chore(dummy): add comments + better visualization

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

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

@@ -41,7 +41,7 @@ jobs:
 
       - name: Get changed files
         id: changed-files
-        uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
+        uses: tj-actions/changed-files@v44
         with:
           files: docker/**
           json: "true"

.github/workflows/test.yml

@@ -126,7 +126,7 @@ jobs:
       # portaudio19-dev is needed to install pyaudio
         run: |
           sudo apt-get update && \
-          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
+          sudo apt-get install -y libegl1-mesa-dev portaudio19-dev
 
       - name: Install uv and python
         uses: astral-sh/setup-uv@v5

.pre-commit-config.yaml

@@ -37,7 +37,7 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1
+    rev: v1.30.2
     hooks:
       - id: typos
         args: [--force-exclude]
@@ -48,7 +48,7 @@ repos:
     -   id: pyupgrade
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.4
+    rev: v0.9.10
     hooks:
       - id: ruff
         args: [--fix]
@@ -57,12 +57,12 @@ repos:
 
   ##### Security #####
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.24.2
+    rev: v8.24.0
     hooks:
       - id: gitleaks
 
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.5.2
+    rev: v1.4.1
     hooks:
       - id: zizmor
 

README.md

@@ -98,18 +98,14 @@ conda create -y -n lerobot python=3.10
 conda activate lerobot
 ```
 
-When using `miniconda`, if you don't have `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
 Install 🤗 LeRobot:
 ```bash
-pip install --no-binary=av -e .
+pip install -e .
 ```
 
-> **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
-`sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
+> **NOTE:** Depending on your platform, If you encounter any build errors during this step
+you may need to install `cmake` and `build-essential` for building some of our dependencies.
+On linux: `sudo apt-get install cmake build-essential`
 
 For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
 - [aloha](https://github.com/huggingface/gym-aloha)
@@ -118,7 +114,7 @@ For simulations, 🤗 LeRobot comes with gymnasium environments that can be inst
 
 For instance, to install 🤗 LeRobot with aloha and pusht, use:
 ```bash
-pip install --no-binary=av -e ".[aloha, pusht]"
+pip install -e ".[aloha, pusht]"
 ```
 
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with

benchmarks/video/README.md

@@ -51,7 +51,7 @@ For a comprehensive list and documentation of these parameters, see the ffmpeg d
 ### Decoding parameters
 **Decoder**
 We tested two video decoding backends from torchvision:
-- `pyav`
+- `pyav` (default)
 - `video_reader` (requires to build torchvision from source)
 
 **Requested timestamps**

benchmarks/video/run_video_benchmark.py

@@ -67,7 +67,7 @@ def parse_int_or_none(value) -> int | None:
 def check_datasets_formats(repo_ids: list) -> None:
     for repo_id in repo_ids:
         dataset = LeRobotDataset(repo_id)
-        if len(dataset.meta.video_keys) > 0:
+        if dataset.video:
             raise ValueError(
                 f"Use only image dataset for running this benchmark. Video dataset provided: {repo_id}"
             )

examples/1_load_lerobot_dataset.py

@@ -119,7 +119,7 @@ print(dataset.features[camera_key]["shape"])
 delta_timestamps = {
     # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
     camera_key: [-1, -0.5, -0.20, 0],
-    # loads 6 state vectors: 1.5 seconds before, 1 second before, ... 200 ms, 100 ms, and current frame
+    # loads 8 state vectors: 1.5 seconds before, 1 second before, ... 200 ms, 100 ms, and current frame
     "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, 0],
     # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
     "action": [t / dataset.fps for t in range(64)],
@@ -143,6 +143,6 @@ dataloader = torch.utils.data.DataLoader(
 
 for batch in dataloader:
     print(f"{batch[camera_key].shape=}")  # (32, 4, c, h, w)
-    print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
+    print(f"{batch['observation.state'].shape=}")  # (32, 5, c)
     print(f"{batch['action'].shape=}")  # (32, 64, c)
     break

examples/2_evaluate_pretrained_policy.py

@@ -18,7 +18,7 @@ training outputs directory. In the latter case, you might want to run examples/3
 
 It requires the installation of the 'gym_pusht' simulation environment. Install it by running:
 ```bash
-pip install --no-binary=av -e ".[pusht]"`
+pip install -e ".[pusht]"`
 ```
 """
 

examples/7_get_started_with_real_robot.md

@@ -33,7 +33,7 @@ First, install the additional dependencies required for robots built with dynami
 
 Using `pip`:
 ```bash
-pip install --no-binary=av -e ".[dynamixel]"
+pip install -e ".[dynamixel]"
 ```
 
 Using `poetry`:
@@ -46,6 +46,13 @@ Using `uv`:
 uv sync --extra "dynamixel"
 ```
 
+/!\ For Linux only, ffmpeg and opencv requires conda install for now. Run this exact sequence of commands:
+```bash
+conda install -c conda-forge ffmpeg
+pip uninstall opencv-python
+conda install -c conda-forge "opencv>=4.10.0"
+```
+
 You are now ready to plug the 5V power supply to the motor bus of the leader arm (the smaller one) since all its motors only require 5V.
 
 Then plug the 12V power supply to the motor bus of the follower arm. It has two motors that need 12V, and the rest will be powered with 5V through the voltage convertor.
@@ -285,11 +292,6 @@ Steps:
    - Scan for devices. All 12 motors should appear.
    - Select the motors one by one and move the arm. Check that the graphical indicator near the top right shows the movement.
 
-** There is a common issue with the Dynamixel XL430-W250 motors where the motors become undiscoverable after upgrading their firmware from Mac and Windows Dynamixel Wizard2 applications.  When this occurs, it is required to do a firmware recovery (Select `DYNAMIXEL Firmware Recovery` and follow the prompts).   There are two known workarounds to conduct this firmware reset:
-  1) Install the Dynamixel Wizard on a linux machine and complete the firmware recovery
-  2) Use the Dynamixel U2D2 in order to perform the reset with Windows or Mac.  This U2D2 can be purchased [here](https://www.robotis.us/u2d2/).
-  For either solution, open DYNAMIXEL Wizard 2.0 and select the appropriate port. You will likely be unable to see the motor in the GUI at this time. Select `Firmware Recovery`, carefully choose the correct model, and wait for the process to complete. Finally, re-scan to confirm the firmware recovery was successful.
-
 **Read and Write with DynamixelMotorsBus**
 
 To get familiar with how `DynamixelMotorsBus` communicates with the motors, you can start by reading data from them. Copy past this code in the same interactive python session:
@@ -827,6 +829,11 @@ It contains:
 - `dtRphone:33.84 (29.5hz)` which is the delta time of capturing an image from the phone camera in the thread running asynchronously.
 
 Troubleshooting:
+- On Linux, if you encounter a hanging issue when using cameras, uninstall opencv and re-install it with conda:
+```bash
+pip uninstall opencv-python
+conda install -c conda-forge opencv=4.10.0
+```
 - On Linux, if you encounter any issue during video encoding with `ffmpeg: unknown encoder libsvtav1`, you can:
   - install with conda-forge by running `conda install -c conda-forge ffmpeg` (it should be compiled with `libsvtav1`),
   - or, install [Homebrew](https://brew.sh) and run `brew install ffmpeg` (it should be compiled with `libsvtav1`),

examples/port_datasets/pusht_zarr.py

@@ -0,0 +1,243 @@
+# 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 shutil
+from pathlib import Path
+
+import numpy as np
+from huggingface_hub import HfApi
+
+from lerobot.common.constants import HF_LEROBOT_HOME
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
+from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
+
+PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface."
+PUSHT_FEATURES = {
+    "observation.state": {
+        "dtype": "float32",
+        "shape": (2,),
+        "names": {
+            "axes": ["x", "y"],
+        },
+    },
+    "action": {
+        "dtype": "float32",
+        "shape": (2,),
+        "names": {
+            "axes": ["x", "y"],
+        },
+    },
+    "next.reward": {
+        "dtype": "float32",
+        "shape": (1,),
+        "names": None,
+    },
+    "next.success": {
+        "dtype": "bool",
+        "shape": (1,),
+        "names": None,
+    },
+    "observation.environment_state": {
+        "dtype": "float32",
+        "shape": (16,),
+        "names": [
+            "keypoints",
+        ],
+    },
+    "observation.image": {
+        "dtype": None,
+        "shape": (3, 96, 96),
+        "names": [
+            "channels",
+            "height",
+            "width",
+        ],
+    },
+}
+
+
+def build_features(mode: str) -> dict:
+    features = PUSHT_FEATURES
+    if mode == "keypoints":
+        features.pop("observation.image")
+    else:
+        features.pop("observation.environment_state")
+        features["observation.image"]["dtype"] = mode
+
+    return features
+
+
+def load_raw_dataset(zarr_path: Path):
+    try:
+        from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import (
+            ReplayBuffer as DiffusionPolicyReplayBuffer,
+        )
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+
+    zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)
+    return zarr_data
+
+
+def calculate_coverage(zarr_data):
+    try:
+        import pymunk
+        from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+
+    block_pos = zarr_data["state"][:, 2:4]
+    block_angle = zarr_data["state"][:, 4]
+
+    num_frames = len(block_pos)
+
+    coverage = np.zeros((num_frames,), dtype=np.float32)
+    # 8 keypoints with 2 coords each
+    keypoints = np.zeros((num_frames, 16), dtype=np.float32)
+
+    # Set x, y, theta (in radians)
+    goal_pos_angle = np.array([256, 256, np.pi / 4])
+    goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
+
+    for i in range(num_frames):
+        space = pymunk.Space()
+        space.gravity = 0, 0
+        space.damping = 0
+
+        # Add walls.
+        walls = [
+            PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
+            PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
+            PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
+            PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
+        ]
+        space.add(*walls)
+
+        block_body, block_shapes = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
+        goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
+        block_geom = pymunk_to_shapely(block_body, block_body.shapes)
+        intersection_area = goal_geom.intersection(block_geom).area
+        goal_area = goal_geom.area
+        coverage[i] = intersection_area / goal_area
+        keypoints[i] = PushTEnv.get_keypoints(block_shapes).flatten()
+
+    return coverage, keypoints
+
+
+def calculate_success(coverage: float, success_threshold: float):
+    return coverage > success_threshold
+
+
+def calculate_reward(coverage: float, success_threshold: float):
+    return np.clip(coverage / success_threshold, 0, 1)
+
+
+def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = True):
+    if mode not in ["video", "image", "keypoints"]:
+        raise ValueError(mode)
+
+    if (HF_LEROBOT_HOME / repo_id).exists():
+        shutil.rmtree(HF_LEROBOT_HOME / repo_id)
+
+    if not raw_dir.exists():
+        download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw")
+
+    zarr_data = load_raw_dataset(zarr_path=raw_dir / "pusht_cchi_v7_replay.zarr")
+
+    env_state = zarr_data["state"][:]
+    agent_pos = env_state[:, :2]
+
+    action = zarr_data["action"][:]
+    image = zarr_data["img"]  # (b, h, w, c)
+
+    if image.dtype == np.float32 and image.max() == np.float32(255):
+        # HACK: images are loaded as float32 but they actually encode uint8 data
+        image = image.astype(np.uint8)
+
+    episode_data_index = {
+        "from": np.concatenate(([0], zarr_data.meta["episode_ends"][:-1])),
+        "to": zarr_data.meta["episode_ends"],
+    }
+
+    # Calculate success and reward based on the overlapping area
+    # of the T-object and the T-area.
+    coverage, keypoints = calculate_coverage(zarr_data)
+    success = calculate_success(coverage, success_threshold=0.95)
+    reward = calculate_reward(coverage, success_threshold=0.95)
+
+    features = build_features(mode)
+    dataset = LeRobotDataset.create(
+        repo_id=repo_id,
+        fps=10,
+        robot_type="2d pointer",
+        features=features,
+        image_writer_threads=4,
+    )
+    episodes = range(len(episode_data_index["from"]))
+    for ep_idx in episodes:
+        from_idx = episode_data_index["from"][ep_idx]
+        to_idx = episode_data_index["to"][ep_idx]
+        num_frames = to_idx - from_idx
+
+        for frame_idx in range(num_frames):
+            i = from_idx + frame_idx
+            idx = i + (frame_idx < num_frames - 1)
+            frame = {
+                "action": action[i],
+                # Shift reward and success by +1 until the last item of the episode
+                "next.reward": reward[idx : idx + 1],
+                "next.success": success[idx : idx + 1],
+                "task": PUSHT_TASK,
+            }
+
+            frame["observation.state"] = agent_pos[i]
+
+            if mode == "keypoints":
+                frame["observation.environment_state"] = keypoints[i]
+            else:
+                frame["observation.image"] = image[i]
+
+            dataset.add_frame(frame)
+
+        dataset.save_episode()
+
+    if push_to_hub:
+        dataset.push_to_hub()
+        hub_api = HfApi()
+        hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
+
+
+if __name__ == "__main__":
+    # To try this script, modify the repo id with your own HuggingFace user (e.g cadene/pusht)
+    repo_id = "lerobot/pusht"
+
+    modes = ["video", "image", "keypoints"]
+    # Uncomment if you want to try with a specific mode
+    # modes = ["video"]
+    # modes = ["image"]
+    # modes = ["keypoints"]
+
+    raw_dir = Path("data/lerobot-raw/pusht_raw")
+    for mode in modes:
+        if mode in ["image", "keypoints"]:
+            repo_id += f"_{mode}"
+
+        # download and load raw dataset, create LeRobotDataset, populate it, push to hub
+        main(raw_dir, repo_id=repo_id, mode=mode)
+
+        # Uncomment if you want to load the local dataset and explore it
+        # dataset = LeRobotDataset(repo_id=repo_id)
+        # breakpoint()

lerobot/common/cameras/__init__.py

@@ -0,0 +1,4 @@
+from .camera import Camera
+from .configs import CameraConfig
+
+__all__ = ["Camera", "CameraConfig"]

lerobot/common/cameras/camera.py

@@ -0,0 +1,25 @@
+import abc
+
+import numpy as np
+
+
+class Camera(abc.ABC):
+    @abc.abstractmethod
+    def connect(self):
+        pass
+
+    @abc.abstractmethod
+    def read(self, temporary_color: str | None = None) -> np.ndarray:
+        pass
+
+    @abc.abstractmethod
+    def async_read(self) -> np.ndarray:
+        pass
+
+    @abc.abstractmethod
+    def disconnect(self):
+        pass
+
+    def __del__(self):
+        if getattr(self, "is_connected", False):
+            self.disconnect()

lerobot/common/cameras/configs.py

@@ -0,0 +1,11 @@
+import abc
+from dataclasses import dataclass
+
+import draccus
+
+
+@dataclass
+class CameraConfig(draccus.ChoiceRegistry, abc.ABC):
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)

lerobot/common/cameras/intel/__init__.py

@@ -0,0 +1,4 @@
+from .camera_realsense import RealSenseCamera
+from .configuration_realsense import RealSenseCameraConfig
+
+__all__ = ["RealSenseCamera", "RealSenseCameraConfig"]

lerobot/common/cameras/intel/camera_realsense.py

@@ -31,14 +31,15 @@ from threading import Thread
 import numpy as np
 from PIL import Image
 
-from lerobot.common.robot_devices.cameras.configs import IntelRealSenseCameraConfig
-from lerobot.common.robot_devices.utils import (
-    RobotDeviceAlreadyConnectedError,
-    RobotDeviceNotConnectedError,
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.utils.robot_utils import (
     busy_wait,
 )
 from lerobot.common.utils.utils import capture_timestamp_utc
 
+from ..camera import Camera
+from .configuration_realsense import RealSenseCameraConfig
+
 SERIAL_NUMBER_INDEX = 1
 
 
@@ -108,13 +109,11 @@ def save_images_from_cameras(
     cameras = []
     for cam_sn in serial_numbers:
         print(f"{cam_sn=}")
-        config = IntelRealSenseCameraConfig(
-            serial_number=cam_sn, fps=fps, width=width, height=height, mock=mock
-        )
-        camera = IntelRealSenseCamera(config)
+        config = RealSenseCameraConfig(serial_number=cam_sn, fps=fps, width=width, height=height, mock=mock)
+        camera = RealSenseCamera(config)
         camera.connect()
         print(
-            f"IntelRealSenseCamera({camera.serial_number}, fps={camera.fps}, width={camera.capture_width}, height={camera.capture_height}, color_mode={camera.color_mode})"
+            f"RealSenseCamera({camera.serial_number}, fps={camera.fps}, width={camera.capture_width}, height={camera.capture_height}, color_mode={camera.color_mode})"
         )
         cameras.append(camera)
 
@@ -166,11 +165,11 @@ def save_images_from_cameras(
             camera.disconnect()
 
 
-class IntelRealSenseCamera:
+class RealSenseCamera(Camera):
     """
-    The IntelRealSenseCamera class is similar to OpenCVCamera class but adds additional features for Intel Real Sense cameras:
+    The RealSenseCamera class is similar to OpenCVCamera class but adds additional features for Intel Real Sense cameras:
     - is instantiated with the serial number of the camera - won't randomly change as it can be the case of OpenCVCamera for Linux,
-    - can also be instantiated with the camera's name — if it's unique — using IntelRealSenseCamera.init_from_name(),
+    - can also be instantiated with the camera's name — if it's unique — using RealSenseCamera.init_from_name(),
     - depth map can be returned.
 
     To find the camera indices of your cameras, you can run our utility script that will save a few frames for each camera:
@@ -178,15 +177,15 @@ class IntelRealSenseCamera:
     python lerobot/common/robot_devices/cameras/intelrealsense.py --images-dir outputs/images_from_intelrealsense_cameras
     ```
 
-    When an IntelRealSenseCamera is instantiated, if no specific config is provided, the default fps, width, height and color_mode
+    When an RealSenseCamera is instantiated, if no specific config is provided, the default fps, width, height and color_mode
     of the given camera will be used.
 
     Example of instantiating with a serial number:
     ```python
-    from lerobot.common.robot_devices.cameras.configs import IntelRealSenseCameraConfig
+    from lerobot.common.robot_devices.cameras.configs import RealSenseCameraConfig
 
-    config = IntelRealSenseCameraConfig(serial_number=128422271347)
-    camera = IntelRealSenseCamera(config)
+    config = RealSenseCameraConfig(serial_number=128422271347)
+    camera = RealSenseCamera(config)
     camera.connect()
     color_image = camera.read()
     # when done using the camera, consider disconnecting
@@ -195,21 +194,21 @@ class IntelRealSenseCamera:
 
     Example of instantiating with a name if it's unique:
     ```
-    config = IntelRealSenseCameraConfig(name="Intel RealSense D405")
+    config = RealSenseCameraConfig(name="Intel RealSense D405")
     ```
 
     Example of changing default fps, width, height and color_mode:
     ```python
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, fps=30, width=1280, height=720)
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, fps=90, width=640, height=480)
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, fps=90, width=640, height=480, color_mode="bgr")
+    config = RealSenseCameraConfig(serial_number=128422271347, fps=30, width=1280, height=720)
+    config = RealSenseCameraConfig(serial_number=128422271347, fps=90, width=640, height=480)
+    config = RealSenseCameraConfig(serial_number=128422271347, fps=90, width=640, height=480, color_mode="bgr")
     # Note: might error out upon `camera.connect()` if these settings are not compatible with the camera
     ```
 
     Example of returning depth:
     ```python
-    config = IntelRealSenseCameraConfig(serial_number=128422271347, use_depth=True)
-    camera = IntelRealSenseCamera(config)
+    config = RealSenseCameraConfig(serial_number=128422271347, use_depth=True)
+    camera = RealSenseCamera(config)
     camera.connect()
     color_image, depth_map = camera.read()
     ```
@@ -217,7 +216,7 @@ class IntelRealSenseCamera:
 
     def __init__(
         self,
-        config: IntelRealSenseCameraConfig,
+        config: RealSenseCameraConfig,
     ):
         self.config = config
         if config.name is not None:
@@ -282,9 +281,7 @@ class IntelRealSenseCamera:
 
     def connect(self):
         if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is already connected."
-            )
+            raise DeviceAlreadyConnectedError(f"RealSenseCamera({self.serial_number}) is already connected.")
 
         if self.mock:
             import tests.cameras.mock_pyrealsense2 as rs
@@ -330,7 +327,7 @@ class IntelRealSenseCamera:
                     "To find the serial number you should use, run `python lerobot/common/robot_devices/cameras/intelrealsense.py`."
                 )
 
-            raise OSError(f"Can't access IntelRealSenseCamera({self.serial_number}).")
+            raise OSError(f"Can't access RealSenseCamera({self.serial_number}).")
 
         color_stream = profile.get_stream(rs.stream.color)
         color_profile = color_stream.as_video_stream_profile()
@@ -342,15 +339,15 @@ class IntelRealSenseCamera:
         if self.fps is not None and not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
             # Using `OSError` since it's a broad that encompasses issues related to device communication
             raise OSError(
-                f"Can't set {self.fps=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_fps}."
+                f"Can't set {self.fps=} for RealSenseCamera({self.serial_number}). Actual value is {actual_fps}."
             )
         if self.capture_width is not None and self.capture_width != actual_width:
             raise OSError(
-                f"Can't set {self.capture_width=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_width}."
+                f"Can't set {self.capture_width=} for RealSenseCamera({self.serial_number}). Actual value is {actual_width}."
             )
         if self.capture_height is not None and self.capture_height != actual_height:
             raise OSError(
-                f"Can't set {self.capture_height=} for IntelRealSenseCamera({self.serial_number}). Actual value is {actual_height}."
+                f"Can't set {self.capture_height=} for RealSenseCamera({self.serial_number}). Actual value is {actual_height}."
             )
 
         self.fps = round(actual_fps)
@@ -370,8 +367,8 @@ class IntelRealSenseCamera:
         If you are reading data from other sensors, we advise to use `camera.async_read()` which is non blocking version of `camera.read()`.
         """
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
+            raise DeviceNotConnectedError(
+                f"RealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
             )
 
         if self.mock:
@@ -386,7 +383,7 @@ class IntelRealSenseCamera:
         color_frame = frame.get_color_frame()
 
         if not color_frame:
-            raise OSError(f"Can't capture color image from IntelRealSenseCamera({self.serial_number}).")
+            raise OSError(f"Can't capture color image from RealSenseCamera({self.serial_number}).")
 
         color_image = np.asanyarray(color_frame.get_data())
 
@@ -418,7 +415,7 @@ class IntelRealSenseCamera:
         if self.use_depth:
             depth_frame = frame.get_depth_frame()
             if not depth_frame:
-                raise OSError(f"Can't capture depth image from IntelRealSenseCamera({self.serial_number}).")
+                raise OSError(f"Can't capture depth image from RealSenseCamera({self.serial_number}).")
 
             depth_map = np.asanyarray(depth_frame.get_data())
 
@@ -445,8 +442,8 @@ class IntelRealSenseCamera:
     def async_read(self):
         """Access the latest color image"""
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
+            raise DeviceNotConnectedError(
+                f"RealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
             )
 
         if self.thread is None:
@@ -472,8 +469,8 @@ class IntelRealSenseCamera:
 
     def disconnect(self):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
-                f"IntelRealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
+            raise DeviceNotConnectedError(
+                f"RealSenseCamera({self.serial_number}) is not connected. Try running `camera.connect()` first."
             )
 
         if self.thread is not None and self.thread.is_alive():
@@ -495,14 +492,14 @@ class IntelRealSenseCamera:
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Save a few frames using `IntelRealSenseCamera` for all cameras connected to the computer, or a selected subset."
+        description="Save a few frames using `RealSenseCamera` for all cameras connected to the computer, or a selected subset."
     )
     parser.add_argument(
         "--serial-numbers",
         type=int,
         nargs="*",
         default=None,
-        help="List of serial numbers used to instantiate the `IntelRealSenseCamera`. If not provided, find and use all available camera indices.",
+        help="List of serial numbers used to instantiate the `RealSenseCamera`. If not provided, find and use all available camera indices.",
     )
     parser.add_argument(
         "--fps",

lerobot/common/cameras/intel/configuration_realsense.py

@@ -12,67 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import abc
 from dataclasses import dataclass
 
-import draccus
-
-
-@dataclass
-class CameraConfig(draccus.ChoiceRegistry, abc.ABC):
-    @property
-    def type(self) -> str:
-        return self.get_choice_name(self.__class__)
-
-
-@CameraConfig.register_subclass("opencv")
-@dataclass
-class OpenCVCameraConfig(CameraConfig):
-    """
-    Example of tested options for Intel Real Sense D405:
-
-    ```python
-    OpenCVCameraConfig(0, 30, 640, 480)
-    OpenCVCameraConfig(0, 60, 640, 480)
-    OpenCVCameraConfig(0, 90, 640, 480)
-    OpenCVCameraConfig(0, 30, 1280, 720)
-    ```
-    """
-
-    camera_index: int
-    fps: int | None = None
-    width: int | None = None
-    height: int | None = None
-    color_mode: str = "rgb"
-    channels: int | None = None
-    rotation: int | None = None
-    mock: bool = False
-
-    def __post_init__(self):
-        if self.color_mode not in ["rgb", "bgr"]:
-            raise ValueError(
-                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
-            )
-
-        self.channels = 3
-
-        if self.rotation not in [-90, None, 90, 180]:
-            raise ValueError(f"`rotation` must be in [-90, None, 90, 180] (got {self.rotation})")
+from ..configs import CameraConfig
 
 
 @CameraConfig.register_subclass("intelrealsense")
 @dataclass
-class IntelRealSenseCameraConfig(CameraConfig):
+class RealSenseCameraConfig(CameraConfig):
     """
     Example of tested options for Intel Real Sense D405:
 
     ```python
-    IntelRealSenseCameraConfig(128422271347, 30, 640, 480)
-    IntelRealSenseCameraConfig(128422271347, 60, 640, 480)
-    IntelRealSenseCameraConfig(128422271347, 90, 640, 480)
-    IntelRealSenseCameraConfig(128422271347, 30, 1280, 720)
-    IntelRealSenseCameraConfig(128422271347, 30, 640, 480, use_depth=True)
-    IntelRealSenseCameraConfig(128422271347, 30, 640, 480, rotation=90)
+    RealSenseCameraConfig(128422271347, 30, 640, 480)
+    RealSenseCameraConfig(128422271347, 60, 640, 480)
+    RealSenseCameraConfig(128422271347, 90, 640, 480)
+    RealSenseCameraConfig(128422271347, 30, 1280, 720)
+    RealSenseCameraConfig(128422271347, 30, 640, 480, use_depth=True)
+    RealSenseCameraConfig(128422271347, 30, 640, 480, rotation=90)
     ```
     """
 

lerobot/common/cameras/opencv/__init__.py

@@ -0,0 +1,4 @@
+from .camera_opencv import OpenCVCamera
+from .configuration_opencv import OpenCVCameraConfig
+
+__all__ = ["OpenCVCamera", "OpenCVCameraConfig"]

lerobot/common/cameras/opencv/camera_opencv.py

@@ -29,14 +29,15 @@ from threading import Thread
 import numpy as np
 from PIL import Image
 
-from lerobot.common.robot_devices.cameras.configs import OpenCVCameraConfig
-from lerobot.common.robot_devices.utils import (
-    RobotDeviceAlreadyConnectedError,
-    RobotDeviceNotConnectedError,
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.utils.robot_utils import (
     busy_wait,
 )
 from lerobot.common.utils.utils import capture_timestamp_utc
 
+from ..camera import Camera
+from .configuration_opencv import OpenCVCameraConfig
+
 # The maximum opencv device index depends on your operating system. For instance,
 # if you have 3 cameras, they should be associated to index 0, 1, and 2. This is the case
 # on MacOS. However, on Ubuntu, the indices are different like 6, 16, 23.
@@ -190,7 +191,7 @@ def save_images_from_cameras(
     print(f"Images have been saved to {images_dir}")
 
 
-class OpenCVCamera:
+class OpenCVCamera(Camera):
     """
     The OpenCVCamera class allows to efficiently record images from cameras. It relies on opencv2 to communicate
     with the cameras. Most cameras are compatible. For more info, see the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
@@ -283,7 +284,7 @@ class OpenCVCamera:
 
     def connect(self):
         if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(f"OpenCVCamera({self.camera_index}) is already connected.")
+            raise DeviceAlreadyConnectedError(f"OpenCVCamera({self.camera_index}) is already connected.")
 
         if self.mock:
             import tests.cameras.mock_cv2 as cv2
@@ -375,7 +376,7 @@ class OpenCVCamera:
         If you are reading data from other sensors, we advise to use `camera.async_read()` which is non blocking version of `camera.read()`.
         """
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"OpenCVCamera({self.camera_index}) is not connected. Try running `camera.connect()` first."
             )
 
@@ -432,7 +433,7 @@ class OpenCVCamera:
 
     def async_read(self):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"OpenCVCamera({self.camera_index}) is not connected. Try running `camera.connect()` first."
             )
 
@@ -454,7 +455,7 @@ class OpenCVCamera:
 
     def disconnect(self):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"OpenCVCamera({self.camera_index}) is not connected. Try running `camera.connect()` first."
             )
 

lerobot/common/cameras/opencv/configuration_opencv.py

@@ -0,0 +1,38 @@
+from dataclasses import dataclass
+
+from ..configs import CameraConfig
+
+
+@CameraConfig.register_subclass("opencv")
+@dataclass
+class OpenCVCameraConfig(CameraConfig):
+    """
+    Example of tested options for Intel Real Sense D405:
+
+    ```python
+    OpenCVCameraConfig(0, 30, 640, 480)
+    OpenCVCameraConfig(0, 60, 640, 480)
+    OpenCVCameraConfig(0, 90, 640, 480)
+    OpenCVCameraConfig(0, 30, 1280, 720)
+    ```
+    """
+
+    camera_index: int
+    fps: int | None = None
+    width: int | None = None
+    height: int | None = None
+    color_mode: str = "rgb"
+    channels: int | None = None
+    rotation: int | None = None
+    mock: bool = False
+
+    def __post_init__(self):
+        if self.color_mode not in ["rgb", "bgr"]:
+            raise ValueError(
+                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
+            )
+
+        self.channels = 3
+
+        if self.rotation not in [-90, None, 90, 180]:
+            raise ValueError(f"`rotation` must be in [-90, None, 90, 180] (got {self.rotation})")

lerobot/common/cameras/utils.py

@@ -0,0 +1,21 @@
+from .camera import Camera
+from .configs import CameraConfig
+
+
+def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[str, Camera]:
+    cameras = {}
+
+    for key, cfg in camera_configs.items():
+        if cfg.type == "opencv":
+            from .opencv import OpenCVCamera
+
+            cameras[key] = OpenCVCamera(cfg)
+
+        elif cfg.type == "intelrealsense":
+            from .intel.camera_realsense import RealSenseCamera
+
+            cameras[key] = RealSenseCamera(cfg)
+        else:
+            raise ValueError(f"The motor type '{cfg.type}' is not valid.")
+
+    return cameras

lerobot/common/constants.py

@@ -17,12 +17,15 @@ from pathlib import Path
 
 from huggingface_hub.constants import HF_HOME
 
-OBS_ENV = "observation.environment_state"
-OBS_ROBOT = "observation.state"
+OBS_ENV_STATE = "observation.environment_state"
+OBS_STATE = "observation.state"
 OBS_IMAGE = "observation.image"
 OBS_IMAGES = "observation.images"
 ACTION = "action"
 
+ROBOTS = "robots"
+TELEOPERATORS = "teleoperators"
+
 # files & directories
 CHECKPOINTS_DIR = "checkpoints"
 LAST_CHECKPOINT_LINK = "last"
@@ -34,12 +37,16 @@ OPTIMIZER_STATE = "optimizer_state.safetensors"
 OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
 SCHEDULER_STATE = "scheduler_state.json"
 
-# cache dir
-default_cache_path = Path(HF_HOME) / "lerobot"
-HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser()
-
 if "LEROBOT_HOME" in os.environ:
     raise ValueError(
         f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n"
         "'LEROBOT_HOME' is deprecated, please use 'HF_LEROBOT_HOME' instead."
     )
+
+# cache dir
+default_cache_path = Path(HF_HOME) / "lerobot"
+HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser()
+
+# calibration dir
+default_calibration_path = HF_LEROBOT_HOME / ".calibration"
+HF_LEROBOT_CALIBRATION = Path(os.getenv("HF_LEROBOT_CALIBRATION", default_calibration_path)).expanduser()

lerobot/common/datasets/lerobot_dataset.py

@@ -67,12 +67,11 @@ from lerobot.common.datasets.utils import (
 )
 from lerobot.common.datasets.video_utils import (
     VideoFrame,
-    decode_video_frames,
+    decode_video_frames_torchvision,
     encode_video_frames,
-    get_safe_default_codec,
     get_video_info,
 )
-from lerobot.common.robot_devices.robots.utils import Robot
+from lerobot.common.robots.utils import Robot
 
 CODEBASE_VERSION = "v2.1"
 
@@ -463,8 +462,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
             download_videos (bool, optional): Flag to download the videos. Note that when set to True but the
                 video files are already present on local disk, they won't be downloaded again. Defaults to
                 True.
-            video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec when available int the platform; otherwise, defaults to 'pyav'.
-                You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
+            video_backend (str | None, optional): Video backend to use for decoding videos. There is currently
+                a single option which is the pyav decoder used by Torchvision. Defaults to pyav.
         """
         super().__init__()
         self.repo_id = repo_id
@@ -474,7 +473,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.episodes = episodes
         self.tolerance_s = tolerance_s
         self.revision = revision if revision else CODEBASE_VERSION
-        self.video_backend = video_backend if video_backend else get_safe_default_codec()
+        self.video_backend = video_backend if video_backend else "pyav"
         self.delta_indices = None
 
         # Unused attributes
@@ -708,7 +707,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
         item = {}
         for vid_key, query_ts in query_timestamps.items():
             video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key)
-            frames = decode_video_frames(video_path, query_ts, self.tolerance_s, self.video_backend)
+            frames = decode_video_frames_torchvision(
+                video_path, query_ts, self.tolerance_s, self.video_backend
+            )
             item[vid_key] = frames.squeeze(0)
 
         return item
@@ -1028,7 +1029,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.delta_timestamps = None
         obj.delta_indices = None
         obj.episode_data_index = None
-        obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec()
+        obj.video_backend = video_backend if video_backend is not None else "pyav"
         return obj
 
 
@@ -1053,7 +1054,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         super().__init__()
         self.repo_ids = repo_ids
         self.root = Path(root) if root else HF_LEROBOT_HOME
-        self.tolerances_s = tolerances_s if tolerances_s else dict.fromkeys(repo_ids, 0.0001)
+        self.tolerances_s = tolerances_s if tolerances_s else {repo_id: 1e-4 for repo_id in repo_ids}
         # Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
         # are handled by this class.
         self._datasets = [

lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/mobile_cabinet.txt

@@ -0,0 +1,85 @@
+https://drive.google.com/file/d/1_SOJkgfP5yZyVjMhTt3nwhvyUjcnlI51/view?usp=drive_link
+https://drive.google.com/file/d/1rmgN8UUzph1qwJnzG1d-uOafodn-gLvb/view?usp=drive_link
+https://drive.google.com/file/d/1NYQ-XxsBVinB6dUoZmVWweT83367P3i2/view?usp=drive_link
+https://drive.google.com/file/d/1oAv_j74zxxCJieMG7r5Vl2BeHK1__3s3/view?usp=drive_link
+https://drive.google.com/file/d/1wFUJQROsrTJt64YRuIeExhFjr2wnK5uu/view?usp=drive_link
+https://drive.google.com/file/d/1KzL3Tt0Le7jVl58XVRUcmigmXjyiuhbK/view?usp=drive_link
+https://drive.google.com/file/d/1qy_YBladeHtianSSGtgAPSHtMin7msvf/view?usp=drive_link
+https://drive.google.com/file/d/1rA_F0V_qL_nyuC_0aBKCisF4-0TIkF2Y/view?usp=drive_link
+https://drive.google.com/file/d/1hw-8qMpz9VgSt62XoASqNRuPECpCwJQP/view?usp=drive_link
+https://drive.google.com/file/d/1BpHOl9rKMzdvNGka6js7C0s40hH6vnDA/view?usp=drive_link
+https://drive.google.com/file/d/1PazhkhiDnJ-OUMyDVDFxEZNKQQqHiNWS/view?usp=drive_link
+https://drive.google.com/file/d/1lZ665R6ATl57dypxH4dGJ2NSt6XYnbuz/view?usp=drive_link
+https://drive.google.com/file/d/1V9HzLaf-tlG15wUzT7KrTDCS_z1vi5NV/view?usp=drive_link
+https://drive.google.com/file/d/1aKauWiXoKqbNwn_2xs4MrmLlaNYlVNmO/view?usp=drive_link
+https://drive.google.com/file/d/1WVD5DFhriO1YmmOgiVHhacR6HWoTPxav/view?usp=drive_link
+https://drive.google.com/file/d/1_X43WgeBAsfkhH9EmpyPki8U9joMeAGC/view?usp=drive_link
+https://drive.google.com/file/d/1t8x0GqWoNKWtnBsB7_D40Z34nL9ak4kf/view?usp=drive_link
+https://drive.google.com/file/d/15V_f26WaKOXjKnq2T3HRWAmtQUi4lbu2/view?usp=drive_link
+https://drive.google.com/file/d/11VFIAsiSDsMOBANgrOcZBpKB9AFWnLy7/view?usp=drive_link
+https://drive.google.com/file/d/1M0NS7vVaxJv3FHnuRYtdwTFYF7We4LxP/view?usp=drive_link
+https://drive.google.com/file/d/1mR0OItTNqFnVLoczcyKYlm6drAy778lO/view?usp=drive_link
+https://drive.google.com/file/d/1NbVFWDQAh-z4JJ4D-Zw6Lps9kdvpqh2j/view?usp=drive_link
+https://drive.google.com/file/d/1JQoZGBzl4W3QG26-n39tefcGN0fDRMbB/view?usp=drive_link
+https://drive.google.com/file/d/1VBjHl-TvZpncopvasIP5G9gecbB2a5f6/view?usp=drive_link
+https://drive.google.com/file/d/1VzSf6zaB21nahm7MsPwroXbJ84NIwq0b/view?usp=drive_link
+https://drive.google.com/file/d/1OtNnfMEydNtZOcivs4k6E_uJSpf8PkGy/view?usp=drive_link
+https://drive.google.com/file/d/14nVvpvsrFr_03Pa_N7MKzwnRwibOUYM6/view?usp=drive_link
+https://drive.google.com/file/d/1M8li6duiO2r3lv_9HhF_XJn0oZUIEK5F/view?usp=drive_link
+https://drive.google.com/file/d/1Cpzea6fO14lxAaNfSBifqoa4ekhCiLD1/view?usp=drive_link
+https://drive.google.com/file/d/1mbxRTm5vlbsY9UJ0jfjM6j9D7kPJjBpG/view?usp=drive_link
+https://drive.google.com/file/d/1RXD1i6IfWsHRlCxVmG04h2h5Ycm_WwZN/view?usp=drive_link
+https://drive.google.com/file/d/1QFqFSwDGOk1BkgGmqgCcc2BRWnJ6R3MA/view?usp=drive_link
+https://drive.google.com/file/d/1bFqWR8DQM0ZUxxtS2bl-RANQvukeFLzp/view?usp=drive_link
+https://drive.google.com/file/d/1pR-rH3yNGoyPdD4hJ6-3lXQ-PstBx9du/view?usp=drive_link
+https://drive.google.com/file/d/107OAwLY-hva9HeQLIK7VCh-ytdDabVjr/view?usp=drive_link
+https://drive.google.com/file/d/1Tpl08QOaSZ37GTO4awFWSdD8wBR9xdlT/view?usp=drive_link
+https://drive.google.com/file/d/1MR164AOM-0S1T6RX8xKTV2IHyaCvpqAW/view?usp=drive_link
+https://drive.google.com/file/d/1_wknJfVnStIhJ82lU_QtcrwahsqYIsr8/view?usp=drive_link
+https://drive.google.com/file/d/1ZuEktWrbYkTx0l5pj3WiZ2CJrfbDOHNo/view?usp=drive_link
+https://drive.google.com/file/d/15G_10hkkkq6yxvyI5NGZirlF-RzduR2F/view?usp=drive_link
+https://drive.google.com/file/d/1DBKxg3ONqh7dhLuX6oh1Yyo2x383V1Hp/view?usp=drive_link
+https://drive.google.com/file/d/1B5iDBkTUr5vopDddV_fHud18SqAHhauS/view?usp=drive_link
+https://drive.google.com/file/d/1acwFV0eenRkki1QcjSKH5xqOtys-P3Pr/view?usp=drive_link
+https://drive.google.com/file/d/1S47BI83xyrh-FKXsvAQqer98Biu_p8XK/view?usp=drive_link
+https://drive.google.com/file/d/1JL6DmBZl3uyq9dyLfgSqtGF06e7E9JwM/view?usp=drive_link
+https://drive.google.com/file/d/16WvRS4Kjog8Pxgr0E3sGGnI01YwL9Uql/view?usp=drive_link
+https://drive.google.com/file/d/12ttGqL33IPWg0-s1SD44rr22M6LiSQBr/view?usp=drive_link
+https://drive.google.com/file/d/1OyZqqnldTU_DliRbr6x0C4a_iWPwIN7j/view?usp=drive_link
+https://drive.google.com/file/d/1oYk00IpLnR9fesLfD15Ebe7nVBffEbcS/view?usp=drive_link
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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/mobile_chair.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/mobile_elevator.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/mobile_shrimp.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/mobile_wash_pan.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/mobile_wipe_wine.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/sim_insertion_human.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/sim_insertion_scripted.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/sim_transfer_cube_human.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/sim_transfer_cube_scripted.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_battery.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_candy.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_coffee.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_coffee_new.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_cups_open.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_fork_pick_up.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_pingpong_test.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_pro_pencil.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_screw_driver.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_tape.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_thread_velcro.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_towel.txt

