trim episodes

2026-07-29 20:49:42 +00:00 · 2026-02-25 23:09:33 +01:00
122 changed files with 2172 additions and 3772 deletions
@@ -44,7 +44,7 @@ permissions:
 # Sets up the environment variables
 env:
  UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:
@@ -61,7 +61,6 @@ jobs:
      MUJOCO_GL: egl
      HF_HOME: /mnt/cache/.cache/huggingface
      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
      - uses: actions/checkout@v6
        with:
@@ -90,11 +89,5 @@ jobs:
      - name: Install lerobot with test extras
        run: uv sync --extra "test"
      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
        run: |
          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          uv run hf auth whoami
      - name: Run pytest
        run: uv run pytest tests -vv --maxfail=10
@@ -37,7 +37,7 @@ permissions:
 # Sets up the environment variables
 env:
  UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 # Ensures that only the latest action is built, canceling older runs.
@@ -60,7 +60,6 @@ jobs:
      MUJOCO_GL: egl
      HF_HOME: /mnt/cache/.cache/huggingface
      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
      - uses: actions/checkout@v6
        with:
@@ -88,12 +87,6 @@ jobs:
      - name: Install lerobot with all extras
        run: uv sync --extra all # TODO(Steven): Make flash-attn optional
      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
        run: |
          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          uv run hf auth whoami
      - name: Run pytest (all extras)
        run: uv run pytest tests -vv --maxfail=10
@@ -169,7 +162,6 @@ jobs:
      HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
      TORCH_HOME: /home/user_lerobot/.cache/torch
      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    container:
      image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -181,13 +173,8 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
        run: |
          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          hf auth whoami
      - name: Fix ptxas permissions
-        run: chmod +x /lerobot/.venv/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas
+        run: chmod +x /lerobot/.venv/lib/python3.10/site-packages/triton/backends/nvidia/bin/ptxas
      - name: Run pytest on GPU
        run: pytest tests -vv --maxfail=10
      - name: Run end-to-end tests
@@ -28,7 +28,7 @@ on:
 # Sets up the environment variables
 env:
  UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
  DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
  DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest
@@ -119,7 +119,6 @@ jobs:
      HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
      TORCH_HOME: /home/user_lerobot/.cache/torch
      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    container:
      image: ${{ needs.build-docker-cpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --shm-size "16gb"
@@ -131,11 +130,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
        run: |
          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          hf auth whoami
      - name: Run pytest on CPU
        run: pytest tests -vv --maxfail=10
      - name: Run end-to-end tests
@@ -152,7 +146,6 @@ jobs:
      HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
      TORCH_HOME: /home/user_lerobot/.cache/torch
      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    container:
      image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -164,11 +157,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
        run: |
          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          hf auth whoami
      - name: Run pytest on GPU
        run: pytest tests -vv --maxfail=10
      - name: Run end-to-end tests
@@ -186,7 +174,6 @@ jobs:
      TORCH_HOME: /home/user_lerobot/.cache/torch
      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
      CUDA_VISIBLE_DEVICES: "0,1,2,3"
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    container:
      image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -198,15 +185,12 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
        run: |
          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          hf auth whoami
      - name: Verify GPU availability
        run: |
          nvidia-smi
          python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
      - name: Run multi-GPU training tests
-        run: pytest -vv tests/training/
+      # TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
        run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
        timeout-minutes: 10
@@ -50,7 +50,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v6
        with:
-          python-version: '3.12'
+          python-version: '3.10'
      - name: Run pre-commit hooks
        uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]
@@ -22,7 +22,7 @@ on:
 # Sets up the environment variables
 env:
  UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
 jobs:
  # This job builds the Python package and publishes it to PyPI
@@ -45,7 +45,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v6
        with:
-          python-version: '3.12'
+          python-version: '3.10'
      - name: Extract Version
        id: extract_info
@@ -83,6 +83,14 @@ jobs:
            exit 1
          fi
      - name: Remove Tags with Git dependencies
        # TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
        run: |
          echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
          grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
          sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
          echo "::info:: Git dependencies removed. Proceeding with build."
      - name: Install build dependencies
        run: python -m pip install build
@@ -29,7 +29,7 @@ permissions:
 # Sets up the environment variables
 env:
  UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:unbound
 # Ensures that only the latest action is built, canceling older runs.
@@ -48,7 +48,6 @@ jobs:
      MUJOCO_GL: egl
      HF_HOME: /mnt/cache/.cache/huggingface
      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
      - uses: actions/checkout@v6
        with:
@@ -80,11 +79,7 @@ jobs:
      - name: Install lerobot with all extras
        run: uv sync --extra all # TODO(Steven): Make flash-attn optional
-      - name: Login to Hugging Face
+
        if: env.HF_USER_TOKEN != ''
        run: |
          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          uv run hf auth whoami
      - name: Run pytest (all extras)
        run: uv run pytest tests -vv
@@ -142,7 +137,6 @@ jobs:
      HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
      TORCH_HOME: /home/user_lerobot/.cache/torch
      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    container:
      image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -154,11 +148,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
        run: |
          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          hf auth whoami
      - name: Run pytest on GPU
        run: pytest tests -vv
      - name: Run end-to-end tests
@@ -13,7 +13,7 @@
 # limitations under the License.
 default_language_version:
-    python: python3.12
+    python: python3.10
 exclude: "tests/artifacts/.*\\.safetensors$"
@@ -55,7 +55,7 @@ repos:
    rev: v3.21.0
    hooks:
    -   id: pyupgrade
-        args: [--py312-plus]
+        args: [--py310-plus]
  ##### Markdown Quality #####
  - repo: https://github.com/rbubley/mirrors-prettier
@@ -1,25 +0,0 @@
 # AI Usage Policy
 The LeRobot project welcomes contributions from everyone, and we have a few guidelines regarding AI usage to ensure high code quality, clear communication, and a healthy open-source ecosystem:
 - **Please disclose significant AI assistance.** If you used AI tools (e.g., Copilot, Claude, Cursor, ChatGPT) to generate a substantial portion of your code or text, let us know in your PR description. Transparency helps us review your changes more effectively.
 - **Own your code (The Human-in-the-Loop).** You must fully understand all the changes you are proposing. If you cannot explain what your AI-assisted code does or how it interacts with LeRobot's broader architecture, please take the time to learn and test it before submitting.
 - **Keep issues and discussions focused.** You are welcome to use AI to help draft issues or PR descriptions, but please review and edit them carefully before posting. AI can often be overly verbose; trimming the noise and getting straight to the point helps our maintainers address your needs faster.
 Our core maintainers also use AI tools to aid their workflows, but they do so while bringing deep contextual knowledge of the LeRobot codebase to validate the output. We ask all contributors to apply that same level of rigor.
 ## Remember the Human Maintainers
 Please remember that LeRobot is maintained by a dedicated team of humans.
 Every discussion, issue, and pull request is read and reviewed by real people. While AI tools can generate thousands of lines of code in seconds, reviewing that code still takes human time and energy. Submitting unverified or low-effort AI output puts an unfair burden on our maintainers.
 Today, the quality of the AI output still heavily depends on the developer driving the tool. We ask that you respect our maintainers' time by thoroughly vetting, testing, and refining your submissions.
 ## AI is Welcome Here
 LeRobot operates at the cutting edge of AI and robotics, and many of our maintainers actively embrace AI coding assistants as valuable productivity tools. We are a pro-AI project!
 Our reason for having an AI policy is not an anti-AI stance. Rather, it exists to ensure that AI is used to enhance human contributions, not replace them with unverified noise. It's about how the tools are used, not the tools themselves.
 We value the unique human insight you bring to the LeRobot community. Let AI empower your workflow, but always let your own judgment take the wheel.
@@ -2,7 +2,7 @@
 Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out, and improving the documentation are immensely valuable.
-Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md) and our [AI policy](./AI_POLICY.md).
+Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md).
 ## Ways to Contribute
@@ -135,7 +135,7 @@ Learn how to implement your own simulation environment or benchmark and distribu
 ## Citation
-If you use LeRobot in your project, please cite the GitHub repository to acknowledge the ongoing development and contributors:
+If you use LeRobot in your research, please cite:
 ```bibtex
@misc{cadene2024lerobot,
@@ -146,23 +146,6 @@ If you use LeRobot in your project, please cite the GitHub repository to acknowl
 }
 ```
 If you are referencing our research or the academic paper, please also cite our ICLR publication:
 <details>
 <summary><b>ICLR 2026 Paper</b></summary>
 ```bibtex
@inproceedings{cadenelerobot,
  title={LeRobot: An Open-Source Library for End-to-End Robot Learning},
  author={Cadene, Remi and Alibert, Simon and Capuano, Francesco and Aractingi, Michel and Zouitine, Adil and Kooijmans, Pepijn and Choghari, Jade and Russi, Martino and Pascal, Caroline and Palma, Steven and Shukor, Mustafa and Moss, Jess and Soare, Alexander and Aubakirova, Dana and Lhoest, Quentin and Gallou\'edec, Quentin and Wolf, Thomas},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://arxiv.org/abs/2602.22818}
 }
 ```
 </details>
 ## Contribute
 We welcome contributions from everyone in the community! To get started, please read our [CONTRIBUTING.md](./CONTRIBUTING.md) guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!
@@ -24,7 +24,7 @@ ARG OS_VERSION=22.04
 FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
 # Define Python version argument
-ARG PYTHON_VERSION=3.12
+ARG PYTHON_VERSION=3.10
 # Configure environment variables
 ENV DEBIAN_FRONTEND=noninteractive \
@@ -19,7 +19,7 @@
 # docker run -it --rm lerobot-user
 # Configure the base image
-ARG PYTHON_VERSION=3.12
+ARG PYTHON_VERSION=3.10
 FROM python:${PYTHON_VERSION}-slim
 # Configure environment variables
@@ -48,7 +48,7 @@ python -m lerobot.async_inference.robot_client \
    --task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
    --policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
    --pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
-    --policy_device=mps \ # POLICY: the device to run the policy on, on the server (cuda, mps, xpu, cpu)
+    --policy_device=mps \ # POLICY: the device to run the policy on, on the server
    --actions_per_chunk=50 \ # POLICY: the number of actions to output at once
    --chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
    --aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions
@@ -32,7 +32,7 @@ version = "0.1.0"
 dependencies = [
    # your policy-specific dependencies
 ]
-requires-python = ">= 3.12"
+requires-python = ">= 3.11"
 [build-system]
 build-backend = # your-build-backend
@@ -82,7 +82,7 @@ Create your policy implementation by inheriting from LeRobot's base `PreTrainedP
 # modeling_my_custom_policy.py
 import torch
 import torch.nn as nn
-from typing import Any
+from typing import Dict, Any
 from lerobot.policies.pretrained import PreTrainedPolicy
 from .configuration_my_custom_policy import MyCustomPolicyConfig
@@ -91,7 +91,7 @@ class MyCustomPolicy(PreTrainedPolicy):
    config_class = MyCustomPolicyConfig
    name = "my_custom_policy"
-    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: dict[str, Any] = None):
+    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: Dict[str, Any] = None):
        super().__init__(config, dataset_stats)
        ...
 ```
@@ -102,7 +102,7 @@ Create processor functions:
 ```python
 # processor_my_custom_policy.py
-from typing import Any
+from typing import Dict, Any
 import torch
@@ -13,7 +13,7 @@ The EarthRover Mini Plus is a fully open source mobile robot that connects throu
 ### Hardware
 - EarthRover Mini robot
- Computer with Python 3.12 or newer
+- Computer with Python 3.10 or newer
 - Internet connection
 ### Setting Up the Frodobots SDK
@@ -170,13 +170,13 @@ Once you can drive the robot well, you can start recording data to train AI mode
 We use Hugging Face to store your data online. First, log in with your token from [Hugging Face settings](https://huggingface.co/settings/tokens):
 ```bash
-hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 Store your Hugging Face username:
 ```bash
-HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
+HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
 ```
@@ -155,10 +155,10 @@ Upload your repository to Hugging Face:
 pip install huggingface_hub
 # Login to Hugging Face
-hf auth login
+huggingface-cli login
 # Create a new repository
-hf repo create my-org/my-custom-env
+huggingface-cli repo create my-custom-env --type space --org my-org
 # Initialize git and push
 git init
@@ -159,7 +159,7 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:
 ```bash
-hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 Then store your Hugging Face repository name in a variable:
@@ -327,7 +327,7 @@ You can look for other LeRobot datasets on the hub by searching for `LeRobot` [t
 You can also push your local dataset to the Hub manually, running:
 ```bash
-hf upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
+huggingface-cli upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
 ```
 #### Record function
@@ -491,7 +491,7 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
 Once training is done, upload the latest checkpoint with:
 ```bash
-hf upload ${HF_USER}/act_so101_test \
+huggingface-cli upload ${HF_USER}/act_so101_test \
  outputs/train/act_so101_test/checkpoints/last/pretrained_model
 ```
@@ -499,7 +499,7 @@ You can also upload intermediate checkpoints with:
 ```bash
 CKPT=010000
-hf upload ${HF_USER}/act_so101_test${CKPT} \
+huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
  outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
 ```
@@ -1,8 +1,8 @@
 # Installation
-This guide uses `conda` (via miniforge) to manage environments (recommended). If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.12 and `ffmpeg` installed with the `libsvtav1` encoder, then skip ahead to [Environment Setup](#step-2-environment-setup).
+This guide uses conda (via miniforge) to manage environments. If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.10 and ffmpeg installed with the `libsvtav1` encoder, then skip ahead to [Install LeRobot](#step-3-install-lerobot-).
-## Step 1 (`conda` only): Install [`miniforge`](https://conda-forge.org/download/)
+## Step 1: Install [`miniforge`](https://conda-forge.org/download/)
 ```bash
 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
@@ -11,47 +11,22 @@ bash Miniforge3-$(uname)-$(uname -m).sh
 ## Step 2: Environment Setup
-Create a virtual environment with Python 3.12:
+Create a virtual environment with Python 3.10, using conda:
 <!-- prettier-ignore-start -->
 <hfoptions id="create_venv">
 <hfoption id="conda">
 ```bash
-conda create -y -n lerobot python=3.12
+conda create -y -n lerobot python=3.10
 ```
-</hfoption>
+
-<hfoption id="uv">
+Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
 ```bash
 uv python install 3.12
 uv venv --python 3.12
 ```
 </hfoption>
 </hfoptions>
 <!-- prettier-ignore-end -->
 Then activate your virtual environment, you have to do this each time you open a shell to use lerobot:
 <!-- prettier-ignore-start -->
 <hfoptions id="activate_venv">
 <hfoption id="conda">```bash
 conda activate lerobot
 ```</hfoption>
 <hfoption id="uv">
 ```bash
 # Linux/macOSsource
 source .venv/bin/activate
 # Windows PowerShell
 source .venv\Scripts\Activate.ps1
 ```
 </hfoption>
 </hfoptions>
 <!-- prettier-ignore-end -->
 When using `conda`, install `ffmpeg` in your environment:
 ```bash
 conda install ffmpeg -c conda-forge
 ffmpeg -version  # ffmpeg 8.X is not yet supported !
 ```
 > [!TIP]
@@ -72,9 +47,6 @@ ffmpeg -version  # ffmpeg 8.X is not yet supported !
 > conda install evdev -c conda-forge
 > ```
 > [!IMPORTANT]
 > If you are using `uv` you will have to install `ffmpeg` system-wide (outside of the virtual environment). You rely on `uv` and `torchcodec` ability to dynamically link to the system `ffmpeg`.
 ## Step 3: Install LeRobot 🤗
 ### From Source
@@ -88,45 +60,23 @@ cd lerobot
 Then, install the library in editable mode. This is useful if you plan to contribute to the code.
 <!-- prettier-ignore-start -->
 <hfoptions id="install_lerobot_src">
 <hfoption id="conda">
 ```bash
 pip install -e .
 ```
 </hfoption>
 <hfoption id="uv">
 ```bash
 uv pip install -e .
 ```
 </hfoption>
 </hfoptions>
 <!-- prettier-ignore-end -->
 ### Installation from PyPI
 **Core Library:**
 Install the base package with:
 <!-- prettier-ignore-start -->
 <hfoptions id="install_lerobot_pypi">
 <hfoption id="conda">
 ```bash
 pip install lerobot
 ```
 </hfoption>
 <hfoption id="uv">
 ```bash
 uv pip install lerobot
 ```
 </hfoption>
 </hfoptions>
 <!-- prettier-ignore-end -->
 _This installs only the default dependencies._
 **Extra Features:**
-To install additional functionality, use one of the following (If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.):
+To install additional functionality, use one of the following:
 ```bash
 pip install 'lerobot[all]'          # All available features
@@ -140,10 +90,13 @@ _Replace `[...]` with your desired features._
 For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 > [!NOTE]
 > For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
 ### Troubleshooting
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
-To install these for Linux run:
+To install these for linux run:
 ```bash
 sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev
@@ -153,7 +106,7 @@ For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/
 ## Optional dependencies
-LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`. If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.
+LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
 ### Simulations
@@ -279,13 +279,13 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:
 ```bash
-hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 Then store your Hugging Face repository name in a variable:
 ```bash
-HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
+HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
 ```
@@ -34,6 +34,11 @@ As described by Physical Intelligence, while AI has achieved remarkable success
   pip install -e ".[pi]"
   ```
   > [!NOTE]
   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
   >
   > This will be solved in the next patch release
 ## Training Data and Capabilities
 π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:
@@ -36,6 +36,11 @@ This diverse training mixture creates a "curriculum" that enables generalization
   pip install -e ".[pi]"
   ```
   > [!NOTE]
   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
   >
   > This will be solved in the next patch release
 ## Usage
 To use π₀.₅ in your LeRobot configuration, specify the policy type as:
@@ -43,11 +43,16 @@ This approach can transform **any existing VLM** into a VLA by training it to pr
   pip install -e ".[pi]"
   ```
   > [!NOTE]
   > For lerobot 0.4.0, if you want to install the pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
   >
   > This will be solved in the next patch release
 ## Training a Custom FAST Tokenizer
 You have two options for the FAST tokenizer:
-1. **Use the pre-trained tokenizer**: The `lerobot/fast-action-tokenizer` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
+1. **Use the pre-trained tokenizer**: The `physical-intelligence/fast` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
 2. **Train your own tokenizer**: For maximum performance on your specific dataset, you can finetune the tokenizer on your own data.
@@ -109,15 +114,15 @@ lerobot-train \
 ### Key Training Parameters
-| Parameter                              | Description                                        | Default                         |
+| Parameter                              | Description                                        | Default                      |
-| -------------------------------------- | -------------------------------------------------- | ------------------------------- |
+| -------------------------------------- | -------------------------------------------------- | ---------------------------- |
-| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false`                         |
+| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false`                      |
-| `--policy.dtype=bfloat16`              | Use mixed precision training for efficiency        | `float32`                       |
+| `--policy.dtype=bfloat16`              | Use mixed precision training for efficiency        | `float32`                    |
-| `--policy.chunk_size`                  | Number of action steps to predict (action horizon) | `50`                            |
+| `--policy.chunk_size`                  | Number of action steps to predict (action horizon) | `50`                         |
-| `--policy.n_action_steps`              | Number of action steps to execute                  | `50`                            |
+| `--policy.n_action_steps`              | Number of action steps to execute                  | `50`                         |
-| `--policy.max_action_tokens`           | Maximum number of FAST tokens per action chunk     | `256`                           |
+| `--policy.max_action_tokens`           | Maximum number of FAST tokens per action chunk     | `256`                        |
-| `--policy.action_tokenizer_name`       | FAST tokenizer to use                              | `lerobot/fast-action-tokenizer` |
+| `--policy.action_tokenizer_name`       | FAST tokenizer to use                              | `physical-intelligence/fast` |
-| `--policy.compile_model=true`          | Enable torch.compile for faster training           | `false`                         |
+| `--policy.compile_model=true`          | Enable torch.compile for faster training           | `false`                      |
 ## Inference
@@ -1,49 +1,23 @@
 # Unitree G1
-<img
+This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.
  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/unitree_thumbnail.jpg"
  alt="Unitree G1 locomanipulation demo"
  style={{ width: "100%" }}
 />
-The Unitree G1 humanoid is now supported in LeRobot! You can teleoperate, train locomanipulation policies, test in sim, and more. Both 29 and 23 DoF variants are supported.
+## About
 We support both 29 and 23 DOF G1 EDU version. We introduce:
 - **`unitree g1` robot class, handling low level read/write from/to the humanoid**
 - **ZMQ socket bridge** for remote communication and camera streaming, allowing for remote policy deployment over wlan, eth or directly on the robot
 - **Locomotion policies** from NVIDIA gr00t and Amazon FAR Holosoma
 - **Simulation mode** for testing policies without the physical robot in mujoco
 ---
-## Part 1: Getting Started
+## Connection guide
-### Install LeRobot on Your Machine
+### Step 1: Configure Ethernet Interface
-```bash
+Set a static IP on the same subnet as the robot:
 conda create -y -n lerobot python=3.12
 conda activate lerobot
 git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
 cd unitree_sdk2_python && pip install -e .
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
 pip install -e '.[unitree_g1]'
 ```
 ### Test the Installation (Simulation)
 ```bash
 lerobot-teleoperate \
  --robot.type=unitree_g1 \
  --robot.is_simulation=true \
  --teleop.type=unitree_g1 \
  --teleop.id=wbc_unitree \
  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
  --display_data=true
 ```
 This will launch a [MuJoCo sim instance](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main) for the G1.
 - Press `9` to release the robot
 - Press `7` / `8` to increase / decrease waist height
 ### Connect to the Robot
 The G1's Ethernet IP is fixed at `192.168.123.164`. Your machine must have a static IP on the same subnet: `192.168.123.x` where `x ≠ 164`.
 ```bash
 # Replace 'enp131s0' with your ethernet interface name (check with `ip a`)
@@ -52,200 +26,272 @@ sudo ip addr add 192.168.123.200/24 dev enp131s0
 sudo ip link set enp131s0 up
 ```
-### SSH into the Robot
+**Note**: The G1's Ethernet IP is fixed at `192.168.123.164`. Your computer must use `192.168.123.x` with x ≠ 164.
 ### Step 2: SSH into the Robot
 ```bash
 ssh unitree@192.168.123.164
 # Password: 123
 ```
-### Install LeRobot on the G1
+You should now be connected to the G1's Orin.
 From the robot:
 ```bash
 conda create -y -n lerobot python=3.12
 conda activate lerobot
 git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
 cd unitree_sdk2_python && pip install -e .
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
 pip install -e '.[unitree_g1]'
 ```
 > **Note:** The Unitree SDK requires CycloneDDS v0.10.2. See the [Unitree SDK docs](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
 ---
 ## Part 2: Enable WiFi on the Robot
-Wi-Fi connectivity is blocked by default on the G1. To activate:
+Wlan0 is disabled by default on the G1. To enable it:
 ### Step 1: Enable WiFi Hardware
 ```bash
 sudo rfkill unblock wifi
 sudo rfkill unblock all
 # Bring up wlan0
 sudo ip link set wlan0 up
 # Enable NetworkManager control of wlan0
 sudo nmcli radio wifi on
 sudo nmcli device set wlan0 managed yes
 sudo systemctl restart NetworkManager
 ```
-**On your laptop** (share internet via Ethernet):
+### Step 2: Enable Internet Forwarding
 **On your laptop:**
 ```bash
 # Enable IP forwarding
 sudo sysctl -w net.ipv4.ip_forward=1
-# Replace wlp132s0f0 with your WiFi interface name
+# Set up NAT (replace wlp132s0f0 with your WiFi interface)
 sudo iptables -t nat -A POSTROUTING -o wlp132s0f0 -s 192.168.123.0/24 -j MASQUERADE
 sudo iptables -A FORWARD -i wlp132s0f0 -o enp131s0 -m state --state RELATED,ESTABLISHED -j ACCEPT
 sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT
 ```
-**On the G1** (set default route through your laptop):
+**On the G1:**
 ```bash
 # Add laptop as default gateway
 sudo ip route del default 2>/dev/null || true
 sudo ip route add default via 192.168.123.200 dev eth0
 echo "nameserver 8.8.8.8" | sudo tee /etc/resolv.conf
-# Verify
+# Test connection
 ping -c 3 8.8.8.8
 ```
-**Connect to a WiFi network:**
+### Step 3: Connect to WiFi Network
 ```bash
 # List available networks
 nmcli device wifi list
 # Connect to your WiFi (example)
 sudo nmcli connection add type wifi ifname wlan0 con-name "YourNetwork" ssid "YourNetwork"
 sudo nmcli connection modify "YourNetwork" wifi-sec.key-mgmt wpa-psk
 sudo nmcli connection modify "YourNetwork" wifi-sec.psk "YourPassword"
 sudo nmcli connection modify "YourNetwork" connection.autoconnect yes
 sudo nmcli connection up "YourNetwork"
 # Check WiFi IP address
 ip a show wlan0
 ```
-You can now SSH over WiFi:
+### Step 4: SSH Over WiFi
 Once connected to WiFi, note the robot's IP address and disconnect the Ethernet cable. You can now SSH over WiFi:
 ```bash
-ssh unitree@<ROBOT_WIFI_IP>
+ssh unitree@<YOUR_ROBOT_IP>
 # Password: 123
 ```
 Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address.
 ---
-## Part 3: Teleoperation & Locomotion
+## Part 3: Robot Server Setup
-### Run the Robot Server
+### Step 1: Install LeRobot on the Orin
 SSH into the robot and install LeRobot:
 ```bash
 ssh unitree@<YOUR_ROBOT_IP>
 conda create -y -n lerobot python=3.10
 conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
 pip install -e '.[unitree_g1]'
 git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
 cd unitree_sdk2_python  && pip install -e .
 ```
 **Note**: The Unitree SDK requires CycloneDDS v0.10.2 to be installed. See the [Unitree SDK documentation](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
 ### Step 2: Run the Robot Server
 On the robot:
 ```bash
-python src/lerobot/robots/unitree_g1/run_g1_server.py --camera
+python src/lerobot/robots/unitree_g1/run_g1_server.py
 ```
-### Run the Locomotion Policy
+**Important**: Keep this terminal running. The server must be active for remote control.
 ```bash
 lerobot-teleoperate \
  --robot.type=unitree_g1 \
  --robot.is_simulation=false \
  --robot.robot_ip=<ROBOT_IP> \
  --teleop.type=unitree_g1 \
  --teleop.id=wbc_unitree \
  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
  --display_data=true \
  --robot.controller=HolosomaLocomotionController
 ```
 We support both [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) and [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl).
 ---
-## Part 4: Loco-Manipulation with the Homunculus Exoskeleton
+## Part 4: Controlling the robot
-We provide a loco-manipulation solution via the Homunculus Exoskeleton — an open-source 7 DoF exoskeleton for whole-body control. Assembly instructions [here](https://github.com/nepyope/hmc_exo).
+With the robot server running, you can now control the robot remotely. Let's launch a locomotion policy
-### Calibrate
+### Step 1: Install LeRobot on your machine
 ```bash
 conda create -y -n lerobot python=3.10
 conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
 pip install -e '.[unitree_g1]'
 git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
 cd unitree_sdk2_python  && pip install -e .
 ```
 ### Step 2: Update Robot IP in Config
 Edit the config file to match your robot's WiFi IP:
 ```python
 # In src/lerobot/robots/unitree_g1/config_unitree_g1.py
 robot_ip: str = "<YOUR_ROBOT_IP>"  # Replace with your robot's WiFi IP.
 ```
 ### Step 3: Run the Locomotion Policy
 ```bash
 # Run GR00T locomotion controller
 python examples/unitree_g1/gr00t_locomotion.py --repo-id "nepyope/GR00T-WholeBodyControl_g1"
 # Run Holosoma locomotion controller
 python examples/unitree_g1/holosoma_locomotion.py
 ```
 Press `Ctrl+C` to stop the policy.
 ---
 ## Running in Simulation Mode (MuJoCo)
 You can test policies before deploying on the physical robot using MuJoCo simulation. Set `is_simulation=True` in config or pass `--robot.is_simulation=true` via CLI.
 ### Calibrate Exoskeleton Teleoperator
 ```bash
 lerobot-calibrate \
-  --teleop.type=unitree_g1 \
+    --teleop.type=unitree_g1 \
-  --teleop.left_arm_config.port=/dev/ttyACM1 \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
-  --teleop.right_arm_config.port=/dev/ttyACM0 \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
-  --teleop.id=exo
+    --teleop.id=exo
 ```
-During calibration move each joint through its entire range. After fitting, move the joint in a neutral position and press `n` to advance.
+### Teleoperate in Simulation
-### Record a Dataset
+```bash
 lerobot-teleoperate \
    --robot.type=unitree_g1 \
    --robot.is_simulation=true \
    --teleop.type=unitree_g1 \
    --teleop.left_arm_config.port=/dev/ttyACM1 \
    --teleop.right_arm_config.port=/dev/ttyACM0 \
    --teleop.id=exo \
    --fps=100
 ```
 ### Record Dataset in Simulation
 ```bash
 lerobot-record \
-  --robot.type=unitree_g1 \
+    --robot.type=unitree_g1 \
-  --robot.is_simulation=true \
+    --robot.is_simulation=true \
-  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
+    --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-  --teleop.type=unitree_g1 \
+    --teleop.type=unitree_g1 \
-  --teleop.left_arm_config.port=/dev/ttyACM1 \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
-  --teleop.right_arm_config.port=/dev/ttyACM0 \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
-  --teleop.id=exo \
+    --teleop.id=exo \
-  --dataset.repo_id=your-username/dataset-name \
+    --dataset.repo_id=your-username/dataset-name \
-  --dataset.single_task="Test" \
+    --dataset.single_task="Test" \
-  --dataset.num_episodes=2 \
+    --dataset.num_episodes=2 \
-  --dataset.episode_time_s=5 \
+    --dataset.episode_time_s=5 \
-  --dataset.reset_time_s=5 \
+    --dataset.reset_time_s=5 \
-  --dataset.push_to_hub=true \
+    --dataset.push_to_hub=true \
-  --dataset.streaming_encoding=true \
+    --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2
+    # --dataset.vcodec=auto \
    --dataset.encoder_threads=2
 ```
-> **Note:** Omit `--teleop.left_arm_config.port` and `--teleop.right_arm_config.port` if you're only using the joystick.
+Example simulation dataset: [nepyope/teleop_test_sim](https://huggingface.co/datasets/nepyope/teleop_test_sim)
 Example dataset: [nepyope/unitree_box_move_blue_full](https://huggingface.co/datasets/nepyope/unitree_box_move_blue_full)
 ---
-## Part 5: Training & Inference
+## Running on Real Robot
-### Train
+Once the robot server is running on the G1 (see Part 3), you can teleoperate and record on the real robot.
 ### Start the Camera Server
 On the robot, start the ZMQ image server:
 ```bash
-python src/lerobot/scripts/lerobot_train.py \
+python src/lerobot/cameras/zmq/image_server.py
  --dataset.repo_id=your-username/dataset-name  \
  --policy.type=pi05 \
  --output_dir=./outputs/pi05_training \
  --job_name=pi05_training \
  --policy.repo_id=your-username/your-repo-id \
  --policy.pretrained_path=lerobot/pi05_base \
  --policy.compile_model=true \
  --policy.gradient_checkpointing=true \
  --wandb.enable=true \
  --policy.dtype=bfloat16 \
  --policy.freeze_vision_encoder=false \
  --policy.train_expert_only=false \
  --steps=3000 \
  --policy.device=cuda \
  --batch_size=32
 ```
-### Inference with RTC
+Keep this running in a separate terminal for camera streaming during recording.
-Once trained, we recommend deploying policies using inference-time RTC:
+### Teleoperate Real Robot
 ```bash
-python examples/rtc/eval_with_real_robot.py \
+lerobot-teleoperate \
-  --policy.path=your-username/your-repo-id \
+    --robot.type=unitree_g1 \
-  --policy.device=cuda \
+    --robot.is_simulation=false \
-  --robot.type=unitree_g1 \
+    --teleop.type=unitree_g1 \
-  --robot.is_simulation=false \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
-  --robot.controller=HolosomaLocomotionController \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
-  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
+    --teleop.id=exo \
-  --task="task_description" \
+    --fps=100
  --duration=1000 \
  --fps=30 \
  --rtc.enabled=true
 ```
 ### Record Dataset on Real Robot
 ```bash
 lerobot-record \
    --robot.type=unitree_g1 \
    --robot.is_simulation=false \
    --robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
    --teleop.type=unitree_g1 \
    --teleop.left_arm_config.port=/dev/ttyACM1 \
    --teleop.right_arm_config.port=/dev/ttyACM0 \
    --teleop.id=exo \
    --dataset.repo_id=your-username/dataset-name \
    --dataset.single_task="Test" \
    --dataset.num_episodes=2 \
    --dataset.episode_time_s=5 \
    --dataset.reset_time_s=5 \
    --dataset.push_to_hub=true \
    --dataset.streaming_encoding=true \
    # --dataset.vcodec=auto \
    --dataset.encoder_threads=2
 ```
 **Note**: Update `server_address` to match your robot's camera server IP.
 Example real robot dataset: [nepyope/teleop_test_real](https://huggingface.co/datasets/nepyope/teleop_test_real)
 ---
 ## Additional Resources
@@ -254,8 +300,8 @@ python examples/rtc/eval_with_real_robot.py \
 - [GR00T-WholeBodyControl](https://github.com/NVlabs/GR00T-WholeBodyControl)
 - [Holosoma](https://github.com/amazon-far/holosoma)
 - [LeRobot Documentation](https://github.com/huggingface/lerobot)
- [Unitree IL LeRobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
+- [Unitree_IL_Lerobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
 ---
-_Last updated: March 2026_
+_Last updated: December 2025_
@@ -57,7 +57,7 @@ class DatasetReplayConfig:
    repo_id: str
    # Episode to replay.
    episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
    root: str | Path | None = None
    # Limit the frames per second. By default, uses the policy fps.
    fps: int = 30
@@ -1,490 +0,0 @@
 #!/usr/bin/env python
 # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
 SLURM-distributed SARM RA-BC annotation pipeline.
 Computes SARM progress values for all frames in a dataset, distributed across
 SLURM workers, then merges the shards into a single sarm_progress.parquet.
