable_device(str(self.serial_number))
+
+        if self.fps and self.width and self.height:
+            # TODO(rcadene): can we set rgb8 directly?
+            config.enable_stream(rs.stream.color, self.width, self.height, rs.format.rgb8, self.fps)
+        else:
+            config.enable_stream(rs.stream.color)
+
+        if self.use_depth:
+            if self.fps and self.width and self.height:
+                config.enable_stream(rs.stream.depth, self.width, self.height, rs.format.z16, self.fps)
+            else:
+                config.enable_stream(rs.stream.depth)
+
+        self.camera = rs.pipeline()
+        try:
+            profile = self.camera.start(config)
+            is_camera_open = True
+        except RuntimeError:
+            is_camera_open = False
+            traceback.print_exc()
+
+        # If the camera doesn't work, display the camera indices corresponding to
+        # valid cameras.
+        if not is_camera_open:
+            # Verify that the provided `serial_number` is valid before printing the traceback
+            camera_infos = find_cameras()
+            serial_numbers = [cam["serial_number"] for cam in camera_infos]
+            if self.serial_number not in serial_numbers:
+                raise ValueError(
+                    f"`serial_number` is expected to be one of these available cameras {serial_numbers}, but {self.serial_number} is provided instead. "
+                    "To find the serial number you should use, run `python lerobot/common/robot_devices/cameras/intelrealsense.py`."
+                )
+
+            raise OSError(f"Can't access IntelRealSenseCamera({self.serial_number}).")
+
+        color_stream = profile.get_stream(rs.stream.color)
+        color_profile = color_stream.as_video_stream_profile()
+        actual_fps = color_profile.fps()
+        actual_width = color_profile.width()
+        actual_height = color_profile.height()
+
+        # Using `math.isclose` since actual fps can be a float (e.g. 29.9 instead of 30)
+        if self.fps is not None and not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
+            # Using `OSError` since it's a broad that encompasses issues related to device communication
+            raise OSError(
+                f"Can't set {self.fps=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_fps}."
+            )
+        if self.width is not None and self.width != actual_width:
+            raise OSError(
+                f"Can't set {self.width=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_width}."
+            )
+        if self.height is not None and self.height != actual_height:
+            raise OSError(
+                f"Can't set {self.height=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_height}."
+            )
+
+        self.fps = round(actual_fps)
+        self.width = round(actual_width)
+        self.height = round(actual_height)
+
+        self.is_connected = True
+
+    def read(self, temporary_color: str | None = None) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
+        """Read a frame from the camera returned in the format height x width x channels (e.g. 480 x 640 x 3)
+        of type `np.uint8`, contrarily to the pytorch format which is float channel first.
+
+        When `use_depth=True`, returns a tuple `(color_image, depth_map)` with a depth map in the format
+        height x width (e.g. 480 x 640) of type np.uint16.
+
+        Note: Reading a frame is done every `camera.fps` times per second, and it is blocking.
+        If you are reading data from other sensors, we advise to use `camera.async_read()` which is non blocking version of `camera.read()`.
+        """
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
+            )
+
+        if self.mock:
+            import tests.mock_cv2 as cv2
+        else:
+            import cv2
+
+        start_time = time.perf_counter()
+
+        frame = self.camera.wait_for_frames(timeout_ms=5000)
+
+        color_frame = frame.get_color_frame()
+
+        if not color_frame:
+            raise OSError(f"Can't capture color image from IntelRealSenseCamera({self.serial_number}).")
+
+        color_image = np.asanyarray(color_frame.get_data())
+
+        requested_color_mode = self.color_mode if temporary_color is None else temporary_color
+        if requested_color_mode not in ["rgb", "bgr"]:
+            raise ValueError(
+                f"Expected color values are 'rgb' or 'bgr', but {requested_color_mode} is provided."
+            )
+
+        # IntelRealSense uses RGB format as default (red, green, blue).
+        if requested_color_mode == "bgr":
+            color_image = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)
+
+        h, w, _ = color_image.shape
+        if h != self.height or w != self.width:
+            raise OSError(
+                f"Can't capture color image with expected height and width ({self.height} x {self.width}). ({h} x {w}) returned instead."
+            )
+
+        if self.rotation is not None:
+            color_image = cv2.rotate(color_image, self.rotation)
+
+        # log the number of seconds it took to read the image
+        self.logs["delta_timestamp_s"] = time.perf_counter() - start_time
+
+        # log the utc time at which the image was received
+        self.logs["timestamp_utc"] = capture_timestamp_utc()
+
+        if self.use_depth:
+            depth_frame = frame.get_depth_frame()
+            if not depth_frame:
+                raise OSError(f"Can't capture depth image from IntelRealSenseCamera({self.serial_number}).")
+
+            depth_map = np.asanyarray(depth_frame.get_data())
+
+            h, w = depth_map.shape
+            if h != self.height or w != self.width:
+                raise OSError(
+                    f"Can't capture depth map with expected height and width ({self.height} x {self.width}). ({h} x {w}) returned instead."
+                )
+
+            if self.rotation is not None:
+                depth_map = cv2.rotate(depth_map, self.rotation)
+
+            return color_image, depth_map
+        else:
+            return color_image
+
+    def read_loop(self):
+        while not self.stop_event.is_set():
+            if self.use_depth:
+                self.color_image, self.depth_map = self.read()
+            else:
+                self.color_image = self.read()
+
+    def async_read(self):
+        """Access the latest color image"""
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
+            )
+
+        if self.thread is None:
+            self.stop_event = threading.Event()
+            self.thread = Thread(target=self.read_loop, args=())
+            self.thread.daemon = True
+            self.thread.start()
+
+        num_tries = 0
+        while self.color_image is None:
+            # TODO(rcadene, aliberts): intelrealsense has diverged compared to opencv over here
+            num_tries += 1
+            time.sleep(1 / self.fps)
+            if num_tries > self.fps and (self.thread.ident is None or not self.thread.is_alive()):
+                raise Exception(
+                    "The thread responsible for `self.async_read()` took too much time to start. There might be an issue. Verify that `self.thread.start()` has been called."
+                )
+
+        if self.use_depth:
+            return self.color_image, self.depth_map
+        else:
+            return self.color_image
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
+            )
+
+        if self.thread is not None and self.thread.is_alive():
+            # wait for the thread to finish
+            self.stop_event.set()
+            self.thread.join()
+            self.thread = None
+            self.stop_event = None
+
+        self.camera.stop()
+        self.camera = None
+
+        self.is_connected = False
+
+    def __del__(self):
+        if getattr(self, "is_connected", False):
+            self.disconnect()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Save a few frames using `IntelRealSenseCamera` for all cameras connected to the computer, or a selected subset."
+    )
+    parser.add_argument(
+        "--serial-numbers",
+        type=int,
+        nargs="*",
+        default=None,
+        help="List of serial numbers used to instantiate the `IntelRealSenseCamera`. If not provided, find and use all available camera indices.",
+    )
+    parser.add_argument(
+        "--fps",
+        type=int,
+        default=30,
+        help="Set the number of frames recorded per seconds for all cameras. If not provided, use the default fps of each camera.",
+    )
+    parser.add_argument(
+        "--width",
+        type=str,
+        default=640,
+        help="Set the width for all cameras. If not provided, use the default width of each camera.",
+    )
+    parser.add_argument(
+        "--height",
+        type=str,
+        default=480,
+        help="Set the height for all cameras. If not provided, use the default height of each camera.",
+    )
+    parser.add_argument(
+        "--images-dir",
+        type=Path,
+        default="outputs/images_from_intelrealsense_cameras",
+        help="Set directory to save a few frames for each camera.",
+    )
+    parser.add_argument(
+        "--record-time-s",
+        type=float,
+        default=2.0,
+        help="Set the number of seconds used to record the frames. By default, 2 seconds.",
+    )
+    args = parser.parse_args()
+    save_images_from_cameras(**vars(args))

lerobot/common/robot_devices/cameras/opencv.py

@@ -13,17 +13,15 @@ from dataclasses import dataclass, replace
 from pathlib import Path
 from threading import Thread
 
-import cv2
 import numpy as np
 from PIL import Image
 
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
+from lerobot.common.robot_devices.utils import (
+    RobotDeviceAlreadyConnectedError,
+    RobotDeviceNotConnectedError,
+    busy_wait,
+)
 from lerobot.common.utils.utils import capture_timestamp_utc
-from lerobot.scripts.control_robot import busy_wait
-
-# Use 1 thread to avoid blocking the main thread. Especially useful during data collection
-# when other threads are used to save the images.
-cv2.setNumThreads(1)
 
 # The maximum opencv device index depends on your operating system. For instance,
 # if you have 3 cameras, they should be associated to index 0, 1, and 2. This is the case
@@ -33,20 +31,44 @@ cv2.setNumThreads(1)
 MAX_OPENCV_INDEX = 60
 
 
-def find_camera_indices(raise_when_empty=False, max_index_search_range=MAX_OPENCV_INDEX):
+def find_cameras(raise_when_empty=False, max_index_search_range=MAX_OPENCV_INDEX, mock=False) -> list[dict]:
+    cameras = []
     if platform.system() == "Linux":
-        # Linux uses camera ports
         print("Linux detected. Finding available camera indices through scanning '/dev/video*' ports")
-        possible_camera_ids = []
-        for port in Path("/dev").glob("video*"):
-            camera_idx = int(str(port).replace("/dev/video", ""))
-            possible_camera_ids.append(camera_idx)
+        possible_ports = [str(port) for port in Path("/dev").glob("video*")]
+        ports = _find_cameras(possible_ports, mock=mock)
+        for port in ports:
+            cameras.append(
+                {
+                    "port": port,
+                    "index": int(port.removeprefix("/dev/video")),
+                }
+            )
     else:
         print(
             "Mac or Windows detected. Finding available camera indices through "
             f"scanning all indices from 0 to {MAX_OPENCV_INDEX}"
         )
-        possible_camera_ids = range(max_index_search_range)
+        possible_indices = range(max_index_search_range)
+        indices = _find_cameras(possible_indices, mock=mock)
+        for index in indices:
+            cameras.append(
+                {
+                    "port": None,
+                    "index": index,
+                }
+            )
+
+    return cameras
+
+
+def _find_cameras(
+    possible_camera_ids: list[int | str], raise_when_empty=False, mock=False
+) -> list[int | str]:
+    if mock:
+        import tests.mock_cv2 as cv2
+    else:
+        import cv2
 
     camera_ids = []
     for camera_idx in possible_camera_ids:
@@ -67,6 +89,16 @@ def find_camera_indices(raise_when_empty=False, max_index_search_range=MAX_OPENC
     return camera_ids
 
 
+def is_valid_unix_path(path: str) -> bool:
+    """Note: if 'path' points to a symlink, this will return True only if the target exists"""
+    p = Path(path)
+    return p.is_absolute() and p.exists()
+
+
+def get_camera_index_from_unix_port(port: Path) -> int:
+    return int(str(port.resolve()).removeprefix("/dev/video"))
+
+
 def save_image(img_array, camera_index, frame_index, images_dir):
     img = Image.fromarray(img_array)
     path = images_dir / f"camera_{camera_index:02d}_frame_{frame_index:06d}.png"
@@ -75,15 +107,26 @@ def save_image(img_array, camera_index, frame_index, images_dir):
 
 
 def save_images_from_cameras(
-    images_dir: Path, camera_ids: list[int] | None = None, fps=None, width=None, height=None, record_time_s=2
+    images_dir: Path,
+    camera_ids: list | None = None,
+    fps=None,
+    width=None,
+    height=None,
+    record_time_s=2,
+    mock=False,
 ):
-    if camera_ids is None:
-        camera_ids = find_camera_indices()
+    """
+    Initializes all the cameras and saves images to the directory. Useful to visually identify the camera
+    associated to a given camera index.
+    """
+    if camera_ids is None or len(camera_ids) == 0:
+        camera_infos = find_cameras(mock=mock)
+        camera_ids = [cam["index"] for cam in camera_infos]
 
     print("Connecting cameras")
     cameras = []
     for cam_idx in camera_ids:
-        camera = OpenCVCamera(cam_idx, fps=fps, width=width, height=height)
+        camera = OpenCVCamera(cam_idx, fps=fps, width=width, height=height, mock=mock)
         camera.connect()
         print(
             f"OpenCVCamera({camera.camera_index}, fps={camera.fps}, width={camera.width}, "
@@ -101,7 +144,7 @@ def save_images_from_cameras(
     print(f"Saving images to {images_dir}")
     frame_index = 0
     start_time = time.perf_counter()
-    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
+    with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
         while True:
             now = time.perf_counter()
 
@@ -122,11 +165,11 @@ def save_images_from_cameras(
                 dt_s = time.perf_counter() - now
                 busy_wait(1 / fps - dt_s)
 
+            print(f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}")
+
             if time.perf_counter() - start_time > record_time_s:
                 break
 
-            print(f"Frame: {frame_index:04d}\tLatency (ms): {(time.perf_counter() - now) * 1000:.2f}")
-
             frame_index += 1
 
     print(f"Images have been saved to {images_dir}")
@@ -149,13 +192,18 @@ class OpenCVCameraConfig:
     width: int | None = None
     height: int | None = None
     color_mode: str = "rgb"
+    rotation: int | None = None
+    mock: bool = False
 
     def __post_init__(self):
         if self.color_mode not in ["rgb", "bgr"]:
             raise ValueError(
-                f"Expected color_mode values are 'rgb' or 'bgr', but {self.color_mode} is provided."
+                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
             )
 
+        if self.rotation not in [-90, None, 90, 180]:
+            raise ValueError(f"`rotation` must be in [-90, None, 90, 180] (got {self.rotation})")
+
 
 class OpenCVCamera:
     """
@@ -196,17 +244,32 @@ class OpenCVCamera:
     ```
     """
 
-    def __init__(self, camera_index: int, config: OpenCVCameraConfig | None = None, **kwargs):
+    def __init__(self, camera_index: int | str, config: OpenCVCameraConfig | None = None, **kwargs):
         if config is None:
             config = OpenCVCameraConfig()
+
         # Overwrite config arguments using kwargs
         config = replace(config, **kwargs)
 
         self.camera_index = camera_index
+        self.port = None
+
+        # Linux uses ports for connecting to cameras
+        if platform.system() == "Linux":
+            if isinstance(self.camera_index, int):
+                self.port = Path(f"/dev/video{self.camera_index}")
+            elif isinstance(self.camera_index, str) and is_valid_unix_path(self.camera_index):
+                self.port = Path(self.camera_index)
+                # Retrieve the camera index from a potentially symlinked path
+                self.camera_index = get_camera_index_from_unix_port(self.port)
+            else:
+                raise ValueError(f"Please check the provided camera_index: {camera_index}")
+
         self.fps = config.fps
         self.width = config.width
         self.height = config.height
         self.color_mode = config.color_mode
+        self.mock = config.mock
 
         self.camera = None
         self.is_connected = False
@@ -215,43 +278,60 @@ class OpenCVCamera:
         self.color_image = None
         self.logs = {}
 
+        if self.mock:
+            import tests.mock_cv2 as cv2
+        else:
+            import cv2
+
+        # TODO(aliberts): Do we keep original width/height or do we define them after rotation?
+        self.rotation = None
+        if config.rotation == -90:
+            self.rotation = cv2.ROTATE_90_COUNTERCLOCKWISE
+        elif config.rotation == 90:
+            self.rotation = cv2.ROTATE_90_CLOCKWISE
+        elif config.rotation == 180:
+            self.rotation = cv2.ROTATE_180
+
     def connect(self):
         if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(f"Camera {self.camera_index} is already connected.")
+            raise RobotDeviceAlreadyConnectedError(f"OpenCVCamera({self.camera_index}) is already connected.")
 
+        if self.mock:
+            import tests.mock_cv2 as cv2
+        else:
+            import cv2
+
+            # Use 1 thread to avoid blocking the main thread. Especially useful during data collection
+            # when other threads are used to save the images.
+            cv2.setNumThreads(1)
+
+        camera_idx = f"/dev/video{self.camera_index}" if platform.system() == "Linux" else self.camera_index
         # First create a temporary camera trying to access `camera_index`,
         # and verify it is a valid camera by calling `isOpened`.
-
-        if platform.system() == "Linux":
-            # Linux uses ports for connecting to cameras
-            tmp_camera = cv2.VideoCapture(f"/dev/video{self.camera_index}")
-        else:
-            tmp_camera = cv2.VideoCapture(self.camera_index)
-
+        tmp_camera = cv2.VideoCapture(camera_idx)
         is_camera_open = tmp_camera.isOpened()
         # Release camera to make it accessible for `find_camera_indices`
+        tmp_camera.release()
         del tmp_camera
 
         # If the camera doesn't work, display the camera indices corresponding to
         # valid cameras.
         if not is_camera_open:
             # Verify that the provided `camera_index` is valid before printing the traceback
-            available_cam_ids = find_camera_indices()
+            cameras_info = find_cameras()
+            available_cam_ids = [cam["index"] for cam in cameras_info]
             if self.camera_index not in available_cam_ids:
                 raise ValueError(
                     f"`camera_index` is expected to be one of these available cameras {available_cam_ids}, but {self.camera_index} is provided instead. "
                     "To find the camera index you should use, run `python lerobot/common/robot_devices/cameras/opencv.py`."
                 )
 
-            raise OSError(f"Can't access camera {self.camera_index}.")
+            raise OSError(f"Can't access OpenCVCamera({camera_idx}).")
 
         # Secondly, create the camera that will be used downstream.
         # Note: For some unknown reason, calling `isOpened` blocks the camera which then
         # needs to be re-created.
-        if platform.system() == "Linux":
-            self.camera = cv2.VideoCapture(f"/dev/video{self.camera_index}")
-        else:
-            self.camera = cv2.VideoCapture(self.camera_index)
+        self.camera = cv2.VideoCapture(camera_idx)
 
         if self.fps is not None:
             self.camera.set(cv2.CAP_PROP_FPS, self.fps)
@@ -264,28 +344,30 @@ class OpenCVCamera:
         actual_width = self.camera.get(cv2.CAP_PROP_FRAME_WIDTH)
         actual_height = self.camera.get(cv2.CAP_PROP_FRAME_HEIGHT)
 
+        # Using `math.isclose` since actual fps can be a float (e.g. 29.9 instead of 30)
         if self.fps is not None and not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
+            # Using `OSError` since it's a broad that encompasses issues related to device communication
             raise OSError(
-                f"Can't set {self.fps=} for camera {self.camera_index}. Actual value is {actual_fps}."
+                f"Can't set {self.fps=} for OpenCVCamera({self.camera_index}). Actual value is {actual_fps}."
             )
-        if self.width is not None and self.width != actual_width:
+        if self.width is not None and not math.isclose(self.width, actual_width, rel_tol=1e-3):
             raise OSError(
-                f"Can't set {self.width=} for camera {self.camera_index}. Actual value is {actual_width}."
+                f"Can't set {self.width=} for OpenCVCamera({self.camera_index}). Actual value is {actual_width}."
             )
-        if self.height is not None and self.height != actual_height:
+        if self.height is not None and not math.isclose(self.height, actual_height, rel_tol=1e-3):
             raise OSError(
-                f"Can't set {self.height=} for camera {self.camera_index}. Actual value is {actual_height}."
+                f"Can't set {self.height=} for OpenCVCamera({self.camera_index}). Actual value is {actual_height}."
             )
 
-        self.fps = actual_fps
-        self.width = actual_width
-        self.height = actual_height
+        self.fps = round(actual_fps)
+        self.width = round(actual_width)
+        self.height = round(actual_height)
 
         self.is_connected = True
 
     def read(self, temporary_color_mode: str | None = None) -> np.ndarray:
         """Read a frame from the camera returned in the format (height, width, channels)
-        (e.g. (640, 480, 3)), contrarily to the pytorch format which is channel first.
+        (e.g. 480 x 640 x 3), contrarily to the pytorch format which is channel first.
 
         Note: Reading a frame is done every `camera.fps` times per second, and it is blocking.
         If you are reading data from other sensors, we advise to use `camera.async_read()` which is non blocking version of `camera.read()`.
@@ -298,6 +380,7 @@ class OpenCVCamera:
         start_time = time.perf_counter()
 
         ret, color_image = self.camera.read()
+
         if not ret:
             raise OSError(f"Can't capture color image from camera {self.camera_index}.")
 
@@ -308,10 +391,15 @@ class OpenCVCamera:
                 f"Expected color values are 'rgb' or 'bgr', but {requested_color_mode} is provided."
             )
 
-        # OpenCV uses BGR format as default (blue, green red) for all operations, including displaying images.
+        # OpenCV uses BGR format as default (blue, green, red) for all operations, including displaying images.
         # However, Deep Learning framework such as LeRobot uses RGB format as default to train neural networks,
         # so we convert the image color from BGR to RGB.
         if requested_color_mode == "rgb":
+            if self.mock:
+                import tests.mock_cv2 as cv2
+            else:
+                import cv2
+
             color_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB)
 
         h, w, _ = color_image.shape
@@ -320,17 +408,25 @@ class OpenCVCamera:
                 f"Can't capture color image with expected height and width ({self.height} x {self.width}). ({h} x {w}) returned instead."
             )
 
+        if self.rotation is not None:
+            color_image = cv2.rotate(color_image, self.rotation)
+
         # log the number of seconds it took to read the image
         self.logs["delta_timestamp_s"] = time.perf_counter() - start_time
 
         # log the utc time at which the image was received
         self.logs["timestamp_utc"] = capture_timestamp_utc()
 
+        self.color_image = color_image
+
         return color_image
 
     def read_loop(self):
-        while self.stop_event is None or not self.stop_event.is_set():
-            self.color_image = self.read()
+        while not self.stop_event.is_set():
+            try:
+                self.color_image = self.read()
+            except Exception as e:
+                print(f"Error reading in thread: {e}")
 
     def async_read(self):
         if not self.is_connected:
@@ -345,15 +441,14 @@ class OpenCVCamera:
             self.thread.start()
 
         num_tries = 0
-        while self.color_image is None:
-            num_tries += 1
-            time.sleep(1 / self.fps)
-            if num_tries > self.fps and (self.thread.ident is None or not self.thread.is_alive()):
-                raise Exception(
-                    "The thread responsible for `self.async_read()` took too much time to start. There might be an issue. Verify that `self.thread.start()` has been called."
-                )
+        while True:
+            if self.color_image is not None:
+                return self.color_image
 
-        return self.color_image
+            time.sleep(1 / self.fps)
+            num_tries += 1
+            if num_tries > self.fps * 2:
+                raise TimeoutError("Timed out waiting for async_read() to start.")
 
     def disconnect(self):
         if not self.is_connected:
@@ -361,16 +456,14 @@ class OpenCVCamera:
                 f"OpenCVCamera({self.camera_index}) is not connected. Try running `camera.connect()` first."
             )
 
-        if self.thread is not None and self.thread.is_alive():
-            # wait for the thread to finish
+        if self.thread is not None:
             self.stop_event.set()
-            self.thread.join()
+            self.thread.join()  # wait for the thread to finish
             self.thread = None
             self.stop_event = None
 
         self.camera.release()
         self.camera = None
-
         self.is_connected = False
 
     def __del__(self):
@@ -416,7 +509,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--record-time-s",
         type=float,
-        default=2.0,
+        default=4.0,
         help="Set the number of seconds used to record the frames. By default, 2 seconds.",
     )
     args = parser.parse_args()

lerobot/common/robot_devices/cameras/utils.py

@@ -1,55 +1,8 @@
-from pathlib import Path
 from typing import Protocol
 
-import cv2
-import einops
 import numpy as np
 
 
-def write_shape_on_image_inplace(image):
-    height, width = image.shape[:2]
-    text = f"Width: {width} Height: {height}"
-
-    # Define the font, scale, color, and thickness
-    font = cv2.FONT_HERSHEY_SIMPLEX
-    font_scale = 1
-    color = (255, 0, 0)  # Blue in BGR
-    thickness = 2
-
-    position = (10, height - 10)  # 10 pixels from the bottom-left corner
-    cv2.putText(image, text, position, font, font_scale, color, thickness)
-
-
-def save_color_image(image, path, write_shape=False):
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-    if write_shape:
-        write_shape_on_image_inplace(image)
-    cv2.imwrite(str(path), image)
-
-
-def save_depth_image(depth, path, write_shape=False):
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-
-    # Apply colormap on depth image (image must be converted to 8-bit per pixel first)
-    depth_image = cv2.applyColorMap(cv2.convertScaleAbs(depth, alpha=0.03), cv2.COLORMAP_JET)
-
-    if write_shape:
-        write_shape_on_image_inplace(depth_image)
-    cv2.imwrite(str(path), depth_image)
-
-
-def convert_torch_image_to_cv2(tensor, rgb_to_bgr=True):
-    assert tensor.ndim == 3
-    c, h, w = tensor.shape
-    assert c < h and c < w
-    color_image = einops.rearrange(tensor, "c h w -> h w c").numpy()
-    if rgb_to_bgr:
-        color_image = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)
-    return color_image
-
-
 # Defines a camera type
 class Camera(Protocol):
     def connect(self): ...

lerobot/common/robot_devices/control_utils.py

@@ -0,0 +1,330 @@
+########################################################################################
+# Utilities
+########################################################################################
+
+
+import logging
+import time
+import traceback
+from contextlib import nullcontext
+from copy import copy
+from functools import cache
+
+import cv2
+import torch
+import tqdm
+from termcolor import colored
+
+from lerobot.common.datasets.populate_dataset import add_frame, safe_stop_image_writer
+from lerobot.common.policies.factory import make_policy
+from lerobot.common.robot_devices.robots.utils import Robot
+from lerobot.common.robot_devices.utils import busy_wait
+from lerobot.common.utils.utils import get_safe_torch_device, init_hydra_config, set_global_seed
+from lerobot.scripts.eval import get_pretrained_policy_path
+
+
+def log_control_info(robot: Robot, dt_s, episode_index=None, frame_index=None, fps=None):
+    log_items = []
+    if episode_index is not None:
+        log_items.append(f"ep:{episode_index}")
+    if frame_index is not None:
+        log_items.append(f"frame:{frame_index}")
+
+    def log_dt(shortname, dt_val_s):
+        nonlocal log_items, fps
+        info_str = f"{shortname}:{dt_val_s * 1000:5.2f} ({1/ dt_val_s:3.1f}hz)"
+        if fps is not None:
+            actual_fps = 1 / dt_val_s
+            if actual_fps < fps - 1:
+                info_str = colored(info_str, "yellow")
+        log_items.append(info_str)
+
+    # total step time displayed in milliseconds and its frequency
+    log_dt("dt", dt_s)
+
+    # TODO(aliberts): move robot-specific logs logic in robot.print_logs()
+    if not robot.robot_type.startswith("stretch"):
+        for name in robot.leader_arms:
+            key = f"read_leader_{name}_pos_dt_s"
+            if key in robot.logs:
+                log_dt("dtRlead", robot.logs[key])
+
+        for name in robot.follower_arms:
+            key = f"write_follower_{name}_goal_pos_dt_s"
+            if key in robot.logs:
+                log_dt("dtWfoll", robot.logs[key])
+
+            key = f"read_follower_{name}_pos_dt_s"
+            if key in robot.logs:
+                log_dt("dtRfoll", robot.logs[key])
+
+        for name in robot.cameras:
+            key = f"read_camera_{name}_dt_s"
+            if key in robot.logs:
+                log_dt(f"dtR{name}", robot.logs[key])
+
+    info_str = " ".join(log_items)
+    logging.info(info_str)
+
+
+@cache
+def is_headless():
+    """Detects if python is running without a monitor."""
+    try:
+        import pynput  # noqa
+
+        return False
+    except Exception:
+        print(
+            "Error trying to import pynput. Switching to headless mode. "
+            "As a result, the video stream from the cameras won't be shown, "
+            "and you won't be able to change the control flow with keyboards. "
+            "For more info, see traceback below.\n"
+        )
+        traceback.print_exc()
+        print()
+        return True
+
+
+def has_method(_object: object, method_name: str):
+    return hasattr(_object, method_name) and callable(getattr(_object, method_name))
+
+
+def predict_action(observation, policy, device, use_amp):
+    observation = copy(observation)
+    with (
+        torch.inference_mode(),
+        torch.autocast(device_type=device.type) if device.type == "cuda" and use_amp else nullcontext(),
+    ):
+        # 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")
+
+    return action
+
+
+def init_keyboard_listener():
+    # Allow to exit early while recording an episode or resetting the environment,
+    # by tapping the right arrow key '->'. This might require a sudo permission
+    # to allow your terminal to monitor keyboard events.
+    events = {}
+    events["exit_early"] = False
+    events["rerecord_episode"] = False
+    events["stop_recording"] = False
+
+    if is_headless():
+        logging.warning(
+            "Headless environment detected. On-screen cameras display and keyboard inputs will not be available."
+        )
+        listener = None
+        return listener, events
+
+    # Only import pynput if not in a headless environment
+    from pynput import keyboard
+
+    def on_press(key):
+        try:
+            if key == keyboard.Key.right:
+                print("Right arrow key pressed. Exiting loop...")
+                events["exit_early"] = True
+            elif key == keyboard.Key.left:
+                print("Left arrow key pressed. Exiting loop and rerecord the last episode...")
+                events["rerecord_episode"] = True
+                events["exit_early"] = True
+            elif key == keyboard.Key.esc:
+                print("Escape key pressed. Stopping data recording...")
+                events["stop_recording"] = True
+                events["exit_early"] = True
+        except Exception as e:
+            print(f"Error handling key press: {e}")
+
+    listener = keyboard.Listener(on_press=on_press)
+    listener.start()
+
+    return listener, events
+
+
+def init_policy(pretrained_policy_name_or_path, policy_overrides):
+    """Instantiate the policy and load fps, device and use_amp from config yaml"""
+    pretrained_policy_path = get_pretrained_policy_path(pretrained_policy_name_or_path)
+    hydra_cfg = init_hydra_config(pretrained_policy_path / "config.yaml", policy_overrides)
+    policy = make_policy(hydra_cfg=hydra_cfg, pretrained_policy_name_or_path=pretrained_policy_path)
+
+    # Check device is available
+    device = get_safe_torch_device(hydra_cfg.device, log=True)
+    use_amp = hydra_cfg.use_amp
+    policy_fps = hydra_cfg.env.fps
+
+    policy.eval()
+    policy.to(device)
+
+    torch.backends.cudnn.benchmark = True
+    torch.backends.cuda.matmul.allow_tf32 = True
+    set_global_seed(hydra_cfg.seed)
+    return policy, policy_fps, device, use_amp
+
+
+def warmup_record(
+    robot,
+    events,
+    enable_teloperation,
+    warmup_time_s,
+    display_cameras,
+    fps,
+):
+    control_loop(
+        robot=robot,
+        control_time_s=warmup_time_s,
+        display_cameras=display_cameras,
+        events=events,
+        fps=fps,
+        teleoperate=enable_teloperation,
+    )
+
+
+def record_episode(
+    robot,
+    dataset,
+    events,
+    episode_time_s,
+    display_cameras,
+    policy,
+    device,
+    use_amp,
+    fps,
+):
+    control_loop(
+        robot=robot,
+        control_time_s=episode_time_s,
+        display_cameras=display_cameras,
+        dataset=dataset,
+        events=events,
+        policy=policy,
+        device=device,
+        use_amp=use_amp,
+        fps=fps,
+        teleoperate=policy is None,
+    )
+
+
+@safe_stop_image_writer
+def control_loop(
+    robot,
+    control_time_s=None,
+    teleoperate=False,
+    display_cameras=False,
+    dataset=None,
+    events=None,
+    policy=None,
+    device=None,
+    use_amp=None,
+    fps=None,
+):
+    # TODO(rcadene): Add option to record logs
+    if not robot.is_connected:
+        robot.connect()
+
+    if events is None:
+        events = {"exit_early": False}
+
+    if control_time_s is None:
+        control_time_s = float("inf")
+
+    if teleoperate and policy is not None:
+        raise ValueError("When `teleoperate` is True, `policy` should be None.")
+
+    if dataset is not None and fps is not None and dataset["fps"] != fps:
+        raise ValueError(f"The dataset fps should be equal to requested fps ({dataset['fps']} != {fps}).")
+
+    timestamp = 0
+    start_episode_t = time.perf_counter()
+    while timestamp < control_time_s:
+        start_loop_t = time.perf_counter()
+
+        if teleoperate:
+            observation, action = robot.teleop_step(record_data=True)
+        else:
+            observation = robot.capture_observation()
+
+            if policy is not None:
+                pred_action = predict_action(observation, policy, device, use_amp)
+                # Action can eventually be clipped using `max_relative_target`,
+                # so action actually sent is saved in the dataset.
+                action = robot.send_action(pred_action)
+                action = {"action": action}
+
+        if dataset is not None:
+            add_frame(dataset, observation, action)
+
+        if display_cameras and not is_headless():
+            image_keys = [key for key in observation if "image" in key]
+            for key in image_keys:
+                cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR))
+            cv2.waitKey(1)
+
+        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)
+
+        timestamp = time.perf_counter() - start_episode_t
+        if events["exit_early"]:
+            events["exit_early"] = False
+            break
+
+
+def reset_environment(robot, events, reset_time_s):
+    # TODO(rcadene): refactor warmup_record and reset_environment
+    # TODO(alibets): allow for teleop during reset
+    if has_method(robot, "teleop_safety_stop"):
+        robot.teleop_safety_stop()
+
+    timestamp = 0
+    start_vencod_t = time.perf_counter()
+
+    # Wait if necessary
+    with tqdm.tqdm(total=reset_time_s, desc="Waiting") as pbar:
+        while timestamp < reset_time_s:
+            time.sleep(1)
+            timestamp = time.perf_counter() - start_vencod_t
+            pbar.update(1)
+            if events["exit_early"]:
+                events["exit_early"] = False
+                break
+
+
+def stop_recording(robot, listener, display_cameras):
+    robot.disconnect()
+
+    if not is_headless():
+        if listener is not None:
+            listener.stop()
+
+        if display_cameras:
+            cv2.destroyAllWindows()
+
+
+def sanity_check_dataset_name(repo_id, policy):
+    _, dataset_name = repo_id.split("/")
+    # either repo_id doesnt start with "eval_" and there is no policy
+    # or repo_id starts with "eval_" and there is a policy
+    if dataset_name.startswith("eval_") == (policy is None):
+        raise ValueError(
+            f"Your dataset name begins by 'eval_' ({dataset_name}) but no policy is provided ({policy})."
+        )

lerobot/common/robot_devices/motors/dynamixel.py

@@ -1,22 +1,12 @@
 import enum
+import logging
+import math
 import time
 import traceback
 from copy import deepcopy
-from pathlib import Path
 
 import numpy as np
 import tqdm
-from dynamixel_sdk import (
-    COMM_SUCCESS,
-    DXL_HIBYTE,
-    DXL_HIWORD,
-    DXL_LOBYTE,
-    DXL_LOWORD,
-    GroupSyncRead,
-    GroupSyncWrite,
-    PacketHandler,
-    PortHandler,
-)
 
 from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
 from lerobot.common.utils.utils import capture_timestamp_utc
@@ -27,11 +17,28 @@ TIMEOUT_MS = 1000
 