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lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_vinh_cup.txt

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+https://drive.google.com/file/d/1mdmJsDGO-YtJAOF_yPKl6lq4PJOIbQhT/view?usp=drive_link
+https://drive.google.com/file/d/11m9bwfop_sPmnQr_8amB6EEsrbAeG_z5/view?usp=drive_link
+https://drive.google.com/file/d/19tyYt5FMn5kru0g9o2nMJhKPnsDqkIZv/view?usp=drive_link
+https://drive.google.com/file/d/1XvTpUdsVTZ-vydvdYYmynbma--HfUGSl/view?usp=drive_link
+https://drive.google.com/file/d/1MO3hFu68J6NohTzr9aB_fY02VA6QSOqj/view?usp=drive_link
+https://drive.google.com/file/d/1Lh-UjwAk__04YOTWINF_QGVU8SjetVaY/view?usp=drive_link
+https://drive.google.com/file/d/1jkSOUwZV5GJ7rZlVeErjcu0DBQs8Np0d/view?usp=drive_link
+https://drive.google.com/file/d/1VIN1eLI-93WrVQwCjsv6XQr353DqqBYA/view?usp=drive_link

lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls/static_ziploc_slide.txt

@@ -0,0 +1,8 @@
+https://drive.google.com/drive/folders/1EgKar7rWBmTIRmeJYZciSwjZx3uP2mHO
+https://drive.google.com/file/d/12eYWQO15atK2hBjXhynPJd9MKAj_42pz/view?usp=drive_link
+https://drive.google.com/file/d/1Ul4oEeICJDjgfYTl4H1uaisTzVYIM6wd/view?usp=drive_link
+https://drive.google.com/file/d/1WSF-OG8lKSe2wVYCv5D1aJNipxpgddk-/view?usp=drive_link
+https://drive.google.com/file/d/1_ppD5j5sFh26aWW0JmhLzJMeNB-lCArk/view?usp=drive_link
+https://drive.google.com/file/d/1WUp846dgWXYhu4oJfhHxiU6YL_7N6s4W/view?usp=drive_link
+https://drive.google.com/file/d/1HRZNAIoAQw_uYiPwnBvtBioQoqiqoXdA/view?usp=drive_link
+https://drive.google.com/file/d/1hedGq-QDMnIn8GlXXBC3GiEJ_Y-LTxyt/view?usp=drive_link

lerobot/common/datasets/push_dataset_to_hub/_diffusion_policy_replay_buffer.py

@@ -0,0 +1,634 @@
+#!/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.
+"""Helper code for loading PushT dataset from Diffusion Policy (https://diffusion-policy.cs.columbia.edu/)
+
+Copied from the original Diffusion Policy repository and used in our `download_and_upload_dataset.py` script.
+"""
+
+from __future__ import annotations
+
+import math
+import numbers
+import os
+from functools import cached_property
+
+import numcodecs
+import numpy as np
+import zarr
+
+
+def check_chunks_compatible(chunks: tuple, shape: tuple):
+    assert len(shape) == len(chunks)
+    for c in chunks:
+        assert isinstance(c, numbers.Integral)
+        assert c > 0
+
+
+def rechunk_recompress_array(group, name, chunks=None, chunk_length=None, compressor=None, tmp_key="_temp"):
+    old_arr = group[name]
+    if chunks is None:
+        chunks = (chunk_length,) + old_arr.chunks[1:] if chunk_length is not None else old_arr.chunks
+    check_chunks_compatible(chunks, old_arr.shape)
+
+    if compressor is None:
+        compressor = old_arr.compressor
+
+    if (chunks == old_arr.chunks) and (compressor == old_arr.compressor):
+        # no change
+        return old_arr
+
+    # rechunk recompress
+    group.move(name, tmp_key)
+    old_arr = group[tmp_key]
+    n_copied, n_skipped, n_bytes_copied = zarr.copy(
+        source=old_arr,
+        dest=group,
+        name=name,
+        chunks=chunks,
+        compressor=compressor,
+    )
+    del group[tmp_key]
+    arr = group[name]
+    return arr
+
+
+def get_optimal_chunks(shape, dtype, target_chunk_bytes=2e6, max_chunk_length=None):
+    """
+    Common shapes
+    T,D
+    T,N,D
+    T,H,W,C
+    T,N,H,W,C
+    """
+    itemsize = np.dtype(dtype).itemsize
+    # reversed
+    rshape = list(shape[::-1])
+    if max_chunk_length is not None:
+        rshape[-1] = int(max_chunk_length)
+    split_idx = len(shape) - 1
+    for i in range(len(shape) - 1):
+        this_chunk_bytes = itemsize * np.prod(rshape[:i])
+        next_chunk_bytes = itemsize * np.prod(rshape[: i + 1])
+        if this_chunk_bytes <= target_chunk_bytes and next_chunk_bytes > target_chunk_bytes:
+            split_idx = i
+
+    rchunks = rshape[:split_idx]
+    item_chunk_bytes = itemsize * np.prod(rshape[:split_idx])
+    this_max_chunk_length = rshape[split_idx]
+    next_chunk_length = min(this_max_chunk_length, math.ceil(target_chunk_bytes / item_chunk_bytes))
+    rchunks.append(next_chunk_length)
+    len_diff = len(shape) - len(rchunks)
+    rchunks.extend([1] * len_diff)
+    chunks = tuple(rchunks[::-1])
+    # print(np.prod(chunks) * itemsize / target_chunk_bytes)
+    return chunks
+
+
+class ReplayBuffer:
+    """
+    Zarr-based temporal datastructure.
+    Assumes first dimension to be time. Only chunk in time dimension.
+    """
+
+    def __init__(self, root: zarr.Group | dict[str, dict]):
+        """
+        Dummy constructor. Use copy_from* and create_from* class methods instead.
+        """
+        assert "data" in root
+        assert "meta" in root
+        assert "episode_ends" in root["meta"]
+        for value in root["data"].values():
+            assert value.shape[0] == root["meta"]["episode_ends"][-1]
+        self.root = root
+
+    # ============= create constructors ===============
+    @classmethod
+    def create_empty_zarr(cls, storage=None, root=None):
+        if root is None:
+            if storage is None:
+                storage = zarr.MemoryStore()
+            root = zarr.group(store=storage)
+        root.require_group("data", overwrite=False)
+        meta = root.require_group("meta", overwrite=False)
+        if "episode_ends" not in meta:
+            meta.zeros("episode_ends", shape=(0,), dtype=np.int64, compressor=None, overwrite=False)
+        return cls(root=root)
+
+    @classmethod
+    def create_empty_numpy(cls):
+        root = {"data": {}, "meta": {"episode_ends": np.zeros((0,), dtype=np.int64)}}
+        return cls(root=root)
+
+    @classmethod
+    def create_from_group(cls, group, **kwargs):
+        if "data" not in group:
+            # create from stratch
+            buffer = cls.create_empty_zarr(root=group, **kwargs)
+        else:
+            # already exist
+            buffer = cls(root=group, **kwargs)
+        return buffer
+
+    @classmethod
+    def create_from_path(cls, zarr_path, mode="r", **kwargs):
+        """
+        Open a on-disk zarr directly (for dataset larger than memory).
+        Slower.
+        """
+        group = zarr.open(os.path.expanduser(zarr_path), mode)
+        return cls.create_from_group(group, **kwargs)
+
+    # ============= copy constructors ===============
+    @classmethod
+    def copy_from_store(
+        cls,
+        src_store,
+        store=None,
+        keys=None,
+        chunks: dict[str, tuple] | None = None,
+        compressors: dict | str | numcodecs.abc.Codec | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        """
+        Load to memory.
+        """
+        src_root = zarr.group(src_store)
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        root = None
+        if store is None:
+            # numpy backend
+            meta = {}
+            for key, value in src_root["meta"].items():
+                if len(value.shape) == 0:
+                    meta[key] = np.array(value)
+                else:
+                    meta[key] = value[:]
+
+            if keys is None:
+                keys = src_root["data"].keys()
+            data = {}
+            for key in keys:
+                arr = src_root["data"][key]
+                data[key] = arr[:]
+
+            root = {"meta": meta, "data": data}
+        else:
+            root = zarr.group(store=store)
+            # copy without recompression
+            n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                source=src_store, dest=store, source_path="/meta", dest_path="/meta", if_exists=if_exists
+            )
+            data_group = root.create_group("data", overwrite=True)
+            if keys is None:
+                keys = src_root["data"].keys()
+            for key in keys:
+                value = src_root["data"][key]
+                cks = cls._resolve_array_chunks(chunks=chunks, key=key, array=value)
+                cpr = cls._resolve_array_compressor(compressors=compressors, key=key, array=value)
+                if cks == value.chunks and cpr == value.compressor:
+                    # copy without recompression
+                    this_path = "/data/" + key
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                        source=src_store,
+                        dest=store,
+                        source_path=this_path,
+                        dest_path=this_path,
+                        if_exists=if_exists,
+                    )
+                else:
+                    # copy with recompression
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy(
+                        source=value,
+                        dest=data_group,
+                        name=key,
+                        chunks=cks,
+                        compressor=cpr,
+                        if_exists=if_exists,
+                    )
+        buffer = cls(root=root)
+        return buffer
+
+    @classmethod
+    def copy_from_path(
+        cls,
+        zarr_path,
+        backend=None,
+        store=None,
+        keys=None,
+        chunks: dict[str, tuple] | None = None,
+        compressors: dict | str | numcodecs.abc.Codec | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        """
+        Copy a on-disk zarr to in-memory compressed.
+        Recommended
+        """
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        if backend == "numpy":
+            print("backend argument is deprecated!")
+            store = None
+        group = zarr.open(os.path.expanduser(zarr_path), "r")
+        return cls.copy_from_store(
+            src_store=group.store,
+            store=store,
+            keys=keys,
+            chunks=chunks,
+            compressors=compressors,
+            if_exists=if_exists,
+            **kwargs,
+        )
+
+    # ============= save methods ===============
+    def save_to_store(
+        self,
+        store,
+        chunks: dict[str, tuple] | None = None,
+        compressors: str | numcodecs.abc.Codec | dict | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        root = zarr.group(store)
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        if self.backend == "zarr":
+            # recompression free copy
+            n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                source=self.root.store,
+                dest=store,
+                source_path="/meta",
+                dest_path="/meta",
+                if_exists=if_exists,
+            )
+        else:
+            meta_group = root.create_group("meta", overwrite=True)
+            # save meta, no chunking
+            for key, value in self.root["meta"].items():
+                _ = meta_group.array(name=key, data=value, shape=value.shape, chunks=value.shape)
+
+        # save data, chunk
+        data_group = root.create_group("data", overwrite=True)
+        for key, value in self.root["data"].items():
+            cks = self._resolve_array_chunks(chunks=chunks, key=key, array=value)
+            cpr = self._resolve_array_compressor(compressors=compressors, key=key, array=value)
+            if isinstance(value, zarr.Array):
+                if cks == value.chunks and cpr == value.compressor:
+                    # copy without recompression
+                    this_path = "/data/" + key
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                        source=self.root.store,
+                        dest=store,
+                        source_path=this_path,
+                        dest_path=this_path,
+                        if_exists=if_exists,
+                    )
+                else:
+                    # copy with recompression
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy(
+                        source=value,
+                        dest=data_group,
+                        name=key,
+                        chunks=cks,
+                        compressor=cpr,
+                        if_exists=if_exists,
+                    )
+            else:
+                # numpy
+                _ = data_group.array(name=key, data=value, chunks=cks, compressor=cpr)
+        return store
+
+    def save_to_path(
+        self,
+        zarr_path,
+        chunks: dict[str, tuple] | None = None,
+        compressors: str | numcodecs.abc.Codec | dict | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        store = zarr.DirectoryStore(os.path.expanduser(zarr_path))
+        return self.save_to_store(
+            store, chunks=chunks, compressors=compressors, if_exists=if_exists, **kwargs
+        )
+
+    @staticmethod
+    def resolve_compressor(compressor="default"):
+        if compressor == "default":
+            compressor = numcodecs.Blosc(cname="lz4", clevel=5, shuffle=numcodecs.Blosc.NOSHUFFLE)
+        elif compressor == "disk":
+            compressor = numcodecs.Blosc("zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
+        return compressor
+
+    @classmethod
+    def _resolve_array_compressor(cls, compressors: dict | str | numcodecs.abc.Codec, key, array):
+        # allows compressor to be explicitly set to None
+        cpr = "nil"
+        if isinstance(compressors, dict):
+            if key in compressors:
+                cpr = cls.resolve_compressor(compressors[key])
+            elif isinstance(array, zarr.Array):
+                cpr = array.compressor
+        else:
+            cpr = cls.resolve_compressor(compressors)
+        # backup default
+        if cpr == "nil":
+            cpr = cls.resolve_compressor("default")
+        return cpr
+
+    @classmethod
+    def _resolve_array_chunks(cls, chunks: dict | tuple, key, array):
+        cks = None
+        if isinstance(chunks, dict):
+            if key in chunks:
+                cks = chunks[key]
+            elif isinstance(array, zarr.Array):
+                cks = array.chunks
+        elif isinstance(chunks, tuple):
+            cks = chunks
+        else:
+            raise TypeError(f"Unsupported chunks type {type(chunks)}")
+        # backup default
+        if cks is None:
+            cks = get_optimal_chunks(shape=array.shape, dtype=array.dtype)
+        # check
+        check_chunks_compatible(chunks=cks, shape=array.shape)
+        return cks
+
+    # ============= properties =================
+    @cached_property
+    def data(self):
+        return self.root["data"]
+
+    @cached_property
+    def meta(self):
+        return self.root["meta"]
+
+    def update_meta(self, data):
+        # sanitize data
+        np_data = {}
+        for key, value in data.items():
+            if isinstance(value, np.ndarray):
+                np_data[key] = value
+            else:
+                arr = np.array(value)
+                if arr.dtype == object:
+                    raise TypeError(f"Invalid value type {type(value)}")
+                np_data[key] = arr
+
+        meta_group = self.meta
+        if self.backend == "zarr":
+            for key, value in np_data.items():
+                _ = meta_group.array(
+                    name=key, data=value, shape=value.shape, chunks=value.shape, overwrite=True
+                )
+        else:
+            meta_group.update(np_data)
+
+        return meta_group
+
+    @property
+    def episode_ends(self):
+        return self.meta["episode_ends"]
+
+    def get_episode_idxs(self):
+        import numba
+
+        numba.jit(nopython=True)
+
+        def _get_episode_idxs(episode_ends):
+            result = np.zeros((episode_ends[-1],), dtype=np.int64)
+            for i in range(len(episode_ends)):
+                start = 0
+                if i > 0:
+                    start = episode_ends[i - 1]
+                end = episode_ends[i]
+                for idx in range(start, end):
+                    result[idx] = i
+            return result
+
+        return _get_episode_idxs(self.episode_ends)
+
+    @property
+    def backend(self):
+        backend = "numpy"
+        if isinstance(self.root, zarr.Group):
+            backend = "zarr"
+        return backend
+
+    # =========== dict-like API ==============
+    def __repr__(self) -> str:
+        if self.backend == "zarr":
+            return str(self.root.tree())
+        else:
+            return super().__repr__()
+
+    def keys(self):
+        return self.data.keys()
+
+    def values(self):
+        return self.data.values()
+
+    def items(self):
+        return self.data.items()
+
+    def __getitem__(self, key):
+        return self.data[key]
+
+    def __contains__(self, key):
+        return key in self.data
+
+    # =========== our API ==============
+    @property
+    def n_steps(self):
+        if len(self.episode_ends) == 0:
+            return 0
+        return self.episode_ends[-1]
+
+    @property
+    def n_episodes(self):
+        return len(self.episode_ends)
+
+    @property
+    def chunk_size(self):
+        if self.backend == "zarr":
+            return next(iter(self.data.arrays()))[-1].chunks[0]
+        return None
+
+    @property
+    def episode_lengths(self):
+        ends = self.episode_ends[:]
+        ends = np.insert(ends, 0, 0)
+        lengths = np.diff(ends)
+        return lengths
+
+    def add_episode(
+        self,
+        data: dict[str, np.ndarray],
+        chunks: dict[str, tuple] | None = None,
+        compressors: str | numcodecs.abc.Codec | dict | None = None,
+    ):
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        assert len(data) > 0
+        is_zarr = self.backend == "zarr"
+
+        curr_len = self.n_steps
+        episode_length = None
+        for value in data.values():
+            assert len(value.shape) >= 1
+            if episode_length is None:
+                episode_length = len(value)
+            else:
+                assert episode_length == len(value)
+        new_len = curr_len + episode_length
+
+        for key, value in data.items():
+            new_shape = (new_len,) + value.shape[1:]
+            # create array
+            if key not in self.data:
+                if is_zarr:
+                    cks = self._resolve_array_chunks(chunks=chunks, key=key, array=value)
+                    cpr = self._resolve_array_compressor(compressors=compressors, key=key, array=value)
+                    arr = self.data.zeros(
+                        name=key, shape=new_shape, chunks=cks, dtype=value.dtype, compressor=cpr
+                    )
+                else:
+                    # copy data to prevent modify
+                    arr = np.zeros(shape=new_shape, dtype=value.dtype)
+                    self.data[key] = arr
+            else:
+                arr = self.data[key]
+                assert value.shape[1:] == arr.shape[1:]
+                # same method for both zarr and numpy
+                if is_zarr:
+                    arr.resize(new_shape)
+                else:
+                    arr.resize(new_shape, refcheck=False)
+            # copy data
+            arr[-value.shape[0] :] = value
+
+        # append to episode ends
+        episode_ends = self.episode_ends
+        if is_zarr:
+            episode_ends.resize(episode_ends.shape[0] + 1)
+        else:
+            episode_ends.resize(episode_ends.shape[0] + 1, refcheck=False)
+        episode_ends[-1] = new_len
+
+        # rechunk
+        if is_zarr and episode_ends.chunks[0] < episode_ends.shape[0]:
+            rechunk_recompress_array(self.meta, "episode_ends", chunk_length=int(episode_ends.shape[0] * 1.5))
+
+    def drop_episode(self):
+        is_zarr = self.backend == "zarr"
+        episode_ends = self.episode_ends[:].copy()
+        assert len(episode_ends) > 0
+        start_idx = 0
+        if len(episode_ends) > 1:
+            start_idx = episode_ends[-2]
+        for value in self.data.values():
+            new_shape = (start_idx,) + value.shape[1:]
+            if is_zarr:
+                value.resize(new_shape)
+            else:
+                value.resize(new_shape, refcheck=False)
+        if is_zarr:
+            self.episode_ends.resize(len(episode_ends) - 1)
+        else:
+            self.episode_ends.resize(len(episode_ends) - 1, refcheck=False)
+
+    def pop_episode(self):
+        assert self.n_episodes > 0
+        episode = self.get_episode(self.n_episodes - 1, copy=True)
+        self.drop_episode()
+        return episode
+
+    def extend(self, data):
+        self.add_episode(data)
+
+    def get_episode(self, idx, copy=False):
+        idx = list(range(len(self.episode_ends)))[idx]
+        start_idx = 0
+        if idx > 0:
+            start_idx = self.episode_ends[idx - 1]
+        end_idx = self.episode_ends[idx]
+        result = self.get_steps_slice(start_idx, end_idx, copy=copy)
+        return result
+
+    def get_episode_slice(self, idx):
+        start_idx = 0
+        if idx > 0:
+            start_idx = self.episode_ends[idx - 1]
+        end_idx = self.episode_ends[idx]
+        return slice(start_idx, end_idx)
+
+    def get_steps_slice(self, start, stop, step=None, copy=False):
+        _slice = slice(start, stop, step)
+
+        result = {}
+        for key, value in self.data.items():
+            x = value[_slice]
+            if copy and isinstance(value, np.ndarray):
+                x = x.copy()
+            result[key] = x
+        return result
+
+    # =========== chunking =============
+    def get_chunks(self) -> dict:
+        assert self.backend == "zarr"
+        chunks = {}
+        for key, value in self.data.items():
+            chunks[key] = value.chunks
+        return chunks
+
+    def set_chunks(self, chunks: dict):
+        assert self.backend == "zarr"
+        for key, value in chunks.items():
+            if key in self.data:
+                arr = self.data[key]
+                if value != arr.chunks:
+                    check_chunks_compatible(chunks=value, shape=arr.shape)
+                    rechunk_recompress_array(self.data, key, chunks=value)
+
+    def get_compressors(self) -> dict:
+        assert self.backend == "zarr"
+        compressors = {}
+        for key, value in self.data.items():
+            compressors[key] = value.compressor
+        return compressors
+
+    def set_compressors(self, compressors: dict):
+        assert self.backend == "zarr"
+        for key, value in compressors.items():
+            if key in self.data:
+                arr = self.data[key]
+                compressor = self.resolve_compressor(value)
+                if compressor != arr.compressor:
+                    rechunk_recompress_array(self.data, key, compressor=compressor)

lerobot/common/datasets/push_dataset_to_hub/_download_raw.py

@@ -0,0 +1,202 @@
+#!/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.
+"""
+This file contains download scripts for raw datasets.
+
+Example of usage:
+```
+python lerobot/common/datasets/push_dataset_to_hub/_download_raw.py \
+--raw-dir data/lerobot-raw/pusht_raw \
+--repo-id lerobot-raw/pusht_raw
+```
+"""
+
+import argparse
+import logging
+import warnings
+from pathlib import Path
+
+from huggingface_hub import snapshot_download
+
+from lerobot.common.datasets.push_dataset_to_hub.utils import check_repo_id
+
+# {raw_repo_id: raw_format}
+AVAILABLE_RAW_REPO_IDS = {
+    "lerobot-raw/aloha_mobile_cabinet_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_mobile_chair_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_mobile_elevator_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_mobile_shrimp_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_mobile_wash_pan_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_mobile_wipe_wine_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_sim_insertion_human_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_sim_insertion_scripted_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_sim_transfer_cube_human_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_sim_transfer_cube_scripted_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_battery_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_candy_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_coffee_new_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_coffee_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_cups_open_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_fork_pick_up_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_pingpong_test_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_pro_pencil_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_screw_driver_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_tape_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_thread_velcro_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_towel_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_vinh_cup_left_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_vinh_cup_raw": "aloha_hdf5",
+    "lerobot-raw/aloha_static_ziploc_slide_raw": "aloha_hdf5",
+    "lerobot-raw/umi_cup_in_the_wild_raw": "umi_zarr",
+    "lerobot-raw/pusht_raw": "pusht_zarr",
+    "lerobot-raw/unitreeh1_fold_clothes_raw": "aloha_hdf5",
+    "lerobot-raw/unitreeh1_rearrange_objects_raw": "aloha_hdf5",
+    "lerobot-raw/unitreeh1_two_robot_greeting_raw": "aloha_hdf5",
+    "lerobot-raw/unitreeh1_warehouse_raw": "aloha_hdf5",
+    "lerobot-raw/xarm_lift_medium_raw": "xarm_pkl",
+    "lerobot-raw/xarm_lift_medium_replay_raw": "xarm_pkl",
+    "lerobot-raw/xarm_push_medium_raw": "xarm_pkl",
+    "lerobot-raw/xarm_push_medium_replay_raw": "xarm_pkl",
+    "lerobot-raw/fractal20220817_data_raw": "openx_rlds.fractal20220817_data",
+    "lerobot-raw/kuka_raw": "openx_rlds.kuka",
+    "lerobot-raw/bridge_openx_raw": "openx_rlds.bridge_openx",
+    "lerobot-raw/taco_play_raw": "openx_rlds.taco_play",
+    "lerobot-raw/jaco_play_raw": "openx_rlds.jaco_play",
+    "lerobot-raw/berkeley_cable_routing_raw": "openx_rlds.berkeley_cable_routing",
+    "lerobot-raw/roboturk_raw": "openx_rlds.roboturk",
+    "lerobot-raw/nyu_door_opening_surprising_effectiveness_raw": "openx_rlds.nyu_door_opening_surprising_effectiveness",
+    "lerobot-raw/viola_raw": "openx_rlds.viola",
+    "lerobot-raw/berkeley_autolab_ur5_raw": "openx_rlds.berkeley_autolab_ur5",
+    "lerobot-raw/toto_raw": "openx_rlds.toto",
+    "lerobot-raw/language_table_raw": "openx_rlds.language_table",
+    "lerobot-raw/columbia_cairlab_pusht_real_raw": "openx_rlds.columbia_cairlab_pusht_real",
+    "lerobot-raw/stanford_kuka_multimodal_dataset_raw": "openx_rlds.stanford_kuka_multimodal_dataset",
+    "lerobot-raw/nyu_rot_dataset_raw": "openx_rlds.nyu_rot_dataset",
+    "lerobot-raw/io_ai_tech_raw": "openx_rlds.io_ai_tech",
+    "lerobot-raw/stanford_hydra_dataset_raw": "openx_rlds.stanford_hydra_dataset",
+    "lerobot-raw/austin_buds_dataset_raw": "openx_rlds.austin_buds_dataset",
+    "lerobot-raw/nyu_franka_play_dataset_raw": "openx_rlds.nyu_franka_play_dataset",
+    "lerobot-raw/maniskill_dataset_raw": "openx_rlds.maniskill_dataset",
+    "lerobot-raw/furniture_bench_dataset_raw": "openx_rlds.furniture_bench_dataset",
+    "lerobot-raw/cmu_franka_exploration_dataset_raw": "openx_rlds.cmu_franka_exploration_dataset",
+    "lerobot-raw/ucsd_kitchen_dataset_raw": "openx_rlds.ucsd_kitchen_dataset",
+    "lerobot-raw/ucsd_pick_and_place_dataset_raw": "openx_rlds.ucsd_pick_and_place_dataset",
+    "lerobot-raw/spoc_raw": "openx_rlds.spoc",
+    "lerobot-raw/austin_sailor_dataset_raw": "openx_rlds.austin_sailor_dataset",
+    "lerobot-raw/austin_sirius_dataset_raw": "openx_rlds.austin_sirius_dataset",
+    "lerobot-raw/bc_z_raw": "openx_rlds.bc_z",
+    "lerobot-raw/utokyo_pr2_opening_fridge_raw": "openx_rlds.utokyo_pr2_opening_fridge",
+    "lerobot-raw/utokyo_pr2_tabletop_manipulation_raw": "openx_rlds.utokyo_pr2_tabletop_manipulation",
+    "lerobot-raw/utokyo_xarm_pick_and_place_raw": "openx_rlds.utokyo_xarm_pick_and_place",
+    "lerobot-raw/utokyo_xarm_bimanual_raw": "openx_rlds.utokyo_xarm_bimanual",
+    "lerobot-raw/utokyo_saytap_raw": "openx_rlds.utokyo_saytap",
+    "lerobot-raw/robo_net_raw": "openx_rlds.robo_net",
+    "lerobot-raw/robo_set_raw": "openx_rlds.robo_set",
+    "lerobot-raw/berkeley_mvp_raw": "openx_rlds.berkeley_mvp",
+    "lerobot-raw/berkeley_rpt_raw": "openx_rlds.berkeley_rpt",
+    "lerobot-raw/kaist_nonprehensile_raw": "openx_rlds.kaist_nonprehensile",
+    "lerobot-raw/stanford_mask_vit_raw": "openx_rlds.stanford_mask_vit",
+    "lerobot-raw/tokyo_u_lsmo_raw": "openx_rlds.tokyo_u_lsmo",
+    "lerobot-raw/dlr_sara_pour_raw": "openx_rlds.dlr_sara_pour",
+    "lerobot-raw/dlr_sara_grid_clamp_raw": "openx_rlds.dlr_sara_grid_clamp",
+    "lerobot-raw/dlr_edan_shared_control_raw": "openx_rlds.dlr_edan_shared_control",
+    "lerobot-raw/asu_table_top_raw": "openx_rlds.asu_table_top",
+    "lerobot-raw/stanford_robocook_raw": "openx_rlds.stanford_robocook",
+    "lerobot-raw/imperialcollege_sawyer_wrist_cam_raw": "openx_rlds.imperialcollege_sawyer_wrist_cam",
+    "lerobot-raw/iamlab_cmu_pickup_insert_raw": "openx_rlds.iamlab_cmu_pickup_insert",
+    "lerobot-raw/uiuc_d3field_raw": "openx_rlds.uiuc_d3field",
+    "lerobot-raw/utaustin_mutex_raw": "openx_rlds.utaustin_mutex",
+    "lerobot-raw/berkeley_fanuc_manipulation_raw": "openx_rlds.berkeley_fanuc_manipulation",
+    "lerobot-raw/cmu_playing_with_food_raw": "openx_rlds.cmu_playing_with_food",
+    "lerobot-raw/cmu_play_fusion_raw": "openx_rlds.cmu_play_fusion",
+    "lerobot-raw/cmu_stretch_raw": "openx_rlds.cmu_stretch",
+    "lerobot-raw/berkeley_gnm_recon_raw": "openx_rlds.berkeley_gnm_recon",
+    "lerobot-raw/berkeley_gnm_cory_hall_raw": "openx_rlds.berkeley_gnm_cory_hall",
+    "lerobot-raw/berkeley_gnm_sac_son_raw": "openx_rlds.berkeley_gnm_sac_son",
+    "lerobot-raw/droid_raw": "openx_rlds.droid",
+    "lerobot-raw/droid_100_raw": "openx_rlds.droid100",
+    "lerobot-raw/fmb_raw": "openx_rlds.fmb",
+    "lerobot-raw/dobbe_raw": "openx_rlds.dobbe",
+    "lerobot-raw/usc_cloth_sim_raw": "openx_rlds.usc_cloth_sim",
+    "lerobot-raw/plex_robosuite_raw": "openx_rlds.plex_robosuite",
+    "lerobot-raw/conq_hose_manipulation_raw": "openx_rlds.conq_hose_manipulation",
+    "lerobot-raw/vima_raw": "openx_rlds.vima",
+    "lerobot-raw/robot_vqa_raw": "openx_rlds.robot_vqa",
+    "lerobot-raw/mimic_play_raw": "openx_rlds.mimic_play",
+    "lerobot-raw/tidybot_raw": "openx_rlds.tidybot",
+    "lerobot-raw/eth_agent_affordances_raw": "openx_rlds.eth_agent_affordances",
+}
+
+
+def download_raw(raw_dir: Path, repo_id: str):
+    check_repo_id(repo_id)
+    user_id, dataset_id = repo_id.split("/")
+
+    if not dataset_id.endswith("_raw"):
+        warnings.warn(
+            f"""`dataset_id` ({dataset_id}) doesn't end with '_raw' (e.g. 'lerobot/pusht_raw'). Following this
+             naming convention by renaming your repository is advised, but not mandatory.""",
+            stacklevel=1,
+        )
+
+    # Send warning if raw_dir isn't well formatted
+    if raw_dir.parts[-2] != user_id or raw_dir.parts[-1] != dataset_id:
+        warnings.warn(
+            f"""`raw_dir` ({raw_dir}) doesn't contain a community or user id `/` the name of the dataset that
+             match the `repo_id` (e.g. 'data/lerobot/pusht_raw'). Following this naming convention is advised,
+             but not mandatory.""",
+            stacklevel=1,
+        )
+    raw_dir.mkdir(parents=True, exist_ok=True)
+
+    logging.info(f"Start downloading from huggingface.co/{user_id} for {dataset_id}")
+    snapshot_download(repo_id, repo_type="dataset", local_dir=raw_dir)
+    logging.info(f"Finish downloading from huggingface.co/{user_id} for {dataset_id}")
+
+
+def download_all_raw_datasets(data_dir: Path | None = None):
+    if data_dir is None:
+        data_dir = Path("data")
+    for repo_id in AVAILABLE_RAW_REPO_IDS:
+        raw_dir = data_dir / repo_id
+        download_raw(raw_dir, repo_id)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=f"""A script to download raw datasets from Hugging Face hub to a local directory. Here is a
+            non exhaustive list of available repositories to use in `--repo-id`: {list(AVAILABLE_RAW_REPO_IDS.keys())}""",
+    )
+
+    parser.add_argument(
+        "--raw-dir",
+        type=Path,
+        required=True,
+        help="Directory containing input raw datasets (e.g. `data/aloha_mobile_chair_raw` or `data/pusht_raw).",
+    )
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="""Repositery identifier on Hugging Face: a community or a user name `/` the name of
+        the dataset (e.g. `lerobot/pusht_raw`, `cadene/aloha_sim_insertion_human_raw`).""",
+    )
+    args = parser.parse_args()
+    download_raw(**vars(args))
+
+
+if __name__ == "__main__":
+    main()

lerobot/common/datasets/push_dataset_to_hub/_encode_datasets.py

@@ -0,0 +1,184 @@
+#!/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.
+"""
+Use this script to batch encode lerobot dataset from their raw format to LeRobotDataset and push their updated
+version to the hub. Under the hood, this script reuses 'push_dataset_to_hub.py'. It assumes that you already
+downloaded raw datasets, which you can do with the related '_download_raw.py' script.
+
+For instance, for codebase_version = 'v1.6', the following command was run, assuming raw datasets from
+lerobot-raw were downloaded in 'raw/datasets/directory':
+```bash
+python lerobot/common/datasets/push_dataset_to_hub/_encode_datasets.py \
+  --raw-dir raw/datasets/directory \
+  --raw-repo-ids lerobot-raw \
+  --local-dir push/datasets/directory \
+  --tests-data-dir tests/data \
+  --push-repo lerobot \
+  --vcodec libsvtav1 \
+  --pix-fmt yuv420p \
+  --g 2 \
+  --crf 30
+```
+"""
+
+import argparse
+from pathlib import Path
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub._download_raw import AVAILABLE_RAW_REPO_IDS
+from lerobot.common.datasets.push_dataset_to_hub.utils import check_repo_id
+from lerobot.scripts.push_dataset_to_hub import push_dataset_to_hub
+
+
+def get_push_repo_id_from_raw(raw_repo_id: str, push_repo: str) -> str:
+    dataset_id_raw = raw_repo_id.split("/")[1]
+    dataset_id = dataset_id_raw.removesuffix("_raw")
+    return f"{push_repo}/{dataset_id}"
+
+
+def encode_datasets(
+    raw_dir: Path,
+    raw_repo_ids: list[str],
+    push_repo: str,
+    vcodec: str,
+    pix_fmt: str,
+    g: int,
+    crf: int,
+    local_dir: Path | None = None,
+    tests_data_dir: Path | None = None,
+    raw_format: str | None = None,
+    dry_run: bool = False,
+) -> None:
+    if len(raw_repo_ids) == 1 and raw_repo_ids[0].lower() == "lerobot-raw":
+        raw_repo_ids_format = AVAILABLE_RAW_REPO_IDS
+    else:
+        if raw_format is None:
+            raise ValueError(raw_format)
+        raw_repo_ids_format = {id_: raw_format for id_ in raw_repo_ids}
+
+    for raw_repo_id, repo_raw_format in raw_repo_ids_format.items():
+        check_repo_id(raw_repo_id)
+        dataset_repo_id_push = get_push_repo_id_from_raw(raw_repo_id, push_repo)
+        dataset_raw_dir = raw_dir / raw_repo_id
+        dataset_dir = local_dir / dataset_repo_id_push if local_dir is not None else None
+        encoding = {
+            "vcodec": vcodec,
+            "pix_fmt": pix_fmt,
+            "g": g,
+            "crf": crf,
+        }
+
+        if not (dataset_raw_dir).is_dir():
+            raise NotADirectoryError(dataset_raw_dir)
+
+        if not dry_run:
+            push_dataset_to_hub(
+                dataset_raw_dir,
+                raw_format=repo_raw_format,
+                repo_id=dataset_repo_id_push,
+                local_dir=dataset_dir,
+                resume=True,
+                encoding=encoding,
+                tests_data_dir=tests_data_dir,
+            )
+        else:
+            print(
+                f"DRY RUN: {dataset_raw_dir}  -->  {dataset_dir}  -->  {dataset_repo_id_push}@{CODEBASE_VERSION}"
+            )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--raw-dir",
+        type=Path,
+        default=Path("data"),
+        help="Directory where raw datasets are located.",
+    )
+    parser.add_argument(
+        "--raw-repo-ids",
+        type=str,
+        nargs="*",
+        default=["lerobot-raw"],
+        help="""Raw dataset repo ids. if 'lerobot-raw', the keys from `AVAILABLE_RAW_REPO_IDS` will be
+            used and raw datasets will be fetched from the 'lerobot-raw/' repo and pushed with their
+            associated format. It is assumed that each dataset is located at `raw_dir / raw_repo_id` """,
+    )
+    parser.add_argument(
+        "--raw-format",
+        type=str,
+        default=None,
+        help="""Raw format to use for the raw repo-ids. Must be specified if --raw-repo-ids is not
+            'lerobot-raw'""",
+    )
+    parser.add_argument(
+        "--local-dir",
+        type=Path,
+        default=None,
+        help="""When provided, writes the dataset converted to LeRobotDataset format in this directory
+        (e.g. `data/lerobot/aloha_mobile_chair`).""",
+    )
+    parser.add_argument(
+        "--push-repo",
+        type=str,
+        default="lerobot",
+        help="Repo to upload datasets to",
+    )
+    parser.add_argument(
+        "--vcodec",
+        type=str,
+        default="libsvtav1",
+        help="Codec to use for encoding videos",
+    )
+    parser.add_argument(
+        "--pix-fmt",
+        type=str,
+        default="yuv420p",
+        help="Pixel formats (chroma subsampling) to be used for encoding",
+    )
+    parser.add_argument(
+        "--g",
+        type=int,
+        default=2,
+        help="Group of pictures sizes to be used for encoding.",
+    )
+    parser.add_argument(
+        "--crf",
+        type=int,
+        default=30,
+        help="Constant rate factors to be used for encoding.",
+    )
+    parser.add_argument(
+        "--tests-data-dir",
+        type=Path,
+        default=None,
+        help=(
+            "When provided, save tests artifacts into the given directory "
+            "(e.g. `--tests-data-dir tests/data` will save to tests/data/{--repo-id})."
+        ),
+    )
+    parser.add_argument(
+        "--dry-run",
+        type=int,
+        default=0,
+        help="If not set to 0, this script won't download or upload anything.",
+    )
+    args = parser.parse_args()
+    encode_datasets(**vars(args))
+
+
+if __name__ == "__main__":
+    main()