 Two subcommands, each a separate SLURM submission:
  compute    – N workers, each computes progress for a subset of episodes
  aggregate  – 1 worker, merges N shards into sarm_progress.parquet, pushes to hub
 Usage:
    python slurm_compute_rabc.py compute \\
        --repo-id user/dataset --reward-model-path user/sarm_model \\
        --stride 10 --device cpu --workers 50 --partition cpu
    python slurm_compute_rabc.py aggregate \\
        --repo-id user/dataset --reward-model-path user/sarm_model \\
        --partition cpu --push-to-hub
 """
 import argparse
 from pathlib import Path
 from datatrove.executor import LocalPipelineExecutor
 from datatrove.executor.slurm import SlurmPipelineExecutor
 from datatrove.pipeline.base import PipelineStep
 class ComputeProgressShards(PipelineStep):
    """Each worker computes SARM progress for its assigned episodes."""
    def __init__(
        self, repo_id, reward_model_path, stride=1, head_mode="sparse", device="cpu", shard_dir="rabc_shards"
    ):
        super().__init__()
        if stride < 1:
            raise ValueError(f"stride must be >= 1, got {stride}")
        self.repo_id = repo_id
        self.reward_model_path = reward_model_path
        self.stride = stride
        self.head_mode = head_mode
        self.device = device
        self.shard_dir = shard_dir
    def run(self, data=None, rank: int = 0, world_size: int = 1):
        import logging
        from pathlib import Path
        import numpy as np
        import pyarrow as pa
        import pyarrow.parquet as pq
        import torch
        from tqdm import tqdm
        from lerobot.policies.sarm.compute_rabc_weights import (
            generate_all_frame_indices,
            interpolate_progress,
            load_sarm_resources,
        )
        from lerobot.utils.utils import init_logging
        init_logging()
        dataset, reward_model, preprocess = load_sarm_resources(
            self.repo_id,
            self.reward_model_path,
            self.device,
        )
        if hasattr(preprocess, "eval"):
            preprocess.eval()
        for step in preprocess.steps:
            if hasattr(step, "eval"):
                step.eval()
        image_key = reward_model.config.image_key
        state_key = reward_model.config.state_key
        frame_gap = reward_model.config.frame_gap
        center_idx = reward_model.config.n_obs_steps // 2
        dual_mode = reward_model.config.uses_dual_heads
        compute_sparse = self.head_mode in ("sparse", "both") or not dual_mode
        compute_dense = self.head_mode in ("dense", "both") and dual_mode
        my_episodes = list(range(dataset.num_episodes))[rank::world_size]
        if not my_episodes:
            logging.info(f"Rank {rank}: no episodes assigned")
            return
        logging.info(f"Rank {rank}: {len(my_episodes)} / {dataset.num_episodes} episodes")
        all_rows = []
        for ep_idx in tqdm(my_episodes, desc=f"Rank {rank}"):
            ep = dataset.meta.episodes[ep_idx]
            ep_start, ep_end = ep["dataset_from_index"], ep["dataset_to_index"]
            task = dataset[ep_start].get("task", "perform the task")
            all_ep_indices = generate_all_frame_indices(ep_start, ep_end, frame_gap)
            if self.stride > 1:
                compute_indices = [i for i in all_ep_indices if (i - ep_start) % self.stride == 0]
                if (ep_end - 1) not in compute_indices:
                    compute_indices.append(ep_end - 1)
                compute_indices = sorted(set(compute_indices))
            else:
                compute_indices = all_ep_indices
            frame_results = {}
            for qi in tqdm(compute_indices, desc=f"  Ep {ep_idx}", leave=False):
                try:
                    sample = dataset[qi]
                    batch = {
                        image_key: sample[image_key],
                        "task": task,
                        "index": qi,
                        "episode_index": ep_idx,
                    }
                    if state_key in sample:
                        batch[state_key] = sample[state_key]
                    with torch.no_grad():
                        processed = preprocess(batch)
                        vf = processed["video_features"].to(self.device)
                        tf = processed["text_features"].to(self.device)
                        sf = processed.get("state_features")
                        if sf is not None:
                            sf = sf.to(self.device)
                        lengths = processed.get("lengths")
                        sparse_val = dense_val = np.nan
                        if compute_sparse:
                            r = reward_model.calculate_rewards(
                                text_embeddings=tf,
                                video_embeddings=vf,
                                state_features=sf,
                                lengths=lengths,
                                return_all_frames=True,
                                head_mode="sparse",
                            )
                            sparse_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
                        if compute_dense:
                            r = reward_model.calculate_rewards(
                                text_embeddings=tf,
                                video_embeddings=vf,
                                state_features=sf,
                                lengths=lengths,
                                return_all_frames=True,
                                head_mode="dense",
                            )
                            dense_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
                        frame_results[qi] = (sparse_val, dense_val)
                except Exception as e:
                    logging.warning(f"Failed frame {qi}: {e}")
            if not frame_results:
                logging.warning(f"Episode {ep_idx}: all frames failed, skipping")
                continue
            # Interpolate to all frames in this episode
            computed_idx = np.array(sorted(frame_results.keys()))
            all_frame_arr = np.arange(ep_start, ep_end)
            sparse_vals = np.array([frame_results[i][0] for i in computed_idx]) if compute_sparse else None
            dense_vals = np.array([frame_results[i][1] for i in computed_idx]) if compute_dense else None
            if self.stride > 1 and len(computed_idx) > 1:
                if compute_sparse:
                    sparse_vals = interpolate_progress(computed_idx, sparse_vals, all_frame_arr)
                if compute_dense:
                    dense_vals = interpolate_progress(computed_idx, dense_vals, all_frame_arr)
                output_frames = all_frame_arr
            else:
                # Use only successfully computed frames to avoid indexing mismatch on failures
                output_frames = computed_idx
            for i, fi in enumerate(output_frames):
                row = {"index": int(fi), "episode_index": ep_idx, "frame_index": int(fi - ep_start)}
                if compute_sparse:
                    row["progress_sparse"] = float(sparse_vals[i])
                if compute_dense:
                    row["progress_dense"] = float(dense_vals[i])
                all_rows.append(row)
        if all_rows:
            import pandas as pd
            df = pd.DataFrame(all_rows).sort_values("index").reset_index(drop=True)
            table = pa.Table.from_pandas(df, preserve_index=False)
            table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
            shard_dir = Path(self.shard_dir)
            shard_dir.mkdir(parents=True, exist_ok=True)
            out = shard_dir / f"shard_{rank:05d}.parquet"
            pq.write_table(table, out)
            logging.info(f"Rank {rank}: saved {len(df)} rows to {out}")
 class AggregateProgress(PipelineStep):
    """Merge all shard parquets into final sarm_progress.parquet."""
    def __init__(self, repo_id, reward_model_path, shard_dir="rabc_shards", push_to_hub=False):
        super().__init__()
        self.repo_id = repo_id
        self.reward_model_path = reward_model_path
        self.shard_dir = shard_dir
        self.push_to_hub = push_to_hub
    def run(self, data=None, rank: int = 0, world_size: int = 1):
        import datetime
        import logging
        import os
        from pathlib import Path
        import pandas as pd
        import pyarrow as pa
        import pyarrow.parquet as pq
        from lerobot.datasets.lerobot_dataset import LeRobotDataset
        from lerobot.utils.utils import init_logging
        init_logging()
        if rank != 0:
            return
        shard_dir = Path(self.shard_dir)
        shards = sorted(shard_dir.glob("shard_*.parquet"))
        if not shards:
            raise FileNotFoundError(f"No shards found in {shard_dir}")
        # Log shard modification time range to help detect stale files
        mtimes = [os.path.getmtime(s) for s in shards]
        oldest = datetime.datetime.fromtimestamp(min(mtimes)).isoformat(timespec="seconds")
        newest = datetime.datetime.fromtimestamp(max(mtimes)).isoformat(timespec="seconds")
        logging.info(f"Aggregating {len(shards)} shards (oldest: {oldest}, newest: {newest})")
        df = pd.concat([pd.read_parquet(s) for s in shards], ignore_index=True)
        df = df.sort_values("index").reset_index(drop=True)
        table = pa.Table.from_pandas(df, preserve_index=False)
        table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
        temp_ds = LeRobotDataset(self.repo_id, download_videos=False)
        out_path = Path(temp_ds.root) / "sarm_progress.parquet"
        out_path.parent.mkdir(parents=True, exist_ok=True)
        pq.write_table(table, out_path)
        logging.info(f"Saved {len(df)} rows to {out_path}")
        for col in ["progress_sparse", "progress_dense"]:
            if col in df.columns:
                v = df[col].dropna()
                logging.info(
                    f"{col}: mean={v.mean():.4f} std={v.std():.4f} min={v.min():.4f} max={v.max():.4f}"
                )
        if self.push_to_hub:
            from huggingface_hub import HfApi
            api = HfApi()
            hub_path = "sarm_progress.parquet"
            logging.info(f"Uploading to {self.repo_id}/{hub_path}")
            api.upload_file(
                path_or_fileobj=str(out_path),
                path_in_repo=hub_path,
                repo_id=self.repo_id,
                repo_type="dataset",
            )
            logging.info(f"Uploaded: https://huggingface.co/datasets/{self.repo_id}/blob/main/{hub_path}")
 def make_compute_executor(
    repo_id,
    reward_model_path,
    stride,
    head_mode,
    device,
    shard_dir,
    logs_dir,
    job_name,
    slurm,
    workers,
    partition,
    cpus_per_task,
    mem_per_cpu,
 ):
    kwargs = {
        "pipeline": [
            ComputeProgressShards(repo_id, reward_model_path, stride, head_mode, device, str(shard_dir)),
        ],
        "logging_dir": str(logs_dir / job_name),
    }
    if slurm:
        kwargs.update(
            {
                "job_name": job_name,
                "tasks": workers,
                "workers": workers,
                "time": "24:00:00",
                "partition": partition,
                "cpus_per_task": cpus_per_task,
                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
            }
        )
        return SlurmPipelineExecutor(**kwargs)
    kwargs.update({"tasks": workers, "workers": 1})
    return LocalPipelineExecutor(**kwargs)
 def make_aggregate_executor(
    repo_id,
    reward_model_path,
    shard_dir,
    logs_dir,
    job_name,
    slurm,
    partition,
    cpus_per_task,
    mem_per_cpu,
    push_to_hub,
 ):
    kwargs = {
        "pipeline": [
            AggregateProgress(repo_id, reward_model_path, str(shard_dir), push_to_hub),
        ],
        "logging_dir": str(logs_dir / job_name),
    }
    if slurm:
        kwargs.update(
            {
                "job_name": job_name,
                "tasks": 1,
                "workers": 1,
                "time": "02:00:00",
                "partition": partition,
                "cpus_per_task": cpus_per_task,
                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
            }
        )
        return SlurmPipelineExecutor(**kwargs)
    kwargs.update({"tasks": 1, "workers": 1})
    return LocalPipelineExecutor(**kwargs)
 def _add_shared_args(p):
    p.add_argument(
        "--repo-id",
        type=str,
        required=True,
        help="Hugging Face repository identifier, e.g. 'user/dataset'.",
    )
    p.add_argument(
        "--shard-dir",
        type=Path,
        default=Path("rabc_shards"),
        help="Directory to read/write per-rank parquet shards.",
    )
    p.add_argument(
        "--logs-dir",
        type=Path,
        default=Path("logs"),
        help="Directory for datatrove logs.",
    )
    p.add_argument(
        "--job-name",
        type=str,
        default=None,
        help="SLURM job name (defaults to rabc_<subcommand>).",
    )
    p.add_argument(
        "--slurm",
        type=int,
        default=1,
        help="1 = submit via SLURM; 0 = run locally (useful for debugging).",
    )
    p.add_argument(
        "--partition",
        type=str,
        default=None,
        help="SLURM partition to submit to.",
    )
    p.add_argument(
        "--cpus-per-task",
        type=int,
        default=4,
        help="Number of CPUs per SLURM task.",
    )
    p.add_argument(
        "--mem-per-cpu",
        type=str,
        default="4G",
        help="Memory per CPU, e.g. '4G' or '1950M'.",
    )
 def main():
    parser = argparse.ArgumentParser(
        description="SLURM-distributed SARM RA-BC annotation pipeline",
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    sub = parser.add_subparsers(dest="command", required=True)
    # compute subcommand
    cp = sub.add_parser(
        "compute",
        help="Distribute progress computation across SLURM workers.",
    )
    _add_shared_args(cp)
    cp.add_argument(
        "--reward-model-path",
        type=str,
        required=True,
        help="Path or HF repo id of the SARM reward model.",
    )
    cp.add_argument(
        "--stride",
        type=int,
        default=1,
        help="Compute every Nth frame; intermediate frames are interpolated (must be >= 1).",
    )
    cp.add_argument(
        "--head-mode",
        type=str,
        default="sparse",
        choices=["sparse", "dense", "both"],
        help="Which reward head(s) to compute.",
    )
    cp.add_argument(
        "--device",
        type=str,
        default="cpu",
        help="Device for reward model inference, e.g. 'cpu' or 'cuda'.",
    )
    cp.add_argument(
        "--workers",
        type=int,
        default=50,
        help="Number of parallel SLURM tasks (one shard per worker).",
    )
    # aggregate subcommand
    ap = sub.add_parser(
        "aggregate",
        help="Merge per-rank shards into a single sarm_progress.parquet.",
    )
    _add_shared_args(ap)
    ap.add_argument(
        "--reward-model-path",
        type=str,
        required=True,
        help="Path or HF repo id of the SARM reward model (stored in parquet metadata).",
    )
    ap.add_argument(
        "--push-to-hub",
        action="store_true",
        help="Upload sarm_progress.parquet to the Hugging Face Hub after aggregation.",
    )
    args = parser.parse_args()
    job_name = args.job_name or f"rabc_{args.command}"
    kwargs = vars(args)
    kwargs["slurm"] = kwargs.pop("slurm") == 1
    kwargs["job_name"] = job_name
    command = kwargs.pop("command")
    executor = make_compute_executor(**kwargs) if command == "compute" else make_aggregate_executor(**kwargs)
    executor.run()
 if __name__ == "__main__":
    main()
@@ -78,7 +78,6 @@ from torch import Tensor
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
 from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import RTCAttentionSchedule
@@ -98,7 +97,6 @@ from lerobot.robots import (  # noqa: F401
    bi_so_follower,
    koch_follower,
    so_follower,
    unitree_g1,
 )
 from lerobot.robots.utils import make_robot_from_config
 from lerobot.utils.constants import OBS_IMAGES
@@ -14,20 +14,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
 import logging
 import time
 from collections import deque
 import numpy as np
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
-from lerobot.robots.unitree_g1.g1_utils import (
+from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
-    REMOTE_AXES,
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
-    REMOTE_BUTTONS,
+from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
    G1_29_JointIndex,
    get_gravity_orientation,
 )
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -36,13 +36,18 @@ GROOT_DEFAULT_ANGLES[[0, 6]] = -0.1  # Hip pitch
 GROOT_DEFAULT_ANGLES[[3, 9]] = 0.3  # Knee
 GROOT_DEFAULT_ANGLES[[4, 10]] = -0.2  # Ankle pitch
 MISSING_JOINTS = []
 G1_MODEL = "g1_23"  # Or "g1_29"
 if G1_MODEL == "g1_23":
    MISSING_JOINTS = [12, 14, 20, 21, 27, 28]  # Waist yaw/pitch, wrist pitch/yaw
 # Control parameters
 ACTION_SCALE = 0.25
 CONTROL_DT = 0.02  # 50Hz
 ANG_VEL_SCALE: float = 0.25
 DOF_POS_SCALE: float = 1.0
 DOF_VEL_SCALE: float = 0.05
-CMD_SCALE: list[float] = [2.0, 2.0, 0.25]
+CMD_SCALE: list = [2.0, 2.0, 0.25]
 DEFAULT_GROOT_REPO_ID = "nepyope/GR00T-WholeBodyControl_g1"
@@ -80,11 +85,11 @@ def load_groot_policies(
 class GrootLocomotionController:
    """GR00T lower-body locomotion controller for the Unitree G1."""
-    control_dt = CONTROL_DT  # Expose for unitree_g1.py
+    def __init__(self, policy_balance, policy_walk, robot, config):
-
+        self.policy_balance = policy_balance
-    def __init__(self):
+        self.policy_walk = policy_walk
-        # Load policies
+        self.robot = robot
-        self.policy_balance, self.policy_walk = load_groot_policies()
+        self.config = config
        self.cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)  # vx, vy, theta_dot
@@ -104,60 +109,45 @@ class GrootLocomotionController:
        logger.info("GrootLocomotionController initialized")
-    def reset(self) -> None:
+    def run_step(self):
-        """Reset internal state for a new episode."""
+        # Get current observation
-        self.cmd[:] = 0.0
+        obs = self.robot.get_observation()
        self.groot_qj_all[:] = 0.0
        self.groot_dqj_all[:] = 0.0
        self.groot_action[:] = 0.0
        self.groot_obs_single[:] = 0.0
        self.groot_obs_stacked[:] = 0.0
        self.groot_height_cmd = 0.74
        self.groot_orientation_cmd[:] = 0.0
        self.groot_obs_history.clear()
        for _ in range(6):
            self.groot_obs_history.append(np.zeros(86, dtype=np.float32))
-    def run_step(self, action: dict, lowstate) -> dict:
+        if not obs:
-        """Run one step of the locomotion controller.
+            return
-        Args:
+        # Get command from remote controller
-            action: Action dict containing remote.lx/ly/rx/ry and buttons
+        if obs["remote.buttons"][0]:  # R1 - raise waist
            lowstate: Robot lowstate containing motor positions/velocities and IMU
        Returns:
            Action dict for lower body joints (0-14)
        """
        if lowstate is None:
            return {}
        buttons = [int(action.get(k, 0)) for k in REMOTE_BUTTONS]
        if buttons[0]:  # R1 - raise waist
            self.groot_height_cmd += 0.001
            self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
-        if buttons[4]:  # R2 - lower waist
+        if obs["remote.buttons"][4]:  # R2 - lower waist
            self.groot_height_cmd -= 0.001
            self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
-        lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
+        self.cmd[0] = obs["remote.ly"]  # Forward/backward
-        self.cmd[0] = ly  # Forward/backward
+        self.cmd[1] = obs["remote.lx"] * -1  # Left/right
-        self.cmd[1] = -lx  # Left/right (negated)
+        self.cmd[2] = obs["remote.rx"] * -1  # Rotation rate
        self.cmd[2] = -rx  # Rotation rate (negated)
-        # Get joint positions and velocities from lowstate
+        # Get joint positions and velocities from flat dict
        for motor in G1_29_JointIndex:
            name = motor.name
            idx = motor.value
-            self.groot_qj_all[idx] = lowstate.motor_state[idx].q
+            self.groot_qj_all[idx] = obs[f"{name}.q"]
-            self.groot_dqj_all[idx] = lowstate.motor_state[idx].dq
+            self.groot_dqj_all[idx] = obs[f"{name}.dq"]
        # Adapt observation for g1_23dof
        for idx in MISSING_JOINTS:
            self.groot_qj_all[idx] = 0.0
            self.groot_dqj_all[idx] = 0.0
        # Scale joint positions and velocities
        qj_obs = self.groot_qj_all.copy()
        dqj_obs = self.groot_dqj_all.copy()
        # Express IMU data in gravity frame of reference
-        quat = lowstate.imu_state.quaternion
+        quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
-        ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
+        ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
-        gravity_orientation = get_gravity_orientation(quat)
+        gravity_orientation = self.robot.get_gravity_orientation(quat)
        # Scale joint positions and velocities before policy inference
        qj_obs = (qj_obs - GROOT_DEFAULT_ANGLES) * DOF_POS_SCALE
@@ -196,10 +186,73 @@ class GrootLocomotionController:
        # Transform action back to target joint positions
        target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * ACTION_SCALE
-        # Build action dict
+        # Build action dict (only first 15 joints for GR00T)
        action_dict = {}
        for i in range(15):
            motor_name = G1_29_JointIndex(i).name
            action_dict[f"{motor_name}.q"] = float(target_dof_pos_15[i])
-        return action_dict
+        # Zero out missing joints for g1_23dof
        for joint_idx in MISSING_JOINTS:
            motor_name = G1_29_JointIndex(joint_idx).name
            action_dict[f"{motor_name}.q"] = 0.0
        # Send action to robot
        self.robot.send_action(action_dict)
 def run(repo_id: str = DEFAULT_GROOT_REPO_ID) -> None:
    """Main function to run the GR00T locomotion controller.
    Args:
        repo_id: Hugging Face Hub repository ID for GR00T policies.
    """
    # Load policies
    policy_balance, policy_walk = load_groot_policies(repo_id=repo_id)
    # Initialize robot
    config = UnitreeG1Config()
    robot = UnitreeG1(config)
    robot.connect()
    # Initialize gr00T locomotion controller
    groot_controller = GrootLocomotionController(
        policy_balance=policy_balance,
        policy_walk=policy_walk,
        robot=robot,
        config=config,
    )
    try:
        robot.reset(CONTROL_DT, GROOT_DEFAULT_ANGLES)
        logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate, R1=raise waist, R2=lower waist")
        logger.info("Press Ctrl+C to stop")
        # Run step
        while not robot._shutdown_event.is_set():
            start_time = time.time()
            groot_controller.run_step()
            elapsed = time.time() - start_time
            sleep_time = max(0, CONTROL_DT - elapsed)
            time.sleep(sleep_time)
    except KeyboardInterrupt:
        logger.info("Stopping locomotion...")
    finally:
        if robot.is_connected:
            robot.disconnect()
        logger.info("Done!")
 if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="GR00T Locomotion Controller for Unitree G1")
    parser.add_argument(
        "--repo-id",
        type=str,
        default=DEFAULT_GROOT_REPO_ID,
        help=f"Hugging Face Hub repo ID for GR00T policies (default: {DEFAULT_GROOT_REPO_ID})",
    )
    args = parser.parse_args()
    run(repo_id=args.repo_id)
@@ -14,21 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import argparse
 import json
 import logging
 import time
 import numpy as np
 import onnx
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
-from lerobot.robots.unitree_g1.g1_utils import (
+from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
-    REMOTE_AXES,
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
-    G1_29_JointArmIndex,
+from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
    G1_29_JointIndex,
    get_gravity_orientation,
 )
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 DEFAULT_ANGLES = np.zeros(29, dtype=np.float32)
@@ -40,13 +40,18 @@ DEFAULT_ANGLES[16] = 0.2  # Left shoulder roll
 DEFAULT_ANGLES[23] = -0.2  # Right shoulder roll
 DEFAULT_ANGLES[[18, 25]] = 0.6  # Elbow
 MISSING_JOINTS = []
 G1_MODEL = "g1_23"  # Or "g1_29"
 if G1_MODEL == "g1_23":
    MISSING_JOINTS = [12, 14, 20, 21, 27, 28]  # Waist yaw/pitch, wrist pitch/yaw
 # Control parameters
 ACTION_SCALE = 0.25
-CONTROL_DT = 0.005  # 200Hz
+CONTROL_DT = 0.02  # 50Hz
 ANG_VEL_SCALE = 0.25
 DOF_POS_SCALE = 1.0
 DOF_VEL_SCALE = 0.05
-GAIT_PERIOD = 0.5
+GAIT_PERIOD = 1.0
 DEFAULT_HOLOSOMA_REPO_ID = "nepyope/holosoma_locomotion"
@@ -82,7 +87,7 @@ def load_policy(
    logger.info(f"Policy loaded: {policy.get_inputs()[0].shape} → {policy.get_outputs()[0].shape}")
    # Extract KP/KD from ONNX metadata
-    model = onnx.load(policy_path, load_external_data=False)
+    model = onnx.load(policy_path)
    metadata = {prop.key: prop.value for prop in model.metadata_props}
    if "kp" not in metadata or "kd" not in metadata:
@@ -96,13 +101,15 @@ def load_policy(
 class HolosomaLocomotionController:
-    """Holosoma lower-body locomotion controller for Unitree G1."""
+    """Holosoma whole-body locomotion controller for Unitree G1."""
-    control_dt = CONTROL_DT  # Expose for unitree_g1.py
+    def __init__(self, policy, robot, kp: np.ndarray, kd: np.ndarray):
        self.policy = policy
        self.robot = robot
-    def __init__(self):
+        # Override robot's PD gains with policy gains
-        # Load policy and gains
+        self.robot.kp = kp
-        self.policy, self.kp, self.kd = load_policy()
+        self.robot.kd = kd
        self.cmd = np.zeros(3, dtype=np.float32)
@@ -117,55 +124,35 @@ class HolosomaLocomotionController:
        self.phase_dt = 2 * np.pi / ((1.0 / CONTROL_DT) * GAIT_PERIOD)
        self.is_standing = True
-        logger.info("HolosomaLocomotionController initialized")
+    def run_step(self):
        # Get current observation
        obs = self.robot.get_observation()
-    def reset(self) -> None:
+        if not obs:
-        """Reset internal state for a new episode."""
+            return
        self.cmd[:] = 0.0
        self.qj[:] = 0.0
        self.dqj[:] = 0.0
        self.obs[:] = 0.0
        self.last_action[:] = 0.0
        self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
        self.is_standing = True
-    def run_step(self, action: dict, lowstate) -> dict:
+        # Get command from remote controller
-        """Run one step of the locomotion controller.
+        ly = obs["remote.ly"] if abs(obs["remote.ly"]) > 0.1 else 0.0
-
+        lx = obs["remote.lx"] if abs(obs["remote.lx"]) > 0.1 else 0.0
-        Args:
+        rx = obs["remote.rx"] if abs(obs["remote.rx"]) > 0.1 else 0.0
            action: Action dict containing remote.lx/ly/rx/ry
            lowstate: Robot lowstate containing motor positions/velocities and IMU
        Returns:
            Action dict for lower body joints (0-14)
        """
        if lowstate is None:
            return {}
        lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
        ly = ly if abs(ly) > 0.1 else 0.0
        lx = lx if abs(lx) > 0.1 else 0.0
        rx = rx if abs(rx) > 0.1 else 0.0
        ly = np.clip(ly, -0.3, 0.3)
        lx = np.clip(lx, -0.3, 0.3)
        self.cmd[:] = [ly, -lx, -rx]
-        # Get joint positions and velocities from lowstate
+        # Get joint positions and velocities
        for motor in G1_29_JointIndex:
            name = motor.name
            idx = motor.value
-            self.qj[idx] = lowstate.motor_state[idx].q
+            self.qj[idx] = obs[f"{name}.q"]
-            self.dqj[idx] = lowstate.motor_state[idx].dq
+            self.dqj[idx] = obs[f"{name}.dq"]
-        # Hide arm positions from policy (show DEFAULT_ANGLES instead)
+        # Adapt observation for g1_23dof
-        # This prevents policy from reacting to teleop arm movements
+        for idx in MISSING_JOINTS:
-        for arm_joint in G1_29_JointArmIndex:
+            self.qj[idx] = 0.0
-            self.qj[arm_joint.value] = DEFAULT_ANGLES[arm_joint.value]
+            self.dqj[idx] = 0.0
            self.dqj[arm_joint.value] = 0.0
        # Express IMU data in gravity frame of reference
-        quat = lowstate.imu_state.quaternion
+        quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
-        ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
+        ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
-        gravity = get_gravity_orientation(quat)
+        gravity = self.robot.get_gravity_orientation(quat)
        # Scale joint positions and velocities before policy inference
        qj_obs = (self.qj - DEFAULT_ANGLES) * DOF_POS_SCALE
@@ -199,16 +186,79 @@ class HolosomaLocomotionController:
        # Run policy inference
        ort_in = {self.policy.get_inputs()[0].name: self.obs.reshape(1, -1).astype(np.float32)}
        raw_action = self.policy.run(None, ort_in)[0].squeeze()
-        policy_action = np.clip(raw_action, -100.0, 100.0)
+        action = np.clip(raw_action, -100.0, 100.0)
-        self.last_action = policy_action.copy()
+        self.last_action = action.copy()
        # Transform action back to target joint positions
-        target = DEFAULT_ANGLES + policy_action * ACTION_SCALE
+        target = DEFAULT_ANGLES + action * ACTION_SCALE
-        # Build action dict (first 15 joints only)
+        # Build action dict
        action_dict = {}
-        for i in range(15):
+        for motor in G1_29_JointIndex:
-            motor_name = G1_29_JointIndex(i).name
+            action_dict[f"{motor.name}.q"] = float(target[motor.value])
            action_dict[f"{motor_name}.q"] = float(target[i])
-        return action_dict
+        # Zero out missing joints for g1_23dof
        for joint_idx in MISSING_JOINTS:
            motor_name = G1_29_JointIndex(joint_idx).name
            action_dict[f"{motor_name}.q"] = 0.0
        # Send action to robot
        self.robot.send_action(action_dict)
 def run(repo_id: str = DEFAULT_HOLOSOMA_REPO_ID, policy_type: str = "fastsac") -> None:
    """Main function to run the Holosoma locomotion controller.
    Args:
        repo_id: Hugging Face Hub repository ID for Holosoma policies.
        policy_type: Policy type to use ('fastsac' or 'ppo').
    """
    # Load policy and gains
    policy, kp, kd = load_policy(repo_id=repo_id, policy_type=policy_type)
    # Initialize robot
    config = UnitreeG1Config()
    robot = UnitreeG1(config)
    robot.connect()
    holosoma_controller = HolosomaLocomotionController(policy, robot, kp, kd)
    try:
        robot.reset(CONTROL_DT, DEFAULT_ANGLES)
        logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate")
        logger.info("Press Ctrl+C to stop")
        # Run step
        while not robot._shutdown_event.is_set():
            start_time = time.time()
            holosoma_controller.run_step()
            elapsed = time.time() - start_time
            sleep_time = max(0, CONTROL_DT - elapsed)
            time.sleep(sleep_time)
    except KeyboardInterrupt:
        logger.info("Stopping locomotion...")
    finally:
        if robot.is_connected:
            robot.disconnect()
        logger.info("Done!")
 if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Holosoma Locomotion Controller for Unitree G1")
    parser.add_argument(
        "--repo-id",
        type=str,
        default=DEFAULT_HOLOSOMA_REPO_ID,
        help=f"Hugging Face Hub repo ID for Holosoma policies (default: {DEFAULT_HOLOSOMA_REPO_ID})",
    )
    parser.add_argument(
        "--policy",
        type=str,
        choices=["fastsac", "ppo"],
        default="fastsac",
        help="Policy type to use: 'fastsac' (default) or 'ppo'",
    )
    args = parser.parse_args()
    run(repo_id=args.repo_id, policy_type=args.policy)
@@ -25,11 +25,11 @@ discord = "https://discord.gg/s3KuuzsPFb"
 [project]
 name = "lerobot"
-version = "0.4.5"
+version = "0.4.4"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
-requires-python = ">=3.12"
+requires-python = ">=3.10"
 authors = [
    { name = "Rémi Cadène", email = "re.cadene@gmail.com" },
    { name = "Simon Alibert", email = "alibert.sim@gmail.com" },
@@ -50,8 +50,7 @@ classifiers = [
    "Intended Audience :: Education",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: Apache Software License",
-    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.13",
    "Topic :: Software Development :: Build Tools",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
@@ -62,28 +61,26 @@ dependencies = [
    # Hugging Face dependencies
    "datasets>=4.0.0,<5.0.0",
    "diffusers>=0.27.2,<0.36.0",
-    "huggingface-hub>=1.0.0,<2.0.0",
+    "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0",
    "accelerate>=1.10.0,<2.0.0",
    # Core dependencies
    "numpy>=2.0.0,<2.3.0", # NOTE: Explicitly listing numpy helps the resolver converge faster. Upper bound imposed by opencv-python-headless.
    "setuptools>=71.0.0,<81.0.0",
    "cmake>=3.29.0.1,<4.2.0",
    "packaging>=24.2,<26.0",
    "torch>=2.2.1,<2.11.0",
    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
    "torchvision>=0.21.0,<0.26.0",
    "einops>=0.8.0,<0.9.0",
    "opencv-python-headless>=4.9.0,<4.13.0",
    "av>=15.0.0,<16.0.0",
    "jsonlines>=4.0.0,<5.0.0",
-    "pynput>=1.7.8,<1.9.0",
+    "packaging>=24.2,<26.0",
    "pynput>=1.7.7,<1.9.0",
    "pyserial>=3.5,<4.0",
    "wandb>=0.24.0,<0.25.0",
-    "draccus==0.10.0", # TODO: Relax version constraint
+
    "torch>=2.2.1,<2.11.0", # TODO: Bump dependency
    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bump dependency
    "torchvision>=0.21.0,<0.26.0", # TODO: Bump dependency
    "draccus==0.10.0", # TODO: Remove ==
    "gymnasium>=1.1.1,<2.0.0",
    "rerun-sdk>=0.24.0,<0.27.0",
@@ -98,14 +95,10 @@ dependencies = [
 # Common
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
-placo-dep = ["placo>=0.9.6,<0.9.17"]
+placo-dep = ["placo>=0.9.6,<0.10.0"]
-transformers-dep = ["transformers>=5.3.0,<6.0.0"]
+transformers-dep = ["transformers>=4.57.1,<5.0.0"]
-grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.34.0"]
+grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
 peft-dep = ["peft>=0.18.0,<1.0.0"]
 scipy-dep = ["scipy>=1.14.0,<2.0.0"]
 qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
 matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
@@ -119,36 +112,34 @@ gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
 hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
 lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
 unitree_g1 = [
    "unitree-sdk2==1.0.1",
    "pyzmq>=26.2.1,<28.0.0",
    "onnxruntime>=1.16.0,<2.0.0",
    "pin>=3.0.0,<4.0.0",
    "meshcat>=0.3.0,<0.4.0",
-    "lerobot[matplotlib-dep]",
+    "matplotlib>=3.9.0,<4.0.0",
    "lerobot[pygame-dep]",
    "casadi>=3.6.0,<4.0.0",
 ]
 reachy2 = ["reachy2_sdk>=1.0.15,<1.1.0"]
 kinematics = ["lerobot[placo-dep]"]
 intelrealsense = [
    "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'",
-    "pyrealsense2-macosx>=2.54,<2.57.0 ; sys_platform == 'darwin'",
+    "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'",
 ]
-phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0", "lerobot[scipy-dep]"]
+phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
 # Policies
 wallx = [
-    "lerobot[transformers-dep]",
+    "transformers==4.49.0",
-    "lerobot[peft]",
+    "peft==0.17.1",
-    "lerobot[scipy-dep]",
+    "scipy==1.15.3",
-    "torchdiffeq>=0.2.4,<0.3.0",
+    "torchdiffeq==0.2.5",
-    "lerobot[qwen-vl-utils-dep]",
+    "qwen_vl_utils==0.0.11"
 ]
-pi = ["lerobot[transformers-dep]", "lerobot[scipy-dep]"]
+pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi", "scipy>=1.10.1,<1.15"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]
 groot = [
    "lerobot[transformers-dep]",
-    "lerobot[peft]",
+    "peft>=0.13.0,<1.0.0",
    "dm-tree>=0.1.8,<1.0.0",
    "timm>=1.0.0,<1.1.0",
    "safetensors>=0.4.3,<1.0.0",
@@ -157,13 +148,13 @@ groot = [
    "ninja>=1.11.1,<2.0.0",
    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
+sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "qwen-vl-utils>=0.0.14,<0.1.0"]
 xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 # Features
-async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
+async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
-peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
+peft = ["lerobot[transformers-dep]", "peft>=0.18.0,<1.0.0"]
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
@@ -171,19 +162,13 @@ test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 # Simulation
-# NOTE: Explicitly listing scipy helps flatten the dependecy tree.