 MAX_ID_RANGE = 252
 
+# The following bounds define the lower and upper joints range (after calibration).
+# For joints in degree (i.e. revolute joints), their nominal range is [-180, 180] degrees
+# which corresponds to a half rotation on the left and half rotation on the right.
+# Some joints might require higher range, so we allow up to [-270, 270] degrees until
+# an error is raised.
+LOWER_BOUND_DEGREE = -270
+UPPER_BOUND_DEGREE = 270
+# For joints in percentage (i.e. joints that move linearly like the prismatic joint of a gripper),
+# their nominal range is [0, 100] %. For instance, for Aloha gripper, 0% is fully
+# closed, and 100% is fully open. To account for slight calibration issue, we allow up to
+# [-10, 110] until an error is raised.
+LOWER_BOUND_LINEAR = -10
+UPPER_BOUND_LINEAR = 110
+
+HALF_TURN_DEGREE = 180
+
 # https://emanual.robotis.com/docs/en/dxl/x/xl330-m077
 # https://emanual.robotis.com/docs/en/dxl/x/xl330-m288
 # https://emanual.robotis.com/docs/en/dxl/x/xl430-w250
 # https://emanual.robotis.com/docs/en/dxl/x/xm430-w350
 # https://emanual.robotis.com/docs/en/dxl/x/xm540-w270
+# https://emanual.robotis.com/docs/en/dxl/x/xc430-w150
 
 # data_name: (address, size_byte)
 X_SERIES_CONTROL_TABLE = {
@@ -109,6 +116,7 @@ MODEL_CONTROL_TABLE = {
     "xl430-w250": X_SERIES_CONTROL_TABLE,
     "xm430-w350": X_SERIES_CONTROL_TABLE,
     "xm540-w270": X_SERIES_CONTROL_TABLE,
+    "xc430-w150": X_SERIES_CONTROL_TABLE,
 }
 
 MODEL_RESOLUTION = {
@@ -118,6 +126,7 @@ MODEL_RESOLUTION = {
     "xl430-w250": 4096,
     "xm430-w350": 4096,
     "xm540-w270": 4096,
+    "xc430-w150": 4096,
 }
 
 MODEL_BAUDRATE_TABLE = {
@@ -127,44 +136,47 @@ MODEL_BAUDRATE_TABLE = {
     "xl430-w250": X_SERIES_BAUDRATE_TABLE,
     "xm430-w350": X_SERIES_BAUDRATE_TABLE,
     "xm540-w270": X_SERIES_BAUDRATE_TABLE,
+    "xc430-w150": X_SERIES_BAUDRATE_TABLE,
 }
 
 NUM_READ_RETRY = 10
 NUM_WRITE_RETRY = 10
 
 
-def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]):
-    """This function convert the degree range to the step range for indicating motors rotation.
-    It assums a motor achieves a full rotation by going from -180 degree position to +180.
+def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]) -> np.ndarray:
+    """This function converts the degree range to the step range for indicating motors rotation.
+    It assumes a motor achieves a full rotation by going from -180 degree position to +180.
     The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
     """
-    if isinstance(degrees, float):
-        degrees = np.array(degrees)
-
     resolutions = [MODEL_RESOLUTION[model] for model in models]
     steps = degrees / 180 * np.array(resolutions) / 2
     steps = steps.astype(int)
     return steps
 
 
-def convert_to_bytes(value, bytes):
+def convert_to_bytes(value, bytes, mock=False):
+    if mock:
+        return value
+
+    import dynamixel_sdk as dxl
+
     # Note: No need to convert back into unsigned int, since this byte preprocessing
     # already handles it for us.
     if bytes == 1:
         data = [
-            DXL_LOBYTE(DXL_LOWORD(value)),
+            dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)),
         ]
     elif bytes == 2:
         data = [
-            DXL_LOBYTE(DXL_LOWORD(value)),
-            DXL_HIBYTE(DXL_LOWORD(value)),
+            dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)),
+            dxl.DXL_HIBYTE(dxl.DXL_LOWORD(value)),
         ]
     elif bytes == 4:
         data = [
-            DXL_LOBYTE(DXL_LOWORD(value)),
-            DXL_HIBYTE(DXL_LOWORD(value)),
-            DXL_LOBYTE(DXL_HIWORD(value)),
-            DXL_HIBYTE(DXL_HIWORD(value)),
+            dxl.DXL_LOBYTE(dxl.DXL_LOWORD(value)),
+            dxl.DXL_HIBYTE(dxl.DXL_LOWORD(value)),
+            dxl.DXL_LOBYTE(dxl.DXL_HIWORD(value)),
+            dxl.DXL_HIBYTE(dxl.DXL_HIWORD(value)),
         ]
     else:
         raise NotImplementedError(
@@ -216,54 +228,29 @@ def assert_same_address(model_ctrl_table, motor_models, data_name):
         )
 
 
-def find_available_ports():
-    ports = []
-    for path in Path("/dev").glob("tty*"):
-        ports.append(str(path))
-    return ports
-
-
-def find_port():
-    print("Finding all available ports for the DynamixelMotorsBus.")
-    ports_before = find_available_ports()
-    print(ports_before)
-
-    print("Remove the usb cable from your DynamixelMotorsBus and press Enter when done.")
-    input()
-
-    time.sleep(0.5)
-    ports_after = find_available_ports()
-    ports_diff = list(set(ports_before) - set(ports_after))
-
-    if len(ports_diff) == 1:
-        port = ports_diff[0]
-        print(f"The port of this DynamixelMotorsBus is '{port}'")
-        print("Reconnect the usb cable.")
-    elif len(ports_diff) == 0:
-        raise OSError(f"Could not detect the port. No difference was found ({ports_diff}).")
-    else:
-        raise OSError(f"Could not detect the port. More than one port was found ({ports_diff}).")
-
-
 class TorqueMode(enum.Enum):
     ENABLED = 1
     DISABLED = 0
 
 
-class OperatingMode(enum.Enum):
-    VELOCITY = 1
-    POSITION = 3
-    EXTENDED_POSITION = 4
-    CURRENT_CONTROLLED_POSITION = 5
-    PWM = 16
-    UNKNOWN = -1
-
-
 class DriveMode(enum.Enum):
     NON_INVERTED = 0
     INVERTED = 1
 
 
+class CalibrationMode(enum.Enum):
+    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
+    DEGREE = 0
+    # Joints with linear motions (like gripper of Aloha) are experessed in nominal range of [0, 100]
+    LINEAR = 1
+
+
+class JointOutOfRangeError(Exception):
+    def __init__(self, message="Joint is out of range"):
+        self.message = message
+        super().__init__(self.message)
+
+
 class DynamixelMotorsBus:
     # TODO(rcadene): Add a script to find the motor indices without DynamixelWizzard2
     """
@@ -273,8 +260,8 @@ class DynamixelMotorsBus:
     A DynamixelMotorsBus instance requires a port (e.g. `DynamixelMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
     To find the port, you can run our utility script:
     ```bash
-    python lerobot/common/robot_devices/motors/dynamixel.py
-    >>> Finding all available ports for the DynamixelMotorsBus.
+    python lerobot/scripts/find_motors_bus_port.py
+    >>> Finding all available ports for the MotorBus.
     >>> ['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
     >>> Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
     >>> The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751.
@@ -310,9 +297,11 @@ class DynamixelMotorsBus:
         motors: dict[str, tuple[int, str]],
         extra_model_control_table: dict[str, list[tuple]] | None = None,
         extra_model_resolution: dict[str, int] | None = None,
+        mock=False,
     ):
         self.port = port
         self.motors = motors
+        self.mock = mock
 
         self.model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
         if extra_model_control_table:
@@ -336,8 +325,13 @@ class DynamixelMotorsBus:
                 f"DynamixelMotorsBus({self.port}) is already connected. Do not call `motors_bus.connect()` twice."
             )
 
-        self.port_handler = PortHandler(self.port)
-        self.packet_handler = PacketHandler(PROTOCOL_VERSION)
+        if self.mock:
+            import tests.mock_dynamixel_sdk as dxl
+        else:
+            import dynamixel_sdk as dxl
+
+        self.port_handler = dxl.PortHandler(self.port)
+        self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
 
         try:
             if not self.port_handler.openPort():
@@ -345,7 +339,7 @@ class DynamixelMotorsBus:
         except Exception:
             traceback.print_exc()
             print(
-                "\nTry running `python lerobot/common/robot_devices/motors/dynamixel.py` to make sure you are using the correct port.\n"
+                "\nTry running `python lerobot/scripts/find_motors_bus_port.py` to make sure you are using the correct port.\n"
             )
             raise
 
@@ -354,25 +348,18 @@ class DynamixelMotorsBus:
 
         self.port_handler.setPacketTimeoutMillis(TIMEOUT_MS)
 
-        # Set expected baudrate for the bus
-        self.set_bus_baudrate(BAUDRATE)
-
-        if not self.are_motors_configured():
-            input(
-                "\n/!\\ A configuration issue has been detected with your motors: \n"
-                "If it's the first time that you use 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.\n"
-            )
-            print()
-            self.configure_motors()
-
     def reconnect(self):
-        self.port_handler = PortHandler(self.port)
-        self.packet_handler = PacketHandler(PROTOCOL_VERSION)
+        if self.mock:
+            import tests.mock_dynamixel_sdk as dxl
+        else:
+            import dynamixel_sdk as dxl
+
+        self.port_handler = dxl.PortHandler(self.port)
+        self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
+
         if not self.port_handler.openPort():
             raise OSError(f"Failed to open port '{self.port}'.")
+
         self.is_connected = True
 
     def are_motors_configured(self):
@@ -384,120 +371,14 @@ class DynamixelMotorsBus:
             print(e)
             return False
 
-    def configure_motors(self):
-        # TODO(rcadene): This script assumes motors follow the X_SERIES baudrates
-        # TODO(rcadene): Refactor this function with intermediate high-level functions
-
-        print("Scanning all baudrates and motor indices")
-        all_baudrates = set(X_SERIES_BAUDRATE_TABLE.values())
-        ids_per_baudrate = {}
-        for baudrate in all_baudrates:
-            self.set_bus_baudrate(baudrate)
-            present_ids = self.find_motor_indices()
-            if len(present_ids) > 0:
-                ids_per_baudrate[baudrate] = present_ids
-        print(f"Motor indices detected: {ids_per_baudrate}")
-        print()
-
-        possible_baudrates = list(ids_per_baudrate.keys())
-        possible_ids = list({idx for sublist in ids_per_baudrate.values() for idx in sublist})
-        untaken_ids = list(set(range(MAX_ID_RANGE)) - set(possible_ids) - set(self.motor_indices))
-
-        # Connect successively one motor to the chain and write a unique random index for each
-        for i in range(len(self.motors)):
-            self.disconnect()
-            input(
-                "1. Unplug the power cord\n"
-                "2. Plug/unplug minimal number of cables to only have the first "
-                f"{i+1} motor(s) ({self.motor_names[:i+1]}) connected.\n"
-                "3. Re-plug the power cord\n"
-                "Press Enter to continue..."
-            )
-            print()
-            self.reconnect()
-
-            if i > 0:
-                try:
-                    self._read_with_motor_ids(self.motor_models, untaken_ids[:i], "ID")
-                except ConnectionError:
-                    print(f"Failed to read from {untaken_ids[:i+1]}. Make sure the power cord is plugged in.")
-                    input("Press Enter to continue...")
-                    print()
-                    self.reconnect()
-
-            print("Scanning possible baudrates and motor indices")
-            motor_found = False
-            for baudrate in possible_baudrates:
-                self.set_bus_baudrate(baudrate)
-                present_ids = self.find_motor_indices(possible_ids)
-                if len(present_ids) == 1:
-                    present_idx = present_ids[0]
-                    print(f"Detected motor with index {present_idx}")
-
-                    if baudrate != BAUDRATE:
-                        print(f"Setting its baudrate to {BAUDRATE}")
-                        baudrate_idx = list(X_SERIES_BAUDRATE_TABLE.values()).index(BAUDRATE)
-
-                        # The write can fail, so we allow retries
-                        for _ in range(NUM_WRITE_RETRY):
-                            self._write_with_motor_ids(
-                                self.motor_models, present_idx, "Baud_Rate", baudrate_idx
-                            )
-                            time.sleep(0.5)
-                            self.set_bus_baudrate(BAUDRATE)
-                            try:
-                                present_baudrate_idx = self._read_with_motor_ids(
-                                    self.motor_models, present_idx, "Baud_Rate"
-                                )
-                            except ConnectionError:
-                                print("Failed to write baudrate. Retrying.")
-                                self.set_bus_baudrate(baudrate)
-                                continue
-                            break
-                        else:
-                            raise
-
-                        if present_baudrate_idx != baudrate_idx:
-                            raise OSError("Failed to write baudrate.")
-
-                    print(f"Setting its index to a temporary untaken index ({untaken_ids[i]})")
-                    self._write_with_motor_ids(self.motor_models, present_idx, "ID", untaken_ids[i])
-
-                    present_idx = self._read_with_motor_ids(self.motor_models, untaken_ids[i], "ID")
-                    if present_idx != untaken_ids[i]:
-                        raise OSError("Failed to write index.")
-
-                    motor_found = True
-                    break
-                elif len(present_ids) > 1:
-                    raise OSError(f"More than one motor detected ({present_ids}), but only one was expected.")
-
-            if not motor_found:
-                raise OSError(
-                    "No motor found, but one new motor expected. Verify power cord is plugged in and retry."
-                )
-            print()
-
-        print(f"Setting expected motor indices: {self.motor_indices}")
-        self.set_bus_baudrate(BAUDRATE)
-        self._write_with_motor_ids(
-            self.motor_models, untaken_ids[: len(self.motors)], "ID", self.motor_indices
-        )
-        print()
-
-        if (self.read("ID") != self.motor_indices).any():
-            raise OSError("Failed to write motors indices.")
-
-        print("Configuration is done!")
-
-    def find_motor_indices(self, possible_ids=None):
+    def find_motor_indices(self, possible_ids=None, num_retry=2):
         if possible_ids is None:
             possible_ids = range(MAX_ID_RANGE)
 
         indices = []
         for idx in tqdm.tqdm(possible_ids):
             try:
-                present_idx = self._read_with_motor_ids(self.motor_models, [idx], "ID")[0]
+                present_idx = self.read_with_motor_ids(self.motor_models, [idx], "ID", num_retry=num_retry)[0]
             except ConnectionError:
                 continue
 
@@ -531,9 +412,22 @@ class DynamixelMotorsBus:
     def motor_indices(self) -> list[int]:
         return [idx for idx, _ in self.motors.values()]
 
-    def set_calibration(self, calibration: dict[str, tuple[int, bool]]):
+    def set_calibration(self, calibration: dict[str, list]):
         self.calibration = calibration
 
+    def apply_calibration_autocorrect(self, values: np.ndarray | list, motor_names: list[str] | None):
+        """This function applies the calibration, automatically detects out of range errors for motors values and attempts to correct.
+
+        For more info, see docstring of `apply_calibration` and `autocorrect_calibration`.
+        """
+        try:
+            values = self.apply_calibration(values, motor_names)
+        except JointOutOfRangeError as e:
+            print(e)
+            self.autocorrect_calibration(values, motor_names)
+            values = self.apply_calibration(values, motor_names)
+        return values
+
     def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
         """Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
         a "zero position" at 0 degree.
@@ -551,56 +445,205 @@ class DynamixelMotorsBus:
         if motor_names is None:
             motor_names = self.motor_names
 
-        # Convert from unsigned int32 original range [0, 2**32[ to centered signed int32 range [-2**31, 2**31[
-        values = values.astype(np.int32)
-
-        for i, name in enumerate(motor_names):
-            homing_offset, drive_mode = self.calibration[name]
-
-            # Update direction of rotation of the motor to match between leader and follower. In fact, the motor of the leader for a given joint
-            # can be assembled in an opposite direction in term of rotation than the motor of the follower on the same joint.
-            if drive_mode:
-                values[i] *= -1
-
-            # Convert from range [-2**31, 2**31[ to nominal range ]-resolution, resolution[ (e.g. ]-2048, 2048[)
-            values[i] += homing_offset
-
-        # Convert from range ]-resolution, resolution[ to the universal float32 centered degree range ]-180, 180[
+        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
         values = values.astype(np.float32)
+
         for i, name in enumerate(motor_names):
-            _, model = self.motors[name]
-            resolution = self.model_resolution[model]
-            values[i] = values[i] / (resolution // 2) * 180
+            calib_idx = self.calibration["motor_names"].index(name)
+            calib_mode = self.calibration["calib_mode"][calib_idx]
+
+            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                drive_mode = self.calibration["drive_mode"][calib_idx]
+                homing_offset = self.calibration["homing_offset"][calib_idx]
+                _, model = self.motors[name]
+                resolution = self.model_resolution[model]
+
+                # Update direction of rotation of the motor to match between leader and follower.
+                # In fact, the motor of the leader for a given joint can be assembled in an
+                # opposite direction in term of rotation than the motor of the follower on the same joint.
+                if drive_mode:
+                    values[i] *= -1
+
+                # Convert from range [-2**31, 2**31] to
+                # nominal range [-resolution//2, resolution//2] (e.g. [-2048, 2048])
+                values[i] += homing_offset
+
+                # Convert from range [-resolution//2, resolution//2] to
+                # universal float32 centered degree range [-180, 180]
+                # (e.g. 2048 / (4096 // 2) * 180 = 180)
+                values[i] = values[i] / (resolution // 2) * HALF_TURN_DEGREE
+
+                if (values[i] < LOWER_BOUND_DEGREE) or (values[i] > UPPER_BOUND_DEGREE):
+                    raise JointOutOfRangeError(
+                        f"Wrong motor position range detected for {name}. "
+                        f"Expected to be in nominal range of [-{HALF_TURN_DEGREE}, {HALF_TURN_DEGREE}] degrees (a full rotation), "
+                        f"with a maximum range of [{LOWER_BOUND_DEGREE}, {UPPER_BOUND_DEGREE}] degrees to account for joints that can rotate a bit more, "
+                        f"but present value is {values[i]} degree. "
+                        "This might be due to a cable connection issue creating an artificial 360 degrees jump in motor values. "
+                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
+                    )
+
+            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                start_pos = self.calibration["start_pos"][calib_idx]
+                end_pos = self.calibration["end_pos"][calib_idx]
+
+                # Rescale the present position to a nominal range [0, 100] %,
+                # useful for joints with linear motions like Aloha gripper
+                values[i] = (values[i] - start_pos) / (end_pos - start_pos) * 100
+
+                if (values[i] < LOWER_BOUND_LINEAR) or (values[i] > UPPER_BOUND_LINEAR):
+                    raise JointOutOfRangeError(
+                        f"Wrong motor position range detected for {name}. "
+                        f"Expected to be in nominal range of [0, 100] % (a full linear translation), "
+                        f"with a maximum range of [{LOWER_BOUND_LINEAR}, {UPPER_BOUND_LINEAR}] % to account for some imprecision during calibration, "
+                        f"but present value is {values[i]} %. "
+                        "This might be due to a cable connection issue creating an artificial jump in motor values. "
+                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
+                    )
 
         return values
 
+    def autocorrect_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
+        """This function automatically detects issues with values of motors after calibration, and correct for these issues.
+
+        Some motors might have values outside of expected maximum bounds after calibration.
+        For instance, for a joint in degree, its value can be outside [-270, 270] degrees, which is totally unexpected given
+        a nominal range of [-180, 180] degrees, which represents half a turn to the left or right starting from zero position.
+
+        Known issues:
+        #1: Motor value randomly shifts of a full turn, caused by hardware/connection errors.
+        #2: Motor internal homing offset is shifted by a full turn, caused by using default calibration (e.g Aloha).
+        #3: motor internal homing offset is shifted by less or more than a full turn, caused by using default calibration
+            or by human error during manual calibration.
+
+        Issues #1 and #2 can be solved by shifting the calibration homing offset by a full turn.
+        Issue #3 will be visually detected by user and potentially captured by the safety feature `max_relative_target`,
+        that will slow down the motor, raise an error asking to recalibrate. Manual recalibrating will solve the issue.
+
+        Note: A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
+        """
+        if motor_names is None:
+            motor_names = self.motor_names
+
+        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
+        values = values.astype(np.float32)
+
+        for i, name in enumerate(motor_names):
+            calib_idx = self.calibration["motor_names"].index(name)
+            calib_mode = self.calibration["calib_mode"][calib_idx]
+
+            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                drive_mode = self.calibration["drive_mode"][calib_idx]
+                homing_offset = self.calibration["homing_offset"][calib_idx]
+                _, model = self.motors[name]
+                resolution = self.model_resolution[model]
+
+                # Update direction of rotation of the motor to match between leader and follower.
+                # In fact, the motor of the leader for a given joint can be assembled in an
+                # opposite direction in term of rotation than the motor of the follower on the same joint.
+                if drive_mode:
+                    values[i] *= -1
+
+                # Convert from initial range to range [-180, 180] degrees
+                calib_val = (values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
+                in_range = (calib_val > LOWER_BOUND_DEGREE) and (calib_val < UPPER_BOUND_DEGREE)
+
+                # Solve this inequality to find the factor to shift the range into [-180, 180] degrees
+                # values[i] = (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE
+                # - HALF_TURN_DEGREE <= (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE <= HALF_TURN_DEGREE
+                # (- (resolution // 2) - values[i] - homing_offset) / resolution <= factor <= ((resolution // 2) - values[i] - homing_offset) / resolution
+                low_factor = (-(resolution // 2) - values[i] - homing_offset) / resolution
+                upp_factor = ((resolution // 2) - values[i] - homing_offset) / resolution
+
+            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                start_pos = self.calibration["start_pos"][calib_idx]
+                end_pos = self.calibration["end_pos"][calib_idx]
+
+                # Convert from initial range to range [0, 100] in %
+                calib_val = (values[i] - start_pos) / (end_pos - start_pos) * 100
+                in_range = (calib_val > LOWER_BOUND_LINEAR) and (calib_val < UPPER_BOUND_LINEAR)
+
+                # Solve this inequality to find the factor to shift the range into [0, 100] %
+                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos + resolution * factor - start_pos - resolution * factor) * 100
+                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100
+                # 0 <= (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100 <= 100
+                # (start_pos - values[i]) / resolution <= factor <= (end_pos - values[i]) / resolution
+                low_factor = (start_pos - values[i]) / resolution
+                upp_factor = (end_pos - values[i]) / resolution
+
+            if not in_range:
+                # Get first integer between the two bounds
+                if low_factor < upp_factor:
+                    factor = math.ceil(low_factor)
+
+                    if factor > upp_factor:
+                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
+                else:
+                    factor = math.ceil(upp_factor)
+
+                    if factor > low_factor:
+                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
+
+                if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                    out_of_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
+                    in_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
+                elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                    out_of_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
+                    in_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
+
+                logging.warning(
+                    f"Auto-correct calibration of motor '{name}' by shifting value by {abs(factor)} full turns, "
+                    f"from '{out_of_range_str}' to '{in_range_str}'."
+                )
+
+                # A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
+                self.calibration["homing_offset"][calib_idx] += resolution * factor
+
     def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
         """Inverse of `apply_calibration`."""
         if motor_names is None:
             motor_names = self.motor_names
 
-        # Convert from the universal float32 centered degree range ]-180, 180[ to resolution range ]-resolution, resolution[
         for i, name in enumerate(motor_names):
-            _, model = self.motors[name]
-            resolution = self.model_resolution[model]
-            values[i] = values[i] / 180 * (resolution // 2)
+            calib_idx = self.calibration["motor_names"].index(name)
+            calib_mode = self.calibration["calib_mode"][calib_idx]
+
+            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                drive_mode = self.calibration["drive_mode"][calib_idx]
+                homing_offset = self.calibration["homing_offset"][calib_idx]
+                _, model = self.motors[name]
+                resolution = self.model_resolution[model]
+
+                # Convert from nominal 0-centered degree range [-180, 180] to
+                # 0-centered resolution range (e.g. [-2048, 2048] for resolution=4096)
+                values[i] = values[i] / HALF_TURN_DEGREE * (resolution // 2)
+
+                # Substract the homing offsets to come back to actual motor range of values
+                # which can be arbitrary.
+                values[i] -= homing_offset
+
+                # Remove drive mode, which is the rotation direction of the motor, to come back to
+                # actual motor rotation direction which can be arbitrary.
+                if drive_mode:
+                    values[i] *= -1
+
+            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                start_pos = self.calibration["start_pos"][calib_idx]
+                end_pos = self.calibration["end_pos"][calib_idx]
+
+                # Convert from nominal lnear range of [0, 100] % to
+                # actual motor range of values which can be arbitrary.
+                values[i] = values[i] / 100 * (end_pos - start_pos) + start_pos
 
         values = np.round(values).astype(np.int32)
-
-        # Convert from nominal range ]-resolution, resolution[ to centered signed int32 range [-2**31, 2**31[
-        for i, name in enumerate(motor_names):
-            homing_offset, drive_mode = self.calibration[name]
-            values[i] -= homing_offset
-
-            # Update direction of rotation of the motor that was matching between leader and follower to their original direction.
-            # In fact, the motor of the leader for a given joint can be assembled in an opposite direction in term of rotation
-            # than the motor of the follower on the same joint.
-            if drive_mode:
-                values[i] *= -1
-
         return values
 
-    def _read_with_motor_ids(self, motor_models, motor_ids, data_name):
+    def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
+        if self.mock:
+            import tests.mock_dynamixel_sdk as dxl
+        else:
+            import dynamixel_sdk as dxl
+
         return_list = True
         if not isinstance(motor_ids, list):
             return_list = False
@@ -608,12 +651,16 @@ class DynamixelMotorsBus:
 
         assert_same_address(self.model_ctrl_table, self.motor_models, data_name)
         addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
-        group = GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
+        group = dxl.GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
         for idx in motor_ids:
             group.addParam(idx)
 
-        comm = group.txRxPacket()
-        if comm != COMM_SUCCESS:
+        for _ in range(num_retry):
+            comm = group.txRxPacket()
+            if comm == dxl.COMM_SUCCESS:
+                break
+
+        if comm != dxl.COMM_SUCCESS:
             raise ConnectionError(
                 f"Read failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
                 f"{self.packet_handler.getTxRxResult(comm)}"
@@ -637,6 +684,11 @@ class DynamixelMotorsBus:
 
         start_time = time.perf_counter()
 
+        if self.mock:
+            import tests.mock_dynamixel_sdk as dxl
+        else:
+            import dynamixel_sdk as dxl
+
         if motor_names is None:
             motor_names = self.motor_names
 
@@ -656,16 +708,18 @@ class DynamixelMotorsBus:
 
         if data_name not in self.group_readers:
             # create new group reader
-            self.group_readers[group_key] = GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
+            self.group_readers[group_key] = dxl.GroupSyncRead(
+                self.port_handler, self.packet_handler, addr, bytes
+            )
             for idx in motor_ids:
                 self.group_readers[group_key].addParam(idx)
 
         for _ in range(NUM_READ_RETRY):
             comm = self.group_readers[group_key].txRxPacket()
-            if comm == COMM_SUCCESS:
+            if comm == dxl.COMM_SUCCESS:
                 break
 
-        if comm != COMM_SUCCESS:
+        if comm != dxl.COMM_SUCCESS:
             raise ConnectionError(
                 f"Read failed due to communication error on port {self.port} for group_key {group_key}: "
                 f"{self.packet_handler.getTxRxResult(comm)}"
@@ -683,19 +737,7 @@ class DynamixelMotorsBus:
             values = values.astype(np.int32)
 
         if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
-            values = self.apply_calibration(values, motor_names)
-
-            # We expect our motors to stay in a nominal range of [-180, 180] degrees
-            # which corresponds to a half turn rotation.
-            # However, some motors can turn a bit more, hence we extend the nominal range to [-270, 270]
-            # which is less than a full 360 degree rotation.
-            if not np.all((values > -270) & (values < 270)):
-                raise ValueError(
-                    f"Wrong motor position range detected. "
-                    f"Expected to be in [-270, +270] but in [{values.min()}, {values.max()}]. "
-                    "This might be due to a cable connection issue creating an artificial 360 degrees jump in motor values. "
-                    "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
-                )
+            values = self.apply_calibration_autocorrect(values, motor_names)
 
         # log the number of seconds it took to read the data from the motors
         delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
@@ -707,7 +749,12 @@ class DynamixelMotorsBus:
 
         return values
 
-    def _write_with_motor_ids(self, motor_models, motor_ids, data_name, values):
+    def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
+        if self.mock:
+            import tests.mock_dynamixel_sdk as dxl
+        else:
+            import dynamixel_sdk as dxl
+
         if not isinstance(motor_ids, list):
             motor_ids = [motor_ids]
         if not isinstance(values, list):
@@ -715,13 +762,17 @@ class DynamixelMotorsBus:
 
         assert_same_address(self.model_ctrl_table, motor_models, data_name)
         addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
-        group = GroupSyncWrite(self.port_handler, self.packet_handler, addr, bytes)
+        group = dxl.GroupSyncWrite(self.port_handler, self.packet_handler, addr, bytes)
         for idx, value in zip(motor_ids, values, strict=True):
-            data = convert_to_bytes(value, bytes)
+            data = convert_to_bytes(value, bytes, self.mock)
             group.addParam(idx, data)
 