lerobot/common/datasets/push_dataset_to_hub/_umi_imagecodecs_numcodecs.py

@@ -0,0 +1,326 @@
+#!/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.
+# imagecodecs/numcodecs.py
+
+# Copyright (c) 2021-2022, Christoph Gohlke
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice,
+#    this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+#    this list of conditions and the following disclaimer in the documentation
+#    and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+#    contributors may be used to endorse or promote products derived from
+#    this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+# Copied from: https://github.com/real-stanford/universal_manipulation_interface/blob/298776ce251f33b6b3185a98d6e7d1f9ad49168b/diffusion_policy/codecs/imagecodecs_numcodecs.py#L1
+"""Additional numcodecs implemented using imagecodecs."""
+
+__version__ = "2022.9.26"
+
+__all__ = ("register_codecs",)
+
+import imagecodecs
+import numpy
+from numcodecs.abc import Codec
+from numcodecs.registry import get_codec, register_codec
+
+# TODO (azouitine): Remove useless codecs
+
+
+def protective_squeeze(x: numpy.ndarray):
+    """
+    Squeeze dim only if it's not the last dim.
+    Image dim expected to be *, H, W, C
+    """
+    img_shape = x.shape[-3:]
+    if len(x.shape) > 3:
+        n_imgs = numpy.prod(x.shape[:-3])
+        if n_imgs > 1:
+            img_shape = (-1,) + img_shape
+    return x.reshape(img_shape)
+
+
+def get_default_image_compressor(**kwargs):
+    if imagecodecs.JPEGXL:
+        # has JPEGXL
+        this_kwargs = {
+            "effort": 3,
+            "distance": 0.3,
+            # bug in libjxl, invalid codestream for non-lossless
+            # when decoding speed > 1
+            "decodingspeed": 1,
+        }
+        this_kwargs.update(kwargs)
+        return JpegXl(**this_kwargs)
+    else:
+        this_kwargs = {"level": 50}
+        this_kwargs.update(kwargs)
+        return Jpeg2k(**this_kwargs)
+
+
+class Jpeg2k(Codec):
+    """JPEG 2000 codec for numcodecs."""
+
+    codec_id = "imagecodecs_jpeg2k"
+
+    def __init__(
+        self,
+        level=None,
+        codecformat=None,
+        colorspace=None,
+        tile=None,
+        reversible=None,
+        bitspersample=None,
+        resolutions=None,
+        numthreads=None,
+        verbose=0,
+    ):
+        self.level = level
+        self.codecformat = codecformat
+        self.colorspace = colorspace
+        self.tile = None if tile is None else tuple(tile)
+        self.reversible = reversible
+        self.bitspersample = bitspersample
+        self.resolutions = resolutions
+        self.numthreads = numthreads
+        self.verbose = verbose
+
+    def encode(self, buf):
+        buf = protective_squeeze(numpy.asarray(buf))
+        return imagecodecs.jpeg2k_encode(
+            buf,
+            level=self.level,
+            codecformat=self.codecformat,
+            colorspace=self.colorspace,
+            tile=self.tile,
+            reversible=self.reversible,
+            bitspersample=self.bitspersample,
+            resolutions=self.resolutions,
+            numthreads=self.numthreads,
+            verbose=self.verbose,
+        )
+
+    def decode(self, buf, out=None):
+        return imagecodecs.jpeg2k_decode(buf, verbose=self.verbose, numthreads=self.numthreads, out=out)
+
+
+class JpegXl(Codec):
+    """JPEG XL codec for numcodecs."""
+
+    codec_id = "imagecodecs_jpegxl"
+
+    def __init__(
+        self,
+        # encode
+        level=None,
+        effort=None,
+        distance=None,
+        lossless=None,
+        decodingspeed=None,
+        photometric=None,
+        planar=None,
+        usecontainer=None,
+        # decode
+        index=None,
+        keeporientation=None,
+        # both
+        numthreads=None,
+    ):
+        """
+        Return JPEG XL image from numpy array.
+        Float must be in nominal range 0..1.
+
+        Currently L, LA, RGB, RGBA images are supported in contig mode.
+        Extra channels are only supported for grayscale images in planar mode.
+
+        Parameters
+        ----------
+        level : Default to None, i.e. not overwriting lossess and decodingspeed options.
+            When < 0: Use lossless compression
+            When in [0,1,2,3,4]: Sets the decoding speed tier for the provided options.
+                Minimum is 0 (slowest to decode, best quality/density), and maximum
+                is 4 (fastest to decode, at the cost of some quality/density).
+        effort : Default to 3.
+            Sets encoder effort/speed level without affecting decoding speed.
+            Valid values are, from faster to slower speed: 1:lightning 2:thunder
+                3:falcon 4:cheetah 5:hare 6:wombat 7:squirrel 8:kitten 9:tortoise.
+            Speed: lightning, thunder, falcon, cheetah, hare, wombat, squirrel, kitten, tortoise
+            control the encoder effort in ascending order.
+            This also affects memory usage: using lower effort will typically reduce memory
+            consumption during encoding.
+            lightning and thunder are fast modes useful for lossless mode (modular).
+            falcon disables all of the following tools.
+            cheetah enables coefficient reordering, context clustering, and heuristics for selecting DCT sizes and quantization steps.
+            hare enables Gaborish filtering, chroma from luma, and an initial estimate of quantization steps.
+            wombat enables error diffusion quantization and full DCT size selection heuristics.
+            squirrel (default) enables dots, patches, and spline detection, and full context clustering.
+            kitten optimizes the adaptive quantization for a psychovisual metric.
+            tortoise enables a more thorough adaptive quantization search.
+        distance : Default to 1.0
+            Sets the distance level for lossy compression: target max butteraugli distance,
+            lower = higher quality. Range: 0 .. 15. 0.0 = mathematically lossless
+            (however, use JxlEncoderSetFrameLossless instead to use true lossless,
+            as setting distance to 0 alone is not the only requirement).
+            1.0 = visually lossless. Recommended range: 0.5 .. 3.0.
+        lossess : Default to False.
+            Use lossess encoding.
+        decodingspeed : Default to 0.
+            Duplicate to level. [0,4]
+        photometric : Return JxlColorSpace value.
+            Default logic is quite complicated but works most of the time.
+            Accepted value:
+                int: [-1,3]
+                str: ['RGB',
+                    'WHITEISZERO', 'MINISWHITE',
+                    'BLACKISZERO', 'MINISBLACK', 'GRAY',
+                    'XYB', 'KNOWN']
+        planar : Enable multi-channel mode.
+            Default to false.
+        usecontainer :
+            Forces the encoder to use the box-based container format (BMFF)
+            even when not necessary.
+            When using JxlEncoderUseBoxes, JxlEncoderStoreJPEGMetadata or
+            JxlEncoderSetCodestreamLevel with level 10, the encoder will
+            automatically also use the container format, it is not necessary
+            to use JxlEncoderUseContainer for those use cases.
+            By default this setting is disabled.
+        index : Selectively decode frames for animation.
+            Default to 0, decode all frames.
+            When set to > 0, decode that frame index only.
+        keeporientation :
+            Enables or disables preserving of as-in-bitstream pixeldata orientation.
+            Some images are encoded with an Orientation tag indicating that the
+            decoder must perform a rotation and/or mirroring to the encoded image data.
+
+            If skip_reorientation is JXL_FALSE (the default): the decoder will apply
+            the transformation from the orientation setting, hence rendering the image
+            according to its specified intent. When producing a JxlBasicInfo, the decoder
+            will always set the orientation field to JXL_ORIENT_IDENTITY (matching the
+            returned pixel data) and also align xsize and ysize so that they correspond
+            to the width and the height of the returned pixel data.
+
+            If skip_reorientation is JXL_TRUE: the decoder will skip applying the
+            transformation from the orientation setting, returning the image in
+            the as-in-bitstream pixeldata orientation. This may be faster to decode
+            since the decoder doesnt have to apply the transformation, but can
+            cause wrong display of the image if the orientation tag is not correctly
+            taken into account by the user.
+
+            By default, this option is disabled, and the returned pixel data is
+            re-oriented according to the images Orientation setting.
+        threads : Default to 1.
+            If <= 0, use all cores.
+            If > 32, clipped to 32.
+        """
+
+        self.level = level
+        self.effort = effort
+        self.distance = distance
+        self.lossless = bool(lossless)
+        self.decodingspeed = decodingspeed
+        self.photometric = photometric
+        self.planar = planar
+        self.usecontainer = usecontainer
+        self.index = index
+        self.keeporientation = keeporientation
+        self.numthreads = numthreads
+
+    def encode(self, buf):
+        # TODO: only squeeze all but last dim
+        buf = protective_squeeze(numpy.asarray(buf))
+        return imagecodecs.jpegxl_encode(
+            buf,
+            level=self.level,
+            effort=self.effort,
+            distance=self.distance,
+            lossless=self.lossless,
+            decodingspeed=self.decodingspeed,
+            photometric=self.photometric,
+            planar=self.planar,
+            usecontainer=self.usecontainer,
+            numthreads=self.numthreads,
+        )
+
+    def decode(self, buf, out=None):
+        return imagecodecs.jpegxl_decode(
+            buf,
+            index=self.index,
+            keeporientation=self.keeporientation,
+            numthreads=self.numthreads,
+            out=out,
+        )
+
+
+def _flat(out):
+    """Return numpy array as contiguous view of bytes if possible."""
+    if out is None:
+        return None
+    view = memoryview(out)
+    if view.readonly or not view.contiguous:
+        return None
+    return view.cast("B")
+
+
+def register_codecs(codecs=None, force=False, verbose=True):
+    """Register codecs in this module with numcodecs."""
+    for name, cls in globals().items():
+        if not hasattr(cls, "codec_id") or name == "Codec":
+            continue
+        if codecs is not None and cls.codec_id not in codecs:
+            continue
+        try:
+            try:  # noqa: SIM105
+                get_codec({"id": cls.codec_id})
+            except TypeError:
+                # registered, but failed
+                pass
+        except ValueError:
+            # not registered yet
+            pass
+        else:
+            if not force:
+                if verbose:
+                    log_warning(f"numcodec {cls.codec_id!r} already registered")
+                continue
+            if verbose:
+                log_warning(f"replacing registered numcodec {cls.codec_id!r}")
+        register_codec(cls)
+
+
+def log_warning(msg, *args, **kwargs):
+    """Log message with level WARNING."""
+    import logging
+
+    logging.getLogger(__name__).warning(msg, *args, **kwargs)

lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py

@@ -0,0 +1,233 @@
+#!/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.
+"""
+Contains utilities to process raw data format of HDF5 files like in: https://github.com/tonyzhaozh/act
+"""
+
+import gc
+import shutil
+from pathlib import Path
+
+import h5py
+import numpy as np
+import torch
+import tqdm
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    calculate_episode_data_index,
+    concatenate_episodes,
+    get_default_encoding,
+    save_images_concurrently,
+)
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
+
+
+def get_cameras(hdf5_data):
+    # ignore depth channel, not currently handled
+    # TODO(rcadene): add depth
+    rgb_cameras = [key for key in hdf5_data["/observations/images"].keys() if "depth" not in key]  # noqa: SIM118
+    return rgb_cameras
+
+
+def check_format(raw_dir) -> bool:
+    # only frames from simulation are uncompressed
+    compressed_images = "sim" not in raw_dir.name
+
+    hdf5_paths = list(raw_dir.glob("episode_*.hdf5"))
+    assert len(hdf5_paths) != 0
+    for hdf5_path in hdf5_paths:
+        with h5py.File(hdf5_path, "r") as data:
+            assert "/action" in data
+            assert "/observations/qpos" in data
+
+            assert data["/action"].ndim == 2
+            assert data["/observations/qpos"].ndim == 2
+
+            num_frames = data["/action"].shape[0]
+            assert num_frames == data["/observations/qpos"].shape[0]
+
+            for camera in get_cameras(data):
+                assert num_frames == data[f"/observations/images/{camera}"].shape[0]
+
+                if compressed_images:
+                    assert data[f"/observations/images/{camera}"].ndim == 2
+                else:
+                    assert data[f"/observations/images/{camera}"].ndim == 4
+                    b, h, w, c = data[f"/observations/images/{camera}"].shape
+                    assert c < h and c < w, f"Expect (h,w,c) image format but ({h=},{w=},{c=}) provided."
+
+
+def load_from_raw(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int,
+    video: bool,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    # only frames from simulation are uncompressed
+    compressed_images = "sim" not in raw_dir.name
+
+    hdf5_files = sorted(raw_dir.glob("episode_*.hdf5"))
+    num_episodes = len(hdf5_files)
+
+    ep_dicts = []
+    ep_ids = episodes if episodes else range(num_episodes)
+    for ep_idx in tqdm.tqdm(ep_ids):
+        ep_path = hdf5_files[ep_idx]
+        with h5py.File(ep_path, "r") as ep:
+            num_frames = ep["/action"].shape[0]
+
+            # last step of demonstration is considered done
+            done = torch.zeros(num_frames, dtype=torch.bool)
+            done[-1] = True
+
+            state = torch.from_numpy(ep["/observations/qpos"][:])
+            action = torch.from_numpy(ep["/action"][:])
+            if "/observations/qvel" in ep:
+                velocity = torch.from_numpy(ep["/observations/qvel"][:])
+            if "/observations/effort" in ep:
+                effort = torch.from_numpy(ep["/observations/effort"][:])
+
+            ep_dict = {}
+
+            for camera in get_cameras(ep):
+                img_key = f"observation.images.{camera}"
+
+                if compressed_images:
+                    import cv2
+
+                    # load one compressed image after the other in RAM and uncompress
+                    imgs_array = []
+                    for data in ep[f"/observations/images/{camera}"]:
+                        imgs_array.append(cv2.imdecode(data, 1))
+                    imgs_array = np.array(imgs_array)
+
+                else:
+                    # load all images in RAM
+                    imgs_array = ep[f"/observations/images/{camera}"][:]
+
+                if video:
+                    # save png images in temporary directory
+                    tmp_imgs_dir = videos_dir / "tmp_images"
+                    save_images_concurrently(imgs_array, tmp_imgs_dir)
+
+                    # encode images to a mp4 video
+                    fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+                    video_path = videos_dir / fname
+                    encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
+
+                    # clean temporary images directory
+                    shutil.rmtree(tmp_imgs_dir)
+
+                    # store the reference to the video frame
+                    ep_dict[img_key] = [
+                        {"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
+                    ]
+                else:
+                    ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
+
+            ep_dict["observation.state"] = state
+            if "/observations/velocity" in ep:
+                ep_dict["observation.velocity"] = velocity
+            if "/observations/effort" in ep:
+                ep_dict["observation.effort"] = effort
+            ep_dict["action"] = action
+            ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
+            ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+            ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+            ep_dict["next.done"] = done
+            # TODO(rcadene): add reward and success by computing them in sim
+
+            assert isinstance(ep_idx, int)
+            ep_dicts.append(ep_dict)
+
+        gc.collect()
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video) -> Dataset:
+    features = {}
+
+    keys = [key for key in data_dict if "observation.images." in key]
+    for key in keys:
+        if video:
+            features[key] = VideoFrame()
+        else:
+            features[key] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    if "observation.velocity" in data_dict:
+        features["observation.velocity"] = Sequence(
+            length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    if "observation.effort" in data_dict:
+        features["observation.effort"] = Sequence(
+            length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        fps = 50
+
+    data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
+    hf_dataset = to_hf_dataset(data_dict, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    if video:
+        info["encoding"] = get_default_encoding()
+
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/push_dataset_to_hub/cam_png_format.py

@@ -0,0 +1,107 @@
+#!/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.
+"""
+Contains utilities to process raw data format of png images files recorded with capture_camera_feed.py
+"""
+
+from pathlib import Path
+
+import torch
+from datasets import Dataset, Features, Image, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    calculate_episode_data_index,
+    concatenate_episodes,
+)
+from lerobot.common.datasets.utils import hf_transform_to_torch
+from lerobot.common.datasets.video_utils import VideoFrame
+
+
+def check_format(raw_dir: Path) -> bool:
+    image_paths = list(raw_dir.glob("frame_*.png"))
+    if len(image_paths) == 0:
+        raise ValueError
+
+
+def load_from_raw(raw_dir: Path, fps: int, episodes: list[int] | None = None):
+    if episodes is not None:
+        # TODO(aliberts): add support for multi-episodes.
+        raise NotImplementedError()
+
+    ep_dict = {}
+    ep_idx = 0
+
+    image_paths = sorted(raw_dir.glob("frame_*.png"))
+    num_frames = len(image_paths)
+
+    ep_dict["observation.image"] = [PILImage.open(x) for x in image_paths]
+    ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
+    ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+    ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+
+    ep_dicts = [ep_dict]
+    data_dict = concatenate_episodes(ep_dicts)
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video) -> Dataset:
+    features = {}
+    if video:
+        features["observation.image"] = VideoFrame()
+    else:
+        features["observation.image"] = Image()
+
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    if video or episodes or encoding is not None:
+        # TODO(aliberts): support this
+        raise NotImplementedError
+
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        fps = 30
+
+    data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes)
+    hf_dataset = to_hf_dataset(data_dict, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py

@@ -0,0 +1,233 @@
+#!/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.
+"""
+Contains utilities to process raw data format from dora-record
+"""
+
+import re
+import warnings
+from pathlib import Path
+
+import pandas as pd
+import torch
+from datasets import Dataset, Features, Image, Sequence, Value
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub.utils import calculate_episode_data_index
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame
+
+
+def check_format(raw_dir) -> bool:
+    assert raw_dir.exists()
+
+    leader_file = list(raw_dir.glob("*.parquet"))
+    if len(leader_file) == 0:
+        raise ValueError(f"Missing parquet files in '{raw_dir}'")
+    return True
+
+
+def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episodes: list[int] | None = None):
+    # Load data stream that will be used as reference for the timestamps synchronization
+    reference_files = list(raw_dir.glob("observation.images.cam_*.parquet"))
+    if len(reference_files) == 0:
+        raise ValueError(f"Missing reference files for camera, starting with  in '{raw_dir}'")
+    # select first camera in alphanumeric order
+    reference_key = sorted(reference_files)[0].stem
+    reference_df = pd.read_parquet(raw_dir / f"{reference_key}.parquet")
+    reference_df = reference_df[["timestamp_utc", reference_key]]
+
+    # Merge all data stream using nearest backward strategy
+    df = reference_df
+    for path in raw_dir.glob("*.parquet"):
+        key = path.stem  # action or observation.state or ...
+        if key == reference_key:
+            continue
+        if "failed_episode_index" in key:
+            # TODO(rcadene): add support for removing episodes that are tagged as "failed"
+            continue
+        modality_df = pd.read_parquet(path)
+        modality_df = modality_df[["timestamp_utc", key]]
+        df = pd.merge_asof(
+            df,
+            modality_df,
+            on="timestamp_utc",
+            # "nearest" is the best option over "backward", since the latter can desynchronizes camera timestamps by
+            # matching timestamps that are too far apart, in order to fit the backward constraints. It's not the case for "nearest".
+            # However, note that "nearest" might synchronize the reference camera with other cameras on slightly future timestamps.
+            # are too far apart.
+            direction="nearest",
+            tolerance=pd.Timedelta(f"{1 / fps} seconds"),
+        )
+    # Remove rows with episode_index -1 which indicates data that correspond to in-between episodes
+    df = df[df["episode_index"] != -1]
+
+    image_keys = [key for key in df if "observation.images." in key]
+
+    def get_episode_index(row):
+        episode_index_per_cam = {}
+        for key in image_keys:
+            path = row[key][0]["path"]
+            match = re.search(r"_(\d{6}).mp4", path)
+            if not match:
+                raise ValueError(path)
+            episode_index = int(match.group(1))
+            episode_index_per_cam[key] = episode_index
+        if len(set(episode_index_per_cam.values())) != 1:
+            raise ValueError(
+                f"All cameras are expected to belong to the same episode, but getting {episode_index_per_cam}"
+            )
+        return episode_index
+
+    df["episode_index"] = df.apply(get_episode_index, axis=1)
+
+    # dora only use arrays, so single values are encapsulated into a list
+    df["frame_index"] = df.groupby("episode_index").cumcount()
+    df = df.reset_index()
+    df["index"] = df.index
+
+    # set 'next.done' to True for the last frame of each episode
+    df["next.done"] = False
+    df.loc[df.groupby("episode_index").tail(1).index, "next.done"] = True
+
+    df["timestamp"] = df["timestamp_utc"].map(lambda x: x.timestamp())
+    # each episode starts with timestamp 0 to match the ones from the video
+    df["timestamp"] = df.groupby("episode_index")["timestamp"].transform(lambda x: x - x.iloc[0])
+
+    del df["timestamp_utc"]
+
+    # sanity check
+    has_nan = df.isna().any().any()
+    if has_nan:
+        raise ValueError("Dataset contains Nan values.")
+
+    # sanity check episode indices go from 0 to n-1
+    ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
+    expected_ep_ids = list(range(df["episode_index"].max() + 1))
+    if ep_ids != expected_ep_ids:
+        raise ValueError(f"Episodes indices go from {ep_ids} instead of {expected_ep_ids}")
+
+    # Create symlink to raw videos directory (that needs to be absolute not relative)
+    videos_dir.parent.mkdir(parents=True, exist_ok=True)
+    videos_dir.symlink_to((raw_dir / "videos").absolute())
+
+    # sanity check the video paths are well formatted
+    for key in df:
+        if "observation.images." not in key:
+            continue
+        for ep_idx in ep_ids:
+            video_path = videos_dir / f"{key}_episode_{ep_idx:06d}.mp4"
+            if not video_path.exists():
+                raise ValueError(f"Video file not found in {video_path}")
+
+    data_dict = {}
+    for key in df:
+        # is video frame
+        if "observation.images." in key:
+            # we need `[0] because dora only use arrays, so single values are encapsulated into a list.
+            # it is the case for video_frame dictionary = [{"path": ..., "timestamp": ...}]
+            data_dict[key] = [video_frame[0] for video_frame in df[key].values]
+
+            # sanity check the video path is well formatted
+            video_path = videos_dir.parent / data_dict[key][0]["path"]
+            if not video_path.exists():
+                raise ValueError(f"Video file not found in {video_path}")
+        # is number
+        elif df[key].iloc[0].ndim == 0 or df[key].iloc[0].shape[0] == 1:
+            data_dict[key] = torch.from_numpy(df[key].values)
+        # is vector
+        elif df[key].iloc[0].shape[0] > 1:
+            data_dict[key] = torch.stack([torch.from_numpy(x.copy()) for x in df[key].values])
+        else:
+            raise ValueError(key)
+
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video) -> Dataset:
+    features = {}
+
+    keys = [key for key in data_dict if "observation.images." in key]
+    for key in keys:
+        if video:
+            features[key] = VideoFrame()
+        else:
+            features[key] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    if "observation.velocity" in data_dict:
+        features["observation.velocity"] = Sequence(
+            length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    if "observation.effort" in data_dict:
+        features["observation.effort"] = Sequence(
+            length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
+        )
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        fps = 30
+    else:
+        raise NotImplementedError()
+
+    if not video:
+        raise NotImplementedError()
+
+    if encoding is not None:
+        warnings.warn(
+            "Video encoding is currently done outside of LeRobot for the dora_parquet format.",
+            stacklevel=1,
+        )
+
+    data_df = load_from_raw(raw_dir, videos_dir, fps, episodes)
+    hf_dataset = to_hf_dataset(data_df, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    if video:
+        info["encoding"] = "unknown"
+
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/push_dataset_to_hub/openx_rlds_format.py

@@ -0,0 +1,312 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+For all datasets in the RLDS format.
+For https://github.com/google-deepmind/open_x_embodiment (OPENX) datasets.
+
+NOTE: You need to install tensorflow and tensorflow_datasets before running this script.
+
+Example:
+    python lerobot/scripts/push_dataset_to_hub.py \
+        --raw-dir /path/to/data/bridge_dataset/1.0.0/ \
+        --repo-id your_hub/sampled_bridge_data_v2 \
+        --raw-format rlds \
+        --episodes 3 4 5 8 9
+
+Exact dataset fps defined in openx/config.py, obtained from:
+    https://docs.google.com/spreadsheets/d/1rPBD77tk60AEIGZrGSODwyyzs5FgCU9Uz3h-3_t2A9g/edit?gid=0#gid=0&range=R:R
+"""
+
+import shutil
+from pathlib import Path
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import torch
+import tqdm
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    calculate_episode_data_index,
+    concatenate_episodes,
+    get_default_encoding,
+    save_images_concurrently,
+)
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
+
+np.set_printoptions(precision=2)
+
+
+def tf_to_torch(data):
+    return torch.from_numpy(data.numpy())
+
+
+def tf_img_convert(img):
+    if img.dtype == tf.string:
+        img = tf.io.decode_image(img, expand_animations=False, dtype=tf.uint8)
+    elif img.dtype != tf.uint8:
+        raise ValueError(f"Unsupported image dtype: found with dtype {img.dtype}")
+    return img.numpy()
+
+
+def _broadcast_metadata_rlds(i: tf.Tensor, traj: dict) -> dict:
+    """
+    In the RLDS format, each trajectory has some top-level metadata that is explicitly separated out, and a "steps"
+    entry. This function moves the "steps" entry to the top level, broadcasting any metadata to the length of the
+    trajectory. This function also adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`.
+
+    NOTE: adapted from DLimp library https://github.com/kvablack/dlimp/
+    """
+    steps = traj.pop("steps")
+
+    traj_len = tf.shape(tf.nest.flatten(steps)[0])[0]
+
+    # broadcast metadata to the length of the trajectory
+    metadata = tf.nest.map_structure(lambda x: tf.repeat(x, traj_len), traj)
+
+    # put steps back in
+    assert "traj_metadata" not in steps
+    traj = {**steps, "traj_metadata": metadata}
+
+    assert "_len" not in traj
+    assert "_traj_index" not in traj
+    assert "_frame_index" not in traj
+    traj["_len"] = tf.repeat(traj_len, traj_len)
+    traj["_traj_index"] = tf.repeat(i, traj_len)
+    traj["_frame_index"] = tf.range(traj_len)
+
+    return traj
+
+
+def load_from_raw(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int,
+    video: bool,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    """
+    Args:
+        raw_dir (Path): _description_
+        videos_dir (Path): _description_
+        fps (int): _description_
+        video (bool): _description_
+        episodes (list[int] | None, optional): _description_. Defaults to None.
+    """
+    ds_builder = tfds.builder_from_directory(str(raw_dir))
+    dataset = ds_builder.as_dataset(
+        split="all",
+        decoders={"steps": tfds.decode.SkipDecoding()},
+    )
+
+    dataset_info = ds_builder.info
+    print("dataset_info: ", dataset_info)
+
+    ds_length = len(dataset)
+    dataset = dataset.take(ds_length)
+    # "flatten" the dataset as such we can apply trajectory level map() easily
+    # each [obs][key] has a shape of (frame_size, ...)
+    dataset = dataset.enumerate().map(_broadcast_metadata_rlds)
+
+    # we will apply the standardization transform if the dataset_name is provided
+    # if the dataset name is not provided and the goal is to convert any rlds formatted dataset
+    # search for 'image' keys in the observations
+    image_keys = []
+    state_keys = []
+    observation_info = dataset_info.features["steps"]["observation"]
+    for key in observation_info:
+        # check whether the key is for an image or a vector observation
+        if len(observation_info[key].shape) == 3:
+            # only adding uint8 images discards depth images
+            if observation_info[key].dtype == tf.uint8:
+                image_keys.append(key)
+        else:
+            state_keys.append(key)
+
+    lang_key = "language_instruction" if "language_instruction" in dataset.element_spec else None
+
+    print(" - image_keys: ", image_keys)
+    print(" - lang_key: ", lang_key)
+
+    it = iter(dataset)
+
+    ep_dicts = []
+    # Init temp path to save ep_dicts in case of crash
+    tmp_ep_dicts_dir = videos_dir.parent.joinpath("ep_dicts")
+    tmp_ep_dicts_dir.mkdir(parents=True, exist_ok=True)
+
+    # check if ep_dicts have already been saved in /tmp
+    starting_ep_idx = 0
+    saved_ep_dicts = [ep.__str__() for ep in tmp_ep_dicts_dir.iterdir()]
+    if len(saved_ep_dicts) > 0:
+        saved_ep_dicts.sort()
+        # get last ep_idx number
+        starting_ep_idx = int(saved_ep_dicts[-1][-13:-3]) + 1
+        for i in range(starting_ep_idx):
+            episode = next(it)
+            ep_dicts.append(torch.load(saved_ep_dicts[i]))
+
+    # if we user specified episodes, skip the ones not in the list
+    if episodes is not None:
+        if ds_length == 0:
+            raise ValueError("No episodes found.")
+        # convert episodes index to sorted list
+        episodes = sorted(episodes)
+
+    for ep_idx in tqdm.tqdm(range(starting_ep_idx, ds_length)):
+        episode = next(it)
+
+        # if user specified episodes, skip the ones not in the list
+        if episodes is not None:
+            if len(episodes) == 0:
+                break
+            if ep_idx == episodes[0]:
+                # process this episode
+                print(" selecting episode idx: ", ep_idx)
+                episodes.pop(0)
+            else:
+                continue  # skip
+
+        num_frames = episode["action"].shape[0]
+
+        ep_dict = {}
+        for key in state_keys:
+            ep_dict[f"observation.{key}"] = tf_to_torch(episode["observation"][key])
+
+        ep_dict["action"] = tf_to_torch(episode["action"])
+        ep_dict["next.reward"] = tf_to_torch(episode["reward"]).float()
+        ep_dict["next.done"] = tf_to_torch(episode["is_last"])
+        ep_dict["is_terminal"] = tf_to_torch(episode["is_terminal"])
+        ep_dict["is_first"] = tf_to_torch(episode["is_first"])
+        ep_dict["discount"] = tf_to_torch(episode["discount"])
+
+        # If lang_key is present, convert the entire tensor at once
+        if lang_key is not None:
+            ep_dict["language_instruction"] = [x.numpy().decode("utf-8") for x in episode[lang_key]]
+
+        ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+        ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
+        ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+
+        image_array_dict = {key: [] for key in image_keys}
+
+        for im_key in image_keys:
+            imgs = episode["observation"][im_key]
+            image_array_dict[im_key] = [tf_img_convert(img) for img in imgs]
+
+        # loop through all cameras
+        for im_key in image_keys:
+            img_key = f"observation.images.{im_key}"
+            imgs_array = image_array_dict[im_key]
+            imgs_array = np.array(imgs_array)
+            if video:
+                # save png images in temporary directory
+                tmp_imgs_dir = videos_dir / "tmp_images"
+                save_images_concurrently(imgs_array, tmp_imgs_dir)
+
+                # encode images to a mp4 video
+                fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+                video_path = videos_dir / fname
+                encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
+
+                # clean temporary images directory
+                shutil.rmtree(tmp_imgs_dir)
+
+                # store the reference to the video frame
+                ep_dict[img_key] = [
+                    {"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
+                ]
+            else:
+                ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
+
+        path_ep_dict = tmp_ep_dicts_dir.joinpath(
+            "ep_dict_" + "0" * (10 - len(str(ep_idx))) + str(ep_idx) + ".pt"
+        )
+        torch.save(ep_dict, path_ep_dict)
+
+        ep_dicts.append(ep_dict)
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video) -> Dataset:
+    features = {}
+
+    for key in data_dict:
+        # check if vector state obs
+        if key.startswith("observation.") and "observation.images." not in key:
+            features[key] = Sequence(length=data_dict[key].shape[1], feature=Value(dtype="float32", id=None))
+        # check if image obs
+        elif "observation.images." in key:
+            if video:
+                features[key] = VideoFrame()
+            else:
+                features[key] = Image()
+
+    if "language_instruction" in data_dict:
+        features["language_instruction"] = Value(dtype="string", id=None)
+
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+
+    features["is_terminal"] = Value(dtype="bool", id=None)
+    features["is_first"] = Value(dtype="bool", id=None)
+    features["discount"] = Value(dtype="float32", id=None)
+
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.reward"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
+    hf_dataset = to_hf_dataset(data_dict, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    if video:
+        info["encoding"] = get_default_encoding()
+
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py

@@ -0,0 +1,275 @@
+#!/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.
+"""Process zarr files formatted like in: https://github.com/real-stanford/diffusion_policy"""
+
+import shutil
+from pathlib import Path
+
+import numpy as np
+import torch
+import tqdm
+import zarr
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    calculate_episode_data_index,
+    concatenate_episodes,
+    get_default_encoding,
+    save_images_concurrently,
+)
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
+
+
+def check_format(raw_dir):
+    zarr_path = raw_dir / "pusht_cchi_v7_replay.zarr"
+    zarr_data = zarr.open(zarr_path, mode="r")
+
+    required_datasets = {
+        "data/action",
+        "data/img",
+        "data/keypoint",
+        "data/n_contacts",
+        "data/state",
+        "meta/episode_ends",
+    }
+    for dataset in required_datasets:
+        assert dataset in zarr_data
+    nb_frames = zarr_data["data/img"].shape[0]
+
+    required_datasets.remove("meta/episode_ends")
+
+    assert all(nb_frames == zarr_data[dataset].shape[0] for dataset in required_datasets)
+
+
+def load_from_raw(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int,
+    video: bool,
+    episodes: list[int] | None = None,
+    keypoints_instead_of_image: bool = False,
+    encoding: dict | None = None,
+):
+    try:
+        import pymunk
+        from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
+
+        from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import (
+            ReplayBuffer as DiffusionPolicyReplayBuffer,
+        )
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+    # as define in gmy-pusht env: https://github.com/huggingface/gym-pusht/blob/e0684ff988d223808c0a9dcfaba9dc4991791370/gym_pusht/envs/pusht.py#L174
+    success_threshold = 0.95  # 95% coverage,
+
+    zarr_path = raw_dir / "pusht_cchi_v7_replay.zarr"
+    zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)
+
+    episode_ids = torch.from_numpy(zarr_data.get_episode_idxs())
+    assert len(
+        {zarr_data[key].shape[0] for key in zarr_data.keys()}  # noqa: SIM118
+    ), "Some data type dont have the same number of total frames."
+
+    # TODO(rcadene): verify that goal pose is expected to be fixed
+    goal_pos_angle = np.array([256, 256, np.pi / 4])  # x, y, theta (in radians)
+    goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
+
+    imgs = torch.from_numpy(zarr_data["img"])  # b h w c
+    states = torch.from_numpy(zarr_data["state"])
+    actions = torch.from_numpy(zarr_data["action"])
+
+    # load data indices from which each episode starts and ends
+    from_ids, to_ids = [], []
+    from_idx = 0
+    for to_idx in zarr_data.meta["episode_ends"]:
+        from_ids.append(from_idx)
+        to_ids.append(to_idx)
+        from_idx = to_idx
+
+    num_episodes = len(from_ids)
+
+    ep_dicts = []
+    ep_ids = episodes if episodes else range(num_episodes)
+    for ep_idx, selected_ep_idx in tqdm.tqdm(enumerate(ep_ids)):
+        from_idx = from_ids[selected_ep_idx]
+        to_idx = to_ids[selected_ep_idx]
+        num_frames = to_idx - from_idx
+
+        # sanity check
+        assert (episode_ids[from_idx:to_idx] == ep_idx).all()
+
+        # get image
+        if not keypoints_instead_of_image:
+            image = imgs[from_idx:to_idx]
+            assert image.min() >= 0.0
+            assert image.max() <= 255.0
+            image = image.type(torch.uint8)
+
+        # get state
+        state = states[from_idx:to_idx]
+        agent_pos = state[:, :2]
+        block_pos = state[:, 2:4]
+        block_angle = state[:, 4]
+
+        # get reward, success, done, and (maybe) keypoints
+        reward = torch.zeros(num_frames)
+        success = torch.zeros(num_frames, dtype=torch.bool)
+        if keypoints_instead_of_image:
+            keypoints = torch.zeros(num_frames, 16)  # 8 keypoints each with 2 coords
+        done = torch.zeros(num_frames, dtype=torch.bool)
+        for i in range(num_frames):
+            space = pymunk.Space()
+            space.gravity = 0, 0
+            space.damping = 0
+
+            # Add walls.
+            walls = [
+                PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
+                PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
+                PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
+                PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
+            ]
+            space.add(*walls)
+
+            block_body, block_shapes = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
+            goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
+            block_geom = pymunk_to_shapely(block_body, block_body.shapes)
+            intersection_area = goal_geom.intersection(block_geom).area
+            goal_area = goal_geom.area
+            coverage = intersection_area / goal_area
+            reward[i] = np.clip(coverage / success_threshold, 0, 1)
+            success[i] = coverage > success_threshold
+            if keypoints_instead_of_image:
+                keypoints[i] = torch.from_numpy(PushTEnv.get_keypoints(block_shapes).flatten())
+
+        # last step of demonstration is considered done
+        done[-1] = True
+
+        ep_dict = {}
+
+        if not keypoints_instead_of_image:
+            imgs_array = [x.numpy() for x in image]
+            img_key = "observation.image"
+            if video:
+                # save png images in temporary directory
+                tmp_imgs_dir = videos_dir / "tmp_images"
+                save_images_concurrently(imgs_array, tmp_imgs_dir)
+
+                # encode images to a mp4 video
+                fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+                video_path = videos_dir / fname
+                encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
+
+                # clean temporary images directory
+                shutil.rmtree(tmp_imgs_dir)
+
+                # store the reference to the video frame
+                ep_dict[img_key] = [
+                    {"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
+                ]
+            else:
+                ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
+
+        ep_dict["observation.state"] = agent_pos
+        if keypoints_instead_of_image:
+            ep_dict["observation.environment_state"] = keypoints
+        ep_dict["action"] = actions[from_idx:to_idx]
+        ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames, dtype=torch.int64)
+        ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+        ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+        # ep_dict["next.observation.image"] = image[1:],
+        # ep_dict["next.observation.state"] = agent_pos[1:],
+        # TODO(rcadene)] = verify that reward and done are aligned with image and agent_pos
+        ep_dict["next.reward"] = torch.cat([reward[1:], reward[[-1]]])
+        ep_dict["next.done"] = torch.cat([done[1:], done[[-1]]])
+        ep_dict["next.success"] = torch.cat([success[1:], success[[-1]]])
+        ep_dicts.append(ep_dict)
+    data_dict = concatenate_episodes(ep_dicts)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video, keypoints_instead_of_image: bool = False):
+    features = {}
+
+    if not keypoints_instead_of_image:
+        if video:
+            features["observation.image"] = VideoFrame()
+        else:
+            features["observation.image"] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    if keypoints_instead_of_image:
+        features["observation.environment_state"] = Sequence(
+            length=data_dict["observation.environment_state"].shape[1],
+            feature=Value(dtype="float32", id=None),
+        )
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.reward"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["next.success"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    # Manually change this to True to use keypoints of the T instead of an image observation (but don't merge
+    # with True). Also make sure to use video = 0 in the `push_dataset_to_hub.py` script.
+    keypoints_instead_of_image = False
+
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        fps = 10
+
+    data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, keypoints_instead_of_image, encoding)
+    hf_dataset = to_hf_dataset(data_dict, video, keypoints_instead_of_image)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video if not keypoints_instead_of_image else 0,
+    }
+    if video:
+        info["encoding"] = get_default_encoding()
+
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py