+aloha = ["gym-aloha>=0.1.2,<0.2.0"]
 aloha = ["gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
 pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
+libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0"]
-metaworld = ["metaworld==3.0.0", "lerobot[scipy-dep]"]
+metaworld = ["metaworld==3.0.0"]
 # All
 all = [
    # NOTE(resolver hint): scipy is pulled in transitively via lerobot[scipy-dep] through
    # multiple extras (aloha, metaworld, pi, wallx, phone). Listing it explicitly
    # helps pip's resolver converge by constraining scipy early, before it encounters
    # the loose scipy requirements from transitive deps like dm-control and metaworld.
    "scipy>=1.14.0,<2.0.0",
    "lerobot[dynamixel]",
    "lerobot[gamepad]",
    "lerobot[hopejr]",
@@ -191,8 +176,8 @@ all = [
    "lerobot[reachy2]",
    "lerobot[kinematics]",
    "lerobot[intelrealsense]",
-    "lerobot[wallx]",
+    # "lerobot[wallx]",
-    "lerobot[pi]",
+    # "lerobot[pi]", TODO(Pepijn): Update pi to transformers v5
    "lerobot[smolvla]",
    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
    "lerobot[xvla]",
@@ -204,11 +189,10 @@ all = [
    "lerobot[aloha]",
    "lerobot[pusht]",
    "lerobot[phone]",
-    "lerobot[libero]; sys_platform == 'linux'",
+    "lerobot[libero]",
    "lerobot[metaworld]",
    "lerobot[sarm]",
    "lerobot[peft]",
    # "lerobot[unitree_g1]", TODO: Unitree requires specific installation instructions for unitree_sdk2
 ]
 [project.scripts]
@@ -237,7 +221,7 @@ lerobot = ["envs/*.json"]
 where = ["src"]
 [tool.ruff]
-target-version = "py312"
+target-version = "py310"
 line-length = 110
 exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
@@ -329,7 +313,7 @@ default.extend-ignore-identifiers-re = [
 # Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
 [tool.mypy]
-python_version = "3.12"
+python_version = "3.10"
 ignore_missing_imports = true
 follow_imports = "skip"
 # warn_return_any = true
@@ -413,3 +397,85 @@ ignore_errors = false
 # [[tool.mypy.overrides]]
 # module = "lerobot.scripts.*"
 # ignore_errors = false
 [tool.uv]
 # wallx requires transformers==4.49.0 which conflicts with other extras that need >=4.53.0
 conflicts = [
    [
        { extra = "wallx" },
        { extra = "transformers-dep" },
    ],
    [
        { extra = "wallx" },
        { extra = "pi" },
    ],
    [
        { extra = "wallx" },
        { extra = "smolvla" },
    ],
    [
        { extra = "wallx" },
        { extra = "groot" },
    ],
    [
        { extra = "wallx" },
        { extra = "xvla" },
    ],
    [
        { extra = "wallx" },
        { extra = "sarm" },
    ],
    [
        { extra = "wallx" },
        { extra = "hilserl" },
    ],
    [
        { extra = "wallx" },
        { extra = "libero" },
    ],
    [
        { extra = "wallx" },
        { extra = "peft" },
    ],
    [
        { extra = "wallx" },
        { extra = "all" },
    ],
    # pi uses custom branch which conflicts with transformers-dep
    [
        { extra = "pi" },
        { extra = "transformers-dep" },
    ],
    [
        { extra = "pi" },
        { extra = "smolvla" },
    ],
    [
        { extra = "pi" },
        { extra = "groot" },
    ],
    [
        { extra = "pi" },
        { extra = "xvla" },
    ],
    [
        { extra = "pi" },
        { extra = "sarm" },
    ],
    [
        { extra = "pi" },
        { extra = "hilserl" },
    ],
    [
        { extra = "pi" },
        { extra = "libero" },
    ],
    [
        { extra = "pi" },
        { extra = "peft" },
    ],
    [
        { extra = "pi" },
        { extra = "all" },
    ],
 ]
@@ -1,73 +1,76 @@
 #
-# This file is autogenerated by pip-compile with Python 3.12
+# This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
 #    pip-compile --output-file=requirements-macos.txt requirements.in
 #
 -e .[all]
    # via -[all]
-absl-py==2.4.0
+absl-py==2.3.1
    # via
    #   dm-control
    #   dm-env
    #   dm-tree
    #   labmaze
    #   mujoco
-accelerate==1.13.0
+    #   tensorboard
 accelerate==1.11.0
    # via
    #   lerobot
    #   peft
 aiohappyeyeballs==2.6.1
    # via aiohttp
-aiohttp==3.13.3
+aiohttp==3.13.1
    # via fsspec
 aiosignal==1.4.0
    # via aiohttp
 annotated-doc==0.0.4
    # via
    #   fastapi
    #   typer
 annotated-types==0.7.0
    # via pydantic
-anyio==4.12.1
+antlr4-python3-runtime==4.9.3
    # via
    #   hydra-core
    #   omegaconf
 anyio==4.11.0
    # via
    #   httpx
    #   starlette
    #   watchfiles
-asttokens==3.0.1
+asttokens==3.0.0
    # via stack-data
 async-timeout==5.0.1
    # via aiohttp
 attrs==25.4.0
    # via
    #   aiohttp
    #   dm-tree
    #   jsonlines
    #   jsonschema
    #   referencing
    #   rerun-sdk
 av==15.1.0
    # via lerobot
 bddl==1.0.1
    # via libero
 certifi==2025.10.5
    # via
    #   lerobot
    #   qwen-vl-utils
 certifi==2026.2.25
    # via
    #   httpcore
    #   httpx
    #   requests
    #   sentry-sdk
 cffi==2.0.0
    # via pymunk
-cfgv==3.5.0
+cfgv==3.4.0
    # via pre-commit
-charset-normalizer==3.4.5
+charset-normalizer==3.4.4
    # via requests
-click==8.3.1
+click==8.3.0
    # via
    #   typer
    #   uvicorn
    #   wandb
-cloudpickle==3.1.2
+cloudpickle==3.1.1
-    # via gymnasium
+    # via
-cmake==4.1.3
+    #   gymnasium
    #   libero
 cmake==4.1.0
    # via lerobot
-cmeel==0.59.0
+cmeel==0.57.3
    # via
    #   cmeel-assimp
    #   cmeel-boost
@@ -105,17 +108,15 @@ cmeel-zlib==1.3.1
    # via cmeel-assimp
 coal-library==3.0.1
    # via pin
-contourpy==1.3.3
+contourpy==1.3.2
-    # via
+    # via matplotlib
-    #   lerobot
+coverage[toml]==7.11.0
    #   matplotlib
 coverage[toml]==7.13.4
    # via pytest-cov
 cycler==0.12.1
    # via matplotlib
-datasets==4.6.1
+datasets==4.1.1
    # via lerobot
-debugpy==1.8.20
+debugpy==1.8.17
    # via lerobot
 decorator==5.2.1
    # via ipython
@@ -129,7 +130,7 @@ dill==0.4.0
    #   multiprocess
 distlib==0.4.0
    # via virtualenv
-dm-control==1.0.37
+dm-control==1.0.34
    # via gym-aloha
 dm-env==1.6
    # via dm-control
@@ -137,55 +138,69 @@ dm-tree==0.1.9
    # via
    #   dm-control
    #   dm-env
    #   lerobot
 docopt==0.6.2
    # via num2words
 draccus==0.10.0
    # via lerobot
 dynamixel-sdk==3.8.4
    # via lerobot
 easydict==1.13
    # via libero
 egl-probe @ git+https://github.com/huggingface/egl_probe.git
    # via
    #   libero
    #   robomimic
 eigenpy==3.10.3
    # via coal-library
-einops==0.8.2
+einops==0.8.1
    # via lerobot
 eiquadprog==1.2.9
    # via placo
 etils[epath,epy]==1.14.0
    # via mujoco
 executing==2.2.1
    # via stack-data
 faker==34.0.2
    # via lerobot
 farama-notifications==0.0.4
    # via gymnasium
 fastapi==0.135.1
    # via
    #   lerobot
-    #   teleop
+    #   libero
 eiquadprog==1.2.9
    # via placo
 etils[epath,epy]==1.13.0
    # via mujoco
 exceptiongroup==1.3.0
    # via
    #   anyio
    #   ipython
    #   pytest
 executing==2.2.1
    # via stack-data
 farama-notifications==0.0.4
    # via gymnasium
 fastapi==0.119.1
    # via teleop
 fastjsonschema==2.21.2
    # via nbformat
 feetech-servo-sdk==1.0.0
    # via lerobot
-filelock==3.25.0
+filelock==3.20.0
    # via
    #   datasets
    #   diffusers
    #   huggingface-hub
    #   python-discovery
    #   torch
    #   transformers
    #   virtualenv
-fonttools==4.61.1
+fonttools==4.60.1
    # via matplotlib
 frozenlist==1.8.0
    # via
    #   aiohttp
    #   aiosignal
-fsspec[http]==2026.2.0
+fsspec[http]==2025.9.0
    # via
    #   datasets
    #   etils
    #   huggingface-hub
    #   torch
 future==1.0.0
    # via libero
 gitdb==4.0.12
    # via gitpython
-gitpython==3.1.46
+gitpython==3.1.45
    # via wandb
 glfw==2.10.0
    # via
@@ -197,6 +212,7 @@ grpcio==1.73.1
    #   lerobot
    #   reachy2-sdk
    #   reachy2-sdk-api
    #   tensorboard
 grpcio-tools==1.73.1
    # via
    #   lerobot
@@ -207,67 +223,71 @@ gym-hil==0.1.13
    # via lerobot
 gym-pusht==0.1.6
    # via lerobot
-gymnasium==1.2.3
+gymnasium==1.2.1
    # via
    #   gym-aloha
    #   gym-hil
    #   gym-pusht
    #   lerobot
    #   libero
    #   metaworld
 h11==0.16.0
-    # via
+    # via uvicorn
-    #   httpcore
+h5py==3.15.1
-    #   uvicorn
+    # via robomimic
 hebi-py==2.11.0
    # via lerobot
-hf-xet==1.3.2
+hf-transfer==0.1.9
    # via huggingface-hub
 hf-xet==1.1.10
    # via huggingface-hub
 hidapi==0.14.0.post4
    # via
    #   gym-hil
    #   lerobot
 httpcore==1.0.9
    # via httpx
 httptools==0.7.1
    # via uvicorn
-httpx==0.28.1
+huggingface-hub[cli,hf-transfer]==0.35.3
    # via
    #   datasets
    #   huggingface-hub
 huggingface-hub==1.6.0
    # via
    #   accelerate
    #   datasets
    #   diffusers
    #   lerobot
    #   peft
    #   timm
    #   tokenizers
    #   transformers
-identify==2.6.17
+hydra-core==1.3.2
    # via libero
 identify==2.6.15
    # via pre-commit
 idna==3.11
    # via
    #   anyio
    #   httpx
    #   requests
    #   yarl
-imageio[ffmpeg]==2.37.2
+imageio[ffmpeg]==2.37.0
    # via
    #   gym-aloha
    #   gym-hil
    #   lerobot
    #   metaworld
    #   robomimic
    #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via imageio
+    # via
-importlib-metadata==8.7.1
+    #   imageio
    #   robomimic
 importlib-metadata==8.7.0
    # via diffusers
 importlib-resources==6.5.2
    # via etils
 iniconfig==2.3.0
    # via pytest
-ipython==9.11.0
+inquirerpy==0.3.4
    # via huggingface-hub
 ipython==8.37.0
    # via meshcat
 ipython-pygments-lexers==1.1.1
    # via ipython
 ischedule==1.2.7
    # via placo
 jedi==0.19.2
@@ -276,24 +296,44 @@ jinja2==3.1.6
    # via torch
 jsonlines==4.0.0
    # via lerobot
 jsonschema==4.25.1
    # via nbformat
 jsonschema-specifications==2025.9.1
    # via jsonschema
 jupyter-core==5.9.1
    # via nbformat
 jupytext==1.18.1
    # via bddl
 kiwisolver==1.4.9
    # via matplotlib
 labmaze==1.0.6
    # via dm-control
-lazy-loader==0.5
+lazy-loader==0.4
    # via scikit-image
-librt==0.8.1
+libero @ git+https://github.com/huggingface/lerobot-libero.git@main
-    # via mypy
+    # via lerobot
 llvmlite==0.45.1
    # via numba
 lxml==6.0.2
    # via dm-control
 markdown==3.9
    # via tensorboard
 markdown-it-py==4.0.0
-    # via rich
+    # via
    #   jupytext
    #   mdit-py-plugins
 markupsafe==3.0.3
-    # via jinja2
+    # via
-matplotlib==3.10.8
+    #   jinja2
-    # via lerobot
+    #   werkzeug
 matplotlib==3.10.7
    # via
    #   lerobot
    #   libero
 matplotlib-inline==0.2.1
    # via ipython
 mdit-py-plugins==0.5.0
    # via jupytext
 mdurl==0.1.2
    # via markdown-it-py
 mergedeep==1.3.4
@@ -306,35 +346,41 @@ mock-serial==0.0.1
    # via lerobot
 mpmath==1.3.0
    # via sympy
-mujoco==3.5.0
+mujoco==3.3.7
    # via
    #   dm-control
    #   gym-aloha
    #   gym-hil
    #   libero
    #   metaworld
-multidict==6.7.1
+    #   robosuite
 multidict==6.7.0
    # via
    #   aiohttp
    #   yarl
-multiprocess==0.70.18
+multiprocess==0.70.16
    # via datasets
 mypy==1.19.1
    # via lerobot
 mypy-extensions==1.1.0
    # via typing-inspect
 nbformat==5.10.4
    # via jupytext
 networkx==3.4.2
    # via
-    #   mypy
+    #   bddl
    #   typing-inspect
 networkx==3.6.1
    # via
    #   scikit-image
    #   torch
-nodeenv==1.10.0
+ninja==1.13.0
    # via lerobot
 nodeenv==1.9.1
    # via pre-commit
 num2words==0.5.14
    # via lerobot
 numba==0.62.1
    # via robosuite
 numpy==2.2.6
    # via
    #   accelerate
    #   bddl
    #   cmeel-boost
    #   contourpy
    #   datasets
@@ -343,14 +389,16 @@ numpy==2.2.6
    #   dm-env
    #   dm-tree
    #   gymnasium
    #   h5py
    #   hebi-py
    #   imageio
    #   labmaze
-    #   lerobot
+    #   libero
    #   matplotlib
    #   meshcat
    #   metaworld
    #   mujoco
    #   numba
    #   opencv-python
    #   opencv-python-headless
    #   pandas
@@ -358,18 +406,26 @@ numpy==2.2.6
    #   pyquaternion
    #   reachy2-sdk
    #   rerun-sdk
    #   robomimic
    #   robosuite
    #   scikit-image
    #   scipy
    #   shapely
    #   teleop
    #   tensorboard
    #   tensorboardx
    #   tifffile
    #   torchvision
    #   transformers
    #   transforms3d
-opencv-python==4.13.0.92
+omegaconf==2.3.0
    # via hydra-core
 opencv-python==4.12.0.88
    # via
    #   gym-pusht
    #   libero
    #   reachy2-sdk
    #   robosuite
 opencv-python-headless==4.12.0.88
    # via lerobot
 orderly-set==5.5.0
@@ -379,87 +435,97 @@ packaging==25.0
    #   accelerate
    #   datasets
    #   huggingface-hub
    #   hydra-core
    #   jupytext
    #   lazy-loader
    #   lerobot
    #   matplotlib
    #   peft
    #   pytest
    #   qwen-vl-utils
    #   reachy2-sdk
    #   scikit-image
    #   tensorboard
    #   tensorboardx
    #   transformers
    #   wandb
 pandas==2.3.3
    # via
    #   datasets
    #   lerobot
-parso==0.8.6
+parso==0.8.5
    # via jedi
-pathspec==1.0.4
+peft==0.17.1
    # via mypy
 peft==0.18.1
    # via lerobot
 pexpect==4.9.0
    # via ipython
-pillow==12.1.1
+pfzy==0.3.4
    # via inquirerpy
 pillow==12.0.0
    # via
    #   diffusers
    #   imageio
    #   lerobot
    #   matplotlib
    #   meshcat
    #   qwen-vl-utils
    #   rerun-sdk
    #   robosuite
    #   scikit-image
    #   tensorboard
    #   torchvision
 pin==3.4.0
    # via placo
-placo==0.9.16
+placo==0.9.14
    # via lerobot
-platformdirs==4.9.4
+platformdirs==4.5.0
    # via
-    #   python-discovery
+    #   jupyter-core
    #   virtualenv
    #   wandb
 pluggy==1.6.0
    # via
    #   pytest
    #   pytest-cov
-pre-commit==4.5.1
+pre-commit==4.3.0
    # via lerobot
 prompt-toolkit==3.0.52
-    # via ipython
+    # via
    #   inquirerpy
    #   ipython
 propcache==0.4.1
    # via
    #   aiohttp
    #   yarl
-protobuf==6.31.1
+protobuf==6.31.0
    # via
    #   dm-control
    #   grpcio-tools
    #   lerobot
    #   reachy2-sdk
    #   reachy2-sdk-api
    #   tensorboard
    #   tensorboardx
    #   wandb
-psutil==7.2.2
+psutil==7.1.1
    # via
    #   accelerate
    #   imageio
    #   peft
    #   robomimic
 ptyprocess==0.7.0
    # via pexpect
 pure-eval==0.2.3
    # via stack-data
-pyarrow==23.0.1
+pyarrow==21.0.0
    # via
    #   datasets
    #   rerun-sdk
-pycparser==3.0
+pycparser==2.23
    # via cffi
-pydantic==2.12.5
+pydantic==2.12.3
    # via
    #   fastapi
    #   wandb
-pydantic-core==2.41.5
+pydantic-core==2.41.4
    # via pydantic
 pygame==2.6.1
    # via
@@ -469,35 +535,33 @@ pygame==2.6.1
 pygments==2.19.2
    # via
    #   ipython
    #   ipython-pygments-lexers
    #   pytest
    #   rich
 pymunk==6.11.1
    # via
    #   gym-pusht
    #   lerobot
-pyngrok==7.5.1
+pyngrok==7.4.1
    # via meshcat
 pynput==1.8.1
    # via
    #   gym-hil
    #   lerobot
-pyobjc-core==12.1
+pyobjc-core==12.0
    # via
    #   pyobjc-framework-applicationservices
    #   pyobjc-framework-cocoa
    #   pyobjc-framework-coretext
    #   pyobjc-framework-quartz
-pyobjc-framework-applicationservices==12.1
+pyobjc-framework-applicationservices==12.0
    # via pynput
-pyobjc-framework-cocoa==12.1
+pyobjc-framework-cocoa==12.0
    # via
    #   pyobjc-framework-applicationservices
    #   pyobjc-framework-coretext
    #   pyobjc-framework-quartz
-pyobjc-framework-coretext==12.1
+pyobjc-framework-coretext==12.0
    # via pyobjc-framework-applicationservices
-pyobjc-framework-quartz==12.1
+pyobjc-framework-quartz==12.0
    # via
    #   pynput
    #   pyobjc-framework-applicationservices
@@ -506,13 +570,13 @@ pyopengl==3.1.10
    # via
    #   dm-control
    #   mujoco
-pyparsing==3.3.2
+pyparsing==3.2.5
    # via
    #   dm-control
    #   matplotlib
 pyquaternion==0.9.9
    # via reachy2-sdk
-pyrealsense2-macosx==2.56.5
+pyrealsense2-macosx==2.54.2
    # via lerobot
 pyserial==3.5
    # via
@@ -521,6 +585,7 @@ pyserial==3.5
    #   lerobot
 pytest==8.4.2
    # via
    #   bddl
    #   lerobot
    #   pytest-cov
    #   pytest-timeout
@@ -531,14 +596,11 @@ pytest-timeout==2.4.0
    # via lerobot
 python-dateutil==2.9.0.post0
    # via
    #   faker
    #   matplotlib
    #   pandas
-python-discovery==1.1.1
+python-dotenv==1.1.1
    # via virtualenv
 python-dotenv==1.2.2
    # via uvicorn
-pytz==2026.1.post1
+pytz==2025.2
    # via pandas
 pyyaml==6.0.3
    # via
@@ -547,10 +609,13 @@ pyyaml==6.0.3
    #   draccus
    #   hebi-py
    #   huggingface-hub
    #   jupytext
    #   omegaconf
    #   peft
    #   pre-commit
    #   pyngrok
    #   pyyaml-include
    #   timm
    #   transformers
    #   uvicorn
    #   wandb
@@ -560,13 +625,15 @@ pyzmq==27.1.0
    # via
    #   lerobot
    #   meshcat
-qwen-vl-utils==0.0.14
+reachy2-sdk==1.0.14
    # via lerobot
 reachy2-sdk==1.0.15
    # via lerobot
 reachy2-sdk-api==1.0.21
    # via reachy2-sdk
-regex==2026.2.28
+referencing==0.37.0
    # via
    #   jsonschema
    #   jsonschema-specifications
 regex==2025.10.23
    # via
    #   diffusers
    #   transformers
@@ -575,150 +642,184 @@ requests==2.32.5
    #   datasets
    #   diffusers
    #   dm-control
-    #   qwen-vl-utils
+    #   huggingface-hub
    #   teleop
    #   transformers
    #   wandb
-rerun-sdk==0.26.2
+rerun-sdk==0.26.1
    # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
    # via placo
-rich==14.3.3
+robomimic==0.2.0
-    # via typer
+    # via libero
-safetensors==0.7.0
+robosuite==1.4.0
    # via libero
 rpds-py==0.28.0
    # via
    #   jsonschema
    #   referencing
 safetensors==0.6.2
    # via
    #   accelerate
    #   diffusers
    #   lerobot
    #   peft
    #   timm
    #   transformers
 scikit-image==0.25.2
    # via
    #   gym-pusht
    #   lerobot
-scipy==1.17.1
+scipy==1.15.3
    # via
    #   dm-control
    #   lerobot
    #   metaworld
    #   robosuite
    #   scikit-image
-    #   torchdiffeq
+sentry-sdk==2.42.1
 sentry-sdk==2.54.0
    # via wandb
 shapely==2.1.2
    # via gym-pusht
 shellingham==1.5.4
    # via typer
 six==1.17.0
    # via
    #   pynput
    #   python-dateutil
-smmap==5.0.3
+smmap==5.0.2
    # via gitdb
 sniffio==1.3.1
    # via anyio
 stack-data==0.6.3
    # via ipython
-starlette==0.52.1
+starlette==0.48.0
    # via fastapi
 sympy==1.14.0
    # via torch
-teleop==0.1.4
+teleop==0.1.2
    # via lerobot
-termcolor==3.3.0
+tensorboard==2.20.0
-    # via lerobot
+    # via robomimic
-tifffile==2026.3.3
+tensorboard-data-server==0.7.2
    # via tensorboard
 tensorboardx==2.6.4
    # via robomimic
 termcolor==3.1.0
    # via
    #   lerobot
    #   robomimic
 thop==0.1.1.post2209072238
    # via libero
 tifffile==2025.5.10
    # via scikit-image
-tokenizers==0.22.2
+timm==1.0.20
    # via lerobot
 tokenizers==0.22.1
    # via transformers
 toml==0.10.2
    # via draccus
-torch==2.10.0
+tomli==2.3.0
    # via
    #   cmeel
    #   coverage
    #   jupytext
    #   pytest
 torch==2.7.1
    # via
    #   accelerate
    #   lerobot
    #   peft
-    #   torchdiffeq
+    #   robomimic
    #   thop
    #   timm
    #   torchvision
-torchcodec==0.10.0
+torchcodec==0.5
    # via lerobot
-torchdiffeq==0.2.5
+torchvision==0.22.1
-    # via lerobot
+    # via
-torchvision==0.25.0
+    #   lerobot
-    # via lerobot
+    #   robomimic
-tornado==6.5.4
+    #   timm
 tornado==6.5.2
    # via meshcat
-tqdm==4.67.3
+tqdm==4.67.1
    # via
    #   datasets
    #   dm-control
    #   huggingface-hub
    #   peft
    #   robomimic
    #   transformers
 traitlets==5.14.3
    # via
    #   ipython
    #   jupyter-core
    #   matplotlib-inline
-transformers==5.3.0
+    #   nbformat
 transformers==4.57.1
    # via
    #   lerobot
    #   libero
    #   peft
 transforms3d==0.4.2
    # via teleop
 typer==0.24.1
    # via
    #   huggingface-hub
    #   transformers
 typing-extensions==4.15.0
    # via
    #   aiosignal
    #   anyio
    #   etils
-    #   faker
+    #   exceptiongroup
    #   fastapi
    #   gymnasium
    #   huggingface-hub
-    #   mypy
+    #   ipython
    #   multidict
    #   pydantic
    #   pydantic-core
    #   referencing
    #   rerun-sdk
    #   starlette
    #   torch
    #   typing-inspect
    #   typing-inspection
    #   uvicorn
    #   virtualenv
    #   wandb
 typing-inspect==0.9.0
    # via draccus
 typing-inspection==0.4.2
-    # via
+    # via pydantic
-    #   fastapi
+tzdata==2025.2
    #   pydantic
 tzdata==2025.3
    # via pandas
 u-msgpack-python==2.8.0
    # via meshcat
-urllib3==2.6.3
+urllib3==2.5.0
    # via
    #   requests
    #   sentry-sdk
-uvicorn[standard]==0.41.0
+uvicorn[standard]==0.38.0
    # via teleop
 uvloop==0.22.1
    # via uvicorn
-virtualenv==21.1.0
+virtualenv==20.35.3
    # via pre-commit
-wandb==0.24.2
+wandb==0.21.4
-    # via lerobot
+    # via
    #   lerobot
    #   libero
 watchfiles==1.1.1
    # via uvicorn
-wcwidth==0.6.0
+wcwidth==0.2.14
    # via prompt-toolkit
 websocket-client==1.9.0
    # via teleop
-websockets==16.0
+websockets==15.0.1
    # via uvicorn
-wrapt==2.1.2
+werkzeug==3.1.3
    # via tensorboard
 wrapt==2.0.0
    # via dm-tree
 xxhash==3.6.0
    # via datasets
-yarl==1.23.0
+yarl==1.22.0
    # via aiohttp
 zipp==3.23.0
    # via
@@ -1,12 +1,12 @@
 #
-# This file is autogenerated by pip-compile with Python 3.12
+# This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
 #    pip-compile --output-file=requirements-ubuntu.txt requirements.in
 #
 -e .[all]
    # via -[all]
-absl-py==2.4.0
+absl-py==2.3.1
    # via
    #   dm-control
    #   dm-env
@@ -14,33 +14,30 @@ absl-py==2.4.0
    #   labmaze
    #   mujoco
    #   tensorboard
-accelerate==1.13.0
+accelerate==1.11.0
    # via
    #   lerobot
    #   peft
 aiohappyeyeballs==2.6.1
    # via aiohttp
-aiohttp==3.13.3
+aiohttp==3.13.1
    # via fsspec
 aiosignal==1.4.0
    # via aiohttp
 annotated-doc==0.0.4
    # via
    #   fastapi
    #   typer
 annotated-types==0.7.0
    # via pydantic
 antlr4-python3-runtime==4.9.3
    # via
    #   hydra-core
    #   omegaconf
-anyio==4.12.1
+anyio==4.11.0
    # via
    #   httpx
    #   starlette
    #   watchfiles
-asttokens==3.0.1
+asttokens==3.0.0
    # via stack-data
 async-timeout==5.0.1
    # via aiohttp
 attrs==25.4.0
    # via
    #   aiohttp
@@ -50,35 +47,30 @@ attrs==25.4.0
    #   referencing
    #   rerun-sdk
 av==15.1.0
-    # via
+    # via lerobot
    #   lerobot
    #   qwen-vl-utils
 bddl==1.0.1
-    # via hf-libero
+    # via libero
-certifi==2026.2.25
+certifi==2025.10.5
    # via
    #   httpcore
    #   httpx
    #   requests
    #   sentry-sdk
 cffi==2.0.0
    # via pymunk
-cfgv==3.5.0
+cfgv==3.4.0
    # via pre-commit
-charset-normalizer==3.4.5
+charset-normalizer==3.4.4
    # via requests
-click==8.3.1
+click==8.3.0
    # via
    #   typer
    #   uvicorn
    #   wandb
-cloudpickle==3.1.2
+cloudpickle==3.1.1
    # via
    #   gymnasium
-    #   hf-libero
+    #   libero
-cmake==4.1.3
+cmake==4.1.0
    # via lerobot
-cmeel==0.59.0
+cmeel==0.57.3
    # via
    #   cmeel-assimp
    #   cmeel-boost
@@ -116,24 +108,20 @@ cmeel-zlib==1.3.1
    # via cmeel-assimp
 coal-library==3.0.1
    # via pin
-contourpy==1.3.3
+contourpy==1.3.2
-    # via
+    # via matplotlib
-    #   lerobot
+coverage[toml]==7.11.0
    #   matplotlib
 coverage[toml]==7.13.4
    # via pytest-cov
 cuda-bindings==12.9.4
    # via torch
 cuda-pathfinder==1.4.1
    # via cuda-bindings
 cycler==0.12.1
    # via matplotlib
-datasets==4.6.1
+datasets==4.1.1
    # via lerobot
-debugpy==1.8.20
+debugpy==1.8.17
    # via lerobot
 decorator==5.2.1
    # via ipython
 decord==0.6.0
    # via lerobot
 deepdiff==8.6.1
    # via lerobot
 diffusers==0.35.2
@@ -144,7 +132,7 @@ dill==0.4.0
    #   multiprocess
 distlib==0.4.0
    # via virtualenv
-dm-control==1.0.37
+dm-control==1.0.34
    # via gym-aloha
 dm-env==1.6
    # via dm-control
@@ -152,6 +140,7 @@ dm-tree==0.1.9
    # via
    #   dm-control
    #   dm-env
    #   lerobot
 docopt==0.6.2
    # via num2words
 draccus==0.10.0
@@ -159,60 +148,66 @@ draccus==0.10.0
 dynamixel-sdk==3.8.4
    # via lerobot
 easydict==1.13
-    # via hf-libero
+    # via libero
-egl-probe==1.0.2
+egl-probe @ git+https://github.com/huggingface/egl_probe.git
-    # via robomimic
+    # via
    #   libero
    #   robomimic
 eigenpy==3.10.3
    # via coal-library
-einops==0.8.2
+einops==0.8.1
    # via
-    #   hf-libero
+    #   flash-attn
    #   lerobot
    #   libero
 eiquadprog==1.2.9
    # via placo
-etils[epath,epy]==1.14.0
+etils[epath,epy]==1.13.0
    # via mujoco
-evdev==1.9.3
+evdev==1.9.2
    # via pynput
 exceptiongroup==1.3.0
    # via
    #   anyio
    #   ipython
    #   pytest
 executing==2.2.1
    # via stack-data
 faker==34.0.2
    # via lerobot
 farama-notifications==0.0.4
    # via gymnasium
-fastapi==0.135.1
+fastapi==0.119.1
-    # via
+    # via teleop
    #   lerobot
    #   teleop
 fastjsonschema==2.21.2
    # via nbformat
 feetech-servo-sdk==1.0.0
    # via lerobot
-filelock==3.25.0
+filelock==3.20.0
    # via
    #   datasets
    #   diffusers
    #   huggingface-hub
    #   python-discovery
    #   torch
    #   transformers
    #   virtualenv
-fonttools==4.61.1
+flash-attn==2.8.3
    # via lerobot
 fonttools==4.60.1
    # via matplotlib
 frozenlist==1.8.0
    # via
    #   aiohttp
    #   aiosignal
-fsspec[http]==2026.2.0
+fsspec[http]==2025.9.0
    # via
    #   datasets
    #   etils
    #   huggingface-hub
    #   torch
 future==1.0.0
-    # via hf-libero
+    # via libero
 gitdb==4.0.12
    # via gitpython
-gitpython==3.1.46
+gitpython==3.1.45
    # via wandb
 glfw==2.10.0
    # via
@@ -235,60 +230,50 @@ gym-hil==0.1.13
    # via lerobot
 gym-pusht==0.1.6
    # via lerobot
-gymnasium==1.2.3
+gymnasium==1.2.1
    # via
    #   gym-aloha
    #   gym-hil
    #   gym-pusht
    #   hf-libero
    #   lerobot
    #   libero
    #   metaworld
 h11==0.16.0
-    # via
+    # via uvicorn
-    #   httpcore
+h5py==3.15.1
    #   uvicorn
 h5py==3.16.0
    # via robomimic
 hebi-py==2.11.0
    # via lerobot
-hf-egl-probe==1.0.2
+hf-transfer==0.1.9
-    # via hf-libero
+    # via huggingface-hub
-hf-libero==0.1.3
+hf-xet==1.1.10
    # via lerobot
 hf-xet==1.3.2
    # via huggingface-hub
 hidapi==0.14.0.post4
    # via
    #   gym-hil
    #   lerobot
 httpcore==1.0.9
    # via httpx
 httptools==0.7.1
    # via uvicorn
-httpx==0.28.1
+huggingface-hub[cli,hf-transfer]==0.35.3
    # via
    #   datasets
    #   huggingface-hub
 huggingface-hub==1.6.0
    # via
    #   accelerate
    #   datasets
    #   diffusers
    #   lerobot
    #   peft
    #   timm
    #   tokenizers
    #   transformers
 hydra-core==1.3.2
-    # via hf-libero
+    # via libero
-identify==2.6.17
+identify==2.6.15
    # via pre-commit
 idna==3.11
    # via
    #   anyio
    #   httpx
    #   requests
    #   yarl
-imageio[ffmpeg]==2.37.2
+imageio[ffmpeg]==2.37.0
    # via
    #   gym-aloha
    #   gym-hil
@@ -300,14 +285,16 @@ imageio-ffmpeg==0.6.0
    # via
    #   imageio
    #   robomimic
-importlib-metadata==8.7.1
+importlib-metadata==8.7.0
    # via diffusers
 importlib-resources==6.5.2
    # via etils
 iniconfig==2.3.0
    # via pytest
-ipython==9.11.0
+inquirerpy==0.3.4
    # via huggingface-hub
 ipython==8.37.0
    # via meshcat
 ipython-pygments-lexers==1.1.1
    # via ipython
 ischedule==1.2.7
    # via placo
 jedi==0.19.2
@@ -316,41 +303,40 @@ jinja2==3.1.6
    # via torch
 jsonlines==4.0.0
    # via lerobot
-jsonschema==4.26.0
+jsonschema==4.25.1
    # via nbformat
 jsonschema-specifications==2025.9.1
    # via jsonschema
 jupyter-core==5.9.1
    # via nbformat
-jupytext==1.19.1
+jupytext==1.18.1
    # via bddl
 kiwisolver==1.4.9
    # via matplotlib
 labmaze==1.0.6
    # via dm-control
-lazy-loader==0.5
+lazy-loader==0.4
    # via scikit-image
-librt==0.8.1
+libero @ git+https://github.com/huggingface/lerobot-libero.git@main
-    # via mypy
+    # via lerobot
-llvmlite==0.46.0
+llvmlite==0.45.1
    # via numba
 lxml==6.0.2
    # via dm-control
-markdown==3.10.2
+markdown==3.9
    # via tensorboard
 markdown-it-py==4.0.0
    # via
    #   jupytext
    #   mdit-py-plugins
    #   rich
 markupsafe==3.0.3
    # via
    #   jinja2
    #   werkzeug
-matplotlib==3.10.8
+matplotlib==3.10.7
    # via
    #   hf-libero
    #   lerobot
    #   libero
 matplotlib-inline==0.2.1
    # via ipython
 mdit-py-plugins==0.5.0
@@ -367,38 +353,36 @@ mock-serial==0.0.1
    # via lerobot
 mpmath==1.3.0
    # via sympy
-mujoco==3.5.0
+mujoco==3.3.7
    # via
    #   dm-control
    #   gym-aloha
    #   gym-hil
-    #   hf-libero
+    #   libero
    #   metaworld
    #   robosuite
-multidict==6.7.1
+multidict==6.7.0
    # via
    #   aiohttp
    #   yarl
-multiprocess==0.70.18
+multiprocess==0.70.16
    # via datasets
 mypy==1.19.1
    # via lerobot
 mypy-extensions==1.1.0
-    # via
+    # via typing-inspect
    #   mypy
    #   typing-inspect
 nbformat==5.10.4
    # via jupytext
-networkx==3.6.1
+networkx==3.4.2
    # via
    #   bddl
    #   scikit-image
    #   torch
-nodeenv==1.10.0
+ninja==1.13.0
    # via lerobot
 nodeenv==1.9.1
    # via pre-commit
 num2words==0.5.14
    # via lerobot
-numba==0.64.0
+numba==0.62.1
    # via robosuite
 numpy==2.2.6
    # via
@@ -407,6 +391,7 @@ numpy==2.2.6
    #   cmeel-boost
    #   contourpy
    #   datasets
    #   decord
    #   diffusers
    #   dm-control
    #   dm-env
@@ -414,10 +399,9 @@ numpy==2.2.6
    #   gymnasium
    #   h5py
    #   hebi-py
    #   hf-libero
    #   imageio
    #   labmaze
-    #   lerobot
+    #   libero
    #   matplotlib
    #   meshcat
    #   metaworld
@@ -442,51 +426,49 @@ numpy==2.2.6
    #   torchvision
    #   transformers
    #   transforms3d
-nvidia-cublas-cu12==12.8.4.1
+nvidia-cublas-cu12==12.6.4.1
    # via
    #   nvidia-cudnn-cu12
    #   nvidia-cusolver-cu12
    #   torch
-nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-cupti-cu12==12.6.80
    # via torch
-nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-nvrtc-cu12==12.6.77
    # via torch
-nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cuda-runtime-cu12==12.6.77
    # via torch
-nvidia-cudnn-cu12==9.10.2.21
+nvidia-cudnn-cu12==9.5.1.17
    # via torch
-nvidia-cufft-cu12==11.3.3.83
+nvidia-cufft-cu12==11.3.0.4
    # via torch
-nvidia-cufile-cu12==1.13.1.3
+nvidia-cufile-cu12==1.11.1.6
    # via torch
-nvidia-curand-cu12==10.3.9.90
+nvidia-curand-cu12==10.3.7.77
    # via torch
-nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusolver-cu12==11.7.1.2
    # via torch
-nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparse-cu12==12.5.4.2
    # via
    #   nvidia-cusolver-cu12
    #   torch
-nvidia-cusparselt-cu12==0.7.1
+nvidia-cusparselt-cu12==0.6.3
    # via torch
-nvidia-nccl-cu12==2.27.5
+nvidia-nccl-cu12==2.26.2
    # via torch
-nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvjitlink-cu12==12.6.85
    # via
    #   nvidia-cufft-cu12
    #   nvidia-cusolver-cu12
    #   nvidia-cusparse-cu12
    #   torch
-nvidia-nvshmem-cu12==3.4.5
+nvidia-nvtx-cu12==12.6.77
    # via torch
 nvidia-nvtx-cu12==12.8.90
    # via torch
 omegaconf==2.3.0
    # via hydra-core
-opencv-python==4.13.0.92
+opencv-python==4.12.0.88
    # via
    #   gym-pusht
-    #   hf-libero
+    #   libero
    #   reachy2-sdk
    #   robosuite
 opencv-python-headless==4.12.0.88
@@ -505,7 +487,6 @@ packaging==25.0
    #   matplotlib
    #   peft
    #   pytest
    #   qwen-vl-utils
    #   reachy2-sdk
    #   scikit-image
    #   tensorboard
@@ -516,21 +497,21 @@ pandas==2.3.3
    # via
    #   datasets
    #   lerobot
-parso==0.8.6
+parso==0.8.5
    # via jedi
-pathspec==1.0.4
+peft==0.17.1
    # via mypy
 peft==0.18.1
    # via lerobot
 pexpect==4.9.0
    # via ipython
-pillow==12.1.1
+pfzy==0.3.4
    # via inquirerpy
 pillow==12.0.0
    # via
    #   diffusers
    #   imageio
    #   lerobot
    #   matplotlib
    #   meshcat
    #   qwen-vl-utils
    #   rerun-sdk
    #   robosuite
    #   scikit-image
@@ -538,27 +519,28 @@ pillow==12.1.1
    #   torchvision
 pin==3.4.0
    # via placo
-placo==0.9.16
+placo==0.9.14
    # via lerobot
-platformdirs==4.9.4
+platformdirs==4.5.0
    # via
    #   jupyter-core
    #   python-discovery
    #   virtualenv
    #   wandb
 pluggy==1.6.0
    # via
    #   pytest
    #   pytest-cov
-pre-commit==4.5.1
+pre-commit==4.3.0
    # via lerobot
 prompt-toolkit==3.0.52
-    # via ipython
+    # via
    #   inquirerpy
    #   ipython
 propcache==0.4.1
    # via
    #   aiohttp
    #   yarl
-protobuf==6.31.1
+protobuf==6.31.0
    # via
    #   dm-control
    #   grpcio-tools
@@ -568,7 +550,7 @@ protobuf==6.31.1
    #   tensorboard
    #   tensorboardx
    #   wandb
-psutil==7.2.2
+psutil==7.1.1
    # via
    #   accelerate
    #   imageio
@@ -578,17 +560,17 @@ ptyprocess==0.7.0
    # via pexpect
 pure-eval==0.2.3
    # via stack-data
-pyarrow==23.0.1
+pyarrow==21.0.0
    # via
    #   datasets
    #   rerun-sdk
-pycparser==3.0
+pycparser==2.23
    # via cffi
-pydantic==2.12.5
+pydantic==2.12.3
    # via
    #   fastapi
    #   wandb
-pydantic-core==2.41.5
+pydantic-core==2.41.4