-        comm = group.txPacket()
-        if comm != COMM_SUCCESS:
+        for _ in range(num_retry):
+            comm = group.txPacket()
+            if comm == dxl.COMM_SUCCESS:
+                break
+
+        if comm != dxl.COMM_SUCCESS:
             raise ConnectionError(
                 f"Write failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
                 f"{self.packet_handler.getTxRxResult(comm)}"
@@ -735,6 +786,11 @@ class DynamixelMotorsBus:
 
         start_time = time.perf_counter()
 
+        if self.mock:
+            import tests.mock_dynamixel_sdk as dxl
+        else:
+            import dynamixel_sdk as dxl
+
         if motor_names is None:
             motor_names = self.motor_names
 
@@ -764,19 +820,19 @@ class DynamixelMotorsBus:
 
         init_group = data_name not in self.group_readers
         if init_group:
-            self.group_writers[group_key] = GroupSyncWrite(
+            self.group_writers[group_key] = dxl.GroupSyncWrite(
                 self.port_handler, self.packet_handler, addr, bytes
             )
 
         for idx, value in zip(motor_ids, values, strict=True):
-            data = convert_to_bytes(value, bytes)
+            data = convert_to_bytes(value, bytes, self.mock)
             if init_group:
                 self.group_writers[group_key].addParam(idx, data)
             else:
                 self.group_writers[group_key].changeParam(idx, data)
 
         comm = self.group_writers[group_key].txPacket()
-        if comm != COMM_SUCCESS:
+        if comm != dxl.COMM_SUCCESS:
             raise ConnectionError(
                 f"Write failed due to communication error on port {self.port} for group_key {group_key}: "
                 f"{self.packet_handler.getTxRxResult(comm)}"
@@ -809,8 +865,3 @@ class DynamixelMotorsBus:
     def __del__(self):
         if getattr(self, "is_connected", False):
             self.disconnect()
-
-
-if __name__ == "__main__":
-    # Helper to find the usb port associated to all your DynamixelMotorsBus.
-    find_port()

lerobot/common/robot_devices/motors/feetech.py

@@ -0,0 +1,887 @@
+import enum
+import logging
+import math
+import time
+import traceback
+from copy import deepcopy
+
+import numpy as np
+import tqdm
+
+from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
+from lerobot.common.utils.utils import capture_timestamp_utc
+
+PROTOCOL_VERSION = 0
+BAUDRATE = 1_000_000
+TIMEOUT_MS = 1000
+
+MAX_ID_RANGE = 252
+
+# The following bounds define the lower and upper joints range (after calibration).
+# For joints in degree (i.e. revolute joints), their nominal range is [-180, 180] degrees
+# which corresponds to a half rotation on the left and half rotation on the right.
+# Some joints might require higher range, so we allow up to [-270, 270] degrees until
+# an error is raised.
+LOWER_BOUND_DEGREE = -270
+UPPER_BOUND_DEGREE = 270
+# For joints in percentage (i.e. joints that move linearly like the prismatic joint of a gripper),
+# their nominal range is [0, 100] %. For instance, for Aloha gripper, 0% is fully
+# closed, and 100% is fully open. To account for slight calibration issue, we allow up to
+# [-10, 110] until an error is raised.
+LOWER_BOUND_LINEAR = -10
+UPPER_BOUND_LINEAR = 110
+
+HALF_TURN_DEGREE = 180
+
+
+# See this link for STS3215 Memory Table:
+# https://docs.google.com/spreadsheets/d/1GVs7W1VS1PqdhA1nW-abeyAHhTUxKUdR/edit?usp=sharing&ouid=116566590112741600240&rtpof=true&sd=true
+# data_name: (address, size_byte)
+SCS_SERIES_CONTROL_TABLE = {
+    "Model": (3, 2),
+    "ID": (5, 1),
+    "Baud_Rate": (6, 1),
+    "Return_Delay": (7, 1),
+    "Response_Status_Level": (8, 1),
+    "Min_Angle_Limit": (9, 2),
+    "Max_Angle_Limit": (11, 2),
+    "Max_Temperature_Limit": (13, 1),
+    "Max_Voltage_Limit": (14, 1),
+    "Min_Voltage_Limit": (15, 1),
+    "Max_Torque_Limit": (16, 2),
+    "Phase": (18, 1),
+    "Unloading_Condition": (19, 1),
+    "LED_Alarm_Condition": (20, 1),
+    "P_Coefficient": (21, 1),
+    "D_Coefficient": (22, 1),
+    "I_Coefficient": (23, 1),
+    "Minimum_Startup_Force": (24, 2),
+    "CW_Dead_Zone": (26, 1),
+    "CCW_Dead_Zone": (27, 1),
+    "Protection_Current": (28, 2),
+    "Angular_Resolution": (30, 1),
+    "Offset": (31, 2),
+    "Mode": (33, 1),
+    "Protective_Torque": (34, 1),
+    "Protection_Time": (35, 1),
+    "Overload_Torque": (36, 1),
+    "Speed_closed_loop_P_proportional_coefficient": (37, 1),
+    "Over_Current_Protection_Time": (38, 1),
+    "Velocity_closed_loop_I_integral_coefficient": (39, 1),
+    "Torque_Enable": (40, 1),
+    "Acceleration": (41, 1),
+    "Goal_Position": (42, 2),
+    "Goal_Time": (44, 2),
+    "Goal_Speed": (46, 2),
+    "Torque_Limit": (48, 2),
+    "Lock": (55, 1),
+    "Present_Position": (56, 2),
+    "Present_Speed": (58, 2),
+    "Present_Load": (60, 2),
+    "Present_Voltage": (62, 1),
+    "Present_Temperature": (63, 1),
+    "Status": (65, 1),
+    "Moving": (66, 1),
+    "Present_Current": (69, 2),
+    # Not in the Memory Table
+    "Maximum_Acceleration": (85, 2),
+}
+
+SCS_SERIES_BAUDRATE_TABLE = {
+    0: 1_000_000,
+    1: 500_000,
+    2: 250_000,
+    3: 128_000,
+    4: 115_200,
+    5: 57_600,
+    6: 38_400,
+    7: 19_200,
+}
+
+CALIBRATION_REQUIRED = ["Goal_Position", "Present_Position"]
+CONVERT_UINT32_TO_INT32_REQUIRED = ["Goal_Position", "Present_Position"]
+
+
+MODEL_CONTROL_TABLE = {
+    "scs_series": SCS_SERIES_CONTROL_TABLE,
+    "sts3215": SCS_SERIES_CONTROL_TABLE,
+}
+
+MODEL_RESOLUTION = {
+    "scs_series": 4096,
+    "sts3215": 4096,
+}
+
+MODEL_BAUDRATE_TABLE = {
+    "scs_series": SCS_SERIES_BAUDRATE_TABLE,
+    "sts3215": SCS_SERIES_BAUDRATE_TABLE,
+}
+
+# High number of retries is needed for feetech compared to dynamixel motors.
+NUM_READ_RETRY = 20
+NUM_WRITE_RETRY = 20
+
+
+def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]) -> np.ndarray:
+    """This function converts the degree range to the step range for indicating motors rotation.
+    It assumes a motor achieves a full rotation by going from -180 degree position to +180.
+    The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
+    """
+    resolutions = [MODEL_RESOLUTION[model] for model in models]
+    steps = degrees / 180 * np.array(resolutions) / 2
+    steps = steps.astype(int)
+    return steps
+
+
+def convert_to_bytes(value, bytes, mock=False):
+    if mock:
+        return value
+
+    import scservo_sdk as scs
+
+    # Note: No need to convert back into unsigned int, since this byte preprocessing
+    # already handles it for us.
+    if bytes == 1:
+        data = [
+            scs.SCS_LOBYTE(scs.SCS_LOWORD(value)),
+        ]
+    elif bytes == 2:
+        data = [
+            scs.SCS_LOBYTE(scs.SCS_LOWORD(value)),
+            scs.SCS_HIBYTE(scs.SCS_LOWORD(value)),
+        ]
+    elif bytes == 4:
+        data = [
+            scs.SCS_LOBYTE(scs.SCS_LOWORD(value)),
+            scs.SCS_HIBYTE(scs.SCS_LOWORD(value)),
+            scs.SCS_LOBYTE(scs.SCS_HIWORD(value)),
+            scs.SCS_HIBYTE(scs.SCS_HIWORD(value)),
+        ]
+    else:
+        raise NotImplementedError(
+            f"Value of the number of bytes to be sent is expected to be in [1, 2, 4], but "
+            f"{bytes} is provided instead."
+        )
+    return data
+
+
+def get_group_sync_key(data_name, motor_names):
+    group_key = f"{data_name}_" + "_".join(motor_names)
+    return group_key
+
+
+def get_result_name(fn_name, data_name, motor_names):
+    group_key = get_group_sync_key(data_name, motor_names)
+    rslt_name = f"{fn_name}_{group_key}"
+    return rslt_name
+
+
+def get_queue_name(fn_name, data_name, motor_names):
+    group_key = get_group_sync_key(data_name, motor_names)
+    queue_name = f"{fn_name}_{group_key}"
+    return queue_name
+
+
+def get_log_name(var_name, fn_name, data_name, motor_names):
+    group_key = get_group_sync_key(data_name, motor_names)
+    log_name = f"{var_name}_{fn_name}_{group_key}"
+    return log_name
+
+
+def assert_same_address(model_ctrl_table, motor_models, data_name):
+    all_addr = []
+    all_bytes = []
+    for model in motor_models:
+        addr, bytes = model_ctrl_table[model][data_name]
+        all_addr.append(addr)
+        all_bytes.append(bytes)
+
+    if len(set(all_addr)) != 1:
+        raise NotImplementedError(
+            f"At least two motor models use a different address for `data_name`='{data_name}' ({list(zip(motor_models, all_addr, strict=False))}). Contact a LeRobot maintainer."
+        )
+
+    if len(set(all_bytes)) != 1:
+        raise NotImplementedError(
+            f"At least two motor models use a different bytes representation for `data_name`='{data_name}' ({list(zip(motor_models, all_bytes, strict=False))}). Contact a LeRobot maintainer."
+        )
+
+
+class TorqueMode(enum.Enum):
+    ENABLED = 1
+    DISABLED = 0
+
+
+class DriveMode(enum.Enum):
+    NON_INVERTED = 0
+    INVERTED = 1
+
+
+class CalibrationMode(enum.Enum):
+    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
+    DEGREE = 0
+    # Joints with linear motions (like gripper of Aloha) are experessed in nominal range of [0, 100]
+    LINEAR = 1
+
+
+class JointOutOfRangeError(Exception):
+    def __init__(self, message="Joint is out of range"):
+        self.message = message
+        super().__init__(self.message)
+
+
+class FeetechMotorsBus:
+    """
+    The FeetechMotorsBus class allows to efficiently read and write to the attached motors. It relies on
+    the python feetech sdk to communicate with the motors. For more info, see the [feetech SDK Documentation](https://emanual.robotis.com/docs/en/software/feetech/feetech_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20).
+
+    A FeetechMotorsBus instance requires a port (e.g. `FeetechMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
+    To find the port, you can run our utility script:
+    ```bash
+    python lerobot/scripts/find_motors_bus_port.py
+    >>> Finding all available ports for the MotorsBus.
+    >>> ['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+    >>> Remove the usb cable from your FeetechMotorsBus and press Enter when done.
+    >>> The port of this FeetechMotorsBus is /dev/tty.usbmodem575E0031751.
+    >>> Reconnect the usb cable.
+    ```
+
+    Example of usage for 1 motor connected to the bus:
+    ```python
+    motor_name = "gripper"
+    motor_index = 6
+    motor_model = "sts3215"
+
+    motors_bus = FeetechMotorsBus(
+        port="/dev/tty.usbmodem575E0031751",
+        motors={motor_name: (motor_index, motor_model)},
+    )
+    motors_bus.connect()
+
+    position = motors_bus.read("Present_Position")
+
+    # move from a few motor steps as an example
+    few_steps = 30
+    motors_bus.write("Goal_Position", position + few_steps)
+
+    # when done, consider disconnecting
+    motors_bus.disconnect()
+    ```
+    """
+
+    def __init__(
+        self,
+        port: str,
+        motors: dict[str, tuple[int, str]],
+        extra_model_control_table: dict[str, list[tuple]] | None = None,
+        extra_model_resolution: dict[str, int] | None = None,
+        mock=False,
+    ):
+        self.port = port
+        self.motors = motors
+        self.mock = mock
+
+        self.model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
+        if extra_model_control_table:
+            self.model_ctrl_table.update(extra_model_control_table)
+
+        self.model_resolution = deepcopy(MODEL_RESOLUTION)
+        if extra_model_resolution:
+            self.model_resolution.update(extra_model_resolution)
+
+        self.port_handler = None
+        self.packet_handler = None
+        self.calibration = None
+        self.is_connected = False
+        self.group_readers = {}
+        self.group_writers = {}
+        self.logs = {}
+
+        self.track_positions = {}
+
+    def connect(self):
+        if self.is_connected:
+            raise RobotDeviceAlreadyConnectedError(
+                f"FeetechMotorsBus({self.port}) is already connected. Do not call `motors_bus.connect()` twice."
+            )
+
+        if self.mock:
+            import tests.mock_scservo_sdk as scs
+        else:
+            import scservo_sdk as scs
+
+        self.port_handler = scs.PortHandler(self.port)
+        self.packet_handler = scs.PacketHandler(PROTOCOL_VERSION)
+
+        try:
+            if not self.port_handler.openPort():
+                raise OSError(f"Failed to open port '{self.port}'.")
+        except Exception:
+            traceback.print_exc()
+            print(
+                "\nTry running `python lerobot/scripts/find_motors_bus_port.py` to make sure you are using the correct port.\n"
+            )
+            raise
+
+        # Allow to read and write
+        self.is_connected = True
+
+        self.port_handler.setPacketTimeoutMillis(TIMEOUT_MS)
+
+    def reconnect(self):
+        if self.mock:
+            import tests.mock_scservo_sdk as scs
+        else:
+            import scservo_sdk as scs
+
+        self.port_handler = scs.PortHandler(self.port)
+        self.packet_handler = scs.PacketHandler(PROTOCOL_VERSION)
+
+        if not self.port_handler.openPort():
+            raise OSError(f"Failed to open port '{self.port}'.")
+
+        self.is_connected = True
+
+    def are_motors_configured(self):
+        # Only check the motor indices and not baudrate, since if the motor baudrates are incorrect,
+        # a ConnectionError will be raised anyway.
+        try:
+            return (self.motor_indices == self.read("ID")).all()
+        except ConnectionError as e:
+            print(e)
+            return False
+
+    def find_motor_indices(self, possible_ids=None, num_retry=2):
+        if possible_ids is None:
+            possible_ids = range(MAX_ID_RANGE)
+
+        indices = []
+        for idx in tqdm.tqdm(possible_ids):
+            try:
+                present_idx = self.read_with_motor_ids(self.motor_models, [idx], "ID", num_retry=num_retry)[0]
+            except ConnectionError:
+                continue
+
+            if idx != present_idx:
+                # sanity check
+                raise OSError(
+                    "Motor index used to communicate through the bus is not the same as the one present in the motor memory. The motor memory might be damaged."
+                )
+            indices.append(idx)
+
+        return indices
+
+    def set_bus_baudrate(self, baudrate):
+        present_bus_baudrate = self.port_handler.getBaudRate()
+        if present_bus_baudrate != baudrate:
+            print(f"Setting bus baud rate to {baudrate}. Previously {present_bus_baudrate}.")
+            self.port_handler.setBaudRate(baudrate)
+
+            if self.port_handler.getBaudRate() != baudrate:
+                raise OSError("Failed to write bus baud rate.")
+
+    @property
+    def motor_names(self) -> list[str]:
+        return list(self.motors.keys())
+
+    @property
+    def motor_models(self) -> list[str]:
+        return [model for _, model in self.motors.values()]
+
+    @property
+    def motor_indices(self) -> list[int]:
+        return [idx for idx, _ in self.motors.values()]
+
+    def set_calibration(self, calibration: dict[str, list]):
+        self.calibration = calibration
+
+    def apply_calibration_autocorrect(self, values: np.ndarray | list, motor_names: list[str] | None):
+        """This function apply the calibration, automatically detects out of range errors for motors values and attempt to correct.
+
+        For more info, see docstring of `apply_calibration` and `autocorrect_calibration`.
+        """
+        try:
+            values = self.apply_calibration(values, motor_names)
+        except JointOutOfRangeError as e:
+            print(e)
+            self.autocorrect_calibration(values, motor_names)
+            values = self.apply_calibration(values, motor_names)
+        return values
+
+    def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
+        """Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
+        a "zero position" at 0 degree.
+
+        Note: We say "nominal degree range" since the motors can take values outside this range. For instance, 190 degrees, if the motor
+        rotate more than a half a turn from the zero position. However, most motors can't rotate more than 180 degrees and will stay in this range.
+
+        Joints values are original in [0, 2**32[ (unsigned int32). Each motor are expected to complete a full rotation
+        when given a goal position that is + or - their resolution. For instance, feetech xl330-m077 have a resolution of 4096, and
+        at any position in their original range, let's say the position 56734, they complete a full rotation clockwise by moving to 60830,
+        or anticlockwise by moving to 52638. The position in the original range is arbitrary and might change a lot between each motor.
+        To harmonize between motors of the same model, different robots, or even models of different brands, we propose to work
+        in the centered nominal degree range ]-180, 180[.
+        """
+        if motor_names is None:
+            motor_names = self.motor_names
+
+        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
+        values = values.astype(np.float32)
+
+        for i, name in enumerate(motor_names):
+            calib_idx = self.calibration["motor_names"].index(name)
+            calib_mode = self.calibration["calib_mode"][calib_idx]
+
+            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                drive_mode = self.calibration["drive_mode"][calib_idx]
+                homing_offset = self.calibration["homing_offset"][calib_idx]
+                _, model = self.motors[name]
+                resolution = self.model_resolution[model]
+
+                # Update direction of rotation of the motor to match between leader and follower.
+                # In fact, the motor of the leader for a given joint can be assembled in an
+                # opposite direction in term of rotation than the motor of the follower on the same joint.
+                if drive_mode:
+                    values[i] *= -1
+
+                # Convert from range [-2**31, 2**31[ to
+                # nominal range ]-resolution, resolution[ (e.g. ]-2048, 2048[)
+                values[i] += homing_offset
+
+                # Convert from range ]-resolution, resolution[ to
+                # universal float32 centered degree range ]-180, 180[
+                values[i] = values[i] / (resolution // 2) * HALF_TURN_DEGREE
+
+                if (values[i] < LOWER_BOUND_DEGREE) or (values[i] > UPPER_BOUND_DEGREE):
+                    raise JointOutOfRangeError(
+                        f"Wrong motor position range detected for {name}. "
+                        f"Expected to be in nominal range of [-{HALF_TURN_DEGREE}, {HALF_TURN_DEGREE}] degrees (a full rotation), "
+                        f"with a maximum range of [{LOWER_BOUND_DEGREE}, {UPPER_BOUND_DEGREE}] degrees to account for joints that can rotate a bit more, "
+                        f"but present value is {values[i]} degree. "
+                        "This might be due to a cable connection issue creating an artificial 360 degrees jump in motor values. "
+                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
+                    )
+
+            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                start_pos = self.calibration["start_pos"][calib_idx]
+                end_pos = self.calibration["end_pos"][calib_idx]
+
+                # Rescale the present position to a nominal range [0, 100] %,
+                # useful for joints with linear motions like Aloha gripper
+                values[i] = (values[i] - start_pos) / (end_pos - start_pos) * 100
+
+                if (values[i] < LOWER_BOUND_LINEAR) or (values[i] > UPPER_BOUND_LINEAR):
+                    raise JointOutOfRangeError(
+                        f"Wrong motor position range detected for {name}. "
+                        f"Expected to be in nominal range of [0, 100] % (a full linear translation), "
+                        f"with a maximum range of [{LOWER_BOUND_LINEAR}, {UPPER_BOUND_LINEAR}] % to account for some imprecision during calibration, "
+                        f"but present value is {values[i]} %. "
+                        "This might be due to a cable connection issue creating an artificial jump in motor values. "
+                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
+                    )
+
+        return values
+
+    def autocorrect_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
+        """This function automatically detects issues with values of motors after calibration, and correct for these issues.
+
+        Some motors might have values outside of expected maximum bounds after calibration.
+        For instance, for a joint in degree, its value can be outside [-270, 270] degrees, which is totally unexpected given
+        a nominal range of [-180, 180] degrees, which represents half a turn to the left or right starting from zero position.
+
+        Known issues:
+        #1: Motor value randomly shifts of a full turn, caused by hardware/connection errors.
+        #2: Motor internal homing offset is shifted of a full turn, caused by using default calibration (e.g Aloha).
+        #3: motor internal homing offset is shifted of less or more than a full turn, caused by using default calibration
+            or by human error during manual calibration.
+
+        Issues #1 and #2 can be solved by shifting the calibration homing offset by a full turn.
+        Issue #3 will be visually detected by user and potentially captured by the safety feature `max_relative_target`,
+        that will slow down the motor, raise an error asking to recalibrate. Manual recalibrating will solve the issue.
+
+        Note: A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
+        """
+        if motor_names is None:
+            motor_names = self.motor_names
+
+        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
+        values = values.astype(np.float32)
+
+        for i, name in enumerate(motor_names):
+            calib_idx = self.calibration["motor_names"].index(name)
+            calib_mode = self.calibration["calib_mode"][calib_idx]
+
+            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                drive_mode = self.calibration["drive_mode"][calib_idx]
+                homing_offset = self.calibration["homing_offset"][calib_idx]
+                _, model = self.motors[name]
+                resolution = self.model_resolution[model]
+
+                if drive_mode:
+                    values[i] *= -1
+
+                # Convert from initial range to range [-180, 180] degrees
+                calib_val = (values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
+                in_range = (calib_val > LOWER_BOUND_DEGREE) and (calib_val < UPPER_BOUND_DEGREE)
+
+                # Solve this inequality to find the factor to shift the range into [-180, 180] degrees
+                # values[i] = (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE
+                # - HALF_TURN_DEGREE <= (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE <= HALF_TURN_DEGREE
+                # (- HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset) / resolution <= factor <= (HALF_TURN_DEGREE / 180 * (resolution // 2) - values[i] - homing_offset) / resolution
+                low_factor = (
+                    -HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
+                ) / resolution
+                upp_factor = (
+                    HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
+                ) / resolution
+
+            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                start_pos = self.calibration["start_pos"][calib_idx]
+                end_pos = self.calibration["end_pos"][calib_idx]
+
+                # Convert from initial range to range [0, 100] in %
+                calib_val = (values[i] - start_pos) / (end_pos - start_pos) * 100
+                in_range = (calib_val > LOWER_BOUND_LINEAR) and (calib_val < UPPER_BOUND_LINEAR)
+
+                # Solve this inequality to find the factor to shift the range into [0, 100] %
+                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos + resolution * factor - start_pos - resolution * factor) * 100
+                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100
+                # 0 <= (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100 <= 100
+                # (start_pos - values[i]) / resolution <= factor <= (end_pos - values[i]) / resolution
+                low_factor = (start_pos - values[i]) / resolution
+                upp_factor = (end_pos - values[i]) / resolution
+
+            if not in_range:
+                # Get first integer between the two bounds
+                if low_factor < upp_factor:
+                    factor = math.ceil(low_factor)
+
+                    if factor > upp_factor:
+                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
+                else:
+                    factor = math.ceil(upp_factor)
+
+                    if factor > low_factor:
+                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
+
+                if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                    out_of_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
+                    in_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
+                elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                    out_of_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
+                    in_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
+
+                logging.warning(
+                    f"Auto-correct calibration of motor '{name}' by shifting value by {abs(factor)} full turns, "
+                    f"from '{out_of_range_str}' to '{in_range_str}'."
+                )
+
+                # A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
+                self.calibration["homing_offset"][calib_idx] += resolution * factor
+
+    def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
+        """Inverse of `apply_calibration`."""
+        if motor_names is None:
+            motor_names = self.motor_names
+
+        for i, name in enumerate(motor_names):
+            calib_idx = self.calibration["motor_names"].index(name)
+            calib_mode = self.calibration["calib_mode"][calib_idx]
+
+            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
+                drive_mode = self.calibration["drive_mode"][calib_idx]
+                homing_offset = self.calibration["homing_offset"][calib_idx]
+                _, model = self.motors[name]
+                resolution = self.model_resolution[model]
+
+                # Convert from nominal 0-centered degree range [-180, 180] to
+                # 0-centered resolution range (e.g. [-2048, 2048] for resolution=4096)
+                values[i] = values[i] / HALF_TURN_DEGREE * (resolution // 2)
+
+                # Substract the homing offsets to come back to actual motor range of values
+                # which can be arbitrary.
+                values[i] -= homing_offset
+
+                # Remove drive mode, which is the rotation direction of the motor, to come back to
+                # actual motor rotation direction which can be arbitrary.
+                if drive_mode:
+                    values[i] *= -1
+
+            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
+                start_pos = self.calibration["start_pos"][calib_idx]
+                end_pos = self.calibration["end_pos"][calib_idx]
+
+                # Convert from nominal lnear range of [0, 100] % to
+                # actual motor range of values which can be arbitrary.
+                values[i] = values[i] / 100 * (end_pos - start_pos) + start_pos
+
+        values = np.round(values).astype(np.int32)
+        return values
+
+    def avoid_rotation_reset(self, values, motor_names, data_name):
+        if data_name not in self.track_positions:
+            self.track_positions[data_name] = {
+                "prev": [None] * len(self.motor_names),
+                # Assume False at initialization
+                "below_zero": [False] * len(self.motor_names),
+                "above_max": [False] * len(self.motor_names),
+            }
+
+        track = self.track_positions[data_name]
+
+        if motor_names is None:
+            motor_names = self.motor_names
+
+        for i, name in enumerate(motor_names):
+            idx = self.motor_names.index(name)
+
+            if track["prev"][idx] is None:
+                track["prev"][idx] = values[i]
+                continue
+
+            # Detect a full rotation occured
+            if abs(track["prev"][idx] - values[i]) > 2048:
+                # Position went below 0 and got reset to 4095
+                if track["prev"][idx] < values[i]:
+                    # So we set negative value by adding a full rotation
+                    values[i] -= 4096
+
+                # Position went above 4095 and got reset to 0
+                elif track["prev"][idx] > values[i]:
+                    # So we add a full rotation
+                    values[i] += 4096
+
+            track["prev"][idx] = values[i]
+
+        return values
+
+    def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
+        if self.mock:
+            import tests.mock_scservo_sdk as scs
+        else:
+            import scservo_sdk as scs
+
+        return_list = True
+        if not isinstance(motor_ids, list):
+            return_list = False
+            motor_ids = [motor_ids]
+
+        assert_same_address(self.model_ctrl_table, self.motor_models, data_name)
+        addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
+        group = scs.GroupSyncRead(self.port_handler, self.packet_handler, addr, bytes)
+        for idx in motor_ids:
+            group.addParam(idx)
+
+        for _ in range(num_retry):
+            comm = group.txRxPacket()
+            if comm == scs.COMM_SUCCESS:
+                break
+
+        if comm != scs.COMM_SUCCESS:
+            raise ConnectionError(
+                f"Read failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
+                f"{self.packet_handler.getTxRxResult(comm)}"
+            )
+
+        values = []
+        for idx in motor_ids:
+            value = group.getData(idx, addr, bytes)
+            values.append(value)
+
+        if return_list:
+            return values
+        else:
+            return values[0]
+
+    def read(self, data_name, motor_names: str | list[str] | None = None):
+        if self.mock:
+            import tests.mock_scservo_sdk as scs
+        else:
+            import scservo_sdk as scs
+
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
+            )
+
+        start_time = time.perf_counter()
+
+        if motor_names is None:
+            motor_names = self.motor_names
+
+        if isinstance(motor_names, str):
+            motor_names = [motor_names]
+
+        motor_ids = []
+        models = []
+        for name in motor_names:
+            motor_idx, model = self.motors[name]
+            motor_ids.append(motor_idx)
+            models.append(model)
+
+        assert_same_address(self.model_ctrl_table, models, data_name)
+        addr, bytes = self.model_ctrl_table[model][data_name]
+        group_key = get_group_sync_key(data_name, motor_names)
+
+        if data_name not in self.group_readers:
+            # create new group reader
+            self.group_readers[group_key] = scs.GroupSyncRead(
+                self.port_handler, self.packet_handler, addr, bytes
+            )
+            for idx in motor_ids:
+                self.group_readers[group_key].addParam(idx)
+
+        for _ in range(NUM_READ_RETRY):
+            comm = self.group_readers[group_key].txRxPacket()
+            if comm == scs.COMM_SUCCESS:
+                break
+
+        if comm != scs.COMM_SUCCESS:
+            raise ConnectionError(
+                f"Read failed due to communication error on port {self.port} for group_key {group_key}: "
+                f"{self.packet_handler.getTxRxResult(comm)}"
+            )
+
+        values = []
+        for idx in motor_ids:
+            value = self.group_readers[group_key].getData(idx, addr, bytes)
+            values.append(value)
+
+        values = np.array(values)
+
+        # Convert to signed int to use range [-2048, 2048] for our motor positions.
+        if data_name in CONVERT_UINT32_TO_INT32_REQUIRED:
+            values = values.astype(np.int32)
+
+        if data_name in CALIBRATION_REQUIRED:
+            values = self.avoid_rotation_reset(values, motor_names, data_name)
+
+        if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
+            values = self.apply_calibration_autocorrect(values, motor_names)
+
+        # log the number of seconds it took to read the data from the motors
+        delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
+        self.logs[delta_ts_name] = time.perf_counter() - start_time
+
+        # log the utc time at which the data was received
+        ts_utc_name = get_log_name("timestamp_utc", "read", data_name, motor_names)
+        self.logs[ts_utc_name] = capture_timestamp_utc()
+
+        return values
+
+    def write_with_motor_ids(self, motor_models, motor_ids, data_name, values, num_retry=NUM_WRITE_RETRY):
+        if self.mock:
+            import tests.mock_scservo_sdk as scs
+        else:
+            import scservo_sdk as scs
+
+        if not isinstance(motor_ids, list):
+            motor_ids = [motor_ids]
+        if not isinstance(values, list):
+            values = [values]
+
+        assert_same_address(self.model_ctrl_table, motor_models, data_name)
+        addr, bytes = self.model_ctrl_table[motor_models[0]][data_name]
+        group = scs.GroupSyncWrite(self.port_handler, self.packet_handler, addr, bytes)
+        for idx, value in zip(motor_ids, values, strict=True):
+            data = convert_to_bytes(value, bytes, self.mock)
+            group.addParam(idx, data)
+
+        for _ in range(num_retry):
+            comm = group.txPacket()
+            if comm == scs.COMM_SUCCESS:
+                break
+
+        if comm != scs.COMM_SUCCESS:
+            raise ConnectionError(
+                f"Write failed due to communication error on port {self.port_handler.port_name} for indices {motor_ids}: "
+                f"{self.packet_handler.getTxRxResult(comm)}"
+            )
+
+    def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
+            )
+
+        start_time = time.perf_counter()
+
+        if self.mock:
+            import tests.mock_scservo_sdk as scs
+        else:
+            import scservo_sdk as scs
+
+        if motor_names is None:
+            motor_names = self.motor_names
+
+        if isinstance(motor_names, str):
+            motor_names = [motor_names]
+
+        if isinstance(values, (int, float, np.integer)):
+            values = [int(values)] * len(motor_names)
+
+        values = np.array(values)
+
+        motor_ids = []
+        models = []
+        for name in motor_names:
+            motor_idx, model = self.motors[name]
+            motor_ids.append(motor_idx)
+            models.append(model)
+
+        if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
+            values = self.revert_calibration(values, motor_names)
+
+        values = values.tolist()
+
+        assert_same_address(self.model_ctrl_table, models, data_name)
+        addr, bytes = self.model_ctrl_table[model][data_name]
+        group_key = get_group_sync_key(data_name, motor_names)
+
+        init_group = data_name not in self.group_readers
+        if init_group:
+            self.group_writers[group_key] = scs.GroupSyncWrite(
+                self.port_handler, self.packet_handler, addr, bytes
+            )
+
+        for idx, value in zip(motor_ids, values, strict=True):
+            data = convert_to_bytes(value, bytes, self.mock)
+            if init_group:
+                self.group_writers[group_key].addParam(idx, data)
+            else:
+                self.group_writers[group_key].changeParam(idx, data)
+
+        comm = self.group_writers[group_key].txPacket()
+        if comm != scs.COMM_SUCCESS:
+            raise ConnectionError(
+                f"Write failed due to communication error on port {self.port} for group_key {group_key}: "
+                f"{self.packet_handler.getTxRxResult(comm)}"
+            )
+
+        # log the number of seconds it took to write the data to the motors
+        delta_ts_name = get_log_name("delta_timestamp_s", "write", data_name, motor_names)
+        self.logs[delta_ts_name] = time.perf_counter() - start_time
+
+        # TODO(rcadene): should we log the time before sending the write command?
+        # log the utc time when the write has been completed
+        ts_utc_name = get_log_name("timestamp_utc", "write", data_name, motor_names)
+        self.logs[ts_utc_name] = capture_timestamp_utc()
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                f"FeetechMotorsBus({self.port}) is not connected. Try running `motors_bus.connect()` first."
+            )
+
+        if self.port_handler is not None:
+            self.port_handler.closePort()
+            self.port_handler = None
+
+        self.packet_handler = None
+        self.group_readers = {}
+        self.group_writers = {}
+        self.is_connected = False
+
+    def __del__(self):
+        if getattr(self, "is_connected", False):
+            self.disconnect()

