Minor addition

for more information, see https://pre-commit.ci

.github/workflows/build-docker-images.yml

@@ -40,24 +40,24 @@ jobs:
           git lfs install
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
         with:
           cache-binary: false
 
       - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
         with:
           lfs: true
           persist-credentials: false
 
       - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
+        uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_PASSWORD }}
 
       - name: Build and Push CPU
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: ./docker/lerobot-cpu/Dockerfile
@@ -78,24 +78,24 @@ jobs:
           git lfs install
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
         with:
           cache-binary: false
 
       - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
         with:
           lfs: true
           persist-credentials: false
 
       - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
+        uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_PASSWORD }}
 
       - name: Build and Push GPU
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: ./docker/lerobot-gpu/Dockerfile
@@ -110,23 +110,23 @@ jobs:
       group: aws-general-8-plus
     steps:
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
         with:
           cache-binary: false
 
       - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
 
       - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
+        uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_PASSWORD }}
 
       - name: Build and Push GPU dev
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: ./docker/lerobot-gpu-dev/Dockerfile

.github/workflows/nightly-tests.yml

@@ -33,7 +33,7 @@ jobs:
     runs-on:
       group: aws-general-8-plus
     container:
-      image: huggingface/lerobot-cpu:latest  # zizmor: ignore[unpinned-images]
+      image: huggingface/lerobot-cpu:latest
       options: --shm-size "16gb"
       credentials:
         username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -60,7 +60,7 @@ jobs:
       CUDA_VISIBLE_DEVICES: "0"
       TEST_TYPE: "single_gpu"
     container:
-      image: huggingface/lerobot-gpu:latest  # zizmor: ignore[unpinned-images]
+      image: huggingface/lerobot-gpu:latest
       options: --gpus all --shm-size "16gb"
       credentials:
         username: ${{ secrets.DOCKERHUB_USERNAME }}

.github/workflows/quality.yml

@@ -33,12 +33,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout Repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
@@ -64,9 +64,9 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout Repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
 
       - name: typos-action
-        uses: crate-ci/typos@db35ee91e80fbb447f33b0e5fbddb24d2a1a884f # v1.29.10
+        uses: crate-ci/typos@v1.29.10

.github/workflows/test-docker-build.yml

@@ -35,7 +35,7 @@ jobs:
       matrix: ${{ steps.set-matrix.outputs.matrix }}
     steps:
       - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -64,17 +64,17 @@ jobs:
         docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
     steps:
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
         with:
           cache-binary: false
 
       - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
 
       - name: Build Docker image
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
         with:
           file: ${{ matrix.docker-file }}
           context: .

.github/workflows/test.yml

@@ -50,7 +50,7 @@ jobs:
     env:
       MUJOCO_GL: egl
     steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@v4
         with:
           lfs: true  # Ensure LFS files are pulled
           persist-credentials: false
@@ -62,7 +62,7 @@ jobs:
           sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
 
       - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
+        uses: astral-sh/setup-uv@v5
         with:
           enable-cache: true
           version: ${{ env.UV_VERSION }}
@@ -85,7 +85,7 @@ jobs:
     env:
       MUJOCO_GL: egl
     steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@v4
         with:
           lfs: true  # Ensure LFS files are pulled
           persist-credentials: false
@@ -94,7 +94,7 @@ jobs:
         run: sudo apt-get update && sudo apt-get install -y ffmpeg
 
       - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
+        uses: astral-sh/setup-uv@v5
         with:
           enable-cache: true
           version: ${{ env.UV_VERSION }}
@@ -117,7 +117,7 @@ jobs:
     env:
       MUJOCO_GL: egl
     steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@v4
         with:
           lfs: true  # Ensure LFS files are pulled
           persist-credentials: false
@@ -129,7 +129,7 @@ jobs:
           sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
 
       - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
+        uses: astral-sh/setup-uv@v5
         with:
           enable-cache: true
           version: ${{ env.UV_VERSION }}

.github/workflows/trufflehog.yml

@@ -24,12 +24,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Checkout code
-      uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      uses: actions/checkout@v4
       with:
         fetch-depth: 0
         persist-credentials: false
 
     - name: Secret Scanning
-      uses: trufflesecurity/trufflehog@90694bf9af66e7536abc5824e7a87246dbf933cb # v3.88.35
+      uses: trufflesecurity/trufflehog@main
       with:
         extra_args: --only-verified

.gitignore

@@ -29,7 +29,6 @@ outputs
 
 # VS Code
 .vscode
-.devcontainer
 
 # HPC
 nautilus/*.yaml

.pre-commit-config.yaml

@@ -37,17 +37,18 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/adhtruong/mirrors-typos
-    rev: v1.32.0
+    rev: v1.31.1
     hooks:
       - id: typos
         args: [--force-exclude]
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.20.0
+    rev: v3.19.1
     hooks:
     -   id: pyupgrade
+
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.11
+    rev: v0.11.5
     hooks:
       - id: ruff
         args: [--fix]
@@ -56,12 +57,12 @@ repos:
 
   ##### Security #####
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.26.0
+    rev: v8.24.3
     hooks:
       - id: gitleaks
 
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.8.0
+    rev: v1.5.2
     hooks:
       - id: zizmor
 

README.md

@@ -360,7 +360,7 @@ with profile(
 If you want, you can cite this work with:
 ```bibtex
 @misc{cadene2024lerobot,
-    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
+    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Wolf, Thomas},
     title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
     howpublished = "\url{https://github.com/huggingface/lerobot}",
     year = {2024}
@@ -408,19 +408,6 @@ Additionally, if you are using any of the particular policy architecture, pretra
   year={2024}
 }
 ```
-
-
-- [HIL-SERL](https://hil-serl.github.io/)
-```bibtex
-@Article{luo2024hilserl,
-title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
-author={Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
-year={2024},
-eprint={2410.21845},
-archivePrefix={arXiv},
-primaryClass={cs.RO}
-}
-```
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)

docs/source/_toctree.yml

@@ -9,8 +9,6 @@
     title: Getting Started with Real-World Robots
   - local: cameras
     title: Cameras
-  - local: hilserl
-    title: Getting Started with Reinforcement Learning
   title: "Tutorials"
 - sections:
   - local: so101

docs/source/cameras.mdx

@@ -1,14 +1,22 @@
 # Cameras
 
-LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
+Here we describe how to set up and use a camera with LeRobot. We support different ways of capturing videos in LeRobot, such as using a phone camera, an integrated laptop camera, an external webcam, or an Intel realsense camera.
+## Set up Cameras
 
-### Finding your camera
+There are three ways to connect and use a camera with LeRobot:
+1. Use [Camera Class](./setup_cameras?use+phone=Mac#use-opencvcamera), which allows you to use any camera: usb, realsense, laptop webcam
+2. Use [iPhone camera](./setup_cameras?use+phone=Mac#use-your-phone) with MacOS
+3. Use [Phone camera](./setup_cameras?use+phone=Linux#use-your-phone) on Linux
 
-To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system.
+### Use Camera Class
 
-To find the camera indices of the cameras plugged into your system, run the following script:
+In LeRobot, you can efficiently record frames from most cameras using either the OpenCVCamera class or the RealSenseCamera class. For more details on compatibility for the OpenCVCamera class, see [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
+
+To instantiate a camera, you need a camera index. When you only have one camera like a webcam of a laptop, the camera index is usually `0` but it might differ, and the camera index might change if you reboot your computer or re-plug your camera. This behavior depends on your operating system.
+
+To find the camera indices, run the following script:
 ```bash
-python lerobot/find_cameras.py opencv # or realsense for Intel Realsense cameras
+python lerobot/find_cameras.py list-cameras
 ```
 
 The output will look something like this if you have two cameras connected:
@@ -25,89 +33,23 @@ Camera #0:
     Height: 1080
     Fps: 15.0
 --------------------
-(more cameras ...)
+Camera #1:
+  Name: OpenCV Camera @ 1
+  Type: OpenCV
+  Id: 1
+  Backend api: AVFOUNDATION
+  Default stream profile:
+    Format: 16.0
+    Width: 1920
+    Height: 1080
+    Fps: 1.0
+--------------------
 ```
 
 > [!WARNING]
-> When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable.
+> On , you could get this error: `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions.
 
-
-## Use Cameras
-
-Below are two examples, demonstrating how to work with the API.
-
-- **Asynchronous frame capture** using an OpenCV-based camera
-- **Color and depth capture** using an Intel RealSense camera
-
-
-<hfoptions id="shell_restart">
-<hfoption id="Open CV Camera">
-
-```python
-from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera
-from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
-
-# Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
-config = OpenCVCameraConfig(
-    index_or_path=0,
-    fps=15,
-    width=1920,
-    height=1080,
-    color_mode=ColorMode.RGB,
-    rotation=Cv2Rotation.NO_ROTATION
-)
-
-# Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default).
-camera = OpenCVCamera(config)
-camera.connect()
-
-# Read frames asynchronously in a loop via `async_read(timeout_ms)`
-try:
-    for i in range(10):
-        frame = camera.async_read(timeout_ms=200)
-        print(f"Async frame {i} shape:", frame.shape)
-finally:
-    camera.disconnect()
-```
-
-</hfoption>
-<hfoption id="Intel Realsense Camera">
-
-```python
-from lerobot.common.cameras.intel.configuration_realsense import RealSenseCameraConfig
-from lerobot.common.cameras.intel.camera_realsense import RealSenseCamera
-from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
-
-# Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
-config = RealSenseCameraConfig(
-    serial_number="233522074606",
-    fps=15,
-    width=640,
-    height=480,
-    color_mode=ColorMode.RGB,
-    use_depth=True,
-    rotation=Cv2Rotation.NO_ROTATION
-)
-
-# Instantiate and connect a `RealSenseCamera` with warm-up read (default).
-camera = RealSenseCamera(config)
-camera.connect()
-
-# Capture a color frame via `read()` and a depth map via `read_depth()`.
-try:
-    color_frame = camera.read()
-    depth_map = camera.read_depth()
-    print("Color frame shape:", color_frame.shape)
-    print("Depth map shape:", depth_map.shape)
-finally:
-    camera.disconnect()
-```
-</hfoption>
-</hfoptions>
-
-
-## Use your phone
+### Use your phone
 <hfoptions id="use phone">
 <hfoption id="Mac">
 
@@ -171,3 +113,78 @@ If everything is set up correctly, you can proceed with the rest of the tutorial
 
 </hfoption>
 </hfoptions>
+
+## Use Cameras
+
+Below are two examples, demonstrating how to work with the API.
+
+- **Asynchronous frame capture** using an OpenCV-based camera
+- **Color and depth capture** using an Intel RealSense camera
+
+### Asynchronous OpenCV Camera
+
+This snippet shows how to:
+
+* Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
+* Instantiate and connect an `OpenCVCamera`, performing a warm-up read.
+* Read frames asynchronously in a loop via `async_read(timeout_ms)`.
+
+```python
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.common.cameras.opencv.camera_opencv import OpenCVCamera
+from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
+
+config = OpenCVCameraConfig(
+    index_or_path=0,
+    fps=15,
+    width=1920,
+    height=1080,
+    color_mode=ColorMode.RGB,
+    rotation=Cv2Rotation.NO_ROTATION
+)
+
+camera = OpenCVCamera(config)
+camera.connect(do_warmup_read=True)
+
+try:
+    for i in range(10):
+        frame = camera.async_read(timeout_ms=1000)
+        print(f"Async frame {i} shape:", frame.shape)
+finally:
+    camera.disconnect()
+```
+
+### Intel RealSense Camera (Color + Depth)
+
+This snippet shows how to:
+
+* Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
+* Instantiate and connect a `RealSenseCamera` with warm-up.
+* Capture a color frame via `read()` and a depth map via `read_depth()`.
+
+```python
+from lerobot.common.cameras.intel.configuration_realsense import RealSenseCameraConfig
+from lerobot.common.cameras.intel.camera_realsense import RealSenseCamera
+from lerobot.common.cameras.configs import ColorMode, Cv2Rotation
+
+config = RealSenseCameraConfig(
+    serial_number="233522074606",
+    fps=15,
+    width=640,
+    height=480,
+    color_mode=ColorMode.RGB,
+    use_depth=True,
+    rotation=Cv2Rotation.NO_ROTATION
+)
+
+camera = RealSenseCamera(config)
+camera.connect(do_warmup_read=True)
+
+try:
+    color_frame = camera.read()
+    depth_map = camera.read_depth()
+    print("Color frame shape:", color_frame.shape)
+    print("Depth map shape:", depth_map.shape)
+finally:
+    camera.disconnect()
+```

docs/source/getting_started_real_world_robot.mdx

@@ -7,18 +7,7 @@ This tutorial will explain how to train a neural network to control a real robot
 2. How to train a policy using your data and prepare it for evaluation.
 3. How to evaluate your policy and visualize the results.
 
-By following these steps, you'll be able to replicate tasks, such as picking up a Lego block and placing it in a bin with a high success rate, as shown in the video below.
-
-<details>
-<summary><strong>Video: pickup lego block task</strong></summary>
-
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot_task.mp4" type="video/mp4" />
-  </video>
-</div>
-
-</details>
+By following these steps, you'll be able to replicate tasks, such as picking up a Lego block and placing it in a bin with a high success rate, as shown in [this video](https://x.com/RemiCadene/status/1814680760592572934).
 
 This tutorial isn’t tied to a specific robot: we walk you through the commands and API snippets you can adapt for any supported platform.
 
@@ -42,6 +31,7 @@ In this example, we’ll demonstrate how to teleoperate the SO101 robot. For eac
 python -m lerobot.teleoperate \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.cameras="{}" \
     --robot.id=my_red_robot_arm \
     --teleop.type=so101_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \
@@ -135,6 +125,58 @@ while True:
 </hfoption>
 </hfoptions>
 
+## Teleoperate LeKiwi
+
+> [!TIP]
+> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
+
+TODO(pepijn): modify these commands with new API, and explain you can also map the arms to a keyboard with the new API now
+
+To teleoperate SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
+```bash
+python -m lerobot.common.robots.lekiwi.lekiwi_host
+```
+
+Then on your laptop, also run `conda activate lerobot` and this script:
+```bash
+python -m lerobot.teleoperate \
+    --robot.type=lekiwi \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.cameras="{}" \
+    --robot.id=my_lekiwi \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_blue_leader_arm
+```
+
+> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`. For the `--control.type=remote_robot` you will also need to set `--control.viewer_ip` and `--control.viewer_port`
+
+You should see on your laptop something like this: ```[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.``` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:
+
+| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
+| ---------- | ------------------ | ---------------------- |
+| Fast       | 0.4                | 90                     |
+| Medium     | 0.25               | 60                     |
+| Slow       | 0.1                | 30                     |
+
+
+| Key | Action         |
+| --- | -------------- |
+| W   | Move forward   |
+| A   | Move left      |
+| S   | Move backward  |
+| D   | Move right     |
+| Z   | Turn left      |
+| X   | Turn right     |
+| R   | Increase speed |
+| F   | Decrease speed |
+
+> [!TIP]
+>  If you use a different keyboard, you can change the keys for each command in the [`LeKiwiConfig`](../lerobot/common/robot_devices/robots/configs.py).
+
+### Wired version
+If you have the **wired** LeKiwi version, please run all commands on your laptop.
+
 ## Record a dataset
 
 Once you're familiar with teleoperation, you can record your first dataset.
@@ -168,8 +210,25 @@ python -m lerobot.record \
     --dataset.single_task="Grab the black cube"
 ```
 
+You will see a lot of lines appearing like this one:
+```
+INFO 2024-08-10 15:02:58 ol_robot.py:219 dt:33.34 (30.0hz) dtRlead: 5.06 (197.5hz) dtWfoll: 0.25 (3963.7hz) dtRfoll: 6.22 (160.7hz) dtRlaptop: 32.57 (30.7hz) dtRphone: 33.84 (29.5hz)
+```
+
+| Field | Meaning |
+|:---|:---|
+| `2024-08-10 15:02:58` | Timestamp when `print` was called. |
+| `ol_robot.py:219` | Source file and line number of the `print` call (`lerobot/scripts/control_robot.py` at line `219`). |
+| `dt: 33.34 (30.0 Hz)` | Delta time (ms) between teleop steps (target: 30.0 Hz, `--fps 30`). Yellow if step is too slow. |
+| `dtRlead: 5.06 (197.5 Hz)` | Delta time (ms) for reading present position from the **leader arm**. |
+| `dtWfoll: 0.25 (3963.7 Hz)` | Delta time (ms) for writing goal position to the **follower arm** (asynchronous). |
+| `dtRfoll: 6.22 (160.7 Hz)` | Delta time (ms) for reading present position from the **follower arm**. |
+| `dtRlaptop: 32.57 (30.7 Hz)` | Delta time (ms) for capturing an image from the **laptop camera** (async thread). |
+| `dtRphone: 33.84 (29.5 Hz)` | Delta time (ms) for capturing an image from the **phone camera** (async thread). |
+
+
 #### Dataset upload
-Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
+Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}` (e.g. `data/cadene/so101_test`). At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
 ```bash
 echo https://huggingface.co/datasets/${HF_USER}/so101_test
 ```
@@ -193,6 +252,9 @@ The `record` function provides a suite of tools for capturing and managing data
 
 ##### 3. Recording Parameters
 Set the flow of data recording using command-line arguments:
+- `--warmup_time_s=10`
+  Number of seconds before starting data collection (default: **10 seconds**).
+  Allows devices to warm up and synchronize.
 - `--dataset.episode_time_s=60`
   Duration of each data recording episode (default: **60 seconds**).
 - `--dataset.reset_time_s=60`
@@ -300,20 +362,21 @@ huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
 
 ## Evaluate your policy
 
+TODO(pepijn): modify this command further
+
 You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
 ```bash
-python -m lerobot.record  \
-  --robot.type=so100_follower \
-  --robot.port=/dev/ttyACM1 \
-  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
-  --robot.id=blue_follower_arm \
-  --teleop.type=so100_leader \
-  --teleop.port=/dev/ttyACM0 \
-  --teleop.id=red_leader_arm \
-  --display_data=false \
-  --dataset.repo_id=$HF_USER/eval_lego_${EPOCHREALTIME/[^0-9]/} \
-  --dataset.single_task="Put lego brick into the transparent box" \
-  --policy.path=${HF_USER}/act_johns_arm
+python -m lerobot.record \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem585A0076841 \
+    --robot.id=my_red_robot_arm \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_blue_leader_arm \
+    --display_data=true \
+    --dataset.repo_id=aliberts/eval_act \
+    --policy.checkpoint_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model \
 ```
 
 As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:

docs/source/hilserl.mdx

@@ -1,512 +0,0 @@
-# HilSerl Real Robot Training Workflow Guide
-
-Human-in-the-Loop Sample-Efficient Reinforcement Learning (HIL-SERL) with LeRobot workflow for taking a policy from “zero” to real-world robot mastery in just a couple of hours.
-It combines three ingredients:
-	1.	**Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point.
-	2.	**On-robot actor / learner loop with human interventions:** a distributed SAC/RLPD learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour.
-	3.	**Safety & efficiency tools:** joint/EE bounds, impedance control, crop-ROI preprocessing and WandB monitoring keep the data useful and the hardware safe.
-
-Together these elements let HIL-SERL reach near-perfect task success and faster cycle times than imitation-only baselines.
-
-<p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hilserl-main-figure.png" alt="HIL-SERL workflow" title="HIL-SERL workflow" width="100%"></img>
-</p>
-
-<p align="center"><i>HIL-SERL workflow, Luo et al. 2024</i></p>
-
-This guide provides step-by-step instructions for training a robot policy using LeRobot's HilSerl implementation to train on a real robot.
-
-
-# 1. Real Robot Training Workflow
-
-## 1.1 Understanding Configuration
-
-The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/common/envs/configs.py`. Which is defined as:
-
-```python
-class HILSerlRobotEnvConfig(EnvConfig):
-    robot: Optional[RobotConfig] = None    # Main robot agent (defined in `lerobot/common/robots`)
-    teleop: Optional[TeleoperatorConfig] = None    # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/common/teleoperators`)
-    wrapper: Optional[EnvTransformConfig] = None    # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
-    fps: int = 10    # Control frequency
-    name: str = "real_robot"    # Environment name
-    mode: str = None    # "record", "replay", or None (for training)
-    repo_id: Optional[str] = None    # LeRobot dataset repository ID
-    dataset_root: Optional[str] = None    # Local dataset root (optional)
-    task: str = ""    # Task identifier
-    num_episodes: int = 10    # Number of episodes for recording
-    episode: int = 0    # episode index for replay
-    device: str = "cuda"    # Compute device
-    push_to_hub: bool = True    # Whether to push the recorded datasets to Hub
-    pretrained_policy_name_or_path: Optional[str] = None    # For policy loading
-    reward_classifier_pretrained_path: Optional[str] = None    # For reward model
-```
-
-
-## 1.2 Finding Robot Workspace Bounds
-
-Before collecting demonstrations, you need to determine the appropriate operational bounds for your robot.
-
-This helps simplifying the problem of learning on the real robot by limiting the robot's operational space to a specific region that solves the task and avoids unnecessary or unsafe exploration.
-
-### 1.2.1 Using find_joint_limits.py
-
-This script helps you find the safe operational bounds for your robot's end-effector. Given that you have a follower and leader arm, you can use the script to find the bounds for the follower arm that will be applied during training.
-Bounding the action space will reduce the redundant exploration of the agent and guarantees safety.
-
-```bash
-python -m lerobot.scripts.find_joint_limits \
-    --robot.type=so100_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=black \
-    --teleop.type=so100_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=blue
-```
-
-### 1.2.2 Workflow
-
-1. Run the script and move the robot through the space that solves the task
-2. The script will record the minimum and maximum end-effector positions and the joint angles and prints them to the console, for example:
-   ```
-   Max ee position [0.24170487 0.201285   0.10273342]
-   Min ee position [0.16631757 -0.08237468  0.03364977]
-   Max joint positions [-20.0, -20.0, -20.0, -20.0, -20.0, -20.0]
-   Min joint positions [50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
-   ```
-3. Use these values in the configuration of you teleoperation device (TeleoperatorConfig) under the `end_effector_bounds` field
-
-### 1.2.3 Example Configuration
-
-```json
-"end_effector_bounds": {
-    "max": [0.24, 0.20, 0.10],
-    "min": [0.16, -0.08, 0.03]
-}
-```
-
-## 1.3 Collecting Demonstrations
-
-With the bounds defined, you can safely collect demonstrations for training. Training RL with off-policy algorithm allows us to use offline datasets collected in order to improve the efficiency of the learning process.
-
-### 1.3.1 Setting Up Record Mode
-
-Create a configuration file for recording demonstrations (or edit an existing one like `env_config_so100.json`):
-
-1. Set `mode` to `"record"`
-2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name")
-3. Set `num_episodes` to the number of demonstrations you want to collect
-4. Set `crop_params_dict` to `null` initially (we'll determine crops later)
-5. Configure `robot`, `cameras`, and other hardware settings
-
-Example configuration section:
-```json
-"mode": "record",
-"repo_id": "username/pick_lift_cube",
-"dataset_root": null,
-"task": "pick_and_lift",
-"num_episodes": 15,
-"episode": 0,
-"push_to_hub": true
-```
-
-### 1.3.2 Gamepad Controls
-
-<p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
-</p>
-<p align="center"><i>Gamepad button mapping for robot control and episode management</i></p>
-
-
-### 1.3.3 Recording Demonstrations
-
-Start the recording process:
-
-```bash
-python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config_so100.json
-```
-
-During recording:
-1. The robot will reset to the initial position defined in the configuration file `fixed_reset_position`
-2. Use the gamepad to control the robot by setting `"control_mode"="gamepad"` in the configuration file
-3. Complete the task successfully
-4. The episode ends with a reward of 1 when you press the "success" button
-5. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0
-6. You can rerecord an episode by pressing the "rerecord" button
-7. The process automatically continues to the next episode
-8. After recording all episodes, the dataset is pushed to the Hugging Face Hub (optional) and saved locally
-
-
-
-## 1.4 Processing the Dataset
-
-After collecting demonstrations, process them to determine optimal camera crops.
-Reinforcement learning is sensitive to background distractions, so it is important to crop the images to the relevant workspace area.
-Note: If you already know the crop parameters, you can skip this step and just set the `crop_params_dict` in the configuration file during recording.
-
-### 1.4.1 Determining Crop Parameters
-
-Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images:
-
-```bash
-python lerobot/scripts/rl/crop_dataset_roi.py --repo-id username/pick_lift_cube
-```
-
-1. For each camera view, the script will display the first frame
-2. Draw a rectangle around the relevant workspace area
-3. Press 'c' to confirm the selection
-4. Repeat for all camera views
-5. The script outputs cropping parameters and creates a new cropped dataset
-
-Example output:
-```
-Selected Rectangular Regions of Interest (top, left, height, width):
-observation.images.side: [180, 207, 180, 200]
-observation.images.front: [180, 250, 120, 150]
-```
-
-<p align="center">
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/crop_dataset.gif" width="600"/>
-</p>
-
-<p align="center"><i>Interactive cropping tool for selecting regions of interest</i></p>
-
-
-### 1.4.2 Updating Configuration
-
-Add these crop parameters to your training configuration:
-
-```json
-"crop_params_dict": {
-    "observation.images.side": [180, 207, 180, 200],
-    "observation.images.front": [180, 250, 120, 150]
-},
-"resize_size": [128, 128]
-```
-
-## 1.5 Training with Actor-Learner
-
-The LeRobot system uses a distributed actor-learner architecture for training. You will need to start two processes: a learner and an actor.
-
-### 1.5.1 Configuration Setup
-
-Create a training configuration file (See example `train_config_hilserl_so100.json`). The training config is based on the main `TrainPipelineConfig` class in `lerobot/configs/train.py`.
-
-1. Set `mode` to `null` (for training mode)
-2. Configure the policy settings (`type`, `device`, etc.)
-3. Set `dataset` to your cropped dataset
-4. Configure environment settings with crop parameters
-5. Check the other parameters related to SAC.
-6. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task.
-
-### 1.5.2 Starting the Learner
-
-First, start the learner server process:
-
-```bash
-python lerobot/scripts/rl/learner.py --config_path lerobot/configs/train_config_hilserl_so100.json
-```
-
-The learner:
-- Initializes the policy network
-- Prepares replay buffers
-- Opens a gRPC server to communicate with actors
-- Processes transitions and updates the policy
-
-### 1.5.3 Starting the Actor
-
-In a separate terminal, start the actor process with the same configuration:
-
-```bash
-python lerobot/scripts/rl/actor.py --config_path lerobot/configs/train_config_hilserl_so100.json
-```
-
-The actor:
-- Connects to the learner via gRPC
-- Initializes the environment
-- Execute rollouts of the policy to collect experience
-- Sends transitions to the learner
-- Receives updated policy parameters
-
-### 1.5.4 Training Flow
-
-The training proceeds automatically:
-
-1. The actor executes the policy in the environment
-2. Transitions are collected and sent to the learner
-3. The learner updates the policy based on these transitions
-4. Updated policy parameters are sent back to the actor
-5. The process continues until the specified step limit is reached
-
-### 1.5.5 Human in the Loop
-
-- The key to learning efficiently is to have a human interventions to provide corrective feedback and completing the task to aide the policy learning and exploration.
-- To perform human interventions, you can press the upper right trigger button on the gamepad. This will pause the policy actions and allow you to take over.
-- A successful experiment is one where the human has to intervene at the start but then reduces the amount of interventions as the policy improves. You can monitor the intervention rate in the `wandb` dashboard.
-
-<p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hil_effect.png?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
-</p>
-
-<p align="center"><i>Example showing how human interventions help guide policy learning over time</i></p>
-
-- The figure shows the plot of the episodic reward over interaction step. The figure shows the effect of human interventions on the policy learning.
-- The orange curve is an experiment without any human interventions. While the pink and blue curves are experiments with human interventions.
-- We can observe that the number of steps where the policy starts achieving the maximum reward is cut by a quarter when human interventions are present.
-
-#### Guide to Human Interventions
-The strategy to follow is to intervene heavily at the start of training and then reduce the amount of interventions as the training progresses. Some tips and hints:
-- Interevene for almost the length of the entire episode at the first few episodes.
-- When the policy is less chaotic, gradually reduce the intervention time during one episode and let the policy explore for a longer time.
-- Once the policy start guiding the robot towards achieving the task, even if its not perfect, you can limit your interventions to simple quick actions like a grasping command, or grasp and lift command.
-
-## 1.6 Monitoring and Debugging
-
-If you have `wandb.enable` set to `true` in your configuration, you can monitor training progress in real-time through the [Weights & Biases](https://wandb.ai/site/) dashboard.
-
-# 2. Training a Reward Classifier with LeRobot
-
-This guide explains how to train a reward classifier for human-in-the-loop reinforcement learning implementation of  LeRobot. Reward classifiers learn to predict the reward value given a state which can be used in an RL setup to train a policy.
-
-
-The reward classifier implementation in `modeling_classifier.py` uses a pretrained vision model to process the images. It can output either a single value for binary rewards to predict success/fail cases or multiple values for multi-class settings.
-
-## 2.1 Collecting a Dataset
-Before training, you need to collect a dataset with labeled examples. The `record_dataset` function in `gym_manipulator.py` enables the process of collecting a dataset of observations, actions, and rewards.
-
-To collect a dataset, you need to modeify some parameters in the environment configuration based on HILSerlRobotEnvConfig.
-
-```bash
-python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/reward_classifier_train_config.json
-```
-
-### 2.1.1 Key Parameters for Data Collection:
-
-- **mode**: set it to "record" to collect a dataset
-- **repo_id**: "hf_username/dataset_name", name of the dataset and repo on the hub
-- **num_episodes**: Number of episodes to record
-- **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected
-- **fps**: Number of frames per second to record
-- **push_to_hub**: Whether to push the dataset to the hub
-
-The `number_of_steps_after_success` parameter is crucial as it allows you to collect more positive examples. When a success is detected, the system will continue recording for the specified number of steps while maintaining the reward=1 label. Otherwise, there won't be enough states in the dataset labeled to 1 to train a good classifier.
-
-Example configuration section for data collection:
-
-```json
-{
-    "mode": "record",
-    "repo_id": "hf_username/dataset_name",
-    "dataset_root": "data/your_dataset",
-    "num_episodes": 20,
-    "push_to_hub": true,
-    "fps": 10,
-    "number_of_steps_after_success": 15
-}
-```
-
-## 2.2 Reward Classifier Configuration
-
-The reward classifier is configured using `configuration_classifier.py`. Here are the key parameters:
-
-- **model_name**: Base model architecture (e.g., we mainly use "helper2424/resnet10")
-- **model_type**: "cnn" or "transformer"
-- **num_cameras**: Number of camera inputs
-- **num_classes**: Number of output classes (typically 2 for binary success/failure)
-- **hidden_dim**: Size of hidden representation
-- **dropout_rate**: Regularization parameter
-- **learning_rate**: Learning rate for optimizer
-
-Example configuration from `reward_classifier_train_config.json`:
-
-```json
-{
-  "policy": {
-    "type": "reward_classifier",
-    "model_name": "helper2424/resnet10",
-    "model_type": "cnn",
-    "num_cameras": 2,
-    "num_classes": 2,
-    "hidden_dim": 256,
-    "dropout_rate": 0.1,
-    "learning_rate": 1e-4,
-    "device": "cuda",
-    "use_amp": true,
-    "input_features": {
-      "observation.images.front": {
-        "type": "VISUAL",
-        "shape": [3, 128, 128]
-      },
-      "observation.images.side": {
-        "type": "VISUAL",
-        "shape": [3, 128, 128]
-      }
-    }
-  }
-}
-```
-
-## 2.3 Training the Classifier
-
-To train the classifier, use the `train.py` script with your configuration:
-
-```bash
-python lerobot/scripts/train.py --config_path lerobot/configs/reward_classifier_train_config.json
-```
-
-## 2.4 Deploying and Testing the Model
-
-To use your trained reward classifier, configure the `HILSerlRobotEnvConfig` to use your model:
-
-```python
-env_config = HILSerlRobotEnvConfig(
-    reward_classifier_pretrained_path="path_to_your_pretrained_trained_model",
-    # Other environment parameters
-)
-```
-or set the argument in the json config file.
-
-```json
-{
-    "reward_classifier_pretrained_path": "path_to_your_pretrained_model"
-}
-```
-
-Run gym_manipulator.py to test the model.
-```bash
-python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
-```
-
-The reward classifier will automatically provide rewards based on the visual input from the robot's cameras.
-
-## 2.5 Example Workflow
-
-1. **Create the configuration files**:
-   Create the necessary json configuration files for the reward classifier and the environment. Check the `json_examples` directory for examples.
-
-2. **Collect a dataset**:
-   ```bash
-   python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
-   ```
-
-3. **Train the classifier**:
-   ```bash
-   python lerobot/scripts/train.py --config_path lerobot/configs/reward_classifier_train_config.json
-   ```
-
-4. **Test the classifier**:
-   ```bash
-   python lerobot/scripts/rl/gym_manipulator.py --config_path lerobot/configs/env_config.json
-   ```
-# 3. Using gym_hil Simulation Environments with LeRobot
-
-This guide explains how to use the `gym_hil` simulation environments as an alternative to real robots when working with the LeRobot framework for Human-In-the-Loop (HIL) reinforcement learning.
-
-`gym_hil` is a package that provides Gymnasium-compatible simulation environments specifically designed for Human-In-the-Loop reinforcement learning. These environments allow you to:
-
-- Train policies in simulation to test the RL stack before training on real robots
-
-- Collect demonstrations in sim using external devices like gamepads or keyboards
-- Perform human interventions during policy learning
-
-Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube.
-
-## 3.1 Installation
-
-First, install the `gym_hil` package within the LeRobot environment:
-
-```bash
-pip install gym_hil
-
-# Or in LeRobot
-cd lerobot
-pip install -e .[hilserl]
-```
-
-## 3.2 Configuration
-
-To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided in `gym_hil_env.json`. Key configuration sections include:
-
-### 3.2.1 Environment Type and Task
-
-```json
-{
-    "type": "hil",
-    "name": "franka_sim",
-    "task": "PandaPickCubeGamepad-v0",
-    "device": "cuda"
-}
-```
-
-Available tasks:
-- `PandaPickCubeBase-v0`: Basic environment
-- `PandaPickCubeGamepad-v0`: With gamepad control
-- `PandaPickCubeKeyboard-v0`: With keyboard control
-
-### 3.2.2 Gym Wrappers Configuration
-
-```json
-"wrapper": {
-    "gripper_penalty": -0.02,
-    "control_time_s": 15.0,
-    "use_gripper": true,
-    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
-    "end_effector_step_sizes": {
-        "x": 0.025,
-        "y": 0.025,
-        "z": 0.025
-    },
-    "control_mode": "gamepad"
-    }
-```
-
-Important parameters:
-- `gripper_penalty`: Penalty for excessive gripper movement
-- `use_gripper`: Whether to enable gripper control
-- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
-- `control_mode`: Set to "gamepad" to use a gamepad controller
-
-## 3.3 Running with HIL RL of LeRobot
-
-### 3.3.1 Basic Usage
-
-To run the environment, set mode to null:
-
-```python
-python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
-```
-
-### 3.3.2 Recording a Dataset
-
-To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
-
-```python
-python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json
-```
-
-### 3.3.3 Training a Policy
-
-To train a policy, checkout the example json in `train_gym_hil_env.json` and run the actor and learner servers:
-
-```python
-python lerobot/scripts/rl/actor.py --config_path path/to/train_gym_hil_env.json
-```
-
-In a different terminal, run the learner server:
-
-```python
-python lerobot/scripts/rl/learner.py --config_path path/to/train_gym_hil_env.json
-```
-
-The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.
-
-Paper citation:
-
-```
-@article{luo2024precise,
-  title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
-  author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey},
-  journal={arXiv preprint arXiv:2410.21845},
-  year={2024}
-}
-```

docs/source/installation.mdx

@@ -2,8 +2,6 @@
 
 ## Install LeRobot
 
-Currently only available from source.
-
 Download our source code:
 ```bash
 git clone https://github.com/huggingface/lerobot.git
@@ -15,6 +13,28 @@ Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.
 conda create -y -n lerobot python=3.10
 ```
 
+Now restart the shell by running:
+<hfoptions id="shell_restart">
+<hfoption id="Windows">
+
+```bash
+source ~/.bashrc
+```
+</hfoption>
+<hfoption id="Mac">
+
+```bash
+source ~/.bash_profile
+```
+</hfoption>
+<hfoption id="zshell">
+
+```bash
+source ~/.zshrc
+```
+</hfoption>
+</hfoptions>
+
 Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
 ```bash
 conda activate lerobot
@@ -31,14 +51,14 @@ conda install ffmpeg -c conda-forge
 >  ```bash
 >  conda install ffmpeg=7.1.1 -c conda-forge
 >  ```
->  - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+>  - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
 
 Install 🤗 LeRobot:
 ```bash
-pip install -e .
+cd lerobot && pip install
 ```
 
-### Troubleshooting
+## Troubleshooting
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
 To install these for linux run:
 ```bash
@@ -46,24 +66,34 @@ sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev
 ```
 For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
 
-## Optional dependencies
+## Sim
+For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
+- [aloha](https://github.com/huggingface/gym-aloha)
+- [xarm](https://github.com/huggingface/gym-xarm)
+- [pusht](https://github.com/huggingface/gym-pusht)
 
-LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
-
-### Simulations
-Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), `xarm` ([gym-xarm](https://github.com/huggingface/gym-xarm)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
-Example:
+For instance, to install 🤗 LeRobot with aloha and pusht, use:
 ```bash
-pip install -e ".[aloha]" # or "[pusht]" for example
+pip install -e ".[aloha, pusht]"
 ```
 
-### Motor Control
+## Motor SDK
 For Koch v1.1 install the Dynamixel SDK, for SO100/SO101/Moss install the Feetech SDK.
-```bash
-pip install -e ".[feetech]" # or "[dynamixel]" for example
-```
 
-### Experiment Tracking
+<hfoptions id="install motors">
+<hfoption id="Feetech">
+```bash
+pip install -e ".[feetech]"
+```
+</hfoption>
+<hfoption id="Dynamixel">
+```bash
+pip install -e ".[dynamixel]"
+```
+</hfoption>
+</hfoptions>
+
+## W&B
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
 ```bash
 wandb login

docs/source/koch.md

@@ -0,0 +1 @@
+../../lerobot/common/robots/koch_follower/koch.md

docs/source/koch.mdx

@@ -1 +0,0 @@
-../../lerobot/common/robots/koch_follower/koch.mdx

docs/source/lekiwi.md

@@ -0,0 +1 @@
+../../lerobot/common/robots/lekiwi/lekiwi.md

docs/source/lekiwi.mdx

@@ -1 +0,0 @@
-../../lerobot/common/robots/lekiwi/lekiwi.mdx

docs/source/so100.md

@@ -0,0 +1 @@
+../../lerobot/common/robots/so100_follower/so100.md

docs/source/so100.mdx

@@ -1 +0,0 @@
-../../lerobot/common/robots/so100_follower/so100.mdx

docs/source/so101.md

@@ -0,0 +1 @@
+../../lerobot/common/robots/so101_follower/so101.md

docs/source/so101.mdx

@@ -1 +0,0 @@
-../../lerobot/common/robots/so101_follower/so101.mdx

examples/robots/lekiwi_client_app.py

@@ -16,38 +16,42 @@ import logging
 import time
 
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.datasets.utils import hw_to_dataset_features
 from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
-from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.common.robots.lekiwi.lekiwi_client import OBS_STATE, LeKiwiClient
 from lerobot.common.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+from lerobot.common.teleoperators.so100 import SO100Leader, SO100LeaderConfig
 
 NB_CYCLES_CLIENT_CONNECTION = 250
 
 
 def main():
     logging.info("Configuring Teleop Devices")
-    leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem58760433331")
+    leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem58760434171")
     leader_arm = SO100Leader(leader_arm_config)
 
     keyboard_config = KeyboardTeleopConfig()
     keyboard = KeyboardTeleop(keyboard_config)
 
     logging.info("Configuring LeKiwi Client")
-    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+    robot_config = LeKiwiClientConfig(remote_ip="192.0.2.42", id="lekiwi")
     robot = LeKiwiClient(robot_config)
 
     logging.info("Creating LeRobot Dataset")
 
-    action_features = hw_to_dataset_features(robot.action_features, "action")
-    obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-    dataset_features = {**action_features, **obs_features}
+    # The observations that we get are expected to be in body frame (x,y,theta)
+    obs_dict = {f"{OBS_STATE}." + key: value for key, value in robot.state_feature.items()}
+    # The actions that we send are expected to be in wheel frame (motor encoders)
+    act_dict = {"action." + key: value for key, value in robot.action_feature.items()}
 
+    features_dict = {
+        **act_dict,
+        **obs_dict,
+        **robot.camera_features,
+    }
     dataset = LeRobotDataset.create(
         repo_id="user/lekiwi" + str(int(time.time())),
         fps=10,
-        features=dataset_features,
-        robot_type=robot.name,
+        features=features_dict,
     )
 
     logging.info("Connecting Teleop Devices")
@@ -72,10 +76,10 @@ def main():
         observation = robot.get_observation()
 
         frame = {**action_sent, **observation}
-        task = "Dummy Example Task Dataset"
+        frame.update({"task": "Dummy Example Task Dataset"})
 
         logging.info("Saved a frame into the dataset")
-        dataset.add_frame(frame, task)
+        dataset.add_frame(frame)
         i += 1
 
     logging.info("Disconnecting Teleop Devices and LeKiwi Client")

lerobot/calibrate.py

@@ -40,7 +40,7 @@ from lerobot.common.robots import (  # noqa: F401
     lekiwi,
     make_robot_from_config,
     so100_follower,
-    so100_follower_end_effector,
+    so101_follower,
 )
 from lerobot.common.teleoperators import (  # noqa: F401
     Teleoperator,

lerobot/common/cameras/camera.py

@@ -36,6 +36,7 @@ class Camera(abc.ABC):
         fps (int | None): Configured frames per second
         width (int | None): Frame width in pixels
         height (int | None): Frame height in pixels
+        warmup_time (int | None): Time reading frames before returning from connect (in seconds)
 
     Example:
         class MyCamera(Camera):
@@ -55,6 +56,7 @@ class Camera(abc.ABC):
         self.fps: int | None = config.fps
         self.width: int | None = config.width
         self.height: int | None = config.height
+        self.warmup_time: int | None = config.warmup_time
 
     @property
     @abc.abstractmethod

lerobot/common/cameras/configs.py

@@ -38,6 +38,7 @@ class CameraConfig(draccus.ChoiceRegistry, abc.ABC):
     fps: int | None = None
     width: int | None = None
     height: int | None = None
+    warmup_time: int | None = None
 
     @property
     def type(self) -> str:

lerobot/common/cameras/opencv/camera_opencv.py

@@ -16,12 +16,14 @@
 Provides the OpenCVCamera class for capturing frames from cameras using OpenCV.
 """
 
+import contextlib
 import logging
 import math
 import platform
+import queue
 import time
 from pathlib import Path
-from threading import Event, Lock, Thread
+from threading import Event, Thread
 from typing import Any, Dict, List
 
 import cv2
@@ -70,17 +72,18 @@ class OpenCVCamera(Camera):
         # Basic usage with camera index 0
         config = OpenCVCameraConfig(index_or_path=0)
         camera = OpenCVCamera(config)
-        camera.connect()
-
-        # Read 1 frame synchronously
-        color_image = camera.read()
-        print(color_image.shape)
-
-        # Read 1 frame asynchronously
-        async_image = camera.async_read()
-
-        # When done, properly disconnect the camera using
-        camera.disconnect()
+        try:
+            camera.connect()
+            print(f"Connected to {camera}")
+            color_image = camera.read() # Synchronous read
+            print(f"Read frame shape: {color_image.shape}")
+            async_image = camera.async_read() # Asynchronous read
+            print(f"Async read frame shape: {async_image.shape}")
+        except Exception as e:
+            print(f"An error occurred: {e}")
+        finally:
+            camera.disconnect()
+            print(f"Disconnected from {camera}")
 
         # Example with custom settings
         custom_config = OpenCVCameraConfig(
@@ -110,15 +113,13 @@ class OpenCVCamera(Camera):
 
         self.fps = config.fps
         self.color_mode = config.color_mode
-        self.warmup_s = config.warmup_s
+        self.warmup_time = config.warmup_time
 
         self.videocapture: cv2.VideoCapture | None = None
 
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
-        self.frame_lock: Lock = Lock()
-        self.latest_frame: np.ndarray | None = None
-        self.new_frame_event: Event = Event()
+        self.frame_queue: queue.Queue = queue.Queue(maxsize=1)
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
         self.backend: int = get_cv2_backend()
@@ -146,7 +147,8 @@ class OpenCVCamera(Camera):
 
         Raises:
             DeviceAlreadyConnectedError: If the camera is already connected.
-            ConnectionError: If the specified camera index/path is not found or the camera is found but fails to open.
+            ValueError: If the specified camera index/path is not found or accessible.
+            ConnectionError: If the camera is found but fails to open.
             RuntimeError: If the camera opens but fails to apply requested FPS/resolution settings.
         """
         if self.is_connected:
@@ -162,19 +164,24 @@ class OpenCVCamera(Camera):
             self.videocapture.release()
             self.videocapture = None
             raise ConnectionError(
-                f"Failed to open {self}."
-                f"Run `python -m lerobot.find_cameras opencv` to find available cameras."
+                f"Failed to open OpenCV camera {self.index_or_path}."
+                f"Run 'python -m lerobot.find_cameras opencv' for details about the available cameras in your system."
             )
 
         self._configure_capture_settings()
 
         if warmup:
+            if self.warmup_time is None:
+                raise ValueError(
+                    f"Warmup time is not set for {self}. Please set a warmup time in the configuration."
+                )
+            logger.debug(f"Reading a warm-up frames for {self} for {self.warmup_time} seconds...")
             start_time = time.time()
-            while time.time() - start_time < self.warmup_s:
+            while time.time() - start_time < self.warmup_time:
                 self.read()
                 time.sleep(0.1)
 
-        logger.info(f"{self} connected.")
+        logger.debug(f"Camera {self.index_or_path} connected and configured successfully.")
 
     def _configure_capture_settings(self) -> None:
         """
@@ -199,6 +206,7 @@ class OpenCVCamera(Camera):
 
         if self.fps is None:
             self.fps = self.videocapture.get(cv2.CAP_PROP_FPS)
+            logger.info(f"FPS set to camera default: {self.fps}.")
         else:
             self._validate_fps()
 
@@ -212,6 +220,8 @@ class OpenCVCamera(Camera):
             else:
                 self.width, self.height = default_width, default_height
                 self.capture_width, self.capture_height = default_width, default_height
+            logger.info(f"Capture width set to camera default: {self.width}.")
+            logger.info(f"Capture height set to camera default: {self.height}.")
         else:
             self._validate_width_and_height()
 
@@ -222,7 +232,10 @@ class OpenCVCamera(Camera):
         actual_fps = self.videocapture.get(cv2.CAP_PROP_FPS)
         # Use math.isclose for robust float comparison
         if not success or not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
-            raise RuntimeError(f"{self} failed to set fps={self.fps} ({actual_fps=}).")
+            raise RuntimeError(
+                f"Failed to set requested FPS {self.fps} for {self}. Actual value reported: {actual_fps} set success: {success})."
+            )
+        logger.debug(f"FPS set to {actual_fps} for {self}.")
 
     def _validate_width_and_height(self) -> None:
         """Validates and sets the camera's frame capture width and height."""
@@ -230,14 +243,18 @@ class OpenCVCamera(Camera):
         success = self.videocapture.set(cv2.CAP_PROP_FRAME_WIDTH, float(self.capture_width))
         actual_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH)))
         if not success or self.capture_width != actual_width:
-            raise RuntimeError(f"{self} failed to set capture_width={self.capture_width} ({actual_width=}).")
+            raise RuntimeError(
+                f"Failed to set requested capture width {self.capture_width} for {self}. Actual value: {actual_width} (set success: {success})."
+            )
+        logger.debug(f"Capture width set to {actual_width} for {self}.")
 
         success = self.videocapture.set(cv2.CAP_PROP_FRAME_HEIGHT, float(self.capture_height))
         actual_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
         if not success or self.capture_height != actual_height:
             raise RuntimeError(
-                f"{self} failed to set capture_height={self.capture_height} ({actual_height})."
+                f"Failed to set requested capture height {self.capture_height} for {self}. Actual value: {actual_height} (set success: {success})."
             )
+        logger.debug(f"Capture height set to {actual_height} for {self}.")
 
