print update hz

fix build frame
do evaluate with rtc
2026-05-12 15:19:43 +00:00 · 2026-01-09 11:35:51 +01:00 · 2026-01-09 11:31:29 +01:00 · 2026-01-09 11:20:20 +01:00 · 2026-01-09 11:08:02 +01:00 · 2026-01-09 10:21:02 +01:00
529 changed files with 32413 additions and 34594 deletions
@@ -22,21 +22,20 @@ Short, imperative summary (e.g., "fix(robots): handle None in sensor parser"). S
 - Short, concrete bullets of the modifications (files/behaviour).
 - Short note if this introduces breaking changes and migration steps.

-## How was this tested (or how to run locally)
+## How was this tested

 - Tests added: list new tests or test files.
 - Manual checks / dataset runs performed.
- Instructions for the reviewer

-Example:
+## How to run locally (reviewer)

- Ran the relevant tests:
+- Run the relevant tests:

  ```bash
  pytest -q tests/ -k <keyword>
  ```

- Reproduce with a quick example or CLI (if applicable):
+- Run a quick example or CLI (if applicable):

  ```bash
  lerobot-train --some.option=true
@@ -18,11 +18,6 @@ name: Documentation
 on:
  # Allows running this workflow manually from the Actions tab
  workflow_dispatch:
-    inputs:
-      version:
-        description: 'Version tag (e.g. v0.1.2) - Leave empty for standard main build'
-        required: false
-        type: string

  # Triggers the workflow on push events to main for the docs folder
  push:
@@ -59,13 +54,7 @@ jobs:
    with:
      commit_sha: ${{ github.sha }}
      package: lerobot
-      additional_args: >-
-        --not_python_module
-        ${{
-          (github.event_name == 'release' && format('--version {0}', github.event.release.tag_name)) ||
-          (inputs.version != '' && format('--version {0}', inputs.version)) ||
-          ''
-        }}
+      additional_args: --not_python_module ${{ github.event_name == 'release' && format('--version {0}', github.event.release.tag_name) || '' }}
    secrets:
      token: ${{ secrets.HUGGINGFACE_PUSH }}
      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
@@ -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

@@ -108,11 +101,9 @@ jobs:
    runs-on:
      group: aws-general-8-plus
    if: |
-      github.repository == 'huggingface/lerobot' && (
-        (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
-        github.event_name == 'push' ||
-        github.event_name == 'workflow_dispatch'
-      )
+      (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
+      github.event_name == 'push' ||
+      github.event_name == 'workflow_dispatch'
    outputs:
      image_tag: ${{ steps.set_tag.outputs.image_tag }}
    env:
@@ -169,7 +160,6 @@ 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: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -181,13 +171,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
-      - name: Fix ptxas permissions
-        run: chmod +x /lerobot/.venv/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas
      - name: Run pytest on GPU
        run: pytest tests -vv --maxfail=10
      - name: Run end-to-end tests
@@ -203,18 +186,15 @@ jobs:
    steps:
      - name: Get Docker Hub Token and Delete Image
        # zizmor: ignore[template-injection]
-        env:
-          DOCKERHUB_LEROBOT_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          DOCKERHUB_LEROBOT_PASSWORD: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-          IMAGE_FULL: ${{ needs.build-and-push-docker.outputs.image_tag }}
        run: |
-          IMAGE_NAME=$(echo "$IMAGE_FULL" | cut -d':' -f1)
-          IMAGE_TAG=$(echo "$IMAGE_FULL" | cut -d':' -f2-)
+          IMAGE_NAME=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f1)
+          IMAGE_TAG=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f2)
+
          echo "Attempting to delete image: $IMAGE_NAME:$IMAGE_TAG"

          TOKEN=$(curl -s -H "Content-Type: application/json" \
                       -X POST \
-                       -d "{\"username\": \"$DOCKERHUB_LEROBOT_USERNAME\", \"password\": \"$DOCKERHUB_LEROBOT_PASSWORD\"}" \
+                       -d '{"username": "${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}", "password": "${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}"}' \
                       https://hub.docker.com/v2/users/login/ | jq -r .token)

          if [ "$TOKEN" == "null" ] || [ -z "$TOKEN" ]; then
@@ -225,7 +205,7 @@ jobs:
          HTTP_RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" \
                               -H "Authorization: JWT ${TOKEN}" \
                               -X DELETE \
-                               https://hub.docker.com/v2/repositories/${IMAGE_NAME}/tags/$IMAGE_TAG)
+                               https://hub.docker.com/v2/repositories/${IMAGE_NAME}/tags/${IMAGE_TAG}/)

          if [ "$HTTP_RESPONSE" -eq 204 ]; then
            echo "Successfully deleted Docker image tag: $IMAGE_NAME:$IMAGE_TAG"
@@ -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

@@ -20,8 +20,8 @@ on:
  workflow_dispatch:

  # Run on the 1st and 15th of every month at 09:00 UTC
-  # schedule:
-  #  - cron: '0 2 1,15 * *'
+  schedule:
+    - cron: '0 2 1,15 * *'

 permissions:
  contents: read
@@ -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

@@ -96,7 +91,6 @@ jobs:
    name: Build and Push Docker
    runs-on:
      group: aws-general-8-plus
-    if: github.repository == 'huggingface/lerobot'
    outputs:
      image_tag: ${{ env.DOCKER_IMAGE_NAME }}
    env:
@@ -142,7 +136,6 @@ 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: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -154,11 +147,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          hf auth login --token "$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
@@ -174,19 +162,15 @@ jobs:
    steps:
      - name: Get Docker Hub Token and Delete Image
        # zizmor: ignore[template-injection]
-        env:
-          DOCKERHUB_LEROBOT_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          DOCKERHUB_LEROBOT_PASSWORD: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-          IMAGE_FULL: ${{ needs.build-and-push-docker.outputs.image_tag }}
        run: |
-          IMAGE_NAME=$(echo "$IMAGE_FULL" | cut -d':' -f1)
-          IMAGE_TAG=$(echo "$IMAGE_FULL" | cut -d':' -f2)
+          IMAGE_NAME=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f1)
+          IMAGE_TAG=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f2)

          echo "Attempting to delete image: $IMAGE_NAME:$IMAGE_TAG"

          TOKEN=$(curl -s -H "Content-Type: application/json" \
                       -X POST \
-                       -d "{\"username\": \"$DOCKERHUB_LEROBOT_USERNAME\", \"password\": \"$DOCKERHUB_LEROBOT_PASSWORD\"}" \
+                       -d '{"username": "${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}", "password": "${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}"}' \
                       https://hub.docker.com/v2/users/login/ | jq -r .token)

          if [ "$TOKEN" == "null" ] || [ -z "$TOKEN" ]; then
@@ -197,7 +181,7 @@ jobs:
          HTTP_RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" \
                               -H "Authorization: JWT ${TOKEN}" \
                               -X DELETE \
-                               https://hub.docker.com/v2/repositories/${IMAGE_NAME}/tags/$IMAGE_TAG)
+                               https://hub.docker.com/v2/repositories/${IMAGE_NAME}/tags/${IMAGE_TAG}/)

          if [ "$HTTP_RESPONSE" -eq 204 ]; then
            echo "Successfully deleted Docker image tag: $IMAGE_NAME:$IMAGE_TAG"
@@ -173,5 +173,4 @@ outputs/

 # Dev folders
 .cache/*
-*.xml
 *.part
@@ -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](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md) and our [AI policy](https://github.com/huggingface/lerobot/blob/main/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

@@ -14,7 +14,7 @@ You can contribute in many ways:
 - **Documentation:** Improve examples, guides, and docstrings.
 - **Feedback:** Submit tickets related to bugs or desired new features.

-If you are unsure where to start, join our [Discord Channel](https://discord.gg/q8Dzzpym3f).
+If you are unsure where to start, join our [Discord Channel](https://discord.gg/JkrYNdmw).

 ## Development Setup

@@ -32,7 +32,7 @@ git remote add upstream https://github.com/huggingface/lerobot.git

 ### 2. Environment Installation

-Please follow our [Installation Guide](https://huggingface.co/docs/lerobot/installation) for the environment setup & installation from source.
+Please follow our [Installation Guide](./docs/source/installation.mdx) for the environment setup & installation from source.

 ## Running Tests & Quality Checks

@@ -75,8 +75,8 @@ pytest -sv tests/test_specific_feature.py

 Use the templates for required fields and examples.

- **Issues:** Follow the [ticket template](https://github.com/huggingface/lerobot/blob/main/.github/ISSUE_TEMPLATE/bug-report.yml).
- **Pull requests:** Rebase on `upstream/main`, use a descriptive branch (don't work on `main`), run `pre-commit` and tests locally, and follow the [PR template](https://github.com/huggingface/lerobot/blob/main/.github/PULL_REQUEST_TEMPLATE.md).
+- **Issues:** Follow the [ticket template](./.github/ISSUE_TEMPLATE/bug-report.yml).
+- **Pull requests:** Rebase on `upstream/main`, use a descriptive branch (don't work on `main`), run `pre-commit` and tests locally, and follow the [PR template](./.github/PULL_REQUEST_TEMPLATE.md).

 One member of the LeRobot team will then review your contribution.

@@ -1,3 +1,2 @@
 include src/lerobot/templates/lerobot_modelcard_template.md
 include src/lerobot/datasets/card_template.md
-include src/lerobot/envs/metaworld_config.json
@@ -10,7 +10,6 @@
 [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
 [![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/)
 [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md)
-[![Discord](https://img.shields.io/badge/Discord-Join_Us-5865F2?style=flat&logo=discord&logoColor=white)](https://discord.gg/q8Dzzpym3f)

 </div>

@@ -100,11 +99,11 @@ lerobot-train \
  --dataset.repo_id=lerobot/aloha_mobile_cabinet
 ```

-| Category                   | Models                                                                                                                                                                                                                  |
-| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md), [Multitask DiT Policy](./docs/source/policy_multi_task_dit_README.md) |
-| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                                                                             |
-| **VLAs Models**            | [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.5](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx)            |
+| Category                   | Models                                                                                                                                                                 |
+| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md)                       |
+| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                            |
+| **VLAs Models**            | [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.5](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx) |

 Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub

@@ -128,14 +127,13 @@ Learn how to implement your own simulation environment or benchmark and distribu
 ## Resources

 - **[Documentation](https://huggingface.co/docs/lerobot/index):** The complete guide to tutorials & API.
- **[Chinese Tutorials: LeRobot+SO-ARM101中文教程-同济子豪兄](https://zihao-ai.feishu.cn/wiki/space/7589642043471924447)** Detailed doc for assembling, teleoperate, dataset, train, deploy. Verified by Seed Studio and 5 global hackathon players.
- **[Discord](https://discord.gg/q8Dzzpym3f):** Join the `LeRobot` server to discuss with the community.
+- **[Discord](https://discord.gg/3gxM6Avj):** Join the `LeRobot` server to discuss with the community.
 - **[X](https://x.com/LeRobotHF):** Follow us on X to stay up-to-date with the latest developments.
 - **[Robot Learning Tutorial](https://huggingface.co/spaces/lerobot/robot-learning-tutorial):** A free, hands-on course to learn robot learning using LeRobot.

 ## 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,
@@ -146,26 +144,9 @@ 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](https://github.com/huggingface/lerobot/blob/main/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!
+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!

 <p align="center">
  <img alt="SO101 Video" src="./media/readme/so100_video.webp" width="640px">
@@ -1,48 +0,0 @@
-# Security Policy
-
-## Project Status & Philosophy
-
-`lerobot` has so far been primarily a research and prototyping tool, which is why deployment security hasn’t been a strong focus until now. As `lerobot` continues to be adopted and deployed in production, we are paying much closer attention to these kinds of issues.
-
-Fortunately, being an open-source project, the community can also help by reporting and fixing vulnerabilities. We appreciate your efforts to responsibly disclose your findings and will make every effort to acknowledge your contributions.
-
-## Reporting a Vulnerability
-
-To report a security issue, please use the GitHub Security Advisory ["Report a Vulnerability"](https://github.com/huggingface/lerobot/security/advisories/new) tab.
-
-The `lerobot` team will send a response indicating the next steps in handling your report. After the initial reply to your report, the security team will keep you informed of the progress towards a fix and full announcement, and may ask for additional information or guidance.
-
-#### Hugging Face Security Team
-
-Since this project is part of the Hugging Face ecosystem, feel free to submit vulnerability reports directly to: **[security@huggingface.co](mailto:security@huggingface.co)**. Someone from the HF security team will review the report and recommend next steps.
-
-#### Open Source Disclosures
-
-If reporting a vulnerability specific to the open-source codebase (and not the underlying Hub infrastructure), you may also use [Huntr](https://huntr.com), a vulnerability disclosure program for open source software.
-
-## Supported Versions
-
-Currently, we treat `lerobot` as a rolling release. We prioritize security updates for the latest available version (`main` branch).
-
-| Version  | Supported |
-| -------- | --------- |
-| Latest   | ✅        |
-| < Latest | ❌        |
-
-## Secure Usage Guidelines
-
-`lerobot` is tightly coupled to the Hugging Face Hub for sharing data and pretrained policies. When downloading artifacts uploaded by others, you expose yourself to risks. Please read below for recommendations to keep your runtime and robot environment safe.
-
-### Remote Artefacts (Weights & Policies)
-
-Models and policies uploaded to the Hugging Face Hub come in different formats. We heavily recommend uploading and downloading models in the [`safetensors`](https://github.com/huggingface/safetensors) format.
-
-`safetensors` was developed specifically to prevent arbitrary code execution on your system, which is critical when running software on physical hardware/robots.
-
-To avoid loading models from unsafe formats (e.g., `pickle`), you should ensure you are prioritizing `safetensors` files.
-
-### Remote Code
-
-Some models or environments on the Hub may require `trust_remote_code=True` to run custom architecture code.
-
-Please **always** verify the content of the modeling files when using this argument. We recommend setting a specific `revision` (commit hash) when loading remote code to ensure you protect yourself from unverified updates to the repository.
@@ -28,9 +28,9 @@ We don't expect the same optimal settings for a dataset of images from a simulat
 For these reasons, we run this benchmark on four representative datasets:

 - `lerobot/pusht_image`: (96 x 96 pixels) simulation with simple geometric shapes, fixed camera.
- `lerobot/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
- `lerobot/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
- `lerobot/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
+- `aliberts/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
+- `aliberts/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
+- `aliberts/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.

 Note: The datasets used for this benchmark need to be image datasets, not video datasets.

@@ -179,7 +179,7 @@ python benchmark/video/run_video_benchmark.py \
    --output-dir outputs/video_benchmark \
    --repo-ids \
        lerobot/pusht_image \
-        lerobot/aloha_mobile_shrimp_image \
+        aliberts/aloha_mobile_shrimp_image \
    --vcodec libx264 libx265 \
    --pix-fmt yuv444p yuv420p \
    --g 2 20 None \
@@ -203,9 +203,9 @@ python benchmark/video/run_video_benchmark.py \
    --output-dir outputs/video_benchmark \
    --repo-ids \
        lerobot/pusht_image \
-        lerobot/aloha_mobile_shrimp_image \
-        lerobot/paris_street \
-        lerobot/kitchen \
+        aliberts/aloha_mobile_shrimp_image \
+        aliberts/paris_street \
+        aliberts/kitchen \
    --vcodec libx264 libx265 \
    --pix-fmt yuv444p yuv420p \
    --g 1 2 3 4 5 6 10 15 20 40 None \
@@ -221,9 +221,9 @@ python benchmark/video/run_video_benchmark.py \
    --output-dir outputs/video_benchmark \
    --repo-ids \
        lerobot/pusht_image \
-        lerobot/aloha_mobile_shrimp_image \
-        lerobot/paris_street \
-        lerobot/kitchen \
+        aliberts/aloha_mobile_shrimp_image \
+        aliberts/paris_street \
+        aliberts/kitchen \
    --vcodec libsvtav1 \
    --pix-fmt yuv420p \
    --g 1 2 3 4 5 6 10 15 20 40 None \
@@ -252,37 +252,37 @@ Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_read

 These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_frames` and `backend=pyav`

-| video_images_size_ratio           | vcodec     | pix_fmt |           |           |           |
-| --------------------------------- | ---------- | ------- | --------- | --------- | --------- |
-|                                   | libx264    |         | libx265   |           | libsvtav1 |
-| repo_id                           | yuv420p    | yuv444p | yuv420p   | yuv444p   | yuv420p   |
-| lerobot/pusht_image               | **16.97%** | 17.58%  | 18.57%    | 18.86%    | 22.06%    |
-| lerobot/aloha_mobile_shrimp_image | 2.14%      | 2.11%   | 1.38%     | **1.37%** | 5.59%     |
-| lerobot/paris_street              | 2.12%      | 2.13%   | **1.54%** | **1.54%** | 4.43%     |
-| lerobot/kitchen                   | 1.40%      | 1.39%   | **1.00%** | **1.00%** | 2.52%     |
+| video_images_size_ratio            | vcodec     | pix_fmt |           |           |           |
+| ---------------------------------- | ---------- | ------- | --------- | --------- | --------- |
+|                                    | libx264    |         | libx265   |           | libsvtav1 |
+| repo_id                            | yuv420p    | yuv444p | yuv420p   | yuv444p   | yuv420p   |
+| lerobot/pusht_image                | **16.97%** | 17.58%  | 18.57%    | 18.86%    | 22.06%    |
+| aliberts/aloha_mobile_shrimp_image | 2.14%      | 2.11%   | 1.38%     | **1.37%** | 5.59%     |
+| aliberts/paris_street              | 2.12%      | 2.13%   | **1.54%** | **1.54%** | 4.43%     |
+| aliberts/kitchen                   | 1.40%      | 1.39%   | **1.00%** | **1.00%** | 2.52%     |

-| video_images_load_time_ratio      | vcodec  | pix_fmt |          |         |           |
-| --------------------------------- | ------- | ------- | -------- | ------- | --------- |
-|                                   | libx264 |         | libx265  |         | libsvtav1 |
-| repo_id                           | yuv420p | yuv444p | yuv420p  | yuv444p | yuv420p   |
-| lerobot/pusht_image               | 6.45    | 5.19    | **1.90** | 2.12    | 2.47      |
-| lerobot/aloha_mobile_shrimp_image | 11.80   | 7.92    | 0.71     | 0.85    | **0.48**  |
-| lerobot/paris_street              | 2.21    | 2.05    | 0.36     | 0.49    | **0.30**  |
-| lerobot/kitchen                   | 1.46    | 1.46    | 0.28     | 0.51    | **0.26**  |
+| video_images_load_time_ratio       | vcodec  | pix_fmt |          |         |           |
+| ---------------------------------- | ------- | ------- | -------- | ------- | --------- |
+|                                    | libx264 |         | libx265  |         | libsvtav1 |
+| repo_id                            | yuv420p | yuv444p | yuv420p  | yuv444p | yuv420p   |
+| lerobot/pusht_image                | 6.45    | 5.19    | **1.90** | 2.12    | 2.47      |
+| aliberts/aloha_mobile_shrimp_image | 11.80   | 7.92    | 0.71     | 0.85    | **0.48**  |
+| aliberts/paris_street              | 2.21    | 2.05    | 0.36     | 0.49    | **0.30**  |
+| aliberts/kitchen                   | 1.46    | 1.46    | 0.28     | 0.51    | **0.26**  |

-|                                   |          | vcodec   | pix_fmt      |          |           |              |
-| --------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
-|                                   |          | libx264  |              | libx265  |           | libsvtav1    |
-| repo_id                           | metric   | yuv420p  | yuv444p      | yuv420p  | yuv444p   | yuv420p      |
-| lerobot/pusht_image               | avg_mse  | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04  | 2.19E-04     |
-|                                   | avg_psnr | 35.44    | 37.07        | 35.49    | **37.30** | 37.20        |
-|                                   | avg_ssim | 98.28%   | **98.85%**   | 98.31%   | 98.84%    | 98.72%       |
-| lerobot/aloha_mobile_shrimp_image | avg_mse  | 2.76E-04 | 2.59E-04     | 3.17E-04 | 3.06E-04  | **1.30E-04** |
-|                                   | avg_psnr | 35.91    | 36.21        | 35.88    | 36.09     | **40.17**    |
-|                                   | avg_ssim | 95.19%   | 95.18%       | 95.00%   | 95.05%    | **97.73%**   |
-| lerobot/paris_street              | avg_mse  | 6.89E-04 | 6.70E-04     | 4.03E-03 | 4.02E-03  | **3.09E-04** |
-|                                   | avg_psnr | 33.48    | 33.68        | 32.05    | 32.15     | **35.40**    |
-|                                   | avg_ssim | 93.76%   | 93.75%       | 89.46%   | 89.46%    | **95.46%**   |
-| lerobot/kitchen                   | avg_mse  | 2.50E-04 | 2.24E-04     | 4.28E-04 | 4.18E-04  | **1.53E-04** |
-|                                   | avg_psnr | 36.73    | 37.33        | 36.56    | 36.75     | **39.12**    |
-|                                   | avg_ssim | 95.47%   | 95.58%       | 95.52%   | 95.53%    | **96.82%**   |
+|                                    |          | vcodec   | pix_fmt      |          |           |              |
+| ---------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
+|                                    |          | libx264  |              | libx265  |           | libsvtav1    |
+| repo_id                            | metric   | yuv420p  | yuv444p      | yuv420p  | yuv444p   | yuv420p      |
+| lerobot/pusht_image                | avg_mse  | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04  | 2.19E-04     |
+|                                    | avg_psnr | 35.44    | 37.07        | 35.49    | **37.30** | 37.20        |
+|                                    | avg_ssim | 98.28%   | **98.85%**   | 98.31%   | 98.84%    | 98.72%       |
+| aliberts/aloha_mobile_shrimp_image | avg_mse  | 2.76E-04 | 2.59E-04     | 3.17E-04 | 3.06E-04  | **1.30E-04** |
+|                                    | avg_psnr | 35.91    | 36.21        | 35.88    | 36.09     | **40.17**    |
+|                                    | avg_ssim | 95.19%   | 95.18%       | 95.00%   | 95.05%    | **97.73%**   |
+| aliberts/paris_street              | avg_mse  | 6.89E-04 | 6.70E-04     | 4.03E-03 | 4.02E-03  | **3.09E-04** |
+|                                    | avg_psnr | 33.48    | 33.68        | 32.05    | 32.15     | **35.40**    |
+|                                    | avg_ssim | 93.76%   | 93.75%       | 89.46%   | 89.46%    | **95.46%**   |
+| aliberts/kitchen                   | avg_mse  | 2.50E-04 | 2.24E-04     | 4.28E-04 | 4.18E-04  | **1.53E-04** |
+|                                    | avg_psnr | 36.73    | 37.33        | 36.56    | 36.75     | **39.12**    |
+|                                    | avg_ssim | 95.47%   | 95.58%       | 95.52%   | 95.53%    | **96.82%**   |
@@ -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 \
@@ -73,7 +73,7 @@ ENV HOME=/home/user_lerobot \
 RUN uv venv --python python${PYTHON_VERSION}

 # Install Python dependencies for caching
-COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml README.md MANIFEST.in ./
+COPY --chown=user_lerobot:user_lerobot pyproject.toml README.md MANIFEST.in ./
 COPY --chown=user_lerobot:user_lerobot src/ src/

 ARG UNBOUND_DEPS=false
@@ -85,8 +85,6 @@ RUN if [ "$UNBOUND_DEPS" = "true" ]; then \

 RUN uv pip install --no-cache ".[all]"

-RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
-
 # Copy the rest of the application source code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
@@ -18,10 +18,8 @@
 # docker build -f docker/Dockerfile.user -t lerobot-user .
 # docker run -it --rm lerobot-user

-# With USB physical access : docker run -it --device=/dev/ -v /dev/:/dev/ --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
@@ -61,7 +59,7 @@ ENV HOME=/home/user_lerobot \
 RUN uv venv

 # Install Python dependencies for caching
-COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml README.md MANIFEST.in ./
+COPY --chown=user_lerobot:user_lerobot pyproject.toml README.md MANIFEST.in ./
 COPY --chown=user_lerobot:user_lerobot src/ src/

 ARG UNBOUND_DEPS=false
@@ -7,6 +7,8 @@
 - sections:
  - local: il_robots
    title: Imitation Learning for Robots
+  - local: cameras
+    title: Cameras
  - local: bring_your_own_policies
    title: Bring Your Own Policies
  - local: integrate_hardware
@@ -17,12 +19,6 @@
    title: Train RL in Simulation
  - local: multi_gpu_training
    title: Multi GPU training
-  - local: peft_training
-    title: Training with PEFT (e.g., LoRA)
-  - local: rename_map
-    title: Using Rename Map and Empty Cameras
-  - local: umi_pi0_relative_ee
-    title: UMI Data with pi0 Relative EE Actions
  title: "Tutorials"
 - sections:
  - local: lerobot-dataset-v3
@@ -31,10 +27,6 @@
    title: Porting Large Datasets
  - local: using_dataset_tools
    title: Using the Dataset Tools
-  - local: dataset_subtask
-    title: Using Subtasks in the Dataset
-  - local: streaming_video_encoding
-    title: Streaming Video Encoding
  title: "Datasets"
 - sections:
  - local: act
@@ -43,16 +35,12 @@
    title: SmolVLA
  - local: pi0
    title: π₀ (Pi0)
-  - local: pi0fast
-    title: π₀-FAST (Pi0Fast)
  - local: pi05
    title: π₀.₅ (Pi05)
  - local: groot
    title: NVIDIA GR00T N1.5
  - local: xvla
    title: X-VLA
-  - local: multi_task_dit
-    title: Multitask DiT Policy
  - local: walloss
    title: WALL-OSS
  title: "Policies"
@@ -71,8 +59,6 @@
    title: Environments from the Hub
  - local: envhub_leisaac
    title: Control & Train Robots in Sim (LeIsaac)
-  - local: envhub_isaaclab_arena
-    title: NVIDIA IsaacLab Arena Environments
  - local: libero
    title: Using Libero
  - local: metaworld
@@ -89,8 +75,6 @@
    title: Processors for Robots and Teleoperators
  - local: env_processor
    title: Environment Processors
-  - local: action_representations
-    title: Action Representations
  title: "Robot Processors"
 - sections:
  - local: so101
@@ -109,19 +93,11 @@
    title: Unitree G1
  - local: earthrover_mini_plus
    title: Earth Rover Mini
-  - local: omx
-    title: OMX
-  - local: openarm
-    title: OpenArm
  title: "Robots"
 - sections:
  - local: phone_teleop
    title: Phone
  title: "Teleoperators"
- sections:
-  - local: cameras
-    title: Cameras
-  title: "Sensors"
 - sections:
  - local: torch_accelerators
    title: PyTorch accelerators
@@ -131,8 +107,6 @@
    title: Notebooks
  - local: feetech
    title: Updating Feetech Firmware
-  - local: damiao
-    title: Damiao Motors and CAN Bus
  title: "Resources"
 - sections:
  - local: contributing
@@ -88,8 +88,5 @@ lerobot-record \
  --dataset.repo_id=${HF_USER}/eval_act_your_dataset \
  --dataset.num_episodes=10 \
  --dataset.single_task="Your task description" \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
  --policy.path=${HF_USER}/act_policy
 ```
@@ -1,238 +0,0 @@
-# Action Representations
-
-This guide explains the different ways robot actions can be represented in LeRobot, how they relate to each other, and when to use each one.
-
-## Joint Space vs End-Effector Space
-
-Before discussing action representations, it helps to understand the two coordinate spaces actions can live in.
-
-### Joint Space
-
-Joint-space actions directly specify target positions for each motor. For a 6-DOF arm with a gripper, a joint-space action might look like:
-
-```
-action = [shoulder_pan: 45.0, shoulder_lift: -20.0, elbow: -30.0, wrist_pitch: 10.0, wrist_roll: 0.0, wrist_yaw: 5.0, gripper: 0.8]
-```
-
-Joint space is the default in LeRobot. It is simple, requires no kinematics model, and maps directly to motor commands. Most beginner setups (SO-100, Koch) use joint-space actions.
-
-### End-Effector (EE) Space
-
-End-effector-space actions specify the desired position and orientation of the robot's tool tip (gripper) in Cartesian coordinates:
-
-```
-action = [x: 0.25, y: -0.10, z: 0.15, wx: 0.0, wy: 0.0, wz: 0.1, gripper: 0.8]
-```
-
-EE space is more intuitive for tasks like pick-and-place because it directly describes where the gripper should go, but it requires a kinematics model (URDF) to convert between EE poses and joint angles.
-
-### Converting Between Spaces
-
-LeRobot provides processor steps for converting between joint and EE spaces using forward and inverse kinematics. These are built on top of `RobotKinematics`, which loads a URDF model of your robot.
-
-```python
-from lerobot.model.kinematics import RobotKinematics
-from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
-    InverseKinematicsEEToJoints,
-)
-
-kinematics = RobotKinematics(
-    urdf_path="./SO101/so101_new_calib.urdf",
-    target_frame_name="gripper_frame_link",
-    joint_names=["shoulder", "elbow", "wrist_pitch", "wrist_roll", "wrist_yaw"],
-)
-
-# Joints → EE (for observations: "where is my gripper?")
-fk_step = ForwardKinematicsJointsToEE(kinematics=kinematics, motor_names=[...])
-
-# EE → Joints (for actions: "move my gripper here")
-ik_step = InverseKinematicsEEToJoints(kinematics=kinematics, motor_names=[...])
-```
-
-See [`examples/so100_to_so100_EE/`](https://github.com/huggingface/lerobot/tree/main/examples/so100_to_so100_EE) for a complete working example of recording, replaying, and evaluating with EE-space actions on an SO-100 arm.
-
-## Absolute, Relative, and Delta Actions
-
-Regardless of whether you work in joint space or EE space, the action values can be expressed in three different ways. The terminology follows [UMI (Chi et al., 2024)](https://arxiv.org/abs/2402.10329).
-
-### Absolute Actions (LeRobot default)
-
-Each action specifies the target position directly.
-
-**Example** (joint space, chunk of 4):
-
-```
-current_state = [45.0, -30.0, 10.0]
-
-action_chunk = [
-    [46.0, -29.0, 11.0],   # go to 46, -29, 11
-    [47.5, -27.0, 12.0],   # go to 47.5, -27, 12
-    [49.0, -25.0, 13.5],   # go to 49, -25, 13.5
-    [50.0, -24.0, 15.0],   # go to 50, -24, 15
-]
-```
-
-Each value is a target position in the robot's coordinate frame. Simple and direct, but requires a consistent global coordinate frame. This is the default in LeRobot.
-
-### Relative Actions (used by OpenPI / pi0)
-
-Each action in the chunk is an offset from the **current state at the moment of prediction**. All actions in the chunk share the same reference point:
-
-```
-current_state = [45.0, -30.0, 10.0]
-
-relative_chunk = [
-    [1.0,  1.0, 1.0],   # +1 from current → target 46, -29, 11
-    [2.5,  3.0, 2.0],   # +2.5 from current → target 47.5, -27, 12
-    [4.0,  5.0, 3.5],   # +4 from current → target 49, -25, 13.5
-    [5.0,  6.0, 5.0],   # +5 from current → target 50, -24, 15
-]
-```
-
-The conversion is straightforward: `relative = absolute - current_state`. To recover absolute: `absolute = relative + current_state`.
-
-**Why use relative actions?** The model learns to predict offsets centered around zero, which is easier to normalize and leads to more stable training. Because every chunk references the same current state, there is no error accumulation across chunks.
-
-### Delta Actions (sequential differences)
-
-Each action is an offset from the **previous action** (or from the current state for the first step):
-
-```
-current_state = [45.0, -30.0, 10.0]
-
-delta_chunk = [
-    [1.0,  1.0, 1.0],   # current → 46, -29, 11
-    [1.5,  2.0, 1.0],   # previous action → 47.5, -27, 12
-    [1.5,  2.0, 1.5],   # previous action → 49, -25, 13.5
-    [1.0,  1.0, 1.5],   # previous action → 50, -24, 15
-]
-```
-
-Here each step is relative to the one before it. To recover absolute positions you must sum all previous deltas, which means errors accumulate over time. UMI explicitly argues against this representation for this reason.
-
-### Visual Comparison
-
-The figure below (based on a figure from [UMI, Chi et al., 2024](https://arxiv.org/abs/2402.10329)) illustrates the key difference. With **relative trajectory**, every action in the chunk points back to the same origin (current state), so a new inference step cleanly resets the reference. With **delta**, each action depends on the previous one, so errors accumulate. **Absolute** actions require a consistent global coordinate frame.
-
-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/action_representations_umi.png"
-  alt="Relative Trajectory as Action Representation (UMI, Chi et al., 2024)"
-  width="85%"
-/>
-
-## Using Relative Actions in LeRobot
-
-LeRobot provides `RelativeActionsProcessorStep` to convert between absolute and relative actions inside the processor pipeline. This is how pi0, pi0.5, and pi0_fast support relative actions.
-
-> **Note:** All pi models (pi0, pi0.5, pi0*fast) apply relative conversion \_before* normalization (`relative → normalize`), so the normalizer always sees delta (relative) values. This means **relative action stats are required** for all of them when training with `use_relative_actions=true`. In pi0_fast the `RelativeActionsProcessorStep` only modifies the action — the state observation is unchanged — so `NormalizerProcessorStep` still runs before the state tokenizer and the tokenizer continues to receive normalized state as expected.
-
-### How it works
-
-During **training** (preprocessing), actions are converted from absolute to relative before the model sees them:
-
-```
-raw absolute action → RelativeActionsProcessorStep → normalize → model
-```
-
-During **inference** (postprocessing), model predictions are converted back to absolute before being sent to the robot:
-
-```
-model output → unnormalize → AbsoluteActionsProcessorStep → robot
-```
-
-The `AbsoluteActionsProcessorStep` reads the cached current state from its paired `RelativeActionsProcessorStep`, so the two must be wired together (handled automatically by the policy factory).
-
-### Enabling relative actions for the pi family (pi0, pi0.5, pi0_fast)
-
-**Step 1**: Precompute relative action statistics for your dataset:
-
-```bash
-lerobot-edit-dataset \
-    --repo_id your_dataset \
-    --operation.type recompute_stats \
-    --operation.relative_action true \
-    --operation.chunk_size 50 \
-    --operation.relative_exclude_joints "['gripper']"
-```
-
-**Step 2**: Train with relative actions enabled:
-
-```bash
-lerobot-train \
-    --dataset.repo_id=your_dataset \
-    --policy.type=pi0 \
-    --policy.use_relative_actions=true \
-    --policy.relative_exclude_joints='["gripper"]'
-```
-
-The `relative_exclude_joints` parameter specifies joints that should remain in absolute space. For example, gripper commands are typically binary (open/close) and don't benefit from relative encoding.
-
-### Combining relative actions with RTC
-
-[RTC](https://arxiv.org/abs/2506.07339) runs policy inference at high frequency and sends actions to the robot as they are predicted rather than waiting for a full chunk. Relative actions and RTC are fully compatible: because every chunk in relative mode references the **same** current state (captured at the start of inference), each predicted action in the chunk remains a valid offset even if the robot has already moved. No special handling is needed — `RelativeActionsProcessorStep` caches the state once per inference call and `AbsoluteActionsProcessorStep` applies it to every action in the streamed output.
-
-### Combining relative actions with EE space
-
-Relative actions work in both joint space and EE space. For example, if your dataset stores EE actions, relative encoding converts them to offsets from the current EE pose:
-
-```
-current_ee_state = [x: 0.25, y: -0.10, z: 0.15, gripper: 0.8]
-
-absolute_ee_chunk = [
-    [0.26, -0.09, 0.16, 0.8],
-    [0.28, -0.07, 0.18, 0.8],
-]
-
-relative_ee_chunk = [
-    [0.01,  0.01, 0.01, 0.0],   # offset from current EE pose
-    [0.03,  0.03, 0.03, 0.0],   # offset from current EE pose
-]
-```
-
-## Processing Pipeline Summary
-
-Here is how the different processors compose. Each arrow is a processor step, and they can be chained in a `RobotProcessorPipeline` or `PolicyProcessorPipeline`:
-
-```
-                    ┌─────────────────────────────────────────┐
-   Action Space     │   Joint Space  ←──IK──→  EE Space      │
-                    │   ForwardKinematicsJointsToEE           │
-                    │   InverseKinematicsEEToJoints           │
-                    └─────────────────────────────────────────┘
-
-                    ┌─────────────────────────────────────────┐
-   State Derivation │   Action column  ────→  State + Action  │
-                    │   DeriveStateFromActionStep (pre only)  │
-                    │   (UMI-style: state from action chunk)  │
-                    └─────────────────────────────────────────┘
-
-                    ┌─────────────────────────────────────────┐
-   Action Repr.     │   Absolute  ←────→  Relative            │
-                    │   RelativeActionsProcessorStep (pre)    │
-                    │   AbsoluteActionsProcessorStep (post)   │
-                    └─────────────────────────────────────────┘
-
-                    ┌─────────────────────────────────────────┐
-   State Repr.      │   Absolute  ────→  Relative             │
-                    │   RelativeStateProcessorStep (pre only) │
-                    └─────────────────────────────────────────┘
-
-                    ┌─────────────────────────────────────────┐
-   Normalization    │   Raw  ←────→  Normalized               │
-                    │   NormalizerProcessorStep (pre)         │
-                    │   UnnormalizerProcessorStep (post)      │
-                    └─────────────────────────────────────────┘
-```
-
-A typical training preprocessor might chain: `raw absolute joint actions → relative → normalize`. A typical inference postprocessor: `unnormalize → absolute → (optionally IK to joints)`.
-
-With UMI-style relative proprioception (`use_relative_state=True`), the preprocessor also converts observation.state to offsets from the current timestep via `RelativeStateProcessorStep` before normalization. This is a pre-processing-only step (state is an input, not an output).
-
-With `derive_state_from_action=True`, the preprocessor first runs `DeriveStateFromActionStep` to extract a 2-step state from the extended action chunk. This enables full UMI-style training without a separate `observation.state` column. See the [UMI pi0 guide](umi_pi0_relative_ee) for details.
-
-## References
-
- [Universal Manipulation Interface (UMI)](https://arxiv.org/abs/2402.10329) - Chi et al., 2024. Defines the relative trajectory action representation and compares it with absolute and delta actions.
- [Introduction to Processors](./introduction_processors) - How processor pipelines work in LeRobot.
- [`examples/so100_to_so100_EE/`](https://github.com/huggingface/lerobot/tree/main/examples/so100_to_so100_EE) - Complete example of recording and evaluating with EE-space actions.
@@ -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
@@ -169,7 +169,7 @@ python -m lerobot.async_inference.robot_client \
 <!-- prettier-ignore-start -->
 ```python
 import threading
-from lerobot.robots.so_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower import SO100FollowerConfig
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.async_inference.configs import RobotClientConfig
 from lerobot.async_inference.robot_client import RobotClient
@@ -195,7 +195,6 @@ client_cfg = RobotClientConfig(
    robot=robot_cfg,
    server_address="localhost:8080",
    policy_device="mps",
-    client_device="cpu",
    policy_type="smolvla",
    pretrained_name_or_path="<user>/smolvla_async",
    chunk_size_threshold=0.5,
@@ -310,4 +309,4 @@ Asynchronous inference represents a significant advancement in real-time robotic
 - **Universal Compatibility**: Works with all LeRobot-supported policies, from lightweight ACT models to vision-language models like SmolVLA

 Start experimenting with the default parameters, monitor your action queue sizes, and iteratively refine your setup to achieve optimal performance for your specific use case.
-If you want to discuss this further, hop into our [Discord community](https://discord.gg/s3KuuzsPFb), or open an issue on our [GitHub repository](https://github.com/huggingface/lerobot/issues).
+If you want to discuss this further, hop into our [Discord community](https://discord.gg/s3KuuzsPFb), or open an issue on our [GitHub repository](https://github.com/lerobot/lerobot/issues).
@@ -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
@@ -41,15 +41,13 @@ requires = # your-build-system

 ## Step 2: Define the Policy Configuration

-Create a configuration class that inherits from [`PreTrainedConfig`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/configs/policies.py) and registers your policy type:
-Here is a template to get you started, customize the parameters and methods as needed for your policy's architecture and training requirements.
+Create a configuration class that inherits from `PreTrainedConfig` and registers your policy type:

 ```python
 # configuration_my_custom_policy.py
 from dataclasses import dataclass, field
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.optim.optimizers import AdamWConfig
-from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
+from lerobot.configs.types import NormalizationMode

@PreTrainedConfig.register_subclass("my_custom_policy")
@dataclass
@@ -63,131 +61,61 @@ class MyCustomPolicyConfig(PreTrainedConfig):
        hidden_dim: Hidden dimension for the policy network
        # Add your policy-specific parameters here
    """
-
-    horizon: int = 50
-    n_action_steps: int = 50
-    hidden_dim: int = 256
-
-    optimizer_lr: float = 1e-4
-    optimizer_weight_decay: float = 1e-4
+    # ...PreTrainedConfig fields...
+    pass

    def __post_init__(self):
        super().__post_init__()
-        if self.n_action_steps > self.horizon:
-            raise ValueError("n_action_steps cannot exceed horizon")
+        # Add any validation logic here

    def validate_features(self) -> None:
        """Validate input/output feature compatibility."""
-        if not self.image_features:
-            raise ValueError("MyCustomPolicy requires at least one image feature.")
-        if self.action_feature is None:
-            raise ValueError("MyCustomPolicy requires 'action' in output_features.")
-
-    def get_optimizer_preset(self) -> AdamWConfig:
-        return AdamWConfig(lr=self.optimizer_lr, weight_decay=self.optimizer_weight_decay)
-
-    def get_scheduler_preset(self):
-        return None
-
-    @property
-    def observation_delta_indices(self) -> list[int] | None:
-        """Relative timestep offsets the dataset loader provides per observation.
-
-        Return `None` for single-frame policies. For temporal policies that consume
-        multiple past or future frames, return a list of offsets, e.g. `[-20, -10, 0, 10]` for
-        3 past frames at stride 10 and 1 future frame at stride 10.
-        """
-        return None
-
-    @property
-    def action_delta_indices(self) -> list[int]:
-        """Relative timestep offsets for the action chunk the dataset loader returns.
-        """
-        return list(range(self.horizon))
-
-    @property
-    def reward_delta_indices(self) -> None:
-        return None
+        # Implement validation logic for your policy's requirements
+        pass
 ```

 ## Step 3: Implement the Policy Class

-Create your policy implementation by inheriting from [`PreTrainedPolicy`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/pretrained.py):
+Create your policy implementation by inheriting from LeRobot's base `PreTrainedPolicy` class:

 ```python
 # 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 lerobot.utils.constants import ACTION
 from .configuration_my_custom_policy import MyCustomPolicyConfig

 class MyCustomPolicy(PreTrainedPolicy):
-    config_class = MyCustomPolicyConfig  # must match the string in @register_subclass
+    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)
-        config.validate_features()  # not called automatically by the base class
-        self.config = config
-        self.model = ...  # your nn.Module here
-
-    def reset(self):
-        """Reset episode state."""
        ...
-
-    def get_optim_params(self) -> dict:
-        """Return parameters to pass to the optimizer (e.g. with per-group lr/wd)."""
-        return {"params": self.parameters()}
-
-    def predict_action_chunk(self, batch: dict[str, torch.Tensor], **kwargs) -> torch.Tensor:
-        """Return the full action chunk (B, chunk_size, action_dim) for the current observation."""
-        ...
-
-    def select_action(self, batch: dict[str, torch.Tensor], **kwargs) -> torch.Tensor:
-        """Return a single action for the current timestep (called at inference)."""
-        ...
-
-    def forward(self, batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-        """Compute the training loss.
-
-        `batch["action_is_pad"]` is a bool mask of shape (B, horizon) that marks
-        timesteps padded because the episode ended before `horizon` steps, you
-        can exclude those from your loss.
-        """
-        actions = batch[ACTION]
-        action_is_pad = batch.get("action_is_pad")
-        ...
-        return {"loss": ...}
 ```

 ## Step 4: Add Data Processors

-Create processor functions. For a concrete reference, see [processor_act.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/processor_act.py) or [processor_diffusion.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/processor_diffusion.py).
+Create processor functions:

 ```python
 # processor_my_custom_policy.py
-from typing import Any
+from typing import Dict, Any
 import torch

-from lerobot.processor import PolicyAction, PolicyProcessorPipeline
-

 def make_my_custom_policy_pre_post_processors(
    config,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
 ) -> tuple[
    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
    PolicyProcessorPipeline[PolicyAction, PolicyAction],
 ]:
-    preprocessor = ...   # build your PolicyProcessorPipeline for inputs
-    postprocessor = ...  # build your PolicyProcessorPipeline for outputs
-    return preprocessor, postprocessor
-```
+    """Create preprocessing and postprocessing functions for your policy."""
+    pass  # Define your preprocessing and postprocessing logic here

-**Important - function naming:** LeRobot discovers your processor by name. The function **must** be called `make_{policy_name}_pre_post_processors` (matching the string you passed to `@PreTrainedConfig.register_subclass`).
+```

 ## Step 5: Package Initialization

@@ -1,22 +1,12 @@
 # Cameras

-LeRobot offers multiple options for video capture:
+LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).

-| Class             | Supported Cameras                   |
-| ----------------- | ----------------------------------- |
-| `OpenCVCamera`    | Phone, built-in laptop, USB webcams |
-| `ZMQCamera`       | Network-connected cameras           |
-| `RealSenseCamera` | Intel RealSense (with depth)        |
-| `Reachy2Camera`   | Reachy 2 robot cameras              |
+### Finding your camera

-> [!TIP]
-> For `OpenCVCamera` compatibility details, see the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
+To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system.

-### Find your camera
-
-Every camera requires a unique identifier to be instantiated, allowing you to distinguish between multiple connected devices.
-
-`OpenCVCamera` and `RealSenseCamera` support auto-discovery. Run the command below to list available devices and their identifiers. Note that these identifiers may change after rebooting your computer or re-plugging the camera, depending on your operating system.
+To find the camera indices of the cameras plugged into your system, run the following script:

 ```bash
 lerobot-find-cameras opencv # or realsense for Intel Realsense cameras
@@ -24,7 +14,7 @@ lerobot-find-cameras opencv # or realsense for Intel Realsense cameras

 The output will look something like this if you have two cameras connected:

-```bash
+```
 --- Detected Cameras ---
 Camera #0:
  Name: OpenCV Camera @ 0
@@ -43,37 +33,13 @@ Camera #0:
 > [!WARNING]
 > When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable.

-`ZMQCamera` and `Reachy2Camera` do not support auto-discovery. They must be configured manually by providing their network address and port or robot SDK settings.
+## Use Cameras

-## Use cameras
+Below are two examples, demonstrating how to work with the API.