@@ -0,0 +1,234 @@
+#!/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.
+"""Process UMI (Universal Manipulation Interface) data stored in Zarr format like in: https://github.com/real-stanford/universal_manipulation_interface"""
+
+import logging
+import shutil
+from pathlib import Path
+
+import torch
+import tqdm
+import zarr
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub._umi_imagecodecs_numcodecs import register_codecs
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    calculate_episode_data_index,
+    concatenate_episodes,
+    get_default_encoding,
+    save_images_concurrently,
+)
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
+
+
+def check_format(raw_dir) -> bool:
+    zarr_path = raw_dir / "cup_in_the_wild.zarr"
+    zarr_data = zarr.open(zarr_path, mode="r")
+
+    required_datasets = {
+        "data/robot0_demo_end_pose",
+        "data/robot0_demo_start_pose",
+        "data/robot0_eef_pos",
+        "data/robot0_eef_rot_axis_angle",
+        "data/robot0_gripper_width",
+        "meta/episode_ends",
+        "data/camera0_rgb",
+    }
+    for dataset in required_datasets:
+        if dataset not in zarr_data:
+            return False
+
+    # mandatory to access zarr_data
+    register_codecs()
+    nb_frames = zarr_data["data/camera0_rgb"].shape[0]
+
+    required_datasets.remove("meta/episode_ends")
+    assert all(nb_frames == zarr_data[dataset].shape[0] for dataset in required_datasets)
+
+
+def load_from_raw(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int,
+    video: bool,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    zarr_path = raw_dir / "cup_in_the_wild.zarr"
+    zarr_data = zarr.open(zarr_path, mode="r")
+
+    # We process the image data separately because it is too large to fit in memory
+    end_pose = torch.from_numpy(zarr_data["data/robot0_demo_end_pose"][:])
+    start_pos = torch.from_numpy(zarr_data["data/robot0_demo_start_pose"][:])
+    eff_pos = torch.from_numpy(zarr_data["data/robot0_eef_pos"][:])
+    eff_rot_axis_angle = torch.from_numpy(zarr_data["data/robot0_eef_rot_axis_angle"][:])
+    gripper_width = torch.from_numpy(zarr_data["data/robot0_gripper_width"][:])
+
+    states_pos = torch.cat([eff_pos, eff_rot_axis_angle], dim=1)
+    states = torch.cat([states_pos, gripper_width], dim=1)
+
+    episode_ends = zarr_data["meta/episode_ends"][:]
+    num_episodes = episode_ends.shape[0]
+
+    # We convert it in torch tensor later because the jit function does not support torch tensors
+    episode_ends = torch.from_numpy(episode_ends)
+
+    # load data indices from which each episode starts and ends
+    from_ids, to_ids = [], []
+    from_idx = 0
+    for to_idx in episode_ends:
+        from_ids.append(from_idx)
+        to_ids.append(to_idx)
+        from_idx = to_idx
+
+    ep_dicts_dir = videos_dir / "ep_dicts"
+    ep_dicts_dir.mkdir(exist_ok=True, parents=True)
+    ep_dicts = []
+
+    ep_ids = episodes if episodes else range(num_episodes)
+    for ep_idx, selected_ep_idx in tqdm.tqdm(enumerate(ep_ids)):
+        ep_dict_path = ep_dicts_dir / f"{ep_idx}"
+        if not ep_dict_path.is_file():
+            from_idx = from_ids[selected_ep_idx]
+            to_idx = to_ids[selected_ep_idx]
+            num_frames = to_idx - from_idx
+
+            # TODO(rcadene): save temporary images of the episode?
+
+            state = states[from_idx:to_idx]
+
+            ep_dict = {}
+
+            # load 57MB of images in RAM (400x224x224x3 uint8)
+            imgs_array = zarr_data["data/camera0_rgb"][from_idx:to_idx]
+            img_key = "observation.image"
+            if video:
+                fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+                video_path = videos_dir / fname
+                if not video_path.is_file():
+                    # save png images in temporary directory
+                    tmp_imgs_dir = videos_dir / "tmp_images"
+                    save_images_concurrently(imgs_array, tmp_imgs_dir)
+
+                    # encode images to a mp4 video
+                    encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
+
+                    # clean temporary images directory
+                    shutil.rmtree(tmp_imgs_dir)
+
+                # store the reference to the video frame
+                ep_dict[img_key] = [
+                    {"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
+                ]
+            else:
+                ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
+
+            ep_dict["observation.state"] = state
+            ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames, dtype=torch.int64)
+            ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+            ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+            ep_dict["episode_data_index_from"] = torch.tensor([from_idx] * num_frames)
+            ep_dict["episode_data_index_to"] = torch.tensor([from_idx + num_frames] * num_frames)
+            ep_dict["end_pose"] = end_pose[from_idx:to_idx]
+            ep_dict["start_pos"] = start_pos[from_idx:to_idx]
+            ep_dict["gripper_width"] = gripper_width[from_idx:to_idx]
+            torch.save(ep_dict, ep_dict_path)
+        else:
+            ep_dict = torch.load(ep_dict_path)
+
+        ep_dicts.append(ep_dict)
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video):
+    features = {}
+
+    if video:
+        features["observation.image"] = VideoFrame()
+    else:
+        features["observation.image"] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+    features["episode_data_index_from"] = Value(dtype="int64", id=None)
+    features["episode_data_index_to"] = Value(dtype="int64", id=None)
+    # `start_pos` and `end_pos` respectively represent the positions of the end-effector
+    # at the beginning and the end of the episode.
+    # `gripper_width` indicates the distance between the grippers, and this value is included
+    # in the state vector, which comprises the concatenation of the end-effector position
+    # and gripper width.
+    features["end_pose"] = Sequence(
+        length=data_dict["end_pose"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["start_pos"] = Sequence(
+        length=data_dict["start_pos"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["gripper_width"] = Sequence(
+        length=data_dict["gripper_width"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        # For umi cup in the wild: https://arxiv.org/pdf/2402.10329#table.caption.16
+        fps = 10
+
+    if not video:
+        logging.warning(
+            "Generating UMI dataset without `video=True` creates ~150GB on disk and requires ~80GB in RAM."
+        )
+
+    data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
+    hf_dataset = to_hf_dataset(data_dict, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    if video:
+        info["encoding"] = get_default_encoding()
+
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py

@@ -0,0 +1,200 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Process pickle files formatted like in: https://github.com/fyhMer/fowm"""
+
+import pickle
+import shutil
+from pathlib import Path
+
+import einops
+import torch
+import tqdm
+from datasets import Dataset, Features, Image, Sequence, Value
+from PIL import Image as PILImage
+
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
+from lerobot.common.datasets.push_dataset_to_hub.utils import (
+    calculate_episode_data_index,
+    concatenate_episodes,
+    get_default_encoding,
+    save_images_concurrently,
+)
+from lerobot.common.datasets.utils import (
+    hf_transform_to_torch,
+)
+from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
+
+
+def check_format(raw_dir):
+    keys = {"actions", "rewards", "dones"}
+    nested_keys = {"observations": {"rgb", "state"}, "next_observations": {"rgb", "state"}}
+
+    xarm_files = list(raw_dir.glob("*.pkl"))
+    assert len(xarm_files) > 0
+
+    with open(xarm_files[0], "rb") as f:
+        dataset_dict = pickle.load(f)
+
+    assert isinstance(dataset_dict, dict)
+    assert all(k in dataset_dict for k in keys)
+
+    # Check for consistent lengths in nested keys
+    expected_len = len(dataset_dict["actions"])
+    assert all(len(dataset_dict[key]) == expected_len for key in keys if key in dataset_dict)
+
+    for key, subkeys in nested_keys.items():
+        nested_dict = dataset_dict.get(key, {})
+        assert all(len(nested_dict[subkey]) == expected_len for subkey in subkeys if subkey in nested_dict)
+
+
+def load_from_raw(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int,
+    video: bool,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    pkl_path = raw_dir / "buffer.pkl"
+
+    with open(pkl_path, "rb") as f:
+        pkl_data = pickle.load(f)
+
+    # load data indices from which each episode starts and ends
+    from_ids, to_ids = [], []
+    from_idx, to_idx = 0, 0
+    for done in pkl_data["dones"]:
+        to_idx += 1
+        if not done:
+            continue
+        from_ids.append(from_idx)
+        to_ids.append(to_idx)
+        from_idx = to_idx
+
+    num_episodes = len(from_ids)
+
+    ep_dicts = []
+    ep_ids = episodes if episodes else range(num_episodes)
+    for ep_idx, selected_ep_idx in tqdm.tqdm(enumerate(ep_ids)):
+        from_idx = from_ids[selected_ep_idx]
+        to_idx = to_ids[selected_ep_idx]
+        num_frames = to_idx - from_idx
+
+        image = torch.tensor(pkl_data["observations"]["rgb"][from_idx:to_idx])
+        image = einops.rearrange(image, "b c h w -> b h w c")
+        state = torch.tensor(pkl_data["observations"]["state"][from_idx:to_idx])
+        action = torch.tensor(pkl_data["actions"][from_idx:to_idx])
+        # TODO(rcadene): we have a missing last frame which is the observation when the env is done
+        # it is critical to have this frame for tdmpc to predict a "done observation/state"
+        # next_image = torch.tensor(pkl_data["next_observations"]["rgb"][from_idx:to_idx])
+        # next_state = torch.tensor(pkl_data["next_observations"]["state"][from_idx:to_idx])
+        next_reward = torch.tensor(pkl_data["rewards"][from_idx:to_idx])
+        next_done = torch.tensor(pkl_data["dones"][from_idx:to_idx])
+
+        ep_dict = {}
+
+        imgs_array = [x.numpy() for x in image]
+        img_key = "observation.image"
+        if video:
+            # save png images in temporary directory
+            tmp_imgs_dir = videos_dir / "tmp_images"
+            save_images_concurrently(imgs_array, tmp_imgs_dir)
+
+            # encode images to a mp4 video
+            fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
+            video_path = videos_dir / fname
+            encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
+
+            # clean temporary images directory
+            shutil.rmtree(tmp_imgs_dir)
+
+            # store the reference to the video frame
+            ep_dict[img_key] = [{"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)]
+        else:
+            ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
+
+        ep_dict["observation.state"] = state
+        ep_dict["action"] = action
+        ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames, dtype=torch.int64)
+        ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
+        ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
+        # ep_dict["next.observation.image"] = next_image
+        # ep_dict["next.observation.state"] = next_state
+        ep_dict["next.reward"] = next_reward
+        ep_dict["next.done"] = next_done
+        ep_dicts.append(ep_dict)
+
+    data_dict = concatenate_episodes(ep_dicts)
+
+    total_frames = data_dict["frame_index"].shape[0]
+    data_dict["index"] = torch.arange(0, total_frames, 1)
+    return data_dict
+
+
+def to_hf_dataset(data_dict, video):
+    features = {}
+
+    if video:
+        features["observation.image"] = VideoFrame()
+    else:
+        features["observation.image"] = Image()
+
+    features["observation.state"] = Sequence(
+        length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["action"] = Sequence(
+        length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
+    )
+    features["episode_index"] = Value(dtype="int64", id=None)
+    features["frame_index"] = Value(dtype="int64", id=None)
+    features["timestamp"] = Value(dtype="float32", id=None)
+    features["next.reward"] = Value(dtype="float32", id=None)
+    features["next.done"] = Value(dtype="bool", id=None)
+    features["index"] = Value(dtype="int64", id=None)
+    # TODO(rcadene): add success
+    # features["next.success"] = Value(dtype='bool', id=None)
+
+    hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
+    hf_dataset.set_transform(hf_transform_to_torch)
+    return hf_dataset
+
+
+def from_raw_to_lerobot_format(
+    raw_dir: Path,
+    videos_dir: Path,
+    fps: int | None = None,
+    video: bool = True,
+    episodes: list[int] | None = None,
+    encoding: dict | None = None,
+):
+    # sanity check
+    check_format(raw_dir)
+
+    if fps is None:
+        fps = 15
+
+    data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
+    hf_dataset = to_hf_dataset(data_dict, video)
+    episode_data_index = calculate_episode_data_index(hf_dataset)
+    info = {
+        "codebase_version": CODEBASE_VERSION,
+        "fps": fps,
+        "video": video,
+    }
+    if video:
+        info["encoding"] = get_default_encoding()
+
+    return hf_dataset, episode_data_index, info

lerobot/common/datasets/utils.py

@@ -40,7 +40,7 @@ from lerobot.common.datasets.backward_compatibility import (
     BackwardCompatibilityError,
     ForwardCompatibilityError,
 )
-from lerobot.common.robot_devices.robots.utils import Robot
+from lerobot.common.robots.utils import Robot
 from lerobot.common.utils.utils import is_valid_numpy_dtype_string
 from lerobot.configs.types import DictLike, FeatureType, PolicyFeature
 
@@ -240,7 +240,7 @@ def load_episodes_stats(local_dir: Path) -> dict:
 def backward_compatible_episodes_stats(
     stats: dict[str, dict[str, np.ndarray]], episodes: list[int]
 ) -> dict[str, dict[str, np.ndarray]]:
-    return dict.fromkeys(episodes, stats)
+    return {ep_idx: stats for ep_idx in episodes}
 
 
 def load_image_as_numpy(

lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py

@@ -27,7 +27,7 @@ from textwrap import dedent
 
 from lerobot import available_datasets
 from lerobot.common.datasets.v2.convert_dataset_v1_to_v2 import convert_dataset
-from lerobot.common.robot_devices.robots.configs import AlohaRobotConfig
+from lerobot.common.robots.aloha.configuration_aloha import AlohaRobotConfig
 
 LOCAL_DIR = Path("data/")
 

lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py

@@ -141,8 +141,8 @@ from lerobot.common.datasets.video_utils import (
     get_image_pixel_channels,
     get_video_info,
 )
-from lerobot.common.robot_devices.robots.configs import RobotConfig
-from lerobot.common.robot_devices.robots.utils import make_robot_config
+from lerobot.common.robots import RobotConfig
+from lerobot.common.robots.utils import make_robot_config
 
 V16 = "v1.6"
 V20 = "v2.0"
@@ -481,7 +481,7 @@ def convert_dataset(
 
     # Tasks
     if single_task:
-        tasks_by_episodes = dict.fromkeys(episode_indices, single_task)
+        tasks_by_episodes = {ep_idx: single_task for ep_idx in episode_indices}
         dataset, tasks = add_task_index_by_episodes(dataset, tasks_by_episodes)
         tasks_by_episodes = {ep_idx: [task] for ep_idx, task in tasks_by_episodes.items()}
     elif tasks_path:

lerobot/common/datasets/video_utils.py

@@ -13,7 +13,6 @@
 # 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 importlib
 import json
 import logging
 import subprocess
@@ -30,46 +29,6 @@ from datasets.features.features import register_feature
 from PIL import Image
 
 
-def get_safe_default_codec():
-    if importlib.util.find_spec("torchcodec"):
-        return "torchcodec"
-    else:
-        logging.warning(
-            "'torchcodec' is not available in your platform, falling back to 'pyav' as a default decoder"
-        )
-        return "pyav"
-
-
-def decode_video_frames(
-    video_path: Path | str,
-    timestamps: list[float],
-    tolerance_s: float,
-    backend: str | None = None,
-) -> torch.Tensor:
-    """
-    Decodes video frames using the specified backend.
-
-    Args:
-        video_path (Path): Path to the video file.
-        timestamps (list[float]): List of timestamps to extract frames.
-        tolerance_s (float): Allowed deviation in seconds for frame retrieval.
-        backend (str, optional): Backend to use for decoding. Defaults to "torchcodec" when available in the platform; otherwise, defaults to "pyav"..
-
-    Returns:
-        torch.Tensor: Decoded frames.
-
-    Currently supports torchcodec on cpu and pyav.
-    """
-    if backend is None:
-        backend = get_safe_default_codec()
-    if backend == "torchcodec":
-        return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s)
-    elif backend in ["pyav", "video_reader"]:
-        return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
-    else:
-        raise ValueError(f"Unsupported video backend: {backend}")
-
-
 def decode_video_frames_torchvision(
     video_path: Path | str,
     timestamps: list[float],
@@ -168,81 +127,6 @@ def decode_video_frames_torchvision(
     return closest_frames
 
 
-def decode_video_frames_torchcodec(
-    video_path: Path | str,
-    timestamps: list[float],
-    tolerance_s: float,
-    device: str = "cpu",
-    log_loaded_timestamps: bool = False,
-) -> torch.Tensor:
-    """Loads frames associated with the requested timestamps of a video using torchcodec.
-
-    Note: Setting device="cuda" outside the main process, e.g. in data loader workers, will lead to CUDA initialization errors.
-
-    Note: Video benefits from inter-frame compression. Instead of storing every frame individually,
-    the encoder stores a reference frame (or a key frame) and subsequent frames as differences relative to
-    that key frame. As a consequence, to access a requested frame, we need to load the preceding key frame,
-    and all subsequent frames until reaching the requested frame. The number of key frames in a video
-    can be adjusted during encoding to take into account decoding time and video size in bytes.
-    """
-
-    if importlib.util.find_spec("torchcodec"):
-        from torchcodec.decoders import VideoDecoder
-    else:
-        raise ImportError("torchcodec is required but not available.")
-
-    # initialize video decoder
-    decoder = VideoDecoder(video_path, device=device, seek_mode="approximate")
-    loaded_frames = []
-    loaded_ts = []
-    # get metadata for frame information
-    metadata = decoder.metadata
-    average_fps = metadata.average_fps
-
-    # convert timestamps to frame indices
-    frame_indices = [round(ts * average_fps) for ts in timestamps]
-
-    # retrieve frames based on indices
-    frames_batch = decoder.get_frames_at(indices=frame_indices)
-
-    for frame, pts in zip(frames_batch.data, frames_batch.pts_seconds, strict=False):
-        loaded_frames.append(frame)
-        loaded_ts.append(pts.item())
-        if log_loaded_timestamps:
-            logging.info(f"Frame loaded at timestamp={pts:.4f}")
-
-    query_ts = torch.tensor(timestamps)
-    loaded_ts = torch.tensor(loaded_ts)
-
-    # compute distances between each query timestamp and loaded timestamps
-    dist = torch.cdist(query_ts[:, None], loaded_ts[:, None], p=1)
-    min_, argmin_ = dist.min(1)
-
-    is_within_tol = min_ < tolerance_s
-    assert is_within_tol.all(), (
-        f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
-        "It means that the closest frame that can be loaded from the video is too far away in time."
-        "This might be due to synchronization issues with timestamps during data collection."
-        "To be safe, we advise to ignore this item during training."
-        f"\nqueried timestamps: {query_ts}"
-        f"\nloaded timestamps: {loaded_ts}"
-        f"\nvideo: {video_path}"
-    )
-
-    # get closest frames to the query timestamps
-    closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
-    closest_ts = loaded_ts[argmin_]
-
-    if log_loaded_timestamps:
-        logging.info(f"{closest_ts=}")
-
-    # convert to float32 in [0,1] range (channel first)
-    closest_frames = closest_frames.type(torch.float32) / 255
-
-    assert len(timestamps) == len(closest_frames)
-    return closest_frames
-
-
 def encode_video_frames(
     imgs_dir: Path | str,
     video_path: Path | str,
@@ -257,7 +141,6 @@ def encode_video_frames(
 ) -> None:
     """More info on ffmpeg arguments tuning on `benchmark/video/README.md`"""
     video_path = Path(video_path)
-    imgs_dir = Path(imgs_dir)
     video_path.parent.mkdir(parents=True, exist_ok=True)
 
     ffmpeg_args = OrderedDict(

lerobot/common/envs/configs.py

@@ -17,7 +17,7 @@ from dataclasses import dataclass, field
 
 import draccus
 
-from lerobot.common.constants import ACTION, OBS_ENV, OBS_IMAGE, OBS_IMAGES, OBS_ROBOT
+from lerobot.common.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 from lerobot.configs.types import FeatureType, PolicyFeature
 
 
@@ -53,7 +53,7 @@ class AlohaEnv(EnvConfig):
     features_map: dict[str, str] = field(
         default_factory=lambda: {
             "action": ACTION,
-            "agent_pos": OBS_ROBOT,
+            "agent_pos": OBS_STATE,
             "top": f"{OBS_IMAGE}.top",
             "pixels/top": f"{OBS_IMAGES}.top",
         }
@@ -94,8 +94,8 @@ class PushtEnv(EnvConfig):
     features_map: dict[str, str] = field(
         default_factory=lambda: {
             "action": ACTION,
-            "agent_pos": OBS_ROBOT,
-            "environment_state": OBS_ENV,
+            "agent_pos": OBS_STATE,
+            "environment_state": OBS_ENV_STATE,
             "pixels": OBS_IMAGE,
         }
     )
@@ -136,7 +136,7 @@ class XarmEnv(EnvConfig):
     features_map: dict[str, str] = field(
         default_factory=lambda: {
             "action": ACTION,
-            "agent_pos": OBS_ROBOT,
+            "agent_pos": OBS_STATE,
             "pixels": OBS_IMAGE,
         }
     )

lerobot/common/envs/utils.py

@@ -13,11 +13,7 @@
 # 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 warnings
-from typing import Any
-
 import einops
-import gymnasium as gym
 import numpy as np
 import torch
 from torch import Tensor
@@ -90,38 +86,3 @@ def env_to_policy_features(env_cfg: EnvConfig) -> dict[str, PolicyFeature]:
         policy_features[policy_key] = feature
 
     return policy_features
-
-
-def are_all_envs_same_type(env: gym.vector.VectorEnv) -> bool:
-    first_type = type(env.envs[0])  # Get type of first env
-    return all(type(e) is first_type for e in env.envs)  # Fast type check
-
-
-def check_env_attributes_and_types(env: gym.vector.VectorEnv) -> None:
-    with warnings.catch_warnings():
-        warnings.simplefilter("once", UserWarning)  # Apply filter only in this function
-
-        if not (hasattr(env.envs[0], "task_description") and hasattr(env.envs[0], "task")):
-            warnings.warn(
-                "The environment does not have 'task_description' and 'task'. Some policies require these features.",
-                UserWarning,
-                stacklevel=2,
-            )
-        if not are_all_envs_same_type(env):
-            warnings.warn(
-                "The environments have different types. Make sure you infer the right task from each environment. Empty task will be passed instead.",
-                UserWarning,
-                stacklevel=2,
-            )
-
-
-def add_envs_task(env: gym.vector.VectorEnv, observation: dict[str, Any]) -> dict[str, Any]:
-    """Adds task feature to the observation dict with respect to the first environment attribute."""
-    if hasattr(env.envs[0], "task_description"):
-        observation["task"] = env.call("task_description")
-    elif hasattr(env.envs[0], "task"):
-        observation["task"] = env.call("task")
-    else:  #  For envs without language instructions, e.g. aloha transfer cube and etc.
-        num_envs = observation[list(observation.keys())[0]].shape[0]
-        observation["task"] = ["" for _ in range(num_envs)]
-    return observation

lerobot/common/errors.py

@@ -0,0 +1,17 @@
+class DeviceNotConnectedError(ConnectionError):
+    """Exception raised when the device is not connected."""
+
+    def __init__(self, message="This device is not connected. Try calling `connect()` first."):
+        self.message = message
+        super().__init__(self.message)
+
+
+class DeviceAlreadyConnectedError(ConnectionError):
+    """Exception raised when the device is already connected."""
+
+    def __init__(
+        self,
+        message="This device is already connected. Try not calling `connect()` twice.",
+    ):
+        self.message = message
+        super().__init__(self.message)

lerobot/common/motors/__init__.py

@@ -0,0 +1,3 @@
+from .motors_bus import MotorsBus
+
+__all__ = ["MotorsBus"]

lerobot/common/motors/dynamixel/__init__.py

@@ -0,0 +1,4 @@
+from .dynamixel import DynamixelMotorsBus, TorqueMode, set_operating_mode
+from .dynamixel_calibration import run_arm_calibration
+
+__all__ = ["DynamixelMotorsBus", "TorqueMode", "set_operating_mode", "run_arm_calibration"]

lerobot/common/motors/dynamixel/dynamixel.py

@@ -22,8 +22,7 @@ from copy import deepcopy
 import numpy as np
 import tqdm
 
-from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 from lerobot.common.utils.utils import capture_timestamp_utc
 
 PROTOCOL_VERSION = 2.0
@@ -288,11 +287,10 @@ class DynamixelMotorsBus:
     motor_index = 6
     motor_model = "xl330-m288"
 
-    config = DynamixelMotorsBusConfig(
+    motors_bus = DynamixelMotorsBus(
         port="/dev/tty.usbmodem575E0031751",
         motors={motor_name: (motor_index, motor_model)},
     )
-    motors_bus = DynamixelMotorsBus(config)
     motors_bus.connect()
 
     position = motors_bus.read("Present_Position")
@@ -308,11 +306,13 @@ class DynamixelMotorsBus:
 
     def __init__(
         self,
-        config: DynamixelMotorsBusConfig,
+        port: str,
+        motors: dict[str, tuple[int, str]],
+        mock: bool = False,
     ):
-        self.port = config.port
-        self.motors = config.motors
-        self.mock = config.mock
+        self.port = port
+        self.motors = motors
+        self.mock = mock
 
         self.model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
         self.model_resolution = deepcopy(MODEL_RESOLUTION)
@@ -327,7 +327,7 @@ class DynamixelMotorsBus:
 
     def connect(self):
         if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
+            raise DeviceAlreadyConnectedError(
                 f"DynamixelMotorsBus({self.port}) is already connected. Do not call `motors_bus.connect()` twice."
             )
 
@@ -684,7 +684,7 @@ class DynamixelMotorsBus:
 
     def read(self, data_name, motor_names: str | list[str] | None = None):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
             )
 
@@ -786,7 +786,7 @@ class DynamixelMotorsBus:
 
     def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
             )
 
@@ -855,7 +855,7 @@ class DynamixelMotorsBus:
 
     def disconnect(self):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"DynamixelMotorsBus({self.port}) is not connected. Try running `motors_bus.connect()` first."
             )
 
@@ -871,3 +871,25 @@ class DynamixelMotorsBus:
     def __del__(self):
         if getattr(self, "is_connected", False):
             self.disconnect()
+
+
+def set_operating_mode(arm: DynamixelMotorsBus):
+    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
+        raise ValueError("To run set robot preset, the torque must be disabled on all motors.")
+
+    # Use 'extended position mode' for all motors except gripper, because in joint mode the servos can't
+    # rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling the arm,
+    # you could end up with a servo with a position 0 or 4095 at a crucial point See [
+    # https://emanual.robotis.com/docs/en/dxl/x/x_series/#operating-mode11]
+    all_motors_except_gripper = [name for name in arm.motor_names if name != "gripper"]
+    if len(all_motors_except_gripper) > 0:
+        # 4 corresponds to Extended Position on Koch motors
+        arm.write("Operating_Mode", 4, all_motors_except_gripper)
+
+    # Use 'position control current based' for gripper to be limited by the limit of the current.
+    # For the follower gripper, it means it can grasp an object without forcing too much even tho,
+    # it's goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
+    # For the leader gripper, it means we can use it as a physical trigger, since we can force with our finger
+    # to make it move, and it will move back to its original target position when we release the force.
+    # 5 corresponds to Current Controlled Position on Koch gripper motors "xl330-m077, xl330-m288"
+    arm.write("Operating_Mode", 5, "gripper")

lerobot/common/motors/dynamixel/dynamixel_calibration.py

@@ -17,12 +17,12 @@
 
 import numpy as np
 
-from lerobot.common.robot_devices.motors.dynamixel import (
+from ..motors_bus import MotorsBus
+from .dynamixel import (
     CalibrationMode,
     TorqueMode,
     convert_degrees_to_steps,
 )
-from lerobot.common.robot_devices.motors.utils import MotorsBus
 
 URL_TEMPLATE = (
     "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"

lerobot/common/motors/feetech/__init__.py

@@ -0,0 +1,9 @@
+from .feetech import FeetechMotorsBus, TorqueMode
+from .feetech_calibration import apply_feetech_offsets_from_calibration, run_full_arm_calibration
+
+__all__ = [
+    "FeetechMotorsBus",
+    "TorqueMode",
+    "apply_feetech_offsets_from_calibration",
+    "run_full_arm_calibration",
+]

lerobot/common/motors/feetech/feetech.py

@@ -13,8 +13,6 @@
 # limitations under the License.
 
 import enum
-import logging
-import math
 import time
 import traceback
 from copy import deepcopy
@@ -22,8 +20,7 @@ from copy import deepcopy
 import numpy as np
 import tqdm
 
-from lerobot.common.robot_devices.motors.configs import FeetechMotorsBusConfig
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 from lerobot.common.utils.utils import capture_timestamp_utc
 
 PROTOCOL_VERSION = 0
@@ -32,13 +29,6 @@ TIMEOUT_MS = 1000
 
 MAX_ID_RANGE = 252
 
-# The following bounds define the lower and upper joints range (after calibration).
-# For joints in degree (i.e. revolute joints), their nominal range is [-180, 180] degrees
-# which corresponds to a half rotation on the left and half rotation on the right.
-# Some joints might require higher range, so we allow up to [-270, 270] degrees until
-# an error is raised.
-LOWER_BOUND_DEGREE = -270
-UPPER_BOUND_DEGREE = 270
 # For joints in percentage (i.e. joints that move linearly like the prismatic joint of a gripper),
 # their nominal range is [0, 100] %. For instance, for Aloha gripper, 0% is fully
 # closed, and 100% is fully open. To account for slight calibration issue, we allow up to
@@ -48,7 +38,6 @@ UPPER_BOUND_LINEAR = 110
 
 HALF_TURN_DEGREE = 180
 
-
 # See this link for STS3215 Memory Table:
 # https://docs.google.com/spreadsheets/d/1GVs7W1VS1PqdhA1nW-abeyAHhTUxKUdR/edit?usp=sharing&ouid=116566590112741600240&rtpof=true&sd=true
 # data_name: (address, size_byte)
@@ -114,8 +103,6 @@ SCS_SERIES_BAUDRATE_TABLE = {
 }
 
 CALIBRATION_REQUIRED = ["Goal_Position", "Present_Position"]
-CONVERT_UINT32_TO_INT32_REQUIRED = ["Goal_Position", "Present_Position"]
-
 
 MODEL_CONTROL_TABLE = {
     "scs_series": SCS_SERIES_CONTROL_TABLE,
@@ -137,15 +124,63 @@ NUM_READ_RETRY = 20
 NUM_WRITE_RETRY = 20
 
 
-def convert_degrees_to_steps(degrees: float | np.ndarray, models: str | list[str]) -> np.ndarray:
-    """This function converts the degree range to the step range for indicating motors rotation.
-    It assumes a motor achieves a full rotation by going from -180 degree position to +180.
-    The motor resolution (e.g. 4096) corresponds to the number of steps needed to achieve a full rotation.
+def convert_ticks_to_degrees(ticks, model):
+    resolutions = MODEL_RESOLUTION[model]
+    # Convert the ticks to degrees
+    return ticks * (360.0 / resolutions)
+
+
+def convert_degrees_to_ticks(degrees, model):
+    resolutions = MODEL_RESOLUTION[model]
+    # Convert degrees to motor ticks
+    return int(degrees * (resolutions / 360.0))
+
+
+def adjusted_to_homing_ticks(raw_motor_ticks: int, model: str, motorbus, motor_id: int) -> int:
     """
-    resolutions = [MODEL_RESOLUTION[model] for model in models]
-    steps = degrees / 180 * np.array(resolutions) / 2
-    steps = steps.astype(int)
-    return steps
+    Takes a raw reading [0..(res-1)] (e.g. 0..4095) and shifts it so that '2048'
+    becomes 0 in the homed coordinate system ([-2048..+2047] for 4096 resolution).
+    """
+    resolutions = MODEL_RESOLUTION[model]
+
+    # Shift raw ticks by half-resolution so 2048 -> 0, then wrap [0..res-1].
+    ticks = (raw_motor_ticks - (resolutions // 2)) % resolutions
+
+    # If above halfway, fold it into negative territory => [-2048..+2047].
+    if ticks > (resolutions // 2):
+        ticks -= resolutions
+
+    # Flip sign if drive_mode is set.
+    drive_mode = 0
+    if motorbus.calibration is not None:
+        drive_mode = motorbus.calibration["drive_mode"][motor_id - 1]
+
+    if drive_mode:
+        ticks *= -1
+
+    return ticks
+
+
+def adjusted_to_motor_ticks(adjusted_pos: int, model: str, motorbus, motor_id: int) -> int:
+    """
+    Inverse of adjusted_to_homing_ticks(). Takes a 'homed' position in [-2048..+2047]
+    and recovers the raw [0..(res-1)] ticks with 2048 as midpoint.
+    """
+    # Flip sign if drive_mode was set.
+    drive_mode = 0
+    if motorbus.calibration is not None:
+        drive_mode = motorbus.calibration["drive_mode"][motor_id - 1]
+
+    if drive_mode:
+        adjusted_pos *= -1
+
+    resolutions = MODEL_RESOLUTION[model]
+
+    # Shift by +half-resolution and wrap into [0..res-1].
+    # This undoes the earlier shift by -half-resolution.
+    ticks = (adjusted_pos + (resolutions // 2)) % resolutions
+
+    return ticks
 
 
 def convert_to_bytes(value, bytes, mock=False):
@@ -267,11 +302,10 @@ class FeetechMotorsBus:
     motor_index = 6
     motor_model = "sts3215"
 
-    config = FeetechMotorsBusConfig(
+    motors_bus = FeetechMotorsBus(
         port="/dev/tty.usbmodem575E0031751",
         motors={motor_name: (motor_index, motor_model)},
     )
-    motors_bus = FeetechMotorsBus(config)
     motors_bus.connect()
 
     position = motors_bus.read("Present_Position")
@@ -287,11 +321,13 @@ class FeetechMotorsBus:
 
     def __init__(
         self,
-        config: FeetechMotorsBusConfig,
+        port: str,
+        motors: dict[str, tuple[int, str]],
+        mock: bool = False,
     ):
-        self.port = config.port
-        self.motors = config.motors
-        self.mock = config.mock
+        self.port = port
+        self.motors = motors
+        self.mock = mock
 
         self.model_ctrl_table = deepcopy(MODEL_CONTROL_TABLE)
         self.model_resolution = deepcopy(MODEL_RESOLUTION)
@@ -304,11 +340,9 @@ class FeetechMotorsBus:
         self.group_writers = {}
         self.logs = {}
 
-        self.track_positions = {}
-
     def connect(self):
         if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
+            raise DeviceAlreadyConnectedError(
                 f"FeetechMotorsBus({self.port}) is already connected. Do not call `motors_bus.connect()` twice."
             )
 
@@ -402,33 +436,7 @@ class FeetechMotorsBus:
     def set_calibration(self, calibration: dict[str, list]):
         self.calibration = calibration
 
-    def apply_calibration_autocorrect(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """This function apply the calibration, automatically detects out of range errors for motors values and attempt to correct.
-
-        For more info, see docstring of `apply_calibration` and `autocorrect_calibration`.
-        """
-        try:
-            values = self.apply_calibration(values, motor_names)
-        except JointOutOfRangeError as e:
-            print(e)
-            self.autocorrect_calibration(values, motor_names)
-            values = self.apply_calibration(values, motor_names)
-        return values
-
     def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with
-        a "zero position" at 0 degree.
-
-        Note: We say "nominal degree range" since the motors can take values outside this range. For instance, 190 degrees, if the motor
-        rotate more than a half a turn from the zero position. However, most motors can't rotate more than 180 degrees and will stay in this range.
-
-        Joints values are original in [0, 2**32[ (unsigned int32). Each motor are expected to complete a full rotation
-        when given a goal position that is + or - their resolution. For instance, feetech xl330-m077 have a resolution of 4096, and
-        at any position in their original range, let's say the position 56734, they complete a full rotation clockwise by moving to 60830,
-        or anticlockwise by moving to 52638. The position in the original range is arbitrary and might change a lot between each motor.
-        To harmonize between motors of the same model, different robots, or even models of different brands, we propose to work
-        in the centered nominal degree range ]-180, 180[.
-        """
         if motor_names is None:
             motor_names = self.motor_names
 
@@ -440,34 +448,11 @@ class FeetechMotorsBus:
             calib_mode = self.calibration["calib_mode"][calib_idx]
 
             if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
+                motor_idx, model = self.motors[name]
 
-                # Update direction of rotation of the motor to match between leader and follower.
-                # In fact, the motor of the leader for a given joint can be assembled in an
-                # opposite direction in term of rotation than the motor of the follower on the same joint.
-                if drive_mode:
-                    values[i] *= -1
-
-                # Convert from range [-2**31, 2**31[ to
-                # nominal range ]-resolution, resolution[ (e.g. ]-2048, 2048[)
-                values[i] += homing_offset
-
-                # Convert from range ]-resolution, resolution[ to
-                # universal float32 centered degree range ]-180, 180[
-                values[i] = values[i] / (resolution // 2) * HALF_TURN_DEGREE
-
-                if (values[i] < LOWER_BOUND_DEGREE) or (values[i] > UPPER_BOUND_DEGREE):
-                    raise JointOutOfRangeError(
-                        f"Wrong motor position range detected for {name}. "
-                        f"Expected to be in nominal range of [-{HALF_TURN_DEGREE}, {HALF_TURN_DEGREE}] degrees (a full rotation), "
-                        f"with a maximum range of [{LOWER_BOUND_DEGREE}, {UPPER_BOUND_DEGREE}] degrees to account for joints that can rotate a bit more, "
-                        f"but present value is {values[i]} degree. "
-                        "This might be due to a cable connection issue creating an artificial 360 degrees jump in motor values. "
-                        "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`"
-                    )
+                # Convert raw motor ticks to homed ticks, then convert the homed ticks to degrees
+                values[i] = adjusted_to_homing_ticks(values[i], model, self, motor_idx)
+                values[i] = convert_ticks_to_degrees(values[i], model)
 
             elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
                 start_pos = self.calibration["start_pos"][calib_idx]
@@ -489,103 +474,6 @@ class FeetechMotorsBus:
 
         return values
 
-    def autocorrect_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
-        """This function automatically detects issues with values of motors after calibration, and correct for these issues.
-
-        Some motors might have values outside of expected maximum bounds after calibration.
-        For instance, for a joint in degree, its value can be outside [-270, 270] degrees, which is totally unexpected given
-        a nominal range of [-180, 180] degrees, which represents half a turn to the left or right starting from zero position.
-
-        Known issues:
-        #1: Motor value randomly shifts of a full turn, caused by hardware/connection errors.
-        #2: Motor internal homing offset is shifted of a full turn, caused by using default calibration (e.g Aloha).
-        #3: motor internal homing offset is shifted of less or more than a full turn, caused by using default calibration
-            or by human error during manual calibration.
-
-        Issues #1 and #2 can be solved by shifting the calibration homing offset by a full turn.
-        Issue #3 will be visually detected by user and potentially captured by the safety feature `max_relative_target`,
-        that will slow down the motor, raise an error asking to recalibrate. Manual recalibrating will solve the issue.
-
-        Note: A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
-        """
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        # Convert from unsigned int32 original range [0, 2**32] to signed float32 range
-        values = values.astype(np.float32)
-
-        for i, name in enumerate(motor_names):
-            calib_idx = self.calibration["motor_names"].index(name)
-            calib_mode = self.calibration["calib_mode"][calib_idx]
-
-            if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
-
-                if drive_mode:
-                    values[i] *= -1
-
-                # Convert from initial range to range [-180, 180] degrees
-                calib_val = (values[i] + homing_offset) / (resolution // 2) * HALF_TURN_DEGREE
-                in_range = (calib_val > LOWER_BOUND_DEGREE) and (calib_val < UPPER_BOUND_DEGREE)
-
-                # Solve this inequality to find the factor to shift the range into [-180, 180] degrees
-                # values[i] = (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE
-                # - HALF_TURN_DEGREE <= (values[i] + homing_offset + resolution * factor) / (resolution // 2) * HALF_TURN_DEGREE <= HALF_TURN_DEGREE
-                # (- HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset) / resolution <= factor <= (HALF_TURN_DEGREE / 180 * (resolution // 2) - values[i] - homing_offset) / resolution
-                low_factor = (
-                    -HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
-                ) / resolution
-                upp_factor = (
-                    HALF_TURN_DEGREE / HALF_TURN_DEGREE * (resolution // 2) - values[i] - homing_offset
-                ) / resolution
-
-            elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                start_pos = self.calibration["start_pos"][calib_idx]
-                end_pos = self.calibration["end_pos"][calib_idx]
-
-                # Convert from initial range to range [0, 100] in %
-                calib_val = (values[i] - start_pos) / (end_pos - start_pos) * 100
-                in_range = (calib_val > LOWER_BOUND_LINEAR) and (calib_val < UPPER_BOUND_LINEAR)
-
-                # Solve this inequality to find the factor to shift the range into [0, 100] %
-                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos + resolution * factor - start_pos - resolution * factor) * 100
-                # values[i] = (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100
-                # 0 <= (values[i] - start_pos + resolution * factor) / (end_pos - start_pos) * 100 <= 100
-                # (start_pos - values[i]) / resolution <= factor <= (end_pos - values[i]) / resolution
-                low_factor = (start_pos - values[i]) / resolution
-                upp_factor = (end_pos - values[i]) / resolution
-
-            if not in_range:
-                # Get first integer between the two bounds
-                if low_factor < upp_factor:
-                    factor = math.ceil(low_factor)
-
-                    if factor > upp_factor:
-                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
-                else:
-                    factor = math.ceil(upp_factor)
-
-                    if factor > low_factor:
-                        raise ValueError(f"No integer found between bounds [{low_factor=}, {upp_factor=}]")
-
-                if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                    out_of_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
-                    in_range_str = f"{LOWER_BOUND_DEGREE} < {calib_val} < {UPPER_BOUND_DEGREE} degrees"
-                elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
-                    out_of_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
-                    in_range_str = f"{LOWER_BOUND_LINEAR} < {calib_val} < {UPPER_BOUND_LINEAR} %"
-
-                logging.warning(
-                    f"Auto-correct calibration of motor '{name}' by shifting value by {abs(factor)} full turns, "
-                    f"from '{out_of_range_str}' to '{in_range_str}'."
-                )
-
-                # A full turn corresponds to 360 degrees but also to 4096 steps for a motor resolution of 4096.
-                self.calibration["homing_offset"][calib_idx] += resolution * factor
-
     def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
         """Inverse of `apply_calibration`."""
         if motor_names is None:
@@ -596,23 +484,11 @@ class FeetechMotorsBus:
             calib_mode = self.calibration["calib_mode"][calib_idx]
 
             if CalibrationMode[calib_mode] == CalibrationMode.DEGREE:
-                drive_mode = self.calibration["drive_mode"][calib_idx]
-                homing_offset = self.calibration["homing_offset"][calib_idx]
-                _, model = self.motors[name]
-                resolution = self.model_resolution[model]
+                motor_idx, model = self.motors[name]
 