     @staticmethod
     def find_cameras() -> List[Dict[str, Any]]:
@@ -260,6 +277,7 @@ class OpenCVCamera(Camera):
         else:
             targets_to_scan = list(range(MAX_OPENCV_INDEX))
 
+        logger.debug(f"Found potential paths: {targets_to_scan}")
         for target in targets_to_scan:
             camera = cv2.VideoCapture(target)
             if camera.isOpened():
@@ -283,6 +301,10 @@ class OpenCVCamera(Camera):
                 found_cameras_info.append(camera_info)
                 camera.release()
 
+        if not found_cameras_info:
+            logger.warning("No OpenCV devices detected.")
+
+        logger.info(f"Detected OpenCV cameras: {[cam['id'] for cam in found_cameras_info]}")
         return found_cameras_info
 
     def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
@@ -313,15 +335,19 @@ class OpenCVCamera(Camera):
 
         start_time = time.perf_counter()
 
+        # NOTE(Steven): Are we okay with this blocking an undefined amount of time?
         ret, frame = self.videocapture.read()
 
         if not ret or frame is None:
-            raise RuntimeError(f"{self} read failed (status={ret}).")
+            raise RuntimeError(
+                f"Failed to capture frame from {self}. '.read()' returned status={ret} and frame is None."
+            )
 
+        # Post-process the frame (color conversion, dimension check, rotation)
         processed_frame = self._postprocess_image(frame, color_mode)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
-        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+        logger.debug(f"{self} synchronous read took: {read_duration_ms:.1f}ms")
 
         return processed_frame
 
@@ -346,25 +372,24 @@ class OpenCVCamera(Camera):
 
         if requested_color_mode not in (ColorMode.RGB, ColorMode.BGR):
             raise ValueError(
-                f"Invalid color mode '{requested_color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+                f"Invalid requested color mode '{requested_color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
             )
 
         h, w, c = image.shape
 
         if h != self.capture_height or w != self.capture_width:
             raise RuntimeError(
-                f"{self} frame width={w} or height={h} do not match configured width={self.capture_width} or height={self.capture_height}."
+                f"Captured frame dimensions ({h}x{w}) do not match configured capture dimensions ({self.capture_height}x{self.capture_width}) for {self}."
             )
 
-        if c != 3:
-            raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).")
-
         processed_image = image
         if requested_color_mode == ColorMode.RGB:
             processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            logger.debug(f"Converted frame from BGR to RGB for {self}.")
 
         if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
             processed_image = cv2.rotate(processed_image, self.rotation)
+            logger.debug(f"Rotated frame by {self.config.rotation} degrees for {self}.")
 
         return processed_image
 
@@ -372,26 +397,28 @@ class OpenCVCamera(Camera):
         """
         Internal loop run by the background thread for asynchronous reading.
 
-        On each iteration:
-        1. Reads a color frame
-        2. Stores result in latest_frame (thread-safe)
-        3. Sets new_frame_event to notify listeners
-
-        Stops on DeviceNotConnectedError, logs other errors and continues.
+        Continuously reads frames from the camera using the synchronous `read()`
+        method and places the latest frame into the `frame_queue`. It overwrites
+        any previous frame in the queue.
         """
+        logger.debug(f"Starting read loop thread for {self}.")
         while not self.stop_event.is_set():
             try:
                 color_image = self.read()
 
-                with self.frame_lock:
-                    self.latest_frame = color_image
-                self.new_frame_event.set()
+                with contextlib.suppress(queue.Empty):
+                    _ = self.frame_queue.get_nowait()
+                self.frame_queue.put(color_image)
+                logger.debug(f"Frame placed in queue for {self}.")
 
             except DeviceNotConnectedError:
+                logger.error(f"Read loop for {self} stopped: Camera disconnected.")
                 break
             except Exception as e:
                 logger.warning(f"Error reading frame in background thread for {self}: {e}")
 
+        logger.debug(f"Stopping read loop thread for {self}.")
+
     def _start_read_thread(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
         if self.thread is not None and self.thread.is_alive():
@@ -400,9 +427,12 @@ class OpenCVCamera(Camera):
             self.stop_event.set()
 
         self.stop_event = Event()
-        self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
+        self.thread = Thread(
+            target=self._read_loop, args=(), name=f"OpenCVCameraReadLoop-{self}-{self.index_or_path}"
+        )
         self.thread.daemon = True
         self.thread.start()
+        logger.debug(f"Read thread started for {self}.")
 
     def _stop_read_thread(self) -> None:
         """Signals the background read thread to stop and waits for it to join."""
@@ -411,21 +441,27 @@ class OpenCVCamera(Camera):
 
         if self.thread is not None and self.thread.is_alive():
             self.thread.join(timeout=2.0)
+            if self.thread.is_alive():
+                logger.warning(f"Read thread for {self} did not terminate gracefully after 2 seconds.")
+            else:
+                logger.debug(f"Read thread for {self} joined successfully.")
 
         self.thread = None
         self.stop_event = None
+        logger.debug(f"Read thread stopped for {self}.")
 
-    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+    def async_read(self, timeout_ms: float = 2000) -> np.ndarray:
         """
         Reads the latest available frame asynchronously.
 
         This method retrieves the most recent frame captured by the background
         read thread. It does not block waiting for the camera hardware directly,
-        but may wait up to timeout_ms for the background thread to provide a frame.
+        only waits for a frame to appear in the internal queue up to the specified
+        timeout.
 
         Args:
             timeout_ms (float): Maximum time in milliseconds to wait for a frame
-                to become available. Defaults to 200ms (0.2 seconds).
+                to become available in the queue. Defaults to 2000ms (2 seconds).
 
         Returns:
             np.ndarray: The latest captured frame as a NumPy array in the format
@@ -434,7 +470,7 @@ class OpenCVCamera(Camera):
         Raises:
             DeviceNotConnectedError: If the camera is not connected.
             TimeoutError: If no frame becomes available within the specified timeout.
-            RuntimeError: If an unexpected error occurs.
+            RuntimeError: If an unexpected error occurs while retrieving from the queue.
         """
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -442,21 +478,17 @@ class OpenCVCamera(Camera):
         if self.thread is None or not self.thread.is_alive():
             self._start_read_thread()
 
-        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+        try:
+            return self.frame_queue.get(timeout=timeout_ms / 1000.0)
+        except queue.Empty as e:
             thread_alive = self.thread is not None and self.thread.is_alive()
             raise TimeoutError(
                 f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
                 f"Read thread alive: {thread_alive}."
-            )
-
-        with self.frame_lock:
-            frame = self.latest_frame
-            self.new_frame_event.clear()
-
-        if frame is None:
-            raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
-
-        return frame
+            ) from e
+        except Exception as e:
+            logger.exception(f"Unexpected error getting frame from queue for {self}: {e}")
+            raise RuntimeError(f"Error getting frame from queue for camera {self.index_or_path}: {e}") from e
 
     def disconnect(self):
         """

lerobot/common/cameras/opencv/configuration_opencv.py

@@ -45,7 +45,6 @@ class OpenCVCameraConfig(CameraConfig):
         height: Requested frame height in pixels for the color stream.
         color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
-        warmup_s: Time reading frames before returning from connect (in seconds)
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.
@@ -54,7 +53,6 @@ class OpenCVCameraConfig(CameraConfig):
     index_or_path: int | Path
     color_mode: ColorMode = ColorMode.RGB
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
-    warmup_s: int = 1
 
     def __post_init__(self):
         if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):

lerobot/common/cameras/realsense/camera_realsense.py

@@ -16,9 +16,11 @@
 Provides the RealSenseCamera class for capturing frames from Intel RealSense cameras.
 """
 
+import contextlib
 import logging
+import queue
 import time
-from threading import Event, Lock, Thread
+from threading import Event, Thread
 from typing import Any, Dict, List
 
 import cv2
@@ -67,7 +69,7 @@ class RealSenseCamera(Camera):
         from lerobot.common.cameras import ColorMode, Cv2Rotation
 
         # Basic usage with serial number
-        config = RealSenseCameraConfig(serial_number_or_name="0123456789") # Replace with actual SN
+        config = RealSenseCameraConfig(serial_number_or_name="1234567890") # Replace with actual SN
         camera = RealSenseCamera(config)
         camera.connect()
 
@@ -83,7 +85,7 @@ class RealSenseCamera(Camera):
 
         # Example with depth capture and custom settings
         custom_config = RealSenseCameraConfig(
-            serial_number_or_name="0123456789", # Replace with actual SN
+            serial_number_or_name="1234567890", # Replace with actual SN
             fps=30,
             width=1280,
             height=720,
@@ -92,10 +94,12 @@ class RealSenseCamera(Camera):
             use_depth=True
         )
         depth_camera = RealSenseCamera(custom_config)
-        depth_camera.connect()
-
-        # Read 1 depth frame
-        depth_map = depth_camera.read_depth()
+        try:
+            depth_camera.connect()
+            depth_map = depth_camera.read_depth()
+            print(f"Depth shape: {depth_map.shape}")
+        finally:
+            depth_camera.disconnect()
 
         # Example using a unique camera name
         name_config = RealSenseCameraConfig(serial_number_or_name="Intel RealSense D435") # If unique
@@ -116,24 +120,22 @@ class RealSenseCamera(Camera):
 
         self.config = config
 
-        if config.serial_number_or_name.isdigit():
-            self.serial_number = config.serial_number_or_name
+        if isinstance(config.serial_number_or_name, int):
+            self.serial_number = str(config.serial_number_or_name)
         else:
             self.serial_number = self._find_serial_number_from_name(config.serial_number_or_name)
 
         self.fps = config.fps
         self.color_mode = config.color_mode
         self.use_depth = config.use_depth
-        self.warmup_s = config.warmup_s
+        self.warmup_time = config.warmup_time
 
         self.rs_pipeline: rs.pipeline | None = None
         self.rs_profile: rs.pipeline_profile | None = None
 
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
-        self.frame_lock: Lock = Lock()
-        self.latest_frame: np.ndarray | None = None
-        self.new_frame_event: Event = Event()
+        self.frame_queue: queue.Queue = queue.Queue(maxsize=1)
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
 
@@ -168,27 +170,29 @@ class RealSenseCamera(Camera):
             raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         self.rs_pipeline = rs.pipeline()
-        rs_config = rs.config()
-        self._configure_rs_pipeline_config(rs_config)
+        rs_config = self._make_rs_pipeline_config()
 
         try:
             self.rs_profile = self.rs_pipeline.start(rs_config)
+            logger.debug(f"Successfully started pipeline for camera {self.serial_number}.")
         except RuntimeError as e:
             self.rs_profile = None
             self.rs_pipeline = None
             raise ConnectionError(
-                f"Failed to open {self}."
-                "Run `python -m lerobot.find_cameras realsense` to find available cameras."
+                f"Failed to open {self} camera. Run 'python -m lerobot.find_cameras realsense' for details about the available cameras in your system."
             ) from e
 
-        self._configure_capture_settings()
+        logger.debug(f"Validating stream configuration for {self}...")
+        self._validate_capture_settings()
 
         if warmup:
-            time.sleep(
-                1
-            )  # NOTE(Steven): RS cameras need a bit of time to warm up before the first read. If we don't wait, the first read from the warmup will raise.
+            if self.warmup_time is None:
+                raise ValueError(
+                    f"Warmup time is not set for {self}. Please set a warmup time in the configuration."
+                )
+            logger.debug(f"Reading a warm-up frames for {self} for {self.warmup_time} seconds...")
             start_time = time.time()
-            while time.time() - start_time < self.warmup_s:
+            while time.time() - start_time < self.warmup_time:
                 self.read()
                 time.sleep(0.1)
 
@@ -243,6 +247,7 @@ class RealSenseCamera(Camera):
 
             found_cameras_info.append(camera_info)
 
+        logger.info(f"Detected RealSense cameras: {[cam['id'] for cam in found_cameras_info]}")
         return found_cameras_info
 
     def _find_serial_number_from_name(self, name: str) -> str:
@@ -264,33 +269,50 @@ class RealSenseCamera(Camera):
             )
 
         serial_number = str(found_devices[0]["serial_number"])
+        logger.info(f"Found serial number '{serial_number}' for camera name '{name}'.")
         return serial_number
 
-    def _configure_rs_pipeline_config(self, rs_config):
+    def _make_rs_pipeline_config(self) -> rs.config:
         """Creates and configures the RealSense pipeline configuration object."""
+        rs_config = rs.config()
         rs.config.enable_device(rs_config, self.serial_number)
 
         if self.width and self.height and self.fps:
+            logger.debug(
+                f"Requesting Color Stream: {self.capture_width}x{self.capture_height} @ {self.fps} FPS, Format: {rs.format.rgb8}"
+            )
             rs_config.enable_stream(
                 rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
             )
             if self.use_depth:
+                logger.debug(
+                    f"Requesting Depth Stream: {self.capture_width}x{self.capture_height} @ {self.fps} FPS, Format: {rs.format.z16}"
+                )
                 rs_config.enable_stream(
                     rs.stream.depth, self.capture_width, self.capture_height, rs.format.z16, self.fps
                 )
         else:
+            logger.debug(f"Requesting Color Stream: Default settings, Format: {rs.stream.color}")
             rs_config.enable_stream(rs.stream.color)
             if self.use_depth:
+                logger.debug(f"Requesting Depth Stream: Default settings, Format: {rs.stream.depth}")
                 rs_config.enable_stream(rs.stream.depth)
 
-    def _configure_capture_settings(self) -> None:
-        """Sets fps, width, and height from device stream if not already configured.
+        return rs_config
 
-        Uses the color stream profile to update unset attributes. Handles rotation by
-        swapping width/height when needed. Original capture dimensions are always stored.
+    def _validate_capture_settings(self) -> None:
+        """
+        Validates if the actual stream settings match the requested configuration.
+
+        This method compares the requested FPS, width, and height against the
+        actual settings obtained from the active RealSense profile after the
+        pipeline has started.
 
         Raises:
-            DeviceNotConnectedError: If device is not connected.
+            RuntimeError: If the actual camera settings significantly deviate
+                          from the requested ones.
+            DeviceNotConnectedError: If the camera is not connected when attempting
+                                     to validate settings.
         """
         if not self.is_connected:
             raise DeviceNotConnectedError(f"Cannot validate settings for {self} as it is not connected.")
@@ -309,8 +331,33 @@ class RealSenseCamera(Camera):
             else:
                 self.width, self.height = actual_width, actual_height
                 self.capture_width, self.capture_height = actual_width, actual_height
+            logger.info(f"Capture width set to camera default: {self.width}.")
+            logger.info(f"Capture height set to camera default: {self.height}.")
+        else:
+            self._validate_width_and_height(stream)
 
-    def read_depth(self, timeout_ms: int = 200) -> np.ndarray:
+        if self.use_depth:
+            stream = self.rs_profile.get_stream(rs.stream.depth).as_video_stream_profile()
+            self._validate_width_and_height(stream)
+
+    def _validate_width_and_height(self, stream) -> None:
+        """Validates and sets the internal capture width and height based on actual stream width."""
+        actual_width = int(round(stream.width()))
+        actual_height = int(round(stream.height()))
+
+        if self.capture_width != actual_width:
+            raise RuntimeError(
+                f"Failed to set requested capture width {self.capture_width} for {self}. Actual value: {actual_width}."
+            )
+        logger.debug(f"Capture width set to {actual_width} for {self}.")
+
+        if self.capture_height != actual_height:
+            raise RuntimeError(
+                f"Failed to set requested capture height {self.capture_height} for {self}. Actual value: {actual_height}."
+            )
+        logger.debug(f"Capture height set to {actual_height} for {self}.")
+
+    def read_depth(self, timeout_ms: int = 100) -> np.ndarray:
         """
         Reads a single frame (depth) synchronously from the camera.
 
@@ -318,7 +365,7 @@ class RealSenseCamera(Camera):
         from the camera hardware via the RealSense pipeline.
 
         Args:
-            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
+            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 100ms.
 
         Returns:
             np.ndarray: The depth map as a NumPy array (height, width)
@@ -341,7 +388,7 @@ class RealSenseCamera(Camera):
         ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
 
         if not ret or frame is None:
-            raise RuntimeError(f"{self} read_depth failed (status={ret}).")
+            raise RuntimeError(f"{self} failed to capture frame. Returned status='{ret}'.")
 
         depth_frame = frame.get_depth_frame()
         depth_map = np.asanyarray(depth_frame.get_data())
@@ -349,11 +396,11 @@ class RealSenseCamera(Camera):
         depth_map_processed = self._postprocess_image(depth_map, depth_frame=True)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
-        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+        logger.debug(f"{self} synchronous read took: {read_duration_ms:.1f}ms")
 
         return depth_map_processed
 
-    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> np.ndarray:
+    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 100) -> np.ndarray:
         """
         Reads a single frame (color) synchronously from the camera.
 
@@ -361,7 +408,7 @@ class RealSenseCamera(Camera):
         from the camera hardware via the RealSense pipeline.
 
         Args:
-            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
+            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 100ms.
 
         Returns:
             np.ndarray: The captured color frame as a NumPy array
@@ -378,10 +425,14 @@ class RealSenseCamera(Camera):
 
         start_time = time.perf_counter()
 
-        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
+        ret, frame = self.rs_pipeline.try_wait_for_frames(
+            timeout_ms=timeout_ms
+        )  # NOTE(Steven): This read has a timeout while opencv doesn't
 
         if not ret or frame is None:
-            raise RuntimeError(f"{self} read failed (status={ret}).")
+            raise RuntimeError(
+                f"Failed to capture frame from {self}. '.read()' returned status={ret} and frame is None."
+            )
 
         color_frame = frame.get_color_frame()
         color_image_raw = np.asanyarray(color_frame.get_data())
@@ -389,7 +440,7 @@ class RealSenseCamera(Camera):
         color_image_processed = self._postprocess_image(color_image_raw, color_mode)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
-        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+        logger.debug(f"{self} synchronous read took: {read_duration_ms:.1f}ms")
 
         return color_image_processed
 
@@ -421,22 +472,21 @@ class RealSenseCamera(Camera):
         if depth_frame:
             h, w = image.shape
         else:
-            h, w, c = image.shape
-
-            if c != 3:
-                raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).")
+            h, w, _c = image.shape
 
         if h != self.capture_height or w != self.capture_width:
             raise RuntimeError(
-                f"{self} frame width={w} or height={h} do not match configured width={self.capture_width} or height={self.capture_height}."
+                f"Captured frame dimensions ({h}x{w}) do not match configured capture dimensions ({self.capture_height}x{self.capture_width}) for {self}."
             )
 
         processed_image = image
         if self.color_mode == ColorMode.BGR:
             processed_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+            logger.debug(f"Converted frame from RGB to BGR for {self}.")
 
         if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
             processed_image = cv2.rotate(processed_image, self.rotation)
+            logger.debug(f"Rotated frame by {self.config.rotation} degrees for {self}.")
 
         return processed_image
 
@@ -444,26 +494,28 @@ class RealSenseCamera(Camera):
         """
         Internal loop run by the background thread for asynchronous reading.
 
-        On each iteration:
-        1. Reads a color frame with 500ms timeout
-        2. Stores result in latest_frame (thread-safe)
-        3. Sets new_frame_event to notify listeners
-
-        Stops on DeviceNotConnectedError, logs other errors and continues.
+        Continuously reads frames (color and optional depth) using `read()`
+        and places the latest result (single image or tuple) into the `frame_queue`.
+        It overwrites any previous frame in the queue.
         """
+        logger.debug(f"Starting read loop thread for {self}.")
         while not self.stop_event.is_set():
             try:
-                color_image = self.read(timeout_ms=500)
+                frame_data = self.read(timeout_ms=500)
 
-                with self.frame_lock:
-                    self.latest_frame = color_image
-                self.new_frame_event.set()
+                with contextlib.suppress(queue.Empty):
+                    _ = self.frame_queue.get_nowait()
+                self.frame_queue.put(frame_data)
+                logger.debug(f"Frame data placed in queue for {self}.")
 
             except DeviceNotConnectedError:
+                logger.error(f"Read loop for {self} stopped: Camera disconnected.")
                 break
             except Exception as e:
                 logger.warning(f"Error reading frame in background thread for {self}: {e}")
 
+        logger.debug(f"Stopping read loop thread for {self}.")
+
     def _start_read_thread(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
         if self.thread is not None and self.thread.is_alive():
@@ -475,6 +527,7 @@ class RealSenseCamera(Camera):
         self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
         self.thread.daemon = True
         self.thread.start()
+        logger.debug(f"Read thread started for {self}.")
 
     def _stop_read_thread(self):
         """Signals the background read thread to stop and waits for it to join."""
@@ -483,22 +536,28 @@ class RealSenseCamera(Camera):
 
         if self.thread is not None and self.thread.is_alive():
             self.thread.join(timeout=2.0)
+            if self.thread.is_alive():
+                logger.warning(f"Read thread for {self} did not terminate gracefully after 2 seconds.")
+            else:
+                logger.debug(f"Read thread for {self} joined successfully.")
 
         self.thread = None
         self.stop_event = None
+        logger.debug(f"Read thread stopped for {self}.")
 
     # NOTE(Steven): Missing implementation for depth for now
-    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+    def async_read(self, timeout_ms: float = 100) -> np.ndarray:
         """
-        Reads the latest available frame data (color) asynchronously.
+        Reads the latest available frame data (color or color+depth) asynchronously.
 
-        This method retrieves the most recent color frame captured by the background
+        This method retrieves the most recent frame captured by the background
         read thread. It does not block waiting for the camera hardware directly,
-        but may wait up to timeout_ms for the background thread to provide a frame.
+        only waits for a frame to appear in the internal queue up to the specified
+        timeout.
 
         Args:
             timeout_ms (float): Maximum time in milliseconds to wait for a frame
-                to become available. Defaults to 200ms (0.2 seconds).
+                to become available in the queue. Defaults to 100ms (0.1 seconds).
 
         Returns:
             np.ndarray:
@@ -507,7 +566,7 @@ class RealSenseCamera(Camera):
         Raises:
             DeviceNotConnectedError: If the camera is not connected.
             TimeoutError: If no frame data becomes available within the specified timeout.
-            RuntimeError: If the background thread died unexpectedly or another error occurs.
+            RuntimeError: If the background thread died unexpectedly or another queue error occurs.
         """
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -515,21 +574,16 @@ class RealSenseCamera(Camera):
         if self.thread is None or not self.thread.is_alive():
             self._start_read_thread()
 
-        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+        try:
+            return self.frame_queue.get(timeout=timeout_ms / 1000.0)
+        except queue.Empty as e:
             thread_alive = self.thread is not None and self.thread.is_alive()
             raise TimeoutError(
                 f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
                 f"Read thread alive: {thread_alive}."
-            )
-
-        with self.frame_lock:
-            frame = self.latest_frame
-            self.new_frame_event.clear()
-
-        if frame is None:
-            raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
-
-        return frame
+            ) from e
+        except Exception as e:
+            raise RuntimeError(f"Error getting frame data from queue for camera {self}: {e}") from e
 
     def disconnect(self):
         """
@@ -550,6 +604,7 @@ class RealSenseCamera(Camera):
             self._stop_read_thread()
 
         if self.rs_pipeline is not None:
+            logger.debug(f"Stopping RealSense pipeline object for {self}.")
             self.rs_pipeline.stop()
             self.rs_pipeline = None
             self.rs_profile = None

lerobot/common/cameras/realsense/configuration_realsense.py

@@ -28,12 +28,12 @@ class RealSenseCameraConfig(CameraConfig):
     Example configurations for Intel RealSense D405:
     ```python
     # Basic configurations
-    RealSenseCameraConfig("0123456789", 30, 1280, 720)   # 1280x720 @ 30FPS
-    RealSenseCameraConfig("0123456789", 60, 640, 480)   # 640x480 @ 60FPS
+    RealSenseCameraConfig(128422271347, 30, 1280, 720)   # 1280x720 @ 30FPS
+    RealSenseCameraConfig(128422271347, 60, 640, 480)   # 640x480 @ 60FPS
 
     # Advanced configurations
-    RealSenseCameraConfig("0123456789", 30, 640, 480, use_depth=True)  # With depth sensing
-    RealSenseCameraConfig("0123456789", 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)     # With 90° rotation
+    RealSenseCameraConfig(128422271347, 30, 640, 480, use_depth=True)  # With depth sensing
+    RealSenseCameraConfig(128422271347, 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)     # With 90° rotation
     ```
 
     Attributes:
@@ -44,7 +44,6 @@ class RealSenseCameraConfig(CameraConfig):
         color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
         use_depth: Whether to enable depth stream. Defaults to False.
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
-        warmup_s: Time reading frames before returning from connect (in seconds)
 
     Note:
         - Either name or serial_number must be specified.
@@ -53,11 +52,10 @@ class RealSenseCameraConfig(CameraConfig):
         - For `fps`, `width` and `height`, either all of them need to be set, or none of them.
     """
 
-    serial_number_or_name: str
+    serial_number_or_name: int | str
     color_mode: ColorMode = ColorMode.RGB
     use_depth: bool = False
-    rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
-    warmup_s: int = 1
+    rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION  # NOTE(Steven): Check if draccus can parse to an enum
 
     def __post_init__(self):
         if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):

lerobot/common/cameras/utils.py

@@ -18,6 +18,9 @@ import platform
 from pathlib import Path
 from typing import TypeAlias
 
+import numpy as np
+from PIL import Image
+
 from .camera import Camera
 from .configs import CameraConfig, Cv2Rotation
 
@@ -63,3 +66,10 @@ def get_cv2_backend() -> int:
         return cv2.CAP_AVFOUNDATION
     else:
         return cv2.CAP_ANY
+
+
+def save_image(img_array: np.ndarray, camera_index: int, frame_index: int, images_dir: Path):
+    img = Image.fromarray(img_array)
+    path = images_dir / f"camera_{camera_index:02d}_frame_{frame_index:06d}.png"
+    path.parent.mkdir(parents=True, exist_ok=True)
+    img.save(str(path), quality=100)

lerobot/common/datasets/image_writer.py

@@ -106,7 +106,7 @@ def worker_process(queue: queue.Queue, num_threads: int):
 class AsyncImageWriter:
     """
     This class abstract away the initialisation of processes or/and threads to
-    save images on disk asynchronously, which is critical to control a robot and record data
+    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 creates a threads pool of size `num_threads`.

lerobot/common/datasets/lerobot_dataset.py

@@ -932,7 +932,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def stop_image_writer(self) -> None:
         """
         Whenever wrapping this dataset inside a parallelized DataLoader, this needs to be called first to
-        remove the image_writer in order for the LeRobotDataset object to be picklable and parallelized.
+        remove the image_writer in order for the LeRobotDataset object to be pickleable and parallelized.
         """
         if self.image_writer is not None:
             self.image_writer.stop()

lerobot/common/datasets/utils.py

@@ -400,22 +400,15 @@ def hw_to_dataset_features(
     joint_fts = {key: ftype for key, ftype in hw_features.items() if ftype is float}
     cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
 
-    if joint_fts and prefix == "action":
-        features[prefix] = {
-            "dtype": "float32",
-            "shape": (len(joint_fts),),
-            "names": list(joint_fts),
-        }
-
-    if joint_fts and prefix == "observation":
-        features[f"{prefix}.state"] = {
+    if joint_fts:
+        features[f"{prefix}.joints"] = {
             "dtype": "float32",
             "shape": (len(joint_fts),),
             "names": list(joint_fts),
         }
 
     for key, shape in cam_fts.items():
-        features[f"{prefix}.images.{key}"] = {
+        features[f"{prefix}.cameras.{key}"] = {
             "dtype": "video" if use_video else "image",
             "shape": shape,
             "names": ["height", "width", "channels"],
@@ -435,7 +428,7 @@ def build_dataset_frame(
         elif ft["dtype"] == "float32" and len(ft["shape"]) == 1:
             frame[key] = np.array([values[name] for name in ft["names"]], dtype=np.float32)
         elif ft["dtype"] in ["image", "video"]:
-            frame[key] = values[key.removeprefix(f"{prefix}.images.")]
+            frame[key] = values[key.removeprefix(f"{prefix}.cameras.")]
 
     return frame
 
@@ -468,7 +461,7 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
             type = FeatureType.ENV
         elif key.startswith("observation"):
             type = FeatureType.STATE
-        elif key.startswith("action"):
+        elif key == "action":
             type = FeatureType.ACTION
         else:
             continue

lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py

@@ -142,6 +142,7 @@ from lerobot.common.datasets.video_utils import (
     get_video_info,
 )
 from lerobot.common.robots import RobotConfig
+from lerobot.common.robots.utils import make_robot_config
 
 V16 = "v1.6"
 V20 = "v2.0"
@@ -597,30 +598,6 @@ def convert_dataset(
         create_branch(repo_id=repo_id, branch=V20, repo_type="dataset")
 
 
-def make_robot_config(robot_type: str, **kwargs) -> RobotConfig:
-    if robot_type == "aloha":
-        raise NotImplementedError  # TODO
-
-    elif robot_type == "koch_follower":
-        from lerobot.common.robots.koch_follower import KochFollowerConfig
-
-        return KochFollowerConfig(**kwargs)
-    elif robot_type == "so100_follower":
-        from lerobot.common.robots.so100_follower import SO100FollowerConfig
-
-        return SO100FollowerConfig(**kwargs)
-    elif robot_type == "stretch":
-        from lerobot.common.robots.stretch3 import Stretch3RobotConfig
-
-        return Stretch3RobotConfig(**kwargs)
-    elif robot_type == "lekiwi":
-        from lerobot.common.robots.lekiwi import LeKiwiConfig
-
-        return LeKiwiConfig(**kwargs)
-    else:
-        raise ValueError(f"Robot type '{robot_type}' is not available.")
-
-
 def main():
     parser = argparse.ArgumentParser()
     task_args = parser.add_mutually_exclusive_group(required=True)

lerobot/common/datasets/video_utils.py

@@ -101,7 +101,7 @@ def decode_video_frames_torchvision(
     keyframes_only = False
     torchvision.set_video_backend(backend)
     if backend == "pyav":
-        keyframes_only = True  # pyav doesn't support accurate seek
+        keyframes_only = True  # pyav doesnt support accuracte seek
 
     # set a video stream reader
     # TODO(rcadene): also load audio stream at the same time

lerobot/common/envs/configs.py

@@ -14,13 +14,10 @@
 
 import abc
 from dataclasses import dataclass, field
-from typing import Any, Dict, Optional, Tuple
 
 import draccus
 
 from lerobot.common.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
-from lerobot.common.robots import RobotConfig
-from lerobot.common.teleoperators.config import TeleoperatorConfig
 from lerobot.configs.types import FeatureType, PolicyFeature
 
 
@@ -158,125 +155,3 @@ class XarmEnv(EnvConfig):
             "visualization_height": self.visualization_height,
             "max_episode_steps": self.episode_length,
         }
-
-
-@dataclass
-class VideoRecordConfig:
-    """Configuration for video recording in ManiSkill environments."""
-
-    enabled: bool = False
-    record_dir: str = "videos"
-    trajectory_name: str = "trajectory"
-
-
-# @dataclass
-# class EEActionSpaceConfig:
-#     """Configuration parameters for end-effector action space."""
-
-#     x_step_size: float
-#     y_step_size: float
-#     z_step_size: float
-#     bounds: Dict[str, Any]  # Contains 'min' and 'max' keys with position bounds
-#     control_mode: str = "gamepad"
-
-
-@dataclass
-class EnvTransformConfig:
-    """Configuration for environment wrappers."""
-
-    # ee_action_space_params: EEActionSpaceConfig = field(default_factory=EEActionSpaceConfig)
-    control_mode: str = "gamepad"
-    display_cameras: bool = False
-    add_joint_velocity_to_observation: bool = False
-    add_current_to_observation: bool = False
-    add_ee_pose_to_observation: bool = False
-    crop_params_dict: Optional[Dict[str, Tuple[int, int, int, int]]] = None
-    resize_size: Optional[Tuple[int, int]] = None
-    control_time_s: float = 20.0
-    fixed_reset_joint_positions: Optional[Any] = None
-    reset_time_s: float = 5.0
-    use_gripper: bool = False
-    gripper_quantization_threshold: float | None = 0.8
-    gripper_penalty: float = 0.0
-    gripper_penalty_in_reward: bool = False
-
-
-@EnvConfig.register_subclass(name="gym_manipulator")
-@dataclass
-class HILSerlRobotEnvConfig(EnvConfig):
-    """Configuration for the HILSerlRobotEnv environment."""
-
-    robot: Optional[RobotConfig] = None
-    teleop: Optional[TeleoperatorConfig] = None
-    wrapper: Optional[EnvTransformConfig] = None
-    fps: int = 10
-    name: str = "real_robot"
-    mode: str = None  # Either "record", "replay", None
-    repo_id: Optional[str] = None
-    dataset_root: Optional[str] = None
-    task: str = ""
-    num_episodes: int = 10  # only for record mode
-    episode: int = 0
-    device: str = "cuda"
-    push_to_hub: bool = True
-    pretrained_policy_name_or_path: Optional[str] = None
-    reward_classifier_pretrained_path: Optional[str] = None
-    # For the reward classifier, to record more positive examples after a success
-    number_of_steps_after_success: int = 0
-
-    def gym_kwargs(self) -> dict:
-        return {}
-
-
-@EnvConfig.register_subclass("hil")
-@dataclass
-class HILEnvConfig(EnvConfig):
-    """Configuration for the HIL environment."""
-
-    type: str = "hil"
-    name: str = "PandaPickCube"
-    task: str = "PandaPickCubeKeyboard-v0"
-    use_viewer: bool = True
-    gripper_penalty: float = 0.0
-    use_gamepad: bool = True
-    state_dim: int = 18
-    action_dim: int = 4
-    fps: int = 100
-    episode_length: int = 100
-    video_record: VideoRecordConfig = field(default_factory=VideoRecordConfig)
-    features: dict[str, PolicyFeature] = field(
-        default_factory=lambda: {
-            "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)),
-            "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
-            "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(18,)),
-        }
-    )
-    features_map: dict[str, str] = field(
-        default_factory=lambda: {
-            "action": ACTION,
-            "observation.image": OBS_IMAGE,
-            "observation.state": OBS_STATE,
-        }
-    )
-    ################# args from hilserlrobotenv
-    reward_classifier_pretrained_path: Optional[str] = None
-    robot_config: Optional[RobotConfig] = None
-    teleop_config: Optional[TeleoperatorConfig] = None
-    wrapper: Optional[EnvTransformConfig] = None
-    mode: str = None  # Either "record", "replay", None
-    repo_id: Optional[str] = None
-    dataset_root: Optional[str] = None
-    num_episodes: int = 10  # only for record mode
-    episode: int = 0
-    device: str = "cuda"
-    push_to_hub: bool = True
-    pretrained_policy_name_or_path: Optional[str] = None
-    ############################
-
-    @property
-    def gym_kwargs(self) -> dict:
-        return {
-            "use_viewer": self.use_viewer,
-            "use_gamepad": self.use_gamepad,
-            "gripper_penalty": self.gripper_penalty,
-        }

lerobot/common/envs/factory.py

@@ -17,7 +17,7 @@ import importlib
 
 import gymnasium as gym
 
-from lerobot.common.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv
+from lerobot.common.envs.configs import AlohaEnv, EnvConfig, PushtEnv, XarmEnv
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
@@ -27,8 +27,6 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
         return PushtEnv(**kwargs)
     elif env_type == "xarm":
         return XarmEnv(**kwargs)
-    elif env_type == "hil":
-        return HILEnvConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{env_type}' is not available.")
 
@@ -67,8 +65,5 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g
     env = env_cls(
         [lambda: gym.make(gym_handle, disable_env_checker=True, **cfg.gym_kwargs) for _ in range(n_envs)]
     )
-    # TODO: add observation processor wrapper and remove preprocess_observation in the codebase
-    # https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/wrappers/vector/vectorize_observation.py#L19,
-    # env = ObservationProcessorWrapper(env=env)
 
     return env

lerobot/common/envs/utils.py

@@ -47,10 +47,6 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
             # TODO(aliberts, rcadene): use transforms.ToTensor()?
             img = torch.from_numpy(img)
 
-            # When preprocessing observations in a non-vectorized environment, we need to add a batch dimension.
-            # This is the case for human-in-the-loop RL where there is only one environment.
-            if img.ndim == 3:
-                img = img.unsqueeze(0)
             # sanity check that images are channel last
             _, h, w, c = img.shape
             assert c < h and c < w, f"expect channel last images, but instead got {img.shape=}"
@@ -66,18 +62,13 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
             return_observations[imgkey] = img
 
     if "environment_state" in observations:
-        env_state = torch.from_numpy(observations["environment_state"]).float()
-        if env_state.dim() == 1:
-            env_state = env_state.unsqueeze(0)
-
-        return_observations["observation.environment_state"] = env_state
+        return_observations["observation.environment_state"] = torch.from_numpy(
+            observations["environment_state"]
+        ).float()
 
     # TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing
-    agent_pos = torch.from_numpy(observations["agent_pos"]).float()
-    if agent_pos.dim() == 1:
-        agent_pos = agent_pos.unsqueeze(0)
-    return_observations["observation.state"] = agent_pos
-
+    # requirement for "agent_pos"
+    return_observations["observation.state"] = torch.from_numpy(observations["agent_pos"]).float()
     return return_observations
 
 