-### Frame access modes
-
-All camera classes implement three access modes for capturing frames:
-
-| Method                    | Behavior                                                                                                                                                   | Blocks?        | Best For                                 |
-| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------- | ---------------------------------------- |
-| `read()`                  | Waits for the camera hardware to return a frame. May block for a long time depending on the camera and SDK.                                                | Yes            | Simple scripts, sequential capture       |
-| `async_read(timeout_ms)`  | Returns the latest unconsumed frame from background thread. Blocks only if buffer is empty, up to `timeout_ms`. Raises `TimeoutError` if no frame arrives. | With a timeout | Control loops synchronized to camera FPS |
-| `read_latest(max_age_ms)` | Peeks at the most recent frame in buffer (may be stale). Raises `TimeoutError` if frame is older than `max_age_ms`.                                        | No             | UI visualization, logging, monitoring    |
-
-### Usage examples
-
-The following examples show how to use the camera API to configure and capture frames from different camera types.
-
- **Blocking and non-blocking frame capture** using an OpenCV-based camera
+- **Asynchronous frame capture** using an OpenCV-based camera
 - **Color and depth capture** using an Intel RealSense camera

-> [!WARNING]
-> Failing to cleanly disconnect cameras can cause resource leaks. Use the context manager protocol to ensure automatic cleanup:
->
-> ```python
-> with OpenCVCamera(config) as camera:
->     ...
-> ```
->
-> You can also call `connect()` and `disconnect()` manually, but always use a `finally` block for the latter.
-
 <hfoptions id="shell_restart">
 <hfoption id="Open CV Camera">

@@ -94,30 +60,16 @@ config = OpenCVCameraConfig(
 )

 # Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default).
-with OpenCVCamera(config) as camera:
-
-    # Read a frame synchronously — blocks until hardware delivers a new frame
-    frame = camera.read()
-    print(f"read() call returned frame with shape:", frame.shape)
-
-    # Read a frame asynchronously with a timeout — returns the latest unconsumed frame or waits up to timeout_ms for a new one
-    try:
-        for i in range(10):
-            frame = camera.async_read(timeout_ms=200)
-            print(f"async_read call returned frame {i} with shape:", frame.shape)
-    except TimeoutError as e:
-        print(f"No frame received within timeout: {e}")
-
-    # Instantly return a frame - returns the most recent frame captured by the camera
-    try:
-        initial_frame = camera.read_latest(max_age_ms=1000)
-        for i in range(10):
-            frame = camera.read_latest(max_age_ms=1000)
-            print(f"read_latest call returned frame {i} with shape:", frame.shape)
-            print(f"Was a new frame received by the camera? {not (initial_frame == frame).any()}")
-    except TimeoutError as e:
-        print(f"Frame too old: {e}")
+camera = OpenCVCamera(config)
+camera.connect()

+# Read frames asynchronously in a loop via `async_read(timeout_ms)`
+try:
+    for i in range(10):
+        frame = camera.async_read(timeout_ms=200)
+        print(f"Async frame {i} shape:", frame.shape)
+finally:
+    camera.disconnect()
 ```
 <!-- prettier-ignore-end -->

@@ -159,10 +111,10 @@ finally:
 </hfoption>
 </hfoptions>

-## Use your phone's camera
+## Use your phone

 <hfoptions id="use phone">
-<hfoption id="iPhone & macOS">
+<hfoption id="Mac">

 To use your iPhone as a camera on macOS, enable the Continuity Camera feature:

@@ -172,49 +124,83 @@ To use your iPhone as a camera on macOS, enable the Continuity Camera feature:

 For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).

+Your iPhone should be detected automatically when running the camera setup script in the next section.
+
 </hfoption>
-<hfoption id="OBS virtual camera">
+<hfoption id="Linux">

-If you want to use your phone as a camera using OBS, follow these steps to set up a virtual camera.
+If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera

-1. _(Linux only) Install `v4l2loopback-dkms` and `v4l-utils`_. These packages create virtual camera devices and verify their settings. Install with:
+1. _Install `v4l2loopback-dkms` and `v4l-utils`_. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:

-```bash
+<!-- prettier-ignore-start -->
+```python
 sudo apt install v4l2loopback-dkms v4l-utils
 ```
+<!-- prettier-ignore-end -->

-2. _Install the [DroidCam app](https://droidcam.app) on your phone_. This app is available for both iOS and Android.
-3. _Download and install [OBS Studio](https://obsproject.com)_.
-4. _Download and install the [DroidCam OBS plugin](https://droidcam.app/obs)_.
-5. _Start OBS Studio_.
+2. _Install [DroidCam](https://droidcam.app) on your phone_. This app is available for both iOS and Android.
+3. _Install [OBS Studio](https://obsproject.com)_. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):

-6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480` to avoid the watermarks.
-7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video` or `OBS > Preferences... > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it.
+<!-- prettier-ignore-start -->
+```python
+flatpak install flathub com.obsproject.Studio
+```
+<!-- prettier-ignore-end -->
+
+4. _Install the DroidCam OBS plugin_. This plugin integrates DroidCam with OBS Studio. Install it with:
+
+<!-- prettier-ignore-start -->
+```python
+flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
+```
+<!-- prettier-ignore-end -->
+
+5. _Start OBS Studio_. Launch with:
+
+<!-- prettier-ignore-start -->
+```python
+flatpak run com.obsproject.Studio
+```
+<!-- prettier-ignore-end -->
+
+6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
+7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
 8. _Start virtual camera_. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. _Verify the virtual camera setup and resolution_.
-   - **Linux**: Use `v4l2-ctl` to list devices and check resolution:
-     ```bash
-     v4l2-ctl --list-devices  # find VirtualCam and note its /dev/videoX path
-     v4l2-ctl -d /dev/videoX --get-fmt-video  # replace with your VirtualCam path
-     ```
-     You should see `VirtualCam` listed and resolution `640x480`.
-   - **macOS**: Open Photo Booth or FaceTime and select "OBS Virtual Camera" as the input.
-   - **Windows**: The native Camera app doesn't support virtual cameras. Use a video conferencing app (Zoom, Teams) or run `lerobot-find-cameras opencv` directly to verify.
+9. _Verify the virtual camera setup_. Use `v4l2-ctl` to list the devices:

-<details>
-<summary><strong>Troubleshooting</strong></summary>
+<!-- prettier-ignore-start -->
+```python
+v4l2-ctl --list-devices
+```
+<!-- prettier-ignore-end -->

-> The virtual camera resolution is incorrect.
+You should see an entry like:

-Delete the virtual camera source and recreate it. The resolution cannot be changed after creation.
+```
+VirtualCam (platform:v4l2loopback-000):
+/dev/video1
+```

-> Error reading frame in background thread for OpenCVCamera(X): OpenCVCamera(X) frame width=640 or height=480 do not match configured width=1920 or height=1080.
+10. _Check the camera resolution_. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.

-This error is caused by OBS Virtual Camera advertising a `1920x1080` resolution despite rescaling. The only fix for now is to comment out the width and height check in `_postprocess_image()`.
+<!-- prettier-ignore-start -->
+```python
+v4l2-ctl -d /dev/video1 --get-fmt-video
+```
+<!-- prettier-ignore-end -->

-</details>
+You should see an entry like:
+
+```
+>>> Format Video Capture:
+>>>	Width/Height      : 640/480
+>>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
+```
+
+Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
+
+If everything is set up correctly, you can proceed with the rest of the tutorial.

 </hfoption>
 </hfoptions>
-
-If everything is set up correctly, your phone will appear as a standard OpenCV camera and can be used with `OpenCVCamera`.
@@ -1,165 +0,0 @@
-# Damiao Motors and CAN Bus
-
-This guide covers setup and usage of Damiao motors with LeRobot via CAN bus communication.
-
-Currently, only Linux is supported, as the OpenArms CAN adapter only has drivers for Linux.
-
-## Linux CAN Setup
-
-Before using Damiao motors, you need to set up the CAN interface on your Linux system.
-
-### Install CAN Utilities
-
-```bash
-sudo apt-get install can-utils
-```
-
-### Configure CAN Interface (Manual)
-
-For standard CAN FD (recommended for OpenArms):
-
-```bash
-sudo ip link set can0 down
-sudo ip link set can0 type can bitrate 1000000 dbitrate 5000000 fd on
-sudo ip link set can0 up
-```
-
-For standard CAN (without FD):
-
-```bash
-sudo ip link set can0 down
-sudo ip link set can0 type can bitrate 1000000
-sudo ip link set can0 up
-```
-
-### Configure CAN Interface (Using LeRobot)
-
-LeRobot provides a utility script to setup and test CAN interfaces:
-
-```bash
-# Setup multiple interfaces (e.g., OpenArms Followers with 2 CAN buses)
-lerobot-setup-can --mode=setup --interfaces=can0,can1
-```
-
-## Debugging CAN Communication
-
-Use the built-in debug tools to test motor communication:
-
-```bash
-# Test motors on all interfaces
-lerobot-setup-can --mode=test --interfaces=can0,can1
-
-# Run speed/latency test
-lerobot-setup-can --mode=speed --interfaces=can0
-```
-
-The test mode will scan for motors (IDs 0x01-0x08) and report which ones respond. Example output:
-
-```
-can0: UP (CAN FD)
-  Motor 0x01 (joint_1): ✓ FOUND
-    → Response 0x11 [FD]: 00112233...
-  Motor 0x02 (joint_2): ✓ FOUND
-  Motor 0x03 (joint_3): ✗ No response
-  ...
-  Summary: 2/8 motors found
-```
-
-## Usage
-
-### Basic Setup
-
-```python
-from lerobot.motors import Motor
-from lerobot.motors.damiao import DamiaoMotorsBus
-
-# Define your motors with send/receive CAN IDs
-motors = {
-    "joint_1": Motor(id=0x01, motor_type_str="dm8009", recv_id=0x11),
-    "joint_2": Motor(id=0x02, motor_type_str="dm4340", recv_id=0x12),
-    "joint_3": Motor(id=0x03, motor_type_str="dm4310", recv_id=0x13),
-}
-
-# Create the bus
-bus = DamiaoMotorsBus(
-    port="can0",  # Linux socketcan interface
-    motors=motors,
-)
-
-# Connect
-bus.connect()
-```
-
-### Reading Motor States
-
-```python
-# Read single motor position (degrees)
-position = bus.read("Present_Position", "joint_1")
-
-# Read from multiple motors
-positions = bus.sync_read("Present_Position")  # All motors
-positions = bus.sync_read("Present_Position", ["joint_1", "joint_2"])
-
-# Read all states at once (position, velocity, torque)
-states = bus.sync_read_all_states()
-# Returns: {'joint_1': {'position': 45.2, 'velocity': 1.3, 'torque': 0.5}, ...}
-```
-
-### Writing Motor Commands
-
-```python
-# Enable torque
-bus.enable_torque()
-
-# Set goal position (degrees)
-bus.write("Goal_Position", "joint_1", 45.0)
-
-# Set positions for multiple motors
-bus.sync_write("Goal_Position", {
-    "joint_1": 45.0,
-    "joint_2": -30.0,
-    "joint_3": 90.0,
-})
-
-# Disable torque
-bus.disable_torque()
-```
-
-## Configuration Options
-
-| Parameter      | Default   | Description                                                 |
-| -------------- | --------- | ----------------------------------------------------------- |
-| `port`         | -         | CAN interface (`can0`) or serial port (`/dev/cu.usbmodem*`) |
-| `use_can_fd`   | `True`    | Enable CAN FD for higher data rates                         |
-| `bitrate`      | `1000000` | Nominal bitrate (1 Mbps)                                    |
-| `data_bitrate` | `5000000` | CAN FD data bitrate (5 Mbps)                                |
-
-## Motor Configuration
-
-Each motor requires:
-
- `id`: CAN ID for sending commands
- `motor_type`: One of the supported motor types (e.g., `"dm8009"`, `"dm4340"`)
- `recv_id`: CAN ID for receiving responses
-
-OpenArms default IDs follow the pattern: send ID `0x0N`, receive ID `0x1N` where N is the joint number.
-
-## Troubleshooting
-
-### No Response from Motors
-
-1. **Check power**
-2. **Verify CAN wiring**: Check CAN-H, CAN-L, and GND connections
-3. **Check motor IDs**: Use Damiao Debugging Tools to verify/configure IDs
-4. **Test CAN interface**: Run `candump can0` to see if messages are being received
-5. **Run diagnostics**: `lerobot-setup-can --mode=test --interfaces=can0`
-
-### Motor Timeout Parameter
-
-If motors were configured with timeout=0, they won't respond to commands. Use Damiao Debugging Tools to set a non-zero timeout value.
-
-### Verify CAN FD Status
-
-```bash
-ip -d link show can0 | grep fd
-```
@@ -1,278 +0,0 @@
-# Using Subtasks in LeRobot Datasets
-
-Subtask support in robotics datasets has proven effective in improving robot reasoning and understanding. Subtasks are particularly useful for:
-
- **Hierarchical policies**: Building policies that include subtask predictions to visualize robot reasoning in real time
- **Reward modeling**: Helping reward models understand task progression (e.g., SARM-style stage-aware reward models)
- **Task decomposition**: Breaking down complex manipulation tasks into atomic, interpretable steps
-
-LeRobotDataset now supports subtasks as part of its dataset structure, alongside tasks.
-
-## What are Subtasks?
-
-While a **task** describes the overall goal (e.g., "Pick up the apple and place it in the basket"), **subtasks** break down the execution into finer-grained steps:
-
-1. "Approach the apple"
-2. "Grasp the apple"
-3. "Lift the apple"
-4. "Move to basket"
-5. "Release the apple"
-
-Each frame in the dataset can be annotated with its corresponding subtask, enabling models to learn and predict these intermediate stages.
-
-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/subtask-asset.png"
-  alt="An overview of subtask annotation showing how frames are labeled with intermediate subtask stages"
-  width="80%"
-/>
-
-<p>
-  <em>Figure: Overview of subtask annotation.</em>
-</p>
-
-**Reference:** _Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing_, Original Article, Published: 19 April 2022.
-
-## Dataset Structure
-
-Subtask information is stored in the dataset metadata:
-
-```
-my-dataset/
-├── data/
-│   └── ...
-├── meta/
-│   ├── info.json
-│   ├── stats.json
-│   ├── tasks.parquet
-│   ├── subtasks.parquet      # Subtask index → subtask string mapping
-│   └── episodes/
-│       └── ...
-└── videos/
-    └── ...
-```
-
-### Subtasks Parquet File
-
-The `meta/subtasks.parquet` file maps subtask indices to their natural language descriptions:
-
-| subtask_index | subtask (index column) |
-| ------------- | ---------------------- |
-| 0             | "Approach the apple"   |
-| 1             | "Grasp the apple"      |
-| 2             | "Lift the apple"       |
-| ...           | ...                    |
-
-### Frame-Level Annotations
-
-Each frame in the dataset can include a `subtask_index` field that references the subtasks parquet file:
-
-```python
-# Example frame data in the parquet file
-{
-    "index": 42,
-    "timestamp": 1.4,
-    "episode_index": 0,
-    "task_index": 0,
-    "subtask_index": 2,  # References "Lift the apple"
-    "observation.state": [...],
-    "action": [...],
-}
-```
-
-## Annotating Datasets with Subtasks
-
-We provide a HuggingFace Space for easily annotating any LeRobotDataset with subtasks:
-
-**[https://huggingface.co/spaces/lerobot/annotate](https://huggingface.co/spaces/lerobot/annotate)**
-
-After completing your annotation:
-
-1. Click "Push to Hub" to upload your annotated dataset
-2. You can also run the annotation space locally by following the instructions at [github.com/huggingface/lerobot-annotate](https://github.com/huggingface/lerobot-annotate)
-
-## Loading Datasets with Subtasks
-
-When you load a dataset with subtask annotations, the subtask information is automatically available:
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-# Load a dataset with subtask annotations
-dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
-
-# Access a sample
-sample = dataset[100]
-
-# The sample includes both task and subtask information
-print(sample["task"])        # "Collect the fruit"
-print(sample["subtask"])     # "Grasp the apple"
-print(sample["task_index"])  # tensor(0)
-print(sample["subtask_index"])  # tensor(2)
-```
-
-### Checking for Subtask Support
-
-You can check if a dataset has subtask annotations:
-
-```python
-# Check if subtasks are available
-has_subtasks = (
-    "subtask_index" in dataset.features
-    and dataset.meta.subtasks is not None
-)
-
-if has_subtasks:
-    print(f"Dataset has {len(dataset.meta.subtasks)} unique subtasks")
-    print("Subtasks:", list(dataset.meta.subtasks.index))
-```
-
-## Using Subtasks for Training
-
-### With the Tokenizer Processor
-
-The `TokenizerProcessor` automatically handles subtask tokenization for Vision-Language Action (VLA) models:
-
-```python
-from lerobot.processor.tokenizer_processor import TokenizerProcessor
-from lerobot.processor.pipeline import ProcessorPipeline
-
-# Create a tokenizer processor
-tokenizer_processor = TokenizerProcessor(
-    tokenizer_name_or_path="google/paligemma-3b-pt-224",
-    padding="max_length",
-    max_length=64,
-)
-
-# The processor will automatically tokenize subtasks if present in the batch
-# and add them to the observation under:
-# - "observation.subtask.tokens"
-# - "observation.subtask.attention_mask"
-```
-
-When subtasks are available in the batch, the tokenizer processor adds:
-
- `observation.subtask.tokens`: Tokenized subtask text
- `observation.subtask.attention_mask`: Attention mask for the subtask tokens
-
-### DataLoader with Subtasks
-
-```python
-import torch
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
-
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    batch_size=16,
-    shuffle=True,
-)
-
-for batch in dataloader:
-    # Access subtask information in the batch
-    subtasks = batch["subtask"]  # List of subtask strings
-    subtask_indices = batch["subtask_index"]  # Tensor of subtask indices
-
-    # Use for training hierarchical policies or reward models
-    print(f"Batch subtasks: {set(subtasks)}")
-```
-
-## Example Datasets with Subtask Annotations
-
-Try loading a dataset with subtask annotations:
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-# Example dataset with subtask annotations
-dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
-
-# Explore the subtasks
-print("Available subtasks:")
-for subtask_name in dataset.meta.subtasks.index:
-    print(f"  - {subtask_name}")
-
-# Get subtask distribution
-subtask_counts = {}
-for i in range(len(dataset)):
-    sample = dataset[i]
-    subtask = sample["subtask"]
-    subtask_counts[subtask] = subtask_counts.get(subtask, 0) + 1
-
-print("\nSubtask distribution:")
-for subtask, count in sorted(subtask_counts.items(), key=lambda x: -x[1]):
-    print(f"  {subtask}: {count} frames")
-```
-
-## Use Cases
-
-### 1. Hierarchical Policy Training
-
-Train policies that predict both actions and current subtask:
-
-```python
-class HierarchicalPolicy(nn.Module):
-    def __init__(self, num_subtasks):
-        super().__init__()
-        self.action_head = nn.Linear(hidden_dim, action_dim)
-        self.subtask_head = nn.Linear(hidden_dim, num_subtasks)
-
-    def forward(self, observations):
-        features = self.encoder(observations)
-        actions = self.action_head(features)
-        subtask_logits = self.subtask_head(features)
-        return actions, subtask_logits
-```
-
-### 2. Stage-Aware Reward Modeling (SARM)
-
-Build reward models that understand task progression:
-
-```python
-# SARM predicts:
-# - Stage: Which subtask is being executed (discrete)
-# - Progress: How far along the subtask (continuous 0-1)
-
-class SARMRewardModel(nn.Module):
-    def forward(self, observations):
-        features = self.encoder(observations)
-        stage_logits = self.stage_classifier(features)
-        progress = self.progress_regressor(features)
-        return stage_logits, progress
-```
-
-### 3. Progress Visualization
-
-Monitor robot execution by tracking subtask progression:
-
-```python
-def visualize_execution(model, observations):
-    for t, obs in enumerate(observations):
-        action, subtask_logits = model(obs)
-        predicted_subtask = subtask_names[subtask_logits.argmax()]
-        print(f"t={t}: Executing '{predicted_subtask}'")
-```
-
-## API Reference
-
-### LeRobotDataset Properties
-
-| Property                    | Type                   | Description                                |
-| --------------------------- | ---------------------- | ------------------------------------------ |
-| `meta.subtasks`             | `pd.DataFrame \| None` | DataFrame mapping subtask names to indices |
-| `features["subtask_index"]` | `dict`                 | Feature spec for subtask_index if present  |
-
-### Sample Keys
-
-When subtasks are available, each sample includes:
-
-| Key             | Type           | Description                          |
-| --------------- | -------------- | ------------------------------------ |
-| `subtask_index` | `torch.Tensor` | Integer index of the current subtask |
-| `subtask`       | `str`          | Natural language subtask description |
-
-## Related Resources
-
- [SARM Paper](https://arxiv.org/pdf/2509.25358) - Stage-Aware Reward Modeling for Long Horizon Robot Manipulation
- [LeRobot Annotate Space](https://huggingface.co/spaces/lerobot/annotate) - Interactive annotation tool
- [LeRobotDataset v3.0](./lerobot-dataset-v3) - Dataset format documentation
@@ -1,11 +1,5 @@
 # EarthRover Mini Plus

-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Earth_Rover_Mini_5_240c9adc-4f9e-44b7-982f-5d1dc24af1d8.png.webp"
-  alt="EarthRover Mini Plus"
-  width="70%"
-/>
-
 The EarthRover Mini Plus is a fully open source mobile robot that connects through the cloud using the Frodobots SDK. This lets you control the robot and record datasets for training AI models.

 ## What You Need
@@ -13,47 +7,28 @@ 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

-The robot needs the [Frodobots SDK](https://github.com/frodobots-org/earth-rovers-sdk) running on your computer. Here's how:
+The robot needs the [Frodobots SDK](https://github.com/Frodobots/earth-rovers-sdk) running on your computer. Here's how:

 1. Download and install the SDK:

 ```bash
-git clone https://github.com/frodobots-org/earth-rovers-sdk.git
+git clone https://github.com/Frodobots/earth-rovers-sdk.git
 cd earth-rovers-sdk
 pip install -r requirements.txt
 ```

-2. Save Credentials:
-
-Write your .env variables with the SDK API key and bot name provided by the Frodobots team.
-
-```bash
-SDK_API_TOKEN=your_sdk_api_token_here
-BOT_SLUG=your_bot_slug_here
-CHROME_EXECUTABLE_PATH=/path/to/chrome_or_chromium
-# Default value is MAP_ZOOM_LEVEL=18 https://wiki.openstreetmap.org/wiki/Zoom_levels
-MAP_ZOOM_LEVEL=18
-MISSION_SLUG=your_mission_slug_here
-# Image quality between 0.1 and 1.0 (default: 0.8)
-# Recommended: 0.8 for better performance
-IMAGE_QUALITY=0.8
-# Image format: jpeg, png or webp (default: png)
-# Recommended: jpeg for better performance and lower bandwidth usage
-IMAGE_FORMAT=jpeg
-```
-
-3. Start the SDK:
+2. Start the SDK:

 ```bash
 hypercorn main:app --reload
 ```

-4. Open your web browser and go to `http://localhost:8000`, then click "Join"
+3. Open your web browser and go to `http://localhost:8000`, then click "Join"

 The SDK gives you:

@@ -170,13 +145,13 @@ 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
 ```

@@ -185,16 +160,13 @@ echo $HF_USER
 Use the standard recording command:

 ```bash
-lerobot-record \
+python src/lerobot/scripts/lerobot_record.py \
    --robot.type=earthrover_mini_plus \
    --teleop.type=keyboard_rover \
    --dataset.repo_id=your_username/dataset_name \
    --dataset.num_episodes=2 \
    --dataset.fps=10 \
    --dataset.single_task="Navigate around obstacles" \
-    --dataset.streaming_encoding=true \
-    --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
    --display_data=true
 ```

@@ -204,26 +176,22 @@ Replace `your_username/dataset_name` with your Hugging Face username and a name

 Your dataset includes:

-**Your Actions (2 features)**:
+**Your Actions (2 things)**:

- `linear_velocity`: How much you moved forward/backward
- `angular_velocity`: How much you turned left/right
+- How much you moved forward/backward
+- How much you turned left/right

-**Robot Observations (24 features)**:
+**Robot Observations (12 things)**:

 - Front camera video
 - Rear camera video
 - Current speed
 - Battery level
- Orientation
- GPS (latitude, longitude, signal strength)
+- Which way the robot is facing
+- GPS location (latitude, longitude, signal strength)
 - Network signal strength
 - Vibration level
- Lamp state (on/off)
- Accelerometer (x, y, z)
- Gyroscope (x, y, z)
- Magnetometer (x, y, z)
- Wheel RPMs (4 wheels)
+- Lamp status (on/off)

 ### Where Your Data Goes

@@ -2,32 +2,14 @@

 The **EnvHub** feature allows you to load simulation environments directly from the Hugging Face Hub with a single line of code. This unlocks a powerful new model for collaboration: instead of environments being locked away inside monolithic libraries, anyone can publish custom environments and share them with the community.

-## What is EnvHub?
+## Overview

-EnvHub lets you create custom robotics simulation environments with your own robot models and scenarios, and make them easily usable by anyone through the LeRobot framework.
+With EnvHub, you can:

-EnvHub packages are stored on the Hugging Face Hub, and can be seamlessly pulled and used in your AI robotics projects through LeRobot with a single line of code.
-
-Thanks to EnvHub, you can:
-
-1. **Create and publish environments** to the Hugging Face Hub as Git repositories, and distribute complex physics simulations without packaging hassles
-2. **Load environments** dynamically, without installing them as packages
-3. **Version and track** environment changes using Git semantics
-4. **Discover** new simulation tasks shared by the community
-
-This design means you can go from discovering an interesting environment on the Hub to running experiments in seconds, or create your own custom robot and environment without worrying about dependency conflicts or complex installation procedures.
-
-When you create an EnvHub package, you can build anything you want inside it and use any simulation tool you like: this is your own space to play with. The only requirement is that the package contains an `env.py` file that defines the environment and allows LeRobot to load and use your EnvHub package.
-
-This `env.py` file needs to expose a small API so LeRobot can load and run it. In particular, you must provide a `make_env(n_envs: int = 1, use_async_envs: bool = False)` or `make_env(n_envs: int = 1, use_async_envs: bool = False, cfg: EnvConfig)` function, which is the main entry point for LeRobot. It should return one of:
-
- A `gym.vector.VectorEnv` (most common)
- A single `gym.Env` (will be automatically wrapped)
- A dict mapping `{suite_name: {task_id: VectorEnv}}` (for multi-task benchmarks)
-
-You can also pass an `EnvConfig` object to `make_env` to configure the environment (e.g. the number of environments, task, camera name, initial states, control mode, episode length, etc.).
-
-Finally, your environment must implement the standard `gym.vector.VectorEnv` interface so it works with LeRobot, including methods like `reset` and `step`.
+- Load environments from the Hub instantly
+- Share your custom simulation tasks with the community
+- Version control your environments using Git
+- Distribute complex physics simulations without packaging hassles

 ## Quick Start

@@ -47,6 +29,17 @@ env = make_env("lerobot/cartpole-env", trust_remote_code=True)
  hash for reproducibility and security.
 </Tip>

+## What is EnvHub?
+
+EnvHub is a framework that allows researchers and developers to:
+
+1. **Publish environments** to the Hugging Face Hub as Git repositories
+2. **Load environments** dynamically without installing them as packages
+3. **Version and track** environment changes using Git semantics
+4. **Discover** new simulation tasks shared by the community
+
+This design means you can go from discovering an interesting environment on the Hub to running experiments in seconds, without worrying about dependency conflicts or complex installation procedures.
+
 ## Repository Structure

 To make your environment loadable from the Hub, your repository must contain at minimum:
@@ -155,10 +148,10 @@ 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
@@ -1,510 +0,0 @@
-# NVIDIA IsaacLab Arena & LeRobot
-
-LeRobot EnvHub now supports **GPU-accelerated simulation** with IsaacLab Arena for policy evaluation at scale.
-Train and evaluate imitation learning policies with high-fidelity simulation — all integrated into the LeRobot ecosystem.
-
-<img
-  src="https://huggingface.co/nvidia/isaaclab-arena-envs/resolve/main/assets/Gr1OpenMicrowaveEnvironment.png"
-  alt="IsaacLab Arena - GR1 Microwave Environment"
-  style={{ maxWidth: "100%", borderRadius: "8px", marginBottom: "1rem" }}
-/>
-
-[IsaacLab Arena](https://github.com/isaac-sim/IsaacLab-Arena) integrates with NVIDIA IsaacLab to provide:
-
- 🤖 **Humanoid embodiments**: GR1, G1, Galileo with various configurations
- 🎯 **Manipulation & loco-manipulation tasks**: Door opening, pick-and-place, button pressing, and more
- ⚡ **GPU-accelerated rollouts**: Parallel environment execution on NVIDIA GPUs
- 🖼️ **RTX Rendering**: Evaluate vision-based policies with realistic rendering, reflections and refractions
- 📦 **LeRobot-compatible datasets**: Ready for training with GR00T N1x, PI0, SmolVLA, ACT, and Diffusion policies
- 🔄 **EnvHub integration**: Load environments from HuggingFace EnvHub with one line
-
-## Installation
-
-### Prerequisites
-
-Hardware requirements are shared with Isaac Sim, and are detailed in [Isaac Sim Requirements](https://docs.isaacsim.omniverse.nvidia.com/5.1.0/installation/requirements.html).
-
- NVIDIA GPU with CUDA support
- NVIDIA driver compatible with IsaacSim 5.1.0
- Linux (Ubuntu 22.04 / 24.04)
-
-### Setup
-
-```bash
-# 1. Create conda environment
-conda create -y -n lerobot-arena python=3.11
-conda activate lerobot-arena
-conda install -y -c conda-forge ffmpeg=7.1.1
-
-# 2. Install Isaac Sim 5.1.0
-pip install "isaacsim[all,extscache]==5.1.0" --extra-index-url https://pypi.nvidia.com
-
-# Accept NVIDIA EULA (required)
-export ACCEPT_EULA=Y
-export PRIVACY_CONSENT=Y
-
-# 3. Install IsaacLab 2.3.0
-git clone https://github.com/isaac-sim/IsaacLab.git
-cd IsaacLab
-git checkout v2.3.0
-./isaaclab.sh -i
-cd ..
-
-# 4. Install IsaacLab Arena
-git clone https://github.com/isaac-sim/IsaacLab-Arena.git
-cd IsaacLab-Arena
-git checkout release/0.1.1
-pip install -e .
-cd ..
-
-
-# 5. Install LeRobot
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e .
-cd ..
-
-
-# 6. Install additional dependencies
-pip install onnxruntime==1.23.2 lightwheel-sdk==1.0.1 vuer[all]==0.0.70 qpsolvers==4.8.1
-pip install numpy==1.26.0 # Isaac Sim 5.1 depends on numpy==1.26.0, this will be fixed in next release
-```
-
-## Evaluating Policies
-
-### Pre-trained Policies
-
-The following trained policies are available:
-
-| Policy                      | Architecture | Task          | Link                                                                     |
-| :-------------------------- | :----------- | :------------ | :----------------------------------------------------------------------- |
-| pi05-arena-gr1-microwave    | PI0.5        | GR1 Microwave | [HuggingFace](https://huggingface.co/nvidia/pi05-arena-gr1-microwave)    |
-| smolvla-arena-gr1-microwave | SmolVLA      | GR1 Microwave | [HuggingFace](https://huggingface.co/nvidia/smolvla-arena-gr1-microwave) |
-
-### Evaluate SmolVLA
-
-```bash
-pip install -e ".[smolvla]"
-pip install numpy==1.26.0 # revert numpy to version 1.26
-```
-
-```bash
-lerobot-eval \
-    --policy.path=nvidia/smolvla-arena-gr1-microwave \
-    --env.type=isaaclab_arena \
-    --env.hub_path=nvidia/isaaclab-arena-envs \
-    --rename_map='{"observation.images.robot_pov_cam_rgb": "observation.images.robot_pov_cam"}' \
-    --policy.device=cuda \
-    --env.environment=gr1_microwave \
-    --env.embodiment=gr1_pink \
-    --env.object=mustard_bottle \
-    --env.headless=false \
-    --env.enable_cameras=true \
-    --env.video=true \
-    --env.video_length=10 \
-    --env.video_interval=15 \
-    --env.state_keys=robot_joint_pos \
-    --env.camera_keys=robot_pov_cam_rgb \
-    --trust_remote_code=True \
-    --eval.batch_size=1
-```
-
-### Evaluate PI0.5
-
-```bash
-pip install -e ".[pi]"
-pip install numpy==1.26.0 # revert numpy to version 1.26
-```
-
-<Tip>PI0.5 requires disabling torch compile for evaluation:</Tip>
-
-```bash
-TORCH_COMPILE_DISABLE=1 TORCHINDUCTOR_DISABLE=1 lerobot-eval \
-    --policy.path=nvidia/pi05-arena-gr1-microwave \
-    --env.type=isaaclab_arena \
-    --env.hub_path=nvidia/isaaclab-arena-envs \
-    --rename_map='{"observation.images.robot_pov_cam_rgb": "observation.images.robot_pov_cam"}' \
-    --policy.device=cuda \
-    --env.environment=gr1_microwave \
-    --env.embodiment=gr1_pink \
-    --env.object=mustard_bottle \
-    --env.headless=false \
-    --env.enable_cameras=true \
-    --env.video=true \
-    --env.video_length=15 \
-    --env.video_interval=15 \
-    --env.state_keys=robot_joint_pos \
-    --env.camera_keys=robot_pov_cam_rgb \
-    --trust_remote_code=True \
-    --eval.batch_size=1
-```
-
-<Tip>
-  To change the number of parallel environments, use the ```--eval.batch_size```
-  flag.
-</Tip>
-
-### What to Expect
-
-During evaluation, you will see a progress bar showing the running success rate:
-
-```
-Stepping through eval batches:   8%|██████▍    | 4/50 [00:45<08:06, 10.58s/it, running_success_rate=25.0%]
-```
-
-### Video Recording
-
-To enable video recording during evaluation, add the following flags to your command:
-
-```bash
--env.video=true \
--env.video_length=15 \
--env.video_interval=15
-```
-
-For more details on video recording, see the [IsaacLab Recording Documentation](https://isaac-sim.github.io/IsaacLab/main/source/how-to/record_video.html).
-
-<Tip>
-When running headless with `--env.headless=true`, you must also enable cameras explicitly for camera enabled environments:
-
-```bash
--env.headless=true --env.enable_cameras=true
-```
-
-</Tip>
-
-### Output Directory
-
-Evaluation videos are saved to the output directory with the following structure:
-
-```
-outputs/eval/<date>/<timestamp>_<env>_<policy>/videos/<task>_<env_id>/eval_episode_<n>.mp4
-```
-
-For example:
-
-```
-outputs/eval/2026-01-02/14-38-01_isaaclab_arena_smolvla/videos/gr1_microwave_0/eval_episode_0.mp4
-```
-
-## Training Policies
-
-To learn more about training policies with LeRobot, please refer to the training documentation:
-
- [SmolVLA](./smolvla)
- [Pi0.5](./pi05)
- [GR00T N1.5](./groot)
-
-Sample IsaacLab Arena datasets are available on HuggingFace Hub for experimentation:
-
-| Dataset                                                                                                   | Description                | Frames |
-| :-------------------------------------------------------------------------------------------------------- | :------------------------- | :----- |
-| [Arena-GR1-Manipulation-Task](https://huggingface.co/datasets/nvidia/Arena-GR1-Manipulation-Task-v3)      | GR1 microwave manipulation | ~4K    |
-| [Arena-G1-Loco-Manipulation-Task](https://huggingface.co/datasets/nvidia/Arena-G1-Loco-Manipulation-Task) | G1 loco-manipulation       | ~4K    |
-
-## Environment Configuration
-
-### Full Configuration Options
-
-```python
-from lerobot.envs.configs import IsaaclabArenaEnv
-
-config = IsaaclabArenaEnv(
-    # Environment selection
-    environment="gr1_microwave",      # Task environment
-    embodiment="gr1_pink",            # Robot embodiment
-    object="power_drill",             # Object to manipulate
-
-    # Simulation settings
-    episode_length=300,               # Max steps per episode
-    headless=True,                    # Run without GUI
-    device="cuda:0",                  # GPU device
-    seed=42,                          # Random seed
-
-    # Observation configuration
-    state_keys="robot_joint_pos",     # State observation keys (comma-separated)
-    camera_keys="robot_pov_cam_rgb",  # Camera observation keys (comma-separated)
-    state_dim=54,                     # Expected state dimension
-    action_dim=36,                    # Expected action dimension
-    camera_height=512,                # Camera image height
-    camera_width=512,                 # Camera image width
-    enable_cameras=True,              # Enable camera observations
-
-    # Video recording
-    video=False,                      # Enable video recording
-    video_length=100,                 # Frames per video
-    video_interval=200,               # Steps between recordings
-
-    # Advanced
-    mimic=False,                      # Enable mimic mode
-    teleop_device=None,               # Teleoperation device
-    disable_fabric=False,             # Disable fabric optimization
-    enable_pinocchio=True,            # Enable Pinocchio for IK
-)
-```
-
-### Using Environment Hub directly for advanced usage
-
-Create a file called `test_env_load_arena.py` or [download from the EnvHub](https://huggingface.co/nvidia/isaaclab-arena-envs/blob/main/tests/test_env_load_arena.py):
-
-```python
-import logging
-from dataclasses import asdict
-from pprint import pformat
-import torch
-import tqdm
-from lerobot.configs import parser
-from lerobot.configs.eval import EvalPipelineConfig
-
-
-@parser.wrap()
-def main(cfg: EvalPipelineConfig):
-    """Run random action rollout for IsaacLab Arena environment."""
-    logging.info(pformat(asdict(cfg)))
-
-    from lerobot.envs.factory import make_env
-
-    env_dict = make_env(
-        cfg.env,
-        n_envs=cfg.env.num_envs,
-        trust_remote_code=True,
-    )
-    env = next(iter(env_dict.values()))[0]
-    env.reset()
-    for _ in tqdm.tqdm(range(cfg.env.episode_length)):
-        with torch.inference_mode():
-            actions = env.action_space.sample()
-            obs, rewards, terminated, truncated, info = env.step(actions)
-            if terminated.any() or truncated.any():
-                obs, info = env.reset()
-    env.close()
-
-
-if __name__ == "__main__":
-    main()
-```
-
-Run with:
-
-```bash
-python test_env_load_arena.py \
-    --env.environment=g1_locomanip_pnp \
-    --env.embodiment=gr1_pink \
-    --env.object=cracker_box \
-    --env.num_envs=4 \
-    --env.enable_cameras=true \
-    --env.seed=1000 \
-    --env.video=true \
-    --env.video_length=10 \
-    --env.video_interval=15 \
-    --env.headless=false \
-    --env.hub_path=nvidia/isaaclab-arena-envs \
-    --env.type=isaaclab_arena
-```
-
-## Creating New Environments
-
-First create a new IsaacLab Arena environment by following the [IsaacLab Arena Documentation](https://isaac-sim.github.io/IsaacLab-Arena/release/0.1.1/index.html).
-
-Clone our EnvHub repo:
-
-```bash
-git clone https://huggingface.co/nvidia/isaaclab-arena-envs
-```
-
-Modify the `example_envs.yaml` file based on your new environment.
-[Upload](./envhub#step-3-upload-to-the-hub) your modified repo to HuggingFace EnvHub.
-
-<Tip>
-  Your IsaacLab Arena environment code must be locally available during
-  evaluation. Users can clone your environment repository separately, or you can
-  bundle the environment code and assets directly in your EnvHub repo.
-</Tip>
-
-Then, when evaluating, use your new environment:
-
-```bash
-lerobot-eval \
-    --env.hub_path=<your-env-hub-path>/isaaclab-arena-envs \
-    --env.environment=<your new environment> \
-    ...other flags...
-```
-
-We look forward to your contributions!
-
-## Troubleshooting
-
-### CUDA out of memory
-
-Reduce `batch_size` or use a GPU with more VRAM:
-
-```bash
--eval.batch_size=1
-```
-
-### EULA not accepted
-
-Set environment variables before running:
-
-```bash
-export ACCEPT_EULA=Y
-export PRIVACY_CONSENT=Y
-```
-
-### Video recording not working
-
-Enable cameras when running headless:
-
-```bash
--env.video=true --env.enable_cameras=true --env.headless=true
-```
-
-### Policy output dimension mismatch
-
-Ensure `action_dim` matches your policy:
-
-```bash
--env.action_dim=36
-```
-
-### libGLU.so.1 Errors during Isaac Sim initialization
-
-Ensure you have the following dependencies installed, this is likely to happen on headless machines.
-
-```bash
-sudo apt update && sudo apt install -y libglu1-mesa libxt6
-```
-
-## See Also
-
- [EnvHub Documentation](./envhub.mdx) - General EnvHub usage
- [IsaacLab Arena GitHub](https://github.com/isaac-sim/IsaacLab-Arena)
- [IsaacLab Documentation](https://isaac-sim.github.io/IsaacLab/)
-
-## Lightwheel LW-BenchHub
-
-[Lightwheel](https://www.lightwheel.ai) is bringing `Lightwheel-Libero-Tasks` and `Lightwheel-RoboCasa-Tasks` with 268 tasks to the LeRobot ecosystem.
-LW-BenchHub collects and generates large-scale datasets via teleoperation that comply with the LeRobot specification, enabling out-of-the-box training and evaluation workflows.
-With the unified interface provided by EnvHub, developers can quickly build end-to-end experimental pipelines.
-
-### Install
-
-Assuming you followed the [Installation](#installation) steps, you can install LW-BenchHub with:
-
-```bash
-conda install pinocchio -c conda-forge -y
-pip install numpy==1.26.0 # revert numpy to version 1.26
-
-sudo apt-get install git-lfs && git lfs install
-
-git clone https://github.com/LightwheelAI/lw_benchhub
-git lfs pull # Ensure LFS files (e.g., .usd assets) are downloaded
-
-cd lw_benchhub
-pip install -e .
-```
-
-For more detailed instructions, please refer to the [LW-BenchHub Documentation](https://docs.lightwheel.net/lw_benchhub/usage/Installation).
-
-### Lightwheel Tasks Dataset
-
-LW-BenchHub datasets are available on HuggingFace Hub:
-
-| Dataset                                                                                                       | Description             | Tasks | Frames |
-| :------------------------------------------------------------------------------------------------------------ | :---------------------- | :---- | :----- |
-| [Lightwheel-Tasks-X7S](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-X7S)                     | X7S LIBERO and RoboCasa | 117   | ~10.3M |
-| [Lightwheel-Tasks-Double-Piper](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-Double-Piper)   | Double-Piper LIBERO     | 130   | ~6.0M  |
-| [Lightwheel-Tasks-G1-Controller](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-G1-Controller) | G1-Controller LIBERO    | 62    | ~2.7M  |
-| [Lightwheel-Tasks-G1-WBC](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-G1-WBC)               | G1-WBC RoboCasa         | 32    | ~1.5M  |
-
-For training policies, refer to the [Training Policies](#training-policies) section.
-
-### Evaluating Policies
-
-#### Pre-trained Policies
-
-The following trained policies are available:
-
-| Policy                   | Architecture | Task                           | Layout     | Robot           | Link                                                                                  |
-| :----------------------- | :----------- | :----------------------------- | :--------- | :-------------- | :------------------------------------------------------------------------------------ |
-| smolvla-double-piper-pnp | SmolVLA      | L90K1PutTheBlackBowlOnThePlate | libero-1-1 | DoublePiper-Abs | [HuggingFace](https://huggingface.co/LightwheelAI/smolvla-double-piper-pnp/tree/main) |
-
-#### Evaluate SmolVLA
-
-```bash
-lerobot-eval \
-  --policy.path=LightwheelAI/smolvla-double-piper-pnp \
-  --env.type=isaaclab_arena \
-  --rename_map='{"observation.images.left_hand_camera_rgb": "observation.images.left_hand", "observation.images.right_hand_camera_rgb": "observation.images.right_hand", "observation.images.first_person_camera_rgb": "observation.images.first_person"}' \
-  --env.hub_path=LightwheelAI/lw_benchhub_env \
-  --env.kwargs='{"config_path": "configs/envhub/example.yml"}' \
-  --trust_remote_code=true \
-  --env.state_keys=joint_pos \
-  --env.action_dim=12 \
-  --env.camera_keys=left_hand_camera_rgb,right_hand_camera_rgb,first_person_camera_rgb \
-  --policy.device=cuda \
-  --eval.batch_size=10 \
-  --eval.n_episodes=100
-```
-
-### Environment Configuration
-
-Evaluation can be quickly launched by modifying the `robot`, `task`, and `layout` settings in the configuration file.
-
-#### Full Configuration Options
-
-```yml
-# =========================
-# Basic Settings
-# =========================
-disable_fabric: false
-device: cuda:0
-sensitivity: 1.0
-step_hz: 50
-enable_cameras: true
-execute_mode: eval
-episode_length_s: 20.0 # Episode length in seconds, increase if episodes timeout during eval
-
-# =========================
-# Robot Settings
-# =========================
-robot: DoublePiper-Abs # Robot type, DoublePiper-Abs, X7S-Abs, G1-Controller or G1-Controller-DecoupledWBC
-robot_scale: 1.0
-
-# =========================
-# Task & Scene Settings
-# =========================
-task: L90K1PutTheBlackBowlOnThePlate # Task name
-scene_backend: robocasa
-task_backend: robocasa
-debug_assets: null
-layout: libero-1-1 # Layout and style ID
-sources:
-  - objaverse
-  - lightwheel
-  - aigen_objs
-object_projects: []
-usd_simplify: false
-seed: 42
-
-# =========================
-# Object Placement Retry Settings
-# =========================
-max_scene_retry: 4
-max_object_placement_retry: 3
-
-resample_objects_placement_on_reset: true
-resample_robot_placement_on_reset: true
-
-# =========================
-# Replay Configuration Settings
-# =========================
-replay_cfgs:
-  add_camera_to_observation: true
-  render_resolution: [640, 480]
-```
-
-### See Also
-
- [LW-BenchHub GitHub](https://github.com/LightwheelAI/LW-BenchHub)
- [LW-BenchHub Documentation](https://docs.lightwheel.net/lw_benchhub/)
@@ -137,8 +137,7 @@ from lerobot.teleoperators import (  # noqa: F401
    Teleoperator,
    TeleoperatorConfig,
    make_teleoperator_from_config,
-    so_leader,
-    bi_so_leader,
+    so101_leader,
 )
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import init_logging
@@ -197,7 +196,7 @@ def teleop_loop(teleop: Teleoperator, env: gym.Env, fps: int):
            obs, info = env.reset()

        dt_s = time.perf_counter() - loop_start
-        precise_sleep(max(1 / fps - dt_s, 0.0))
+        precise_sleep(1 / fps - dt_s)
        loop_s = time.perf_counter() - loop_start
        print(f"\ntime: {loop_s * 1e3:.2f}ms ({1 / loop_s:.0f} Hz)")

@@ -223,7 +222,7 @@ def teleoperate(cfg: TeleoperateConfig):

 def main():
    teleoperate(TeleoperateConfig(
-        teleop=so_leader.SO101LeaderConfig(
+        teleop=so101_leader.SO101LeaderConfig(
            port="/dev/ttyACM0",
            id='leader',
            use_degrees=False,
@@ -12,12 +12,6 @@ Developers and researchers can post-train GR00T N1.5 with their own real or synt

 GR00T N1.5 (specifically the GR00T-N1.5-3B model) is built using pre-trained vision and language encoders. It utilizes a flow matching action transformer to model a chunk of actions, conditioned on vision, language, and proprioception.

-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-groot-paper1%20(1).png"
-  alt="An overview of GR00T"
-  width="80%"
-/>
-
 Its strong performance comes from being trained on an expansive and diverse humanoid dataset, which includes:

 - Real captured data from robots.
@@ -109,7 +103,7 @@ Once you have trained your model using your parameters you can run inference in

 ```bash
 lerobot-record \
-  --robot.type=bi_so_follower \
+  --robot.type=bi_so100_follower \
  --robot.left_arm_port=/dev/ttyACM1 \
  --robot.right_arm_port=/dev/ttyACM0 \
  --robot.id=bimanual_follower \
@@ -120,12 +114,9 @@ lerobot-record \
  --display_data=true \
  --dataset.repo_id=<user>/eval_groot-bimanual  \
  --dataset.num_episodes=10 \
-  --dataset.single_task="Grab and handover the red cube to the other arm" \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
-  --policy.path=<user>/groot-bimanual \ # your trained model
-  --dataset.episode_time_s=30 \
+  --dataset.single_task="Grab and handover the red cube to the other arm"
+  --policy.path=<user>/groot-bimanual # your trained model
+  --dataset.episode_time_s=30
  --dataset.reset_time_s=10
 ```

@@ -224,15 +224,12 @@ lerobot-record \
    --teleop.port=/dev/tty.usbmodem1201 \
    --teleop.id=right \
    --teleop.side=right \
-    --dataset.repo_id=<USER>/hand_record_test_with_video_data \
+    --dataset.repo_id=nepyope/hand_record_test_with_video_data \
    --dataset.single_task="Hand recording test with video data" \
    --dataset.num_episodes=1 \
    --dataset.episode_time_s=5 \
    --dataset.push_to_hub=true \
    --dataset.private=true \
-    --dataset.streaming_encoding=true \
-    --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
    --display_data=true
 ```

@@ -244,7 +241,7 @@ lerobot-replay \
    --robot.port=/dev/tty.usbmodem58760432281 \
    --robot.id=right \
    --robot.side=right \
-    --dataset.repo_id=<USER>/hand_record_test_with_camera \
+    --dataset.repo_id=nepyope/hand_record_test_with_camera \
    --dataset.episode=0
 ```

@@ -252,13 +249,13 @@ lerobot-replay \

 ```bash
 lerobot-train \
-  --dataset.repo_id=<USER>/hand_record_test_with_video_data \
+  --dataset.repo_id=nepyope/hand_record_test_with_video_data \
  --policy.type=act \
  --output_dir=outputs/train/hopejr_hand \
  --job_name=hopejr \
  --policy.device=mps \
  --wandb.enable=true \
-  --policy.repo_id=<USER>/hand_test_policy
+  --policy.repo_id=nepyope/hand_test_policy
 ```

 ### Evaluate
@@ -273,11 +270,8 @@ lerobot-record \
  --robot.side=right \
  --robot.cameras='{"main": {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30}}' \
  --display_data=false \
-  --dataset.repo_id=<USER>/eval_hopejr \
+  --dataset.repo_id=nepyope/eval_hopejr \
  --dataset.single_task="Evaluate hopejr hand policy" \
  --dataset.num_episodes=10 \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
  --policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
 ```
@@ -58,8 +58,8 @@ lerobot-teleoperate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.teleoperators.so_leader import SO101LeaderConfig, SO101Leader
-from lerobot.robots.so_follower import SO101FollowerConfig, SO101Follower
+from lerobot.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
+from lerobot.robots.so101_follower import SO101FollowerConfig, SO101Follower

 robot_config = SO101FollowerConfig(
    port="/dev/tty.usbmodem58760431541",
@@ -159,13 +159,13 @@ 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=$(NO_COLOR=1 hf auth whoami | awk -F': *' 'NR==1 {print $2}')
+HF_USER=$(hf auth whoami | head -n 1)
 echo $HF_USER
 ```

@@ -185,10 +185,7 @@ lerobot-record \
    --display_data=true \
    --dataset.repo_id=${HF_USER}/record-test \
    --dataset.num_episodes=5 \
-    --dataset.single_task="Grab the black cube" \
-    --dataset.streaming_encoding=true \
-    # --dataset.vcodec=auto \
-    --dataset.encoder_threads=2
+    --dataset.single_task="Grab the black cube"
 ```
 </hfoption>
 <hfoption id="API example">
@@ -198,9 +195,9 @@ lerobot-record \
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.teleoperators.so_leader.config_so100_leader import SO100LeaderConfig
-from lerobot.teleoperators.so_leader.so100_leader import SO100Leader
+from lerobot.robots.so100_follower import SO100Follower, SO100FollowerConfig
+from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
+from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
@@ -327,7 +324,7 @@ 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
@@ -411,8 +408,8 @@ lerobot-replay \
 import time

 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.robots.so_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so_follower.so100_follower import SO100Follower
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say

@@ -424,7 +421,7 @@ robot = SO100Follower(robot_config)
 robot.connect()

 dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[episode_idx])
-actions = dataset.select_columns("action")
+actions = dataset.hf_dataset.select_columns("action")

 log_say(f"Replaying episode {episode_idx}")
 for idx in range(dataset.num_frames):
@@ -435,7 +432,7 @@ for idx in range(dataset.num_frames):
    }
    robot.send_action(action)

-    precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+    precise_sleep(1.0 / dataset.fps - (time.perf_counter() - t0))

 robot.disconnect()
 ```
@@ -491,7 +488,7 @@ 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 +496,7 @@ 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
 ```

@@ -518,9 +515,6 @@ lerobot-record  \
  --display_data=false \
  --dataset.repo_id=${HF_USER}/eval_so100 \
  --dataset.single_task="Put lego brick into the transparent box" \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
  # <- Teleop optional if you want to teleoperate in between episodes \
  # --teleop.type=so100_leader \
  # --teleop.port=/dev/ttyACM0 \
@@ -537,8 +531,8 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import hw_to_dataset_features
 from lerobot.policies.act.modeling_act import ACTPolicy
 from lerobot.policies.factory import make_pre_post_processors
-from lerobot.robots.so_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so_follower.so100_follower import SO100Follower
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
@@ -1,57 +1,30 @@
 # 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).
-
-## Step 1 (`conda` only): Install [`miniforge`](https://conda-forge.org/download/)
+## Install [`miniforge`](https://conda-forge.org/download/)

 ```bash
 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
 bash Miniforge3-$(uname)-$(uname -m).sh
 ```

-## Step 2: Environment Setup
+## 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]
@@ -65,17 +38,7 @@ ffmpeg -version  # ffmpeg 8.X is not yet supported !
 >
 > - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.