    # via pydantic
 pygame==2.6.1
    # via
@@ -598,14 +580,12 @@ pygame==2.6.1
 pygments==2.19.2
    # via
    #   ipython
    #   ipython-pygments-lexers
    #   pytest
    #   rich
 pymunk==6.11.1
    # via
    #   gym-pusht
    #   lerobot
-pyngrok==7.5.1
+pyngrok==7.4.1
    # via meshcat
 pynput==1.8.1
    # via
@@ -615,7 +595,7 @@ pyopengl==3.1.10
    # via
    #   dm-control
    #   mujoco
-pyparsing==3.3.2
+pyparsing==3.2.5
    # via
    #   dm-control
    #   matplotlib
@@ -641,16 +621,13 @@ pytest-timeout==2.4.0
    # via lerobot
 python-dateutil==2.9.0.post0
    # via
    #   faker
    #   matplotlib
    #   pandas
-python-discovery==1.1.1
+python-dotenv==1.1.1
    # via virtualenv
 python-dotenv==1.2.2
    # via uvicorn
 python-xlib==0.33
    # via pynput
-pytz==2026.1.post1
+pytz==2025.2
    # via pandas
 pyyaml==6.0.3
    # via
@@ -665,6 +642,7 @@ pyyaml==6.0.3
    #   pre-commit
    #   pyngrok
    #   pyyaml-include
    #   timm
    #   transformers
    #   uvicorn
    #   wandb
@@ -674,9 +652,7 @@ pyzmq==27.1.0
    # via
    #   lerobot
    #   meshcat
-qwen-vl-utils==0.0.14
+reachy2-sdk==1.0.14
    # via lerobot
 reachy2-sdk==1.0.15
    # via lerobot
 reachy2-sdk-api==1.0.21
    # via reachy2-sdk
@@ -684,7 +660,7 @@ referencing==0.37.0
    # via
    #   jsonschema
    #   jsonschema-specifications
-regex==2026.2.28
+regex==2025.10.23
    # via
    #   diffusers
    #   transformers
@@ -693,62 +669,60 @@ requests==2.32.5
    #   datasets
    #   diffusers
    #   dm-control
-    #   qwen-vl-utils
+    #   huggingface-hub
    #   teleop
    #   transformers
    #   wandb
-rerun-sdk==0.26.2
+rerun-sdk==0.26.1
    # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
    # via placo
 rich==14.3.3
    # via typer
 robomimic==0.2.0
-    # via hf-libero
+    # via libero
 robosuite==1.4.0
-    # via hf-libero
+    # via libero
-rpds-py==0.30.0
+rpds-py==0.28.0
    # via
    #   jsonschema
    #   referencing
-safetensors==0.7.0
+safetensors==0.6.2
    # via
    #   accelerate
    #   diffusers
    #   lerobot
    #   peft
    #   timm
    #   transformers
 scikit-image==0.25.2
    # via
    #   gym-pusht
    #   lerobot
-scipy==1.17.1
+scipy==1.15.3
    # via
    #   dm-control
    #   lerobot
    #   metaworld
    #   robosuite
    #   scikit-image
-    #   torchdiffeq
+sentry-sdk==2.42.1
 sentry-sdk==2.54.0
    # via wandb
 shapely==2.1.2
    # via gym-pusht
 shellingham==1.5.4
    # via typer
 six==1.17.0
    # via
    #   pynput
    #   python-dateutil
    #   python-xlib
-smmap==5.0.3
+smmap==5.0.2
    # via gitdb
 sniffio==1.3.1
    # via anyio
 stack-data==0.6.3
    # via ipython
-starlette==0.52.1
+starlette==0.48.0
    # via fastapi
 sympy==1.14.0
    # via torch
-teleop==0.1.4
+teleop==0.1.2
    # via lerobot
 tensorboard==2.20.0
    # via robomimic
@@ -756,38 +730,46 @@ tensorboard-data-server==0.7.2
    # via tensorboard
 tensorboardx==2.6.4
    # via robomimic
-termcolor==3.3.0
+termcolor==3.1.0
    # via
    #   lerobot
    #   robomimic
 thop==0.1.1.post2209072238
-    # via hf-libero
+    # via libero
-tifffile==2026.3.3
+tifffile==2025.5.10
    # via scikit-image
-tokenizers==0.22.2
+timm==1.0.20
    # via lerobot
 tokenizers==0.22.1
    # via transformers
 toml==0.10.2
    # via draccus
-torch==2.10.0
+tomli==2.3.0
    # via
    #   cmeel
    #   coverage
    #   jupytext
    #   pytest
 torch==2.7.1
    # via
    #   accelerate
    #   flash-attn
    #   lerobot
    #   peft
    #   robomimic
    #   thop
-    #   torchdiffeq
+    #   timm
    #   torchvision
-torchcodec==0.10.0
+torchcodec==0.5
    # via lerobot
-torchdiffeq==0.2.5
+torchvision==0.22.1
    # via lerobot
 torchvision==0.25.0
    # via
    #   lerobot
    #   robomimic
-tornado==6.5.4
+    #   timm
 tornado==6.5.2
    # via meshcat
-tqdm==4.67.3
+tqdm==4.67.1
    # via
    #   datasets
    #   dm-control
@@ -801,29 +783,26 @@ traitlets==5.14.3
    #   jupyter-core
    #   matplotlib-inline
    #   nbformat
-transformers==5.3.0
+transformers==4.57.1
    # via
    #   hf-libero
    #   lerobot
    #   libero
    #   peft
 transforms3d==0.4.2
    # via teleop
-triton==3.6.0
+triton==3.3.1
    # via torch
 typer==0.24.1
    # via
    #   huggingface-hub
    #   transformers
 typing-extensions==4.15.0
    # via
    #   aiosignal
    #   anyio
    #   etils
-    #   faker
+    #   exceptiongroup
    #   fastapi
    #   gymnasium
    #   huggingface-hub
-    #   mypy
+    #   ipython
    #   multidict
    #   pydantic
    #   pydantic-core
    #   referencing
@@ -832,46 +811,46 @@ typing-extensions==4.15.0
    #   torch
    #   typing-inspect
    #   typing-inspection
    #   uvicorn
    #   virtualenv
    #   wandb
 typing-inspect==0.9.0
    # via draccus
 typing-inspection==0.4.2
-    # via
+    # via pydantic
-    #   fastapi
+tzdata==2025.2
    #   pydantic
 tzdata==2025.3
    # via pandas
 u-msgpack-python==2.8.0
    # via meshcat
-urllib3==2.6.3
+urllib3==2.5.0
    # via
    #   requests
    #   sentry-sdk
-uvicorn[standard]==0.41.0
+uvicorn[standard]==0.38.0
    # via teleop
 uvloop==0.22.1
    # via uvicorn
-virtualenv==21.1.0
+virtualenv==20.35.3
    # via pre-commit
-wandb==0.24.2
+wandb==0.21.4
    # via
    #   hf-libero
    #   lerobot
    #   libero
 watchfiles==1.1.1
    # via uvicorn
-wcwidth==0.6.0
+wcwidth==0.2.14
    # via prompt-toolkit
 websocket-client==1.9.0
    # via teleop
-websockets==16.0
+websockets==15.0.1
    # via uvicorn
-werkzeug==3.1.6
+werkzeug==3.1.3
    # via tensorboard
-wrapt==2.1.2
+wrapt==2.0.0
    # via dm-tree
 xxhash==3.6.0
    # via datasets
-yarl==1.23.0
+yarl==1.22.0
    # via aiohttp
 zipp==3.23.0
    # via
@@ -1,9 +1,9 @@
 # requirements.in
-# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.3.1 25D2128 arm64).
+# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.0.1 25A362 arm64).
-# Darwin MacBook-Pro.local 25.3.0 Darwin Kernel Version 25.3.0: Wed Jan 28 20:54:55 PST 2026; root:xnu-12377.91.3~2/RELEASE_ARM64_T8132 arm64
+# Darwin MacBook-Pro.local 25.0.0 Darwin Kernel Version 25.0.0: Wed Sep 17 21:42:08 PDT 2025; root:xnu-12377.1.9~141/RELEASE_ARM64_T8132 arm64
-# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.4 LTS x86_64).
+# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.3 LTS x86_64).
-# Linux lerobot-linux 6.17.0-14-generic #14~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jan 15 15:52:10 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
+# Linux mlerobot-linux 6.14.0-33-generic #33~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Sep 19 17:02:30 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
 -e .[all]
@@ -63,9 +63,9 @@ from lerobot.transport import (
    services_pb2_grpc,  # type: ignore
 )
 from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks
 from lerobot.utils.import_utils import register_third_party_plugins
 from .configs import RobotClientConfig
 from .constants import SUPPORTED_ROBOTS
 from .helpers import (
    Action,
    FPSTracker,
@@ -485,9 +485,8 @@ class RobotClient:
 def async_client(cfg: RobotClientConfig):
    logging.info(pformat(asdict(cfg)))
-    # TODO: Assert if checking robot support is still needed with the plugin system
+    if cfg.robot.type not in SUPPORTED_ROBOTS:
-    # if cfg.robot.type not in SUPPORTED_ROBOTS:
+        raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
    #     raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
    client = RobotClient(cfg)
@@ -513,5 +512,4 @@ def async_client(cfg: RobotClientConfig):
 if __name__ == "__main__":
    register_third_party_plugins()
    async_client()  # run the client
@@ -181,7 +181,7 @@ class ZMQCamera(Camera):
        try:
            message = self.socket.recv_string()
        except Exception as e:
-            # zmq is lazy-imported in connect(), so check by name to avoid a top-level import
+            # Check for ZMQ timeout (EAGAIN/Again) without requiring global zmq import
            if type(e).__name__ == "Again":
                raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e
            raise
@@ -23,7 +23,6 @@ import base64
 import contextlib
 import json
 import logging
 import threading
 import time
 from collections import deque
@@ -43,57 +42,10 @@ def encode_image(image: np.ndarray, quality: int = 80) -> str:
    return base64.b64encode(buffer).decode("utf-8")
 class CameraCaptureThread:
    """Background thread that continuously captures and encodes frames from a camera."""
    def __init__(self, camera: OpenCVCamera, name: str):
        self.camera = camera
        self.name = name
        self.latest_encoded: str | None = None  # Pre-encoded JPEG as base64
        self.latest_timestamp: float = 0.0
        self.frame_lock = threading.Lock()
        self.running = False
        self.thread: threading.Thread | None = None
    def start(self):
        """Start the capture thread."""
        self.running = True
        self.thread = threading.Thread(target=self._capture_loop, daemon=True)
        self.thread.start()
    def stop(self):
        """Stop the capture thread."""
        self.running = False
        if self.thread:
            self.thread.join(timeout=1.0)
    def _capture_loop(self):
        """Continuously capture and encode frames at the camera's native rate."""
        while self.running:
            try:
                frame = self.camera.read()  # Blocks at camera's native rate
                timestamp = time.time()
                # Encode immediately in capture thread (this is the slow part)
                encoded = encode_image(frame)
                with self.frame_lock:
                    self.latest_encoded = encoded
                    self.latest_timestamp = timestamp
            except Exception as e:
                logger.warning(f"Camera {self.name} capture error: {e}")
                time.sleep(0.01)
    def get_latest(self) -> tuple[str | None, float]:
        """Get the latest encoded frame and its timestamp."""
        with self.frame_lock:
            return self.latest_encoded, self.latest_timestamp
 class ImageServer:
    def __init__(self, config: dict, port: int = 5555):
        # fps controls the publish loop rate (how often frames are sent over ZMQ), not the camera capture rate
        self.fps = config.get("fps", 30)
        self.cameras: dict[str, OpenCVCamera] = {}
        self.capture_threads: dict[str, CameraCaptureThread] = {}
        for name, cfg in config.get("cameras", {}).items():
            shape = cfg.get("shape", [480, 640])
@@ -109,10 +61,6 @@ class ImageServer:
            self.cameras[name] = camera
            logger.info(f"Camera {name}: {shape[1]}x{shape[0]}")
            # Create capture thread for this camera
            capture_thread = CameraCaptureThread(camera, name)
            self.capture_threads[name] = capture_thread
        # ZMQ PUB socket
        self.context = zmq.Context()
        self.socket = self.context.socket(zmq.PUB)
@@ -125,18 +73,6 @@ class ImageServer:
    def run(self):
        frame_count = 0
        frame_times = deque(maxlen=60)
        last_published_ts: dict[str, float] = {}
        # Start all capture threads
        for capture_thread in self.capture_threads.values():
            capture_thread.start()
        # Wait for first frames to be captured and encoded
        logger.info("Waiting for cameras to start capturing...")
        for name, capture_thread in self.capture_threads.items():
            while capture_thread.get_latest()[0] is None:
                time.sleep(0.01)
            logger.info(f"Camera {name} ready (capture + encode in background)")
        try:
            while True:
@@ -144,12 +80,10 @@ class ImageServer:
                # Build message
                message = {"timestamps": {}, "images": {}}
-                for name, capture_thread in self.capture_threads.items():
+                for name, cam in self.cameras.items():
-                    encoded, timestamp = capture_thread.get_latest()
+                    frame = cam.read()  # Returns RGB
-                    if encoded is not None and timestamp > last_published_ts.get(name, 0.0):
+                    message["timestamps"][name] = time.time()
-                        message["timestamps"][name] = timestamp
+                    message["images"][name] = encode_image(frame)
                        message["images"][name] = encoded
                        last_published_ts[name] = timestamp
                # Send as JSON string (suppress if buffer full)
                with contextlib.suppress(zmq.Again):
@@ -168,8 +102,6 @@ class ImageServer:
        except KeyboardInterrupt:
            pass
        finally:
            for capture_thread in self.capture_threads.values():
                capture_thread.stop()
            for cam in self.cameras.values():
                cam.disconnect()
            self.socket.close()
@@ -27,7 +27,7 @@ class DatasetConfig:
    # "dataset_index" into the returned item. The index mapping is made according to the order in which the
    # datasets are provided.
    repo_id: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
    root: str | None = None
    episodes: list[int] | None = None
    image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
@@ -50,9 +50,6 @@ class TrainPipelineConfig(HubMixin):
    # `seed` is used for training (eg: model initialization, dataset shuffling)
    # AND for the evaluation environments.
    seed: int | None = 1000
    # Set to True to use deterministic cuDNN algorithms for reproducibility.
    # This disables cudnn.benchmark and may reduce training speed by ~10-20%.
    cudnn_deterministic: bool = False
    # Number of workers for the dataloader.
    num_workers: int = 4
    batch_size: int = 8
@@ -289,9 +289,7 @@ def aggregate_datasets(
    logging.info("Find all tasks")
    unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
-    dst_meta.tasks = pd.DataFrame(
+    dst_meta.tasks = pd.DataFrame({"task_index": range(len(unique_tasks))}, index=unique_tasks)
        {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
    )
    meta_idx = {"chunk": 0, "file": 0}
    data_idx = {"chunk": 0, "file": 0}
@@ -47,6 +47,7 @@ from lerobot.datasets.utils import (
    DEFAULT_EPISODES_PATH,
    get_parquet_file_size_in_mb,
    load_episodes,
    load_info,
    update_chunk_file_indices,
    write_info,
    write_stats,
@@ -89,8 +90,8 @@ def delete_episodes(
    Args:
        dataset: The source LeRobotDataset.
        episode_indices: List of episode indices to delete.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
    """
    if not episode_indices:
        raise ValueError("No episodes to delete")
@@ -152,7 +153,7 @@ def split_dataset(
        dataset: The source LeRobotDataset to split.
        splits: Either a dict mapping split names to episode indices, or a dict mapping
                split names to fractions (must sum to <= 1.0).
-        output_dir: Root directory where the split datasets will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
+        output_dir: Base directory for output datasets. If None, uses default location.
    Examples:
      Split by specific episodes
@@ -243,8 +244,8 @@ def merge_datasets(
    Args:
        datasets: List of LeRobotDatasets to merge.
-        output_repo_id: Merged dataset identifier.
+        output_repo_id: Repository ID for the merged dataset.
-        output_dir: Root directory where the merged dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/output_repo_id.
+        output_dir: Directory to save the merged dataset. If None, uses default location.
    """
    if not datasets:
        raise ValueError("No datasets to merge")
@@ -288,8 +289,8 @@ def modify_features(
        dataset: The source LeRobotDataset.
        add_features: Optional dict mapping feature names to (feature_values, feature_info) tuples.
        remove_features: Optional feature name(s) to remove. Can be a single string or list.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
    Returns:
        New dataset with features modified.
@@ -390,8 +391,8 @@ def add_features(
    Args:
        dataset: The source LeRobotDataset.
        features: Dictionary mapping feature names to (feature_values, feature_info) tuples.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
    Returns:
        New dataset with all features added.
@@ -427,8 +428,8 @@ def remove_feature(
    Args:
        dataset: The source LeRobotDataset.
        feature_names: Name(s) of features to remove. Can be a single string or list.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
    Returns:
        New dataset with features removed.
@@ -1475,9 +1476,7 @@ def modify_tasks(
    # Collect all unique tasks and create new task mapping
    unique_tasks = sorted(set(episode_to_task.values()))
-    new_task_df = pd.DataFrame(
+    new_task_df = pd.DataFrame({"task_index": list(range(len(unique_tasks)))}, index=unique_tasks)
        {"task_index": list(range(len(unique_tasks)))}, index=pd.Index(unique_tasks, name="task")
    )
    task_to_index = {task: idx for idx, task in enumerate(unique_tasks)}
    logging.info(f"Modifying tasks in {dataset.repo_id}")
@@ -1531,7 +1530,7 @@ def modify_tasks(
 def convert_image_to_video_dataset(
    dataset: LeRobotDataset,
-    output_dir: Path | None = None,
+    output_dir: Path,
    repo_id: str | None = None,
    vcodec: str = "libsvtav1",
    pix_fmt: str = "yuv420p",
@@ -1550,8 +1549,8 @@ def convert_image_to_video_dataset(
    Args:
        dataset: The source LeRobot dataset with images
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        output_dir: Directory to save the new video dataset
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        repo_id: Repository ID for the new dataset (default: original_id + "_video")
        vcodec: Video codec (default: libsvtav1)
        pix_fmt: Pixel format (default: yuv420p)
        g: Group of pictures size (default: 2)
@@ -1602,7 +1601,6 @@ def convert_image_to_video_dataset(
            # Video info will be updated after episodes are encoded
    # Create new metadata for video dataset
    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
    new_meta = LeRobotDatasetMetadata.create(
        repo_id=repo_id,
        fps=dataset.meta.fps,
@@ -1777,3 +1775,296 @@ def convert_image_to_video_dataset(
    # Return new dataset
    return LeRobotDataset(repo_id=repo_id, root=output_dir)
 def trim_episodes_by_frames(
    dataset: LeRobotDataset,
    episode_frames_to_keep: dict[int, list[int]],
    output_dir: str | Path | None = None,
    repo_id: str | None = None,
 ) -> LeRobotDataset:
    """Trim multiple episodes to keep only specific frames.
    This function creates a new dataset where the specified episodes contain only
    the frames at the given indices. All other episodes are copied as-is.
    Args:
        dataset: The source LeRobotDataset.
        episode_frames_to_keep: Dict mapping episode indices to lists of global frame indices to keep.
        output_dir: Directory to save the new dataset. If None, uses default location.
        repo_id: Repository ID for the new dataset. If None, appends "_trimmed" to original.
    Returns:
        A new LeRobotDataset with the trimmed episodes.
    """
    if not episode_frames_to_keep:
        raise ValueError("No episodes to trim")
    for ep_idx in episode_frames_to_keep:
        if ep_idx >= dataset.meta.total_episodes:
            raise ValueError(f"Episode {ep_idx} does not exist")
        if not episode_frames_to_keep[ep_idx]:
            raise ValueError(f"No frames to keep for episode {ep_idx}")
    if repo_id is None:
        repo_id = f"{dataset.repo_id}_trimmed"
    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
    total_trimmed = sum(len(frames) for frames in episode_frames_to_keep.values())
    logging.info(f"Trimming {len(episode_frames_to_keep)} episodes, keeping {total_trimmed} frames total")
    # Create new metadata
    new_meta = LeRobotDatasetMetadata.create(
        repo_id=repo_id,
        fps=dataset.meta.fps,
        features=dataset.meta.features,
        robot_type=dataset.meta.robot_type,
        root=output_dir,
        use_videos=len(dataset.meta.video_keys) > 0,
    )
    # Build set of all frames to keep (for episodes being trimmed)
    # and compute new frame counts per episode
    all_keep_frames: set[int] = set()
    trimmed_frame_counts: dict[int, int] = {}
    for ep_idx, frames in episode_frames_to_keep.items():
        all_keep_frames.update(frames)
        trimmed_frame_counts[ep_idx] = len(frames)
    # Copy and filter data
    _copy_and_reindex_data_with_multi_frame_filter(
        dataset, new_meta, episode_frames_to_keep, all_keep_frames
    )
    # Handle videos if present
    if dataset.meta.video_keys:
        _copy_and_reindex_videos_with_multi_frame_filter(
            dataset, new_meta, episode_frames_to_keep
        )
    # Copy episode metadata
    _copy_and_reindex_episodes_metadata_for_multi_trim(
        dataset, new_meta, trimmed_frame_counts
    )
    logging.info(f"Created trimmed dataset with {new_meta.total_frames} frames at {output_dir}")
    # Return the metadata instead of trying to load as LeRobotDataset
    # This avoids Hub validation issues when the repo doesn't exist yet
    return new_meta
 # Keep old function for backward compatibility
 def trim_episode_by_frames(
    dataset: LeRobotDataset,
    episode_index: int,
    keep_frame_indices: list[int],
    output_dir: str | Path | None = None,
    repo_id: str | None = None,
 ) -> LeRobotDataset:
    """Trim a single episode. Wrapper around trim_episodes_by_frames."""
    return trim_episodes_by_frames(
        dataset,
        episode_frames_to_keep={episode_index: keep_frame_indices},
        output_dir=output_dir,
        repo_id=repo_id,
    )
 def _copy_and_reindex_data_with_multi_frame_filter(
    src_dataset: LeRobotDataset,
    dst_meta: LeRobotDatasetMetadata,
    episode_frames_to_keep: dict[int, list[int]],
    all_keep_frames: set[int],
 ) -> None:
    """Copy data files with frame-level filtering for multiple episodes."""
    if src_dataset.meta.episodes is None:
        src_dataset.meta.episodes = load_episodes(src_dataset.meta.root)
    # Copy tasks
    if dst_meta.tasks is None and src_dataset.meta.tasks is not None:
        # Tasks are stored with task string as index
        dst_meta.save_episode_tasks(list(src_dataset.meta.tasks.index))
    # Get all parquet files
    data_dir = src_dataset.root / "data"
    parquet_files = sorted(data_dir.glob("chunk-*/file-*.parquet"))
    trim_episode_set = set(episode_frames_to_keep.keys())
    global_index = 0
    for parquet_path in tqdm(parquet_files, desc="Processing data files"):
        df = pd.read_parquet(parquet_path)
        # Filter: keep all frames from non-trimmed episodes, 
        # and only specified frames from trimmed episodes
        mask = (~df["episode_index"].isin(trim_episode_set)) | (df["index"].isin(all_keep_frames))
        df = df[mask].copy().reset_index(drop=True)
        if len(df) == 0:
            continue
        # Reindex
        df["index"] = range(global_index, global_index + len(df))
        # Recalculate frame_index within each episode
        for ep_idx in df["episode_index"].unique():
            ep_mask = df["episode_index"] == ep_idx
            df.loc[ep_mask, "frame_index"] = range(ep_mask.sum())
        # Recalculate timestamps based on frame_index and fps
        df["timestamp"] = df["frame_index"] / src_dataset.meta.fps
        # Determine output path (keep same structure)
        rel_path = parquet_path.relative_to(src_dataset.root)
        dst_path = dst_meta.root / rel_path
        dst_path.parent.mkdir(parents=True, exist_ok=True)
        _write_parquet(df, dst_path, dst_meta)
        global_index += len(df)
 def _copy_and_reindex_videos_with_multi_frame_filter(
    src_dataset: LeRobotDataset,
    dst_meta: LeRobotDatasetMetadata,
    episode_frames_to_keep: dict[int, list[int]],
 ) -> None:
    """Copy video files for trimmed dataset.
    In v3.0 datasets, multiple episodes are concatenated into single video files.
    Each episode has from_timestamp/to_timestamp indicating its portion of the video.
    For trimming, we copy the original video files as-is and update the metadata
    timestamps in _copy_and_reindex_episodes_metadata_for_multi_trim.
    """
    for video_key in src_dataset.meta.video_keys:
        video_dir = src_dataset.root / "videos" / video_key
        dst_video_dir = dst_meta.root / "videos" / video_key
        if not video_dir.exists():
            logging.warning(f"Video directory not found: {video_dir}")
            continue
        # Copy all video files (they contain concatenated episodes)
        # The metadata timestamps will handle which portions to use
        copied_files = set()
        for chunk_dir in video_dir.glob("chunk-*"):
            dst_chunk_dir = dst_video_dir / chunk_dir.name
            dst_chunk_dir.mkdir(parents=True, exist_ok=True)
            for video_file in chunk_dir.glob("*.mp4"):
                if video_file.name not in copied_files:
                    dst_path = dst_chunk_dir / video_file.name
                    if not dst_path.exists():
                        shutil.copy(video_file, dst_path)
                        copied_files.add(video_file.name)
        logging.info(f"Copied {len(copied_files)} video files for {video_key}")
 def _trim_video_frames(
    src_path: Path,
    dst_path: Path,
    keep_frame_indices: list[int],
    fps: float,
    episode_start_idx: int,
 ) -> None:
    """Trim a video to keep only specific frames using ffmpeg."""
    import subprocess
    # Convert global indices to local indices within the episode
    local_indices = sorted([idx - episode_start_idx for idx in keep_frame_indices])
    if not local_indices:
        logging.warning(f"No frames to keep for video {src_path}")
        return
    # Calculate start and end times
    start_frame = local_indices[0]
    end_frame = local_indices[-1]
    start_time = start_frame / fps
    duration = (end_frame - start_frame + 1) / fps
    # Use ffmpeg to trim
    cmd = [
        "ffmpeg", "-y",
        "-ss", str(start_time),
        "-i", str(src_path),
        "-t", str(duration),
        "-c", "copy",  # Fast copy without re-encoding
        str(dst_path)
    ]
    try:
        subprocess.run(cmd, check=True, capture_output=True)
    except subprocess.CalledProcessError as e:
        logging.error(f"Failed to trim video: {e.stderr.decode()}")
        # Fallback: copy the whole video
        shutil.copy(src_path, dst_path)
 def _copy_and_reindex_episodes_metadata_for_multi_trim(
    src_dataset: LeRobotDataset,
    dst_meta: LeRobotDatasetMetadata,
    trimmed_frame_counts: dict[int, int],
 ) -> None:
    """Copy and update episode metadata for trimmed dataset."""
    if src_dataset.meta.episodes is None:
        src_dataset.meta.episodes = load_episodes(src_dataset.meta.root)
    # Calculate new frame counts and indices
    episodes_data = []
    global_idx = 0
    for old_ep_idx in range(src_dataset.meta.total_episodes):
        src_ep = src_dataset.meta.episodes[old_ep_idx]
        if old_ep_idx in trimmed_frame_counts:
            ep_length = trimmed_frame_counts[old_ep_idx]
        else:
            ep_length = src_ep["length"]
        ep_data = {
            "episode_index": old_ep_idx,
            "tasks": src_ep["tasks"],
            "length": ep_length,
            "data/chunk_index": src_ep["data/chunk_index"],
            "data/file_index": src_ep["data/file_index"],
            "dataset_from_index": global_idx,
            "dataset_to_index": global_idx + ep_length,
        }
        # Copy video metadata - preserve timestamps for concatenated videos
        for video_key in src_dataset.meta.video_keys:
            ep_data[f"videos/{video_key}/chunk_index"] = src_ep[f"videos/{video_key}/chunk_index"]
            ep_data[f"videos/{video_key}/file_index"] = src_ep[f"videos/{video_key}/file_index"]
            # Keep original from_timestamp (start position in concatenated video)
            orig_from_ts = src_ep[f"videos/{video_key}/from_timestamp"]
            ep_data[f"videos/{video_key}/from_timestamp"] = orig_from_ts
            # For trimmed episodes, update to_timestamp based on new length
            # For non-trimmed episodes, keep original to_timestamp
            if old_ep_idx in trimmed_frame_counts:
                ep_data[f"videos/{video_key}/to_timestamp"] = orig_from_ts + (ep_length / src_dataset.meta.fps)
            else:
                ep_data[f"videos/{video_key}/to_timestamp"] = src_ep[f"videos/{video_key}/to_timestamp"]
        ep_data["meta/episodes/chunk_index"] = 0
        ep_data["meta/episodes/file_index"] = 0
        episodes_data.append(ep_data)
        global_idx += ep_length
    # Save episodes metadata
    df = pd.DataFrame(episodes_data)
    episodes_path = dst_meta.root / DEFAULT_EPISODES_PATH.format(chunk_index=0, file_index=0)
    episodes_path.parent.mkdir(parents=True, exist_ok=True)
    df.to_parquet(episodes_path)
    # Update info.json
    info = load_info(src_dataset.root)
    info["total_episodes"] = len(episodes_data)
    info["total_frames"] = global_idx
    write_info(info, dst_meta.root)
@@ -314,7 +314,7 @@ class LeRobotDatasetMetadata:
        if self.tasks is None:
            new_tasks = tasks
            task_indices = range(len(tasks))
-            self.tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(tasks, name="task"))
+            self.tasks = pd.DataFrame({"task_index": task_indices}, index=tasks)
        else:
            new_tasks = [task for task in tasks if task not in self.tasks.index]
            new_task_indices = range(len(self.tasks), len(self.tasks) + len(new_tasks))
@@ -664,11 +664,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
        for the README).
        Args:
-            repo_id (str): This is the repo id that will be used to fetch the dataset.
+            repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset
-            root (Path | None, optional): Local directory where the dataset will be downloaded and
+                will be stored under root/repo_id.
-                stored. If set, all dataset files will be stored directly under this path. If not set, the
+            root (Path | None, optional): Local directory to use for downloading/writing files. You can also
-                dataset files will be stored under $HF_LEROBOT_HOME/repo_id (configurable via the
+                set the HF_LEROBOT_HOME environment variable to point to a different location. Defaults to
-                HF_LEROBOT_HOME environment variable).
+                '~/.cache/huggingface/lerobot'.
            episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                their episode_index in this list. Defaults to None.
            image_transforms (Callable | None, optional): You can pass standard v2 image transforms from
@@ -1771,12 +1771,11 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
            )
        for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
            extra_keys = set(ds.features).difference(intersection_features)
-            if extra_keys:
+            logging.warning(
-                logging.warning(
+                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
-                    f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                "other datasets."
-                    "other datasets."
+            )
-                )
+            self.disabled_features.update(extra_keys)
                self.disabled_features.update(extra_keys)
        self.image_transforms = image_transforms
        self.delta_timestamps = delta_timestamps
@@ -21,7 +21,7 @@ from collections import deque
 from collections.abc import Iterable, Iterator
 from pathlib import Path
 from pprint import pformat
-from typing import Any
+from typing import Any, Generic, TypeVar
 import datasets
 import numpy as np
@@ -78,6 +78,8 @@ DEFAULT_FEATURES = {
    "task_index": {"dtype": "int64", "shape": (1,), "names": None},
 }
 T = TypeVar("T")
 def get_parquet_file_size_in_mb(parquet_path: str | Path) -> float:
    metadata = pq.read_metadata(parquet_path)
@@ -339,7 +341,6 @@ def write_tasks(tasks: pandas.DataFrame, local_dir: Path) -> None:
 def load_tasks(local_dir: Path) -> pandas.DataFrame:
    tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
    tasks.index.name = "task"
    return tasks
@@ -1232,7 +1233,7 @@ class LookAheadError(Exception):
    pass
-class Backtrackable[T]:
+class Backtrackable(Generic[T]):
    """
    Wrap any iterator/iterable so you can step back up to `history` items
    and look ahead up to `lookahead` items.