lerobot/common/robot_devices/robots/dynamixel_calibration.py

@@ -0,0 +1,130 @@
+"""Logic to calibrate a robot arm built with dynamixel motors"""
+# TODO(rcadene, aliberts): move this logic into the robot code when refactoring
+
+import numpy as np
+
+from lerobot.common.robot_devices.motors.dynamixel import (
+    CalibrationMode,
+    TorqueMode,
+    convert_degrees_to_steps,
+)
+from lerobot.common.robot_devices.motors.utils import MotorsBus
+
+URL_TEMPLATE = (
+    "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
+)
+
+# The following positions are provided in nominal degree range ]-180, +180[
+# For more info on these constants, see comments in the code where they get used.
+ZERO_POSITION_DEGREE = 0
+ROTATED_POSITION_DEGREE = 90
+
+
+def assert_drive_mode(drive_mode):
+    # `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
+    if not np.all(np.isin(drive_mode, [0, 1])):
+        raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
+
+
+def apply_drive_mode(position, drive_mode):
+    assert_drive_mode(drive_mode)
+    # Convert `drive_mode` from [0, 1] with 0 indicates original rotation direction and 1 inverted,
+    # to [-1, 1] with 1 indicates original rotation direction and -1 inverted.
+    signed_drive_mode = -(drive_mode * 2 - 1)
+    position *= signed_drive_mode
+    return position
+
+
+def compute_nearest_rounded_position(position, models):
+    delta_turn = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, models)
+    nearest_pos = np.round(position.astype(float) / delta_turn) * delta_turn
+    return nearest_pos.astype(position.dtype)
+
+
+def run_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
+    """This function ensures that a neural network trained on data collected on a given robot
+    can work on another robot. For instance before calibration, setting a same goal position
+    for each motor of two different robots will get two very different positions. But after calibration,
+    the two robots will move to the same position.To this end, this function computes the homing offset
+    and the drive mode for each motor of a given robot.
+
+    Homing offset is used to shift the motor position to a ]-2048, +2048[ nominal range (when the motor uses 2048 steps
+    to complete a half a turn). This range is set around an arbitrary "zero position" corresponding to all motor positions
+    being 0. During the calibration process, you will need to manually move the robot to this "zero position".
+
+    Drive mode is used to invert the rotation direction of the motor. This is useful when some motors have been assembled
+    in the opposite orientation for some robots. During the calibration process, you will need to manually move the robot
+    to the "rotated position".
+
+    After calibration, the homing offsets and drive modes are stored in a cache.
+
+    Example of usage:
+    ```python
+    run_arm_calibration(arm, "koch", "left", "follower")
+    ```
+    """
+    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
+        raise ValueError("To run calibration, the torque must be disabled on all motors.")
+
+    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
+
+    print("\nMove arm to zero position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero"))
+    input("Press Enter to continue...")
+
+    # We arbitrarily chose our zero target position to be a straight horizontal position with gripper upwards and closed.
+    # It is easy to identify and all motors are in a "quarter turn" position. Once calibration is done, this position will
+    # correspond to every motor angle being 0. If you set all 0 as Goal Position, the arm will move in this position.
+    zero_target_pos = convert_degrees_to_steps(ZERO_POSITION_DEGREE, arm.motor_models)
+
+    # Compute homing offset so that `present_position + homing_offset ~= target_position`.
+    zero_pos = arm.read("Present_Position")
+    zero_nearest_pos = compute_nearest_rounded_position(zero_pos, arm.motor_models)
+    homing_offset = zero_target_pos - zero_nearest_pos
+
+    # The rotated target position corresponds to a rotation of a quarter turn from the zero position.
+    # This allows to identify the rotation direction of each motor.
+    # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
+    # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
+    # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
+    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarely rotate clockwise from the point of view
+    # of the previous motor in the kinetic chain.
+    print("\nMove arm to rotated target position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
+    input("Press Enter to continue...")
+
+    rotated_target_pos = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
+
+    # Find drive mode by rotating each motor by a quarter of a turn.
+    # Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
+    rotated_pos = arm.read("Present_Position")
+    drive_mode = (rotated_pos < zero_pos).astype(np.int32)
+
+    # Re-compute homing offset to take into account drive mode
+    rotated_drived_pos = apply_drive_mode(rotated_pos, drive_mode)
+    rotated_nearest_pos = compute_nearest_rounded_position(rotated_drived_pos, arm.motor_models)
+    homing_offset = rotated_target_pos - rotated_nearest_pos
+
+    print("\nMove arm to rest position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest"))
+    input("Press Enter to continue...")
+    print()
+
+    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
+    calib_mode = [CalibrationMode.DEGREE.name] * len(arm.motor_names)
+
+    # TODO(rcadene): make type of joints (DEGREE or LINEAR) configurable from yaml?
+    if robot_type in ["aloha"] and "gripper" in arm.motor_names:
+        # Joints with linear motions (like gripper of Aloha) are experessed in nominal range of [0, 100]
+        calib_idx = arm.motor_names.index("gripper")
+        calib_mode[calib_idx] = CalibrationMode.LINEAR.name
+
+    calib_data = {
+        "homing_offset": homing_offset.tolist(),
+        "drive_mode": drive_mode.tolist(),
+        "start_pos": zero_pos.tolist(),
+        "end_pos": rotated_pos.tolist(),
+        "calib_mode": calib_mode,
+        "motor_names": arm.motor_names,
+    }
+    return calib_data

lerobot/common/robot_devices/robots/factory.py

@@ -1,7 +1,9 @@
 import hydra
 from omegaconf import DictConfig
 
+from lerobot.common.robot_devices.robots.utils import Robot
 
-def make_robot(cfg: DictConfig):
+
+def make_robot(cfg: DictConfig) -> Robot:
     robot = hydra.utils.instantiate(cfg)
     return robot

lerobot/common/robot_devices/robots/feetech_calibration.py

@@ -0,0 +1,484 @@
+"""Logic to calibrate a robot arm built with feetech motors"""
+# TODO(rcadene, aliberts): move this logic into the robot code when refactoring
+
+import time
+
+import numpy as np
+
+from lerobot.common.robot_devices.motors.feetech import (
+    CalibrationMode,
+    TorqueMode,
+    convert_degrees_to_steps,
+)
+from lerobot.common.robot_devices.motors.utils import MotorsBus
+
+URL_TEMPLATE = (
+    "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
+)
+
+# The following positions are provided in nominal degree range ]-180, +180[
+# For more info on these constants, see comments in the code where they get used.
+ZERO_POSITION_DEGREE = 0
+ROTATED_POSITION_DEGREE = 90
+
+
+def assert_drive_mode(drive_mode):
+    # `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
+    if not np.all(np.isin(drive_mode, [0, 1])):
+        raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
+
+
+def apply_drive_mode(position, drive_mode):
+    assert_drive_mode(drive_mode)
+    # Convert `drive_mode` from [0, 1] with 0 indicates original rotation direction and 1 inverted,
+    # to [-1, 1] with 1 indicates original rotation direction and -1 inverted.
+    signed_drive_mode = -(drive_mode * 2 - 1)
+    position *= signed_drive_mode
+    return position
+
+
+def move_until_block(arm, motor_name, positive_direction=True, while_move_hook=None):
+    count = 0
+    while True:
+        present_pos = arm.read("Present_Position", motor_name)
+        if positive_direction:
+            # Move +100 steps every time. Lower the steps to lower the speed at which the arm moves.
+            arm.write("Goal_Position", present_pos + 100, motor_name)
+        else:
+            arm.write("Goal_Position", present_pos - 100, motor_name)
+
+        if while_move_hook is not None:
+            while_move_hook()
+
+        present_pos = arm.read("Present_Position", motor_name).item()
+        present_speed = arm.read("Present_Speed", motor_name).item()
+        present_current = arm.read("Present_Current", motor_name).item()
+        # present_load = arm.read("Present_Load", motor_name).item()
+        # present_voltage = arm.read("Present_Voltage", motor_name).item()
+        # present_temperature = arm.read("Present_Temperature", motor_name).item()
+
+        # print(f"{present_pos=}")
+        # print(f"{present_speed=}")
+        # print(f"{present_current=}")
+        # print(f"{present_load=}")
+        # print(f"{present_voltage=}")
+        # print(f"{present_temperature=}")
+
+        if present_speed == 0 and present_current > 40:
+            count += 1
+            if count > 100 or present_current > 300:
+                return present_pos
+        else:
+            count = 0
+
+
+def move_to_calibrate(
+    arm,
+    motor_name,
+    invert_drive_mode=False,
+    positive_first=True,
+    in_between_move_hook=None,
+    while_move_hook=None,
+):
+    initial_pos = arm.read("Present_Position", motor_name)
+
+    if positive_first:
+        p_present_pos = move_until_block(
+            arm, motor_name, positive_direction=True, while_move_hook=while_move_hook
+        )
+    else:
+        n_present_pos = move_until_block(
+            arm, motor_name, positive_direction=False, while_move_hook=while_move_hook
+        )
+
+    if in_between_move_hook is not None:
+        in_between_move_hook()
+
+    if positive_first:
+        n_present_pos = move_until_block(
+            arm, motor_name, positive_direction=False, while_move_hook=while_move_hook
+        )
+    else:
+        p_present_pos = move_until_block(
+            arm, motor_name, positive_direction=True, while_move_hook=while_move_hook
+        )
+
+    zero_pos = (n_present_pos + p_present_pos) / 2
+
+    calib_data = {
+        "initial_pos": initial_pos,
+        "homing_offset": zero_pos if invert_drive_mode else -zero_pos,
+        "invert_drive_mode": invert_drive_mode,
+        "drive_mode": -1 if invert_drive_mode else 0,
+        "zero_pos": zero_pos,
+        "start_pos": n_present_pos if invert_drive_mode else p_present_pos,
+        "end_pos": p_present_pos if invert_drive_mode else n_present_pos,
+    }
+    return calib_data
+
+
+def apply_offset(calib, offset):
+    calib["zero_pos"] += offset
+    if calib["drive_mode"]:
+        calib["homing_offset"] += offset
+    else:
+        calib["homing_offset"] -= offset
+    return calib
+
+
+def run_arm_auto_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
+    if robot_type == "so100":
+        return run_arm_auto_calibration_so100(arm, robot_type, arm_name, arm_type)
+    elif robot_type == "moss":
+        return run_arm_auto_calibration_moss(arm, robot_type, arm_name, arm_type)
+    else:
+        raise ValueError(robot_type)
+
+
+def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
+    """All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
+    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
+        raise ValueError("To run calibration, the torque must be disabled on all motors.")
+
+    if not (robot_type == "so100" and arm_type == "follower"):
+        raise NotImplementedError("Auto calibration only supports the follower of so100 arms for now.")
+
+    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
+
+    print("\nMove arm to initial position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
+    input("Press Enter to continue...")
+
+    # Lower the acceleration of the motors (in [0,254])
+    initial_acceleration = arm.read("Acceleration")
+    arm.write("Lock", 0)
+    arm.write("Acceleration", 10)
+    time.sleep(1)
+
+    arm.write("Torque_Enable", TorqueMode.ENABLED.value)
+
+    print(f'{arm.read("Present_Position", "elbow_flex")=}')
+
+    calib = {}
+
+    init_wf_pos = arm.read("Present_Position", "wrist_flex")
+    init_sl_pos = arm.read("Present_Position", "shoulder_lift")
+    init_ef_pos = arm.read("Present_Position", "elbow_flex")
+    arm.write("Goal_Position", init_wf_pos - 800, "wrist_flex")
+    arm.write("Goal_Position", init_sl_pos + 150 + 1024, "shoulder_lift")
+    arm.write("Goal_Position", init_ef_pos - 2048, "elbow_flex")
+    time.sleep(2)
+
+    print("Calibrate shoulder_pan")
+    calib["shoulder_pan"] = move_to_calibrate(arm, "shoulder_pan")
+    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
+    time.sleep(1)
+
+    print("Calibrate gripper")
+    calib["gripper"] = move_to_calibrate(arm, "gripper", invert_drive_mode=True)
+    time.sleep(1)
+
+    print("Calibrate wrist_flex")
+    calib["wrist_flex"] = move_to_calibrate(arm, "wrist_flex")
+    calib["wrist_flex"] = apply_offset(calib["wrist_flex"], offset=80)
+
+    def in_between_move_hook():
+        nonlocal arm, calib
+        time.sleep(2)
+        ef_pos = arm.read("Present_Position", "elbow_flex")
+        sl_pos = arm.read("Present_Position", "shoulder_lift")
+        arm.write("Goal_Position", ef_pos + 1024, "elbow_flex")
+        arm.write("Goal_Position", sl_pos - 1024, "shoulder_lift")
+        time.sleep(2)
+
+    print("Calibrate elbow_flex")
+    calib["elbow_flex"] = move_to_calibrate(
+        arm, "elbow_flex", positive_first=False, in_between_move_hook=in_between_move_hook
+    )
+    calib["elbow_flex"] = apply_offset(calib["elbow_flex"], offset=80 - 1024)
+
+    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 1024 + 512, "elbow_flex")
+    time.sleep(1)
+
+    def in_between_move_hook():
+        nonlocal arm, calib
+        arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"], "elbow_flex")
+
+    print("Calibrate shoulder_lift")
+    calib["shoulder_lift"] = move_to_calibrate(
+        arm,
+        "shoulder_lift",
+        invert_drive_mode=True,
+        positive_first=False,
+        in_between_move_hook=in_between_move_hook,
+    )
+    # add an 30 steps as offset to align with body
+    calib["shoulder_lift"] = apply_offset(calib["shoulder_lift"], offset=1024 - 50)
+
+    def while_move_hook():
+        nonlocal arm, calib
+        positions = {
+            "shoulder_lift": round(calib["shoulder_lift"]["zero_pos"] - 1600),
+            "elbow_flex": round(calib["elbow_flex"]["zero_pos"] + 1700),
+            "wrist_flex": round(calib["wrist_flex"]["zero_pos"] + 800),
+            "gripper": round(calib["gripper"]["end_pos"]),
+        }
+        arm.write("Goal_Position", list(positions.values()), list(positions.keys()))
+
+    arm.write("Goal_Position", round(calib["shoulder_lift"]["zero_pos"] - 1600), "shoulder_lift")
+    time.sleep(2)
+    arm.write("Goal_Position", round(calib["elbow_flex"]["zero_pos"] + 1700), "elbow_flex")
+    time.sleep(2)
+    arm.write("Goal_Position", round(calib["wrist_flex"]["zero_pos"] + 800), "wrist_flex")
+    time.sleep(2)
+    arm.write("Goal_Position", round(calib["gripper"]["end_pos"]), "gripper")
+    time.sleep(2)
+
+    print("Calibrate wrist_roll")
+    calib["wrist_roll"] = move_to_calibrate(
+        arm, "wrist_roll", invert_drive_mode=True, positive_first=False, while_move_hook=while_move_hook
+    )
+
+    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["gripper"]["start_pos"], "gripper")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"], "wrist_flex")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 2048, "elbow_flex")
+    arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] - 2048, "shoulder_lift")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
+    time.sleep(1)
+
+    calib_modes = []
+    for name in arm.motor_names:
+        if name == "gripper":
+            calib_modes.append(CalibrationMode.LINEAR.name)
+        else:
+            calib_modes.append(CalibrationMode.DEGREE.name)
+
+    calib_dict = {
+        "homing_offset": [calib[name]["homing_offset"] for name in arm.motor_names],
+        "drive_mode": [calib[name]["drive_mode"] for name in arm.motor_names],
+        "start_pos": [calib[name]["start_pos"] for name in arm.motor_names],
+        "end_pos": [calib[name]["end_pos"] for name in arm.motor_names],
+        "calib_mode": calib_modes,
+        "motor_names": arm.motor_names,
+    }
+
+    # Re-enable original accerlation
+    arm.write("Lock", 0)
+    arm.write("Acceleration", initial_acceleration)
+    time.sleep(1)
+
+    return calib_dict
+
+
+def run_arm_auto_calibration_moss(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
+    """All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
+    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
+        raise ValueError("To run calibration, the torque must be disabled on all motors.")
+
+    if not (robot_type == "moss" and arm_type == "follower"):
+        raise NotImplementedError("Auto calibration only supports the follower of moss arms for now.")
+
+    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
+
+    print("\nMove arm to initial position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
+    input("Press Enter to continue...")
+
+    # Lower the acceleration of the motors (in [0,254])
+    initial_acceleration = arm.read("Acceleration")
+    arm.write("Lock", 0)
+    arm.write("Acceleration", 10)
+    time.sleep(1)
+
+    arm.write("Torque_Enable", TorqueMode.ENABLED.value)
+
+    sl_pos = arm.read("Present_Position", "shoulder_lift")
+    arm.write("Goal_Position", sl_pos - 1024 - 450, "shoulder_lift")
+    ef_pos = arm.read("Present_Position", "elbow_flex")
+    arm.write("Goal_Position", ef_pos + 1024 + 450, "elbow_flex")
+    time.sleep(2)
+
+    calib = {}
+
+    print("Calibrate shoulder_pan")
+    calib["shoulder_pan"] = move_to_calibrate(arm, "shoulder_pan")
+    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
+    time.sleep(1)
+
+    print("Calibrate gripper")
+    calib["gripper"] = move_to_calibrate(arm, "gripper", invert_drive_mode=True)
+    time.sleep(1)
+
+    print("Calibrate wrist_flex")
+    calib["wrist_flex"] = move_to_calibrate(arm, "wrist_flex", invert_drive_mode=True)
+    calib["wrist_flex"] = apply_offset(calib["wrist_flex"], offset=-210 + 1024)
+
+    wr_pos = arm.read("Present_Position", "wrist_roll")
+    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
+    time.sleep(1)
+    arm.write("Goal_Position", wr_pos - 1024, "wrist_roll")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 2048, "wrist_flex")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["gripper"]["end_pos"], "gripper")
+    time.sleep(1)
+
+    print("Calibrate wrist_roll")
+    calib["wrist_roll"] = move_to_calibrate(arm, "wrist_roll", invert_drive_mode=True)
+    calib["wrist_roll"] = apply_offset(calib["wrist_roll"], offset=790)
+
+    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"] - 1024, "wrist_roll")
+    arm.write("Goal_Position", calib["gripper"]["start_pos"], "gripper")
+    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")
+    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 2048, "wrist_flex")
+
+    def in_between_move_elbow_flex_hook():
+        nonlocal arm, calib
+        arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"], "wrist_flex")
+
+    print("Calibrate elbow_flex")
+    calib["elbow_flex"] = move_to_calibrate(
+        arm,
+        "elbow_flex",
+        invert_drive_mode=True,
+        in_between_move_hook=in_between_move_elbow_flex_hook,
+    )
+    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
+
+    def in_between_move_shoulder_lift_hook():
+        nonlocal arm, calib
+        sl = arm.read("Present_Position", "shoulder_lift")
+        arm.write("Goal_Position", sl - 1500, "shoulder_lift")
+        time.sleep(1)
+        arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 1536, "elbow_flex")
+        time.sleep(1)
+        arm.write("Goal_Position", calib["wrist_flex"]["start_pos"], "wrist_flex")
+        time.sleep(1)
+
+    print("Calibrate shoulder_lift")
+    calib["shoulder_lift"] = move_to_calibrate(
+        arm, "shoulder_lift", in_between_move_hook=in_between_move_shoulder_lift_hook
+    )
+    calib["shoulder_lift"] = apply_offset(calib["shoulder_lift"], offset=-1024)
+
+    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
+    time.sleep(1)
+    arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] + 2048, "shoulder_lift")
+    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] - 1024 - 400, "elbow_flex")
+    time.sleep(2)
+
+    calib_modes = []
+    for name in arm.motor_names:
+        if name == "gripper":
+            calib_modes.append(CalibrationMode.LINEAR.name)
+        else:
+            calib_modes.append(CalibrationMode.DEGREE.name)
+
+    calib_dict = {
+        "homing_offset": [calib[name]["homing_offset"] for name in arm.motor_names],
+        "drive_mode": [calib[name]["drive_mode"] for name in arm.motor_names],
+        "start_pos": [calib[name]["start_pos"] for name in arm.motor_names],
+        "end_pos": [calib[name]["end_pos"] for name in arm.motor_names],
+        "calib_mode": calib_modes,
+        "motor_names": arm.motor_names,
+    }
+
+    # Re-enable original accerlation
+    arm.write("Lock", 0)
+    arm.write("Acceleration", initial_acceleration)
+    time.sleep(1)
+
+    return calib_dict
+
+
+def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
+    """This function ensures that a neural network trained on data collected on a given robot
+    can work on another robot. For instance before calibration, setting a same goal position
+    for each motor of two different robots will get two very different positions. But after calibration,
+    the two robots will move to the same position.To this end, this function computes the homing offset
+    and the drive mode for each motor of a given robot.
+
+    Homing offset is used to shift the motor position to a ]-2048, +2048[ nominal range (when the motor uses 2048 steps
+    to complete a half a turn). This range is set around an arbitrary "zero position" corresponding to all motor positions
+    being 0. During the calibration process, you will need to manually move the robot to this "zero position".
+
+    Drive mode is used to invert the rotation direction of the motor. This is useful when some motors have been assembled
+    in the opposite orientation for some robots. During the calibration process, you will need to manually move the robot
+    to the "rotated position".
+
+    After calibration, the homing offsets and drive modes are stored in a cache.
+
+    Example of usage:
+    ```python
+    run_arm_calibration(arm, "so100", "left", "follower")
+    ```
+    """
+    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
+        raise ValueError("To run calibration, the torque must be disabled on all motors.")
+
+    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
+
+    print("\nMove arm to zero position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero"))
+    input("Press Enter to continue...")
+
+    # We arbitrarily chose our zero target position to be a straight horizontal position with gripper upwards and closed.
+    # It is easy to identify and all motors are in a "quarter turn" position. Once calibration is done, this position will
+    # correspond to every motor angle being 0. If you set all 0 as Goal Position, the arm will move in this position.
+    zero_target_pos = convert_degrees_to_steps(ZERO_POSITION_DEGREE, arm.motor_models)
+
+    # Compute homing offset so that `present_position + homing_offset ~= target_position`.
+    zero_pos = arm.read("Present_Position")
+    homing_offset = zero_target_pos - zero_pos
+
+    # The rotated target position corresponds to a rotation of a quarter turn from the zero position.
+    # This allows to identify the rotation direction of each motor.
+    # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
+    # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
+    # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
+    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarely rotate clockwise from the point of view
+    # of the previous motor in the kinetic chain.
+    print("\nMove arm to rotated target position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
+    input("Press Enter to continue...")
+
+    rotated_target_pos = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
+
+    # Find drive mode by rotating each motor by a quarter of a turn.
+    # Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
+    rotated_pos = arm.read("Present_Position")
+    drive_mode = (rotated_pos < zero_pos).astype(np.int32)
+
+    # Re-compute homing offset to take into account drive mode
+    rotated_drived_pos = apply_drive_mode(rotated_pos, drive_mode)
+    homing_offset = rotated_target_pos - rotated_drived_pos
+
+    print("\nMove arm to rest position")
+    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest"))
+    input("Press Enter to continue...")
+    print()
+
+    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
+    calib_modes = []
+    for name in arm.motor_names:
+        if name == "gripper":
+            calib_modes.append(CalibrationMode.LINEAR.name)
+        else:
+            calib_modes.append(CalibrationMode.DEGREE.name)
+
+    calib_dict = {
+        "homing_offset": homing_offset.tolist(),
+        "drive_mode": drive_mode.tolist(),
+        "start_pos": zero_pos.tolist(),
+        "end_pos": rotated_pos.tolist(),
+        "calib_mode": calib_modes,
+        "motor_names": arm.motor_names,
+    }
+    return calib_dict