-> [!NOTE]
-> When installing LeRobot inside WSL (Windows Subsystem for Linux), make sure to install `evdev` with the following command:
->
-> ```bash
-> 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 🤗
+## Install LeRobot 🤗

 ### From Source

@@ -88,45 +51,23 @@ 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 +81,13 @@ _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 +97,7 @@ 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

@@ -18,7 +18,7 @@ If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus inter
 - [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos

 Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/motors_bus.py) abstract class to learn about its API.
-For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so_follower/so101_follower/so101_follower.py)
+For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so101_follower/so101_follower.py)

 Use these if compatible. Otherwise, you'll need to find or write a Python interface (not covered in this tutorial):

@@ -1,11 +1,5 @@
 # LeKiwi

-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/1740517739083.jpeg"
-  alt="LeKiwi"
-  width="70%"
-/>
-
 In the steps below, we explain how to assemble the LeKiwi mobile robot.

 ## Source the parts
@@ -210,7 +204,7 @@ lerobot-calibrate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.teleoperators.so_leader import SO100LeaderConfig, SO100Leader
+from lerobot.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader

 config = SO100LeaderConfig(
    port="/dev/tty.usbmodem58760431551",
@@ -279,13 +273,13 @@ 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
 ```

@@ -41,10 +41,7 @@ lerobot-record \
  --display_data=true \
  --dataset.repo_id=${HF_USER}/record-test \
  --dataset.num_episodes=5 \
-  --dataset.single_task="Grab the black cube" \
-  --dataset.streaming_encoding=true \
-  # --dataset.vcodec=auto \
-  --dataset.encoder_threads=2
+  --dataset.single_task="Grab the black cube"
 ```

 See the [recording guide](./il_robots#record-a-dataset) for more details.
@@ -42,7 +42,6 @@ lerobot-eval \
 ```

 - `--env.task` picks the suite (`libero_object`, `libero_spatial`, etc.).
- `--env.task_ids` picks task ids to run (`[0]`, `[1,2,3]`, etc.). Omit this flag (or set it to `null`) to run all tasks in the suite.
 - `--eval.batch_size` controls how many environments run in parallel.
 - `--eval.n_episodes` sets how many episodes to run in total.

@@ -1,340 +0,0 @@
-# Multitask DiT Policy
-
-Multitask Diffusion Transformer (DiT) Policy is an evolution of the original Diffusion Policy architecture, which leverages a large DiT with text and vision conditioning for multitask robot learning. This implementation supports both diffusion and flow matching objectives for action generation, enabling robots to perform diverse manipulation tasks conditioned on language instructions.
-
-## Model Overview
-
-The model uses:
-
- **CLIP Vision Encoder**: Processes RGB images from multiple camera views
- **CLIP Text Encoder**: Encodes language task instructions (frozen weights with learnable projection)
- **Diffusion Transformer**: Predicts action sequences conditioned on observations and language
- **Two Objectives**: Supports both diffusion (DDPM/DDIM) and flow matching for action generation
-
-This model is exciting because you can achieve extremely high dexterity, competitive with multi-billion parameter
-VLAs, with only ~450M parameters and significantly less training.
-
-## Installation Requirements
-
-Multitask DiT Policy has additional dependencies. Install it with:
-
-```bash
-pip install lerobot[multi_task_dit]
-```
-
-This will install all necessary dependencies including the HuggingFace Transformers library for CLIP models.
-
-## Usage
-
-To use Multitask DiT in your LeRobot configuration, specify the policy type as:
-
-```python
-policy.type=multi_task_dit
-```
-
-## Training
-
-### Basic Training Command
-
-Here's a complete training command for training Multitask DiT on your dataset:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=YOUR_DATASET \
-  --output_dir=./outputs/multitask_dit_training \
-  --batch_size=32 \
-  --steps=5000 \
-  --save_freq=500 \
-  --log_freq=100 \
-  --policy.type=multi_task_dit \
-  --policy.device=cuda \
-  --policy.repo_id="HF_USER/multitask-dit-your-robot" \
-  --wandb.enable=true
-```
-
-### Recommended Hyperparameters and Dataset Details (30Hz Control Frequency)
-
-For reliable performance, start with these suggested default hyperparameters:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=YOUR_DATASET \
-  --output_dir=./outputs/mutitask_dit_training \
-  --batch_size=320 \
-  --steps=30000 \
-  --policy.type=multi_task_dit \
-  --policy.device=cuda \
-  --policy.horizon=32 \
-  --policy.n_action_steps=24 \
-  --policy.objective=diffusion \
-  --policy.noise_scheduler_type=DDPM \
-  --policy.num_train_timesteps=100 \
-  --policy.repo_id="HF_USER/multitask-dit-your-robot" \
-  --wandb.enable=true
-```
-
-**Key Parameters:**
-
- **Batch Size**: 192-320 - If you have access to a GPU that can support this, you will get the best training dynamics
- **Horizon**: 32 - number of action steps to predict, ~1.0 sec at 30Hz
- **n_action_steps**: 24 - ~0.8 seconds at 30Hz
- **Objective**: `diffusion` - start with diffusion and experiment with flow matching if generation quality is poor
- **Training Steps**: >30k steps recommended for a single task
-
-### Training Configuration Parameters
-
-#### Objective Selection
-
-Choose between diffusion and flow matching:
-
-```bash
-# Diffusion objective (default)
--policy.objective=diffusion \
--policy.noise_scheduler_type=DDPM \  # or "DDIM"
--policy.num_train_timesteps=100 \
--policy.num_inference_steps=10 \  # For faster inference
--policy.beta_schedule=squaredcos_cap_v2 \  # Noise schedule type
--policy.prediction_type=epsilon \  # "epsilon" (predict noise) or "sample" (predict clean)
--policy.clip_sample=true \  # Clip samples during denoising
--policy.clip_sample_range=1.0  # Clipping range [-x, x]
-
-# Flow matching objective
--policy.objective=flow_matching \
--policy.timestep_sampling_strategy=beta \  # or "uniform" | the beta sampling strategy performance appears much better in practice
--policy.num_integration_steps=100 \
--policy.integration_method=euler \  # or "rk4"
--policy.sigma_min=0.0  # Minimum noise in flow interpolation path
-```
-
-#### Transformer Architecture
-
-Adjust model capacity based on dataset size:
-
-```bash
-# Small datasets (< 100 examples)
--policy.num_layers=4 \
--policy.hidden_dim=512 \
--policy.num_heads=8  # should ideally be hidden_dim // 64
-
-# Medium datasets (100-5k examples) - default
--policy.num_layers=6 \
--policy.hidden_dim=512 \
--policy.num_heads=8  # should ideally be hidden_dim // 64
-
-# Large datasets (> 5k examples)
--policy.num_layers=8 \
--policy.hidden_dim=512 \
--policy.num_heads=8   # should ideally be hidden_dim // 64
-```
-
-**Positional Encoding Options:**
-
-The model supports two positional encoding methods for action sequences:
-
-```bash
-# Rotary Position Embedding (RoPE) - default, recommended
--policy.use_rope=true \
--policy.rope_base=10000.0  # Base frequency for RoPE
-
-# Absolute positional encoding
--policy.use_positional_encoding=true  # Disables RoPE when true
-```
-
-**Other Transformer Parameters:**
-
-```bash
--policy.dropout=0.1  # Dropout rate for DiT blocks (0.0-1.0)
--policy.timestep_embed_dim=256  # Timestep embedding dimension
-```
-
-#### Vision Encoder Configuration
-
-```bash
-# Use different CLIP model for more expressivity at the cost of inference time
-# experiment with larger or smaller models depending on the complexity of your tasks and size of dataset
--policy.vision_encoder_name=openai/clip-vit-large-patch14
-
-# Use separate vision encoder per camera
-# This may be useful when cameras have significantly different characteristics, but
-# be wary of increased VRAM footprint.
--policy.use_separate_rgb_encoder_per_camera=true
-
-# Image preprocessing
--policy.image_resize_shape=[XXX,YYY] \ # you may need to resize your images for inference speed ups
--policy.image_crop_shape=[224,224] \
--policy.image_crop_is_random=true  # Random during training, center at inference
-```
-
-#### Text Encoder Configuration
-
-```bash
-# Use different CLIP text encoder model
-# same as vision: experiment with larger or smaller models depending on the
-# complexity of your tasks and size of dataset
--policy.text_encoder_name=openai/clip-vit-large-patch14
-```
-
-#### Learning Rate Configuration
-
-The vision encoder uses a separate learning rate multiplier, where 1/10th is suggested to be the ideal staritng point:
-
-```bash
--policy.optimizer_lr=2e-5 \
--policy.vision_encoder_lr_multiplier=0.1  # Vision encoder LR = 0.1 * optimizer_lr
-```
-
-### Training Tuning Guidelines
-
-#### 1. Flow Matching with Beta Sampling
-
-The original diffusion implementation here is based on the work described in [TRI's LBM paper](https://arxiv.org/abs/2507.05331)
-
-Additionally, we have implemented a flow-matching objective, which is described at a high-level in [Boston Dynamics blog post](https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/).
-
-Consider testing the flow-matching objective and evaluating performance differences for your task:
-
-```bash
--policy.objective=flow_matching \
--policy.timestep_sampling_strategy=beta \
--policy.timestep_sampling_alpha=1.5 \
--policy.timestep_sampling_beta=1.0 \
--policy.timestep_sampling_s=0.999
-```
-
-This hasn't been shown to be a silver bullet across every user case, but it occasionally results in smoother and more consistent actions.
-
-#### 2. Number of Transformer Layers
-
-Match model capacity to your dataset size:
-
- **Small datasets** (< 100 examples): Reduce to 4 layers
- **Large datasets** (> 5k examples): Increase to 8 layers
-
-#### 3. `horizon` Tuning
-
-The model can be sensitive to the horizon you choose. Start with around a 1 second horizon based on your control frequency:
-
- **30 Hz frequency**: `horizon=30`
- **10 Hz frequency**: `horizon=10`
-
-Then experiment with increasing from there. The horizon determines how far into the future the model predicts actions.
-
-#### 4. `n_action_steps` Sensitivity
-
-The model can also be very sensitive to `n_action_steps`. Start with it being around 0.8 seconds based on your control frequency and tune from there:
-
- **Lower values**: More reactive but potentially less stable for long-horizon tasks
- **Higher values**: Better for long-horizon execution but open-loop failures are limited in their recovery
-
-### Inference Tuning
-
-For faster inference, use DDIM with fewer sampling steps:
-
-```bash
--policy.noise_scheduler_type=DDIM \
--policy.num_inference_steps=10
-```
-
-### Resuming Training
-
-To resume training from a checkpoint:
-
-```bash
-lerobot-train \
-  --config_path=./outputs/mutitask_dit_training/checkpoints/last/pretrained_model/train_config.json \
-  --resume=true
-```
-
-The checkpoint directory should contain `model.safetensors` and `config.json` files (saved automatically during training). When resuming, the configuration is loaded from the checkpoint, so you don't need to specify other parameters.
-
-## Common Failure Modes and Debugging
-
-Training these models can be finicky. Here are common failure modes and debugging approaches:
-
-### Idling / No Motion
-
-The model may "collapse" during inference, resulting in static or no motion. This can occur when:
-
-1. **Insufficient training data**: If you only have 20-50 examples, try to roughly double your dataset size. Once you have above 300 examples, if you're still seeing this, the task may be too complex.
-
-2. **Multiple similar tasks**: When your dataset contains multiple similar tasks (e.g., picking up 2 different objects), the model may rely too heavily on language conditioning which might not be rich enough.
-
-**Debugging tips:**
-
- Increase dataset size (double until you get to over 300 examples)
- Train for longer, up to 100k steps, even when the loss flatlines
- Check if the model is receiving proper language instructions or increase diversity of instruction
-
-### Executing the Wrong Task
-
-Sometimes the robot will completely ignore your instruction and perform some other task. This generally only happens if you have trained on multiple tasks.
-
-**Potential causes:**
-
- Language instruction ambiguity
- Insufficient task-specific training data
- Model confusion between similar tasks in the multitask dataset
-
-**Debugging tips:**
-
- Verify language instruction specificity, especially if descriptions are similar between multiple tasks
- Check task distribution in your training dataset and add weighting to the failing/ignored task
- Consider task-specific fine-tuning
-
-### Training Instability
-
-If training loss is unstable or diverging:
-
- Try adjusting learning rate between `1e-5` and `3e-4`
- Increase batch size if possible
- Check that your dataset normalization is correct
- Verify image preprocessing is working correctly
-
-## Performance Considerations
-
-### GPU Requirements
-
- **Inference**: At least an RTX 5070 Ti (or equivalent GPU) is recommended for reasonable speed performance
- **Training**: A GPU with enough VRAM to load batch sizes of >64 is ideal, which will vary depending on the number of image observations, etc
-
-### Batch Size Recommendations
-
- **Minimum**: 64 (less than this may result in unstable training)
- **Recommended**: 256-320 (best performance, requires larger GPU)
-
-## Example: Training on Custom Dataset
-
-Here's a complete example training on a custom dataset:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=YOUR_DATASET \
-  --output_dir=./outputs/mutitask_dit_training \
-  --batch_size=320 \
-  --steps=30000 \
-  --save_freq=1000 \
-  --log_freq=100 \
-  --eval_freq=1000 \
-  --policy.type=multi_task_dit \
-  --policy.device=cuda \
-  --policy.horizon=32 \
-  --policy.n_action_steps=24 \
-  --policy.objective=diffusion \
-  --policy.noise_scheduler_type=DDPM \
-  --policy.num_layers=6 \
-  --policy.hidden_dim=512 \
-  --policy.vision_encoder_name=openai/clip-vit-base-patch16 \
-  --policy.image_resize_shape=[320,240] \
-  --policy.image_crop_shape=[224,224] \
-  --policy.repo_id="HF_USER/multitask-dit-your-robot" \
-  --wandb.enable=true \
-  --wandb.project=multitask_dit
-```
-
-## References
-
-For more details on the technical implementation and architecture, see:
-
- [A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation](https://arxiv.org/abs/2507.05331)
- [Large Behavior Models and Atlas Find New Footing](https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/)
- [Dissecting and Open-Sourcing Multitask Diffusion Transformer Policy](https://brysonkjones.substack.com/p/dissecting-and-open-sourcing-multitask-diffusion-transformer-policy)
@@ -1,197 +0,0 @@
-## Order and Assemble the parts
-
-First, assemble the OMX hardware following the official assembly guide.
-
-OMX Assembly Guide: https://ai.robotis.com/omx/assembly_guide_omx.html
-
-OMX robots are shipped preconfigured from the factory. Motor IDs, communication parameters, and joint offsets are already set, so no additional motor setup or calibration is required before using LeRobot.
-
-## Install LeRobot 🤗
-
-To install LeRobot, follow our [Installation Guide](./installation)
-
-In addition to these instructions, you need to install the Dynamixel SDK:
-
-```bash
-pip install -e ".[dynamixel]"
-```
-
-## Connect the robot
-
-To find the port for each bus servo adapter, run this script:
-
-```bash
-lerobot-find-port
-```
-
-This command runs and when prompted, disconnect the USB cable from either the leader or follower arm and press Enter. The output will show 'The port of this MotorsBus is [port]'. This identifies the port for the disconnected arm. Repeat for the other arm to identify both ports.
-
-<hfoptions id="find_port">
-<hfoption id="Mac">
-
-Example output on macOS:
-
-```
-Finding all available ports for the MotorBus.
-['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
-Remove the USB cable from your MotorsBus and press Enter when done.
-
-[...Disconnect corresponding leader or follower arm and press Enter...]
-
-The port of this MotorsBus is /dev/tty.usbmodem575E0032081
-Reconnect the USB cable.
-```
-
-Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
-
-</hfoption>
-<hfoption id="Linux">
-
-On Linux, we strongly recommend using udev rules to assign persistent and human-readable device names to the OMX leader and follower arms. This avoids issues where device names such as ttyACM0 and ttyACM1 change when the robot is unplugged, replugged, or when the system is rebooted.
-
-#### 1. Find your device serial numbers
-
-You should have obtained the port numbers like ../../ttyACM? for the leader and follower using `lerobot-find-port`. You can match those results with the serial numbers using the `ls -l /dev/serial/by-id/` command.
-To create udev rules, you need the unique serial number for each OMX device. The easiest way is to list devices under:
-
-```bash
-ls -l /dev/serial/by-id/
-```
-
-You will see output similar to:
-
-```bash
-usb-ROBOTIS_OpenRB-150_228BDD7B503059384C2E3120FF0A2B19-if00 -> ../../ttyACM0
-usb-ROBOTIS_OpenRB-150_67E1ED68503059384C2E3120FF092234-if00 -> ../../ttyACM1
-```
-
-In each line, the serial number is the long string after `usb-ROBOTIS_OpenRB-150_` and before `-if00`.
-
-Follower serial: `228BDD7B503059384C2E3120FF0A2B19`
-
-Leader serial: `67E1ED68503059384C2E3120FF092234`
-
-#### 2. Create the udev rule
-
-Create a new udev rule file:
-
-```bash
-sudo nano /etc/udev/rules.d/99-omx.rules
-```
-
-Paste the following lines, replacing the serial numbers with the values you found above:
-
-```bash
-SUBSYSTEM=="tty", ATTRS{idVendor}=="0403", ATTRS{serial}=="228BDD7B503059384C2E3120FF0A2B19", SYMLINK+="omx_follower"
-SUBSYSTEM=="tty", ATTRS{idVendor}=="0403", ATTRS{serial}=="67E1ED68503059384C2E3120FF092234", SYMLINK+="omx_leader"
-```
-
-Save the file and reload udev rules:
-
-```bash
-sudo udevadm control --reload-rules
-sudo udevadm trigger
-```
-
-Now unplug and replug both devices once.
-
-#### 3. Verify the symlinks
-
-Check that the persistent device names exist:
-
-```bash
-ls -l /dev/omx_follower /dev/omx_leader
-```
-
-You should see them pointing to ttyACM\* devices:
-
-```bash
-/dev/omx_follower -> ttyACM*
-/dev/omx_leader   -> ttyACM*
-```
-
-These names remain stable across reboots and reconnections.
-
-</hfoption>
-</hfoptions>
-
-## Teleoperate
-
-After identifying the correct ports, you can directly teleoperate the follower arm using the leader arm.
-
-<hfoptions id="teleoperate">
-<hfoption id="Mac">
-
-### Teleoperate without camera
-
-```bash
-lerobot-teleoperate \
-  --robot.type=omx_follower \
-  --robot.port=<your_follower_port> \
-  --robot.id=omx_follower_arm \
-  --teleop.type=omx_leader \
-  --teleop.port=<your_leader_port> \
-  --teleop.id=omx_leader_arm
-```
-
-During teleoperation, motions of the leader arm are mirrored in real time by the follower arm. OMX is already preconfigured, teleoperation can begin immediately without any calibration steps.
-
-### Teleoperate with camera
-
-You can also enable camera input during teleoperation by providing a camera configuration for the follower arm.
-
-```bash
-lerobot-teleoperate \
-  --robot.type=omx_follower \
-  --robot.port=<your_follower_port> \
-  --robot.id=omx_follower_arm \
-  --robot.cameras="{front: {type: opencv, index_or_path: '/dev/video0', width: 640, height: 480, fps: 30}}" \
-  --teleop.type=omx_leader \
-  --teleop.port=<your_leader_port> \
-  --teleop.id=omx_leader_arm \
-  --display_data=true
-```
-
-When the camera is enabled, the camera stream is displayed in real time and synchronized with the robot state. This setup is useful for visual monitoring and can be reused later for demonstration recording and imitation learning.
-
-</hfoption>
-<hfoption id="Linux">
-
-### Teleoperate without camera
-
-```bash
-lerobot-teleoperate \
-  --robot.type=omx_follower \
-  --robot.port=/dev/omx_follower \
-  --robot.id=omx_follower_arm \
-  --teleop.type=omx_leader \
-  --teleop.port=/dev/omx_leader \
-  --teleop.id=omx_leader_arm
-```
-
-During teleoperation, motions of the leader arm are mirrored in real time by the follower arm. OMX is already preconfigured, teleoperation can begin immediately without any calibration steps.
-
-### Teleoperate with camera
-
-You can also enable camera input during teleoperation by providing a camera configuration for the follower arm.
-
-```bash
-lerobot-teleoperate \
-  --robot.type=omx_follower \
-  --robot.port=/dev/omx_follower \
-  --robot.id=omx_follower_arm \
-  --robot.cameras="{front: {type: opencv, index_or_path: '/dev/video0', width: 640, height: 480, fps: 30}}" \
-  --teleop.type=omx_leader \
-  --teleop.port=/dev/omx_leader \
-  --teleop.id=omx_leader_arm \
-  --display_data=true
-```
-
-When the camera is enabled, the camera stream is displayed in real time and synchronized with the robot state. This setup is useful for visual monitoring and can be reused later for demonstration recording and imitation learning.
-
-</hfoption>
-</hfoptions>
-
-Congrats 🎉, your robot is all set to learn a task on its own.
-
-> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/robotis).
@@ -1,276 +0,0 @@
-# OpenArm
-
-[OpenArm](https://openarm.dev) is an open-source 7DOF humanoid arm designed for physical AI research and deployment.
-
-To get your OpenArm, assembled or DIY, and join the global community, browse verified and certified manufacturers worldwide at [openarm.dev](https://openarm.dev).
-
-## What's Unique?
-
- **Human-Scale Design**: OpenArm is designed with human-like proportions, scaled for a person around 160-165cm tall. This provides an optimal balance between practical reach and manageable inertia for safe, responsive operation.
-
- **Safety-First Architecture**: Built with QDD backdrivable motors and high compliance, OpenArm prioritizes safe human-robot interaction while maintaining practical payload capabilities (6.0kg peak / 4.1kg nominal) for real-world tasks.
-
- **Built for Durability**: Critical structural components use aluminum and stainless steel construction, ensuring robust performance for repetitive data collection and continuous research use.
-
- **Fully Accessible & Buildable**: Every component, from CNC parts and 3D-printed casings to electrical wiring is designed to be purchasable and buildable by individual researchers and labs, with complete fabrication data provided.
-
- **Practical & Affordable**: At $6,500 USD for a complete bimanual system, OpenArm delivers research-grade capabilities at a fraction of traditional humanoid robot costs.
-
-## Platform Requirements
-
-<Tip warning={true}>
-  **Linux Only**: OpenArm currently only works on Linux. The CAN bus USB adapter
-  does not have macOS drivers and has not been tested on Windows.
-</Tip>
-
-## Safety Guide
-
-Before operating OpenArm, please read the [official safety guide](https://docs.openarm.dev/getting-started/safety-guide). Key points:
-
- **Secure installation**: Fasten the arm to a flat, stable surface with screws or clamps
- **Safe distance**: Keep body parts and objects outside the range of motion during operation
- **Protective equipment**: Always wear safety goggles; use additional PPE as needed
- **Payload limits**: Do not exceed specified payload limits (6.0kg peak / 4.1kg nominal per arm)
- **Emergency stop**: Know the location and operation of the emergency stop device
- **Regular inspection**: Check for loose screws, damaged mechanical limits, unusual noises, and wiring damage
-
-## Hardware Setup
-
-Follow the official [OpenArm hardware documentation](https://docs.openarm.dev) for:
-
- Bill of materials and sourcing
- 3D printing instructions
- Mechanical assembly
- Electrical wiring
-
-The hardware repositories are available at [github.com/enactic/openarm](https://github.com/enactic/openarm).
-
-## CAN Bus Setup
-
-OpenArm uses CAN bus communication with Damiao motors. Once you have the CAN bus USB adapter plugged into your Linux PC, follow the [Damiao Motors and CAN Bus guide](./damiao) to configure the interface.
-
-Quick setup:
-
-```bash
-# Setup CAN interfaces
-lerobot-setup-can --mode=setup --interfaces=can0,can1
-
-# Test motor communication
-lerobot-setup-can --mode=test --interfaces=can0,can1
-```
-
-## Install LeRobot 🤗
-
-Follow our [Installation Guide](./installation), then install the Damiao motor support:
-
-```bash
-pip install -e ".[damiao]"
-```
-
-## Usage
-
-### Follower Arm (Robot)
-
-<hfoptions id="follower">
-<hfoption id="Command">
-
-```bash
-lerobot-calibrate \
-    --robot.type=openarm_follower \
-    --robot.port=can0 \
-    --robot.side=right \
-    --robot.id=my_openarm_follower
-```
-
-</hfoption>
-<hfoption id="API example">
-
-```python
-from lerobot.robots.openarm_follower import OpenArmFollower, OpenArmFollowerConfig
-
-config = OpenArmFollowerConfig(
-    port="can0",
-    side="right",  # or "left" for left arm
-    id="my_openarm_follower",
-)
-
-follower = OpenArmFollower(config)
-follower.connect()
-
-# Read current state
-obs = follower.get_observation()
-print(obs)
-
-# Send action (position in degrees)
-action = {
-    "joint_1.pos": 0.0,
-    "joint_2.pos": 0.0,
-    "joint_3.pos": 0.0,
-    "joint_4.pos": 45.0,
-    "joint_5.pos": 0.0,
-    "joint_6.pos": 0.0,
-    "joint_7.pos": 0.0,
-    "gripper.pos": 0.0,
-}
-follower.send_action(action)
-
-follower.disconnect()
-```
-
-</hfoption>
-</hfoptions>
-
-### Leader Arm (Teleoperator)
-
-The leader arm is used for teleoperation - manually moving it to control the follower arm.
-
-<hfoptions id="leader">
-<hfoption id="Command">
-
-```bash
-lerobot-calibrate \
-    --teleop.type=openarm_leader \
-    --teleop.port=can1 \
-    --teleop.id=my_openarm_leader
-```
-
-</hfoption>
-<hfoption id="API example">
-
-```python
-from lerobot.teleoperators.openarm_leader import OpenArmLeader, OpenArmLeaderConfig
-
-config = OpenArmLeaderConfig(
-    port="can1",
-    id="my_openarm_leader",
-    manual_control=True,  # Disable torque for manual movement
-)
-
-leader = OpenArmLeader(config)
-leader.connect()
-
-# Read current position (as action to send to follower)
-action = leader.get_action()
-print(action)
-
-leader.disconnect()
-```
-
-</hfoption>
-</hfoptions>
-
-### Teleoperation
-
-To teleoperate OpenArm with leader-follower control:
-
-```bash
-lerobot-teleoperate \
-    --robot.type=openarm_follower \
-    --robot.port=can0 \
-    --robot.side=right \
-    --robot.id=my_follower \
-    --teleop.type=openarm_leader \
-    --teleop.port=can1 \
-    --teleop.id=my_leader
-```
-
-### Bimanual Teleoperation
-
-To teleoperate a bimanual OpenArm setup with two leader and two follower arms:
-
-```bash
-lerobot-teleoperate \
-    --robot.type=bi_openarm_follower \
-    --robot.left_arm_config.port=can0 \
-    --robot.left_arm_config.side=left \
-    --robot.right_arm_config.port=can1 \
-    --robot.right_arm_config.side=right \
-    --robot.id=my_bimanual_follower \
-    --teleop.type=bi_openarm_leader \
-    --teleop.left_arm_config.port=can2 \
-    --teleop.right_arm_config.port=can3 \
-    --teleop.id=my_bimanual_leader
-```
-
-### Recording Data
-
-To record a dataset during teleoperation:
-
-```bash
-lerobot-record \
-    --robot.type=openarm_follower \
-    --robot.port=can0 \
-    --robot.side=right \
-    --robot.id=my_follower \
-    --teleop.type=openarm_leader \
-    --teleop.port=can1 \
-    --teleop.id=my_leader \
-    --repo-id=my_hf_username/my_openarm_dataset \
-    --fps=30 \
-    --num-episodes=10
-```
-
-## Configuration Options
-
-### Follower Configuration
-
-| Parameter             | Default   | Description                                                |
-| --------------------- | --------- | ---------------------------------------------------------- |
-| `port`                | -         | CAN interface (e.g., `can0`)                               |
-| `side`                | `None`    | Arm side: `"left"`, `"right"`, or `None` for custom limits |
-| `use_can_fd`          | `True`    | Enable CAN FD for higher data rates                        |
-| `can_bitrate`         | `1000000` | Nominal bitrate (1 Mbps)                                   |
-| `can_data_bitrate`    | `5000000` | CAN FD data bitrate (5 Mbps)                               |
-| `max_relative_target` | `None`    | Safety limit for relative target positions                 |
-| `position_kp`         | Per-joint | Position control proportional gains                        |
-| `position_kd`         | Per-joint | Position control derivative gains                          |
-
-### Leader Configuration
-
-| Parameter          | Default   | Description                         |
-| ------------------ | --------- | ----------------------------------- |
-| `port`             | -         | CAN interface (e.g., `can1`)        |
-| `manual_control`   | `True`    | Disable torque for manual movement  |
-| `use_can_fd`       | `True`    | Enable CAN FD for higher data rates |
-| `can_bitrate`      | `1000000` | Nominal bitrate (1 Mbps)            |
-| `can_data_bitrate` | `5000000` | CAN FD data bitrate (5 Mbps)        |
-
-## Motor Configuration
-
-OpenArm uses Damiao motors with the following default configuration:
-
-| Joint                       | Motor Type | Send ID | Recv ID |
-| --------------------------- | ---------- | ------- | ------- |
-| joint_1 (Shoulder pan)      | DM8009     | 0x01    | 0x11    |
-| joint_2 (Shoulder lift)     | DM8009     | 0x02    | 0x12    |
-| joint_3 (Shoulder rotation) | DM4340     | 0x03    | 0x13    |
-| joint_4 (Elbow flex)        | DM4340     | 0x04    | 0x14    |
-| joint_5 (Wrist roll)        | DM4310     | 0x05    | 0x15    |
-| joint_6 (Wrist pitch)       | DM4310     | 0x06    | 0x16    |
-| joint_7 (Wrist rotation)    | DM4310     | 0x07    | 0x17    |
-| gripper                     | DM4310     | 0x08    | 0x18    |
-
-## Troubleshooting
-
-### No Response from Motors
-
-1. Check power supply connections
-2. Verify CAN wiring (CAN-H, CAN-L, GND)
-3. Run diagnostics: `lerobot-setup-can --mode=test --interfaces=can0`
-4. See the [Damiao troubleshooting guide](./damiao#troubleshooting) for more details
-
-### CAN Interface Not Found
-
-Ensure the CAN interface is configured:
-
-```bash
-ip link show can0
-```
-
-## Resources
-
- [OpenArm Website](https://openarm.dev)
- [OpenArm Documentation](https://docs.openarm.dev)
- [OpenArm GitHub](https://github.com/enactic/openarm)
- [Safety Guide](https://docs.openarm.dev/getting-started/safety-guide)
- [Damiao Motors and CAN Bus](./damiao)
@@ -0,0 +1,328 @@
+# OpenArms Robot
+
+OpenArms is a 7 DOF robotic arm with a gripper, designed by [Enactic, Inc.](https://www.enactic.com/) It uses Damiao motors controlled via CAN bus communication and MIT control mode for smooth, precise motion.
+
+## Hardware Overview
+
+- **7 DOF per arm** (14 DOF total for dual arm setup)
+- **1 gripper per arm** (2 grippers total)
+- **Damiao motors** with 4 different types:
+  - **DM8009** (DM-J8009P-2EC) for shoulders (J1, J2) - high torque
+  - **DM4340** for shoulder rotation and elbow (J3, J4)
+  - **DM4310** (DM-J4310-2EC V1.1) for wrist (J5, J6, J7) and gripper (J8)
+- **24V power supply** required
+- **CAN interface device**:
+  - **Linux**: Any SocketCAN-compatible adapter
+  - **macOS**: CANable, PEAK PCAN-USB, or Kvaser USBcan
+- Proper CAN wiring (CANH, CANL, 120Ω termination)
+
+
+## Motor Configuration
+
+Each arm has the following motor configuration based on the [OpenArm setup guide](https://docs.openarm.dev/software/setup/):
+
+| Joint | Motor | Motor Type | Sender CAN ID | Receiver ID | Description |
+|-------|-------|------------|---------------|-------------|-------------|
+| J1 | joint_1 | DM8009 | 0x01 | 0x11 | Shoulder pan |
+| J2 | joint_2 | DM8009 | 0x02 | 0x12 | Shoulder lift |
+| J3 | joint_3 | DM4340 | 0x03 | 0x13 | Shoulder rotation |
+| J4 | joint_4 | DM4340 | 0x04 | 0x14 | Elbow flex |
+| J5 | joint_5 | DM4310 | 0x05 | 0x15 | Wrist roll |
+| J6 | joint_6 | DM4310 | 0x06 | 0x16 | Wrist pitch |
+| J7 | joint_7 | DM4310 | 0x07 | 0x17 | Wrist rotation |
+| J8 | gripper | DM4310 | 0x08 | 0x18 | Gripper |
+
+For dual arm setups, the left arm uses IDs 0x09-0x10 for joints 1-8 with the same motor types.
+
+## Quick Start 
+
+```bash
+# Install system dependencies
+sudo apt install can-utils iproute2
+
+# Install LeRobot with OpenArms support
+pip install -e ".[openarms]"
+```
+
+## Setup Guide
+
+### Step 1: Motor ID Configuration
+
+**IMPORTANT**: Before using the robot, motors must be configured with the correct CAN IDs.
+
+Refer to the [OpenArm Motor ID Configuration Guide](https://docs.openarm.dev/software/setup/motor-id) for detailed instructions using the Damiao Debugging Tools on Windows.
+
+Key points:
+- Each motor needs a unique **Sender CAN ID** (0x01-0x08)
+- Each motor needs a unique **Receiver/Master ID** (0x11-0x18)
+- Use the Damiao Debugging Tools to set these IDs
+
+### Step 2: Setup CAN Interface
+
+Configure your CAN interface as described in the [OpenArm CAN Setup Guide](https://docs.openarm.dev/software/setup/can-setup):
+
+#### Linux (SocketCAN)
+
+```bash
+# Find your CAN interface
+ip link show
+
+# Configure can0, 1, 2, 3
+sudo ip link set can0 down
+sudo ip link set can0 type can bitrate 1000000
+sudo ip link set can0 up
+
+sudo ip link set can1 down
+sudo ip link set can1 type can bitrate 1000000
+sudo ip link set can1 up
+
+sudo ip link set can2 down
+sudo ip link set can2 type can bitrate 1000000
+sudo ip link set can2 up
+
+sudo ip link set can3 down
+sudo ip link set can3 type can bitrate 1000000
+sudo ip link set can3 up
+
+# Verify configuration
+ip link show can0
+```
+
+or run:
+
+`examples/openarms/setup_can.sh`
+
+### Testing canbus and motor connection
+
+Please run this script to check if all motors can be found and to find your can-fd speed: `python examples/openarms/debug_can_communication.py`
+
+## Usage
+
+### Basic Setup
+
+
+```python
+from lerobot.robots.openarms import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+
+# Configure for dual arm setup
+config = OpenArmsFollowerConfig(
+    port="can0",
+    can_interface="socketcan",  # Or "auto" for auto-detection
+    id="openarms_dual",
+    is_dual_arm=True,
+)
+
+robot = OpenArmsFollower(config)
+robot.connect()
+```
+
+### Calibration
+
+On first use, you'll need to calibrate the robot:
+
+```python
+robot.calibrate()
+```
+
+The calibration process will:
+1. Disable torque on all motors
+2. Ask you to position arms in **hanging position with grippers closed**
+3. Set this as the zero position
+4. Ask you to move each joint through its full range
+5. Record min/max positions for each joint
+6. Save calibration to file
+
+### Reading Observations
+
+The robot provides comprehensive state information:
+
+```python
+observation = robot.get_observation()
+
+# Observation includes for each motor:
+# - {motor_name}.pos: Position in degrees
+# - {motor_name}.vel: Velocity in degrees/second  
+# - {motor_name}.torque: Motor torque
+# - {camera_name}: Camera images (if configured)
+
+print(f"Right arm joint 1 position: {observation['right_joint_1.pos']:.1f}°")
+print(f"Right arm joint 1 velocity: {observation['right_joint_1.vel']:.1f}°/s")
+print(f"Right arm joint 1 torque: {observation['right_joint_1.torque']:.3f} N·m")
+```
+
+### Sending Actions
+
+```python
+# Send target positions (in degrees)
+action = {
+    "right_joint_1.pos": 45.0,
+    "right_joint_2.pos": -30.0,
+    # ... all joints
+    "right_gripper.pos": 45.0,  # Half-closed
+}
+
+actual_action = robot.send_action(action)
+```
+
+### Gripper Control
+
+```python
+# Open gripper
+robot.open_gripper(arm="right")
+
+# Close gripper  
+robot.close_gripper(arm="right")
+```
+
+## Safety Features
+
+### 1. Maximum Relative Target
+
+Limits how far a joint can move in a single command to prevent sudden movements:
+
+```python
+config = OpenArmsFollowerConfig(
+    port="can0",
+    # Limit all joints to 10 degrees per command
+    max_relative_target=10.0,
+    
+    # Or set per-motor limits
+    max_relative_target={
+        "right_joint_1": 15.0,  # Slower moving joint
+        "right_joint_2": 10.0,
+        "right_gripper": 5.0,   # Very slow gripper
+    }
+)
+```
+
+**How it works**: If current position is 50° and you command 80°, with `max_relative_target=10.0`, the robot will only move to 60° in that step.
+
+### 2. Torque Limits
+
+Control maximum torque output, especially important for grippers and teleoperation:
+
+```python
+config = OpenArmsFollowerConfig(
+    port="can0",
+    # Gripper torque limit (fraction of motor's max torque)
+    gripper_torque_limit=0.5,  # 50% of max torque
+)
+```
+
+Lower torque limits prevent damage when gripping delicate objects.
+
+### 3. MIT Control Gains
+
+Control responsiveness and stability via PID-like gains:
+
+```python
+config = OpenArmsFollowerConfig(
+    port="can0",
+    position_kp=10.0,  # Position gain (higher = more responsive)
+    position_kd=0.5,   # Velocity damping (higher = more damped)
+)
+```
+
+**Guidelines**:
+- **For following (robot)**: Higher gains for responsiveness
+  - `position_kp=10.0`, `position_kd=0.5`
+- **For teleoperation (leader)**: Lower gains or disable torque for manual movement
+  - `manual_control=True` (torque disabled)
+
+### 4. Velocity Limits
+
+Velocity limits are enforced by the Damiao motors based on motor type. For DM4310:
+- Max velocity: 30 rad/s ≈ 1718°/s
+
+The motors will automatically limit velocity to safe values.
+
+## Teleoperation
+
+### Leader Arm Setup
+
+The leader arm is moved manually (torque disabled) to generate commands:
+
+```python
+from lerobot.teleoperators.openarms import OpenArmsLeader
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+
+config = OpenArmsLeaderConfig(
+    port="can1",  # Separate CAN interface for leader
+    id="openarms_leader",
+    manual_control=True,  # Torque disabled for manual movement
+    is_dual_arm=True,
+)
+
+leader = OpenArmsLeader(config)
+leader.connect()
+
+# Read current position as action
+action = leader.get_action()
+# action contains positions for all joints in degrees
+```
+
+### Safety Considerations for Teleoperation
+
+1. **Use separate CAN interfaces** for leader and follower to avoid conflicts
+2. **Enable max_relative_target** on follower to smooth abrupt movements
+3. **Lower torque limits** on follower to prevent damage from tracking errors
+4. **Test with one arm** before enabling dual arm teleoperation
+5. **Have emergency stop** ready (power switch or CAN disable)
+
+```python
+# Recommended follower config for teleoperation
+follower_config = OpenArmsFollowerConfig(
+    port="can0",
+    max_relative_target=5.0,  # Small steps for smooth following
+    gripper_torque_limit=0.3, # Low torque for safety
+    position_kp=5.0,  # Lower gains for gentler following
+    position_kd=0.3,
+)
+```
+
+## Troubleshooting
+
+### Motor Shaking/Unstable
+
+- **Lower control gains**: Reduce `position_kp` and `position_kd`
+- **Check calibration**: Re-run calibration procedure
+- **Verify power**: Insufficient current can cause instability
+- **Check mechanical**: Loose connections, binding, or damaged components
+
+### CAN Bus Errors
+
+```bash
+# Check for errors
+ip -s link show can0
+
+# Reset CAN interface
+sudo ip link set can0 down
+sudo ip link set can0 up
+```
+
+### Control Mode
+
+OpenArms uses **MIT control mode** which allows simultaneous control of:
+- Position (degrees)
+- Velocity (degrees/second)
+- Torque (N·m)
+- Position gain (Kp)
+- Velocity damping (Kd)
+
+### Communication
+
+- **Protocol**: CAN 2.0 at 1 Mbps (or CAN-FD at 5 Mbps)
+- **Frame format**: Standard 11-bit IDs
+- **Update rate**: Typically 50-100 Hz depending on motor count
+- **Latency**: ~10-20ms per motor command
+
+## References
+
+- [OpenArm Official Documentation](https://docs.openarm.dev/)
+- [OpenArm Setup Guide](https://docs.openarm.dev/software/setup/)
+- [Motor ID Configuration](https://docs.openarm.dev/software/setup/motor-id)
+- [CAN Interface Setup](https://docs.openarm.dev/software/setup/can-setup)
+- [Motor Communication Test](https://docs.openarm.dev/software/setup/configure-test)
+- [Damiao Motor Documentation](https://wiki.seeedstudio.com/damiao_series/)
+- [Enactic GitHub](https://github.com/enactic/openarm_can)
@@ -1,62 +0,0 @@
-# Parameter efficient fine-tuning with 🤗 PEFT
-
-[🤗 PEFT](https://github.com/huggingface/peft) (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting
-large pretrained models such as pre-trained policies (e.g., SmolVLA, π₀, ...) to new tasks without training all
-of the model's parameters while yielding comparable performance.
-
-Install the `lerobot[peft]` optional package to enable PEFT support.
-
-To read about all the possible methods of adaption, please refer to the [🤗 PEFT docs](https://huggingface.co/docs/peft/index).
-
-## Training SmolVLA
-
-In this section we'll show you how to train a pre-trained SmolVLA policy with PEFT on the libero dataset.
-For brevity we're only training on the `libero_spatial` subset. We will use `lerobot/smolvla_base` as the model
-to parameter efficiently fine-tune:
-
-```
-lerobot-train \
- --policy.path=lerobot/smolvla_base \
- --policy.repo_id=your_hub_name/my_libero_smolvla \
- --dataset.repo_id=HuggingFaceVLA/libero \
- --policy.output_features=null \
- --policy.input_features=null \
- --policy.optimizer_lr=1e-3 \
- --policy.scheduler_decay_lr=1e-4 \
- --env.type=libero \
- --env.task=libero_spatial \
- --steps=100000 \
- --batch_size=32 \
- --peft.method_type=LORA \
- --peft.r=64
-```
-
-Note the `--peft.method_type` parameter that let's you select which PEFT method to use. Here we use
-[LoRA](https://huggingface.co/docs/peft/main/en/package_reference/lora) (Low-Rank Adapter) which is probably the most
-popular fine-tuning method to date. Low-rank adaption means that we only fine-tune a matrix with comparably low rank
-instead of the full weight matrix. This rank can be specified using the `--peft.r` parameter. The higher the rank
-the closer you get to full fine-tuning
-
-There are more complex methods that have more parameters. These are not yet supported, feel free to raise an issue
-if you want to see a specific PEFT method supported.
-
-By default, PEFT will target the `q_proj` and `v_proj` layers of the LM expert in SmolVLA. It will also target the
-state and action projection matrices as they are most likely task-dependent. If you need to target different layers
-you can use `--peft.target_modules` to specify which layers to target. You can refer to the respective PEFT method's
-documentation to see what inputs are supported, (e.g., [LoRA's target_modules documentation](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraConfig.target_modules)).
-Usually a list of suffixes or a regex are supported. For example, to target the MLPs of the `lm_expert` instead of
-the `q` and `v` projections, use:
-
-```
--peft.target_modules='(model\.vlm_with_expert\.lm_expert\..*\.(down|gate|up)_proj|.*\.(state_proj|action_in_proj|action_out_proj|action_time_mlp_in|action_time_mlp_out))'
-```
-
-In case you need to fully fine-tune a layer instead of just adapting it, you can supply a list of layer suffixes
-to the `--peft.full_training_modules` parameter:
-
-```
--peft.full_training_modules=["state_proj"]
-```
-
-The learning rate and the scheduled target learning rate can usually be scaled by a factor of 10 compared to the
-learning rate used for full fine-tuning (e.g., 1e-4 normal, so 1e-3 using LoRA).
@@ -44,7 +44,7 @@ Modify the examples to use `PhoneOS.IOS` or `PhoneOS.ANDROID` in `PhoneConfig`.

 Teleoperation example:

-```python
+```36:43:examples/phone_so100_teleop.py
 from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS

 teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
@@ -66,13 +66,12 @@ Run on of the examples scripts to teleoperate, record a dataset, replay a datase

 All scripts assume you configured your robot (e.g., SO-100 follower) and set the correct serial port.

-Additionally you need to **copy the URDF of the robot into the examples folder**. For the examples in this tutorial (using SO100/SO101), copy the `SO101` folder from the [SO-ARM100 repo](https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101) into the `examples/phone_to_so100/` directory, so that the URDF file path becomes `examples/phone_to_so100/SO101/so101_new_calib.urdf`.
+Additionally you need to **copy the urdf of the robot to the examples folder**. For the examples in this tutorial (Using SO100/SO101) it is highly recommended to use the urdf in the [SO-ARM100 repo](https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf)

 - Run this example to teleoperate:

  ```bash
-  cd examples/phone_to_so100
-  python teleoperate.py
+  python examples/phone_to_so100/teleoperate.py
  ```

 After running the example:
@@ -85,29 +84,26 @@ Additionally you can customize mapping or safety limits by editing the processor
 - Run this example to record a dataset, which saves absolute end effector observations and actions:

  ```bash
-  cd examples/phone_to_so100
-  python record.py
+  python examples/phone_to_so100/record.py
  ```

 - Run this example to replay recorded episodes:

  ```bash
-  cd examples/phone_to_so100
-  python replay.py
+  python examples/phone_to_so100/replay.py
  ```

 - Run this example to evaluate a pretrained policy:

  ```bash
-  cd examples/phone_to_so100
-  python evaluate.py
+  python examples/phone_to_so100/evaluate.py
  ```

 ### Important pipeline steps and options

 - Kinematics are used in multiple steps. We use [Placo](https://github.com/Rhoban/placo) which is a wrapper around Pinocchio for handling our kinematics. We construct the kinematics object by passing the robot's URDF and target frame. We set `target_frame_name` to the gripper frame.