-                # Convert from nominal 0-centered degree range [-180, 180] to
-                # 0-centered resolution range (e.g. [-2048, 2048] for resolution=4096)
-                values[i] = values[i] / HALF_TURN_DEGREE * (resolution // 2)
-
-                # Subtract the homing offsets to come back to actual motor range of values
-                # which can be arbitrary.
-                values[i] -= homing_offset
-
-                # Remove drive mode, which is the rotation direction of the motor, to come back to
-                # actual motor rotation direction which can be arbitrary.
-                if drive_mode:
-                    values[i] *= -1
+                # Convert degrees to homed ticks, then convert the homed ticks to raw ticks
+                values[i] = convert_degrees_to_ticks(values[i], model)
+                values[i] = adjusted_to_motor_ticks(values[i], model, self, motor_idx)
 
             elif CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
                 start_pos = self.calibration["start_pos"][calib_idx]
@@ -625,43 +501,6 @@ class FeetechMotorsBus:
         values = np.round(values).astype(np.int32)
         return values
 
-    def avoid_rotation_reset(self, values, motor_names, data_name):
-        if data_name not in self.track_positions:
-            self.track_positions[data_name] = {
-                "prev": [None] * len(self.motor_names),
-                # Assume False at initialization
-                "below_zero": [False] * len(self.motor_names),
-                "above_max": [False] * len(self.motor_names),
-            }
-
-        track = self.track_positions[data_name]
-
-        if motor_names is None:
-            motor_names = self.motor_names
-
-        for i, name in enumerate(motor_names):
-            idx = self.motor_names.index(name)
-
-            if track["prev"][idx] is None:
-                track["prev"][idx] = values[i]
-                continue
-
-            # Detect a full rotation occurred
-            if abs(track["prev"][idx] - values[i]) > 2048:
-                # Position went below 0 and got reset to 4095
-                if track["prev"][idx] < values[i]:
-                    # So we set negative value by adding a full rotation
-                    values[i] -= 4096
-
-                # Position went above 4095 and got reset to 0
-                elif track["prev"][idx] > values[i]:
-                    # So we add a full rotation
-                    values[i] += 4096
-
-            track["prev"][idx] = values[i]
-
-        return values
-
     def read_with_motor_ids(self, motor_models, motor_ids, data_name, num_retry=NUM_READ_RETRY):
         if self.mock:
             import tests.motors.mock_scservo_sdk as scs
@@ -707,7 +546,7 @@ class FeetechMotorsBus:
             import scservo_sdk as scs
 
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
             )
 
@@ -735,7 +574,7 @@ class FeetechMotorsBus:
             self.port_handler.ser.reset_output_buffer()
             self.port_handler.ser.reset_input_buffer()
 
-            # create new group reader
+            # Create new group reader
             self.group_readers[group_key] = scs.GroupSyncRead(
                 self.port_handler, self.packet_handler, addr, bytes
             )
@@ -760,15 +599,8 @@ class FeetechMotorsBus:
 
         values = np.array(values)
 
-        # Convert to signed int to use range [-2048, 2048] for our motor positions.
-        if data_name in CONVERT_UINT32_TO_INT32_REQUIRED:
-            values = values.astype(np.int32)
-
-        if data_name in CALIBRATION_REQUIRED:
-            values = self.avoid_rotation_reset(values, motor_names, data_name)
-
         if data_name in CALIBRATION_REQUIRED and self.calibration is not None:
-            values = self.apply_calibration_autocorrect(values, motor_names)
+            values = self.apply_calibration(values, motor_names)
 
         # log the number of seconds it took to read the data from the motors
         delta_ts_name = get_log_name("delta_timestamp_s", "read", data_name, motor_names)
@@ -811,7 +643,7 @@ class FeetechMotorsBus:
 
     def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."
             )
 
@@ -880,7 +712,7 @@ class FeetechMotorsBus:
 
     def disconnect(self):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 f"FeetechMotorsBus({self.port}) is not connected. Try running `motors_bus.connect()` first."
             )
 

lerobot/common/motors/feetech/feetech_calibration.py

@@ -0,0 +1,254 @@
+# 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 ..motors_bus import MotorsBus
+from .feetech import (
+    CalibrationMode,
+    FeetechMotorsBus,
+    TorqueMode,
+)
+
+URL_TEMPLATE = (
+    "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
+)
+
+
+def disable_torque(arm: MotorsBus):
+    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
+        raise ValueError("To run calibration, the torque must be disabled on all motors.")
+
+
+def get_calibration_modes(arm: MotorsBus):
+    """Returns calibration modes for each motor (DEGREE for rotational, LINEAR for gripper)."""
+    return [
+        CalibrationMode.LINEAR.name if name == "gripper" else CalibrationMode.DEGREE.name
+        for name in arm.motor_names
+    ]
+
+
+def reset_offset(motor_id, motor_bus):
+    # Open the write lock, changes to EEPROM do NOT persist yet
+    motor_bus.write("Lock", 1)
+
+    # Set offset to 0
+    motor_name = motor_bus.motor_names[motor_id - 1]
+    motor_bus.write("Offset", 0, motor_names=[motor_name])
+
+    # Close the write lock, changes to EEPROM do persist
+    motor_bus.write("Lock", 0)
+
+    # Confirm that the offset is zero by reading it back
+    confirmed_offset = motor_bus.read("Offset")[motor_id - 1]
+    print(f"Offset for motor {motor_id} reset to: {confirmed_offset}")
+    return confirmed_offset
+
+
+def calibrate_homing_motor(motor_id, motor_bus):
+    reset_offset(motor_id, motor_bus)
+
+    home_ticks = motor_bus.read("Present_Position")[motor_id - 1]  # Read index starts at 0
+    print(f"Encoder offset (present position in homing position): {home_ticks}")
+
+    return home_ticks
+
+
+def calibrate_linear_motor(motor_id, motor_bus):
+    motor_names = motor_bus.motor_names
+    motor_name = motor_names[motor_id - 1]
+
+    reset_offset(motor_id, motor_bus)
+
+    input(f"Close the {motor_name}, then press Enter...")
+    start_pos = motor_bus.read("Present_Position")[motor_id - 1]  # Read index starts ar 0
+    print(f"  [Motor {motor_id}] start position recorded: {start_pos}")
+
+    input(f"Open the {motor_name} fully, then press Enter...")
+    end_pos = motor_bus.read("Present_Position")[motor_id - 1]  # Read index starts ar 0
+    print(f"  [Motor {motor_id}] end position recorded: {end_pos}")
+
+    return start_pos, end_pos
+
+
+def single_motor_calibration(arm: MotorsBus, motor_id: int):
+    """Calibrates a single motor and returns its calibration data for updating the calibration file."""
+
+    disable_torque(arm)
+    print(f"\n--- Calibrating Motor {motor_id} ---")
+
+    start_pos = 0
+    end_pos = 0
+    encoder_offset = 0
+
+    if motor_id == 6:
+        start_pos, end_pos = calibrate_linear_motor(motor_id, arm)
+    else:
+        input("Move the motor to (zero) position, then press Enter...")
+        encoder_offset = calibrate_homing_motor(motor_id, arm)
+
+    print(f"Calibration for motor ID:{motor_id} done.")
+
+    # Create a calibration dictionary for the single motor
+    calib_dict = {
+        "homing_offset": int(encoder_offset),
+        "drive_mode": 0,
+        "start_pos": int(start_pos),
+        "end_pos": int(end_pos),
+        "calib_mode": get_calibration_modes(arm)[motor_id - 1],
+        "motor_name": arm.motor_names[motor_id - 1],
+    }
+
+    return calib_dict
+
+
+def run_full_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
+    """
+    Runs a full calibration process for all motors in a robotic arm.
+
+    This function calibrates each motor in the arm, determining encoder offsets and
+    start/end positions for linear and rotational motors. The calibration data is then
+    stored in a dictionary for later use.
+
+    **Calibration Process:**
+    - The user is prompted to move the arm to its homing position before starting.
+    - Motors with rotational motion are calibrated using a homing method.
+    - Linear actuators (e.g., grippers) are calibrated separately.
+    - Encoder offsets, start positions, and end positions are recorded.
+
+    **Example Usage:**
+    ```python
+    run_full_arm_calibration(arm, "so100", "left", "follower")
+    ```
+    """
+    disable_torque(arm)
+
+    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
+
+    print("\nMove arm to homing position (middle)")
+    print(
+        "See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero")
+    )  # TODO(pepijn): replace with new instruction homing pos (all motors in middle) in tutorial
+    input("Press Enter to continue...")
+
+    start_positions = np.zeros(len(arm.motor_indices))
+    end_positions = np.zeros(len(arm.motor_indices))
+    encoder_offsets = np.zeros(len(arm.motor_indices))
+
+    modes = get_calibration_modes(arm)
+
+    for i, motor_id in enumerate(arm.motor_indices):
+        if modes[i] == CalibrationMode.DEGREE.name:
+            encoder_offsets[i] = calibrate_homing_motor(motor_id, arm)
+            start_positions[i] = 0
+            end_positions[i] = 0
+
+    for i, motor_id in enumerate(arm.motor_indices):
+        if modes[i] == CalibrationMode.LINEAR.name:
+            start_positions[i], end_positions[i] = calibrate_linear_motor(motor_id, arm)
+            encoder_offsets[i] = 0
+
+    print("\nMove arm to rest position")
+    input("Press Enter to continue...")
+
+    print(f"\n calibration of {robot_type} {arm_name} {arm_type} done!")
+
+    # Force drive_mode values (can be static)
+    drive_modes = [0, 1, 0, 0, 1, 0]
+
+    calib_dict = {
+        "homing_offset": encoder_offsets.astype(int).tolist(),
+        "drive_mode": drive_modes,
+        "start_pos": start_positions.astype(int).tolist(),
+        "end_pos": end_positions.astype(int).tolist(),
+        "calib_mode": get_calibration_modes(arm),
+        "motor_names": arm.motor_names,
+    }
+    return calib_dict
+
+
+def run_full_auto_arm_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
+    """TODO(pepijn): Add this method later as extra
+    Example of usage:
+    ```python
+    run_full_auto_arm_calibration(arm, "so100", "left", "follower")
+    ```
+    """
+    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
+
+
+def apply_feetech_offsets_from_calibration(motorsbus: FeetechMotorsBus, calibration_dict: dict):
+    """
+    Reads 'calibration_dict' containing 'homing_offset' and 'motor_names',
+    then writes each motor's offset to the servo's internal Offset (0x1F) in EPROM.
+
+    This version is modified so each homed position (originally 0) will now read
+    2047, i.e. 180° away from 0 in the 4096-count circle. Offsets are permanently
+    stored in EEPROM, so the servo's Present_Position is hardware-shifted even
+    after power cycling.
+
+    Steps:
+      1) Subtract 2047 from the old offset (so 0 -> 2047).
+      2) Clamp to [-2047..+2047].
+      3) Encode sign bit and magnitude into a 12-bit number.
+    """
+
+    homing_offsets = calibration_dict["homing_offset"]
+    motor_names = calibration_dict["motor_names"]
+    start_pos = calibration_dict["start_pos"]
+
+    # Open the write lock, changes to EEPROM do NOT persist yet
+    motorsbus.write("Lock", 1)
+
+    # For each motor, set the 'Offset' parameter
+    for m_name, old_offset in zip(motor_names, homing_offsets, strict=False):
+        # If bus doesn’t have a motor named m_name, skip
+        if m_name not in motorsbus.motors:
+            print(f"Warning: '{m_name}' not found in motorsbus.motors; skipping offset.")
+            continue
+
+        if m_name == "gripper":
+            old_offset = start_pos  # If gripper set the offset to the start position of the gripper
+            continue
+
+        # Shift the offset so the homed position reads 2047
+        new_offset = old_offset - 2047
+
+        # Clamp to [-2047..+2047]
+        if new_offset > 2047:
+            new_offset = 2047
+            print(
+                f"Warning: '{new_offset}' is getting clamped because its larger then 2047; This should not happen!"
+            )
+        elif new_offset < -2047:
+            new_offset = -2047
+            print(
+                f"Warning: '{new_offset}' is getting clamped because its smaller then -2047; This should not happen!"
+            )
+
+        # Determine the direction (sign) bit and magnitude
+        direction_bit = 1 if new_offset < 0 else 0
+        magnitude = abs(new_offset)
+
+        # Combine sign bit (bit 11) with the magnitude (bits 0..10)
+        servo_offset = (direction_bit << 11) | magnitude
+
+        # Write offset to servo
+        motorsbus.write("Offset", servo_offset, motor_names=m_name)
+        print(
+            f"Set offset for {m_name}: "
+            f"old_offset={old_offset}, new_offset={new_offset}, servo_encoded={magnitude} + direction={direction_bit}"
+        )
+
+    motorsbus.write("Lock", 0)
+    print("Offsets have been saved to EEPROM successfully.")

lerobot/common/motors/motors_bus.py

@@ -0,0 +1,46 @@
+import abc
+
+
+class MotorsBus(abc.ABC):
+    """The main LeRobot class for implementing motors buses."""
+
+    def __init__(
+        self,
+        motors: dict[str, tuple[int, str]],
+    ):
+        self.motors = motors
+
+    def __len__(self):
+        return len(self.motors)
+
+    @abc.abstractmethod
+    def connect(self):
+        pass
+
+    @abc.abstractmethod
+    def reconnect(self):
+        pass
+
+    @abc.abstractmethod
+    def set_calibration(self, calibration: dict[str, list]):
+        pass
+
+    @abc.abstractmethod
+    def apply_calibration(self):
+        pass
+
+    @abc.abstractmethod
+    def revert_calibration(self):
+        pass
+
+    @abc.abstractmethod
+    def read(self):
+        pass
+
+    @abc.abstractmethod
+    def write(self):
+        pass
+
+    @abc.abstractmethod
+    def disconnect(self):
+        pass

lerobot/common/motors/utils.py

@@ -12,22 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Protocol
-
-from lerobot.common.robot_devices.motors.configs import (
-    DynamixelMotorsBusConfig,
-    FeetechMotorsBusConfig,
-    MotorsBusConfig,
-)
-
-
-class MotorsBus(Protocol):
-    def motor_names(self): ...
-    def set_calibration(self): ...
-    def apply_calibration(self): ...
-    def revert_calibration(self): ...
-    def read(self): ...
-    def write(self): ...
+from .configs import MotorsBusConfig
+from .motors_bus import MotorsBus
 
 
 def make_motors_buses_from_configs(motors_bus_configs: dict[str, MotorsBusConfig]) -> list[MotorsBus]:
@@ -35,12 +21,12 @@ def make_motors_buses_from_configs(motors_bus_configs: dict[str, MotorsBusConfig
 
     for key, cfg in motors_bus_configs.items():
         if cfg.type == "dynamixel":
-            from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus
+            from .dynamixel import DynamixelMotorsBus
 
             motors_buses[key] = DynamixelMotorsBus(cfg)
 
         elif cfg.type == "feetech":
-            from lerobot.common.robot_devices.motors.feetech import FeetechMotorsBus
+            from lerobot.common.motors.feetech.feetech import FeetechMotorsBus
 
             motors_buses[key] = FeetechMotorsBus(cfg)
 
@@ -52,13 +38,16 @@ def make_motors_buses_from_configs(motors_bus_configs: dict[str, MotorsBusConfig
 
 def make_motors_bus(motor_type: str, **kwargs) -> MotorsBus:
     if motor_type == "dynamixel":
-        from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus
+        from .configs import DynamixelMotorsBusConfig
+        from .dynamixel import DynamixelMotorsBus
 
         config = DynamixelMotorsBusConfig(**kwargs)
         return DynamixelMotorsBus(config)
 
     elif motor_type == "feetech":
-        from lerobot.common.robot_devices.motors.feetech import FeetechMotorsBus
+        from feetech import FeetechMotorsBus
+
+        from .configs import FeetechMotorsBusConfig
 
         config = FeetechMotorsBusConfig(**kwargs)
         return FeetechMotorsBus(config)

lerobot/common/policies/act/modeling_act.py

@@ -119,7 +119,9 @@ class ACTPolicy(PreTrainedPolicy):
         batch = self.normalize_inputs(batch)
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            batch["observation.images"] = [batch[key] for key in self.config.image_features]
+            batch["observation.images"] = torch.stack(
+                [batch[key] for key in self.config.image_features], dim=-4
+            )
 
         # If we are doing temporal ensembling, do online updates where we keep track of the number of actions
         # we are ensembling over.
@@ -147,8 +149,9 @@ class ACTPolicy(PreTrainedPolicy):
         batch = self.normalize_inputs(batch)
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
-            batch["observation.images"] = [batch[key] for key in self.config.image_features]
-
+            batch["observation.images"] = torch.stack(
+                [batch[key] for key in self.config.image_features], dim=-4
+            )
         batch = self.normalize_targets(batch)
         actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
 
@@ -410,10 +413,11 @@ class ACT(nn.Module):
                 "actions must be provided when using the variational objective in training mode."
             )
 
-        if "observation.images" in batch:
-            batch_size = batch["observation.images"][0].shape[0]
-        else:
-            batch_size = batch["observation.environment_state"].shape[0]
+        batch_size = (
+            batch["observation.images"]
+            if "observation.images" in batch
+            else batch["observation.environment_state"]
+        ).shape[0]
 
         # Prepare the latent for input to the transformer encoder.
         if self.config.use_vae and "action" in batch:
@@ -486,21 +490,20 @@ class ACT(nn.Module):
             all_cam_features = []
             all_cam_pos_embeds = []
 
-            # For a list of images, the H and W may vary but H*W is constant.
-            for img in batch["observation.images"]:
-                cam_features = self.backbone(img)["feature_map"]
+            for cam_index in range(batch["observation.images"].shape[-4]):
+                cam_features = self.backbone(batch["observation.images"][:, cam_index])["feature_map"]
+                # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use
+                # buffer
                 cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
-                cam_features = self.encoder_img_feat_input_proj(cam_features)
-
-                # Rearrange features to (sequence, batch, dim).
-                cam_features = einops.rearrange(cam_features, "b c h w -> (h w) b c")
-                cam_pos_embed = einops.rearrange(cam_pos_embed, "b c h w -> (h w) b c")
-
+                cam_features = self.encoder_img_feat_input_proj(cam_features)  # (B, C, h, w)
                 all_cam_features.append(cam_features)
                 all_cam_pos_embeds.append(cam_pos_embed)
-
-            encoder_in_tokens.extend(torch.cat(all_cam_features, axis=0))
-            encoder_in_pos_embed.extend(torch.cat(all_cam_pos_embeds, axis=0))
+            # Concatenate camera observation feature maps and positional embeddings along the width dimension,
+            # and move to (sequence, batch, dim).
+            all_cam_features = torch.cat(all_cam_features, axis=-1)
+            encoder_in_tokens.extend(einops.rearrange(all_cam_features, "b c h w -> (h w) b c"))
+            all_cam_pos_embeds = torch.cat(all_cam_pos_embeds, axis=-1)
+            encoder_in_pos_embed.extend(einops.rearrange(all_cam_pos_embeds, "b c h w -> (h w) b c"))
 
         # Stack all tokens along the sequence dimension.
         encoder_in_tokens = torch.stack(encoder_in_tokens, axis=0)

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -33,7 +33,7 @@ from diffusers.schedulers.scheduling_ddim import DDIMScheduler
 from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
 from torch import Tensor, nn
 
-from lerobot.common.constants import OBS_ENV, OBS_ROBOT
+from lerobot.common.constants import OBS_ENV_STATE, OBS_STATE
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
@@ -238,8 +238,8 @@ class DiffusionModel(nn.Module):
 
     def _prepare_global_conditioning(self, batch: dict[str, Tensor]) -> Tensor:
         """Encode image features and concatenate them all together along with the state vector."""
-        batch_size, n_obs_steps = batch[OBS_ROBOT].shape[:2]
-        global_cond_feats = [batch[OBS_ROBOT]]
+        batch_size, n_obs_steps = batch[OBS_STATE].shape[:2]
+        global_cond_feats = [batch[OBS_STATE]]
         # Extract image features.
         if self.config.image_features:
             if self.config.use_separate_rgb_encoder_per_camera:
@@ -269,7 +269,7 @@ class DiffusionModel(nn.Module):
             global_cond_feats.append(img_features)
 
         if self.config.env_state_feature:
-            global_cond_feats.append(batch[OBS_ENV])
+            global_cond_feats.append(batch[OBS_ENV_STATE])
 
         # Concatenate features then flatten to (B, global_cond_dim).
         return torch.cat(global_cond_feats, dim=-1).flatten(start_dim=1)

lerobot/common/policies/factory.py

@@ -25,7 +25,6 @@ from lerobot.common.envs.utils import env_to_policy_features
 from lerobot.common.policies.act.configuration_act import ACTConfig
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
 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.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
@@ -55,10 +54,6 @@ def get_policy_class(name: str) -> PreTrainedPolicy:
         from lerobot.common.policies.pi0.modeling_pi0 import PI0Policy
 
         return PI0Policy
-    elif name == "pi0fast":
-        from lerobot.common.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy
-
-        return PI0FASTPolicy
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -74,8 +69,6 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return VQBeTConfig(**kwargs)
     elif policy_type == "pi0":
         return PI0Config(**kwargs)
-    elif policy_type == "pi0fast":
-        return PI0FASTConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 

lerobot/common/policies/pi0/modeling_pi0.py

@@ -24,7 +24,7 @@ Designed by Physical Intelligence. Ported from Jax by Hugging Face.
 
 Install pi0 extra dependencies:
 ```bash
-pip install --no-binary=av -e ".[pi0]"
+pip install -e ".[pi0]"
 ```
 
 Example of finetuning the pi0 pretrained model (`pi0_base` in `openpi`):
@@ -57,7 +57,7 @@ import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
 from transformers import AutoTokenizer
 
-from lerobot.common.constants import ACTION, OBS_ROBOT
+from lerobot.common.constants import ACTION, OBS_STATE
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.common.policies.pi0.paligemma_with_expert import (
@@ -271,7 +271,7 @@ class PI0Policy(PreTrainedPolicy):
         self.eval()
 
         if self.config.adapt_to_pi_aloha:
-            batch[OBS_ROBOT] = self._pi_aloha_decode_state(batch[OBS_ROBOT])
+            batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE])
 
         batch = self.normalize_inputs(batch)
 
@@ -303,7 +303,7 @@ class PI0Policy(PreTrainedPolicy):
     def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> tuple[Tensor, dict[str, Tensor]]:
         """Do a full training forward pass to compute the loss"""
         if self.config.adapt_to_pi_aloha:
-            batch[OBS_ROBOT] = self._pi_aloha_decode_state(batch[OBS_ROBOT])
+            batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE])
             batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION])
 
         batch = self.normalize_inputs(batch)
@@ -313,7 +313,7 @@ class PI0Policy(PreTrainedPolicy):
         state = self.prepare_state(batch)
         lang_tokens, lang_masks = self.prepare_language(batch)
         actions = self.prepare_action(batch)
-        actions_is_pad = batch.get("action_is_pad")
+        actions_is_pad = batch.get("actions_is_pad")
 
         loss_dict = {}
         losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time)
@@ -380,7 +380,7 @@ class PI0Policy(PreTrainedPolicy):
 
     def prepare_language(self, batch) -> tuple[Tensor, Tensor]:
         """Tokenize the text input"""
-        device = batch[OBS_ROBOT].device
+        device = batch[OBS_STATE].device
         tasks = batch["task"]
 
         # PaliGemma prompt has to end with a new line
@@ -427,7 +427,7 @@ class PI0Policy(PreTrainedPolicy):
 
     def prepare_state(self, batch):
         """Pad state"""
-        state = pad_vector(batch[OBS_ROBOT], self.config.max_state_dim)
+        state = pad_vector(batch[OBS_STATE], self.config.max_state_dim)
         return state
 
     def prepare_action(self, batch):

lerobot/common/policies/pi0fast/configuration_pi0fast.py

@@ -1,136 +0,0 @@
-from dataclasses import dataclass, field
-
-from lerobot.common.optim.optimizers import AdamWConfig
-from lerobot.common.optim.schedulers import (
-    CosineDecayWithWarmupSchedulerConfig,
-)
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-
-
-@PreTrainedConfig.register_subclass("pi0fast")
-@dataclass
-class PI0FASTConfig(PreTrainedConfig):
-    # Input / output structure.
-    n_obs_steps: int = 1
-    chunk_size: int = 10
-    n_action_steps: int = 5
-
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-            "ACTION": NormalizationMode.MEAN_STD,
-        }
-    )
-
-    # Shorter state and action vectors will be padded
-    max_state_dim: int = 32  # 32
-    max_action_dim: int = 32  # 32
-
-    # Image preprocessing
-    resize_imgs_with_padding: tuple[int, int] = (224, 224)
-    interpolate_like_pi: bool = False
-
-    # Add empty images. Used by pi0_aloha_sim which adds the empty
-    # left and right wrist cameras in addition to the top camera.
-    empty_cameras: int = 0
-
-    # Converts the joint and gripper values from the standard Aloha space to
-    # the space used by the pi internal runtime which was used to train the base model.
-    adapt_to_pi_aloha: bool = False
-
-    # Converts joint dimensions to deltas with respect to the current state before passing to the model.
-    # Gripper dimensions will remain in absolute values.
-    use_delta_joint_actions_aloha: bool = False
-
-    # Tokenizer
-    tokenizer_max_length: int = 48
-
-    # Projector
-    proj_width: int = 1024
-
-    # Decoding
-    max_decoding_steps: int = 256
-    fast_skip_tokens: int = 128  # Skip last 128 tokens in PaliGemma vocab since they are special tokens
-    max_input_seq_len: int = 256  # 512
-
-    # Utils
-    use_cache: bool = True
-
-    # Frozen parameters
-    freeze_vision_encoder: bool = True
-    freeze_lm_head: bool = True
-
-    # Training presets
-    optimizer_lr: float = 1e-4
-    optimizer_betas: tuple[float, float] = (0.9, 0.95)
-    optimizer_eps: float = 1e-8
-    optimizer_weight_decay: float = 1e-5
-
-    scheduler_warmup_steps: int = 1_000
-    scheduler_decay_steps: int = 30_000
-    scheduler_decay_lr: float = 2.5e-6
-
-    checkpoint_path: str = None
-
-    padding_side: str = "right"
-
-    precision: str = "bfloat16"
-    grad_clip_norm: float = 1
-
-    # Allows padding/truncation of generated action tokens during detokenization to ensure decoding.
-    # In the original version, tensors of 0s were generated if shapes didn't match for stable decoding.
-    relaxed_action_decoding: bool = True
-
-    def __post_init__(self):
-        super().__post_init__()
-
-        """Input validation (not exhaustive)."""
-        if self.n_action_steps > self.chunk_size:
-            raise ValueError(
-                f"The chunk size is the upper bound for the number of action steps per model invocation. Got "
-                f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
-            )
-        if self.n_obs_steps != 1:
-            raise ValueError(
-                f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
-            )
-
-    def validate_features(self) -> None:
-        for i in range(self.empty_cameras):
-            key = f"observation.images.empty_camera_{i}"
-            empty_camera = PolicyFeature(
-                type=FeatureType.VISUAL,
-                shape=(3, 480, 640),
-            )
-            self.input_features[key] = empty_camera
-
-    def get_optimizer_preset(self) -> AdamWConfig:
-        return AdamWConfig(
-            lr=self.optimizer_lr,
-            betas=self.optimizer_betas,
-            eps=self.optimizer_eps,
-            weight_decay=self.optimizer_weight_decay,
-            grad_clip_norm=self.grad_clip_norm,
-        )
-
-    def get_scheduler_preset(self):
-        return CosineDecayWithWarmupSchedulerConfig(
-            peak_lr=self.optimizer_lr,
-            decay_lr=self.scheduler_decay_lr,
-            num_warmup_steps=self.scheduler_warmup_steps,
-            num_decay_steps=self.scheduler_decay_steps,
-        )
-
-    @property
-    def observation_delta_indices(self) -> None:
-        return None
-
-    @property
-    def action_delta_indices(self) -> list:
-        return list(range(self.chunk_size))
-
-    @property
-    def reward_delta_indices(self) -> None:
-        return None