lerobot/common/model/kinematics.py

@@ -1,589 +0,0 @@
-# ruff: noqa: N806, N815, N803
-
-# 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 numpy as np
-from scipy.spatial.transform import Rotation
-
-
-def skew_symmetric(w):
-    """Creates the skew-symmetric matrix from a 3D vector."""
-    return np.array([[0, -w[2], w[1]], [w[2], 0, -w[0]], [-w[1], w[0], 0]])
-
-
-def rodrigues_rotation(w, theta):
-    """Computes the rotation matrix using Rodrigues' formula."""
-    w_hat = skew_symmetric(w)
-    return np.eye(3) + np.sin(theta) * w_hat + (1 - np.cos(theta)) * w_hat @ w_hat
-
-
-def screw_axis_to_transform(S, theta):
-    """Converts a screw axis to a 4x4 transformation matrix."""
-    S_w = S[:3]
-    S_v = S[3:]
-    if np.allclose(S_w, 0) and np.linalg.norm(S_v) == 1:  # Pure translation
-        T = np.eye(4)
-        T[:3, 3] = S_v * theta
-    elif np.linalg.norm(S_w) == 1:  # Rotation and translation
-        w_hat = skew_symmetric(S_w)
-        R = np.eye(3) + np.sin(theta) * w_hat + (1 - np.cos(theta)) * w_hat @ w_hat
-        t = (np.eye(3) * theta + (1 - np.cos(theta)) * w_hat + (theta - np.sin(theta)) * w_hat @ w_hat) @ S_v
-        T = np.eye(4)
-        T[:3, :3] = R
-        T[:3, 3] = t
-    else:
-        raise ValueError("Invalid screw axis parameters")
-    return T
-
-
-def pose_difference_se3(pose1, pose2):
-    """
-    Calculates the SE(3) difference between two 4x4 homogeneous transformation matrices.
-    SE(3) (Special Euclidean Group) represents rigid body transformations in 3D space, combining rotation (SO(3)) and translation.
-    Each 4x4 matrix has the following structure, a 3x3 rotation matrix in the top-left and a 3x1 translation vector in the top-right:
-
-    [R11 R12 R13 tx]
-    [R21 R22 R23 ty]
-    [R31 R32 R33 tz]
-    [ 0   0   0   1]
-
-    where Rij is the 3x3 rotation matrix and [tx,ty,tz] is the translation vector.
-
-    pose1 - pose2
-
-    Args:
-        pose1: A 4x4 numpy array representing the first pose.
-        pose2: A 4x4 numpy array representing the second pose.
-
-    Returns:
-        A tuple (translation_diff, rotation_diff) where:
-        - translation_diff is a 3x1 numpy array representing the translational difference.
-        - rotation_diff is a 3x1 numpy array representing the rotational difference in axis-angle representation.
-    """
-
-    # Extract rotation matrices from poses
-    R1 = pose1[:3, :3]
-    R2 = pose2[:3, :3]
-
-    # Calculate translational difference
-    translation_diff = pose1[:3, 3] - pose2[:3, 3]
-
-    # Calculate rotational difference using scipy's Rotation library
-    R_diff = Rotation.from_matrix(R1 @ R2.T)
-    rotation_diff = R_diff.as_rotvec()  # Convert to axis-angle representation
-
-    return np.concatenate([translation_diff, rotation_diff])
-
-
-def se3_error(target_pose, current_pose):
-    pos_error = target_pose[:3, 3] - current_pose[:3, 3]
-    R_target = target_pose[:3, :3]
-    R_current = current_pose[:3, :3]
-    R_error = R_target @ R_current.T
-    rot_error = Rotation.from_matrix(R_error).as_rotvec()
-    return np.concatenate([pos_error, rot_error])
-
-
-class RobotKinematics:
-    """Robot kinematics class supporting multiple robot models."""
-
-    # Robot measurements dictionary
-    ROBOT_MEASUREMENTS = {
-        "koch": {
-            "gripper": [0.239, -0.001, 0.024],
-            "wrist": [0.209, 0, 0.024],
-            "forearm": [0.108, 0, 0.02],
-            "humerus": [0, 0, 0.036],
-            "shoulder": [0, 0, 0],
-            "base": [0, 0, 0.02],
-        },
-        "so100": {
-            "gripper": [0.320, 0, 0.050],
-            "wrist": [0.278, 0, 0.050],
-            "forearm": [0.143, 0, 0.044],
-            "humerus": [0.031, 0, 0.072],
-            "shoulder": [0, 0, 0],
-            "base": [0, 0, 0.02],
-        },
-        "moss": {
-            "gripper": [0.246, 0.013, 0.111],
-            "wrist": [0.245, 0.002, 0.064],
-            "forearm": [0.122, 0, 0.064],
-            "humerus": [0.001, 0.001, 0.063],
-            "shoulder": [0, 0, 0],
-            "base": [0, 0, 0.02],
-        },
-        "so101": {
-            "gripper": [0.33, 0.0, 0.285],
-            "wrist": [0.30, 0.0, 0.267],
-            "forearm": [0.25, 0.0, 0.266],
-            "humerus": [0.06, 0.0, 0.264],
-            "shoulder": [0.0, 0.0, 0.238],
-            "base": [0.0, 0.0, 0.12],
-        },
-    }
-
-    def __init__(self, robot_type="so100"):
-        """Initialize kinematics for the specified robot type.
-
-        Args:
-            robot_type: String specifying the robot model ("koch", "so100", or "moss")
-        """
-        if robot_type not in self.ROBOT_MEASUREMENTS:
-            raise ValueError(
-                f"Unknown robot type: {robot_type}. Available types: {list(self.ROBOT_MEASUREMENTS.keys())}"
-            )
-
-        self.robot_type = robot_type
-        self.measurements = self.ROBOT_MEASUREMENTS[robot_type]
-
-        # Initialize all transformation matrices and screw axes
-        self._setup_transforms()
-
-    def _create_translation_matrix(self, x=0, y=0, z=0):
-        """Create a 4x4 translation matrix."""
-        return np.array([[1, 0, 0, x], [0, 1, 0, y], [0, 0, 1, z], [0, 0, 0, 1]])
-
-    def _setup_transforms(self):
-        """Setup all transformation matrices and screw axes for the robot."""
-        # Set up rotation matrices (constant across robot types)
-
-        # Gripper orientation
-        self.gripper_X0 = np.array(
-            [
-                [1, 0, 0, 0],
-                [0, 0, 1, 0],
-                [0, -1, 0, 0],
-                [0, 0, 0, 1],
-            ]
-        )
-
-        # Wrist orientation
-        self.wrist_X0 = np.array(
-            [
-                [0, -1, 0, 0],
-                [1, 0, 0, 0],
-                [0, 0, 1, 0],
-                [0, 0, 0, 1],
-            ]
-        )
-
-        # Base orientation
-        self.base_X0 = np.array(
-            [
-                [0, 0, 1, 0],
-                [1, 0, 0, 0],
-                [0, 1, 0, 0],
-                [0, 0, 0, 1],
-            ]
-        )
-
-        # Gripper
-        # Screw axis of gripper frame wrt base frame
-        self.S_BG = np.array(
-            [
-                1,
-                0,
-                0,
-                0,
-                self.measurements["gripper"][2],
-                -self.measurements["gripper"][1],
-            ]
-        )
-
-        # Gripper origin to centroid transform
-        self.X_GoGc = self._create_translation_matrix(x=0.07)
-
-        # Gripper origin to tip transform
-        self.X_GoGt = self._create_translation_matrix(x=0.12)
-
-        # 0-position gripper frame pose wrt base
-        self.X_BoGo = self._create_translation_matrix(
-            x=self.measurements["gripper"][0],
-            y=self.measurements["gripper"][1],
-            z=self.measurements["gripper"][2],
-        )
-
-        # Wrist
-        # Screw axis of wrist frame wrt base frame
-        self.S_BR = np.array([0, 1, 0, -self.measurements["wrist"][2], 0, self.measurements["wrist"][0]])
-
-        # 0-position origin to centroid transform
-        self.X_RoRc = self._create_translation_matrix(x=0.0035, y=-0.002)
-
-        # 0-position wrist frame pose wrt base
-        self.X_BR = self._create_translation_matrix(
-            x=self.measurements["wrist"][0],
-            y=self.measurements["wrist"][1],
-            z=self.measurements["wrist"][2],
-        )
-
-        # Forearm
-        # Screw axis of forearm frame wrt base frame
-        self.S_BF = np.array(
-            [
-                0,
-                1,
-                0,
-                -self.measurements["forearm"][2],
-                0,
-                self.measurements["forearm"][0],
-            ]
-        )
-
-        # Forearm origin + centroid transform
-        self.X_FoFc = self._create_translation_matrix(x=0.036)  # spellchecker:disable-line
-
-        # 0-position forearm frame pose wrt base
-        self.X_BF = self._create_translation_matrix(
-            x=self.measurements["forearm"][0],
-            y=self.measurements["forearm"][1],
-            z=self.measurements["forearm"][2],
-        )
-
-        # Humerus
-        # Screw axis of humerus frame wrt base frame
-        self.S_BH = np.array(
-            [
-                0,
-                -1,
-                0,
-                self.measurements["humerus"][2],
-                0,
-                -self.measurements["humerus"][0],
-            ]
-        )
-
-        # Humerus origin to centroid transform
-        self.X_HoHc = self._create_translation_matrix(x=0.0475)
-
-        # 0-position humerus frame pose wrt base
-        self.X_BH = self._create_translation_matrix(
-            x=self.measurements["humerus"][0],
-            y=self.measurements["humerus"][1],
-            z=self.measurements["humerus"][2],
-        )
-
-        # Shoulder
-        # Screw axis of shoulder frame wrt Base frame
-        self.S_BS = np.array([0, 0, -1, 0, 0, 0])
-
-        # Shoulder origin to centroid transform
-        self.X_SoSc = self._create_translation_matrix(x=-0.017, z=0.0235)
-
-        # 0-position shoulder frame pose wrt base
-        self.X_BS = self._create_translation_matrix(
-            x=self.measurements["shoulder"][0],
-            y=self.measurements["shoulder"][1],
-            z=self.measurements["shoulder"][2],
-        )
-
-        # Base
-        # Base origin to centroid transform
-        self.X_BoBc = self._create_translation_matrix(y=0.015)
-
-        # World to base transform
-        self.X_WoBo = self._create_translation_matrix(
-            x=self.measurements["base"][0],
-            y=self.measurements["base"][1],
-            z=self.measurements["base"][2],
-        )
-
-        # Pre-compute gripper post-multiplication matrix
-        self._fk_gripper_post = self.X_GoGc @ self.X_BoGo @ self.gripper_X0
-
-    def fk_base(self):
-        """Forward kinematics for the base frame."""
-        return self.X_WoBo @ self.X_BoBc @ self.base_X0
-
-    def fk_shoulder(self, robot_pos_deg):
-        """Forward kinematics for the shoulder frame."""
-        robot_pos_rad = robot_pos_deg / 180 * np.pi
-        return self.X_WoBo @ screw_axis_to_transform(self.S_BS, robot_pos_rad[0]) @ self.X_SoSc @ self.X_BS
-
-    def fk_humerus(self, robot_pos_deg):
-        """Forward kinematics for the humerus frame."""
-        robot_pos_rad = robot_pos_deg / 180 * np.pi
-
-        theta_shoulder_pan = robot_pos_rad[0]
-        # NOTE: Negate shoulder lift angle for all robot types
-        theta_shoulder_lift = -robot_pos_rad[1]
-
-        return (
-            self.X_WoBo
-            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
-            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
-            @ self.X_HoHc
-            @ self.X_BH
-        )
-
-    def fk_forearm(self, robot_pos_deg):
-        """Forward kinematics for the forearm frame."""
-        robot_pos_rad = robot_pos_deg / 180 * np.pi
-
-        theta_shoulder_pan = robot_pos_rad[0]
-        # NOTE: Negate shoulder lift angle for all robot types
-        theta_shoulder_lift = -robot_pos_rad[1]
-        theta_elbow_flex = robot_pos_rad[2]
-
-        return (
-            self.X_WoBo
-            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
-            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
-            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
-            @ self.X_FoFc  # spellchecker:disable-line
-            @ self.X_BF
-        )
-
-    def fk_wrist(self, robot_pos_deg):
-        """Forward kinematics for the wrist frame."""
-        robot_pos_rad = robot_pos_deg / 180 * np.pi
-
-        theta_shoulder_pan = robot_pos_rad[0]
-        # NOTE: Negate shoulder lift angle for all robot types
-        theta_shoulder_lift = -robot_pos_rad[1]
-        theta_elbow_flex = robot_pos_rad[2]
-        theta_wrist_flex = robot_pos_rad[3]
-
-        return (
-            self.X_WoBo
-            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
-            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
-            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
-            @ screw_axis_to_transform(self.S_BR, theta_wrist_flex)
-            @ self.X_RoRc
-            @ self.X_BR
-            @ self.wrist_X0
-        )
-
-    def fk_gripper(self, robot_pos_deg):
-        """Forward kinematics for the gripper frame."""
-        robot_pos_rad = robot_pos_deg / 180 * np.pi
-
-        theta_shoulder_pan = robot_pos_rad[0]
-        # NOTE: Negate shoulder lift angle for all robot types
-        theta_shoulder_lift = -robot_pos_rad[1]
-        theta_elbow_flex = robot_pos_rad[2]
-        theta_wrist_flex = robot_pos_rad[3]
-        theta_wrist_roll = robot_pos_rad[4]
-
-        return (
-            self.X_WoBo
-            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
-            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
-            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
-            @ screw_axis_to_transform(self.S_BR, theta_wrist_flex)
-            @ screw_axis_to_transform(self.S_BG, theta_wrist_roll)
-            @ self._fk_gripper_post
-        )
-
-    def fk_gripper_tip(self, robot_pos_deg):
-        """Forward kinematics for the gripper tip frame."""
-        robot_pos_rad = robot_pos_deg / 180 * np.pi
-
-        theta_shoulder_pan = robot_pos_rad[0]
-        # Negate shoulder lift angle for all robot types
-        theta_shoulder_lift = -robot_pos_rad[1]
-        theta_elbow_flex = robot_pos_rad[2]
-        theta_wrist_flex = robot_pos_rad[3]
-        theta_wrist_roll = robot_pos_rad[4]
-
-        return (
-            self.X_WoBo
-            @ screw_axis_to_transform(self.S_BS, theta_shoulder_pan)
-            @ screw_axis_to_transform(self.S_BH, theta_shoulder_lift)
-            @ screw_axis_to_transform(self.S_BF, theta_elbow_flex)
-            @ screw_axis_to_transform(self.S_BR, theta_wrist_flex)
-            @ screw_axis_to_transform(self.S_BG, theta_wrist_roll)
-            @ self.X_GoGt
-            @ self.X_BoGo
-            @ self.gripper_X0
-        )
-
-    def compute_jacobian(self, robot_pos_deg, fk_func=None):
-        """Finite differences to compute the Jacobian.
-        J(i, j) represents how the ith component of the end-effector's velocity changes wrt a small change
-        in the jth joint's velocity.
-
-        Args:
-            robot_pos_deg: Current joint positions in degrees
-            fk_func: Forward kinematics function to use (defaults to fk_gripper)
-        """
-        if fk_func is None:
-            fk_func = self.fk_gripper
-
-        eps = 1e-8
-        jac = np.zeros(shape=(6, 5))
-        delta = np.zeros(len(robot_pos_deg[:-1]), dtype=np.float64)
-        for el_ix in range(len(robot_pos_deg[:-1])):
-            delta *= 0
-            delta[el_ix] = eps / 2
-            Sdot = (
-                pose_difference_se3(
-                    fk_func(robot_pos_deg[:-1] + delta),
-                    fk_func(robot_pos_deg[:-1] - delta),
-                )
-                / eps
-            )
-            jac[:, el_ix] = Sdot
-        return jac
-
-    def compute_positional_jacobian(self, robot_pos_deg, fk_func=None):
-        """Finite differences to compute the positional Jacobian.
-        J(i, j) represents how the ith component of the end-effector's position changes wrt a small change
-        in the jth joint's velocity.
-
-        Args:
-            robot_pos_deg: Current joint positions in degrees
-            fk_func: Forward kinematics function to use (defaults to fk_gripper)
-        """
-        if fk_func is None:
-            fk_func = self.fk_gripper
-
-        eps = 1e-8
-        jac = np.zeros(shape=(3, 5))
-        delta = np.zeros(len(robot_pos_deg[:-1]), dtype=np.float64)
-        for el_ix in range(len(robot_pos_deg[:-1])):
-            delta *= 0
-            delta[el_ix] = eps / 2
-            Sdot = (
-                fk_func(robot_pos_deg[:-1] + delta)[:3, 3] - fk_func(robot_pos_deg[:-1] - delta)[:3, 3]
-            ) / eps
-            jac[:, el_ix] = Sdot
-        return jac
-
-    def ik(self, current_joint_pos, desired_ee_pose, position_only=True, fk_func=None):
-        """Inverse kinematics using gradient descent.
-
-        Args:
-            current_joint_state: Initial joint positions in degrees
-            desired_ee_pose: Target end-effector pose as a 4x4 transformation matrix
-            position_only: If True, only match end-effector position, not orientation
-            fk_func: Forward kinematics function to use (defaults to fk_gripper)
-
-        Returns:
-            Joint positions in degrees that achieve the desired end-effector pose
-        """
-        if fk_func is None:
-            fk_func = self.fk_gripper
-
-        # Do gradient descent.
-        current_joint_state = current_joint_pos.copy()
-        max_iterations = 5
-        learning_rate = 1
-        for _ in range(max_iterations):
-            current_ee_pose = fk_func(current_joint_state)
-            if not position_only:
-                error = se3_error(desired_ee_pose, current_ee_pose)
-                jac = self.compute_jacobian(current_joint_state, fk_func)
-            else:
-                error = desired_ee_pose[:3, 3] - current_ee_pose[:3, 3]
-                jac = self.compute_positional_jacobian(current_joint_state, fk_func)
-            delta_angles = np.linalg.pinv(jac) @ error
-            current_joint_state[:-1] += learning_rate * delta_angles
-
-            if np.linalg.norm(error) < 5e-3:
-                return current_joint_state
-        return current_joint_state
-
-
-if __name__ == "__main__":
-    import time
-
-    def run_test(robot_type):
-        """Run test suite for a specific robot type."""
-        print(f"\n--- Testing {robot_type.upper()} Robot ---")
-
-        # Initialize kinematics for this robot
-        robot = RobotKinematics(robot_type)
-
-        # Test 1: Forward kinematics consistency
-        print("Test 1: Forward kinematics consistency")
-        test_angles = np.array([30, 45, -30, 20, 10, 0])  # Example joint angles in degrees
-
-        # Calculate FK for different joints
-        shoulder_pose = robot.fk_shoulder(test_angles)
-        humerus_pose = robot.fk_humerus(test_angles)
-        forearm_pose = robot.fk_forearm(test_angles)
-        wrist_pose = robot.fk_wrist(test_angles)
-        gripper_pose = robot.fk_gripper(test_angles)
-        gripper_tip_pose = robot.fk_gripper_tip(test_angles)
-
-        # Check that poses form a consistent kinematic chain (positions should be progressively further from origin)
-        distances = [
-            np.linalg.norm(shoulder_pose[:3, 3]),
-            np.linalg.norm(humerus_pose[:3, 3]),
-            np.linalg.norm(forearm_pose[:3, 3]),
-            np.linalg.norm(wrist_pose[:3, 3]),
-            np.linalg.norm(gripper_pose[:3, 3]),
-            np.linalg.norm(gripper_tip_pose[:3, 3]),
-        ]
-
-        # Check if distances generally increase along the chain
-        is_consistent = all(distances[i] <= distances[i + 1] for i in range(len(distances) - 1))
-        print(f"  Pose distances from origin: {[round(d, 3) for d in distances]}")
-        print(f"  Kinematic chain consistency: {'PASSED' if is_consistent else 'FAILED'}")
-
-        # Test 2: Jacobian computation
-        print("Test 2: Jacobian computation")
-        jacobian = robot.compute_jacobian(test_angles)
-        positional_jacobian = robot.compute_positional_jacobian(test_angles)
-
-        # Check shapes
-        jacobian_shape_ok = jacobian.shape == (6, 5)
-        pos_jacobian_shape_ok = positional_jacobian.shape == (3, 5)
-
-        print(f"  Jacobian shape: {'PASSED' if jacobian_shape_ok else 'FAILED'}")
-        print(f"  Positional Jacobian shape: {'PASSED' if pos_jacobian_shape_ok else 'FAILED'}")
-
-        # Test 3: Inverse kinematics
-        print("Test 3: Inverse kinematics (position only)")
-
-        # Generate target pose from known joint angles
-        original_angles = np.array([10, 20, 30, -10, 5, 0])
-        target_pose = robot.fk_gripper(original_angles)
-
-        # Start IK from a different position
-        initial_guess = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
-
-        # Measure IK performance
-        start_time = time.time()
-        computed_angles = robot.ik(initial_guess.copy(), target_pose)
-        ik_time = time.time() - start_time
-
-        # Compute resulting pose from IK solution
-        result_pose = robot.fk_gripper(computed_angles)
-
-        # Calculate position error
-        pos_error = np.linalg.norm(target_pose[:3, 3] - result_pose[:3, 3])
-        passed = pos_error < 0.01  # Accept errors less than 1cm
-
-        print(f"  IK computation time: {ik_time:.4f} seconds")
-        print(f"  Position error: {pos_error:.4f}")
-        print(f"  IK position accuracy: {'PASSED' if passed else 'FAILED'}")
-
-        return is_consistent and jacobian_shape_ok and pos_jacobian_shape_ok and passed
-
-    # Run tests for all robot types
-    results = {}
-    for robot_type in ["koch", "so100", "moss", "so101"]:
-        results[robot_type] = run_test(robot_type)
-
-    # Print overall summary
-    print("\n=== Test Summary ===")
-    all_passed = all(results.values())
-    for robot_type, passed in results.items():
-        print(f"{robot_type.upper()}: {'PASSED' if passed else 'FAILED'}")
-    print(f"\nOverall: {'ALL TESTS PASSED' if all_passed else 'SOME TESTS FAILED'}")

lerobot/common/motors/dynamixel/dynamixel.py

@@ -108,7 +108,6 @@ class DynamixelMotorsBus(MotorsBus):
     https://emanual.robotis.com/docs/en/software/dynamixel/dynamixel_sdk/sample_code/python_read_write_protocol_2_0/#python-read-write-protocol-20
     """
 
-    apply_drive_mode = False
     available_baudrates = deepcopy(AVAILABLE_BAUDRATES)
     default_baudrate = DEFAULT_BAUDRATE
     default_timeout = DEFAULT_TIMEOUT_MS
@@ -169,10 +168,6 @@ class DynamixelMotorsBus(MotorsBus):
         for motor in self.motors:
             self.write("Return_Delay_Time", motor, 0)
 
-    @property
-    def is_calibrated(self) -> bool:
-        return self.calibration == self.read_calibration()
-
     def read_calibration(self) -> dict[str, MotorCalibration]:
         offsets = self.sync_read("Homing_Offset", normalize=False)
         mins = self.sync_read("Min_Position_Limit", normalize=False)

lerobot/common/motors/feetech/feetech.py

@@ -102,7 +102,6 @@ class FeetechMotorsBus(MotorsBus):
     python feetech sdk to communicate with the motors, which is itself based on the dynamixel sdk.
     """
 
-    apply_drive_mode = True
     available_baudrates = deepcopy(SCAN_BAUDRATES)
     default_baudrate = DEFAULT_BAUDRATE
     default_timeout = DEFAULT_TIMEOUT_MS
@@ -157,9 +156,7 @@ class FeetechMotorsBus(MotorsBus):
         firmware_versions = self._read_firmware_version(self.ids)
         if len(set(firmware_versions.values())) != 1:
             raise RuntimeError(
-                "Some Motors use different firmware versions:"
-                f"\n{pformat(firmware_versions)}\n"
-                "Update their firmware first using Feetech's software. "
+                "Some Motors use different firmware versions. Update their firmware first using Feetech's software. "
                 "Visit https://www.feetechrc.com/software."
             )
 
@@ -229,35 +226,21 @@ class FeetechMotorsBus(MotorsBus):
             self.write("Maximum_Acceleration", motor, 254)
             self.write("Acceleration", motor, 254)
 
-    @property
-    def is_calibrated(self) -> bool:
-        motors_calibration = self.read_calibration()
-        if set(motors_calibration) != set(self.calibration):
-            return False
-
-        same_ranges = all(
-            self.calibration[motor].range_min == cal.range_min
-            and self.calibration[motor].range_max == cal.range_max
-            for motor, cal in motors_calibration.items()
-        )
-        if self.protocol_version == 1:
-            return same_ranges
-
-        same_offsets = all(
-            self.calibration[motor].homing_offset == cal.homing_offset
-            for motor, cal in motors_calibration.items()
-        )
-        return same_ranges and same_offsets
-
     def read_calibration(self) -> dict[str, MotorCalibration]:
-        offsets, mins, maxes = {}, {}, {}
-        drive_modes = dict.fromkeys(self.motors, 0)
-        for motor in self.motors:
-            mins[motor] = self.read("Min_Position_Limit", motor, normalize=False)
-            maxes[motor] = self.read("Max_Position_Limit", motor, normalize=False)
-            offsets[motor] = (
-                self.read("Homing_Offset", motor, normalize=False) if self.protocol_version == 0 else 0
-            )
+        if self.protocol_version == 0:
+            offsets = self.sync_read("Homing_Offset", normalize=False)
+            mins = self.sync_read("Min_Position_Limit", normalize=False)
+            maxes = self.sync_read("Max_Position_Limit", normalize=False)
+            drive_modes = dict.fromkeys(self.motors, 0)
+        else:
+            offsets, mins, maxes, drive_modes = {}, {}, {}, {}
+            for motor in self.motors:
+                offsets[motor] = 0
+                mins[motor] = self.read("Min_Position_Limit", motor, normalize=False)
+                maxes[motor] = self.read("Max_Position_Limit", motor, normalize=False)
+                drive_modes[motor] = 0
+
+        # TODO(aliberts): add set/get_drive_mode?
 
         calibration = {}
         for motor, m in self.motors.items():

lerobot/common/motors/feetech/tables.py

@@ -207,7 +207,6 @@ MODEL_BAUDRATE_TABLE = {
 STS_SMS_SERIES_ENCODINGS_TABLE = {
     "Homing_Offset": 11,
     "Goal_Velocity": 15,
-    "Present_Velocity": 15,
 }
 
 MODEL_ENCODING_TABLE = {

lerobot/common/motors/motors_bus.py

@@ -252,7 +252,6 @@ class MotorsBus(abc.ABC):
     ```
     """
 
-    apply_drive_mode: bool
     available_baudrates: list[int]
     default_baudrate: int
     default_timeout: int
@@ -373,44 +372,29 @@ class MotorsBus(abc.ABC):
         return error != self._no_error
 
     def _assert_motors_exist(self) -> None:
-        expected_models = {m.id: self.model_number_table[m.model] for m in self.motors.values()}
-
+        # TODO(aliberts): collect all wrong ids/models and display them at once
         found_models = {}
         for id_ in self.ids:
             model_nb = self.ping(id_)
             if model_nb is not None:
                 found_models[id_] = model_nb
+        expected_models = {m.id: self.model_number_table[m.model] for m in self.motors.values()}
+        if set(found_models) != set(self.ids):
+            raise RuntimeError(
+                f"{self.__class__.__name__} is supposed to have these motors: ({{id: model_nb}})"
+                f"\n{pformat(expected_models, indent=4, sort_dicts=False)}\n"
+                f"But it found these motors on port '{self.port}':"
+                f"\n{pformat(found_models, indent=4, sort_dicts=False)}\n"
+            )
 
-        missing_ids = [id_ for id_ in self.ids if id_ not in found_models]
-        wrong_models = {
-            id_: (expected_models[id_], found_models[id_])
-            for id_ in found_models
-            if expected_models.get(id_) != found_models[id_]
-        }
-
-        if missing_ids or wrong_models:
-            error_lines = [f"{self.__class__.__name__} motor check failed on port '{self.port}':"]
-
-            if missing_ids:
-                error_lines.append("\nMissing motor IDs:")
-                error_lines.extend(
-                    f"  - {id_} (expected model: {expected_models[id_]})" for id_ in missing_ids
+        for id_, model in expected_models.items():
+            if found_models[id_] != model:
+                raise RuntimeError(
+                    f"Motor '{self._id_to_name(id_)}' (id={id_}) is supposed to be of model_number={model} "
+                    f"('{self._id_to_model(id_)}') but a model_number={found_models[id_]} "
+                    "was found instead for that id."
                 )
 
-            if wrong_models:
-                error_lines.append("\nMotors with incorrect model numbers:")
-                error_lines.extend(
-                    f"  - {id_} ({self._id_to_name(id_)}): expected {expected}, found {found}"
-                    for id_, (expected, found) in wrong_models.items()
-                )
-
-            error_lines.append("\nFull expected motor list (id: model_number):")
-            error_lines.append(pformat(expected_models, indent=4, sort_dicts=False))
-            error_lines.append("\nFull found motor list (id: model_number):")
-            error_lines.append(pformat(found_models, indent=4, sort_dicts=False))
-
-            raise RuntimeError("\n".join(error_lines))
-
     @abc.abstractmethod
     def _assert_protocol_is_compatible(self, instruction_name: str) -> None:
         pass
@@ -640,10 +624,9 @@ class MotorsBus(abc.ABC):
                 raise RuntimeError("Failed to write bus baud rate.")
 
     @property
-    @abc.abstractmethod
     def is_calibrated(self) -> bool:
         """bool: ``True`` if the cached calibration matches the motors."""
-        pass
+        return self.calibration == self.read_calibration()
 
     @abc.abstractmethod
     def read_calibration(self) -> dict[str, MotorCalibration]:
@@ -772,7 +755,7 @@ class MotorsBus(abc.ABC):
 
         return mins, maxes
 
-    def _normalize(self, ids_values: dict[int, int]) -> dict[int, float]:
+    def _normalize(self, data_name: str, ids_values: dict[int, int]) -> dict[int, float]:
         if not self.calibration:
             raise RuntimeError(f"{self} has no calibration registered.")
 
@@ -781,33 +764,20 @@ class MotorsBus(abc.ABC):
             motor = self._id_to_name(id_)
             min_ = self.calibration[motor].range_min
             max_ = self.calibration[motor].range_max
-            drive_mode = self.apply_drive_mode and self.calibration[motor].drive_mode
-            if max_ == min_:
-                raise ValueError(f"Invalid calibration for motor '{motor}': min and max are equal.")
-
             bounded_val = min(max_, max(min_, val))
             # TODO(Steven): normalization can go boom if max_ == min_, we should add a check probably in record_ranges_of_motions
             # (which probably indicates the user forgot to move a motor, most likely a gripper-like one)
             if self.motors[motor].norm_mode is MotorNormMode.RANGE_M100_100:
-                norm = (((bounded_val - min_) / (max_ - min_)) * 200) - 100
-                normalized_values[id_] = -norm if drive_mode else norm
+                normalized_values[id_] = (((bounded_val - min_) / (max_ - min_)) * 200) - 100
             elif self.motors[motor].norm_mode is MotorNormMode.RANGE_0_100:
-                norm = ((bounded_val - min_) / (max_ - min_)) * 100
-                normalized_values[id_] = 100 - norm if drive_mode else norm
-            elif self.motors[motor].norm_mode is MotorNormMode.DEGREE:
-                resolution = self.model_resolution_table[self.motors[motor].model]
-                if drive_mode:
-                    val *= -1
-                # middle_pos = homing_offset + (resolution - 1) // 2
-                middle_pos = int((max_ + min_) / 2)
-                normalized_values[id_] = ((val - middle_pos) / (resolution // 2)) * 180
+                normalized_values[id_] = ((bounded_val - min_) / (max_ - min_)) * 100
             else:
                 # TODO(alibers): velocity and degree modes
                 raise NotImplementedError
 
         return normalized_values
 
-    def _unnormalize(self, ids_values: dict[int, float]) -> dict[int, int]:
+    def _unnormalize(self, data_name: str, ids_values: dict[int, float]) -> dict[int, int]:
         if not self.calibration:
             raise RuntimeError(f"{self} has no calibration registered.")
 
@@ -816,29 +786,14 @@ class MotorsBus(abc.ABC):
             motor = self._id_to_name(id_)
             min_ = self.calibration[motor].range_min
             max_ = self.calibration[motor].range_max
-            drive_mode = self.apply_drive_mode and self.calibration[motor].drive_mode
-            if max_ == min_:
-                raise ValueError(f"Invalid calibration for motor '{motor}': min and max are equal.")
-
             if self.motors[motor].norm_mode is MotorNormMode.RANGE_M100_100:
-                val = -val if drive_mode else val
                 bounded_val = min(100.0, max(-100.0, val))
                 unnormalized_values[id_] = int(((bounded_val + 100) / 200) * (max_ - min_) + min_)
             elif self.motors[motor].norm_mode is MotorNormMode.RANGE_0_100:
-                val = 100 - val if drive_mode else val
                 bounded_val = min(100.0, max(0.0, val))
                 unnormalized_values[id_] = int((bounded_val / 100) * (max_ - min_) + min_)
-            elif self.motors[motor].norm_mode is MotorNormMode.DEGREE:
-                resolution = self.model_resolution_table[self.motors[motor].model]
-                middle_pos = int((max_ + min_) / 2)
-                unnormalized_values[id_] = int((val / 180) * resolution // 2) + middle_pos
-                if drive_mode:
-                    unnormalized_values[id_] *= -1
-
-                # if unnormalized_values[id_] < 0:
-                #     breakpoint()
             else:
-                # TODO(aliberts): degree mode
+                # TODO(alibers): velocity and degree modes
                 raise NotImplementedError
 
         return unnormalized_values
@@ -959,7 +914,7 @@ class MotorsBus(abc.ABC):
         id_value = self._decode_sign(data_name, {id_: value})
 
         if normalize and data_name in self.normalized_data:
-            id_value = self._normalize(id_value)
+            id_value = self._normalize(data_name, id_value)
 
         return id_value[id_]
 
@@ -1026,7 +981,7 @@ class MotorsBus(abc.ABC):
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
         if normalize and data_name in self.normalized_data:
-            value = self._unnormalize({id_: value})[id_]
+            value = self._unnormalize(data_name, {id_: value})[id_]
 
         value = self._encode_sign(data_name, {id_: value})[id_]
 
@@ -1105,7 +1060,7 @@ class MotorsBus(abc.ABC):
         ids_values = self._decode_sign(data_name, ids_values)
 
         if normalize and data_name in self.normalized_data:
-            ids_values = self._normalize(ids_values)
+            ids_values = self._normalize(data_name, ids_values)
 
         return {self._id_to_name(id_): value for id_, value in ids_values.items()}
 
@@ -1191,7 +1146,7 @@ class MotorsBus(abc.ABC):
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
         if normalize and data_name in self.normalized_data:
-            ids_values = self._unnormalize(ids_values)
+            ids_values = self._unnormalize(data_name, ids_values)
 
         ids_values = self._encode_sign(data_name, ids_values)
 

lerobot/common/optim/optimizers.py

@@ -14,9 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import abc
-from dataclasses import asdict, dataclass, field
+from dataclasses import asdict, dataclass
 from pathlib import Path
-from typing import Any
 
 import draccus
 import torch
@@ -45,16 +44,7 @@ class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
         return "adam"
 
     @abc.abstractmethod
-    def build(self) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]:
-        """
-        Build the optimizer. It can be a single optimizer or a dictionary of optimizers.
-        NOTE: Multiple optimizers are useful when you have different models to optimize.
-        For example, you can have one optimizer for the policy and another one for the value function
-        in reinforcement learning settings.
-
-        Returns:
-            The optimizer or a dictionary of optimizers.
-        """
+    def build(self) -> torch.optim.Optimizer:
         raise NotImplementedError
 
 
@@ -104,76 +94,7 @@ class SGDConfig(OptimizerConfig):
         return torch.optim.SGD(params, **kwargs)
 
 
-@OptimizerConfig.register_subclass("multi_adam")
-@dataclass
-class MultiAdamConfig(OptimizerConfig):
-    """Configuration for multiple Adam optimizers with different parameter groups.
-
-    This creates a dictionary of Adam optimizers, each with its own hyperparameters.
-
-    Args:
-        lr: Default learning rate (used if not specified for a group)
-        weight_decay: Default weight decay (used if not specified for a group)
-        optimizer_groups: Dictionary mapping parameter group names to their hyperparameters
-        grad_clip_norm: Gradient clipping norm
-    """
-
-    lr: float = 1e-3
-    weight_decay: float = 0.0
-    grad_clip_norm: float = 10.0
-    optimizer_groups: dict[str, dict[str, Any]] = field(default_factory=dict)
-
-    def build(self, params_dict: dict[str, list]) -> dict[str, torch.optim.Optimizer]:
-        """Build multiple Adam optimizers.
-
-        Args:
-            params_dict: Dictionary mapping parameter group names to lists of parameters
-                         The keys should match the keys in optimizer_groups
-
-        Returns:
-            Dictionary mapping parameter group names to their optimizers
-        """
-        optimizers = {}
-
-        for name, params in params_dict.items():
-            # Get group-specific hyperparameters or use defaults
-            group_config = self.optimizer_groups.get(name, {})
-
-            # Create optimizer with merged parameters (defaults + group-specific)
-            optimizer_kwargs = {
-                "lr": group_config.get("lr", self.lr),
-                "betas": group_config.get("betas", (0.9, 0.999)),
-                "eps": group_config.get("eps", 1e-5),
-                "weight_decay": group_config.get("weight_decay", self.weight_decay),
-            }
-
-            optimizers[name] = torch.optim.Adam(params, **optimizer_kwargs)
-
-        return optimizers
-
-
-def save_optimizer_state(
-    optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path
-) -> None:
-    """Save optimizer state to disk.
-
-    Args:
-        optimizer: Either a single optimizer or a dictionary of optimizers.
-        save_dir: Directory to save the optimizer state.
-    """
-    if isinstance(optimizer, dict):
-        # Handle dictionary of optimizers
-        for name, opt in optimizer.items():
-            optimizer_dir = save_dir / name
-            optimizer_dir.mkdir(exist_ok=True, parents=True)
-            _save_single_optimizer_state(opt, optimizer_dir)
-    else:
-        # Handle single optimizer
-        _save_single_optimizer_state(optimizer, save_dir)
-
-
-def _save_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> None:
-    """Save a single optimizer's state to disk."""
+def save_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> None:
     state = optimizer.state_dict()
     param_groups = state.pop("param_groups")
     flat_state = flatten_dict(state)
@@ -181,44 +102,11 @@ def _save_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Pat
     write_json(param_groups, save_dir / OPTIMIZER_PARAM_GROUPS)
 
 
-def load_optimizer_state(
-    optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer], save_dir: Path
-) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]:
-    """Load optimizer state from disk.
-
-    Args:
-        optimizer: Either a single optimizer or a dictionary of optimizers.
-        save_dir: Directory to load the optimizer state from.
-
-    Returns:
-        The updated optimizer(s) with loaded state.
-    """
-    if isinstance(optimizer, dict):
-        # Handle dictionary of optimizers
-        loaded_optimizers = {}
-        for name, opt in optimizer.items():
-            optimizer_dir = save_dir / name
-            if optimizer_dir.exists():
-                loaded_optimizers[name] = _load_single_optimizer_state(opt, optimizer_dir)
-            else:
-                loaded_optimizers[name] = opt
-        return loaded_optimizers
-    else:
-        # Handle single optimizer
-        return _load_single_optimizer_state(optimizer, save_dir)
-
-
-def _load_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> torch.optim.Optimizer:
-    """Load a single optimizer's state from disk."""
+def load_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> torch.optim.Optimizer:
     current_state_dict = optimizer.state_dict()
     flat_state = load_file(save_dir / OPTIMIZER_STATE)
     state = unflatten_dict(flat_state)
-
-    # Handle case where 'state' key might not exist (for newly created optimizers)
-    if "state" in state:
-        loaded_state_dict = {"state": {int(k): v for k, v in state["state"].items()}}
-    else:
-        loaded_state_dict = {"state": {}}
+    loaded_state_dict = {"state": {int(k): v for k, v in state["state"].items()}}
 
     if "param_groups" in current_state_dict:
         param_groups = deserialize_json_into_object(

lerobot/common/policies/factory.py

@@ -27,7 +27,6 @@ from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionC
 from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
 from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.policies.reward_model.configuration_classifier import RewardClassifierConfig
 from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.configs.policies import PreTrainedConfig
@@ -60,14 +59,6 @@ def get_policy_class(name: str) -> PreTrainedPolicy:
         from lerobot.common.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
 
         return PI0FASTPolicy
-    elif name == "sac":
-        from lerobot.common.policies.sac.modeling_sac import SACPolicy
-
-        return SACPolicy
-    elif name == "reward_classifier":
-        from lerobot.common.policies.reward_model.modeling_classifier import Classifier
-
-        return Classifier
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -85,8 +76,6 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return PI0Config(**kwargs)
     elif policy_type == "pi0fast":
         return PI0FASTConfig(**kwargs)
-    elif policy_type == "reward_classifier":
-        return RewardClassifierConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 

lerobot/common/policies/normalize.py

@@ -151,7 +151,6 @@ class Normalize(nn.Module):
     # TODO(rcadene): should we remove torch.no_grad?
     @torch.no_grad
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        # TODO: Remove this shallow copy
         batch = dict(batch)  # shallow copy avoids mutating the input batch
         for key, ft in self.features.items():
             if key not in batch:
@@ -253,168 +252,3 @@ class Unnormalize(nn.Module):
             else:
                 raise ValueError(norm_mode)
         return batch
-
-
-# TODO (azouitine): We should replace all normalization on the policies with register_buffer normalization
-#       and remove the `Normalize` and `Unnormalize` classes.
-def _initialize_stats_buffers(
-    module: nn.Module,
-    features: dict[str, PolicyFeature],
-    norm_map: dict[str, NormalizationMode],
-    stats: dict[str, dict[str, Tensor]] | None = None,
-) -> None:
-    """Register statistics buffers (mean/std or min/max) on the given *module*.
-
-    The logic matches the previous constructors of `NormalizeBuffer` and `UnnormalizeBuffer`,
-    but is factored out so it can be reused by both classes and stay in sync.
-    """
-    for key, ft in features.items():
-        norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY)
-        if norm_mode is NormalizationMode.IDENTITY:
-            continue
-
-        shape: tuple[int, ...] = tuple(ft.shape)
-        if ft.type is FeatureType.VISUAL:
-            # reduce spatial dimensions, keep channel dimension only
-            c, *_ = shape
-            shape = (c, 1, 1)
-
-        prefix = key.replace(".", "_")
-
-        if norm_mode is NormalizationMode.MEAN_STD:
-            mean = torch.full(shape, torch.inf, dtype=torch.float32)
-            std = torch.full(shape, torch.inf, dtype=torch.float32)
-
-            if stats and key in stats and "mean" in stats[key] and "std" in stats[key]:
-                mean_data = stats[key]["mean"]
-                std_data = stats[key]["std"]
-                if isinstance(mean_data, torch.Tensor):
-                    # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
-                    # tensors anywhere (for example, when we use the same stats for normalization and
-                    # unnormalization). See the logic here
-                    # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
-                    mean = mean_data.clone().to(dtype=torch.float32)
-                    std = std_data.clone().to(dtype=torch.float32)
-                else:
-                    raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
-
-            module.register_buffer(f"{prefix}_mean", mean)
-            module.register_buffer(f"{prefix}_std", std)
-            continue
-
-        if norm_mode is NormalizationMode.MIN_MAX:
-            min_val = torch.full(shape, torch.inf, dtype=torch.float32)
-            max_val = torch.full(shape, torch.inf, dtype=torch.float32)
-
-            if stats and key in stats and "min" in stats[key] and "max" in stats[key]:
-                min_data = stats[key]["min"]
-                max_data = stats[key]["max"]
-                if isinstance(min_data, torch.Tensor):
-                    min_val = min_data.clone().to(dtype=torch.float32)
-                    max_val = max_data.clone().to(dtype=torch.float32)
-                else:
-                    raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
-
-            module.register_buffer(f"{prefix}_min", min_val)
-            module.register_buffer(f"{prefix}_max", max_val)
-            continue
-
-        raise ValueError(norm_mode)
-
-
-class NormalizeBuffer(nn.Module):
-    """Same as `Normalize` but statistics are stored as registered buffers rather than parameters."""
-
-    def __init__(
-        self,
-        features: dict[str, PolicyFeature],
-        norm_map: dict[str, NormalizationMode],
-        stats: dict[str, dict[str, Tensor]] | None = None,
-    ):
-        super().__init__()
-        self.features = features
-        self.norm_map = norm_map
-
-        _initialize_stats_buffers(self, features, norm_map, stats)
-
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        batch = dict(batch)
-        for key, ft in self.features.items():
-            if key not in batch:
-                continue
-
-            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
-            if norm_mode is NormalizationMode.IDENTITY:
-                continue
-
-            prefix = key.replace(".", "_")
-
-            if norm_mode is NormalizationMode.MEAN_STD:
-                mean = getattr(self, f"{prefix}_mean")
-                std = getattr(self, f"{prefix}_std")
-                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
-                assert not torch.isinf(std).any(), _no_stats_error_str("std")
-                batch[key] = (batch[key] - mean) / (std + 1e-8)
-                continue
-
-            if norm_mode is NormalizationMode.MIN_MAX:
-                min_val = getattr(self, f"{prefix}_min")
-                max_val = getattr(self, f"{prefix}_max")
-                assert not torch.isinf(min_val).any(), _no_stats_error_str("min")
-                assert not torch.isinf(max_val).any(), _no_stats_error_str("max")
-                batch[key] = (batch[key] - min_val) / (max_val - min_val + 1e-8)
-                batch[key] = batch[key] * 2 - 1
-                continue
-
-            raise ValueError(norm_mode)
-
-        return batch
-
-
-class UnnormalizeBuffer(nn.Module):
-    """Inverse operation of `NormalizeBuffer`. Uses registered buffers for statistics."""
-
-    def __init__(
-        self,
-        features: dict[str, PolicyFeature],
-        norm_map: dict[str, NormalizationMode],
-        stats: dict[str, dict[str, Tensor]] | None = None,
-    ):
-        super().__init__()
-        self.features = features
-        self.norm_map = norm_map
-
-        _initialize_stats_buffers(self, features, norm_map, stats)
-
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        # batch = dict(batch)
-        for key, ft in self.features.items():
-            if key not in batch:
-                continue
-
-            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
-            if norm_mode is NormalizationMode.IDENTITY:
-                continue
-
-            prefix = key.replace(".", "_")
-
-            if norm_mode is NormalizationMode.MEAN_STD:
-                mean = getattr(self, f"{prefix}_mean")
-                std = getattr(self, f"{prefix}_std")
-                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
-                assert not torch.isinf(std).any(), _no_stats_error_str("std")
-                batch[key] = batch[key] * std + mean
-                continue
-
-            if norm_mode is NormalizationMode.MIN_MAX:
-                min_val = getattr(self, f"{prefix}_min")
-                max_val = getattr(self, f"{prefix}_max")
-                assert not torch.isinf(min_val).any(), _no_stats_error_str("min")
-                assert not torch.isinf(max_val).any(), _no_stats_error_str("max")
-                batch[key] = (batch[key] + 1) / 2
-                batch[key] = batch[key] * (max_val - min_val) + min_val
-                continue
-
-            raise ValueError(norm_mode)
-
-        return batch

lerobot/common/policies/pi0/modeling_pi0.py

@@ -357,7 +357,7 @@ class PI0Policy(PreTrainedPolicy):
             if self.config.resize_imgs_with_padding is not None:
                 img = resize_with_pad(img, *self.config.resize_imgs_with_padding, pad_value=0)
 
-            # Normalize from range [0,1] to [-1,1] as expected by siglip
+            # Normalize from range [0,1] to [-1,1] as expacted by siglip
             img = img * 2.0 - 1.0
 
             bsize = img.shape[0]

lerobot/common/policies/pi0fast/modeling_pi0fast.py

@@ -516,7 +516,7 @@ class PI0FAST(nn.Module):
                         interpolate_like_pi=self.config.interpolate_like_pi,
                     )
 
-                # Normalize from range [0,1] to [-1,1] as expected by siglip
+                # Normalize from range [0,1] to [-1,1] as expacted by siglip
                 img = img * 2.0 - 1.0
 
                 bsize = img.shape[0]

lerobot/common/policies/reward_model/configuration_classifier.py

@@ -1,62 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from lerobot.common.optim.optimizers import AdamWConfig, OptimizerConfig
-from lerobot.common.optim.schedulers import LRSchedulerConfig
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import NormalizationMode
-
-
-@PreTrainedConfig.register_subclass(name="reward_classifier")
-@dataclass
-class RewardClassifierConfig(PreTrainedConfig):
-    """Configuration for the Reward Classifier model."""
-
-    name: str = "reward_classifier"
-    num_classes: int = 2
-    hidden_dim: int = 256
-    latent_dim: int = 256
-    image_embedding_pooling_dim: int = 8
-    dropout_rate: float = 0.1
-    model_name: str = "helper2424/resnet10"
-    device: str = "cpu"
-    model_type: str = "cnn"  # "transformer" or "cnn"
-    num_cameras: int = 2
-    learning_rate: float = 1e-4
-    weight_decay: float = 0.01
-    grad_clip_norm: float = 1.0
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.MEAN_STD,
-        }
-    )
-
-    @property
-    def observation_delta_indices(self) -> List | None:
-        return None
-
-    @property
-    def action_delta_indices(self) -> List | None:
-        return None
-
-    @property
-    def reward_delta_indices(self) -> List | None:
-        return None
-
-    def get_optimizer_preset(self) -> OptimizerConfig:
-        return AdamWConfig(
-            lr=self.learning_rate,
-            weight_decay=self.weight_decay,
-            grad_clip_norm=self.grad_clip_norm,
-        )
-
-    def get_scheduler_preset(self) -> LRSchedulerConfig | None:
-        return None
-
-    def validate_features(self) -> None:
-        """Validate feature configurations."""
-        has_image = any(key.startswith("observation.image") for key in self.input_features)
-        if not has_image:
-            raise ValueError(
-                "You must provide an image observation (key starting with 'observation.image') in the input features"
-            )

lerobot/common/policies/reward_model/modeling_classifier.py

@@ -1,301 +0,0 @@
-import logging
-from typing import Dict, Optional, Tuple
-
-import torch
-from torch import Tensor, nn
-
-from lerobot.common.constants import OBS_IMAGE
-from lerobot.common.policies.normalize import Normalize, Unnormalize
-from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.policies.reward_model.configuration_classifier import RewardClassifierConfig
-
-
-class ClassifierOutput:
-    """Wrapper for classifier outputs with additional metadata."""
-
-    def __init__(
-        self,
-        logits: Tensor,
-        probabilities: Optional[Tensor] = None,
-        hidden_states: Optional[Tensor] = None,
-    ):
-        self.logits = logits
-        self.probabilities = probabilities
-        self.hidden_states = hidden_states
-
-    def __repr__(self):
-        return (
-            f"ClassifierOutput(logits={self.logits}, "
-            f"probabilities={self.probabilities}, "
-            f"hidden_states={self.hidden_states})"
-        )
-
-
-class SpatialLearnedEmbeddings(nn.Module):
-    def __init__(self, height, width, channel, num_features=8):
-        """
-        PyTorch implementation of learned spatial embeddings
-
-        Args:
-            height: Spatial height of input features
-            width: Spatial width of input features
-            channel: Number of input channels
-            num_features: Number of output embedding dimensions
-        """
-        super().__init__()
-        self.height = height
-        self.width = width
-        self.channel = channel
-        self.num_features = num_features
-
-        self.kernel = nn.Parameter(torch.empty(channel, height, width, num_features))
-
-        nn.init.kaiming_normal_(self.kernel, mode="fan_in", nonlinearity="linear")
-
-    def forward(self, features):
-        """
-        Forward pass for spatial embedding
-
-        Args:
-            features: Input tensor of shape [B, H, W, C] or [H, W, C] if no batch
-        Returns:
-            Output tensor of shape [B, C*F] or [C*F] if no batch
-        """
-
-        features = features.last_hidden_state
-
-        original_shape = features.shape
-        if features.dim() == 3:
-            features = features.unsqueeze(0)  # Add batch dim
-
-        features_expanded = features.unsqueeze(-1)  # [B, H, W, C, 1]
-        kernel_expanded = self.kernel.unsqueeze(0)  # [1, H, W, C, F]
-
-        # Element-wise multiplication and spatial reduction
-        output = (features_expanded * kernel_expanded).sum(dim=(2, 3))  # Sum H,W
-
-        # Reshape to combine channel and feature dimensions
-        output = output.view(output.size(0), -1)  # [B, C*F]
-
-        # Remove batch dim
-        if len(original_shape) == 3:
-            output = output.squeeze(0)
-
-        return output
-
-
-class Classifier(PreTrainedPolicy):
-    """Image classifier built on top of a pre-trained encoder."""
-
-    name = "reward_classifier"
-    config_class = RewardClassifierConfig
-
-    def __init__(
-        self,
-        config: RewardClassifierConfig,
-        dataset_stats: Dict[str, Dict[str, Tensor]] | None = None,
-    ):
-        from transformers import AutoModel
-
-        super().__init__(config)
-        self.config = config
-
-        # Initialize normalization (standardized with the policy framework)
-        self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats)
-        self.normalize_targets = Normalize(
-            config.output_features, config.normalization_mapping, dataset_stats
-        )
-        self.unnormalize_outputs = Unnormalize(
-            config.output_features, config.normalization_mapping, dataset_stats
-        )
-
-        # Set up encoder
-        encoder = AutoModel.from_pretrained(self.config.model_name, trust_remote_code=True)
-        # Extract vision model if we're given a multimodal model
-        if hasattr(encoder, "vision_model"):
-            logging.info("Multimodal model detected - using vision encoder only")
-            self.encoder = encoder.vision_model
-            self.vision_config = encoder.config.vision_config
-        else:
-            self.encoder = encoder
-            self.vision_config = getattr(encoder, "config", None)
-
-        # Model type from config
-        self.is_cnn = self.config.model_type == "cnn"
-
-        # For CNNs, initialize backbone
-        if self.is_cnn:
-            self._setup_cnn_backbone()
-
-        self._freeze_encoder()
-
-        # Extract image keys from input_features
-        self.image_keys = [
-            key.replace(".", "_") for key in config.input_features if key.startswith(OBS_IMAGE)
-        ]
-
-        if self.is_cnn:
-            self.encoders = nn.ModuleDict()
-            for image_key in self.image_keys:
-                encoder = self._create_single_encoder()
-                self.encoders[image_key] = encoder
-
-        self._build_classifier_head()
-
-    def _setup_cnn_backbone(self):
-        """Set up CNN encoder"""
-        if hasattr(self.encoder, "fc"):
-            self.feature_dim = self.encoder.fc.in_features
-            self.encoder = nn.Sequential(*list(self.encoder.children())[:-1])
-        elif hasattr(self.encoder.config, "hidden_sizes"):
-            self.feature_dim = self.encoder.config.hidden_sizes[-1]  # Last channel dimension
-        else:
-            raise ValueError("Unsupported CNN architecture")
-
-    def _freeze_encoder(self) -> None:
-        """Freeze the encoder parameters."""
-        for param in self.encoder.parameters():
-            param.requires_grad = False
-
-    def _create_single_encoder(self):
-        encoder = nn.Sequential(
-            self.encoder,
-            SpatialLearnedEmbeddings(
-                height=4,
-                width=4,
-                channel=self.feature_dim,
-                num_features=self.config.image_embedding_pooling_dim,
-            ),
-            nn.Dropout(self.config.dropout_rate),
-            nn.Linear(self.feature_dim * self.config.image_embedding_pooling_dim, self.config.latent_dim),
-            nn.LayerNorm(self.config.latent_dim),
-            nn.Tanh(),
-        )
-
-        return encoder
-
-    def _build_classifier_head(self) -> None:
-        """Initialize the classifier head architecture."""
-        # Get input dimension based on model type
-        if self.is_cnn:
-            input_dim = self.config.latent_dim
-        else:  # Transformer models
-            if hasattr(self.encoder.config, "hidden_size"):
-                input_dim = self.encoder.config.hidden_size
-            else:
-                raise ValueError("Unsupported transformer architecture since hidden_size is not found")
-
-        self.classifier_head = nn.Sequential(
-            nn.Linear(input_dim * self.config.num_cameras, self.config.hidden_dim),
-            nn.Dropout(self.config.dropout_rate),
-            nn.LayerNorm(self.config.hidden_dim),
-            nn.ReLU(),
-            nn.Linear(
-                self.config.hidden_dim,
-                1 if self.config.num_classes == 2 else self.config.num_classes,
-            ),
-        )
-
-    def _get_encoder_output(self, x: torch.Tensor, image_key: str) -> torch.Tensor:
-        """Extract the appropriate output from the encoder."""
-        with torch.no_grad():
-            if self.is_cnn:
-                # The HF ResNet applies pooling internally
-                outputs = self.encoders[image_key](x)
-                return outputs
-            else:  # Transformer models
-                outputs = self.encoder(x)
-                return outputs.last_hidden_state[:, 0, :]
-
-    def extract_images_and_labels(self, batch: Dict[str, Tensor]) -> Tuple[list, Tensor]:
-        """Extract image tensors and label tensors from batch."""
-        # Check for both OBS_IMAGE and OBS_IMAGES prefixes
-        images = [batch[key] for key in self.config.input_features if key.startswith(OBS_IMAGE)]
-        labels = batch["next.reward"]
-
-        return images, labels
-
-    def predict(self, xs: list) -> ClassifierOutput:
-        """Forward pass of the classifier for inference."""
-        encoder_outputs = torch.hstack(
-            [self._get_encoder_output(x, img_key) for x, img_key in zip(xs, self.image_keys, strict=True)]
-        )
-        logits = self.classifier_head(encoder_outputs)
-
-        if self.config.num_classes == 2:
-            logits = logits.squeeze(-1)
-            probabilities = torch.sigmoid(logits)
-        else:
-            probabilities = torch.softmax(logits, dim=-1)
-
-        return ClassifierOutput(logits=logits, probabilities=probabilities, hidden_states=encoder_outputs)
-
-    def forward(self, batch: Dict[str, Tensor]) -> Tuple[Tensor, Dict[str, Tensor]]:
-        """Standard forward pass for training compatible with train.py."""
-        # Normalize inputs if needed
-        batch = self.normalize_inputs(batch)
-        batch = self.normalize_targets(batch)
-
-        # Extract images and labels
-        images, labels = self.extract_images_and_labels(batch)
-
-        # Get predictions
-        outputs = self.predict(images)
-
-        # Calculate loss
-        if self.config.num_classes == 2:
-            # Binary classification
-            loss = nn.functional.binary_cross_entropy_with_logits(outputs.logits, labels)
-            predictions = (torch.sigmoid(outputs.logits) > 0.5).float()
-        else:
-            # Multi-class classification
-            loss = nn.functional.cross_entropy(outputs.logits, labels.long())
-            predictions = torch.argmax(outputs.logits, dim=1)
-
-        # Calculate accuracy for logging
-        correct = (predictions == labels).sum().item()
-        total = labels.size(0)
-        accuracy = 100 * correct / total
-
-        # Return loss and metrics for logging
-        output_dict = {
-            "accuracy": accuracy,
-            "correct": correct,
-            "total": total,
-        }
-
-        return loss, output_dict
-
-    def predict_reward(self, batch, threshold=0.5):
-        """Eval method. Returns predicted reward with the decision threshold as argument."""
-        # Check for both OBS_IMAGE and OBS_IMAGES prefixes
-        batch = self.normalize_inputs(batch)
-        batch = self.normalize_targets(batch)
-
-        # Extract images from batch dict
-        images = [batch[key] for key in self.config.input_features if key.startswith(OBS_IMAGE)]
-
-        if self.config.num_classes == 2:
-            probs = self.predict(images).probabilities
-            logging.debug(f"Predicted reward images: {probs}")
-            return (probs > threshold).float()
-        else:
-            return torch.argmax(self.predict(images).probabilities, dim=1)
-
-    def get_optim_params(self):
-        """Return optimizer parameters for the policy."""
-        return self.parameters()
-
-    def select_action(self, batch: Dict[str, Tensor]) -> Tensor:
-        """
-        This method is required by PreTrainedPolicy but not used for reward classifiers.
-        The reward classifier is not an actor and does not select actions.
-        """
-        raise NotImplementedError("Reward classifiers do not select actions")
-
-    def reset(self):
-        """
-        This method is required by PreTrainedPolicy but not used for reward classifiers.
-        The reward classifier is not an actor and does not select actions.
-        """
-        pass