-  ```python
+  ```examples/phone_to_so100/teleoperate.py
  kinematics_solver = RobotKinematics(
    urdf_path="./SO101/so101_new_calib.urdf",
    target_frame_name="gripper_frame_link",
@@ -118,7 +114,7 @@ Additionally you can customize mapping or safety limits by editing the processor

 - The `MapPhoneActionToRobotAction` step converts the calibrated phone pose and inputs into target deltas and gripper commands, below is shown what the step outputs.

-  ```python
+  ```src/lerobot/teleoperators/phone/phone_processor.py
  action["enabled"] = enabled
        action["target_x"] = -pos[1] if enabled else 0.0
        action["target_y"] = pos[0] if enabled else 0.0
@@ -131,7 +127,7 @@ Additionally you can customize mapping or safety limits by editing the processor

 - The `EEReferenceAndDelta` step converts target deltas to an absolute desired EE pose, storing a reference on enable, the `end_effector_step_sizes` are the step sizes for the EE pose and can be modified to change the motion speed.

-  ```python
+  ```examples/phone_to_so100/teleoperate.py
  EEReferenceAndDelta(
      kinematics=kinematics_solver,
      end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
@@ -142,7 +138,7 @@ Additionally you can customize mapping or safety limits by editing the processor

 - The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` are the step limits for the EE pose and can be modified to change the safety limits.

-  ```python
+  ```examples/phone_to_so100/teleoperate.py
  EEBoundsAndSafety(
      end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
      max_ee_step_m=0.10,
@@ -151,7 +147,7 @@ Additionally you can customize mapping or safety limits by editing the processor

 - The `GripperVelocityToJoint` step turns a velocity‑like gripper input into absolute gripper position using the current measured state. The `speed_factor` is the factor by which the velocity is multiplied.

-  ```python
+  ```examples/phone_to_so100/teleoperate.py
  GripperVelocityToJoint(speed_factor=20.0)
  ```

@@ -161,7 +157,7 @@ We use different IK initial guesses in the kinematic steps. As initial guess eit

 - Closed loop (used in record/eval): sets `initial_guess_current_joints=True` so IK starts from the measured joints each frame.

-  ```python
+  ```examples/phone_to_so100/record.py
  InverseKinematicsEEToJoints(
      kinematics=kinematics_solver,
      motor_names=list(robot.bus.motors.keys()),
@@ -171,7 +167,7 @@ We use different IK initial guesses in the kinematic steps. As initial guess eit

 - Open loop (used in replay): sets `initial_guess_current_joints=False` so IK continues from the previous IK solution rather than the measured state. This preserves action stability when we replay without feedback.

-  ```python
+  ```examples/phone_to_so100/replay.py
  InverseKinematicsEEToJoints(
      kinematics=kinematics_solver,
      motor_names=list(robot.bus.motors.keys()),
@@ -6,12 +6,6 @@

 π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi0). Unlike traditional robot programs that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.

-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-pi0%20(1).png"
-  alt="An overview of Pi0"
-  width="85%"
-/>
-
 ### The Vision for Physical Intelligence

 As described by Physical Intelligence, while AI has achieved remarkable success in digital domains, from chess-playing to drug discovery, human intelligence still dramatically outpaces AI in the physical world. To paraphrase Moravec's paradox, winning a game of chess represents an "easy" problem for AI, but folding a shirt or cleaning up a table requires solving some of the most difficult engineering problems ever conceived. π₀ represents a first step toward developing artificial physical intelligence that enables users to simply ask robots to perform any task they want, just like they can with large language models.
@@ -34,6 +28,11 @@ 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:
@@ -55,7 +54,7 @@ policy.type=pi0
 For training π₀, you can use the standard LeRobot training script with the appropriate configuration:

 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
    --dataset.repo_id=your_dataset \
    --policy.type=pi0 \
    --output_dir=./outputs/pi0_training \
@@ -65,8 +64,6 @@ lerobot-train \
    --policy.compile_model=true \
    --policy.gradient_checkpointing=true \
    --policy.dtype=bfloat16 \
-    --policy.freeze_vision_encoder=false \
-    --policy.train_expert_only=false \
    --steps=3000 \
    --policy.device=cuda \
    --batch_size=32
@@ -82,55 +79,6 @@ lerobot-train \
  - [lerobot/pi0_base](https://huggingface.co/lerobot/pi0_base)
  - [lerobot/pi0_libero](https://huggingface.co/lerobot/pi0_libero) (specifically trained on the Libero dataset)

-### Training Parameters Explained
-
-| Parameter               | Default | Description                                 |
-| ----------------------- | ------- | ------------------------------------------- |
-| `freeze_vision_encoder` | `false` | Do not freeze the vision encoder            |
-| `train_expert_only`     | `false` | Do not freeze the VLM, train all parameters |
-
-**💡 Tip**: Setting `train_expert_only=true` freezes the VLM and trains only the action expert and projections, allowing finetuning with reduced memory usage.
-
-## Relative Actions
-
-By default, π₀ predicts absolute actions. You can enable **relative actions** so the model predicts offsets relative to the current robot state. This can improve training stability for certain setups.
-
-To use relative actions, first recompute your dataset stats in relative space via the CLI:
-
-```bash
-lerobot-edit-dataset \
-    --repo_id your_dataset \
-    --operation.type recompute_stats \
-    --operation.relative_action true \
-    --operation.chunk_size 50 \
-    --operation.relative_exclude_joints "['gripper']" \
-    --push_to_hub true
-```
-
-Or equivalently in Python:
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.dataset_tools import recompute_stats
-
-dataset = LeRobotDataset("your_dataset")
-recompute_stats(dataset, relative_action=True, chunk_size=50, relative_exclude_joints=["gripper"])
-dataset.push_to_hub()
-```
-
-The `chunk_size` should match your policy's `chunk_size` (default 50 for π₀). `relative_exclude_joints` lists joint names that should remain in absolute space (e.g. gripper commands). Use `--push_to_hub true` to upload the updated stats to the Hub.
-
-Then train with relative actions enabled:
-
-```bash
-lerobot-train \
-    --dataset.repo_id=your_dataset \
-    --policy.type=pi0 \
-    --policy.use_relative_actions=true \
-    --policy.relative_exclude_joints='["gripper"]' \
-    ...
-```
-
 ## License

 This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).
@@ -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:
@@ -51,7 +56,7 @@ policy.type=pi05
 Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:

 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py\
    --dataset.repo_id=your_dataset \
    --policy.type=pi05 \
    --output_dir=./outputs/pi05_training \
@@ -62,8 +67,6 @@ lerobot-train \
    --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
@@ -79,15 +82,6 @@ lerobot-train \
  - [lerobot/pi05_base](https://huggingface.co/lerobot/pi05_base)
  - [lerobot/pi05_libero](https://huggingface.co/lerobot/pi05_libero) (specifically trained on the Libero dataset)

-### Training Parameters Explained
-
-| Parameter               | Default | Description                                 |
-| ----------------------- | ------- | ------------------------------------------- |
-| `freeze_vision_encoder` | `false` | Do not freeze the vision encoder            |
-| `train_expert_only`     | `false` | Do not freeze the VLM, train all parameters |
-
-**💡 Tip**: Setting `train_expert_only=true` freezes the VLM and trains only the action expert and projections, allowing finetuning with reduced memory usage.
-
 If your dataset is not converted with `quantiles`, you can convert it with the following command:

 ```bash
@@ -97,46 +91,6 @@ python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \

 Or train pi05 with this normalization mapping: `--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'`

-## Relative Actions
-
-By default, π₀.₅ predicts absolute actions. You can enable **relative actions** so the model predicts offsets relative to the current robot state. This can improve training stability for certain setups.
-
-To use relative actions, first recompute your dataset stats in relative space via the CLI:
-
-```bash
-lerobot-edit-dataset \
-    --repo_id your_dataset \
-    --operation.type recompute_stats \
-    --operation.relative_action true \
-    --operation.chunk_size 50 \
-    --operation.relative_exclude_joints "['gripper']" \
-    --push_to_hub true
-```
-
-Or equivalently in Python:
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.dataset_tools import recompute_stats
-
-dataset = LeRobotDataset("your_dataset")
-recompute_stats(dataset, relative_action=True, chunk_size=50, relative_exclude_joints=["gripper"])
-dataset.push_to_hub()
-```
-
-The `chunk_size` should match your policy's `chunk_size` (default 50 for π₀.₅). `relative_exclude_joints` lists joint names that should remain in absolute space (e.g. gripper commands). Use `--push_to_hub true` to upload the updated stats to the Hub.
-
-Then train with relative actions enabled:
-
-```bash
-lerobot-train \
-    --dataset.repo_id=your_dataset \
-    --policy.type=pi05 \
-    --policy.use_relative_actions=true \
-    --policy.relative_exclude_joints='["gripper"]' \
-    ...
-```
-
 ## Performance Results

 ### Libero Benchmark Results
@@ -1,241 +0,0 @@
-# π₀-FAST (Pi0-FAST)
-
-π₀-FAST is a **Vision-Language-Action model for general robot control** that uses autoregressive next-token prediction to model continuous robot actions.
-
-## Model Overview
-
-π₀-FAST combines the power of Vision-Language Models with a novel action tokenization approach called **FAST (Frequency-space Action Sequence Tokenization)**. This enables training autoregressive VLAs on highly dexterous tasks that are impossible with standard binning-based discretization, while training **up to 5x faster** than diffusion-based approaches like π₀.
-
-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-pifast.png"
-  alt="An overview of Pi0-FAST"
-  width="85%"
-/>
-
-### Why FAST?
-
-Standard approaches for robot action tokenization use simple per-dimension, per-timestep binning schemes. While passable for simple behaviors, this rapidly breaks down for complex and dexterous skills that require precision and high-frequency control.
-
-FAST solves this by compressing action sequences using signal processing techniques, resulting in a dense sequence of action tokens that can be predicted autoregressively—just like language tokens.
-
-### How FAST Tokenization Works
-
-The FAST tokenizer compresses action sequences through the following steps:
-
-1. **Normalize**: Take a continuous action chunk of shape `(H, D)` where `H` is the horizon and `D` is the action dimension. Normalize using one of the supported normalization methods (Quantiles recommended to handle outliers).
-
-2. **Discrete Cosine Transform (DCT)**: Apply DCT (via scipy) to each action dimension separately. DCT is a compression algorithm commonly used in image and audio codecs (JPEG, MP3).
-
-3. **Quantization**: Round and remove insignificant coefficients for each action dimension, producing a sparse frequency matrix.
-
-4. **Flatten**: Flatten the matrix into a 1D vector, with low-frequency components first.
-
-5. **Byte Pair Encoding (BPE)**: Train a BPE tokenizer to compress the DCT coefficients into dense action tokens, typically achieving **10x compression** over prior tokenization approaches.
-
-This approach can transform **any existing VLM** into a VLA by training it to predict these FAST tokens.
-
-## Installation Requirements
-
-1. Install LeRobot by following our [Installation Guide](./installation).
-2. Install π₀-FAST dependencies by running:
-
-   ```bash
-   pip install -e ".[pi]"
-   ```
-
-## 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.
-
-2. **Train your own tokenizer**: For maximum performance on your specific dataset, you can finetune the tokenizer on your own data.
-
-### Training Your Own Tokenizer
-
-```bash
-lerobot-train-tokenizer \
-    --repo_id "user/my-lerobot-dataset" \
-    --action_horizon 10 \
-    --encoded_dims "0:6" \
-    --vocab_size 1024 \
-    --scale 10.0 \
-    --normalization_mode QUANTILES \
-    --output_dir "./my_fast_tokenizer" \
-    --push_to_hub \
-    --hub_repo_id "username/my-action-tokenizer"
-```
-
-### Key Tokenizer Parameters
-
-| Parameter              | Description                                                                       | Default      |
-| ---------------------- | --------------------------------------------------------------------------------- | ------------ |
-| `--repo_id`            | LeRobot dataset repository ID                                                     | Required     |
-| `--action_horizon`     | Number of future actions in each chunk                                            | `10`         |
-| `--encoded_dims`       | Comma-separated dimension ranges to encode (e.g., `"0:6,7:23"`)                   | `"0:6,7:23"` |
-| `--vocab_size`         | BPE vocabulary size                                                               | `1024`       |
-| `--scale`              | DCT scaling factor for quantization                                               | `10.0`       |
-| `--normalization_mode` | Normalization mode (`MEAN_STD`, `MIN_MAX`, `QUANTILES`, `QUANTILE10`, `IDENTITY`) | `QUANTILES`  |
-| `--sample_fraction`    | Fraction of chunks to sample per episode                                          | `0.1`        |
-
-## Usage
-
-To use π₀-FAST in LeRobot, specify the policy type as:
-
-```python
-policy.type=pi0_fast
-```
-
-## Training
-
-For training π₀-FAST, you can use the LeRobot training script:
-
-```bash
-lerobot-train \
-    --dataset.repo_id=your_dataset \
-    --policy.type=pi0_fast \
-    --output_dir=./outputs/pi0fast_training \
-    --job_name=pi0fast_training \
-    --policy.pretrained_path=lerobot/pi0_fast_base \
-    --policy.dtype=bfloat16 \
-    --policy.gradient_checkpointing=true \
-    --policy.chunk_size=10 \
-    --policy.n_action_steps=10 \
-    --policy.max_action_tokens=256 \
-    --steps=100000 \
-    --batch_size=4 \
-    --policy.device=cuda
-```
-
-### Key Training Parameters
-
-| Parameter                              | Description                                        | Default                         |
-| -------------------------------------- | -------------------------------------------------- | ------------------------------- |
-| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false`                         |
-| `--policy.dtype=bfloat16`              | Use mixed precision training for efficiency        | `float32`                       |
-| `--policy.chunk_size`                  | Number of action steps to predict (action horizon) | `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.action_tokenizer_name`       | FAST tokenizer to use                              | `lerobot/fast-action-tokenizer` |
-| `--policy.compile_model=true`          | Enable torch.compile for faster training           | `false`                         |
-
-## Inference
-
-### KV-Caching for Fast Inference
-
-π₀-FAST supports **KV-caching**, a widely used optimization in LLM inference. This caches the key-value pairs from the attention mechanism, avoiding redundant computation during autoregressive decoding.
-
-```python
-# KV-caching is enabled by default
-policy.use_kv_cache=true
-```
-
-### Inference Example
-
-```python
-from lerobot.policies.pi0_fast import PI0FastPolicy, PI0FastConfig
-
-# Load the policy
-policy = PI0FastPolicy.from_pretrained("your-model-path")
-
-# During inference
-actions = policy.predict_action_chunk(batch)
-```
-
-## Model Architecture
-
-π₀-FAST uses a PaliGemma-based architecture:
-
- **Vision Encoder**: SigLIP vision tower for image understanding
- **Language Model**: Gemma 2B for processing language instructions and predicting action tokens
-
-The model takes images, text instructions, and robot state as input, and outputs discrete FAST tokens that are decoded back to continuous actions.
-
-## Configuration Options
-
-| Parameter            | Description                                     | Default    |
-| -------------------- | ----------------------------------------------- | ---------- |
-| `paligemma_variant`  | VLM backbone variant (`gemma_300m`, `gemma_2b`) | `gemma_2b` |
-| `max_state_dim`      | Maximum state vector dimension (padded)         | `32`       |
-| `max_action_dim`     | Maximum action vector dimension (padded)        | `32`       |
-| `temperature`        | Sampling temperature (0.0 for greedy)           | `0.0`      |
-| `max_decoding_steps` | Maximum decoding steps                          | `256`      |
-| `use_kv_cache`       | Enable KV caching for faster inference          | `true`     |
-
-## Comparison with π₀
-
-| Feature               | π₀                        | π₀-FAST                      |
-| --------------------- | ------------------------- | ---------------------------- |
-| Action Representation | Flow Matching (Diffusion) | Autoregressive Tokens (FAST) |
-| Training Speed        | 1x                        | **5x faster**                |
-| Dexterity             | High                      | High                         |
-| Inference Method      | Iterative Denoising       | Autoregressive Decoding      |
-| KV-Caching            | N/A                       | Supported                    |
-
-## Reproducing π₀Fast results
-
-We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40kk steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
-
-The finetuned model can be found here:
-
- **π₀Fast LIBERO**: [lerobot/pi0fast-libero](https://huggingface.co/lerobot/pi0fast-libero)
-
-With the following training command:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=lerobot/libero \
-  --output_dir=outputs/libero_pi0fast \
-  --job_name=libero_pi0fast \
-  --policy.path=lerobot/pi0fast_base \
-  --policy.dtype=bfloat16 \
-  --steps=100000 \
-  --save_freq=20000 \
-  --batch_size=4 \
-  --policy.device=cuda \
-  --policy.scheduler_warmup_steps=4000 \
-  --policy.scheduler_decay_steps=100000 \
-  --policy.scheduler_decay_lr=1e-5 \
-  --policy.gradient_checkpointing=true \
-  --policy.chunk_size=10 \
-  --policy.n_action_steps=10 \
-  --policy.max_action_tokens=256 \
-  --policy.empty_cameras=1 \
-```
-
-We then evaluate the finetuned model using the LeRobot LIBERO implementation, by running the following command:
-
-```bash
-tasks="libero_object,libero_spatial,libero_goal,libero_10"
-lerobot-eval \
-  --policy.path=lerobot/pi0fast-libero \
-  --policy.max_action_tokens=256 \
-  --env.type=libero \
-  --policy.gradient_checkpointing=false \
-  --env.task=${tasks} \
-  --eval.batch_size=1 \
-  --eval.n_episodes=1 \
-  --rename_map='{"observation.images.image":"observation.images.base_0_rgb","observation.images.image2":"observation.images.left_wrist_0_rgb"}'
-```
-
-**Note:** We set `n_action_steps=10`, similar to the original OpenPI implementation.
-
-### Results
-
-We obtain the following results on the LIBERO benchmark:
-
-| Model       | LIBERO Spatial | LIBERO Object | LIBERO Goal | LIBERO 10 | Average  |
-| ----------- | -------------- | ------------- | ----------- | --------- | -------- |
-| **π₀-fast** | 70.0           | 100.0         | 100.0       | 60.0      | **82.5** |
-
-The full evaluation output folder, including videos, is available [here](https://drive.google.com/drive/folders/1HXpwPTRm4hx6g1sF2P7OOqGG0TwPU7LQ?usp=sharing)
-
-## License
-
-This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).
-
-## References
-
- [FAST: Efficient Robot Action Tokenization](https://www.physicalintelligence.company/research/fast) - Physical Intelligence Blog
- [OpenPI Repository](https://github.com/Physical-Intelligence/openpi) - Original implementation
- [FAST Tokenizer on Hugging Face](https://huggingface.co/physical-intelligence/fast) - Pre-trained tokenizer
@@ -1,37 +0,0 @@
-# Multitask DiT Policy
-
-## Citation
-
-If you use this work, please cite the following works:
-
-```bibtex
-@misc{jones2025multitaskditpolicy,
-  author = {Bryson Jones},
-  title = {Dissecting and Open-Sourcing Multitask Diffusion Transformer Policy},
-  year = {2025},
-  url = {https://brysonkjones.substack.com/p/dissecting-and-open-sourcing-multitask-diffusion-transformer-policy},
-  note = {Blog post}
-}
-```
-
-```bibtex
-@misc{trilbmteam2025carefulexaminationlargebehaviormodels,
-  author       = {TRI LBM Team},
-  title        = {A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation},
-  year         = {2025},
-  eprint       = {arXiv:2507.05331},
-  archivePrefix = {arXiv},
-  primaryClass = {cs.RO},
-  url          = {https://arxiv.org/abs/2507.05331}
-}
-```
-
-```bibtex
-@misc{bostondynamics2025largebehaviormodelsatlas,
-  author       = {Boston Dynamics and TRI Research Team},
-  title        = {Large Behavior Models and Atlas Find New Footing},
-  year         = {2025},
-  url          = {https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/},
-  note         = {Blog post}
-}
-```
@@ -1,30 +1,20 @@
 # WALL-OSS

-This repository contains the Hugging Face port of [**WALL-OSS**](https://x2robot.com/en/research/68bc2cde8497d7f238dde690), a Vision-Language-Action model for cross-embodiment robotic control based on Qwen2.5-VL with flow matching/FAST action prediction.
+This repository contains the Hugging Face port of **WALL-OSS**, a Vision-Language-Action model for cross-embodiment robotic control based on Qwen2.5-VL with flow matching/FAST action prediction.

 ---

 ## Model Overview

 | Feature            | Description                                           |
-| ------------------ | ----------------------------------------------------- |
+| ------------------ | ----------------------------------------------------- | --- |
 | Base Model         | Qwen2.5-VL (Vision-Language Model)                    |
 | Action Prediction  | Flow Matching (diffusion) or FAST (discrete tokens)   |
-| Architecture       | Mixture of Experts (MoE) with action-specific routing |
+| Architecture       | Mixture of Experts (MoE) with action-specific routing |     |
 | Multi-Modal Inputs | Vision (images/videos), Language, Proprioception      |

 ---

-## Additional Resources
-
-Paper: https://arxiv.org/pdf/2509.11766
-
-Official Repository: https://github.com/X-Square-Robot/wall-x
-
-Hugging Face: https://huggingface.co/x-square-robot
-
---
-
 ## Citation

 If you use this work, please cite:
@@ -42,4 +32,4 @@ If you use this work, please cite:

 ## License

-This model follows the **Apache 2.0 License**, consistent with the original [WallX repository](https://github.com/X-Square-Robot/wall-x).
+This port follows the **Apache 2.0 License**.
@@ -30,7 +30,7 @@ Each of these pipelines handle different conversions between different action an

 Below is an example of the three pipelines that we use in the phone to SO-100 follower examples:

-```python
+```69:90:examples/phone_so100_record.py
 phone_to_robot_ee_pose_processor = RobotProcessorPipeline[RobotAction, RobotAction]( # teleop -> dataset action
    steps=[
        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
@@ -84,7 +84,7 @@ Dataset features are determined by the keys saved in the dataset. Each step can

 Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:

-```python
+```src/lerobot/robots/so100_follower/robot_kinematic_processor.py
    def transform_features(
        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
@@ -103,7 +103,7 @@ Here we declare what PolicyFeatures we modify in this step, so we know what feat

 Below is an example of how we aggregate and merge features in the phone to SO-100 record example:

-```python
+```121:145:examples/phone_so100_record.py
 features=combine_feature_dicts(
        # Run the feature contract of the pipelines
        # This tells you how the features would look like after the pipeline steps
@@ -0,0 +1,291 @@
+# RaC: Recovery and Correction Training
+
+RaC (Recovery and Correction) is a human-in-the-loop data collection and training paradigm that improves robot policy performance on long-horizon tasks by explicitly teaching recovery and correction behaviors.
+
+**Key References:**
+- [RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction](https://arxiv.org/abs/2509.07953) (Hu et al., 2025)
+- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://arxiv.org/abs/1810.02890) (Kelly et al., 2019)
+- [π∗0.6: a VLA That Learns From Experience](https://pi.website/blog/pistar06) (Physical Intelligence, 2025)
+- [SARM: Stage-Aware Reward Modeling](https://arxiv.org/abs/2509.25358) (Chen et al., 2025)
+
+---
+
+## Why RaC? The Problem with Standard Data Collection
+
+### Standard Behavioral Cloning Data Collection Limitations
+
+Standard behavior cloning trains policies on successful demonstrations. This approach can be sensitive to distribution shift and compounding errors. Because during deployment small errors can cascade and push the robot into states never seen during training.
+This is where RaC and methods like Dagger and HG-DAgger come in.
+
+### Prior Human-in-the-Loop Methods
+
+**DAgger** (Dataset Aggregation) addresses distribution shift by:
+- Running the novice policy to collect states
+- Querying expert for correct actions at those states
+- Aggregating new labels into training set
+
+**HG-DAgger** (Human-Gated DAgger) improves on DAgger by:
+- Giving human full control authority during interventions
+- Human takes over when unsafe, provides correction, returns control
+- Better action labels because human has uninterrupted control
+
+### RaC
+
+RaC explicitly collects **recovery + correction** data:
+
+```
+BC/DAgger:   policy → mistake → human corrects → continue
+RaC:         policy → mistake → human RECOVERS (teleop back) → CORRECTS → END
+```
+
+The critical insight is **Rule 1 (Recover then Correct)**:
+- Every intervention starts with human teleoperating back to an in-distribution state
+- Then human provides correction to complete the current subtask
+- Both segments are recorded as training data
+- This teaches the policy: "when things go wrong, go back and retry"
+
+**Rule 2 (Terminate after Intervention)**:
+- Episode ends after correction completes
+- Avoids mixed policy/human data on later subtasks
+- Keeps data distribution clean
+
+---
+
+## Comparison Table
+
+| Method | Data Type | Recovery Behavior | Correction Behavior |
+|--------|-----------|-------------------|---------------------|
+| BC | Success only | ✗ | ✗ |
+| DAgger | Success + corrections | ✗ | ✓ |
+| HG-DAgger | Success + corrections | Sometimes | ✓ |
+| RaC | Success + recovery + correction | ✓ Explicit | ✓ |
+
+---
+
+## The RaC Pipeline
+
+```
+┌─────────────────────────────────────────────────────────────────────────┐
+│                         RaC Training Pipeline                           │
+├─────────────────────────────────────────────────────────────────────────┤
+│                                                                         │
+│  1. PRE-TRAINING (Standard BC)                                          │
+│     └─> Train initial policy on clean demonstrations                    │
+│                                                                         │
+│  2. RAC DATA COLLECTION (Human-in-the-loop)                             │
+│     ├─> Policy runs autonomously                                        │
+│     ├─> Human monitors and intervenes when failure imminent             │
+│     │   ├─> RECOVERY: Human teleoperates robot back to good state       │
+│     │   └─> CORRECTION: Human completes the current subtask             │
+│     └─> Episode terminates after correction (Rule 2)                    │
+│                                                                         │
+│  3. REWARD LABELING (Optional: SARM)                                    │
+│     └─> Compute progress rewards for advantage-weighted training        │
+│                                                                         │
+│  4. FINE-TUNING                                                         │
+│     └─> Train on combined demos + RaC data (optionally with RA-BC)      │
+│                                                                         │
+└─────────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## Step-by-Step Guide
+
+### Step 1: Pre-train a Base Policy
+
+First, train a policy on your demonstration dataset:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+    --dataset.repo_id=your-username/demo-dataset \
+    --policy.type=pi0 \
+    --output_dir=outputs/pretrain \
+    --batch_size=32 \
+    --steps=50000
+```
+
+### Step 2: Collect RaC Data
+
+Run the RaC data collection script with your pre-trained policy:
+
+```bash
+python examples/rac/rac_data_collection.py \
+    --robot.type=so100_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --dataset.repo_id=your-username/rac-dataset \
+    --dataset.single_task="Pick up the cube and place it in the bowl" \
+    --dataset.num_episodes=50
+```
+
+**Controls (Keyboard + Foot Pedal):**
+
+| Key / Pedal | Action |
+|-------------|--------|
+| **SPACE** / Right pedal | Pause policy (teleop mirrors robot, no recording) |
+| **c** / Left pedal | Take control (start correction, recording resumes) |
+| **→** / Right pedal | End episode (save) - when in correction mode |
+| **←** | Re-record episode |
+| **ESC** | Stop session and push to hub |
+| Any key/pedal during reset | Start next episode |
+
+**The RaC Protocol:**
+
+1. Watch the policy run autonomously (teleop is idle/free)
+2. When you see imminent failure, press **SPACE** or **right pedal** to pause
+   - Policy stops
+   - Teleoperator moves to match robot position (torque enabled)
+   - No frames recorded during pause
+3. Press **c** or **left pedal** to take control
+   - Teleoperator torque disabled, free to move
+   - **RECOVERY**: Teleoperate back to a good state
+   - **CORRECTION**: Complete the subtask
+   - All movements are recorded
+4. Press **→** or **right pedal** to save and end episode
+5. **RESET**: Teleop moves to robot position, you can move robot to starting position
+6. Press any key/pedal to start next episode
+
+The recovery and correction segments teach the policy how to recover from errors.
+
+**Foot Pedal Setup (Linux):**
+
+If using a USB foot pedal (PCsensor FootSwitch), ensure access:
+```bash
+sudo setfacl -m u:$USER:rw /dev/input/by-id/usb-PCsensor_FootSwitch-event-kbd
+```
+
+### Step 3: (Optional) Compute SARM Rewards
+
+For advantage-weighted training (RA-BC / Pi0.6-style), compute SARM progress values:
+
+```bash
+python src/lerobot/policies/sarm/compute_rabc_weights.py \
+    --dataset-repo-id your-username/rac-dataset \
+    --reward-model-path your-username/sarm-model \
+    --head-mode sparse \
+    --push-to-hub
+```
+
+### Step 4: Fine-tune Policy
+
+Fine-tune on the RaC data:
+
+```bash
+# Without RA-BC (standard fine-tuning)
+python src/lerobot/scripts/lerobot_train.py \
+    --dataset.repo_id=your-username/rac-dataset \
+    --policy.type=pi0 \
+    --policy.pretrained_path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --output_dir=outputs/rac_finetune \
+    --steps=20000
+
+# With RA-BC (advantage-weighted, Pi0.6-style)
+python src/lerobot/scripts/lerobot_train.py \
+    --dataset.repo_id=your-username/rac-dataset \
+    --policy.type=pi0 \
+    --policy.pretrained_path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --output_dir=outputs/rac_finetune_rabc \
+    --use_rabc=true \
+    --rabc_kappa=0.01 \
+    --steps=20000
+```
+
+---
+
+## Connection to Pi0.6 / RECAP
+
+Pi0.6's RECAP method shares similar principles:
+- Collect autonomous rollouts + expert interventions
+- Use value function to compute **advantages**: A(s,a) = V(s') - V(s)
+- **Advantage conditioning**: Weight training based on expected improvement
+
+In LeRobot, we can use **SARM** as the value function:
+- SARM progress φ(s) ∈ [0,1] measures task completion
+- Progress delta = φ(s') - φ(s) approximates advantage
+- RA-BC uses these to weight training samples (higher weight for good corrections)
+
+---
+
+## Tips for Effective RaC Collection
+
+### When to Intervene
+
+Intervene when you see:
+- Robot about to make an irreversible mistake
+- Robot hesitating or showing uncertain behavior
+- Robot deviating from expected trajectory
+
+### Recovery: Teleoperating Back to Good State
+
+During recovery, teleoperate the robot back to a state where:
+- The robot is in a familiar, in-distribution configuration
+- The current subtask can still be completed
+- The recovery trajectory itself is informative training data
+
+### Quality of Corrections
+
+During correction:
+- Provide **confident, clean** trajectories
+- Complete the current subtask fully
+- Don't overcorrect or add unnecessary movements
+
+---
+
+## Iterative Improvement
+
+RaC can be applied iteratively:
+
+```
+┌─────────────────────────────────────────────────────────────────────────┐
+│  Policy v0 (demos)                                                      │
+│       ↓                                                                 │
+│  RaC Collection (target current failure modes) → Policy v1              │
+│       ↓                                                                 │
+│  RaC Collection (target new failure modes) → Policy v2                  │
+│       ↓                                                                 │
+│  ... (repeat until satisfactory performance)                            │
+└─────────────────────────────────────────────────────────────────────────┘
+```
+
+Each iteration:
+1. Deploy current policy
+2. Collect RaC interventions on failure cases
+3. Fine-tune on accumulated data
+
+---
+
+## References
+
+```bibtex
+@article{hu2025rac,
+  title={RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction},
+  author={Hu, Zheyuan and Wu, Robyn and Enock, Naveen and Li, Jasmine and Kadakia, Riya and Erickson, Zackory and Kumar, Aviral},
+  journal={arXiv preprint arXiv:2509.07953},
+  year={2025}
+}
+
+@article{kelly2019hgdagger,
+  title={HG-DAgger: Interactive Imitation Learning with Human Experts},
+  author={Kelly, Michael and Sidrane, Chelsea and Driggs-Campbell, Katherine and Kochenderfer, Mykel J},
+  journal={arXiv preprint arXiv:1810.02890},
+  year={2019}
+}
+
+@article{pi2025recap,
+  title={π∗0.6: a VLA That Learns From Experience},
+  author={Physical Intelligence},
+  year={2025}
+}
+
+@article{chen2025sarm,
+  title={SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation},
+  author={Chen, Qianzhong and Yu, Justin and Schwager, Mac and Abbeel, Pieter and Shentu, Yide and Wu, Philipp},
+  journal={arXiv preprint arXiv:2509.25358},
+  year={2025}
+}
+```
+
@@ -38,7 +38,6 @@ docker run --rm -it \
  start_rviz:=true start_sdk_server:=true mujoco:=true
 ```

-> [!NOTE]
 > If MuJoCo runs slowly (low simulation frequency), append `-e LD_LIBRARY_PATH="/opt/host-libs:$LD_LIBRARY_PATH" \` to the previous command to improve performance:
 >
 > ```
@@ -142,7 +141,7 @@ If you choose this option but still want to use the VR teleoperation application
 First add reachy2 and reachy2_teleoperator to the imports of the record script. Then you can use the following command:

 ```bash
-lerobot-record \
+python -m lerobot.record \
    --robot.type=reachy2 \
    --robot.ip_address=192.168.0.200 \
    --robot.id=r2-0000 \
@@ -151,7 +150,6 @@ lerobot-record \
    --teleop.type=reachy2_teleoperator \
    --teleop.ip_address=192.168.0.200 \
    --teleop.with_mobile_base=false \
-    --robot.with_torso_camera=true \
    --dataset.repo_id=pollen_robotics/record_test \
    --dataset.single_task="Reachy 2 recording test" \
    --dataset.num_episodes=1 \
@@ -159,9 +157,6 @@ lerobot-record \
    --dataset.fps=15 \
    --dataset.push_to_hub=true \
    --dataset.private=true \
-    --dataset.streaming_encoding=true \
-    --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
    --display_data=true
 ```

@@ -170,7 +165,7 @@ lerobot-record \
 **Extended setup overview (all options included):**

 ```bash
-lerobot-record \
+python -m lerobot.record \
    --robot.type=reachy2 \
    --robot.ip_address=192.168.0.200 \
    --robot.use_external_commands=true \
@@ -182,8 +177,6 @@ lerobot-record \
    --robot.with_left_teleop_camera=true \
    --robot.with_right_teleop_camera=true \
    --robot.with_torso_camera=false \
-    --robot.camera_width=640 \
-    --robot.camera_height=480 \
    --robot.disable_torque_on_disconnect=false \
    --robot.max_relative_target=5.0 \
    --teleop.type=reachy2_teleoperator \
@@ -201,9 +194,6 @@ lerobot-record \
    --dataset.fps=15 \
    --dataset.push_to_hub=true \
    --dataset.private=true \
-    --dataset.streaming_encoding=true \
-    --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
    --display_data=true
 ```

@@ -222,10 +212,9 @@ Must be set to true if a compliant Reachy 2 is used to control another one.
 From our initial tests, recording **all** joints when only some are moving can reduce model quality with certain policies.
 To avoid this, you can exclude specific parts from recording and replay using:

-```bash
+````
 --robot.with_<part>=false
-```
-
+```,
 with `<part>` being one of : `mobile_base`, `l_arm`, `r_arm", `neck`, `antennas`.
 It determine whether the corresponding part is recorded in the observations. True if not set.

@@ -233,60 +222,49 @@ By default, **all parts are recorded**.

 The same per-part mechanism is available in `reachy2_teleoperator` as well.

-```bash
--teleop.with\_<part>
-```
+````

+--teleop.with\_<part>
+
+```
 with `<part>` being one of : `mobile_base`, `l_arm`, `r_arm", `neck`, `antennas`.
 Determine whether the corresponding part is recorded in the actions. True if not set.

 > **Important:** In a given session, the **enabled parts must match** on both the robot and the teleoperator.
-> For example, if the robot runs with `--robot.with_mobile_base=false`, the teleoperator must disable the same part `--teleoperator.with_mobile_base=false`.
+For example, if the robot runs with `--robot.with_mobile_base=false`, the teleoperator must disable the same part `--teleoperator.with_mobile_base=false`.

 ##### Use the relevant cameras

-You can do the same for **cameras**. Enable or disable each camera with default parameters using:
+You can do the same for **cameras**. By default, only the **teleoperation cameras** are recorded (both `left_teleop_camera` and `right_teleop_camera`). Enable or disable each camera with:

-```bash
--robot.with_left_teleop_camera=<true|false> \
--robot.with_right_teleop_camera=<true|false> \
+```
+
+--robot.with_left_teleop_camera=<true|false>
+--robot.with_right_teleop_camera=<true|false>
 --robot.with_torso_camera=<true|false>
-```

-By default, no camera is recorded, all camera arguments are set to `false`.
-If you want to, you can use custom `width` and `height` parameters for Reachy 2's cameras using the `--robot.camera_width` & `--robot.camera_height` argument:
+````

-```bash
--robot.camera_width=1920 \
--robot.camera_height=1080
-```
-
-This will change the resolution of all 3 default robot cameras (enabled by the above bool arguments).
-
-If you want, you can add additional cameras other than the ones in the robot as usual with:
-
-```bash
--robot.cameras="{ extra: {type: opencv, index_or_path: 42, width: 640, height: 480, fps: 30}}" \
-```

 ## Step 2: Replay

 Make sure the robot is configured with the same parts as the dataset:

 ```bash
-lerobot-replay \
+python -m lerobot.replay \
    --robot.type=reachy2 \
    --robot.ip_address=192.168.0.200 \
    --robot.use_external_commands=false \
    --robot.with_mobile_base=false \
    --dataset.repo_id=pollen_robotics/record_test \
    --dataset.episode=0
-```
+    --display_data=true
+````

 ## Step 3: Train

 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
  --dataset.repo_id=pollen_robotics/record_test \
  --policy.type=act \
  --output_dir=outputs/train/reachy2_test \
@@ -299,9 +277,10 @@ lerobot-train \
 ## Step 4: Evaluate

 ```bash
-lerobot-eval \
+python -m lerobot.record \
  --robot.type=reachy2 \
  --robot.ip_address=192.168.0.200 \
+  --display_data=false \
  --dataset.repo_id=pollen_robotics/eval_record_test \
  --dataset.single_task="Evaluate reachy2 policy" \
  --dataset.num_episodes=10 \
@@ -1,114 +0,0 @@
-# Rename Map and Empty Cameras
-
-When you train, evaluate, or record with a robot policy, your **dataset** or **environment** provides observations under one set of keys (e.g. `observation.images.front`, `observation.images.eagle`), while your **policy** expects another (e.g. `observation.images.image`, `observation.images.image2`). The **rename map** bridges that gap without changing the policy or data source.
-
-> **Scope:** The rename map only renames **observation** keys (images and state). Action keys are not affected.
-
-## Why observation keys don't always match
-
-Policies have a fixed set of **input feature names** baked into their pretrained config. For example:
-
- [pi0fast-libero](https://huggingface.co/lerobot/pi0fast-libero) expects `observation.images.base_0_rgb` and `observation.images.left_wrist_0_rgb`.
- [xvla-base](https://huggingface.co/lerobot/xvla-base) expects `observation.images.image`, `observation.images.image2`, and `observation.images.image3`.
-
-Your dataset might use different names entirely (e.g. `observation.images.front`, `observation.images.eagle`, `observation.images.glove`), and your eval environment might use yet another set. Rather than editing the policy config or renaming columns in the dataset, you pass a **rename map**: a JSON dictionary that maps source keys to the keys the policy expects. Renaming happens inside the preprocessor pipeline, so the policy always sees its expected keys.
-
-## Using the rename map
-
-Pass the mapping as a JSON string on the command line. The convention is always:
-
-```
--rename_map='{"source_key": "policy_key", ...}'
-```
-
-where **source_key** is what the dataset or environment provides, and **policy_key** is what the policy expects.
-
-Only listed keys are renamed; everything else passes through unchanged. Order of entries doesn't matter.
-
-Supported policies: **PI0**, **PI05**, **PI0Fast**, **SmolVLA**, and **XVLA**.
-
-### Training
-
-Suppose you fine-tune [lerobot/xvla-base](https://huggingface.co/lerobot/xvla-base) on a dataset with images under `observation.images.front`, `observation.images.eagle`, and `observation.images.glove`. XVLA expects `observation.images.image`, `observation.images.image2`, and `observation.images.image3`:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=YOUR_DATASET \
-  --output_dir=./outputs/xvla_training \
-  --job_name=xvla_training \
-  --policy.path="lerobot/xvla-base" \
-  --policy.repo_id="HF_USER/xvla-your-robot" \
-  --policy.dtype=bfloat16 \
-  --policy.action_mode=auto \
-  --steps=20000 \
-  --policy.device=cuda \
-  --policy.freeze_vision_encoder=false \
-  --policy.freeze_language_encoder=false \
-  --policy.train_policy_transformer=true \
-  --policy.train_soft_prompts=true \
-  --rename_map='{"observation.images.front": "observation.images.image", "observation.images.eagle": "observation.images.image2", "observation.images.glove": "observation.images.image3"}'
-```
-
-### Evaluation
-
-A policy that expects `observation.images.base_0_rgb` and `observation.images.left_wrist_0_rgb` (e.g. [pi0fast-libero](https://huggingface.co/lerobot/pi0fast-libero)), but the LIBERO environment returns `observation.images.image` and `observation.images.image2`:
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/pi0fast-libero \
-  --env.type=libero \
-  ... \
-  --rename_map='{"observation.images.image": "observation.images.base_0_rgb", "observation.images.image2": "observation.images.left_wrist_0_rgb"}'
-```
-
-### Recording
-
-`lerobot-record` also supports rename maps, nested under the dataset config:
-
-```bash
-lerobot-record \ # When running inference
-  --policy.path="<user>/smolVLA_finetuned" \
-  ... \
-  --dataset.rename_map='{"observation.images.glove2": "observation.images.image"}'
-```
-
-## Alternative: edit the policy config directly
-
-If you always use the same dataset or environment, you can **edit the policy's `config.json`** so its observation keys match your data source. Then no rename map is needed.
-
-The tradeoff: modifying the policy config ties it to one data source. A rename map keeps one policy usable across many datasets and environments.
-
-## Empty cameras: fewer views than the policy expects
-
-Some policies are built for a fixed number of image inputs. If your dataset has fewer cameras, you can set **`empty_cameras`** in the policy config instead of modifying the model architecture.
-
-### How it works
-
-Setting `empty_cameras=N` adds N placeholder image features to the policy config, named:
-
-```
-observation.images.empty_camera_0
-observation.images.empty_camera_1
-...
-```
-
-At runtime, these keys have no corresponding data in the batch. The policy fills them with masked dummy tensors (padded with `-1` for SigLIP-based vision encoders, with a zero attention mask), so the extra image slots are effectively ignored during training and inference.
-
-### Example
-
-XVLA-base has three visual inputs and `empty_cameras=0` by default. Your dataset only has two cameras:
-
-1. Set `--policy.empty_cameras=1`.
-2. The config adds a third key: `observation.images.empty_camera_0`.
-3. Use the rename map for your two real cameras as usual.
-4. The third slot is masked out — no fake images needed in your dataset.
-
-## Quick reference
-
-| Goal                                      | What to do                                                                  |
-| ----------------------------------------- | --------------------------------------------------------------------------- |
-| Dataset keys ≠ policy keys                | `--rename_map='{"dataset_key": "policy_key", ...}'`                         |
-| Env keys ≠ policy keys (eval)             | `--rename_map='{"env_key": "policy_key", ...}'`                             |
-| Recording with different keys (inference) | `--dataset.rename_map='{"source_key": "policy_key", ...}'`.                 |
-| Fewer cameras than policy expects         | `--policy.empty_cameras=N` (supported by PI0, PI05, PI0Fast, SmolVLA, XVLA) |
-| Avoid passing a rename map                | Edit the policy's `config.json` so its keys match your data source          |
@@ -4,12 +4,6 @@ SARM (Stage-Aware Reward Modeling) is a video-based reward modeling framework fo

 **Paper**: [SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation](https://arxiv.org/abs/2509.25358)

-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-sarm.png"
-  alt="An overview of SARM"
-  width="80%"
-/>
-
 ## Why Reward Models?

 Standard behavior cloning treats all demonstration frames equally, but real-world robot datasets are messy. They contain hesitations, corrections, and variable-quality trajectories. Reward models solve this by learning a generalizable notion of **task progress** from demonstrations: given video frames and a task description, they predict how close the robot is to completing the task (0→1). This learned "progress signal" can be used in multiple ways, two promising applications are: (1) **weighted imitation learning** (RA-BC), where high-progress frames receive more weight during policy training, and (2) **reinforcement learning**, where the reward model provides dense rewards for online or offline policy improvement.
@@ -269,7 +263,7 @@ This generates visualizations showing video frames with subtask boundaries overl
 Train with **no annotations** - uses linear progress from 0 to 1:

 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
  --dataset.repo_id=your-username/your-dataset \
  --policy.type=sarm \
  --policy.annotation_mode=single_stage \
@@ -288,7 +282,7 @@ lerobot-train \
 Train with **dense annotations only** (sparse auto-generated):

 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
  --dataset.repo_id=your-username/your-dataset \
  --policy.type=sarm \
  --policy.annotation_mode=dense_only \
@@ -307,7 +301,7 @@ lerobot-train \
 Train with **both sparse and dense annotations**:

 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
  --dataset.repo_id=your-username/your-dataset \
  --policy.type=sarm \
  --policy.annotation_mode=dual \
@@ -468,7 +462,7 @@ This script:
 Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`). Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:

 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
  --dataset.repo_id=your-username/your-dataset \
  --policy.type=pi0 \
  --use_rabc=true \
@@ -106,9 +106,6 @@ lerobot-record \
  --dataset.repo_id=${HF_USER}/eval_DATASET_NAME_test \  # <- This will be the dataset name on HF Hub
  --dataset.episode_time_s=50 \
  --dataset.num_episodes=10 \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
  # <- Teleop optional if you want to teleoperate in between episodes \
  # --teleop.type=so100_leader \
  # --teleop.port=/dev/ttyACM0 \
@@ -103,7 +103,7 @@ lerobot-setup-motors \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.robots.so100_follower import SO100Follower, SO100FollowerConfig

 config = SO100FollowerConfig(
    port="/dev/tty.usbmodem585A0076841",
@@ -177,7 +177,7 @@ lerobot-setup-motors \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig

 config = SO100LeaderConfig(
    port="/dev/tty.usbmodem585A0076841",
@@ -579,7 +579,7 @@ lerobot-calibrate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.robots.so_follower import SO100FollowerConfig, SO100Follower
+from lerobot.robots.so100_follower import SO100FollowerConfig, SO100Follower

 config = SO100FollowerConfig(
    port="/dev/tty.usbmodem585A0076891",
@@ -617,7 +617,7 @@ lerobot-calibrate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.teleoperators.so_leader import SO100LeaderConfig, SO100Leader
+from lerobot.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader

 config = SO100LeaderConfig(
    port="/dev/tty.usbmodem58760431551",
@@ -1,18 +1,5 @@
 # SO-101

-<div style="display: flex; align-items: center; gap: 10px;">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/SO101_Follower.webp"
-    alt="SO-101"
-    width="60%"
-  />
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/SO101_Leader.webp"
-    alt="SO-101"
-    width="60%"
-  />
-</div>
-
 In the steps below, we explain how to assemble our flagship robot, the SO-101.