lerobot/common/robot_devices/robots/koch.py

@@ -1,515 +0,0 @@
-import pickle
-import time
-from dataclasses import dataclass, field, replace
-from pathlib import Path
-
-import numpy as np
-import torch
-
-from lerobot.common.robot_devices.cameras.utils import Camera
-from lerobot.common.robot_devices.motors.dynamixel import (
-    OperatingMode,
-    TorqueMode,
-    convert_degrees_to_steps,
-)
-from lerobot.common.robot_devices.motors.utils import MotorsBus
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
-
-########################################################################
-# Calibration logic
-########################################################################
-
-URL_TEMPLATE = (
-    "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
-)
-
-# In nominal degree range ]-180, +180[
-ZERO_POSITION_DEGREE = 0
-ROTATED_POSITION_DEGREE = 90
-GRIPPER_OPEN_DEGREE = 35.156
-
-
-def assert_drive_mode(drive_mode):
-    # `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
-    if not np.all(np.isin(drive_mode, [0, 1])):
-        raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
-
-
-def apply_drive_mode(position, drive_mode):
-    assert_drive_mode(drive_mode)
-    # Convert `drive_mode` from [0, 1] with 0 indicates original rotation direction and 1 inverted,
-    # to [-1, 1] with 1 indicates original rotation direction and -1 inverted.
-    signed_drive_mode = -(drive_mode * 2 - 1)
-    position *= signed_drive_mode
-    return position
-
-
-def reset_torque_mode(arm: MotorsBus):
-    # To be configured, all servos must be in "torque disable" mode
-    arm.write("Torque_Enable", TorqueMode.DISABLED.value)
-
-    # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't
-    # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm,
-    # you could end up with a servo with a position 0 or 4095 at a crucial point See [
-    # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
-    all_motors_except_gripper = [name for name in arm.motor_names if name != "gripper"]
-    if len(all_motors_except_gripper) > 0:
-        arm.write("Operating_Mode", OperatingMode.EXTENDED_POSITION.value, all_motors_except_gripper)
-
-    # Use 'position control current based' for gripper to be limited by the limit of the current.
-    # For the follower gripper, it means it can grasp an object without forcing too much even tho,
-    # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
-    # For the leader gripper, it means we can use it as a physical trigger, since we can force with our finger
-    # to make it move, and it will move back to its original target position when we release the force.
-    arm.write("Operating_Mode", OperatingMode.CURRENT_CONTROLLED_POSITION.value, "gripper")
-
-
-def run_arm_calibration(arm: MotorsBus, name: str, arm_type: str):
-    """This function ensures that a neural network trained on data collected on a given robot
-    can work on another robot. For instance before calibration, setting a same goal position
-    for each motor of two different robots will get two very different positions. But after calibration,
-    the two robots will move to the same position.To this end, this function computes the homing offset
-    and the drive mode for each motor of a given robot.
-
-    Homing offset is used to shift the motor position to a ]-2048, +2048[ nominal range (when the motor uses 2048 steps
-    to complete a half a turn). This range is set around an arbitrary "zero position" corresponding to all motor positions
-    being 0. During the calibration process, you will need to manually move the robot to this "zero position".
-
-    Drive mode is used to invert the rotation direction of the motor. This is useful when some motors have been assembled
-    in the opposite orientation for some robots. During the calibration process, you will need to manually move the robot
-    to the "rotated position".
-
-    After calibration, the homing offsets and drive modes are stored in a cache.
-
-    Example of usage:
-    ```python
-    run_arm_calibration(arm, "left", "follower")
-    ```
-    """
-    reset_torque_mode(arm)
-
-    print(f"\nRunning calibration of {name} {arm_type}...")
-
-    print("\nMove arm to zero position")
-    print("See: " + URL_TEMPLATE.format(robot="koch", arm=arm_type, position="zero"))
-    input("Press Enter to continue...")
-
-    # We arbitrarely choosed our zero target position to be a straight horizontal position with gripper upwards and closed.
-    # It is easy to identify and all motors are in a "quarter turn" position. Once calibration is done, this position will
-    # corresponds to every motor angle being 0. If you set all 0 as Goal Position, the arm will move in this position.
-    zero_position = convert_degrees_to_steps(ZERO_POSITION_DEGREE, arm.motor_models)
-
-    def _compute_nearest_rounded_position(position, models):
-        # TODO(rcadene): Rework this function since some motors cant physically rotate a quarter turn
-        # (e.g. the gripper of Aloha arms can only rotate ~50 degree)
-        quarter_turn_degree = 90
-        quarter_turn = convert_degrees_to_steps(quarter_turn_degree, models)
-        nearest_pos = np.round(position.astype(float) / quarter_turn) * quarter_turn
-        return nearest_pos.astype(position.dtype)
-
-    # Compute homing offset so that `present_position + homing_offset ~= target_position`.
-    position = arm.read("Present_Position")
-    position = _compute_nearest_rounded_position(position, arm.motor_models)
-    homing_offset = zero_position - position
-
-    print("\nMove arm to rotated target position")
-    print("See: " + URL_TEMPLATE.format(robot="koch", arm=arm_type, position="rotated"))
-    input("Press Enter to continue...")
-
-    # The rotated target position corresponds to a rotation of a quarter turn from the zero position.
-    # This allows to identify the rotation direction of each motor.
-    # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
-    # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
-    # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
-    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarely rotate clockwise from the point of view
-    # of the previous motor in the kinetic chain.
-    rotated_position = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
-
-    # Find drive mode by rotating each motor by a quarter of a turn.
-    # Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
-    position = arm.read("Present_Position")
-    position += homing_offset
-    position = _compute_nearest_rounded_position(position, arm.motor_models)
-    drive_mode = (position != rotated_position).astype(np.int32)
-
-    # Re-compute homing offset to take into account drive mode
-    position = arm.read("Present_Position")
-    position = apply_drive_mode(position, drive_mode)
-    position = _compute_nearest_rounded_position(position, arm.motor_models)
-    homing_offset = rotated_position - position
-
-    print("\nMove arm to rest position")
-    print("See: " + URL_TEMPLATE.format(robot="koch", arm=arm_type, position="rest"))
-    input("Press Enter to continue...")
-    print()
-
-    return homing_offset, drive_mode
-
-
-########################################################################
-# Alexander Koch robot arm
-########################################################################
-
-
-@dataclass
-class KochRobotConfig:
-    """
-    Example of usage:
-    ```python
-    KochRobotConfig()
-    ```
-    """
-
-    # Define all components of the robot
-    leader_arms: dict[str, MotorsBus] = field(default_factory=lambda: {})
-    follower_arms: dict[str, MotorsBus] = field(default_factory=lambda: {})
-    cameras: dict[str, Camera] = field(default_factory=lambda: {})
-
-
-class KochRobot:
-    # TODO(rcadene): Implement force feedback
-    """This class allows to control any Koch robot of various number of motors.
-
-    A few versions are available:
-    - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow expansion, which was developed
-    by Alexander Koch from [Tau Robotics](https://tau-robotics.com): [Github for sourcing and assembly](
-    - [Koch v1.1])https://github.com/jess-moss/koch-v1-1), which was developed by Jess Moss.
-
-    Example of highest frequency teleoperation without camera:
-    ```python
-    # Defines how to communicate with the motors of the leader and follower arms
-    leader_arms = {
-        "main": DynamixelMotorsBus(
-            port="/dev/tty.usbmodem575E0031751",
-            motors={
-                # name: (index, model)
-                "shoulder_pan": (1, "xl330-m077"),
-                "shoulder_lift": (2, "xl330-m077"),
-                "elbow_flex": (3, "xl330-m077"),
-                "wrist_flex": (4, "xl330-m077"),
-                "wrist_roll": (5, "xl330-m077"),
-                "gripper": (6, "xl330-m077"),
-            },
-        ),
-    }
-    follower_arms = {
-        "main": DynamixelMotorsBus(
-            port="/dev/tty.usbmodem575E0032081",
-            motors={
-                # name: (index, model)
-                "shoulder_pan": (1, "xl430-w250"),
-                "shoulder_lift": (2, "xl430-w250"),
-                "elbow_flex": (3, "xl330-m288"),
-                "wrist_flex": (4, "xl330-m288"),
-                "wrist_roll": (5, "xl330-m288"),
-                "gripper": (6, "xl330-m288"),
-            },
-        ),
-    }
-    robot = KochRobot(leader_arms, follower_arms)
-
-    # Connect motors buses and cameras if any (Required)
-    robot.connect()
-
-    while True:
-        robot.teleop_step()
-    ```
-
-    Example of highest frequency data collection without camera:
-    ```python
-    # Assumes leader and follower arms have been instantiated already (see first example)
-    robot = KochRobot(leader_arms, follower_arms)
-    robot.connect()
-    while True:
-        observation, action = robot.teleop_step(record_data=True)
-    ```
-
-    Example of highest frequency data collection with cameras:
-    ```python
-    # Defines how to communicate with 2 cameras connected to the computer.
-    # Here, the webcam of the laptop and the phone (connected in USB to the laptop)
-    # can be reached respectively using the camera indices 0 and 1. These indices can be
-    # arbitrary. See the documentation of `OpenCVCamera` to find your own camera indices.
-    cameras = {
-        "laptop": OpenCVCamera(camera_index=0, fps=30, width=640, height=480),
-        "phone": OpenCVCamera(camera_index=1, fps=30, width=640, height=480),
-    }
-
-    # Assumes leader and follower arms have been instantiated already (see first example)
-    robot = KochRobot(leader_arms, follower_arms, cameras)
-    robot.connect()
-    while True:
-        observation, action = robot.teleop_step(record_data=True)
-    ```
-
-    Example of controlling the robot with a policy (without running multiple policies in parallel to ensure highest frequency):
-    ```python
-    # Assumes leader and follower arms + cameras have been instantiated already (see previous example)
-    robot = KochRobot(leader_arms, follower_arms, cameras)
-    robot.connect()
-    while True:
-        # Uses the follower arms and cameras to capture an observation
-        observation = robot.capture_observation()
-
-        # Assumes a policy has been instantiated
-        with torch.inference_mode():
-            action = policy.select_action(observation)
-
-        # Orders the robot to move
-        robot.send_action(action)
-    ```
-
-    Example of disconnecting which is not mandatory since we disconnect when the object is deleted:
-    ```python
-    robot.disconnect()
-    ```
-    """
-
-    def __init__(
-        self,
-        config: KochRobotConfig | None = None,
-        calibration_path: Path = ".cache/calibration/koch.pkl",
-        **kwargs,
-    ):
-        if config is None:
-            config = KochRobotConfig()
-        # Overwrite config arguments using kwargs
-        self.config = replace(config, **kwargs)
-        self.calibration_path = Path(calibration_path)
-
-        self.leader_arms = self.config.leader_arms
-        self.follower_arms = self.config.follower_arms
-        self.cameras = self.config.cameras
-        self.is_connected = False
-        self.logs = {}
-
-    def connect(self):
-        if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
-                "KochRobot is already connected. Do not run `robot.connect()` twice."
-            )
-
-        if not self.leader_arms and not self.follower_arms and not self.cameras:
-            raise ValueError(
-                "KochRobot doesn't have any device to connect. See example of usage in docstring of the class."
-            )
-
-        # Connect the arms
-        for name in self.follower_arms:
-            print(f"Connecting {name} follower arm.")
-            self.follower_arms[name].connect()
-            print(f"Connecting {name} leader arm.")
-            self.leader_arms[name].connect()
-
-        # Reset the arms and load or run calibration
-        if self.calibration_path.exists():
-            # Reset all arms before setting calibration
-            for name in self.follower_arms:
-                reset_torque_mode(self.follower_arms[name])
-            for name in self.leader_arms:
-                reset_torque_mode(self.leader_arms[name])
-
-            with open(self.calibration_path, "rb") as f:
-                calibration = pickle.load(f)
-        else:
-            print(f"Missing calibration file '{self.calibration_path}'. Starting calibration precedure.")
-            # Run calibration process which begins by reseting all arms
-            calibration = self.run_calibration()
-
-            print(f"Calibration is done! Saving calibration file '{self.calibration_path}'")
-            self.calibration_path.parent.mkdir(parents=True, exist_ok=True)
-            with open(self.calibration_path, "wb") as f:
-                pickle.dump(calibration, f)
-
-        # Set calibration
-        for name in self.follower_arms:
-            self.follower_arms[name].set_calibration(calibration[f"follower_{name}"])
-        for name in self.leader_arms:
-            self.leader_arms[name].set_calibration(calibration[f"leader_{name}"])
-
-        # Set better PID values to close the gap between recored states and actions
-        # TODO(rcadene): Implement an automatic procedure to set optimial PID values for each motor
-        for name in self.follower_arms:
-            self.follower_arms[name].write("Position_P_Gain", 1500, "elbow_flex")
-            self.follower_arms[name].write("Position_I_Gain", 0, "elbow_flex")
-            self.follower_arms[name].write("Position_D_Gain", 600, "elbow_flex")
-
-        # Enable torque on all motors of the follower arms
-        for name in self.follower_arms:
-            print(f"Activating torque on {name} follower arm.")
-            self.follower_arms[name].write("Torque_Enable", 1)
-
-        # Enable torque on the gripper of the leader arms, and move it to 45 degrees,
-        # so that we can use it as a trigger to close the gripper of the follower arms.
-        for name in self.leader_arms:
-            self.leader_arms[name].write("Torque_Enable", 1, "gripper")
-            self.leader_arms[name].write("Goal_Position", GRIPPER_OPEN_DEGREE, "gripper")
-
-        # Connect the cameras
-        for name in self.cameras:
-            self.cameras[name].connect()
-
-        self.is_connected = True
-
-    def run_calibration(self):
-        calibration = {}
-
-        for name in self.follower_arms:
-            homing_offset, drive_mode = run_arm_calibration(self.follower_arms[name], name, "follower")
-
-            calibration[f"follower_{name}"] = {}
-            for idx, motor_name in enumerate(self.follower_arms[name].motor_names):
-                calibration[f"follower_{name}"][motor_name] = (homing_offset[idx], drive_mode[idx])
-
-        for name in self.leader_arms:
-            homing_offset, drive_mode = run_arm_calibration(self.leader_arms[name], name, "leader")
-
-            calibration[f"leader_{name}"] = {}
-            for idx, motor_name in enumerate(self.leader_arms[name].motor_names):
-                calibration[f"leader_{name}"][motor_name] = (homing_offset[idx], drive_mode[idx])
-
-        return calibration
-
-    def teleop_step(
-        self, record_data=False
-    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "KochRobot is not connected. You need to run `robot.connect()`."
-            )
-
-        # Prepare to assign the position of the leader to the follower
-        leader_pos = {}
-        for name in self.leader_arms:
-            before_lread_t = time.perf_counter()
-            leader_pos[name] = self.leader_arms[name].read("Present_Position")
-            self.logs[f"read_leader_{name}_pos_dt_s"] = time.perf_counter() - before_lread_t
-
-        follower_goal_pos = {}
-        for name in self.leader_arms:
-            follower_goal_pos[name] = leader_pos[name]
-
-        # Send action
-        for name in self.follower_arms:
-            before_fwrite_t = time.perf_counter()
-            self.follower_arms[name].write("Goal_Position", follower_goal_pos[name])
-            self.logs[f"write_follower_{name}_goal_pos_dt_s"] = time.perf_counter() - before_fwrite_t
-
-        # Early exit when recording data is not requested
-        if not record_data:
-            return
-
-        # TODO(rcadene): Add velocity and other info
-        # Read follower position
-        follower_pos = {}
-        for name in self.follower_arms:
-            before_fread_t = time.perf_counter()
-            follower_pos[name] = self.follower_arms[name].read("Present_Position")
-            self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
-
-        # Create state by concatenating follower current position
-        state = []
-        for name in self.follower_arms:
-            if name in follower_pos:
-                state.append(follower_pos[name])
-        state = np.concatenate(state)
-
-        # Create action by concatenating follower goal position
-        action = []
-        for name in self.follower_arms:
-            if name in follower_goal_pos:
-                action.append(follower_goal_pos[name])
-        action = np.concatenate(action)
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionnaries and format to pytorch
-        obs_dict, action_dict = {}, {}
-        obs_dict["observation.state"] = torch.from_numpy(state)
-        action_dict["action"] = torch.from_numpy(action)
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = torch.from_numpy(images[name])
-
-        return obs_dict, action_dict
-
-    def capture_observation(self):
-        """The returned observations do not have a batch dimension."""
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "KochRobot is not connected. You need to run `robot.connect()`."
-            )
-
-        # Read follower position
-        follower_pos = {}
-        for name in self.follower_arms:
-            before_fread_t = time.perf_counter()
-            follower_pos[name] = self.follower_arms[name].read("Present_Position")
-            self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
-
-        # Create state by concatenating follower current position
-        state = []
-        for name in self.follower_arms:
-            if name in follower_pos:
-                state.append(follower_pos[name])
-        state = np.concatenate(state)
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionnaries and format to pytorch
-        obs_dict = {}
-        obs_dict["observation.state"] = torch.from_numpy(state)
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = torch.from_numpy(images[name])
-        return obs_dict
-
-    def send_action(self, action: torch.Tensor):
-        """The provided action is expected to be a vector."""
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "KochRobot is not connected. You need to run `robot.connect()`."
-            )
-
-        from_idx = 0
-        to_idx = 0
-        follower_goal_pos = {}
-        for name in self.follower_arms:
-            if name in self.follower_arms:
-                to_idx += len(self.follower_arms[name].motor_names)
-                follower_goal_pos[name] = action[from_idx:to_idx].numpy()
-                from_idx = to_idx
-
-        for name in self.follower_arms:
-            self.follower_arms[name].write("Goal_Position", follower_goal_pos[name].astype(np.int32))
-
-    def disconnect(self):
-        if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                "KochRobot is not connected. You need to run `robot.connect()` before disconnecting."
-            )
-
-        for name in self.follower_arms:
-            self.follower_arms[name].disconnect()
-
-        for name in self.leader_arms:
-            self.leader_arms[name].disconnect()
-
-        for name in self.cameras:
-            self.cameras[name].disconnect()
-
-        self.is_connected = False
-
-    def __del__(self):
-        if getattr(self, "is_connected", False):
-            self.disconnect()

lerobot/common/robot_devices/robots/manipulator.py

@@ -0,0 +1,650 @@
+"""Contains logic to instantiate a robot, read information from its motors and cameras,
+and send orders to its motors.
+"""
+# TODO(rcadene, aliberts): reorganize the codebase into one file per robot, with the associated
+# calibration procedure, to make it easy for people to add their own robot.
+
+import json
+import logging
+import time
+import warnings
+from dataclasses import dataclass, field, replace
+from pathlib import Path
+from typing import Sequence
+
+import numpy as np
+import torch
+
+from lerobot.common.robot_devices.cameras.utils import Camera
+from lerobot.common.robot_devices.motors.utils import MotorsBus
+from lerobot.common.robot_devices.robots.utils import get_arm_id
+from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
+
+
+def ensure_safe_goal_position(
+    goal_pos: torch.Tensor, present_pos: torch.Tensor, max_relative_target: float | list[float]
+):
+    # Cap relative action target magnitude for safety.
+    diff = goal_pos - present_pos
+    max_relative_target = torch.tensor(max_relative_target)
+    safe_diff = torch.minimum(diff, max_relative_target)
+    safe_diff = torch.maximum(safe_diff, -max_relative_target)
+    safe_goal_pos = present_pos + safe_diff
+
+    if not torch.allclose(goal_pos, safe_goal_pos):
+        logging.warning(
+            "Relative goal position magnitude had to be clamped to be safe.\n"
+            f"  requested relative goal position target: {diff}\n"
+            f"    clamped relative goal position target: {safe_diff}"
+        )
+
+    return safe_goal_pos
+
+
+@dataclass
+class ManipulatorRobotConfig:
+    """
+    Example of usage:
+    ```python
+    ManipulatorRobotConfig()
+    ```
+    """
+
+    # Define all components of the robot
+    robot_type: str = "koch"
+    leader_arms: dict[str, MotorsBus] = field(default_factory=lambda: {})
+    follower_arms: dict[str, MotorsBus] = field(default_factory=lambda: {})
+    cameras: dict[str, Camera] = field(default_factory=lambda: {})
+
+    # Optionally limit the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length
+    # as the number of motors in your follower arms (assumes all follower arms have the same number of
+    # motors).
+    max_relative_target: list[float] | float | None = None
+
+    # Optionally set the leader arm in torque mode with the gripper motor set to this angle. This makes it
+    # possible to squeeze the gripper and have it spring back to an open position on its own. If None, the
+    # gripper is not put in torque mode.
+    gripper_open_degree: float | None = None
+
+    def __setattr__(self, prop: str, val):
+        if prop == "max_relative_target" and val is not None and isinstance(val, Sequence):
+            for name in self.follower_arms:
+                if len(self.follower_arms[name].motors) != len(val):
+                    raise ValueError(
+                        f"len(max_relative_target)={len(val)} but the follower arm with name {name} has "
+                        f"{len(self.follower_arms[name].motors)} motors. Please make sure that the "
+                        f"`max_relative_target` list has as many parameters as there are motors per arm. "
+                        "Note: This feature does not yet work with robots where different follower arms have "
+                        "different numbers of motors."
+                    )
+        super().__setattr__(prop, val)
+
+    def __post_init__(self):
+        if self.robot_type not in ["koch", "koch_bimanual", "aloha", "so100", "moss"]:
+            raise ValueError(f"Provided robot type ({self.robot_type}) is not supported.")
+
+
+class ManipulatorRobot:
+    # TODO(rcadene): Implement force feedback
+    """This class allows to control any manipulator robot of various number of motors.
+
+    Non exaustive list of robots:
+    - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow expansion, developed
+    by Alexander Koch from [Tau Robotics](https://tau-robotics.com)
+    - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss
+    - [Aloha](https://www.trossenrobotics.com/aloha-kits) developed by Trossen Robotics
+
+    Example of highest frequency teleoperation without camera:
+    ```python
+    # Defines how to communicate with the motors of the leader and follower arms
+    leader_arms = {
+        "main": DynamixelMotorsBus(
+            port="/dev/tty.usbmodem575E0031751",
+            motors={
+                # name: (index, model)
+                "shoulder_pan": (1, "xl330-m077"),
+                "shoulder_lift": (2, "xl330-m077"),
+                "elbow_flex": (3, "xl330-m077"),
+                "wrist_flex": (4, "xl330-m077"),
+                "wrist_roll": (5, "xl330-m077"),
+                "gripper": (6, "xl330-m077"),
+            },
+        ),
+    }
+    follower_arms = {
+        "main": DynamixelMotorsBus(
+            port="/dev/tty.usbmodem575E0032081",
+            motors={
+                # name: (index, model)
+                "shoulder_pan": (1, "xl430-w250"),
+                "shoulder_lift": (2, "xl430-w250"),
+                "elbow_flex": (3, "xl330-m288"),
+                "wrist_flex": (4, "xl330-m288"),
+                "wrist_roll": (5, "xl330-m288"),
+                "gripper": (6, "xl330-m288"),
+            },
+        ),
+    }
+    robot = ManipulatorRobot(
+        robot_type="koch",
+        calibration_dir=".cache/calibration/koch",
+        leader_arms=leader_arms,
+        follower_arms=follower_arms,
+    )
+
+    # Connect motors buses and cameras if any (Required)
+    robot.connect()
+
+    while True:
+        robot.teleop_step()
+    ```
+
+    Example of highest frequency data collection without camera:
+    ```python
+    # Assumes leader and follower arms have been instantiated already (see first example)
+    robot = ManipulatorRobot(
+        robot_type="koch",
+        calibration_dir=".cache/calibration/koch",
+        leader_arms=leader_arms,
+        follower_arms=follower_arms,
+    )
+    robot.connect()
+    while True:
+        observation, action = robot.teleop_step(record_data=True)
+    ```
+
+    Example of highest frequency data collection with cameras:
+    ```python
+    # Defines how to communicate with 2 cameras connected to the computer.
+    # Here, the webcam of the laptop and the phone (connected in USB to the laptop)
+    # can be reached respectively using the camera indices 0 and 1. These indices can be
+    # arbitrary. See the documentation of `OpenCVCamera` to find your own camera indices.
+    cameras = {
+        "laptop": OpenCVCamera(camera_index=0, fps=30, width=640, height=480),
+        "phone": OpenCVCamera(camera_index=1, fps=30, width=640, height=480),
+    }
+
+    # Assumes leader and follower arms have been instantiated already (see first example)
+    robot = ManipulatorRobot(
+        robot_type="koch",
+        calibration_dir=".cache/calibration/koch",
+        leader_arms=leader_arms,
+        follower_arms=follower_arms,
+        cameras=cameras,
+    )
+    robot.connect()
+    while True:
+        observation, action = robot.teleop_step(record_data=True)
+    ```
+
+    Example of controlling the robot with a policy (without running multiple policies in parallel to ensure highest frequency):
+    ```python
+    # Assumes leader and follower arms + cameras have been instantiated already (see previous example)
+    robot = ManipulatorRobot(
+        robot_type="koch",
+        calibration_dir=".cache/calibration/koch",
+        leader_arms=leader_arms,
+        follower_arms=follower_arms,
+        cameras=cameras,
+    )
+    robot.connect()
+    while True:
+        # Uses the follower arms and cameras to capture an observation
+        observation = robot.capture_observation()
+
+        # Assumes a policy has been instantiated
+        with torch.inference_mode():
+            action = policy.select_action(observation)
+
+        # Orders the robot to move
+        robot.send_action(action)
+    ```
+
+    Example of disconnecting which is not mandatory since we disconnect when the object is deleted:
+    ```python
+    robot.disconnect()
+    ```
+    """
+
+    def __init__(
+        self,
+        config: ManipulatorRobotConfig | None = None,
+        calibration_dir: Path = ".cache/calibration/koch",
+        **kwargs,
+    ):
+        if config is None:
+            config = ManipulatorRobotConfig()
+        # Overwrite config arguments using kwargs
+        self.config = replace(config, **kwargs)
+        self.calibration_dir = Path(calibration_dir)
+
+        self.robot_type = self.config.robot_type
+        self.leader_arms = self.config.leader_arms
+        self.follower_arms = self.config.follower_arms
+        self.cameras = self.config.cameras
+        self.is_connected = False
+        self.logs = {}
+
+    @property
+    def has_camera(self):
+        return len(self.cameras) > 0
+
+    @property
+    def num_cameras(self):
+        return len(self.cameras)
+
+    @property
+    def available_arms(self):
+        available_arms = []
+        for name in self.follower_arms:
+            arm_id = get_arm_id(name, "follower")
+            available_arms.append(arm_id)
+        for name in self.leader_arms:
+            arm_id = get_arm_id(name, "leader")
+            available_arms.append(arm_id)
+        return available_arms
+
+    def connect(self):
+        if self.is_connected:
+            raise RobotDeviceAlreadyConnectedError(
+                "ManipulatorRobot is already connected. Do not run `robot.connect()` twice."
+            )
+
+        if not self.leader_arms and not self.follower_arms and not self.cameras:
+            raise ValueError(
+                "ManipulatorRobot doesn't have any device to connect. See example of usage in docstring of the class."
+            )
+
+        # Connect the arms
+        for name in self.follower_arms:
+            print(f"Connecting {name} follower arm.")
+            self.follower_arms[name].connect()
+        for name in self.leader_arms:
+            print(f"Connecting {name} leader arm.")
+            self.leader_arms[name].connect()
+
+        if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
+            from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
+        elif self.robot_type in ["so100", "moss"]:
+            from lerobot.common.robot_devices.motors.feetech import TorqueMode
+
+        # We assume that at connection time, arms are in a rest position, and torque can
+        # be safely disabled to run calibration and/or set robot preset configurations.
+        for name in self.follower_arms:
+            self.follower_arms[name].write("Torque_Enable", TorqueMode.DISABLED.value)
+        for name in self.leader_arms:
+            self.leader_arms[name].write("Torque_Enable", TorqueMode.DISABLED.value)
+
+        self.activate_calibration()
+
+        # Set robot preset (e.g. torque in leader gripper for Koch v1.1)
+        if self.robot_type in ["koch", "koch_bimanual"]:
+            self.set_koch_robot_preset()
+        elif self.robot_type == "aloha":
+            self.set_aloha_robot_preset()
+        elif self.robot_type in ["so100", "moss"]:
+            self.set_so100_robot_preset()
+
+        # Enable torque on all motors of the follower arms
+        for name in self.follower_arms:
+            print(f"Activating torque on {name} follower arm.")
+            self.follower_arms[name].write("Torque_Enable", 1)
+
+        if self.config.gripper_open_degree is not None:
+            if self.robot_type not in ["koch", "koch_bimanual"]:
+                raise NotImplementedError(
+                    f"{self.robot_type} does not support position AND current control in the handle, which is require to set the gripper open."
+                )
+            # Set the leader arm in torque mode with the gripper motor set to an angle. This makes it possible
+            # to squeeze the gripper and have it spring back to an open position on its own.
+            for name in self.leader_arms:
+                self.leader_arms[name].write("Torque_Enable", 1, "gripper")
+                self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper")
+
+        # Check both arms can be read
+        for name in self.follower_arms:
+            self.follower_arms[name].read("Present_Position")
+        for name in self.leader_arms:
+            self.leader_arms[name].read("Present_Position")
+
+        # Connect the cameras
+        for name in self.cameras:
+            self.cameras[name].connect()
+
+        self.is_connected = True
+
+    def activate_calibration(self):
+        """After calibration all motors function in human interpretable ranges.
+        Rotations are expressed in degrees in nominal range of [-180, 180],
+        and linear motions (like gripper of Aloha) in nominal range of [0, 100].
+        """
+
+        def load_or_run_calibration_(name, arm, arm_type):
+            arm_id = get_arm_id(name, arm_type)
+            arm_calib_path = self.calibration_dir / f"{arm_id}.json"
+
+            if arm_calib_path.exists():
+                with open(arm_calib_path) as f:
+                    calibration = json.load(f)
+            else:
+                # TODO(rcadene): display a warning in __init__ if calibration file not available
+                print(f"Missing calibration file '{arm_calib_path}'")
+
+                if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
+                    from lerobot.common.robot_devices.robots.dynamixel_calibration import run_arm_calibration
+
+                    calibration = run_arm_calibration(arm, self.robot_type, name, arm_type)
+
+                elif self.robot_type in ["so100", "moss"]:
+                    from lerobot.common.robot_devices.robots.feetech_calibration import (
+                        run_arm_manual_calibration,
+                    )
+
+                    calibration = run_arm_manual_calibration(arm, self.robot_type, name, arm_type)
+
+                print(f"Calibration is done! Saving calibration file '{arm_calib_path}'")
+                arm_calib_path.parent.mkdir(parents=True, exist_ok=True)
+                with open(arm_calib_path, "w") as f:
+                    json.dump(calibration, f)
+
+            return calibration
+
+        for name, arm in self.follower_arms.items():
+            calibration = load_or_run_calibration_(name, arm, "follower")
+            arm.set_calibration(calibration)
+        for name, arm in self.leader_arms.items():
+            calibration = load_or_run_calibration_(name, arm, "leader")
+            arm.set_calibration(calibration)
+
+    def set_koch_robot_preset(self):
+        def set_operating_mode_(arm):
+            from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
+
+            if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
+                raise ValueError("To run set robot preset, the torque must be disabled on all motors.")
+
+            # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't
+            # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm,
+            # you could end up with a servo with a position 0 or 4095 at a crucial point See [
+            # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
+            all_motors_except_gripper = [name for name in arm.motor_names if name != "gripper"]
+            if len(all_motors_except_gripper) > 0:
+                # 4 corresponds to Extended Position on Koch motors
+                arm.write("Operating_Mode", 4, all_motors_except_gripper)
+
+            # Use 'position control current based' for gripper to be limited by the limit of the current.
+            # For the follower gripper, it means it can grasp an object without forcing too much even tho,
+            # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
+            # For the leader gripper, it means we can use it as a physical trigger, since we can force with our finger
+            # to make it move, and it will move back to its original target position when we release the force.
+            # 5 corresponds to Current Controlled Position on Koch gripper motors "xl330-m077, xl330-m288"
+            arm.write("Operating_Mode", 5, "gripper")
+
+        for name in self.follower_arms:
+            set_operating_mode_(self.follower_arms[name])
+
+            # Set better PID values to close the gap between recorded states and actions
+            # TODO(rcadene): Implement an automatic procedure to set optimial PID values for each motor
+            self.follower_arms[name].write("Position_P_Gain", 1500, "elbow_flex")
+            self.follower_arms[name].write("Position_I_Gain", 0, "elbow_flex")
+            self.follower_arms[name].write("Position_D_Gain", 600, "elbow_flex")
+
+        if self.config.gripper_open_degree is not None:
+            for name in self.leader_arms:
+                set_operating_mode_(self.leader_arms[name])
+
+                # Enable torque on the gripper of the leader arms, and move it to 45 degrees,
+                # so that we can use it as a trigger to close the gripper of the follower arms.
+                self.leader_arms[name].write("Torque_Enable", 1, "gripper")
+                self.leader_arms[name].write("Goal_Position", self.config.gripper_open_degree, "gripper")
+
+    def set_aloha_robot_preset(self):
+        def set_shadow_(arm):
+            # Set secondary/shadow ID for shoulder and elbow. These joints have two motors.
+            # As a result, if only one of them is required to move to a certain position,
+            # the other will follow. This is to avoid breaking the motors.
+            if "shoulder_shadow" in arm.motor_names:
+                shoulder_idx = arm.read("ID", "shoulder")
+                arm.write("Secondary_ID", shoulder_idx, "shoulder_shadow")
+
+            if "elbow_shadow" in arm.motor_names:
+                elbow_idx = arm.read("ID", "elbow")
+                arm.write("Secondary_ID", elbow_idx, "elbow_shadow")
+
+        for name in self.follower_arms:
+            set_shadow_(self.follower_arms[name])
+
+        for name in self.leader_arms:
+            set_shadow_(self.leader_arms[name])
+
+        for name in self.follower_arms:
+            # Set a velocity limit of 131 as advised by Trossen Robotics
+            self.follower_arms[name].write("Velocity_Limit", 131)
+
+            # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't
+            # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm,
+            # you could end up with a servo with a position 0 or 4095 at a crucial point See [
+            # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
+            all_motors_except_gripper = [
+                name for name in self.follower_arms[name].motor_names if name != "gripper"
+            ]
+            if len(all_motors_except_gripper) > 0:
+                # 4 corresponds to Extended Position on Aloha motors
+                self.follower_arms[name].write("Operating_Mode", 4, all_motors_except_gripper)
+
+            # Use 'position control current based' for follower gripper to be limited by the limit of the current.
+            # It can grasp an object without forcing too much even tho,
+            # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
+            # 5 corresponds to Current Controlled Position on Aloha gripper follower "xm430-w350"
+            self.follower_arms[name].write("Operating_Mode", 5, "gripper")
+
+            # Note: We can't enable torque on the leader gripper since "xc430-w150" doesn't have
+            # a Current Controlled Position mode.
+
+        if self.config.gripper_open_degree is not None:
+            warnings.warn(
+                f"`gripper_open_degree` is set to {self.config.gripper_open_degree}, but None is expected for Aloha instead",
+                stacklevel=1,
+            )
+
+    def set_so100_robot_preset(self):
+        for name in self.follower_arms:
+            # Mode=0 for Position Control
+            self.follower_arms[name].write("Mode", 0)
+            # Set P_Coefficient to lower value to avoid shakiness (Default is 32)
+            self.follower_arms[name].write("P_Coefficient", 16)
+            # Set I_Coefficient and D_Coefficient to default value 0 and 32
+            self.follower_arms[name].write("I_Coefficient", 0)
+            self.follower_arms[name].write("D_Coefficient", 32)
+            # Close the write lock so that Maximum_Acceleration gets written to EPROM address,
+            # which is mandatory for Maximum_Acceleration to take effect after rebooting.
+            self.follower_arms[name].write("Lock", 0)
+            # Set Maximum_Acceleration to 254 to speedup acceleration and deceleration of
+            # the motors. Note: this configuration is not in the official STS3215 Memory Table
+            self.follower_arms[name].write("Maximum_Acceleration", 254)
+            self.follower_arms[name].write("Acceleration", 254)
+
+    def teleop_step(
+        self, record_data=False
+    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
+            )
+
+        # Prepare to assign the position of the leader to the follower
+        leader_pos = {}
+        for name in self.leader_arms:
+            before_lread_t = time.perf_counter()
+            leader_pos[name] = self.leader_arms[name].read("Present_Position")
+            leader_pos[name] = torch.from_numpy(leader_pos[name])
+            self.logs[f"read_leader_{name}_pos_dt_s"] = time.perf_counter() - before_lread_t
+
+        # Send goal position to the follower
+        follower_goal_pos = {}
+        for name in self.follower_arms:
+            before_fwrite_t = time.perf_counter()
+            goal_pos = leader_pos[name]
+
+            # Cap goal position when too far away from present position.
+            # Slower fps expected due to reading from the follower.
+            if self.config.max_relative_target is not None:
+                present_pos = self.follower_arms[name].read("Present_Position")
+                present_pos = torch.from_numpy(present_pos)
+                goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
+
+            # Used when record_data=True
+            follower_goal_pos[name] = goal_pos
+
+            goal_pos = goal_pos.numpy().astype(np.int32)
+            self.follower_arms[name].write("Goal_Position", goal_pos)
+            self.logs[f"write_follower_{name}_goal_pos_dt_s"] = time.perf_counter() - before_fwrite_t
+
+        # Early exit when recording data is not requested
+        if not record_data:
+            return
+
+        # TODO(rcadene): Add velocity and other info
+        # Read follower position
+        follower_pos = {}
+        for name in self.follower_arms:
+            before_fread_t = time.perf_counter()
+            follower_pos[name] = self.follower_arms[name].read("Present_Position")
+            follower_pos[name] = torch.from_numpy(follower_pos[name])
+            self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
+
+        # Create state by concatenating follower current position
+        state = []
+        for name in self.follower_arms:
+            if name in follower_pos:
+                state.append(follower_pos[name])
+        state = torch.cat(state)
+
+        # Create action by concatenating follower goal position
+        action = []
+        for name in self.follower_arms:
+            if name in follower_goal_pos:
+                action.append(follower_goal_pos[name])
+        action = torch.cat(action)
+
+        # Capture images from cameras
+        images = {}
+        for name in self.cameras:
+            before_camread_t = time.perf_counter()
+            images[name] = self.cameras[name].async_read()
+            images[name] = torch.from_numpy(images[name])
+            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
+            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
+
+        # Populate output dictionnaries
+        obs_dict, action_dict = {}, {}
+        obs_dict["observation.state"] = state
+        action_dict["action"] = action
+        for name in self.cameras:
+            obs_dict[f"observation.images.{name}"] = images[name]
+
+        return obs_dict, action_dict
+
+    def capture_observation(self):
+        """The returned observations do not have a batch dimension."""
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
+            )
+
+        # Read follower position
+        follower_pos = {}
+        for name in self.follower_arms:
+            before_fread_t = time.perf_counter()
+            follower_pos[name] = self.follower_arms[name].read("Present_Position")
+            follower_pos[name] = torch.from_numpy(follower_pos[name])
+            self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t
+
+        # Create state by concatenating follower current position
+        state = []
+        for name in self.follower_arms:
+            if name in follower_pos:
+                state.append(follower_pos[name])
+        state = torch.cat(state)
+
+        # Capture images from cameras
+        images = {}
+        for name in self.cameras:
+            before_camread_t = time.perf_counter()
+            images[name] = self.cameras[name].async_read()
+            images[name] = torch.from_numpy(images[name])
+            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
+            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
+
+        # Populate output dictionnaries and format to pytorch
+        obs_dict = {}
+        obs_dict["observation.state"] = state
+        for name in self.cameras:
+            obs_dict[f"observation.images.{name}"] = images[name]
+        return obs_dict
+
+    def send_action(self, action: torch.Tensor) -> torch.Tensor:
+        """Command the follower arms to move to a target joint configuration.
+
+        The relative action magnitude may be clipped depending on the configuration parameter
+        `max_relative_target`. In this case, the action sent differs from original action.
+        Thus, this function always returns the action actually sent.
+
+        Args:
+            action: tensor containing the concatenated goal positions for the follower arms.
+        """
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
+            )
+
+        from_idx = 0
+        to_idx = 0
+        action_sent = []
+        for name in self.follower_arms:
+            # Get goal position of each follower arm by splitting the action vector
+            to_idx += len(self.follower_arms[name].motor_names)
+            goal_pos = action[from_idx:to_idx]
+            from_idx = to_idx
+
+            # Cap goal position when too far away from present position.
+            # Slower fps expected due to reading from the follower.
+            if self.config.max_relative_target is not None:
+                present_pos = self.follower_arms[name].read("Present_Position")
+                present_pos = torch.from_numpy(present_pos)
+                goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
+
+            # Save tensor to concat and return
+            action_sent.append(goal_pos)
+
+            # Send goal position to each follower
+            goal_pos = goal_pos.numpy().astype(np.int32)
+            self.follower_arms[name].write("Goal_Position", goal_pos)
+
+        return torch.cat(action_sent)
+
+    def print_logs(self):
+        pass
+        # TODO(aliberts): move robot-specific logs logic here
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise RobotDeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting."
+            )
+
+        for name in self.follower_arms:
+            self.follower_arms[name].disconnect()
+
+        for name in self.leader_arms:
+            self.leader_arms[name].disconnect()
+
+        for name in self.cameras:
+            self.cameras[name].disconnect()
+
+        self.is_connected = False
+
+    def __del__(self):
+        if getattr(self, "is_connected", False):
+            self.disconnect()