lerobot/common/policies/sac/configuration_sac.py

@@ -1,243 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass, field
-
-from lerobot.common.optim.optimizers import MultiAdamConfig
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import NormalizationMode
-
-
-def is_image_feature(key: str) -> bool:
-    """Check if a feature key represents an image feature.
-
-    Args:
-        key: The feature key to check
-
-    Returns:
-        True if the key represents an image feature, False otherwise
-    """
-    return key.startswith("observation.image")
-
-
-@dataclass
-class ConcurrencyConfig:
-    """Configuration for the concurrency of the actor and learner.
-    Possible values are:
-    - "threads": Use threads for the actor and learner.
-    - "processes": Use processes for the actor and learner.
-    """
-
-    actor: str = "threads"
-    learner: str = "threads"
-
-
-@dataclass
-class ActorLearnerConfig:
-    learner_host: str = "127.0.0.1"
-    learner_port: int = 50051
-    policy_parameters_push_frequency: int = 4
-
-
-@dataclass
-class CriticNetworkConfig:
-    hidden_dims: list[int] = field(default_factory=lambda: [256, 256])
-    activate_final: bool = True
-    final_activation: str | None = None
-
-
-@dataclass
-class ActorNetworkConfig:
-    hidden_dims: list[int] = field(default_factory=lambda: [256, 256])
-    activate_final: bool = True
-
-
-@dataclass
-class PolicyConfig:
-    use_tanh_squash: bool = True
-    log_std_min: float = 1e-5
-    log_std_max: float = 10.0
-    init_final: float = 0.05
-
-
-@PreTrainedConfig.register_subclass("sac")
-@dataclass
-class SACConfig(PreTrainedConfig):
-    """Soft Actor-Critic (SAC) configuration.
-
-    SAC is an off-policy actor-critic deep RL algorithm based on the maximum entropy
-    reinforcement learning framework. It learns a policy and a Q-function simultaneously
-    using experience collected from the environment.
-
-    This configuration class contains all the parameters needed to define a SAC agent,
-    including network architectures, optimization settings, and algorithm-specific
-    hyperparameters.
-    """
-
-    # Mapping of feature types to normalization modes
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.MEAN_STD,
-            "STATE": NormalizationMode.MIN_MAX,
-            "ENV": NormalizationMode.MIN_MAX,
-            "ACTION": NormalizationMode.MIN_MAX,
-        }
-    )
-
-    # Statistics for normalizing different types of inputs
-    dataset_stats: dict[str, dict[str, list[float]]] | None = field(
-        default_factory=lambda: {
-            "observation.image": {
-                "mean": [0.485, 0.456, 0.406],
-                "std": [0.229, 0.224, 0.225],
-            },
-            "observation.state": {
-                "min": [0.0, 0.0],
-                "max": [1.0, 1.0],
-            },
-            "action": {
-                "min": [0.0, 0.0, 0.0],
-                "max": [1.0, 1.0, 1.0],
-            },
-        }
-    )
-
-    # Architecture specifics
-    # Device to run the model on (e.g., "cuda", "cpu")
-    device: str = "cpu"
-    # Device to store the model on
-    storage_device: str = "cpu"
-    # Name of the vision encoder model (Set to "helper2424/resnet10" for hil serl resnet10)
-    vision_encoder_name: str | None = None
-    # Whether to freeze the vision encoder during training
-    freeze_vision_encoder: bool = True
-    # Hidden dimension size for the image encoder
-    image_encoder_hidden_dim: int = 32
-    # Whether to use a shared encoder for actor and critic
-    shared_encoder: bool = True
-    # Number of discrete actions, eg for gripper actions
-    num_discrete_actions: int | None = None
-    # Dimension of the image embedding pooling
-    image_embedding_pooling_dim: int = 8
-
-    # Training parameter
-    # Number of steps for online training
-    online_steps: int = 1000000
-    # Seed for the online environment
-    online_env_seed: int = 10000
-    # Capacity of the online replay buffer
-    online_buffer_capacity: int = 100000
-    # Capacity of the offline replay buffer
-    offline_buffer_capacity: int = 100000
-    # Whether to use asynchronous prefetching for the buffers
-    async_prefetch: bool = False
-    # Number of steps before learning starts
-    online_step_before_learning: int = 100
-    # Frequency of policy updates
-    policy_update_freq: int = 1
-
-    # SAC algorithm parameters
-    # Discount factor for the SAC algorithm
-    discount: float = 0.99
-    # Initial temperature value
-    temperature_init: float = 1.0
-    # Number of critics in the ensemble
-    num_critics: int = 2
-    # Number of subsampled critics for training
-    num_subsample_critics: int | None = None
-    # Learning rate for the critic network
-    critic_lr: float = 3e-4
-    # Learning rate for the actor network
-    actor_lr: float = 3e-4
-    # Learning rate for the temperature parameter
-    temperature_lr: float = 3e-4
-    # Weight for the critic target update
-    critic_target_update_weight: float = 0.005
-    # Update-to-data ratio for the UTD algorithm (If you want enable utd_ratio, you need to set it to >1)
-    utd_ratio: int = 1
-    # Hidden dimension size for the state encoder
-    state_encoder_hidden_dim: int = 256
-    # Dimension of the latent space
-    latent_dim: int = 256
-    # Target entropy for the SAC algorithm
-    target_entropy: float | None = None
-    # Whether to use backup entropy for the SAC algorithm
-    use_backup_entropy: bool = True
-    # Gradient clipping norm for the SAC algorithm
-    grad_clip_norm: float = 40.0
-
-    # Network configuration
-    # Configuration for the critic network architecture
-    critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
-    # Configuration for the actor network architecture
-    actor_network_kwargs: ActorNetworkConfig = field(default_factory=ActorNetworkConfig)
-    # Configuration for the policy parameters
-    policy_kwargs: PolicyConfig = field(default_factory=PolicyConfig)
-    # Configuration for the discrete critic network
-    discrete_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
-    # Configuration for actor-learner architecture
-    actor_learner_config: ActorLearnerConfig = field(default_factory=ActorLearnerConfig)
-    # Configuration for concurrency settings (you can use threads or processes for the actor and learner)
-    concurrency: ConcurrencyConfig = field(default_factory=ConcurrencyConfig)
-
-    # Optimizations
-    use_torch_compile: bool = True
-
-    def __post_init__(self):
-        super().__post_init__()
-        # Any validation specific to SAC configuration
-
-    def get_optimizer_preset(self) -> MultiAdamConfig:
-        return MultiAdamConfig(
-            weight_decay=0.0,
-            optimizer_groups={
-                "actor": {"lr": self.actor_lr},
-                "critic": {"lr": self.critic_lr},
-                "temperature": {"lr": self.temperature_lr},
-            },
-        )
-
-    def get_scheduler_preset(self) -> None:
-        return None
-
-    def validate_features(self) -> None:
-        has_image = any(is_image_feature(key) for key in self.input_features)
-        has_state = "observation.state" in self.input_features
-
-        if not (has_state or has_image):
-            raise ValueError(
-                "You must provide either 'observation.state' or an image observation (key starting with 'observation.image') in the input features"
-            )
-
-        if "action" not in self.output_features:
-            raise ValueError("You must provide 'action' in the output features")
-
-    @property
-    def image_features(self) -> list[str]:
-        return [key for key in self.input_features if is_image_feature(key)]
-
-    @property
-    def observation_delta_indices(self) -> list:
-        return None
-
-    @property
-    def action_delta_indices(self) -> list:
-        return None  # SAC typically predicts one action at a time
-
-    @property
-    def reward_delta_indices(self) -> None:
-        return None

lerobot/common/robots/koch_follower/koch.md

@@ -89,11 +89,10 @@ If you are repurposing motors from another robot, you will probably also need to
 
 Connect the usb cable from your computer and the 5V power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
 
-For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
+For a visual reference on how to set the motor ids please refer to [this video](http://localhost:5173/so101#setup-motors-video) where we follow the process for the SO101 arm.
 
 <hfoptions id="setup_motors">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.setup_motors \
     --robot.type=koch_follower \
@@ -101,7 +100,6 @@ python -m lerobot.setup_motors \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.robots.koch_follower import KochFollower, KochFollowerConfig
 
@@ -159,7 +157,6 @@ Do the same steps for the leader arm but modify the command or script accordingl
 
 <hfoptions id="setup_motors">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.setup_motors \
     --teleop.type=koch_leader \
@@ -167,7 +164,6 @@ python -m lerobot.setup_motors \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.teleoperators.koch_leader import KochLeader, KochLeaderConfig
 
@@ -192,7 +188,6 @@ Run the following command or API example to calibrate the follower arm:
 
 <hfoptions id="calibrate_follower">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --robot.type=koch_follower \
@@ -201,7 +196,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.robots.koch_follower import KochFollowerConfig, KochFollower
 
@@ -218,7 +212,7 @@ follower.disconnect()
 </hfoption>
 </hfoptions>
 
-We unified the calibration method for most robots. Thus, the calibration steps for this Koch arm are the same as the steps for the SO100 and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
+We unified the calibration method for most robots. Thus, the calibration steps for this Koch arm are the same as the steps for the SO100 and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](http://localhost:5173/so101#calibration-video)
 
 #### Leader
 
@@ -226,7 +220,6 @@ Do the same steps to calibrate the leader arm, run the following command or API
 
 <hfoptions id="calibrate_leader">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --teleop.type=koch_leader \
@@ -235,7 +228,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.teleoperators.koch_leader import KochLeaderConfig, KochLeader
 

lerobot/common/robots/koch_follower/koch_follower.py

@@ -82,7 +82,8 @@ class KochFollower(Robot):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        # TODO(aliberts): add cam.is_connected for cam in self.cameras
+        return self.bus.is_connected
 
     def connect(self, calibrate: bool = True) -> None:
         """

lerobot/common/robots/lekiwi/lekiwi.md

@@ -46,7 +46,7 @@ First, we will assemble the two SO100/SO101 arms. One to attach to the mobile ba
 ### Configure motors
 The instructions for configuring the motors can be found in the SO101 [docs](./so101#configure-the-motors). Besides the ids for the arm motors, we also need to set the motor ids for the mobile base. These need to be in a specific order to work. Below an image of the motor ids and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ids for the wheels are 7, 8 and 9.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/motor_ids.webp" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/motor_ids.webp" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
 
 ### Troubleshoot communication
 
@@ -90,7 +90,6 @@ The calibration process is very important because it allows a neural network tra
 Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:
 <hfoptions id="calibrate_follower">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --robot.type=lekiwi \
@@ -99,7 +98,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
 
@@ -116,7 +114,7 @@ lekiwi.disconnect()
 </hfoption>
 </hfoptions>
 
-We unified the calibration method for most robots, thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
+We unified the calibration method for most robots, thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](http://localhost:5173/so101#calibration-video)
 
 ### Wired version
 If you have the **wired** LeKiwi version, please run all commands on your laptop.
@@ -125,7 +123,6 @@ If you have the **wired** LeKiwi version, please run all commands on your laptop
 Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the following command of API example on your laptop:
 <hfoptions id="calibrate_leader">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --teleop.type=so100_leader \
@@ -134,7 +131,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
 
@@ -151,98 +147,7 @@ leader.disconnect()
 </hfoption>
 </hfoptions>
 
-## Teleoperate LeKiwi
-
-> [!TIP]
-> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
-
-To teleoperate, SSH into your Raspberry Pi, and run `conda activate lerobot` and this command:
-```bash
-python -m lerobot.common.robots.lekiwi.lekiwi_host
-```
-
-Then on your laptop, also run `conda activate lerobot` and this command or API example:
-
-<hfoptions id="teleoperate_koch_camera">
-<hfoption id="Command">
-
-```bash
-python -m lerobot.teleoperate \
-    --robot.type=lekiwi \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.cameras="{}" \
-    --robot.id=my_lekiwi \
-    --teleop.type=so101_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_blue_leader_arm
-```
-</hfoption>
-<hfoption id="API example">
-
-```python
-from lerobot.common.teleoperators.keyboard.teleop_keyboard import KeyboardTeleopConfig, KeyboardTeleop
-from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
-from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-
-robot_config = LeKiwiClientConfig(
-    remote_ip="172.18.133.90",
-    id="my_red_lekiwi"
-)
-
-teleop__arm_config = SO100LeaderConfig(
-    port="/dev/tty.usbmodem58760431551",
-    id="my_blue_leader_arm",
-)
-
-teleop_keyboard_config = KeyboardTeleopConfig(
-    id="my_laptop_keyboard",
-)
-
-robot = LeKiwiClient(robot_config)
-teleop_arm = SO100Leader(teleop__arm_config)
-telep_keyboard = KeyboardTeleop(teleop_keyboard_config)
-robot.connect()
-teleop_arm.connect()
-telep_keyboard.connect()
-
-while True:
-    observation = robot.get_observation()
-    action_arm = teleop_arm.get_action()
-    action_base = telep_keyboard.get_action()
-    robot.send_action(action_arm | action_base)
-```
-</hfoption>
-</hfoptions>
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`. For the `--control.type=remote_robot` you will also need to set `--control.viewer_ip` and `--control.viewer_port`
-
-You should see on your laptop something like this: ```[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.``` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:
-
-| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
-| ---------- | ------------------ | ---------------------- |
-| Fast       | 0.4                | 90                     |
-| Medium     | 0.25               | 60                     |
-| Slow       | 0.1                | 30                     |
-
-
-| Key | Action         |
-| --- | -------------- |
-| W   | Move forward   |
-| A   | Move left      |
-| S   | Move backward  |
-| D   | Move right     |
-| Z   | Turn left      |
-| X   | Turn right     |
-| R   | Increase speed |
-| F   | Decrease speed |
-
-> [!TIP]
->  If you use a different keyboard, you can change the keys for each command in the [`LeKiwiConfig`](../lerobot/common/robot_devices/robots/configs.py).
-
-### Wired version
-If you have the **wired** LeKiwi version, please run all commands on your laptop.
-
-Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial (you can skip the teleoperation part): [Getting started with real-world robots](./getting_started_real_world_robot)
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
 
 > [!TIP]
 >  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

lerobot/common/robots/lekiwi/lekiwi.py

@@ -16,12 +16,9 @@
 
 import logging
 import time
-from functools import cached_property
 from itertools import chain
 from typing import Any
 
-import numpy as np
-
 from lerobot.common.cameras.utils import make_cameras_from_configs
 from lerobot.common.constants import OBS_IMAGES, OBS_STATE
 from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
@@ -74,39 +71,39 @@ class LeKiwi(Robot):
         self.cameras = make_cameras_from_configs(config.cameras)
 
     @property
-    def _state_ft(self) -> dict[str, type]:
-        return dict.fromkeys(
-            (
-                "arm_shoulder_pan.pos",
-                "arm_shoulder_lift.pos",
-                "arm_elbow_flex.pos",
-                "arm_wrist_flex.pos",
-                "arm_wrist_roll.pos",
-                "arm_gripper.pos",
-                "x.vel",
-                "y.vel",
-                "theta.vel",
-            ),
-            float,
-        )
+    def state_feature(self) -> dict:
+        state_ft = {
+            "arm_shoulder_pan": {"dtype": "float32"},
+            "arm_shoulder_lift": {"dtype": "float32"},
+            "arm_elbow_flex": {"dtype": "float32"},
+            "arm_wrist_flex": {"dtype": "float32"},
+            "arm_wrist_roll": {"dtype": "float32"},
+            "arm_gripper": {"dtype": "float32"},
+            "base_left_wheel": {"dtype": "float32"},
+            "base_right_wheel": {"dtype": "float32"},
+            "base_back_wheel": {"dtype": "float32"},
+        }
+        return state_ft
 
     @property
-    def _cameras_ft(self) -> dict[str, tuple]:
-        return {
-            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
-        }
+    def action_feature(self) -> dict:
+        return self.state_feature
 
-    @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._state_ft, **self._cameras_ft}
-
-    @cached_property
-    def action_features(self) -> dict[str, type]:
-        return self._state_ft
+    @property
+    def camera_features(self) -> dict[str, dict]:
+        cam_ft = {}
+        for cam_key, cam in self.cameras.items():
+            cam_ft[cam_key] = {
+                "shape": (cam.height, cam.width, cam.channels),
+                "names": ["height", "width", "channels"],
+                "info": None,
+            }
+        return cam_ft
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        # TODO(aliberts): add cam.is_connected for cam in self.cameras
+        return self.bus.is_connected
 
     def connect(self, calibrate: bool = True) -> None:
         if self.is_connected:
@@ -193,142 +190,6 @@ class LeKiwi(Robot):
             self.bus.setup_motor(motor)
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
-    @staticmethod
-    def _degps_to_raw(degps: float) -> int:
-        steps_per_deg = 4096.0 / 360.0
-        speed_in_steps = degps * steps_per_deg
-        speed_int = int(round(speed_in_steps))
-        # Cap the value to fit within signed 16-bit range (-32768 to 32767)
-        if speed_int > 0x7FFF:
-            speed_int = 0x7FFF  # 32767 -> maximum positive value
-        elif speed_int < -0x8000:
-            speed_int = -0x8000  # -32768 -> minimum negative value
-        return speed_int
-
-    @staticmethod
-    def _raw_to_degps(raw_speed: int) -> float:
-        steps_per_deg = 4096.0 / 360.0
-        magnitude = raw_speed
-        degps = magnitude / steps_per_deg
-        return degps
-
-    def _body_to_wheel_raw(
-        self,
-        x: float,
-        y: float,
-        theta: float,
-        wheel_radius: float = 0.05,
-        base_radius: float = 0.125,
-        max_raw: int = 3000,
-    ) -> dict:
-        """
-        Convert desired body-frame velocities into wheel raw commands.
-
-        Parameters:
-          x_cmd      : Linear velocity in x (m/s).
-          y_cmd      : Linear velocity in y (m/s).
-          theta_cmd  : Rotational velocity (deg/s).
-          wheel_radius: Radius of each wheel (meters).
-          base_radius : Distance from the center of rotation to each wheel (meters).
-          max_raw    : Maximum allowed raw command (ticks) per wheel.
-
-        Returns:
-          A dictionary with wheel raw commands:
-             {"base_left_wheel": value, "base_back_wheel": value, "base_right_wheel": value}.
-
-        Notes:
-          - Internally, the method converts theta_cmd to rad/s for the kinematics.
-          - The raw command is computed from the wheels angular speed in deg/s
-            using _degps_to_raw(). If any command exceeds max_raw, all commands
-            are scaled down proportionally.
-        """
-        # Convert rotational velocity from deg/s to rad/s.
-        theta_rad = theta * (np.pi / 180.0)
-        # Create the body velocity vector [x, y, theta_rad].
-        velocity_vector = np.array([x, y, theta_rad])
-
-        # Define the wheel mounting angles with a -90° offset.
-        angles = np.radians(np.array([240, 120, 0]) - 90)
-        # Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
-        # The third column (base_radius) accounts for the effect of rotation.
-        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
-
-        # Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s).
-        wheel_linear_speeds = m.dot(velocity_vector)
-        wheel_angular_speeds = wheel_linear_speeds / wheel_radius
-
-        # Convert wheel angular speeds from rad/s to deg/s.
-        wheel_degps = wheel_angular_speeds * (180.0 / np.pi)
-
-        # Scaling
-        steps_per_deg = 4096.0 / 360.0
-        raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps]
-        max_raw_computed = max(raw_floats)
-        if max_raw_computed > max_raw:
-            scale = max_raw / max_raw_computed
-            wheel_degps = wheel_degps * scale
-
-        # Convert each wheel’s angular speed (deg/s) to a raw integer.
-        wheel_raw = [self._degps_to_raw(deg) for deg in wheel_degps]
-
-        return {
-            "base_left_wheel": wheel_raw[0],
-            "base_back_wheel": wheel_raw[1],
-            "base_right_wheel": wheel_raw[2],
-        }
-
-    def _wheel_raw_to_body(
-        self,
-        left_wheel_speed,
-        back_wheel_speed,
-        right_wheel_speed,
-        wheel_radius: float = 0.05,
-        base_radius: float = 0.125,
-    ) -> dict[str, Any]:
-        """
-        Convert wheel raw command feedback back into body-frame velocities.
-
-        Parameters:
-          wheel_raw   : Vector with raw wheel commands ("base_left_wheel", "base_back_wheel", "base_right_wheel").
-          wheel_radius: Radius of each wheel (meters).
-          base_radius : Distance from the robot center to each wheel (meters).
-
-        Returns:
-          A dict (x_cmd, y_cmd, theta_cmd) where:
-             OBS_STATE.x_cmd      : Linear velocity in x (m/s).
-             OBS_STATE.y_cmd      : Linear velocity in y (m/s).
-             OBS_STATE.theta_cmd  : Rotational velocity in deg/s.
-        """
-
-        # Convert each raw command back to an angular speed in deg/s.
-        wheel_degps = np.array(
-            [
-                self._raw_to_degps(left_wheel_speed),
-                self._raw_to_degps(back_wheel_speed),
-                self._raw_to_degps(right_wheel_speed),
-            ]
-        )
-
-        # Convert from deg/s to rad/s.
-        wheel_radps = wheel_degps * (np.pi / 180.0)
-        # Compute each wheel’s linear speed (m/s) from its angular speed.
-        wheel_linear_speeds = wheel_radps * wheel_radius
-
-        # Define the wheel mounting angles with a -90° offset.
-        angles = np.radians(np.array([240, 120, 0]) - 90)
-        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
-
-        # Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
-        m_inv = np.linalg.inv(m)
-        velocity_vector = m_inv.dot(wheel_linear_speeds)
-        x, y, theta_rad = velocity_vector
-        theta = theta_rad * (180.0 / np.pi)
-        return {
-            "x.vel": x,
-            "y.vel": y,
-            "theta.vel": theta,
-        }  # m/s and deg/s
-
     def get_observation(self) -> dict[str, Any]:
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -336,19 +197,9 @@ class LeKiwi(Robot):
         # Read actuators position for arm and vel for base
         start = time.perf_counter()
         arm_pos = self.bus.sync_read("Present_Position", self.arm_motors)
-        base_wheel_vel = self.bus.sync_read("Present_Velocity", self.base_motors)
-
-        base_vel = self._wheel_raw_to_body(
-            base_wheel_vel["base_left_wheel"],
-            base_wheel_vel["base_back_wheel"],
-            base_wheel_vel["base_right_wheel"],
-        )
-
-        arm_state = {f"{k}.pos": v for k, v in arm_pos.items()}
-
-        flat_states = {**arm_state, **base_vel}
-
-        obs_dict = {f"{OBS_STATE}": flat_states}
+        base_vel = self.bus.sync_read("Present_Velocity", self.base_motors)
+        obs_dict = {**arm_pos, **base_vel}
+        obs_dict = {f"{OBS_STATE}." + key: value for key, value in obs_dict.items()}
         dt_ms = (time.perf_counter() - start) * 1e3
         logger.debug(f"{self} read state: {dt_ms:.1f}ms")
 
@@ -377,12 +228,8 @@ class LeKiwi(Robot):
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
-        arm_goal_pos = {k: v for k, v in action.items() if k.endswith(".pos")}
-        base_goal_vel = {k: v for k, v in action.items() if k.endswith(".vel")}
-
-        base_wheel_goal_vel = self._body_to_wheel_raw(
-            base_goal_vel["x.vel"], base_goal_vel["y.vel"], base_goal_vel["theta.vel"]
-        )
+        arm_goal_pos = {k: v for k, v in action.items() if k in self.arm_motors}
+        base_goal_vel = {k: v for k, v in action.items() if k in self.base_motors}
 
         # Cap goal position when too far away from present position.
         # /!\ Slower fps expected due to reading from the follower.
@@ -393,9 +240,8 @@ class LeKiwi(Robot):
             arm_goal_pos = arm_safe_goal_pos
 
         # Send goal position to the actuators
-        arm_goal_pos_raw = {k.replace(".pos", ""): v for k, v in arm_goal_pos.items()}
-        self.bus.sync_write("Goal_Position", arm_goal_pos_raw)
-        self.bus.sync_write("Goal_Velocity", base_wheel_goal_vel)
+        self.bus.sync_write("Goal_Position", arm_goal_pos)
+        self.bus.sync_write("Goal_Velocity", base_goal_vel)
 
         return {**arm_goal_pos, **base_goal_vel}
 

lerobot/common/robots/lekiwi/lekiwi_client.py

@@ -15,7 +15,6 @@
 import base64
 import json
 import logging
-from functools import cached_property
 from typing import Any, Dict, Optional, Tuple
 
 import cv2
@@ -55,7 +54,8 @@ class LeKiwiClient(Robot):
 
         self.last_frames = {}
 
-        self.last_remote_state = {}
+        self.last_remote_arm_state = {}
+        self.last_remote_base_state = {"base_left_wheel": 0, "base_back_wheel": 0, "base_right_wheel": 0}
 
         # Define three speed levels and a current index
         self.speed_levels = [
@@ -68,41 +68,53 @@ class LeKiwiClient(Robot):
         self._is_connected = False
         self.logs = {}
 
-    @cached_property
-    def _state_ft(self) -> dict[str, type]:
-        return dict.fromkeys(
-            (
-                "arm_shoulder_pan.pos",
-                "arm_shoulder_lift.pos",
-                "arm_elbow_flex.pos",
-                "arm_wrist_flex.pos",
-                "arm_wrist_roll.pos",
-                "arm_gripper.pos",
-                "x.vel",
-                "y.vel",
-                "theta.vel",
-            ),
-            float,
-        )
-
-    @cached_property
-    def _state_order(self) -> tuple[str, ...]:
-        return tuple(self._state_ft.keys())
-
-    @cached_property
-    def _cameras_ft(self) -> dict[str, tuple]:
-        return {
-            "front": (480, 640, 3),
-            "wrist": (640, 480, 3),
+    @property
+    def state_feature(self) -> dict:
+        state_ft = {
+            "arm_shoulder_pan": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_shoulder_lift": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_elbow_flex": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_wrist_flex": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_wrist_roll": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_gripper": {"shape": (1,), "info": None, "dtype": "float32"},
+            "x_cmd": {"shape": (1,), "info": None, "dtype": "float32"},
+            "y_cmd": {"shape": (1,), "info": None, "dtype": "float32"},
+            "theta_cmd": {"shape": (1,), "info": None, "dtype": "float32"},
         }
+        return state_ft
 
-    @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._state_ft, **self._cameras_ft}
+    @property
+    def action_feature(self) -> dict:
+        action_ft = {
+            "arm_shoulder_pan": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_shoulder_lift": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_elbow_flex": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_wrist_flex": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_wrist_roll": {"shape": (1,), "info": None, "dtype": "float32"},
+            "arm_gripper": {"shape": (1,), "info": None, "dtype": "float32"},
+            "base_left_wheel": {"shape": (1,), "info": None, "dtype": "float32"},
+            "base_right_wheel": {"shape": (1,), "info": None, "dtype": "float32"},
+            "base_back_wheel": {"shape": (1,), "info": None, "dtype": "float32"},
+        }
+        return action_ft
 
-    @cached_property
-    def action_features(self) -> dict[str, type]:
-        return self._state_ft
+    @property
+    def camera_features(self) -> dict[str, dict]:
+        cam_ft = {
+            f"{OBS_IMAGES}.front": {
+                "shape": (480, 640, 3),
+                "names": ["height", "width", "channels"],
+                "info": None,
+                "dtype": "image",
+            },
+            f"{OBS_IMAGES}.wrist": {
+                "shape": (480, 640, 3),
+                "names": ["height", "width", "channels"],
+                "dtype": "image",
+                "info": None,
+            },
+        }
+        return cam_ft
 
     @property
     def is_connected(self) -> bool:
@@ -142,6 +154,130 @@ class LeKiwiClient(Robot):
     def calibrate(self) -> None:
         pass
 
+    @staticmethod
+    def _degps_to_raw(degps: float) -> int:
+        steps_per_deg = 4096.0 / 360.0
+        speed_in_steps = degps * steps_per_deg
+        speed_int = int(round(speed_in_steps))
+        # Cap the value to fit within signed 16-bit range (-32768 to 32767)
+        if speed_int > 0x7FFF:
+            speed_int = 0x7FFF  # 32767 -> maximum positive value
+        elif speed_int < -0x8000:
+            speed_int = -0x8000  # -32768 -> minimum negative value
+        return speed_int
+
+    @staticmethod
+    def _raw_to_degps(raw_speed: int) -> float:
+        steps_per_deg = 4096.0 / 360.0
+        magnitude = raw_speed
+        degps = magnitude / steps_per_deg
+        return degps
+
+    def _body_to_wheel_raw(
+        self,
+        x_cmd: float,
+        y_cmd: float,
+        theta_cmd: float,
+        wheel_radius: float = 0.05,
+        base_radius: float = 0.125,
+        max_raw: int = 3000,
+    ) -> dict:
+        """
+        Convert desired body-frame velocities into wheel raw commands.
+
+        Parameters:
+          x_cmd      : Linear velocity in x (m/s).
+          y_cmd      : Linear velocity in y (m/s).
+          theta_cmd  : Rotational velocity (deg/s).
+          wheel_radius: Radius of each wheel (meters).
+          base_radius : Distance from the center of rotation to each wheel (meters).
+          max_raw    : Maximum allowed raw command (ticks) per wheel.
+
+        Returns:
+          A dictionary with wheel raw commands:
+             {"base_left_wheel": value, "base_back_wheel": value, "base_right_wheel": value}.
+
+        Notes:
+          - Internally, the method converts theta_cmd to rad/s for the kinematics.
+          - The raw command is computed from the wheels angular speed in deg/s
+            using _degps_to_raw(). If any command exceeds max_raw, all commands
+            are scaled down proportionally.
+        """
+        # Convert rotational velocity from deg/s to rad/s.
+        theta_rad = theta_cmd * (np.pi / 180.0)
+        # Create the body velocity vector [x, y, theta_rad].
+        velocity_vector = np.array([x_cmd, y_cmd, theta_rad])
+
+        # Define the wheel mounting angles with a -90° offset.
+        angles = np.radians(np.array([240, 120, 0]) - 90)
+        # Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
+        # The third column (base_radius) accounts for the effect of rotation.
+        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
+
+        # Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s).
+        wheel_linear_speeds = m.dot(velocity_vector)
+        wheel_angular_speeds = wheel_linear_speeds / wheel_radius
+
+        # Convert wheel angular speeds from rad/s to deg/s.
+        wheel_degps = wheel_angular_speeds * (180.0 / np.pi)
+
+        # Scaling
+        steps_per_deg = 4096.0 / 360.0
+        raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps]
+        max_raw_computed = max(raw_floats)
+        if max_raw_computed > max_raw:
+            scale = max_raw / max_raw_computed
+            wheel_degps = wheel_degps * scale
+
+        # Convert each wheel’s angular speed (deg/s) to a raw integer.
+        wheel_raw = [self._degps_to_raw(deg) for deg in wheel_degps]
+
+        return {
+            "base_left_wheel": wheel_raw[0],
+            "base_back_wheel": wheel_raw[1],
+            "base_right_wheel": wheel_raw[2],
+        }
+
+    def _wheel_raw_to_body(
+        self, wheel_raw: dict[str, Any], wheel_radius: float = 0.05, base_radius: float = 0.125
+    ) -> dict[str, Any]:
+        """
+        Convert wheel raw command feedback back into body-frame velocities.
+
+        Parameters:
+          wheel_raw   : Vector with raw wheel commands ("base_left_wheel", "base_back_wheel", "base_right_wheel").
+          wheel_radius: Radius of each wheel (meters).
+          base_radius : Distance from the robot center to each wheel (meters).
+
+        Returns:
+          A dict (x_cmd, y_cmd, theta_cmd) where:
+             OBS_STATE.x_cmd      : Linear velocity in x (m/s).
+             OBS_STATE.y_cmd      : Linear velocity in y (m/s).
+             OBS_STATE.theta_cmd  : Rotational velocity in deg/s.
+        """
+
+        # Convert each raw command back to an angular speed in deg/s.
+        wheel_degps = np.array([LeKiwiClient._raw_to_degps(int(v)) for _, v in wheel_raw.items()])
+        # Convert from deg/s to rad/s.
+        wheel_radps = wheel_degps * (np.pi / 180.0)
+        # Compute each wheel’s linear speed (m/s) from its angular speed.
+        wheel_linear_speeds = wheel_radps * wheel_radius
+
+        # Define the wheel mounting angles with a -90° offset.
+        angles = np.radians(np.array([240, 120, 0]) - 90)
+        m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
+
+        # Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
+        m_inv = np.linalg.inv(m)
+        velocity_vector = m_inv.dot(wheel_linear_speeds)
+        x_cmd, y_cmd, theta_rad = velocity_vector
+        theta_cmd = theta_rad * (180.0 / np.pi)
+        return {
+            f"{OBS_STATE}.x_cmd": x_cmd * 1000,
+            f"{OBS_STATE}.y_cmd": y_cmd * 1000,
+            f"{OBS_STATE}.theta_cmd": theta_cmd,
+        }  # Convert to mm/s
+
     def _poll_and_get_latest_message(self) -> Optional[str]:
         """Polls the ZMQ socket for a limited time and returns the latest message string."""
         poller = zmq.Poller()
@@ -195,24 +331,25 @@ class LeKiwiClient(Robot):
 
     def _remote_state_from_obs(
         self, observation: Dict[str, Any]
-    ) -> Tuple[Dict[str, np.ndarray], Dict[str, Any]]:
-        """Extracts frames, and state from the parsed observation."""
-        flat_state = observation[OBS_STATE]
+    ) -> Tuple[Dict[str, np.ndarray], Dict[str, Any], Dict[str, Any]]:
+        """Extracts frames, speed, and arm state from the parsed observation."""
 
-        state_vec = np.array(
-            [flat_state.get(k, 0.0) for k in self._state_order],
-            dtype=np.float32,
-        )
+        # Separate image and state data
+        image_observation = {k: v for k, v in observation.items() if k.startswith(OBS_IMAGES)}
+        state_observation = {k: v for k, v in observation.items() if k.startswith(OBS_STATE)}
 
         # Decode images
-        image_observation = {k: v for k, v in observation.items() if k.startswith(OBS_IMAGES)}
         current_frames: Dict[str, np.ndarray] = {}
         for cam_name, image_b64 in image_observation.items():
             frame = self._decode_image_from_b64(image_b64)
             if frame is not None:
                 current_frames[cam_name] = frame
 
-        return current_frames, {"observation.state": state_vec}
+        # Extract state components
+        current_arm_state = {k: v for k, v in state_observation.items() if k.startswith(f"{OBS_STATE}.arm")}
+        current_base_state = {k: v for k, v in state_observation.items() if k.startswith(f"{OBS_STATE}.base")}
+
+        return current_frames, current_arm_state, current_base_state
 
     def _get_data(self) -> Tuple[Dict[str, np.ndarray], Dict[str, Any], Dict[str, Any]]:
         """
@@ -228,26 +365,27 @@ class LeKiwiClient(Robot):
 
         # 2. If no message, return cached data
         if latest_message_str is None:
-            return self.last_frames, self.last_remote_state
+            return self.last_frames, self.last_remote_arm_state, self.last_remote_base_state
 
         # 3. Parse the JSON message
         observation = self._parse_observation_json(latest_message_str)
 
         # 4. If JSON parsing failed, return cached data
         if observation is None:
-            return self.last_frames, self.last_remote_state
+            return self.last_frames, self.last_remote_arm_state, self.last_remote_base_state
 
         # 5. Process the valid observation data
         try:
-            new_frames, new_state = self._remote_state_from_obs(observation)
+            new_frames, new_arm_state, new_base_state = self._remote_state_from_obs(observation)
         except Exception as e:
             logging.error(f"Error processing observation data, serving last observation: {e}")
-            return self.last_frames, self.last_remote_state
+            return self.last_frames, self.last_remote_arm_state, self.last_remote_base_state
 
         self.last_frames = new_frames
-        self.last_remote_state = new_state
+        self.last_remote_arm_state = new_arm_state
+        self.last_remote_base_state = new_base_state
 
-        return new_frames, new_state
+        return new_frames, new_arm_state, new_base_state
 
     def get_observation(self) -> dict[str, Any]:
         """
@@ -258,7 +396,13 @@ class LeKiwiClient(Robot):
         if not self._is_connected:
             raise DeviceNotConnectedError("LeKiwiClient is not connected. You need to run `robot.connect()`.")
 
-        frames, obs_dict = self._get_data()
+        frames, remote_arm_state, remote_base_state = self._get_data()
+        remote_body_state = self._wheel_raw_to_body(remote_base_state)
+
+        obs_dict = {**remote_arm_state, **remote_body_state}
+
+        # TODO(Steven): Remove this when it is possible to record a non-numpy array value
+        obs_dict = {k: np.array([v], dtype=np.float32) for k, v in obs_dict.items()}
 
         # Loop over each configured camera
         for cam_name, frame in frames.items():
@@ -269,7 +413,7 @@ class LeKiwiClient(Robot):
 
         return obs_dict
 
-    def _from_keyboard_to_base_action(self, pressed_keys: np.ndarray):
+    def _from_keyboard_to_wheel_action(self, pressed_keys: np.ndarray):
         # Speed control
         if self.teleop_keys["speed_up"] in pressed_keys:
             self.speed_index = min(self.speed_index + 1, 2)
@@ -295,11 +439,7 @@ class LeKiwiClient(Robot):
             theta_cmd += theta_speed
         if self.teleop_keys["rotate_right"] in pressed_keys:
             theta_cmd -= theta_speed
-        return {
-            "x.vel": x_cmd,
-            "y.vel": y_cmd,
-            "theta.vel": theta_cmd,
-        }
+        return self._body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
 
     def configure(self):
         pass
@@ -321,23 +461,26 @@ class LeKiwiClient(Robot):
                 "ManipulatorRobot is not connected. You need to run `robot.connect()`."
             )
 
+        goal_pos = {}
+
         common_keys = [
             key
             for key in action
-            if key in (motor.replace("arm_", "") for motor, _ in self.action_features.items())
+            if key in (motor.replace("arm_", "") for motor, _ in self.action_feature.items())
         ]
 
         arm_actions = {"arm_" + arm_motor: action[arm_motor] for arm_motor in common_keys}
+        goal_pos = arm_actions
 
         keyboard_keys = np.array(list(set(action.keys()) - set(common_keys)))
-        base_actions = self._from_keyboard_to_base_action(keyboard_keys)
-        goal_pos = {**arm_actions, **base_actions}
+        wheel_actions = self._from_keyboard_to_wheel_action(keyboard_keys)
+        goal_pos = {**arm_actions, **wheel_actions}
 
         self.zmq_cmd_socket.send_string(json.dumps(goal_pos))  # action is in motor space
 
         # TODO(Steven): Remove the np conversion when it is possible to record a non-numpy array value
-        actions = np.array([goal_pos.get(k, 0.0) for k in self._state_order], dtype=np.float32)
-        return {"action.state": actions}
+        goal_pos = {"action." + k: np.array([v], dtype=np.float32) for k, v in goal_pos.items()}
+        return goal_pos
 
     def disconnect(self):
         """Cleans ZMQ comms"""

lerobot/common/robots/so100_follower/so100.md

@@ -22,11 +22,7 @@ pip install -e ".[feetech]"
 <details>
 <summary><strong>Video removing gears</strong></summary>
 
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7" type="video/mp4" />
-  </video>
-</div>
+<video src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7"></video>
 
 </details>
 
@@ -40,11 +36,7 @@ Remove all support material from the 3D-printed parts. The easiest way to do thi
 <details>
 <summary><strong>Video assembling arms</strong></summary>
 
-<div class="video-container">
-  <video controls width="600">
-    <source src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca" type="video/mp4" />
-  </video>
-</div>
+<video src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca"></video>
 
 </details>
 
@@ -58,12 +50,12 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 2: Insert Wires**
 - Insert two wires into the first motor.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_1.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_1.jpg" style="height:300px;">
 
 **Step 3: Install in Base**
 - Place the first motor into the base.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_2.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_2.jpg" style="height:300px;">
 
 **Step 4: Secure Motor**
 - Fasten the motor with 4 screws. Two from the bottom and two from top.
@@ -71,13 +63,12 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 5: Attach Motor Holder**
 - Slide over the first motor holder and fasten it using two screws (one on each side).
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_4.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_4.jpg" style="height:300px;">
 
 **Step 6: Attach Motor Horns**
 - Install both motor horns, securing the top horn with a screw. Try not to move the motor position when attaching the motor horn, especially for the leader arms, where we removed the gears.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_5.webp" style="height:300px;"/>
-
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_5.jpg" style="height:300px;">
 <details>
   <summary><strong>Video adding motor horn</strong></summary>
   <video src="https://github.com/user-attachments/assets/ef3391a4-ad05-4100-b2bd-1699bf86c969"></video>
@@ -87,7 +78,7 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 - Route one wire to the back of the robot and the other to the left or towards you (see photo).
 - Attach the shoulder part.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_6.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_6.jpg" style="height:300px;">
 
 **Step 8: Secure Shoulder**
 - Tighten the shoulder part with 4 screws on top and 4 on the bottom
@@ -100,18 +91,18 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 9: Install Motor 2**
 - Slide the second motor in from the top and link the wire from motor 1 to motor 2.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_8.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_8.jpg" style="height:300px;">
 
 **Step 10: Attach Shoulder Holder**
 - Add the shoulder motor holder.
 - Ensure the wire from motor 1 to motor 2 goes behind the holder while the other wire is routed upward (see photo).
 - This part can be tight to assemble, you can use a workbench like the image or a similar setup to push the part around the motor.
 
-<div style="display: flex;">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_9.webp" style="height:250px;"/>
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_10.webp" style="height:250px;"/>
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_12.webp" style="height:250px;"/>
-</div>
+  <div style="display: flex;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_9.jpg" style="height:250px;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_10.jpg" style="height:250px;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_12.jpg" style="height:250px;">
+  </div>
 
 **Step 11: Secure Motor 2**
 - Fasten the second motor with 4 screws.
@@ -122,12 +113,12 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 13: Attach Base**
 - Install the base attachment using 2 screws.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_11.webp" style="height:300px;">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_11.jpg" style="height:300px;">
 
 **Step 14: Attach Upper Arm**
 - Attach the upper arm with 4 screws on each side.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_13.webp" style="height:300px;">
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_13.jpg" style="height:300px;">
 
 ---
 
@@ -139,12 +130,12 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 16: Attach Motor Horn**
 - Attach both motor horns to motor 3 and secure one again with a horn screw.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_14.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_14.jpg" style="height:300px;">
 
 **Step 17: Attach Forearm**
 - Connect the forearm to motor 3 using 4 screws on each side.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_15.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_15.jpg" style="height:300px;">
 
 ---
 
@@ -153,20 +144,20 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 18: Install Motor 4**
 - Slide in motor 4, attach the cable from motor 3, and secure the cable in its holder with a screw.
 