 ## Source the parts
@@ -138,7 +125,7 @@ lerobot-setup-motors \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
+from lerobot.robots.so101_follower import SO101Follower, SO101FollowerConfig

 config = SO101FollowerConfig(
    port="/dev/tty.usbmodem585A0076841",
@@ -214,7 +201,7 @@ lerobot-setup-motors \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.teleoperators.so_leader import SO101Leader, SO101LeaderConfig
+from lerobot.teleoperators.so101_leader import SO101Leader, SO101LeaderConfig

 config = SO101LeaderConfig(
    port="/dev/tty.usbmodem585A0076841",
@@ -236,10 +223,10 @@ It is advisable to install one 3-pin cable in the motor after placing them befor

 ### Joint 1

- Install both motor horns. Secure the top horn with a M3x6mm screw. No screws are required for the bottom horn.
 - Place the first motor into the base.
 - Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from the bottom.
 - Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
+- Install both motor horns, securing the top horn with a M3x6mm screw.
 - Attach the shoulder part.
 - Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
 - Add the shoulder motor holder.
@@ -255,9 +242,9 @@ It is advisable to install one 3-pin cable in the motor after placing them befor

 ### Joint 2

- Install both motor horns. Secure the top horn with a M3x6mm screw. No screws are required for the bottom horn.
 - Slide the second motor in from the top.
 - Fasten the second motor with 4 M2x6mm screws.
+- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
 - Attach the upper arm with 4 M3x6mm screws on each side.

 <div class="video-container">
@@ -271,8 +258,8 @@ It is advisable to install one 3-pin cable in the motor after placing them befor

 ### Joint 3

- Install both motor horns. Secure the top horn with a M3x6mm screw. No screws are required for the bottom horn.
- Insert motor 3 and fasten using 4 M2x6mm screws.
+- Insert motor 3 and fasten using 4 M2x6mm screws
+- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
 - Connect the forearm to motor 3 using 4 M3x6mm screws on each side.

 <div class="video-container">
@@ -286,10 +273,9 @@ It is advisable to install one 3-pin cable in the motor after placing them befor

 ### Joint 4

- Install both motor horns. Secure the top horn with a M3x6mm screw. No screws are required for the bottom horn.
 - Slide over motor holder 4.
 - Slide in motor 4.
- Fasten motor 4 with 4 M2x6mm screws.
+- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.

 <div class="video-container">
  <video controls width="600">
@@ -322,7 +308,7 @@ It is advisable to install one 3-pin cable in the motor after placing them befor

 - Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
 - Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
- Install both motor horns on the gripper motor. Secure the top horn with a M3x6mm screw; no screws are required for the bottom horn.
+- Attach the motor horns and again use a M3x6mm horn screw.
 - Install the gripper claw and secure it with 4 M3x6mm screws on both sides.

 <div class="video-container">
@@ -378,7 +364,7 @@ lerobot-calibrate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.robots.so_follower import SO101FollowerConfig, SO101Follower
+from lerobot.robots.so101_follower import SO101FollowerConfig, SO101Follower

 config = SO101FollowerConfig(
    port="/dev/tty.usbmodem585A0076891",
@@ -427,7 +413,7 @@ lerobot-calibrate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.teleoperators.so_leader import SO101LeaderConfig, SO101Leader
+from lerobot.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader

 config = SO101LeaderConfig(
    port="/dev/tty.usbmodem58760431551",
@@ -1,155 +0,0 @@
-# Streaming Video Encoding Guide
-
-## 1. Overview
-
-Streaming video encoding eliminates the traditional PNG round-trip during video dataset recording. Instead of:
-
-1. Capture frame -> write PNG to disk -> (at episode end) read PNG's -> encode to MP4 -> delete PNG's
-
-Frames can be encoded in real-time during capture:
-
-1. Capture frame -> queue to encoder thread -> encode to MP4 directly
-
-This makes `save_episode()` near-instant (the video is already encoded by the time the episode ends) and removes the blocking wait that previously occurred between episodes, especially with multiple cameras in long episodes.
-
-## 2. Tuning Parameters
-
-| Parameter               | CLI Flag                          | Type          | Default       | Description                                                       |
-| ----------------------- | --------------------------------- | ------------- | ------------- | ----------------------------------------------------------------- |
-| `streaming_encoding`    | `--dataset.streaming_encoding`    | `bool`        | `True`        | Enable real-time encoding during capture                          |
-| `vcodec`                | `--dataset.vcodec`                | `str`         | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder                     |
-| `encoder_threads`       | `--dataset.encoder_threads`       | `int \| None` | `None` (auto) | Threads per encoder instance. `None` will leave the vcoded decide |
-| `encoder_queue_maxsize` | `--dataset.encoder_queue_maxsize` | `int`         | `60`          | Max buffered frames per camera (~2s at 30fps). Consumes RAM       |
-
-## 3. Performance Considerations
-
-Streaming encoding means the CPU is encoding video **during** the capture loop, not after. This creates a CPU budget that must be shared between:
-
- **Control loop** (reading cameras, control the robot, writing non-video data)
- **Encoder threads** (one pool per camera)
- **Rerun visualization** (if enabled)
- **OS and other processes**
-
-### Resolution & Number of Cameras Impact
-
-| Setup                     | Throughput (px/sec) | CPU Encoding Load | Notes                          |
-| ------------------------- | ------------------- | ----------------- | ------------------------------ |
-| 2camsx 640x480x3 @30fps   | 55M                 | Low               | Works on most systems          |
-| 2camsx 1280x720x3 @30fps  | 165M                | Moderate          | Comfortable on modern systems  |
-| 2camsx 1920x1080x3 @30fps | 373M                | High              | Requires powerful high-end CPU |
-
-### `encoder_threads` Tuning
-
-This parameter controls how many threads each encoder instance uses internally:
-
- **Higher values** (e.g., 4-5): Faster encoding, but uses more CPU cores per camera. Good for high-end systems with many cores.
- **Lower values** (e.g., 1-2): Less CPU per camera, freeing cores for capture and visualization. Good for low-res images and capable CPUs.
- **`None` (default)**: Lets the codec decide. Information available in the codec logs.
-
-### Backpressure and Frame Dropping
-
-Each camera has a bounded queue (`encoder_queue_maxsize`, default 60 frames). When the encoder can't keep up:
-
-1. The queue fills up (consuming RAM)
-2. New frames are **dropped** (not blocked) — the capture loop continues uninterrupted
-3. A warning is logged: `"Encoder queue full for {camera}, dropped N frame(s)"`
-4. At episode end, total dropped frames per camera are reported
-
-### Symptoms of Encoder Falling Behind
-
- **System feels laggy and freezes**: all CPUs are at 100%
- **Dropped frame warnings** in the log or lower frames/FPS than expected in the recorded dataset
- **Choppy robot movement**: If CPU is severely overloaded, even the capture loop may be affected
- **Accumulated rerun lag**: Visualization falls behind real-time
-
-## 4. Hardware-Accelerated Encoding
-
-### When to Use
-
-Use HW encoding when:
-
- CPU is the bottleneck (dropped frames, choppy robot, rerun lag)
- You have compatible hardware (GPU or dedicated encoder)
- You're recording at high throughput (high resolution or with many cameras)
-
-### Choosing a Codec
-
-| Codec                 | CPU Usage | File Size      | Quality | Notes                                                            |
-| --------------------- | --------- | -------------- | ------- | ---------------------------------------------------------------- |
-| `libsvtav1` (default) | High      | Smallest       | Best    | Default. Best compression but most CPU-intensive                 |
-| `h264`                | Medium    | ~30-50% larger | Good    | Software H.264. Lower CPU                                        |
-| HW encoders           | Very Low  | Largest        | Good    | Offloads to dedicated hardware. Best for CPU-constrained systems |
-
-### Available HW Encoders
-
-| Encoder             | Platform      | Hardware                                                                                         | CLI Value                            |
-| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------ |
-| `h264_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.vcodec=h264_videotoolbox` |
-| `hevc_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.vcodec=hevc_videotoolbox` |
-| `h264_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.vcodec=h264_nvenc`        |
-| `hevc_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.vcodec=hevc_nvenc`        |
-| `h264_vaapi`        | Linux         | Intel/AMD GPU                                                                                    | `--dataset.vcodec=h264_vaapi`        |
-| `h264_qsv`          | Linux/Windows | Intel Quick Sync                                                                                 | `--dataset.vcodec=h264_qsv`          |
-| `auto`              | Any           | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.vcodec=auto`              |
-
-> [!NOTE]
-> In order to use the HW accelerated encoders you might need to upgrade your GPU drivers.
-
-> [!NOTE]
-> `libsvtav1` is the default because it provides the best training performance; other vcodecs can reduce CPU usage and be faster, but they typically produce larger files and may affect training time.
-
-## 5. Troubleshooting
-
-| Symptom                                                            | Likely Cause                                 | Fix                                                                                                                                                                                                                                                                                  |
-| ------------------------------------------------------------------ | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage)                | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.vcodec=auto`) |
-| "Encoder queue full" warnings or dropped frames in dataset         | Encoder can't keep up (Queue overflow)       | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.vcodec=auto`).                                                                                                                                                    |
-| High RAM usage                                                     | Queue filling faster than encoding           | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding                                                                                                                                                                                     |
-| Large video files                                                  | Using HW encoder or H.264                    | Expected trade-off. Switch to `libsvtav1` if CPU allows                                                                                                                                                                                                                              |
-| `save_episode()` still slow                                        | `streaming_encoding` is `False`              | Set `--dataset.streaming_encoding=true`                                                                                                                                                                                                                                              |
-| Encoder thread crash                                               | Codec not available or invalid settings      | Check `vcodec` is installed, try `--dataset.vcodec=auto`                                                                                                                                                                                                                             |
-| Recorded dataset is missing frames                                 | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected.                                   |
-
-## 6. Recommended Configurations
-
-These estimates are conservative; we recommend testing them on your setup—start with a low load and increase it gradually.
-
-### High-End Systems: modern 12+ cores (24+ threads)
-
-A throughput between ~250-500M px/sec should be comfortable in CPU. For even better results try HW encoding if available.
-
-```bash
-# 3camsx 1280x720x3 @30fps: Defaults work well. Optionally increase encoder parallelism.
-# 2camsx 1920x1080x3 @30fps: Defaults work well. Optionally increase encoder parallelism.
-lerobot-record --dataset.encoder_threads=5 ...
-
-# 3camsx 1920x1080x3 @30fps: Might require some tuning.
-```
-
-### Mid-Range Systems: modern 8+ cores (16+ threads) or Apple Silicon
-
-A throughput between ~80-300M px/sec should be possible in CPU.
-
-```bash
-# 3camsx 640x480x3 @30fps: Defaults work well. Optionally decrease encoder parallelism.
-# 2camsx 1280x720x3 @30fps: Defaults work well. Optionally decrease encoder parallelism.
-lerobot-record --dataset.encoder_threads=2 ...
-
-# 2camsx 1920x1080x3 @30fps: Might require some tuning.
-```
-
-### Low-Resource Systems: modern 4+ cores (8+ threads) or Raspberry Pi 5
-
-On very constrained systems, streaming encoding may compete too heavily with the capture loop. Disabling it falls back to the PNG-based approach where encoding happens between episodes (blocking, but doesn't interfere with capture). Alternatively, record at a lower throughput to reduce both capture and encoding load. Consider also changing codec to `h264` and using batch encoding.
-
-```bash
-# 2camsx 640x480x3 @30fps: Requires some tuning.
-
-# Use H.264, disable streaming, consider batching encoding
-lerobot-record --dataset.vcodec=h264 --dataset.streaming_encoding=false ...
-```
-
-## 7. Closing note
-
-Performance ultimately depends on your exact setup — frames-per-second, resolution, CPU cores and load, available memory, episode length, and the encoder you choose. Always test with your target workload, be mindful about your CPU & system capabilities and tune `encoder_threads`, `encoder_queue_maxsize`, and
-`vcodec` reasonably. That said, a common practical configuration (for many applications) is three cameras at 640×480x3 @30fps; this usually runs fine with the default streaming video encoding settings in modern systems. Always verify your recorded dataset is healthy by comparing the video duration to the CLI episode duration and confirming the row count equals FPS × CLI duration.
@@ -1,227 +0,0 @@
-# UMI Data with pi0 Relative EE Actions
-
-This guide explains how to train a pi0 policy with UMI-style relative end-effector (EE) actions and deploy it on a real OpenArm robot.
-
-**What we will do:**
-
-1. Prepare the dataset (EE pose + gripper in the action column).
-2. Recompute statistics for relative actions.
-3. Train pi0 with `derive_state_from_action=true`.
-4. Evaluate the trained policy on a real robot.
-
-## Background
-
-[UMI (Universal Manipulation Interface)](https://umi-gripper.github.io) collects manipulation data with hand-held grippers, recovering 6-DoF EE poses via SLAM. The key insight from UMI (Chi et al., 2024) is that the action space must include **both EE trajectory and gripper width**, and actions should be expressed as **relative trajectories** (offsets from the current pose).
-
-### Dataset layout
-
-The dataset should have this structure:
-
-| Feature                   | Shape     | Content                                                  |
-| ------------------------- | --------- | -------------------------------------------------------- |
-| `observation.images.cam0` | `[3,H,W]` | Wrist camera image                                       |
-| `action`                  | `[8]`     | `[x, y, z, ax, ay, az, proximal, distal]` (EE + gripper) |
-
-No separate `observation.pose` or `observation.joints` columns are needed — the model derives its proprioception state directly from the action column (`derive_state_from_action=true`).
-
-### Why relative actions?
-
-With relative actions, each action in a chunk is an **offset from the current state** rather than an absolute target:
-
-```
-relative_action[i] = absolute_action[t + i] − state[t]
-```
-
-UMI ablations show this is critical: absolute actions achieve only 25% success vs 100% for relative trajectory on the cup arrangement task. Compared to delta actions (each step relative to the previous), relative trajectory avoids error accumulation. See the [Action Representations](action_representations) guide for details.
-
-### `derive_state_from_action`
-
-When `derive_state_from_action=true`, pi0 derives `observation.state` from the action column during training — no separate state column needed. Under the hood:
-
- `action_delta_indices` extends to `[-1, 0, 1, ..., chunk_size-1]` (one extra leading timestep).
- `DeriveStateFromActionStep` extracts `[action[t-1], action[t]]` as a 2-step state and strips the extra timestep from the action chunk.
- `RelativeActionsProcessorStep` converts actions to offsets from `state[t]`.
- `RelativeStateProcessorStep` converts the 2-step state to relative proprioception (velocity + zeros) and flattens.
-
-This implies `use_relative_state=true` and `state_obs_steps=2`.
-
-During **inference**, `DeriveStateFromActionStep` is a no-op — state comes from the robot via forward kinematics. `RelativeStateProcessorStep` buffers the previous state and applies the same conversion automatically.
-
-## Step 1: Recompute Stats
-
-After preparing the dataset with EE pose in the action column, recompute statistics with `derive_state_from_action=true`. This computes relative action and state stats so the normalizer sees offset distributions:
-
-```bash
-lerobot-edit-dataset \
-    --repo-id=glannuzel/grabette-dataset \
-    --operation=recompute_stats \
-    --operation.relative_action=true \
-    --operation.relative_exclude_joints='["proximal", "distal"]' \
-    --operation.derive_state_from_action=true \
-    --operation.chunk_size=30 \
-    --push_to_hub=true
-```
-
-| Flag                            | Purpose                                                                         |
-| ------------------------------- | ------------------------------------------------------------------------------- |
-| `relative_action=true`          | Compute stats on `action − state` (relative actions)                            |
-| `relative_exclude_joints`       | Keep gripper dims absolute (they don't benefit from relative encoding)          |
-| `derive_state_from_action=true` | Derive state from action column (implies `relative_state`, `state_obs_steps=2`) |
-| `chunk_size=30`                 | Must match training chunk size                                                  |
-
-## Step 2: Train
-
-```bash
-#!/bin/bash
-set -euo pipefail
-
-export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:${LD_LIBRARY_PATH:-}
-
-DATASET="glannuzel/grabette-dataset"
-NUM_PROCESSES=8
-
-echo "=== Training pi0 on $DATASET (UMI relative EE, ${NUM_PROCESSES} GPUs) ==="
-accelerate launch --multi_gpu --num_processes=$NUM_PROCESSES \
-    -m lerobot.scripts.lerobot_train \
-    --dataset.repo_id="$DATASET" \
-    --dataset.video_backend=pyav \
-    --policy.type=pi0 \
-    --policy.pretrained_path=lerobot/pi0_base \
-    --policy.repo_id=pepijn/grabette-umi-pi0 \
-    --policy.chunk_size=30 \
-    --policy.n_action_steps=30 \
-    --policy.derive_state_from_action=true \
-    --use_relative_actions=true \
-    --policy.relative_exclude_joints='["proximal", "distal"]' \
-    --batch_size=32 \
-    --steps=5000 \
-    --policy.scheduler_decay_steps=5000 \
-    --policy.dtype=bfloat16 \
-    --policy.compile_model=false \
-    --policy.gradient_checkpointing=true \
-    --policy.device=cuda \
-    --output_dir=/fsx/pepijn/outputs/grabette-umi \
-    --job_name=grabette-umi-v2 \
-    --wandb.enable=true \
-    --wandb.disable_artifact=true \
-    --wandb.project=grabette-umi \
-    --log_freq=100 \
-    --save_freq=5000
-```
-
-Key flags:
-
-| Flag                            | Purpose                                                                |
-| ------------------------------- | ---------------------------------------------------------------------- |
-| `derive_state_from_action=true` | Derive proprioception from action column (full UMI mode)               |
-| `use_relative_actions=true`     | Actions are offsets from current state                                 |
-| `relative_exclude_joints`       | `["proximal", "distal"]` — gripper stays absolute, EE pose is relative |
-| `chunk_size=30`                 | Action horizon: 30 steps (~0.65s at 46 FPS)                            |
-| `n_action_steps=30`             | Execute full chunk before replanning                                   |
-
-Note: `derive_state_from_action=true` automatically implies `use_relative_state=true` and `state_obs_steps=2`. No `rename_map` is needed since there are no separate observation columns to rename.
-
-## Step 3: Evaluate
-
-The evaluation script in `examples/umi_pi0_relative_ee/evaluate.py` runs inference on a real OpenArm robot:
-
-```bash
-python examples/umi_pi0_relative_ee/evaluate.py
-```
-
-Edit `HF_MODEL_ID`, camera index, and robot configuration at the top of the file.
-
-### How inference works
-
-At inference, the training dataset has no `observation.state` — it was derived from actions. The evaluate script provides `observation.state` from the robot via forward kinematics:
-
-1. **Robot → FK** — Arm joint positions → EE pose `[x,y,z,ax,ay,az]`, gripper → `[proximal, distal]`. Combined into `observation.state` (8D).
-2. **Preprocessor** (loaded from checkpoint) — `DeriveStateFromActionStep` is a no-op. `RelativeStateProcessorStep` buffers previous state, stacks `[prev, current]`, subtracts current → velocity info. `RelativeActionsProcessorStep` caches state. `NormalizerProcessorStep` normalizes.
-3. **pi0 inference** — Predicts normalized relative action chunk (30 steps).
-4. **Postprocessor** — `UnnormalizerProcessorStep` unnormalizes, `AbsoluteActionsProcessorStep` adds cached state → absolute EE targets.
-5. **IK → Robot** — Absolute `[x,y,z,ax,ay,az]` → arm joint targets with full 6-DOF IK (orientation weight = 1.0). `[proximal, distal]` → direct gripper position commands.
-
-### Latency compensation
-
-Set `LATENCY_SKIP_STEPS` to skip the first few predicted action steps, compensating for system latency:
-
-```python
-LATENCY_SKIP_STEPS = 7  # ceil(total_latency_ms / (1000 / FPS))
-```
-
-At 46 FPS (~22ms/step) with ~150ms total latency: `ceil(150/22) ≈ 7`. Start with 0 for a safe first test.
-
-## Replay Viewer
-
-Visualize any dataset episode in a browser-based 3D viewer before running on hardware. The viewer shows the EE trajectory overlaid on the OpenArm URDF model.
-
-### Quick start
-
-```bash
-python examples/umi_pi0_relative_ee/replay.py
-```
-
-### Options
-
-| Flag        | Default                      | Description                          |
-| ----------- | ---------------------------- | ------------------------------------ |
-| `--repo-id` | `glannuzel/grabette-dataset` | HuggingFace dataset repo to load     |
-| `--episode` | `0`                          | Episode index to replay              |
-| `--port`    | `8765`                       | HTTP server port                     |
-| `--force`   | off                          | Re-extract trajectory even if cached |
-
-### Viewer controls
-
-The panel in the top-left corner shows live EE coordinates and gripper state. Transport controls:
-
- **Play / Pause** — toggle automatic playback.
- **Step buttons** (◀ ▶) — advance or rewind one frame.
- **Reset** (⟳) — jump to frame 0.
- **Scrubber** — drag to seek.
- **Speed selector** — 0.25× to 4× playback speed.
-
-### Color legend
-
-| Color              | Meaning                                       |
-| ------------------ | --------------------------------------------- |
-| Red sphere         | Current EE position                           |
-| Yellow trail       | Past trajectory                               |
-| Dark trail         | Future trajectory                             |
-| Orange ring + axes | URDF `ee_target` frame (zero-joint reference) |
-
-## How the Pieces Fit Together
-
-```
-Training (derive_state_from_action=true):
-  DataLoader loads action: [B, 31, 8]  (chunk_size=30 + 1 leading)
-      → DeriveStateFromActionStep
-          state  = action[:, :2, :]     → [B, 2, 8]
-          action = action[:, 1:, :]     → [B, 30, 8]
-      → RelativeActionsProcessorStep    (action -= state[:, -1, :])
-      → RelativeStateProcessorStep      (state offsets from current, flatten → [B, 16])
-      → NormalizerProcessorStep         → pi0 model
-
-Inference:
-  arm joints → FK → observation.state [8D: x,y,z,ax,ay,az,prox,dist]
-                        ↓
-                DeriveStateFromActionStep (no-op)
-                        ↓
-                RelativeActionsProcessorStep (caches state)
-                        ↓
-                RelativeStateProcessorStep (buffers prev, stacks, subtracts, flattens)
-                        ↓
-                NormalizerProcessorStep → pi0 model → relative action chunk [30, 8]
-                        ↓
-                UnnormalizerProcessorStep
-                        ↓
-                AbsoluteActionsProcessorStep (+ cached state → absolute EE)
-                        ↓
-                IK → joint targets → robot
-```
-
-## References
-
- [UMI: Universal Manipulation Interface](https://umi-gripper.github.io) — Chi et al., 2024. Defines relative trajectory actions.
- [Action Representations](action_representations) — LeRobot guide comparing absolute, relative, and delta actions.
- [pi0 documentation](pi0) — Full pi0 configuration including `use_relative_actions`.
- [`examples/so100_to_so100_EE/`](https://github.com/huggingface/lerobot/tree/main/examples/so100_to_so100_EE) — EE-space evaluation example this builds on.
@@ -1,72 +1,23 @@
-# Unitree G1
+# Unitree G1 Robot Setup and Control

-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/unitree_thumbnail.jpg"
-  alt="Unitree G1 locomanipulation demo"
-  style={{ width: "100%" }}
-/>
+This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.

-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 the Unitree G1
+
+We offer support for both 29 and 23 DOF G1. We introduce:
+
+- **`unitree g1` robot class, handling low level communication with the humanoid**
+- **ZMQ socket bridge** for remote communication over WiFi, allowing one to deploy policies remotely instead of over ethernet or directly on the Orin
+- **GR00T locomotion policy** for bipedal walking and balance
+- **MuJoCo simulation mode** for testing policies without the physical robot

 ---

-## Part 1: Getting Started
+## Part 1: Connect to Robot over Ethernet

-### Install the Unitree SDK
+### Step 1: Configure Your Computer's Ethernet Interface

-Follow the [unitree_sdk2_python installation guide](https://github.com/unitreerobotics/unitree_sdk2_python#installation). Tested with `unitree_sdk2py==1.0.1` and `cyclonedds==0.10.2`:
-
-```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 .
-cd ..
-```
-
-### Install LeRobot
-
-```bash
-conda install ffmpeg -c conda-forge
-conda install -c conda-forge "pinocchio>=3.0.0,<4.0.0"
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e '.[unitree_g1]'
-```
-
-<Tip>
-  For now, pinocchio must be installed from conda-forge (not pip) to include the
-  CasADi bindings needed for arm IK.
-</Tip>
-
-### Test the Installation (Simulation)
-
-The simulation environment has its own dependencies. Check the Simulation environment dependencies: [Unitree G1 Mujoco EnvHub](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main).
-
-```bash
-pip install mujoco loguru msgpack msgpack-numpy
-```
-
-```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, "warmup_s": 5}}' \
-  --display_data=true \
-  --robot.controller=GrootLocomotionController
-```
-
-This will launch a [MuJoCo sim instance](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main) for the G1. You can connect a gamepad to your machine before launching in order to control the robot's locomotion in sim. We support both [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) and [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl) via `--robot.controller`.
-
- Press `9` to release the robot
- Press `7` / `8` to increase / decrease waist height
-
-### Connect to the Physical 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`.
+Set a static IP on the same subnet as the robot:

 ```bash
 # Replace 'enp131s0' with your ethernet interface name (check with `ip a`)
@@ -75,228 +26,183 @@ sudo ip addr add 192.168.123.200/24 dev enp131s0
 sudo ip link set enp131s0 up
 ```

-### SSH into the Robot
+**Note**: The robot's Ethernet IP is fixed at `192.168.123.164`. Your computer must use `192.168.123.x` where x ≠ 164.
+
+### Step 2: SSH into the Robot

 ```bash
 ssh unitree@192.168.123.164
 # Password: 123
 ```

-### Share Internet via Ethernet
+You should now be connected to the robot's onboard computer.

-The G1 needs internet access to clone repos and install packages. Share your laptop's connection over Ethernet:
+---

-**On your laptop:**
-
-```bash
-sudo sysctl -w net.ipv4.ip_forward=1
-
-# Replace wlp132s0f0 with your WiFi interface name
-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:**
-
-```bash
-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
-ping -c 3 8.8.8.8
-```
-
-### Install the Unitree SDK on the G1
-
-Follow the [unitree_sdk2_python installation guide](https://github.com/unitreerobotics/unitree_sdk2_python#installation):
-
-```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
-python -m pip install -e .
-cd ..
-```
-
-### Install LeRobot on the G1
-
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-conda install -c conda-forge "pinocchio>=3.0.0,<4.0.0"
-python -m pip install -e '.[unitree_g1]'
-```
-
-<Tip>
-  For now, pinocchio must be installed from conda-forge (not pip) to include the
-  CasADi bindings needed for arm IK.
-</Tip>
-
-### (Optional) Enable WiFi on the Robot
-
-For wireless SSH access, you can enable WiFi on the G1 (it's blocked by default):
+## Part 2: Enable WiFi on the Robot
+
+Once connected via Ethernet, follow these steps to enable WiFi:
+
+### Step 1: Enable WiFi Hardware

 ```bash
+# Unblock WiFi radio
+sudo rfkill unblock wifi
 sudo rfkill unblock all
+
+# Bring up WiFi interface
 sudo ip link set wlan0 up
+
+# Enable NetworkManager control
 sudo nmcli radio wifi on
 sudo nmcli device set wlan0 managed yes
 sudo systemctl restart NetworkManager
 ```

-**Connect to a WiFi network:**
+### Step 2: Enable Internet Forwarding
+
+**On your laptop:**

 ```bash
+# Enable IP forwarding
+sudo sysctl -w net.ipv4.ip_forward=1
+
+# 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 robot:**
+
+```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
+
+# Test connection
+ping -c 3 8.8.8.8
+```
+
+### 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 then SSH over WiFi instead of Ethernet:
+### 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
 ```

---
-
-## Part 2: Teleoperation & Locomotion
-
-### Run the Robot Server
-
-On the robot (from `~/lerobot`):
-
-```bash
-cd ~/lerobot
-python src/lerobot/robots/unitree_g1/run_g1_server.py --camera
-```
-
-### Run the Locomotion Policy
-
-You can run the teleoperation client from your laptop over Ethernet, over WiFi (experimental), or directly on the robot itself. Mind potential latency introduced by your network.
-
-**From your laptop:**
-
-```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 [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl) and [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) via `--robot.controller`.
+Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address (e.g., `172.18.129.215`).

 ---

-## Part 3: Loco-Manipulation with the Homunculus Exoskeleton
+## Part 3: Robot Server Setup

-We provide a loco-manipulation solution via the Homunculus Exoskeleton — an open-source 7 DoF exoskeleton for whole-body control. Check it out [here](https://github.com/nepyope/hmc_exo).
+### Step 1: Install LeRobot on the Orin

-### Calibrate
+SSH into the robot and install LeRobot:

 ```bash
-lerobot-calibrate \
-  --teleop.type=unitree_g1 \
-  --teleop.left_arm_config.port=/dev/ttyACM1 \
-  --teleop.right_arm_config.port=/dev/ttyACM0 \
-  --teleop.id=exo
+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 .
 ```

-During calibration move each joint through its entire range. After fitting, move the joint in a neutral position and press `n` to advance.
+**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.

-### Record a Dataset
+### Step 2: Run the Robot Server
+
+On the robot:

 ```bash
-lerobot-record \
-  --robot.type=unitree_g1 \
-  --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}}' \
-  --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.encoder_threads=2
+python src/lerobot/robots/unitree_g1/run_g1_server.py
 ```

-> **Note:** Omit `--teleop.left_arm_config.port` and `--teleop.right_arm_config.port` if you're only using the joystick.
-
-Example dataset: [nepyope/unitree_box_move_blue_full](https://huggingface.co/datasets/nepyope/unitree_box_move_blue_full)
+**Important**: Keep this terminal running. The server must be active for remote control.

 ---

-## Part 4: Training & Inference
+## Part 4: Running GR00T Locomotion

-### Train
+With the robot server running, you can now control the robot from your laptop.
+
+### Step 1: Install LeRobot on your machine

 ```bash
-python src/lerobot/scripts/lerobot_train.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
+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 .
 ```

-### Inference with RTC
+### Step 2: Update Robot IP in Config

-Once trained, we recommend deploying policies using inference-time RTC:
+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.
+```
+
+**Note**: When running directly on the G1 (not remotely), set `robot_ip: str = "127.0.0.1"` instead.
+
+### Step 3: Run the Locomotion Policy

 ```bash
-python examples/rtc/eval_with_real_robot.py \
-  --policy.path=your-username/your-repo-id \
-  --policy.device=cuda \
-  --robot.type=unitree_g1 \
-  --robot.is_simulation=false \
-  --robot.controller=HolosomaLocomotionController \
-  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-  --task="task_description" \
-  --duration=1000 \
-  --fps=30 \
-  --rtc.enabled=true
+# Run GR00T locomotion controller
+python examples/unitree_g1/gr00t_locomotion.py --repo-id "nepyope/GR00T-WholeBodyControl_g1"
 ```

+### Step 4: Control with Remote
+
+- **Left stick**: Forward/backward and left/right movement
+- **Right stick**: Rotation
+- **R1 button**: Raise waist height
+- **R2 button**: Lower waist height
+
+Press `Ctrl+C` to stop the policy.
+
 ---

+## Extra: Running in Simulation Mode (MuJoCo)
+
+You can now test and develop policies without a physical robot using MuJoCo. to do so set `is_simulation=True` in config.
+
 ## Additional Resources

 - [Unitree SDK Documentation](https://github.com/unitreerobotics/unitree_sdk2_python)
- [GR00T-WholeBodyControl](https://github.com/NVlabs/GR00T-WholeBodyControl)
- [Holosoma](https://github.com/amazon-far/holosoma)
+- [GR00T Policy Repository](https://huggingface.co/nepyope/GR00T-WholeBodyControl_g1)
 - [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_
@@ -12,7 +12,6 @@ LeRobot provides several utilities for manipulating datasets:
 4. **Add Features** - Add new features to a dataset
 5. **Remove Features** - Remove features from a dataset
 6. **Convert to Video** - Convert image-based datasets to video format for efficient storage
-7. **Show the Info of Datasets** - Show the summary of datasets information such as number of episode etc.

 The core implementation is in `lerobot.datasets.dataset_tools`.
 An example script detailing how to use the tools API is available in `examples/dataset/use_dataset_tools.py`.
@@ -96,26 +95,26 @@ Convert an image-based dataset to video format, creating a new LeRobotDataset wh
 # Local-only: Save to a custom output directory (no hub push)
 lerobot-edit-dataset \
    --repo_id lerobot/pusht_image \
-    --operation.type convert_image_to_video \
+    --operation.type convert_to_video \
    --operation.output_dir /path/to/output/pusht_video

 # Save with new repo_id (local storage)
 lerobot-edit-dataset \
    --repo_id lerobot/pusht_image \
    --new_repo_id lerobot/pusht_video \
-    --operation.type convert_image_to_video
+    --operation.type convert_to_video

 # Convert and push to Hugging Face Hub
 lerobot-edit-dataset \
    --repo_id lerobot/pusht_image \
    --new_repo_id lerobot/pusht_video \
-    --operation.type convert_image_to_video \
+    --operation.type convert_to_video \
    --push_to_hub true

 # Convert with custom video codec and quality settings
 lerobot-edit-dataset \
    --repo_id lerobot/pusht_image \
-    --operation.type convert_image_to_video \
+    --operation.type convert_to_video \
    --operation.output_dir outputs/pusht_video \
    --operation.vcodec libsvtav1 \
    --operation.pix_fmt yuv420p \
@@ -125,23 +124,16 @@ lerobot-edit-dataset \
 # Convert only specific episodes
 lerobot-edit-dataset \
    --repo_id lerobot/pusht_image \
-    --operation.type convert_image_to_video \
+    --operation.type convert_to_video \
    --operation.output_dir outputs/pusht_video \
    --operation.episode_indices "[0, 1, 2, 5, 10]"

 # Convert with multiple workers for parallel processing
 lerobot-edit-dataset \
    --repo_id lerobot/pusht_image \
-    --operation.type convert_image_to_video \
+    --operation.type convert_to_video \
    --operation.output_dir outputs/pusht_video \
    --operation.num_workers 8
-
-# For memory-constrained systems, users can now specify limits:
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --operation.type convert_to_video \
-    --operation.max_episodes_per_batch 50 \
-    --operation.max_frames_per_batch 10000
 ```

 **Parameters:**
@@ -157,30 +149,6 @@ lerobot-edit-dataset \

 **Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). All episodes, stats, and tasks are preserved.

-### Show the information of datasets
-
-Show the information of datasets such as number of episode, number of frame, File size and so on.
-No change will be made to the dataset
-
-```bash
-
-# Show dataset information without feature details
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --operation.type info \
-
-# Show dataset information with feature details
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --operation.type info \
-    --operation.show_features true
-
-```
-
-**Parameters:**
-
- `parameters`: The flag to control show or no show dataset information with feature details.(default=false)
-
 ### Push to Hub

 Add the `--push_to_hub true` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:
@@ -8,12 +8,6 @@ X Square Robot’s WALL-OSS is now integrated into Hugging Face’s LeRobot ecos

 The WALL-OSS team is building the embodied foundation model to capture and compress the world's most valuable data: the continuous, high-fidelity stream of physical interaction. By creating a direct feedback loop between the model's decisions and the body's lived experience, the emergence of a truly generalizable intelligence is enabled—one that understands not just how the world works, but how to act effectively within it.

-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/walloss-lerobot-paper.png"
-  alt="An overview of WALL-OSS"
-  width="85%"
-/>
-
 Technically, WALL-OSS introduces a tightly coupled multimodal architecture (tightly-coupled MoE structure) that integrates both discrete and continuous action modeling strategies. Through a two-stage training pipeline (Inspiration → Integration), the model gradually unifies semantic reasoning and high-frequency action generation. Its core innovations include:

 - **Embodied perception–enhanced multimodal pretraining**: Large-scale training on unified vision–language–action data to strengthen spatial, causal, and manipulation understanding.
@@ -45,7 +39,7 @@ policy.type=wall_x
 For training WallX, you can use the standard LeRobot training script with the appropriate configuration:

 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
    --dataset.repo_id=your_dataset \
    --policy.type=wall_x \
    --output_dir=./outputs/wallx_training \
@@ -154,7 +154,7 @@ lerobot-train \