lerobot/common/robot_devices/robots/stretch.py

@@ -0,0 +1,216 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+from dataclasses import dataclass, field, replace
+
+import torch
+from stretch_body.gamepad_teleop import GamePadTeleop
+from stretch_body.robot import Robot as StretchAPI
+from stretch_body.robot_params import RobotParams
+
+from lerobot.common.robot_devices.cameras.utils import Camera
+
+
+@dataclass
+class StretchRobotConfig:
+    robot_type: str | None = "stretch"
+    cameras: dict[str, Camera] = field(default_factory=lambda: {})
+    # TODO(aliberts): add feature with max_relative target
+    # TODO(aliberts): add comment on max_relative target
+    max_relative_target: list[float] | float | None = None
+
+
+class StretchRobot(StretchAPI):
+    """Wrapper of stretch_body.robot.Robot"""
+
+    def __init__(self, config: StretchRobotConfig | None = None, **kwargs):
+        super().__init__()
+        if config is None:
+            config = StretchRobotConfig()
+        # Overwrite config arguments using kwargs
+        self.config = replace(config, **kwargs)
+
+        self.robot_type = self.config.robot_type
+        self.cameras = self.config.cameras
+        self.is_connected = False
+        self.teleop = None
+        self.logs = {}
+
+        # TODO(aliberts): test this
+        RobotParams.set_logging_level("WARNING")
+        RobotParams.set_logging_formatter("brief_console_formatter")
+
+        self.state_keys = None
+        self.action_keys = None
+
+    def connect(self) -> None:
+        self.is_connected = self.startup()
+        if not self.is_connected:
+            print("Another process is already using Stretch. Try running 'stretch_free_robot_process.py'")
+            raise ConnectionError()
+
+        for name in self.cameras:
+            self.cameras[name].connect()
+            self.is_connected = self.is_connected and self.cameras[name].is_connected
+
+        if not self.is_connected:
+            print("Could not connect to the cameras, check that all cameras are plugged-in.")
+            raise ConnectionError()
+
+        self.run_calibration()
+
+    def run_calibration(self) -> None:
+        if not self.is_homed():
+            self.home()
+
+    def teleop_step(
+        self, record_data=False
+    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
+        # TODO(aliberts): return ndarrays instead of torch.Tensors
+        if not self.is_connected:
+            raise ConnectionError()
+
+        if self.teleop is None:
+            self.teleop = GamePadTeleop(robot_instance=False)
+            self.teleop.startup(robot=self)
+
+        before_read_t = time.perf_counter()
+        state = self.get_state()
+        action = self.teleop.gamepad_controller.get_state()
+        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
+
+        before_write_t = time.perf_counter()
+        self.teleop.do_motion(robot=self)
+        self.push_command()
+        self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
+
+        if self.state_keys is None:
+            self.state_keys = list(state)
+
+        if not record_data:
+            return
+
+        state = torch.as_tensor(list(state.values()))
+        action = torch.as_tensor(list(action.values()))
+
+        # Capture images from cameras
+        images = {}
+        for name in self.cameras:
+            before_camread_t = time.perf_counter()
+            images[name] = self.cameras[name].async_read()
+            images[name] = torch.from_numpy(images[name])
+            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
+            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
+
+        # Populate output dictionnaries
+        obs_dict, action_dict = {}, {}
+        obs_dict["observation.state"] = state
+        action_dict["action"] = action
+        for name in self.cameras:
+            obs_dict[f"observation.images.{name}"] = images[name]
+
+        return obs_dict, action_dict
+
+    def get_state(self) -> dict:
+        status = self.get_status()
+        return {
+            "head_pan.pos": status["head"]["head_pan"]["pos"],
+            "head_tilt.pos": status["head"]["head_tilt"]["pos"],
+            "lift.pos": status["lift"]["pos"],
+            "arm.pos": status["arm"]["pos"],
+            "wrist_pitch.pos": status["end_of_arm"]["wrist_pitch"]["pos"],
+            "wrist_roll.pos": status["end_of_arm"]["wrist_roll"]["pos"],
+            "wrist_yaw.pos": status["end_of_arm"]["wrist_yaw"]["pos"],
+            "gripper.pos": status["end_of_arm"]["stretch_gripper"]["pos"],
+            "base_x.vel": status["base"]["x_vel"],
+            "base_y.vel": status["base"]["y_vel"],
+            "base_theta.vel": status["base"]["theta_vel"],
+        }
+
+    def capture_observation(self) -> dict:
+        # TODO(aliberts): return ndarrays instead of torch.Tensors
+        before_read_t = time.perf_counter()
+        state = self.get_state()
+        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
+
+        if self.state_keys is None:
+            self.state_keys = list(state)
+
+        state = torch.as_tensor(list(state.values()))
+
+        # Capture images from cameras
+        images = {}
+        for name in self.cameras:
+            before_camread_t = time.perf_counter()
+            images[name] = self.cameras[name].async_read()
+            images[name] = torch.from_numpy(images[name])
+            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
+            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
+
+        # Populate output dictionnaries
+        obs_dict = {}
+        obs_dict["observation.state"] = state
+        for name in self.cameras:
+            obs_dict[f"observation.images.{name}"] = images[name]
+
+        return obs_dict
+
+    def send_action(self, action: torch.Tensor) -> torch.Tensor:
+        # TODO(aliberts): return ndarrays instead of torch.Tensors
+        if not self.is_connected:
+            raise ConnectionError()
+
+        if self.teleop is None:
+            self.teleop = GamePadTeleop(robot_instance=False)
+            self.teleop.startup(robot=self)
+
+        if self.action_keys is None:
+            dummy_action = self.teleop.gamepad_controller.get_state()
+            self.action_keys = list(dummy_action.keys())
+
+        action_dict = dict(zip(self.action_keys, action.tolist(), strict=True))
+
+        before_write_t = time.perf_counter()
+        self.teleop.do_motion(state=action_dict, robot=self)
+        self.push_command()
+        self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
+
+        # TODO(aliberts): return action_sent when motion is limited
+        return action
+
+    def print_logs(self) -> None:
+        pass
+        # TODO(aliberts): move robot-specific logs logic here
+
+    def teleop_safety_stop(self) -> None:
+        if self.teleop is not None:
+            self.teleop._safety_stop(robot=self)
+
+    def disconnect(self) -> None:
+        self.stop()
+        if self.teleop is not None:
+            self.teleop.gamepad_controller.stop()
+            self.teleop.stop()
+
+        if len(self.cameras) > 0:
+            for cam in self.cameras.values():
+                cam.disconnect()
+
+        self.is_connected = False
+
+    def __del__(self):
+        self.disconnect()

lerobot/common/robot_devices/robots/utils.py

@@ -1,9 +1,20 @@
 from typing import Protocol
 
 
+def get_arm_id(name, arm_type):
+    """Returns the string identifier of a robot arm. For instance, for a bimanual manipulator
+    like Aloha, it could be left_follower, right_follower, left_leader, or right_leader.
+    """
+    return f"{name}_{arm_type}"
+
+
 class Robot(Protocol):
-    def init_teleop(self): ...
+    # TODO(rcadene, aliberts): Add unit test checking the protocol is implemented in the corresponding classes
+    robot_type: str
+
+    def connect(self): ...
     def run_calibration(self): ...
     def teleop_step(self, record_data=False): ...
     def capture_observation(self): ...
     def send_action(self, action): ...
+    def disconnect(self): ...

lerobot/common/robot_devices/utils.py

@@ -1,3 +1,35 @@
+import platform
+import time
+
+
+def busy_wait(seconds):
+    if platform.system() == "Darwin":
+        # On Mac, `time.sleep` is not accurate and we need to use this while loop trick,
+        # but it consumes CPU cycles.
+        # TODO(rcadene): find an alternative: from python 11, time.sleep is precise
+        end_time = time.perf_counter() + seconds
+        while time.perf_counter() < end_time:
+            pass
+    else:
+        # On Linux time.sleep is accurate
+        if seconds > 0:
+            time.sleep(seconds)
+
+
+def safe_disconnect(func):
+    # TODO(aliberts): Allow to pass custom exceptions
+    # (e.g. ThreadServiceExit, KeyboardInterrupt, SystemExit, UnpluggedError, DynamixelCommError)
+    def wrapper(robot, *args, **kwargs):
+        try:
+            return func(robot, *args, **kwargs)
+        except Exception as e:
+            if robot.is_connected:
+                robot.disconnect()
+            raise e
+
+    return wrapper
+
+
 class RobotDeviceNotConnectedError(Exception):
     """Exception raised when the robot device is not connected."""
 

lerobot/common/utils/utils.py

@@ -14,7 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
+import os
 import os.path as osp
+import platform
 import random
 from contextlib import contextmanager
 from datetime import datetime, timezone
@@ -27,6 +29,18 @@ import torch
 from omegaconf import DictConfig
 
 
+def none_or_int(value):
+    if value == "None":
+        return None
+    return int(value)
+
+
+def inside_slurm():
+    """Check whether the python process was launched through slurm"""
+    # TODO(rcadene): return False for interactive mode `--pty bash`
+    return "SLURM_JOB_ID" in os.environ
+
+
 def get_safe_torch_device(cfg_device: str, log: bool = False) -> torch.device:
     """Given a string, return a torch.device with checks on whether the device is available."""
     match cfg_device:
@@ -158,7 +172,6 @@ def init_hydra_config(config_path: str, overrides: list[str] | None = None) -> D
         version_base="1.2",
     )
     cfg = hydra.compose(Path(config_path).stem, overrides)
-
     return cfg
 
 
@@ -177,3 +190,30 @@ def print_cuda_memory_usage():
 
 def capture_timestamp_utc():
     return datetime.now(timezone.utc)
+
+
+def say(text, blocking=False):
+    # Check if mac, linux, or windows.
+    if platform.system() == "Darwin":
+        cmd = f'say "{text}"'
+        if not blocking:
+            cmd += " &"
+    elif platform.system() == "Linux":
+        cmd = f'spd-say "{text}"'
+        if blocking:
+            cmd += "  --wait"
+    elif platform.system() == "Windows":
+        # TODO(rcadene): Make blocking option work for Windows
+        cmd = (
+            'PowerShell -Command "Add-Type -AssemblyName System.Speech; '
+            f"(New-Object System.Speech.Synthesis.SpeechSynthesizer).Speak('{text}')\""
+        )
+
+    os.system(cmd)
+
+
+def log_say(text, play_sounds, blocking=False):
+    logging.info(text)
+
+    if play_sounds:
+        say(text, blocking)

lerobot/configs/env/aloha_real.yaml

@@ -0,0 +1,10 @@
+# @package _global_
+
+fps: 30
+
+env:
+  name: real_world
+  task: null
+  state_dim: 18
+  action_dim: 18
+  fps: ${fps}

lerobot/configs/env/moss_real.yaml

@@ -0,0 +1,10 @@
+# @package _global_
+
+fps: 30
+
+env:
+  name: real_world
+  task: null
+  state_dim: 6
+  action_dim: 6
+  fps: ${fps}

lerobot/configs/env/so100_real.yaml

@@ -0,0 +1,10 @@
+# @package _global_
+
+fps: 30
+
+env:
+  name: real_world
+  task: null
+  state_dim: 6
+  action_dim: 6
+  fps: ${fps}

lerobot/configs/policy/act_aloha_real.yaml

@@ -1,16 +1,22 @@
 # @package _global_
 
-# Use `act_real.yaml` to train on real-world Aloha/Aloha2 datasets.
-# Compared to `act.yaml`, it contains 4 cameras (i.e. cam_right_wrist, cam_left_wrist, images,
-# cam_low) instead of 1 camera (i.e. top). Also, `training.eval_freq` is set to -1. This config is used
-# to evaluate checkpoints at a certain frequency of training steps. When it is set to -1, it deactivates evaluation.
-# This is because real-world evaluation is done through [dora-lerobot](https://github.com/dora-rs/dora-lerobot).
-# Look at its README for more information on how to evaluate a checkpoint in the real-world.
+# Use `act_aloha_real.yaml` to train on real-world datasets collected on Aloha or Aloha-2 robots.
+# Compared to `act.yaml`, it contains 4 cameras (i.e. cam_right_wrist, cam_left_wrist, cam_high, cam_low) instead of 1 camera (i.e. top).
+# Also, `training.eval_freq` is set to -1. This config is used to evaluate checkpoints at a certain frequency of training steps.
+# When it is set to -1, it deactivates evaluation. This is because real-world evaluation is done through our `control_robot.py` script.
+# Look at the documentation in header of `control_robot.py` for more information on how to collect data , train and evaluate a policy.
 #
-# Example of usage for training:
+# Example of usage for training and inference with `control_robot.py`:
 # ```bash
 # python lerobot/scripts/train.py \
-#   policy=act_real \
+#   policy=act_aloha_real \
+#   env=aloha_real
+# ```
+#
+# Example of usage for training and inference with [Dora-rs](https://github.com/dora-rs/dora-lerobot):
+# ```bash
+# python lerobot/scripts/train.py \
+#   policy=act_aloha_real \
 #   env=dora_aloha_real
 # ```
 
@@ -36,10 +42,11 @@ override_dataset_stats:
     std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
 
 training:
-  offline_steps: 100000
+  offline_steps: 80000
   online_steps: 0
   eval_freq: -1
-  save_freq: 20000
+  save_freq: 10000
+  log_freq: 100
   save_checkpoint: true
 
   batch_size: 8
@@ -62,7 +69,7 @@ policy:
 
   # Input / output structure.
   n_obs_steps: 1
-  chunk_size: 100 # chunk_size
+  chunk_size: 100
   n_action_steps: 100
 
   input_shapes:
@@ -107,7 +114,7 @@ policy:
   n_vae_encoder_layers: 4
 
   # Inference.
-  temporal_ensemble_coeff: null
+  temporal_ensemble_momentum: null
 
   # Training and loss computation.
   dropout: 0.1

lerobot/configs/policy/act_moss_real.yaml

@@ -1,43 +1,37 @@
 # @package _global_
 
-# Use `act_real_no_state.yaml` to train on real-world Aloha/Aloha2 datasets when cameras are moving (e.g. wrist cameras)
-# Compared to `act_real.yaml`, it is camera only and does not use the state as input which is vector of robot joint positions.
-# We validated experimentaly that not using state reaches better success rate. Our hypothesis is that `act_real.yaml` might
-# overfits to the state, because the images are more complex to learn from since they are moving.
+# Use `act_koch_real.yaml` to train on real-world datasets collected on Alexander Koch's robots.
+# Compared to `act.yaml`, it contains 2 cameras (i.e. laptop, phone) instead of 1 camera (i.e. top).
+# Also, `training.eval_freq` is set to -1. This config is used to evaluate checkpoints at a certain frequency of training steps.
+# When it is set to -1, it deactivates evaluation. This is because real-world evaluation is done through our `control_robot.py` script.
+# Look at the documentation in header of `control_robot.py` for more information on how to collect data , train and evaluate a policy.
 #
 # Example of usage for training:
 # ```bash
 # python lerobot/scripts/train.py \
-#   policy=act_real_no_state \
-#   env=dora_aloha_real
+#   policy=act_koch_real \
+#   env=koch_real
 # ```
 
 seed: 1000
-dataset_repo_id: lerobot/aloha_static_vinh_cup
+dataset_repo_id: lerobot/moss_pick_place_lego
 
 override_dataset_stats:
-  observation.images.cam_right_wrist:
+  observation.images.laptop:
     # stats from imagenet, since we use a pretrained vision model
     mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
     std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-  observation.images.cam_left_wrist:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-  observation.images.cam_high:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-  observation.images.cam_low:
+  observation.images.phone:
     # stats from imagenet, since we use a pretrained vision model
     mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
     std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
 
 training:
-  offline_steps: 100000
+  offline_steps: 80000
   online_steps: 0
   eval_freq: -1
-  save_freq: 20000
+  save_freq: 10000
+  log_freq: 100
   save_checkpoint: true
 
   batch_size: 8
@@ -60,24 +54,22 @@ policy:
 
   # Input / output structure.
   n_obs_steps: 1
-  chunk_size: 100 # chunk_size
+  chunk_size: 100
   n_action_steps: 100
 
   input_shapes:
     # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
-    observation.images.cam_right_wrist: [3, 480, 640]
-    observation.images.cam_left_wrist: [3, 480, 640]
-    observation.images.cam_high: [3, 480, 640]
-    observation.images.cam_low: [3, 480, 640]
+    observation.images.laptop: [3, 480, 640]
+    observation.images.phone: [3, 480, 640]
+    observation.state: ["${env.state_dim}"]
   output_shapes:
     action: ["${env.action_dim}"]
 
   # Normalization / Unnormalization
   input_normalization_modes:
-    observation.images.cam_right_wrist: mean_std
-    observation.images.cam_left_wrist: mean_std
-    observation.images.cam_high: mean_std
-    observation.images.cam_low: mean_std
+    observation.images.laptop: mean_std
+    observation.images.phone: mean_std
+    observation.state: mean_std
   output_normalization_modes:
     action: mean_std
 
@@ -103,7 +95,7 @@ policy:
   n_vae_encoder_layers: 4
 
   # Inference.
-  temporal_ensemble_coeff: null
+  temporal_ensemble_momentum: null
 
   # Training and loss computation.
   dropout: 0.1

lerobot/configs/policy/act_so100_real.yaml

@@ -0,0 +1,102 @@
+# @package _global_
+
+# Use `act_koch_real.yaml` to train on real-world datasets collected on Alexander Koch's robots.
+# Compared to `act.yaml`, it contains 2 cameras (i.e. laptop, phone) instead of 1 camera (i.e. top).
+# Also, `training.eval_freq` is set to -1. This config is used to evaluate checkpoints at a certain frequency of training steps.
+# When it is set to -1, it deactivates evaluation. This is because real-world evaluation is done through our `control_robot.py` script.
+# Look at the documentation in header of `control_robot.py` for more information on how to collect data , train and evaluate a policy.
+#
+# Example of usage for training:
+# ```bash
+# python lerobot/scripts/train.py \
+#   policy=act_koch_real \
+#   env=koch_real
+# ```
+
+seed: 1000
+dataset_repo_id: lerobot/so100_pick_place_lego
+
+override_dataset_stats:
+  observation.images.laptop:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.phone:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+
+training:
+  offline_steps: 80000
+  online_steps: 0
+  eval_freq: -1
+  save_freq: 10000
+  log_freq: 100
+  save_checkpoint: true
+
+  batch_size: 8
+  lr: 1e-5
+  lr_backbone: 1e-5
+  weight_decay: 1e-4
+  grad_clip_norm: 10
+  online_steps_between_rollouts: 1
+
+  delta_timestamps:
+    action: "[i / ${fps} for i in range(${policy.chunk_size})]"
+
+eval:
+  n_episodes: 50
+  batch_size: 50
+
+# See `configuration_act.py` for more details.
+policy:
+  name: act
+
+  # Input / output structure.
+  n_obs_steps: 1
+  chunk_size: 100
+  n_action_steps: 100
+
+  input_shapes:
+    # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
+    observation.images.laptop: [3, 480, 640]
+    observation.images.phone: [3, 480, 640]
+    observation.state: ["${env.state_dim}"]
+  output_shapes:
+    action: ["${env.action_dim}"]
+
+  # Normalization / Unnormalization
+  input_normalization_modes:
+    observation.images.laptop: mean_std
+    observation.images.phone: mean_std
+    observation.state: mean_std
+  output_normalization_modes:
+    action: mean_std
+
+  # Architecture.
+  # Vision backbone.
+  vision_backbone: resnet18
+  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+  replace_final_stride_with_dilation: false
+  # Transformer layers.
+  pre_norm: false
+  dim_model: 512
+  n_heads: 8
+  dim_feedforward: 3200
+  feedforward_activation: relu
+  n_encoder_layers: 4
+  # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
+  # that means only the first layer is used. Here we match the original implementation by setting this to 1.
+  # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
+  n_decoder_layers: 1
+  # VAE.
+  use_vae: true
+  latent_dim: 32
+  n_vae_encoder_layers: 4
+
+  # Inference.
+  temporal_ensemble_momentum: null
+
+  # Training and loss computation.
+  dropout: 0.1
+  kl_weight: 10.0

lerobot/configs/policy/tdmpc.yaml

@@ -12,6 +12,7 @@ training:
   grad_clip_norm: 10.0
   lr: 3e-4
 
+  save_freq: 10000
   eval_freq: 5000
   log_freq: 100
 

lerobot/configs/robot/aloha.yaml

@@ -0,0 +1,117 @@
+# [Aloha: A Low-Cost Hardware for Bimanual Teleoperation](https://www.trossenrobotics.com/aloha-stationary)
+# https://aloha-2.github.io
+
+# Requires installing extras packages
+# With pip: `pip install -e ".[dynamixel intelrealsense]"`
+# With poetry: `poetry install --sync --extras "dynamixel intelrealsense"`
+
+# See [tutorial](https://github.com/huggingface/lerobot/blob/main/examples/9_use_aloha.md)
+
+
+_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
+robot_type: aloha
+# Specific to Aloha, LeRobot comes with default calibration files. Assuming the motors have been
+# properly assembled, no manual calibration step is expected. If you need to run manual calibration,
+# simply update this path to ".cache/calibration/aloha"
+calibration_dir: .cache/calibration/aloha_default
+
+# /!\ FOR SAFETY, READ THIS /!\
+# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+# the number of motors in your follower arms.
+# For Aloha, for every goal position request, motor rotations are capped at 5 degrees by default.
+# When you feel more confident with teleoperation or running the policy, you can extend
+# this safety limit and even removing it by setting it to `null`.
+# Also, everything is expected to work safely out-of-the-box, but we highly advise to
+# first try to teleoperate the grippers only (by commenting out the rest of the motors in this yaml),
+# then to gradually add more motors (by uncommenting), until you can teleoperate both arms fully
+max_relative_target: 5
+
+leader_arms:
+  left:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/ttyDXL_leader_left
+    motors:  # window_x
+      # name: (index, model)
+      waist: [1, xm430-w350]
+      shoulder: [2, xm430-w350]
+      shoulder_shadow: [3, xm430-w350]
+      elbow: [4, xm430-w350]
+      elbow_shadow: [5, xm430-w350]
+      forearm_roll: [6, xm430-w350]
+      wrist_angle: [7, xm430-w350]
+      wrist_rotate: [8, xl430-w250]
+      gripper: [9, xc430-w150]
+  right:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/ttyDXL_leader_right
+    motors:  # window_x
+      # name: (index, model)
+      waist: [1, xm430-w350]
+      shoulder: [2, xm430-w350]
+      shoulder_shadow: [3, xm430-w350]
+      elbow: [4, xm430-w350]
+      elbow_shadow: [5, xm430-w350]
+      forearm_roll: [6, xm430-w350]
+      wrist_angle: [7, xm430-w350]
+      wrist_rotate: [8, xl430-w250]
+      gripper: [9, xc430-w150]
+
+follower_arms:
+  left:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/ttyDXL_follower_left
+    motors:
+      # name: [index, model]
+      waist: [1, xm540-w270]
+      shoulder: [2, xm540-w270]
+      shoulder_shadow: [3, xm540-w270]
+      elbow: [4, xm540-w270]
+      elbow_shadow: [5, xm540-w270]
+      forearm_roll: [6, xm540-w270]
+      wrist_angle: [7, xm540-w270]
+      wrist_rotate: [8, xm430-w350]
+      gripper: [9, xm430-w350]
+  right:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/ttyDXL_follower_right
+    motors:
+      # name: [index, model]
+      waist: [1, xm540-w270]
+      shoulder: [2, xm540-w270]
+      shoulder_shadow: [3, xm540-w270]
+      elbow: [4, xm540-w270]
+      elbow_shadow: [5, xm540-w270]
+      forearm_roll: [6, xm540-w270]
+      wrist_angle: [7, xm540-w270]
+      wrist_rotate: [8, xm430-w350]
+      gripper: [9, xm430-w350]
+
+# Troubleshooting: If one of your IntelRealSense cameras freeze during
+# data recording due to bandwidth limit, you might need to plug the camera
+# on another USB hub or PCIe card.
+cameras:
+  cam_high:
+    _target_: lerobot.common.robot_devices.cameras.intelrealsense.IntelRealSenseCamera
+    serial_number: 128422271347
+    fps: 30
+    width: 640
+    height: 480
+  cam_low:
+    _target_: lerobot.common.robot_devices.cameras.intelrealsense.IntelRealSenseCamera
+    serial_number: 130322270656
+    fps: 30
+    width: 640
+    height: 480
+  cam_left_wrist:
+    _target_: lerobot.common.robot_devices.cameras.intelrealsense.IntelRealSenseCamera
+    serial_number: 218622272670
+    fps: 30
+    width: 640
+    height: 480
+  cam_right_wrist:
+    _target_: lerobot.common.robot_devices.cameras.intelrealsense.IntelRealSenseCamera
+    serial_number: 130322272300
+    fps: 30
+    width: 640
+    height: 480

lerobot/configs/robot/koch.yaml

@@ -1,5 +1,12 @@
-_target_: lerobot.common.robot_devices.robots.koch.KochRobot
-calibration_path: .cache/calibration/koch.pkl
+_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
+robot_type: koch
+calibration_dir: .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: null
+
 leader_arms:
   main:
     _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
@@ -12,6 +19,7 @@ leader_arms:
       wrist_flex: [4, "xl330-m077"]
       wrist_roll: [5, "xl330-m077"]
       gripper: [6, "xl330-m077"]
+
 follower_arms:
   main:
     _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
@@ -24,6 +32,7 @@ follower_arms:
       wrist_flex: [4, "xl330-m288"]
       wrist_roll: [5, "xl330-m288"]
       gripper: [6, "xl330-m288"]
+
 cameras:
   laptop:
     _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
@@ -37,3 +46,8 @@ cameras:
     fps: 30
     width: 640
     height: 480
+
+# ~ Koch specific settings ~
+# Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
+# to squeeze the gripper and have it spring back to an open position on its own.
+gripper_open_degree: 35.156

lerobot/configs/robot/koch_bimanual.yaml

@@ -0,0 +1,75 @@
+_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
+robot_type: koch_bimanual
+calibration_dir: .cache/calibration/koch_bimanual
+
+# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+# the number of motors in your follower arms.
+max_relative_target: null
+
+leader_arms:
+  left:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/tty.usbmodem585A0085511
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "xl330-m077"]
+      shoulder_lift: [2, "xl330-m077"]
+      elbow_flex: [3, "xl330-m077"]
+      wrist_flex: [4, "xl330-m077"]
+      wrist_roll: [5, "xl330-m077"]
+      gripper: [6, "xl330-m077"]
+  right:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/tty.usbmodem575E0031751
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "xl330-m077"]
+      shoulder_lift: [2, "xl330-m077"]
+      elbow_flex: [3, "xl330-m077"]
+      wrist_flex: [4, "xl330-m077"]
+      wrist_roll: [5, "xl330-m077"]
+      gripper: [6, "xl330-m077"]
+
+follower_arms:
+  left:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/tty.usbmodem585A0076891
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "xl430-w250"]
+      shoulder_lift: [2, "xl430-w250"]
+      elbow_flex: [3, "xl330-m288"]
+      wrist_flex: [4, "xl330-m288"]
+      wrist_roll: [5, "xl330-m288"]
+      gripper: [6, "xl330-m288"]
+  right:
+    _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus
+    port: /dev/tty.usbmodem575E0032081
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "xl430-w250"]
+      shoulder_lift: [2, "xl430-w250"]
+      elbow_flex: [3, "xl330-m288"]
+      wrist_flex: [4, "xl330-m288"]
+      wrist_roll: [5, "xl330-m288"]
+      gripper: [6, "xl330-m288"]
+
+cameras:
+  laptop:
+    _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
+    camera_index: 0
+    fps: 30
+    width: 640
+    height: 480
+  phone:
+    _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
+    camera_index: 1
+    fps: 30
+    width: 640
+    height: 480
+
+# ~ Koch specific settings ~
+# Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
+# to squeeze the gripper and have it spring back to an open position on its own.
+gripper_open_degree: 35.156

lerobot/configs/robot/moss.yaml

@@ -0,0 +1,56 @@
+# [Moss v1 robot arm](https://github.com/jess-moss/moss-robot-arms)
+
+# Requires installing extras packages
+# With pip: `pip install -e ".[feetech]"`
+# With poetry: `poetry install --sync --extras "feetech"`
+
+# See [tutorial](https://github.com/huggingface/lerobot/blob/main/examples/11_use_moss.md)
+
+_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
+robot_type: moss
+calibration_dir: .cache/calibration/moss
+
+# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+# the number of motors in your follower arms.
+max_relative_target: null
+
+leader_arms:
+  main:
+    _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
+    port: /dev/tty.usbmodem58760431091
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "sts3215"]
+      shoulder_lift: [2, "sts3215"]
+      elbow_flex: [3, "sts3215"]
+      wrist_flex: [4, "sts3215"]
+      wrist_roll: [5, "sts3215"]
+      gripper: [6, "sts3215"]
+
+follower_arms:
+  main:
+    _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
+    port: /dev/tty.usbmodem58760431191
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "sts3215"]
+      shoulder_lift: [2, "sts3215"]
+      elbow_flex: [3, "sts3215"]
+      wrist_flex: [4, "sts3215"]
+      wrist_roll: [5, "sts3215"]
+      gripper: [6, "sts3215"]
+
+cameras:
+  laptop:
+    _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
+    camera_index: 0
+    fps: 30
+    width: 640
+    height: 480
+  phone:
+    _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
+    camera_index: 1
+    fps: 30
+    width: 640
+    height: 480