-<div style="display: flex;">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_16.webp" style="height:300px;"/>
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_19.webp" style="height:300px;"/>
-</div>
+  <div style="display: flex;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_16.jpg" style="height:300px;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_19.jpg" style="height:300px;">
+  </div>
 
 **Step 19: Attach Motor Holder 4**
 - Install the fourth motor holder (a tight fit). Ensure one wire is routed upward and the wire from motor 3 is routed downward (see photo).
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_17.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_17.jpg" style="height:300px;">
 
 **Step 20: Secure Motor 4 & Attach Horn**
 - Fasten motor 4 with 4 screws and attach its motor horns, use for one a horn screw.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_18.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_18.jpg" style="height:300px;">
 
 ---
 
@@ -175,18 +166,18 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 21: Install Motor 5**
 - Insert motor 5 into the wrist holder and secure it with 2 front screws.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_20.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_20.jpg" style="height:300px;">
 
 **Step 22: Attach Wrist**
 - Connect the wire from motor 4 to motor 5. And already insert the other wire for the gripper.
 - Secure the wrist to motor 4 using 4 screws on both sides.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_22.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_22.jpg" style="height:300px;">
 
 **Step 23: Attach Wrist Horn**
 - Install only one motor horn on the wrist motor and secure it with a horn screw.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_23.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_23.jpg" style="height:300px;">
 
 ---
 
@@ -195,26 +186,26 @@ This video provides visual guidance for assembling the arms, but it doesn't spec
 **Step 24: Attach Gripper**
 - Attach the gripper to motor 5.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_24.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_24.jpg" style="height:300px;">
 
 **Step 25: Install Gripper Motor**
 - Insert the gripper motor, connect the motor wire from motor 5 to motor 6, and secure it with 3 screws on each side.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_25.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_25.jpg" style="height:300px;">
 
 **Step 26: Attach Gripper Horn & Claw**
 - Attach the motor horns and again use a horn screw.
 - Install the gripper claw and secure it with 4 screws on both sides.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_26.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_26.jpg" style="height:300px;">
 
 **Step 27: Mount Controller**
 - Attach the motor controller to the back of the robot.
 
-<div style="display: flex;">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp" style="height:300px;"/>
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp" style="height:300px;"/>
-</div>
+  <div style="display: flex;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_27.jpg" style="height:300px;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_28.jpg" style="height:300px;">
+  </div>
 
 *Assembly complete – proceed to Leader arm assembly.*
 
@@ -227,30 +218,30 @@ For the leader configuration, perform **Steps 1–23**. Make sure that you remov
 **Step 24: Attach Leader Holder**
 - Mount the leader holder onto the wrist and secure it with a screw.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_29.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_29.jpg" style="height:300px;">
 
 **Step 25: Attach Handle**
 - Attach the handle to motor 5 using 4 screws.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_30.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_30.jpg" style="height:300px;">
 
 **Step 26: Install Gripper Motor**
 - Insert the gripper motor, secure it with 3 screws on each side, attach a motor horn using a horn screw, and connect the motor wire.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_31.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_31.jpg" style="height:300px;">
 
 **Step 27: Attach Trigger**
 - Attach the follower trigger with 4 screws.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_32.webp" style="height:300px;"/>
+  <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_32.jpg" style="height:300px;">
 
 **Step 28: Mount Controller**
 - Attach the motor controller to the back of the robot.
 
-<div style="display: flex;">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp" style="height:300px;"/>
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp" style="height:300px;"/>
-</div>
+  <div style="display: flex;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_27.jpg" style="height:300px;">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/so100_assembly_28.jpg" style="height:300px;">
+  </div>
 
 ## Configure the motors
 
@@ -318,11 +309,10 @@ If you are repurposing motors from another robot, you will probably also need to
 
 Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
 
-For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
+For a visual reference on how to set the motor ids please refer to [this video](http://localhost:5173/so101#setup-motors-video) where we follow the process for the SO101 arm.
 
 <hfoptions id="setup_motors">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.setup_motors \
     --robot.type=so100_follower \
@@ -330,7 +320,6 @@ python -m lerobot.setup_motors \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.robots.so100_follower import SO100Follower, SO100FollowerConfig
 
@@ -395,7 +384,6 @@ python -m lerobot.setup_motors \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
 
@@ -420,7 +408,6 @@ Run the following command or API example to calibrate the follower arm:
 
 <hfoptions id="calibrate_follower">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --robot.type=so100_follower \
@@ -429,7 +416,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.robots.so100_follower import SO100FollowerConfig, SO100Follower
 
@@ -446,7 +432,7 @@ follower.disconnect()
 </hfoption>
 </hfoptions>
 
-We unified the calibration method for most robots. Thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video)
+We unified the calibration method for most robots. Thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](http://localhost:5173/so101#calibration-video)
 
 #### Leader
 
@@ -454,7 +440,6 @@ Do the same steps to calibrate the leader arm, run the following command or API
 
 <hfoptions id="calibrate_leader">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --teleop.type=so100_leader \
@@ -463,7 +448,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
 

lerobot/common/robots/so100_follower/so100_follower.py

@@ -79,7 +79,8 @@ class SO100Follower(Robot):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        # TODO(aliberts): add cam.is_connected for cam in self.cameras
+        return self.bus.is_connected
 
     def connect(self, calibrate: bool = True) -> None:
         """
@@ -93,6 +94,7 @@ class SO100Follower(Robot):
         if not self.is_calibrated and calibrate:
             self.calibrate()
 
+        # Connect the cameras
         for cam in self.cameras.values():
             cam.connect()
 

lerobot/common/robots/so100_follower_end_effector/__init__.py

@@ -1,2 +0,0 @@
-from .config_so100_follower_end_effector import SO100FollowerEndEffectorConfig
-from .so100_follower_end_effector import SO100FollowerEndEffector

lerobot/common/robots/so100_follower_end_effector/config_so100_follower_end_effector.py

@@ -1,61 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass, field
-from typing import Dict, List
-
-from lerobot.common.cameras import CameraConfig
-
-from ..config import RobotConfig
-
-
-@RobotConfig.register_subclass("so100_follower_end_effector")
-@dataclass
-class SO100FollowerEndEffectorConfig(RobotConfig):
-    """Configuration for the SO100FollowerEndEffector robot."""
-
-    # Port to connect to the arm
-    port: str
-
-    disable_torque_on_disconnect: bool = True
-
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    # cameras
-    cameras: dict[str, CameraConfig] = field(default_factory=dict)
-
-    # Default bounds for the end-effector position (in meters)
-    end_effector_bounds: Dict[str, List[float]] = field(
-        default_factory=lambda: {
-            "min": [-1.0, -1.0, -1.0],  # min x, y, z
-            "max": [1.0, 1.0, 1.0],  # max x, y, z
-        }
-    )
-
-    max_gripper_pos: float = 50
-
-    end_effector_step_sizes: Dict[str, float] = field(
-        default_factory=lambda: {
-            "x": 0.02,
-            "y": 0.02,
-            "z": 0.02,
-        }
-    )
-
-    urdf_path: str = "/Users/michel_aractingi/code/SO-ARM100/Simulation/SO101/so101_new_calib.urdf"

lerobot/common/robots/so100_follower_end_effector/so100_follower_end_effector.py

@@ -1,203 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import time
-from typing import Any, Dict
-
-import numpy as np
-
-from lerobot.common.cameras import make_cameras_from_configs
-from lerobot.common.errors import DeviceNotConnectedError
-from lerobot.common.model.kinematics import RobotKinematics
-from lerobot.common.motors import Motor, MotorNormMode
-from lerobot.common.motors.feetech import FeetechMotorsBus
-
-from ..so100_follower import SO100Follower
-from .config_so100_follower_end_effector import SO100FollowerEndEffectorConfig
-
-logger = logging.getLogger(__name__)
-
-
-class SO100FollowerEndEffector(SO100Follower):
-    """
-    SO100Follower robot with end-effector space control.
-
-    This robot inherits from SO100Follower but transforms actions from
-    end-effector space to joint space before sending them to the motors.
-    """
-
-    config_class = SO100FollowerEndEffectorConfig
-    name = "so100_follower_end_effector"
-
-    def __init__(self, config: SO100FollowerEndEffectorConfig):
-        super().__init__(config)
-        self.bus = FeetechMotorsBus(
-            port=self.config.port,
-            motors={
-                "shoulder_pan": Motor(1, "sts3215", MotorNormMode.DEGREE),
-                "shoulder_lift": Motor(2, "sts3215", MotorNormMode.DEGREE),
-                "elbow_flex": Motor(3, "sts3215", MotorNormMode.DEGREE),
-                "wrist_flex": Motor(4, "sts3215", MotorNormMode.DEGREE),
-                "wrist_roll": Motor(5, "sts3215", MotorNormMode.DEGREE),
-                "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100),
-            },
-            calibration=self.calibration,
-        )
-
-        self.cameras = make_cameras_from_configs(config.cameras)
-
-        self.config = config
-
-        # Initialize the kinematics module for the so100 robot
-        self.kinematics = RobotKinematics(robot_type="so101")
-
-        # Set the forward kinematics function
-        self.fk_function = self.kinematics.fk_gripper
-
-        # Store the bounds for end-effector position
-        self.end_effector_bounds = self.config.end_effector_bounds
-
-        # Store the joint mins and maxs
-        self.joint_mins = None
-        self.joint_maxs = None
-
-        self.current_ee_pos = None
-        self.current_joint_pos = None
-
-    @property
-    def action_features(self) -> Dict[str, Any]:
-        """
-        Define action features for end-effector control.
-        Returns dictionary with dtype, shape, and names.
-        """
-        return {
-            "dtype": "float32",
-            "shape": (4,),
-            "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2, "gripper": 3},
-        }
-
-    def connect(self):
-        super().connect()
-        self.joint_mins = self.bus.sync_read("Min_Position_Limit")
-        self.joint_maxs = self.bus.sync_read("Max_Position_Limit")
-
-    def send_action(self, action: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Transform action from end-effector space to joint space and send to motors.
-
-        Args:
-            action: Dictionary with keys 'delta_x', 'delta_y', 'delta_z' for end-effector control
-                   or a numpy array with [delta_x, delta_y, delta_z]
-
-        Returns:
-            The joint-space action that was sent to the motors
-        """
-
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
-        # Convert action to numpy array if not already
-        if isinstance(action, dict):
-            if all(k in action for k in ["delta_x", "delta_y", "delta_z", "gripper"]):
-                action = np.array(
-                    [
-                        action["delta_x"] * self.config.end_effector_step_sizes["x"],
-                        action["delta_y"] * self.config.end_effector_step_sizes["y"],
-                        action["delta_z"] * self.config.end_effector_step_sizes["z"],
-                        action["gripper"],
-                    ],
-                    dtype=np.float32,
-                )
-            else:
-                logger.warning(
-                    f"Expected action keys 'delta_x', 'delta_y', 'delta_z', got {list(action.keys())}"
-                )
-                action = np.zeros(4, dtype=np.float32)
-
-        if self.current_joint_pos is None:
-            # Read current joint positions
-            current_joint_pos = self.bus.sync_read("Present_Position")
-            self.current_joint_pos = np.array([current_joint_pos[name] for name in self.bus.motors])
-
-        # Calculate current end-effector position using forward kinematics
-        if self.current_ee_pos is None:
-            self.current_ee_pos = self.fk_function(self.current_joint_pos)
-
-        # Set desired end-effector position by adding delta
-        desired_ee_pos = np.eye(4)
-        desired_ee_pos[:3, :3] = self.current_ee_pos[:3, :3]  # Keep orientation
-
-        # Add delta to position and clip to bounds
-        desired_ee_pos[:3, 3] = self.current_ee_pos[:3, 3] + action[:3]
-        if self.end_effector_bounds is not None:
-            desired_ee_pos[:3, 3] = np.clip(
-                desired_ee_pos[:3, 3],
-                self.end_effector_bounds["min"],
-                self.end_effector_bounds["max"],
-            )
-
-        # Compute inverse kinematics to get joint positions
-        target_joint_values_in_degrees = self.kinematics.ik(
-            self.current_joint_pos,
-            desired_ee_pos,
-            position_only=True,
-            fk_func=self.fk_function,
-        )
-
-        target_joint_values_in_degrees = np.clip(target_joint_values_in_degrees, -180.0, 180.0)
-        # Create joint space action dictionary
-        joint_action = {
-            f"{key}.pos": target_joint_values_in_degrees[i] for i, key in enumerate(self.bus.motors.keys())
-        }
-
-        # Handle gripper separately if included in action
-        joint_action["gripper.pos"] = np.clip(
-            self.current_joint_pos[-1] + (action[-1] - 1) * self.config.max_gripper_pos,
-            5,
-            self.config.max_gripper_pos,
-        )
-
-        self.current_ee_pos = desired_ee_pos.copy()
-        self.current_joint_pos = target_joint_values_in_degrees.copy()
-        self.current_joint_pos[-1] = joint_action["gripper.pos"]
-
-        # Send joint space action to parent class
-        return super().send_action(joint_action)
-
-    def get_observation(self) -> dict[str, Any]:
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
-        # Read arm position
-        start = time.perf_counter()
-        obs_dict = self.bus.sync_read("Present_Position")
-        obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
-        dt_ms = (time.perf_counter() - start) * 1e3
-        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
-
-        # Capture images from cameras
-        for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
-            dt_ms = (time.perf_counter() - start) * 1e3
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
-
-        return obs_dict
-
-    def reset(self):
-        self.current_ee_pos = None
-        self.current_joint_pos = None

lerobot/common/robots/so101_follower/so101.md

@@ -198,7 +198,7 @@ The video below shows the sequence of steps for setting the motor ids.
 
 <div class="video-container">
   <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/setup_motors_so101_2.mp4" type="video/mp4" />
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/setup_motors_so101-2-2.mp4" type="video/mp4" />
   </video>
 </div>
 
@@ -208,7 +208,6 @@ Connect the usb cable from your computer and the power supply to the follower ar
 
 <hfoptions id="setup_motors">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.setup_motors \
     --robot.type=so101_follower \
@@ -216,7 +215,6 @@ python -m lerobot.setup_motors \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.robots.so101_follower import SO101Follower, SO101FollowerConfig
 
@@ -274,7 +272,6 @@ Do the same steps for the leader arm.
 
 <hfoptions id="setup_motors">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.setup_motors \
     --teleop.type=so101_leader \
@@ -282,7 +279,6 @@ python -m lerobot.setup_motors \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.teleoperators.so101_leader import SO101Leader, SO101LeaderConfig
 
@@ -307,7 +303,6 @@ Run the following command or API example to calibrate the follower arm:
 
 <hfoptions id="calibrate_follower">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --robot.type=so101_follower \
@@ -316,7 +311,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.robots.so101_follower import SO101FollowerConfig, SO101Follower
 
@@ -339,7 +333,7 @@ The video below shows how to perform the calibration. First you need to move the
 
 <div class="video-container">
   <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/calibrate_so101_2.mp4" type="video/mp4" />
+    <source src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/lerobot/calibrate_so101-2-2.mp4" type="video/mp4" />
   </video>
 </div>
 
@@ -349,7 +343,6 @@ Do the same steps to calibrate the leader arm, run the following command or API
 