@@ -36,11 +36,8 @@ Convert a local dataset (works in place):
 ```bash
 python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
    --repo-id=lerobot/pusht \
-    --root=/path/to/local/dataset/directory \
+    --root=/path/to/local/dataset/directory
    --push-to-hub=false
 N.B. Path semantics (v2): --root is the exact dataset folder containing
 meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
 ```
 """
@@ -108,7 +105,7 @@ episodes.jsonl
 {"episode_index": 1, "tasks": ["Put the blue block in the green bowl"], "length": 266}
 NEW
-meta/episodes/chunk-000/file_000.parquet
+meta/episodes/chunk-000/episodes_000.parquet
 episode_index | video_chunk_index | video_file_index | data_chunk_index | data_file_index | tasks | length
 -------------------------
 OLD
@@ -116,16 +113,15 @@ tasks.jsonl
 {"task_index": 1, "task": "Put the blue block in the green bowl"}
 NEW
-meta/tasks.parquet
+meta/tasks/chunk-000/file_000.parquet
 task_index | task
 -------------------------
 OLD
 episodes_stats.jsonl
 {"episode_index": 1, "stats": {"feature_name": {"min": ..., "max": ..., "mean": ..., "std": ..., "count": ...}}}
 NEW
-meta/episodes/chunk-000/file_000.parquet
+meta/episodes_stats/chunk-000/file_000.parquet
-episode_index | feature_name/min | feature_name/max | feature_name/mean | feature_name/std | feature_name/count
+episode_index | mean | std | min | max
 -------------------------
 UPDATE
 meta/info.json
@@ -174,7 +170,7 @@ def convert_tasks(root, new_root):
    tasks, _ = legacy_load_tasks(root)
    task_indices = tasks.keys()
    task_strings = tasks.values()
-    df_tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(task_strings, name="task"))
+    df_tasks = pd.DataFrame({"task_index": task_indices}, index=task_strings)
    write_tasks(df_tasks, new_root)
@@ -205,6 +201,7 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
    image_keys = get_image_keys(root)
    ep_idx = 0
    chunk_idx = 0
    file_idx = 0
    size_in_mb = 0
@@ -214,23 +211,9 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
    logging.info(f"Converting data files from {len(ep_paths)} episodes")
-    for ep_idx, ep_path in enumerate(tqdm.tqdm(ep_paths, desc="convert data files")):
+    for ep_path in tqdm.tqdm(ep_paths, desc="convert data files"):
        ep_size_in_mb = get_parquet_file_size_in_mb(ep_path)
        ep_num_frames = get_parquet_num_frames(ep_path)
        # Check if we need to start a new file BEFORE creating metadata
        if size_in_mb + ep_size_in_mb >= data_file_size_in_mb and len(paths_to_cat) > 0:
            # Write the accumulated data files
            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
            # Move to next file
            chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
            # Reset for the next file
            size_in_mb = 0
            paths_to_cat = []
        # Now create metadata with correct chunk/file indices
        ep_metadata = {
            "episode_index": ep_idx,
            "data/chunk_index": chunk_idx,
@@ -241,7 +224,20 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
        size_in_mb += ep_size_in_mb
        num_frames += ep_num_frames
        episodes_metadata.append(ep_metadata)
-        paths_to_cat.append(ep_path)
+        ep_idx += 1
        if size_in_mb < data_file_size_in_mb:
            paths_to_cat.append(ep_path)
            continue
        if paths_to_cat:
            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
        # Reset for the next file
        size_in_mb = ep_size_in_mb
        paths_to_cat = [ep_path]
        chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
    # Write remaining data if any
    if paths_to_cat:
@@ -473,7 +469,7 @@ def convert_dataset(
    # Set root based on whether local dataset path is provided
    use_local_dataset = False
-    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root)
+    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root) / repo_id
    if root.exists():
        validate_local_dataset_version(root)
        use_local_dataset = True
@@ -557,7 +553,7 @@ if __name__ == "__main__":
        "--root",
        type=str,
        default=None,
-        help="Local directory to use for downloading/writing the dataset. Defaults to $HF_LEROBOT_HOME/repo_id.",
+        help="Local directory to use for downloading/writing the dataset.",
    )
    parser.add_argument(
        "--push-to-hub",
@@ -29,7 +29,7 @@ from dataclasses import dataclass
 from enum import Enum
 from functools import cached_property
 from pprint import pformat
-from typing import Protocol
+from typing import Protocol, TypeAlias
 import serial
 from deepdiff import DeepDiff
@@ -38,8 +38,8 @@ from tqdm import tqdm
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.utils import enter_pressed, move_cursor_up
-type NameOrID = str | int
+NameOrID: TypeAlias = str | int
-type Value = int | float
+Value: TypeAlias = int | float
 logger = logging.getLogger(__name__)
@@ -1277,4 +1277,4 @@ class SerialMotorsBus(MotorsBusBase):
 # Backward compatibility alias
-MotorsBus = SerialMotorsBus
+MotorsBus: TypeAlias = SerialMotorsBus
@@ -55,16 +55,10 @@ class DiffusionConfig(PreTrainedConfig):
        normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
            a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
        vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
-        resize_shape: (H, W) shape to resize images to as a preprocessing step for the vision
+        crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
-            backbone. If None, no resizing is done and the original image resolution is used.
+            within the image size. If None, no cropping is done.
-        crop_ratio: Ratio in (0, 1] used to derive the crop size from resize_shape
+        crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval
-            (crop_h = int(resize_shape[0] * crop_ratio), likewise for width).
+            mode).
            Set to 1.0 to disable cropping. Only takes effect when resize_shape is not None.
        crop_shape: (H, W) shape to crop images to. When resize_shape is set and crop_ratio < 1.0,
            this is computed automatically. Can also be set directly for legacy configs that use
            crop-only (without resize). If None and no derivation applies, no cropping is done.
        crop_is_random: Whether the crop should be random at training time (it's always a center
            crop in eval mode).
        pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
            `None` means no pretrained weights.
        use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
@@ -120,9 +114,7 @@ class DiffusionConfig(PreTrainedConfig):
    # Architecture / modeling.
    # Vision backbone.
    vision_backbone: str = "resnet18"
-    resize_shape: tuple[int, int] | None = None
+    crop_shape: tuple[int, int] | None = (84, 84)
    crop_ratio: float = 1.0
    crop_shape: tuple[int, int] | None = None
    crop_is_random: bool = True
    pretrained_backbone_weights: str | None = None
    use_group_norm: bool = True
@@ -183,25 +175,6 @@ class DiffusionConfig(PreTrainedConfig):
                f"Got {self.noise_scheduler_type}."
            )
        if self.resize_shape is not None and (
            len(self.resize_shape) != 2 or any(d <= 0 for d in self.resize_shape)
        ):
            raise ValueError(f"`resize_shape` must be a pair of positive integers. Got {self.resize_shape}.")
        if not (0 < self.crop_ratio <= 1.0):
            raise ValueError(f"`crop_ratio` must be in (0, 1]. Got {self.crop_ratio}.")
        if self.resize_shape is not None:
            if self.crop_ratio < 1.0:
                self.crop_shape = (
                    int(self.resize_shape[0] * self.crop_ratio),
                    int(self.resize_shape[1] * self.crop_ratio),
                )
            else:
                # Explicitly disable cropping for resize+ratio path when crop_ratio == 1.0.
                self.crop_shape = None
        if self.crop_shape is not None and (self.crop_shape[0] <= 0 or self.crop_shape[1] <= 0):
            raise ValueError(f"`crop_shape` must have positive dimensions. Got {self.crop_shape}.")
        # Check that the horizon size and U-Net downsampling is compatible.
        # U-Net downsamples by 2 with each stage.
        downsampling_factor = 2 ** len(self.down_dims)
@@ -229,12 +202,13 @@ class DiffusionConfig(PreTrainedConfig):
        if len(self.image_features) == 0 and self.env_state_feature is None:
            raise ValueError("You must provide at least one image or the environment state among the inputs.")
-        if self.resize_shape is None and self.crop_shape is not None:
+        if self.crop_shape is not None:
            for key, image_ft in self.image_features.items():
                if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]:
                    raise ValueError(
-                        f"`crop_shape` should fit within the image shapes. Got {self.crop_shape} "
+                        f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} "
-                        f"for `crop_shape` and {image_ft.shape} for `{key}`."
+                        f"for `crop_shape` and {image_ft.shape} for "
                        f"`{key}`."
                    )
        # Check that all input images have the same shape.
@@ -454,18 +454,12 @@ class DiffusionRgbEncoder(nn.Module):
    def __init__(self, config: DiffusionConfig):
        super().__init__()
        # Set up optional preprocessing.
-        if config.resize_shape is not None:
+        if config.crop_shape is not None:
            self.resize = torchvision.transforms.Resize(config.resize_shape)
        else:
            self.resize = None
        crop_shape = config.crop_shape
        if crop_shape is not None:
            self.do_crop = True
            # Always use center crop for eval
-            self.center_crop = torchvision.transforms.CenterCrop(crop_shape)
+            self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape)
            if config.crop_is_random:
-                self.maybe_random_crop = torchvision.transforms.RandomCrop(crop_shape)
+                self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape)
            else:
                self.maybe_random_crop = self.center_crop
        else:
@@ -491,16 +485,13 @@ class DiffusionRgbEncoder(nn.Module):
        # Set up pooling and final layers.
        # Use a dry run to get the feature map shape.
-        # The dummy shape mirrors the runtime preprocessing order: resize -> crop.
+        # The dummy input should take the number of image channels from `config.image_features` and it should
        # use the height and width from `config.crop_shape` if it is provided, otherwise it should use the
        # height and width from `config.image_features`.
        # Note: we have a check in the config class to make sure all images have the same shape.
        images_shape = next(iter(config.image_features.values())).shape
-        if config.crop_shape is not None:
+        dummy_shape_h_w = config.crop_shape if config.crop_shape is not None else images_shape[1:]
            dummy_shape_h_w = config.crop_shape
        elif config.resize_shape is not None:
            dummy_shape_h_w = config.resize_shape
        else:
            dummy_shape_h_w = images_shape[1:]
        dummy_shape = (1, images_shape[0], *dummy_shape_h_w)
        feature_map_shape = get_output_shape(self.backbone, dummy_shape)[1:]
@@ -516,10 +507,7 @@ class DiffusionRgbEncoder(nn.Module):
        Returns:
            (B, D) image feature.
        """
-        # Preprocess: resize if configured, then crop if configured.
+        # Preprocess: maybe crop (if it was set up in the __init__).
        if self.resize is not None:
            x = self.resize(x)
        if self.do_crop:
            if self.training:  # noqa: SIM108
                x = self.maybe_random_crop(x)
@@ -18,9 +18,10 @@ from __future__ import annotations
 import importlib
 import logging
-from typing import Any, TypedDict, Unpack
+from typing import Any, TypedDict
 import torch
 from typing_extensions import Unpack
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import FeatureType
@@ -4,16 +4,17 @@
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
 from __future__ import annotations
 # copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
 from typing import Optional
 from transformers.image_processing_utils import (
    BatchFeature,
    get_patch_output_size,
 )
 from transformers.image_processing_utils_fast import (
    BaseImageProcessorFast,
-    ImagesKwargs,
+    DefaultFastImageProcessorKwargs,
    group_images_by_shape,
    reorder_images,
 )
@@ -76,7 +77,7 @@ def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> tor
    return img[:, top:bottom, left:right]
-class Eagle25VLFastImageProcessorKwargs(ImagesKwargs):
+class Eagle25VLFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
    max_dynamic_tiles: int | None
    min_dynamic_tiles: int | None
    use_thumbnail: bool | None
@@ -164,11 +165,11 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
    def _resize_for_patching(
        self,
-        image: torch.Tensor,
+        image: "torch.Tensor",
        target_resolution: tuple,
-        interpolation: F.InterpolationMode,
+        interpolation: "F.InterpolationMode",
        input_data_format: ChannelDimension,
-    ) -> torch.Tensor:
+    ) -> "torch.Tensor":
        """
        Resizes an image to a target resolution while maintaining aspect ratio.
@@ -218,8 +219,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
        return best_ratio
    def _pad_for_patching(
-        self, image: torch.Tensor, target_resolution: tuple, input_data_format: ChannelDimension
+        self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
-    ) -> torch.Tensor:
+    ) -> "torch.Tensor":
        """
        Pad an image to a target resolution while maintaining aspect ratio.
        """
@@ -235,15 +236,15 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
    def _get_image_patches(
        self,
-        image: torch.Tensor,
+        image: "torch.Tensor",
        min_num: int,
        max_num: int,
        size: tuple,
        tile_size: int,
        use_thumbnail: bool,
-        interpolation: F.InterpolationMode,
+        interpolation: "F.InterpolationMode",
        pad_during_tiling: bool,
-    ) -> list[torch.Tensor]:
+    ) -> list["torch.Tensor"]:
        image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
        orig_height, orig_width = image_size
        aspect_ratio = orig_width / orig_height
@@ -304,8 +305,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
    def _pad_for_batching(
        self,
-        pixel_values: list[torch.Tensor],
+        pixel_values: list["torch.Tensor"],
-    ) -> list[torch.Tensor]:
+    ) -> list["torch.Tensor"]:
        """
        Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
@@ -326,14 +327,14 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
    def _preprocess(
        self,
-        images: list[torch.Tensor],
+        images: list["torch.Tensor"],
        do_resize: bool,
        size: SizeDict,
        max_dynamic_tiles: int,
        min_dynamic_tiles: int,
        use_thumbnail: bool,
        pad_during_tiling: bool,
-        interpolation: F.InterpolationMode | None,
+        interpolation: Optional["F.InterpolationMode"],
        do_center_crop: bool,
        crop_size: SizeDict,
        do_rescale: bool,
@@ -15,16 +15,16 @@
 # limitations under the License.
 import builtins
 import copy
 import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
+from typing import TYPE_CHECKING, Literal, TypedDict
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
 from typing_extensions import Unpack
 from lerobot.utils.import_utils import _transformers_available
@@ -32,21 +32,13 @@ from lerobot.utils.import_utils import _transformers_available
 if TYPE_CHECKING or _transformers_available:
    from transformers.models.auto import CONFIG_MAPPING
    from transformers.models.gemma import modeling_gemma
-
+    from transformers.models.gemma.modeling_gemma import GemmaForCausalLM
-    from lerobot.policies.pi_gemma import (
+    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
        PaliGemmaForConditionalGenerationWithPiGemma,
        PiGemmaForCausalLM,
        _gated_residual,
        layernorm_forward,
    )
 else:
    CONFIG_MAPPING = None
    modeling_gemma = None
-    PiGemmaForCausalLM = None
+    GemmaForCausalLM = None
-    _gated_residual = None
+    PaliGemmaForConditionalGeneration = None
    layernorm_forward = None
    PaliGemmaForConditionalGenerationWithPiGemma = None
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0.configuration_pi0 import DEFAULT_IMAGE_SIZE, PI0Config
@@ -199,7 +191,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
    if images.dtype == torch.uint8:
        resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
    elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(0.0, 1.0)
+        resized_images = resized_images.clamp(-1.0, 1.0)
    else:
        raise ValueError(f"Unsupported image dtype: {images.dtype}")
@@ -210,7 +202,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
    pad_w1 = pad_w0 + remainder_w
    # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else 0.0
+    constant_value = 0 if images.dtype == torch.uint8 else -1.0
    padded_images = F.pad(
        resized_images,
        (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -229,14 +221,14 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
 def compute_layer_complete(
    layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
 ):
-    models = [paligemma.model.language_model, gemma_expert.model]
+    models = [paligemma.language_model, gemma_expert.model]
    query_states = []
    key_states = []
    value_states = []
    gates = []
    for i, hidden_states in enumerate(inputs_embeds):
        layer = models[i].layers[layer_idx]
-        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
+        hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i])  # noqa: PLW2901
        gates.append(gate)
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
@@ -262,10 +254,10 @@ def compute_layer_complete(
        query_states, key_states, cos, sin, unsqueeze_dim=1
    )
    batch_size = query_states.shape[0]
-    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
+    scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling
    # Attention computation
    att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.model.language_model.layers[layer_idx].self_attn,
+        paligemma.language_model.layers[layer_idx].self_attn,
        query_states,
        key_states,
        value_states,
@@ -273,7 +265,7 @@ def compute_layer_complete(
        scaling,
    )
    # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim
    att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
    # Process layer outputs
    outputs_embeds = []
@@ -285,15 +277,15 @@ def compute_layer_complete(
            att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
        out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos])
        # first residual
-        out_emb = _gated_residual(hidden_states, out_emb, gates[i])
+        out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i])  # noqa: SLF001
        after_first_residual = out_emb.clone()
-        out_emb, gate = layernorm_forward(layer.post_attention_layernorm, out_emb, adarms_cond[i])
+        out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i])
        # Convert to bfloat16 if the next layer (mlp) uses bfloat16
        if layer.mlp.up_proj.weight.dtype == torch.bfloat16:
            out_emb = out_emb.to(dtype=torch.bfloat16)
        out_emb = layer.mlp(out_emb)
        # second residual
-        out_emb = _gated_residual(after_first_residual, out_emb, gate)
+        out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate)  # noqa: SLF001
        outputs_embeds.append(out_emb)
        start_pos = end_pos
    return outputs_embeds
@@ -366,7 +358,7 @@ class PaliGemmaWithExpertModel(
        vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
        vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
        vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.torch_dtype = "float32"
        vlm_config_hf.text_config.vocab_size = 257152
        vlm_config_hf.text_config.use_adarms = use_adarms[0]
        vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
@@ -374,7 +366,7 @@ class PaliGemmaWithExpertModel(
        vlm_config_hf.vision_config.intermediate_size = 4304
        vlm_config_hf.vision_config.projection_dim = 2048
        vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.dtype = "float32"
+        vlm_config_hf.vision_config.torch_dtype = "float32"
        action_expert_config_hf = CONFIG_MAPPING["gemma"](
            head_dim=action_expert_config.head_dim,
@@ -385,13 +377,13 @@ class PaliGemmaWithExpertModel(
            num_key_value_heads=action_expert_config.num_kv_heads,
            vocab_size=257152,
            hidden_activation="gelu_pytorch_tanh",
-            dtype="float32",
+            torch_dtype="float32",
            use_adarms=use_adarms[1],
            adarms_cond_dim=action_expert_config.width if use_adarms[1] else None,
        )
-        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
+        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
-        self.gemma_expert = PiGemmaForCausalLM(config=action_expert_config_hf)
+        self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf)
        self.gemma_expert.model.embed_tokens = None
        self.to_bfloat16_for_selected_params(precision)
@@ -406,11 +398,10 @@ class PaliGemmaWithExpertModel(
        else:
            raise ValueError(f"Invalid precision: {precision}")
        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
        # "same dtype" error). Align with PI05.
        params_to_keep_float32 = [
-            "vision_tower",
+            "vision_tower.vision_model.embeddings.patch_embedding.weight",
-            "multi_modal_projector",
+            "vision_tower.vision_model.embeddings.patch_embedding.bias",
            "vision_tower.vision_model.embeddings.position_embedding.weight",
            "input_layernorm",
            "post_attention_layernorm",
            "model.norm",
@@ -422,8 +413,8 @@ class PaliGemmaWithExpertModel(
    def _set_requires_grad(self):
        if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
+            self.paligemma.vision_tower.eval()
-            for param in self.paligemma.model.vision_tower.parameters():
+            for param in self.paligemma.vision_tower.parameters():
                param.requires_grad = False
        if self.train_expert_only:
            self.paligemma.eval()
@@ -433,23 +424,15 @@ class PaliGemmaWithExpertModel(
    def train(self, mode: bool = True):
        super().train(mode)
        if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
+            self.paligemma.vision_tower.eval()
        if self.train_expert_only:
            self.paligemma.eval()
    def embed_image(self, image: torch.Tensor):
-        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32). Align with PI05.
+        return self.paligemma.model.get_image_features(image)
        out_dtype = image.dtype
        if image.dtype != torch.float32:
            image = image.to(torch.float32)
        image_outputs = self.paligemma.model.get_image_features(image)
        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
        if features.dtype != out_dtype:
            features = features.to(out_dtype)
        return features
    def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.language_model.embed_tokens(tokens)
    def forward(
        self,
@@ -463,7 +446,7 @@ class PaliGemmaWithExpertModel(
        if adarms_cond is None:
            adarms_cond = [None, None]
        if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.model.language_model.forward(
+            prefix_output = self.paligemma.language_model.forward(
                inputs_embeds=inputs_embeds[0],
                attention_mask=attention_mask,
                position_ids=position_ids,
@@ -487,7 +470,7 @@ class PaliGemmaWithExpertModel(
            prefix_output = None
            prefix_past_key_values = None
        else:
-            models = [self.paligemma.model.language_model, self.gemma_expert.model]
+            models = [self.paligemma.language_model, self.gemma_expert.model]
            num_layers = self.paligemma.config.text_config.num_hidden_layers
            # Check if gradient checkpointing is enabled for any of the models
@@ -527,7 +510,7 @@ class PaliGemmaWithExpertModel(
            def compute_final_norms(inputs_embeds, adarms_cond):
                outputs_embeds = []
                for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
+                    out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i])
                    outputs_embeds.append(out_emb)
                return outputs_embeds
@@ -593,19 +576,29 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
            # Also compile the main forward pass used during training
            self.forward = torch.compile(self.forward, mode=config.compile_mode)
        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
        try:
            from transformers.models.siglip import check
            if not check.check_whether_transformers_replace_is_installed_correctly():
                raise ValueError(msg)
        except ImportError:
            raise ValueError(msg) from None
    def gradient_checkpointing_enable(self):
        """Enable gradient checkpointing for memory optimization."""
        self.gradient_checkpointing_enabled = True
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True
        self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True
        logging.info("Enabled gradient checkpointing for PI0Pytorch model")
    def gradient_checkpointing_disable(self):
        """Disable gradient checkpointing."""
        self.gradient_checkpointing_enabled = False
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False
        self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False
        logging.info("Disabled gradient checkpointing for PI0Pytorch model")
@@ -767,7 +760,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, time)
        if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
            == torch.bfloat16
        ):
            suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
@@ -841,7 +834,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
        prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
-        self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
+        self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"  # noqa: SLF001
        _, past_key_values = self.paligemma_with_expert.forward(
            attention_mask=prefix_att_2d_masks_4d,
@@ -915,7 +908,6 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
        self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
        past_key_values = copy.deepcopy(past_key_values)
        outputs_embeds, _ = self.paligemma_with_expert.forward(
            attention_mask=full_att_2d_masks_4d,
            position_ids=position_ids,
@@ -1005,12 +997,14 @@ class PI0Policy(PreTrainedPolicy):
        # Check if dataset_stats were provided in kwargs
        model = cls(config, **kwargs)
-        # Load state dict (expects keys with "model." prefix)
+        # Now manually load and remap the state dict
        try:
            # Try to load the pytorch_model.bin or model.safetensors file
            print(f"Loading model from: {pretrained_name_or_path}")
            try:
                from transformers.utils import cached_file
                # Try safetensors first
                resolved_file = cached_file(
                    pretrained_name_or_path,
                    "model.safetensors",
@@ -1018,7 +1012,7 @@ class PI0Policy(PreTrainedPolicy):
                    force_download=kwargs.get("force_download", False),
                    resume_download=kwargs.get("resume_download"),
                    proxies=kwargs.get("proxies"),
-                    token=kwargs.get("token"),
+                    use_auth_token=kwargs.get("use_auth_token"),
                    revision=kwargs.get("revision"),
                    local_files_only=kwargs.get("local_files_only", False),
                )
@@ -1031,7 +1025,7 @@ class PI0Policy(PreTrainedPolicy):
                print("Returning model without loading pretrained weights")
                return model
-            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
+            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
            fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
            # Then add "model." prefix for all keys that don't already have it
@@ -1076,7 +1070,7 @@ class PI0Policy(PreTrainedPolicy):
                print("All keys loaded successfully!")
        except Exception as e:
-            print(f"Warning: Could not load state dict: {e}")
+            print(f"Warning: Could not remap state dict keys: {e}")
        return model
@@ -1126,14 +1120,6 @@ class PI0Policy(PreTrainedPolicy):
                # Some checkpoints might have this, but current model expects different structure
                logging.warning(f"Vision embedding key might need handling: {key}")
            if (
                key == "model.paligemma_with_expert.paligemma.lm_head.weight"
                or key == "paligemma_with_expert.paligemma.lm_head.weight"
            ):
                fixed_state_dict[
                    "model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
                ] = value.clone()
            fixed_state_dict[new_key] = value
        return fixed_state_dict
@@ -15,16 +15,16 @@
 # limitations under the License.
 import builtins
 import copy
 import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
+from typing import TYPE_CHECKING, Literal, TypedDict
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
 from typing_extensions import Unpack
 from lerobot.utils.import_utils import _transformers_available
@@ -32,20 +32,14 @@ from lerobot.utils.import_utils import _transformers_available
 if TYPE_CHECKING or _transformers_available:
    from transformers.models.auto import CONFIG_MAPPING
    from transformers.models.gemma import modeling_gemma
-
+    from transformers.models.gemma.modeling_gemma import GemmaForCausalLM
-    from lerobot.policies.pi_gemma import (
+    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
        PaliGemmaForConditionalGenerationWithPiGemma,
        PiGemmaForCausalLM,
        _gated_residual,
        layernorm_forward,
    )
 else:
    CONFIG_MAPPING = None
    modeling_gemma = None
-    PiGemmaForCausalLM = None
+    GemmaForCausalLM = None
-    _gated_residual = None
+    PaliGemmaForConditionalGeneration = None
-    layernorm_forward = None
+
    PaliGemmaForConditionalGenerationWithPiGemma = None
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi05.configuration_pi05 import DEFAULT_IMAGE_SIZE, PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy, T
@@ -98,11 +92,10 @@ def create_sinusoidal_pos_embedding(  # see openpi `create_sinusoidal_pos_embedd
 def sample_beta(alpha, beta, bsize, device):  # see openpi `sample_beta` (exact copy)
-    # Beta sampling uses _sample_dirichlet which isn't implemented for MPS, so sample on CPU
+    alpha_t = torch.as_tensor(alpha, dtype=torch.float32, device=device)
-    alpha_t = torch.tensor(alpha, dtype=torch.float32)
+    beta_t = torch.as_tensor(beta, dtype=torch.float32, device=device)
    beta_t = torch.tensor(beta, dtype=torch.float32)
    dist = torch.distributions.Beta(alpha_t, beta_t)
-    return dist.sample((bsize,)).to(device)
+    return dist.sample((bsize,))
 def make_att_2d_masks(pad_masks, att_masks):  # see openpi `make_att_2d_masks` (exact copy)
@@ -196,7 +189,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
    if images.dtype == torch.uint8:
        resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
    elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(0.0, 1.0)
+        resized_images = resized_images.clamp(-1.0, 1.0)
    else:
        raise ValueError(f"Unsupported image dtype: {images.dtype}")
@@ -207,7 +200,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
    pad_w1 = pad_w0 + remainder_w
    # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else 0.0
+    constant_value = 0 if images.dtype == torch.uint8 else -1.0
    padded_images = F.pad(
        resized_images,
        (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -226,14 +219,14 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
 def compute_layer_complete(
    layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
 ):
-    models = [paligemma.model.language_model, gemma_expert.model]
+    models = [paligemma.language_model, gemma_expert.model]
    query_states = []
    key_states = []
    value_states = []
    gates = []
    for i, hidden_states in enumerate(inputs_embeds):
        layer = models[i].layers[layer_idx]
-        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
+        hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i])  # noqa: PLW2901
        gates.append(gate)
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
@@ -259,10 +252,10 @@ def compute_layer_complete(
        query_states, key_states, cos, sin, unsqueeze_dim=1
    )
    batch_size = query_states.shape[0]
-    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
+    scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling
    # Attention computation
    att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.model.language_model.layers[layer_idx].self_attn,
+        paligemma.language_model.layers[layer_idx].self_attn,
        query_states,
        key_states,
        value_states,
@@ -270,7 +263,7 @@ def compute_layer_complete(
        scaling,
    )
    # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim
    att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
    # Process layer outputs
    outputs_embeds = []
@@ -282,15 +275,15 @@ def compute_layer_complete(
            att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
        out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos])
        # first residual
-        out_emb = _gated_residual(hidden_states, out_emb, gates[i])
+        out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i])  # noqa: SLF001
        after_first_residual = out_emb.clone()
-        out_emb, gate = layernorm_forward(layer.post_attention_layernorm, out_emb, adarms_cond[i])
+        out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i])
        # Convert to bfloat16 if the next layer (mlp) uses bfloat16
        if layer.mlp.up_proj.weight.dtype == torch.bfloat16:
            out_emb = out_emb.to(dtype=torch.bfloat16)
        out_emb = layer.mlp(out_emb)
        # second residual
-        out_emb = _gated_residual(after_first_residual, out_emb, gate)
+        out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate)  # noqa: SLF001
        outputs_embeds.append(out_emb)
        start_pos = end_pos
    return outputs_embeds
@@ -363,7 +356,7 @@ class PaliGemmaWithExpertModel(
        vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
        vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
        vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.torch_dtype = "float32"
        vlm_config_hf.text_config.vocab_size = 257152
        vlm_config_hf.text_config.use_adarms = use_adarms[0]
        vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
@@ -371,7 +364,7 @@ class PaliGemmaWithExpertModel(
        vlm_config_hf.vision_config.intermediate_size = 4304
        vlm_config_hf.vision_config.projection_dim = 2048
        vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.dtype = "float32"
+        vlm_config_hf.vision_config.torch_dtype = "float32"
        action_expert_config_hf = CONFIG_MAPPING["gemma"](
            head_dim=action_expert_config.head_dim,
@@ -382,13 +375,13 @@ class PaliGemmaWithExpertModel(
            num_key_value_heads=action_expert_config.num_kv_heads,
            vocab_size=257152,
            hidden_activation="gelu_pytorch_tanh",
-            dtype="float32",
+            torch_dtype="float32",
            use_adarms=use_adarms[1],
            adarms_cond_dim=action_expert_config.width if use_adarms[1] else None,
        )
-        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
+        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
-        self.gemma_expert = PiGemmaForCausalLM(config=action_expert_config_hf)
+        self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf)
        self.gemma_expert.model.embed_tokens = None
        self.to_bfloat16_for_selected_params(precision)
@@ -403,11 +396,10 @@ class PaliGemmaWithExpertModel(
        else:
            raise ValueError(f"Invalid precision: {precision}")
        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
        # "same dtype" error). Saves memory vs full float32; more memory than only 3 params.
        params_to_keep_float32 = [
-            "vision_tower",
+            "vision_tower.vision_model.embeddings.patch_embedding.weight",
-            "multi_modal_projector",
+            "vision_tower.vision_model.embeddings.patch_embedding.bias",
            "vision_tower.vision_model.embeddings.position_embedding.weight",
            "input_layernorm",
            "post_attention_layernorm",
            "model.norm",
@@ -419,8 +411,8 @@ class PaliGemmaWithExpertModel(
    def _set_requires_grad(self):
        if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
+            self.paligemma.vision_tower.eval()
-            for param in self.paligemma.model.vision_tower.parameters():
+            for param in self.paligemma.vision_tower.parameters():
                param.requires_grad = False
        if self.train_expert_only:
            self.paligemma.eval()
@@ -430,23 +422,15 @@ class PaliGemmaWithExpertModel(
    def train(self, mode: bool = True):
        super().train(mode)
        if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
+            self.paligemma.vision_tower.eval()
        if self.train_expert_only:
            self.paligemma.eval()
    def embed_image(self, image: torch.Tensor):
-        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32).