 ```bash
 lerobot-train \
-  --dataset.repo_id=<USER>/bimanual-so100-handover-cube \
+  --dataset.repo_id=pepijn223/bimanual-so100-handover-cube \
  --output_dir=./outputs/xvla_bimanual \
  --job_name=xvla_so101_training \
  --policy.path="lerobot/xvla-base" \
@@ -22,7 +22,7 @@ lerobot-replay \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.id=black \
-    --dataset.repo_id=<USER>/record-test \
+    --dataset.repo_id=aliberts/record-test \
    --dataset.episode=2
 ```
 """
@@ -41,7 +41,8 @@ from lerobot.robots import (  # noqa: F401
    RobotConfig,
    koch_follower,
    make_robot_from_config,
-    so_follower,
+    so100_follower,
+    so101_follower,
 )
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
@@ -57,7 +58,7 @@ 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
@@ -78,28 +79,27 @@ def replay(cfg: ReplayConfig):

    robot = make_robot_from_config(cfg.robot)
    dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode])
-    actions = dataset.select_columns(ACTION)
+    actions = dataset.hf_dataset.select_columns(ACTION)
    robot.connect()

-    try:
-        log_say("Replaying episode", cfg.play_sounds, blocking=True)
-        for idx in range(dataset.num_frames):
-            start_episode_t = time.perf_counter()
+    log_say("Replaying episode", cfg.play_sounds, blocking=True)
+    for idx in range(dataset.num_frames):
+        start_episode_t = time.perf_counter()

-            action_array = actions[idx][ACTION]
-            action = {}
-            for i, name in enumerate(dataset.features[ACTION]["names"]):
-                key = f"{name.removeprefix('main_')}.pos"
-                action[key] = action_array[i].item()
+        action_array = actions[idx][ACTION]
+        action = {}
+        for i, name in enumerate(dataset.features[ACTION]["names"]):
+            key = f"{name.removeprefix('main_')}.pos"
+            action[key] = action_array[i].item()

-            action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
-            action["elbow_flex.pos"] -= 90
-            robot.send_action(action)
+        action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
+        action["elbow_flex.pos"] -= 90
+        robot.send_action(action)

-            dt_s = time.perf_counter() - start_episode_t
-            precise_sleep(max(1 / dataset.fps - dt_s, 0.0))
-    finally:
-        robot.disconnect()
+        dt_s = time.perf_counter() - start_episode_t
+        precise_sleep(1 / dataset.fps - dt_s)
+
+    robot.disconnect()


 if __name__ == "__main__":
@@ -32,8 +32,7 @@ import torch
 from huggingface_hub import HfApi

 import lerobot
-from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata


 def main():
@@ -88,8 +87,9 @@ def main():
    # The previous metadata class is contained in the 'meta' attribute of the dataset:
    print(dataset.meta)

-    # You can inspect the dataset using its repr:
-    print(dataset)
+    # LeRobotDataset actually wraps an underlying Hugging Face dataset
+    # (see https://huggingface.co/docs/datasets for more information).
+    print(dataset.hf_dataset)

    # LeRobot datasets also subclasses PyTorch datasets so you can do everything you know and love from working
    # with the latter, like iterating through the dataset.
@@ -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()
@@ -14,8 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.datasets.feature_utils import hw_to_dataset_features
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
 from lerobot.policies.act.modeling_act import ACTPolicy
 from lerobot.policies.factory import make_pre_post_processors
 from lerobot.processor import make_default_processors
@@ -78,24 +78,40 @@ def main():
    listener, events = init_keyboard_listener()
    init_rerun(session_name="lekiwi_evaluate")

-    try:
-        if not robot.is_connected:
-            raise ValueError("Robot is not connected!")
+    if not robot.is_connected:
+        raise ValueError("Robot is not connected!")

-        print("Starting evaluate loop...")
-        recorded_episodes = 0
-        while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-            log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
+    print("Starting evaluate loop...")
+    recorded_episodes = 0
+    while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+        log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")

-            # Main record loop
+        # Main record loop
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            policy=policy,
+            preprocessor=preprocessor,  # Pass the pre and post policy processors
+            postprocessor=postprocessor,
+            dataset=dataset,
+            control_time_s=EPISODE_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
+        )
+
+        # Reset the environment if not stopping or re-recording
+        if not events["stop_recording"] and (
+            (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+        ):
+            log_say("Reset the environment")
            record_loop(
                robot=robot,
                events=events,
                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
                control_time_s=EPISODE_TIME_SEC,
                single_task=TASK_DESCRIPTION,
                display_data=True,
@@ -104,42 +120,24 @@ def main():
                robot_observation_processor=robot_observation_processor,
            )

-            # Reset the environment if not stopping or re-recording
-            if not events["stop_recording"] and (
-                (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-            ):
-                log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
-                )
+        if events["rerecord_episode"]:
+            log_say("Re-record episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue

-            if events["rerecord_episode"]:
-                log_say("Re-record episode")
-                events["rerecord_episode"] = False
-                events["exit_early"] = False
-                dataset.clear_episode_buffer()
-                continue
+        # Save episode
+        dataset.save_episode()
+        recorded_episodes += 1

-            # Save episode
-            dataset.save_episode()
-            recorded_episodes += 1
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    listener.stop()

-    finally:
-        # Clean up
-        log_say("Stop recording")
-        robot.disconnect()
-        listener.stop()
-
-        dataset.finalize()
-        dataset.push_to_hub()
+    dataset.finalize()
+    dataset.push_to_hub()


 if __name__ == "__main__":
@@ -14,14 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.datasets.feature_utils import hw_to_dataset_features
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
 from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
 from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
@@ -74,23 +74,40 @@ def main():
    listener, events = init_keyboard_listener()
    init_rerun(session_name="lekiwi_record")

-    try:
-        if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-            raise ValueError("Robot or teleop is not connected!")
+    if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+        raise ValueError("Robot or teleop is not connected!")

-        print("Starting record loop...")
-        recorded_episodes = 0
-        while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-            log_say(f"Recording episode {recorded_episodes}")
+    print("Starting record loop...")
+    recorded_episodes = 0
+    while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+        log_say(f"Recording episode {recorded_episodes}")

-            # Main record loop
+        # Main record loop
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            dataset=dataset,
+            teleop=[leader_arm, keyboard],
+            control_time_s=EPISODE_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
+        )
+
+        # Reset the environment if not stopping or re-recording
+        if not events["stop_recording"] and (
+            (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+        ):
+            log_say("Reset the environment")
            record_loop(
                robot=robot,
                events=events,
                fps=FPS,
-                dataset=dataset,
                teleop=[leader_arm, keyboard],
-                control_time_s=EPISODE_TIME_SEC,
+                control_time_s=RESET_TIME_SEC,
                single_task=TASK_DESCRIPTION,
                display_data=True,
                teleop_action_processor=teleop_action_processor,
@@ -98,44 +115,26 @@ def main():
                robot_observation_processor=robot_observation_processor,
            )

-            # Reset the environment if not stopping or re-recording
-            if not events["stop_recording"] and (
-                (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-            ):
-                log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    teleop=[leader_arm, keyboard],
-                    control_time_s=RESET_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
-                )
+        if events["rerecord_episode"]:
+            log_say("Re-record episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue

-            if events["rerecord_episode"]:
-                log_say("Re-record episode")
-                events["rerecord_episode"] = False
-                events["exit_early"] = False
-                dataset.clear_episode_buffer()
-                continue
+        # Save episode
+        dataset.save_episode()
+        recorded_episodes += 1

-            # Save episode
-            dataset.save_episode()
-            recorded_episodes += 1
-    finally:
-        # Clean up
-        log_say("Stop recording")
-        robot.disconnect()
-        leader_arm.disconnect()
-        keyboard.disconnect()
-        listener.stop()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    leader_arm.disconnect()
+    keyboard.disconnect()
+    listener.stop()

-        dataset.finalize()
-        dataset.push_to_hub()
+    dataset.finalize()
+    dataset.push_to_hub()


 if __name__ == "__main__":
@@ -35,32 +35,32 @@ def main():

    # Fetch the dataset to replay
    dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[EPISODE_IDX])
-    actions = dataset.select_columns(ACTION)
+    # Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
+    episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
+    actions = episode_frames.select_columns(ACTION)

    # Connect to the robot
    robot.connect()

-    try:
-        if not robot.is_connected:
-            raise ValueError("Robot is not connected!")
+    if not robot.is_connected:
+        raise ValueError("Robot is not connected!")

-        print("Starting replay loop...")
-        log_say(f"Replaying episode {EPISODE_IDX}")
-        for idx in range(dataset.num_frames):
-            t0 = time.perf_counter()
+    print("Starting replay loop...")
+    log_say(f"Replaying episode {EPISODE_IDX}")
+    for idx in range(len(episode_frames)):
+        t0 = time.perf_counter()

-            # Get recorded action from dataset
-            action = {
-                name: float(actions[idx][ACTION][i])
-                for i, name in enumerate(dataset.features[ACTION]["names"])
-            }
+        # Get recorded action from dataset
+        action = {
+            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+        }

-            # Send action to robot
-            _ = robot.send_action(action)
+        # Send action to robot
+        _ = robot.send_action(action)

-            precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
-    finally:
-        robot.disconnect()
+        precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+
+    robot.disconnect()


 if __name__ == "__main__":
@@ -18,7 +18,7 @@ import time

 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
 from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data

@@ -0,0 +1,257 @@
+#!/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.
+
+"""
+Convert a joint-space OpenArms dataset to end-effector space.
+
+For each frame, converts joint positions to EE poses (x, y, z, wx, wy, wz) using FK.
+Grippers are kept as-is. Applies to both observation.state and action.
+
+Example usage:
+    python examples/openarms/convert_joints_to_ee.py \
+        --input-dataset lerobot-data-collection/rac_blackf0 \
+        --output-repo-id my_user/rac_blackf0_ee \
+        --output-dir ./outputs/rac_blackf0_ee
+"""
+
+import argparse
+import shutil
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+
+from lerobot.datasets.compute_stats import get_feature_stats
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import write_info, write_stats
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.utils.rotation import Rotation
+
+DEFAULT_URDF = "src/lerobot/robots/openarms/urdf/openarm_bimanual_pybullet.urdf"
+DEFAULT_LEFT_EE_FRAME = "openarm_left_hand_tcp"
+DEFAULT_RIGHT_EE_FRAME = "openarm_right_hand_tcp"
+
+LEFT_URDF_JOINTS = [f"openarm_left_joint{i}" for i in range(1, 8)]
+RIGHT_URDF_JOINTS = [f"openarm_right_joint{i}" for i in range(1, 8)]
+
+JOINT_NAMES = [f"joint_{i}" for i in range(1, 8)]
+EE_COMPONENTS = ["x", "y", "z", "wx", "wy", "wz"]
+
+
+def compute_fk_for_arm(kinematics: RobotKinematics, joint_values: np.ndarray) -> dict[str, float]:
+    """Compute FK for one arm, returns EE pose as dict."""
+    t = kinematics.forward_kinematics(joint_values)
+    pos = t[:3, 3]
+    rotvec = Rotation.from_matrix(t[:3, :3]).as_rotvec()
+    return {
+        "x": float(pos[0]),
+        "y": float(pos[1]),
+        "z": float(pos[2]),
+        "wx": float(rotvec[0]),
+        "wy": float(rotvec[1]),
+        "wz": float(rotvec[2]),
+    }
+
+
+def convert_joints_to_ee(
+    values: np.ndarray,
+    names: list[str],
+    left_kin: RobotKinematics,
+    right_kin: RobotKinematics,
+) -> tuple[np.ndarray, list[str]]:
+    """
+    Convert joint values to EE values.
+    
+    Args:
+        values: Array of shape (N,) with joint values for one frame
+        names: List of feature names corresponding to values
+        left_kin: Left arm kinematics solver
+        right_kin: Right arm kinematics solver
+    
+    Returns:
+        (new_values, new_names) with joints replaced by EE poses
+    """
+    name_to_idx = {n: i for i, n in enumerate(names)}
+    
+    new_values = []
+    new_names = []
+    
+    for prefix, kinematics in [("right", right_kin), ("left", left_kin)]:
+        joint_vals = []
+        for jname in JOINT_NAMES:
+            key = f"{prefix}_{jname}.pos"
+            if key in name_to_idx:
+                joint_vals.append(values[name_to_idx[key]])
+        
+        if len(joint_vals) == 7:
+            ee_pose = compute_fk_for_arm(kinematics, np.array(joint_vals, dtype=float))
+            for comp in EE_COMPONENTS:
+                new_names.append(f"{prefix}_ee.{comp}")
+                new_values.append(ee_pose[comp])
+        
+        gripper_key = f"{prefix}_gripper.pos"
+        if gripper_key in name_to_idx:
+            new_names.append(f"{prefix}_ee.gripper_pos")
+            new_values.append(values[name_to_idx[gripper_key]])
+    
+    return np.array(new_values, dtype=np.float32), new_names
+
+
+def transform_feature_info(old_info: dict, new_names: list[str]) -> dict:
+    """Create new feature info with EE names instead of joint names."""
+    return {
+        "dtype": old_info.get("dtype", "float32"),
+        "shape": (len(new_names),),
+        "names": new_names,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert joint-space dataset to EE-space")
+    parser.add_argument("--input-dataset", type=str, required=True, help="Input dataset repo ID")
+    parser.add_argument("--output-repo-id", type=str, required=True, help="Output dataset repo ID")
+    parser.add_argument("--output-dir", type=str, required=True, help="Output directory")
+    parser.add_argument("--urdf", type=str, default=DEFAULT_URDF, help="Path to URDF file")
+    parser.add_argument("--left-ee-frame", type=str, default=DEFAULT_LEFT_EE_FRAME)
+    parser.add_argument("--right-ee-frame", type=str, default=DEFAULT_RIGHT_EE_FRAME)
+    parser.add_argument("--push-to-hub", action="store_true", help="Push converted dataset to HF Hub")
+    args = parser.parse_args()
+
+    output_dir = Path(args.output_dir)
+    if output_dir.exists():
+        shutil.rmtree(output_dir)
+
+    urdf_path = args.urdf
+    if not Path(urdf_path).is_absolute():
+        urdf_path = str(Path(__file__).parent.parent.parent / urdf_path)
+
+    print(f"Loading dataset: {args.input_dataset}")
+    dataset = LeRobotDataset(args.input_dataset)
+    
+    print(f"Initializing kinematics from {urdf_path}")
+    left_kin = RobotKinematics(urdf_path, args.left_ee_frame, LEFT_URDF_JOINTS)
+    right_kin = RobotKinematics(urdf_path, args.right_ee_frame, RIGHT_URDF_JOINTS)
+
+    action_info = dataset.meta.features.get("action", {})
+    state_info = dataset.meta.features.get("observation.state", {})
+    action_names = action_info.get("names", [])
+    state_names = state_info.get("names", [])
+
+    print(f"Original action names ({len(action_names)}): {action_names[:8]}...")
+    print(f"Original state names ({len(state_names)}): {state_names[:8]}...")
+
+    sample_action = np.zeros(len(action_names), dtype=np.float32)
+    _, new_action_names = convert_joints_to_ee(sample_action, action_names, left_kin, right_kin)
+    sample_state = np.zeros(len(state_names), dtype=np.float32)
+    _, new_state_names = convert_joints_to_ee(sample_state, state_names, left_kin, right_kin)
+
+    print(f"New action names ({len(new_action_names)}): {new_action_names}")
+    print(f"New state names ({len(new_state_names)}): {new_state_names}")
+
+    new_features = dataset.meta.features.copy()
+    new_features["action"] = transform_feature_info(action_info, new_action_names)
+    new_features["observation.state"] = transform_feature_info(state_info, new_state_names)
+
+    new_meta = LeRobotDatasetMetadata.create(
+        repo_id=args.output_repo_id,
+        fps=dataset.meta.fps,
+        features=new_features,
+        robot_type=dataset.meta.robot_type,
+        root=output_dir,
+        use_videos=len(dataset.meta.video_keys) > 0,
+    )
+
+    data_dir = dataset.root / "data"
+    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+    print(f"Processing {len(parquet_files)} parquet files...")
+
+    all_actions = []
+    all_states = []
+
+    for src_path in tqdm(parquet_files, desc="Converting"):
+        df = pd.read_parquet(src_path).reset_index(drop=True)
+        
+        new_actions = []
+        new_states = []
+        
+        for idx in range(len(df)):
+            action_vals = np.array(df.iloc[idx]["action"], dtype=np.float32)
+            state_vals = np.array(df.iloc[idx]["observation.state"], dtype=np.float32)
+            
+            new_action, _ = convert_joints_to_ee(action_vals, action_names, left_kin, right_kin)
+            new_state, _ = convert_joints_to_ee(state_vals, state_names, left_kin, right_kin)
+            
+            new_actions.append(new_action.tolist())
+            new_states.append(new_state.tolist())
+            all_actions.append(new_action)
+            all_states.append(new_state)
+        
+        df["action"] = new_actions
+        df["observation.state"] = new_states
+        
+        relative_path = src_path.relative_to(dataset.root)
+        out_path = output_dir / relative_path
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_parquet(out_path)
+
+    print("Computing statistics...")
+    all_actions_arr = np.stack(all_actions)
+    all_states_arr = np.stack(all_states)
+    
+    stats = {}
+    stats["action"] = get_feature_stats(all_actions_arr, axis=0, keepdims=True)
+    stats["observation.state"] = get_feature_stats(all_states_arr, axis=0, keepdims=True)
+    write_stats(stats, output_dir)
+
+    print("Updating metadata...")
+    new_meta.info["total_episodes"] = dataset.meta.total_episodes
+    new_meta.info["total_frames"] = dataset.meta.total_frames
+    new_meta.info["total_tasks"] = dataset.meta.total_tasks
+    write_info(new_meta.info, output_dir)
+
+    print("Copying episode metadata...")
+    src_episodes_dir = dataset.root / "meta" / "episodes"
+    dst_episodes_dir = output_dir / "meta" / "episodes"
+    if src_episodes_dir.exists():
+        shutil.copytree(src_episodes_dir, dst_episodes_dir, dirs_exist_ok=True)
+
+    print("Copying tasks metadata...")
+    src_tasks = dataset.root / "meta" / "tasks.parquet"
+    dst_tasks = output_dir / "meta" / "tasks.parquet"
+    if src_tasks.exists():
+        shutil.copy2(src_tasks, dst_tasks)
+
+    if dataset.meta.video_keys:
+        print("Copying videos...")
+        src_videos = dataset.root / "videos"
+        dst_videos = output_dir / "videos"
+        if src_videos.exists():
+            shutil.copytree(src_videos, dst_videos, dirs_exist_ok=True)
+
+    print(f"\nDone! Dataset saved to: {output_dir}")
+    print(f"Repo ID: {args.output_repo_id}")
+
+    if args.push_to_hub:
+        print("\nPushing to Hub...")
+        output_dataset = LeRobotDataset(args.output_repo_id, root=output_dir)
+        output_dataset.push_to_hub()
+        print(f"Pushed to: https://huggingface.co/datasets/{args.output_repo_id}")
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,416 @@
+#!/usr/bin/env python3
+"""
+Comprehensive debug script for OpenArms CAN FD communication.
+Tests all 4 CAN interfaces with CAN FD support.
+"""
+
+import can
+import time
+import sys
+import subprocess
+
+def check_can_interface(port):
+    """Check if CAN interface is UP and configured."""
+    try:
+        result = subprocess.run(['ip', 'link', 'show', port], 
+                              capture_output=True, text=True)
+        if result.returncode != 0:
+            return False, "Interface not found", None
+        
+        output = result.stdout
+        if 'UP' not in output:
+            return False, "Interface is DOWN", None
+        
+        # Check if CAN FD is enabled
+        is_fd = 'fd on' in output.lower() or 'canfd' in output.lower()
+        
+        return True, "Interface is UP", is_fd
+    except FileNotFoundError:
+        return None, "Cannot check (ip command not found)", None
+
+
+def test_motor_on_interface(bus, motor_id, timeout=2.0, use_fd=False):
+    """
+    Test a single motor and return all responses.
+    
+    Returns:
+        list of (arbitration_id, data) tuples for all responses received
+    """
+    # Send enable command
+    enable_msg = can.Message(
+        arbitration_id=motor_id,
+        data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC],
+        is_extended_id=False,
+        is_fd=use_fd
+    )
+    
+    try:
+        bus.send(enable_msg)
+    except Exception as e:
+        return None, f"Send error: {e}"
+    
+    # Listen for responses
+    responses = []
+    start_time = time.time()
+    
+    while time.time() - start_time < timeout:
+        msg = bus.recv(timeout=0.1)
+        if msg:
+            responses.append((msg.arbitration_id, msg.data, msg.is_fd if hasattr(msg, 'is_fd') else False))
+    
+    # Send disable command
+    disable_msg = can.Message(
+        arbitration_id=motor_id,
+        data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD],
+        is_extended_id=False,
+        is_fd=use_fd
+    )
+    try:
+        bus.send(disable_msg)
+    except:
+        pass
+    
+    return responses, None
+
+
+def test_interface(port, interface_type="socketcan", use_can_fd=True):
+    """Test all 8 motors on a single CAN interface."""
+    
+    results = {
+        'interface': port,
+        'status': None,
+        'is_fd': use_can_fd,
+        'motors': {}
+    }
+    
+    # Check interface status
+    status_ok, status_msg, interface_has_fd = check_can_interface(port)
+    
+    if interface_has_fd is not None:
+        results['interface_fd_enabled'] = interface_has_fd
+        if use_can_fd and not interface_has_fd:
+            status_msg += " (CAN FD NOT enabled on interface!)"
+        elif interface_has_fd:
+            status_msg += " (CAN FD enabled)"
+    
+    results['status'] = status_msg
+    
+    if status_ok is False:
+        return results
+    
+    # Try to connect
+    try:
+        if use_can_fd:
+            print(f"  Connecting to {port} with CAN FD (1 Mbps / 5 Mbps)...")
+            bus = can.interface.Bus(
+                channel=port,
+                interface=interface_type,
+                bitrate=1000000,
+                data_bitrate=5000000,
+                fd=True
+            )
+        else:
+            print(f"  Connecting to {port} with CAN 2.0 (1 Mbps)...")
+            bus = can.interface.Bus(
+                channel=port,
+                interface=interface_type,
+                bitrate=1000000
+            )
+    except Exception as e:
+        results['status'] = f"Connection failed: {e}"
+        return results
+    
+    try:
+        # Clear any pending messages
+        while bus.recv(timeout=0.01):
+            pass
+        
+        # Test each motor (0x01 to 0x08)
+        for motor_id in range(0x01, 0x09):
+            responses, error = test_motor_on_interface(bus, motor_id, timeout=1.0, use_fd=use_can_fd)
+            
+            if error:
+                results['motors'][motor_id] = {'error': error}
+            elif responses:
+                results['motors'][motor_id] = {
+                    'found': True,
+                    'responses': responses
+                }
+            else:
+                results['motors'][motor_id] = {
+                    'found': False,
+                    'responses': []
+                }
+            
+            time.sleep(0.05)  # Small delay between motors
+        
+    finally:
+        bus.shutdown()
+    
+    return results
+
+
+def print_results(all_results):
+    """Print formatted results for all interfaces."""
+    
+    print("SUMMARY - Motors Found on Each Interface")
+    
+    motor_names = {
+        0x01: "joint_1 (Shoulder pan)",
+        0x02: "joint_2 (Shoulder lift)",
+        0x03: "joint_3 (Shoulder rotation)",
+        0x04: "joint_4 (Elbow flex)",
+        0x05: "joint_5 (Wrist roll)",
+        0x06: "joint_6 (Wrist pitch)",
+        0x07: "joint_7 (Wrist rotation)",
+        0x08: "gripper",
+    }
+    
+    total_found = 0
+    
+    for result in all_results:
+        interface = result['interface']
+        status = result['status']
+        
+        print(f"{interface}: {status}")
+        if result.get('is_fd'):
+            print(f"  Mode: CAN FD")
+        else:
+            print(f"  Mode: CAN 2.0")
+        
+        if 'Connection failed' in status or 'DOWN' in status:
+            print(f"  ⚠ Cannot test {interface}")
+            continue
+        
+        motors_found = 0
+        
+        for motor_id in range(0x01, 0x09):
+            motor_data = result['motors'].get(motor_id, {})
+            motor_name = motor_names.get(motor_id, "Unknown")
+            
+            if motor_data.get('error'):
+                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✗ {motor_data['error']}")
+            elif motor_data.get('found'):
+                motors_found += 1
+                total_found += 1
+                responses = motor_data['responses']
+                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✓ FOUND")
+                
+                for resp_id, data, is_fd in responses:
+                    data_hex = data.hex()
+                    fd_flag = " [FD]" if is_fd else " [2.0]"
+                    print(f"    → Response from 0x{resp_id:02X}{fd_flag}: {data_hex}")
+            else:
+                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✗ No response")
+        
+        print(f"\n  Summary: {motors_found}/8 motors found on {interface}")
+    
+    # Overall summary
+    print("OVERALL SUMMARY")
+    print(f"Total motors found across all interfaces: {total_found}")
+    
+    # Analyze configuration
+    print("DIAGNOSIS")
+    
+    for result in all_results:
+        interface = result['interface']
+        motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
+        
+        if motors_found == 0:
+            print(f"\n⚠ {interface}: NO MOTORS FOUND")
+            print("  Possible issues:")
+            print("    1. CAN FD mode mismatch (interface vs motor configuration)")
+            print("    2. Missing 120Ω termination resistors at BOTH cable ends")
+            print("    3. Motor timeout parameter set incorrectly (should NOT be 0)")
+            print("    4. CANH/CANL wiring issue")
+            print("    5. Cable too long (>40m for CAN FD at 5Mbps)")
+            
+            # Check FD mismatch
+            if result.get('is_fd') and not result.get('interface_fd_enabled'):
+                print("    ⚠️ CRITICAL: Trying CAN FD but interface NOT configured for FD!")
+                print(f"       Fix: sudo ip link set {interface} type can bitrate 1000000 dbitrate 5000000 fd on")
+                
+        elif motors_found < 8:
+            print(f"\n⚠ {interface}: Only {motors_found}/8 motors responding")
+            print("  Check power and connections for missing motors")
+        else:
+            print(f"\n✓ {interface}: All 8 motors responding correctly!")
+    
+    # Check for unexpected response IDs
+    print("RESPONSE ID ANALYSIS")
+    
+    for result in all_results:
+        interface = result['interface']
+        unexpected = []
+        
+        for motor_id, motor_data in result['motors'].items():
+            if motor_data.get('found'):
+                expected_id = motor_id + 0x10
+                actual_ids = [resp[0] for resp in motor_data['responses']]
+                
+                if expected_id not in actual_ids:
+                    unexpected.append((motor_id, actual_ids))
+        
+        if unexpected:
+            print(f"\n⚠ {interface}: Unexpected response IDs detected")
+            for motor_id, actual_ids in unexpected:
+                expected_id = motor_id + 0x10
+                print(f"  Motor 0x{motor_id:02X}: Expected 0x{expected_id:02X}, "
+                      f"got {[f'0x{id:02X}' for id in actual_ids]}")
+            print("  → Motor Master IDs need reconfiguration")
+        else:
+            motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
+            if motors_found > 0:
+                print(f"\n✓ {interface}: All responding motors use correct IDs")
+
+
+def test_communication_speed(interface, motor_id, num_iterations=100):
+    """
+    Test communication speed with a motor.
+    
+    Returns:
+        tuple: (hz, avg_latency_ms) or (None, None) if test failed
+    """
+    try:
+        # Connect to interface
+        bus = can.interface.Bus(
+            channel=interface,
+            interface="socketcan",
+            bitrate=1000000,
+            data_bitrate=5000000,
+            fd=True
+        )
+        
+        # Send refresh commands and measure round-trip time
+        latencies = []
+        successful = 0
+        
+        for _ in range(num_iterations):
+            start = time.perf_counter()
+            
+            # Send enable command (lightweight operation)
+            enable_msg = can.Message(
+                arbitration_id=motor_id,
+                data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC],
+                is_extended_id=False,
+                is_fd=True
+            )
+            bus.send(enable_msg)
+            
+            # Wait for response
+            msg = bus.recv(timeout=0.1)
+            
+            if msg:
+                latency = (time.perf_counter() - start) * 1000  # Convert to ms
+                latencies.append(latency)
+                successful += 1
+        
+        bus.shutdown()
+        
+        if successful > 0:
+            avg_latency = sum(latencies) / len(latencies)
+            hz = 1000.0 / avg_latency if avg_latency > 0 else 0
+            return hz, avg_latency
+        
+        return None, None
+        
+    except Exception as e:
+        print(f"    Speed test error: {e}")
+        return None, None
+
+
+def main():
+    """Main function to test all CAN interfaces with CAN FD."""
+    
+    print("\nThis will test all 4 CAN interfaces (can0-can3) with CAN FD")
+    print("Testing motors 0x01-0x08 on each interface")
+    print()
+    print("Make sure:")
+    print("  ✓ Motors are powered (24V)")
+    print("  ✓ CAN interfaces configured with FD mode:")
+    print("    ./examples/openarms/setup_can.sh")
+    print("  ✓ Motor 'timeout' parameter NOT set to 0 (use Damiao tools)")
+    print("  ✓ CAN wiring includes 120Ω termination at BOTH ends")
+    print()
+    
+    input("Press ENTER to start testing...")
+    
+    # Test all 4 interfaces with CAN FD
+    all_results = []
+    
+    for i in range(4):
+        interface = f"can{i}"
+        print(f"Testing {interface}...")
+        
+        result = test_interface(interface, use_can_fd=True)
+        all_results.append(result)
+        
+        # Quick status
+        if 'Connection failed' in result['status'] or 'DOWN' in result['status']:
+            print(f"  ⚠ {interface}: {result['status']}")
+        else:
+            motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
+            print(f"  {interface}: {motors_found}/8 motors found")
+        
+        time.sleep(0.2)
+    
+    # Print detailed results
+    print_results(all_results)
+    
+    print("Testing Complete!")
+    
+    all_found = sum(sum(1 for m in r['motors'].values() if m.get('found')) for r in all_results)
+    
+    if all_found == 0:
+        print("\n⚠️ CRITICAL: No motors found on any interface!")
+        print("\nTop issues to check:")
+        print("  1. Motor 'timeout' parameter (use Damiao tools to set > 0)")
+        print("  2. CAN FD not enabled (run ./examples/openarms/setup_can.sh)")
+        print("  3. Missing termination resistors")
+        print("\nTry:")
+        print("  a) Check motor parameters with Damiao Debugging Tools")
+        print("  b) Verify CAN FD is enabled: ip -d link show can0 | grep fd")
+        print("  c) Run setup script: ./examples/openarms/setup_can.sh")
+    else:
+        # Run speed test on interfaces with motors
+        print("COMMUNICATION SPEED TEST")
+        print("\nTesting maximum communication frequency...")
+        
+        for result in all_results:
+            interface = result['interface']
+            
+            # Find first responding motor
+            responding_motor = None
+            for motor_id, motor_data in result['motors'].items():
+                if motor_data.get('found'):
+                    responding_motor = motor_id
+                    break
+            
+            if responding_motor:
+                print(f"\n{interface}: Testing with motor 0x{responding_motor:02X}...")
+                hz, latency = test_communication_speed(interface, responding_motor, num_iterations=100)
+                
+                if hz:
+                    print(f"  ✓ Max frequency: {hz:.1f} Hz")
+                    print(f"  ✓ Avg latency: {latency:.2f} ms")
+                    print(f"  ✓ Commands per second: ~{int(hz)}")
+                else:
+                    print(f"  ✗ Speed test failed")
+            else:
+                print(f"\n{interface}: No motors found, skipping speed test")
+
+        print()
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\n\nTesting interrupted by user.")
+        sys.exit(1)
+    except Exception as e:
+        print(f"\nUnexpected error: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
@@ -0,0 +1,360 @@
+#!/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.
+
+"""
+OpenArms Policy Evaluation
+
+Evaluates a trained policy on the OpenArms robot by running inference and recording
+the evaluation episodes to a dataset. Supports optional leader arm for manual resets.
+
+Example usage:
+    python examples/openarms/evaluate.py
+"""
+
+import time
+from pathlib import Path
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.processor import make_default_processors
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun
+
+
+HF_MODEL_ID = "lerobot-data-collection/three-folds-pi0"  # TODO: Replace with your trained model
+HF_EVAL_DATASET_ID = "lerobot-data-collection/three-folds-pi0_eval7"  # TODO: Replace with your eval dataset name
+TASK_DESCRIPTION = "three-folds-dataset"  # TODO: Replace with your task, this should match!!
+
+NUM_EPISODES = 1
+FPS = 30
+EPISODE_TIME_SEC = 300
+RESET_TIME_SEC = 60
+
+# Robot CAN interfaces
+FOLLOWER_LEFT_PORT = "can0"
+FOLLOWER_RIGHT_PORT = "can1"
+
+# If enabled, you can manually reset the environment between evaluation episodes
+USE_LEADER_FOR_RESETS = True  # Set to False if you don't want to use leader
+LEADER_LEFT_PORT = "can2"
+LEADER_RIGHT_PORT = "can3"
+
+# Camera configuration
+CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=FPS),
+}
+
+def main():
+    """Main evaluation function."""
+    print("OpenArms Policy Evaluation")
+    print(f"\nModel: {HF_MODEL_ID}")
+    print(f"Evaluation Dataset: {HF_EVAL_DATASET_ID}")
+    print(f"Task: {TASK_DESCRIPTION}")
+    print(f"Episodes: {NUM_EPISODES}")
+    print(f"Episode Duration: {EPISODE_TIME_SEC}s")
+    print(f"Reset Duration: {RESET_TIME_SEC}s")
+    print(f"Use Leader for Resets: {USE_LEADER_FOR_RESETS}")
+    
+    follower_config = OpenArmsFollowerConfig(
+        port_left=FOLLOWER_LEFT_PORT,
+        port_right=FOLLOWER_RIGHT_PORT,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=CAMERA_CONFIG,
+    )
+    
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+    
+    if not follower.is_connected:
+        raise RuntimeError("Follower robot failed to connect!")
+
+    
+    leader = None
+    if USE_LEADER_FOR_RESETS:
+        leader_config = OpenArmsLeaderConfig(
+            port_left=LEADER_LEFT_PORT,
+            port_right=LEADER_RIGHT_PORT,
+            can_interface="socketcan",
+            id="openarms_leader",
+            manual_control=False,  # Enable torque control for gravity compensation
+        )
+        
+        leader = OpenArmsLeader(leader_config)
+        leader.connect(calibrate=False)
+        
+        if not leader.is_connected:
+            raise RuntimeError("Leader robot failed to connect!")
+        
+        # Enable gravity compensation
+        if leader.pin_robot is not None:
+            leader.bus_right.enable_torque()
+            leader.bus_left.enable_torque()
+            time.sleep(0.1)
+            print(f"Leader connected with gravity compensation ({LEADER_LEFT_PORT}, {LEADER_RIGHT_PORT})")
+        else:
+            print(f"Leader connected but gravity compensation unavailable (no URDF)")
+
+    # Build default processors for action and observation
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    
+    # Build dataset features from robot features and processors
+    # For actions, only include positions (no velocity or torque)
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+    
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+    
+    # Check if dataset already exists
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
+    if dataset_path.exists():
+        print(f"Evaluation dataset already exists at: {dataset_path}")
+        print("This will append new episodes to the existing dataset.")
+        choice = input("  Continue? (y/n): ").strip().lower()
+        if choice != 'y':
+            print("  Aborting evaluation.")
+            follower.disconnect()
+            if leader:
+                leader.disconnect()
+            return
+    
+    # Create dataset
+    dataset = LeRobotDataset.create(
+        repo_id=HF_EVAL_DATASET_ID,
+        fps=FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_processes=0,
+        image_writer_threads=12, 
+    )
+    
+    # Load policy config from pretrained model and create policy using factory
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+    policy = make_policy(policy_config, ds_meta=dataset.meta)
+    
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy.config,
+        pretrained_path=HF_MODEL_ID,
+        dataset_stats=dataset.meta.stats,
+        preprocessor_overrides={
+            "device_processor": {"device": str(policy.config.device)}
+        },
+    )
+
+    print(f"\nRunning evaluation...")
+    # Initialize keyboard listener and visualization
+    listener, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_evaluation")
+    episode_idx = 0
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Evaluating episode {episode_idx + 1} of {NUM_EPISODES}")
+            print(f"\nRunning inference for episode {episode_idx + 1}...")
+            
+            # Run inference with policy
+            record_loop(
+                robot=follower,
+                events=events,
+                fps=FPS,
+                teleop_action_processor=teleop_action_processor,
+                robot_action_processor=robot_action_processor,
+                robot_observation_processor=robot_observation_processor,
+                policy=policy,
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
+                dataset=dataset,
+                control_time_s=EPISODE_TIME_SEC,
+                single_task=TASK_DESCRIPTION,
+                display_data=True,
+            )
+            
+            # Handle re-recording
+            if events["rerecord_episode"]:
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            # Save episode
+            if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                print(f"Saving episode {episode_idx + 1} ({dataset.episode_buffer['size']} frames)...")
+                dataset.save_episode()
+                episode_idx += 1
+            
+            # Reset environment between episodes (if not last episode)
+            if not events["stop_recording"] and episode_idx < NUM_EPISODES:
+                if USE_LEADER_FOR_RESETS and leader:
+                    log_say("Reset the environment using leader arms")
+                    print(f"\nManual reset period ({RESET_TIME_SEC}s)...")
+                    
+                    # Use leader for manual reset with gravity compensation
+                    import numpy as np
+                    
+                    dt = 1 / FPS
+                    reset_start_time = time.perf_counter()
+                    
+                    while time.perf_counter() - reset_start_time < RESET_TIME_SEC:
+                        if events["exit_early"] or events["stop_recording"]:
+                            break
+                        
+                        loop_start = time.perf_counter()
+                        
+                        # Get leader state
+                        leader_action = leader.get_action()
+                        
+                        # Extract positions and velocities
+                        leader_positions_deg = {}
+                        leader_velocities_deg_per_sec = {}
+                        
+                        for motor in leader.bus_right.motors:
+                            pos_key = f"right_{motor}.pos"
+                            vel_key = f"right_{motor}.vel"
+                            if pos_key in leader_action:
+                                leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
+                            if vel_key in leader_action:
+                                leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
+                        
+                        for motor in leader.bus_left.motors:
+                            pos_key = f"left_{motor}.pos"
+                            vel_key = f"left_{motor}.vel"
+                            if pos_key in leader_action:
+                                leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
+                            if vel_key in leader_action:
+                                leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
+                        
+                        # Calculate gravity and friction torques
+                        leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
+                        leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
+                        
+                        leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
+                        leader_friction_torques_nm = leader._friction_from_velocity(
+                            leader_velocities_rad_per_sec,
+                            friction_scale=1.0
+                        )
+                        
+                        # Combine torques
+                        leader_total_torques_nm = {}
+                        for motor_name in leader_gravity_torques_nm:
+                            gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
+                            friction = leader_friction_torques_nm.get(motor_name, 0.0)
+                            leader_total_torques_nm[motor_name] = gravity + friction
+                        
+                        # Apply compensation
+                        for motor in leader.bus_right.motors:
+                            full_name = f"right_{motor}"
+                            position = leader_positions_deg.get(full_name, 0.0)
+                            torque = leader_total_torques_nm.get(full_name, 0.0)
+                            kd = leader.get_damping_kd(motor)
+                            
+                            leader.bus_right._mit_control(
+                                motor=motor, kp=0.0, kd=kd,
+                                position_degrees=position,
+                                velocity_deg_per_sec=0.0,
+                                torque=torque,
+                            )
+                        
+                        for motor in leader.bus_left.motors:
+                            full_name = f"left_{motor}"
+                            position = leader_positions_deg.get(full_name, 0.0)
+                            torque = leader_total_torques_nm.get(full_name, 0.0)
+                            kd = leader.get_damping_kd(motor)
+                            
+                            leader.bus_left._mit_control(
+                                motor=motor, kp=0.0, kd=kd,
+                                position_degrees=position,
+                                velocity_deg_per_sec=0.0,
+                                torque=torque,
+                            )
+                        
+                        # Send leader positions to follower
+                        follower_action = {}
+                        for joint in leader_positions_deg.keys():
+                            pos_key = f"{joint}.pos"
+                            if pos_key in leader_action:
+                                follower_action[pos_key] = leader_action[pos_key]
+                        
+                        if follower_action:
+                            follower.send_action(follower_action)
+                        
+                        # Maintain loop rate
+                        loop_duration = time.perf_counter() - loop_start
+                        sleep_time = dt - loop_duration
+                        if sleep_time > 0:
+                            time.sleep(sleep_time)
+                    
+                    print("Reset complete")
+                else:
+                    log_say("Waiting for manual reset")
+                    print(f"Manually reset the environment and press ENTER to continue")
+                    input("Press ENTER when ready...")
+        
+        print(f"Evaluation complete! {episode_idx} episodes recorded")
+        log_say("Evaluation complete", blocking=True)
+    
+    except KeyboardInterrupt:
+        print("\n\nEvaluation interrupted by user")
+    
+    finally:
+        if leader:
+            leader.bus_right.disable_torque()
+            leader.bus_left.disable_torque()
+            time.sleep(0.1)
+            leader.disconnect()
+
+        follower.disconnect()
+        
+        if listener is not None:
+            listener.stop()
+        
+        dataset.finalize()
+        print("\nUploading to Hugging Face Hub...")
+        dataset.push_to_hub(private=True)
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,650 @@
+#!/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.
+
+"""
+OpenArms End-Effector Policy Evaluation
+
+Evaluates a policy trained on end-effector (EE) space by:
+1. Converting robot joint observations to EE poses (FK)
+2. Running policy inference with EE state
+3. Converting EE action output back to joint positions (IK)
+4. Sending joint commands to robot
+
+Example usage:
+    python examples/openarms/evaluate_ee.py
+    python examples/openarms/evaluate_ee.py --model lerobot/my-ee-policy
+"""
+
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline, make_default_processors
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.control_utils import predict_action
+from lerobot.utils.relative_actions import (
+    convert_state_to_relative,
+    convert_from_relative_actions,
+    PerTimestepNormalizer,
+)
+from lerobot.utils.utils import get_safe_torch_device
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+from lerobot.processor.converters import (
+    robot_action_observation_to_transition,
+    robot_action_to_transition,
+    transition_to_robot_action,
+)
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.robot_kinematic_processor import (
+    BimanualEEBoundsAndSafety,
+    BimanualForwardKinematicsJointsToEE,
+    BimanualInverseKinematicsEEToJoints,
+)
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+
+# Configuration
+HF_MODEL_ID = "lerobot-data-collection/pi0_ee"  # TODO: Replace with your EE-trained model
+HF_EVAL_DATASET_ID = "your-org/your-ee-eval-dataset"  # TODO: Replace with your eval dataset
+TASK_DESCRIPTION = "ee-policy-task"  # TODO: Replace with your task
+
+NUM_EPISODES = 1
+FPS = 30
+EPISODE_TIME_SEC = 1000
+RESET_TIME_SEC = 60
+
+# Robot CAN interfaces
+FOLLOWER_LEFT_PORT = "can0"
+FOLLOWER_RIGHT_PORT = "can1"
+
+# Leader for manual resets (disabled by default)
+USE_LEADER_FOR_RESETS = False
+LEADER_LEFT_PORT = "can2"
+LEADER_RIGHT_PORT = "can3"
+
+# Camera configuration
+CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=FPS),
+}
+
+# Kinematics configuration
+DEFAULT_URDF = "src/lerobot/robots/openarms/urdf/openarm_bimanual_pybullet.urdf"
+DEFAULT_LEFT_EE_FRAME = "openarm_left_hand_tcp"
+DEFAULT_RIGHT_EE_FRAME = "openarm_right_hand_tcp"
+
+MOTOR_NAMES = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7", "gripper"]
+LEFT_URDF_JOINTS = [f"openarm_left_joint{i}" for i in range(1, 8)]
+RIGHT_URDF_JOINTS = [f"openarm_right_joint{i}" for i in range(1, 8)]
+
+
+def load_relative_config(model_path: Path | str) -> tuple[PerTimestepNormalizer | None, bool, bool]:
+    """Auto-detect relative action/state settings and load normalizer from checkpoint."""
+    model_path = Path(model_path) if isinstance(model_path, str) else model_path
+    normalizer = None
+    use_relative_actions = False
+    use_relative_state = False
+    
+    # Try local path first
+    if model_path.exists():
+        stats_path = model_path / "relative_stats.pt"
+        if stats_path.exists():
+            normalizer = PerTimestepNormalizer.load(stats_path)
+            use_relative_actions = True
+            print(f"  Loaded per-timestep stats from: {stats_path}")
+        
+        config_path = model_path / "train_config.json"
+        if config_path.exists():
+            cfg = TrainPipelineConfig.from_pretrained(model_path)
+            use_relative_actions = getattr(cfg, "use_relative_actions", use_relative_actions)
+            use_relative_state = getattr(cfg, "use_relative_state", False)
+    else:
+        # Try hub
+        try:
+            from huggingface_hub import hf_hub_download
+            try:
+                stats_file = hf_hub_download(repo_id=str(model_path), filename="relative_stats.pt")
+                normalizer = PerTimestepNormalizer.load(stats_file)
+                use_relative_actions = True
+                print("  Loaded per-timestep stats from hub")
+            except Exception:
+                pass  # No stats file means no relative actions
+            
+            try:
+                config_file = hf_hub_download(repo_id=str(model_path), filename="train_config.json")
+                cfg = TrainPipelineConfig.from_pretrained(Path(config_file).parent)
+                use_relative_actions = getattr(cfg, "use_relative_actions", use_relative_actions)
+                use_relative_state = getattr(cfg, "use_relative_state", False)
+            except Exception:
+                pass
+        except Exception as e:
+            print(f"  Warning: Could not load relative config: {e}")
+    
+    return normalizer, use_relative_actions, use_relative_state
+
+
+def build_kinematics_pipelines(urdf_path: str, left_ee_frame: str, right_ee_frame: str):
+    """Build FK and IK pipelines for bimanual robot."""
+    left_kinematics = RobotKinematics(
+        urdf_path=urdf_path,
+        target_frame_name=left_ee_frame,
+        joint_names=LEFT_URDF_JOINTS,
+    )
+    right_kinematics = RobotKinematics(
+        urdf_path=urdf_path,
+        target_frame_name=right_ee_frame,
+        joint_names=RIGHT_URDF_JOINTS,
+    )
+
+    # Joints -> EE (Forward Kinematics)
+    joints_to_ee = RobotProcessorPipeline[RobotAction, RobotAction](
+        steps=[
+            BimanualForwardKinematicsJointsToEE(
+                left_kinematics=left_kinematics,
+                right_kinematics=right_kinematics,
+                motor_names=MOTOR_NAMES,
+            ),
+        ],
+        to_transition=robot_action_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    # EE -> Joints (Inverse Kinematics)
+    ee_to_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            BimanualEEBoundsAndSafety(
+                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+                max_ee_step_m=0.10,
+            ),
+            BimanualInverseKinematicsEEToJoints(
+                left_kinematics=left_kinematics,
+                right_kinematics=right_kinematics,
+                motor_names=MOTOR_NAMES,
+                initial_guess_current_joints=True,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    return joints_to_ee, ee_to_joints
+
+
+def convert_obs_joints_to_ee(obs: dict, joints_to_ee_pipeline) -> dict:
+    """Convert joint observations to EE space."""
+    # Extract joint positions from observation
+    joint_positions = {}
+    for key, value in obs.items():
+        if key.startswith("observation.state.") and key.endswith(".pos"):
+            # e.g., observation.state.left_joint_1.pos -> left_joint_1.pos
+            motor_key = key.replace("observation.state.", "")
+            joint_positions[motor_key] = value
+    
+    if not joint_positions:
+        return obs
+    
+    # Apply FK to get EE poses
+    ee_poses = joints_to_ee_pipeline(joint_positions)
+    
+    # Build new observation with EE state
+    new_obs = {}
+    for key, value in obs.items():
+        if not (key.startswith("observation.state.") and key.endswith(".pos")):
+            new_obs[key] = value
+    
+    # Add EE poses as state
+    for key, value in ee_poses.items():
+        new_obs[f"observation.state.{key}"] = value
+    
+    return new_obs
+
+
+def convert_action_ee_to_joints(
+    ee_action: dict,
+    current_obs: dict,
+    ee_to_joints_pipeline,
+) -> dict:
+    """Convert EE action to joint positions using IK."""
+    # Extract EE components from action
+    ee_action_dict = {}
+    for key, value in ee_action.items():
+        if "ee." in key:
+            # e.g., action.left_ee.x -> left_ee.x
+            ee_key = key.replace("action.", "")
+            ee_action_dict[ee_key] = value
+    
+    if not ee_action_dict:
+        return ee_action
+    
+    # Build current observation for IK (joint positions)
+    current_joints = {}
+    for key, value in current_obs.items():
+        if key.startswith("observation.state.") and "joint" in key and key.endswith(".pos"):
+            motor_key = key.replace("observation.state.", "")
+            current_joints[motor_key] = value
+    
+    # Apply IK
+    joint_action = ee_to_joints_pipeline((ee_action_dict, current_joints))
+    
+    # Format as action dict
+    result = {}
+    for key, value in joint_action.items():
+        result[f"action.{key}"] = value
+    
+    return result
+
+
+def run_ee_inference_loop(
+    robot: OpenArmsFollower,
+    policy,
+    preprocessor,
+    postprocessor,
+    joints_to_ee,
+    ee_to_joints,
+    dataset: LeRobotDataset,
+    fps: int,
+    duration_s: float,
+    events: dict,
+    task: str,
+    use_relative_actions: bool = False,
+    use_relative_state: bool = False,
+    relative_normalizer: PerTimestepNormalizer | None = None,
+    display_data: bool = True,
+):
+    """Run inference loop with EE conversion and optional UMI-style relative actions."""
+    device = get_safe_torch_device(policy.config.device)
+    
+    # Reset policy and processors
+    policy.reset()
+    preprocessor.reset()
+    postprocessor.reset()
+    
+    dt = 1.0 / fps
+    timestamp = 0
+    start_time = time.perf_counter()
+    step = 0
+    
+    mode_str = ""
+    if use_relative_actions:
+        mode_str += " [relative actions]"
+    if use_relative_state:
+        mode_str += " [relative state]"
+    print(f"\nRunning EE inference for {duration_s}s...{mode_str}")
+    
+    while timestamp < duration_s:
+        loop_start = time.perf_counter()
+        
+        if events.get("exit_early"):
+            events["exit_early"] = False
+            break
+        
+        # 1. Get robot observation (joint positions)
+        robot_obs = robot.get_observation()
+        
+        # 2. Convert joint observation to EE space using FK
+        joint_state = {}
+        for key, value in robot_obs.items():
+            if key.endswith(".pos"):
+                joint_state[key] = value
+        
+        ee_state = joints_to_ee(joint_state.copy())
+        
+        # 3. Build observation frame with EE state for policy input
+        # Filter to only EE keys (FK may include other keys in output)
+        # Expected: left_ee.{x,y,z,wx,wy,wz,gripper_pos}, right_ee.{...} = 14 total
+        ee_keys = sorted([k for k in ee_state.keys() if "_ee." in k])
+        ee_values = [ee_state[k] for k in ee_keys]
+        
+        # Debug: print on first step
+        if step == 0:
+            print(f"  FK output keys ({len(ee_keys)}): {ee_keys}")
+            state_feature = policy.config.input_features.get("observation.state")
+            if state_feature:
+                print(f"  Policy expects state dim: {state_feature.shape[0]}")
+        
+        # Store current EE position for relative action conversion (using same order)
+        current_ee_pos = torch.tensor(ee_values)
+        
+        # Convert to relative state if enabled (UMI-style)
+        if use_relative_state:
+            ee_state_tensor = torch.tensor(ee_values)
+            relative_state = convert_state_to_relative(ee_state_tensor.unsqueeze(0))
+            ee_values = [float(relative_state[0, i]) for i in range(len(ee_values))]
+        
+        # Build observation dict for policy (images + state as numpy arrays)
+        observation_frame = {}
+        
+        # Add images - robot.cameras contains camera names as keys
+        for cam_name in robot.cameras:
+            if cam_name in robot_obs:
+                observation_frame[f"observation.images.{cam_name}"] = robot_obs[cam_name]