 <hfoptions id="calibrate_leader">
 <hfoption id="Command">
-
 ```bash
 python -m lerobot.calibrate \
     --teleop.type=so101_leader \
@@ -358,7 +351,6 @@ python -m lerobot.calibrate \
 ```
 </hfoption>
 <hfoption id="API example">
-
 ```python
 from lerobot.common.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
 

lerobot/common/robots/so101_follower/so101_follower.py

@@ -79,7 +79,8 @@ class SO101Follower(Robot):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        # TODO(aliberts): add cam.is_connected for cam in self.cameras
+        return self.bus.is_connected
 
     def connect(self, calibrate: bool = True) -> None:
         """
@@ -93,6 +94,7 @@ class SO101Follower(Robot):
         if not self.is_calibrated and calibrate:
             self.calibrate()
 
+        # Connect the cameras
         for cam in self.cameras.values():
             cam.connect()
 

lerobot/common/robots/utils.py

@@ -32,12 +32,6 @@ def make_robot_config(robot_type: str, **kwargs) -> RobotConfig:
         from .so100_follower.config_so100_follower import SO100FollowerConfig
 
         return SO100FollowerConfig(**kwargs)
-    elif robot_type == "so100_follower_end_effector":
-        from .so100_follower_end_effector.config_so100_follower_end_effector import (
-            SO100FollowerEndEffectorConfig,
-        )
-
-        return SO100FollowerEndEffectorConfig(**kwargs)
     elif robot_type == "stretch":
         from .stretch3.configuration_stretch3 import Stretch3RobotConfig
 
@@ -59,10 +53,6 @@ def make_robot_from_config(config: RobotConfig) -> Robot:
         from .so100_follower import SO100Follower
 
         return SO100Follower(config)
-    elif config.type == "so100_follower_end_effector":
-        from .so100_follower_end_effector import SO100FollowerEndEffector
-
-        return SO100FollowerEndEffector(config)
     elif config.type == "so101_follower":
         from .so101_follower import SO101Follower
 

lerobot/common/robots/viperx/viperx.py

@@ -83,7 +83,8 @@ class ViperX(Robot):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+        # TODO(aliberts): add cam.is_connected for cam in self.cameras
+        return self.bus.is_connected
 
     def connect(self, calibrate: bool = True) -> None:
         """

lerobot/common/teleoperators/gamepad/__init__.py

@@ -1,4 +0,0 @@
-from .configuration_gamepad import GamepadTeleopConfig
-from .teleop_gamepad import GamepadTeleop
-
-__all__ = ["GamepadTeleopConfig", "GamepadTeleop"]

lerobot/common/teleoperators/gamepad/configuration_gamepad.py

@@ -1,28 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass
-
-from ..config import TeleoperatorConfig
-
-
-@TeleoperatorConfig.register_subclass("gamepad")
-@dataclass
-class GamepadTeleopConfig(TeleoperatorConfig):
-    # TODO(Steven): Consider setting in here the keys that we want to capture/listen
-    mock: bool = False
-
-    use_gripper: bool = True

lerobot/common/teleoperators/gamepad/gamepad_utils.py

@@ -1,716 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import time
-
-import numpy as np
-import torch
-
-from lerobot.common.model.kinematics import RobotKinematics
-from lerobot.common.utils.robot_utils import busy_wait
-
-
-class InputController:
-    """Base class for input controllers that generate motion deltas."""
-
-    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0):
-        """
-        Initialize the controller.
-
-        Args:
-            x_step_size: Base movement step size in meters
-            y_step_size: Base movement step size in meters
-            z_step_size: Base movement step size in meters
-        """
-        self.x_step_size = x_step_size
-        self.y_step_size = y_step_size
-        self.z_step_size = z_step_size
-        self.running = True
-        self.episode_end_status = None  # None, "success", or "failure"
-        self.intervention_flag = False
-        self.open_gripper_command = False
-        self.close_gripper_command = False
-
-    def start(self):
-        """Start the controller and initialize resources."""
-        pass
-
-    def stop(self):
-        """Stop the controller and release resources."""
-        pass
-
-    def get_deltas(self):
-        """Get the current movement deltas (dx, dy, dz) in meters."""
-        return 0.0, 0.0, 0.0
-
-    def should_quit(self):
-        """Return True if the user has requested to quit."""
-        return not self.running
-
-    def update(self):
-        """Update controller state - call this once per frame."""
-        pass
-
-    def __enter__(self):
-        """Support for use in 'with' statements."""
-        self.start()
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        """Ensure resources are released when exiting 'with' block."""
-        self.stop()
-
-    def get_episode_end_status(self):
-        """
-        Get the current episode end status.
-
-        Returns:
-            None if episode should continue, "success" or "failure" otherwise
-        """
-        status = self.episode_end_status
-        self.episode_end_status = None  # Reset after reading
-        return status
-
-    def should_intervene(self):
-        """Return True if intervention flag was set."""
-        return self.intervention_flag
-
-    def gripper_command(self):
-        """Return the current gripper command."""
-        if self.open_gripper_command == self.close_gripper_command:
-            return "no-op"
-        elif self.open_gripper_command:
-            return "open"
-        elif self.close_gripper_command:
-            return "close"
-
-
-class KeyboardController(InputController):
-    """Generate motion deltas from keyboard input."""
-
-    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0):
-        super().__init__(x_step_size, y_step_size, z_step_size)
-        self.key_states = {
-            "forward_x": False,
-            "backward_x": False,
-            "forward_y": False,
-            "backward_y": False,
-            "forward_z": False,
-            "backward_z": False,
-            "quit": False,
-            "success": False,
-            "failure": False,
-        }
-        self.listener = None
-
-    def start(self):
-        """Start the keyboard listener."""
-        from pynput import keyboard
-
-        def on_press(key):
-            try:
-                if key == keyboard.Key.up:
-                    self.key_states["forward_x"] = True
-                elif key == keyboard.Key.down:
-                    self.key_states["backward_x"] = True
-                elif key == keyboard.Key.left:
-                    self.key_states["forward_y"] = True
-                elif key == keyboard.Key.right:
-                    self.key_states["backward_y"] = True
-                elif key == keyboard.Key.shift:
-                    self.key_states["backward_z"] = True
-                elif key == keyboard.Key.shift_r:
-                    self.key_states["forward_z"] = True
-                elif key == keyboard.Key.esc:
-                    self.key_states["quit"] = True
-                    self.running = False
-                    return False
-                elif key == keyboard.Key.enter:
-                    self.key_states["success"] = True
-                    self.episode_end_status = "success"
-                elif key == keyboard.Key.backspace:
-                    self.key_states["failure"] = True
-                    self.episode_end_status = "failure"
-            except AttributeError:
-                pass
-
-        def on_release(key):
-            try:
-                if key == keyboard.Key.up:
-                    self.key_states["forward_x"] = False
-                elif key == keyboard.Key.down:
-                    self.key_states["backward_x"] = False
-                elif key == keyboard.Key.left:
-                    self.key_states["forward_y"] = False
-                elif key == keyboard.Key.right:
-                    self.key_states["backward_y"] = False
-                elif key == keyboard.Key.shift:
-                    self.key_states["backward_z"] = False
-                elif key == keyboard.Key.shift_r:
-                    self.key_states["forward_z"] = False
-                elif key == keyboard.Key.enter:
-                    self.key_states["success"] = False
-                elif key == keyboard.Key.backspace:
-                    self.key_states["failure"] = False
-            except AttributeError:
-                pass
-
-        self.listener = keyboard.Listener(on_press=on_press, on_release=on_release)
-        self.listener.start()
-
-        print("Keyboard controls:")
-        print("  Arrow keys: Move in X-Y plane")
-        print("  Shift and Shift_R: Move in Z axis")
-        print("  Enter: End episode with SUCCESS")
-        print("  Backspace: End episode with FAILURE")
-        print("  ESC: Exit")
-
-    def stop(self):
-        """Stop the keyboard listener."""
-        if self.listener and self.listener.is_alive():
-            self.listener.stop()
-
-    def get_deltas(self):
-        """Get the current movement deltas from keyboard state."""
-        delta_x = delta_y = delta_z = 0.0
-
-        if self.key_states["forward_x"]:
-            delta_x += self.x_step_size
-        if self.key_states["backward_x"]:
-            delta_x -= self.x_step_size
-        if self.key_states["forward_y"]:
-            delta_y += self.y_step_size
-        if self.key_states["backward_y"]:
-            delta_y -= self.y_step_size
-        if self.key_states["forward_z"]:
-            delta_z += self.z_step_size
-        if self.key_states["backward_z"]:
-            delta_z -= self.z_step_size
-
-        return delta_x, delta_y, delta_z
-
-    def should_quit(self):
-        """Return True if ESC was pressed."""
-        return self.key_states["quit"]
-
-    def should_save(self):
-        """Return True if Enter was pressed (save episode)."""
-        return self.key_states["success"] or self.key_states["failure"]
-
-
-class GamepadController(InputController):
-    """Generate motion deltas from gamepad input."""
-
-    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0, deadzone=0.1):
-        super().__init__(x_step_size, y_step_size, z_step_size)
-        self.deadzone = deadzone
-        self.joystick = None
-        self.intervention_flag = False
-
-    def start(self):
-        """Initialize pygame and the gamepad."""
-        import pygame
-
-        pygame.init()
-        pygame.joystick.init()
-
-        if pygame.joystick.get_count() == 0:
-            logging.error("No gamepad detected. Please connect a gamepad and try again.")
-            self.running = False
-            return
-
-        self.joystick = pygame.joystick.Joystick(0)
-        self.joystick.init()
-        logging.info(f"Initialized gamepad: {self.joystick.get_name()}")
-
-        print("Gamepad controls:")
-        print("  Left analog stick: Move in X-Y plane")
-        print("  Right analog stick (vertical): Move in Z axis")
-        print("  B/Circle button: Exit")
-        print("  Y/Triangle button: End episode with SUCCESS")
-        print("  A/Cross button: End episode with FAILURE")
-        print("  X/Square button: Rerecord episode")
-
-    def stop(self):
-        """Clean up pygame resources."""
-        import pygame
-
-        if pygame.joystick.get_init():
-            if self.joystick:
-                self.joystick.quit()
-            pygame.joystick.quit()
-        pygame.quit()
-
-    def update(self):
-        """Process pygame events to get fresh gamepad readings."""
-        import pygame
-
-        for event in pygame.event.get():
-            if event.type == pygame.JOYBUTTONDOWN:
-                if event.button == 3:
-                    self.episode_end_status = "success"
-                # A button (1) for failure
-                elif event.button == 1:
-                    self.episode_end_status = "failure"
-                # X button (0) for rerecord
-                elif event.button == 0:
-                    self.episode_end_status = "rerecord_episode"
-
-                # RB button (6) for closing gripper
-                elif event.button == 6:
-                    self.close_gripper_command = True
-
-                # LT button (7) for opening gripper
-                elif event.button == 7:
-                    self.open_gripper_command = True
-
-            # Reset episode status on button release
-            elif event.type == pygame.JOYBUTTONUP:
-                if event.button in [0, 2, 3]:
-                    self.episode_end_status = None
-
-                elif event.button == 6:
-                    self.close_gripper_command = False
-
-                elif event.button == 7:
-                    self.open_gripper_command = False
-
-            # Check for RB button (typically button 5) for intervention flag
-            if self.joystick.get_button(5):
-                self.intervention_flag = True
-            else:
-                self.intervention_flag = False
-
-    def get_deltas(self):
-        """Get the current movement deltas from gamepad state."""
-        import pygame
-
-        try:
-            # Read joystick axes
-            # Left stick X and Y (typically axes 0 and 1)
-            x_input = self.joystick.get_axis(0)  # Left/Right
-            y_input = self.joystick.get_axis(1)  # Up/Down (often inverted)
-
-            # Right stick Y (typically axis 3 or 4)
-            z_input = self.joystick.get_axis(3)  # Up/Down for Z
-
-            # Apply deadzone to avoid drift
-            x_input = 0 if abs(x_input) < self.deadzone else x_input
-            y_input = 0 if abs(y_input) < self.deadzone else y_input
-            z_input = 0 if abs(z_input) < self.deadzone else z_input
-
-            # Calculate deltas (note: may need to invert axes depending on controller)
-            delta_x = -y_input * self.y_step_size  # Forward/backward
-            delta_y = -x_input * self.x_step_size  # Left/right
-            delta_z = -z_input * self.z_step_size  # Up/down
-
-            return delta_x, delta_y, delta_z
-
-        except pygame.error:
-            logging.error("Error reading gamepad. Is it still connected?")
-            return 0.0, 0.0, 0.0
-
-
-class GamepadControllerHID(InputController):
-    """Generate motion deltas from gamepad input using HIDAPI."""
-
-    def __init__(
-        self,
-        x_step_size=1.0,
-        y_step_size=1.0,
-        z_step_size=1.0,
-        deadzone=0.1,
-        vendor_id=0x046D,
-        product_id=0xC219,
-    ):
-        """
-        Initialize the HID gamepad controller.
-
-        Args:
-            step_size: Base movement step size in meters
-            z_scale: Scaling factor for Z-axis movement
-            deadzone: Joystick deadzone to prevent drift
-            vendor_id: USB vendor ID of the gamepad (default: Logitech)
-            product_id: USB product ID of the gamepad (default: RumblePad 2)
-        """
-        super().__init__(x_step_size, y_step_size, z_step_size)
-        self.deadzone = deadzone
-        self.vendor_id = vendor_id
-        self.product_id = product_id
-        self.device = None
-        self.device_info = None
-
-        # Movement values (normalized from -1.0 to 1.0)
-        self.left_x = 0.0
-        self.left_y = 0.0
-        self.right_x = 0.0
-        self.right_y = 0.0
-
-        # Button states
-        self.buttons = {}
-        self.quit_requested = False
-        self.save_requested = False
-
-    def find_device(self):
-        """Look for the gamepad device by vendor and product ID."""
-        import hid
-
-        devices = hid.enumerate()
-        for device in devices:
-            if device["vendor_id"] == self.vendor_id and device["product_id"] == self.product_id:
-                logging.info(f"Found gamepad: {device.get('product_string', 'Unknown')}")
-                return device
-
-        logging.error(
-            f"No gamepad with vendor ID 0x{self.vendor_id:04X} and product ID 0x{self.product_id:04X} found"
-        )
-        return None
-
-    def start(self):
-        """Connect to the gamepad using HIDAPI."""
-        import hid
-
-        self.device_info = self.find_device()
-        if not self.device_info:
-            self.running = False
-            return
-
-        try:
-            logging.info(f"Connecting to gamepad at path: {self.device_info['path']}")
-            self.device = hid.device()
-            self.device.open_path(self.device_info["path"])
-            self.device.set_nonblocking(1)
-
-            manufacturer = self.device.get_manufacturer_string()
-            product = self.device.get_product_string()
-            logging.info(f"Connected to {manufacturer} {product}")
-
-            logging.info("Gamepad controls (HID mode):")
-            logging.info("  Left analog stick: Move in X-Y plane")
-            logging.info("  Right analog stick: Move in Z axis (vertical)")
-            logging.info("  Button 1/B/Circle: Exit")
-            logging.info("  Button 2/A/Cross: End episode with SUCCESS")
-            logging.info("  Button 3/X/Square: End episode with FAILURE")
-
-        except OSError as e:
-            logging.error(f"Error opening gamepad: {e}")
-            logging.error("You might need to run this with sudo/admin privileges on some systems")
-            self.running = False
-
-    def stop(self):
-        """Close the HID device connection."""
-        if self.device:
-            self.device.close()
-            self.device = None
-
-    def update(self):
-        """
-        Read and process the latest gamepad data.
-        Due to an issue with the HIDAPI, we need to read the read the device several times in order to get a stable reading
-        """
-        for _ in range(10):
-            self._update()
-
-    def _update(self):
-        """Read and process the latest gamepad data."""
-        if not self.device or not self.running:
-            return
-
-        try:
-            # Read data from the gamepad
-            data = self.device.read(64)
-            # Interpret gamepad data - this will vary by controller model
-            # These offsets are for the Logitech RumblePad 2
-            if data and len(data) >= 8:
-                # Normalize joystick values from 0-255 to -1.0-1.0
-                self.left_x = (data[1] - 128) / 128.0
-                self.left_y = (data[2] - 128) / 128.0
-                self.right_x = (data[3] - 128) / 128.0
-                self.right_y = (data[4] - 128) / 128.0
-
-                # Apply deadzone
-                self.left_x = 0 if abs(self.left_x) < self.deadzone else self.left_x
-                self.left_y = 0 if abs(self.left_y) < self.deadzone else self.left_y
-                self.right_x = 0 if abs(self.right_x) < self.deadzone else self.right_x
-                self.right_y = 0 if abs(self.right_y) < self.deadzone else self.right_y
-
-                # Parse button states (byte 5 in the Logitech RumblePad 2)
-                buttons = data[5]
-
-                # Check if RB is pressed then the intervention flag should be set
-                self.intervention_flag = data[6] in [2, 6, 10, 14]
-
-                # Check if RT is pressed
-                self.open_gripper_command = data[6] in [8, 10, 12]
-
-                # Check if LT is pressed
-                self.close_gripper_command = data[6] in [4, 6, 12]
-
-                # Check if Y/Triangle button (bit 7) is pressed for saving
-                # Check if X/Square button (bit 5) is pressed for failure
-                # Check if A/Cross button (bit 4) is pressed for rerecording
-                if buttons & 1 << 7:
-                    self.episode_end_status = "success"
-                elif buttons & 1 << 5:
-                    self.episode_end_status = "failure"
-                elif buttons & 1 << 4:
-                    self.episode_end_status = "rerecord_episode"
-                else:
-                    self.episode_end_status = None
-
-        except OSError as e:
-            logging.error(f"Error reading from gamepad: {e}")
-
-    def get_deltas(self):
-        """Get the current movement deltas from gamepad state."""
-        # Calculate deltas - invert as needed based on controller orientation
-        delta_x = -self.left_y * self.x_step_size  # Forward/backward
-        delta_y = -self.left_x * self.y_step_size  # Left/right
-        delta_z = -self.right_y * self.z_step_size  # Up/down
-
-        return delta_x, delta_y, delta_z
-
-    def should_quit(self):
-        """Return True if quit button was pressed."""
-        return self.quit_requested
-
-    def should_save(self):
-        """Return True if save button was pressed."""
-        return self.save_requested
-
-
-def test_forward_kinematics(robot, fps=10):
-    logging.info("Testing Forward Kinematics")
-    timestep = time.perf_counter()
-    kinematics = RobotKinematics(robot.robot_type)
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-        robot.teleop_step()
-        obs = robot.capture_observation()
-        joint_positions = obs["observation.state"].cpu().numpy()
-        ee_pos = kinematics.fk_gripper_tip(joint_positions)
-        logging.info(f"EE Position: {ee_pos[:3, 3]}")
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def test_inverse_kinematics(robot, fps=10):
-    logging.info("Testing Inverse Kinematics")
-    timestep = time.perf_counter()
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-        obs = robot.capture_observation()
-        joint_positions = obs["observation.state"].cpu().numpy()
-        ee_pos = RobotKinematics.fk_gripper_tip(joint_positions)
-        desired_ee_pos = ee_pos
-        target_joint_state = RobotKinematics.ik(joint_positions, desired_ee_pos, position_only=True)
-        robot.send_action(torch.from_numpy(target_joint_state))
-        logging.info(f"Target Joint State: {target_joint_state}")
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_inverse_kinematics_with_leader(robot, fps=10):
-    logging.info("Testing Inverse Kinematics")
-    kinematics = RobotKinematics(robot.robot_type)
-    timestep = time.perf_counter()
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-        obs = robot.capture_observation()
-        joint_positions = obs["observation.state"].cpu().numpy()
-        ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-        leader_joint_positions = robot.leader_arms["main"].read("Present_Position")
-        leader_ee = kinematics.fk_gripper_tip(leader_joint_positions)
-
-        desired_ee_pos = leader_ee
-        target_joint_state = kinematics.ik(
-            joint_positions, desired_ee_pos, position_only=True, fk_func=kinematics.fk_gripper_tip
-        )
-        robot.send_action(torch.from_numpy(target_joint_state))
-        logging.info(f"Leader EE: {leader_ee[:3, 3]}, Follower EE: {ee_pos[:3, 3]}")
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_delta_inverse_kinematics_with_leader(robot, fps=10):
-    logging.info("Testing Delta End-Effector Control")
-    timestep = time.perf_counter()
-
-    # Initial position capture
-    obs = robot.capture_observation()
-    joint_positions = obs["observation.state"].cpu().numpy()
-
-    kinematics = RobotKinematics(robot.robot_type)
-
-    leader_joint_positions = robot.leader_arms["main"].read("Present_Position")
-    initial_leader_ee = kinematics.fk_gripper_tip(leader_joint_positions)
-
-    desired_ee_pos = np.diag(np.ones(4))
-    joint_positions = robot.follower_arms["main"].read("Present_Position")
-    fixed_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-
-        # Get leader state for teleoperation
-        leader_joint_positions = robot.leader_arms["main"].read("Present_Position")
-        leader_ee = kinematics.fk_gripper_tip(leader_joint_positions)
-
-        # Get current state
-        # obs = robot.capture_observation()
-        # joint_positions = obs["observation.state"].cpu().numpy()
-        joint_positions = robot.follower_arms["main"].read("Present_Position")
-        current_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-        # Calculate delta between leader and follower end-effectors
-        # Scaling factor can be adjusted for sensitivity
-        scaling_factor = 1.0
-        ee_delta = -np.clip((leader_ee - initial_leader_ee) * scaling_factor, -0.05, 0.05)
-
-        # Apply delta to current position
-        desired_ee_pos[0, 3] = fixed_ee_pos[0, 3]  # current_ee_pos[0, 3] + ee_delta[0, 3] * 0
-        desired_ee_pos[1, 3] = fixed_ee_pos[1, 3]  # current_ee_pos[1, 3] + ee_delta[1, 3] * 0
-        desired_ee_pos[2, 3] = current_ee_pos[2, 3] - ee_delta[2, 3]
-
-        # Compute joint targets via inverse kinematics
-        target_joint_state = kinematics.ik(
-            joint_positions, desired_ee_pos, position_only=True, fk_func=kinematics.fk_gripper_tip
-        )
-
-        initial_leader_ee = leader_ee.copy()
-
-        # Send command to robot
-        robot.send_action(torch.from_numpy(target_joint_state))
-
-        # Logging
-        logging.info(f"Current EE: {current_ee_pos[:3, 3]}, Desired EE: {desired_ee_pos[:3, 3]}")
-        logging.info(f"Delta EE: {ee_delta[:3, 3]}")
-
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_delta_inverse_kinematics(robot, controller, fps=10, bounds=None, fk_func=None):
-    """
-    Control a robot using delta end-effector movements from any input controller.
-
-    Args:
-        robot: Robot instance to control
-        controller: InputController instance (keyboard, gamepad, etc.)
-        fps: Control frequency in Hz
-        bounds: Optional position limits
-        fk_func: Forward kinematics function to use
-    """
-    if fk_func is None:
-        fk_func = RobotKinematics.fk_gripper_tip
-
-    logging.info(f"Testing Delta End-Effector Control with {controller.__class__.__name__}")
-
-    # Initial position capture
-    obs = robot.capture_observation()
-    joint_positions = obs["observation.state"].cpu().numpy()
-    kinematics = RobotKinematics(robot.robot_type)
-    current_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-    # Initialize desired position with current position
-    desired_ee_pos = np.eye(4)  # Identity matrix
-
-    timestep = time.perf_counter()
-    with controller:
-        while not controller.should_quit() and time.perf_counter() - timestep < 60.0:
-            loop_start_time = time.perf_counter()
-
-            # Process input events
-            controller.update()
-
-            # Get current robot state
-            joint_positions = robot.follower_arms["main"].read("Present_Position")
-            current_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-            # Get movement deltas from the controller
-            delta_x, delta_y, delta_z = controller.get_deltas()
-
-            # Update desired position
-            desired_ee_pos[0, 3] = current_ee_pos[0, 3] + delta_x
-            desired_ee_pos[1, 3] = current_ee_pos[1, 3] + delta_y
-            desired_ee_pos[2, 3] = current_ee_pos[2, 3] + delta_z
-
-            # Apply bounds if provided
-            if bounds is not None:
-                desired_ee_pos[:3, 3] = np.clip(desired_ee_pos[:3, 3], bounds["min"], bounds["max"])
-
-            # Only send commands if there's actual movement
-            if any(abs(v) > 0.001 for v in [delta_x, delta_y, delta_z]):
-                # Compute joint targets via inverse kinematics
-                target_joint_state = kinematics.ik(joint_positions, desired_ee_pos, position_only=True)
-
-                # Send command to robot
-                robot.send_action(torch.from_numpy(target_joint_state))
-
-            busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_gym_env(env, controller, fps: int = 30):
-    """
-    Control a robot through a gym environment using keyboard inputs.
-
-    Args:
-        env: A gym environment created with make_robot_env
-        fps: Target control frequency
-    """
-
-    logging.info("Testing Keyboard Control of Gym Environment")
-    print("Keyboard controls:")
-    print("  Arrow keys: Move in X-Y plane")
-    print("  Shift and Shift_R: Move in Z axis")
-    print("  ESC: Exit")
-
-    # Reset the environment to get initial observation
-    obs, info = env.reset()
-
-    try:
-        with controller:
-            while not controller.should_quit():
-                loop_start_time = time.perf_counter()
-
-                # Process input events
-                controller.update()
-
-                # Get movement deltas from the controller
-                delta_x, delta_y, delta_z = controller.get_deltas()
-
-                # Create the action vector
-                action = np.array([delta_x, delta_y, delta_z])
-
-                # Skip if no movement
-                if any(abs(v) > 0.001 for v in [delta_x, delta_y, delta_z]):
-                    # Step the environment - pass action as a tensor with intervention flag
-                    action_tensor = torch.from_numpy(action.astype(np.float32))
-                    obs, reward, terminated, truncated, info = env.step((action_tensor, False))
-
-                    # Log information
-                    logging.info(f"Action: [{delta_x:.4f}, {delta_y:.4f}, {delta_z:.4f}]")
-                    logging.info(f"Reward: {reward}")
-
-                    # Reset if episode ended
-                    if terminated or truncated:
-                        logging.info("Episode ended, resetting environment")
-                        obs, info = env.reset()
-
-                # Maintain target frame rate
-                busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-    finally:
-        # Close the environment
-        env.close()

lerobot/common/teleoperators/gamepad/teleop_gamepad.py

@@ -1,134 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import sys
-from enum import IntEnum
-from queue import Queue
-from typing import Any
-
-import numpy as np
-
-from ..teleoperator import Teleoperator
-from .configuration_gamepad import GamepadTeleopConfig
-
-
-class GripperAction(IntEnum):
-    CLOSE = 0
-    STAY = 1
-    OPEN = 2
-
-
-gripper_action_map = {
-    "close": GripperAction.CLOSE.value,
-    "open": GripperAction.OPEN.value,
-    "stay": GripperAction.STAY.value,
-}
-
-
-class GamepadTeleop(Teleoperator):
-    """
-    Teleop class to use gamepad inputs for control.
-    """
-
-    config_class = GamepadTeleopConfig
-    name = "gamepad"
-
-    def __init__(self, config: GamepadTeleopConfig):
-        super().__init__(config)
-        self.config = config
-        self.robot_type = config.type
-
-        self.event_queue = Queue()
-        self.current_pressed = {}
-        self.listener = None
-        self.logs = {}
-
-        self.gamepad = None
-
-    @property
-    def action_features(self) -> dict:
-        if self.config.use_gripper:
-            return {
-                "dtype": "float32",
-                "shape": (4,),
-                "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2, "gripper": 3},
-            }
-        else:
-            return {
-                "dtype": "float32",
-                "shape": (3,),
-                "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2},
-            }
-
-    @property
-    def feedback_features(self) -> dict:
-        return {}
-
-    @property
-    def is_connected(self) -> bool:
-        pass
-
-    @property
-    def is_calibrated(self) -> bool:
-        return True
-
-    def connect(self) -> None:
-        # use HidApi for macos
-        if sys.platform == "darwin":
-            # NOTE: On macOS, pygame doesn’t reliably detect input from some controllers so we fall back to hidapi
-            from lerobot.common.utils.end_effector_control import GamepadControllerHID as Gamepad
-        else:
-            from lerobot.common.utils.end_effector_control import GamepadController as Gamepad
-
-        self.gamepad = Gamepad(x_step_size=1.0, y_step_size=1.0, z_step_size=1.0)
-        self.gamepad.start()
-
-    def calibrate(self) -> None:
-        pass
-
-    def configure(self):
-        pass
-
-    def get_action(self) -> dict[str, Any]:
-        # Update the controller to get fresh inputs
-        self.gamepad.update()
-
-        # Get movement deltas from the controller
-        delta_x, delta_y, delta_z = self.gamepad.get_deltas()
-
-        # Create action from gamepad input
-        gamepad_action = np.array([delta_x, delta_y, delta_z], dtype=np.float32)
-
-        action_dict = {
-            "delta_x": gamepad_action[0],
-            "delta_y": gamepad_action[1],
-            "delta_z": gamepad_action[2],
-        }
-
-        # Default gripper action is to stay
-        gripper_action = GripperAction.STAY.value
-        if self.config.use_gripper:
-            gripper_command = self.gamepad.gripper_command()
-            gripper_action = gripper_action_map[gripper_command]
-            action_dict["gripper"] = gripper_action
-
-        return action_dict
-
-    def send_feedback(self, feedback: dict[str, Any]) -> None:
-        pass
-
-    def disconnect(self) -> None:
-        pass

lerobot/common/teleoperators/so101_leader/so101_leader.py

@@ -44,11 +44,11 @@ class SO101Leader(Teleoperator):
         self.bus = FeetechMotorsBus(
             port=self.config.port,
             motors={
-                "shoulder_pan": Motor(1, "sts3215", MotorNormMode.DEGREE),
-                "shoulder_lift": Motor(2, "sts3215", MotorNormMode.DEGREE),
-                "elbow_flex": Motor(3, "sts3215", MotorNormMode.DEGREE),
-                "wrist_flex": Motor(4, "sts3215", MotorNormMode.DEGREE),
-                "wrist_roll": Motor(5, "sts3215", MotorNormMode.DEGREE),
+                "shoulder_pan": Motor(1, "sts3215", MotorNormMode.RANGE_M100_100),
+                "shoulder_lift": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100),
+                "elbow_flex": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100),
+                "wrist_flex": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100),
+                "wrist_roll": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100),
                 "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100),
             },
             calibration=self.calibration,

lerobot/common/teleoperators/utils.py

@@ -45,9 +45,5 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator:
         from tests.mocks.mock_teleop import MockTeleop
 
         return MockTeleop(config)
-    elif config.type == "gamepad":
-        from .gamepad.teleop_gamepad import GamepadTeleop
-
-        return GamepadTeleop(config)
     else:
         raise ValueError(config.type)

lerobot/common/transport/services.proto

@@ -1,60 +0,0 @@
-//  Copyright 2024 The HuggingFace Inc. team.
-//  All rights reserved.
-
-//  Licensed under the Apache License, Version 2.0 (the "License");
-//  you may not use this file except in compliance with the License.
-//  You may obtain a copy of the License at
-
-//      http://www.apache.org/licenses/LICENSE-2.0
-
-//  Unless required by applicable law or agreed to in writing, software
-//  distributed under the License is distributed on an "AS IS" BASIS,
-//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-//  See the License for the specific language governing permissions and
-//  limitations under the License.
-
-// To generate a classes for transport part (services_pb2.py and services_pb2_grpc.py) use the following command:
-//
-// python -m grpc_tools.protoc -I . --python_out=. --grpc_python_out=. lerobot/common/transport/services.proto
-//
-// The command should be launched from the root of the project.
-
-syntax = "proto3";
-
-package transport;
-
-// LearnerService: the Actor calls this to push transitions.
-// The Learner implements this service.
-service LearnerService {
-  // Actor -> Learner to store transitions
-  rpc SendInteractionMessage(InteractionMessage) returns (Empty);
-  rpc StreamParameters(Empty) returns (stream Parameters);
-  rpc SendTransitions(stream Transition) returns (Empty);
-  rpc SendInteractions(stream InteractionMessage) returns (Empty);
-  rpc Ready(Empty) returns (Empty);
-}
-
-enum TransferState {
-    TRANSFER_UNKNOWN = 0;
-    TRANSFER_BEGIN = 1;
-    TRANSFER_MIDDLE = 2;
-    TRANSFER_END = 3;
-}
-
-// Messages
-message Transition {
-  TransferState transfer_state = 1;
-  bytes data = 2;
-}
-
-message Parameters {
-  TransferState transfer_state = 1;
-  bytes data = 2;
-}
-
-message InteractionMessage {
-  TransferState transfer_state = 1;
-  bytes data = 2;
-}
-
-message Empty {}

lerobot/common/transport/services_pb2.py

@@ -1,45 +0,0 @@
-# Generated by the protocol buffer compiler.  DO NOT EDIT!
-# NO CHECKED-IN PROTOBUF GENCODE
-# source: lerobot/common/transport/services.proto
-# Protobuf Python Version: 5.29.0
-"""Generated protocol buffer code."""
-from google.protobuf import descriptor as _descriptor
-from google.protobuf import descriptor_pool as _descriptor_pool
-from google.protobuf import runtime_version as _runtime_version
-from google.protobuf import symbol_database as _symbol_database
-from google.protobuf.internal import builder as _builder
-_runtime_version.ValidateProtobufRuntimeVersion(
-    _runtime_version.Domain.PUBLIC,
-    5,
-    29,
-    0,
-    '',
-    'lerobot/common/transport/services.proto'
-)
-# @@protoc_insertion_point(imports)
-
-_sym_db = _symbol_database.Default()
-
-
-
-
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'lerobot/common/transport/services.proto\x12\ttransport\"L\n\nTransition\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"L\n\nParameters\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"T\n\x12InteractionMessage\x12\x30\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x18.transport.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"\x07\n\x05\x45mpty*`\n\rTransferState\x12\x14\n\x10TRANSFER_UNKNOWN\x10\x00\x12\x12\n\x0eTRANSFER_BEGIN\x10\x01\x12\x13\n\x0fTRANSFER_MIDDLE\x10\x02\x12\x10\n\x0cTRANSFER_END\x10\x03\x32\xcc\x02\n\x0eLearnerService\x12I\n\x16SendInteractionMessage\x12\x1d.transport.InteractionMessage\x1a\x10.transport.Empty\x12=\n\x10StreamParameters\x12\x10.transport.Empty\x1a\x15.transport.Parameters0\x01\x12<\n\x0fSendTransitions\x12\x15.transport.Transition\x1a\x10.transport.Empty(\x01\x12\x45\n\x10SendInteractions\x12\x1d.transport.InteractionMessage\x1a\x10.transport.Empty(\x01\x12+\n\x05Ready\x12\x10.transport.Empty\x1a\x10.transport.Emptyb\x06proto3')
-
-_globals = globals()
-_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
-_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'lerobot.common.transport.services_pb2', _globals)
-if not _descriptor._USE_C_DESCRIPTORS:
-  DESCRIPTOR._loaded_options = None
-  _globals['_TRANSFERSTATE']._serialized_start=305
-  _globals['_TRANSFERSTATE']._serialized_end=401
-  _globals['_TRANSITION']._serialized_start=54
-  _globals['_TRANSITION']._serialized_end=130
-  _globals['_PARAMETERS']._serialized_start=132
-  _globals['_PARAMETERS']._serialized_end=208
-  _globals['_INTERACTIONMESSAGE']._serialized_start=210
-  _globals['_INTERACTIONMESSAGE']._serialized_end=294
-  _globals['_EMPTY']._serialized_start=296
-  _globals['_EMPTY']._serialized_end=303
-  _globals['_LEARNERSERVICE']._serialized_start=404
-  _globals['_LEARNERSERVICE']._serialized_end=736
-# @@protoc_insertion_point(module_scope)

lerobot/common/transport/services_pb2_grpc.py

@@ -1,276 +0,0 @@
-# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
-"""Client and server classes corresponding to protobuf-defined services."""
-import grpc
-import warnings
-
-from lerobot.common.transport import services_pb2 as lerobot_dot_common_dot_transport_dot_services__pb2
-
-GRPC_GENERATED_VERSION = '1.71.0'
-GRPC_VERSION = grpc.__version__
-_version_not_supported = False
-
-try:
-    from grpc._utilities import first_version_is_lower
-    _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION)
-except ImportError:
-    _version_not_supported = True
-
-if _version_not_supported:
-    raise RuntimeError(
-        f'The grpc package installed is at version {GRPC_VERSION},'
-        + f' but the generated code in lerobot/common/transport/services_pb2_grpc.py depends on'
-        + f' grpcio>={GRPC_GENERATED_VERSION}.'
-        + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}'
-        + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.'
-    )
-
-
-class LearnerServiceStub:
-    """LearnerService: the Actor calls this to push transitions.
-    The Learner implements this service.
-    """
-
-    def __init__(self, channel):
-        """Constructor.
-
-        Args:
-            channel: A grpc.Channel.
-        """
-        self.SendInteractionMessage = channel.unary_unary(
-                '/transport.LearnerService/SendInteractionMessage',
-                request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.SerializeToString,
-                response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-                _registered_method=True)
-        self.StreamParameters = channel.unary_stream(
-                '/transport.LearnerService/StreamParameters',
-                request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-                response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Parameters.FromString,
-                _registered_method=True)
-        self.SendTransitions = channel.stream_unary(
-                '/transport.LearnerService/SendTransitions',
-                request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Transition.SerializeToString,
-                response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-                _registered_method=True)
-        self.SendInteractions = channel.stream_unary(
-                '/transport.LearnerService/SendInteractions',
-                request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.SerializeToString,
-                response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-                _registered_method=True)
-        self.Ready = channel.unary_unary(
-                '/transport.LearnerService/Ready',
-                request_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-                response_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-                _registered_method=True)
-
-
-class LearnerServiceServicer:
-    """LearnerService: the Actor calls this to push transitions.
-    The Learner implements this service.
-    """
-
-    def SendInteractionMessage(self, request, context):
-        """Actor -> Learner to store transitions
-        """
-        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-        context.set_details('Method not implemented!')
-        raise NotImplementedError('Method not implemented!')
-
-    def StreamParameters(self, request, context):
-        """Missing associated documentation comment in .proto file."""
-        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-        context.set_details('Method not implemented!')
-        raise NotImplementedError('Method not implemented!')
-
-    def SendTransitions(self, request_iterator, context):
-        """Missing associated documentation comment in .proto file."""
-        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-        context.set_details('Method not implemented!')
-        raise NotImplementedError('Method not implemented!')
-
-    def SendInteractions(self, request_iterator, context):
-        """Missing associated documentation comment in .proto file."""
-        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-        context.set_details('Method not implemented!')
-        raise NotImplementedError('Method not implemented!')
-
-    def Ready(self, request, context):
-        """Missing associated documentation comment in .proto file."""
-        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-        context.set_details('Method not implemented!')
-        raise NotImplementedError('Method not implemented!')
-
-
-def add_LearnerServiceServicer_to_server(servicer, server):
-    rpc_method_handlers = {
-            'SendInteractionMessage': grpc.unary_unary_rpc_method_handler(
-                    servicer.SendInteractionMessage,
-                    request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.FromString,
-                    response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-            ),
-            'StreamParameters': grpc.unary_stream_rpc_method_handler(
-                    servicer.StreamParameters,
-                    request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-                    response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Parameters.SerializeToString,
-            ),
-            'SendTransitions': grpc.stream_unary_rpc_method_handler(
-                    servicer.SendTransitions,
-                    request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Transition.FromString,
-                    response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-            ),
-            'SendInteractions': grpc.stream_unary_rpc_method_handler(
-                    servicer.SendInteractions,
-                    request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.FromString,
-                    response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-            ),
-            'Ready': grpc.unary_unary_rpc_method_handler(
-                    servicer.Ready,
-                    request_deserializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-                    response_serializer=lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-            ),
-    }
-    generic_handler = grpc.method_handlers_generic_handler(
-            'transport.LearnerService', rpc_method_handlers)
-    server.add_generic_rpc_handlers((generic_handler,))
-    server.add_registered_method_handlers('transport.LearnerService', rpc_method_handlers)
-
-
- # This class is part of an EXPERIMENTAL API.
-class LearnerService:
-    """LearnerService: the Actor calls this to push transitions.
-    The Learner implements this service.
-    """
-
-    @staticmethod
-    def SendInteractionMessage(request,
-            target,
-            options=(),
-            channel_credentials=None,
-            call_credentials=None,
-            insecure=False,
-            compression=None,
-            wait_for_ready=None,
-            timeout=None,
-            metadata=None):
-        return grpc.experimental.unary_unary(
-            request,
-            target,
-            '/transport.LearnerService/SendInteractionMessage',
-            lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.SerializeToString,
-            lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-            options,
-            channel_credentials,
-            insecure,
-            call_credentials,
-            compression,
-            wait_for_ready,
-            timeout,
-            metadata,
-            _registered_method=True)
-
-    @staticmethod
-    def StreamParameters(request,
-            target,
-            options=(),
-            channel_credentials=None,
-            call_credentials=None,
-            insecure=False,
-            compression=None,
-            wait_for_ready=None,
-            timeout=None,
-            metadata=None):
-        return grpc.experimental.unary_stream(
-            request,
-            target,
-            '/transport.LearnerService/StreamParameters',
-            lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-            lerobot_dot_common_dot_transport_dot_services__pb2.Parameters.FromString,
-            options,
-            channel_credentials,
-            insecure,
-            call_credentials,
-            compression,
-            wait_for_ready,
-            timeout,
-            metadata,
-            _registered_method=True)
-
-    @staticmethod
-    def SendTransitions(request_iterator,
-            target,
-            options=(),
-            channel_credentials=None,
-            call_credentials=None,
-            insecure=False,
-            compression=None,
-            wait_for_ready=None,
-            timeout=None,
-            metadata=None):
-        return grpc.experimental.stream_unary(
-            request_iterator,
-            target,
-            '/transport.LearnerService/SendTransitions',
-            lerobot_dot_common_dot_transport_dot_services__pb2.Transition.SerializeToString,
-            lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-            options,
-            channel_credentials,
-            insecure,
-            call_credentials,
-            compression,
-            wait_for_ready,
-            timeout,
-            metadata,
-            _registered_method=True)
-
-    @staticmethod
-    def SendInteractions(request_iterator,
-            target,
-            options=(),
-            channel_credentials=None,
-            call_credentials=None,
-            insecure=False,
-            compression=None,
-            wait_for_ready=None,
-            timeout=None,
-            metadata=None):
-        return grpc.experimental.stream_unary(
-            request_iterator,
-            target,
-            '/transport.LearnerService/SendInteractions',
-            lerobot_dot_common_dot_transport_dot_services__pb2.InteractionMessage.SerializeToString,
-            lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-            options,
-            channel_credentials,
-            insecure,
-            call_credentials,
-            compression,
-            wait_for_ready,
-            timeout,
-            metadata,
-            _registered_method=True)
-
-    @staticmethod
-    def Ready(request,
-            target,
-            options=(),
-            channel_credentials=None,
-            call_credentials=None,
-            insecure=False,
-            compression=None,
-            wait_for_ready=None,
-            timeout=None,
-            metadata=None):
-        return grpc.experimental.unary_unary(
-            request,
-            target,
-            '/transport.LearnerService/Ready',
-            lerobot_dot_common_dot_transport_dot_services__pb2.Empty.SerializeToString,
-            lerobot_dot_common_dot_transport_dot_services__pb2.Empty.FromString,
-            options,
-            channel_credentials,
-            insecure,
-            call_credentials,
-            compression,
-            wait_for_ready,
-            timeout,
-            metadata,
-            _registered_method=True)

lerobot/common/transport/utils.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import io
-import logging
-import pickle  # nosec B403: Safe usage for internal serialization only
-from multiprocessing import Event, Queue
-from typing import Any
-
-import torch
-
-from lerobot.common.transport import services_pb2
-from lerobot.common.utils.transition import Transition
-
-CHUNK_SIZE = 2 * 1024 * 1024  # 2 MB
-
-
-def bytes_buffer_size(buffer: io.BytesIO) -> int:
-    buffer.seek(0, io.SEEK_END)
-    result = buffer.tell()
-    buffer.seek(0)
-    return result
-
-
-def send_bytes_in_chunks(buffer: bytes, message_class: Any, log_prefix: str = "", silent: bool = True):
-    buffer = io.BytesIO(buffer)
-    size_in_bytes = bytes_buffer_size(buffer)
-
-    sent_bytes = 0
-
-    logging_method = logging.info if not silent else logging.debug
-
-    logging_method(f"{log_prefix} Buffer size {size_in_bytes / 1024 / 1024} MB with")
-
-    while sent_bytes < size_in_bytes:
-        transfer_state = services_pb2.TransferState.TRANSFER_MIDDLE
-
-        if sent_bytes + CHUNK_SIZE >= size_in_bytes:
-            transfer_state = services_pb2.TransferState.TRANSFER_END
-        elif sent_bytes == 0:
-            transfer_state = services_pb2.TransferState.TRANSFER_BEGIN
-
-        size_to_read = min(CHUNK_SIZE, size_in_bytes - sent_bytes)
-        chunk = buffer.read(size_to_read)
-
-        yield message_class(transfer_state=transfer_state, data=chunk)
-        sent_bytes += size_to_read
-        logging_method(f"{log_prefix} Sent {sent_bytes}/{size_in_bytes} bytes with state {transfer_state}")
-
-    logging_method(f"{log_prefix} Published {sent_bytes / 1024 / 1024} MB")
-
-
-def receive_bytes_in_chunks(iterator, queue: Queue, shutdown_event: Event, log_prefix: str = ""):  # type: ignore
-    bytes_buffer = io.BytesIO()
-    step = 0
-
-    logging.info(f"{log_prefix} Starting receiver")
-    for item in iterator:
-        logging.debug(f"{log_prefix} Received item")
-        if shutdown_event.is_set():
-            logging.info(f"{log_prefix} Shutting down receiver")
-            return
-
-        if item.transfer_state == services_pb2.TransferState.TRANSFER_BEGIN:
-            bytes_buffer.seek(0)
-            bytes_buffer.truncate(0)
-            bytes_buffer.write(item.data)
-            logging.debug(f"{log_prefix} Received data at step 0")
-            step = 0
-            continue
-        elif item.transfer_state == services_pb2.TransferState.TRANSFER_MIDDLE:
-            bytes_buffer.write(item.data)
-            step += 1
-            logging.debug(f"{log_prefix} Received data at step {step}")
-        elif item.transfer_state == services_pb2.TransferState.TRANSFER_END:
-            bytes_buffer.write(item.data)
-            logging.debug(f"{log_prefix} Received data at step end size {bytes_buffer_size(bytes_buffer)}")
-
-            queue.put(bytes_buffer.getvalue())
-
-            bytes_buffer.seek(0)
-            bytes_buffer.truncate(0)
-            step = 0
-
-            logging.debug(f"{log_prefix} Queue updated")
-
-
-def state_to_bytes(state_dict: dict[str, torch.Tensor]) -> bytes:
-    """Convert model state dict to flat array for transmission"""
-    buffer = io.BytesIO()
-
-    torch.save(state_dict, buffer)
-
-    return buffer.getvalue()
-
-
-def bytes_to_state_dict(buffer: bytes) -> dict[str, torch.Tensor]:
-    buffer = io.BytesIO(buffer)
-    buffer.seek(0)
-    return torch.load(buffer, weights_only=False)  # nosec B614: Using weights_only=False relies on pickle which has security implications.
-    # This is currently safe as we only deserialize trusted internal data.
-    # TODO: Verify if weights_only=True would work for our use case (safer default in torch 2.6+)
-
-
-def python_object_to_bytes(python_object: Any) -> bytes:
-    return pickle.dumps(python_object)
-
-
-def bytes_to_python_object(buffer: bytes) -> Any:
-    buffer = io.BytesIO(buffer)
-    buffer.seek(0)
-    obj = pickle.load(buffer)  # nosec B301: Safe usage of pickle.load
-    # Add validation checks here
-    return obj
-
-
-def bytes_to_transitions(buffer: bytes) -> list[Transition]:
-    buffer = io.BytesIO(buffer)
-    buffer.seek(0)
-    transitions = torch.load(buffer, weights_only=False)  # nosec B614: Safe usage of torch.load
-    # Add validation checks here
-    return transitions
-
-
-def transitions_to_bytes(transitions: list[Transition]) -> bytes:
-    buffer = io.BytesIO()
-    torch.save(transitions, buffer)
-    return buffer.getvalue()

lerobot/common/utils/buffer.py

@@ -1,816 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import functools
-from contextlib import suppress
-from typing import Callable, Optional, Sequence, TypedDict
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from tqdm import tqdm
-
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.utils.transition import Transition
-
-
-class BatchTransition(TypedDict):
-    state: dict[str, torch.Tensor]
-    action: torch.Tensor
-    reward: torch.Tensor
-    next_state: dict[str, torch.Tensor]
-    done: torch.Tensor
-    truncated: torch.Tensor
-    complementary_info: dict[str, torch.Tensor | float | int] | None = None
-
-
-def random_crop_vectorized(images: torch.Tensor, output_size: tuple) -> torch.Tensor:
-    """
-    Perform a per-image random crop over a batch of images in a vectorized way.
-    (Same as shown previously.)
-    """
-    B, C, H, W = images.shape  # noqa: N806
-    crop_h, crop_w = output_size
-
-    if crop_h > H or crop_w > W:
-        raise ValueError(
-            f"Requested crop size ({crop_h}, {crop_w}) is bigger than the image size ({H}, {W})."
-        )
-
-    tops = torch.randint(0, H - crop_h + 1, (B,), device=images.device)
-    lefts = torch.randint(0, W - crop_w + 1, (B,), device=images.device)
-
-    rows = torch.arange(crop_h, device=images.device).unsqueeze(0) + tops.unsqueeze(1)
-    cols = torch.arange(crop_w, device=images.device).unsqueeze(0) + lefts.unsqueeze(1)
-
-    rows = rows.unsqueeze(2).expand(-1, -1, crop_w)  # (B, crop_h, crop_w)
-    cols = cols.unsqueeze(1).expand(-1, crop_h, -1)  # (B, crop_h, crop_w)
-
-    images_hwcn = images.permute(0, 2, 3, 1)  # (B, H, W, C)
-
-    # Gather pixels
-    cropped_hwcn = images_hwcn[torch.arange(B, device=images.device).view(B, 1, 1), rows, cols, :]
-    # cropped_hwcn => (B, crop_h, crop_w, C)
-
-    cropped = cropped_hwcn.permute(0, 3, 1, 2)  # (B, C, crop_h, crop_w)
-    return cropped
-
-
-def random_shift(images: torch.Tensor, pad: int = 4):
-    """Vectorized random shift, imgs: (B,C,H,W), pad: #pixels"""
-    _, _, h, w = images.shape
-    images = F.pad(input=images, pad=(pad, pad, pad, pad), mode="replicate")
-    return random_crop_vectorized(images=images, output_size=(h, w))
-
-
-class ReplayBuffer:
-    def __init__(
-        self,
-        capacity: int,
-        device: str = "cuda:0",
-        state_keys: Optional[Sequence[str]] = None,
-        image_augmentation_function: Optional[Callable] = None,
-        use_drq: bool = True,
-        storage_device: str = "cpu",
-        optimize_memory: bool = False,
-    ):
-        """
-        Replay buffer for storing transitions.
-        It will allocate tensors on the specified device, when the first transition is added.
-        NOTE: If you encounter memory issues, you can try to use the `optimize_memory` flag to save memory or
-        and use the `storage_device` flag to store the buffer on a different device.
-        Args:
-            capacity (int): Maximum number of transitions to store in the buffer.
-            device (str): The device where the tensors will be moved when sampling ("cuda:0" or "cpu").
-            state_keys (List[str]): The list of keys that appear in `state` and `next_state`.
-            image_augmentation_function (Optional[Callable]): A function that takes a batch of images
-                and returns a batch of augmented images. If None, a default augmentation function is used.
-            use_drq (bool): Whether to use the default DRQ image augmentation style, when sampling in the buffer.
-            storage_device: The device (e.g. "cpu" or "cuda:0") where the data will be stored.
-                Using "cpu" can help save GPU memory.
-            optimize_memory (bool): If True, optimizes memory by not storing duplicate next_states when
-                they can be derived from states. This is useful for large datasets where next_state[i] = state[i+1].
-        """
-        if capacity <= 0:
-            raise ValueError("Capacity must be greater than 0.")
-
-        self.capacity = capacity
-        self.device = device
-        self.storage_device = storage_device
-        self.position = 0
-        self.size = 0
-        self.initialized = False
-        self.optimize_memory = optimize_memory
-
-        # Track episode boundaries for memory optimization
-        self.episode_ends = torch.zeros(capacity, dtype=torch.bool, device=storage_device)
-
-        # If no state_keys provided, default to an empty list
-        self.state_keys = state_keys if state_keys is not None else []
-
-        self.image_augmentation_function = image_augmentation_function
-
-        if image_augmentation_function is None:
-            base_function = functools.partial(random_shift, pad=4)
-            self.image_augmentation_function = torch.compile(base_function)
-        self.use_drq = use_drq
-
-    def _initialize_storage(
-        self,
-        state: dict[str, torch.Tensor],
-        action: torch.Tensor,
-        complementary_info: Optional[dict[str, torch.Tensor]] = None,
-    ):
-        """Initialize the storage tensors based on the first transition."""
-        # Determine shapes from the first transition
-        state_shapes = {key: val.squeeze(0).shape for key, val in state.items()}
-        action_shape = action.squeeze(0).shape
-
-        # Pre-allocate tensors for storage
-        self.states = {
-            key: torch.empty((self.capacity, *shape), device=self.storage_device)
-            for key, shape in state_shapes.items()
-        }
-        self.actions = torch.empty((self.capacity, *action_shape), device=self.storage_device)
-        self.rewards = torch.empty((self.capacity,), device=self.storage_device)
-
-        if not self.optimize_memory:
-            # Standard approach: store states and next_states separately
-            self.next_states = {
-                key: torch.empty((self.capacity, *shape), device=self.storage_device)
-                for key, shape in state_shapes.items()
-            }
-        else:
-            # Memory-optimized approach: don't allocate next_states buffer
-            # Just create a reference to states for consistent API
-            self.next_states = self.states  # Just a reference for API consistency
-
-        self.dones = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device)
-        self.truncateds = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device)
-
-        # Initialize storage for complementary_info
-        self.has_complementary_info = complementary_info is not None
-        self.complementary_info_keys = []
-        self.complementary_info = {}
-
-        if self.has_complementary_info:
-            self.complementary_info_keys = list(complementary_info.keys())
-            # Pre-allocate tensors for each key in complementary_info
-            for key, value in complementary_info.items():
-                if isinstance(value, torch.Tensor):
-                    value_shape = value.squeeze(0).shape
-                    self.complementary_info[key] = torch.empty(
-                        (self.capacity, *value_shape), device=self.storage_device
-                    )
-                elif isinstance(value, (int, float)):
-                    # Handle scalar values similar to reward
-                    self.complementary_info[key] = torch.empty((self.capacity,), device=self.storage_device)
-                else:
-                    raise ValueError(f"Unsupported type {type(value)} for complementary_info[{key}]")
-
-        self.initialized = True
-
-    def __len__(self):
-        return self.size
-
-    def add(
-        self,
-        state: dict[str, torch.Tensor],
-        action: torch.Tensor,
-        reward: float,
-        next_state: dict[str, torch.Tensor],
-        done: bool,
-        truncated: bool,
-        complementary_info: Optional[dict[str, torch.Tensor]] = None,
-    ):
-        """Saves a transition, ensuring tensors are stored on the designated storage device."""
-        # Initialize storage if this is the first transition
-        if not self.initialized:
-            self._initialize_storage(state=state, action=action, complementary_info=complementary_info)
-
-        # Store the transition in pre-allocated tensors
-        for key in self.states:
-            self.states[key][self.position].copy_(state[key].squeeze(dim=0))
-
-            if not self.optimize_memory:
-                # Only store next_states if not optimizing memory
-                self.next_states[key][self.position].copy_(next_state[key].squeeze(dim=0))
-
-        self.actions[self.position].copy_(action.squeeze(dim=0))
-        self.rewards[self.position] = reward
-        self.dones[self.position] = done
-        self.truncateds[self.position] = truncated
-
-        # Handle complementary_info if provided and storage is initialized
-        if complementary_info is not None and self.has_complementary_info:
-            # Store the complementary_info
-            for key in self.complementary_info_keys:
-                if key in complementary_info:
-                    value = complementary_info[key]
-                    if isinstance(value, torch.Tensor):
-                        self.complementary_info[key][self.position].copy_(value.squeeze(dim=0))
-                    elif isinstance(value, (int, float)):
-                        self.complementary_info[key][self.position] = value
-
-        self.position = (self.position + 1) % self.capacity
-        self.size = min(self.size + 1, self.capacity)
-
-    def sample(self, batch_size: int) -> BatchTransition:
-        """Sample a random batch of transitions and collate them into batched tensors."""
-        if not self.initialized:
-            raise RuntimeError("Cannot sample from an empty buffer. Add transitions first.")
-
-        batch_size = min(batch_size, self.size)
-        high = max(0, self.size - 1) if self.optimize_memory and self.size < self.capacity else self.size
-
-        # Random indices for sampling - create on the same device as storage
-        idx = torch.randint(low=0, high=high, size=(batch_size,), device=self.storage_device)
-
-        # Identify image keys that need augmentation
-        image_keys = [k for k in self.states if k.startswith("observation.image")] if self.use_drq else []
-
-        # Create batched state and next_state
-        batch_state = {}
-        batch_next_state = {}
-
-        # First pass: load all state tensors to target device
-        for key in self.states:
-            batch_state[key] = self.states[key][idx].to(self.device)
-
-            if not self.optimize_memory:
-                # Standard approach - load next_states directly
-                batch_next_state[key] = self.next_states[key][idx].to(self.device)
-            else:
-                # Memory-optimized approach - get next_state from the next index
-                next_idx = (idx + 1) % self.capacity
-                batch_next_state[key] = self.states[key][next_idx].to(self.device)
-
-        # Apply image augmentation in a batched way if needed
-        if self.use_drq and image_keys:
-            # Concatenate all images from state and next_state
-            all_images = []
-            for key in image_keys:
-                all_images.append(batch_state[key])
-                all_images.append(batch_next_state[key])
-
-            # Optimization: Batch all images and apply augmentation once
-            all_images_tensor = torch.cat(all_images, dim=0)
-            augmented_images = self.image_augmentation_function(all_images_tensor)
-
-            # Split the augmented images back to their sources
-            for i, key in enumerate(image_keys):
-                # Calculate offsets for the current image key:
-                # For each key, we have 2*batch_size images (batch_size for states, batch_size for next_states)
-                # States start at index i*2*batch_size and take up batch_size slots
-                batch_state[key] = augmented_images[i * 2 * batch_size : (i * 2 + 1) * batch_size]
-                # Next states start after the states at index (i*2+1)*batch_size and also take up batch_size slots
-                batch_next_state[key] = augmented_images[(i * 2 + 1) * batch_size : (i + 1) * 2 * batch_size]
-
-        # Sample other tensors
-        batch_actions = self.actions[idx].to(self.device)
-        batch_rewards = self.rewards[idx].to(self.device)
-        batch_dones = self.dones[idx].to(self.device).float()
-        batch_truncateds = self.truncateds[idx].to(self.device).float()
-
-        # Sample complementary_info if available
-        batch_complementary_info = None
-        if self.has_complementary_info:
-            batch_complementary_info = {}
-            for key in self.complementary_info_keys:
-                batch_complementary_info[key] = self.complementary_info[key][idx].to(self.device)
-
-        return BatchTransition(
-            state=batch_state,
-            action=batch_actions,
-            reward=batch_rewards,
-            next_state=batch_next_state,
-            done=batch_dones,
-            truncated=batch_truncateds,
-            complementary_info=batch_complementary_info,
-        )
-
-    def get_iterator(
-        self,
-        batch_size: int,
-        async_prefetch: bool = True,
-        queue_size: int = 2,
-    ):
-        """
-        Creates an infinite iterator that yields batches of transitions.
-        Will automatically restart when internal iterator is exhausted.
-
-        Args:
-            batch_size (int): Size of batches to sample
-            async_prefetch (bool): Whether to use asynchronous prefetching with threads (default: True)
-            queue_size (int): Number of batches to prefetch (default: 2)
-
-        Yields:
-            BatchTransition: Batched transitions
-        """
-        while True:  # Create an infinite loop
-            if async_prefetch:
-                # Get the standard iterator
-                iterator = self._get_async_iterator(queue_size=queue_size, batch_size=batch_size)
-            else:
-                iterator = self._get_naive_iterator(batch_size=batch_size, queue_size=queue_size)
-
-            # Yield all items from the iterator
-            with suppress(StopIteration):
-                yield from iterator
-
-    def _get_async_iterator(self, batch_size: int, queue_size: int = 2):
-        """
-        Creates an iterator that prefetches batches in a background thread.
-
-        Args:
-            queue_size (int): Number of batches to prefetch (default: 2)
-            batch_size (int): Size of batches to sample (default: 128)
-
-        Yields:
-            BatchTransition: Prefetched batch transitions
-        """
-        import queue
-        import threading
-
-        # Use thread-safe queue
-        data_queue = queue.Queue(maxsize=queue_size)
-        running = [True]  # Use list to allow modification in nested function
-
-        def prefetch_worker():
-            while running[0]:
-                try:
-                    # Sample data and add to queue
-                    data = self.sample(batch_size)
-                    data_queue.put(data, block=True, timeout=0.5)
-                except queue.Full:
-                    continue
-                except Exception as e:
-                    print(f"Prefetch error: {e}")
-                    break
-
-        # Start prefetching thread
-        thread = threading.Thread(target=prefetch_worker, daemon=True)
-        thread.start()
-
-        try:
-            while running[0]:
-                try:
-                    yield data_queue.get(block=True, timeout=0.5)
-                except queue.Empty:
-                    if not thread.is_alive():
-                        break
-        finally:
-            # Clean up
-            running[0] = False
-            thread.join(timeout=1.0)
-
-    def _get_naive_iterator(self, batch_size: int, queue_size: int = 2):
-        """
-        Creates a simple non-threaded iterator that yields batches.
-
-        Args:
-            batch_size (int): Size of batches to sample
-            queue_size (int): Number of initial batches to prefetch
-
-        Yields:
-            BatchTransition: Batch transitions
-        """
-        import collections
-
-        queue = collections.deque()
-
-        def enqueue(n):
-            for _ in range(n):
-                data = self.sample(batch_size)
-                queue.append(data)
-
-        enqueue(queue_size)
-        while queue:
-            yield queue.popleft()
-            enqueue(1)
-
-    @classmethod
-    def from_lerobot_dataset(
-        cls,
-        lerobot_dataset: LeRobotDataset,
-        device: str = "cuda:0",
-        state_keys: Optional[Sequence[str]] = None,
-        capacity: Optional[int] = None,
-        image_augmentation_function: Optional[Callable] = None,
-        use_drq: bool = True,
-        storage_device: str = "cpu",
-        optimize_memory: bool = False,
-    ) -> "ReplayBuffer":
-        """
-        Convert a LeRobotDataset into a ReplayBuffer.
-
-        Args:
-            lerobot_dataset (LeRobotDataset): The dataset to convert.
-            device (str): The device for sampling tensors. Defaults to "cuda:0".
-            state_keys (Optional[Sequence[str]]): The list of keys that appear in `state` and `next_state`.
-            capacity (Optional[int]): Buffer capacity. If None, uses dataset length.
-            action_mask (Optional[Sequence[int]]): Indices of action dimensions to keep.
-            image_augmentation_function (Optional[Callable]): Function for image augmentation.
-                If None, uses default random shift with pad=4.
-            use_drq (bool): Whether to use DrQ image augmentation when sampling.
-            storage_device (str): Device for storing tensor data. Using "cpu" saves GPU memory.
-            optimize_memory (bool): If True, reduces memory usage by not duplicating state data.
-
-        Returns:
-            ReplayBuffer: The replay buffer with dataset transitions.
-        """
-        if capacity is None:
-            capacity = len(lerobot_dataset)
-
-        if capacity < len(lerobot_dataset):
-            raise ValueError(
-                "The capacity of the ReplayBuffer must be greater than or equal to the length of the LeRobotDataset."
-            )
-
-        # Create replay buffer with image augmentation and DrQ settings
-        replay_buffer = cls(
-            capacity=capacity,
-            device=device,
-            state_keys=state_keys,
-            image_augmentation_function=image_augmentation_function,
-            use_drq=use_drq,
-            storage_device=storage_device,
-            optimize_memory=optimize_memory,
-        )
-
-        # Convert dataset to transitions
-        list_transition = cls._lerobotdataset_to_transitions(dataset=lerobot_dataset, state_keys=state_keys)
-
-        # Initialize the buffer with the first transition to set up storage tensors
-        if list_transition:
-            first_transition = list_transition[0]
-            first_state = {k: v.to(device) for k, v in first_transition["state"].items()}
-            first_action = first_transition["action"].to(device)
-
-            # Get complementary info if available
-            first_complementary_info = None
-            if (
-                "complementary_info" in first_transition
-                and first_transition["complementary_info"] is not None
-            ):
-                first_complementary_info = {
-                    k: v.to(device) for k, v in first_transition["complementary_info"].items()
-                }
-
-            replay_buffer._initialize_storage(
-                state=first_state, action=first_action, complementary_info=first_complementary_info
-            )
-
-        # Fill the buffer with all transitions
-        for data in list_transition:
-            for k, v in data.items():
-                if isinstance(v, dict):
-                    for key, tensor in v.items():
-                        v[key] = tensor.to(storage_device)
-                elif isinstance(v, torch.Tensor):
-                    data[k] = v.to(storage_device)
-
-            action = data["action"]
-
-            replay_buffer.add(
-                state=data["state"],
-                action=action,
-                reward=data["reward"],
-                next_state=data["next_state"],
-                done=data["done"],
-                truncated=False,  # NOTE: Truncation are not supported yet in lerobot dataset
-                complementary_info=data.get("complementary_info", None),
-            )
-
-        return replay_buffer
-
-    def to_lerobot_dataset(
-        self,
-        repo_id: str,
-        fps=1,
-        root=None,
-        task_name="from_replay_buffer",
-    ) -> LeRobotDataset:
-        """
-        Converts all transitions in this ReplayBuffer into a single LeRobotDataset object.
-        """
-        if self.size == 0:
-            raise ValueError("The replay buffer is empty. Cannot convert to a dataset.")
-
-        # Create features dictionary for the dataset
-        features = {
-            "index": {"dtype": "int64", "shape": [1]},  # global index across episodes
-            "episode_index": {"dtype": "int64", "shape": [1]},  # which episode
-            "frame_index": {"dtype": "int64", "shape": [1]},  # index inside an episode
-            "timestamp": {"dtype": "float32", "shape": [1]},  # for now we store dummy
-            "task_index": {"dtype": "int64", "shape": [1]},
-        }
-
-        # Add "action"
-        sample_action = self.actions[0]
-        act_info = guess_feature_info(t=sample_action, name="action")
-        features["action"] = act_info
-
-        # Add "reward" and "done"
-        features["next.reward"] = {"dtype": "float32", "shape": (1,)}
-        features["next.done"] = {"dtype": "bool", "shape": (1,)}
-
-        # Add state keys
-        for key in self.states:
-            sample_val = self.states[key][0]
-            f_info = guess_feature_info(t=sample_val, name=key)
-            features[key] = f_info
-
-        # Add complementary_info keys if available
-        if self.has_complementary_info:
-            for key in self.complementary_info_keys:
-                sample_val = self.complementary_info[key][0]
-                if isinstance(sample_val, torch.Tensor) and sample_val.ndim == 0:
-                    sample_val = sample_val.unsqueeze(0)
-                f_info = guess_feature_info(t=sample_val, name=f"complementary_info.{key}")
-                features[f"complementary_info.{key}"] = f_info
-
-        # Create an empty LeRobotDataset
-        lerobot_dataset = LeRobotDataset.create(
-            repo_id=repo_id,
-            fps=fps,
-            root=root,
-            robot_type=None,
-            features=features,
-            use_videos=True,
-        )
-
-        # Start writing images if needed
-        lerobot_dataset.start_image_writer(num_processes=0, num_threads=3)
-
-        # Convert transitions into episodes and frames
-        episode_index = 0
-        lerobot_dataset.episode_buffer = lerobot_dataset.create_episode_buffer(episode_index=episode_index)
-
-        frame_idx_in_episode = 0
-        for idx in range(self.size):
-            actual_idx = (self.position - self.size + idx) % self.capacity
-
-            frame_dict = {}
-
-            # Fill the data for state keys
-            for key in self.states:
-                frame_dict[key] = self.states[key][actual_idx].cpu()
-
-            # Fill action, reward, done
-            frame_dict["action"] = self.actions[actual_idx].cpu()
-            frame_dict["next.reward"] = torch.tensor([self.rewards[actual_idx]], dtype=torch.float32).cpu()
-            frame_dict["next.done"] = torch.tensor([self.dones[actual_idx]], dtype=torch.bool).cpu()
-
-            # Add complementary_info if available
-            if self.has_complementary_info:
-                for key in self.complementary_info_keys:
-                    val = self.complementary_info[key][actual_idx]
-                    # Convert tensors to CPU
-                    if isinstance(val, torch.Tensor):
-                        if val.ndim == 0:
-                            val = val.unsqueeze(0)
-                        frame_dict[f"complementary_info.{key}"] = val.cpu()
-                    # Non-tensor values can be used directly
-                    else:
-                        frame_dict[f"complementary_info.{key}"] = val
-
-            # Add to the dataset's buffer
-            lerobot_dataset.add_frame(frame_dict, task=task_name)
-
-            # Move to next frame
-            frame_idx_in_episode += 1
-
-            # If we reached an episode boundary, call save_episode, reset counters
-            if self.dones[actual_idx] or self.truncateds[actual_idx]:
-                lerobot_dataset.save_episode()
-                episode_index += 1
-                frame_idx_in_episode = 0
-                lerobot_dataset.episode_buffer = lerobot_dataset.create_episode_buffer(
-                    episode_index=episode_index
-                )
-
-        # Save any remaining frames in the buffer
-        if lerobot_dataset.episode_buffer["size"] > 0:
-            lerobot_dataset.save_episode()
-
-        lerobot_dataset.stop_image_writer()
-
-        return lerobot_dataset
-
-    @staticmethod
-    def _lerobotdataset_to_transitions(
-        dataset: LeRobotDataset,
-        state_keys: Optional[Sequence[str]] = None,
-    ) -> list[Transition]:
-        """
-        Convert a LeRobotDataset into a list of RL (s, a, r, s', done) transitions.
-
-        Args:
-            dataset (LeRobotDataset):
-                The dataset to convert. Each item in the dataset is expected to have
-                at least the following keys:
-                {
-                    "action": ...
-                    "next.reward": ...
-                    "next.done": ...
-                    "episode_index": ...
-                }
-                plus whatever your 'state_keys' specify.
-
-            state_keys (Optional[Sequence[str]]):
-                The dataset keys to include in 'state' and 'next_state'. Their names
-                will be kept as-is in the output transitions. E.g.
-                ["observation.state", "observation.environment_state"].
-                If None, you must handle or define default keys.
-
-        Returns:
-            transitions (List[Transition]):
-                A list of Transition dictionaries with the same length as `dataset`.
-        """
-        if state_keys is None:
-            raise ValueError("State keys must be provided when converting LeRobotDataset to Transitions.")
-
-        transitions = []
-        num_frames = len(dataset)
-
-        # Check if the dataset has "next.done" key
-        sample = dataset[0]
-        has_done_key = "next.done" in sample
-
-        # Check for complementary_info keys
-        complementary_info_keys = [key for key in sample if key.startswith("complementary_info.")]
-        has_complementary_info = len(complementary_info_keys) > 0
-
-        # If not, we need to infer it from episode boundaries
-        if not has_done_key:
-            print("'next.done' key not found in dataset. Inferring from episode boundaries...")
-
-        for i in tqdm(range(num_frames)):
-            current_sample = dataset[i]
-
-            # ----- 1) Current state -----
-            current_state: dict[str, torch.Tensor] = {}
-            for key in state_keys:
-                val = current_sample[key]
-                current_state[key] = val.unsqueeze(0)  # Add batch dimension
-
-            # ----- 2) Action -----
-            action = current_sample["action"].unsqueeze(0)  # Add batch dimension
-
-            # ----- 3) Reward and done -----
-            reward = float(current_sample["next.reward"].item())  # ensure float
-
-            # Determine done flag - use next.done if available, otherwise infer from episode boundaries
-            if has_done_key:
-                done = bool(current_sample["next.done"].item())  # ensure bool
-            else:
-                # If this is the last frame or if next frame is in a different episode, mark as done
-                done = False
-                if i == num_frames - 1:
-                    done = True
-                elif i < num_frames - 1:
-                    next_sample = dataset[i + 1]
-                    if next_sample["episode_index"] != current_sample["episode_index"]:
-                        done = True
-
-            # TODO: (azouitine) Handle truncation (using the same value as done for now)
-            truncated = done
-
-            # ----- 4) Next state -----
-            # If not done and the next sample is in the same episode, we pull the next sample's state.
-            # Otherwise (done=True or next sample crosses to a new episode), next_state = current_state.
-            next_state = current_state  # default
-            if not done and (i < num_frames - 1):
-                next_sample = dataset[i + 1]
-                if next_sample["episode_index"] == current_sample["episode_index"]:
-                    # Build next_state from the same keys
-                    next_state_data: dict[str, torch.Tensor] = {}
-                    for key in state_keys:
-                        val = next_sample[key]
-                        next_state_data[key] = val.unsqueeze(0)  # Add batch dimension
-                    next_state = next_state_data
-
-            # ----- 5) Complementary info (if available) -----
-            complementary_info = None
-            if has_complementary_info:
-                complementary_info = {}
-                for key in complementary_info_keys:
-                    # Strip the "complementary_info." prefix to get the actual key
-                    clean_key = key[len("complementary_info.") :]
-                    val = current_sample[key]
-                    # Handle tensor and non-tensor values differently
-                    if isinstance(val, torch.Tensor):
-                        complementary_info[clean_key] = val.unsqueeze(0)  # Add batch dimension
-                    else:
-                        # TODO: (azouitine) Check if it's necessary to convert to tensor
-                        # For non-tensor values, use directly
-                        complementary_info[clean_key] = val
-
-            # ----- Construct the Transition -----
-            transition = Transition(
-                state=current_state,
-                action=action,
-                reward=reward,
-                next_state=next_state,
-                done=done,
-                truncated=truncated,
-                complementary_info=complementary_info,
-            )
-            transitions.append(transition)
-
-        return transitions
-
-
-# Utility function to guess shapes/dtypes from a tensor
-def guess_feature_info(t, name: str):
-    """
-    Return a dictionary with the 'dtype' and 'shape' for a given tensor or scalar value.
-    If it looks like a 3D (C,H,W) shape, we might consider it an 'image'.
-    Otherwise default to appropriate dtype for numeric.
-    """
-
-    shape = tuple(t.shape)
-    # Basic guess: if we have exactly 3 dims and shape[0] in {1, 3}, guess 'image'
-    if len(shape) == 3 and shape[0] in [1, 3]:
-        return {
-            "dtype": "image",
-            "shape": shape,
-        }
-    else:
-        # Otherwise treat as numeric
-        return {
-            "dtype": "float32",
-            "shape": shape,
-        }
-
-
-def concatenate_batch_transitions(
-    left_batch_transitions: BatchTransition, right_batch_transition: BatchTransition
-) -> BatchTransition:
-    """NOTE: Be careful it change the left_batch_transitions in place"""
-    # Concatenate state fields
-    left_batch_transitions["state"] = {
-        key: torch.cat(
-            [left_batch_transitions["state"][key], right_batch_transition["state"][key]],
-            dim=0,
-        )
-        for key in left_batch_transitions["state"]
-    }
-
-    # Concatenate basic fields
-    left_batch_transitions["action"] = torch.cat(
-        [left_batch_transitions["action"], right_batch_transition["action"]], dim=0
-    )
-    left_batch_transitions["reward"] = torch.cat(
-        [left_batch_transitions["reward"], right_batch_transition["reward"]], dim=0
-    )
-
-    # Concatenate next_state fields
-    left_batch_transitions["next_state"] = {
-        key: torch.cat(
-            [left_batch_transitions["next_state"][key], right_batch_transition["next_state"][key]],
-            dim=0,
-        )
-        for key in left_batch_transitions["next_state"]
-    }
-
-    # Concatenate done and truncated fields
-    left_batch_transitions["done"] = torch.cat(
-        [left_batch_transitions["done"], right_batch_transition["done"]], dim=0
-    )
-    left_batch_transitions["truncated"] = torch.cat(
-        [left_batch_transitions["truncated"], right_batch_transition["truncated"]],
-        dim=0,
-    )
-
-    # Handle complementary_info
-    left_info = left_batch_transitions.get("complementary_info")
-    right_info = right_batch_transition.get("complementary_info")
-
-    # Only process if right_info exists
-    if right_info is not None:
-        # Initialize left complementary_info if needed
-        if left_info is None:
-            left_batch_transitions["complementary_info"] = right_info
-        else:
-            # Concatenate each field
-            for key in right_info:
-                if key in left_info:
-                    left_info[key] = torch.cat([left_info[key], right_info[key]], dim=0)
-                else:
-                    left_info[key] = right_info[key]
-
-    return left_batch_transitions

lerobot/common/utils/control_utils.py

@@ -18,20 +18,24 @@
 
 
 import logging
+import time
 import traceback
 from contextlib import nullcontext
 from copy import copy
 from functools import cache
 
-import numpy as np
+import rerun as rr
 import torch
 from deepdiff import DeepDiff
 from termcolor import colored
 
+from lerobot.common.datasets.image_writer import safe_stop_image_writer
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.datasets.utils import DEFAULT_FEATURES
 from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.robots import Robot
+from lerobot.common.utils.robot_utils import busy_wait
+from lerobot.common.utils.utils import get_safe_torch_device, has_method
 
 
 def log_control_info(robot: Robot, dt_s, episode_index=None, frame_index=None, fps=None):
@@ -97,9 +101,7 @@ def is_headless():
         return True
 
 
-def predict_action(
-    observation: dict[str, np.ndarray], policy: PreTrainedPolicy, device: torch.device, use_amp: bool
-):
+def predict_action(observation, policy, device, use_amp):
     observation = copy(observation)
     with (
         torch.inference_mode(),
@@ -107,7 +109,6 @@ def predict_action(
     ):
         # Convert to pytorch format: channel first and float32 in [0,1] with batch dimension
         for name in observation:
-            observation[name] = torch.from_numpy(observation[name])
             if "image" in name:
                 observation[name] = observation[name].type(torch.float32) / 255
                 observation[name] = observation[name].permute(2, 0, 1).contiguous()
@@ -168,6 +169,147 @@ def init_keyboard_listener():
     return listener, events
 
 
+def warmup_record(
+    robot,
+    events,
+    enable_teleoperation,
+    warmup_time_s,
+    display_data,
+    fps,
+):
+    control_loop(
+        robot=robot,
+        control_time_s=warmup_time_s,
+        display_data=display_data,
+        events=events,
+        fps=fps,
+        teleoperate=enable_teleoperation,
+    )
+
+
+def record_episode(
+    robot,
+    dataset,
+    events,
+    episode_time_s,
+    display_data,
+    policy,
+    fps,
+    single_task,
+):
+    control_loop(
+        robot=robot,
+        control_time_s=episode_time_s,
+        display_data=display_data,
+        dataset=dataset,
+        events=events,
+        policy=policy,
+        fps=fps,
+        teleoperate=policy is None,
+        single_task=single_task,
+    )
+
+
+@safe_stop_image_writer
+def control_loop(
+    robot,
+    control_time_s=None,
+    teleoperate=False,
+    display_data=False,
+    dataset: LeRobotDataset | None = None,
+    events=None,
+    policy: PreTrainedPolicy = None,
+    fps: int | None = None,
+    single_task: str | None = 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 single_task is None:
+        raise ValueError("You need to provide a task as argument in `single_task`.")
+
+    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()
+            action = None
+
+            if policy is not None:
+                pred_action = predict_action(
+                    observation, policy, get_safe_torch_device(policy.config.device), policy.config.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:
+            frame = {**observation, **action, "task": single_task}
+            dataset.add_frame(frame)
+
+        # TODO(Steven): This should be more general (for RemoteRobot instead of checking the name, but anyways it will change soon)
+        if (display_data and not is_headless()) or (display_data and robot.robot_type.startswith("lekiwi")):
+            if action is not None:
+                for k, v in action.items():
+                    for i, vv in enumerate(v):
+                        rr.log(f"sent_{k}_{i}", rr.Scalar(vv.numpy()))
+
+            image_keys = [key for key in observation if "image" in key]
+            for key in image_keys:
+                rr.log(key, rr.Image(observation[key].numpy()), static=True)
+
+        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, fps):
+    # TODO(rcadene): refactor warmup_record and reset_environment
+    if has_method(robot, "teleop_safety_stop"):
+        robot.teleop_safety_stop()
+
+    control_loop(
+        robot=robot,
+        control_time_s=reset_time_s,
+        events=events,
+        fps=fps,
+        teleoperate=True,
+    )
+
+
+def stop_recording(robot, listener, display_data):
+    robot.disconnect()
+
+    if not is_headless() and listener is not None:
+        listener.stop()
+
+
 def sanity_check_dataset_name(repo_id, policy_cfg):
     _, dataset_name = repo_id.split("/")
     # either repo_id doesnt start with "eval_" and there is no policy

lerobot/common/utils/end_effector_control.py

@@ -1,802 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import logging
-import sys
-import time
-
-import numpy as np
-import torch
-
-from lerobot.common.model.kinematics import RobotKinematics
-from lerobot.common.utils.robot_utils import busy_wait
-from lerobot.common.utils.utils import init_logging
-
-
-class InputController:
-    """Base class for input controllers that generate motion deltas."""
-
-    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0):
-        """
-        Initialize the controller.
-
-        Args:
-            x_step_size: Base movement step size in meters
-            y_step_size: Base movement step size in meters
-            z_step_size: Base movement step size in meters
-        """
-        self.x_step_size = x_step_size
-        self.y_step_size = y_step_size
-        self.z_step_size = z_step_size
-        self.running = True
-        self.episode_end_status = None  # None, "success", or "failure"
-        self.intervention_flag = False
-        self.open_gripper_command = False
-        self.close_gripper_command = False
-
-    def start(self):
-        """Start the controller and initialize resources."""
-        pass
-
-    def stop(self):
-        """Stop the controller and release resources."""
-        pass
-
-    def get_deltas(self):
-        """Get the current movement deltas (dx, dy, dz) in meters."""
-        return 0.0, 0.0, 0.0
-
-    def should_quit(self):
-        """Return True if the user has requested to quit."""
-        return not self.running
-
-    def update(self):
-        """Update controller state - call this once per frame."""
-        pass
-
-    def __enter__(self):
-        """Support for use in 'with' statements."""
-        self.start()
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        """Ensure resources are released when exiting 'with' block."""
-        self.stop()
-
-    def get_episode_end_status(self):
-        """
-        Get the current episode end status.
-
-        Returns:
-            None if episode should continue, "success" or "failure" otherwise
-        """
-        status = self.episode_end_status
-        self.episode_end_status = None  # Reset after reading
-        return status
-
-    def should_intervene(self):
-        """Return True if intervention flag was set."""
-        return self.intervention_flag
-
-    def gripper_command(self):
-        """Return the current gripper command."""
-        if self.open_gripper_command == self.close_gripper_command:
-            return "stay"
-        elif self.open_gripper_command:
-            return "open"
-        elif self.close_gripper_command:
-            return "close"
-
-
-class KeyboardController(InputController):
-    """Generate motion deltas from keyboard input."""
-
-    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0):
-        super().__init__(x_step_size, y_step_size, z_step_size)
-        self.key_states = {
-            "forward_x": False,
-            "backward_x": False,
-            "forward_y": False,
-            "backward_y": False,
-            "forward_z": False,
-            "backward_z": False,
-            "quit": False,
-            "success": False,
-            "failure": False,
-        }
-        self.listener = None
-
-    def start(self):
-        """Start the keyboard listener."""
-        from pynput import keyboard
-
-        def on_press(key):
-            try:
-                if key == keyboard.Key.up:
-                    self.key_states["forward_x"] = True
-                elif key == keyboard.Key.down:
-                    self.key_states["backward_x"] = True
-                elif key == keyboard.Key.left:
-                    self.key_states["forward_y"] = True
-                elif key == keyboard.Key.right:
-                    self.key_states["backward_y"] = True
-                elif key == keyboard.Key.shift:
-                    self.key_states["backward_z"] = True
-                elif key == keyboard.Key.shift_r:
-                    self.key_states["forward_z"] = True
-                elif key == keyboard.Key.esc:
-                    self.key_states["quit"] = True
-                    self.running = False
-                    return False
-                elif key == keyboard.Key.enter:
-                    self.key_states["success"] = True
-                    self.episode_end_status = "success"
-                elif key == keyboard.Key.backspace:
-                    self.key_states["failure"] = True
-                    self.episode_end_status = "failure"
-            except AttributeError:
-                pass
-
-        def on_release(key):
-            try:
-                if key == keyboard.Key.up:
-                    self.key_states["forward_x"] = False
-                elif key == keyboard.Key.down:
-                    self.key_states["backward_x"] = False
-                elif key == keyboard.Key.left:
-                    self.key_states["forward_y"] = False
-                elif key == keyboard.Key.right:
-                    self.key_states["backward_y"] = False
-                elif key == keyboard.Key.shift:
-                    self.key_states["backward_z"] = False
-                elif key == keyboard.Key.shift_r:
-                    self.key_states["forward_z"] = False
-                elif key == keyboard.Key.enter:
-                    self.key_states["success"] = False
-                elif key == keyboard.Key.backspace:
-                    self.key_states["failure"] = False
-            except AttributeError:
-                pass
-
-        self.listener = keyboard.Listener(on_press=on_press, on_release=on_release)
-        self.listener.start()
-
-        print("Keyboard controls:")
-        print("  Arrow keys: Move in X-Y plane")
-        print("  Shift and Shift_R: Move in Z axis")
-        print("  Enter: End episode with SUCCESS")
-        print("  Backspace: End episode with FAILURE")
-        print("  ESC: Exit")
-
-    def stop(self):
-        """Stop the keyboard listener."""
-        if self.listener and self.listener.is_alive():
-            self.listener.stop()
-
-    def get_deltas(self):
-        """Get the current movement deltas from keyboard state."""
-        delta_x = delta_y = delta_z = 0.0
-
-        if self.key_states["forward_x"]:
-            delta_x += self.x_step_size
-        if self.key_states["backward_x"]:
-            delta_x -= self.x_step_size
-        if self.key_states["forward_y"]:
-            delta_y += self.y_step_size
-        if self.key_states["backward_y"]:
-            delta_y -= self.y_step_size
-        if self.key_states["forward_z"]:
-            delta_z += self.z_step_size
-        if self.key_states["backward_z"]:
-            delta_z -= self.z_step_size
-
-        return delta_x, delta_y, delta_z
-
-    def should_quit(self):
-        """Return True if ESC was pressed."""
-        return self.key_states["quit"]
-
-    def should_save(self):
-        """Return True if Enter was pressed (save episode)."""
-        return self.key_states["success"] or self.key_states["failure"]
-
-
-class GamepadController(InputController):
-    """Generate motion deltas from gamepad input."""
-
-    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0, deadzone=0.1):
-        super().__init__(x_step_size, y_step_size, z_step_size)
-        self.deadzone = deadzone
-        self.joystick = None
-        self.intervention_flag = False
-
-    def start(self):
-        """Initialize pygame and the gamepad."""
-        import pygame
-
-        pygame.init()
-        pygame.joystick.init()
-
-        if pygame.joystick.get_count() == 0:
-            logging.error("No gamepad detected. Please connect a gamepad and try again.")
-            self.running = False
-            return
-
-        self.joystick = pygame.joystick.Joystick(0)
-        self.joystick.init()
-        logging.info(f"Initialized gamepad: {self.joystick.get_name()}")
-
-        print("Gamepad controls:")
-        print("  Left analog stick: Move in X-Y plane")
-        print("  Right analog stick (vertical): Move in Z axis")
-        print("  B/Circle button: Exit")
-        print("  Y/Triangle button: End episode with SUCCESS")
-        print("  A/Cross button: End episode with FAILURE")
-        print("  X/Square button: Rerecord episode")
-
-    def stop(self):
-        """Clean up pygame resources."""
-        import pygame
-
-        if pygame.joystick.get_init():
-            if self.joystick:
-                self.joystick.quit()
-            pygame.joystick.quit()
-        pygame.quit()
-
-    def update(self):
-        """Process pygame events to get fresh gamepad readings."""
-        import pygame
-
-        for event in pygame.event.get():
-            if event.type == pygame.JOYBUTTONDOWN:
-                if event.button == 3:
-                    self.episode_end_status = "success"
-                # A button (1) for failure
-                elif event.button == 1:
-                    self.episode_end_status = "failure"
-                # X button (0) for rerecord
-                elif event.button == 0:
-                    self.episode_end_status = "rerecord_episode"
-
-                # RB button (6) for closing gripper
-                elif event.button == 6:
-                    self.close_gripper_command = True
-
-                # LT button (7) for opening gripper
-                elif event.button == 7:
-                    self.open_gripper_command = True
-
-            # Reset episode status on button release
-            elif event.type == pygame.JOYBUTTONUP:
-                if event.button in [0, 2, 3]:
-                    self.episode_end_status = None
-
-                elif event.button == 6:
-                    self.close_gripper_command = False
-
-                elif event.button == 7:
-                    self.open_gripper_command = False
-
-            # Check for RB button (typically button 5) for intervention flag
-            if self.joystick.get_button(5):
-                self.intervention_flag = True
-            else:
-                self.intervention_flag = False
-
-    def get_deltas(self):
-        """Get the current movement deltas from gamepad state."""
-        import pygame
-
-        try:
-            # Read joystick axes
-            # Left stick X and Y (typically axes 0 and 1)
-            x_input = self.joystick.get_axis(0)  # Left/Right
-            y_input = self.joystick.get_axis(1)  # Up/Down (often inverted)
-
-            # Right stick Y (typically axis 3 or 4)
-            z_input = self.joystick.get_axis(3)  # Up/Down for Z
-
-            # Apply deadzone to avoid drift
-            x_input = 0 if abs(x_input) < self.deadzone else x_input
-            y_input = 0 if abs(y_input) < self.deadzone else y_input
-            z_input = 0 if abs(z_input) < self.deadzone else z_input
-
-            # Calculate deltas (note: may need to invert axes depending on controller)
-            delta_x = -y_input * self.y_step_size  # Forward/backward
-            delta_y = -x_input * self.x_step_size  # Left/right
-            delta_z = -z_input * self.z_step_size  # Up/down
-
-            return delta_x, delta_y, delta_z
-
-        except pygame.error:
-            logging.error("Error reading gamepad. Is it still connected?")
-            return 0.0, 0.0, 0.0
-
-
-class GamepadControllerHID(InputController):
-    """Generate motion deltas from gamepad input using HIDAPI."""
-
-    def __init__(
-        self,
-        x_step_size=1.0,
-        y_step_size=1.0,
-        z_step_size=1.0,
-        deadzone=0.1,
-        vendor_id=0x046D,
-        product_id=0xC219,
-    ):
-        """
-        Initialize the HID gamepad controller.
-
-        Args:
-            step_size: Base movement step size in meters
-            z_scale: Scaling factor for Z-axis movement
-            deadzone: Joystick deadzone to prevent drift
-            vendor_id: USB vendor ID of the gamepad (default: Logitech)
-            product_id: USB product ID of the gamepad (default: RumblePad 2)
-        """
-        super().__init__(x_step_size, y_step_size, z_step_size)
-        self.deadzone = deadzone
-        self.vendor_id = vendor_id
-        self.product_id = product_id
-        self.device = None
-        self.device_info = None
-
-        # Movement values (normalized from -1.0 to 1.0)
-        self.left_x = 0.0
-        self.left_y = 0.0
-        self.right_x = 0.0
-        self.right_y = 0.0
-
-        # Button states
-        self.buttons = {}
-        self.quit_requested = False
-        self.save_requested = False
-
-    def find_device(self):
-        """Look for the gamepad device by vendor and product ID."""
-        import hid
-
-        devices = hid.enumerate()
-        for device in devices:
-            if device["vendor_id"] == self.vendor_id and device["product_id"] == self.product_id:
-                logging.info(f"Found gamepad: {device.get('product_string', 'Unknown')}")
-                return device
-
-        logging.error(
-            f"No gamepad with vendor ID 0x{self.vendor_id:04X} and product ID 0x{self.product_id:04X} found"
-        )
-        return None
-
-    def start(self):
-        """Connect to the gamepad using HIDAPI."""
-        import hid
-
-        self.device_info = self.find_device()
-        if not self.device_info:
-            self.running = False
-            return
-
-        try:
-            logging.info(f"Connecting to gamepad at path: {self.device_info['path']}")
-            self.device = hid.device()
-            self.device.open_path(self.device_info["path"])
-            self.device.set_nonblocking(1)
-
-            manufacturer = self.device.get_manufacturer_string()
-            product = self.device.get_product_string()
-            logging.info(f"Connected to {manufacturer} {product}")
-
-            logging.info("Gamepad controls (HID mode):")
-            logging.info("  Left analog stick: Move in X-Y plane")
-            logging.info("  Right analog stick: Move in Z axis (vertical)")
-            logging.info("  Button 1/B/Circle: Exit")
-            logging.info("  Button 2/A/Cross: End episode with SUCCESS")
-            logging.info("  Button 3/X/Square: End episode with FAILURE")
-
-        except OSError as e:
-            logging.error(f"Error opening gamepad: {e}")
-            logging.error("You might need to run this with sudo/admin privileges on some systems")
-            self.running = False
-
-    def stop(self):
-        """Close the HID device connection."""
-        if self.device:
-            self.device.close()
-            self.device = None
-
-    def update(self):
-        """
-        Read and process the latest gamepad data.
-        Due to an issue with the HIDAPI, we need to read the read the device several times in order to get a stable reading
-        """
-        for _ in range(10):
-            self._update()
-
-    def _update(self):
-        """Read and process the latest gamepad data."""
-        if not self.device or not self.running:
-            return
-
-        try:
-            # Read data from the gamepad
-            data = self.device.read(64)
-            # Interpret gamepad data - this will vary by controller model
-            # These offsets are for the Logitech RumblePad 2
-            if data and len(data) >= 8:
-                # Normalize joystick values from 0-255 to -1.0-1.0
-                self.left_x = (data[1] - 128) / 128.0
-                self.left_y = (data[2] - 128) / 128.0
-                self.right_x = (data[3] - 128) / 128.0
-                self.right_y = (data[4] - 128) / 128.0
-
-                # Apply deadzone
-                self.left_x = 0 if abs(self.left_x) < self.deadzone else self.left_x
-                self.left_y = 0 if abs(self.left_y) < self.deadzone else self.left_y
-                self.right_x = 0 if abs(self.right_x) < self.deadzone else self.right_x
-                self.right_y = 0 if abs(self.right_y) < self.deadzone else self.right_y
-
-                # Parse button states (byte 5 in the Logitech RumblePad 2)
-                buttons = data[5]
-
-                # Check if RB is pressed then the intervention flag should be set
-                self.intervention_flag = data[6] in [2, 6, 10, 14]
-
-                # Check if RT is pressed
-                self.open_gripper_command = data[6] in [8, 10, 12]
-
-                # Check if LT is pressed
-                self.close_gripper_command = data[6] in [4, 6, 12]
-
-                # Check if Y/Triangle button (bit 7) is pressed for saving
-                # Check if X/Square button (bit 5) is pressed for failure
-                # Check if A/Cross button (bit 4) is pressed for rerecording
-                if buttons & 1 << 7:
-                    self.episode_end_status = "success"
-                elif buttons & 1 << 5:
-                    self.episode_end_status = "failure"
-                elif buttons & 1 << 4:
-                    self.episode_end_status = "rerecord_episode"
-                else:
-                    self.episode_end_status = None
-
-        except OSError as e:
-            logging.error(f"Error reading from gamepad: {e}")
-
-    def get_deltas(self):
-        """Get the current movement deltas from gamepad state."""
-        # Calculate deltas - invert as needed based on controller orientation
-        delta_x = -self.left_y * self.x_step_size  # Forward/backward
-        delta_y = -self.left_x * self.y_step_size  # Left/right
-        delta_z = -self.right_y * self.z_step_size  # Up/down
-
-        return delta_x, delta_y, delta_z
-
-    def should_quit(self):
-        """Return True if quit button was pressed."""
-        return self.quit_requested
-
-    def should_save(self):
-        """Return True if save button was pressed."""
-        return self.save_requested
-
-
-def test_forward_kinematics(robot, fps=10):
-    logging.info("Testing Forward Kinematics")
-    timestep = time.perf_counter()
-    kinematics = RobotKinematics(robot.robot_type)
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-        robot.teleop_step()
-        obs = robot.capture_observation()
-        joint_positions = obs["observation.state"].cpu().numpy()
-        ee_pos = kinematics.fk_gripper_tip(joint_positions)
-        logging.info(f"EE Position: {ee_pos[:3, 3]}")
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def test_inverse_kinematics(robot, fps=10):
-    logging.info("Testing Inverse Kinematics")
-    timestep = time.perf_counter()
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-        obs = robot.capture_observation()
-        joint_positions = obs["observation.state"].cpu().numpy()
-        ee_pos = RobotKinematics.fk_gripper_tip(joint_positions)
-        desired_ee_pos = ee_pos
-        target_joint_state = RobotKinematics.ik(joint_positions, desired_ee_pos, position_only=True)
-        robot.send_action(torch.from_numpy(target_joint_state))
-        logging.info(f"Target Joint State: {target_joint_state}")
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_inverse_kinematics_with_leader(robot, fps=10):
-    logging.info("Testing Inverse Kinematics")
-    kinematics = RobotKinematics(robot.robot_type)
-    timestep = time.perf_counter()
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-        obs = robot.capture_observation()
-        joint_positions = obs["observation.state"].cpu().numpy()
-        ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-        leader_joint_positions = robot.leader_arms["main"].read("Present_Position")
-        leader_ee = kinematics.fk_gripper_tip(leader_joint_positions)
-
-        desired_ee_pos = leader_ee
-        target_joint_state = kinematics.ik(
-            joint_positions, desired_ee_pos, position_only=True, fk_func=kinematics.fk_gripper_tip
-        )
-        robot.send_action(torch.from_numpy(target_joint_state))
-        logging.info(f"Leader EE: {leader_ee[:3, 3]}, Follower EE: {ee_pos[:3, 3]}")
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_delta_inverse_kinematics_with_leader(robot, fps=10):
-    logging.info("Testing Delta End-Effector Control")
-    timestep = time.perf_counter()
-
-    # Initial position capture
-    obs = robot.capture_observation()
-    joint_positions = obs["observation.state"].cpu().numpy()
-
-    kinematics = RobotKinematics(robot.robot_type)
-
-    leader_joint_positions = robot.leader_arms["main"].read("Present_Position")
-    initial_leader_ee = kinematics.fk_gripper_tip(leader_joint_positions)
-
-    desired_ee_pos = np.diag(np.ones(4))
-    joint_positions = robot.follower_arms["main"].read("Present_Position")
-    fixed_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-    while time.perf_counter() - timestep < 60.0:
-        loop_start_time = time.perf_counter()
-
-        # Get leader state for teleoperation
-        leader_joint_positions = robot.leader_arms["main"].read("Present_Position")
-        leader_ee = kinematics.fk_gripper_tip(leader_joint_positions)
-
-        # Get current state
-        # obs = robot.capture_observation()
-        # joint_positions = obs["observation.state"].cpu().numpy()
-        joint_positions = robot.follower_arms["main"].read("Present_Position")
-        current_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-        # Calculate delta between leader and follower end-effectors
-        # Scaling factor can be adjusted for sensitivity
-        scaling_factor = 1.0
-        ee_delta = -np.clip((leader_ee - initial_leader_ee) * scaling_factor, -0.05, 0.05)
-
-        # Apply delta to current position
-        desired_ee_pos[0, 3] = fixed_ee_pos[0, 3]  # current_ee_pos[0, 3] + ee_delta[0, 3] * 0
-        desired_ee_pos[1, 3] = fixed_ee_pos[1, 3]  # current_ee_pos[1, 3] + ee_delta[1, 3] * 0
-        desired_ee_pos[2, 3] = current_ee_pos[2, 3] - ee_delta[2, 3]
-
-        # Compute joint targets via inverse kinematics
-        target_joint_state = kinematics.ik(
-            joint_positions, desired_ee_pos, position_only=True, fk_func=kinematics.fk_gripper_tip
-        )
-
-        initial_leader_ee = leader_ee.copy()
-
-        # Send command to robot
-        robot.send_action(torch.from_numpy(target_joint_state))
-
-        # Logging
-        logging.info(f"Current EE: {current_ee_pos[:3, 3]}, Desired EE: {desired_ee_pos[:3, 3]}")
-        logging.info(f"Delta EE: {ee_delta[:3, 3]}")
-
-        busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_delta_inverse_kinematics(robot, controller, fps=10, bounds=None, fk_func=None):
-    """
-    Control a robot using delta end-effector movements from any input controller.
-
-    Args:
-        robot: Robot instance to control
-        controller: InputController instance (keyboard, gamepad, etc.)
-        fps: Control frequency in Hz
-        bounds: Optional position limits
-        fk_func: Forward kinematics function to use
-    """
-    if fk_func is None:
-        fk_func = RobotKinematics.fk_gripper_tip
-
-    logging.info(f"Testing Delta End-Effector Control with {controller.__class__.__name__}")
-
-    # Initial position capture
-    obs = robot.capture_observation()
-    joint_positions = obs["observation.state"].cpu().numpy()
-    kinematics = RobotKinematics(robot.robot_type)
-    current_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-    # Initialize desired position with current position
-    desired_ee_pos = np.eye(4)  # Identity matrix
-
-    timestep = time.perf_counter()
-    with controller:
-        while not controller.should_quit() and time.perf_counter() - timestep < 60.0:
-            loop_start_time = time.perf_counter()
-
-            # Process input events
-            controller.update()
-
-            # Get current robot state
-            joint_positions = robot.follower_arms["main"].read("Present_Position")
-            current_ee_pos = kinematics.fk_gripper_tip(joint_positions)
-
-            # Get movement deltas from the controller
-            delta_x, delta_y, delta_z = controller.get_deltas()
-
-            # Update desired position
-            desired_ee_pos[0, 3] = current_ee_pos[0, 3] + delta_x
-            desired_ee_pos[1, 3] = current_ee_pos[1, 3] + delta_y
-            desired_ee_pos[2, 3] = current_ee_pos[2, 3] + delta_z
-
-            # Apply bounds if provided
-            if bounds is not None:
-                desired_ee_pos[:3, 3] = np.clip(desired_ee_pos[:3, 3], bounds["min"], bounds["max"])
-
-            # Only send commands if there's actual movement
-            if any(abs(v) > 0.001 for v in [delta_x, delta_y, delta_z]):
-                # Compute joint targets via inverse kinematics
-                target_joint_state = kinematics.ik(joint_positions, desired_ee_pos, position_only=True)
-
-                # Send command to robot
-                robot.send_action(torch.from_numpy(target_joint_state))
-
-            busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-
-def teleoperate_gym_env(env, controller, fps: int = 30):
-    """
-    Control a robot through a gym environment using keyboard inputs.
-
-    Args:
-        env: A gym environment created with make_robot_env
-        fps: Target control frequency
-    """
-
-    logging.info("Testing Keyboard Control of Gym Environment")
-    print("Keyboard controls:")
-    print("  Arrow keys: Move in X-Y plane")
-    print("  Shift and Shift_R: Move in Z axis")
-    print("  ESC: Exit")
-
-    # Reset the environment to get initial observation
-    obs, info = env.reset()
-
-    try:
-        with controller:
-            while not controller.should_quit():
-                loop_start_time = time.perf_counter()
-
-                # Process input events
-                controller.update()
-
-                # Get movement deltas from the controller
-                delta_x, delta_y, delta_z = controller.get_deltas()
-
-                # Create the action vector
-                action = np.array([delta_x, delta_y, delta_z])
-
-                # Skip if no movement
-                if any(abs(v) > 0.001 for v in [delta_x, delta_y, delta_z]):
-                    # Step the environment - pass action as a tensor with intervention flag
-                    action_tensor = torch.from_numpy(action.astype(np.float32))
-                    obs, reward, terminated, truncated, info = env.step((action_tensor, False))
-
-                    # Log information
-                    logging.info(f"Action: [{delta_x:.4f}, {delta_y:.4f}, {delta_z:.4f}]")
-                    logging.info(f"Reward: {reward}")
-
-                    # Reset if episode ended
-                    if terminated or truncated:
-                        logging.info("Episode ended, resetting environment")
-                        obs, info = env.reset()
-
-                # Maintain target frame rate
-                busy_wait(1 / fps - (time.perf_counter() - loop_start_time))
-
-    finally:
-        # Close the environment
-        env.close()
-
-
-if __name__ == "__main__":
-    from lerobot.common.envs.configs import EEActionSpaceConfig, EnvTransformConfig, HILSerlRobotEnvConfig
-    from lerobot.common.robot_devices.robots.configs import RobotConfig
-    from lerobot.common.robot_devices.robots.utils import make_robot_from_config
-    from lerobot.scripts.server.gym_manipulator import make_robot_env
-
-    init_logging()
-
-    parser = argparse.ArgumentParser(description="Test end-effector control")
-    parser.add_argument(
-        "--mode",
-        type=str,
-        default="keyboard",
-        choices=[
-            "keyboard",
-            "gamepad",
-            "keyboard_gym",
-            "gamepad_gym",
-            "leader_delta",
-            "leader",
-        ],
-        help="Control mode to use",
-    )
-    parser.add_argument(
-        "--robot-type",
-        type=str,
-        default="so100",
-        help="Robot type (so100, koch, aloha, etc.)",
-    )
-
-    args = parser.parse_args()
-
-    robot_config = RobotConfig.get_choice_class(args.robot_type)(mock=False)
-    robot = make_robot_from_config(robot_config)
-
-    if not robot.is_connected:
-        robot.connect()
-
-    # Example bounds
-    bounds = {
-        "max": np.array([0.32170487, 0.201285, 0.10273342]),
-        "min": np.array([0.16631757, -0.08237468, 0.03364977]),
-    }
-
-    try:
-        # Determine controller type based on mode prefix
-        controller = None
-        if args.mode.startswith("keyboard"):
-            controller = KeyboardController(x_step_size=0.01, y_step_size=0.01, z_step_size=0.05)
-        elif args.mode.startswith("gamepad"):
-            if sys.platform == "darwin":
-                controller = GamepadControllerHID(x_step_size=0.01, y_step_size=0.01, z_step_size=0.05)
-            else:
-                controller = GamepadController(x_step_size=0.01, y_step_size=0.01, z_step_size=0.05)
-
-        # Handle mode categories
-        if args.mode in ["keyboard", "gamepad"]:
-            # Direct robot control modes
-            teleoperate_delta_inverse_kinematics(robot, controller, bounds=bounds, fps=10)
-
-        elif args.mode in ["keyboard_gym", "gamepad_gym"]:
-            # Gym environment control modes
-            cfg = HILSerlRobotEnvConfig(robot=robot_config, wrapper=EnvTransformConfig())
-            cfg.wrapper.ee_action_space_params = EEActionSpaceConfig(
-                x_step_size=0.03, y_step_size=0.03, z_step_size=0.03, bounds=bounds
-            )
-            cfg.wrapper.ee_action_space_params.use_gamepad = False
-            cfg.device = "cpu"
-            env = make_robot_env(cfg, robot)
-            teleoperate_gym_env(env, controller, fps=cfg.fps)
-
-        elif args.mode == "leader_delta":
-            # Leader-follower modes don't use controllers
-            teleoperate_delta_inverse_kinematics_with_leader(robot)
-
-        elif args.mode == "leader":
-            teleoperate_inverse_kinematics_with_leader(robot)
-
-    finally:
-        if robot.is_connected:
-            robot.disconnect()

lerobot/common/utils/process.py

@@ -1,53 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import signal
-import sys
-
-shutdown_event_counter = 0
-
-
-def setup_process_handlers(use_threads: bool) -> any:
-    if use_threads:
-        from threading import Event
-    else:
-        from multiprocessing import Event
-
-    shutdown_event = Event()
-
-    # Define signal handler
-    def signal_handler(signum, frame):
-        logging.info("Shutdown signal received. Cleaning up...")
-        shutdown_event.set()
-        global shutdown_event_counter
-        shutdown_event_counter += 1
-
-        if shutdown_event_counter > 1:
-            logging.info("Force shutdown")
-            sys.exit(1)
-
-    signal.signal(signal.SIGINT, signal_handler)  # Ctrl+C
-    signal.signal(signal.SIGTERM, signal_handler)  # Termination request (kill)
-    signal.signal(signal.SIGHUP, signal_handler)  # Terminal closed/Hangup
-    signal.signal(signal.SIGQUIT, signal_handler)  # Ctrl+\
-
-    def signal_handler(signum, frame):
-        logging.info("Shutdown signal received. Cleaning up...")
-        shutdown_event.set()
-
-    return shutdown_event

lerobot/common/utils/queue.py

@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-from queue import Empty, Queue
-
-
-def get_last_item_from_queue(queue: Queue):
-    item = queue.get()
-    counter = 1
-
-    # Drain queue and keep only the most recent parameters
-    try:
-        while True:
-            item = queue.get_nowait()
-            counter += 1
-    except Empty:
-        pass
-
-    logging.debug(f"Drained {counter} items from queue")
-
-    return item

lerobot/common/utils/transition.py

@@ -1,85 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import TypedDict
-
-import torch
-
-
-class Transition(TypedDict):
-    state: dict[str, torch.Tensor]
-    action: torch.Tensor
-    reward: float
-    next_state: dict[str, torch.Tensor]
-    done: bool
-    truncated: bool
-    complementary_info: dict[str, torch.Tensor | float | int] | None = None
-
-
-def move_transition_to_device(transition: Transition, device: str = "cpu") -> Transition:
-    device = torch.device(device)
-    non_blocking = device.type == "cuda"
-
-    # Move state tensors to device
-    transition["state"] = {
-        key: val.to(device, non_blocking=non_blocking) for key, val in transition["state"].items()
-    }
-
-    # Move action to device
-    transition["action"] = transition["action"].to(device, non_blocking=non_blocking)
-
-    # Move reward and done if they are tensors
-    if isinstance(transition["reward"], torch.Tensor):
-        transition["reward"] = transition["reward"].to(device, non_blocking=non_blocking)
-
-    if isinstance(transition["done"], torch.Tensor):
-        transition["done"] = transition["done"].to(device, non_blocking=non_blocking)
-
-    if isinstance(transition["truncated"], torch.Tensor):
-        transition["truncated"] = transition["truncated"].to(device, non_blocking=non_blocking)
-
-    # Move next_state tensors to device
-    transition["next_state"] = {
-        key: val.to(device, non_blocking=non_blocking) for key, val in transition["next_state"].items()
-    }
-
-    # Move complementary_info tensors if present
-    if transition.get("complementary_info") is not None:
-        for key, val in transition["complementary_info"].items():
-            if isinstance(val, torch.Tensor):
-                transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking)
-            elif isinstance(val, (int, float, bool)):
-                transition["complementary_info"][key] = torch.tensor(val, device=device)
-            else:
-                raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]")
-    return transition
-
-
-def move_state_dict_to_device(state_dict, device="cpu"):
-    """
-    Recursively move all tensors in a (potentially) nested
-    dict/list/tuple structure to the CPU.
-    """
-    if isinstance(state_dict, torch.Tensor):
-        return state_dict.to(device)
-    elif isinstance(state_dict, dict):
-        return {k: move_state_dict_to_device(v, device=device) for k, v in state_dict.items()}
-    elif isinstance(state_dict, list):
-        return [move_state_dict_to_device(v, device=device) for v in state_dict]
-    elif isinstance(state_dict, tuple):
-        return tuple(move_state_dict_to_device(v, device=device) for v in state_dict)
-    else:
-        return state_dict

lerobot/common/utils/utils.py

@@ -20,11 +20,9 @@ import platform
 import select
 import subprocess
 import sys
-import time
-from copy import copy, deepcopy
+from copy import copy
 from datetime import datetime, timezone
 from pathlib import Path
-from statistics import mean
 
 import numpy as np
 import torch
@@ -111,17 +109,11 @@ def is_amp_available(device: str):
         raise ValueError(f"Unknown device '{device}.")
 
 
-def init_logging(log_file: Path | None = None, display_pid: bool = False):
+def init_logging():
     def custom_format(record):
         dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         fnameline = f"{record.pathname}:{record.lineno}"
-
-        # NOTE: Display PID is useful for multi-process logging.
-        if display_pid:
-            pid_str = f"[PID: {os.getpid()}]"
-            message = f"{record.levelname} {pid_str} {dt} {fnameline[-15:]:>15} {record.msg}"
-        else:
-            message = f"{record.levelname} {dt} {fnameline[-15:]:>15} {record.msg}"
+        message = f"{record.levelname} {dt} {fnameline[-15:]:>15} {record.msg}"
         return message
 
     logging.basicConfig(level=logging.INFO)
@@ -135,12 +127,6 @@ def init_logging(log_file: Path | None = None, display_pid: bool = False):
     console_handler.setFormatter(formatter)
     logging.getLogger().addHandler(console_handler)
 
-    if log_file is not None:
-        # Additionally write logs to file
-        file_handler = logging.FileHandler(log_file)
-        file_handler.setFormatter(formatter)
-        logging.getLogger().addHandler(file_handler)
-
 
 def format_big_number(num, precision=0):
     suffixes = ["", "K", "M", "B", "T", "Q"]
@@ -253,114 +239,3 @@ def enter_pressed() -> bool:
 def move_cursor_up(lines):
     """Move the cursor up by a specified number of lines."""
     print(f"\033[{lines}A", end="")
-
-
-class TimerManager:
-    """
-    Lightweight utility to measure elapsed time.
-
-    Examples
-    --------
-    ```python
-    # Example 1: Using context manager
-    timer = TimerManager("Policy", log=False)
-    for _ in range(3):
-        with timer:
-            time.sleep(0.01)
-    print(timer.last, timer.fps_avg, timer.percentile(90))  # Prints: 0.01 100.0 0.01
-    ```
-
-    ```python
-    # Example 2: Using start/stop methods
-    timer = TimerManager("Policy", log=False)
-    timer.start()
-    time.sleep(0.01)
-    timer.stop()
-    print(timer.last, timer.fps_avg, timer.percentile(90))  # Prints: 0.01 100.0 0.01
-    ```
-    """
-
-    def __init__(
-        self,
-        label: str = "Elapsed-time",
-        log: bool = True,
-        logger: logging.Logger | None = None,
-    ):
-        self.label = label
-        self.log = log
-        self.logger = logger
-        self._start: float | None = None
-        self._history: list[float] = []
-
-    def __enter__(self):
-        return self.start()
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.stop()
-
-    def start(self):
-        self._start = time.perf_counter()
-        return self
-
-    def stop(self) -> float:
-        if self._start is None:
-            raise RuntimeError("Timer was never started.")
-        elapsed = time.perf_counter() - self._start
-        self._history.append(elapsed)
-        self._start = None
-        if self.log:
-            if self.logger is not None:
-                self.logger.info(f"{self.label}: {elapsed:.6f} s")
-            else:
-                logging.info(f"{self.label}: {elapsed:.6f} s")
-        return elapsed
-
-    def reset(self):
-        self._history.clear()
-
-    @property
-    def last(self) -> float:
-        return self._history[-1] if self._history else 0.0
-
-    @property
-    def avg(self) -> float:
-        return mean(self._history) if self._history else 0.0
-
-    @property
-    def total(self) -> float:
-        return sum(self._history)
-
-    @property
-    def count(self) -> int:
-        return len(self._history)
-
-    @property
-    def history(self) -> list[float]:
-        return deepcopy(self._history)
-
-    @property
-    def fps_history(self) -> list[float]:
-        return [1.0 / t for t in self._history]
-
-    @property
-    def fps_last(self) -> float:
-        return 0.0 if self.last == 0 else 1.0 / self.last
-
-    @property
-    def fps_avg(self) -> float:
-        return 0.0 if self.avg == 0 else 1.0 / self.avg
-
-    def percentile(self, p: float) -> float:
-        """
-        Return the p-th percentile of recorded times.
-        """
-        if not self._history:
-            return 0.0
-        return float(np.percentile(self._history, p))
-
-    def fps_percentile(self, p: float) -> float:
-        """
-        FPS corresponding to the p-th percentile time.
-        """
-        val = self.percentile(p)
-        return 0.0 if val == 0 else 1.0 / val

lerobot/common/utils/wandb_utils.py

@@ -30,10 +30,9 @@ def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[st
     """Return a group name for logging. Optionally returns group name as list."""
     lst = [
         f"policy:{cfg.policy.type}",
+        f"dataset:{cfg.dataset.repo_id}",
         f"seed:{cfg.seed}",
     ]
-    if cfg.dataset is not None:
-        lst.append(f"dataset:{cfg.dataset.repo_id}")
     if cfg.env is not None:
         lst.append(f"env:{cfg.env.type}")
     return lst if return_list else "-".join(lst)
@@ -93,12 +92,6 @@ class WandBLogger:
             resume="must" if cfg.resume else None,
             mode=self.cfg.mode if self.cfg.mode in ["online", "offline", "disabled"] else "online",
         )
-        run_id = wandb.run.id
-        # NOTE: We will override the cfg.wandb.run_id with the wandb run id.
-        # This is because we want to be able to resume the run from the wandb run id.
-        cfg.wandb.run_id = run_id
-        # Handle custom step key for rl asynchronous training.
-        self._wandb_custom_step_key: set[str] | None = None
         print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"]))
         logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}")
         self._wandb = wandb
@@ -115,26 +108,9 @@ class WandBLogger:
         artifact.add_file(checkpoint_dir / PRETRAINED_MODEL_DIR / SAFETENSORS_SINGLE_FILE)
         self._wandb.log_artifact(artifact)
 
-    def log_dict(
-        self, d: dict, step: int | None = None, mode: str = "train", custom_step_key: str | None = None
-    ):
+    def log_dict(self, d: dict, step: int, mode: str = "train"):
         if mode not in {"train", "eval"}:
             raise ValueError(mode)
-        if step is None and custom_step_key is None:
-            raise ValueError("Either step or custom_step_key must be provided.")
-
-        # NOTE: This is not simple. Wandb step must always monotonically increase and it
-        # increases with each wandb.log call, but in the case of asynchronous RL for example,
-        # multiple time steps is possible. For example, the interaction step with the environment,
-        # the training step, the evaluation step, etc. So we need to define a custom step key
-        # to log the correct step for each metric.
-        if custom_step_key is not None:
-            if self._wandb_custom_step_key is None:
-                self._wandb_custom_step_key = set()
-            new_custom_key = f"{mode}/{custom_step_key}"
-            if new_custom_key not in self._wandb_custom_step_key:
-                self._wandb_custom_step_key.add(new_custom_key)
-                self._wandb.define_metric(new_custom_key, hidden=True)
 
         for k, v in d.items():
             if not isinstance(v, (int, float, str)):
@@ -142,18 +118,7 @@ class WandBLogger:
                     f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.'
                 )
                 continue
-
-            # Do not log the custom step key itself.
-            if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
-                continue
-
-            if custom_step_key is not None:
-                value_custom_step = d[custom_step_key]
-                data = {f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step}
-                self._wandb.log(data)
-                continue
-
-            self._wandb.log(data={f"{mode}/{k}": v}, step=step)
+            self._wandb.log({f"{mode}/{k}": v}, step=step)
 
     def log_video(self, video_path: str, step: int, mode: str = "train"):
         if mode not in {"train", "eval"}:

lerobot/configs/control.py

@@ -87,8 +87,6 @@ class RecordControlConfig(ControlConfig):
     play_sounds: bool = True
     # Resume recording on an existing dataset.
     resume: bool = False
-    # Reset follower arms to an initial position.
-    reset_follower_arms: bool = False
 
     def __post_init__(self):
         # HACK: We parse again the cli args here to get the pretrained path if there was one.

lerobot/configs/train.py

@@ -34,10 +34,11 @@ TRAIN_CONFIG_NAME = "train_config.json"
 
 @dataclass
 class TrainPipelineConfig(HubMixin):
-    dataset: DatasetConfig | None = None  # NOTE: In RL, we don't need an offline dataset
+    dataset: DatasetConfig
     env: envs.EnvConfig | None = None
     policy: PreTrainedConfig | None = None
-    # Set `dir` to where you would like to save all of the run outputs. If you run another training session # with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
+    # Set `dir` to where you would like to save all of the run outputs. If you run another training session
+    # with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
     output_dir: Path | None = None
     job_name: str | None = None
     # Set `resume` to true to resume a previous run. In order for this to work, you will need to make sure
@@ -106,7 +107,7 @@ class TrainPipelineConfig(HubMixin):
             train_dir = f"{now:%Y-%m-%d}/{now:%H-%M-%S}_{self.job_name}"
             self.output_dir = Path("outputs/train") / train_dir
 
-        if self.dataset is not None and isinstance(self.dataset.repo_id, list):
+        if isinstance(self.dataset.repo_id, list):
             raise NotImplementedError("LeRobotMultiDataset is not currently implemented.")
 
         if not self.use_policy_training_preset and (self.optimizer is None or self.scheduler is None):

lerobot/configs/types.py

@@ -23,7 +23,6 @@ class FeatureType(str, Enum):
     VISUAL = "VISUAL"
     ENV = "ENV"
     ACTION = "ACTION"
-    REWARD = "REWARD"
 
 
 class NormalizationMode(str, Enum):

lerobot/find_cameras.py

@@ -24,12 +24,10 @@ python -m lerobot.find_cameras
 ```
 """
 