+        return self.paligemma.model.get_image_features(image)
        out_dtype = image.dtype
        if image.dtype != torch.float32:
            image = image.to(torch.float32)
        image_outputs = self.paligemma.model.get_image_features(image)
        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
        if features.dtype != out_dtype:
            features = features.to(out_dtype)
        return features
    def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.language_model.embed_tokens(tokens)
    def forward(
        self,
@@ -460,7 +444,7 @@ class PaliGemmaWithExpertModel(
        if adarms_cond is None:
            adarms_cond = [None, None]
        if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.model.language_model.forward(
+            prefix_output = self.paligemma.language_model.forward(
                inputs_embeds=inputs_embeds[0],
                attention_mask=attention_mask,
                position_ids=position_ids,
@@ -484,7 +468,7 @@ class PaliGemmaWithExpertModel(
            prefix_output = None
            prefix_past_key_values = None
        else:
-            models = [self.paligemma.model.language_model, self.gemma_expert.model]
+            models = [self.paligemma.language_model, self.gemma_expert.model]
            num_layers = self.paligemma.config.text_config.num_hidden_layers
            # Check if gradient checkpointing is enabled for any of the models
@@ -524,7 +508,7 @@ class PaliGemmaWithExpertModel(
            def compute_final_norms(inputs_embeds, adarms_cond):
                outputs_embeds = []
                for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
+                    out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i])
                    outputs_embeds.append(out_emb)
                return outputs_embeds
@@ -589,19 +573,29 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
            # Also compile the main forward pass used during training
            self.forward = torch.compile(self.forward, mode=config.compile_mode)
        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
        try:
            from transformers.models.siglip import check
            if not check.check_whether_transformers_replace_is_installed_correctly():
                raise ValueError(msg)
        except ImportError:
            raise ValueError(msg) from None
    def gradient_checkpointing_enable(self):
        """Enable gradient checkpointing for memory optimization."""
        self.gradient_checkpointing_enabled = True
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True
        self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True
        logging.info("Enabled gradient checkpointing for PI05Pytorch model")
    def gradient_checkpointing_disable(self):
        """Disable gradient checkpointing."""
        self.gradient_checkpointing_enabled = False
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False
        self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False
        logging.info("Disabled gradient checkpointing for PI05Pytorch model")
@@ -743,7 +737,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(x_t, time)
        if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
            == torch.bfloat16
        ):
            suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
@@ -814,7 +808,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
        prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
-        self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
+        self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"  # noqa: SLF001
        _, past_key_values = self.paligemma_with_expert.forward(
            attention_mask=prefix_att_2d_masks_4d,
@@ -886,7 +880,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
        self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
        past_key_values = copy.deepcopy(past_key_values)
        outputs_embeds, _ = self.paligemma_with_expert.forward(
            attention_mask=full_att_2d_masks_4d,
            position_ids=position_ids,
@@ -976,12 +969,14 @@ class PI05Policy(PreTrainedPolicy):
        # Check if dataset_stats were provided in kwargs
        model = cls(config, **kwargs)
-        # Load state dict (expects keys with "model." prefix)
+        # Now manually load and remap the state dict
        try:
            # Try to load the pytorch_model.bin or model.safetensors file
            print(f"Loading model from: {pretrained_name_or_path}")
            try:
                from transformers.utils import cached_file
                # Try safetensors first
                resolved_file = cached_file(
                    pretrained_name_or_path,
                    "model.safetensors",
@@ -989,7 +984,7 @@ class PI05Policy(PreTrainedPolicy):
                    force_download=kwargs.get("force_download", False),
                    resume_download=kwargs.get("resume_download"),
                    proxies=kwargs.get("proxies"),
-                    token=kwargs.get("token"),
+                    use_auth_token=kwargs.get("use_auth_token"),
                    revision=kwargs.get("revision"),
                    local_files_only=kwargs.get("local_files_only", False),
                )
@@ -1002,7 +997,7 @@ class PI05Policy(PreTrainedPolicy):
                print("Returning model without loading pretrained weights")
                return model
-            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
+            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
            fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
            # Then add "model." prefix for all keys that don't already have it
@@ -1014,6 +1009,8 @@ class PI05Policy(PreTrainedPolicy):
                    new_key = f"model.{key}"
                    remapped_state_dict[new_key] = value
                    remap_count += 1
                    if remap_count <= 10:  # Only print first 10 to avoid spam
                        print(f"Remapped: {key} -> {new_key}")
                else:
                    remapped_state_dict[key] = value
@@ -1047,7 +1044,7 @@ class PI05Policy(PreTrainedPolicy):
                print("All keys loaded successfully!")
        except Exception as e:
-            print(f"Warning: Could not load state dict: {e}")
+            print(f"Warning: Could not remap state dict keys: {e}")
        return model
@@ -1101,14 +1098,6 @@ class PI05Policy(PreTrainedPolicy):
                # Some checkpoints might have this, but current model expects different structure
                logging.warning(f"Vision embedding key might need handling: {key}")
            if (
                key == "model.paligemma_with_expert.paligemma.lm_head.weight"
                or key == "paligemma_with_expert.paligemma.lm_head.weight"
            ):
                fixed_state_dict[
                    "model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
                ] = value.clone()
            fixed_state_dict[new_key] = value
        return fixed_state_dict
@@ -23,6 +23,7 @@ import torch
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
 from lerobot.policies.pi05.modeling_pi05 import pad_vector
 from lerobot.processor import (
    AddBatchDimensionProcessorStep,
    DeviceProcessorStep,
@@ -67,6 +68,9 @@ class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
        # TODO: check if this necessary
        state = deepcopy(state)
        # Prepare state (pad to max_state_dim)
        state = pad_vector(state, self.max_state_dim)
        # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
        state_np = state.cpu().numpy()
@@ -54,7 +54,7 @@ class PI0FastConfig(PreTrainedConfig):
    tokenizer_max_length: int = 200  # see openpi `__post_init__`
    text_tokenizer_name: str = "google/paligemma-3b-pt-224"
-    action_tokenizer_name: str = "lerobot/fast-action-tokenizer"
+    action_tokenizer_name: str = "physical-intelligence/fast"
    temperature: float = 0.0
    max_decoding_steps: int = 256
    fast_skip_tokens: int = 128
@@ -19,12 +19,13 @@ import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
+from typing import TYPE_CHECKING, Literal, TypedDict
 import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
 from typing_extensions import Unpack
 from lerobot.utils.import_utils import _scipy_available, _transformers_available
@@ -37,16 +38,11 @@ else:
 if TYPE_CHECKING or _transformers_available:
    from transformers import AutoTokenizer
    from transformers.models.auto import CONFIG_MAPPING
-
+    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
    from lerobot.policies.pi_gemma import (
        PaliGemmaForConditionalGenerationWithPiGemma,
        PiGemmaModel,
    )
 else:
    CONFIG_MAPPING = None
    PaliGemmaForConditionalGeneration = None
    AutoTokenizer = None
    PiGemmaModel = None
    PaliGemmaForConditionalGenerationWithPiGemma = None
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
@@ -125,7 +121,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
    if images.dtype == torch.uint8:
        resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
    elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(0.0, 1.0)
+        resized_images = resized_images.clamp(-1.0, 1.0)
    else:
        raise ValueError(f"Unsupported image dtype: {images.dtype}")
@@ -136,7 +132,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
    pad_w1 = pad_w0 + remainder_w
    # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else 0.0
+    constant_value = 0 if images.dtype == torch.uint8 else -1.0
    padded_images = F.pad(
        resized_images,
        (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -210,22 +206,16 @@ class PI0FastPaliGemma(nn.Module):
        vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
        vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
        vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.torch_dtype = "float32"
        vlm_config_hf.text_config.vocab_size = 257152
        vlm_config_hf.text_config.use_adarms = use_adarms[0]
        vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
        vlm_config_hf.vision_config.intermediate_size = 4304
        vlm_config_hf.vision_config.projection_dim = 2048
        vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.dtype = "float32"
+        vlm_config_hf.vision_config.torch_dtype = "float32"
-        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
+        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
        # Use PI Gemma (AdaRMS) as language model when use_adarms[0] is True so that
        # forward(..., adarms_cond=...) is supported (same as pi0/pi05).
        if use_adarms[0]:
            text_config = self.paligemma.config.text_config
            self.paligemma.model.language_model = PiGemmaModel(text_config)
        self.to_bfloat16_for_selected_params(precision)
@@ -238,11 +228,10 @@ class PI0FastPaliGemma(nn.Module):
        else:
            raise ValueError(f"Invalid precision: {precision}")
        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
        # "same dtype" error). Align with PI05.
        params_to_keep_float32 = [
-            "vision_tower",
+            "vision_tower.vision_model.embeddings.patch_embedding.weight",
-            "multi_modal_projector",
+            "vision_tower.vision_model.embeddings.patch_embedding.bias",
            "vision_tower.vision_model.embeddings.position_embedding.weight",
            "input_layernorm",
            "post_attention_layernorm",
            "model.norm",
@@ -253,18 +242,10 @@ class PI0FastPaliGemma(nn.Module):
                param.data = param.data.to(dtype=torch.float32)
    def embed_image(self, image: torch.Tensor):
-        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32). Align with PI05.
+        return self.paligemma.model.get_image_features(image)
        out_dtype = image.dtype
        if image.dtype != torch.float32:
            image = image.to(torch.float32)
        image_outputs = self.paligemma.model.get_image_features(image)
        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
        if features.dtype != out_dtype:
            features = features.to(out_dtype)
        return features
    def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.language_model.embed_tokens(tokens)
    def forward(
        self,
@@ -278,7 +259,7 @@ class PI0FastPaliGemma(nn.Module):
        if adarms_cond is None:
            adarms_cond = [None, None]
        if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.model.language_model.forward(
+            prefix_output = self.paligemma.language_model.forward(
                inputs_embeds=inputs_embeds[0],
                attention_mask=attention_mask,
                position_ids=position_ids,
@@ -325,14 +306,24 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
            self.sample_actions_fast = torch.compile(self.sample_actions_fast, mode=config.compile_mode)
            self.forward = torch.compile(self.forward, mode=config.compile_mode)
        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
        try:
            from transformers.models.siglip import check
            if not check.check_whether_transformers_replace_is_installed_correctly():
                raise ValueError(msg)
        except ImportError:
            raise ValueError(msg) from None
    def gradient_checkpointing_enable(self):
        """Enable gradient checkpointing for memory optimization."""
        self.gradient_checkpointing_enabled = True
        # Call the proper gradient_checkpointing_enable() method with use_reentrant=False for better memory efficiency
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing_enable(
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing_enable(
            gradient_checkpointing_kwargs={"use_reentrant": False}
        )
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing_enable(
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing_enable(
            gradient_checkpointing_kwargs={"use_reentrant": False}
        )
        logging.info("Enabled gradient checkpointing for PI0FastPytorch model")
@@ -341,8 +332,8 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
        """Disable gradient checkpointing."""
        self.gradient_checkpointing_enabled = False
        # Call the proper gradient_checkpointing_disable() method
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing_disable()
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing_disable()
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing_disable()
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing_disable()
        logging.info("Disabled gradient checkpointing for PI0FastPytorch model")
    def _apply_checkpoint(self, func, *args, **kwargs):
@@ -532,7 +523,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
        # Convert embeddings to bfloat16 if needed
        if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
            == torch.bfloat16
        ):
            prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -625,7 +616,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
        )
        if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
            == torch.bfloat16
        ):
            prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -723,7 +714,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
        # Ensure correct precision (bfloat16/float32)
        if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
            == torch.bfloat16
        ):
            prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -906,12 +897,14 @@ class PI0FastPolicy(PreTrainedPolicy):
        # Check if dataset_stats were provided in kwargs
        model = cls(config, **kwargs)
-        # Load state dict (expects keys with "model." prefix)
+        # Now manually load and remap the state dict
        try:
            # Try to load the pytorch_model.bin or model.safetensors file
            print(f"Loading model from: {pretrained_name_or_path}")
            try:
                from transformers.utils import cached_file
                # Try safetensors first
                resolved_file = cached_file(
                    pretrained_name_or_path,
                    "model.safetensors",
@@ -919,7 +912,7 @@ class PI0FastPolicy(PreTrainedPolicy):
                    force_download=kwargs.get("force_download", False),
                    resume_download=kwargs.get("resume_download"),
                    proxies=kwargs.get("proxies"),
-                    token=kwargs.get("token"),
+                    use_auth_token=kwargs.get("use_auth_token"),
                    revision=kwargs.get("revision"),
                    local_files_only=kwargs.get("local_files_only", False),
                )
@@ -932,9 +925,8 @@ class PI0FastPolicy(PreTrainedPolicy):
                print("Returning model without loading pretrained weights")
                return model
-            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
+            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
            fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
            # Then add "model." prefix for all keys that don't already have it
            remapped_state_dict = {}
            remap_count = 0
@@ -944,6 +936,8 @@ class PI0FastPolicy(PreTrainedPolicy):
                    new_key = f"model.{key}"
                    remapped_state_dict[new_key] = value
                    remap_count += 1
                    if remap_count <= 10:  # Only print first 10 to avoid spam
                        print(f"Remapped: {key} -> {new_key}")
                else:
                    remapped_state_dict[key] = value
@@ -977,7 +971,7 @@ class PI0FastPolicy(PreTrainedPolicy):
                print("All keys loaded successfully!")
        except Exception as e:
-            print(f"Warning: Could not load state dict: {e}")
+            print(f"Warning: Could not remap state dict keys: {e}")
        return model
@@ -23,6 +23,7 @@ import torch
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
 from lerobot.policies.pi0_fast.modeling_pi0_fast import pad_vector
 from lerobot.processor import (
    ActionTokenizerProcessorStep,
    AddBatchDimensionProcessorStep,
@@ -68,6 +69,9 @@ class Pi0FastPrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
        # TODO: check if this necessary
        state = deepcopy(state)
        # Prepare state (pad to max_state_dim)
        state = pad_vector(state, self.max_state_dim)
        # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
        state_np = state.cpu().numpy()
@@ -1,363 +0,0 @@
 # 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.
 from __future__ import annotations
 from typing import TYPE_CHECKING
 import torch
 from torch import nn
 from lerobot.utils.import_utils import _transformers_available
 if TYPE_CHECKING or _transformers_available:
    from transformers.cache_utils import DynamicCache
    from transformers.masking_utils import create_causal_mask
    from transformers.modeling_layers import GradientCheckpointingLayer
    from transformers.modeling_outputs import BaseModelOutputWithPast
    from transformers.models.gemma.modeling_gemma import (
        GemmaAttention,
        GemmaConfig,
        GemmaForCausalLM,
        GemmaMLP,
        GemmaModel,
    )
    from transformers.models.paligemma.modeling_paligemma import (
        PaliGemmaForConditionalGeneration,
        PaliGemmaModel,
    )
 else:
    GemmaAttention = None
    GemmaConfig = None
    GemmaForCausalLM = None
    GemmaMLP = None
    GemmaModel = None
    PaliGemmaModel = None
    PaliGemmaForConditionalGeneration = None
    DynamicCache = None
    GradientCheckpointingLayer = None
    BaseModelOutputWithPast = None
    create_causal_mask = None
 def _gated_residual(
    x: torch.Tensor | None,
    y: torch.Tensor | None,
    gate: torch.Tensor | None,
 ) -> torch.Tensor | None:
    """Gated residual: x + y when gate is None, else x + y * gate."""
    if x is None and y is None:
        return None
    if x is None or y is None:
        return x if x is not None else y
    if gate is None:
        return x + y
    return x + y * gate
 def layernorm_forward(
    layernorm: nn.Module,
    x: torch.Tensor,
    cond: torch.Tensor | None = None,
 ):
    """
    call layernorm and return hidden states and gate
    if cond is not None, use conditional norm
    otherwise, use normal gemma norm
    """
    if cond is not None:
        return layernorm(x, cond=cond)
    else:
        return layernorm(x)
 class PiGemmaRMSNorm(nn.Module):
    """
    Adaptive RMSNorm for PI Gemma (AdaRMS).
    When cond_dim is set, uses cond to modulate scale/shift/gate; otherwise behaves like standard GemmaRMSNorm.
    forward(x, cond=None) returns (output, gate) for use with _gated_residual.
    """
    def __init__(self, dim: int, eps: float = 1e-6, cond_dim: int | None = None):
        super().__init__()
        self.eps = eps
        self.dim = dim
        self.cond_dim = cond_dim
        if cond_dim is not None:
            self.dense = nn.Linear(cond_dim, dim * 3, bias=True)
            nn.init.zeros_(self.dense.weight)
        else:
            self.weight = nn.Parameter(torch.zeros(dim))
            self.dense = None
    def _norm(self, x):
        # Compute variance in float32 (like the source implementation)
        var = torch.mean(torch.square(x.float()), dim=-1, keepdim=True)
        # Compute normalization in float32
        normed_inputs = x * torch.rsqrt(var + self.eps)
        return normed_inputs
    def forward(
        self,
        x: torch.Tensor,
        cond: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        dtype = x.dtype
        normed = self._norm(x)
        if cond is None or self.dense is None:
            normed = normed * (1.0 + self.weight.float())
            return normed.type_as(x), None
        if cond.shape[-1] != self.cond_dim:
            raise ValueError(f"Expected cond dim {self.cond_dim}, got {cond.shape[-1]}")
        modulation = self.dense(cond)
        if len(x.shape) == 3:
            modulation = modulation.unsqueeze(1)
        scale, shift, gate = modulation.chunk(3, dim=-1)
        normed = normed * (1 + scale.float()) + shift.float()
        return normed.to(dtype), gate.to(dtype)
    def extra_repr(self) -> str:
        if self.dense is not None:
            return f"dim={self.dim}, eps={self.eps}, adaptive=True, cond_dim={self.cond_dim}"
        return f"dim={self.dim}, eps={self.eps}"
 def _get_pi_gemma_decoder_layer_base():
    """base for PiGemmaDecoderLayer"""
    class _PiGemmaDecoderLayerBase(GradientCheckpointingLayer):
        """Decoder layer that uses PiGemmaRMSNorm and _gated_residual, compatible with v5 Gemma."""
        def __init__(self, config: GemmaConfig, layer_idx: int):
            super().__init__()
            self.hidden_size = config.hidden_size
            self.self_attn = GemmaAttention(config=config, layer_idx=layer_idx)
            self.mlp = GemmaMLP(config)
            cond_dim = (
                getattr(config, "adarms_cond_dim", None) if getattr(config, "use_adarms", False) else None
            )
            self.input_layernorm = PiGemmaRMSNorm(
                config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim
            )
            self.post_attention_layernorm = PiGemmaRMSNorm(
                config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim
            )
        def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: torch.Tensor | None = None,
            position_ids: torch.LongTensor | None = None,
            past_key_values=None,
            use_cache: bool = False,
            cache_position: torch.LongTensor | None = None,
            position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
            adarms_cond: torch.Tensor | None = None,
            **kwargs,
        ) -> torch.Tensor:
            residual = hidden_states
            hidden_states, gate = self.input_layernorm(hidden_states, cond=adarms_cond)
            hidden_states, _ = self.self_attn(
                hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **kwargs,
            )
            hidden_states = _gated_residual(residual, hidden_states, gate)
            residual = hidden_states
            hidden_states, gate = self.post_attention_layernorm(hidden_states, cond=adarms_cond)
            hidden_states = self.mlp(hidden_states)
            hidden_states = _gated_residual(residual, hidden_states, gate)
            return hidden_states
    return _PiGemmaDecoderLayerBase
 class PiGemmaModel(GemmaModel):  # type: ignore[misc]
    """
    GemmaModel extended with AdaRMS (adaptive RMSNorm) and gated residuals when config.use_adarms is True.
    """
    def __init__(self, config: GemmaConfig, **kwargs):
        super().__init__(config, **kwargs)
        # if not getattr(config, "use_adarms", False):
        #     return
        cond_dim = getattr(config, "adarms_cond_dim", None)
        pi_gemma_decoder_layer_base = _get_pi_gemma_decoder_layer_base()
        self.layers = nn.ModuleList(
            [pi_gemma_decoder_layer_base(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = PiGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim)
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: DynamicCache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        adarms_cond: torch.Tensor | None = None,
        **kwargs,
    ) -> BaseModelOutputWithPast:
        """
        adarms_cond (`torch.Tensor` of shape `(batch_size, cond_dim)`, *optional*):
            Condition for ADARMS.
        """
        output_attentions = (
            output_attentions if output_attentions is not None else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
        if self.gradient_checkpointing and self.training and use_cache:
            import logging
            logging.warning(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
            )
            use_cache = False
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()
        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)
        causal_mask = create_causal_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )
        # embed positions
        hidden_states = inputs_embeds
        # Convert to bfloat16 if the first layer uses bfloat16
        if len(self.layers) > 0 and self.layers[0].self_attn.q_proj.weight.dtype == torch.bfloat16:
            hidden_states = hidden_states.to(torch.bfloat16)
        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)
        # normalized
        # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
        # See https://github.com/huggingface/transformers/pull/29402
        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            layer_outputs = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                adarms_cond=adarms_cond,
                **kwargs,
            )
            hidden_states = layer_outputs
            if output_attentions:
                all_self_attns += (layer_outputs[1],)
        hidden_states, _ = self.norm(hidden_states, adarms_cond)
        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )
 class PiGemmaForCausalLM(GemmaForCausalLM):  # type: ignore[misc]
    """
    Causal LM wrapper using PiGemmaModel as the backbone, for consistency with GemmaForCausalLM
    and the language model used in pi0_fast. Use this for the action expert in pi0/pi05.
    """
    def __init__(self, config: GemmaConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.model = PiGemmaModel(config)
 class PaliGemmaModelWithPiGemma(PaliGemmaModel):
    """PaliGemmaModel whose language_model is PiGemmaModel (custom decoder with PiGemmaRMSNorm and gated residuals)."""
    def __init__(self, config):
        super().__init__(config)
        self.language_model = PiGemmaModel(config.text_config)
 class PaliGemmaForConditionalGenerationWithPiGemma(PaliGemmaForConditionalGeneration):
    """PaliGemmaForConditionalGeneration using PiGemma decoder for the language model."""
    def __init__(self, config):
        super().__init__(config)
        self.model = PaliGemmaModelWithPiGemma(config)
    # Make modules available through conditional class for BC
    @property
    def language_model(self):
        return self.model.language_model
 __all__ = [
    "PiGemmaModel",
    "PiGemmaForCausalLM",
    "PiGemmaRMSNorm",
    "_gated_residual",
    "layernorm_forward",
    "PaliGemmaModelWithPiGemma",
    "PaliGemmaForConditionalGenerationWithPiGemma",
 ]
@@ -19,7 +19,7 @@ import os
 from importlib.resources import files
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import TypedDict, TypeVar, Unpack
+from typing import TypedDict, TypeVar
 import packaging
 import safetensors
@@ -28,6 +28,7 @@ from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from huggingface_hub.errors import HfHubHTTPError
 from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor
 from torch import Tensor, nn
 from typing_extensions import Unpack
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig
@@ -33,7 +33,7 @@ class RewardClassifierConfig(PreTrainedConfig):
    latent_dim: int = 256
    image_embedding_pooling_dim: int = 8
    dropout_rate: float = 0.1
-    model_name: str = "helper2424/resnet10"  # TODO: This needs to be updated. The model on the Hub doesn't call self.post_init() in its __init__, which is required by transformers v5 to set all_tied_weights_keys. The from_pretrained call fails when it tries to access this attribute during _finalize_model_loading.
+    model_name: str = "helper2424/resnet10"
    device: str = "cpu"
    model_type: str = "cnn"  # "transformer" or "cnn"
    num_cameras: int = 2
@@ -106,9 +106,6 @@ class SmolVLAConfig(PreTrainedConfig):
    # Real-Time Chunking (RTC) configuration
    rtc_config: RTCConfig | None = None
    compile_model: bool = False  # Whether to use torch.compile for model optimization
    compile_mode: str = "max-autotune"  # Torch compile mode
    def __post_init__(self):
        super().__post_init__()
@@ -54,11 +54,12 @@ policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
 import math
 from collections import deque
-from typing import TypedDict, Unpack
+from typing import TypedDict
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
 from typing_extensions import Unpack
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
@@ -592,12 +593,6 @@ class VLAFlowMatching(nn.Module):
        self.prefix_length = self.config.prefix_length
        self.rtc_processor = rtc_processor
        # Compile model if requested
        if config.compile_model:
            torch.set_float32_matmul_precision("high")
            self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode)
            self.forward = torch.compile(self.forward, mode=config.compile_mode)
    def _rtc_enabled(self):
        return self.config.rtc_config is not None and self.config.rtc_config.enabled
@@ -77,6 +77,7 @@ class SmolVLMWithExpertModel(nn.Module):
            print(f"Loading  {model_id} weights ...")
            self.vlm = AutoModelForImageTextToText.from_pretrained(
                model_id,
                device_map=device,
                torch_dtype="bfloat16",
                low_cpu_mem_usage=True,
            )
@@ -55,7 +55,7 @@ class WallXConfig(PreTrainedConfig):
    pretrained_name_or_path: str = "x-square-robot/wall-oss-flow"
    # Tokenizer settings
-    action_tokenizer_path: str | None = "lerobot/fast-action-tokenizer"
+    action_tokenizer_path: str | None = "physical-intelligence/fast"
    # Action prediction mode: "diffusion" or "fast"
    prediction_mode: str = "diffusion"
@@ -261,15 +261,10 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
    and optional LoRA fine-tuning support.
    """
-    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tied_weights_keys = ["lm_head.weight"]
    config_class = Qwen2_5_VLConfig
    _no_split_modules = ["Qwen2_5_VLDecoderLayer_with_MoE", "Qwen2_5_VLVisionBlock"]
    def init_weights(self):
        if getattr(self.model, "language_model", None) is not None:
            return
        super().init_weights()
    @classmethod
    def from_pretrained(
        cls,
@@ -317,11 +312,6 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
            processor.action_processor = action_tokenizer
        else:
            action_tokenizer = None
        # add pad_token_id to config
        config.pad_token_id = processor.tokenizer.pad_token_id
        config.text_config.pad_token_id = processor.tokenizer.pad_token_id
        # Initialize model with configuration and processor
        model = cls(config, processor=processor, action_tokenizer=action_tokenizer, **kwargs)
@@ -341,7 +331,7 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
                force_download=kwargs.get("force_download", False),
                resume_download=kwargs.get("resume_download"),
                proxies=kwargs.get("proxies"),
-                token=kwargs.get("token"),
+                use_auth_token=kwargs.get("use_auth_token"),
                revision=kwargs.get("revision"),
                local_files_only=kwargs.get("local_files_only", False),
            )
@@ -21,7 +21,6 @@ class Qwen2_5_VLVisionConfig(PretrainedConfig):
        window_size=112,
        out_hidden_size=3584,
        fullatt_block_indexes=[7, 15, 23, 31],
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)
@@ -39,7 +38,6 @@ class Qwen2_5_VLVisionConfig(PretrainedConfig):
        self.window_size = window_size
        self.fullatt_block_indexes = fullatt_block_indexes
        self.out_hidden_size = out_hidden_size
        self.initializer_range = initializer_range
 class Qwen2_5_VLConfig(PretrainedConfig):
@@ -11,6 +11,7 @@ from transformers.activations import ACT2FN
 from transformers.cache_utils import (
    Cache,
    DynamicCache,
    SlidingWindowCache,
    StaticCache,
 )
 from transformers.generation import GenerationMixin
@@ -30,15 +31,6 @@ from transformers.utils import (
 from .configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
 # TODO(Steven): SlidingWindowCache was removed in transformers v5. Define a placeholder so isinstance checks
 # always return False (which is the correct behavior when no sliding window cache is in use).
 class _SlidingWindowCachePlaceholder:
    pass
 SlidingWindowCache = _SlidingWindowCachePlaceholder
 if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.layers.rotary import apply_rotary_emb
@@ -602,40 +594,19 @@ class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel):
        return hidden_states
 def _compute_default_rope_parameters_qwen2_5_vl(config, device=None):
    """
    compute default rope parameters for Qwen2_5_VL
    """
    base = config.text_config.rope_parameters["rope_theta"]
    dim = config.hidden_size // config.num_attention_heads
    inv_freq = 1.0 / (
        base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
    )
    return inv_freq, 1.0
 class Qwen2_5_VLRotaryEmbedding(nn.Module):
    def __init__(self, config: Qwen2_5_VLConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        elif hasattr(config, "rope_parameters") and config.rope_parameters is not None:
            self.rope_type = config.rope_parameters.get("rope_type", "default")
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings
        self.config = config
-
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        if self.rope_type == "default":
            self.rope_init_fn = _compute_default_rope_parameters_qwen2_5_vl
            self.rope_kwargs = {}
        else:
            rope_type_key = "linear" if self.rope_type == "linear" else self.rope_type
            self.rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type_key]
            self.rope_kwargs = {}
        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
@@ -1596,7 +1567,7 @@ QWEN2_5_VL_INPUTS_DOCSTRING = r"""
 class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tied_weights_keys = ["lm_head.weight"]
    config_class = Qwen2_5_VLConfig
    _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"]
@@ -144,7 +144,7 @@ def preprocesser_call(
    """
    # Process image inputs
    if images is not None and len(images) > 0:
-        image_inputs = processor.image_processor(images=images, return_tensors=return_tensors)
+        image_inputs = processor.image_processor(images=images, videos=None, return_tensors=return_tensors)
        image_grid_thw = image_inputs["image_grid_thw"]
    else:
        image_inputs = {}
@@ -152,7 +152,7 @@ def preprocesser_call(
    # Process video inputs
    if videos is not None:
-        videos_inputs = processor.image_processor(videos=videos, return_tensors=return_tensors)
+        videos_inputs = processor.image_processor(images=None, videos=videos, return_tensors=return_tensors)
        video_grid_thw = videos_inputs["video_grid_thw"]
    else:
        videos_inputs = {}
@@ -276,8 +276,6 @@ class Florence2LanguageConfig(PretrainedConfig):
        )
        # ensure backward compatibility for BART CNN models
        if not hasattr(self, "forced_bos_token_id"):
            self.forced_bos_token_id = None
        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
            self.forced_bos_token_id = self.bos_token_id
            warnings.warn(
@@ -1951,10 +1951,7 @@ class Florence2Decoder(Florence2LanguagePreTrainedModel):
 class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
-    _tied_weights_keys = {
+    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
        "encoder.embed_tokens.weight": "shared.weight",
        "decoder.embed_tokens.weight": "shared.weight",
    }
    def __init__(self, config: Florence2LanguageConfig):
        super().__init__(config)
@@ -2079,10 +2076,7 @@ class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
 class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel, GenerationMixin):
    base_model_prefix = "model"
-    _tied_weights_keys = {
+    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
        "model.encoder.embed_tokens.weight": "model.shared.weight",
        "model.decoder.embed_tokens.weight": "model.shared.weight",
    }
    _keys_to_ignore_on_load_missing = ["final_logits_bias"]
    def __init__(self, config: Florence2LanguageConfig):
@@ -2442,10 +2436,11 @@ FLORENCE2_INPUTS_DOCSTRING = r"""
    FLORENCE2_START_DOCSTRING,
 )
 class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
-    _tied_weights_keys = {
+    _tied_weights_keys = [
-        "language_model.model.encoder.embed_tokens.weight": "language_model.model.shared.weight",
+        "language_model.encoder.embed_tokens.weight",
-        "language_model.model.decoder.embed_tokens.weight": "language_model.model.shared.weight",
+        "language_model.decoder.embed_tokens.weight",
-    }
+        "language_model.lm_head.weight",
    ]
    def __init__(self, config: Florence2Config):
        super().__init__(config)
@@ -17,7 +17,7 @@
 from __future__ import annotations
 from enum import Enum
-from typing import Any, TypedDict
+from typing import Any, TypeAlias, TypedDict
 import numpy as np
 import torch
@@ -36,10 +36,10 @@ class TransitionKey(str, Enum):
    COMPLEMENTARY_DATA = "complementary_data"
-PolicyAction = torch.Tensor
+PolicyAction: TypeAlias = torch.Tensor
-RobotAction = dict[str, Any]
+RobotAction: TypeAlias = dict[str, Any]
-EnvAction = np.ndarray
+EnvAction: TypeAlias = np.ndarray
-RobotObservation = dict[str, Any]
+RobotObservation: TypeAlias = dict[str, Any]
 EnvTransition = TypedDict(
@@ -39,7 +39,7 @@ from collections.abc import Callable, Iterable, Sequence
 from copy import deepcopy
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, TypedDict, TypeVar, cast
+from typing import Any, Generic, TypeAlias, TypedDict, TypeVar, cast
 import torch
 from huggingface_hub import hf_hub_download
@@ -251,7 +251,7 @@ class ProcessorMigrationError(Exception):
@dataclass
-class DataProcessorPipeline[TInput, TOutput](HubMixin):
+class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]):
    """A sequential pipeline for processing data, integrated with the Hugging Face Hub.
    This class chains together multiple `ProcessorStep` instances to form a complete
@@ -1432,8 +1432,8 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
 # Type aliases for semantic clarity.
-RobotProcessorPipeline = DataProcessorPipeline[TInput, TOutput]
+RobotProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput]
-PolicyProcessorPipeline = DataProcessorPipeline[TInput, TOutput]
+PolicyProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput]
 class ObservationProcessorStep(ProcessorStep, ABC):
@@ -336,7 +336,7 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
    Requires the `transformers` library to be installed.
    Attributes:
-        tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "lerobot/fast-action-tokenizer").
+        tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "physical-intelligence/fast").
        tokenizer: A pre-initialized processor/tokenizer object. If provided, `tokenizer_name` is ignored.
        trust_remote_code: Whether to trust remote code when loading the tokenizer (required for some tokenizers).
        action_tokenizer: The internal tokenizer/processor instance, loaded during initialization.
@@ -15,6 +15,7 @@
 # limitations under the License.
 from dataclasses import dataclass, field
 from typing import TypeAlias
 from lerobot.cameras import CameraConfig
@@ -49,5 +50,5 @@ class SOFollowerRobotConfig(RobotConfig, SOFollowerConfig):
    pass
-SO100FollowerConfig = SOFollowerRobotConfig
+SO100FollowerConfig: TypeAlias = SOFollowerRobotConfig
-SO101FollowerConfig = SOFollowerRobotConfig
+SO101FollowerConfig: TypeAlias = SOFollowerRobotConfig
@@ -17,6 +17,7 @@
 import logging
 import time
 from functools import cached_property
 from typing import TypeAlias
 from lerobot.cameras.utils import make_cameras_from_configs
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
@@ -229,5 +230,5 @@ class SOFollower(Robot):
        logger.info(f"{self} disconnected.")
-SO100Follower = SOFollower
+SO100Follower: TypeAlias = SOFollower
-SO101Follower = SOFollower
+SO101Follower: TypeAlias = SOFollower
@@ -16,5 +16,3 @@
 from .config_unitree_g1 import UnitreeG1Config
 from .unitree_g1 import UnitreeG1
 __all__ = ["UnitreeG1", "UnitreeG1Config"]
@@ -27,10 +27,11 @@ _GAINS: dict[str, dict[str, list[float]]] = {
    },  # pitch, roll, yaw, knee, ankle_pitch, ankle_roll
    "right_leg": {"kp": [150, 150, 150, 300, 40, 40], "kd": [2, 2, 2, 4, 2, 2]},
    "waist": {"kp": [250, 250, 250], "kd": [5, 5, 5]},  # yaw, roll, pitch
-    "left_arm": {"kp": [50, 50, 80, 80], "kd": [3, 3, 3, 3]},  # shoulder_pitch/roll/yaw, elbow
+    "left_arm": {"kp": [80, 80, 80, 80], "kd": [3, 3, 3, 3]},  # shoulder_pitch/roll/yaw, elbow
    "left_wrist": {"kp": [40, 40, 40], "kd": [1.5, 1.5, 1.5]},  # roll, pitch, yaw
-    "right_arm": {"kp": [50, 50, 80, 80], "kd": [3, 3, 3, 3]},
+    "right_arm": {"kp": [80, 80, 80, 80], "kd": [3, 3, 3, 3]},
    "right_wrist": {"kp": [40, 40, 40], "kd": [1.5, 1.5, 1.5]},
    "other": {"kp": [80, 80, 80, 80, 80, 80], "kd": [3, 3, 3, 3, 3, 3]},
 }
@@ -67,7 +68,3 @@ class UnitreeG1Config(RobotConfig):
    # Compensates for gravity on the unitree's arms using the arm ik solver
    gravity_compensation: bool = False
    # Lower-body controller class name, e.g. "GrootLocomotionController" or
    # "HolosomaLocomotionController". None disables it.
    controller: str | None = None
@@ -14,34 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import importlib
 from enum import IntEnum
 import numpy as np
 # ruff: noqa: N801, N815
 NUM_MOTORS = 29
 REMOTE_AXES = ("remote.lx", "remote.ly", "remote.rx", "remote.ry")
 REMOTE_BUTTONS = tuple(f"remote.button.{i}" for i in range(16))
 REMOTE_KEYS = REMOTE_AXES + REMOTE_BUTTONS
 def default_remote_input() -> dict[str, float]:
    """Return a zeroed-out remote input dict (axes + buttons)."""
    return dict.fromkeys(REMOTE_KEYS, 0.0)
 def get_gravity_orientation(quaternion: list[float] | np.ndarray) -> np.ndarray:
    """Get gravity orientation from quaternion [w, x, y, z]."""
    qw, qx, qy, qz = quaternion
    gravity_orientation = np.zeros(3, dtype=np.float32)
    gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
    gravity_orientation[1] = -2 * (qz * qy + qw * qx)
    gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
    return gravity_orientation
 class G1_29_JointArmIndex(IntEnum):
    # Left arm
@@ -51,7 +29,7 @@ class G1_29_JointArmIndex(IntEnum):
    kLeftElbow = 18
    kLeftWristRoll = 19
    kLeftWristPitch = 20
-    kLeftWristYaw = 21
+    kLeftWristyaw = 21
    # Right arm
    kRightShoulderPitch = 22
@@ -63,21 +41,6 @@ class G1_29_JointArmIndex(IntEnum):
    kRightWristYaw = 28
 def make_locomotion_controller(name: str | None):
    """Instantiate a locomotion controller by class name. Returns None if name is None."""
    if name is None:
        return None
    controllers = {
        "GrootLocomotionController": "lerobot.robots.unitree_g1.gr00t_locomotion",
        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.holosoma_locomotion",
    }
    module_path = controllers.get(name)
    if module_path is None:
        raise ValueError(f"Unknown controller: {name!r}. Available: {list(controllers)}")
    module = importlib.import_module(module_path)
    return getattr(module, name)()
 class G1_29_JointIndex(IntEnum):
    # Left leg
    kLeftHipPitch = 0
@@ -106,7 +69,7 @@ class G1_29_JointIndex(IntEnum):
    kLeftElbow = 18
    kLeftWristRoll = 19
    kLeftWristPitch = 20
-    kLeftWristYaw = 21
+    kLeftWristyaw = 21
    # Right arm
    kRightShoulderPitch = 22
@@ -16,11 +16,13 @@
 import logging
 import os
-from collections import deque
+import sys
 import numpy as np
 logger = logging.getLogger(__name__)
 parent2_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.append(parent2_dir)
 class WeightedMovingFilter:
@@ -29,14 +31,18 @@ class WeightedMovingFilter:
        self._weights = np.array(weights)
        self._data_size = data_size
        self._filtered_data = np.zeros(self._data_size)
-        self._data_queue = deque(maxlen=self._window_size)
+        self._data_queue = []
    def _apply_filter(self):
        if len(self._data_queue) < self._window_size:
            return self._data_queue[-1]
        data_array = np.array(self._data_queue)
-        return data_array.T @ self._weights
+        temp_filtered_data = np.zeros(self._data_size)
        for i in range(self._data_size):
            temp_filtered_data[i] = np.convolve(data_array[:, i], self._weights, mode="valid")[-1]
        return temp_filtered_data
    def add_data(self, new_data):
        assert len(new_data) == self._data_size
@@ -46,6 +52,9 @@ class WeightedMovingFilter:
        ):  # skip duplicate data
            return
        if len(self._data_queue) >= self._window_size:
            self._data_queue.pop(0)
        self._data_queue.append(new_data)
        self._filtered_data = self._apply_filter()
@@ -62,6 +71,8 @@ class G1_29_ArmIK:  # noqa: N801
        from pinocchio import casadi as cpin
        self._pin = pin
        np.set_printoptions(precision=5, suppress=True, linewidth=200)
        self.unit_test = unit_test
        self.repo_path = snapshot_download("lerobot/unitree-g1-mujoco")
@@ -238,35 +249,50 @@ class G1_29_ArmIK:  # noqa: N801
        self.opti.set_value(self.param_tf_r, right_wrist)
        self.opti.set_value(self.var_q_last, self.init_data)  # for smooth
        converged = True
        try:
            self.opti.solve()
            sol_q = self.opti.value(self.var_q)
            self.smooth_filter.add_data(sol_q)
            sol_q = self.smooth_filter.filtered_data
            if current_lr_arm_motor_dq is not None:
                v = current_lr_arm_motor_dq * 0.0
            else:
                v = (sol_q - self.init_data) * 0.0
            self.init_data = sol_q
            sol_tauff = self._pin.rnea(
                self.reduced_robot.model,
                self.reduced_robot.data,
                sol_q,
                v,
                np.zeros(self.reduced_robot.model.nv),
            )
            return sol_q, sol_tauff
        except Exception as e:
-            converged = False
+            logger.error(f"ERROR in convergence, plotting debug info.{e}")
-            logger.error(f"IK convergence error: {e}")
+
            sol_q = self.opti.debug.value(self.var_q)
            self.smooth_filter.add_data(sol_q)
            sol_q = self.smooth_filter.filtered_data
-        self.smooth_filter.add_data(sol_q)
+            if current_lr_arm_motor_dq is not None:
-        sol_q = self.smooth_filter.filtered_data
+                v = current_lr_arm_motor_dq * 0.0
-        self.init_data = sol_q
+            else:
                v = (sol_q - self.init_data) * 0.0
            self.init_data = sol_q
        if not converged:
            logger.error(
                f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}"
            )
            return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv)
        sol_tauff = self._pin.rnea(
            self.reduced_robot.model,
            self.reduced_robot.data,
            sol_q,
            np.zeros(self.reduced_robot.model.nv),
            np.zeros(self.reduced_robot.model.nv),
        )
        return sol_q, sol_tauff
    def solve_tau(self, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None):
        try:
            q_g1 = np.array(current_lr_arm_motor_q, dtype=float)
@@ -24,7 +24,6 @@ This server runs on the robot and forwards:
 Uses JSON for secure serialization instead of pickle.