+        
+        # Add state as numpy array
+        observation_frame["observation.state"] = np.array(ee_values, dtype=np.float32)
+        
+        # 4. Run policy inference using predict_action
+        action_tensor = predict_action(
+            observation=observation_frame,
+            policy=policy,
+            device=device,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            use_amp=policy.config.use_amp,
+            task=task,
+            robot_type=robot.robot_type,
+        )
+        
+        # 5. Convert action tensor to dict using EE keys (not joint keys from eval dataset)
+        action_tensor = action_tensor.squeeze(0).cpu()
+        while action_tensor.dim() > 1:
+            action_tensor = action_tensor[0]
+        # Use the same EE keys we used for state (truncated to match policy's action dim)
+        ee_action = {ee_keys[i]: float(action_tensor[i]) for i in range(len(action_tensor))}
+        
+        # 6. Convert relative action back to absolute if needed
+        if use_relative_actions:
+            action_keys = sorted(ee_action.keys())
+            action_vals = torch.tensor([ee_action[k] for k in action_keys])
+            
+            # Unnormalize if we have a normalizer
+            if relative_normalizer is not None:
+                action_vals = relative_normalizer.unnormalize(action_vals.unsqueeze(0).unsqueeze(0))
+                action_vals = action_vals.squeeze(0).squeeze(0)
+            
+            # Convert from relative to absolute
+            absolute_action = convert_from_relative_actions(action_vals.unsqueeze(0), current_ee_pos)
+            
+            # Convert back to dict
+            ee_action = {k: float(absolute_action[0, i]) for i, k in enumerate(action_keys)}
+        
+        # 7. Convert EE action to joint positions using IK
+        joint_action = ee_to_joints((ee_action.copy(), joint_state.copy()))
+        
+        # 8. Send joint commands to robot
+        robot.send_action(joint_action)
+        
+        # 9. Save frame to dataset (save original robot obs + joint action)
+        if dataset is not None:
+            obs_frame = build_dataset_frame(dataset.features, robot_obs, prefix=OBS_STR)
+            act_frame = build_dataset_frame(dataset.features, joint_action, prefix=ACTION)
+            frame = {**obs_frame, **act_frame, "task": task}
+            dataset.add_frame(frame)
+        
+        # 10. Visualization
+        if display_data:
+            log_rerun_data(observation=robot_obs, action=joint_action)
+        
+        # Progress logging
+        step += 1
+        if step % (fps * 5) == 0:
+            elapsed = time.perf_counter() - start_time
+            print(f"  Step {step}, elapsed: {elapsed:.1f}s")
+        
+        # Maintain loop rate
+        loop_duration = time.perf_counter() - loop_start
+        sleep_time = dt - loop_duration
+        if sleep_time > 0:
+            precise_sleep(sleep_time)
+        
+        timestamp = time.perf_counter() - start_time
+    
+    print(f"  Completed {step} steps")
+
+
+def main():
+    """Main evaluation function for EE policies."""
+    print("=" * 70)
+    print("OpenArms End-Effector Policy Evaluation")
+    print("=" * 70)
+    print(f"\nModel: {HF_MODEL_ID}")
+    print(f"Dataset: {HF_EVAL_DATASET_ID}")
+    print(f"Task: {TASK_DESCRIPTION}")
+    print(f"Episodes: {NUM_EPISODES}")
+    print(f"Episode Duration: {EPISODE_TIME_SEC}s")
+    print("=" * 70)
+    
+    # Resolve URDF path
+    urdf_path = Path(__file__).parent.parent.parent / DEFAULT_URDF
+    if not urdf_path.exists():
+        raise FileNotFoundError(f"URDF not found: {urdf_path}")
+    urdf_path = str(urdf_path)
+    
+    # Build kinematics pipelines
+    print("\n[1/5] Building kinematics pipelines...")
+    joints_to_ee, ee_to_joints = build_kinematics_pipelines(
+        urdf_path, DEFAULT_LEFT_EE_FRAME, DEFAULT_RIGHT_EE_FRAME
+    )
+    print("  FK and IK pipelines ready")
+    
+    # Initialize robot
+    print("\n[2/5] Connecting to robot...")
+    follower_config = OpenArmsFollowerConfig(
+        port_left=FOLLOWER_LEFT_PORT,
+        port_right=FOLLOWER_RIGHT_PORT,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=CAMERA_CONFIG,
+    )
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+    
+    if not follower.is_connected:
+        raise RuntimeError("Robot failed to connect!")
+    print("  Robot connected")
+    
+    # Initialize leader for resets
+    leader = None
+    if USE_LEADER_FOR_RESETS:
+        print("\n  Connecting leader for resets...")
+        leader_config = OpenArmsLeaderConfig(
+            port_left=LEADER_LEFT_PORT,
+            port_right=LEADER_RIGHT_PORT,
+            can_interface="socketcan",
+            id="openarms_leader",
+            manual_control=False,
+        )
+        leader = OpenArmsLeader(leader_config)
+        leader.connect(calibrate=False)
+        
+        if leader.is_connected and leader.pin_robot is not None:
+            leader.bus_right.enable_torque()
+            leader.bus_left.enable_torque()
+            print("  Leader connected with gravity compensation")
+    
+    # Create dataset for saving evaluation data
+    print(f"\n[3/5] Creating evaluation dataset...")
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    action_features_hw = {k: v for k, v in follower.action_features.items() if k.endswith(".pos")}
+    
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+    
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
+    if dataset_path.exists():
+        print(f"  Dataset exists at: {dataset_path}")
+        if input("  Continue and overwrite? (y/n): ").strip().lower() != 'y':
+            follower.disconnect()
+            return
+    
+    dataset = LeRobotDataset.create(
+        repo_id=HF_EVAL_DATASET_ID,
+        fps=FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_processes=0,
+        image_writer_threads=12,
+    )
+    print("  Dataset created")
+    
+    # Load policy directly using from_pretrained to preserve original EE features
+    # (make_policy would overwrite output_features with joint features from eval dataset)
+    print(f"\n[4/5] Loading policy from {HF_MODEL_ID}...")
+    from lerobot.policies.factory import get_policy_class
+    
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_cls = get_policy_class(policy_config.type)
+    policy = policy_cls.from_pretrained(HF_MODEL_ID)
+    
+    # Load preprocessor/postprocessor from pretrained model
+    # (uses the trained EE features, not joint features from eval dataset)
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy.config,
+        pretrained_path=HF_MODEL_ID,
+        preprocessor_overrides={
+            "device_processor": {"device": str(policy.config.device)}
+        },
+    )
+    print("  Policy loaded")
+    print(f"  State dim: {policy.config.input_features['observation.state'].shape[0]}")
+    print(f"  Action dim: {policy.config.output_features['action'].shape[0]}")
+    
+    # Auto-detect relative action/state settings from checkpoint
+    relative_normalizer, use_relative_actions, use_relative_state = load_relative_config(HF_MODEL_ID)
+    
+    mode = "absolute"
+    if use_relative_actions:
+        mode = "relative actions + state" if use_relative_state else "relative actions only"
+    print(f"  Mode: {mode}")
+    
+    # Initialize keyboard listener and visualization
+    print("\n[5/5] Starting evaluation...")
+    listener, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_eval_ee")
+    
+    print("\nControls: ESC=stop, →=next episode, ←=rerecord")
+    episode_idx = 0
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events.get("stop_recording"):
+            log_say(f"Episode {episode_idx + 1} of {NUM_EPISODES}")
+            print(f"\n{'='*50}")
+            print(f"Episode {episode_idx + 1}/{NUM_EPISODES}")
+            print(f"{'='*50}")
+            
+            input("\nPress ENTER to start episode...")
+            events["exit_early"] = False
+            
+            # Run inference with EE conversion
+            run_ee_inference_loop(
+                robot=follower,
+                policy=policy,
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
+                joints_to_ee=joints_to_ee,
+                ee_to_joints=ee_to_joints,
+                dataset=dataset,
+                fps=FPS,
+                duration_s=EPISODE_TIME_SEC,
+                events=events,
+                task=TASK_DESCRIPTION,
+                use_relative_actions=use_relative_actions,
+                use_relative_state=use_relative_state,
+                relative_normalizer=relative_normalizer,
+            )
+            
+            # Handle re-recording
+            if events.get("rerecord_episode", False):
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            # Save episode if we have data
+            if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                print(f"  Saving episode {episode_idx + 1}...")
+                dataset.save_episode()
+                episode_idx += 1
+            
+            events["exit_early"] = False
+            
+            # Reset between episodes
+            if episode_idx < NUM_EPISODES and not events.get("stop_recording"):
+                if USE_LEADER_FOR_RESETS and leader and leader.is_connected:
+                    log_say("Reset environment using leader arms")
+                    print(f"\nManual reset ({RESET_TIME_SEC}s) - use leader arms...")
+                    
+                    reset_start = time.perf_counter()
+                    while time.perf_counter() - reset_start < RESET_TIME_SEC:
+                        if events.get("exit_early") or events.get("stop_recording"):
+                            break
+                        
+                        leader_action = leader.get_action()
+                        follower_action = {k: v for k, v in leader_action.items() if k.endswith(".pos")}
+                        if follower_action:
+                            follower.send_action(follower_action)
+                        time.sleep(1/FPS)
+                else:
+                    input("\nReset environment and press ENTER...")
+        
+        print(f"\n✓ Evaluation complete! {episode_idx} episodes recorded")
+        log_say("Evaluation complete", blocking=True)
+    
+    except KeyboardInterrupt:
+        print("\n\nEvaluation interrupted")
+    
+    finally:
+        if leader:
+            if hasattr(leader, 'bus_right'):
+                leader.bus_right.disable_torque()
+            if hasattr(leader, 'bus_left'):
+                leader.bus_left.disable_torque()
+            leader.disconnect()
+        
+        follower.disconnect()
+        
+        if listener is not None:
+            listener.stop()
+        
+        # Finalize and push dataset
+        dataset.finalize()
+        print("Uploading to Hub...")
+        dataset.push_to_hub(private=True)
+        
+        print("✓ Done!")
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,587 @@
+#!/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.
+
+"""
+OpenArms Policy Evaluation with Interpolation
+
+Evaluates a trained policy with smooth action interpolation:
+- Decoupled camera capture (CAMERA_FPS) from robot control (ROBOT_FPS)
+- Speed multiplier to execute actions faster than training
+- Velocity feedforward for smoother tracking
+- Adjustable PID gains
+
+Example usage:
+    python examples/openarms/evaluate_interpolation.py
+"""
+
+import time
+from collections import deque
+from pathlib import Path
+
+import numpy as np
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.processor import make_default_processors
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.utils.control_utils import init_keyboard_listener, predict_action
+from lerobot.utils.utils import log_say, get_safe_torch_device
+from lerobot.utils.visualization_utils import init_rerun
+
+
+# ======================== MODEL & TASK CONFIG ========================
+HF_MODEL_ID = "lerobot-data-collection/three-folds-pi0"  # TODO: Replace with your trained model
+HF_EVAL_DATASET_ID = "lerobot-data-collection/three-folds-pi0_eval_interp"  # TODO: Replace
+TASK_DESCRIPTION = "three-folds-dataset"  # TODO: Replace with your task
+
+# ======================== TIMING CONFIG ========================
+CAMERA_FPS = 30           # Camera hardware limit (fixed)
+POLICY_FPS = 30           # What the policy was trained with
+SPEED_MULTIPLIER = 1.2    # Execute actions faster (1.0 = normal, 1.2 = 20% faster)
+ROBOT_FPS = 50            # Robot command rate (higher = smoother interpolation)
+
+# Derived values
+EFFECTIVE_POLICY_FPS = int(POLICY_FPS * SPEED_MULTIPLIER)  # How fast we consume actions (36Hz at 1.2x)
+
+NUM_EPISODES = 1
+EPISODE_TIME_SEC = 300
+RESET_TIME_SEC = 60
+
+# ======================== PID TUNING ========================
+# Set to None to use robot config defaults
+CUSTOM_KP_SCALE = 0.7     # Scale factor for position gain (0.5-1.0, lower = smoother)
+CUSTOM_KD_SCALE = 1.3     # Scale factor for damping gain (1.0-2.0, higher = less overshoot)
+USE_VELOCITY_FEEDFORWARD = True  # Enable velocity feedforward for smoother tracking
+
+# ======================== ROBOT CONFIG ========================
+FOLLOWER_LEFT_PORT = "can0"
+FOLLOWER_RIGHT_PORT = "can1"
+
+USE_LEADER_FOR_RESETS = True
+LEADER_LEFT_PORT = "can2"
+LEADER_RIGHT_PORT = "can3"
+
+# Camera config uses CAMERA_FPS (hardware limit)
+CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=CAMERA_FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=CAMERA_FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=CAMERA_FPS),
+}
+
+
+class ActionInterpolator:
+    """Interpolate between policy actions for smoother robot control with velocity estimation."""
+    
+    def __init__(self, effective_policy_fps: int, robot_fps: int):
+        self.effective_policy_fps = effective_policy_fps
+        self.robot_fps = robot_fps
+        self.substeps_per_policy_step = robot_fps / effective_policy_fps
+        self.prev_action: dict | None = None
+        self.curr_action: dict | None = None
+        self.substep = 0
+        self.last_interpolated: dict | None = None
+        
+    def update(self, new_action: dict) -> None:
+        self.prev_action = self.curr_action
+        self.curr_action = new_action
+        self.substep = 0
+        
+    def get_interpolated_action(self) -> tuple[dict | None, dict | None]:
+        """Returns (interpolated_position, estimated_velocity_deg_per_sec)"""
+        if self.curr_action is None:
+            return None, None
+        if self.prev_action is None:
+            self.last_interpolated = self.curr_action.copy()
+            return self.curr_action, {k: 0.0 for k in self.curr_action}
+            
+        t = min(self.substep / self.substeps_per_policy_step, 1.0)
+        self.substep += 1
+        
+        interpolated = {}
+        velocity = {}
+        dt = 1.0 / self.robot_fps
+        
+        for key in self.curr_action:
+            prev = self.prev_action.get(key, self.curr_action[key])
+            curr = self.curr_action[key]
+            interpolated[key] = prev * (1 - t) + curr * t
+            
+            if self.last_interpolated is not None and key in self.last_interpolated:
+                velocity[key] = (interpolated[key] - self.last_interpolated[key]) / dt
+            else:
+                velocity[key] = (curr - prev) * self.effective_policy_fps
+        
+        self.last_interpolated = interpolated.copy()
+        return interpolated, velocity
+    
+    def reset(self):
+        self.prev_action = None
+        self.curr_action = None
+        self.substep = 0
+        self.last_interpolated = None
+
+
+class HzTracker:
+    """Track and display actual loop frequency."""
+    
+    def __init__(self, name: str = "Robot", window_size: int = 100, print_interval: float = 2.0):
+        self.name = name
+        self.timestamps = deque(maxlen=window_size)
+        self.last_print_time = 0
+        self.print_interval = print_interval
+        
+    def tick(self) -> float | None:
+        now = time.perf_counter()
+        self.timestamps.append(now)
+        
+        if len(self.timestamps) < 2:
+            return None
+            
+        hz = (len(self.timestamps) - 1) / (self.timestamps[-1] - self.timestamps[0])
+        
+        if now - self.last_print_time >= self.print_interval:
+            print(f"{self.name} Hz: {hz:.1f}")
+            self.last_print_time = now
+            
+        return hz
+    
+    def get_avg_hz(self) -> float | None:
+        if len(self.timestamps) < 2:
+            return None
+        return (len(self.timestamps) - 1) / (self.timestamps[-1] - self.timestamps[0])
+    
+    def reset(self):
+        self.timestamps.clear()
+        self.last_print_time = 0
+
+
+def interpolated_eval_loop(
+    robot,
+    policy,
+    preprocessor,
+    postprocessor,
+    robot_observation_processor,
+    robot_action_processor,
+    dataset,
+    events,
+    interpolator: ActionInterpolator,
+    robot_hz_tracker: HzTracker,
+    camera_fps: int,
+    effective_policy_fps: int,
+    robot_fps: int,
+    control_time_s: float,
+    task: str,
+    kp_scale: float | None = None,
+    kd_scale: float | None = None,
+    use_velocity_ff: bool = False,
+):
+    """
+    Run evaluation with decoupled camera and robot control:
+    - Camera captures at camera_fps (hardware limit)
+    - Policy inference runs when new camera frame is available
+    - Actions are consumed at effective_policy_fps (sped up by SPEED_MULTIPLIER)
+    - Robot receives interpolated commands at robot_fps (smoothest)
+    """
+    from lerobot.scripts.lerobot_record import build_dataset_frame, make_robot_action
+    from lerobot.utils.visualization_utils import log_rerun_data
+    
+    camera_dt = 1.0 / camera_fps
+    policy_dt = 1.0 / effective_policy_fps
+    robot_dt = 1.0 / robot_fps
+    
+    interpolator.reset()
+    robot_hz_tracker.reset()
+    policy.reset()
+    
+    # Build custom gains if scaling is enabled
+    custom_kp = None
+    custom_kd = None
+    if kp_scale is not None or kd_scale is not None:
+        custom_kp = {}
+        custom_kd = {}
+        for arm in ["right", "left"]:
+            bus = robot.bus_right if arm == "right" else robot.bus_left
+            for i, motor_name in enumerate(bus.motors):
+                full_name = f"{arm}_{motor_name}"
+                default_kp = robot.config.position_kp[i] if isinstance(robot.config.position_kp, list) else robot.config.position_kp
+                default_kd = robot.config.position_kd[i] if isinstance(robot.config.position_kd, list) else robot.config.position_kd
+                custom_kp[full_name] = default_kp * (kp_scale or 1.0)
+                custom_kd[full_name] = default_kd * (kd_scale or 1.0)
+        print(f"Custom gains: kp_scale={kp_scale}, kd_scale={kd_scale}")
+    
+    if use_velocity_ff:
+        print("Velocity feedforward: enabled")
+    
+    last_camera_time = -camera_dt
+    last_policy_action_time = -policy_dt
+    cached_observation = None
+    cached_robot_action = None
+    
+    start_time = time.perf_counter()
+    
+    print(f"\nStarting interpolated eval loop:")
+    print(f"  Camera: {camera_fps}Hz | Policy actions consumed: {effective_policy_fps}Hz | Robot: {robot_fps}Hz")
+    
+    while time.perf_counter() - start_time < control_time_s:
+        if events["exit_early"] or events["stop_recording"]:
+            break
+            
+        loop_start = time.perf_counter()
+        elapsed = loop_start - start_time
+        
+        # === CAMERA CAPTURE (at camera_fps, decoupled from robot) ===
+        if elapsed - last_camera_time >= camera_dt:
+            obs = robot.get_observation()
+            obs_processed = robot_observation_processor(obs)
+            observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix="observation")
+            
+            # Run policy inference with fresh observation
+            action_values = predict_action(
+                observation=observation_frame,
+                policy=policy,
+                device=get_safe_torch_device(policy.config.device),
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
+                use_amp=policy.config.use_amp,
+                task=task,
+                robot_type=robot.robot_type,
+            )
+            
+            act_processed = make_robot_action(action_values, dataset.features)
+            cached_robot_action = robot_action_processor((act_processed, obs))
+            cached_observation = (obs_processed, observation_frame, act_processed)
+            
+            last_camera_time = elapsed
+        
+        # === ACTION UPDATE (at effective_policy_fps, faster than camera if speed > 1) ===
+        if elapsed - last_policy_action_time >= policy_dt and cached_robot_action is not None:
+            interpolator.update(cached_robot_action)
+            last_policy_action_time = elapsed
+            
+            # Save to dataset at effective policy rate
+            if dataset is not None and cached_observation is not None:
+                obs_processed, observation_frame, act_processed = cached_observation
+                action_frame = build_dataset_frame(dataset.features, act_processed, prefix="action")
+                frame = {**observation_frame, **action_frame, "task": task}
+                dataset.add_frame(frame)
+                log_rerun_data(observation=obs_processed, action=act_processed)
+        
+        # === ROBOT COMMAND (at robot_fps, highest rate for smoothness) ===
+        smooth_action, velocity = interpolator.get_interpolated_action()
+        if smooth_action is not None:
+            vel_ff = velocity if use_velocity_ff else None
+            robot.send_action(smooth_action, custom_kp=custom_kp, custom_kd=custom_kd, velocity_feedforward=vel_ff)
+        
+        robot_hz_tracker.tick()
+        
+        # Maintain robot control rate
+        sleep_time = robot_dt - (time.perf_counter() - loop_start)
+        if sleep_time > 0:
+            time.sleep(sleep_time)
+    
+    # Print final stats
+    robot_hz = robot_hz_tracker.get_avg_hz()
+    if robot_hz:
+        print(f"\nFinal average robot Hz: {robot_hz:.1f}")
+
+
+def main():
+    """Main evaluation function."""
+    print("=" * 60)
+    print("OpenArms Policy Evaluation with Interpolation")
+    print("=" * 60)
+    print(f"\nModel: {HF_MODEL_ID}")
+    print(f"Dataset: {HF_EVAL_DATASET_ID}")
+    print(f"Task: {TASK_DESCRIPTION}")
+    print(f"\n--- Timing ---")
+    print(f"Camera FPS: {CAMERA_FPS} (hardware limit)")
+    print(f"Policy trained at: {POLICY_FPS}Hz")
+    print(f"Speed multiplier: {SPEED_MULTIPLIER}x")
+    print(f"Effective policy FPS: {EFFECTIVE_POLICY_FPS}Hz (actions consumed)")
+    print(f"Robot FPS: {ROBOT_FPS}Hz (interpolated commands)")
+    print(f"\n--- PID Tuning ---")
+    print(f"KP scale: {CUSTOM_KP_SCALE}")
+    print(f"KD scale: {CUSTOM_KD_SCALE}")
+    print(f"Velocity feedforward: {USE_VELOCITY_FEEDFORWARD}")
+    print(f"\n--- Episodes ---")
+    print(f"Episodes: {NUM_EPISODES}")
+    print(f"Duration: {EPISODE_TIME_SEC}s per episode")
+    print(f"Reset time: {RESET_TIME_SEC}s")
+    print(f"Leader for resets: {USE_LEADER_FOR_RESETS}")
+    print("=" * 60)
+    
+    follower_config = OpenArmsFollowerConfig(
+        port_left=FOLLOWER_LEFT_PORT,
+        port_right=FOLLOWER_RIGHT_PORT,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=CAMERA_CONFIG,
+    )
+    
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+    
+    if not follower.is_connected:
+        raise RuntimeError("Follower robot failed to connect!")
+
+    leader = None
+    if USE_LEADER_FOR_RESETS:
+        leader_config = OpenArmsLeaderConfig(
+            port_left=LEADER_LEFT_PORT,
+            port_right=LEADER_RIGHT_PORT,
+            can_interface="socketcan",
+            id="openarms_leader",
+            manual_control=False,
+        )
+        
+        leader = OpenArmsLeader(leader_config)
+        leader.connect(calibrate=False)
+        
+        if not leader.is_connected:
+            raise RuntimeError("Leader robot failed to connect!")
+        
+        if leader.pin_robot is not None:
+            leader.bus_right.enable_torque()
+            leader.bus_left.enable_torque()
+            time.sleep(0.1)
+            print(f"Leader connected with gravity compensation")
+        else:
+            print(f"Leader connected (no gravity compensation)")
+
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+    
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+    
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
+    if dataset_path.exists():
+        print(f"\nDataset exists at: {dataset_path}")
+        choice = input("Continue and append? (y/n): ").strip().lower()
+        if choice != 'y':
+            print("Aborting.")
+            follower.disconnect()
+            if leader:
+                leader.disconnect()
+            return
+    
+    # Dataset uses effective policy FPS (sped up rate)
+    dataset = LeRobotDataset.create(
+        repo_id=HF_EVAL_DATASET_ID,
+        fps=EFFECTIVE_POLICY_FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_processes=0,
+        image_writer_threads=12, 
+    )
+    
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+    policy = make_policy(policy_config, ds_meta=dataset.meta)
+    
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy.config,
+        pretrained_path=HF_MODEL_ID,
+        dataset_stats=dataset.meta.stats,
+        preprocessor_overrides={
+            "device_processor": {"device": str(policy.config.device)}
+        },
+    )
+
+    print(f"\nRunning evaluation...")
+    listener, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_evaluation_interp")
+    
+    interpolator = ActionInterpolator(effective_policy_fps=EFFECTIVE_POLICY_FPS, robot_fps=ROBOT_FPS)
+    robot_hz_tracker = HzTracker(name="Robot", window_size=100, print_interval=2.0)
+    
+    episode_idx = 0
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Evaluating episode {episode_idx + 1} of {NUM_EPISODES}")
+            print(f"\n--- Episode {episode_idx + 1}/{NUM_EPISODES} ---")
+            
+            interpolated_eval_loop(
+                robot=follower,
+                policy=policy,
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
+                robot_observation_processor=robot_observation_processor,
+                robot_action_processor=robot_action_processor,
+                dataset=dataset,
+                events=events,
+                interpolator=interpolator,
+                robot_hz_tracker=robot_hz_tracker,
+                camera_fps=CAMERA_FPS,
+                effective_policy_fps=EFFECTIVE_POLICY_FPS,
+                robot_fps=ROBOT_FPS,
+                control_time_s=EPISODE_TIME_SEC,
+                task=TASK_DESCRIPTION,
+                kp_scale=CUSTOM_KP_SCALE,
+                kd_scale=CUSTOM_KD_SCALE,
+                use_velocity_ff=USE_VELOCITY_FEEDFORWARD,
+            )
+            
+            if events["rerecord_episode"]:
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                print(f"Saving episode ({dataset.episode_buffer['size']} frames)...")
+                dataset.save_episode()
+                episode_idx += 1
+            
+            if not events["stop_recording"] and episode_idx < NUM_EPISODES:
+                if USE_LEADER_FOR_RESETS and leader:
+                    log_say("Reset the environment using leader arms")
+                    print(f"\nManual reset ({RESET_TIME_SEC}s)...")
+                    
+                    dt = 1 / CAMERA_FPS
+                    reset_start_time = time.perf_counter()
+                    
+                    while time.perf_counter() - reset_start_time < RESET_TIME_SEC:
+                        if events["exit_early"] or events["stop_recording"]:
+                            break
+                        
+                        loop_start = time.perf_counter()
+                        leader_action = leader.get_action()
+                        
+                        leader_positions_deg = {}
+                        leader_velocities_deg_per_sec = {}
+                        
+                        for motor in leader.bus_right.motors:
+                            pos_key = f"right_{motor}.pos"
+                            vel_key = f"right_{motor}.vel"
+                            if pos_key in leader_action:
+                                leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
+                            if vel_key in leader_action:
+                                leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
+                        
+                        for motor in leader.bus_left.motors:
+                            pos_key = f"left_{motor}.pos"
+                            vel_key = f"left_{motor}.vel"
+                            if pos_key in leader_action:
+                                leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
+                            if vel_key in leader_action:
+                                leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
+                        
+                        leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
+                        leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
+                        
+                        leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
+                        leader_friction_torques_nm = leader._friction_from_velocity(
+                            leader_velocities_rad_per_sec, friction_scale=1.0
+                        )
+                        
+                        leader_total_torques_nm = {}
+                        for motor_name in leader_gravity_torques_nm:
+                            gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
+                            friction = leader_friction_torques_nm.get(motor_name, 0.0)
+                            leader_total_torques_nm[motor_name] = gravity + friction
+                        
+                        for motor in leader.bus_right.motors:
+                            full_name = f"right_{motor}"
+                            position = leader_positions_deg.get(full_name, 0.0)
+                            torque = leader_total_torques_nm.get(full_name, 0.0)
+                            kd = leader.get_damping_kd(motor)
+                            leader.bus_right._mit_control(
+                                motor=motor, kp=0.0, kd=kd,
+                                position_degrees=position, velocity_deg_per_sec=0.0, torque=torque,
+                            )
+                        
+                        for motor in leader.bus_left.motors:
+                            full_name = f"left_{motor}"
+                            position = leader_positions_deg.get(full_name, 0.0)
+                            torque = leader_total_torques_nm.get(full_name, 0.0)
+                            kd = leader.get_damping_kd(motor)
+                            leader.bus_left._mit_control(
+                                motor=motor, kp=0.0, kd=kd,
+                                position_degrees=position, velocity_deg_per_sec=0.0, torque=torque,
+                            )
+                        
+                        follower_action = {}
+                        for joint in leader_positions_deg.keys():
+                            pos_key = f"{joint}.pos"
+                            if pos_key in leader_action:
+                                follower_action[pos_key] = leader_action[pos_key]
+                        
+                        if follower_action:
+                            follower.send_action(follower_action)
+                        
+                        loop_duration = time.perf_counter() - loop_start
+                        sleep_time = dt - loop_duration
+                        if sleep_time > 0:
+                            time.sleep(sleep_time)
+                    
+                    print("Reset complete")
+                else:
+                    log_say("Waiting for manual reset")
+                    input("Press ENTER when ready...")
+        
+        print(f"\nEvaluation complete! {episode_idx} episodes recorded")
+        log_say("Evaluation complete", blocking=True)
+    
+    except KeyboardInterrupt:
+        print("\n\nInterrupted by user")
+    
+    finally:
+        if leader:
+            leader.bus_right.disable_torque()
+            leader.bus_left.disable_torque()
+            time.sleep(0.1)
+            leader.disconnect()
+
+        follower.disconnect()
+        
+        if listener is not None:
+            listener.stop()
+        
+        dataset.finalize()
+        print("\nUploading to Hugging Face Hub...")
+        dataset.push_to_hub(private=True)
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,317 @@
+#!/usr/bin/env python
+"""
+OpenArms Policy Evaluation with Relative Actions
+
+Two modes supported (based on training config):
+  Mode 1: Relative actions only (use_relative_state=False)
+    - Policy outputs relative action deltas
+    - State input is absolute
+  Mode 2: Relative actions + state (use_relative_state=True)
+    - Policy outputs relative action deltas  
+    - State input is also converted to relative
+
+Example usage:
+    python examples/openarms/evaluate_relative.py
+"""
+
+import time
+from pathlib import Path
+
+import torch
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.processor import make_default_processors
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.control_utils import init_keyboard_listener, predict_action
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import get_safe_torch_device
+from lerobot.utils.relative_actions import (
+    convert_from_relative_actions_dict,
+    convert_state_to_relative,
+    PerTimestepNormalizer,
+)
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+
+# Configuration
+HF_MODEL_ID = "your-org/your-relative-policy"
+HF_EVAL_DATASET_ID = "your-org/your-eval-dataset"
+TASK_DESCRIPTION = "your task description"
+
+NUM_EPISODES = 1
+FPS = 30
+EPISODE_TIME_SEC = 1000
+
+FOLLOWER_LEFT_PORT = "can0"
+FOLLOWER_RIGHT_PORT = "can1"
+
+CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=FPS),
+}
+
+
+def load_relative_config(model_path: Path | str) -> tuple[PerTimestepNormalizer | None, bool]:
+    """Load normalizer and relative_state setting from checkpoint."""
+    model_path = Path(model_path) if isinstance(model_path, str) else model_path
+    normalizer = None
+    use_relative_state = False
+    
+    # Try local path first
+    if model_path.exists():
+        stats_path = model_path / "relative_stats.pt"
+        if stats_path.exists():
+            normalizer = PerTimestepNormalizer.load(stats_path)
+            print(f"Loaded per-timestep stats from: {stats_path}")
+        
+        config_path = model_path / "train_config.json"
+        if config_path.exists():
+            cfg = TrainPipelineConfig.from_pretrained(model_path)
+            use_relative_state = getattr(cfg, "use_relative_state", False)
+    else:
+        # Try hub
+        try:
+            from huggingface_hub import hf_hub_download
+            stats_file = hf_hub_download(repo_id=str(model_path), filename="relative_stats.pt")
+            normalizer = PerTimestepNormalizer.load(stats_file)
+            print("Loaded per-timestep stats from hub")
+            
+            config_file = hf_hub_download(repo_id=str(model_path), filename="train_config.json")
+            cfg = TrainPipelineConfig.from_pretrained(Path(config_file).parent)
+            use_relative_state = getattr(cfg, "use_relative_state", False)
+        except Exception as e:
+            print(f"Warning: Could not load relative config: {e}")
+    
+    return normalizer, use_relative_state
+
+
+def inference_loop_relative(
+    robot,
+    policy,
+    preprocessor,
+    postprocessor,
+    dataset,
+    events,
+    fps: int,
+    control_time_s: float,
+    single_task: str,
+    display_data: bool = True,
+    state_key: str = "observation.state",
+    relative_normalizer: PerTimestepNormalizer | None = None,
+    use_relative_state: bool = False,
+):
+    """
+    Inference loop for relative action policies.
+    
+    If use_relative_state=True, also converts observation state to relative.
+    """
+    device = get_safe_torch_device(policy.config.device)
+    timestamp = 0
+    start_t = time.perf_counter()
+    
+    while timestamp < control_time_s:
+        loop_start = time.perf_counter()
+        
+        if events["exit_early"] or events["stop_recording"]:
+            break
+        
+        obs = robot.get_observation()
+        observation_frame = build_dataset_frame(dataset.features, obs, prefix=OBS_STR)
+        current_pos = {k: v for k, v in obs.items() if k.endswith(".pos")}
+        
+        # Convert state to relative if using full UMI mode
+        if use_relative_state and state_key in observation_frame:
+            state_tensor = observation_frame[state_key]
+            if isinstance(state_tensor, torch.Tensor):
+                observation_frame[state_key] = convert_state_to_relative(state_tensor)
+        
+        # Policy inference (outputs action tensor)
+        action_tensor = predict_action(
+            observation=observation_frame,
+            policy=policy,
+            device=device,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            use_amp=policy.config.use_amp,
+            task=single_task,
+            robot_type=robot.robot_type,
+        )
+        
+        # Unnormalize relative actions if normalizer exists
+        if relative_normalizer is not None:
+            # action_tensor shape: [1, action_dim] or [action_dim]
+            if action_tensor.dim() == 1:
+                action_tensor = action_tensor.unsqueeze(0).unsqueeze(0)  # [1, 1, action_dim]
+            elif action_tensor.dim() == 2:
+                action_tensor = action_tensor.unsqueeze(1)  # [batch, 1, action_dim]
+            action_tensor = relative_normalizer.unnormalize(action_tensor)
+        
+        # Flatten to 1D: take first timestep if chunks, squeeze batch dims
+        while action_tensor.dim() > 1:
+            action_tensor = action_tensor[0]
+        
+        # Manually convert to dict (tensor_to_robot_action expects specific shape)
+        action_names = dataset.features[ACTION]["names"]
+        relative_action = {name: float(action_tensor[i]) for i, name in enumerate(action_names)}
+        
+        # Convert relative to absolute
+        absolute_action = convert_from_relative_actions_dict(relative_action, current_pos)
+        
+        robot.send_action(absolute_action)
+        
+        if dataset is not None:
+            action_frame = build_dataset_frame(dataset.features, absolute_action, prefix=ACTION)
+            frame = {**observation_frame, **action_frame, "task": single_task}
+            dataset.add_frame(frame)
+        
+        if display_data:
+            log_rerun_data(observation=obs, action=absolute_action)
+        
+        dt = time.perf_counter() - loop_start
+        precise_sleep(1 / fps - dt)
+        timestamp = time.perf_counter() - start_t
+
+
+def main():
+    print("=" * 60)
+    print("  OpenArms Evaluation - Relative Actions")
+    print("=" * 60)
+    print(f"\nModel: {HF_MODEL_ID}")
+    print(f"Dataset: {HF_EVAL_DATASET_ID}")
+    print(f"Episodes: {NUM_EPISODES}, Duration: {EPISODE_TIME_SEC}s")
+    
+    # Load relative action config
+    relative_normalizer, use_relative_state = load_relative_config(HF_MODEL_ID)
+    mode = "actions + state" if use_relative_state else "actions only"
+    print(f"Mode: relative {mode}")
+    
+    # Setup robot
+    follower_config = OpenArmsFollowerConfig(
+        port_left=FOLLOWER_LEFT_PORT,
+        port_right=FOLLOWER_RIGHT_PORT,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=CAMERA_CONFIG,
+    )
+    
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+    
+    if not follower.is_connected:
+        raise RuntimeError("Robot failed to connect!")
+
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    action_features_hw = {k: v for k, v in follower.action_features.items() if k.endswith(".pos")}
+    
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+    
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
+    if dataset_path.exists():
+        print(f"\nDataset exists at: {dataset_path}")
+        if input("Continue? (y/n): ").strip().lower() != 'y':
+            follower.disconnect()
+            return
+    
+    dataset = LeRobotDataset.create(
+        repo_id=HF_EVAL_DATASET_ID,
+        fps=FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_processes=0,
+        image_writer_threads=12, 
+    )
+    
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+    policy = make_policy(policy_config, ds_meta=dataset.meta)
+    
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy.config,
+        pretrained_path=HF_MODEL_ID,
+        dataset_stats=dataset.meta.stats,
+        preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+    )
+
+    listener, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_eval_relative")
+    episode_idx = 0
+    
+    print("\nControls: ESC=stop, →=next episode, ←=rerecord")
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Episode {episode_idx + 1} of {NUM_EPISODES}")
+            
+            inference_loop_relative(
+                robot=follower,
+                policy=policy,
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
+                dataset=dataset,
+                events=events,
+                fps=FPS,
+                control_time_s=EPISODE_TIME_SEC,
+                single_task=TASK_DESCRIPTION,
+                display_data=True,
+                relative_normalizer=relative_normalizer,
+                use_relative_state=use_relative_state,
+            )
+            
+            if events.get("rerecord_episode", False):
+                log_say("Re-recording")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                print(f"Saving episode {episode_idx + 1}...")
+                dataset.save_episode()
+                episode_idx += 1
+            
+            events["exit_early"] = False
+            
+            if not events["stop_recording"] and episode_idx < NUM_EPISODES:
+                input("Press ENTER for next episode...")
+        
+        print(f"\nDone! {episode_idx} episodes recorded")
+        log_say("Complete", blocking=True)
+    
+    except KeyboardInterrupt:
+        print("\n\nInterrupted")
+    
+    finally:
+        follower.disconnect()
+        if listener is not None:
+            listener.stop()
+        dataset.finalize()
+        print("Uploading to Hub...")
+        dataset.push_to_hub(private=True)
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,653 @@
+#!/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.
+
+"""
+OpenArms Policy Evaluation with Real-Time Chunking (RTC)
+
+Evaluates a trained policy on the OpenArms robot using RTC for smooth, continuous motion.
+RTC enables large flow-matching policies (Pi0, Pi0.5, SmolVLA) to produce reactive motion
+despite high inference latency by asynchronously generating action chunks.
+
+Features:
+- Thread-based asynchronous action generation and execution
+- RTC for smooth transitions between action chunks
+- Dataset recording for evaluation episodes
+
+Example usage:
+    python examples/openarms/evaluate_with_rtc.py
+
+    # With custom RTC parameters
+    python examples/openarms/evaluate_with_rtc.py \
+        --rtc.execution_horizon=12 \
+        --rtc.max_guidance_weight=10.0
+"""
+
+import logging
+import math
+import sys
+import time
+import traceback
+from dataclasses import dataclass, field
+from pathlib import Path
+from threading import Event, Lock, Thread
+
+import torch
+from torch import Tensor
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs import parser
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import RTCAttentionSchedule
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts, hw_to_dataset_features
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
+from lerobot.policies.rtc.action_queue import ActionQueue
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.policies.rtc.latency_tracker import LatencyTracker
+from lerobot.processor import make_default_processors
+from lerobot.rl.process import ProcessSignalHandler
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.utils.hub import HubMixin
+from lerobot.utils.utils import init_logging, log_say
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# ============================================================================
+# Default Configuration Constants
+# ============================================================================
+
+DEFAULT_HF_MODEL_ID = "lerobot-data-collection/three-folds-pi0"
+DEFAULT_HF_EVAL_DATASET_ID = "lerobot-data-collection/three-folds-pi0_eval_rtc"
+DEFAULT_TASK_DESCRIPTION = "three-folds-dataset"
+
+DEFAULT_NUM_EPISODES = 1
+DEFAULT_FPS = 30
+DEFAULT_EPISODE_TIME_SEC = 300
+DEFAULT_RESET_TIME_SEC = 60
+
+DEFAULT_FOLLOWER_LEFT_PORT = "can0"
+DEFAULT_FOLLOWER_RIGHT_PORT = "can1"
+
+DEFAULT_CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=DEFAULT_FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=DEFAULT_FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=DEFAULT_FPS),
+}
+
+
+# ============================================================================
+# Thread-Safe Robot Wrapper
+# ============================================================================
+
+
+class RobotWrapper:
+    """Thread-safe wrapper for robot operations."""
+
+    def __init__(self, robot: OpenArmsFollower):
+        self.robot = robot
+        self.lock = Lock()
+
+    def get_observation(self) -> dict[str, Tensor]:
+        with self.lock:
+            return self.robot.get_observation()
+
+    def send_action(self, action: dict) -> None:
+        with self.lock:
+            self.robot.send_action(action)
+
+    @property
+    def observation_features(self) -> dict:
+        with self.lock:
+            return self.robot.observation_features
+
+    @property
+    def action_features(self) -> dict:
+        with self.lock:
+            return self.robot.action_features
+
+    @property
+    def name(self) -> str:
+        return self.robot.name
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+
+@dataclass
+class OpenArmsRTCEvalConfig(HubMixin):
+    """Configuration for OpenArms evaluation with RTC."""
+
+    policy: PreTrainedConfig | None = None
+
+    rtc: RTCConfig = field(
+        default_factory=lambda: RTCConfig(
+            enabled=True,
+            execution_horizon=10,
+            max_guidance_weight=10.0,
+            prefix_attention_schedule=RTCAttentionSchedule.EXP,
+        )
+    )
+
+    model_id: str = DEFAULT_HF_MODEL_ID
+    eval_dataset_id: str = DEFAULT_HF_EVAL_DATASET_ID
+    task: str = DEFAULT_TASK_DESCRIPTION
+
+    num_episodes: int = DEFAULT_NUM_EPISODES
+    fps: float = DEFAULT_FPS
+    episode_time_sec: float = DEFAULT_EPISODE_TIME_SEC
+    reset_time_sec: float = DEFAULT_RESET_TIME_SEC
+
+    follower_left_port: str = DEFAULT_FOLLOWER_LEFT_PORT
+    follower_right_port: str = DEFAULT_FOLLOWER_RIGHT_PORT
+
+    device: str = "cuda"
+
+    # Should be higher than inference_delay + execution_horizon
+    action_queue_size_to_get_new_actions: int = 30
+
+    record_dataset: bool = True
+    push_to_hub: bool = True
+
+    use_torch_compile: bool = False
+    torch_compile_backend: str = "inductor"
+    torch_compile_mode: str = "default"
+    torch_compile_disable_cudagraphs: bool = True
+
+    def __post_init__(self):
+        policy_path = parser.get_path_arg("policy")
+        if policy_path:
+            cli_overrides = parser.get_cli_overrides("policy")
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+            self.model_id = policy_path
+        elif self.model_id:
+            self.policy = PreTrainedConfig.from_pretrained(self.model_id)
+            self.policy.pretrained_path = self.model_id
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        return ["policy"]
+
+
+# ============================================================================
+# Action Generation Thread
+# ============================================================================
+
+
+def get_actions_thread(
+    policy,
+    robot: RobotWrapper,
+    robot_observation_processor,
+    action_queue: ActionQueue,
+    shutdown_event: Event,
+    cfg: OpenArmsRTCEvalConfig,
+    episode_active: Event,
+):
+    """Thread function to asynchronously generate action chunks from the policy."""
+    try:
+        logger.info("[GET_ACTIONS] Starting action generation thread")
+
+        latency_tracker = LatencyTracker()
+        time_per_chunk = 1.0 / cfg.fps
+
+        hw_features = hw_to_dataset_features(robot.observation_features, "observation")
+        policy_device = policy.config.device
+
+        logger.info(f"[GET_ACTIONS] Loading preprocessor/postprocessor from {cfg.policy.pretrained_path}")
+
+        preprocessor, postprocessor = make_pre_post_processors(
+            policy_cfg=cfg.policy,
+            pretrained_path=cfg.policy.pretrained_path,
+            dataset_stats=None,
+            preprocessor_overrides={
+                "device_processor": {"device": cfg.device},
+            },
+        )
+
+        logger.info("[GET_ACTIONS] Preprocessor/postprocessor loaded successfully")
+
+        get_actions_threshold = cfg.action_queue_size_to_get_new_actions
+        if not cfg.rtc.enabled:
+            get_actions_threshold = 0
+
+        while not shutdown_event.is_set():
+            if not episode_active.is_set():
+                time.sleep(0.01)
+                continue
+
+            if action_queue.qsize() <= get_actions_threshold:
+                current_time = time.perf_counter()
+                action_index_before_inference = action_queue.get_action_index()
+                prev_actions = action_queue.get_left_over()
+
+                inference_latency = latency_tracker.max()
+                inference_delay = math.ceil(inference_latency / time_per_chunk) if inference_latency else 0
+
+                obs = robot.get_observation()
+                obs_processed = robot_observation_processor(obs)
+
+                obs_with_policy_features = build_dataset_frame(
+                    hw_features, obs_processed, prefix="observation"
+                )
+
+                for name in obs_with_policy_features:
+                    obs_with_policy_features[name] = torch.from_numpy(obs_with_policy_features[name])
+                    if "image" in name:
+                        obs_with_policy_features[name] = (
+                            obs_with_policy_features[name].type(torch.float32) / 255
+                        )
+                        obs_with_policy_features[name] = (
+                            obs_with_policy_features[name].permute(2, 0, 1).contiguous()
+                        )
+                    obs_with_policy_features[name] = obs_with_policy_features[name].unsqueeze(0)
+                    obs_with_policy_features[name] = obs_with_policy_features[name].to(policy_device)
+
+                obs_with_policy_features["task"] = [cfg.task]
+                obs_with_policy_features["robot_type"] = robot.name
+
+                preprocessed_obs = preprocessor(obs_with_policy_features)
+
+                actions = policy.predict_action_chunk(
+                    preprocessed_obs,
+                    inference_delay=inference_delay,
+                    prev_chunk_left_over=prev_actions,
+                )
+
+                original_actions = actions.squeeze(0).clone()
+                postprocessed_actions = postprocessor(actions).squeeze(0)
+
+                new_latency = time.perf_counter() - current_time
+                new_delay = math.ceil(new_latency / time_per_chunk)
+                latency_tracker.add(new_latency)
+
+                if cfg.action_queue_size_to_get_new_actions < cfg.rtc.execution_horizon + new_delay:
+                    logger.warning(
+                        "[GET_ACTIONS] action_queue_size_to_get_new_actions too small. "
+                        "Should be higher than inference delay + execution horizon."
+                    )
+
+                action_queue.merge(
+                    original_actions, postprocessed_actions, new_delay, action_index_before_inference
+                )
+
+                logger.debug(
+                    f"[GET_ACTIONS] Generated chunk, latency={new_latency:.3f}s, "
+                    f"delay={new_delay}, queue_size={action_queue.qsize()}"
+                )
+            else:
+                time.sleep(0.01)
+
+        logger.info("[GET_ACTIONS] Action generation thread shutting down")
+    except Exception as e:
+        logger.error(f"[GET_ACTIONS] Fatal exception: {e}")
+        logger.error(traceback.format_exc())
+        shutdown_event.set()
+        sys.exit(1)
+
+
+# ============================================================================
+# Action Execution Thread
+# ============================================================================
+
+
+def actor_thread(
+    robot: RobotWrapper,
+    robot_action_processor,
+    action_queue: ActionQueue,
+    shutdown_event: Event,
+    cfg: OpenArmsRTCEvalConfig,
+    episode_active: Event,
+    dataset: LeRobotDataset | None,
+    dataset_lock: Lock,
+    teleop_action_processor,
+    robot_observation_processor,
+):
+    """Thread function to execute actions on the robot."""
+    try:
+        logger.info("[ACTOR] Starting actor thread")
+
+        action_count = 0
+        action_interval = 1.0 / cfg.fps
+        action_keys = [k for k in robot.action_features.keys() if k.endswith(".pos")]
+
+        while not shutdown_event.is_set():
+            if not episode_active.is_set():
+                time.sleep(0.01)
+                continue
+
+            start_time = time.perf_counter()
+            action = action_queue.get()
+
+            if action is not None:
+                action = action.cpu()
+
+                action_dict = {}
+                for i, key in enumerate(action_keys):
+                    if i < len(action):
+                        action_dict[key] = action[i].item()
+
+                action_processed = robot_action_processor((action_dict, None))
+                robot.send_action(action_processed)
+
+                if cfg.record_dataset and dataset is not None:
+                    with dataset_lock:
+                        obs = robot.get_observation()
+                        obs_processed = robot_observation_processor(obs)
+                        action_for_dataset = teleop_action_processor((action_dict, None))
+
+                        frame = {}
+                        for key, value in obs_processed.items():
+                            frame[f"observation.{key}"] = value
+                        for key, value in action_for_dataset.items():
+                            frame[f"action.{key}"] = value
+                        frame["task"] = cfg.task
+
+                        dataset.add_frame(frame)
+
+                action_count += 1
+
+            dt_s = time.perf_counter() - start_time
+            sleep_time = max(0, action_interval - dt_s - 0.001)
+            if sleep_time > 0:
+                time.sleep(sleep_time)
+
+        logger.info(f"[ACTOR] Actor thread shutting down. Total actions executed: {action_count}")
+    except Exception as e:
+        logger.error(f"[ACTOR] Fatal exception: {e}")
+        logger.error(traceback.format_exc())
+        shutdown_event.set()
+        sys.exit(1)
+
+
+# ============================================================================
+# Main Evaluation Function
+# ============================================================================
+
+
+def _apply_torch_compile(policy, cfg: OpenArmsRTCEvalConfig):
+    """Apply torch.compile to the policy's predict_action_chunk method."""
+    if policy.name in ["pi05", "pi0"]:
+        return policy
+
+    try:
+        if not hasattr(torch, "compile"):
+            logger.warning(
+                f"torch.compile not available. Requires PyTorch 2.0+. "
+                f"Current version: {torch.__version__}. Skipping compilation."
+            )
+            return policy
+
+        logger.info("Applying torch.compile to predict_action_chunk...")
+
+        compile_kwargs = {
+            "backend": cfg.torch_compile_backend,
+            "mode": cfg.torch_compile_mode,
+        }
+
+        if cfg.torch_compile_disable_cudagraphs:
+            compile_kwargs["options"] = {"triton.cudagraphs": False}
+
+        original_method = policy.predict_action_chunk
+        compiled_method = torch.compile(original_method, **compile_kwargs)
+        policy.predict_action_chunk = compiled_method
+        logger.info("Successfully compiled predict_action_chunk")
+
+    except Exception as e:
+        logger.error(f"Failed to apply torch.compile: {e}")
+        logger.warning("Continuing without torch.compile")
+
+    return policy
+
+
+@parser.wrap()
+def main(cfg: OpenArmsRTCEvalConfig):
+    """Main evaluation function with RTC."""
+    init_logging()
+
+    print("=" * 60)
+    print("OpenArms Policy Evaluation with RTC")
+    print("=" * 60)
+    print(f"\nModel: {cfg.model_id}")
+    print(f"Evaluation Dataset: {cfg.eval_dataset_id}")
+    print(f"Task: {cfg.task}")
+    print(f"Episodes: {cfg.num_episodes}")
+    print(f"Episode Duration: {cfg.episode_time_sec}s")
+    print(f"RTC Enabled: {cfg.rtc.enabled}")
+    print(f"RTC Execution Horizon: {cfg.rtc.execution_horizon}")
+    print(f"RTC Max Guidance Weight: {cfg.rtc.max_guidance_weight}")
+    print(f"Device: {cfg.device}")
+    print("=" * 60)
+
+    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
+    shutdown_event = signal_handler.shutdown_event
+    episode_active = Event()
+
+    # Initialize Robot
+    follower_config = OpenArmsFollowerConfig(
+        port_left=cfg.follower_left_port,
+        port_right=cfg.follower_right_port,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=DEFAULT_CAMERA_CONFIG,
+    )
+
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+
+    if not follower.is_connected:
+        raise RuntimeError("Follower robot failed to connect!")
+
+    robot = RobotWrapper(follower)
+    logger.info("Follower robot connected")
+
+    # Build Processors and Dataset Features
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+
+    # Create or Load Dataset
+    dataset = None
+    dataset_lock = Lock()
+
+    if cfg.record_dataset:
+        dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / cfg.eval_dataset_id
+        if dataset_path.exists():
+            logger.info(f"Evaluation dataset exists at: {dataset_path}")
+            logger.info("New episodes will be appended.")
+            choice = input("Continue? (y/n): ").strip().lower()
+            if choice != "y":
+                logger.info("Aborting evaluation.")
+                follower.disconnect()
+                return
+
+        dataset = LeRobotDataset.create(
+            repo_id=cfg.eval_dataset_id,
+            fps=int(cfg.fps),
+            features=dataset_features,
+            robot_type=follower.name,
+            use_videos=True,
+            image_writer_processes=0,
+            image_writer_threads=12,
+        )
+        logger.info(f"Dataset created: {cfg.eval_dataset_id}")
+
+    # Load Policy
+    logger.info(f"Loading policy from: {cfg.model_id}")
+
+    policy_class = get_policy_class(cfg.policy.type)
+    config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