lerobot/common/policies/pi0fast/modeling_pi0fast.py

@@ -1,973 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-π0+FAST: Efficient Action Tokenization for Vision-Language-Action Models
-
-[Paper](https://arxiv.org/abs/2501.09747)
-[Jax code](https://github.com/Physical-Intelligence/openpi)
-
-Designed by Physical Intelligence. Ported from Jax by Hugging Face.
-
-Example of finetuning the pi0+FAST pretrained model (`pi0_fast_base` in `openpi`):
-```bash
-python lerobot/scripts/train.py \
---policy.path=lerobot/pi0fast_base \
---dataset.repo_id=danaaubakirova/koch_test
-```
-
-Example of training the pi0+FAST neural network with from scratch:
-```bash
-python lerobot/scripts/train.py \
---policy.type=pi0fast \
---dataset.repo_id=danaaubakirova/koch_test
-```
-
-Example of using the pi0 pretrained model outside LeRobot training framework:
-```python
-policy = PI0FASTPolicy.from_pretrained("lerobot/pi0fast_base")
-```
-
-"""
-
-from collections import deque
-from functools import partial
-
-import numpy as np
-import torch
-import torch.nn.functional as F  # noqa: N812
-from PIL import Image
-from scipy.fft import idct
-from torch import Tensor, nn
-from transformers import AutoProcessor, AutoTokenizer, PaliGemmaForConditionalGeneration
-from transformers.cache_utils import HybridCache, StaticCache
-from transformers.models.auto import CONFIG_MAPPING
-
-from lerobot.common.constants import ACTION, OBS_ROBOT
-from lerobot.common.policies.normalize import Normalize, Unnormalize
-from lerobot.common.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
-from lerobot.common.policies.pretrained import PreTrainedPolicy
-
-PRECISION = {
-    "float16": torch.float16,
-    "float32": torch.float32,
-    "bfloat16": torch.bfloat16,
-}
-
-
-def normalize(x, min_val, max_val):
-    return (x - min_val) / (max_val - min_val)
-
-
-def unnormalize(x, min_val, max_val):
-    return x * (max_val - min_val) + min_val
-
-
-def safe_arcsin(value):
-    # This ensures that the input stays within
-    # [−1,1] to avoid invalid values for arcsin
-    return torch.arcsin(torch.clamp(value, -1.0, 1.0))
-
-
-def aloha_gripper_to_angular(value):
-    # Aloha transforms the gripper positions into a linear space. The following code
-    # reverses this transformation to be consistent with pi0 which is pretrained in
-    # angular space.
-    #
-    # These values are coming from the Aloha code:
-    # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED
-    value = unnormalize(value, min_val=0.01844, max_val=0.05800)
-
-    # This is the inverse of the angular to linear transformation inside the Interbotix code.
-    def linear_to_radian(linear_position, arm_length, horn_radius):
-        value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position)
-        return safe_arcsin(value)
-
-    # The constants are taken from the Interbotix code.
-    value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022)
-
-    # Normalize to [0, 1].
-    # The values 0.4 and 1.5 were measured on an actual Trossen robot.
-    return normalize(value, min_val=0.4, max_val=1.5)
-
-
-def aloha_gripper_from_angular(value):
-    # Convert from the gripper position used by pi0 to the gripper position that is used by Aloha.
-    # Note that the units are still angular but the range is different.
-
-    # The values 0.4 and 1.5 were measured on an actual Trossen robot.
-    value = unnormalize(value, min_val=0.4, max_val=1.5)
-
-    # These values are coming from the Aloha code:
-    # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE
-    return normalize(value, min_val=-0.6213, max_val=1.4910)
-
-
-def aloha_gripper_from_angular_inv(value):
-    # Directly inverts the gripper_from_angular function.
-    value = unnormalize(value, min_val=-0.6213, max_val=1.4910)
-    return normalize(value, min_val=0.4, max_val=1.5)
-
-
-class PI0FASTPolicy(PreTrainedPolicy):
-    """Wrapper class around PI0FAST tokenizer and model to train and run inference within LeRobot."""
-
-    config_class = PI0FASTConfig
-    name = "pi0fast"
-
-    def __init__(
-        self,
-        config: PI0FASTConfig,
-        dataset_stats: dict[str, dict[str, Tensor]] | None = None,
-    ):
-        """
-        Args:
-            config: Policy configuration class instance or None, in which case the default instantiation of
-                    the configuration class is used.
-            dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected
-                that they will be passed with a call to `load_state_dict` before the policy is used.
-        """
-
-        super().__init__(config)
-        config.validate_features()
-        self.config = config
-
-        self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats)
-        self.normalize_targets = Normalize(
-            config.output_features, config.normalization_mapping, dataset_stats
-        )
-        self.unnormalize_outputs = Unnormalize(
-            config.output_features, config.normalization_mapping, dataset_stats
-        )
-
-        self.language_tokenizer = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224")
-        self.model = PI0FAST(config)
-
-        self.reset()
-
-    def reset(self):
-        """This should be called whenever the environment is reset."""
-        self._action_queue = deque([], maxlen=self.config.n_action_steps)
-
-    def get_optim_params(self) -> dict:
-        return self.parameters()
-
-    def _pi_aloha_decode_state(self, state):
-        # Flip the joints.
-        for motor_idx in [1, 2, 8, 9]:
-            state[:, motor_idx] *= -1
-        # Reverse the gripper transformation that is being applied by the Aloha runtime.
-        for motor_idx in [6, 13]:
-            state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx])
-        return state
-
-    def _pi_aloha_encode_actions(self, actions):
-        # Flip the joints.
-        for motor_idx in [1, 2, 8, 9]:
-            actions[:, :, motor_idx] *= -1
-        # Reverse the gripper transformation that is being applied by the Aloha runtime.
-        for motor_idx in [6, 13]:
-            actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx])
-        return actions
-
-    def _pi_aloha_encode_actions_inv(self, actions):
-        # Flip the joints again.
-        for motor_idx in [1, 2, 8, 9]:
-            actions[:, :, motor_idx] *= -1
-        # Reverse the gripper transformation that is being applied by the Aloha runtime.
-        for motor_idx in [6, 13]:
-            actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx])
-        return actions
-
-    @torch.no_grad
-    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
-        """Select a single action given environment observations.
-
-        This method wraps `select_actions` in order to return one action at a time for execution in the
-        environment. It works by managing the actions in a queue and only calling `select_actions` when the
-        queue is empty.
-        """
-        self.eval()
-
-        if self.config.adapt_to_pi_aloha:
-            batch[OBS_ROBOT] = self._pi_aloha_decode_state(batch[OBS_ROBOT])
-
-        batch = self.normalize_inputs(batch)
-
-        # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by
-        # querying the policy.
-        if len(self._action_queue) == 0:
-            actions = self.model.generate_actions(batch)
-
-            actions = actions[:, : self.config.n_action_steps]
-
-            original_action_dim = self.config.action_feature.shape[
-                0
-            ]  # self.config.max_action_dim  # self.config.action_feature.shape[0]
-            actions = actions[:, :, :original_action_dim]
-
-            actions = self.unnormalize_outputs({"action": actions})["action"]
-
-            if self.config.adapt_to_pi_aloha:
-                actions = self._pi_aloha_encode_actions(actions)
-
-            # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
-            # effectively has shape (n_action_steps, batch_size, *), hence the transpose.
-            self._action_queue.extend(actions.transpose(0, 1))
-        return self._action_queue.popleft()
-
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        if self.config.adapt_to_pi_aloha:
-            batch[OBS_ROBOT] = self._pi_aloha_decode_state(batch[OBS_ROBOT])
-            batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION])
-        batch = self.normalize_inputs(batch)
-        batch = self.normalize_targets(batch)
-        loss_dict = self.model.forward(batch)
-        return loss_dict["loss"], loss_dict
-
-
-def block_causal_update_causal_mask(
-    attention_mask,
-    token_type_ids=None,
-    past_key_values=None,
-    cache_position=None,
-    input_tensor=None,
-    attn_implementation: str = "eager",
-    dtype: torch.dtype = "float32",
-):
-    """
-    Update the causal mask during training and generation. It can be customized to different attention masks.
-    """
-    if attn_implementation == "flash_attention_2":
-        if attention_mask is not None and 0.0 in attention_mask:
-            return attention_mask
-        return None
-    using_static_cache = isinstance(past_key_values, StaticCache)
-    min_dtype = torch.finfo(dtype).min
-
-    if input_tensor is None:
-        input_tensor = attention_mask
-
-    inputs_lead_dim, sequence_length = input_tensor.shape[:2]
-
-    if using_static_cache or isinstance(past_key_values, HybridCache):
-        target_length = past_key_values.get_max_cache_shape()
-    else:
-        target_length = (
-            attention_mask.shape[-1]
-            if isinstance(attention_mask, torch.Tensor)
-            else cache_position[0] + sequence_length + 1
-        )
-
-    # Handle precomputed attention masks
-    if attention_mask is not None and attention_mask.dim() == 4:
-        return attention_mask
-
-    # Causal mask initialization
-    causal_mask = torch.full(
-        (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
-    )
-
-    # Standard causal masking (triu ensures tokens can only attend to past)
-    if sequence_length != 1:
-        causal_mask = torch.triu(causal_mask, diagonal=1)
-
-        # Apply block causal mask
-        if token_type_ids is not None:
-            token_type_ids = token_type_ids.to(causal_mask.device).bool()
-            cumsum = torch.cumsum(token_type_ids, dim=1)
-            block_causal_mask = cumsum[:, None, :] <= cumsum[:, :, None]
-
-            # Combine causal_mask with block-wise attention mask
-            causal_mask = torch.where(block_causal_mask, 0.0, causal_mask)
-            causal_mask = causal_mask[:, None, :, :]
-        else:
-            # Apply past cache position constraint
-            causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
-                -1, 1
-            )
-            causal_mask = causal_mask[None, None, :, :].expand(inputs_lead_dim, 1, -1, -1)
-    else:
-        # Apply past cache position constraint
-        causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
-            -1, 1
-        )
-        causal_mask = causal_mask[None, None, :, :].expand(inputs_lead_dim, 1, -1, -1)
-
-    if attention_mask is not None:
-        causal_mask = causal_mask.clone()  # Copy to contiguous memory for in-place edits
-        mask_length = attention_mask.shape[-1]
-
-        # Apply padding mask
-        padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
-            causal_mask.device
-        )
-        padding_mask = padding_mask == 0
-        causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-            padding_mask, min_dtype
-        )
-
-    return causal_mask
-
-
-def prepare_inputs_for_generation(
-    # self,
-    input_ids,
-    past_key_values=None,
-    inputs_embeds=None,
-    cache_position=None,
-    position_ids=None,
-    pixel_values=None,
-    attention_mask=None,
-    token_type_ids=None,
-    use_cache=True,
-    num_logits_to_keep=None,
-    labels=None,
-    self=None,
-    **kwargs,
-):
-    # create block causal attention
-    if cache_position[0] > 0 and input_ids.shape[1] > 0:
-        input_tensor = input_ids[:, -1:]
-        new_positions = (
-            torch.ones(
-                (position_ids.shape[0], input_ids.shape[1]),
-                dtype=position_ids.dtype,
-                device=position_ids.device,
-            ).cumsum(-1)
-            + position_ids[:, -1:]
-        )
-        position_ids = torch.cat([position_ids, new_positions], dim=-1)
-    else:
-        input_tensor = inputs_embeds
-    attention_mask = block_causal_update_causal_mask(
-        attention_mask=attention_mask,
-        past_key_values=past_key_values,
-        cache_position=cache_position,
-        input_tensor=input_tensor,
-        token_type_ids=token_type_ids,
-        dtype=self.dtype,
-        attn_implementation=self.config.text_config._attn_implementation,
-    )
-    # Overwritten -- custom `position_ids` and `pixel_values` handling
-    model_inputs = self.language_model.prepare_inputs_for_generation(
-        input_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        cache_position=cache_position,
-        use_cache=use_cache,
-        num_logits_to_keep=num_logits_to_keep,
-        token_type_ids=token_type_ids,
-        **kwargs,
-    )
-
-    # Position_ids in Paligemma are 1-indexed
-    if model_inputs.get("position_ids") is not None:
-        model_inputs["position_ids"] += 1
-    # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
-    # Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always
-    if cache_position[0] == 0:
-        model_inputs["pixel_values"] = pixel_values
-    is_training = token_type_ids is not None and labels is not None
-    if cache_position[0] == 0 and isinstance(past_key_values, HybridCache):
-        input_tensor = inputs_embeds if inputs_embeds is not None else input_ids
-        causal_mask = self._update_causal_mask(
-            attention_mask, token_type_ids, past_key_values, cache_position, input_tensor, is_training
-        )
-        model_inputs["attention_mask"] = causal_mask
-
-    return model_inputs
-
-
-class PI0FAST(nn.Module):
-    def __init__(self, config: PI0FASTConfig):
-        super().__init__()
-        self.config = config
-
-        # TODO: move tokenizers in Policy
-        fast_tokenizer_path = "physical-intelligence/fast"
-        pi0_paligemma_path = "google/paligemma-3b-pt-224"
-        self.paligemma_tokenizer = AutoTokenizer.from_pretrained(pi0_paligemma_path)
-        self.processor = AutoProcessor.from_pretrained(pi0_paligemma_path)
-        self.fast_tokenizer = AutoProcessor.from_pretrained(fast_tokenizer_path, trust_remote_code=True)
-        self.fast_skip_tokens = self.config.fast_skip_tokens
-        self.max_input_seq_len = self.config.max_input_seq_len
-        self.action_horizon = self.config.chunk_size
-        self.action_dim = self.config.action_feature.shape[
-            0
-        ]  # self.config.max_action_dim  # self.config.action_feature.shape[0]
-        precision = config.precision
-        torch_precision = PRECISION.get(precision, torch.float32)
-        self.pad_token_id = (
-            self.paligemma_tokenizer.pad_token_id
-            if hasattr(self.paligemma_tokenizer, "pad_token_id")
-            else self.paligemma_tokenizer.eos_token_id
-        )
-
-        paligemma_config = CONFIG_MAPPING["paligemma"](
-            transformers_version="4.48.1",
-            _vocab_size=257152,
-            bos_token_id=2,
-            eos_token_id=1,
-            hidden_size=2048,
-            image_token_index=257152,
-            model_type="paligemma",
-            pad_token_id=0,
-            projection_dim=2048,
-            text_config={
-                "hidden_activation": "gelu_pytorch_tanh",
-                "hidden_size": 2048,
-                "intermediate_size": 16384,
-                "model_type": "gemma",
-                "num_attention_heads": 8,
-                "num_hidden_layers": 18,
-                "num_image_tokens": 256,
-                "num_key_value_heads": 1,
-                "torch_dtype": precision,
-                "vocab_size": 257152,
-                "_attn_implementation": "eager",
-            },
-            vision_config={
-                "hidden_size": 1152,
-                "intermediate_size": 4304,
-                "model_type": "siglip_vision_model",
-                "num_attention_heads": 16,
-                "num_hidden_layers": 27,
-                "num_image_tokens": 256,
-                "patch_size": 14,
-                "projection_dim": 2048,
-                "projector_hidden_act": "gelu_pytorch_tanh",
-                "torch_dtype": precision,
-                "vision_use_head": False,
-            },
-        )
-        self.pi0_paligemma = PaliGemmaForConditionalGeneration(config=paligemma_config)
-
-        self.pi0_paligemma.prepare_inputs_for_generation = partial(
-            prepare_inputs_for_generation, self=self.pi0_paligemma
-        )
-        # change important stuff in bf16
-        params_to_change_dtype = [
-            "language_model",
-            "vision_tower",
-            "multi_modal",
-        ]
-        for name, param in self.pi0_paligemma.named_parameters():
-            if any(selector in name for selector in params_to_change_dtype):
-                param.data = param.data.to(dtype=torch_precision)
-        self.set_requires_grad()
-        self.image_keys = self.config.image_features.keys()
-        self.ignore_index = self.pi0_paligemma.config.ignore_index
-        self.padding_side = self.config.padding_side
-
-    def set_requires_grad(self):
-        if self.config.freeze_vision_encoder:
-            self.pi0_paligemma.vision_tower.eval()
-            for params in self.pi0_paligemma.vision_tower.parameters():
-                params.requires_grad = False
-        # To avoid unused params issue with distributed training
-        if self.config.freeze_lm_head:
-            for name, params in self.pi0_paligemma.named_parameters():
-                if "embed_tokens" in name:  # lm heads and embedding layer are tied
-                    params.requires_grad = False
-
-    def embed_tokens(self, tokens: torch.Tensor):
-        return self.pi0_paligemma.language_model.model.embed_tokens(tokens)
-
-    def prepare_inputs_for_generation(self, *args, **kwargs):
-        return self.pi0_paligemma.prepare_inputs_for_generation(*args, **kwargs)
-
-    def prepare_images(self, batch):
-        """Preprocess LeRobot batch into Pi0 inputs"""
-        images = []
-        img_masks = []
-        present_img_keys = [key for key in self.image_keys if key in batch]
-        if len(present_img_keys) == 0:
-            raise ValueError(
-                f"All image features are missing from the batch. At least one expected. (batch: {batch.keys()}) (image_features:{self.config.image_features})"
-            )
-
-        # Preprocess image features present in the batch
-        num_empty_cameras = 0
-        for key in self.image_keys:
-            if key in present_img_keys:
-                img = batch[key]
-
-                if self.config.resize_imgs_with_padding is not None:
-                    img = resize_with_pad(
-                        img,
-                        *self.config.resize_imgs_with_padding,
-                        pad_value=0,
-                        interpolate_like_pi=self.config.interpolate_like_pi,
-                    )
-
-                # Normalize from range [0,1] to [-1,1] as expacted by siglip
-                img = img * 2.0 - 1.0
-
-                bsize = img.shape[0]
-                device = img.device
-                mask = torch.ones(bsize, dtype=torch.bool, device=device)
-            else:
-                if num_empty_cameras >= self.config.empty_cameras:
-                    continue
-                img = torch.ones_like(img) * -1
-                bsize = img.shape[0]
-                device = img.device
-                mask = torch.ones(bsize, dtype=torch.bool, device=device)
-                num_empty_cameras += 1
-
-            images.append(img)
-            img_masks.append(mask)
-        return images, img_masks
-
-    def normalize_actions(self, actions: torch.Tensor) -> torch.Tensor:
-        mins = actions.amin(dim=(1, 2), keepdim=True)  # [0]
-        maxs = actions.amax(dim=(1, 2), keepdim=True)  # [0]
-        return 2 * (actions - mins) / (maxs - mins + 1e-8) - 1
-
-    def _act_tokens_to_paligemma_tokens(self, tokens: torch.Tensor) -> torch.Tensor:
-        out = self.paligemma_tokenizer.vocab_size - 1 - self.fast_skip_tokens - tokens
-        return out
-
-    def fast_tokenizer_wrapper(self, actions_norm):
-        """
-        A wrapper for self.fast_tokenizer that ensures batch processing,
-        conversion to PyTorch tensors, and returns a dictionary without padding.
-        """
-        batch_tokens = self.fast_tokenizer(actions_norm)
-        fast_out = self.processor.tokenizer.pad({"input_ids": batch_tokens}, return_tensors="pt")
-
-        return fast_out
-
-    def create_token_type_ids(self, padded_mask: torch.Tensor, prefix_len: int) -> torch.Tensor:
-        token_type_ids = torch.zeros_like(padded_mask, dtype=torch.bool)
-        # Compute cumulative sum mask
-        cumsum_mask = (padded_mask != 0).cumsum(dim=1)
-        # Suffix block (everything after prefix_len)
-        suffix_mask = cumsum_mask > prefix_len
-        token_type_ids = suffix_mask
-        return token_type_ids
-
-    def create_input_tokens(self, state, lang_text, actions=None):
-        bsize = state.shape[0]
-        device = state.device
-        bins = torch.linspace(-1, 1, 256 + 1, device=device)[:-1]
-        discretized = torch.bucketize(state, bins) - 1
-        discretized = discretized[:, :32]
-
-        prefix_texts = []
-        state_text = []
-        for txt, disc in zip(lang_text, discretized, strict=False):
-            cleaned = txt.lower().strip().replace("_", " ")
-            state_str = " ".join(str(val.item()) for val in disc)
-            prefix_texts.append(f"Task: {cleaned}, State: {state_str};\n")
-            state_text.append(f"State: {state_str};\n")
-
-        prefix_out = self.paligemma_tokenizer(
-            prefix_texts, add_special_tokens=True, return_tensors="pt", padding="longest", truncation=False
-        )
-        prefix_ids = prefix_out["input_ids"].to(device)
-        prefix_mask = prefix_out["attention_mask"].to(device)
-        prefix_lens = prefix_mask.sum(dim=1)[:, None].cpu()
-
-        if actions is not None:
-            actions_norm = self.normalize_actions(actions)
-            actions_pad = F.pad(
-                actions_norm, (0, max(0, self.config.max_action_dim - actions_norm.shape[2])), value=0
-            )[:, :, : self.config.max_action_dim]
-            fast_out = self.fast_tokenizer_wrapper(
-                actions_pad.cpu(),
-            )
-            act_ids = fast_out["input_ids"]
-            act_mask = fast_out["attention_mask"].to(device)
-
-            act_ids = self._act_tokens_to_paligemma_tokens(act_ids).to(device)
-            # Replace action with 0 to pad tokens
-            act_ids = torch.where(
-                act_ids == self.paligemma_tokenizer.vocab_size - 1 - self.fast_skip_tokens,
-                self.pad_token_id,
-                act_ids,
-            )
-
-            eos_token = torch.tensor(
-                [self.paligemma_tokenizer.eos_token_id], dtype=torch.long, device=device
-            ).expand(bsize, -1)
-            eos_mask = torch.tensor([1], dtype=torch.long, device=device).expand(bsize, -1)
-            bos = self.paligemma_tokenizer("Action: ", add_special_tokens=False, return_tensors="pt")
-            bos_token = bos["input_ids"].expand(act_ids.shape[0], -1).to(device)
-            bos_mask = bos["attention_mask"].expand(act_ids.shape[0], -1).to(device)
-            act_ids = torch.cat([bos_token, act_ids, eos_token], dim=1)
-            act_mask = torch.cat([bos_mask, act_mask, eos_mask], dim=1)
-            act_mask = act_mask.to(device)
-        else:
-            act_ids = torch.empty(bsize, self.pad_token_id, dtype=torch.long, device=device)
-            act_mask = torch.empty(bsize, 0, dtype=torch.long, device=device)
-        final_ids = torch.cat([prefix_ids, act_ids], dim=1)
-
-        final_mask = torch.cat([prefix_mask, act_mask], dim=1)
-        batch_inputs = {"input_ids": final_ids.tolist(), "attention_mask": final_mask.tolist()}
-
-        # Use tokenizer pad function
-        padded_output = self.paligemma_tokenizer.pad(
-            batch_inputs, padding="longest", max_length=180, return_tensors="pt"
-        )
-        padded_mask = padded_output["attention_mask"]
-
-        # define tensor of padding lengths
-        att_mask = (padded_mask != 0).cumsum(dim=1) > prefix_lens
-
-        token_type_ids = self.create_token_type_ids(padded_mask=padded_mask, prefix_len=prefix_lens)
-
-        padded_output["padded_mask"] = padded_output.pop("attention_mask")
-        padded_output["attention_mask"] = att_mask
-        # loss is computed not on prefix, and not on padding
-        padded_output["loss_mask"] = att_mask & padded_output["padded_mask"]
-        padded_output["token_type_ids"] = token_type_ids
-        return padded_output
-
-    def shift_padding_side(
-        self,
-        tokens: torch.Tensor,
-        ar_mask: torch.Tensor,
-        padding_mask: torch.Tensor,
-        loss_mask: torch.Tensor,
-        targets: torch.Tensor,
-        token_type_ids: torch.Tensor,
-        padding_side: str = "right",
-    ) -> tuple[torch.Tensor]:
-        if padding_side not in ["right", "left"]:
-            return tokens, ar_mask, padding_mask, loss_mask, targets, token_type_ids
-
-        new_tokens = torch.empty_like(tokens)
-        new_ar_masks = torch.empty_like(ar_mask)
-        new_padding_mask = torch.empty_like(padding_mask)
-        new_loss_mask = torch.empty_like(loss_mask)
-        new_targets = torch.empty_like(targets)
-        new_token_type_ids = torch.empty_like(token_type_ids)
-        batch_size = tokens.shape[0]
-        for i in range(batch_size):
-            padding_indices = torch.where(padding_mask[i] == 0)[0]
-            non_padding_indices = torch.where(padding_mask[i] == 1)[0]
-            if padding_side == "left":
-                new_indices = torch.cat((padding_indices, non_padding_indices), dim=0)
-            else:
-                new_indices = torch.cat((non_padding_indices, padding_indices), dim=0)
-            new_tokens[i] = tokens[i].index_select(0, new_indices)
-            new_ar_masks[i] = ar_mask[i].index_select(0, new_indices)
-            new_padding_mask[i] = padding_mask[i].index_select(0, new_indices)
-            new_loss_mask[i] = loss_mask[i].index_select(0, new_indices)
-            new_targets[i] = targets[i].index_select(0, new_indices)
-            new_token_type_ids[i] = token_type_ids[i].index_select(0, new_indices)
-
-        return new_tokens, new_ar_masks, new_padding_mask, new_loss_mask, new_targets, new_token_type_ids
-
-    def forward(self, batch: dict[str, Tensor]):
-        device = batch[OBS_ROBOT].device
-        # TODO: keep like this or move to the policy .forward
-        images, img_masks = self.prepare_images(batch)
-
-        padded_outs = self.create_input_tokens(
-            state=batch[OBS_ROBOT],
-            lang_text=batch["task"],
-            actions=batch[ACTION],
-        )
-
-        embs, pad_masks, _, targets, loss_mask, token_type_ids = self.embed_inputs(
-            images,
-            img_masks,
-            padded_outs["input_ids"],
-            padded_outs["padded_mask"],
-            padded_outs["attention_mask"],
-            padded_outs["loss_mask"],
-            padded_outs["token_type_ids"],
-            padding_side=self.padding_side,
-        )
-        position_ids = torch.cumsum(pad_masks, dim=1) - 1
-        token_type_ids = token_type_ids.to(dtype=torch.int64)
-        past_seen_tokens = 0
-        cache_position = torch.arange(past_seen_tokens, past_seen_tokens + embs.shape[1], device=embs.device)
-        pad_masks = block_causal_update_causal_mask(
-            attention_mask=pad_masks,
-            past_key_values=None,
-            cache_position=cache_position,
-            input_tensor=embs,
-            token_type_ids=token_type_ids,
-            dtype=self.pi0_paligemma.dtype,
-            attn_implementation=self.pi0_paligemma.config.text_config._attn_implementation,
-        )
-        outputs = self.pi0_paligemma.forward(
-            input_ids=None,
-            token_type_ids=None,
-            attention_mask=pad_masks,
-            position_ids=position_ids,
-            past_key_values=None,
-            inputs_embeds=embs,
-            use_cache=False,
-            labels=None,
-        )
-
-        logits = outputs.logits
-
-        loss_fct = nn.CrossEntropyLoss(reduction="none")
-
-        # Shift left for next-step prediction
-        logits = logits[:, :-1, :]
-        targets = targets[:, 1:].to(device)  # Shift targets
-        loss_mask = loss_mask[:, 1:].to(device)  # Ensure correct shape
-
-        # Compute per-token loss
-        token_loss = loss_fct(logits.reshape(-1, logits.shape[-1]), targets.reshape(-1))
-
-        # Apply loss mask
-        token_loss = token_loss * loss_mask.reshape(-1)
-
-        # Compute final loss
-        loss = token_loss.sum() / torch.clamp(loss_mask.sum(), min=1)
-
-        # Return loss dictionary
-        loss_dict = {"ce_loss": loss.item(), "loss": loss}
-        return loss_dict
-
-    def decode_actions_with_fast(
-        self,
-        tokens: list[list[int]],
-        *,
-        time_horizon: int | None = None,
-        action_dim: int | None = None,
-        relaxed_decoding: bool = True,
-    ) -> np.array:
-        """
-        Adapt original decoding in FAST to always return actions instead of zeros.
-        """
-        self.time_horizon = (
-            time_horizon or self.fast_tokenizer.time_horizon or self.fast_tokenizer.called_time_horizon
-        )
-        self.action_dim = (
-            action_dim or self.fast_tokenizer.action_dim or self.fast_tokenizer.called_action_dim
-        )
-
-        # Cache the time horizon and action dimension for the next call
-        self.called_time_horizon = self.time_horizon
-        self.called_action_dim = self.action_dim
-
-        assert self.time_horizon is not None and self.action_dim is not None, (
-            "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
-        )
-
-        decoded_actions = []
-        for token in tokens:
-            try:
-                decoded_tokens = self.fast_tokenizer.bpe_tokenizer.decode(token)
-                decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.fast_tokenizer.min_token
-                if relaxed_decoding:
-                    # Expected sequence length
-                    expected_seq_len = self.time_horizon * self.action_dim
-                    diff = expected_seq_len - decoded_dct_coeff.shape[0]
-                    # Apply truncation if too long
-                    if diff < 0:
-                        decoded_dct_coeff = decoded_dct_coeff[:expected_seq_len]  # Truncate on the right
-                    # Apply padding if too short
-                    elif diff > 0:
-                        decoded_dct_coeff = np.pad(
-                            decoded_dct_coeff, (0, diff), mode="constant", constant_values=0
-                        )
-
-                decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
-                assert decoded_dct_coeff.shape == (
-                    self.time_horizon,
-                    self.action_dim,
-                ), (
-                    f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
-                )
-            except Exception as e:
-                print(f"Error decoding tokens: {e}")
-                print(f"Tokens: {token}")
-                decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
-            decoded_actions.append(idct(decoded_dct_coeff / self.fast_tokenizer.scale, axis=0, norm="ortho"))
-        return np.stack(decoded_actions)
-
-    def extract_actions(self, tokens: torch.Tensor, action_horizon: int, action_dim: int) -> torch.Tensor:
-        """
-        Extracts actions from predicted output tokens using the FAST model.
-
-        Args:
-            tokens (torch.Tensor): The input tensor of tokenized outputs.
-            action_horizon (int): The number of timesteps for actions.
-            action_dim (int): The dimensionality of each action.
-
-        Returns:
-            torch.Tensor: The extracted actions as a tensor of shape (action_horizon, action_dim).
-        """
-        # Decode predicted output tokens
-        decoded_tokens = self.paligemma_tokenizer.batch_decode(tokens, skip_special_tokens=True)
-        cleaned_tokens = [
-            tokens_sequence.replace("Action:", "").replace(":", "").strip().split("|")[0].strip()
-            for tokens_sequence in decoded_tokens
-        ]
-        raw_action_tokens = [
-            self.processor.tokenizer.encode(sample_tokens, return_tensors="pt", padding=False)
-            for sample_tokens in cleaned_tokens
-        ]  # something like this should be robust #looks good
-        action_tokens = [
-            self._act_tokens_to_paligemma_tokens(raw_action_token) for raw_action_token in raw_action_tokens
-        ]
-        # returns the tensor of decoded actions per sample in a list
-        decoded_actions = [
-            torch.tensor(
-                self.decode_actions_with_fast(
-                    tok.tolist(),
-                    time_horizon=action_horizon,
-                    action_dim=action_dim,
-                    relaxed_decoding=self.config.relaxed_action_decoding,
-                ),
-                device=tokens.device,
-            ).squeeze(0)
-            for tok in action_tokens
-        ]
-
-        return torch.stack(
-            decoded_actions,
-            dim=0,
-        )
-
-    def generate_actions(self, batch: dict[str, Tensor]):
-        # TODO: keep like this or move to the policy .forward
-        images, img_masks = self.prepare_images(batch)
-
-        padded_outs = self.create_input_tokens(state=batch[OBS_ROBOT], lang_text=batch["task"], actions=None)
-        embs, pad_masks, att_masks2, targets, loss_mask, token_type_ids = self.embed_inputs(
-            images,
-            img_masks,
-            padded_outs["input_ids"],
-            padded_outs["padded_mask"],
-            padded_outs["attention_mask"],
-            padded_outs["loss_mask"],
-            padded_outs["token_type_ids"],
-            padding_side="left",
-        )
-        token_type_ids = token_type_ids.to(dtype=torch.int64)
-        prefix_position_ids = torch.cumsum(pad_masks, dim=1) - 1
-        output_tokens = self.pi0_paligemma.generate(
-            input_ids=None,
-            attention_mask=pad_masks,
-            position_ids=prefix_position_ids,
-            past_key_values=None,
-            inputs_embeds=embs,
-            use_cache=self.config.use_cache,
-            max_new_tokens=self.config.max_decoding_steps,
-            do_sample=False,
-            num_beams=1,
-            token_type_ids=token_type_ids,
-        )
-        actions = self.extract_actions(output_tokens, self.action_horizon, self.action_dim)
-        return actions
-
-    def embed_image(self, image: torch.Tensor):
-        return self.pi0_paligemma.get_image_features(image)
-
-    def embed_inputs(
-        self,
-        images,
-        img_masks,
-        tokens,
-        pad_mask,
-        ar_mask,
-        loss_mask,
-        token_type_ids,
-        padding_side: str = "right",
-    ):
-        # TODO: avoid list in python and torch.cat ; prefer pre-allocation with torch.empty
-        # images are a list of same size
-        # vectorizing everything!
-        device = images[0].device
-        image_embedding_dim = images[0].shape[-1]  # TODO should be from self.config
-        all_images = torch.stack(images, dim=1).to(device)
-        b, n, c, h, w = all_images.shape
-        all_images = all_images.view(b * n, c, h, w)
-        embedded = self.embed_image(all_images).to(device)
-        b_n, p, image_embedding_dim = embedded.shape  # Extract current dimensions
-        m = b_n // b  # Compute the number of images per sample dynamically
-
-        # Reshape dynamically
-        embedded = embedded.view(b, m, p, image_embedding_dim)
-        tokens_embs = self.embed_tokens(tokens.to(device))
-
-        img_masks = torch.stack(img_masks, dim=1).unsqueeze(-1).to(device)
-        num_img_emb = embedded.shape[2]
-        img_pad_masks = img_masks.repeat(1, 1, num_img_emb).view(b, -1)
-        img_att_masks = torch.zeros((b, n, num_img_emb), dtype=torch.long, device=device).reshape(b, -1)
-
-        image_target_tokens = (
-            torch.ones((b, n, num_img_emb), dtype=torch.long, device=device) * self.pad_token_id
-        ).reshape(b, -1)
-        image_loss_mask = torch.zeros((b, n, num_img_emb), dtype=torch.long, device=device).reshape(b, -1)
-
-        embedded = embedded.reshape(b, n * num_img_emb, image_embedding_dim)  # Shape: (B, N*P, D)
-
-        embs = torch.cat([embedded, tokens_embs], dim=1).to(device)
-        pad_masks = torch.cat([img_pad_masks, pad_mask.to(device)], dim=1)
-        att_masks = torch.cat([img_att_masks, ar_mask.to(device)], dim=1)
-        loss_masks = torch.cat([image_loss_mask, loss_mask.to(device)], dim=1)
-        targets = torch.cat([image_target_tokens, tokens.to(device)], dim=1)
-        token_type_ids = torch.cat([img_att_masks, token_type_ids.to(device)], dim=1)
-
-        # Shift pad tokens to the left (.generate()) or right (.train())
-        embs, att_masks, pad_masks, loss_masks, targets, token_type_ids = self.shift_padding_side(
-            embs, att_masks, pad_masks, loss_masks, targets, token_type_ids, padding_side=padding_side
-        )
-
-        targets = torch.where(targets == self.pad_token_id, self.ignore_index, targets)
-        return embs, pad_masks, att_masks, targets, loss_masks, token_type_ids
-
-
-def resize_with_pad(img, width, height, pad_value=0, interpolate_like_pi=True):
-    # assume no-op when width height fits already
-    if img.ndim != 4:
-        raise ValueError(f"(b,c,h,w) expected, but {img.shape}")
-
-    cur_height, cur_width = img.shape[2:]
-
-    ratio = max(cur_width / width, cur_height / height)
-    resized_height = int(cur_height / ratio)
-    resized_width = int(cur_width / ratio)
-
-    if interpolate_like_pi:
-        img = (img * 255.0).to(dtype=torch.uint8)
-        img = img.permute(0, 2, 3, 1)
-        original_device = img.device
-        img = img.to(device="cpu").numpy()
-        imgs = []
-        for sub_img in img:
-            sub_img = Image.fromarray(sub_img)
-            resized_img = sub_img.resize((resized_width, resized_height), resample=2)
-            resized_img = torch.from_numpy(np.array(resized_img))
-            imgs.append(resized_img)
-        img = torch.stack(imgs, dim=0)
-        img = img.permute(0, 3, 1, 2)
-        resized_img = img.to(device=original_device, dtype=torch.float32) / 255.0
-    else:
-        resized_img = F.interpolate(
-            img, size=(resized_height, resized_width), mode="bilinear", align_corners=False
-        )
-
-    pad_height = max(0, int(height - resized_height))
-    pad_width = max(0, int(width - resized_width))
-
-    # pad on left and top of image
-    padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value)
-    return padded_img

lerobot/common/policies/tdmpc/modeling_tdmpc.py

@@ -35,7 +35,7 @@ import torch.nn as nn
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor
 
-from lerobot.common.constants import OBS_ENV, OBS_ROBOT
+from lerobot.common.constants import OBS_ENV_STATE, OBS_STATE
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
@@ -122,7 +122,7 @@ class TDMPCPolicy(PreTrainedPolicy):
 
         # When the action queue is depleted, populate it again by querying the policy.
         if len(self._queues["action"]) == 0:
-            batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch if key in self._queues}
+            batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
 
             # Remove the time dimensions as it is not handled yet.
             for key in batch:
@@ -753,9 +753,9 @@ class TDMPCObservationEncoder(nn.Module):
                 )
             )
         if self.config.env_state_feature:
-            feat.append(self.env_state_enc_layers(obs_dict[OBS_ENV]))
+            feat.append(self.env_state_enc_layers(obs_dict[OBS_ENV_STATE]))
         if self.config.robot_state_feature:
-            feat.append(self.state_enc_layers(obs_dict[OBS_ROBOT]))
+            feat.append(self.state_enc_layers(obs_dict[OBS_STATE]))
         return torch.stack(feat, dim=0).mean(0)
 
 

lerobot/common/robot_devices/cameras/utils.py

@@ -1,67 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Protocol
-
-import numpy as np
-
-from lerobot.common.robot_devices.cameras.configs import (
-    CameraConfig,
-    IntelRealSenseCameraConfig,
-    OpenCVCameraConfig,
-)
-
-
-# Defines a camera type
-class Camera(Protocol):
-    def connect(self): ...
-    def read(self, temporary_color: str | None = None) -> np.ndarray: ...
-    def async_read(self) -> np.ndarray: ...
-    def disconnect(self): ...
-
-
-def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> list[Camera]:
-    cameras = {}
-
-    for key, cfg in camera_configs.items():
-        if cfg.type == "opencv":
-            from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
-
-            cameras[key] = OpenCVCamera(cfg)
-
-        elif cfg.type == "intelrealsense":
-            from lerobot.common.robot_devices.cameras.intelrealsense import IntelRealSenseCamera
-
-            cameras[key] = IntelRealSenseCamera(cfg)
-        else:
-            raise ValueError(f"The camera type '{cfg.type}' is not valid.")
-
-    return cameras
-
-
-def make_camera(camera_type, **kwargs) -> Camera:
-    if camera_type == "opencv":
-        from lerobot.common.robot_devices.cameras.opencv import OpenCVCamera
-
-        config = OpenCVCameraConfig(**kwargs)
-        return OpenCVCamera(config)
-
-    elif camera_type == "intelrealsense":
-        from lerobot.common.robot_devices.cameras.intelrealsense import IntelRealSenseCamera
-
-        config = IntelRealSenseCameraConfig(**kwargs)
-        return IntelRealSenseCamera(config)
-
-    else:
-        raise ValueError(f"The camera type '{camera_type}' is not valid.")

lerobot/common/robot_devices/robots/configs.py

@@ -1,613 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import abc
-from dataclasses import dataclass, field
-from typing import Sequence
-
-import draccus
-
-from lerobot.common.robot_devices.cameras.configs import (
-    CameraConfig,
-    IntelRealSenseCameraConfig,
-    OpenCVCameraConfig,
-)
-from lerobot.common.robot_devices.motors.configs import (
-    DynamixelMotorsBusConfig,
-    FeetechMotorsBusConfig,
-    MotorsBusConfig,
-)
-
-
-@dataclass
-class RobotConfig(draccus.ChoiceRegistry, abc.ABC):
-    @property
-    def type(self) -> str:
-        return self.get_choice_name(self.__class__)
-
-
-# TODO(rcadene, aliberts): remove ManipulatorRobotConfig abstraction
-@dataclass
-class ManipulatorRobotConfig(RobotConfig):
-    leader_arms: dict[str, MotorsBusConfig] = field(default_factory=lambda: {})
-    follower_arms: dict[str, MotorsBusConfig] = field(default_factory=lambda: {})
-    cameras: dict[str, CameraConfig] = field(default_factory=lambda: {})
-
-    # Optionally limit the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length
-    # as the number of motors in your follower arms (assumes all follower arms have the same number of
-    # motors).
-    max_relative_target: list[float] | float | None = None
-
-    # Optionally set the leader arm in torque mode with the gripper motor set to this angle. This makes it
-    # possible to squeeze the gripper and have it spring back to an open position on its own. If None, the
-    # gripper is not put in torque mode.
-    gripper_open_degree: float | None = None
-
-    mock: bool = False
-
-    def __post_init__(self):
-        if self.mock:
-            for arm in self.leader_arms.values():
-                if not arm.mock:
-                    arm.mock = True
-            for arm in self.follower_arms.values():
-                if not arm.mock:
-                    arm.mock = True
-            for cam in self.cameras.values():
-                if not cam.mock:
-                    cam.mock = True
-
-        if self.max_relative_target is not None and isinstance(self.max_relative_target, Sequence):
-            for name in self.follower_arms:
-                if len(self.follower_arms[name].motors) != len(self.max_relative_target):
-                    raise ValueError(
-                        f"len(max_relative_target)={len(self.max_relative_target)} but the follower arm with name {name} has "
-                        f"{len(self.follower_arms[name].motors)} motors. Please make sure that the "
-                        f"`max_relative_target` list has as many parameters as there are motors per arm. "
-                        "Note: This feature does not yet work with robots where different follower arms have "
-                        "different numbers of motors."
-                    )
-
-
-@RobotConfig.register_subclass("aloha")
-@dataclass
-class AlohaRobotConfig(ManipulatorRobotConfig):
-    # Specific to Aloha, LeRobot comes with default calibration files. Assuming the motors have been
-    # properly assembled, no manual calibration step is expected. If you need to run manual calibration,
-    # simply update this path to ".cache/calibration/aloha"
-    calibration_dir: str = ".cache/calibration/aloha_default"
-
-    # /!\ FOR SAFETY, READ THIS /!\
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    # For Aloha, for every goal position request, motor rotations are capped at 5 degrees by default.
-    # When you feel more confident with teleoperation or running the policy, you can extend
-    # this safety limit and even removing it by setting it to `null`.
-    # Also, everything is expected to work safely out-of-the-box, but we highly advise to
-    # first try to teleoperate the grippers only (by commenting out the rest of the motors in this yaml),
-    # then to gradually add more motors (by uncommenting), until you can teleoperate both arms fully
-    max_relative_target: int | None = 5
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                # window_x
-                port="/dev/ttyDXL_leader_left",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm430-w350"],
-                    "shoulder": [2, "xm430-w350"],
-                    "shoulder_shadow": [3, "xm430-w350"],
-                    "elbow": [4, "xm430-w350"],
-                    "elbow_shadow": [5, "xm430-w350"],
-                    "forearm_roll": [6, "xm430-w350"],
-                    "wrist_angle": [7, "xm430-w350"],
-                    "wrist_rotate": [8, "xl430-w250"],
-                    "gripper": [9, "xc430-w150"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                # window_x
-                port="/dev/ttyDXL_leader_right",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm430-w350"],
-                    "shoulder": [2, "xm430-w350"],
-                    "shoulder_shadow": [3, "xm430-w350"],
-                    "elbow": [4, "xm430-w350"],
-                    "elbow_shadow": [5, "xm430-w350"],
-                    "forearm_roll": [6, "xm430-w350"],
-                    "wrist_angle": [7, "xm430-w350"],
-                    "wrist_rotate": [8, "xl430-w250"],
-                    "gripper": [9, "xc430-w150"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                port="/dev/ttyDXL_follower_left",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm540-w270"],
-                    "shoulder": [2, "xm540-w270"],
-                    "shoulder_shadow": [3, "xm540-w270"],
-                    "elbow": [4, "xm540-w270"],
-                    "elbow_shadow": [5, "xm540-w270"],
-                    "forearm_roll": [6, "xm540-w270"],
-                    "wrist_angle": [7, "xm540-w270"],
-                    "wrist_rotate": [8, "xm430-w350"],
-                    "gripper": [9, "xm430-w350"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                port="/dev/ttyDXL_follower_right",
-                motors={
-                    # name: (index, model)
-                    "waist": [1, "xm540-w270"],
-                    "shoulder": [2, "xm540-w270"],
-                    "shoulder_shadow": [3, "xm540-w270"],
-                    "elbow": [4, "xm540-w270"],
-                    "elbow_shadow": [5, "xm540-w270"],
-                    "forearm_roll": [6, "xm540-w270"],
-                    "wrist_angle": [7, "xm540-w270"],
-                    "wrist_rotate": [8, "xm430-w350"],
-                    "gripper": [9, "xm430-w350"],
-                },
-            ),
-        }
-    )
-
-    # Troubleshooting: If one of your IntelRealSense cameras freeze during
-    # data recording due to bandwidth limit, you might need to plug the camera
-    # on another USB hub or PCIe card.
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "cam_high": IntelRealSenseCameraConfig(
-                serial_number=128422271347,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "cam_low": IntelRealSenseCameraConfig(
-                serial_number=130322270656,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "cam_left_wrist": IntelRealSenseCameraConfig(
-                serial_number=218622272670,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "cam_right_wrist": IntelRealSenseCameraConfig(
-                serial_number=130322272300,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("koch")
-@dataclass
-class KochRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/koch"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0085511",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl330-m077"],
-                    "shoulder_lift": [2, "xl330-m077"],
-                    "elbow_flex": [3, "xl330-m077"],
-                    "wrist_flex": [4, "xl330-m077"],
-                    "wrist_roll": [5, "xl330-m077"],
-                    "gripper": [6, "xl330-m077"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl430-w250"],
-                    "shoulder_lift": [2, "xl430-w250"],
-                    "elbow_flex": [3, "xl330-m288"],
-                    "wrist_flex": [4, "xl330-m288"],
-                    "wrist_roll": [5, "xl330-m288"],
-                    "gripper": [6, "xl330-m288"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    # ~ Koch specific settings ~
-    # Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
-    # to squeeze the gripper and have it spring back to an open position on its own.
-    gripper_open_degree: float = 35.156
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("koch_bimanual")
-@dataclass
-class KochBimanualRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/koch_bimanual"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0085511",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl330-m077"],
-                    "shoulder_lift": [2, "xl330-m077"],
-                    "elbow_flex": [3, "xl330-m077"],
-                    "wrist_flex": [4, "xl330-m077"],
-                    "wrist_roll": [5, "xl330-m077"],
-                    "gripper": [6, "xl330-m077"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem575E0031751",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl330-m077"],
-                    "shoulder_lift": [2, "xl330-m077"],
-                    "elbow_flex": [3, "xl330-m077"],
-                    "wrist_flex": [4, "xl330-m077"],
-                    "wrist_roll": [5, "xl330-m077"],
-                    "gripper": [6, "xl330-m077"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "left": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl430-w250"],
-                    "shoulder_lift": [2, "xl430-w250"],
-                    "elbow_flex": [3, "xl330-m288"],
-                    "wrist_flex": [4, "xl330-m288"],
-                    "wrist_roll": [5, "xl330-m288"],
-                    "gripper": [6, "xl330-m288"],
-                },
-            ),
-            "right": DynamixelMotorsBusConfig(
-                port="/dev/tty.usbmodem575E0032081",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "xl430-w250"],
-                    "shoulder_lift": [2, "xl430-w250"],
-                    "elbow_flex": [3, "xl330-m288"],
-                    "wrist_flex": [4, "xl330-m288"],
-                    "wrist_roll": [5, "xl330-m288"],
-                    "gripper": [6, "xl330-m288"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    # ~ Koch specific settings ~
-    # Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible
-    # to squeeze the gripper and have it spring back to an open position on its own.
-    gripper_open_degree: float = 35.156
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("moss")
-@dataclass
-class MossRobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/moss"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431091",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("so100")
-@dataclass
-class So100RobotConfig(ManipulatorRobotConfig):
-    calibration_dir: str = ".cache/calibration/so100"
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem58760431091",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0076891",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "laptop": OpenCVCameraConfig(
-                camera_index=0,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-            "phone": OpenCVCameraConfig(
-                camera_index=1,
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("stretch")
-@dataclass
-class StretchRobotConfig(RobotConfig):
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "navigation": OpenCVCameraConfig(
-                camera_index="/dev/hello-nav-head-camera",
-                fps=10,
-                width=1280,
-                height=720,
-                rotation=-90,
-            ),
-            "head": IntelRealSenseCameraConfig(
-                name="Intel RealSense D435I",
-                fps=30,
-                width=640,
-                height=480,
-                rotation=90,
-            ),
-            "wrist": IntelRealSenseCameraConfig(
-                name="Intel RealSense D405",
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )
-
-    mock: bool = False
-
-
-@RobotConfig.register_subclass("lekiwi")
-@dataclass
-class LeKiwiRobotConfig(RobotConfig):
-    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
-    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
-    # the number of motors in your follower arms.
-    max_relative_target: int | None = None
-
-    # Network Configuration
-    ip: str = "192.168.0.193"
-    port: int = 5555
-    video_port: int = 5556
-
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "front": OpenCVCameraConfig(
-                camera_index="/dev/video0", fps=30, width=640, height=480, rotation=90
-            ),
-            "wrist": OpenCVCameraConfig(
-                camera_index="/dev/video2", fps=30, width=640, height=480, rotation=180
-            ),
-        }
-    )
-
-    calibration_dir: str = ".cache/calibration/lekiwi"
-
-    leader_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/tty.usbmodem585A0077581",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                },
-            ),
-        }
-    )
-
-    follower_arms: dict[str, MotorsBusConfig] = field(
-        default_factory=lambda: {
-            "main": FeetechMotorsBusConfig(
-                port="/dev/ttyACM0",
-                motors={
-                    # name: (index, model)
-                    "shoulder_pan": [1, "sts3215"],
-                    "shoulder_lift": [2, "sts3215"],
-                    "elbow_flex": [3, "sts3215"],
-                    "wrist_flex": [4, "sts3215"],
-                    "wrist_roll": [5, "sts3215"],
-                    "gripper": [6, "sts3215"],
-                    "left_wheel": (7, "sts3215"),
-                    "back_wheel": (8, "sts3215"),
-                    "right_wheel": (9, "sts3215"),
-                },
-            ),
-        }
-    )
-
-    teleop_keys: dict[str, str] = field(
-        default_factory=lambda: {
-            # Movement
-            "forward": "w",
-            "backward": "s",
-            "left": "a",
-            "right": "d",
-            "rotate_left": "z",
-            "rotate_right": "x",
-            # Speed control
-            "speed_up": "r",
-            "speed_down": "f",
-            # quit teleop
-            "quit": "q",
-        }
-    )
-
-    mock: bool = False