 """
 import argparse
 import base64
 import contextlib
 import json
@@ -39,8 +38,6 @@ from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
 from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_ as hg_LowCmd, LowState_ as hg_LowState
 from unitree_sdk2py.utils.crc import CRC
 from lerobot.cameras.zmq.image_server import ImageServer
 # DDS topic names follow Unitree SDK naming conventions
 # ruff: noqa: N816
 kTopicLowCommand_Debug = "rt/lowcmd"  # action to robot
@@ -153,32 +150,6 @@ def cmd_forward_loop(
 def main() -> None:
    """Main entry point for the robot server bridge."""
    parser = argparse.ArgumentParser(description="DDS-to-ZMQ bridge server for Unitree G1")
    parser.add_argument("--camera", action="store_true", help="Also launch camera server")
    parser.add_argument("--camera-device", type=int, default=4, help="Camera device ID (default: 4)")
    parser.add_argument("--camera-fps", type=int, default=30, help="Camera FPS (default: 30)")
    parser.add_argument("--camera-width", type=int, default=640, help="Camera width (default: 640)")
    parser.add_argument("--camera-height", type=int, default=480, help="Camera height (default: 480)")
    parser.add_argument("--camera-port", type=int, default=5555, help="Camera ZMQ port (default: 5555)")
    args = parser.parse_args()
    # Optionally start camera server in background thread
    camera_thread = None
    if args.camera:
        camera_config = {
            "fps": args.camera_fps,
            "cameras": {
                "head_camera": {
                    "device_id": args.camera_device,
                    "shape": [args.camera_height, args.camera_width],
                }
            },
        }
        camera_server = ImageServer(camera_config, port=args.camera_port)
        camera_thread = threading.Thread(target=camera_server.run, daemon=True)
        camera_thread.start()
        print(f"Camera server started on port {args.camera_port} (device {args.camera_device})")
    # initialize DDS
    ChannelFactoryInitialize(0)
@@ -235,8 +206,6 @@ def main() -> None:
        shutdown_event.set()
        ctx.term()  # terminates blocking zmq.recv() calls
        t_state.join(timeout=2.0)
        if camera_thread is not None:
            camera_thread.join(timeout=2.0)
 if __name__ == "__main__":
@@ -14,67 +14,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from __future__ import annotations
 import logging
 import struct
 import threading
 import time
 from dataclasses import dataclass, field
 from functools import cached_property
-from typing import TYPE_CHECKING, Protocol, runtime_checkable
+from typing import Any
 import numpy as np
 from lerobot.cameras.utils import make_cameras_from_configs
 from lerobot.envs.factory import make_env
 from lerobot.processor import RobotAction, RobotObservation
-from lerobot.robots.unitree_g1.g1_kinematics import G1_29_ArmIK
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointArmIndex, G1_29_JointIndex
-from lerobot.robots.unitree_g1.g1_utils import (
+from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK
    REMOTE_AXES,
    REMOTE_KEYS,
    G1_29_JointArmIndex,
    G1_29_JointIndex,
    default_remote_input,
    make_locomotion_controller,
 )
 from lerobot.utils.import_utils import _unitree_sdk_available
 from ..robot import Robot
 from .config_unitree_g1 import UnitreeG1Config
 if TYPE_CHECKING or _unitree_sdk_available:
    from unitree_sdk2py.core.channel import (
        ChannelFactoryInitialize as _SDKChannelFactoryInitialize,
        ChannelPublisher as _SDKChannelPublisher,
        ChannelSubscriber as _SDKChannelSubscriber,
    )
    from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
    from unitree_sdk2py.idl.unitree_hg.msg.dds_ import (
        LowCmd_ as hg_LowCmd,
        LowState_ as hg_LowState,
    )
    from unitree_sdk2py.utils.crc import CRC
 else:
    _SDKChannelFactoryInitialize = None
    _SDKChannelPublisher = None
    _SDKChannelSubscriber = None
    unitree_hg_msg_dds__LowCmd_ = None
    hg_LowCmd = None
    hg_LowState = None
    CRC = None
 logger = logging.getLogger(__name__)
@runtime_checkable
 class LocomotionController(Protocol):
    control_dt: float
    def run_step(self, action: dict, lowstate) -> dict: ...
    def reset(self) -> None: ...
 # DDS topic names follow Unitree SDK naming conventions
 # ruff: noqa: N816
 kTopicLowCommand_Debug = "rt/lowcmd"
@@ -103,7 +63,7 @@ class IMUState:
 class G1_29_LowState:  # noqa: N801
    motor_state: list[MotorState] = field(default_factory=lambda: [MotorState() for _ in G1_29_JointIndex])
    imu_state: IMUState = field(default_factory=IMUState)
-    wireless_remote: bytes | None = None  # Raw wireless remote data
+    wireless_remote: Any = None  # Raw wireless remote data
    mode_machine: int = 0  # Robot mode
@@ -111,6 +71,25 @@ class UnitreeG1(Robot):
    config_class = UnitreeG1Config
    name = "unitree_g1"
    # unitree remote controller
    class RemoteController:
        def __init__(self):
            self.lx = 0
            self.ly = 0
            self.rx = 0
            self.ry = 0
            self.button = [0] * 16
        def set(self, data):
            # wireless_remote
            keys = struct.unpack("H", data[2:4])[0]
            for i in range(16):
                self.button[i] = (keys & (1 << i)) >> i
            self.lx = struct.unpack("f", data[4:8])[0]
            self.rx = struct.unpack("f", data[8:12])[0]
            self.ry = struct.unpack("f", data[12:16])[0]
            self.ly = struct.unpack("f", data[20:24])[0]
    def __init__(self, config: UnitreeG1Config):
        super().__init__(config)
@@ -124,9 +103,11 @@ class UnitreeG1(Robot):
        # Import channel classes based on mode
        if config.is_simulation:
-            self._ChannelFactoryInitialize = _SDKChannelFactoryInitialize
+            from unitree_sdk2py.core.channel import (
-            self._ChannelPublisher = _SDKChannelPublisher
+                ChannelFactoryInitialize,
-            self._ChannelSubscriber = _SDKChannelSubscriber
+                ChannelPublisher,
                ChannelSubscriber,
            )
        else:
            from lerobot.robots.unitree_g1.unitree_sdk2_socket import (
                ChannelFactoryInitialize,
@@ -134,30 +115,22 @@ class UnitreeG1(Robot):
                ChannelSubscriber,
            )
-            self._ChannelFactoryInitialize = ChannelFactoryInitialize
+        # Store for use in connect()
-            self._ChannelPublisher = ChannelPublisher
+        self._ChannelFactoryInitialize = ChannelFactoryInitialize
-            self._ChannelSubscriber = ChannelSubscriber
+        self._ChannelPublisher = ChannelPublisher
        self._ChannelSubscriber = ChannelSubscriber
        # Initialize state variables
        self.sim_env = None
        self._env_wrapper = None
        self._lowstate = None
        self._lowstate_lock = threading.Lock()
        self._shutdown_event = threading.Event()
        self.subscribe_thread = None
        self.remote_controller = self.RemoteController()
-        self.arm_ik = G1_29_ArmIK() if config.gravity_compensation else None
+        self.arm_ik = G1_29_ArmIK()
-        # Lower-body controller loaded dynamically
+    def _subscribe_motor_state(self):  # polls robot state @ 250Hz
        self.controller: LocomotionController | None = make_locomotion_controller(config.controller)
        # Controller thread state
        self._controller_thread = None
        self._controller_action_lock = threading.Lock()
        self.controller_input = default_remote_input()
        self.controller_output = {}
    def _subscribe_lowstate(self):  # polls robot state @ 250Hz
        while not self._shutdown_event.is_set():
            start_time = time.time()
@@ -170,11 +143,11 @@ class UnitreeG1(Robot):
                lowstate = G1_29_LowState()
                # Capture motor states using jointindex
-                for joint in G1_29_JointIndex:
+                for id in G1_29_JointIndex:
-                    lowstate.motor_state[joint].q = msg.motor_state[joint].q
+                    lowstate.motor_state[id].q = msg.motor_state[id].q
-                    lowstate.motor_state[joint].dq = msg.motor_state[joint].dq
+                    lowstate.motor_state[id].dq = msg.motor_state[id].dq
-                    lowstate.motor_state[joint].tau_est = msg.motor_state[joint].tau_est
+                    lowstate.motor_state[id].tau_est = msg.motor_state[id].tau_est
-                    lowstate.motor_state[joint].temperature = msg.motor_state[joint].temperature
+                    lowstate.motor_state[id].temperature = msg.motor_state[id].temperature
                # Capture IMU state
                lowstate.imu_state.quaternion = list(msg.imu_state.quaternion)
@@ -189,106 +162,31 @@ class UnitreeG1(Robot):
                # Capture mode_machine
                lowstate.mode_machine = msg.mode_machine
-                with self._lowstate_lock:
+                self._lowstate = lowstate
                    self._lowstate = lowstate
            current_time = time.time()
            all_t_elapsed = current_time - start_time
            sleep_time = max(0, (self.control_dt - all_t_elapsed))  # maintain constant control dt
            time.sleep(sleep_time)
    def publish_lowcmd(
        self,
        action: RobotAction,
        kp: np.ndarray | list[float] | None = None,
        kd: np.ndarray | list[float] | None = None,
        tau: np.ndarray | list[float] | None = None,
    ) -> None:  # writes robot command whenever requested
        for motor in G1_29_JointIndex:
            key = f"{motor.name}.q"
            if key in action:
                self.msg.motor_cmd[motor.value].q = action[key]
                self.msg.motor_cmd[motor.value].qd = 0
                self.msg.motor_cmd[motor.value].kp = (
                    kp[motor.value] if kp is not None else self.kp[motor.value]
                )
                self.msg.motor_cmd[motor.value].kd = (
                    kd[motor.value] if kd is not None else self.kd[motor.value]
                )
                self.msg.motor_cmd[motor.value].tau = tau[motor.value] if tau is not None else 0.0
        self.msg.crc = self.crc.Crc(self.msg)
        self.lowcmd_publisher.Write(self.msg)
    @property
    def _cameras_ft(self) -> dict[str, tuple]:
        return {
            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
        }
    @cached_property
    def observation_features(self) -> dict[str, type | tuple]:
        return {**self._motors_ft, **self._cameras_ft}
    @cached_property
    def action_features(self) -> dict[str, type]:
-        if self.controller is None:
+        return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
            return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
-        arm_features = {f"{G1_29_JointArmIndex(motor).name}.q": float for motor in G1_29_JointArmIndex}
+    def calibrate(self) -> None:  # robot is already calibrated
        remote_features = dict.fromkeys(REMOTE_AXES, float)
        return {**arm_features, **remote_features}
    def _controller_loop(self):
        """Background thread that runs controller at policy's control_dt."""
        control_dt = self.controller.control_dt
        logger.info(f"Controller loop starting with control_dt={control_dt} ({1.0 / control_dt:.1f}Hz)")
        loop_count = 0
        last_log_time = time.time()
        while not self._shutdown_event.is_set():
            start_time = time.time()
            with self._lowstate_lock:
                lowstate = self._lowstate
            if lowstate is not None and self.controller is not None:
                loop_count += 1
                if time.time() - last_log_time >= 5.0:  # Log every 5 seconds
                    actual_hz = loop_count / (time.time() - last_log_time)
                    logger.info(
                        f"Controller actual rate: {actual_hz:.1f}Hz (target: {1.0 / control_dt:.1f}Hz)"
                    )
                    loop_count = 0
                    last_log_time = time.time()
                # Read controller input snapshot
                with self._controller_action_lock:
                    controller_input = dict(self.controller_input)
                # Run controller step
                controller_action = self.controller.run_step(controller_input, lowstate)
                # Write controller output snapshot
                with self._controller_action_lock:
                    self.controller_output = dict(controller_action)
                ctrl_kp = self.controller.kp if hasattr(self.controller, "kp") else None
                ctrl_kd = self.controller.kd if hasattr(self.controller, "kd") else None
                self.publish_lowcmd(controller_action, kp=ctrl_kp, kd=ctrl_kd)
            elapsed = time.time() - start_time
            sleep_time = max(0, control_dt - elapsed)
            time.sleep(sleep_time)
    def calibrate(self) -> None:
        # TODO: implement g1_29 calibration
        pass
    def configure(self) -> None:
        pass
    def connect(self, calibrate: bool = True) -> None:  # connect to DDS
        from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
        from unitree_sdk2py.idl.unitree_hg.msg.dds_ import (
            LowCmd_ as hg_LowCmd,
            LowState_ as hg_LowState,
        )
        from unitree_sdk2py.utils.crc import CRC
        # Initialize DDS channel and simulation environment
        if self.config.is_simulation:
            self._ChannelFactoryInitialize(0, "lo")
@@ -296,7 +194,7 @@ class UnitreeG1(Robot):
            # Extract the actual gym env from the dict structure
            self.sim_env = self._env_wrapper["hub_env"][0].envs[0]
        else:
-            self._ChannelFactoryInitialize(0, config=self.config)
+            self._ChannelFactoryInitialize(0)
        # Initialize direct motor control interface
        self.lowcmd_publisher = self._ChannelPublisher(kTopicLowCommand_Debug, hg_LowCmd)
@@ -305,7 +203,7 @@ class UnitreeG1(Robot):
        self.lowstate_subscriber.Init()
        # Start subscribe thread to read robot state
-        self.subscribe_thread = threading.Thread(target=self._subscribe_lowstate)
+        self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state)
        self.subscribe_thread.start()
        # Connect cameras
@@ -322,53 +220,25 @@ class UnitreeG1(Robot):
        # Wait for first state message to arrive
        lowstate = None
        deadline = time.time() + 10.0
        while lowstate is None:
-            with self._lowstate_lock:
+            lowstate = self._lowstate
                lowstate = self._lowstate
            if lowstate is None:
                if time.time() > deadline:
                    raise TimeoutError("Timed out waiting for robot state (10s)")
                logger.warning("[UnitreeG1] Waiting for robot state...")
                time.sleep(0.01)
-        logger.info("[UnitreeG1] Connected to robot.")
+            logger.warning("[UnitreeG1] Waiting for robot state...")
        logger.warning("[UnitreeG1] Connected to robot.")
        self.msg.mode_machine = lowstate.mode_machine
        # Initialize all motors with unified kp/kd from config
        self.kp = np.array(self.config.kp, dtype=np.float32)
        self.kd = np.array(self.config.kd, dtype=np.float32)
-        for joint in G1_29_JointIndex:
+        for id in G1_29_JointIndex:
-            self.msg.motor_cmd[joint].mode = 1
+            self.msg.motor_cmd[id].mode = 1
-            self.msg.motor_cmd[joint].kp = self.kp[joint.value]
+            self.msg.motor_cmd[id].kp = self.kp[id.value]
-            self.msg.motor_cmd[joint].kd = self.kd[joint.value]
+            self.msg.motor_cmd[id].kd = self.kd[id.value]
-            self.msg.motor_cmd[joint].q = lowstate.motor_state[joint.value].q
+            self.msg.motor_cmd[id].q = lowstate.motor_state[id.value].q
        # Start controller thread if enabled
        if self.controller is not None:
            self._controller_thread = threading.Thread(target=self._controller_loop, daemon=True)
            self._controller_thread.start()
            fps = int(1.0 / self.controller.control_dt)
            logger.info(f"Controller thread started ({fps}Hz)")
    def _send_zero_torque(self) -> None:
        """Send a zero-gain command to make joints passive before shutting down."""
        try:
            with self._lowstate_lock:
                lowstate = self._lowstate
            if lowstate is None:
                return
            action = {f"{motor.name}.q": lowstate.motor_state[motor.value].q for motor in G1_29_JointIndex}
            zero_gains = np.zeros(29, dtype=np.float32)
            self.publish_lowcmd(action, kp=zero_gains, kd=zero_gains, tau=zero_gains)
            logger.info("Sent zero-torque command for safe shutdown")
        except Exception as e:
            logger.warning(f"Failed to send zero-torque on disconnect: {e}")
    def disconnect(self):
        # Put robot in passive mode before stopping threads
        if not self.config.is_simulation:
            self._send_zero_torque()
        # Signal thread to stop and unblock any waits
        self._shutdown_event.set()
@@ -378,12 +248,6 @@ class UnitreeG1(Robot):
            if self.subscribe_thread.is_alive():
                logger.warning("Subscribe thread did not stop cleanly")
        # Wait for controller thread to finish
        if self._controller_thread is not None:
            self._controller_thread.join(timeout=2.0)
            if self._controller_thread.is_alive():
                logger.warning("Controller thread did not stop cleanly")
        # Close simulation environment
        if self.config.is_simulation and self.sim_env is not None:
            try:
@@ -410,8 +274,7 @@ class UnitreeG1(Robot):
            cam.disconnect()
    def get_observation(self) -> RobotObservation:
-        with self._lowstate_lock:
+        lowstate = self._lowstate
            lowstate = self._lowstate
        if lowstate is None:
            return {}
@@ -450,9 +313,14 @@ class UnitreeG1(Robot):
            obs["imu.rpy.pitch"] = lowstate.imu_state.rpy[1]
            obs["imu.rpy.yaw"] = lowstate.imu_state.rpy[2]
-        # Wireless remote (raw bytes for teleoperator)
+        # Controller - parse wireless_remote and add to obs
-        if lowstate.wireless_remote:
+        if lowstate.wireless_remote and len(lowstate.wireless_remote) >= 24:
-            obs["wireless_remote"] = lowstate.wireless_remote
+            self.remote_controller.set(lowstate.wireless_remote)
        obs["remote.buttons"] = self.remote_controller.button.copy()
        obs["remote.lx"] = self.remote_controller.lx
        obs["remote.ly"] = self.remote_controller.ly
        obs["remote.rx"] = self.remote_controller.rx
        obs["remote.ry"] = self.remote_controller.ry
        # Cameras - read images from ZMQ cameras
        for cam_name, cam in self._cameras.items():
@@ -460,63 +328,73 @@ class UnitreeG1(Robot):
        return obs
    def send_action(self, action: RobotAction) -> RobotAction:
        action_to_publish = action
        if self.controller is not None:
            # Controller thread owns legs/waist. Here we only update joystick inputs
            # and publish arm targets from the teleoperator.
            self._update_controller_action(action)
            arm_prefixes = tuple(j.name for j in G1_29_JointArmIndex)
            action_to_publish = {
                key: value
                for key, value in action.items()
                if key.endswith(".q") and key.startswith(arm_prefixes)
            }
        tau = None
        if self.config.gravity_compensation and self.arm_ik is not None:
            tau = np.zeros(29, dtype=np.float32)
            action_np = np.array(
                [
                    action_to_publish.get(f"{joint.name}.q", self.msg.motor_cmd[joint.value].q)
                    for joint in G1_29_JointArmIndex
                ],
                dtype=np.float32,
            )
            arm_tau = self.arm_ik.solve_tau(action_np)
            arm_start_idx = G1_29_JointArmIndex.kLeftShoulderPitch.value
            for joint in G1_29_JointArmIndex:
                local_idx = joint.value - arm_start_idx
                tau[joint.value] = arm_tau[local_idx]
        self.publish_lowcmd(action_to_publish, tau=tau)
        return action
    def _update_controller_action(self, action: RobotAction) -> None:
        """Update controller input state from incoming teleop action."""
        with self._controller_action_lock:
            for key in REMOTE_KEYS:
                if key in action:
                    self.controller_input[key] = action[key]
    @property
    def is_calibrated(self) -> bool:
        return True
    @property
    def is_connected(self) -> bool:
-        with self._lowstate_lock:
+        return self._lowstate is not None
            return self._lowstate is not None
    @property
    def _motors_ft(self) -> dict[str, type]:
        """Joint positions for all 29 joints."""
        return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
    @property
    def cameras(self) -> dict:
        return self._cameras
    @property
    def _cameras_ft(self) -> dict[str, tuple]:
        return {
            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
        }
    @cached_property
    def observation_features(self) -> dict[str, type | tuple]:
        return {**self._motors_ft, **self._cameras_ft}
    def send_action(self, action: RobotAction) -> RobotAction:
        for motor in G1_29_JointIndex:
            key = f"{motor.name}.q"
            if key in action:
                self.msg.motor_cmd[motor.value].q = action[key]
                self.msg.motor_cmd[motor.value].qd = 0
                self.msg.motor_cmd[motor.value].kp = self.kp[motor.value]
                self.msg.motor_cmd[motor.value].kd = self.kd[motor.value]
                self.msg.motor_cmd[motor.value].tau = 0
        if self.config.gravity_compensation:
            # Build action_np from motor commands (arm joints are indices 15-28, local indices 0-13)
            action_np = np.zeros(14)
            arm_start_idx = G1_29_JointArmIndex.kLeftShoulderPitch.value  # 15
            for joint in G1_29_JointArmIndex:
                local_idx = joint.value - arm_start_idx
                action_np[local_idx] = self.msg.motor_cmd[joint.value].q
            tau = self.arm_ik.solve_tau(action_np)
            # Apply tau back to motor commands
            for joint in G1_29_JointArmIndex:
                local_idx = joint.value - arm_start_idx
                self.msg.motor_cmd[joint.value].tau = tau[local_idx]
        self.msg.crc = self.crc.Crc(self.msg)
        self.lowcmd_publisher.Write(self.msg)
        return action
    def get_gravity_orientation(self, quaternion):  # get gravity orientation from quaternion
        """Get gravity orientation from quaternion."""
        qw = quaternion[0]
        qx = quaternion[1]
        qy = quaternion[2]
        qz = quaternion[3]
        gravity_orientation = np.zeros(3)
        gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
        gravity_orientation[1] = -2 * (qz * qy + qw * qx)
        gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
        return gravity_orientation
    def reset(
        self,
        control_dt: float | None = None,
@@ -529,9 +407,15 @@ class UnitreeG1(Robot):
        if self.config.is_simulation and self.sim_env is not None:
            self.sim_env.reset()
-            self.publish_lowcmd(
+
-                {f"{motor.name}.q": float(default_positions[motor.value]) for motor in G1_29_JointIndex}
+            for motor in G1_29_JointIndex:
-            )
+                self.msg.motor_cmd[motor.value].q = default_positions[motor.value]
                self.msg.motor_cmd[motor.value].qd = 0
                self.msg.motor_cmd[motor.value].kp = self.kp[motor.value]
                self.msg.motor_cmd[motor.value].kd = self.kd[motor.value]
                self.msg.motor_cmd[motor.value].tau = 0
            self.msg.crc = self.crc.Crc(self.msg)
            self.lowcmd_publisher.Write(self.msg)
        else:
            total_time = 3.0
            num_steps = int(total_time / control_dt)
@@ -562,8 +446,4 @@ class UnitreeG1(Robot):
                sleep_time = max(0, control_dt - elapsed)
                time.sleep(sleep_time)
        # Reset controller internal state (gait phase, obs history, etc.)
        if self.controller is not None and hasattr(self.controller, "reset"):
            self.controller.reset()
        logger.info("Reached default position")
@@ -22,8 +22,6 @@ import zmq
 from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
 # Module-level ZMQ state mirrors the Unitree SDK's global ChannelFactory Singleton.
 # Only one robot connection per process is supported.
 _ctx: zmq.Context | None = None
 _lowcmd_sock: zmq.Socket | None = None
 _lowstate_sock: zmq.Socket | None = None
@@ -99,22 +97,17 @@ def lowcmd_to_dict(topic: str, msg: Any) -> dict[str, Any]:
    }
-def ChannelFactoryInitialize(domain_id: int = 0, config: Any = None) -> None:  # noqa: N802
+def ChannelFactoryInitialize(*args: Any, **kwargs: Any) -> None:  # noqa: N802
    """
    Initialize ZMQ sockets for robot communication.
    This function mimics the Unitree SDK's ChannelFactoryInitialize but uses
    ZMQ sockets to connect to the robot server bridge instead of DDS.
    Args:
        domain_id: Ignored (for API compatibility with Unitree SDK)
        config: UnitreeG1Config instance with robot_ip
    """
    global _ctx, _lowcmd_sock, _lowstate_sock
    # read socket config
-    if config is None:
+    config = UnitreeG1Config()
        config = UnitreeG1Config()
    robot_ip = config.robot_ip
    ctx = zmq.Context.instance()
@@ -56,7 +56,6 @@ from lerobot.teleoperators import (  # noqa: F401
    make_teleoperator_from_config,
    omx_leader,
    openarm_leader,
    openarm_mini,
    so_leader,
    unitree_g1,
 )
@@ -132,13 +132,10 @@ def visualize_dataset(
    logging.info("Logging to Rerun")
    first_index = None
    for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
        if first_index is None:
            first_index = batch["index"][0].item()
        # iterate over the batch
        for i in range(len(batch["index"])):
-            rr.set_time("frame_index", sequence=batch["index"][i].item() - first_index)
+            rr.set_time("frame_index", sequence=batch["frame_index"][i].item())
            rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())
            # display each camera image
@@ -21,9 +21,6 @@ This script allows you to delete episodes, split datasets, merge datasets,
 remove features, modify tasks, and convert image datasets to video format.
 When new_repo_id is specified, creates a new dataset.
 Path semantics (v2): --root and --new_root are exact dataset folders containing
 meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
 Usage Examples:
 Delete episodes 0, 2, and 5 from a dataset:
@@ -32,34 +29,19 @@ Delete episodes 0, 2, and 5 from a dataset:
        --operation.type delete_episodes \
        --operation.episode_indices "[0, 2, 5]"
-Delete episodes from a local dataset at a specific path:
+Delete episodes and save to a new dataset:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --root /path/to/pusht \
        --operation.type delete_episodes \
        --operation.episode_indices "[0, 2, 5]"
 Delete episodes and save to a new dataset at a specific path and with a new repo_id:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --new_repo_id lerobot/pusht_filtered \
        --new_root /path/to/pusht_filtered \
        --operation.type delete_episodes \
        --operation.episode_indices "[0, 2, 5]"
-Split dataset by fractions (pusht_train, pusht_val):
+Split dataset by fractions:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --operation.type split \
        --operation.splits '{"train": 0.8, "val": 0.2}'
 Split dataset by fractions and save split datasets to a specific folder (base_folder/train, base_folder/val):
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --new_root /path/to/base_folder \
        --operation.type split \
        --operation.splits '{"train": 0.8, "val": 0.2}'
 Split dataset by episode indices:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
@@ -74,29 +56,15 @@ Split into more than two splits:
 Merge multiple datasets:
    lerobot-edit-dataset \
-        --new_repo_id lerobot/pusht_merged \
+        --repo_id lerobot/pusht_merged \
        --operation.type merge \
        --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
 Merge multiple datasets to a specific output path:
    lerobot-edit-dataset \
        --new_repo_id lerobot/pusht_merged \
        --new_root /path/to/pusht_merged \
        --operation.type merge \
        --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
 Merge multiple datasets from a list of local dataset paths:
    lerobot-edit-dataset \
        --new_repo_id lerobot/pusht_merged \
        --operation.type merge \
        --operation.repo_ids "['pusht_train', 'pusht_val']" \
        --operation.roots "['/path/to/pusht_train', '/path/to/pusht_val']"
 Remove camera feature:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --operation.type remove_feature \
-        --operation.feature_names "['observation.image']"
+        --operation.feature_names "['observation.images.top']"
 Modify tasks - set a single task for all episodes (WARNING: modifies in-place):
    lerobot-edit-dataset \
@@ -120,8 +88,8 @@ Modify tasks - set default task with overrides for specific episodes (WARNING: m
 Convert image dataset to video format and save locally:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht_image \
-        --new_root /path/to/output/pusht_video \
+        --operation.type convert_image_to_video \
-        --operation.type convert_image_to_video
+        --operation.output_dir /path/to/output/pusht_video
 Convert image dataset to video format and save with new repo_id:
    lerobot-edit-dataset \
@@ -136,6 +104,28 @@ Convert image dataset to video format and push to hub:
        --operation.type convert_image_to_video \
        --push_to_hub true
 Trim single episode to keep only frames within timestamp range:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --new_repo_id lerobot/pusht_trimmed \
        --operation.type trim_episode \
        --operation.episode_index 0 \
        --operation.start_timestamp 10.0 \
        --operation.end_timestamp 30.0
 Trim multiple episodes at once (use null for no limit):
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --operation.type trim_episode \
        --operation.episode_trims '{"0": [10.0, 30.0], "2": [5.0, null], "3": [null, 20.0]}'
 Trim and re-upload to same repo (overwrites original):
    python -m lerobot.scripts.lerobot_edit_dataset \
        --repo_id lerobot/pusht \
        --operation.type trim_episode \
        --operation.episode_index 0 \
        --operation.start_timestamp 10.0 \
        --push_to_hub true
 Show dataset information:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht_image \
@@ -199,7 +189,6 @@ class SplitConfig(OperationConfig):
@dataclass
 class MergeConfig(OperationConfig):
    repo_ids: list[str] | None = None
    roots: list[str] | None = None
@OperationConfig.register_subclass("remove_feature")
@@ -233,46 +222,59 @@ class ConvertImageToVideoConfig(OperationConfig):
@OperationConfig.register_subclass("info")
@dataclass
 class InfoConfig(OperationConfig):
    type: str = "info"
    show_features: bool = False
@dataclass
 class TrimEpisodeConfig:
    """Trim episodes to keep only frames within timestamp ranges.
    Supports multiple episodes via episode_trims dict:
        --operation.episode_trims '{"0": [10.0, 30.0], "2": [5.0, 20.0]}'
    Or single episode via legacy parameters:
        --operation.episode_index 0 --operation.start_timestamp 10.0 --operation.end_timestamp 30.0
    """
    type: str = "trim_episode"
    # Multi-episode support: dict mapping episode_index -> [start_timestamp, end_timestamp]
    # Use null for no limit, e.g. {"0": [10.0, null], "2": [null, 30.0]}
    episode_trims: dict[str, list[float | None]] | None = None
    # Legacy single-episode parameters (used if episode_trims is None)
    episode_index: int | None = None
    start_timestamp: float | None = None  # Keep frames from this timestamp (inclusive)
    end_timestamp: float | None = None    # Keep frames until this timestamp (inclusive)
@dataclass
 class EditDatasetConfig:
-    # Operation configuration.
+    repo_id: str
    operation: (
        DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig | ConvertImageToVideoConfig | TrimEpisodeConfig
    )
    operation: OperationConfig
    # Input dataset identifier. Always required unless for Merge operation.
    repo_id: str | None = None
    # Root directory where the input dataset is stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
    root: str | None = None
    # Edited dataset identifier. When both new_repo_id (resp. new_root) and repo_id (resp. root) are identical, modifications are applied in-place and a backup of the original dataset is created. Required for Merge operation.
    new_repo_id: str | None = None
    # Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/new_repo_id. For Split operation, this is the base directory for the split datasets.
    new_root: str | None = None
    # Upload dataset to Hugging Face hub.
    push_to_hub: bool = False
-def get_output_path(
+def get_output_path(repo_id: str, new_repo_id: str | None, root: Path | None) -> tuple[str, Path]:
-    repo_id: str,
+    if new_repo_id:
-    new_repo_id: str | None,
+        output_repo_id = new_repo_id
-    root: Path | str | None,
+        output_dir = root / new_repo_id if root else HF_LEROBOT_HOME / new_repo_id
-    new_root: Path | str | None,
+    else:
-) -> tuple[str, Path]:
+        output_repo_id = repo_id
-    input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
+        dataset_path = root / repo_id if root else HF_LEROBOT_HOME / repo_id
        old_path = Path(str(dataset_path) + "_old")
-    output_repo_id = new_repo_id if new_repo_id else repo_id
+        if dataset_path.exists():
-    output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
+            if old_path.exists():
                shutil.rmtree(old_path)
            shutil.move(str(dataset_path), str(old_path))
-    # In case of in-place modification, create a backup of the original dataset (if it exists)
+        output_dir = dataset_path
    if output_path == input_path:
        backup_path = input_path.with_name(input_path.name + "_old")
-        if input_path.exists():
+    return output_repo_id, output_dir
            if backup_path.exists():
                shutil.rmtree(backup_path)
            shutil.move(input_path, backup_path)
    return output_repo_id, output_path
 def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
@@ -284,15 +286,11 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    output_repo_id, output_dir = get_output_path(
-        cfg.repo_id,
+        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
        new_repo_id=cfg.new_repo_id,
        root=cfg.root,
        new_root=cfg.new_root,
    )
-    # In case of in-place modification, make the dataset point to the backup directory
+    if cfg.new_repo_id is None:
-    if output_dir == dataset.root:
+        dataset.root = Path(str(dataset.root) + "_old")
        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
    logging.info(f"Deleting episodes {cfg.operation.episode_indices} from {cfg.repo_id}")
    new_dataset = delete_episodes(
@@ -319,27 +317,19 @@ def handle_split(cfg: EditDatasetConfig) -> None:
            "splits dict must be specified with split names as keys and fractions/episode lists as values"
        )
    if cfg.new_repo_id is not None:
        logging.warning(
            "split uses the original dataset identifier --repo_id to generate split names. The --new_repo_id parameter is ignored."