lerobot/configs/robot/so100.yaml

@@ -0,0 +1,56 @@
+# [SO-100 robot arm](https://github.com/TheRobotStudio/SO-ARM100)
+
+# Requires installing extras packages
+# With pip: `pip install -e ".[feetech]"`
+# With poetry: `poetry install --sync --extras "feetech"`
+
+# See [tutorial](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md)
+
+_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot
+robot_type: so100
+calibration_dir: .cache/calibration/so100
+
+# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+# the number of motors in your follower arms.
+max_relative_target: null
+
+leader_arms:
+  main:
+    _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
+    port: /dev/tty.usbmodem585A0077581
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "sts3215"]
+      shoulder_lift: [2, "sts3215"]
+      elbow_flex: [3, "sts3215"]
+      wrist_flex: [4, "sts3215"]
+      wrist_roll: [5, "sts3215"]
+      gripper: [6, "sts3215"]
+
+follower_arms:
+  main:
+    _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus
+    port: /dev/tty.usbmodem585A0080971
+    motors:
+      # name: (index, model)
+      shoulder_pan: [1, "sts3215"]
+      shoulder_lift: [2, "sts3215"]
+      elbow_flex: [3, "sts3215"]
+      wrist_flex: [4, "sts3215"]
+      wrist_roll: [5, "sts3215"]
+      gripper: [6, "sts3215"]
+
+cameras:
+  laptop:
+    _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
+    camera_index: 0
+    fps: 30
+    width: 640
+    height: 480
+  phone:
+    _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
+    camera_index: 1
+    fps: 30
+    width: 640
+    height: 480

lerobot/configs/robot/stretch.yaml

@@ -0,0 +1,33 @@
+# [Stretch3 from Hello Robot](https://hello-robot.com/stretch-3-product)
+
+# Requires installing extras packages
+# With pip: `pip install -e ".[stretch]"`
+# With poetry: `poetry install --sync --extras "stretch"`
+
+# See [tutorial](https://github.com/huggingface/lerobot/blob/main/examples/8_use_stretch.md)
+
+
+_target_: lerobot.common.robot_devices.robots.stretch.StretchRobot
+robot_type: stretch3
+
+cameras:
+  navigation:
+    _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera
+    camera_index: /dev/hello-nav-head-camera
+    fps: 10
+    width: 1280
+    height: 720
+    rotation: -90
+  head:
+    _target_: lerobot.common.robot_devices.cameras.intelrealsense.IntelRealSenseCamera.init_from_name
+    name: Intel RealSense D435I
+    fps: 30
+    width: 640
+    height: 480
+    rotation: 90
+  wrist:
+    _target_: lerobot.common.robot_devices.cameras.intelrealsense.IntelRealSenseCamera.init_from_name
+    name: Intel RealSense D405
+    fps: 30
+    width: 640
+    height: 480

lerobot/scripts/configure_motor.py

@@ -0,0 +1,145 @@
+"""
+This script configure a single motor at a time to a given ID and baudrate.
+
+Example of usage:
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem585A0080521 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 1
+```
+"""
+
+import argparse
+import time
+
+
+def configure_motor(port, brand, model, motor_idx_des, baudrate_des):
+    if brand == "feetech":
+        from lerobot.common.robot_devices.motors.feetech import MODEL_BAUDRATE_TABLE
+        from lerobot.common.robot_devices.motors.feetech import (
+            SCS_SERIES_BAUDRATE_TABLE as SERIES_BAUDRATE_TABLE,
+        )
+        from lerobot.common.robot_devices.motors.feetech import FeetechMotorsBus as MotorsBusClass
+    elif brand == "dynamixel":
+        from lerobot.common.robot_devices.motors.dynamixel import MODEL_BAUDRATE_TABLE
+        from lerobot.common.robot_devices.motors.dynamixel import (
+            X_SERIES_BAUDRATE_TABLE as SERIES_BAUDRATE_TABLE,
+        )
+        from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus as MotorsBusClass
+    else:
+        raise ValueError(
+            f"Currently we do not support this motor brand: {brand}. We currently support feetech and dynamixel motors."
+        )
+
+    # Check if the provided model exists in the model_baud_rate_table
+    if model not in MODEL_BAUDRATE_TABLE:
+        raise ValueError(
+            f"Invalid model '{model}' for brand '{brand}'. Supported models: {list(MODEL_BAUDRATE_TABLE.keys())}"
+        )
+
+    # Setup motor names, indices, and models
+    motor_name = "motor"
+    motor_index_arbitrary = motor_idx_des  # Use the motor ID passed via argument
+    motor_model = model  # Use the motor model passed via argument
+
+    # Initialize the MotorBus with the correct port and motor configurations
+    motor_bus = MotorsBusClass(port=port, motors={motor_name: (motor_index_arbitrary, motor_model)})
+
+    # Try to connect to the motor bus and handle any connection-specific errors
+    try:
+        motor_bus.connect()
+        print(f"Connected on port {motor_bus.port}")
+    except OSError as e:
+        print(f"Error occurred when connecting to the motor bus: {e}")
+        return
+
+    # Motor bus is connected, proceed with the rest of the operations
+    try:
+        print("Scanning all baudrates and motor indices")
+        all_baudrates = set(SERIES_BAUDRATE_TABLE.values())
+        motor_index = -1  # Set the motor index to an out-of-range value.
+
+        for baudrate in all_baudrates:
+            motor_bus.set_bus_baudrate(baudrate)
+            present_ids = motor_bus.find_motor_indices(list(range(1, 10)))
+            if len(present_ids) > 1:
+                raise ValueError(
+                    "Error: More than one motor ID detected. This script is designed to only handle one motor at a time. Please disconnect all but one motor."
+                )
+
+            if len(present_ids) == 1:
+                if motor_index != -1:
+                    raise ValueError(
+                        "Error: More than one motor ID detected. This script is designed to only handle one motor at a time. Please disconnect all but one motor."
+                    )
+                motor_index = present_ids[0]
+
+        if motor_index == -1:
+            raise ValueError("No motors detected. Please ensure you have one motor connected.")
+
+        print(f"Motor index found at: {motor_index}")
+
+        if brand == "feetech":
+            # Allows ID and BAUDRATE to be written in memory
+            motor_bus.write_with_motor_ids(motor_bus.motor_models, motor_index, "Lock", 0)
+
+        if baudrate != baudrate_des:
+            print(f"Setting its baudrate to {baudrate_des}")
+            baudrate_idx = list(SERIES_BAUDRATE_TABLE.values()).index(baudrate_des)
+
+            # The write can fail, so we allow retries
+            motor_bus.write_with_motor_ids(motor_bus.motor_models, motor_index, "Baud_Rate", baudrate_idx)
+            time.sleep(0.5)
+            motor_bus.set_bus_baudrate(baudrate_des)
+            present_baudrate_idx = motor_bus.read_with_motor_ids(
+                motor_bus.motor_models, motor_index, "Baud_Rate", num_retry=2
+            )
+
+            if present_baudrate_idx != baudrate_idx:
+                raise OSError("Failed to write baudrate.")
+
+        print(f"Setting its index to desired index {motor_idx_des}")
+        motor_bus.write_with_motor_ids(motor_bus.motor_models, motor_index, "Lock", 0)
+        motor_bus.write_with_motor_ids(motor_bus.motor_models, motor_index, "ID", motor_idx_des)
+
+        present_idx = motor_bus.read_with_motor_ids(motor_bus.motor_models, motor_idx_des, "ID", num_retry=2)
+        if present_idx != motor_idx_des:
+            raise OSError("Failed to write index.")
+
+        if brand == "feetech":
+            # Set Maximum_Acceleration to 254 to speedup acceleration and deceleration of
+            # the motors. Note: this configuration is not in the official STS3215 Memory Table
+            motor_bus.write("Lock", 0)
+            motor_bus.write("Maximum_Acceleration", 254)
+
+            motor_bus.write("Goal_Position", 2048)
+            time.sleep(4)
+            print("Present Position", motor_bus.read("Present_Position"))
+
+            motor_bus.write("Offset", 0)
+            time.sleep(4)
+            print("Offset", motor_bus.read("Offset"))
+
+    except Exception as e:
+        print(f"Error occurred during motor configuration: {e}")
+
+    finally:
+        motor_bus.disconnect()
+        print("Disconnected from motor bus.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=str, required=True, help="Motors bus port (e.g. dynamixel,feetech)")
+    parser.add_argument("--brand", type=str, required=True, help="Motor brand (e.g. dynamixel,feetech)")
+    parser.add_argument("--model", type=str, required=True, help="Motor model (e.g. xl330-m077,sts3215)")
+    parser.add_argument("--ID", type=int, required=True, help="Desired ID of the current motor (e.g. 1,2,3)")
+    parser.add_argument(
+        "--baudrate", type=int, default=1000000, help="Desired baudrate for the motor (default: 1000000)"
+    )
+    args = parser.parse_args()
+
+    configure_motor(args.port, args.brand, args.model, args.ID, args.baudrate)

lerobot/scripts/control_robot.py

@@ -99,160 +99,76 @@ python lerobot/scripts/control_robot.py record \
 """
 
 import argparse
-import concurrent.futures
-import json
 import logging
-import os
-import platform
-import shutil
 import time
-import traceback
-from contextlib import nullcontext
-from functools import cache
 from pathlib import Path
-
-import cv2
-import torch
-import tqdm
-from omegaconf import DictConfig
-from PIL import Image
-from termcolor import colored
+from typing import List
 
 # from safetensors.torch import load_file, save_file
-from lerobot.common.datasets.compute_stats import compute_stats
-from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
-from lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format import to_hf_dataset
-from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes, get_default_encoding
-from lerobot.common.datasets.utils import calculate_episode_data_index, create_branch
-from lerobot.common.datasets.video_utils import encode_video_frames
-from lerobot.common.policies.factory import make_policy
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.populate_dataset import (
+    create_lerobot_dataset,
+    delete_current_episode,
+    init_dataset,
+    save_current_episode,
+)
+from lerobot.common.robot_devices.control_utils import (
+    control_loop,
+    has_method,
+    init_keyboard_listener,
+    init_policy,
+    log_control_info,
+    record_episode,
+    reset_environment,
+    sanity_check_dataset_name,
+    stop_recording,
+    warmup_record,
+)
 from lerobot.common.robot_devices.robots.factory import make_robot
 from lerobot.common.robot_devices.robots.utils import Robot
-from lerobot.common.utils.utils import get_safe_torch_device, init_hydra_config, init_logging, set_global_seed
-from lerobot.scripts.eval import get_pretrained_policy_path
-from lerobot.scripts.push_dataset_to_hub import (
-    push_dataset_card_to_hub,
-    push_meta_data_to_hub,
-    push_videos_to_hub,
-    save_meta_data,
-)
-
-########################################################################################
-# Utilities
-########################################################################################
-
-
-def say(text, blocking=False):
-    # Check if mac, linux, or windows.
-    if platform.system() == "Darwin":
-        cmd = f'say "{text}"'
-    elif platform.system() == "Linux":
-        cmd = f'spd-say "{text}"'
-    elif platform.system() == "Windows":
-        cmd = (
-            'PowerShell -Command "Add-Type -AssemblyName System.Speech; '
-            f"(New-Object System.Speech.Synthesis.SpeechSynthesizer).Speak('{text}')\""
-        )
-
-    if not blocking and platform.system() in ["Darwin", "Linux"]:
-        # TODO(rcadene): Make it work for Windows
-        # Use the ampersand to run command in the background
-        cmd += " &"
-
-    os.system(cmd)
-
-
-def save_image(img_tensor, key, frame_index, episode_index, videos_dir):
-    img = Image.fromarray(img_tensor.numpy())
-    path = videos_dir / f"{key}_episode_{episode_index:06d}" / f"frame_{frame_index:06d}.png"
-    path.parent.mkdir(parents=True, exist_ok=True)
-    img.save(str(path), quality=100)
-
-
-def busy_wait(seconds):
-    # Significantly more accurate than `time.sleep`, and mendatory for our use case,
-    # but it consumes CPU cycles.
-    # TODO(rcadene): find an alternative: from python 11, time.sleep is precise
-    end_time = time.perf_counter() + seconds
-    while time.perf_counter() < end_time:
-        pass
-
-
-def none_or_int(value):
-    if value == "None":
-        return None
-    return int(value)
-
-
-def log_control_info(robot, dt_s, episode_index=None, frame_index=None, fps=None):
-    log_items = []
-    if episode_index is not None:
-        log_items += [f"ep:{episode_index}"]
-    if frame_index is not None:
-        log_items += [f"frame:{frame_index}"]
-
-    def log_dt(shortname, dt_val_s):
-        nonlocal log_items
-        log_items += [f"{shortname}:{dt_val_s * 1000:5.2f} ({1/ dt_val_s:3.1f}hz)"]
-
-    # total step time displayed in milliseconds and its frequency
-    log_dt("dt", dt_s)
-
-    for name in robot.leader_arms:
-        key = f"read_leader_{name}_pos_dt_s"
-        if key in robot.logs:
-            log_dt("dtRlead", robot.logs[key])
-
-    for name in robot.follower_arms:
-        key = f"write_follower_{name}_goal_pos_dt_s"
-        if key in robot.logs:
-            log_dt("dtWfoll", robot.logs[key])
-
-        key = f"read_follower_{name}_pos_dt_s"
-        if key in robot.logs:
-            log_dt("dtRfoll", robot.logs[key])
-
-    for name in robot.cameras:
-        key = f"read_camera_{name}_dt_s"
-        if key in robot.logs:
-            log_dt(f"dtR{name}", robot.logs[key])
-
-    info_str = " ".join(log_items)
-    if fps is not None:
-        actual_fps = 1 / dt_s
-        if actual_fps < fps - 1:
-            info_str = colored(info_str, "yellow")
-    logging.info(info_str)
-
-
-@cache
-def is_headless():
-    """Detects if python is running without a monitor."""
-    try:
-        import pynput  # noqa
-
-        return False
-    except Exception:
-        print(
-            "Error trying to import pynput. Switching to headless mode. "
-            "As a result, the video stream from the cameras won't be shown, "
-            "and you won't be able to change the control flow with keyboards. "
-            "For more info, see traceback below.\n"
-        )
-        traceback.print_exc()
-        print()
-        return True
-
+from lerobot.common.robot_devices.utils import busy_wait, safe_disconnect
+from lerobot.common.utils.utils import init_hydra_config, init_logging, log_say, none_or_int
 
 ########################################################################################
 # Control modes
 ########################################################################################
 
 
-def calibrate(robot: Robot):
-    if robot.calibration_path.exists():
-        print(f"Removing '{robot.calibration_path}'")
-        robot.calibration_path.unlink()
+@safe_disconnect
+def calibrate(robot: Robot, arms: list[str] | None):
+    # TODO(aliberts): move this code in robots' classes
+    if robot.robot_type.startswith("stretch"):
+        if not robot.is_connected:
+            robot.connect()
+        if not robot.is_homed():
+            robot.home()
+        return
+
+    if arms is None:
+        arms = robot.available_arms
+
+    unknown_arms = [arm_id for arm_id in arms if arm_id not in robot.available_arms]
+    available_arms_str = " ".join(robot.available_arms)
+    unknown_arms_str = " ".join(unknown_arms)
+
+    if arms is None or len(arms) == 0:
+        raise ValueError(
+            "No arm provided. Use `--arms` as argument with one or more available arms.\n"
+            f"For instance, to recalibrate all arms add: `--arms {available_arms_str}`"
+        )
+
+    if len(unknown_arms) > 0:
+        raise ValueError(
+            f"Unknown arms provided ('{unknown_arms_str}'). Available arms are `{available_arms_str}`."
+        )
+
+    for arm_id in arms:
+        arm_calib_path = robot.calibration_dir / f"{arm_id}.json"
+        if arm_calib_path.exists():
+            print(f"Removing '{arm_calib_path}'")
+            arm_calib_path.unlink()
+        else:
+            print(f"Calibration file not found '{arm_calib_path}'")
 
     if robot.is_connected:
         robot.disconnect()
@@ -260,36 +176,31 @@ def calibrate(robot: Robot):
     # Calling `connect` automatically runs calibration
     # when the calibration file is missing
     robot.connect()
+    robot.disconnect()
+    print("Calibration is done! You can now teleoperate and record datasets!")
 
 
-def teleoperate(robot: Robot, fps: int | None = None, teleop_time_s: float | None = None):
-    # TODO(rcadene): Add option to record logs
-    if not robot.is_connected:
-        robot.connect()
-
-    start_teleop_t = time.perf_counter()
-    while True:
-        start_loop_t = time.perf_counter()
-        robot.teleop_step()
-
-        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)
-
-        if teleop_time_s is not None and time.perf_counter() - start_teleop_t > teleop_time_s:
-            break
+@safe_disconnect
+def teleoperate(
+    robot: Robot, fps: int | None = None, teleop_time_s: float | None = None, display_cameras: bool = False
+):
+    control_loop(
+        robot,
+        control_time_s=teleop_time_s,
+        fps=fps,
+        teleoperate=True,
+        display_cameras=display_cameras,
+    )
 
 
+@safe_disconnect
 def record(
     robot: Robot,
-    policy: torch.nn.Module | None = None,
-    hydra_cfg: DictConfig | None = None,
+    root: str,
+    repo_id: str,
+    pretrained_policy_name_or_path: str | None = None,
+    policy_overrides: List[str] | None = None,
     fps: int | None = None,
-    root="data",
-    repo_id="lerobot/debug",
     warmup_time_s=2,
     episode_time_s=10,
     reset_time_s=5,
@@ -298,372 +209,115 @@ def record(
     run_compute_stats=True,
     push_to_hub=True,
     tags=None,
-    num_image_writers=8,
+    num_image_writer_processes=0,
+    num_image_writer_threads_per_camera=4,
     force_override=False,
+    display_cameras=True,
+    play_sounds=True,
 ):
     # TODO(rcadene): Add option to record logs
-    # TODO(rcadene): Clean this function via decomposition in higher level functions
+    listener = None
+    events = None
+    policy = None
+    device = None
+    use_amp = None
 
-    _, dataset_name = repo_id.split("/")
-    if dataset_name.startswith("eval_") and policy is None:
-        raise ValueError(
-            f"Your dataset name begins by 'eval_' ({dataset_name}) but no policy is provided ({policy})."
-        )
+    # Load pretrained policy
+    if pretrained_policy_name_or_path is not None:
+        policy, policy_fps, device, use_amp = init_policy(pretrained_policy_name_or_path, policy_overrides)
 
-    if not video:
-        raise NotImplementedError()
+        if fps is None:
+            fps = policy_fps
+            logging.warning(f"No fps provided, so using the fps from policy config ({policy_fps}).")
+        elif fps != policy_fps:
+            logging.warning(
+                f"There is a mismatch between the provided fps ({fps}) and the one from policy config ({policy_fps})."
+            )
+
+    # Create empty dataset or load existing saved episodes
+    sanity_check_dataset_name(repo_id, policy)
+    dataset = init_dataset(
+        repo_id,
+        root,
+        force_override,
+        fps,
+        video,
+        write_images=robot.has_camera,
+        num_image_writer_processes=num_image_writer_processes,
+        num_image_writer_threads=num_image_writer_threads_per_camera * robot.num_cameras,
+    )
 
     if not robot.is_connected:
         robot.connect()
 
-    local_dir = Path(root) / repo_id
-    if local_dir.exists() and force_override:
-        shutil.rmtree(local_dir)
+    listener, events = init_keyboard_listener()
 
-    episodes_dir = local_dir / "episodes"
-    episodes_dir.mkdir(parents=True, exist_ok=True)
+    # 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", play_sounds)
+    warmup_record(robot, events, enable_teleoperation, warmup_time_s, display_cameras, fps)
 
-    videos_dir = local_dir / "videos"
-    videos_dir.mkdir(parents=True, exist_ok=True)
+    if has_method(robot, "teleop_safety_stop"):
+        robot.teleop_safety_stop()
 
-    # Logic to resume data recording
-    rec_info_path = episodes_dir / "data_recording_info.json"
-    if rec_info_path.exists():
-        with open(rec_info_path) as f:
-            rec_info = json.load(f)
-        episode_index = rec_info["last_episode_index"] + 1
-    else:
-        episode_index = 0
+    while True:
+        if dataset["num_episodes"] >= num_episodes:
+            break
 
-    if is_headless():
-        logging.info(
-            "Headless environment detected. On-screen cameras display and keyboard inputs will not be available."
+        episode_index = dataset["num_episodes"]
+        log_say(f"Recording episode {episode_index}", play_sounds)
+        record_episode(
+            dataset=dataset,
+            robot=robot,
+            events=events,
+            episode_time_s=episode_time_s,
+            display_cameras=display_cameras,
+            policy=policy,
+            device=device,
+            use_amp=use_amp,
+            fps=fps,
         )
 
-    # Allow to exit early while recording an episode or resetting the environment,
-    # by tapping the right arrow key '->'. This might require a sudo permission
-    # to allow your terminal to monitor keyboard events.
-    exit_early = False
-    rerecord_episode = False
-    stop_recording = False
+        # Execute a few seconds without recording to give time to manually reset the environment
+        # Current code logic doesn't allow to teleoperate during this time.
+        # TODO(rcadene): add an option to enable teleoperation during reset
+        # Skip reset for the last episode to be recorded
+        if not events["stop_recording"] and (
+            (episode_index < num_episodes - 1) or events["rerecord_episode"]
+        ):
+            log_say("Reset the environment", play_sounds)
+            reset_environment(robot, events, reset_time_s)
 
-    # Only import pynput if not in a headless environment
-    if not is_headless():
-        from pynput import keyboard
+        if events["rerecord_episode"]:
+            log_say("Re-record episode", play_sounds)
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            delete_current_episode(dataset)
+            continue
 
-        def on_press(key):
-            nonlocal exit_early, rerecord_episode, stop_recording
-            try:
-                if key == keyboard.Key.right:
-                    print("Right arrow key pressed. Exiting loop...")
-                    exit_early = True
-                elif key == keyboard.Key.left:
-                    print("Left arrow key pressed. Exiting loop and rerecord the last episode...")
-                    rerecord_episode = True
-                    exit_early = True
-                elif key == keyboard.Key.esc:
-                    print("Escape key pressed. Stopping data recording...")
-                    stop_recording = True
-                    exit_early = True
-            except Exception as e:
-                print(f"Error handling key press: {e}")
+        # Increment by one dataset["current_episode_index"]
+        save_current_episode(dataset)
 
-        listener = keyboard.Listener(on_press=on_press)
-        listener.start()
+        if events["stop_recording"]:
+            break
 
-    # Load policy if any
-    if policy is not None:
-        # Check device is available
-        device = get_safe_torch_device(hydra_cfg.device, log=True)
+    log_say("Stop recording", play_sounds, blocking=True)
+    stop_recording(robot, listener, display_cameras)
 
-        policy.eval()
-        policy.to(device)
+    lerobot_dataset = create_lerobot_dataset(dataset, run_compute_stats, push_to_hub, tags, play_sounds)
 
-        torch.backends.cudnn.benchmark = True
-        torch.backends.cuda.matmul.allow_tf32 = True
-        set_global_seed(hydra_cfg.seed)
-
-        # override fps using policy fps
-        fps = hydra_cfg.env.fps
-
-    # Execute a few seconds without recording data, to give times
-    # to the robot devices to connect and start synchronizing.
-    timestamp = 0
-    start_warmup_t = time.perf_counter()
-    is_warmup_print = False
-    while timestamp < warmup_time_s:
-        if not is_warmup_print:
-            logging.info("Warming up (no data recording)")
-            say("Warming up")
-            is_warmup_print = True
-
-        start_loop_t = time.perf_counter()
-
-        if policy is None:
-            observation, action = robot.teleop_step(record_data=True)
-        else:
-            observation = robot.capture_observation()
-
-        if not is_headless():
-            image_keys = [key for key in observation if "image" in key]
-            for key in image_keys:
-                cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR))
-            cv2.waitKey(1)
-
-        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)
-
-        timestamp = time.perf_counter() - start_warmup_t
-
-    # Save images using threads to reach high fps (30 and more)
-    # Using `with` to exist smoothly if an execption is raised.
-    # Using only 4 worker threads to avoid blocking the main thread.
-    futures = []
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_image_writers) as executor:
-        # Start recording all episodes
-        while episode_index < num_episodes:
-            logging.info(f"Recording episode {episode_index}")
-            say(f"Recording episode {episode_index}")
-            ep_dict = {}
-            frame_index = 0
-            timestamp = 0
-            start_episode_t = time.perf_counter()
-            while timestamp < episode_time_s:
-                start_loop_t = time.perf_counter()
-
-                if policy is None:
-                    observation, action = robot.teleop_step(record_data=True)
-                else:
-                    observation = robot.capture_observation()
-
-                image_keys = [key for key in observation if "image" in key]
-                not_image_keys = [key for key in observation if "image" not in key]
-
-                for key in image_keys:
-                    futures += [
-                        executor.submit(
-                            save_image, observation[key], key, frame_index, episode_index, videos_dir
-                        )
-                    ]
-
-                if not is_headless():
-                    image_keys = [key for key in observation if "image" in key]
-                    for key in image_keys:
-                        cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR))
-                    cv2.waitKey(1)
-
-                for key in not_image_keys:
-                    if key not in ep_dict:
-                        ep_dict[key] = []
-                    ep_dict[key].append(observation[key])
-
-                if policy is not None:
-                    with (
-                        torch.inference_mode(),
-                        torch.autocast(device_type=device.type)
-                        if device.type == "cuda" and hydra_cfg.use_amp
-                        else nullcontext(),
-                    ):
-                        # 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)
-                    action = {"action": action}
-
-                for key in action:
-                    if key not in ep_dict:
-                        ep_dict[key] = []
-                    ep_dict[key].append(action[key])
-
-                frame_index += 1
-
-                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)
-
-                timestamp = time.perf_counter() - start_episode_t
-                if exit_early:
-                    exit_early = False
-                    break
-
-            if not stop_recording:
-                # Start resetting env while the executor are finishing
-                logging.info("Reset the environment")
-                say("Reset the environment")
-
-            timestamp = 0
-            start_vencod_t = time.perf_counter()
-
-            # During env reset we save the data and encode the videos
-            num_frames = frame_index
-
-            for key in image_keys:
-                tmp_imgs_dir = videos_dir / f"{key}_episode_{episode_index:06d}"
-                fname = f"{key}_episode_{episode_index:06d}.mp4"
-                video_path = local_dir / "videos" / fname
-                if video_path.exists():
-                    video_path.unlink()
-                # Store the reference to the video frame, even tho the videos are not yet encoded
-                ep_dict[key] = []
-                for i in range(num_frames):
-                    ep_dict[key].append({"path": f"videos/{fname}", "timestamp": i / fps})
-
-            for key in not_image_keys:
-                ep_dict[key] = torch.stack(ep_dict[key])
-
-            for key in action:
-                ep_dict[key] = torch.stack(ep_dict[key])
-
-            ep_dict["episode_index"] = torch.tensor([episode_index] * num_frames)
-            ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
-            ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
-
-            done = torch.zeros(num_frames, dtype=torch.bool)
-            done[-1] = True
-            ep_dict["next.done"] = done
-
-            ep_path = episodes_dir / f"episode_{episode_index}.pth"
-            print("Saving episode dictionary...")
-            torch.save(ep_dict, ep_path)
-
-            rec_info = {
-                "last_episode_index": episode_index,
-            }
-            with open(rec_info_path, "w") as f:
-                json.dump(rec_info, f)
-
-            is_last_episode = stop_recording or (episode_index == (num_episodes - 1))
-
-            # Wait if necessary
-            with tqdm.tqdm(total=reset_time_s, desc="Waiting") as pbar:
-                while timestamp < reset_time_s and not is_last_episode:
-                    time.sleep(1)
-                    timestamp = time.perf_counter() - start_vencod_t
-                    pbar.update(1)
-                    if exit_early:
-                        exit_early = False
-                        break
-
-            # Skip updating episode index which forces re-recording episode
-            if rerecord_episode:
-                rerecord_episode = False
-                continue
-
-            episode_index += 1
-
-            if is_last_episode:
-                logging.info("Done recording")
-                say("Done recording", blocking=True)
-                if not is_headless():
-                    listener.stop()
-
-                logging.info("Waiting for threads writing the images on disk to terminate...")
-                for _ in tqdm.tqdm(
-                    concurrent.futures.as_completed(futures), total=len(futures), desc="Writting images"
-                ):
-                    pass
-                break
-
-    robot.disconnect()
-    if not is_headless():
-        cv2.destroyAllWindows()
-
-    num_episodes = episode_index
-
-    logging.info("Encoding videos")
-    say("Encoding videos")
-    # Use ffmpeg to convert frames stored as png into mp4 videos
-    for episode_index in tqdm.tqdm(range(num_episodes)):
-        for key in image_keys:
-            tmp_imgs_dir = videos_dir / f"{key}_episode_{episode_index:06d}"
-            fname = f"{key}_episode_{episode_index:06d}.mp4"
-            video_path = local_dir / "videos" / fname
-            if video_path.exists():
-                # Skip if video is already encoded. Could be the case when resuming data recording.
-                continue
-            # note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
-            # since video encoding with ffmpeg is already using multithreading.
-            encode_video_frames(tmp_imgs_dir, video_path, fps, overwrite=True)
-            shutil.rmtree(tmp_imgs_dir)
-
-    logging.info("Concatenating episodes")
-    ep_dicts = []
-    for episode_index in tqdm.tqdm(range(num_episodes)):
-        ep_path = episodes_dir / f"episode_{episode_index}.pth"
-        ep_dict = torch.load(ep_path)
-        ep_dicts.append(ep_dict)
-    data_dict = concatenate_episodes(ep_dicts)
-
-    total_frames = data_dict["frame_index"].shape[0]
-    data_dict["index"] = torch.arange(0, total_frames, 1)
-
-    hf_dataset = to_hf_dataset(data_dict, video)
-    episode_data_index = calculate_episode_data_index(hf_dataset)
-    info = {
-        "codebase_version": CODEBASE_VERSION,
-        "fps": fps,
-        "video": video,
-    }
-    if video:
-        info["encoding"] = get_default_encoding()
-
-    lerobot_dataset = LeRobotDataset.from_preloaded(
-        repo_id=repo_id,
-        hf_dataset=hf_dataset,
-        episode_data_index=episode_data_index,
-        info=info,
-        videos_dir=videos_dir,
-    )
-    if run_compute_stats:
-        logging.info("Computing dataset statistics")
-        say("Computing dataset statistics")
-        stats = compute_stats(lerobot_dataset)
-        lerobot_dataset.stats = stats
-    else:
-        stats = {}
-        logging.info("Skipping computation of the dataset statistics")
-
-    hf_dataset = hf_dataset.with_format(None)  # to remove transforms that cant be saved
-    hf_dataset.save_to_disk(str(local_dir / "train"))
-
-    meta_data_dir = local_dir / "meta_data"
-    save_meta_data(info, stats, episode_data_index, meta_data_dir)
-
-    if push_to_hub:
-        hf_dataset.push_to_hub(repo_id, revision="main")
-        push_meta_data_to_hub(repo_id, meta_data_dir, revision="main")
-        push_dataset_card_to_hub(repo_id, revision="main", tags=tags)
-        if video:
-            push_videos_to_hub(repo_id, videos_dir, revision="main")
-        create_branch(repo_id, repo_type="dataset", branch=CODEBASE_VERSION)
-
-    logging.info("Exiting")
-    say("Exiting")
+    log_say("Exiting", play_sounds)
     return lerobot_dataset
 
 
-def replay(robot: Robot, episode: int, fps: int | None = None, root="data", repo_id="lerobot/debug"):
+@safe_disconnect
+def replay(
+    robot: Robot, episode: int, fps: int | None = None, root="data", repo_id="lerobot/debug", play_sounds=True
+):
+    # TODO(rcadene, aliberts): refactor with control_loop, once `dataset` is an instance of LeRobotDataset
     # TODO(rcadene): Add option to record logs
     local_dir = Path(root) / repo_id
     if not local_dir.exists():
@@ -677,8 +331,7 @@ def replay(robot: Robot, episode: int, fps: int | None = None, root="data", repo
     if not robot.is_connected:
         robot.connect()
 
-    logging.info("Replaying episode")
-    say("Replaying episode", blocking=True)
+    log_say("Replaying episode", play_sounds, blocking=True)
     for idx in range(from_idx, to_idx):
         start_episode_t = time.perf_counter()
 
@@ -712,11 +365,23 @@ if __name__ == "__main__":
     )
 
     parser_calib = subparsers.add_parser("calibrate", parents=[base_parser])
+    parser_calib.add_argument(
+        "--arms",
+        type=str,
+        nargs="*",
+        help="List of arms to calibrate (e.g. `--arms left_follower right_follower left_leader`)",
+    )
 
     parser_teleop = subparsers.add_parser("teleoperate", parents=[base_parser])
     parser_teleop.add_argument(
         "--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
     )
+    parser_teleop.add_argument(
+        "--display-cameras",
+        type=int,
+        default=1,
+        help="Display all cameras on screen (set to 1 to display or 0).",
+    )
 
     parser_record = subparsers.add_parser("record", parents=[base_parser])
     parser_record.add_argument(
@@ -772,10 +437,25 @@ if __name__ == "__main__":
         help="Add tags to your dataset on the hub.",
     )
     parser_record.add_argument(
-        "--num-image-writers",
+        "--num-image-writer-processes",
         type=int,
-        default=8,
-        help="Number of threads writing the frames as png images on disk. Don't set too much as you might get unstable fps due to main thread being blocked.",
+        default=0,
+        help=(
+            "Number of subprocesses handling the saving of frames as PNGs. Set to 0 to use threads only; "
+            "set to ≥1 to use subprocesses, each using threads to write images. The best number of processes "
+            "and threads depends on your system. We recommend 4 threads per camera with 0 processes. "
+            "If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses."
+        ),
+    )
+    parser_record.add_argument(
+        "--num-image-writer-threads-per-camera",
+        type=int,
+        default=4,
+        help=(
+            "Number of threads writing the frames as png images on disk, per camera. "
+            "Too many threads might cause unstable teleoperation fps due to main thread being blocked. "
+            "Not enough threads might cause low camera fps."
+        ),
     )
     parser_record.add_argument(
         "--force-override",
@@ -839,19 +519,7 @@ if __name__ == "__main__":
         teleoperate(robot, **kwargs)
 
     elif control_mode == "record":
-        pretrained_policy_name_or_path = args.pretrained_policy_name_or_path
-        policy_overrides = args.policy_overrides
-        del kwargs["pretrained_policy_name_or_path"]
-        del kwargs["policy_overrides"]
-
-        policy_cfg = None
-        if pretrained_policy_name_or_path is not None:
-            pretrained_policy_path = get_pretrained_policy_path(pretrained_policy_name_or_path)
-            policy_cfg = init_hydra_config(pretrained_policy_path / "config.yaml", policy_overrides)
-            policy = make_policy(hydra_cfg=policy_cfg, pretrained_policy_name_or_path=pretrained_policy_path)
-            record(robot, policy, policy_cfg, **kwargs)
-        else:
-            record(robot, **kwargs)
+        record(robot, **kwargs)
 
     elif control_mode == "replay":
         replay(robot, **kwargs)

lerobot/scripts/eval.py

@@ -57,7 +57,7 @@ import gymnasium as gym
 import numpy as np
 import torch
 from huggingface_hub import snapshot_download
-from huggingface_hub.utils._errors import RepositoryNotFoundError
+from huggingface_hub.errors import RepositoryNotFoundError
 from huggingface_hub.utils._validators import HFValidationError
 from torch import Tensor, nn
 from tqdm import trange
@@ -70,7 +70,13 @@ from lerobot.common.policies.factory import make_policy
 from lerobot.common.policies.policy_protocol import Policy
 from lerobot.common.policies.utils import get_device_from_parameters
 from lerobot.common.utils.io_utils import write_video
-from lerobot.common.utils.utils import get_safe_torch_device, init_hydra_config, init_logging, set_global_seed
+from lerobot.common.utils.utils import (
+    get_safe_torch_device,
+    init_hydra_config,
+    init_logging,
+    inside_slurm,
+    set_global_seed,
+)
 
 
 def rollout(
@@ -79,7 +85,6 @@ def rollout(
     seeds: list[int] | None = None,
     return_observations: bool = False,
     render_callback: Callable[[gym.vector.VectorEnv], None] | None = None,
-    enable_progbar: bool = False,
 ) -> dict:
     """Run a batched policy rollout once through a batch of environments.
 