-# NOTE(Steven): RealSense can also be identified/opened as OpenCV cameras. If you know the camera is a RealSense, use the `lerobot.find_cameras realsense` flag to avoid confusion.
-# NOTE(Steven): macOS cameras sometimes report different FPS at init time, not an issue here as we don't specify FPS when opening the cameras, but the information displayed might not be truthful.
-
 import argparse
 import concurrent.futures
 import logging
+import shutil
 import time
 from pathlib import Path
 from typing import Any, Dict, List
@@ -153,6 +151,17 @@ def save_image(
         logger.error(f"Failed to save image for camera {camera_identifier} (type {camera_type}): {e}")
 
 
+def initialize_output_directory(output_dir: str | Path) -> Path:
+    """Initialize and clean the output directory."""
+    output_dir = Path(output_dir)
+    if output_dir.exists():
+        logger.info(f"Output directory {output_dir} exists. Removing previous content.")
+        shutil.rmtree(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    logger.info(f"Saving images to {output_dir}")
+    return output_dir
+
+
 def create_camera_instance(cam_meta: Dict[str, Any]) -> Dict[str, Any] | None:
     """Create and connect to a camera instance based on metadata."""
     cam_type = cam_meta.get("type")
@@ -228,9 +237,9 @@ def cleanup_cameras(cameras_to_use: List[Dict[str, Any]]):
 
 
 def save_images_from_all_cameras(
-    output_dir: Path,
+    output_dir: str | Path,
     record_time_s: float = 2.0,
-    camera_type: str | None = None,
+    camera_type_filter: str | None = None,
 ):
     """
     Connects to detected cameras (optionally filtered by type) and saves images from each.
@@ -239,12 +248,11 @@ def save_images_from_all_cameras(
     Args:
         output_dir: Directory to save images.
         record_time_s: Duration in seconds to record images.
-        camera_type: Optional string to filter cameras ("realsense" or "opencv").
+        camera_type_filter: Optional string to filter cameras ("realsense" or "opencv").
                             If None, uses all detected cameras.
     """
-    output_dir.mkdir(parents=True, exist_ok=True)
-    logger.info(f"Saving images to {output_dir}")
-    all_camera_metadata = find_and_print_cameras(camera_type_filter=camera_type)
+    output_dir = initialize_output_directory(output_dir)
+    all_camera_metadata = find_and_print_cameras(camera_type_filter=camera_type_filter)
 
     if not all_camera_metadata:
         logger.warning("No cameras detected matching the criteria. Cannot save images.")
@@ -286,6 +294,10 @@ def save_images_from_all_cameras(
             logger.info(f"Image capture finished. Images saved to {output_dir}")
 
 
+# NOTE(Steven):
+# * realsense identified as opencv -> consistent in linux, we can even capture images
+# * opencv mac cams reporting different fps at init, not an issue as we don't enforce fps here
+# * opencv not opening in linux if we specify a backend
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Unified camera utility script for listing cameras and capturing images."
@@ -311,5 +323,14 @@ if __name__ == "__main__":
         default=6.0,
         help="Time duration to attempt capturing frames. Default: 6 seconds.",
     )
+    parser.set_defaults(
+        func=lambda args: save_images_from_all_cameras(
+            output_dir=args.output_dir,
+            record_time_s=args.record_time_s,
+            camera_type_filter=args.camera_type,
+        )
+    )
+
     args = parser.parse_args()
-    save_images_from_all_cameras(**vars(args))
+
+    args.func(args)

lerobot/record.py

@@ -38,6 +38,7 @@ from dataclasses import asdict, dataclass
 from pathlib import Path
 from pprint import pformat
 
+import draccus
 import numpy as np
 import rerun as rr
 
@@ -131,6 +132,8 @@ class RecordConfig:
     teleop: TeleoperatorConfig | None = None
     # Whether to control the robot with a policy
     policy: PreTrainedConfig | None = None
+    # Number of seconds before starting data collection. It allows the robot devices to warmup and synchronize.
+    warmup_time_s: int | float = 10
     # Display all cameras on screen
     display_data: bool = False
     # Use vocal synthesis to read events.
@@ -149,11 +152,6 @@ class RecordConfig:
             self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
             self.policy.pretrained_path = policy_path
 
-    @classmethod
-    def __get_path_fields__(cls) -> list[str]:
-        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
-        return ["policy"]
-
 
 @safe_stop_image_writer
 def record_loop(
@@ -170,10 +168,6 @@ def record_loop(
     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}).")
 