lerobot/common/robot_devices/robots/feetech_calibration.py

@@ -1,498 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Logic to calibrate a robot arm built with feetech motors"""
-# TODO(rcadene, aliberts): move this logic into the robot code when refactoring
-
-import time
-
-import numpy as np
-
-from lerobot.common.robot_devices.motors.feetech import (
-    CalibrationMode,
-    TorqueMode,
-    convert_degrees_to_steps,
-)
-from lerobot.common.robot_devices.motors.utils import MotorsBus
-
-URL_TEMPLATE = (
-    "https://raw.githubusercontent.com/huggingface/lerobot/main/media/{robot}/{arm}_{position}.webp"
-)
-
-# The following positions are provided in nominal degree range ]-180, +180[
-# For more info on these constants, see comments in the code where they get used.
-ZERO_POSITION_DEGREE = 0
-ROTATED_POSITION_DEGREE = 90
-
-
-def assert_drive_mode(drive_mode):
-    # `drive_mode` is in [0,1] with 0 means original rotation direction for the motor, and 1 means inverted.
-    if not np.all(np.isin(drive_mode, [0, 1])):
-        raise ValueError(f"`drive_mode` contains values other than 0 or 1: ({drive_mode})")
-
-
-def apply_drive_mode(position, drive_mode):
-    assert_drive_mode(drive_mode)
-    # Convert `drive_mode` from [0, 1] with 0 indicates original rotation direction and 1 inverted,
-    # to [-1, 1] with 1 indicates original rotation direction and -1 inverted.
-    signed_drive_mode = -(drive_mode * 2 - 1)
-    position *= signed_drive_mode
-    return position
-
-
-def move_until_block(arm, motor_name, positive_direction=True, while_move_hook=None):
-    count = 0
-    while True:
-        present_pos = arm.read("Present_Position", motor_name)
-        if positive_direction:
-            # Move +100 steps every time. Lower the steps to lower the speed at which the arm moves.
-            arm.write("Goal_Position", present_pos + 100, motor_name)
-        else:
-            arm.write("Goal_Position", present_pos - 100, motor_name)
-
-        if while_move_hook is not None:
-            while_move_hook()
-
-        present_pos = arm.read("Present_Position", motor_name).item()
-        present_speed = arm.read("Present_Speed", motor_name).item()
-        present_current = arm.read("Present_Current", motor_name).item()
-        # present_load = arm.read("Present_Load", motor_name).item()
-        # present_voltage = arm.read("Present_Voltage", motor_name).item()
-        # present_temperature = arm.read("Present_Temperature", motor_name).item()
-
-        # print(f"{present_pos=}")
-        # print(f"{present_speed=}")
-        # print(f"{present_current=}")
-        # print(f"{present_load=}")
-        # print(f"{present_voltage=}")
-        # print(f"{present_temperature=}")
-
-        if present_speed == 0 and present_current > 40:
-            count += 1
-            if count > 100 or present_current > 300:
-                return present_pos
-        else:
-            count = 0
-
-
-def move_to_calibrate(
-    arm,
-    motor_name,
-    invert_drive_mode=False,
-    positive_first=True,
-    in_between_move_hook=None,
-    while_move_hook=None,
-):
-    initial_pos = arm.read("Present_Position", motor_name)
-
-    if positive_first:
-        p_present_pos = move_until_block(
-            arm, motor_name, positive_direction=True, while_move_hook=while_move_hook
-        )
-    else:
-        n_present_pos = move_until_block(
-            arm, motor_name, positive_direction=False, while_move_hook=while_move_hook
-        )
-
-    if in_between_move_hook is not None:
-        in_between_move_hook()
-
-    if positive_first:
-        n_present_pos = move_until_block(
-            arm, motor_name, positive_direction=False, while_move_hook=while_move_hook
-        )
-    else:
-        p_present_pos = move_until_block(
-            arm, motor_name, positive_direction=True, while_move_hook=while_move_hook
-        )
-
-    zero_pos = (n_present_pos + p_present_pos) / 2
-
-    calib_data = {
-        "initial_pos": initial_pos,
-        "homing_offset": zero_pos if invert_drive_mode else -zero_pos,
-        "invert_drive_mode": invert_drive_mode,
-        "drive_mode": -1 if invert_drive_mode else 0,
-        "zero_pos": zero_pos,
-        "start_pos": n_present_pos if invert_drive_mode else p_present_pos,
-        "end_pos": p_present_pos if invert_drive_mode else n_present_pos,
-    }
-    return calib_data
-
-
-def apply_offset(calib, offset):
-    calib["zero_pos"] += offset
-    if calib["drive_mode"]:
-        calib["homing_offset"] += offset
-    else:
-        calib["homing_offset"] -= offset
-    return calib
-
-
-def run_arm_auto_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    if robot_type == "so100":
-        return run_arm_auto_calibration_so100(arm, robot_type, arm_name, arm_type)
-    elif robot_type == "moss":
-        return run_arm_auto_calibration_moss(arm, robot_type, arm_name, arm_type)
-    else:
-        raise ValueError(robot_type)
-
-
-def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    """All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
-    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-        raise ValueError("To run calibration, the torque must be disabled on all motors.")
-
-    if not (robot_type == "so100" and arm_type == "follower"):
-        raise NotImplementedError("Auto calibration only supports the follower of so100 arms for now.")
-
-    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
-
-    print("\nMove arm to initial position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
-    input("Press Enter to continue...")
-
-    # Lower the acceleration of the motors (in [0,254])
-    initial_acceleration = arm.read("Acceleration")
-    arm.write("Lock", 0)
-    arm.write("Acceleration", 10)
-    time.sleep(1)
-
-    arm.write("Torque_Enable", TorqueMode.ENABLED.value)
-
-    print(f'{arm.read("Present_Position", "elbow_flex")=}')
-
-    calib = {}
-
-    init_wf_pos = arm.read("Present_Position", "wrist_flex")
-    init_sl_pos = arm.read("Present_Position", "shoulder_lift")
-    init_ef_pos = arm.read("Present_Position", "elbow_flex")
-    arm.write("Goal_Position", init_wf_pos - 800, "wrist_flex")
-    arm.write("Goal_Position", init_sl_pos + 150 + 1024, "shoulder_lift")
-    arm.write("Goal_Position", init_ef_pos - 2048, "elbow_flex")
-    time.sleep(2)
-
-    print("Calibrate shoulder_pan")
-    calib["shoulder_pan"] = move_to_calibrate(arm, "shoulder_pan")
-    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
-    time.sleep(1)
-
-    print("Calibrate gripper")
-    calib["gripper"] = move_to_calibrate(arm, "gripper", invert_drive_mode=True)
-    time.sleep(1)
-
-    print("Calibrate wrist_flex")
-    calib["wrist_flex"] = move_to_calibrate(arm, "wrist_flex")
-    calib["wrist_flex"] = apply_offset(calib["wrist_flex"], offset=80)
-
-    def in_between_move_hook():
-        nonlocal arm, calib
-        time.sleep(2)
-        ef_pos = arm.read("Present_Position", "elbow_flex")
-        sl_pos = arm.read("Present_Position", "shoulder_lift")
-        arm.write("Goal_Position", ef_pos + 1024, "elbow_flex")
-        arm.write("Goal_Position", sl_pos - 1024, "shoulder_lift")
-        time.sleep(2)
-
-    print("Calibrate elbow_flex")
-    calib["elbow_flex"] = move_to_calibrate(
-        arm, "elbow_flex", positive_first=False, in_between_move_hook=in_between_move_hook
-    )
-    calib["elbow_flex"] = apply_offset(calib["elbow_flex"], offset=80 - 1024)
-
-    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 1024 + 512, "elbow_flex")
-    time.sleep(1)
-
-    def in_between_move_hook():
-        nonlocal arm, calib
-        arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"], "elbow_flex")
-
-    print("Calibrate shoulder_lift")
-    calib["shoulder_lift"] = move_to_calibrate(
-        arm,
-        "shoulder_lift",
-        invert_drive_mode=True,
-        positive_first=False,
-        in_between_move_hook=in_between_move_hook,
-    )
-    # add an 30 steps as offset to align with body
-    calib["shoulder_lift"] = apply_offset(calib["shoulder_lift"], offset=1024 - 50)
-
-    def while_move_hook():
-        nonlocal arm, calib
-        positions = {
-            "shoulder_lift": round(calib["shoulder_lift"]["zero_pos"] - 1600),
-            "elbow_flex": round(calib["elbow_flex"]["zero_pos"] + 1700),
-            "wrist_flex": round(calib["wrist_flex"]["zero_pos"] + 800),
-            "gripper": round(calib["gripper"]["end_pos"]),
-        }
-        arm.write("Goal_Position", list(positions.values()), list(positions.keys()))
-
-    arm.write("Goal_Position", round(calib["shoulder_lift"]["zero_pos"] - 1600), "shoulder_lift")
-    time.sleep(2)
-    arm.write("Goal_Position", round(calib["elbow_flex"]["zero_pos"] + 1700), "elbow_flex")
-    time.sleep(2)
-    arm.write("Goal_Position", round(calib["wrist_flex"]["zero_pos"] + 800), "wrist_flex")
-    time.sleep(2)
-    arm.write("Goal_Position", round(calib["gripper"]["end_pos"]), "gripper")
-    time.sleep(2)
-
-    print("Calibrate wrist_roll")
-    calib["wrist_roll"] = move_to_calibrate(
-        arm, "wrist_roll", invert_drive_mode=True, positive_first=False, while_move_hook=while_move_hook
-    )
-
-    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["gripper"]["start_pos"], "gripper")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"], "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 2048, "elbow_flex")
-    arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] - 2048, "shoulder_lift")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
-    time.sleep(1)
-
-    calib_modes = []
-    for name in arm.motor_names:
-        if name == "gripper":
-            calib_modes.append(CalibrationMode.LINEAR.name)
-        else:
-            calib_modes.append(CalibrationMode.DEGREE.name)
-
-    calib_dict = {
-        "homing_offset": [calib[name]["homing_offset"] for name in arm.motor_names],
-        "drive_mode": [calib[name]["drive_mode"] for name in arm.motor_names],
-        "start_pos": [calib[name]["start_pos"] for name in arm.motor_names],
-        "end_pos": [calib[name]["end_pos"] for name in arm.motor_names],
-        "calib_mode": calib_modes,
-        "motor_names": arm.motor_names,
-    }
-
-    # Re-enable original accerlation
-    arm.write("Lock", 0)
-    arm.write("Acceleration", initial_acceleration)
-    time.sleep(1)
-
-    return calib_dict
-
-
-def run_arm_auto_calibration_moss(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    """All the offsets and magic numbers are hand tuned, and are unique to SO-100 follower arms"""
-    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-        raise ValueError("To run calibration, the torque must be disabled on all motors.")
-
-    if not (robot_type == "moss" and arm_type == "follower"):
-        raise NotImplementedError("Auto calibration only supports the follower of moss arms for now.")
-
-    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
-
-    print("\nMove arm to initial position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="initial"))
-    input("Press Enter to continue...")
-
-    # Lower the acceleration of the motors (in [0,254])
-    initial_acceleration = arm.read("Acceleration")
-    arm.write("Lock", 0)
-    arm.write("Acceleration", 10)
-    time.sleep(1)
-
-    arm.write("Torque_Enable", TorqueMode.ENABLED.value)
-
-    sl_pos = arm.read("Present_Position", "shoulder_lift")
-    arm.write("Goal_Position", sl_pos - 1024 - 450, "shoulder_lift")
-    ef_pos = arm.read("Present_Position", "elbow_flex")
-    arm.write("Goal_Position", ef_pos + 1024 + 450, "elbow_flex")
-    time.sleep(2)
-
-    calib = {}
-
-    print("Calibrate shoulder_pan")
-    calib["shoulder_pan"] = move_to_calibrate(arm, "shoulder_pan")
-    arm.write("Goal_Position", calib["shoulder_pan"]["zero_pos"], "shoulder_pan")
-    time.sleep(1)
-
-    print("Calibrate gripper")
-    calib["gripper"] = move_to_calibrate(arm, "gripper", invert_drive_mode=True)
-    time.sleep(1)
-
-    print("Calibrate wrist_flex")
-    calib["wrist_flex"] = move_to_calibrate(arm, "wrist_flex", invert_drive_mode=True)
-    calib["wrist_flex"] = apply_offset(calib["wrist_flex"], offset=-210 + 1024)
-
-    wr_pos = arm.read("Present_Position", "wrist_roll")
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", wr_pos - 1024, "wrist_roll")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 2048, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["gripper"]["end_pos"], "gripper")
-    time.sleep(1)
-
-    print("Calibrate wrist_roll")
-    calib["wrist_roll"] = move_to_calibrate(arm, "wrist_roll", invert_drive_mode=True)
-    calib["wrist_roll"] = apply_offset(calib["wrist_roll"], offset=790)
-
-    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"] - 1024, "wrist_roll")
-    arm.write("Goal_Position", calib["gripper"]["start_pos"], "gripper")
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 2048, "wrist_flex")
-
-    def in_between_move_elbow_flex_hook():
-        nonlocal arm, calib
-        arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"], "wrist_flex")
-
-    print("Calibrate elbow_flex")
-    calib["elbow_flex"] = move_to_calibrate(
-        arm,
-        "elbow_flex",
-        invert_drive_mode=True,
-        in_between_move_hook=in_between_move_elbow_flex_hook,
-    )
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-
-    def in_between_move_shoulder_lift_hook():
-        nonlocal arm, calib
-        sl = arm.read("Present_Position", "shoulder_lift")
-        arm.write("Goal_Position", sl - 1500, "shoulder_lift")
-        time.sleep(1)
-        arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] + 1536, "elbow_flex")
-        time.sleep(1)
-        arm.write("Goal_Position", calib["wrist_flex"]["start_pos"], "wrist_flex")
-        time.sleep(1)
-
-    print("Calibrate shoulder_lift")
-    calib["shoulder_lift"] = move_to_calibrate(
-        arm, "shoulder_lift", in_between_move_hook=in_between_move_shoulder_lift_hook
-    )
-    calib["shoulder_lift"] = apply_offset(calib["shoulder_lift"], offset=-1024)
-
-    arm.write("Goal_Position", calib["wrist_flex"]["zero_pos"] - 1024, "wrist_flex")
-    time.sleep(1)
-    arm.write("Goal_Position", calib["shoulder_lift"]["zero_pos"] + 2048, "shoulder_lift")
-    arm.write("Goal_Position", calib["elbow_flex"]["zero_pos"] - 1024 - 400, "elbow_flex")
-    time.sleep(2)
-
-    calib_modes = []
-    for name in arm.motor_names:
-        if name == "gripper":
-            calib_modes.append(CalibrationMode.LINEAR.name)
-        else:
-            calib_modes.append(CalibrationMode.DEGREE.name)
-
-    calib_dict = {
-        "homing_offset": [calib[name]["homing_offset"] for name in arm.motor_names],
-        "drive_mode": [calib[name]["drive_mode"] for name in arm.motor_names],
-        "start_pos": [calib[name]["start_pos"] for name in arm.motor_names],
-        "end_pos": [calib[name]["end_pos"] for name in arm.motor_names],
-        "calib_mode": calib_modes,
-        "motor_names": arm.motor_names,
-    }
-
-    # Re-enable original accerlation
-    arm.write("Lock", 0)
-    arm.write("Acceleration", initial_acceleration)
-    time.sleep(1)
-
-    return calib_dict
-
-
-def run_arm_manual_calibration(arm: MotorsBus, robot_type: str, arm_name: str, arm_type: str):
-    """This function ensures that a neural network trained on data collected on a given robot
-    can work on another robot. For instance before calibration, setting a same goal position
-    for each motor of two different robots will get two very different positions. But after calibration,
-    the two robots will move to the same position.To this end, this function computes the homing offset
-    and the drive mode for each motor of a given robot.
-
-    Homing offset is used to shift the motor position to a ]-2048, +2048[ nominal range (when the motor uses 2048 steps
-    to complete a half a turn). This range is set around an arbitrary "zero position" corresponding to all motor positions
-    being 0. During the calibration process, you will need to manually move the robot to this "zero position".
-
-    Drive mode is used to invert the rotation direction of the motor. This is useful when some motors have been assembled
-    in the opposite orientation for some robots. During the calibration process, you will need to manually move the robot
-    to the "rotated position".
-
-    After calibration, the homing offsets and drive modes are stored in a cache.
-
-    Example of usage:
-    ```python
-    run_arm_calibration(arm, "so100", "left", "follower")
-    ```
-    """
-    if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
-        raise ValueError("To run calibration, the torque must be disabled on all motors.")
-
-    print(f"\nRunning calibration of {robot_type} {arm_name} {arm_type}...")
-
-    print("\nMove arm to zero position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="zero"))
-    input("Press Enter to continue...")
-
-    # We arbitrarily chose our zero target position to be a straight horizontal position with gripper upwards and closed.
-    # It is easy to identify and all motors are in a "quarter turn" position. Once calibration is done, this position will
-    # correspond to every motor angle being 0. If you set all 0 as Goal Position, the arm will move in this position.
-    zero_target_pos = convert_degrees_to_steps(ZERO_POSITION_DEGREE, arm.motor_models)
-
-    # Compute homing offset so that `present_position + homing_offset ~= target_position`.
-    zero_pos = arm.read("Present_Position")
-    homing_offset = zero_target_pos - zero_pos
-
-    # The rotated target position corresponds to a rotation of a quarter turn from the zero position.
-    # This allows to identify the rotation direction of each motor.
-    # For instance, if the motor rotates 90 degree, and its value is -90 after applying the homing offset, then we know its rotation direction
-    # is inverted. However, for the calibration being successful, we need everyone to follow the same target position.
-    # Sometimes, there is only one possible rotation direction. For instance, if the gripper is closed, there is only one direction which
-    # corresponds to opening the gripper. When the rotation direction is ambiguous, we arbitrarily rotate clockwise from the point of view
-    # of the previous motor in the kinetic chain.
-    print("\nMove arm to rotated target position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rotated"))
-    input("Press Enter to continue...")
-
-    rotated_target_pos = convert_degrees_to_steps(ROTATED_POSITION_DEGREE, arm.motor_models)
-
-    # Find drive mode by rotating each motor by a quarter of a turn.
-    # Drive mode indicates if the motor rotation direction should be inverted (=1) or not (=0).
-    rotated_pos = arm.read("Present_Position")
-    drive_mode = (rotated_pos < zero_pos).astype(np.int32)
-
-    # Re-compute homing offset to take into account drive mode
-    rotated_drived_pos = apply_drive_mode(rotated_pos, drive_mode)
-    homing_offset = rotated_target_pos - rotated_drived_pos
-
-    print("\nMove arm to rest position")
-    print("See: " + URL_TEMPLATE.format(robot=robot_type, arm=arm_type, position="rest"))
-    input("Press Enter to continue...")
-    print()
-
-    # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
-    calib_modes = []
-    for name in arm.motor_names:
-        if name == "gripper":
-            calib_modes.append(CalibrationMode.LINEAR.name)
-        else:
-            calib_modes.append(CalibrationMode.DEGREE.name)
-
-    calib_dict = {
-        "homing_offset": homing_offset.tolist(),
-        "drive_mode": drive_mode.tolist(),
-        "start_pos": zero_pos.tolist(),
-        "end_pos": rotated_pos.tolist(),
-        "calib_mode": calib_modes,
-        "motor_names": arm.motor_names,
-    }
-    return calib_dict

lerobot/common/robot_devices/robots/stretch.py

@@ -1,208 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import time
-from dataclasses import replace
-
-import torch
-from stretch_body.gamepad_teleop import GamePadTeleop
-from stretch_body.robot import Robot as StretchAPI
-from stretch_body.robot_params import RobotParams
-
-from lerobot.common.robot_devices.robots.configs import StretchRobotConfig
-
-
-class StretchRobot(StretchAPI):
-    """Wrapper of stretch_body.robot.Robot"""
-
-    def __init__(self, config: StretchRobotConfig | None = None, **kwargs):
-        super().__init__()
-        if config is None:
-            self.config = StretchRobotConfig(**kwargs)
-        else:
-            # Overwrite config arguments using kwargs
-            self.config = replace(config, **kwargs)
-
-        self.robot_type = self.config.type
-        self.cameras = self.config.cameras
-        self.is_connected = False
-        self.teleop = None
-        self.logs = {}
-
-        # TODO(aliberts): test this
-        RobotParams.set_logging_level("WARNING")
-        RobotParams.set_logging_formatter("brief_console_formatter")
-
-        self.state_keys = None
-        self.action_keys = None
-
-    def connect(self) -> None:
-        self.is_connected = self.startup()
-        if not self.is_connected:
-            print("Another process is already using Stretch. Try running 'stretch_free_robot_process.py'")
-            raise ConnectionError()
-
-        for name in self.cameras:
-            self.cameras[name].connect()
-            self.is_connected = self.is_connected and self.cameras[name].is_connected
-
-        if not self.is_connected:
-            print("Could not connect to the cameras, check that all cameras are plugged-in.")
-            raise ConnectionError()
-
-        self.run_calibration()
-
-    def run_calibration(self) -> None:
-        if not self.is_homed():
-            self.home()
-
-    def teleop_step(
-        self, record_data=False
-    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
-        # TODO(aliberts): return ndarrays instead of torch.Tensors
-        if not self.is_connected:
-            raise ConnectionError()
-
-        if self.teleop is None:
-            self.teleop = GamePadTeleop(robot_instance=False)
-            self.teleop.startup(robot=self)
-
-        before_read_t = time.perf_counter()
-        state = self.get_state()
-        action = self.teleop.gamepad_controller.get_state()
-        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
-
-        before_write_t = time.perf_counter()
-        self.teleop.do_motion(robot=self)
-        self.push_command()
-        self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
-
-        if self.state_keys is None:
-            self.state_keys = list(state)
-
-        if not record_data:
-            return
-
-        state = torch.as_tensor(list(state.values()))
-        action = torch.as_tensor(list(action.values()))
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionaries
-        obs_dict, action_dict = {}, {}
-        obs_dict["observation.state"] = state
-        action_dict["action"] = action
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-
-        return obs_dict, action_dict
-
-    def get_state(self) -> dict:
-        status = self.get_status()
-        return {
-            "head_pan.pos": status["head"]["head_pan"]["pos"],
-            "head_tilt.pos": status["head"]["head_tilt"]["pos"],
-            "lift.pos": status["lift"]["pos"],
-            "arm.pos": status["arm"]["pos"],
-            "wrist_pitch.pos": status["end_of_arm"]["wrist_pitch"]["pos"],
-            "wrist_roll.pos": status["end_of_arm"]["wrist_roll"]["pos"],
-            "wrist_yaw.pos": status["end_of_arm"]["wrist_yaw"]["pos"],
-            "gripper.pos": status["end_of_arm"]["stretch_gripper"]["pos"],
-            "base_x.vel": status["base"]["x_vel"],
-            "base_y.vel": status["base"]["y_vel"],
-            "base_theta.vel": status["base"]["theta_vel"],
-        }
-
-    def capture_observation(self) -> dict:
-        # TODO(aliberts): return ndarrays instead of torch.Tensors
-        before_read_t = time.perf_counter()
-        state = self.get_state()
-        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
-
-        if self.state_keys is None:
-            self.state_keys = list(state)
-
-        state = torch.as_tensor(list(state.values()))
-
-        # Capture images from cameras
-        images = {}
-        for name in self.cameras:
-            before_camread_t = time.perf_counter()
-            images[name] = self.cameras[name].async_read()
-            images[name] = torch.from_numpy(images[name])
-            self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
-
-        # Populate output dictionaries
-        obs_dict = {}
-        obs_dict["observation.state"] = state
-        for name in self.cameras:
-            obs_dict[f"observation.images.{name}"] = images[name]
-
-        return obs_dict
-
-    def send_action(self, action: torch.Tensor) -> torch.Tensor:
-        # TODO(aliberts): return ndarrays instead of torch.Tensors
-        if not self.is_connected:
-            raise ConnectionError()
-
-        if self.teleop is None:
-            self.teleop = GamePadTeleop(robot_instance=False)
-            self.teleop.startup(robot=self)
-
-        if self.action_keys is None:
-            dummy_action = self.teleop.gamepad_controller.get_state()
-            self.action_keys = list(dummy_action.keys())
-
-        action_dict = dict(zip(self.action_keys, action.tolist(), strict=True))
-
-        before_write_t = time.perf_counter()
-        self.teleop.do_motion(state=action_dict, robot=self)
-        self.push_command()
-        self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
-
-        # TODO(aliberts): return action_sent when motion is limited
-        return action
-
-    def print_logs(self) -> None:
-        pass
-        # TODO(aliberts): move robot-specific logs logic here
-
-    def teleop_safety_stop(self) -> None:
-        if self.teleop is not None:
-            self.teleop._safety_stop(robot=self)
-
-    def disconnect(self) -> None:
-        self.stop()
-        if self.teleop is not None:
-            self.teleop.gamepad_controller.stop()
-            self.teleop.stop()
-
-        if len(self.cameras) > 0:
-            for cam in self.cameras.values():
-                cam.disconnect()
-
-        self.is_connected = False
-
-    def __del__(self):
-        self.disconnect()

lerobot/common/robot_devices/robots/utils.py

@@ -1,86 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Protocol
-
-from lerobot.common.robot_devices.robots.configs import (
-    AlohaRobotConfig,
-    KochBimanualRobotConfig,
-    KochRobotConfig,
-    LeKiwiRobotConfig,
-    ManipulatorRobotConfig,
-    MossRobotConfig,
-    RobotConfig,
-    So100RobotConfig,
-    StretchRobotConfig,
-)
-
-
-def get_arm_id(name, arm_type):
-    """Returns the string identifier of a robot arm. For instance, for a bimanual manipulator
-    like Aloha, it could be left_follower, right_follower, left_leader, or right_leader.
-    """
-    return f"{name}_{arm_type}"
-
-
-class Robot(Protocol):
-    # TODO(rcadene, aliberts): Add unit test checking the protocol is implemented in the corresponding classes
-    robot_type: str
-    features: dict
-
-    def connect(self): ...
-    def run_calibration(self): ...
-    def teleop_step(self, record_data=False): ...
-    def capture_observation(self): ...
-    def send_action(self, action): ...
-    def disconnect(self): ...
-
-
-def make_robot_config(robot_type: str, **kwargs) -> RobotConfig:
-    if robot_type == "aloha":
-        return AlohaRobotConfig(**kwargs)
-    elif robot_type == "koch":
-        return KochRobotConfig(**kwargs)
-    elif robot_type == "koch_bimanual":
-        return KochBimanualRobotConfig(**kwargs)
-    elif robot_type == "moss":
-        return MossRobotConfig(**kwargs)
-    elif robot_type == "so100":
-        return So100RobotConfig(**kwargs)
-    elif robot_type == "stretch":
-        return StretchRobotConfig(**kwargs)
-    elif robot_type == "lekiwi":
-        return LeKiwiRobotConfig(**kwargs)
-    else:
-        raise ValueError(f"Robot type '{robot_type}' is not available.")
-
-
-def make_robot_from_config(config: RobotConfig):
-    if isinstance(config, ManipulatorRobotConfig):
-        from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot
-
-        return ManipulatorRobot(config)
-    elif isinstance(config, LeKiwiRobotConfig):
-        from lerobot.common.robot_devices.robots.mobile_manipulator import MobileManipulator
-
-        return MobileManipulator(config)
-    else:
-        from lerobot.common.robot_devices.robots.stretch import StretchRobot
-
-        return StretchRobot(config)
-
-
-def make_robot(robot_type: str, **kwargs) -> Robot:
-    config = make_robot_config(robot_type, **kwargs)
-    return make_robot_from_config(config)

lerobot/common/robots/__init__.py

@@ -0,0 +1,4 @@
+from .config import RobotConfig
+from .robot import Robot
+
+__all__ = ["RobotConfig", "Robot"]

lerobot/common/robots/config.py

@@ -0,0 +1,17 @@
+import abc
+from dataclasses import dataclass
+from pathlib import Path
+
+import draccus
+
+
+@dataclass(kw_only=True)
+class RobotConfig(draccus.ChoiceRegistry, abc.ABC):
+    # Allows to distinguish between different robots of the same type
+    id: str | None = None
+    # Directory to store calibration file
+    calibration_dir: Path | None = None
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)

lerobot/common/robots/dummy/configuration_dummy.py

@@ -0,0 +1,31 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.common.cameras.configs import CameraConfig
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.common.robots.config import RobotConfig
+
+
+@RobotConfig.register_subclass("dummy")
+@dataclass
+class DummyConfig(RobotConfig):
+    id = "dummy"
+
+    cameras: dict[str, CameraConfig] = field(
+        default_factory=lambda: {
+            "cam": OpenCVCameraConfig(camera_index=0, fps=30, width=1280, height=720),
+        }
+    )

lerobot/common/robots/dummy/dummy.py

@@ -0,0 +1,101 @@
+# 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 numpy as np
+
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.constants import OBS_STATE
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+
+from ..robot import Robot
+from .configuration_dummy import DummyConfig
+
+
+class Dummy(Robot):
+    config_class = DummyConfig
+    name = "dummy"
+
+    def __init__(self, config: DummyConfig):
+        super().__init__(config)
+        self.cameras = make_cameras_from_configs(config.cameras)
+        self.is_connected = False
+
+    @property
+    def state_feature(self) -> dict:
+        logging.warning("Dummy has nothing to send.")
+
+    @property
+    def action_feature(self) -> dict:
+        logging.warning("Dummy has nothing to send.")
+
+    @property
+    def camera_features(self) -> dict[str, dict]:
+        cam_ft = {
+            "cam": {
+                "shape": (480, 640, 3),
+                "names": ["height", "width", "channels"],
+                "info": None,
+            },
+        }
+        return cam_ft
+
+    def connect(self) -> None:
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(
+                "Dummy is already connected. Do not run `robot.connect()` twice."
+            )
+
+        logging.info("Connecting cameras.")
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.is_connected = True
+
+    def calibrate(self) -> None:
+        logging.warning("Dummy has nothing to calibrate.")
+        return
+
+    def get_observation(self) -> dict[str, np.ndarray]:
+        if not self.is_connected:
+            raise DeviceNotConnectedError("Dummy is not connected. You need to run `robot.connect()`.")
+
+        obs_dict = {}
+
+        for cam_key, cam in self.cameras.items():
+            frame = cam.async_read()
+            obs_dict[f"{OBS_STATE}.{cam_key}"] = frame
+
+        return obs_dict
+
+    def send_action(self, action: np.ndarray) -> np.ndarray:
+        logging.warning("Dummy has nothing to send.")
+
+    def print_logs(self):
+        pass
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError(
+                "Dummy is not connected. You need to run `robot.connect()` before disconnecting."
+            )
+        for cam in self.cameras.values():
+            cam.disconnect()
+        self.is_connected = False
+
+    def __del__(self):
+        if getattr(self, "is_connected", False):
+            self.disconnect()

lerobot/common/robots/dummy/dummy_app.py

@@ -0,0 +1,71 @@
+# 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 rerun as rr
+
+from lerobot.common.robots.dummy.configuration_dummy import DummyConfig
+from lerobot.common.robots.dummy.dummy import Dummy
+from lerobot.common.teleoperators.so100 import SO100Teleop, SO100TeleopConfig
+
+
+# IMO, it's better to use rerun in the application code instead of the library code
+def main():
+    logging.info("Configuring Devices")
+    leader_arm_config = SO100TeleopConfig(port="/dev/tty.usbmodem58760434171")
+    leader_arm = SO100Teleop(leader_arm_config)
+
+    robot_config = DummyConfig()
+    robot = Dummy(robot_config)
+
+    logging.info("Connecting SO100 Devices")
+    leader_arm.connect()
+
+    logging.info("Connecting Dummy")
+    robot.connect()
+
+    rr.init("rerun_dummy_data")
+    # If data source and visualizer are in different host, use .connect() instead to establish a tcp connection
+    # We can define a custom blueprint configuration for the visualizer panels
+    # Memory limit will make sure no more than 5% of the memory is used by the visualizer
+    rr.spawn(memory_limit="5%")
+
+    logging.info("Starting...")
+    i = 0
+    while i < 10000:
+        # An alternative would be do rerun log inside of these methods. But then that means embedding rerun into the library
+        arm_action = leader_arm.get_action()
+        observation = robot.get_observation()
+
+        for j in range(arm_action.size):
+            # If you want to disable batching we can do it: export RERUN_FLUSH_NUM_BYTES=256
+            current_time = time.time()
+            rr.set_time_seconds(f"arm_action_{j}", current_time)
+            rr.log(f"arm_action_{j}", rr.Scalar(arm_action[j]))
+
+        for k, v in observation.items():
+            # This discards all previous image frames
+            rr.set_time_seconds(k, 0.0)
+            rr.log(k, rr.Image(v))
+
+        i += 1
+
+    robot.disconnect()
+    leader_arm.disconnect()
+
+
+if __name__ == "__main__":
+    main()

lerobot/common/robots/koch/__init__.py

@@ -0,0 +1,4 @@
+from .configuration_koch import KochRobotConfig
+from .robot_koch import KochRobot
+
+__all__ = ["KochRobotConfig", "KochRobot"]

lerobot/common/robots/koch/configuration_koch.py

@@ -0,0 +1,20 @@
+from dataclasses import dataclass, field
+
+from lerobot.common.cameras import CameraConfig
+
+from ..config import RobotConfig
+
+
+@RobotConfig.register_subclass("koch")
+@dataclass
+class KochRobotConfig(RobotConfig):
+    # Port to connect to the robot
+    port: str
+
+    # `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)

lerobot/common/robots/koch/robot_koch.py

@@ -0,0 +1,217 @@
+#!/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 json
+import logging
+import time
+
+import numpy as np
+
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.constants import OBS_IMAGES, OBS_STATE
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.motors.dynamixel import (
+    DynamixelMotorsBus,
+    TorqueMode,
+    run_arm_calibration,
+    set_operating_mode,
+)
+
+from ..robot import Robot
+from ..utils import ensure_safe_goal_position
+from .configuration_koch import KochRobotConfig
+
+
+class KochRobot(Robot):
+    """
+    - [Koch v1.0](https://github.com/AlexanderKoch-Koch/low_cost_robot), with and without the wrist-to-elbow
+        expansion, developed by Alexander Koch from [Tau Robotics](https://tau-robotics.com)
+    - [Koch v1.1](https://github.com/jess-moss/koch-v1-1) developed by Jess Moss
+    """
+
+    config_class = KochRobotConfig
+    name = "koch"
+
+    def __init__(self, config: KochRobotConfig):
+        super().__init__(config)
+        self.config = config
+        self.robot_type = config.type
+
+        self.arm = DynamixelMotorsBus(
+            port=self.config.port,
+            motors={
+                "shoulder_pan": (1, "xl430-w250"),
+                "shoulder_lift": (2, "xl430-w250"),
+                "elbow_flex": (3, "xl330-m288"),
+                "wrist_flex": (4, "xl330-m288"),
+                "wrist_roll": (5, "xl330-m288"),
+                "gripper": (6, "xl330-m288"),
+            },
+        )
+        self.cameras = make_cameras_from_configs(config.cameras)
+
+        self.is_connected = False
+        self.logs = {}
+
+    @property
+    def state_feature(self) -> dict:
+        return {
+            "dtype": "float32",
+            "shape": (len(self.arm),),
+            "names": {"motors": list(self.arm.motors)},
+        }
+
+    @property
+    def action_feature(self) -> dict:
+        return self.state_feature
+
+    @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
+
+    def connect(self) -> None:
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(
+                "ManipulatorRobot is already connected. Do not run `robot.connect()` twice."
+            )
+
+        logging.info("Connecting arm.")
+        self.arm.connect()
+
+        # We assume that at connection time, arm is in a rest position,
+        # and torque can be safely disabled to run calibration.
+        self.arm.write("Torque_Enable", TorqueMode.DISABLED.value)
+        self.calibrate()
+
+        set_operating_mode(self.arm)
+
+        # Set better PID values to close the gap between recorded states and actions
+        # TODO(rcadene): Implement an automatic procedure to set optimal PID values for each motor
+        self.arm.write("Position_P_Gain", 1500, "elbow_flex")
+        self.arm.write("Position_I_Gain", 0, "elbow_flex")
+        self.arm.write("Position_D_Gain", 600, "elbow_flex")
+
+        logging.info("Activating torque.")
+        self.arm.write("Torque_Enable", TorqueMode.ENABLED.value)
+
+        # Check arm can be read
+        self.arm.read("Present_Position")
+
+        # Connect the cameras
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.is_connected = True
+
+    def calibrate(self) -> None:
+        """After calibration all motors function in human interpretable ranges.
+        Rotations are expressed in degrees in nominal range of [-180, 180],
+        and linear motions (like gripper of Aloha) in nominal range of [0, 100].
+        """
+        if self.calibration_fpath.exists():
+            with open(self.calibration_fpath) as f:
+                calibration = json.load(f)
+        else:
+            # TODO(rcadene): display a warning in __init__ if calibration file not available
+            logging.info(f"Missing calibration file '{self.calibration_fpath}'")
+            calibration = run_arm_calibration(self.arm, self.robot_type, self.name, "follower")
+
+            logging.info(f"Calibration is done! Saving calibration file '{self.calibration_fpath}'")
+            self.calibration_fpath.parent.mkdir(parents=True, exist_ok=True)
+            with open(self.calibration_fpath, "w") as f:
+                json.dump(calibration, f)
+
+        self.arm.set_calibration(calibration)
+
+    def get_observation(self) -> dict[str, np.ndarray]:
+        """The returned observations do not have a batch dimension."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
+            )
+
+        obs_dict = {}
+
+        # Read arm position
+        before_read_t = time.perf_counter()
+        obs_dict[OBS_STATE] = self.arm.read("Present_Position")
+        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
+
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            before_camread_t = time.perf_counter()
+            obs_dict[f"{OBS_IMAGES}.{cam_key}"] = cam.async_read()
+            self.logs[f"read_camera_{cam_key}_dt_s"] = cam.logs["delta_timestamp_s"]
+            self.logs[f"async_read_camera_{cam_key}_dt_s"] = time.perf_counter() - before_camread_t
+
+        return obs_dict
+
+    def send_action(self, action: np.ndarray) -> np.ndarray:
+        """Command arm to move to a target joint configuration.
+
+        The relative action magnitude may be clipped depending on the configuration parameter
+        `max_relative_target`. In this case, the action sent differs from original action.
+        Thus, this function always returns the action actually sent.
+
+        Args:
+            action (np.ndarray): array containing the goal positions for the motors.
+
+        Raises:
+            RobotDeviceNotConnectedError: if robot is not connected.
+
+        Returns:
+            np.ndarray: the action sent to the motors, potentially clipped.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()`."
+            )
+
+        goal_pos = action
+
+        # Cap goal position when too far away from present position.
+        # /!\ Slower fps expected due to reading from the follower.
+        if self.config.max_relative_target is not None:
+            present_pos = self.arm.read("Present_Position")
+            goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
+
+        # Send goal position to the arm
+        self.arm.write("Goal_Position", goal_pos.astype(np.int32))
+
+        return goal_pos
+
+    def print_logs(self):
+        # TODO(aliberts): move robot-specific logs logic here
+        pass
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError(
+                "ManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting."
+            )
+
+        self.arm.disconnect()
+        for cam in self.cameras.values():
+            cam.disconnect()
+
+        self.is_connected = False

lerobot/common/robots/lekiwi/README.md

@@ -69,7 +69,7 @@ git clone https://github.com/huggingface/lerobot.git ~/lerobot
 
 #### 5. Install LeRobot with dependencies for the feetech motors:
 ```bash
-cd ~/lerobot && pip install --no-binary=av -e ".[feetech]"
+cd ~/lerobot && pip install -e ".[feetech]"
 ```
 