@@ -109,7 +114,6 @@ def rollout(
             are returned optionally because they typically take more memory to cache. Defaults to False.
         render_callback: Optional rendering callback to be used after the environments are reset, and after
             every step.
-        enable_progbar: Enable a progress bar over rollout steps.
     Returns:
         The dictionary described above.
     """
@@ -136,7 +140,7 @@ def rollout(
     progbar = trange(
         max_steps,
         desc=f"Running rollout with at most {max_steps} steps",
-        disable=not enable_progbar,
+        disable=inside_slurm(),  # we dont want progress bar when we use slurm, since it clutters the logs
         leave=False,
     )
     while not np.all(done):
@@ -210,8 +214,6 @@ def eval_policy(
     videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
-    enable_progbar: bool = False,
-    enable_inner_progbar: bool = False,
 ) -> dict:
     """
     Args:
@@ -224,8 +226,6 @@ def eval_policy(
             the "episodes" key of the returned dictionary.
         start_seed: The first seed to use for the first individual rollout. For all subsequent rollouts the
             seed is incremented by 1. If not provided, the environments are not manually seeded.
-        enable_progbar: Enable progress bar over batches.
-        enable_inner_progbar: Enable progress bar over steps in each batch.
     Returns:
         Dictionary with metrics and data regarding the rollouts.
     """
@@ -266,7 +266,8 @@ def eval_policy(
     if return_episode_data:
         episode_data: dict | None = None
 
-    progbar = trange(n_batches, desc="Stepping through eval batches", disable=not enable_progbar)
+    # we dont want progress bar when we use slurm, since it clutters the logs
+    progbar = trange(n_batches, desc="Stepping through eval batches", disable=inside_slurm())
     for batch_ix in progbar:
         # Cache frames for rendering videos. Each item will be (b, h, w, c), and the list indexes the rollout
         # step.
@@ -285,7 +286,6 @@ def eval_policy(
             seeds=list(seeds) if seeds else None,
             return_observations=return_episode_data,
             render_callback=render_frame if max_episodes_rendered > 0 else None,
-            enable_progbar=enable_inner_progbar,
         )
 
         # Figure out where in each rollout sequence the first done condition was encountered (results after
@@ -454,6 +454,16 @@ def main(
     else:
         hydra_cfg = init_hydra_config(hydra_cfg_path, config_overrides)
 
+    if hydra_cfg.eval.batch_size > hydra_cfg.eval.n_episodes:
+        raise ValueError(
+            "The eval batch size is greater than the number of eval episodes "
+            f"({hydra_cfg.eval.batch_size} > {hydra_cfg.eval.n_episodes}). As a result, {hydra_cfg.eval.batch_size} "
+            f"eval environments will be instantiated, but only {hydra_cfg.eval.n_episodes} will be used. "
+            "This might significantly slow down evaluation. To fix this, you should update your command "
+            f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={hydra_cfg.eval.batch_size}`), "
+            f"or lower the batch size (e.g. `eval.batch_size={hydra_cfg.eval.n_episodes}`)."
+        )
+
     if out_dir is None:
         out_dir = f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{hydra_cfg.env.name}_{hydra_cfg.policy.name}"
 
@@ -487,8 +497,6 @@ def main(
             max_episodes_rendered=10,
             videos_dir=Path(out_dir) / "videos",
             start_seed=hydra_cfg.seed,
-            enable_progbar=True,
-            enable_inner_progbar=True,
         )
     print(info["aggregated"])
 

lerobot/scripts/find_motors_bus_port.py

@@ -0,0 +1,36 @@
+import time
+from pathlib import Path
+
+
+def find_available_ports():
+    ports = []
+    for path in Path("/dev").glob("tty*"):
+        ports.append(str(path))
+    return ports
+
+
+def find_port():
+    print("Finding all available ports for the MotorsBus.")
+    ports_before = find_available_ports()
+    print(ports_before)
+
+    print("Remove the usb cable from your MotorsBus and press Enter when done.")
+    input()
+
+    time.sleep(0.5)
+    ports_after = find_available_ports()
+    ports_diff = list(set(ports_before) - set(ports_after))
+
+    if len(ports_diff) == 1:
+        port = ports_diff[0]
+        print(f"The port of this MotorsBus is '{port}'")
+        print("Reconnect the usb cable.")
+    elif len(ports_diff) == 0:
+        raise OSError(f"Could not detect the port. No difference was found ({ports_diff}).")
+    else:
+        raise OSError(f"Could not detect the port. More than one port was found ({ports_diff}).")
+
+
+if __name__ == "__main__":
+    # Helper to find the usb port associated to all your MotorsBus.
+    find_port()

lerobot/scripts/push_dataset_to_hub.py

@@ -66,6 +66,8 @@ def get_from_raw_to_lerobot_format_fn(raw_format: str):
         from lerobot.common.datasets.push_dataset_to_hub.umi_zarr_format import from_raw_to_lerobot_format
     elif raw_format == "aloha_hdf5":
         from lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format import from_raw_to_lerobot_format
+    elif "openx_rlds" in raw_format:
+        from lerobot.common.datasets.push_dataset_to_hub.openx_rlds_format import from_raw_to_lerobot_format
     elif raw_format == "dora_parquet":
         from lerobot.common.datasets.push_dataset_to_hub.dora_parquet_format import from_raw_to_lerobot_format
     elif raw_format == "xarm_pkl":
@@ -197,9 +199,25 @@ def push_dataset_to_hub(
 
     # convert dataset from original raw format to LeRobot format
     from_raw_to_lerobot_format = get_from_raw_to_lerobot_format_fn(raw_format)
-    hf_dataset, episode_data_index, info = from_raw_to_lerobot_format(
-        raw_dir, videos_dir, fps, video, episodes, encoding
-    )
+
+    fmt_kwgs = {
+        "raw_dir": raw_dir,
+        "videos_dir": videos_dir,
+        "fps": fps,
+        "video": video,
+        "episodes": episodes,
+        "encoding": encoding,
+    }
+
+    if "openx_rlds." in raw_format:
+        # Support for official OXE dataset name inside `raw_format`.
+        # For instance, `raw_format="oxe_rlds"` uses the default formating (TODO what does that mean?),
+        # and `raw_format="oxe_rlds.bridge_orig"` uses the brdige_orig formating
+        _, openx_dataset_name = raw_format.split(".")
+        print(f"Converting dataset [{openx_dataset_name}] from 'openx_rlds' to LeRobot format.")
+        fmt_kwgs["openx_dataset_name"] = openx_dataset_name
+
+    hf_dataset, episode_data_index, info = from_raw_to_lerobot_format(**fmt_kwgs)
 
     lerobot_dataset = LeRobotDataset.from_preloaded(
         repo_id=repo_id,
@@ -268,7 +286,7 @@ def main():
         "--raw-format",
         type=str,
         required=True,
-        help="Dataset type (e.g. `pusht_zarr`, `umi_zarr`, `aloha_hdf5`, `xarm_pkl`, `dora_parquet`).",
+        help="Dataset type (e.g. `pusht_zarr`, `umi_zarr`, `aloha_hdf5`, `xarm_pkl`, `dora_parquet`, `openx_rlds`).",
     )
     parser.add_argument(
         "--repo-id",
@@ -328,6 +346,13 @@ def main():
         default=0,
         help="When set to 1, resumes a previous run.",
     )
+    parser.add_argument(
+        "--cache-dir",
+        type=Path,
+        required=False,
+        default="/tmp",
+        help="Directory to store the temporary videos and images generated while creating the dataset.",
+    )
     parser.add_argument(
         "--tests-data-dir",
         type=Path,

lerobot/scripts/train.py

@@ -93,6 +93,18 @@ def make_optimizer_and_scheduler(cfg, policy):
     elif policy.name == "tdmpc":
         optimizer = torch.optim.Adam(policy.parameters(), cfg.training.lr)
         lr_scheduler = None
+
+    elif policy.name == "tdmpc2":
+        params_group = [
+            {"params": policy.model._encoder.parameters(), "lr": cfg.training.lr * cfg.training.enc_lr_scale},
+            {"params": policy.model._dynamics.parameters()},
+            {"params": policy.model._reward.parameters()},
+            {"params": policy.model._Qs.parameters()},
+            {"params": policy.model._pi.parameters(), "eps": 1e-5},
+        ]
+        optimizer = torch.optim.Adam(params_group, lr=cfg.training.lr)
+        lr_scheduler = None
+
     elif cfg.policy.name == "vqbet":
         from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTOptimizer, VQBeTScheduler
 
@@ -241,6 +253,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
         raise NotImplementedError()
 
     init_logging()
+    logging.info(pformat(OmegaConf.to_container(cfg)))
 
     if cfg.training.online_steps > 0 and isinstance(cfg.dataset_repo_id, ListConfig):
         raise NotImplementedError("Online training with LeRobotMultiDataset is not implemented.")
@@ -287,6 +300,16 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             "you meant to resume training, please use `resume=true` in your command or yaml configuration."
         )
 
+    if cfg.eval.batch_size > cfg.eval.n_episodes:
+        raise ValueError(
+            "The eval batch size is greater than the number of eval episodes "
+            f"({cfg.eval.batch_size} > {cfg.eval.n_episodes}). As a result, {cfg.eval.batch_size} "
+            f"eval environments will be instantiated, but only {cfg.eval.n_episodes} will be used. "
+            "This might significantly slow down evaluation. To fix this, you should update your command "
+            f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={cfg.eval.batch_size}`), "
+            f"or lower the batch size (e.g. `eval.batch_size={cfg.eval.n_episodes}`)."
+        )
+
     # log metrics to terminal and wandb
     logger = Logger(cfg, out_dir, wandb_job_name=job_name)
 
@@ -372,7 +395,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             logging.info(f"Checkpoint policy after step {step}")
             # Note: Save with step as the identifier, and format it to have at least 6 digits but more if
             # needed (choose 6 as a minimum for consistency without being overkill).
-            logger.save_checkpont(
+            logger.save_checkpoint(
                 step,
                 policy,
                 optimizer,

lerobot/scripts/visualize_dataset_html.py

@@ -57,7 +57,6 @@ import logging
 import shutil
 from pathlib import Path
 
-import torch
 import tqdm
 from flask import Flask, redirect, render_template, url_for
 
@@ -65,19 +64,6 @@ from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.utils.utils import init_logging
 
 
-class EpisodeSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, episode_index):
-        from_idx = dataset.episode_data_index["from"][episode_index].item()
-        to_idx = dataset.episode_data_index["to"][episode_index].item()
-        self.frame_ids = range(from_idx, to_idx)
-
-    def __iter__(self):
-        return iter(self.frame_ids)
-
-    def __len__(self):
-        return len(self.frame_ids)
-
-
 def run_server(
     dataset: LeRobotDataset,
     episodes: list[int],
@@ -112,10 +98,14 @@ def run_server(
             "fps": dataset.fps,
         }
         video_paths = get_episode_video_paths(dataset, episode_id)
+        language_instruction = get_episode_language_instruction(dataset, episode_id)
         videos_info = [
             {"url": url_for("static", filename=video_path), "filename": Path(video_path).name}
             for video_path in video_paths
         ]
+        if language_instruction:
+            videos_info[0]["language_instruction"] = language_instruction
+
         ep_csv_url = url_for("static", filename=get_ep_csv_fname(episode_id))
         return render_template(
             "visualize_dataset_template.html",
@@ -186,6 +176,20 @@ def get_episode_video_paths(dataset: LeRobotDataset, ep_index: int) -> list[str]
     ]
 
 
+def get_episode_language_instruction(dataset: LeRobotDataset, ep_index: int) -> list[str]:
+    # check if the dataset has language instructions
+    if "language_instruction" not in dataset.hf_dataset.features:
+        return None
+
+    # get first frame index
+    first_frame_idx = dataset.episode_data_index["from"][ep_index].item()
+
+    language_instruction = dataset.hf_dataset[first_frame_idx]["language_instruction"]
+    # TODO (michel-aractingi) hack to get the sentence, some strings in openx are badly stored
+    # with the tf.tensor appearing in the string
+    return language_instruction.removeprefix("tf.Tensor(b'").removesuffix("', shape=(), dtype=string)")
+
+
 def visualize_dataset_html(
     repo_id: str,
     root: Path | None = None,

lerobot/templates/visualize_dataset_template.html

@@ -14,7 +14,7 @@
 <!-- Use [Alpin.js](https://alpinejs.dev), a lightweight and easy to learn JS framework -->
 <!-- Use [tailwindcss](https://tailwindcss.com/), CSS classes for styling html -->
 <!-- Use [dygraphs](https://dygraphs.com/), a lightweight JS charting library -->
-<body class="flex h-screen max-h-screen bg-slate-950 text-gray-200" x-data="createAlpineData()" @keydown.window="(e) => {
+<body class="flex flex-col md:flex-row h-screen max-h-screen bg-slate-950 text-gray-200" x-data="createAlpineData()" @keydown.window="(e) => {
     // Use the space bar to play and pause, instead of default action (e.g. scrolling)
     const { keyCode, key } = e;
     if (keyCode === 32 || key === ' ') {
@@ -30,7 +30,7 @@
     }
 }">
     <!-- Sidebar -->
-    <div x-ref="sidebar" class="w-60 bg-slate-900 p-5 break-words max-h-screen overflow-y-auto">
+    <div x-ref="sidebar" class="bg-slate-900 p-5 break-words overflow-y-auto shrink-0 md:shrink md:w-60 md:max-h-screen">
         <h1 class="mb-4 text-xl font-semibold">{{ dataset_info.repo_id }}</h1>
 
         <ul>
@@ -46,7 +46,8 @@
         </ul>
 
         <p>Episodes:</p>
-        <ul class="ml-2">
+        <!-- episodes menu for medium & large screens -->
+        <ul class="ml-2 hidden md:block">
             {% for episode in episodes %}
             <li class="font-mono text-sm mt-0.5">
                 <a href="episode_{{ episode }}" class="underline {% if episode_id == episode %}font-bold -ml-1{% endif %}">
@@ -56,26 +57,47 @@
             {% endfor %}
         </ul>
 
+        <!-- episodes menu for small screens -->
+        <div class="flex overflow-x-auto md:hidden">
+            {% for episode in episodes %}
+            <p class="font-mono text-sm mt-0.5 border-r last:border-r-0 px-2 {% if episode_id == episode %}font-bold{% endif %}">
+                <a href="episode_{{ episode }}" class="">
+                    {{ episode }}
+                </a>
+            </p>
+            {% endfor %}
+        </div>
+
     </div>
 
     <!-- Toggle sidebar button -->
-    <button class="flex items-center opacity-50 hover:opacity-100 mx-1"
+    <button class="flex items-center opacity-50 hover:opacity-100 mx-1 hidden md:block"
         @click="() => ($refs.sidebar.classList.toggle('hidden'))" title="Toggle sidebar">
         <div class="bg-slate-500 w-2 h-10 rounded-full"></div>
     </button>
 
     <!-- Content -->
-    <div class="flex-1 max-h-screen flex flex-col gap-4 overflow-y-auto">
+    <div class="max-h-screen flex flex-col gap-4 overflow-y-auto md:flex-1">
         <h1 class="text-xl font-bold mt-4 font-mono">
             Episode {{ episode_id }}
         </h1>
 
+        <!-- Error message -->
+        <div class="font-medium text-orange-700 hidden" :class="{ 'hidden': !videoCodecError }">
+            <p>Videos could NOT play because <a href="https://en.wikipedia.org/wiki/AV1" target="_blank" class="underline">AV1</a> decoding is not available on your browser.</p>
+            <ul class="list-decimal list-inside">
+                <li>If iPhone: <span class="italic">It is supported with A17 chip or higher.</span></li>
+                <li>If Mac with Safari: <span class="italic">It is supported on most browsers except Safari with M1 chip or higher and on Safari with M3 chip or higher.</span></li>
+                <li>Other: <span class="italic">Contact the maintainers on LeRobot discord channel:</span> <a href="https://discord.com/invite/s3KuuzsPFb" target="_blank" class="underline">https://discord.com/invite/s3KuuzsPFb</a></li>
+            </ul>
+        </div>
+
         <!-- Videos -->
         <div class="flex flex-wrap gap-1">
             {% for video_info in videos_info %}
-            <div class="max-w-96">
+            <div x-show="!videoCodecError" class="max-w-96">
                 <p class="text-sm text-gray-300 bg-gray-800 px-2 rounded-t-xl truncate">{{ video_info.filename }}</p>
-                <video autoplay muted loop type="video/mp4" class="min-w-64" @timeupdate="() => {
+                <video muted loop type="video/mp4" class="object-contain w-full h-full" @canplaythrough="videoCanPlay" @timeupdate="() => {
                     if (video.duration) {
                       const time = video.currentTime;
                       const pc = (100 / video.duration) * time;
@@ -100,6 +122,13 @@
             {% endfor %}
         </div>
 
+        <!-- Language instruction -->
+        {% if videos_info[0].language_instruction %}
+        <p class="font-medium mt-2">
+            Language Instruction: <span class="italic">{{ videos_info[0].language_instruction }}</span>
+        </p>
+        {% endif %}
+
         <!-- Shortcuts info -->
         <div class="text-sm hidden md:block">
             Hotkeys: <span class="font-mono">Space</span> to pause/unpause, <span class="font-mono">Arrow Down</span> to go to next episode, <span class="font-mono">Arrow Up</span> to go to previous episode.
@@ -107,12 +136,12 @@
 
         <!-- Controllers -->
         <div class="flex gap-1 text-3xl items-center">
-            <button x-ref="btnPlay" class="-rotate-90 hidden" class="-rotate-90" title="Play. Toggle with Space" @click="() => {
+            <button x-ref="btnPlay" class="-rotate-90" class="-rotate-90" title="Play. Toggle with Space" @click="() => {
                 videos.forEach(video => video.play());
                 $refs.btnPlay.classList.toggle('hidden');
                 $refs.btnPause.classList.toggle('hidden');
             }">🔽</button>
-            <button x-ref="btnPause" title="Pause. Toggle with Space" @click="() => {
+            <button x-ref="btnPause" class="hidden" title="Pause. Toggle with Space" @click="() => {
                 videos.forEach(video => video.pause());
                 $refs.btnPlay.classList.toggle('hidden');
                 $refs.btnPause.classList.toggle('hidden');
@@ -125,7 +154,6 @@
                 @click="() => (videos.forEach(video => (video.currentTime = 0.0)))">↩️</button>
             <input x-ref="slider" max="100" min="0" step="1" type="range" value="0" class="w-80 mx-2" @input="() => {
                 const sliderValue = $refs.slider.value;
-                $refs.btnPause.click();
                 videos.forEach(video => {
                     const time = (video.duration * sliderValue) / 100;
                     video.currentTime = time;
@@ -177,9 +205,9 @@
                             </td>
                             <template x-for="(cell, colIndex) in row">
                                 <td x-show="cell" class="border border-slate-700">
-                                    <div class="flex gap-x-2 w-24 justify-between px-2">
+                                    <div class="flex gap-x-2 w-24 justify-between px-2" :class="{ 'hidden': cell.isNull }">
                                         <input type="checkbox" x-model="cell.checked" @change="updateTableValues()">
-                                        <span x-text="`${cell.value.toFixed(2)}`"
+                                        <span x-text="`${!cell.isNull ? cell.value.toFixed(2) : null}`"
                                             :style="`color: ${cell.color}`"></span>
                                     </div>
                                 </td>
@@ -201,16 +229,29 @@
                 dygraph: null,
                 currentFrameData: null,
                 columnNames: ["state", "action", "pred action"],
-                nColumns: {% if has_policy %}3{% else %}2{% endif %},
+                nColumns: 2,
+                nStates: 0,
+                nActions: 0,
                 checked: [],
                 dygraphTime: 0.0,
                 dygraphIndex: 0,
                 videos: null,
                 video: null,
                 colors: null,
+                nVideos: {{ videos_info | length }},
+                nVideoReadyToPlay: 0,
+                videoCodecError: false,
 
                 // alpine initialization
                 init() {
+                    // check if videos can play
+                    const dummyVideo = document.createElement('video');
+                    const canPlayVideos = dummyVideo.canPlayType('video/mp4; codecs="av01.0.05M.08"'); // codec source: https://huggingface.co/blog/video-encoding#results
+                    if(!canPlayVideos){
+                        this.videoCodecError = true;
+                    }
+
+                    // process CSV data
                     this.videos = document.querySelectorAll('video');
                     this.video = this.videos[0];
                     this.dygraph = new Dygraph(document.getElementById("graph"), '{{ ep_csv_url }}', {
@@ -235,17 +276,19 @@
                                 this.checked = Array(this.colors.length).fill(true);
 
                                 const seriesNames = this.dygraph.getLabels().slice(1);
+                                this.nStates = seriesNames.findIndex(item => item.startsWith('action_'));
+                                this.nActions = seriesNames.length - this.nStates;
                                 const colors = [];
                                 const LIGHTNESS = [30, 65, 85]; // state_lightness, action_lightness, pred_action_lightness
-                                let lightnessIdx = 0;
-                                const chunkSize = Math.ceil(seriesNames.length / this.nColumns);
-                                for (let i = 0; i < seriesNames.length; i += chunkSize) {
-                                    const lightness = LIGHTNESS[lightnessIdx];
-                                    for (let hue = 0; hue < 360; hue += parseInt(360/chunkSize)) {
-                                        const color = `hsl(${hue}, 100%, ${lightness}%)`;
-                                        colors.push(color);
-                                    }
-                                    lightnessIdx += 1;
+                                // colors for "state" lines
+                                for (let hue = 0; hue < 360; hue += parseInt(360/this.nStates)) {
+                                    const color = `hsl(${hue}, 100%, ${LIGHTNESS[0]}%)`;
+                                    colors.push(color);
+                                }
+                                // colors for "action" lines
+                                for (let hue = 0; hue < 360; hue += parseInt(360/this.nActions)) {
+                                    const color = `hsl(${hue}, 100%, ${LIGHTNESS[1]}%)`;
+                                    colors.push(color);
                                 }
                                 this.dygraph.updateOptions({ colors });
                                 this.colors = colors;
@@ -272,37 +315,40 @@
                     if (!this.currentFrameData) {
                         return [];
                     }
-                    const columnSize = Math.ceil(this.currentFrameData.length / this.nColumns);
-                    return Array.from({
-                        length: columnSize
-                    }, (_, rowIndex) => {
-                        const row = [
-                            this.currentFrameData[rowIndex] || null,
-                            this.currentFrameData[rowIndex + columnSize] || null,
-                        ];
-                        if (this.nColumns === 3) {
-                            row.push(this.currentFrameData[rowIndex + 2 * columnSize] || null)
-                        }
-                        return row;
-                    });
+                    const rows = [];
+                    const nRows = Math.max(this.nStates, this.nActions);
+                    let rowIndex = 0;
+                    while(rowIndex < nRows){
+                        const row = [];
+                        // number of states may NOT match number of actions. In this case, we null-pad the 2D array to make a fully rectangular 2d array
+                        const nullCell = { isNull: true };
+                        const stateValueIdx = rowIndex;
+                        const actionValueIdx = stateValueIdx + this.nStates; // because this.currentFrameData = [state0, state1, ..., stateN, action0, action1, ..., actionN]
+                        // row consists of [state value, action value]
+                        row.push(rowIndex < this.nStates ? this.currentFrameData[stateValueIdx] : nullCell); // push "state value" to row
+                        row.push(rowIndex < this.nActions ? this.currentFrameData[actionValueIdx] : nullCell); // push "action value" to row
+                        rowIndex += 1;
+                        rows.push(row);
+                    }
+                    return rows;
                 },
                 isRowChecked(rowIndex) {
-                    return this.rows[rowIndex].every(cell => cell && cell.checked);
+                    return this.rows[rowIndex].every(cell => cell && (cell.isNull || cell.checked));
                 },
                 isColumnChecked(colIndex) {
-                    return this.rows.every(row => row[colIndex] && row[colIndex].checked);
+                    return this.rows.every(row => row[colIndex] && (row[colIndex].isNull || row[colIndex].checked));
                 },
                 toggleRow(rowIndex) {
                     const newState = !this.isRowChecked(rowIndex);
                     this.rows[rowIndex].forEach(cell => {
-                        if (cell) cell.checked = newState;
+                        if (cell && !cell.isNull) cell.checked = newState;
                     });
                     this.updateTableValues();
                 },
                 toggleColumn(colIndex) {
                     const newState = !this.isColumnChecked(colIndex);
                     this.rows.forEach(row => {
-                        if (row[colIndex]) row[colIndex].checked = newState;
+                        if (row[colIndex] && !row[colIndex].isNull) row[colIndex].checked = newState;
                     });
                     this.updateTableValues();
                 },
@@ -343,7 +389,6 @@
                     window.history.replaceState({}, '', url.toString());
                 },
 
-
                 formatTime(time) {
                     var hours = Math.floor(time / 3600);
                     var minutes = Math.floor((time % 3600) / 60);
@@ -351,6 +396,14 @@
                     return (hours > 0 ? hours + ':' : '') + (minutes < 10 ? '0' + minutes : minutes) + ':' + (seconds <
                         10 ?
                         '0' + seconds : seconds);
+                },
+
+                videoCanPlay() {
+                    this.nVideoReadyToPlay += 1;
+                    if(this.nVideoReadyToPlay == this.nVideos) {
+                        // start autoplay all videos in sync
+                        this.$refs.btnPlay.click();
+                    }
                 }
             };
         }

pyproject.toml

@@ -43,8 +43,9 @@ opencv-python = ">=4.9.0"
 diffusers = ">=0.27.2"
 torchvision = ">=0.17.1"
 h5py = ">=3.10.0"
-huggingface-hub = {extras = ["hf-transfer", "cli"], version = ">=0.23.0"}
-gymnasium = ">=0.29.1"
+huggingface-hub = {extras = ["hf-transfer", "cli"], version = ">=0.25.0"}
+# TODO(rcadene, aliberts): Make gym 1.0.0 work
+gymnasium = "==0.29.1"
 cmake = ">=3.29.0.1"
 gym-dora = { git = "https://github.com/dora-rs/dora-lerobot.git", subdirectory = "gym_dora", optional = true }
 gym-pusht = { version = ">=0.1.5", optional = true}
@@ -64,9 +65,12 @@ pandas = {version = ">=2.2.2", optional = true}
 scikit-image = {version = ">=0.23.2", optional = true}
 dynamixel-sdk = {version = ">=3.7.31", optional = true}
 pynput = {version = ">=1.7.7", optional = true}
-# TODO(rcadene, salibert): 71.0.1 has a bug
-setuptools = {version = "!=71.0.1", optional = true}
-
+feetech-servo-sdk = {version = ">=1.0.0", optional = true}
+setuptools = {version = "!=71.0.1", optional = true}  # TODO(rcadene, aliberts): 71.0.1 has a bug
+pyrealsense2 = {version = ">=2.55.1.6486", markers = "sys_platform != 'darwin'", optional = true}  # TODO(rcadene, aliberts): Fix on Mac
+pyrender = {git = "https://github.com/mmatl/pyrender.git", markers = "sys_platform == 'linux'", optional = true}
+hello-robot-stretch-body = {version = ">=0.7.27", markers = "sys_platform == 'linux'", optional = true}
+pyserial = {version = ">=3.5", optional = true}
 
 
 [tool.poetry.extras]
@@ -75,10 +79,13 @@ pusht = ["gym-pusht"]
 xarm = ["gym-xarm"]
 aloha = ["gym-aloha"]
 dev = ["pre-commit", "debugpy"]
-test = ["pytest", "pytest-cov"]
+test = ["pytest", "pytest-cov", "pyserial"]
 umi = ["imagecodecs"]
 video_benchmark = ["scikit-image", "pandas"]
-koch = ["dynamixel-sdk", "pynput"]
+dynamixel = ["dynamixel-sdk", "pynput"]
+feetech = ["feetech-servo-sdk", "pynput"]
+intelrealsense = ["pyrealsense2"]
+stretch = ["hello-robot-stretch-body", "pyrender", "pyrealsense2", "pynput"]
 
 [tool.ruff]
 line-length = 110