-    # if policy is given it needs cleaning up
-    if policy is not None:
-        policy.reset()
-
     timestamp = 0
     start_episode_t = time.perf_counter()
     while timestamp < control_time_s:
@@ -181,14 +175,10 @@ def record_loop(
 
         observation = robot.get_observation()
 
-        if policy is not None or dataset is not None:
-            observation_frame = build_dataset_frame(dataset.features, observation, prefix="observation")
-
         if policy is not None:
-            action_values = predict_action(
-                observation_frame, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
+            action = predict_action(
+                observation, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
             )
-            action = {key: action_values[i] for i, key in enumerate(robot.action_features)}
         else:
             action = teleop.get_action()
 
@@ -197,6 +187,7 @@ def record_loop(
         sent_action = robot.send_action(action)
 
         if dataset is not None:
+            observation_frame = build_dataset_frame(dataset.features, observation, prefix="observation")
             action_frame = build_dataset_frame(dataset.features, sent_action, prefix="action")
             frame = {**observation_frame, **action_frame}
             dataset.add_frame(frame, task=single_task)
@@ -224,7 +215,7 @@ def record_loop(
             break
 
 
-@parser.wrap()
+@draccus.wrap()
 def record(cfg: RecordConfig) -> LeRobotDataset:
     init_logging()
     logging.info(pformat(asdict(cfg)))
@@ -322,6 +313,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                 single_task=cfg.dataset.single_task,
                 display_data=cfg.display_data,
             )
+            # reset_environment(robot, events, cfg.dataset.reset_time_s, cfg.dataset.fps)
 
         if events["rerecord_episode"]:
             log_say("Re-record episode", cfg.play_sounds)

lerobot/replay.py

@@ -78,16 +78,16 @@ def replay(cfg: ReplayConfig):
 
     robot = make_robot_from_config(cfg.robot)
     dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode])
-    actions = dataset.hf_dataset.select_columns("action")
+    actions = dataset.hf_dataset.select_columns("action.joints")
     robot.connect()
 
     log_say("Replaying episode", cfg.play_sounds, blocking=True)
     for idx in range(dataset.num_frames):
         start_episode_t = time.perf_counter()
 
-        action_array = actions[idx]["action"]
+        action_array = actions[idx]["action.joints"]
         action = {}
-        for i, name in enumerate(dataset.features["action"]["names"]):
+        for i, name in enumerate(dataset.features["action.joints"]["names"]):
             action[name] = action_array[i]
 
         robot.send_action(action)

lerobot/scripts/find_joint_limits.py

@@ -1,105 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Simple script to control a robot from teleoperation.
-
-Example:
-
-```shell
-python -m lerobot.scripts.server.find_joint_limits \
-    --robot.type=so100_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=black \
-    --teleop.type=so100_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=blue
-```
-"""
-
-import time
-from dataclasses import dataclass
-
-import draccus
-import numpy as np
-
-from lerobot.common.model.kinematics_utils import RobotKinematics
-from lerobot.common.robots import (  # noqa: F401
-    RobotConfig,
-    koch_follower,
-    make_robot_from_config,
-    so100_follower,
-    so100_follower_end_effector,
-)
-from lerobot.common.teleoperators import (  # noqa: F401
-    TeleoperatorConfig,
-    gamepad,
-    koch_leader,
-    make_teleoperator_from_config,
-    so100_leader,
-)
-
-
-@dataclass
-class FindJointLimitsConfig:
-    teleop: TeleoperatorConfig
-    robot: RobotConfig
-    # Limit the maximum frames per second. By default, no limit.
-    fps: int | None = None
-    teleop_time_s: float | None = None
-    # Display all cameras on screen
-    display_data: bool = False
-
-    urdf_path: str = "/Users/michel_aractingi/code/SO-ARM100/Simulation/SO101/so101_new_calib.urdf"
-
-
-@draccus.wrap()
-def find_joint_and_ee_bounds(cfg: FindJointLimitsConfig):
-    teleop = make_teleoperator_from_config(cfg.teleop)
-    robot = make_robot_from_config(cfg.robot)
-
-    teleop.connect()
-    robot.connect()
-
-    start_episode_t = time.perf_counter()
-    ee_list = []
-    pos_list = []
-    kinematics = RobotKinematics(cfg.urdf_path)
-    control_time_s = 10
-    while True:
-        action = teleop.get_action()
-        robot.send_action(action)
-
-        joint_positions = robot.bus.sync_read("Present_Position")
-        joint_positions = np.array([joint_positions[key] for key in joint_positions])
-        ee_pos, _, _ = kinematics.forward_kinematics(joint_positions * np.pi / 180)
-        ee_list.append(ee_pos.copy())
-        pos_list.append(joint_positions)
-
-        if time.perf_counter() - start_episode_t > control_time_s:
-            max_ee = np.max(np.stack(ee_list), 0)
-            min_ee = np.min(np.stack(ee_list), 0)
-            max_pos = np.max(np.stack(pos_list), 0)
-            min_pos = np.min(np.stack(pos_list), 0)
-            print(f"Max ee position {max_ee}")
-            print(f"Min ee position {min_ee}")
-            print(f"Max joint pos position {max_pos}")
-            print(f"Min joint pos position {min_pos}")
-            break
-
-
-if __name__ == "__main__":
-    find_joint_and_ee_bounds()

lerobot/scripts/rl/actor.py

@@ -1,726 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Actor server runner for distributed HILSerl robot policy training.
-
-This script implements the actor component of the distributed HILSerl architecture.
-It executes the policy in the robot environment, collects experience,
-and sends transitions to the learner server for policy updates.
-
-Examples of usage:
-
-- Start an actor server for real robot training with human-in-the-loop intervention:
-```bash
-python lerobot/scripts/rl/actor.py --config_path lerobot/configs/train_config_hilserl_so100.json
-```
-
-- Run with a specific robot type for a pick and place task:
-```bash
-python lerobot/scripts/rl/actor.py \
-    --config_path lerobot/configs/train_config_hilserl_so100.json \
-    --robot.type=so100 \
-    --task=pick_and_place
-```
-
-- Set a custom workspace bound for the robot's end-effector:
-```bash
-python lerobot/scripts/rl/actor.py \
-    --config_path lerobot/configs/train_config_hilserl_so100.json \
-    --env.ee_action_space_params.bounds.max="[0.24, 0.20, 0.10]" \
-    --env.ee_action_space_params.bounds.min="[0.16, -0.08, 0.03]"
-```
-
-- Run with specific camera crop parameters:
-```bash
-python lerobot/scripts/rl/actor.py \
-    --config_path lerobot/configs/train_config_hilserl_so100.json \
-    --env.crop_params_dict="{'observation.images.side': [180, 207, 180, 200], 'observation.images.front': [180, 250, 120, 150]}"
-```
-
-**NOTE**: The actor server requires a running learner server to connect to. Ensure the learner
-server is started before launching the actor.
-
-**NOTE**: Human intervention is key to HILSerl training. Press the upper right trigger button on the
-gamepad to take control of the robot during training. Initially intervene frequently, then gradually
-reduce interventions as the policy improves.
-
-**WORKFLOW**:
-1. Determine robot workspace bounds using `find_joint_limits.py`
-2. Record demonstrations with `gym_manipulator.py` in record mode
-3. Process the dataset and determine camera crops with `crop_dataset_roi.py`
-4. Start the learner server with the training configuration
-5. Start this actor server with the same configuration
-6. Use human interventions to guide policy learning
-
-For more details on the complete HILSerl training workflow, see:
-https://github.com/michel-aractingi/lerobot-hilserl-guide
-"""
-
-import logging
-import os
-import time
-from functools import lru_cache
-from queue import Empty
-
-import grpc
-import torch
-from torch import nn
-from torch.multiprocessing import Event, Queue
-
-from lerobot.common.cameras import opencv  # noqa: F401
-from lerobot.common.policies.factory import make_policy
-from lerobot.common.policies.sac.modeling_sac import SACPolicy
-from lerobot.common.robots import so100_follower_end_effector  # noqa: F401
-from lerobot.common.teleoperators import gamepad, so100_leader  # noqa: F401
-from lerobot.common.transport import services_pb2, services_pb2_grpc
-from lerobot.common.transport.utils import (
-    bytes_to_state_dict,
-    python_object_to_bytes,
-    receive_bytes_in_chunks,
-    send_bytes_in_chunks,
-    transitions_to_bytes,
-)
-from lerobot.common.utils.process import setup_process_handlers
-from lerobot.common.utils.queue import get_last_item_from_queue
-from lerobot.common.utils.random_utils import set_seed
-from lerobot.common.utils.robot_utils import busy_wait
-from lerobot.common.utils.transition import (
-    Transition,
-    move_state_dict_to_device,
-    move_transition_to_device,
-)
-from lerobot.common.utils.utils import (
-    TimerManager,
-    get_safe_torch_device,
-    init_logging,
-)
-from lerobot.configs import parser
-from lerobot.configs.train import TrainPipelineConfig
-from lerobot.scripts.rl import learner_service
-from lerobot.scripts.rl.gym_manipulator import make_robot_env
-
-ACTOR_SHUTDOWN_TIMEOUT = 30
-
-
-#################################################
-# Main entry point #
-#################################################
-
-
-@parser.wrap()
-def actor_cli(cfg: TrainPipelineConfig):
-    cfg.validate()
-    display_pid = False
-    if not use_threads(cfg):
-        import torch.multiprocessing as mp
-
-        mp.set_start_method("spawn")
-        display_pid = True
-
-    # Create logs directory to ensure it exists
-    log_dir = os.path.join(cfg.output_dir, "logs")
-    os.makedirs(log_dir, exist_ok=True)
-    log_file = os.path.join(log_dir, f"actor_{cfg.job_name}.log")
-
-    # Initialize logging with explicit log file
-    init_logging(log_file=log_file, display_pid=display_pid)
-    logging.info(f"Actor logging initialized, writing to {log_file}")
-
-    shutdown_event = setup_process_handlers(use_threads(cfg))
-
-    learner_client, grpc_channel = learner_service_client(
-        host=cfg.policy.actor_learner_config.learner_host,
-        port=cfg.policy.actor_learner_config.learner_port,
-    )
-
-    logging.info("[ACTOR] Establishing connection with Learner")
-    if not establish_learner_connection(learner_client, shutdown_event):
-        logging.error("[ACTOR] Failed to establish connection with Learner")
-        return
-
-    if not use_threads(cfg):
-        # If we use multithreading, we can reuse the channel
-        grpc_channel.close()
-        grpc_channel = None
-
-    logging.info("[ACTOR] Connection with Learner established")
-
-    parameters_queue = Queue()
-    transitions_queue = Queue()
-    interactions_queue = Queue()
-
-    concurrency_entity = None
-    if use_threads(cfg):
-        from threading import Thread
-
-        concurrency_entity = Thread
-    else:
-        from multiprocessing import Process
-
-        concurrency_entity = Process
-
-    receive_policy_process = concurrency_entity(
-        target=receive_policy,
-        args=(cfg, parameters_queue, shutdown_event, grpc_channel),
-        daemon=True,
-    )
-
-    transitions_process = concurrency_entity(
-        target=send_transitions,
-        args=(cfg, transitions_queue, shutdown_event, grpc_channel),
-        daemon=True,
-    )
-
-    interactions_process = concurrency_entity(
-        target=send_interactions,
-        args=(cfg, interactions_queue, shutdown_event, grpc_channel),
-        daemon=True,
-    )
-
-    transitions_process.start()
-    interactions_process.start()
-    receive_policy_process.start()
-
-    act_with_policy(
-        cfg=cfg,
-        shutdown_event=shutdown_event,
-        parameters_queue=parameters_queue,
-        transitions_queue=transitions_queue,
-        interactions_queue=interactions_queue,
-    )
-    logging.info("[ACTOR] Policy process joined")
-
-    logging.info("[ACTOR] Closing queues")
-    transitions_queue.close()
-    interactions_queue.close()
-    parameters_queue.close()
-
-    transitions_process.join()
-    logging.info("[ACTOR] Transitions process joined")
-    interactions_process.join()
-    logging.info("[ACTOR] Interactions process joined")
-    receive_policy_process.join()
-    logging.info("[ACTOR] Receive policy process joined")
-
-    logging.info("[ACTOR] join queues")
-    transitions_queue.cancel_join_thread()
-    interactions_queue.cancel_join_thread()
-    parameters_queue.cancel_join_thread()
-
-    logging.info("[ACTOR] queues closed")
-
-
-#################################################
-# Core algorithm functions #
-#################################################
-
-
-def act_with_policy(
-    cfg: TrainPipelineConfig,
-    shutdown_event: any,  # Event,
-    parameters_queue: Queue,
-    transitions_queue: Queue,
-    interactions_queue: Queue,
-):
-    """
-    Executes policy interaction within the environment.
-
-    This function rolls out the policy in the environment, collecting interaction data and pushing it to a queue for streaming to the learner.
-    Once an episode is completed, updated network parameters received from the learner are retrieved from a queue and loaded into the network.
-
-    Args:
-        cfg: Configuration settings for the interaction process.
-        shutdown_event: Event to check if the process should shutdown.
-        parameters_queue: Queue to receive updated network parameters from the learner.
-        transitions_queue: Queue to send transitions to the learner.
-        interactions_queue: Queue to send interactions to the learner.
-    """
-    # Initialize logging for multiprocessing
-    if not use_threads(cfg):
-        log_dir = os.path.join(cfg.output_dir, "logs")
-        os.makedirs(log_dir, exist_ok=True)
-        log_file = os.path.join(log_dir, f"actor_policy_{os.getpid()}.log")
-        init_logging(log_file=log_file, display_pid=True)
-        logging.info("Actor policy process logging initialized")
-
-    logging.info("make_env online")
-
-    online_env = make_robot_env(cfg=cfg.env)
-
-    set_seed(cfg.seed)
-    device = get_safe_torch_device(cfg.policy.device, log=True)
-
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cuda.matmul.allow_tf32 = True
-
-    logging.info("make_policy")
-
-    ### Instantiate the policy in both the actor and learner processes
-    ### To avoid sending a SACPolicy object through the port, we create a policy instance
-    ### on both sides, the learner sends the updated parameters every n steps to update the actor's parameters
-    policy: SACPolicy = make_policy(
-        cfg=cfg.policy,
-        env_cfg=cfg.env,
-    )
-    policy = policy.eval()
-    assert isinstance(policy, nn.Module)
-
-    obs, info = online_env.reset()
-
-    # NOTE: For the moment we will solely handle the case of a single environment
-    sum_reward_episode = 0
-    list_transition_to_send_to_learner = []
-    episode_intervention = False
-    # Add counters for intervention rate calculation
-    episode_intervention_steps = 0
-    episode_total_steps = 0
-
-    policy_timer = TimerManager("Policy inference", log=False)
-
-    for interaction_step in range(cfg.policy.online_steps):
-        start_time = time.perf_counter()
-        if shutdown_event.is_set():
-            logging.info("[ACTOR] Shutting down act_with_policy")
-            return
-
-        if interaction_step >= cfg.policy.online_step_before_learning:
-            # Time policy inference and check if it meets FPS requirement
-            with policy_timer:
-                action = policy.select_action(batch=obs)
-            policy_fps = policy_timer.fps_last
-
-            log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
-
-        else:
-            action = online_env.action_space.sample()
-
-        next_obs, reward, done, truncated, info = online_env.step(action)
-
-        sum_reward_episode += float(reward)
-        # Increment total steps counter for intervention rate
-        episode_total_steps += 1
-
-        # NOTE: We override the action if the intervention is True, because the action applied is the intervention action
-        if "is_intervention" in info and info["is_intervention"]:
-            # NOTE: The action space for demonstration before hand is with the full action space
-            # but sometimes for example we want to deactivate the gripper
-            action = info["action_intervention"]
-            episode_intervention = True
-            # Increment intervention steps counter
-            episode_intervention_steps += 1
-
-        list_transition_to_send_to_learner.append(
-            Transition(
-                state=obs,
-                action=action,
-                reward=reward,
-                next_state=next_obs,
-                done=done,
-                truncated=truncated,  # TODO: (azouitine) Handle truncation properly
-                complementary_info=info,
-            )
-        )
-        # assign obs to the next obs and continue the rollout
-        obs = next_obs
-
-        if done or truncated:
-            logging.info(f"[ACTOR] Global step {interaction_step}: Episode reward: {sum_reward_episode}")
-
-            update_policy_parameters(policy=policy.actor, parameters_queue=parameters_queue, device=device)
-
-            if len(list_transition_to_send_to_learner) > 0:
-                push_transitions_to_transport_queue(
-                    transitions=list_transition_to_send_to_learner,
-                    transitions_queue=transitions_queue,
-                )
-                list_transition_to_send_to_learner = []
-
-            stats = get_frequency_stats(policy_timer)
-            policy_timer.reset()
-
-            # Calculate intervention rate
-            intervention_rate = 0.0
-            if episode_total_steps > 0:
-                intervention_rate = episode_intervention_steps / episode_total_steps
-
-            # Send episodic reward to the learner
-            interactions_queue.put(
-                python_object_to_bytes(
-                    {
-                        "Episodic reward": sum_reward_episode,
-                        "Interaction step": interaction_step,
-                        "Episode intervention": int(episode_intervention),
-                        "Intervention rate": intervention_rate,
-                        **stats,
-                    }
-                )
-            )
-
-            # Reset intervention counters
-            sum_reward_episode = 0.0
-            episode_intervention = False
-            episode_intervention_steps = 0
-            episode_total_steps = 0
-            obs, info = online_env.reset()
-
-        if cfg.env.fps is not None:
-            dt_time = time.perf_counter() - start_time
-            busy_wait(1 / cfg.env.fps - dt_time)
-
-
-#################################################
-#  Communication Functions - Group all gRPC/messaging functions  #
-#################################################
-
-
-def establish_learner_connection(
-    stub: services_pb2_grpc.LearnerServiceStub,
-    shutdown_event: Event,  # type: ignore
-    attempts: int = 30,
-):
-    """Establish a connection with the learner.
-
-    Args:
-        stub (services_pb2_grpc.LearnerServiceStub): The stub to use for the connection.
-        shutdown_event (Event): The event to check if the connection should be established.
-        attempts (int): The number of attempts to establish the connection.
-    Returns:
-        bool: True if the connection is established, False otherwise.
-    """
-    for _ in range(attempts):
-        if shutdown_event.is_set():
-            logging.info("[ACTOR] Shutting down establish_learner_connection")
-            return False
-
-        # Force a connection attempt and check state
-        try:
-            logging.info("[ACTOR] Send ready message to Learner")
-            if stub.Ready(services_pb2.Empty()) == services_pb2.Empty():
-                return True
-        except grpc.RpcError as e:
-            logging.error(f"[ACTOR] Waiting for Learner to be ready... {e}")
-            time.sleep(2)
-    return False
-
-
-@lru_cache(maxsize=1)
-def learner_service_client(
-    host: str = "127.0.0.1",
-    port: int = 50051,
-) -> tuple[services_pb2_grpc.LearnerServiceStub, grpc.Channel]:
-    import json
-
-    """
-    Returns a client for the learner service.
-
-    GRPC uses HTTP/2, which is a binary protocol and multiplexes requests over a single connection.
-    So we need to create only one client and reuse it.
-    """
-
-    service_config = {
-        "methodConfig": [
-            {
-                "name": [{}],  # Applies to ALL methods in ALL services
-                "retryPolicy": {
-                    "maxAttempts": 5,  # Max retries (total attempts = 5)
-                    "initialBackoff": "0.1s",  # First retry after 0.1s
-                    "maxBackoff": "2s",  # Max wait time between retries
-                    "backoffMultiplier": 2,  # Exponential backoff factor
-                    "retryableStatusCodes": [
-                        "UNAVAILABLE",
-                        "DEADLINE_EXCEEDED",
-                    ],  # Retries on network failures
-                },
-            }
-        ]
-    }
-
-    service_config_json = json.dumps(service_config)
-
-    channel = grpc.insecure_channel(
-        f"{host}:{port}",
-        options=[
-            ("grpc.max_receive_message_length", learner_service.MAX_MESSAGE_SIZE),
-            ("grpc.max_send_message_length", learner_service.MAX_MESSAGE_SIZE),
-            ("grpc.enable_retries", 1),
-            ("grpc.service_config", service_config_json),
-        ],
-    )
-    stub = services_pb2_grpc.LearnerServiceStub(channel)
-    logging.info("[ACTOR] Learner service client created")
-    return stub, channel
-
-
-def receive_policy(
-    cfg: TrainPipelineConfig,
-    parameters_queue: Queue,
-    shutdown_event: Event,  # type: ignore
-    learner_client: services_pb2_grpc.LearnerServiceStub | None = None,
-    grpc_channel: grpc.Channel | None = None,
-):
-    """Receive parameters from the learner.
-
-    Args:
-        cfg (TrainPipelineConfig): The configuration for the actor.
-        parameters_queue (Queue): The queue to receive the parameters.
-        shutdown_event (Event): The event to check if the process should shutdown.
-    """
-    logging.info("[ACTOR] Start receiving parameters from the Learner")
-    if not use_threads(cfg):
-        # Create a process-specific log file
-        log_dir = os.path.join(cfg.output_dir, "logs")
-        os.makedirs(log_dir, exist_ok=True)
-        log_file = os.path.join(log_dir, f"actor_receive_policy_{os.getpid()}.log")
-
-        # Initialize logging with explicit log file
-        init_logging(log_file=log_file, display_pid=True)
-        logging.info("Actor receive policy process logging initialized")
-
-        # Setup process handlers to handle shutdown signal
-        # But use shutdown event from the main process
-        setup_process_handlers(use_threads=False)
-
-    if grpc_channel is None or learner_client is None:
-        learner_client, grpc_channel = learner_service_client(
-            host=cfg.policy.actor_learner_config.learner_host,
-            port=cfg.policy.actor_learner_config.learner_port,
-        )
-
-    try:
-        iterator = learner_client.StreamParameters(services_pb2.Empty())
-        receive_bytes_in_chunks(
-            iterator,
-            parameters_queue,
-            shutdown_event,
-            log_prefix="[ACTOR] parameters",
-        )
-
-    except grpc.RpcError as e:
-        logging.error(f"[ACTOR] gRPC error: {e}")
-
-    if not use_threads(cfg):
-        grpc_channel.close()
-    logging.info("[ACTOR] Received policy loop stopped")
-
-
-def send_transitions(
-    cfg: TrainPipelineConfig,
-    transitions_queue: Queue,
-    shutdown_event: any,  # Event,
-    learner_client: services_pb2_grpc.LearnerServiceStub | None = None,
-    grpc_channel: grpc.Channel | None = None,
-) -> services_pb2.Empty:
-    """
-    Sends transitions to the learner.
-
-    This function continuously retrieves messages from the queue and processes:
-
-    - Transition Data:
-        - A batch of transitions (observation, action, reward, next observation) is collected.
-        - Transitions are moved to the CPU and serialized using PyTorch.
-        - The serialized data is wrapped in a `services_pb2.Transition` message and sent to the learner.
-    """
-
-    if not use_threads(cfg):
-        # Create a process-specific log file
-        log_dir = os.path.join(cfg.output_dir, "logs")
-        os.makedirs(log_dir, exist_ok=True)
-        log_file = os.path.join(log_dir, f"actor_transitions_{os.getpid()}.log")
-
-        # Initialize logging with explicit log file
-        init_logging(log_file=log_file, display_pid=True)
-        logging.info("Actor transitions process logging initialized")
-
-        # Setup process handlers to handle shutdown signal
-        # But use shutdown event from the main process
-        setup_process_handlers(False)
-
-    if grpc_channel is None or learner_client is None:
-        learner_client, grpc_channel = learner_service_client(
-            host=cfg.policy.actor_learner_config.learner_host,
-            port=cfg.policy.actor_learner_config.learner_port,
-        )
-
-    try:
-        learner_client.SendTransitions(transitions_stream(shutdown_event, transitions_queue))
-    except grpc.RpcError as e:
-        logging.error(f"[ACTOR] gRPC error: {e}")
-
-    logging.info("[ACTOR] Finished streaming transitions")
-
-    if not use_threads(cfg):
-        grpc_channel.close()
-    logging.info("[ACTOR] Transitions process stopped")
-
-
-def send_interactions(
-    cfg: TrainPipelineConfig,
-    interactions_queue: Queue,
-    shutdown_event: Event,  # type: ignore
-    learner_client: services_pb2_grpc.LearnerServiceStub | None = None,
-    grpc_channel: grpc.Channel | None = None,
-) -> services_pb2.Empty:
-    """
-    Sends interactions to the learner.
-
-    This function continuously retrieves messages from the queue and processes:
-
-    - Interaction Messages:
-        - Contains useful statistics about episodic rewards and policy timings.
-        - The message is serialized using `pickle` and sent to the learner.
-    """
-
-    if not use_threads(cfg):
-        # Create a process-specific log file
-        log_dir = os.path.join(cfg.output_dir, "logs")
-        os.makedirs(log_dir, exist_ok=True)
-        log_file = os.path.join(log_dir, f"actor_interactions_{os.getpid()}.log")
-
-        # Initialize logging with explicit log file
-        init_logging(log_file=log_file, display_pid=True)
-        logging.info("Actor interactions process logging initialized")
-
-        # Setup process handlers to handle shutdown signal
-        # But use shutdown event from the main process
-        setup_process_handlers(False)
-
-    if grpc_channel is None or learner_client is None:
-        learner_client, grpc_channel = learner_service_client(
-            host=cfg.policy.actor_learner_config.learner_host,
-            port=cfg.policy.actor_learner_config.learner_port,
-        )
-
-    try:
-        learner_client.SendInteractions(interactions_stream(shutdown_event, interactions_queue))
-    except grpc.RpcError as e:
-        logging.error(f"[ACTOR] gRPC error: {e}")
-
-    logging.info("[ACTOR] Finished streaming interactions")
-
-    if not use_threads(cfg):
-        grpc_channel.close()
-    logging.info("[ACTOR] Interactions process stopped")
-
-
-def transitions_stream(shutdown_event: Event, transitions_queue: Queue) -> services_pb2.Empty:  # type: ignore
-    while not shutdown_event.is_set():
-        try:
-            message = transitions_queue.get(block=True, timeout=5)
-        except Empty:
-            logging.debug("[ACTOR] Transition queue is empty")
-            continue
-
-        yield from send_bytes_in_chunks(
-            message, services_pb2.Transition, log_prefix="[ACTOR] Send transitions"
-        )
-
-    return services_pb2.Empty()
-
-
-def interactions_stream(
-    shutdown_event: Event,  # type: ignore
-    interactions_queue: Queue,
-) -> services_pb2.Empty:
-    while not shutdown_event.is_set():
-        try:
-            message = interactions_queue.get(block=True, timeout=5)
-        except Empty:
-            logging.debug("[ACTOR] Interaction queue is empty")
-            continue
-
-        yield from send_bytes_in_chunks(
-            message,
-            services_pb2.InteractionMessage,
-            log_prefix="[ACTOR] Send interactions",
-        )
-
-    return services_pb2.Empty()
-
-
-#################################################
-#  Policy functions #
-#################################################
-
-
-def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device):
-    if not parameters_queue.empty():
-        logging.info("[ACTOR] Load new parameters from Learner.")
-        bytes_state_dict = get_last_item_from_queue(parameters_queue)
-        state_dict = bytes_to_state_dict(bytes_state_dict)
-        state_dict = move_state_dict_to_device(state_dict, device=device)
-        policy.load_state_dict(state_dict)
-
-
-#################################################
-#  Utilities functions #
-#################################################
-
-
-def push_transitions_to_transport_queue(transitions: list, transitions_queue):
-    """Send transitions to learner in smaller chunks to avoid network issues.
-
-    Args:
-        transitions: List of transitions to send
-        message_queue: Queue to send messages to learner
-        chunk_size: Size of each chunk to send
-    """
-    transition_to_send_to_learner = []
-    for transition in transitions:
-        tr = move_transition_to_device(transition=transition, device="cpu")
-        for key, value in tr["state"].items():
-            if torch.isnan(value).any():
-                logging.warning(f"Found NaN values in transition {key}")
-
-        transition_to_send_to_learner.append(tr)
-
-    transitions_queue.put(transitions_to_bytes(transition_to_send_to_learner))
-
-
-def get_frequency_stats(timer: TimerManager) -> dict[str, float]:
-    """Get the frequency statistics of the policy.
-
-    Args:
-        timer (TimerManager): The timer with collected metrics.
-
-    Returns:
-        dict[str, float]: The frequency statistics of the policy.
-    """
-    stats = {}
-    if timer.count > 1:
-        avg_fps = timer.fps_avg
-        p90_fps = timer.fps_percentile(90)
-        logging.debug(f"[ACTOR] Average policy frame rate: {avg_fps}")
-        logging.debug(f"[ACTOR] Policy frame rate 90th percentile: {p90_fps}")
-        stats = {
-            "Policy frequency [Hz]": avg_fps,
-            "Policy frequency 90th-p [Hz]": p90_fps,
-        }
-    return stats
-
-
-def log_policy_frequency_issue(policy_fps: float, cfg: TrainPipelineConfig, interaction_step: int):
-    if policy_fps < cfg.env.fps:
-        logging.warning(
-            f"[ACTOR] Policy FPS {policy_fps:.1f} below required {cfg.env.fps} at step {interaction_step}"
-        )
-
-
-def use_threads(cfg: TrainPipelineConfig) -> bool:
-    return cfg.policy.concurrency.actor == "threads"
-
-
-if __name__ == "__main__":
-    actor_cli()

lerobot/scripts/rl/crop_dataset_roi.py

@@ -1,303 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import json
-from copy import deepcopy
-from pathlib import Path
-from typing import Dict, Tuple
-
-import cv2
-
-# import torch.nn.functional as F  # noqa: N812
-import torchvision.transforms.functional as F  # type: ignore  # noqa: N812
-from tqdm import tqdm  # type: ignore
-
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-
-
-def select_rect_roi(img):
-    """
-    Allows the user to draw a rectangular ROI on the image.
-
-    The user must click and drag to draw the rectangle.
-    - While dragging, the rectangle is dynamically drawn.
-    - On mouse button release, the rectangle is fixed.
-    - Press 'c' to confirm the selection.
-    - Press 'r' to reset the selection.
-    - Press ESC to cancel.
-
-    Returns:
-        A tuple (top, left, height, width) representing the rectangular ROI,
-        or None if no valid ROI is selected.
-    """
-    # Create a working copy of the image
-    clone = img.copy()
-    working_img = clone.copy()
-
-    roi = None  # Will store the final ROI as (top, left, height, width)
-    drawing = False
-    index_x, index_y = -1, -1  # Initial click coordinates
-
-    def mouse_callback(event, x, y, flags, param):
-        nonlocal index_x, index_y, drawing, roi, working_img
-
-        if event == cv2.EVENT_LBUTTONDOWN:
-            # Start drawing: record starting coordinates
-            drawing = True
-            index_x, index_y = x, y
-
-        elif event == cv2.EVENT_MOUSEMOVE:
-            if drawing:
-                # Compute the top-left and bottom-right corners regardless of drag direction
-                top = min(index_y, y)
-                left = min(index_x, x)
-                bottom = max(index_y, y)
-                right = max(index_x, x)
-                # Show a temporary image with the current rectangle drawn
-                temp = working_img.copy()
-                cv2.rectangle(temp, (left, top), (right, bottom), (0, 255, 0), 2)
-                cv2.imshow("Select ROI", temp)
-
-        elif event == cv2.EVENT_LBUTTONUP:
-            # Finish drawing
-            drawing = False
-            top = min(index_y, y)
-            left = min(index_x, x)
-            bottom = max(index_y, y)
-            right = max(index_x, x)
-            height = bottom - top
-            width = right - left
-            roi = (top, left, height, width)  # (top, left, height, width)
-            # Draw the final rectangle on the working image and display it
-            working_img = clone.copy()
-            cv2.rectangle(working_img, (left, top), (right, bottom), (0, 255, 0), 2)
-            cv2.imshow("Select ROI", working_img)
-
-    # Create the window and set the callback
-    cv2.namedWindow("Select ROI")
-    cv2.setMouseCallback("Select ROI", mouse_callback)
-    cv2.imshow("Select ROI", working_img)
-
-    print("Instructions for ROI selection:")
-    print("  - Click and drag to draw a rectangular ROI.")
-    print("  - Press 'c' to confirm the selection.")
-    print("  - Press 'r' to reset and draw again.")
-    print("  - Press ESC to cancel the selection.")
-
-    # Wait until the user confirms with 'c', resets with 'r', or cancels with ESC
-    while True:
-        key = cv2.waitKey(1) & 0xFF
-        # Confirm ROI if one has been drawn
-        if key == ord("c") and roi is not None:
-            break
-        # Reset: clear the ROI and restore the original image
-        elif key == ord("r"):
-            working_img = clone.copy()
-            roi = None
-            cv2.imshow("Select ROI", working_img)
-        # Cancel selection for this image
-        elif key == 27:  # ESC key
-            roi = None
-            break
-
-    cv2.destroyWindow("Select ROI")
-    return roi
-
-
-def select_square_roi_for_images(images: dict) -> dict:
-    """
-    For each image in the provided dictionary, open a window to allow the user
-    to select a rectangular ROI. Returns a dictionary mapping each key to a tuple
-    (top, left, height, width) representing the ROI.
-
-    Parameters:
-        images (dict): Dictionary where keys are identifiers and values are OpenCV images.
-
-    Returns:
-        dict: Mapping of image keys to the selected rectangular ROI.
-    """
-    selected_rois = {}
-
-    for key, img in images.items():
-        if img is None:
-            print(f"Image for key '{key}' is None, skipping.")
-            continue
-
-        print(f"\nSelect rectangular ROI for image with key: '{key}'")
-        roi = select_rect_roi(img)
-
-        if roi is None:
-            print(f"No valid ROI selected for '{key}'.")
-        else:
-            selected_rois[key] = roi
-            print(f"ROI for '{key}': {roi}")
-
-    return selected_rois
-
-
-def get_image_from_lerobot_dataset(dataset: LeRobotDataset):
-    """
-    Find the first row in the dataset and extract the image in order to be used for the crop.
-    """
-    row = dataset[0]
-    image_dict = {}
-    for k in row:
-        if "image" in k:
-            image_dict[k] = deepcopy(row[k])
-    return image_dict
-
-
-def convert_lerobot_dataset_to_cropper_lerobot_dataset(
-    original_dataset: LeRobotDataset,
-    crop_params_dict: Dict[str, Tuple[int, int, int, int]],
-    new_repo_id: str,
-    new_dataset_root: str,
-    resize_size: Tuple[int, int] = (128, 128),
-    push_to_hub: bool = False,
-) -> LeRobotDataset:
-    """
-    Converts an existing LeRobotDataset by iterating over its episodes and frames,
-    applying cropping and resizing to image observations, and saving a new dataset
-    with the transformed data.
-
-    Args:
-        original_dataset (LeRobotDataset): The source dataset.
-        crop_params_dict (Dict[str, Tuple[int, int, int, int]]):
-            A dictionary mapping observation keys to crop parameters (top, left, height, width).
-        new_repo_id (str): Repository id for the new dataset.
-        new_dataset_root (str): The root directory where the new dataset will be written.
-        resize_size (Tuple[int, int], optional): The target size (height, width) after cropping.
-            Defaults to (128, 128).
-
-    Returns:
-        LeRobotDataset: A new LeRobotDataset where the specified image observations have been cropped
-                        and resized.
-    """
-    # 1. Create a new (empty) LeRobotDataset for writing.
-    new_dataset = LeRobotDataset.create(
-        repo_id=new_repo_id,
-        fps=original_dataset.fps,
-        root=new_dataset_root,
-        robot_type=original_dataset.meta.robot_type,
-        features=original_dataset.meta.info["features"],
-        use_videos=len(original_dataset.meta.video_keys) > 0,
-    )
-
-    # Update the metadata for every image key that will be cropped:
-    # (Here we simply set the shape to be the final resize_size.)
-    for key in crop_params_dict:
-        if key in new_dataset.meta.info["features"]:
-            new_dataset.meta.info["features"][key]["shape"] = [3] + list(resize_size)
-
-    # TODO:  Directly modify the mp4 video + meta info features, instead of recreating a dataset
-    prev_episode_index = 0
-    for frame_idx in tqdm(range(len(original_dataset))):
-        frame = original_dataset[frame_idx]
-
-        # Create a copy of the frame to add to the new dataset
-        new_frame = {}
-        for key, value in frame.items():
-            if key in ("task_index", "timestamp", "episode_index", "frame_index", "index"):
-                continue
-            if key in ("next.done", "next.reward"):
-                # if not isinstance(value, str) and len(value.shape) == 0:
-                value = value.unsqueeze(0)
-
-            if key in crop_params_dict:
-                top, left, height, width = crop_params_dict[key]
-                # Apply crop then resize.
-                cropped = F.crop(value, top, left, height, width)
-                value = F.resize(cropped, resize_size)
-                value = value.clamp(0, 1)
-
-            new_frame[key] = value
-
-        new_dataset.add_frame(new_frame)
-
-        if frame["episode_index"].item() != prev_episode_index:
-            # Save the episode
-            new_dataset.save_episode()
-            prev_episode_index = frame["episode_index"].item()
-
-    if push_to_hub:
-        new_dataset.push_to_hub()
-
-    return new_dataset
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Crop rectangular ROIs from a LeRobot dataset.")
-    parser.add_argument(
-        "--repo-id",
-        type=str,
-        default="lerobot",
-        help="The repository id of the LeRobot dataset to process.",
-    )
-    parser.add_argument(
-        "--root",
-        type=str,
-        default=None,
-        help="The root directory of the LeRobot dataset.",
-    )
-    parser.add_argument(
-        "--crop-params-path",
-        type=str,
-        default=None,
-        help="The path to the JSON file containing the ROIs.",
-    )
-    parser.add_argument(
-        "--push-to-hub",
-        type=bool,
-        default=False,
-        help="Whether to push the new dataset to the hub.",
-    )
-    args = parser.parse_args()
-
-    dataset = LeRobotDataset(repo_id=args.repo_id, root=args.root)
-
-    images = get_image_from_lerobot_dataset(dataset)
-    images = {k: v.cpu().permute(1, 2, 0).numpy() for k, v in images.items()}
-    images = {k: (v * 255).astype("uint8") for k, v in images.items()}
-
-    if args.crop_params_path is None:
-        rois = select_square_roi_for_images(images)
-    else:
-        with open(args.crop_params_path) as f:
-            rois = json.load(f)
-
-    # Print the selected rectangular ROIs
-    print("\nSelected Rectangular Regions of Interest (top, left, height, width):")
-    for key, roi in rois.items():
-        print(f"{key}: {roi}")
-
-    new_repo_id = args.repo_id + "_cropped_resized"
-    new_dataset_root = Path(str(dataset.root) + "_cropped_resized")
-
-    cropped_resized_dataset = convert_lerobot_dataset_to_cropper_lerobot_dataset(
-        original_dataset=dataset,
-        crop_params_dict=rois,
-        new_repo_id=new_repo_id,
-        new_dataset_root=new_dataset_root,
-        resize_size=(128, 128),
-        push_to_hub=args.push_to_hub,
-    )
-
-    meta_dir = new_dataset_root / "meta"
-    meta_dir.mkdir(exist_ok=True)
-
-    with open(meta_dir / "crop_params.json", "w") as f:
-        json.dump(rois, f, indent=4)

lerobot/scripts/rl/learner_service.py

@@ -1,84 +0,0 @@
-import logging
-from multiprocessing import Event, Queue
-
-from lerobot.common.transport import services_pb2, services_pb2_grpc
-from lerobot.common.transport.utils import receive_bytes_in_chunks, send_bytes_in_chunks
-
-MAX_MESSAGE_SIZE = 4 * 1024 * 1024  # 4 MB
-MAX_WORKERS = 3  # Stream parameters, send transitions and interactions
-SHUTDOWN_TIMEOUT = 10
-
-
-class LearnerService(services_pb2_grpc.LearnerServiceServicer):
-    """
-    Implementation of the LearnerService gRPC service
-    This service is used to send parameters to the Actor and receive transitions and interactions from the Actor
-    check transport.proto for the gRPC service definition
-    """
-
-    def __init__(
-        self,
-        shutdown_event: Event,  # type: ignore
-        parameters_queue: Queue,
-        seconds_between_pushes: float,
-        transition_queue: Queue,
-        interaction_message_queue: Queue,
-    ):
-        self.shutdown_event = shutdown_event
-        self.parameters_queue = parameters_queue
-        self.seconds_between_pushes = seconds_between_pushes
-        self.transition_queue = transition_queue
-        self.interaction_message_queue = interaction_message_queue
-
-    def StreamParameters(self, request, context):  # noqa: N802
-        # TODO: authorize the request
-        logging.info("[LEARNER] Received request to stream parameters from the Actor")
-
-        while not self.shutdown_event.is_set():
-            logging.info("[LEARNER] Push parameters to the Actor")
-            buffer = self.parameters_queue.get()
-
-            yield from send_bytes_in_chunks(
-                buffer,
-                services_pb2.Parameters,
-                log_prefix="[LEARNER] Sending parameters",
-                silent=True,
-            )
-
-            logging.info("[LEARNER] Parameters sent")
-
-            self.shutdown_event.wait(self.seconds_between_pushes)
-
-        logging.info("[LEARNER] Stream parameters finished")
-        return services_pb2.Empty()
-
-    def SendTransitions(self, request_iterator, _context):  # noqa: N802
-        # TODO: authorize the request
-        logging.info("[LEARNER] Received request to receive transitions from the Actor")
-
-        receive_bytes_in_chunks(
-            request_iterator,
-            self.transition_queue,
-            self.shutdown_event,
-            log_prefix="[LEARNER] transitions",
-        )
-
-        logging.debug("[LEARNER] Finished receiving transitions")
-        return services_pb2.Empty()
-
-    def SendInteractions(self, request_iterator, _context):  # noqa: N802
-        # TODO: authorize the request
-        logging.info("[LEARNER] Received request to receive interactions from the Actor")
-
-        receive_bytes_in_chunks(
-            request_iterator,
-            self.interaction_message_queue,
-            self.shutdown_event,
-            log_prefix="[LEARNER] interactions",
-        )
-
-        logging.debug("[LEARNER] Finished receiving interactions")
-        return services_pb2.Empty()
-
-    def Ready(self, request, context):  # noqa: N802
-        return services_pb2.Empty()

lerobot/teleoperate.py

@@ -47,7 +47,6 @@ from lerobot.common.robots import (  # noqa: F401
     koch_follower,
     make_robot_from_config,
     so100_follower,
-    so100_follower_end_effector,
     so101_follower,
 )
 from lerobot.common.teleoperators import (
@@ -58,7 +57,7 @@ from lerobot.common.teleoperators import (
 from lerobot.common.utils.utils import init_logging, move_cursor_up
 from lerobot.common.utils.visualization_utils import _init_rerun
 
-from .common.teleoperators import gamepad, koch_leader, so100_leader  # noqa: F401
+from .common.teleoperators import koch_leader, so100_leader, so101_leader  # noqa: F401
 
 
 @dataclass

pyproject.toml

@@ -53,7 +53,7 @@ dependencies = [
     "einops>=0.8.0",
     "flask>=3.0.3",
     "gdown>=5.1.0",
-    "gymnasium==0.29.1",                                                 # TODO(rcadene, aliberts): Make gym 1.0.0 work
+    "gymnasium==0.29.1", # TODO(rcadene, aliberts): Make gym 1.0.0 work
     "h5py>=3.10.0",
     "huggingface-hub[hf-transfer,cli]>=0.27.1 ; python_version < '4.0'",
     "imageio[ffmpeg]>=2.34.0",
@@ -63,13 +63,13 @@ dependencies = [
     "opencv-python-headless>=4.9.0",
     "packaging>=24.2",
     "av>=14.2.0",
-    "pymunk>=6.6.0,<7.0.0",
+    "pymunk>=6.6.0",
     "pynput>=1.7.7",
     "pyzmq>=26.2.1",
     "rerun-sdk>=0.21.0",
     "termcolor>=2.4.0",
-    "torch>=2.2.1",
-    "torchcodec>=0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
+    "torch>=2.2.1,<2.7",
+    "torchcodec==0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
     "torchvision>=0.21.0",
     "wandb>=0.16.3",
     "zarr>=2.17.0",
@@ -82,57 +82,31 @@ dev = ["pre-commit>=3.7.0", "debugpy>=1.8.1"]
 dora = [
     "gym-dora @ git+https://github.com/dora-rs/dora-lerobot.git#subdirectory=gym_dora ; python_version < '4.0'",
 ]
-dynamixel = ["dynamixel-sdk>=3.7.31", "pynput>=1.7.7"]
-feetech = ["feetech-servo-sdk>=1.0.0", "pynput>=1.7.7"]
-hilserl = ["transformers>=4.48", "gym-hil>=0.1.3", "protobuf>=5.29.3", "grpcio>=1.70.0"]
-intelrealsense = ["pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'"]
+dynamixel = ["dynamixel-sdk>=3.7.31"]
+feetech = ["feetech-servo-sdk>=1.0.0"]
+intelrealsense = [
+    "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'",
+    "pyrealsense2-macosx>=2.54 ; sys_platform == 'darwin'",
+]
 pi0 = ["transformers>=4.48.0"]
 pusht = ["gym-pusht>=0.1.5 ; python_version < '4.0'"]
 stretch = [
     "hello-robot-stretch-body>=0.7.27 ; python_version < '4.0' and sys_platform == 'linux'",
     "pyrender @ git+https://github.com/mmatl/pyrender.git ; sys_platform == 'linux'",
-    "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'",
-    "pynput>=1.7.7",
+    "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'"
 ]
-test = ["pytest>=8.1.0", "pytest-cov>=5.0.0", "pyserial>=3.5"]
+test = ["pytest>=8.1.0", "pytest-cov>=5.0.0", "pyserial>=3.5", "mock-serial>=0.0.1 ; sys_platform != 'win32'"]
 umi = ["imagecodecs>=2024.1.1"]
 video_benchmark = ["scikit-image>=0.23.2", "pandas>=2.2.2"]
 xarm = ["gym-xarm>=0.1.1 ; python_version < '4.0'"]
 
-
 [tool.poetry]
 requires-poetry = ">=2.1"
 
 [tool.ruff]
 line-length = 110
 target-version = "py310"
-exclude = [
-    "tests/data",
-    ".bzr",
-    ".direnv",
-    ".eggs",
-    ".git",
-    ".git-rewrite",
-    ".hg",
-    ".mypy_cache",
-    ".nox",
-    ".pants.d",
-    ".pytype",
-    ".ruff_cache",
-    ".svn",
-    ".tox",
-    ".venv",
-    "__pypackages__",
-    "_build",
-    "buck-out",
-    "build",
-    "dist",
-    "node_modules",
-    "venv",
-    "*_pb2.py",
-    "*_pb2_grpc.py",
-]
-
+exclude = ["tests/artifacts/**/*.safetensors"]
 
 [tool.ruff.lint]
 select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]

tests/artifacts/image_transforms/default_transforms.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6b1e600768a8771c5fe650e038a1193597e3810f032041b2a0d021e4496381c1
+oid sha256:0389a716d51c1c615fb2a3bfa386d89f00b0deca08c4fa21b23e020a939d0213
 size 3686488