 ## C. Install LeRobot on laptop
@@ -110,9 +110,15 @@ git clone https://github.com/huggingface/lerobot.git ~/lerobot
 
 #### 5. Install LeRobot with dependencies for the feetech motors:
 ```bash
-cd ~/lerobot && pip install --no-binary=av -e ".[feetech]"
+cd ~/lerobot && pip install -e ".[feetech]"
 ```
 
+*EXTRA: For Linux only (not Mac)*: install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
 Great :hugs:! You are now done installing LeRobot and we can begin assembling the SO100 arms and Mobile base :robot:.
 Every time you now want to use LeRobot you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
 
@@ -393,10 +399,6 @@ python lerobot/scripts/control_robot.py \
 ```
 
 # F. Teleoperate
-
-> [!TIP]
-> If you're using a Mac, you might need to give Terminal permission to access your keyboard. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
-
 To teleoperate SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
 ```bash
 python lerobot/scripts/control_robot.py \

lerobot/common/robots/lekiwi/configuration_lekiwi.py

@@ -0,0 +1,89 @@
+from dataclasses import dataclass, field
+
+from lerobot.common.cameras.configs import CameraConfig
+from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.common.motors.configs import FeetechMotorsBusConfig, MotorsBusConfig
+from lerobot.common.robots.config import RobotConfig
+
+
+@RobotConfig.register_subclass("lekiwi")
+@dataclass
+class LeKiwiRobotConfig(RobotConfig):
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    # Network Configuration
+    ip: str = "192.168.0.193"
+    port: int = 5555
+    video_port: int = 5556
+
+    cameras: dict[str, CameraConfig] = field(
+        default_factory=lambda: {
+            "front": OpenCVCameraConfig(
+                camera_index="/dev/video0", fps=30, width=640, height=480, rotation=90
+            ),
+            "wrist": OpenCVCameraConfig(
+                camera_index="/dev/video2", fps=30, width=640, height=480, rotation=180
+            ),
+        }
+    )
+
+    calibration_dir: str = ".cache/calibration/lekiwi"
+
+    leader_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem585A0077581",
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                },
+            ),
+        }
+    )
+
+    follower_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/ttyACM0",
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                    "left_wheel": (7, "sts3215"),
+                    "back_wheel": (8, "sts3215"),
+                    "right_wheel": (9, "sts3215"),
+                },
+            ),
+        }
+    )
+
+    teleop_keys: dict[str, str] = field(
+        default_factory=lambda: {
+            # Movement
+            "forward": "w",
+            "backward": "s",
+            "left": "a",
+            "right": "d",
+            "rotate_left": "z",
+            "rotate_right": "x",
+            # Speed control
+            "speed_up": "r",
+            "speed_down": "f",
+            # quit teleop
+            "quit": "q",
+        }
+    )
+
+    mock: bool = False

lerobot/common/robots/lekiwi/lekiwi_remote.py

@@ -21,7 +21,7 @@ from pathlib import Path
 import cv2
 import zmq
 
-from lerobot.common.robot_devices.robots.mobile_manipulator import LeKiwi
+from lerobot.common.robots.mobile_manipulator import LeKiwi
 
 
 def setup_zmq_sockets(config):
@@ -61,7 +61,7 @@ def calibrate_follower_arm(motors_bus, calib_dir_str):
     calib_dir.mkdir(parents=True, exist_ok=True)
     calib_file = calib_dir / "main_follower.json"
     try:
-        from lerobot.common.robot_devices.robots.feetech_calibration import run_arm_manual_calibration
+        from lerobot.common.motors.feetech.feetech_calibration import run_full_arm_calibration
     except ImportError:
         print("[WARNING] Calibration function not available. Skipping calibration.")
         return
@@ -72,7 +72,7 @@ def calibrate_follower_arm(motors_bus, calib_dir_str):
         print(f"[INFO] Loaded calibration from {calib_file}")
     else:
         print("[INFO] Calibration file not found. Running manual calibration...")
-        calibration = run_arm_manual_calibration(motors_bus, "lekiwi", "follower_arm", "follower")
+        calibration = run_full_arm_calibration(motors_bus, "lekiwi", "follower_arm", "follower")
         print(f"[INFO] Calibration complete. Saving to {calib_file}")
         with open(calib_file, "w") as f:
             json.dump(calibration, f)
@@ -93,8 +93,8 @@ def run_lekiwi(robot_config):
       - Processes incoming commands (arm and wheel commands) and sends back sensor and camera data.
     """
     # Import helper functions and classes
-    from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
-    from lerobot.common.robot_devices.motors.feetech import FeetechMotorsBus, TorqueMode
+    from lerobot.common.cameras.utils import make_cameras_from_configs
+    from lerobot.common.motors.feetech.feetech import FeetechMotorsBus, TorqueMode
 
     # Initialize cameras from the robot configuration.
     cameras = make_cameras_from_configs(robot_config.cameras)

lerobot/common/robots/lekiwi/robot_lekiwi.py

@@ -0,0 +1,692 @@
+import base64
+import json
+import os
+import sys
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+import zmq
+
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.errors import DeviceNotConnectedError
+from lerobot.common.motors.feetech.feetech import TorqueMode
+from lerobot.common.motors.feetech.feetech_calibration import run_full_arm_calibration
+from lerobot.common.motors.motors_bus import MotorsBus
+from lerobot.common.motors.utils import make_motors_buses_from_configs
+from lerobot.common.robots.lekiwi.configuration_lekiwi import LeKiwiRobotConfig
+from lerobot.common.robots.utils import get_arm_id
+
+PYNPUT_AVAILABLE = True
+try:
+    # Only import if there's a valid X server or if we're not on a Pi
+    if ("DISPLAY" not in os.environ) and ("linux" in sys.platform):
+        print("No DISPLAY set. Skipping pynput import.")
+        raise ImportError("pynput blocked intentionally due to no display.")
+
+    from pynput import keyboard
+except ImportError:
+    keyboard = None
+    PYNPUT_AVAILABLE = False
+except Exception as e:
+    keyboard = None
+    PYNPUT_AVAILABLE = False
+    print(f"Could not import pynput: {e}")
+
+
+class MobileManipulator:
+    """
+    MobileManipulator is a class for connecting to and controlling a remote mobile manipulator robot.
+    The robot includes a three omniwheel mobile base and a remote follower arm.
+    The leader arm is connected locally (on the laptop) and its joint positions are recorded and then
+    forwarded to the remote follower arm (after applying a safety clamp).
+    In parallel, keyboard teleoperation is used to generate raw velocity commands for the wheels.
+    """
+
+    def __init__(self, config: LeKiwiRobotConfig):
+        """
+        Expected keys in config:
+          - ip, port, video_port for the remote connection.
+          - calibration_dir, leader_arms, follower_arms, max_relative_target, etc.
+        """
+        self.robot_type = config.type
+        self.config = config
+        self.remote_ip = config.ip
+        self.remote_port = config.port
+        self.remote_port_video = config.video_port
+        self.calibration_dir = Path(self.config.calibration_dir)
+        self.logs = {}
+
+        self.teleop_keys = self.config.teleop_keys
+
+        # For teleoperation, the leader arm (local) is used to record the desired arm pose.
+        self.leader_arms = make_motors_buses_from_configs(self.config.leader_arms)
+
+        self.follower_arms = make_motors_buses_from_configs(self.config.follower_arms)
+
+        self.cameras = make_cameras_from_configs(self.config.cameras)
+
+        self.is_connected = False
+
+        self.last_frames = {}
+        self.last_present_speed = {}
+        self.last_remote_arm_state = torch.zeros(6, dtype=torch.float32)
+
+        # Define three speed levels and a current index
+        self.speed_levels = [
+            {"xy": 0.1, "theta": 30},  # slow
+            {"xy": 0.2, "theta": 60},  # medium
+            {"xy": 0.3, "theta": 90},  # fast
+        ]
+        self.speed_index = 0  # Start at slow
+
+        # ZeroMQ context and sockets.
+        self.context = None
+        self.cmd_socket = None
+        self.video_socket = None
+
+        # Keyboard state for base teleoperation.
+        self.running = True
+        self.pressed_keys = {
+            "forward": False,
+            "backward": False,
+            "left": False,
+            "right": False,
+            "rotate_left": False,
+            "rotate_right": False,
+        }
+
+        if PYNPUT_AVAILABLE:
+            print("pynput is available - enabling local keyboard listener.")
+            self.listener = keyboard.Listener(
+                on_press=self.on_press,
+                on_release=self.on_release,
+            )
+            self.listener.start()
+        else:
+            print("pynput not available - skipping local keyboard listener.")
+            self.listener = None
+
+    def get_motor_names(self, arms: dict[str, MotorsBus]) -> list:
+        return [f"{arm}_{motor}" for arm, bus in arms.items() for motor in bus.motors]
+
+    @property
+    def camera_features(self) -> dict:
+        cam_ft = {}
+        for cam_key, cam in self.cameras.items():
+            key = f"observation.images.{cam_key}"
+            cam_ft[key] = {
+                "shape": (cam.height, cam.width, cam.channels),
+                "names": ["height", "width", "channels"],
+                "info": None,
+            }
+        return cam_ft
+
+    @property
+    def motor_features(self) -> dict:
+        follower_arm_names = [
+            "shoulder_pan",
+            "shoulder_lift",
+            "elbow_flex",
+            "wrist_flex",
+            "wrist_roll",
+            "gripper",
+        ]
+        observations = ["x_mm", "y_mm", "theta"]
+        combined_names = follower_arm_names + observations
+        return {
+            "action": {
+                "dtype": "float32",
+                "shape": (len(combined_names),),
+                "names": combined_names,
+            },
+            "observation.state": {
+                "dtype": "float32",
+                "shape": (len(combined_names),),
+                "names": combined_names,
+            },
+        }
+
+    @property
+    def features(self):
+        return {**self.motor_features, **self.camera_features}
+
+    @property
+    def has_camera(self):
+        return len(self.cameras) > 0
+
+    @property
+    def num_cameras(self):
+        return len(self.cameras)
+
+    @property
+    def available_arms(self):
+        available = []
+        for name in self.leader_arms:
+            available.append(get_arm_id(name, "leader"))
+        for name in self.follower_arms:
+            available.append(get_arm_id(name, "follower"))
+        return available
+
+    def on_press(self, key):
+        try:
+            # Movement
+            if key.char == self.teleop_keys["forward"]:
+                self.pressed_keys["forward"] = True
+            elif key.char == self.teleop_keys["backward"]:
+                self.pressed_keys["backward"] = True
+            elif key.char == self.teleop_keys["left"]:
+                self.pressed_keys["left"] = True
+            elif key.char == self.teleop_keys["right"]:
+                self.pressed_keys["right"] = True
+            elif key.char == self.teleop_keys["rotate_left"]:
+                self.pressed_keys["rotate_left"] = True
+            elif key.char == self.teleop_keys["rotate_right"]:
+                self.pressed_keys["rotate_right"] = True
+
+            # Quit teleoperation
+            elif key.char == self.teleop_keys["quit"]:
+                self.running = False
+                return False
+
+            # Speed control
+            elif key.char == self.teleop_keys["speed_up"]:
+                self.speed_index = min(self.speed_index + 1, 2)
+                print(f"Speed index increased to {self.speed_index}")
+            elif key.char == self.teleop_keys["speed_down"]:
+                self.speed_index = max(self.speed_index - 1, 0)
+                print(f"Speed index decreased to {self.speed_index}")
+
+        except AttributeError:
+            # e.g., if key is special like Key.esc
+            if key == keyboard.Key.esc:
+                self.running = False
+                return False
+
+    def on_release(self, key):
+        try:
+            if hasattr(key, "char"):
+                if key.char == self.teleop_keys["forward"]:
+                    self.pressed_keys["forward"] = False
+                elif key.char == self.teleop_keys["backward"]:
+                    self.pressed_keys["backward"] = False
+                elif key.char == self.teleop_keys["left"]:
+                    self.pressed_keys["left"] = False
+                elif key.char == self.teleop_keys["right"]:
+                    self.pressed_keys["right"] = False
+                elif key.char == self.teleop_keys["rotate_left"]:
+                    self.pressed_keys["rotate_left"] = False
+                elif key.char == self.teleop_keys["rotate_right"]:
+                    self.pressed_keys["rotate_right"] = False
+        except AttributeError:
+            pass
+
+    def connect(self):
+        if not self.leader_arms:
+            raise ValueError("MobileManipulator has no leader arm to connect.")
+        for name in self.leader_arms:
+            print(f"Connecting {name} leader arm.")
+            self.calibrate_leader()
+
+        # Set up ZeroMQ sockets to communicate with the remote mobile robot.
+        self.context = zmq.Context()
+        self.cmd_socket = self.context.socket(zmq.PUSH)
+        connection_string = f"tcp://{self.remote_ip}:{self.remote_port}"
+        self.cmd_socket.connect(connection_string)
+        self.cmd_socket.setsockopt(zmq.CONFLATE, 1)
+        self.video_socket = self.context.socket(zmq.PULL)
+        video_connection = f"tcp://{self.remote_ip}:{self.remote_port_video}"
+        self.video_socket.connect(video_connection)
+        self.video_socket.setsockopt(zmq.CONFLATE, 1)
+        print(
+            f"[INFO] Connected to remote robot at {connection_string} and video stream at {video_connection}."
+        )
+        self.is_connected = True
+
+    def load_or_run_calibration_(self, name, arm, arm_type):
+        arm_id = get_arm_id(name, arm_type)
+        arm_calib_path = self.calibration_dir / f"{arm_id}.json"
+
+        if arm_calib_path.exists():
+            with open(arm_calib_path) as f:
+                calibration = json.load(f)
+        else:
+            print(f"Missing calibration file '{arm_calib_path}'")
+            calibration = run_full_arm_calibration(arm, self.robot_type, name, arm_type)
+            print(f"Calibration is done! Saving calibration file '{arm_calib_path}'")
+            arm_calib_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(arm_calib_path, "w") as f:
+                json.dump(calibration, f)
+
+        return calibration
+
+    def calibrate_leader(self):
+        for name, arm in self.leader_arms.items():
+            # Connect the bus
+            arm.connect()
+
+            # Disable torque on all motors
+            for motor_id in arm.motors:
+                arm.write("Torque_Enable", TorqueMode.DISABLED.value, motor_id)
+
+            # Now run calibration
+            calibration = self.load_or_run_calibration_(name, arm, "leader")
+            arm.set_calibration(calibration)
+
+    def calibrate_follower(self):
+        for name, bus in self.follower_arms.items():
+            bus.connect()
+
+            # Disable torque on all motors
+            for motor_id in bus.motors:
+                bus.write("Torque_Enable", 0, motor_id)
+
+            # Then filter out wheels
+            arm_only_dict = {k: v for k, v in bus.motors.items() if not k.startswith("wheel_")}
+            if not arm_only_dict:
+                continue
+
+            original_motors = bus.motors
+            bus.motors = arm_only_dict
+
+            calibration = self.load_or_run_calibration_(name, bus, "follower")
+            bus.set_calibration(calibration)
+
+            bus.motors = original_motors
+
+    def _get_data(self):
+        """
+        Polls the video socket for up to 15 ms. If data arrives, decode only
+        the *latest* message, returning frames, speed, and arm state. If
+        nothing arrives for any field, use the last known values.
+        """
+        frames = {}
+        present_speed = {}
+        remote_arm_state_tensor = torch.zeros(6, dtype=torch.float32)
+
+        # Poll up to 15 ms
+        poller = zmq.Poller()
+        poller.register(self.video_socket, zmq.POLLIN)
+        socks = dict(poller.poll(15))
+        if self.video_socket not in socks or socks[self.video_socket] != zmq.POLLIN:
+            # No new data arrived → reuse ALL old data
+            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
+
+        # Drain all messages, keep only the last
+        last_msg = None
+        while True:
+            try:
+                obs_string = self.video_socket.recv_string(zmq.NOBLOCK)
+                last_msg = obs_string
+            except zmq.Again:
+                break
+
+        if not last_msg:
+            # No new message → also reuse old
+            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
+
+        # Decode only the final message
+        try:
+            observation = json.loads(last_msg)
+
+            images_dict = observation.get("images", {})
+            new_speed = observation.get("present_speed", {})
+            new_arm_state = observation.get("follower_arm_state", None)
+
+            # Convert images
+            for cam_name, image_b64 in images_dict.items():
+                if image_b64:
+                    jpg_data = base64.b64decode(image_b64)
+                    np_arr = np.frombuffer(jpg_data, dtype=np.uint8)
+                    frame_candidate = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+                    if frame_candidate is not None:
+                        frames[cam_name] = frame_candidate
+
+            # If remote_arm_state is None and frames is None there is no message then use the previous message
+            if new_arm_state is not None and frames is not None:
+                self.last_frames = frames
+
+                remote_arm_state_tensor = torch.tensor(new_arm_state, dtype=torch.float32)
+                self.last_remote_arm_state = remote_arm_state_tensor
+
+                present_speed = new_speed
+                self.last_present_speed = new_speed
+            else:
+                frames = self.last_frames
+
+                remote_arm_state_tensor = self.last_remote_arm_state
+
+                present_speed = self.last_present_speed
+
+        except Exception as e:
+            print(f"[DEBUG] Error decoding video message: {e}")
+            # If decode fails, fall back to old data
+            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
+
+        return frames, present_speed, remote_arm_state_tensor
+
+    def _process_present_speed(self, present_speed: dict) -> torch.Tensor:
+        state_tensor = torch.zeros(3, dtype=torch.int32)
+        if present_speed:
+            decoded = {key: MobileManipulator.raw_to_degps(value) for key, value in present_speed.items()}
+            if "1" in decoded:
+                state_tensor[0] = decoded["1"]
+            if "2" in decoded:
+                state_tensor[1] = decoded["2"]
+            if "3" in decoded:
+                state_tensor[2] = decoded["3"]
+        return state_tensor
+
+    def teleop_step(
+        self, record_data: bool = False
+    ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
+        if not self.is_connected:
+            raise DeviceNotConnectedError("MobileManipulator is not connected. Run `connect()` first.")
+
+        speed_setting = self.speed_levels[self.speed_index]
+        xy_speed = speed_setting["xy"]  # e.g. 0.1, 0.25, or 0.4
+        theta_speed = speed_setting["theta"]  # e.g. 30, 60, or 90
+
+        # Prepare to assign the position of the leader to the follower
+        arm_positions = []
+        for name in self.leader_arms:
+            pos = self.leader_arms[name].read("Present_Position")
+            pos_tensor = torch.from_numpy(pos).float()
+            # Instead of pos_tensor.item(), use tolist() to convert the entire tensor to a list
+            arm_positions.extend(pos_tensor.tolist())
+
+        # (The rest of your code for generating wheel commands remains unchanged)
+        x_cmd = 0.0  # m/s forward/backward
+        y_cmd = 0.0  # m/s lateral
+        theta_cmd = 0.0  # deg/s rotation
+        if self.pressed_keys["forward"]:
+            x_cmd += xy_speed
+        if self.pressed_keys["backward"]:
+            x_cmd -= xy_speed
+        if self.pressed_keys["left"]:
+            y_cmd += xy_speed
+        if self.pressed_keys["right"]:
+            y_cmd -= xy_speed
+        if self.pressed_keys["rotate_left"]:
+            theta_cmd += theta_speed
+        if self.pressed_keys["rotate_right"]:
+            theta_cmd -= theta_speed
+
+        wheel_commands = self.body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
+
+        message = {"raw_velocity": wheel_commands, "arm_positions": arm_positions}
+        self.cmd_socket.send_string(json.dumps(message))
+
+        if not record_data:
+            return
+
+        obs_dict = self.capture_observation()
+
+        arm_state_tensor = torch.tensor(arm_positions, dtype=torch.float32)
+
+        wheel_velocity_tuple = self.wheel_raw_to_body(wheel_commands)
+        wheel_velocity_mm = (
+            wheel_velocity_tuple[0] * 1000.0,
+            wheel_velocity_tuple[1] * 1000.0,
+            wheel_velocity_tuple[2],
+        )
+        wheel_tensor = torch.tensor(wheel_velocity_mm, dtype=torch.float32)
+        action_tensor = torch.cat([arm_state_tensor, wheel_tensor])
+        action_dict = {"action": action_tensor}
+
+        return obs_dict, action_dict
+
+    def capture_observation(self) -> dict:
+        """
+        Capture observations from the remote robot: current follower arm positions,
+        present wheel speeds (converted to body-frame velocities: x, y, theta),
+        and a camera frame.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError("Not connected. Run `connect()` first.")
+
+        frames, present_speed, remote_arm_state_tensor = self._get_data()
+
+        body_state = self.wheel_raw_to_body(present_speed)
+
+        body_state_mm = (body_state[0] * 1000.0, body_state[1] * 1000.0, body_state[2])  # Convert x,y to mm/s
+        wheel_state_tensor = torch.tensor(body_state_mm, dtype=torch.float32)
+        combined_state_tensor = torch.cat((remote_arm_state_tensor, wheel_state_tensor), dim=0)
+
+        obs_dict = {"observation.state": combined_state_tensor}
+
+        # Loop over each configured camera
+        for cam_name, cam in self.cameras.items():
+            frame = frames.get(cam_name, None)
+            if frame is None:
+                # Create a black image using the camera's configured width, height, and channels
+                frame = np.zeros((cam.height, cam.width, cam.channels), dtype=np.uint8)
+            obs_dict[f"observation.images.{cam_name}"] = torch.from_numpy(frame)
+
+        return obs_dict
+
+    def send_action(self, action: torch.Tensor) -> torch.Tensor:
+        if not self.is_connected:
+            raise DeviceNotConnectedError("Not connected. Run `connect()` first.")
+
+        # Ensure the action tensor has at least 9 elements:
+        #   - First 6: arm positions.
+        #   - Last 3: base commands.
+        if action.numel() < 9:
+            # Pad with zeros if there are not enough elements.
+            padded = torch.zeros(9, dtype=action.dtype)
+            padded[: action.numel()] = action
+            action = padded
+
+        # Extract arm and base actions.
+        arm_actions = action[:6].flatten()
+        base_actions = action[6:].flatten()
+
+        x_cmd_mm = base_actions[0].item()  # mm/s
+        y_cmd_mm = base_actions[1].item()  # mm/s
+        theta_cmd = base_actions[2].item()  # deg/s
+
+        # Convert mm/s to m/s for the kinematics calculations.
+        x_cmd = x_cmd_mm / 1000.0  # m/s
+        y_cmd = y_cmd_mm / 1000.0  # m/s
+
+        # Compute wheel commands from body commands.
+        wheel_commands = self.body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
+
+        arm_positions_list = arm_actions.tolist()
+
+        message = {"raw_velocity": wheel_commands, "arm_positions": arm_positions_list}
+        self.cmd_socket.send_string(json.dumps(message))
+
+        return action
+
+    def print_logs(self):
+        pass
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError("Not connected.")
+        if self.cmd_socket:
+            stop_cmd = {
+                "raw_velocity": {"left_wheel": 0, "back_wheel": 0, "right_wheel": 0},
+                "arm_positions": {},
+            }
+            self.cmd_socket.send_string(json.dumps(stop_cmd))
+            self.cmd_socket.close()
+        if self.video_socket:
+            self.video_socket.close()
+        if self.context:
+            self.context.term()
+        if PYNPUT_AVAILABLE:
+            self.listener.stop()
+        self.is_connected = False
+        print("[INFO] Disconnected from remote robot.")
+
+    def __del__(self):
+        if getattr(self, "is_connected", False):
+            self.disconnect()
+        if PYNPUT_AVAILABLE:
+            self.listener.stop()
+
+    @staticmethod
+    def degps_to_raw(degps: float) -> int:
+        steps_per_deg = 4096.0 / 360.0
+        speed_in_steps = abs(degps) * steps_per_deg
+        speed_int = int(round(speed_in_steps))
+        if speed_int > 0x7FFF:
+            speed_int = 0x7FFF
+        if degps < 0:
+            return speed_int | 0x8000
+        else:
+            return speed_int & 0x7FFF
+
+    @staticmethod
+    def raw_to_degps(raw_speed: int) -> float:
+        steps_per_deg = 4096.0 / 360.0
+        magnitude = raw_speed & 0x7FFF
+        degps = magnitude / steps_per_deg
+        if raw_speed & 0x8000:
+            degps = -degps
+        return degps
+
+    def body_to_wheel_raw(
+        self,
+        x_cmd: float,
+        y_cmd: float,
+        theta_cmd: float,
+        wheel_radius: float = 0.05,
+        base_radius: float = 0.125,
+        max_raw: int = 3000,
+    ) -> dict:
+        """
+        Convert desired body-frame velocities into wheel raw commands.
+
+        Parameters:
+          x_cmd      : Linear velocity in x (m/s).
+          y_cmd      : Linear velocity in y (m/s).
+          theta_cmd  : Rotational velocity (deg/s).
+          wheel_radius: Radius of each wheel (meters).
+          base_radius : Distance from the center of rotation to each wheel (meters).
+          max_raw    : Maximum allowed raw command (ticks) per wheel.
+
+        Returns:
+          A dictionary with wheel raw commands:
+             {"left_wheel": value, "back_wheel": value, "right_wheel": value}.
+
+        Notes:
+          - Internally, the method converts theta_cmd to rad/s for the kinematics.
+          - The raw command is computed from the wheels angular speed in deg/s
+            using degps_to_raw(). If any command exceeds max_raw, all commands
+            are scaled down proportionally.
+        """
+        # Convert rotational velocity from deg/s to rad/s.
+        theta_rad = theta_cmd * (np.pi / 180.0)
+        # Create the body velocity vector [x, y, theta_rad].
+        velocity_vector = np.array([x_cmd, y_cmd, theta_rad])
+
+        # Define the wheel mounting angles 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 = [MobileManipulator.degps_to_raw(deg) for deg in wheel_degps]
+
+        return {"left_wheel": wheel_raw[0], "back_wheel": wheel_raw[1], "right_wheel": wheel_raw[2]}
+
+    def wheel_raw_to_body(
+        self, wheel_raw: dict, wheel_radius: float = 0.05, base_radius: float = 0.125
+    ) -> tuple:
+        """
+        Convert wheel raw command feedback back into body-frame velocities.
+
+        Parameters:
+          wheel_raw   : Dictionary with raw wheel commands (keys: "left_wheel", "back_wheel", "right_wheel").
+          wheel_radius: Radius of each wheel (meters).
+          base_radius : Distance from the robot center to each wheel (meters).
+
+        Returns:
+          A tuple (x_cmd, y_cmd, theta_cmd) where:
+             x_cmd      : Linear velocity in x (m/s).
+             y_cmd      : Linear velocity in y (m/s).
+             theta_cmd  : Rotational velocity in deg/s.
+        """
+        # Extract the raw values in order.
+        raw_list = [
+            int(wheel_raw.get("left_wheel", 0)),
+            int(wheel_raw.get("back_wheel", 0)),
+            int(wheel_raw.get("right_wheel", 0)),
+        ]
+
+        # Convert each raw command back to an angular speed in deg/s.
+        wheel_degps = np.array([MobileManipulator.raw_to_degps(r) for r in raw_list])
+        # Convert from deg/s to rad/s.
+        wheel_radps = wheel_degps * (np.pi / 180.0)
+        # Compute each wheel’s linear speed (m/s) from its angular speed.
+        wheel_linear_speeds = wheel_radps * wheel_radius
+
+        # Define the wheel mounting angles 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 (x_cmd, y_cmd, theta_cmd)
+
+
+class LeKiwi:
+    def __init__(self, motor_bus):
+        """
+        Initializes the LeKiwi with Feetech motors bus.
+        """
+        self.motor_bus = motor_bus
+        self.motor_ids = ["left_wheel", "back_wheel", "right_wheel"]
+
+        # Initialize motors in velocity mode.
+        self.motor_bus.write("Lock", 0)
+        self.motor_bus.write("Mode", [1, 1, 1], self.motor_ids)
+        self.motor_bus.write("Lock", 1)
+        print("Motors set to velocity mode.")
+
+    def read_velocity(self):
+        """
+        Reads the raw speeds for all wheels. Returns a dictionary with motor names:
+        """
+        raw_speeds = self.motor_bus.read("Present_Speed", self.motor_ids)
+        return {
+            "left_wheel": int(raw_speeds[0]),
+            "back_wheel": int(raw_speeds[1]),
+            "right_wheel": int(raw_speeds[2]),
+        }
+
+    def set_velocity(self, command_speeds):
+        """
+        Sends raw velocity commands (16-bit encoded values) directly to the motor bus.
+        The order of speeds must correspond to self.motor_ids.
+        """
+        self.motor_bus.write("Goal_Speed", command_speeds, self.motor_ids)
+
+    def stop(self):
+        """Stops the robot by setting all motor speeds to zero."""
+        self.motor_bus.write("Goal_Speed", [0, 0, 0], self.motor_ids)
+        print("Motors stopped.")

lerobot/common/robots/manipulator.py

@@ -19,39 +19,133 @@ and send orders to its motors.
 # calibration procedure, to make it easy for people to add their own robot.
 
 import json
-import logging
 import time
 import warnings
+from dataclasses import dataclass, field
 from pathlib import Path
+from typing import Sequence
 
 import numpy as np
 import torch
 
-from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
-from lerobot.common.robot_devices.motors.utils import MotorsBus, make_motors_buses_from_configs
-from lerobot.common.robot_devices.robots.configs import ManipulatorRobotConfig
-from lerobot.common.robot_devices.robots.utils import get_arm_id
-from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
+from lerobot.common.cameras.configs import CameraConfig
+from lerobot.common.cameras.utils import make_cameras_from_configs
+from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.common.motors.configs import MotorsBusConfig
+from lerobot.common.motors.motors_bus import MotorsBus
+from lerobot.common.motors.utils import make_motors_buses_from_configs
+from lerobot.common.robots.config import RobotConfig
+from lerobot.common.robots.utils import ensure_safe_goal_position, get_arm_id
 
 
-def ensure_safe_goal_position(
-    goal_pos: torch.Tensor, present_pos: torch.Tensor, max_relative_target: float | list[float]
-):
-    # Cap relative action target magnitude for safety.
-    diff = goal_pos - present_pos
-    max_relative_target = torch.tensor(max_relative_target)
-    safe_diff = torch.minimum(diff, max_relative_target)
-    safe_diff = torch.maximum(safe_diff, -max_relative_target)
-    safe_goal_pos = present_pos + safe_diff
+@dataclass
+class ManipulatorRobotConfig(RobotConfig):
+    leader_arms: dict[str, MotorsBusConfig] = field(default_factory=lambda: {})
+    follower_arms: dict[str, MotorsBusConfig] = field(default_factory=lambda: {})
+    cameras: dict[str, CameraConfig] = field(default_factory=lambda: {})
 
-    if not torch.allclose(goal_pos, safe_goal_pos):
-        logging.warning(
-            "Relative goal position magnitude had to be clamped to be safe.\n"
-            f"  requested relative goal position target: {diff}\n"
-            f"    clamped relative goal position target: {safe_diff}"
+    # Optionally limit the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length
+    # as the number of motors in your follower arms (assumes all follower arms have the same number of
+    # motors).
+    max_relative_target: list[float] | float | None = None
+
+    # Optionally set the leader arm in torque mode with the gripper motor set to this angle. This makes it
+    # possible to squeeze the gripper and have it spring back to an open position on its own. If None, the
+    # gripper is not put in torque mode.
+    gripper_open_degree: float | None = None
+
+    mock: bool = False
+
+    def __post_init__(self):
+        if self.mock:
+            for arm in self.leader_arms.values():
+                if not arm.mock:
+                    arm.mock = True
+            for arm in self.follower_arms.values():
+                if not arm.mock:
+                    arm.mock = True
+            for cam in self.cameras.values():
+                if not cam.mock:
+                    cam.mock = True
+
+        if self.max_relative_target is not None and isinstance(self.max_relative_target, Sequence):
+            for name in self.follower_arms:
+                if len(self.follower_arms[name].motors) != len(self.max_relative_target):
+                    raise ValueError(
+                        f"len(max_relative_target)={len(self.max_relative_target)} but the follower arm with name {name} has "
+                        f"{len(self.follower_arms[name].motors)} motors. Please make sure that the "
+                        f"`max_relative_target` list has as many parameters as there are motors per arm. "
+                        "Note: This feature does not yet work with robots where different follower arms have "
+                        "different numbers of motors."
+                    )
+
+
+def apply_feetech_offsets_from_calibration(motorsbus, calibration_dict: dict):
+    """
+    Reads 'calibration_dict' containing 'homing_offset' and 'motor_names',
+    then writes each motor's offset to the servo's internal Offset (0x1F) in EPROM.
+
+    This version is modified so each homed position (originally 0) will now read
+    2047, i.e. 180° away from 0 in the 4096-count circle. Offsets are permanently
+    stored in EEPROM, so the servo's Present_Position is hardware-shifted even
+    after power cycling.
+
+    Steps:
+      1) Subtract 2047 from the old offset (so 0 -> 2047).
+      2) Clamp to [-2047..+2047].
+      3) Encode sign bit and magnitude into a 12-bit number.
+    """
+
+    homing_offsets = calibration_dict["homing_offset"]
+    motor_names = calibration_dict["motor_names"]
+    start_pos = calibration_dict["start_pos"]
+
+    # Open the write lock, changes to EEPROM do NOT persist yet
+    motorsbus.write("Lock", 1)
+
+    # For each motor, set the 'Offset' parameter
+    for m_name, old_offset in zip(motor_names, homing_offsets, strict=False):
+        # If bus doesn’t have a motor named m_name, skip
+        if m_name not in motorsbus.motors:
+            print(f"Warning: '{m_name}' not found in motorsbus.motors; skipping offset.")
+            continue
+
+        if m_name == "gripper":
+            old_offset = start_pos  # If gripper set the offset to the start position of the gripper
+            continue
+
+        # Shift the offset so the homed position reads 2047
+        new_offset = old_offset - 2047
+
+        # Clamp to [-2047..+2047]
+        if new_offset > 2047:
+            new_offset = 2047
+            print(
+                f"Warning: '{new_offset}' is getting clamped because its larger then 2047; This should not happen!"
+            )
+        elif new_offset < -2047:
+            new_offset = -2047
+            print(
+                f"Warning: '{new_offset}' is getting clamped because its smaller then -2047; This should not happen!"
+            )
+
+        # Determine the direction (sign) bit and magnitude
+        direction_bit = 1 if new_offset < 0 else 0
+        magnitude = abs(new_offset)
+
+        # Combine sign bit (bit 11) with the magnitude (bits 0..10)
+        servo_offset = (direction_bit << 11) | magnitude
+
+        # Write offset to servo
+        motorsbus.write("Offset", servo_offset, motor_names=m_name)
+        print(
+            f"Set offset for {m_name}: "
+            f"old_offset={old_offset}, new_offset={new_offset}, servo_encoded={magnitude} + direction={direction_bit}"
         )
 
-    return safe_goal_pos
+    motorsbus.write("Lock", 0)
+    print("Offsets have been saved to EEPROM successfully.")
 
 
 class ManipulatorRobot:
@@ -224,7 +318,7 @@ class ManipulatorRobot:
 
     def connect(self):
         if self.is_connected:
-            raise RobotDeviceAlreadyConnectedError(
+            raise DeviceAlreadyConnectedError(
                 "ManipulatorRobot is already connected. Do not run `robot.connect()` twice."
             )
 
@@ -242,9 +336,9 @@ class ManipulatorRobot:
             self.leader_arms[name].connect()
 
         if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
-            from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
+            from lerobot.common.motors.dynamixel.dynamixel import TorqueMode
         elif self.robot_type in ["so100", "moss", "lekiwi"]:
-            from lerobot.common.robot_devices.motors.feetech import TorqueMode
+            from lerobot.common.motors.feetech.feetech import TorqueMode
 
         # We assume that at connection time, arms are in a rest position, and torque can
         # be safely disabled to run calibration and/or set robot preset configurations.
@@ -309,16 +403,16 @@ class ManipulatorRobot:
                 print(f"Missing calibration file '{arm_calib_path}'")
 
                 if self.robot_type in ["koch", "koch_bimanual", "aloha"]:
-                    from lerobot.common.robot_devices.robots.dynamixel_calibration import run_arm_calibration
+                    from lerobot.common.motors.dynamixel.dynamixel_calibration import run_arm_calibration
 
                     calibration = run_arm_calibration(arm, self.robot_type, name, arm_type)
 
                 elif self.robot_type in ["so100", "moss", "lekiwi"]:
-                    from lerobot.common.robot_devices.robots.feetech_calibration import (
-                        run_arm_manual_calibration,
+                    from lerobot.common.motors.feetech.feetech_calibration import (
+                        run_full_arm_calibration,
                     )
 
-                    calibration = run_arm_manual_calibration(arm, self.robot_type, name, arm_type)
+                    calibration = run_full_arm_calibration(arm, self.robot_type, name, arm_type)
 
                 print(f"Calibration is done! Saving calibration file '{arm_calib_path}'")
                 arm_calib_path.parent.mkdir(parents=True, exist_ok=True)
@@ -327,16 +421,28 @@ class ManipulatorRobot:
 
             return calibration
 
-        for name, arm in self.follower_arms.items():
-            calibration = load_or_run_calibration_(name, arm, "follower")
-            arm.set_calibration(calibration)
-        for name, arm in self.leader_arms.items():
-            calibration = load_or_run_calibration_(name, arm, "leader")
-            arm.set_calibration(calibration)
+            # For each follower arm
+
+        for name, arm_bus in self.follower_arms.items():
+            calibration = load_or_run_calibration_(name, arm_bus, "follower")
+            arm_bus.set_calibration(calibration)
+
+            # If this is a Feetech robot, also set the servo offset into EEPROM
+            if self.robot_type in ["so100", "lekiwi"]:
+                apply_feetech_offsets_from_calibration(arm_bus, calibration)
+
+        # For each leader arm
+        for name, arm_bus in self.leader_arms.items():
+            calibration = load_or_run_calibration_(name, arm_bus, "leader")
+            arm_bus.set_calibration(calibration)
+
+            # Optionally also set offset for leader if you want the servo offsets as well
+            if self.robot_type in ["so100", "lekiwi"]:
+                apply_feetech_offsets_from_calibration(arm_bus, calibration)
 
     def set_koch_robot_preset(self):
         def set_operating_mode_(arm):
-            from lerobot.common.robot_devices.motors.dynamixel import TorqueMode
+            from lerobot.common.motors.dynamixel.dynamixel import TorqueMode
 
             if (arm.read("Torque_Enable") != TorqueMode.DISABLED.value).any():
                 raise ValueError("To run set robot preset, the torque must be disabled on all motors.")
@@ -446,7 +552,7 @@ class ManipulatorRobot:
         self, record_data=False
     ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 "ManipulatorRobot is not connected. You need to run `robot.connect()`."
             )
 
@@ -474,7 +580,7 @@ class ManipulatorRobot:
             # Used when record_data=True
             follower_goal_pos[name] = goal_pos
 
-            goal_pos = goal_pos.numpy().astype(np.float32)
+            goal_pos = goal_pos.numpy().astype(np.int32)
             self.follower_arms[name].write("Goal_Position", goal_pos)
             self.logs[f"write_follower_{name}_goal_pos_dt_s"] = time.perf_counter() - before_fwrite_t
 
@@ -526,7 +632,7 @@ class ManipulatorRobot:
     def capture_observation(self):
         """The returned observations do not have a batch dimension."""
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 "ManipulatorRobot is not connected. You need to run `robot.connect()`."
             )
 
@@ -572,7 +678,7 @@ class ManipulatorRobot:
             action: tensor containing the concatenated goal positions for the follower arms.
         """
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 "ManipulatorRobot is not connected. You need to run `robot.connect()`."
             )
 
@@ -596,7 +702,7 @@ class ManipulatorRobot:
             action_sent.append(goal_pos)
 
             # Send goal position to each follower
-            goal_pos = goal_pos.numpy().astype(np.float32)
+            goal_pos = goal_pos.numpy().astype(np.int32)
             self.follower_arms[name].write("Goal_Position", goal_pos)
 
         return torch.cat(action_sent)
@@ -607,7 +713,7 @@ class ManipulatorRobot:
 
     def disconnect(self):
         if not self.is_connected:
-            raise RobotDeviceNotConnectedError(
+            raise DeviceNotConnectedError(
                 "ManipulatorRobot is not connected. You need to run `robot.connect()` before disconnecting."
             )