        )
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    logging.info(f"Splitting dataset {cfg.repo_id} with splits: {cfg.operation.splits}")
-    split_datasets = split_dataset(
+    split_datasets = split_dataset(dataset, splits=cfg.operation.splits)
        dataset,
        splits=cfg.operation.splits,
        output_dir=cfg.new_root,
    )
    for split_name, split_ds in split_datasets.items():
        split_repo_id = f"{cfg.repo_id}_{split_name}"
        logging.info(
            f"{split_name}: {split_ds.meta.total_episodes} episodes, {split_ds.meta.total_frames} frames"
        )
        if cfg.push_to_hub:
-            logging.info(f"Pushing {split_name} split to hub as {split_ds.repo_id}")
+            logging.info(f"Pushing {split_name} split to hub as {split_repo_id}")
            LeRobotDataset(split_ds.repo_id, root=split_ds.root).push_to_hub()
@@ -350,29 +340,18 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
    if not cfg.operation.repo_ids:
        raise ValueError("repo_ids must be specified for merge operation")
-    if cfg.repo_id is not None or cfg.root is not None:
+    if not cfg.repo_id:
-        logging.warning(
+        raise ValueError("repo_id must be specified as the output repository for merged dataset")
            "merge uses --new_repo_id and --new_root for the merged dataset. The --repo_id and --root parameters are ignored."
        )
-    if cfg.operation.roots:
+    logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
-        if len(cfg.operation.roots) != len(cfg.operation.repo_ids):
+    datasets = [LeRobotDataset(repo_id, root=cfg.root) for repo_id in cfg.operation.repo_ids]
            raise ValueError("repo_ids and roots must have the same length for merge operation")
        logging.info(f"Loading {len(cfg.operation.roots)} datasets to merge")
        datasets = [
            LeRobotDataset(repo_id=repo_id, root=root)
            for repo_id, root in zip(cfg.operation.repo_ids, cfg.operation.roots, strict=True)
        ]
    else:
        logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
        datasets = [LeRobotDataset(repo_id) for repo_id in cfg.operation.repo_ids]
-    output_dir = Path(cfg.new_root) if cfg.new_root else HF_LEROBOT_HOME / cfg.new_repo_id
+    output_dir = Path(cfg.root) / cfg.repo_id if cfg.root else HF_LEROBOT_HOME / cfg.repo_id
-    logging.info(f"Merging datasets into {cfg.new_repo_id}")
+    logging.info(f"Merging datasets into {cfg.repo_id}")
    merged_dataset = merge_datasets(
        datasets,
-        output_repo_id=cfg.new_repo_id,
+        output_repo_id=cfg.repo_id,
        output_dir=output_dir,
    )
@@ -382,7 +361,7 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
    )
    if cfg.push_to_hub:
-        logging.info(f"Pushing to hub as {cfg.new_repo_id}")
+        logging.info(f"Pushing to hub as {cfg.repo_id}")
        LeRobotDataset(merged_dataset.repo_id, root=output_dir).push_to_hub()
@@ -395,15 +374,11 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    output_repo_id, output_dir = get_output_path(
-        cfg.repo_id,
+        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
        new_repo_id=cfg.new_repo_id,
        root=cfg.root,
        new_root=cfg.new_root,
    )
-    # In case of in-place modification, make the dataset point to the backup directory
+    if cfg.new_repo_id is None:
-    if output_dir == dataset.root:
+        dataset.root = Path(str(dataset.root) + "_old")
        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
    logging.info(f"Removing features {cfg.operation.feature_names} from {cfg.repo_id}")
    new_dataset = remove_feature(
@@ -421,6 +396,92 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
 def handle_trim_episode(cfg: EditDatasetConfig) -> None:
    """Trim episodes to keep only frames within timestamp ranges."""
    if not isinstance(cfg.operation, TrimEpisodeConfig):
        raise ValueError("Operation config must be TrimEpisodeConfig")
    # Parse episode trims - support both multi-episode dict and legacy single episode
    episode_trims: dict[int, tuple[float | None, float | None]] = {}
    if cfg.operation.episode_trims is not None:
        # Multi-episode mode
        for ep_str, ts_range in cfg.operation.episode_trims.items():
            ep_idx = int(ep_str)
            start_ts = ts_range[0] if len(ts_range) > 0 else None
            end_ts = ts_range[1] if len(ts_range) > 1 else None
            episode_trims[ep_idx] = (start_ts, end_ts)
    elif cfg.operation.episode_index is not None:
        # Legacy single-episode mode
        if cfg.operation.start_timestamp is None and cfg.operation.end_timestamp is None:
            raise ValueError("At least one of start_timestamp or end_timestamp must be specified")
        episode_trims[cfg.operation.episode_index] = (
            cfg.operation.start_timestamp,
            cfg.operation.end_timestamp,
        )
    else:
        raise ValueError("Either episode_trims or episode_index must be specified")
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    output_repo_id, output_dir = get_output_path(
        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
    )
    if cfg.new_repo_id is None:
        dataset.root = Path(str(dataset.root) + "_old")
    logging.info(f"Trimming {len(episode_trims)} episode(s) from {cfg.repo_id}")
    # Get episode boundaries and find frames to keep for each episode
    episodes_info = dataset.meta.episodes
    all_frames_to_keep: dict[int, list[int]] = {}
    for ep_idx, (start_ts, end_ts) in episode_trims.items():
        if ep_idx >= len(episodes_info["episode_index"]):
            raise ValueError(f"Episode {ep_idx} does not exist (dataset has {len(episodes_info['episode_index'])} episodes)")
        from_frame = episodes_info["dataset_from_index"][ep_idx]
        to_frame = episodes_info["dataset_to_index"][ep_idx]
        logging.info(f"Episode {ep_idx}: trimming to [{start_ts}, {end_ts}]")
        logging.info(f"  Original frames: {from_frame} to {to_frame} ({to_frame - from_frame} frames)")
        # Find frames within timestamp range
        frames_to_keep = []
        for frame_idx in range(from_frame, to_frame):
            frame = dataset.hf_dataset[frame_idx]
            ts = frame["timestamp"]
            in_range = True
            if start_ts is not None and ts < start_ts:
                in_range = False
            if end_ts is not None and ts > end_ts:
                in_range = False
            if in_range:
                frames_to_keep.append(frame_idx)
        if not frames_to_keep:
            raise ValueError(f"Episode {ep_idx}: No frames found in timestamp range [{start_ts}, {end_ts}]")
        logging.info(f"  Keeping {len(frames_to_keep)} frames (indices {frames_to_keep[0]} to {frames_to_keep[-1]})")
        all_frames_to_keep[ep_idx] = frames_to_keep
    from lerobot.datasets.dataset_tools import trim_episodes_by_frames
    new_dataset = trim_episodes_by_frames(
        dataset,
        episode_frames_to_keep=all_frames_to_keep,
        output_dir=output_dir,
        repo_id=output_repo_id,
    )
    logging.info(f"Dataset saved to {output_dir}")
    logging.info(f"Episodes: {new_dataset.meta.total_episodes}, Frames: {new_dataset.meta.total_frames}")
    if cfg.push_to_hub:
        logging.info(f"Pushing to hub as {output_repo_id}")
        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
 def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
    if not isinstance(cfg.operation, ModifyTasksConfig):
        raise ValueError("Operation config must be ModifyTasksConfig")
@@ -431,10 +492,9 @@ def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
    if new_task is None and episode_tasks_raw is None:
        raise ValueError("Must specify at least one of new_task or episode_tasks for modify_tasks operation")
-    if cfg.new_repo_id is not None or cfg.new_root is not None:
+    # Warn about in-place modification behavior
-        logging.warning(
+    if cfg.new_repo_id is not None:
-            "modify_tasks modifies datasets in-place. The --new_repo_id and --new_root parameters are ignored."
+        logging.warning("modify_tasks modifies datasets in-place. The --new_repo_id parameter is ignored.")
        )
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    logging.warning(f"Modifying dataset in-place at {dataset.root}. Original data will be overwritten.")
@@ -470,30 +530,32 @@ def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    # Determine output directory and repo_id
-    # Priority: 1) new_root, 2) new_repo_id, 3) operation.output_dir, 4) auto-generated name
+    # Priority: 1) new_repo_id, 2) operation.output_dir, 3) auto-generated name
    output_dir_config = getattr(cfg.operation, "output_dir", None)
    if output_dir_config:
        logging.warning(
            "--operation.output_dir is deprecated and will be removed in future versions. "
            "Please use --new_root instead."
        )
-    if cfg.new_root:
+    if cfg.new_repo_id:
-        output_dir = Path(cfg.new_root)
+        # Use new_repo_id for both local storage and hub push
        output_repo_id = cfg.new_repo_id or f"{cfg.repo_id}_video"
        logging.info(f"Saving to new_root: {output_dir} as {output_repo_id}")
    elif cfg.new_repo_id:
        output_repo_id = cfg.new_repo_id
-        output_dir = HF_LEROBOT_HOME / cfg.new_repo_id
+        # Place new dataset as a sibling to the original dataset
        # Get the parent of the actual dataset root (not cfg.root which might be the lerobot cache dir)
        # Extract just the dataset name (after last slash) for the local directory
        local_dir_name = cfg.new_repo_id.split("/")[-1]
        output_dir = dataset.root.parent / local_dir_name
        logging.info(f"Saving to new dataset: {cfg.new_repo_id} at {output_dir}")
    elif output_dir_config:
        # Use custom output directory for local-only storage
        output_dir = Path(output_dir_config)
        # Extract repo name from output_dir for the dataset
        output_repo_id = output_dir.name
-        logging.info(f"Saving to local directory: {output_dir} as {output_repo_id}")
+        logging.info(f"Saving to local directory: {output_dir}")
    else:
        # Auto-generate name: append "_video" to original repo_id
        output_repo_id = f"{cfg.repo_id}_video"
-        output_dir = HF_LEROBOT_HOME / output_repo_id
+        # Place new dataset as a sibling to the original dataset
-        logging.info(f"Saving to auto-generated location: {output_dir} as {output_repo_id}")
+        # Extract just the dataset name (after last slash) for the local directory
        local_dir_name = output_repo_id.split("/")[-1]
        output_dir = dataset.root.parent / local_dir_name
        logging.info(f"Saving to auto-generated location: {output_dir}")
    logging.info(f"Converting dataset {cfg.repo_id} to video format")
@@ -568,20 +630,8 @@ def handle_info(cfg: EditDatasetConfig):
        sys.stdout.write(f"{feature_dump_str}\n")
 def _validate_config(cfg: EditDatasetConfig) -> None:
    if isinstance(cfg.operation, MergeConfig):
        if not cfg.new_repo_id:
            raise ValueError("--new_repo_id is required for merge operation (the merged dataset identifier)")
    else:
        if not cfg.repo_id:
            raise ValueError(
                f"--repo_id is required for {cfg.operation.type} operation (the input dataset identifier)"
            )
@parser.wrap()
 def edit_dataset(cfg: EditDatasetConfig) -> None:
    _validate_config(cfg)
    operation_type = cfg.operation.type
    if operation_type == "delete_episodes":
@@ -596,6 +646,8 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
        handle_modify_tasks(cfg)
    elif operation_type == "convert_image_to_video":
        handle_convert_image_to_video(cfg)
    elif operation_type == "trim_episode":
        handle_trim_episode(cfg)
    elif operation_type == "info":
        handle_info(cfg)
    else:
@@ -61,7 +61,6 @@ from lerobot.teleoperators import (  # noqa: F401
    make_teleoperator_from_config,
    omx_leader,
    openarm_leader,
    openarm_mini,
    so_leader,
 )
 from lerobot.utils.robot_utils import precise_sleep
@@ -125,7 +125,6 @@ from lerobot.teleoperators import (  # noqa: F401
    make_teleoperator_from_config,
    omx_leader,
    openarm_leader,
    openarm_mini,
    reachy2_teleoperator,
    so_leader,
    unitree_g1,
@@ -155,7 +154,7 @@ class DatasetRecordConfig:
    repo_id: str
    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
    single_task: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
    root: str | Path | None = None
    # Limit the frames per second.
    fps: int = 30
@@ -334,7 +333,6 @@ def record_loop(
        preprocessor.reset()
        postprocessor.reset()
    no_action_count = 0
    timestamp = 0
    start_episode_t = time.perf_counter()
    while timestamp < control_time_s:
@@ -369,8 +367,6 @@ def record_loop(
            act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
        elif policy is None and isinstance(teleop, Teleoperator):
            if robot.name == "unitree_g1":
                teleop.send_feedback(obs)
            act = teleop.get_action()
            # Applies a pipeline to the raw teleop action, default is IdentityProcessor
@@ -384,13 +380,11 @@ def record_loop(
            act = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
            act_processed_teleop = teleop_action_processor((act, obs))
        else:
-            no_action_count += 1
+            logging.info(
-            if no_action_count == 1 or no_action_count % 10 == 0:
+                "No policy or teleoperator provided, skipping action generation."
-                logging.warning(
+                "This is likely to happen when resetting the environment without a teleop device."
-                    "No policy or teleoperator provided, skipping action generation. "
+                "The robot won't be at its rest position at the start of the next episode."
-                    "This is likely to happen when resetting the environment without a teleop device. "
+            )
                    "The robot won't be at its rest position at the start of the next episode."
                )
            continue
        # Applies a pipeline to the action, default is IdentityProcessor
@@ -558,6 +552,10 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                ):
                    log_say("Reset the environment", cfg.play_sounds)
                    # reset g1 robot
                    if robot.name == "unitree_g1":
                        robot.reset()
                    record_loop(
                        robot=robot,
                        events=events,
@@ -80,7 +80,7 @@ class DatasetReplayConfig:
    repo_id: str
    # Episode to replay.
    episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
    root: str | Path | None = None
    # Limit the frames per second. By default, uses the policy fps.
    fps: int = 30
@@ -60,7 +60,6 @@ import rerun as rr
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
 from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
 from lerobot.processor import (
    RobotAction,
@@ -95,7 +94,6 @@ from lerobot.teleoperators import (  # noqa: F401
    make_teleoperator_from_config,
    omx_leader,
    openarm_leader,
    openarm_mini,
    reachy2_teleoperator,
    so_leader,
    unitree_g1,
@@ -154,6 +152,7 @@ def teleop_loop(
    display_len = max(len(key) for key in robot.action_features)
    start = time.perf_counter()
    while True:
        loop_start = time.perf_counter()
@@ -163,9 +162,6 @@ def teleop_loop(
        # given that it is the identity processor as default
        obs = robot.get_observation()
        if robot.name == "unitree_g1":
            teleop.send_feedback(obs)
        # Get teleop action
        raw_action = teleop.get_action()
@@ -209,11 +209,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
    # Use accelerator's device
    device = accelerator.device
-    if cfg.cudnn_deterministic:
+    torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False
    else:
        torch.backends.cudnn.benchmark = True
    torch.backends.cuda.matmul.allow_tf32 = True
    # Dataset loading synchronization: main process downloads first to avoid race conditions
@@ -384,10 +380,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
        "dataloading_s": AverageMeter("data_s", ":.3f"),
    }
-    # Keep global batch size for logging; MetricsTracker handles world size internally.
+    # Use effective batch size for proper epoch calculation in distributed training
    effective_batch_size = cfg.batch_size * accelerator.num_processes
    train_tracker = MetricsTracker(
-        cfg.batch_size,
+        effective_batch_size,
        dataset.num_frames,
        dataset.num_episodes,
        train_metrics,
@@ -306,7 +306,7 @@ def train_fast_tokenizer(
    # download the tokenizer source code (not pretrained weights)
    # we'll train a new tokenizer on our own data
-    base_tokenizer = AutoProcessor.from_pretrained("lerobot/fast-action-tokenizer", trust_remote_code=True)
+    base_tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
    # convert action_chunks array to list of arrays (expected by .fit())
    action_data_list = [action_chunks[i] for i in range(len(action_chunks))]
@@ -15,6 +15,7 @@
 # limitations under the License.
 from dataclasses import dataclass
 from typing import TypeAlias
 from ..config import TeleoperatorConfig
@@ -37,5 +38,5 @@ class SOLeaderTeleopConfig(TeleoperatorConfig, SOLeaderConfig):
    pass
-SO100LeaderConfig = SOLeaderTeleopConfig
+SO100LeaderConfig: TypeAlias = SOLeaderTeleopConfig
-SO101LeaderConfig = SOLeaderTeleopConfig
+SO101LeaderConfig: TypeAlias = SOLeaderTeleopConfig
@@ -16,6 +16,7 @@
 import logging
 import time
 from typing import TypeAlias
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.feetech import (
@@ -155,5 +156,5 @@ class SOLeader(Teleoperator):
        logger.info(f"{self} disconnected.")
-SO100Leader = SOLeader
+SO100Leader: TypeAlias = SOLeader
-SO101Leader = SOLeader
+SO101Leader: TypeAlias = SOLeader
@@ -19,13 +19,3 @@ from .exo_calib import ExoskeletonCalibration, ExoskeletonJointCalibration
 from .exo_ik import ExoskeletonIKHelper
 from .exo_serial import ExoskeletonArm
 from .unitree_g1 import UnitreeG1Teleoperator
 __all__ = [
    "ExoskeletonArmPortConfig",
    "ExoskeletonCalibration",
    "ExoskeletonIKHelper",
    "ExoskeletonJointCalibration",
    "ExoskeletonArm",
    "UnitreeG1Teleoperator",
    "UnitreeG1TeleoperatorConfig",
 ]
@@ -35,9 +35,6 @@ import serial
 logger = logging.getLogger(__name__)
 ADC_MAX = 2**12 - 1
 ADC_HALF = ADC_MAX / 2
 # exoskeleton joint names -> ADC channel pairs. TODO: add wrist pitch and wrist yaw
 JOINTS = {
    "shoulder_pitch": (0, 1),
@@ -62,7 +59,7 @@ class ExoskeletonCalibration:
    version: int = 2
    side: str = ""
-    adc_max: int = ADC_MAX
+    adc_max: int = 2**12 - 1
    joints: list[ExoskeletonJointCalibration] = field(default_factory=list)
    def to_dict(self) -> dict:
@@ -95,7 +92,7 @@ class ExoskeletonCalibration:
        return cls(
            version=data.get("version", 2),
            side=data.get("side", ""),
-            adc_max=data.get("adc_max", ADC_MAX),
+            adc_max=data.get("adc_max", 2**12 - 1),
            joints=joints,
        )
@@ -115,8 +112,11 @@ class CalibParams:
 def normalize_angle(angle: float) -> float:
-    """Normalize angle to [-pi, pi]."""
+    while angle > np.pi:
-    return float(np.arctan2(np.sin(angle), np.cos(angle)))
+        angle -= 2 * np.pi
    while angle < -np.pi:
        angle += 2 * np.pi
    return angle
 def joint_z_and_angle(raw16: list[int], j: ExoskeletonJointCalibration) -> tuple[np.ndarray, float]:
@@ -125,7 +125,7 @@ def joint_z_and_angle(raw16: list[int], j: ExoskeletonJointCalibration) -> tuple
    """
    pair = JOINTS[j.name]
    s, c = raw16[pair[0]], raw16[pair[1]]  # get sin and cos
-    p = np.array([float(c) - ADC_HALF, float(s) - ADC_HALF])  # center the raw values
+    p = np.array([float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2])  # center the raw values
    z = np.asarray(j.T) @ (
        p - np.asarray(j.center_fit)
    )  # center the ellipse and invert the transformation matrix to get unit circle coords
@@ -167,7 +167,7 @@ def run_exo_calibration(
    def read_joint_point(raw16: list[int], pair: tuple[int, int]):
        s, c = raw16[pair[0]], raw16[pair[1]]
-        return float(c) - ADC_HALF, float(s) - ADC_HALF, float(s), float(c)
+        return float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2, float(s), float(c)
    def select_fit_subset(xs, ys):
        """Select and filter points for ellipse fitting. Trims outliers by radius and downsamples."""
@@ -317,7 +317,7 @@ def run_exo_calibration(
                        calib = ExoskeletonCalibration(
                            version=2,
                            side=side,
-                            adc_max=ADC_MAX,
+                            adc_max=2**12 - 1,
                            joints=[
                                ExoskeletonJointCalibration(
                                    name=j["name"],
@@ -367,8 +367,8 @@ def run_exo_calibration(
                    state["win_s"].append(s_raw)
                    state["win_c"].append(c_raw)
                    if len(state["win_s"]) >= max(3, params.median_window):
-                        state["ys"].append(running_median(state["win_s"]) - ADC_HALF)
+                        state["ys"].append(running_median(state["win_s"]) - (2**12 - 1) / 2)
-                        state["xs"].append(running_median(state["win_c"]) - ADC_HALF)
+                        state["xs"].append(running_median(state["win_c"]) - (2**12 - 1) / 2)
                else:
                    jdata = joints_out[-1]
                    z = np.array(jdata["T"]) @ (np.array([x_raw, y_raw]) - np.array(jdata["center_fit"]))
@@ -25,8 +25,8 @@ from dataclasses import dataclass
 import numpy as np
 from lerobot.robots.unitree_g1.g1_kinematics import G1_29_ArmIK
 from lerobot.robots.unitree_g1.g1_utils import G1_29_JointArmIndex
 from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK
 from .exo_calib import JOINTS
@@ -32,29 +32,25 @@ def parse_raw16(line: bytes) -> list[int] | None:
        if len(parts) < 16:
            return None
        return [int(x) for x in parts[:16]]
-    except (ValueError, IndexError):
+    except Exception:
        return None
 def read_raw_from_serial(ser) -> list[int] | None:
    """Read latest sample from serial; if buffer is backed up, keep only the newest."""
-    try:
+    last = None
-        last = None
+    while ser.in_waiting > 0:
-        while ser.in_waiting > 0:
+        b = ser.readline()
-            b = ser.readline()
+        if not b:
-            if not b:
+            break
-                break
+        raw16 = parse_raw16(b)
-            raw16 = parse_raw16(b)
+        if raw16 is not None:
-            if raw16 is not None:
+            last = raw16
-                last = raw16
+    if last is None:
-        if last is None:
+        b = ser.readline()
-            b = ser.readline()
+        if b:
-            if b:
+            last = parse_raw16(b)
-                last = parse_raw16(b)
+    return last
        return last
    except serial.SerialException as e:
        logger.warning(f"Serial read error: {e}")
        return None
@dataclass
@@ -119,6 +115,5 @@ class ExoskeletonArm:
        return {} if raw is None else exo_raw_to_angles(raw, self.calibration)
    def calibrate(self) -> None:
-        if not self.is_connected:
+        ser = self._ser
-            raise RuntimeError("Cannot calibrate: exoskeleton not connected")
+        self.calibration = run_exo_calibration(ser, self.side, self.calibration_fpath)
        self.calibration = run_exo_calibration(self._ser, self.side, self.calibration_fpath)
@@ -17,22 +17,9 @@
 import logging
 import time
 from functools import cached_property
 from typing import TYPE_CHECKING, Any
-from lerobot.robots.unitree_g1.g1_utils import REMOTE_AXES, G1_29_JointArmIndex
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
 from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, TELEOPERATORS
 from lerobot.utils.import_utils import _unitree_sdk_available
 if TYPE_CHECKING or _unitree_sdk_available:
    from unitree_sdk2py.utils.joystick import Joystick
 else:
    class Joystick:
        def __init__(self):
            raise ImportError(
                "unitree_sdk2py is required for RemoteController. Install with: pip install unitree_sdk2py"
            )
 from ..teleoperator import Teleoperator
 from .config_unitree_g1 import UnitreeG1TeleoperatorConfig
@@ -42,120 +29,6 @@ from .exo_serial import ExoskeletonArm
 logger = logging.getLogger(__name__)
 class RemoteController:
    """Unitree remote controller data parser for joystick and button state."""
    # ADC parameters for exoskeleton joystick (12-bit ADC)
    ADC_MAX = 4095
    ADC_HALF = ADC_MAX / 2
    JOYSTICK_X_IDX = 11  # X axis in raw ADC array
    JOYSTICK_BTN_IDX = 12  # Button in raw ADC array
    JOYSTICK_Y_IDX = 13  # Y axis in raw ADC array
    # Map SDK named buttons to positional indices matching the wireless_remote
    # byte layout (little-endian uint16 from bytes 2-3).
    _BUTTON_MAP: list[str] = [
        "RB",
        "LB",
        "start",
        "back",
        "RT",
        "LT",
        "",
        "",
        "A",
        "B",
        "X",
        "Y",
        "up",
        "right",
        "down",
        "left",
    ]
    def __init__(self):
        self.lx = 0.0
        self.ly = 0.0
        self.rx = 0.0
        self.ry = 0.0
        self.button = [0] * 16
        self.remote_action = dict.fromkeys(REMOTE_AXES, 0.0)
        # SDK joystick parser for wireless remote bytes
        self._joystick = Joystick()
        # Disable axis smoothing and deadzone to preserve raw values
        for axis in (self._joystick.lx, self._joystick.ly, self._joystick.rx, self._joystick.ry):
            axis.smooth = 1.0
            axis.deadzone = 0.0
        # Joystick center calibration (read at connect time)
        self.left_center_x = self.ADC_HALF
        self.left_center_y = self.ADC_HALF
        self.right_center_x = self.ADC_HALF
        self.right_center_y = self.ADC_HALF
        # Whether to use exo joystick (detected at connect time)
        self.use_left_exo_joystick = False
        self.use_right_exo_joystick = False
    def _sync_remote_action(self) -> None:
        self.remote_action.update(zip(REMOTE_AXES, (self.lx, self.ly, self.rx, self.ry), strict=True))
    def calibrate_center(self, raw16: list[int] | None, side: str) -> None:
        if raw16 is None or len(raw16) < 16:
            logger.info(f"{side.capitalize()} exo joystick: no data available")
            return
        btn_val = raw16[self.JOYSTICK_BTN_IDX]
        logger.info(f"{side.capitalize()} exo joystick button ADC: {btn_val} (threshold: {self.ADC_HALF})")
        if btn_val <= self.ADC_HALF:
            logger.info(f"{side.capitalize()} exo joystick not detected (button below threshold)")
            return
        x = raw16[self.JOYSTICK_X_IDX]
        y = raw16[self.JOYSTICK_Y_IDX]
        if side == "left":
            self.use_left_exo_joystick = True
            self.left_center_x, self.left_center_y = x, y
        else:
            self.use_right_exo_joystick = True
            self.right_center_x, self.right_center_y = x, y
        logger.info(f"{side.capitalize()} exo joystick enabled, center: x={x}, y={y}")
    def set_from_exo(self, raw16: list[int] | None, side: str) -> None:
        if raw16 is None or len(raw16) < 16:
            return
        if side == "left":
            if not self.use_left_exo_joystick:
                return
            self.lx = (raw16[self.JOYSTICK_X_IDX] - self.left_center_x) / self.ADC_HALF
            self.ly = (raw16[self.JOYSTICK_Y_IDX] - self.left_center_y) / self.ADC_HALF
            self.button[4] = 1 if raw16[self.JOYSTICK_BTN_IDX] < self.ADC_HALF else 0
            return
        if not self.use_right_exo_joystick:
            return
        self.rx = (raw16[self.JOYSTICK_X_IDX] - self.right_center_x) / self.ADC_HALF
        self.ry = (raw16[self.JOYSTICK_Y_IDX] - self.right_center_y) / self.ADC_HALF
        self.button[0] = 1 if raw16[self.JOYSTICK_BTN_IDX] < self.ADC_HALF else 0
    def set_from_wireless(self, wireless_remote: bytes) -> None:
        """Parse Unitree wireless remote raw bytes into joystick + button state."""
        if len(wireless_remote) < 24:
            return
        self._joystick.extract(wireless_remote)
        self.lx = self._joystick.lx.data
        self.ly = self._joystick.ly.data
        self.rx = self._joystick.rx.data
        self.ry = self._joystick.ry.data
        for i, name in enumerate(self._BUTTON_MAP):
            if name:
                self.button[i] = getattr(self._joystick, name).data
 class UnitreeG1Teleoperator(Teleoperator):
    """
    Bimanual exoskeleton arms teleoperator for Unitree G1 arms.
@@ -170,13 +43,6 @@ class UnitreeG1Teleoperator(Teleoperator):
    def __init__(self, config: UnitreeG1TeleoperatorConfig):
        super().__init__(config)
        self.config = config
        left_exo_enabled = bool(config.left_arm_config.port.strip())
        right_exo_enabled = bool(config.right_arm_config.port.strip())
        if left_exo_enabled != right_exo_enabled:
            raise ValueError(
                "Invalid exo config: set both left/right exo ports, or leave both empty for remote-only mode."
            )
        self._arm_control_enabled = left_exo_enabled and right_exo_enabled
        # Setup calibration directory
        self.calibration_dir = (
@@ -204,37 +70,24 @@ class UnitreeG1Teleoperator(Teleoperator):
        )
        self.ik_helper: ExoskeletonIKHelper | None = None
        self.remote_controller = RemoteController()
    @cached_property
    def action_features(self) -> dict[str, type]:
-        remote_features = dict.fromkeys(self.remote_controller.remote_action, float)
+        return {f"{name}.q": float for name in self._g1_joint_names}
        if not self._arm_control_enabled:
            return remote_features
        joint_features = {f"{name}.q": float for name in self._g1_arm_joint_names}
        return {**joint_features, **remote_features}
    @cached_property
    def feedback_features(self) -> dict[str, type]:
-        return {"wireless_remote": bytes}
+        return {}
    @property
    def is_connected(self) -> bool:
        if not self._arm_control_enabled:
            return True
        return self.left_arm.is_connected and self.right_arm.is_connected
    @property
    def is_calibrated(self) -> bool:
        if not self._arm_control_enabled:
            return True
        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
    def connect(self, calibrate: bool = True) -> None:
        if not self._arm_control_enabled:
            logger.warning("Exo ports not fully configured; teleop will send joystick only (no arm actions)")
            return
        self.left_arm.connect(calibrate)
        self.right_arm.connect(calibrate)
@@ -242,13 +95,6 @@ class UnitreeG1Teleoperator(Teleoperator):
        self.ik_helper = ExoskeletonIKHelper(frozen_joints=frozen_joints)
        logger.info("IK helper initialized")
        time.sleep(0.1)  # Give serial time to populate buffer
        left_raw = self.left_arm.read_raw()
        right_raw = self.right_arm.read_raw()
        self.remote_controller.calibrate_center(left_raw, "left")
        self.remote_controller.calibrate_center(right_raw, "right")
    def calibrate(self) -> None:
        if not self.left_arm.is_calibrated:
            logger.info("Starting calibration for left arm...")
@@ -269,33 +115,12 @@ class UnitreeG1Teleoperator(Teleoperator):
        pass
    def get_action(self) -> dict[str, float]:
-        joint_action = {}
+        left_angles = self.left_arm.get_angles()
-        left_raw = None
+        right_angles = self.right_arm.get_angles()
-        right_raw = None
+        return self.ik_helper.compute_g1_joints_from_exo(left_angles, right_angles)
        if self._arm_control_enabled:
            left_raw = self.left_arm.read_raw()
            right_raw = self.right_arm.read_raw()
-            left_angles = self.left_arm.get_angles()
+    def send_feedback(self, feedback: dict[str, float]) -> None:
-            right_angles = self.right_arm.get_angles()
+        raise NotImplementedError("Exoskeleton arms do not support feedback")
            joint_action = self.ik_helper.compute_g1_joints_from_exo(left_angles, right_angles)
        # Wireless remote has priority when non-zero; otherwise, use exo joystick.
        rc = self.remote_controller
        wireless_active = (
            abs(rc.lx) > 1e-3 or abs(rc.ly) > 1e-3 or abs(rc.rx) > 1e-3 or abs(rc.ry) > 1e-3
        ) or any(rc.button)
        if self._arm_control_enabled and not wireless_active:
            rc.set_from_exo(left_raw, "left")
            rc.set_from_exo(right_raw, "right")
        rc._sync_remote_action()
        return {**joint_action, **rc.remote_action}
    def send_feedback(self, feedback: dict[str, Any]) -> None:
        wireless_remote = feedback.get("wireless_remote")
        if wireless_remote is not None:
            self.remote_controller.set_from_wireless(wireless_remote)
    def disconnect(self) -> None:
        self.left_arm.disconnect()
@@ -328,5 +153,5 @@ class UnitreeG1Teleoperator(Teleoperator):
            print("\n\nVisualization stopped.")
    @cached_property
-    def _g1_arm_joint_names(self) -> list[str]:
+    def _g1_joint_names(self) -> list[str]:
-        return [joint.name for joint in G1_29_JointArmIndex]
+        return [joint.name for joint in G1_29_JointIndex]
@@ -74,8 +74,6 @@ _peft_available = is_package_available("peft")
 _scipy_available = is_package_available("scipy")
 _reachy2_sdk_available = is_package_available("reachy2_sdk")
 _can_available = is_package_available("python-can", "can")
 _unitree_sdk_available = is_package_available("unitree-sdk2", "unitree_sdk2py")
 _pygame_available = is_package_available("pygame")
 def make_device_from_device_class(config: ChoiceRegistry) -> Any:
@@ -16,10 +16,12 @@
 import json
 import warnings
 from pathlib import Path
 from typing import TypeVar
 import imageio
 JsonLike = str | int | float | bool | None | list["JsonLike"] | dict[str, "JsonLike"] | tuple["JsonLike", ...]
 T = TypeVar("T", bound=JsonLike)
 def write_video(video_path, stacked_frames, fps):
@@ -31,7 +33,7 @@ def write_video(video_path, stacked_frames, fps):
        imageio.mimsave(video_path, stacked_frames, fps=fps)
-def deserialize_json_into_object[T: JsonLike](fpath: Path, obj: T) -> T:
+def deserialize_json_into_object(fpath: Path, obj: T) -> T:
    """
    Loads the JSON data from `fpath` and recursively fills `obj` with the
    corresponding values (strictly matching structure and types).
@@ -104,10 +104,9 @@ class MetricsTracker:
        self.metrics = metrics
        self.steps = initial_step
        world_size = accelerator.num_processes if accelerator else 1
        # A sample is an (observation,action) pair, where observation and action
        # can be on multiple timestamps. In a batch, we have `batch_size` number of samples.
-        self.samples = self.steps * self._batch_size * world_size
+        self.samples = self.steps * self._batch_size
        self.episodes = self.samples / self._avg_samples_per_ep
        self.epochs = self.samples / self._num_frames
        self.accelerator = accelerator
@@ -133,8 +132,7 @@ class MetricsTracker:
        Updates metrics that depend on 'step' for one step.
        """
        self.steps += 1
-        world_size = self.accelerator.num_processes if self.accelerator else 1
+        self.samples += self._batch_size * (self.accelerator.num_processes if self.accelerator else 1)
        self.samples += self._batch_size * world_size
        self.episodes = self.samples / self._avg_samples_per_ep
        self.epochs = self.samples / self._num_frames
@@ -1,3 +1,3 @@
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-oid sha256:54aecbc1af72a4cd5e9261492f5e7601890517516257aacdf2a0ffb3ce281f1b
+oid sha256:19eaaa85f66ba4aa6388dbb83819ffad6ea4363247208f871a8dc385689f6fc8
 size 992
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:88a9c3775a2aa1e90a08850521970070a4fcf0f6b82aab43cd8ccc5cf77e0013
+oid sha256:227296eaeeb54acdc3dae2eb8af3d4d08fb87e245337624447140b1e91cfd002
 size 47424
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91a2635e05a75fe187a5081504c5f35ce3417378813fa2deaf9ca4e8200e1819
+oid sha256:271b00cb2f0cd5fd26b1d53463638e3d1a6e92692ec625fcffb420ca190869e5
 size 68
--- a/Show More
+++ b/Show More