+
+    if cfg.policy.type in ["pi05", "pi0"]:
+        config.compile_model = cfg.use_torch_compile
+
+    policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
+
+    policy.config.rtc_config = cfg.rtc
+    policy.init_rtc_processor()
+
+    assert policy.name in ["smolvla", "pi05", "pi0"], "Only smolvla, pi05, and pi0 are supported for RTC"
+
+    policy = policy.to(cfg.device)
+    policy.eval()
+
+    if cfg.use_torch_compile:
+        policy = _apply_torch_compile(policy, cfg)
+
+    logger.info(f"Policy loaded: {policy.name}")
+
+    # Create Action Queue and Start Threads
+    action_queue = ActionQueue(cfg.rtc)
+
+    get_actions_t = Thread(
+        target=get_actions_thread,
+        args=(
+            policy,
+            robot,
+            robot_observation_processor,
+            action_queue,
+            shutdown_event,
+            cfg,
+            episode_active,
+        ),
+        daemon=True,
+        name="GetActions",
+    )
+    get_actions_t.start()
+    logger.info("Started action generation thread")
+
+    actor_t = Thread(
+        target=actor_thread,
+        args=(
+            robot,
+            robot_action_processor,
+            action_queue,
+            shutdown_event,
+            cfg,
+            episode_active,
+            dataset,
+            dataset_lock,
+            teleop_action_processor,
+            robot_observation_processor,
+        ),
+        daemon=True,
+        name="Actor",
+    )
+    actor_t.start()
+    logger.info("Started actor thread")
+
+    # Run Evaluation Episodes
+    episode_idx = 0
+
+    try:
+        while episode_idx < cfg.num_episodes and not shutdown_event.is_set():
+            log_say(f"Evaluating episode {episode_idx + 1} of {cfg.num_episodes}")
+            logger.info(f"\n{'='*40}")
+            logger.info(f"Episode {episode_idx + 1} / {cfg.num_episodes}")
+            logger.info(f"{'='*40}")
+
+            action_queue = ActionQueue(cfg.rtc)
+            episode_active.set()
+            episode_start_time = time.time()
+
+            while (time.time() - episode_start_time) < cfg.episode_time_sec:
+                if shutdown_event.is_set():
+                    break
+
+                elapsed = time.time() - episode_start_time
+                if int(elapsed) % 10 == 0 and int(elapsed) > 0:
+                    logger.info(
+                        f"[MAIN] Episode progress: {elapsed:.0f}/{cfg.episode_time_sec}s, "
+                        f"queue_size={action_queue.qsize()}"
+                    )
+
+                time.sleep(0.5)
+
+            episode_active.clear()
+            logger.info(f"Episode {episode_idx + 1} completed")
+
+            if cfg.record_dataset and dataset is not None:
+                with dataset_lock:
+                    if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                        logger.info(
+                            f"Saving episode {episode_idx + 1} "
+                            f"({dataset.episode_buffer['size']} frames)"
+                        )
+                        dataset.save_episode()
+
+            episode_idx += 1
+
+            # Manual reset between episodes
+            if not shutdown_event.is_set() and episode_idx < cfg.num_episodes:
+                log_say("Waiting for manual reset")
+                logger.info("Manually reset the environment and press ENTER to continue")
+                input("Press ENTER when ready...")
+
+        logger.info(f"Evaluation complete! {episode_idx} episodes recorded")
+        log_say("Evaluation complete", blocking=True)
+
+    except KeyboardInterrupt:
+        logger.info("\n\nEvaluation interrupted by user")
+
+    finally:
+        shutdown_event.set()
+        episode_active.clear()
+
+        if get_actions_t.is_alive():
+            logger.info("Waiting for action generation thread to finish...")
+            get_actions_t.join(timeout=5.0)
+
+        if actor_t.is_alive():
+            logger.info("Waiting for actor thread to finish...")
+            actor_t.join(timeout=5.0)
+
+        follower.disconnect()
+        logger.info("Follower disconnected")
+
+        if cfg.record_dataset and dataset is not None:
+            dataset.finalize()
+            if cfg.push_to_hub:
+                logger.info("Uploading to Hugging Face Hub...")
+                dataset.push_to_hub(private=True)
+
+        logger.info("Cleanup completed")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,894 @@
+#!/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.
+
+"""
+OpenArms Policy Evaluation with RTC + Interpolation
+
+Combines Real-Time Chunking (RTC) with smooth action interpolation:
+- RTC for reactive motion despite high inference latency
+- Action interpolation for smooth robot movements
+- Speed multiplier to execute faster than training
+- Velocity feedforward and PID tuning
+- Decoupled inference (async) from robot control
+
+Example usage:
+    python examples/openarms/evaluate_with_rtc_interpolation.py
+
+    # With custom RTC parameters
+    python examples/openarms/evaluate_with_rtc_interpolation.py \
+        --rtc.execution_horizon=12 \
+        --rtc.max_guidance_weight=10.0
+"""
+
+import logging
+import math
+import sys
+import time
+import traceback
+from collections import deque
+from dataclasses import dataclass, field
+from pathlib import Path
+from threading import Event, Lock, Thread
+
+import torch
+from torch import Tensor
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs import parser
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import RTCAttentionSchedule
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts, hw_to_dataset_features
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
+from lerobot.policies.rtc.action_queue import ActionQueue
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.policies.rtc.latency_tracker import LatencyTracker
+from lerobot.processor import make_default_processors
+from lerobot.rl.process import ProcessSignalHandler
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.utils.hub import HubMixin
+from lerobot.utils.utils import init_logging, log_say
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# ============================================================================
+# Default Configuration Constants
+# ============================================================================
+
+DEFAULT_HF_MODEL_ID = "lerobot-data-collection/three-folds-pi0"
+DEFAULT_HF_EVAL_DATASET_ID = "lerobot-data-collection/three-folds-pi0_eval_rtc_interp"
+DEFAULT_TASK_DESCRIPTION = "three-folds-dataset"
+
+DEFAULT_NUM_EPISODES = 1
+DEFAULT_CAMERA_FPS = 30           # Camera hardware limit
+DEFAULT_POLICY_FPS = 30           # What the policy was trained with
+DEFAULT_SPEED_MULTIPLIER = 1.0    # Execute actions faster (1.0 = normal, 1.2 = 20% faster)
+DEFAULT_ROBOT_FPS = 50            # Robot command rate (higher = smoother)
+DEFAULT_EPISODE_TIME_SEC = 300
+DEFAULT_RESET_TIME_SEC = 60
+
+DEFAULT_FOLLOWER_LEFT_PORT = "can0"
+DEFAULT_FOLLOWER_RIGHT_PORT = "can1"
+
+# PID tuning defaults
+DEFAULT_KP_SCALE = 0.7            # Lower = smoother but slower
+DEFAULT_KD_SCALE = 1.3            # Higher = less overshoot
+DEFAULT_USE_VELOCITY_FF = True    # Velocity feedforward
+
+DEFAULT_CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=DEFAULT_CAMERA_FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=DEFAULT_CAMERA_FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=DEFAULT_CAMERA_FPS),
+}
+
+
+# ============================================================================
+# Action Interpolator
+# ============================================================================
+
+
+class ActionInterpolator:
+    """Interpolate between RTC actions for smoother robot control with velocity estimation."""
+    
+    def __init__(self, robot_fps: int):
+        self.robot_fps = robot_fps
+        self.prev_action: Tensor | None = None
+        self.curr_action: Tensor | None = None
+        self.prev_time: float = 0
+        self.curr_time: float = 0
+        self.last_interpolated: Tensor | None = None
+        
+    def update(self, new_action: Tensor) -> None:
+        self.prev_action = self.curr_action
+        self.prev_time = self.curr_time
+        self.curr_action = new_action
+        self.curr_time = time.perf_counter()
+        
+    def get_interpolated_action(self) -> tuple[Tensor | None, Tensor | None]:
+        """Returns (interpolated_position, estimated_velocity)"""
+        if self.curr_action is None:
+            return None, None
+        if self.prev_action is None:
+            self.last_interpolated = self.curr_action.clone()
+            return self.curr_action, torch.zeros_like(self.curr_action)
+        
+        # Time-based interpolation
+        current_time = time.perf_counter()
+        dt_actions = self.curr_time - self.prev_time
+        if dt_actions <= 0:
+            dt_actions = 1.0 / 30  # Fallback
+            
+        t = (current_time - self.prev_time) / dt_actions
+        t = max(0.0, min(t, 1.5))  # Allow slight extrapolation
+        
+        interpolated = self.prev_action + t * (self.curr_action - self.prev_action)
+        
+        # Estimate velocity
+        dt_robot = 1.0 / self.robot_fps
+        if self.last_interpolated is not None:
+            velocity = (interpolated - self.last_interpolated) / dt_robot
+        else:
+            velocity = (self.curr_action - self.prev_action) / dt_actions
+        
+        self.last_interpolated = interpolated.clone()
+        return interpolated, velocity
+    
+    def reset(self):
+        self.prev_action = None
+        self.curr_action = None
+        self.prev_time = 0
+        self.curr_time = 0
+        self.last_interpolated = None
+
+
+class HzTracker:
+    """Track and display actual loop frequency."""
+    
+    def __init__(self, name: str = "Loop", window_size: int = 100, print_interval: float = 2.0):
+        self.name = name
+        self.timestamps = deque(maxlen=window_size)
+        self.last_print_time = 0
+        self.print_interval = print_interval
+        self.extra_info_fn = None  # Optional callback for extra info
+        
+    def tick(self) -> float | None:
+        now = time.perf_counter()
+        self.timestamps.append(now)
+        
+        if len(self.timestamps) < 2:
+            return None
+            
+        hz = (len(self.timestamps) - 1) / (self.timestamps[-1] - self.timestamps[0])
+        
+        if now - self.last_print_time >= self.print_interval:
+            extra = ""
+            if self.extra_info_fn:
+                extra = self.extra_info_fn()
+            print(f"[CONTROL] {self.name}: {hz:.1f} Hz{extra}", flush=True)
+            self.last_print_time = now
+            
+        return hz
+    
+    def get_avg_hz(self) -> float | None:
+        if len(self.timestamps) < 2:
+            return None
+        return (len(self.timestamps) - 1) / (self.timestamps[-1] - self.timestamps[0])
+    
+    def reset(self):
+        self.timestamps.clear()
+        self.last_print_time = 0
+
+
+# ============================================================================
+# Thread-Safe Robot Wrapper
+# ============================================================================
+
+
+class RobotWrapper:
+    """Thread-safe wrapper for robot operations with custom PID gains."""
+
+    def __init__(
+        self, 
+        robot: OpenArmsFollower,
+        custom_kp: dict | None = None,
+        custom_kd: dict | None = None,
+        use_velocity_ff: bool = False,
+    ):
+        self.robot = robot
+        self.lock = Lock()
+        self.custom_kp = custom_kp
+        self.custom_kd = custom_kd
+        self.use_velocity_ff = use_velocity_ff
+
+    def get_observation(self) -> dict[str, Tensor]:
+        with self.lock:
+            return self.robot.get_observation()
+
+    def send_action(self, action: dict, velocity_ff: dict | None = None) -> None:
+        with self.lock:
+            vel_ff = velocity_ff if self.use_velocity_ff else None
+            self.robot.send_action(
+                action, 
+                custom_kp=self.custom_kp, 
+                custom_kd=self.custom_kd,
+                velocity_feedforward=vel_ff,
+            )
+
+    @property
+    def observation_features(self) -> dict:
+        with self.lock:
+            return self.robot.observation_features
+
+    @property
+    def action_features(self) -> dict:
+        with self.lock:
+            return self.robot.action_features
+
+    @property
+    def name(self) -> str:
+        return self.robot.name
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+
+@dataclass
+class OpenArmsRTCInterpEvalConfig(HubMixin):
+    """Configuration for OpenArms evaluation with RTC + Interpolation."""
+
+    policy: PreTrainedConfig | None = None
+
+    rtc: RTCConfig = field(
+        default_factory=lambda: RTCConfig(
+            enabled=True,
+            execution_horizon=10,
+            max_guidance_weight=10.0,
+            prefix_attention_schedule=RTCAttentionSchedule.EXP,
+        )
+    )
+
+    model_id: str = DEFAULT_HF_MODEL_ID
+    eval_dataset_id: str = DEFAULT_HF_EVAL_DATASET_ID
+    task: str = DEFAULT_TASK_DESCRIPTION
+
+    num_episodes: int = DEFAULT_NUM_EPISODES
+    camera_fps: float = DEFAULT_CAMERA_FPS
+    policy_fps: float = DEFAULT_POLICY_FPS
+    speed_multiplier: float = DEFAULT_SPEED_MULTIPLIER
+    robot_fps: float = DEFAULT_ROBOT_FPS
+    episode_time_sec: float = DEFAULT_EPISODE_TIME_SEC
+    reset_time_sec: float = DEFAULT_RESET_TIME_SEC
+
+    # PID tuning
+    kp_scale: float | None = DEFAULT_KP_SCALE
+    kd_scale: float | None = DEFAULT_KD_SCALE
+    use_velocity_ff: bool = DEFAULT_USE_VELOCITY_FF
+
+    follower_left_port: str = DEFAULT_FOLLOWER_LEFT_PORT
+    follower_right_port: str = DEFAULT_FOLLOWER_RIGHT_PORT
+
+    device: str = "cuda"
+
+    # Should be higher than inference_delay + execution_horizon
+    action_queue_size_to_get_new_actions: int = 30
+
+    record_dataset: bool = True
+    push_to_hub: bool = True
+
+    use_torch_compile: bool = False
+    torch_compile_backend: str = "inductor"
+    torch_compile_mode: str = "default"
+    torch_compile_disable_cudagraphs: bool = True
+
+    @property
+    def effective_policy_fps(self) -> int:
+        return int(self.policy_fps * self.speed_multiplier)
+
+    def __post_init__(self):
+        policy_path = parser.get_path_arg("policy")
+        if policy_path:
+            cli_overrides = parser.get_cli_overrides("policy")
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+            self.model_id = policy_path
+        elif self.model_id:
+            self.policy = PreTrainedConfig.from_pretrained(self.model_id)
+            self.policy.pretrained_path = self.model_id
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        return ["policy"]
+
+
+# ============================================================================
+# Action Generation Thread (RTC)
+# ============================================================================
+
+
+def get_actions_thread(
+    policy,
+    robot: RobotWrapper,
+    robot_observation_processor,
+    action_queue: ActionQueue,
+    shutdown_event: Event,
+    cfg: OpenArmsRTCInterpEvalConfig,
+    episode_active: Event,
+):
+    """Thread function to asynchronously generate action chunks from the policy using RTC."""
+    try:
+        logger.info("[GET_ACTIONS] Starting RTC action generation thread")
+
+        latency_tracker = LatencyTracker()
+        inference_hz_tracker = HzTracker(name="Inference", window_size=20, print_interval=5.0)
+        time_per_chunk = 1.0 / cfg.effective_policy_fps  # Use effective FPS with speed multiplier
+
+        hw_features = hw_to_dataset_features(robot.observation_features, "observation")
+        policy_device = policy.config.device
+
+        logger.info(f"[GET_ACTIONS] Loading preprocessor/postprocessor from {cfg.policy.pretrained_path}")
+
+        preprocessor, postprocessor = make_pre_post_processors(
+            policy_cfg=cfg.policy,
+            pretrained_path=cfg.policy.pretrained_path,
+            dataset_stats=None,
+            preprocessor_overrides={
+                "device_processor": {"device": cfg.device},
+            },
+        )
+
+        logger.info("[GET_ACTIONS] Preprocessor/postprocessor loaded successfully")
+
+        get_actions_threshold = cfg.action_queue_size_to_get_new_actions
+        if not cfg.rtc.enabled:
+            get_actions_threshold = 0
+
+        while not shutdown_event.is_set():
+            if not episode_active.is_set():
+                time.sleep(0.01)
+                continue
+
+            if action_queue.qsize() <= get_actions_threshold:
+                current_time = time.perf_counter()
+                action_index_before_inference = action_queue.get_action_index()
+                prev_actions = action_queue.get_left_over()
+
+                inference_latency = latency_tracker.max()
+                inference_delay = math.ceil(inference_latency / time_per_chunk) if inference_latency else 0
+
+                obs = robot.get_observation()
+                obs_processed = robot_observation_processor(obs)
+
+                obs_with_policy_features = build_dataset_frame(
+                    hw_features, obs_processed, prefix="observation"
+                )
+
+                for name in obs_with_policy_features:
+                    obs_with_policy_features[name] = torch.from_numpy(obs_with_policy_features[name])
+                    if "image" in name:
+                        obs_with_policy_features[name] = (
+                            obs_with_policy_features[name].type(torch.float32) / 255
+                        )
+                        obs_with_policy_features[name] = (
+                            obs_with_policy_features[name].permute(2, 0, 1).contiguous()
+                        )
+                    obs_with_policy_features[name] = obs_with_policy_features[name].unsqueeze(0)
+                    obs_with_policy_features[name] = obs_with_policy_features[name].to(policy_device)
+
+                obs_with_policy_features["task"] = [cfg.task]
+                obs_with_policy_features["robot_type"] = robot.name
+
+                preprocessed_obs = preprocessor(obs_with_policy_features)
+
+                actions = policy.predict_action_chunk(
+                    preprocessed_obs,
+                    inference_delay=inference_delay,
+                    prev_chunk_left_over=prev_actions,
+                )
+
+                original_actions = actions.squeeze(0).clone()
+                postprocessed_actions = postprocessor(actions).squeeze(0)
+
+                new_latency = time.perf_counter() - current_time
+                new_delay = math.ceil(new_latency / time_per_chunk)
+                latency_tracker.add(new_latency)
+                
+                # Set extra info to show latency
+                inference_hz_tracker.extra_info_fn = lambda lat=new_latency, delay=new_delay: f" | Latency: {lat*1000:.0f}ms | Delay: {delay}"
+                inference_hz_tracker.tick()
+
+                if cfg.action_queue_size_to_get_new_actions < cfg.rtc.execution_horizon + new_delay:
+                    logger.warning(
+                        "[GET_ACTIONS] action_queue_size_to_get_new_actions too small. "
+                        "Should be higher than inference delay + execution horizon."
+                    )
+
+                action_queue.merge(
+                    original_actions, postprocessed_actions, new_delay, action_index_before_inference
+                )
+
+                logger.debug(
+                    f"[GET_ACTIONS] Generated chunk, latency={new_latency:.3f}s, "
+                    f"delay={new_delay}, queue_size={action_queue.qsize()}"
+                )
+            else:
+                time.sleep(0.01)
+
+        logger.info("[GET_ACTIONS] Action generation thread shutting down")
+    except Exception as e:
+        logger.error(f"[GET_ACTIONS] Fatal exception: {e}")
+        logger.error(traceback.format_exc())
+        shutdown_event.set()
+        sys.exit(1)
+
+
+# ============================================================================
+# Actor Thread with Interpolation
+# ============================================================================
+
+
+def actor_thread(
+    robot: RobotWrapper,
+    robot_action_processor,
+    action_queue: ActionQueue,
+    shutdown_event: Event,
+    cfg: OpenArmsRTCInterpEvalConfig,
+    episode_active: Event,
+    dataset: LeRobotDataset | None,
+    dataset_lock: Lock,
+    teleop_action_processor,
+    robot_observation_processor,
+    interpolator: ActionInterpolator,
+    hz_tracker: HzTracker,
+    dataset_features: dict,
+):
+    """Thread function to execute interpolated actions on the robot at high frequency."""
+    try:
+        logger.info(f"[ACTOR] Starting actor thread with interpolation at {cfg.robot_fps}Hz")
+
+        action_count = 0
+        robot_interval = 1.0 / cfg.robot_fps  # High frequency robot control
+        effective_policy_interval = 1.0 / cfg.effective_policy_fps  # Action consume rate
+        action_keys = [k for k in robot.action_features.keys() if k.endswith(".pos")]
+        
+        last_action_consume_time = 0
+        interpolator.reset()
+        hz_tracker.reset()
+        
+        # Set up extra info callback to show queue size
+        hz_tracker.extra_info_fn = lambda: f" | Queue: {action_queue.qsize()}"
+
+        while not shutdown_event.is_set():
+            if not episode_active.is_set():
+                time.sleep(0.01)
+                interpolator.reset()
+                hz_tracker.reset()
+                last_action_consume_time = 0
+                continue
+
+            start_time = time.perf_counter()
+            
+            # Consume new action from RTC queue at effective_policy_fps rate
+            current_time = time.perf_counter()
+            if current_time - last_action_consume_time >= effective_policy_interval:
+                action = action_queue.get()
+                
+                if action is not None:
+                    action = action.cpu()
+                    interpolator.update(action)
+                    last_action_consume_time = current_time
+                    
+                    # Record to dataset at action consume rate
+                    if cfg.record_dataset and dataset is not None:
+                        with dataset_lock:
+                            obs = robot.get_observation()
+                            obs_processed = robot_observation_processor(obs)
+                            
+                            action_dict = {}
+                            for i, key in enumerate(action_keys):
+                                if i < len(action):
+                                    action_dict[key] = action[i].item()
+                            
+                            action_for_dataset = teleop_action_processor((action_dict, None))
+
+                            # Use build_dataset_frame to properly format keys
+                            observation_frame = build_dataset_frame(
+                                dataset_features, obs_processed, prefix="observation"
+                            )
+                            action_frame = build_dataset_frame(
+                                dataset_features, action_for_dataset, prefix="action"
+                            )
+                            frame = {**observation_frame, **action_frame, "task": cfg.task}
+                            dataset.add_frame(frame)
+            
+            # Get interpolated action and send to robot at robot_fps (highest rate)
+            interp_action, velocity = interpolator.get_interpolated_action()
+            
+            if interp_action is not None:
+                # Convert tensor to dict
+                action_dict = {}
+                velocity_dict = {}
+                for i, key in enumerate(action_keys):
+                    if i < len(interp_action):
+                        action_dict[key] = interp_action[i].item()
+                        if velocity is not None:
+                            motor_name = key.removesuffix(".pos")
+                            velocity_dict[motor_name] = velocity[i].item()
+                
+                action_processed = robot_action_processor((action_dict, None))
+                robot.send_action(action_processed, velocity_ff=velocity_dict)
+                action_count += 1
+
+            hz_tracker.tick()
+
+            # Maintain robot control rate
+            dt_s = time.perf_counter() - start_time
+            sleep_time = max(0, robot_interval - dt_s - 0.001)
+            if sleep_time > 0:
+                time.sleep(sleep_time)
+
+        final_hz = hz_tracker.get_avg_hz()
+        if final_hz:
+            logger.info(f"[ACTOR] Final robot Hz: {final_hz:.1f}")
+        logger.info(f"[ACTOR] Actor thread shutting down. Total actions executed: {action_count}")
+    except Exception as e:
+        logger.error(f"[ACTOR] Fatal exception: {e}")
+        logger.error(traceback.format_exc())
+        shutdown_event.set()
+        sys.exit(1)
+
+
+# ============================================================================
+# Helper Functions
+# ============================================================================
+
+
+def build_custom_gains(robot: OpenArmsFollower, kp_scale: float | None, kd_scale: float | None) -> tuple[dict | None, dict | None]:
+    """Build custom PID gains dict from robot config."""
+    if kp_scale is None and kd_scale is None:
+        return None, None
+        
+    custom_kp = {}
+    custom_kd = {}
+    for arm in ["right", "left"]:
+        bus = robot.bus_right if arm == "right" else robot.bus_left
+        for i, motor_name in enumerate(bus.motors):
+            full_name = f"{arm}_{motor_name}"
+            default_kp = robot.config.position_kp[i] if isinstance(robot.config.position_kp, list) else robot.config.position_kp
+            default_kd = robot.config.position_kd[i] if isinstance(robot.config.position_kd, list) else robot.config.position_kd
+            custom_kp[full_name] = default_kp * (kp_scale or 1.0)
+            custom_kd[full_name] = default_kd * (kd_scale or 1.0)
+    return custom_kp, custom_kd
+
+
+def _apply_torch_compile(policy, cfg: OpenArmsRTCInterpEvalConfig):
+    """Apply torch.compile to the policy's predict_action_chunk method."""
+    if policy.name in ["pi05", "pi0"]:
+        return policy
+
+    try:
+        if not hasattr(torch, "compile"):
+            logger.warning(
+                f"torch.compile not available. Requires PyTorch 2.0+. "
+                f"Current version: {torch.__version__}. Skipping compilation."
+            )
+            return policy
+
+        logger.info("Applying torch.compile to predict_action_chunk...")
+
+        compile_kwargs = {
+            "backend": cfg.torch_compile_backend,
+            "mode": cfg.torch_compile_mode,
+        }
+
+        if cfg.torch_compile_disable_cudagraphs:
+            compile_kwargs["options"] = {"triton.cudagraphs": False}
+
+        original_method = policy.predict_action_chunk
+        compiled_method = torch.compile(original_method, **compile_kwargs)
+        policy.predict_action_chunk = compiled_method
+        logger.info("Successfully compiled predict_action_chunk")
+
+    except Exception as e:
+        logger.error(f"Failed to apply torch.compile: {e}")
+        logger.warning("Continuing without torch.compile")
+
+    return policy
+
+
+# ============================================================================
+# Main Evaluation Function
+# ============================================================================
+
+
+@parser.wrap()
+def main(cfg: OpenArmsRTCInterpEvalConfig):
+    """Main evaluation function with RTC + Interpolation."""
+    init_logging()
+
+    print("=" * 70)
+    print("OpenArms Policy Evaluation with RTC + Interpolation")
+    print("=" * 70)
+    print(f"\nModel: {cfg.model_id}")
+    print(f"Evaluation Dataset: {cfg.eval_dataset_id}")
+    print(f"Task: {cfg.task}")
+    print(f"\n--- Timing ---")
+    print(f"Camera FPS: {cfg.camera_fps} (hardware limit)")
+    print(f"Policy trained at: {cfg.policy_fps}Hz")
+    print(f"Speed multiplier: {cfg.speed_multiplier}x")
+    print(f"Effective policy FPS: {cfg.effective_policy_fps}Hz (action consume rate)")
+    print(f"Robot FPS: {cfg.robot_fps}Hz (interpolated commands)")
+    print(f"\n--- RTC ---")
+    print(f"RTC Enabled: {cfg.rtc.enabled}")
+    print(f"Execution Horizon: {cfg.rtc.execution_horizon}")
+    print(f"Max Guidance Weight: {cfg.rtc.max_guidance_weight}")
+    print(f"\n--- PID Tuning ---")
+    print(f"KP scale: {cfg.kp_scale}")
+    print(f"KD scale: {cfg.kd_scale}")
+    print(f"Velocity feedforward: {cfg.use_velocity_ff}")
+    print(f"\n--- Episodes ---")
+    print(f"Episodes: {cfg.num_episodes}")
+    print(f"Duration: {cfg.episode_time_sec}s per episode")
+    print(f"Device: {cfg.device}")
+    print("=" * 70)
+
+    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
+    shutdown_event = signal_handler.shutdown_event
+    episode_active = Event()
+
+    # Initialize Robot
+    camera_config = {
+        "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=int(cfg.camera_fps)),
+        "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=int(cfg.camera_fps)),
+        "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=int(cfg.camera_fps)),
+    }
+
+    follower_config = OpenArmsFollowerConfig(
+        port_left=cfg.follower_left_port,
+        port_right=cfg.follower_right_port,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=15.0,
+        cameras=camera_config,
+    )
+
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+
+    if not follower.is_connected:
+        raise RuntimeError("Follower robot failed to connect!")
+
+    # Build custom PID gains
+    custom_kp, custom_kd = build_custom_gains(follower, cfg.kp_scale, cfg.kd_scale)
+    if custom_kp:
+        logger.info(f"Custom gains: kp_scale={cfg.kp_scale}, kd_scale={cfg.kd_scale}")
+    if cfg.use_velocity_ff:
+        logger.info("Velocity feedforward enabled")
+
+    robot = RobotWrapper(
+        follower, 
+        custom_kp=custom_kp, 
+        custom_kd=custom_kd,
+        use_velocity_ff=cfg.use_velocity_ff,
+    )
+    logger.info("Follower robot connected")
+
+    # Build Processors and Dataset Features
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+
+    # Create or Load Dataset
+    dataset = None
+    dataset_lock = Lock()
+
+    if cfg.record_dataset:
+        dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / cfg.eval_dataset_id
+        if dataset_path.exists():
+            logger.info(f"Evaluation dataset exists at: {dataset_path}")
+            logger.info("New episodes will be appended.")
+            choice = input("Continue? (y/n): ").strip().lower()
+            if choice != "y":
+                logger.info("Aborting evaluation.")
+                follower.disconnect()
+                return
+
+        # Dataset uses effective policy FPS
+        dataset = LeRobotDataset.create(
+            repo_id=cfg.eval_dataset_id,
+            fps=cfg.effective_policy_fps,
+            features=dataset_features,
+            robot_type=follower.name,
+            use_videos=True,
+            image_writer_processes=0,
+            image_writer_threads=12,
+        )
+        logger.info(f"Dataset created: {cfg.eval_dataset_id} at {cfg.effective_policy_fps}Hz")
+
+    # Load Policy
+    logger.info(f"Loading policy from: {cfg.model_id}")
+
+    policy_class = get_policy_class(cfg.policy.type)
+    config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
+
+    if cfg.policy.type in ["pi05", "pi0"]:
+        config.compile_model = cfg.use_torch_compile
+
+    policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
+
+    policy.config.rtc_config = cfg.rtc
+    policy.init_rtc_processor()
+
+    assert policy.name in ["smolvla", "pi05", "pi0"], "Only smolvla, pi05, and pi0 are supported for RTC"
+
+    policy = policy.to(cfg.device)
+    policy.eval()
+
+    if cfg.use_torch_compile:
+        policy = _apply_torch_compile(policy, cfg)
+
+    logger.info(f"Policy loaded: {policy.name}")
+
+    # Create Action Queue, Interpolator, and Hz Tracker
+    action_queue = ActionQueue(cfg.rtc)
+    interpolator = ActionInterpolator(robot_fps=int(cfg.robot_fps))
+    hz_tracker = HzTracker(name="Robot", window_size=100, print_interval=2.0)
+
+    # Start Threads
+    get_actions_t = Thread(
+        target=get_actions_thread,
+        args=(
+            policy,
+            robot,
+            robot_observation_processor,
+            action_queue,
+            shutdown_event,
+            cfg,
+            episode_active,
+        ),
+        daemon=True,
+        name="GetActions",
+    )
+    get_actions_t.start()
+    logger.info("Started RTC action generation thread")
+
+    actor_t = Thread(
+        target=actor_thread,
+        args=(
+            robot,
+            robot_action_processor,
+            action_queue,
+            shutdown_event,
+            cfg,
+            episode_active,
+            dataset,
+            dataset_lock,
+            teleop_action_processor,
+            robot_observation_processor,
+            interpolator,
+            hz_tracker,
+            dataset_features,
+        ),
+        daemon=True,
+        name="Actor",
+    )
+    actor_t.start()
+    logger.info(f"Started actor thread with interpolation at {cfg.robot_fps}Hz")
+
+    # Run Evaluation Episodes
+    episode_idx = 0
+
+    try:
+        while episode_idx < cfg.num_episodes and not shutdown_event.is_set():
+            log_say(f"Evaluating episode {episode_idx + 1} of {cfg.num_episodes}")
+            logger.info(f"\n{'='*50}")
+            logger.info(f"Episode {episode_idx + 1} / {cfg.num_episodes}")
+            logger.info(f"{'='*50}")
+
+            action_queue = ActionQueue(cfg.rtc)
+            interpolator.reset()
+            hz_tracker.reset()
+            episode_active.set()
+            episode_start_time = time.time()
+
+            while (time.time() - episode_start_time) < cfg.episode_time_sec:
+                if shutdown_event.is_set():
+                    break
+
+                elapsed = time.time() - episode_start_time
+                if int(elapsed) % 30 == 0 and int(elapsed) > 0:
+                    logger.info(
+                        f"[MAIN] Episode progress: {elapsed:.0f}/{cfg.episode_time_sec}s, "
+                        f"queue_size={action_queue.qsize()}"
+                    )
+
+                time.sleep(0.5)
+
+            episode_active.clear()
+            logger.info(f"Episode {episode_idx + 1} completed")
+
+            if cfg.record_dataset and dataset is not None:
+                with dataset_lock:
+                    if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                        logger.info(
+                            f"Saving episode {episode_idx + 1} "
+                            f"({dataset.episode_buffer['size']} frames)"
+                        )
+                        dataset.save_episode()
+
+            episode_idx += 1
+
+            # Manual reset between episodes
+            if not shutdown_event.is_set() and episode_idx < cfg.num_episodes:
+                log_say("Waiting for manual reset")
+                logger.info("Manually reset the environment and press ENTER to continue")
+                input("Press ENTER when ready...")
+
+        logger.info(f"Evaluation complete! {episode_idx} episodes recorded")
+        log_say("Evaluation complete", blocking=True)
+
+    except KeyboardInterrupt:
+        logger.info("\n\nEvaluation interrupted by user")
+
+    finally:
+        shutdown_event.set()
+        episode_active.clear()
+
+        if get_actions_t.is_alive():
+            logger.info("Waiting for action generation thread to finish...")
+            get_actions_t.join(timeout=5.0)
+
+        if actor_t.is_alive():
+            logger.info("Waiting for actor thread to finish...")
+            actor_t.join(timeout=5.0)
+
+        follower.disconnect()
+        logger.info("Follower disconnected")
+
+        if cfg.record_dataset and dataset is not None:
+            dataset.finalize()
+            if cfg.push_to_hub:
+                logger.info("Uploading to Hugging Face Hub...")
+                dataset.push_to_hub(private=True)
+
+        logger.info("Cleanup completed")
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,216 @@
+import time
+import numpy as np
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+
+
+# Friction model parameters from OpenArms config/follower.yaml
+# τ_fric(ω) = Fo + Fv·ω + Fc·tanh(k·ω)
+# For 8 motors: [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7, gripper]
+FRICTION_PARAMS = {
+    "Fc": [0.306, 0.306, 0.40, 0.166, 0.050, 0.093, 0.172, 0.0512],  # Coulomb friction [Nm]
+    "k": [28.417, 28.417, 29.065, 130.038, 151.771, 242.287, 7.888, 4.000],  # tanh steepness
+    "Fv": [0.063, 0.0630, 0.604, 0.813, 0.029, 0.072, 0.084, 0.084],  # Viscous friction [Nm·s/rad]
+    "Fo": [0.088, 0.088, 0.008, -0.058, 0.005, 0.009, -0.059, -0.050],  # Offset torque [Nm]
+}
+
+# Constants from OpenArms C++ implementation
+AMP_TMP = 1.0
+COEF_TMP = 0.1
+
+FRICTION_SCALE = 1.0  # OpenArms C++ uses 0.3 factor in unilateral mode
+DAMPING_KD = [0.5, 0.5, 0.5, 0.5, 0.1, 0.1, 0.1, 0.1]  # Damping gains for stability
+
+def compute_friction_torque(velocity_rad_per_sec: float, motor_index: int) -> float:
+    """
+    Compute friction torque for a single motor using the tanh friction model.
+    
+    Args:
+        velocity_rad_per_sec: Angular velocity in rad/s
+        motor_index: Index of the motor (0-7)
+        
+    Returns:
+        Friction torque in N·m (scaled for stability)
+    """
+    
+    Fc = FRICTION_PARAMS["Fc"][motor_index]
+    k = FRICTION_PARAMS["k"][motor_index]
+    Fv = FRICTION_PARAMS["Fv"][motor_index]
+    Fo = FRICTION_PARAMS["Fo"][motor_index]
+    
+    # Friction model: τ_fric = amp * Fc * tanh(coef * k * ω) + Fv * ω + Fo
+    friction_torque = (
+        AMP_TMP * Fc * np.tanh(COEF_TMP * k * velocity_rad_per_sec) +
+        Fv * velocity_rad_per_sec +
+        Fo
+    )
+    
+    # Scale down friction compensation for stability at lower control rates
+    # (OpenArms C++ uses 0.3 factor in unilateral mode)!!
+    friction_torque *= FRICTION_SCALE
+    
+    return friction_torque
+
+
+def main() -> None:
+    config = OpenArmsFollowerConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=5.0,
+    )
+    
+    print("Initializing robot...")
+    follower = OpenArmsFollower(config)
+    follower.connect(calibrate=True)
+    
+    print(f"Applying friction compensation")
+    print("  1. Support the arm before starting")
+    print("  2. The arm will be held in place by friction compensation")
+    print("  3. You should be able to move it with gentle force")
+    print("\nPress ENTER when ready to start...")
+    input()
+    
+    print(f"✓ Motors enabled")
+    print("\nStarting friction compensation loop...")
+    print("Press Ctrl+C to stop\n")
+    
+    loop_times = []
+    last_print_time = time.perf_counter()
+    
+    # Motor name to index mapping
+    motor_name_to_index = {
+        "joint_1": 0,
+        "joint_2": 1,
+        "joint_3": 2,
+        "joint_4": 3,
+        "joint_5": 4,
+        "joint_6": 5,
+        "joint_7": 6,
+        "gripper": 7,
+    }
+    
+    try:
+        while True:
+            loop_start = time.perf_counter()
+            
+            # Get current joint positions and velocities from robot
+            obs = follower.get_observation()
+            
+            # Extract velocities in degrees per second
+            velocities_deg_per_sec = {}
+            positions_deg = {}
+            
+            for motor in follower.bus_right.motors:
+                vel_key = f"right_{motor}.vel"
+                pos_key = f"right_{motor}.pos"
+                if vel_key in obs:
+                    velocities_deg_per_sec[f"right_{motor}"] = obs[vel_key]
+                if pos_key in obs:
+                    positions_deg[f"right_{motor}"] = obs[pos_key]
+            
+            for motor in follower.bus_left.motors:
+                vel_key = f"left_{motor}.vel"
+                pos_key = f"left_{motor}.pos"
+                if vel_key in obs:
+                    velocities_deg_per_sec[f"left_{motor}"] = obs[vel_key]
+                if pos_key in obs:
+                    positions_deg[f"left_{motor}"] = obs[pos_key]
+            
+            # Convert velocities to rad/s and compute friction torques
+            friction_torques_nm = {}
+            for motor_full_name, velocity_deg_per_sec in velocities_deg_per_sec.items():
+                # Extract motor name without arm prefix
+                if motor_full_name.startswith("right_"):
+                    motor_name = motor_full_name.removeprefix("right_")
+                elif motor_full_name.startswith("left_"):
+                    motor_name = motor_full_name.removeprefix("left_")
+                else:
+                    continue
+                
+                # Get motor index for friction parameters
+                motor_index = motor_name_to_index.get(motor_name, 0)
+                
+                # Convert velocity to rad/s
+                velocity_rad_per_sec = np.deg2rad(velocity_deg_per_sec)
+                
+                # Compute friction torque
+                friction_torque = compute_friction_torque(velocity_rad_per_sec, motor_index)
+                friction_torques_nm[motor_full_name] = friction_torque
+            
+            # Apply friction compensation to right arm (all joints INCLUDING gripper)
+            for motor in follower.bus_right.motors:
+                full_name = f"right_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = friction_torques_nm.get(full_name, 0.0)
+                
+                # Get motor index for damping gain
+                motor_index = motor_name_to_index.get(motor, 0)
+                kd = DAMPING_KD[motor_index]
+                
+                # Send MIT control command with friction compensation + damping
+                follower.bus_right._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=kd,   # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+
+            # Apply friction compensation to left arm (all joints INCLUDING gripper)
+            for motor in follower.bus_left.motors:
+                full_name = f"left_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = friction_torques_nm.get(full_name, 0.0)
+                
+                # Get motor index for damping gain
+                motor_index = motor_name_to_index.get(motor, 0)
+                kd = DAMPING_KD[motor_index]
+                
+                # Send MIT control command with friction compensation + damping
+                follower.bus_left._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=kd,   # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+            
+            # Measure loop time
+            loop_end = time.perf_counter()
+            loop_time = loop_end - loop_start
+            loop_times.append(loop_time)
+            
+            # Print status every 2 seconds
+            if loop_end - last_print_time >= 2.0:
+                if loop_times:
+                    avg_time = sum(loop_times) / len(loop_times)
+                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
+                    
+                    print(f"{current_hz:.1f} Hz")
+
+                    loop_times = []
+                    last_print_time = loop_end
+            
+            time.sleep(0.001)
+            
+    except KeyboardInterrupt:
+        print("\n\nStopping friction compensation...")
+    
+    finally:
+        print("\nDisabling all motors and disconnecting...")
+        follower.bus_right.disable_torque()
+        follower.bus_left.disable_torque()
+        time.sleep(0.1)
+        follower.disconnect()
+        print("✓ Safe shutdown complete")
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,142 @@
+import time
+import numpy as np
+import pinocchio as pin
+from os.path import join, dirname, exists, expanduser
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+
+
+def main() -> None:
+    config = OpenArmsFollowerConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=5.0,
+    )
+    
+    
+    print("Initializing robot...")
+    follower = OpenArmsFollower(config)
+    follower.connect(calibrate=True)
+    
+    # Load URDF for Pinocchio dynamics
+    urdf_path = "/home/croissant/Documents/openarm_description/openarm_bimanual_pybullet.urdf"
+    
+    pin_robot = pin.RobotWrapper.BuildFromURDF(urdf_path, dirname(urdf_path))
+    pin_robot.data = pin_robot.model.createData()
+    print(f"✓ Loaded Pinocchio model with {pin_robot.nq} DoFs")
+    
+    follower.pin_robot = pin_robot
+    
+    print(f"Applying gravity compensation")
+    print("  1. Support the arm before starting")
+    print("  2. The arm will be held in place by gravity compensation")
+    print("  3. You should be able to move it with gentle force")
+    print("\nPress ENTER when ready to start...")
+    input()
+    
+    print(f"✓ Motors enabled")
+    print("\nStarting gravity compensation loop...")
+    print("Press Ctrl+C to stop\n")
+    
+    loop_times = []
+    last_print_time = time.perf_counter()
+    
+    try:
+        while True:
+            loop_start = time.perf_counter()
+            
+            # Get current joint positions from robot
+            obs = follower.get_observation()
+            
+            # Extract positions in degrees
+            positions_deg = {}
+            for motor in follower.bus_right.motors:
+                key = f"right_{motor}.pos"
+                if key in obs:
+                    positions_deg[f"right_{motor}"] = obs[key]
+            
+            for motor in follower.bus_left.motors:
+                key = f"left_{motor}.pos"
+                if key in obs:
+                    positions_deg[f"left_{motor}"] = obs[key]
+            
+            # Convert to radians and calculate gravity torques
+            # Use the built-in method from OpenArmsFollower
+            positions_rad = {k: np.deg2rad(v) for k, v in positions_deg.items()}
+            torques_nm = follower._gravity_from_q(positions_rad)
+            
+            # Apply gravity compensation to right arm (all joints except gripper)
+            for motor in follower.bus_right.motors:
+                if motor == "gripper":
+                    continue  # Skip gripper
+                    
+                full_name = f"right_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = torques_nm.get(full_name, 0.0)
+                
+                # Send MIT control command with gravity compensation torque
+                follower.bus_right._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=0.0,  # No velocity damping
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+
+            # Apply gravity compensation to left arm (all joints except gripper)
+            for motor in follower.bus_left.motors:
+                if motor == "gripper":
+                    continue  # Skip gripper
+                    
+                full_name = f"left_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = torques_nm.get(full_name, 0.0)
+                
+                # Send MIT control command with gravity compensation torque
+                follower.bus_left._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=0.0,  # No velocity damping
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+            
+            # Measure loop time
+            loop_end = time.perf_counter()
+            loop_time = loop_end - loop_start
+            loop_times.append(loop_time)
+            
+            # Print status every 2 seconds
+            if loop_end - last_print_time >= 2.0:
+                if loop_times:
+                    avg_time = sum(loop_times) / len(loop_times)
+                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
+                    
+                    print(f"{current_hz:.1f} Hz ({avg_time*1000:.1f} ms)")
+
+                    loop_times = []
+                    last_print_time = loop_end
+            
+            time.sleep(0.005)
+            
+    except KeyboardInterrupt:
+        print("\n\nStopping gravity compensation...")
+    
+    finally:
+        print("\nDisabling all motors and disconnecting...")
+        follower.bus_right.disable_torque()
+        follower.bus_left.disable_torque()
+        time.sleep(0.1)
+        follower.disconnect()
+        print("✓ Safe shutdown complete")
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,395 @@
+"""
+OpenArms Dataset Recording with Gravity + Friction Compensation
+
+Records a dataset using OpenArms follower robot with leader teleoperator.
+Leader arms have gravity and friction compensation for weightless, easy movement.
+Includes 3 cameras: left wrist, right wrist, and base camera.
+
+Uses the same compensation approach as teleop_with_compensation.py
+"""
+
+import shutil
+import time
+from pathlib import Path
+
+import numpy as np
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+# Recording parameters
+NUM_EPISODES = 1
+FPS = 30
+EPISODE_TIME_SEC = 600
+RESET_TIME_SEC = 120
+TASK_DESCRIPTION = "OpenArms task description"
+
+# Friction compensation scale factor (1.0 = full, 0.3 = 30% for stability)
+FRICTION_SCALE = 1.0
+
+def record_loop_with_compensation(
+    robot,
+    leader,
+    events,
+    fps,
+    dataset,
+    dataset_features,
+    control_time_s,
+    single_task,
+    display_data=True,
+):
+    """
+    Custom record loop that applies gravity + friction compensation to leader.
+    Based on record_loop but with integrated compensation.
+    """
+    dt = 1 / fps
+    episode_start_time = time.perf_counter()
+    
+    # All joints (both arms)
+    all_joints = []
+    for motor in leader.bus_right.motors:
+        all_joints.append(f"right_{motor}")
+    for motor in leader.bus_left.motors:
+        all_joints.append(f"left_{motor}")
+    
+    while True:
+        loop_start = time.perf_counter()
+        elapsed = loop_start - episode_start_time
+        
+        # Check if we should exit
+        if elapsed >= control_time_s or events["exit_early"] or events["stop_recording"]:
+            break
+        
+        # Get leader state
+        leader_action = leader.get_action()
+        
+        # Extract positions and velocities in degrees
+        leader_positions_deg = {}
+        leader_velocities_deg_per_sec = {}
+        
+        for motor in leader.bus_right.motors:
+            pos_key = f"right_{motor}.pos"
+            vel_key = f"right_{motor}.vel"
+            if pos_key in leader_action:
+                leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
+            if vel_key in leader_action:
+                leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
+        
+        for motor in leader.bus_left.motors:
+            pos_key = f"left_{motor}.pos"
+            vel_key = f"left_{motor}.vel"
+            if pos_key in leader_action:
+                leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
+            if vel_key in leader_action:
+                leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
+        
+        # Calculate gravity torques for leader using built-in method
+        leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
+        leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
+        
+        # Calculate friction torques for leader using built-in method
+        leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
+        leader_friction_torques_nm = leader._friction_from_velocity(
+            leader_velocities_rad_per_sec,
+            friction_scale=FRICTION_SCALE
+        )
+        
+        # Combine gravity + friction torques
+        leader_total_torques_nm = {}
+        for motor_name in leader_gravity_torques_nm:
+            gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
+            friction = leader_friction_torques_nm.get(motor_name, 0.0)
+            leader_total_torques_nm[motor_name] = gravity + friction
+        
+        # Apply gravity + friction compensation to leader RIGHT arm (all joints including gripper)
+        for motor in leader.bus_right.motors:
+            full_name = f"right_{motor}"
+            position = leader_positions_deg.get(full_name, 0.0)
+            torque = leader_total_torques_nm.get(full_name, 0.0)
+            
+            # Get damping gain for stability
+            kd = leader.get_damping_kd(motor)
+            
+            leader.bus_right._mit_control(
+                motor=motor,
+                kp=0.0,
+                kd=kd,  # Add damping for stability
+                position_degrees=position,
+                velocity_deg_per_sec=0.0,
+                torque=torque,
+            )
+        
+        # Apply gravity + friction compensation to leader LEFT arm (all joints including gripper)
+        for motor in leader.bus_left.motors:
+            full_name = f"left_{motor}"
+            position = leader_positions_deg.get(full_name, 0.0)
+            torque = leader_total_torques_nm.get(full_name, 0.0)
+            
+            # Get damping gain for stability
+            kd = leader.get_damping_kd(motor)
+            
+            leader.bus_left._mit_control(
+                motor=motor,
+                kp=0.0,
+                kd=kd,  # Add damping for stability
+                position_degrees=position,
+                velocity_deg_per_sec=0.0,
+                torque=torque,
+            )
+        
+        # Send leader positions to follower (both arms)
+        follower_action = {}
+        for joint in all_joints:
+            pos_key = f"{joint}.pos"
+            if pos_key in leader_action:
+                follower_action[pos_key] = leader_action[pos_key]
+        
+        # Send action to robot
+        if follower_action:
+            robot.send_action(follower_action)
+        
+        # Get observation from robot (includes camera images)
+        observation = robot.get_observation()
+        
+        # Add to dataset if we have a dataset
+        if dataset is not None:
+            # Build properly formatted observation frame
+            obs_frame = build_dataset_frame(dataset_features, observation, prefix="observation")
+            
+            # Build properly formatted action frame (keep .pos suffix - it matches the feature names)
+            action_frame = build_dataset_frame(dataset_features, follower_action, prefix="action")
+            
+            # Combine into single frame
+            frame = {**obs_frame, **action_frame}
+            
+            # Add metadata (task is required, timestamp will be auto-calculated by add_frame)
+            frame["task"] = single_task
+            
+            dataset.add_frame(frame)
+        
+        # Display data if requested
+        if display_data:
+            log_rerun_data(observation=observation, action=follower_action)
+        
+        # Maintain loop rate
+        loop_duration = time.perf_counter() - loop_start
+        sleep_time = dt - loop_duration
+        if sleep_time > 0:
+            time.sleep(sleep_time)
+
+
+def main():
+    """Main recording loop with gravity compensation."""
+    
+    print("=" * 70)
+    print("OpenArms Dataset Recording with Compensation")
+    print("=" * 70)
+    
+    # Create camera configurations (3 cameras: left wrist, right wrist, base)
+    # Using actual device paths found by lerobot-find-cameras opencv
+    camera_config = {
+        "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video0", width=640, height=480, fps=FPS),
+        "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
+        "base": OpenCVCameraConfig(index_or_path="/dev/video7", width=640, height=480, fps=FPS),
+    }
+    
+    # Configure follower robot with cameras
+    follower_config = OpenArmsFollowerConfig(
+        port_left="can2",
+        port_right="can3",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=camera_config,
+    )
+    
+    # Configure leader teleoperator (no cameras needed)
+    leader_config = OpenArmsLeaderConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_leader",
+        manual_control=False,  # Enable torque control for gravity compensation
+    )
+    
+    # Initialize robot and teleoperator
+    print("\nInitializing devices...")
+    follower = OpenArmsFollower(follower_config)
+    leader = OpenArmsLeader(leader_config)
+    
+    # Connect devices
+    print("Connecting and calibrating...")
+    follower.connect(calibrate=True)
+    leader.connect(calibrate=True)
+    
+    # Verify URDF is loaded for gravity compensation
+    if leader.pin_robot is None:
+        raise RuntimeError("URDF model not loaded on leader. Gravity compensation not available.")
+    
+    # Configure the dataset features
+    # For actions, we only want to record positions (not velocity or torque)
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+    
+    action_features = hw_to_dataset_features(action_features_hw, "action")
+    obs_features = hw_to_dataset_features(follower.observation_features, "observation")
+    dataset_features = {**action_features, **obs_features}
+    
+    # Create the dataset
+    print("\nCreating dataset...")
+    repo_id = "<hf_username>/<dataset_repo_id>"  # TODO: Replace with your Hugging Face repo
+    
+    # Check if dataset already exists and prompt user
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / repo_id
+    while dataset_path.exists():
+        print(f"\nDataset already exists at: {dataset_path}")
+        print("\nOptions:")
+        print("  1. Overwrite existing dataset")
+        print("  2. Use a different name")
+        print("  3. Abort")
+        
+        choice = input("\nEnter your choice (1/2/3): ").strip()
+        
+        if choice == '1':
+            print(f"Removing existing dataset...")
+            shutil.rmtree(dataset_path)
+            print("✓ Existing dataset removed")
+            break
+        elif choice == '2':
+            print("\nCurrent repo_id:", repo_id)
+            new_repo_id = input("Enter new repo_id (format: <username>/<dataset_name>): ").strip()
+            if new_repo_id and '/' in new_repo_id:
+                repo_id = new_repo_id
+                dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / repo_id
+                print(f"✓ Using new repo_id: {repo_id}")
+                # Loop will continue if this new path also exists
+            else:
+                print("Invalid repo_id format. Please use format: <username>/<dataset_name>")
+        elif choice == '3':
+            print("Aborting. Please remove the existing dataset manually or restart with a different repo_id.")
+            follower.disconnect()
+            leader.disconnect()
+            return
+        else:
+            print("Invalid choice. Please enter 1, 2, or 3.")
+    
+    dataset = LeRobotDataset.create(
+        repo_id=repo_id,
+        fps=FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_threads=4,
+    )
+    
+    # Initialize keyboard listener and visualization
+    _, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_recording")
+    
+    # Enable motors on both leader arms for gravity compensation
+    leader.bus_right.enable_torque()
+    leader.bus_left.enable_torque()
+    time.sleep(0.1)
+    
+    print("\n" + "=" * 70)
+    print(f"Recording {NUM_EPISODES} episodes")
+    print(f"Task: {TASK_DESCRIPTION}")
+    print("=" * 70)
+    print("\nLeader BOTH arms: Gravity + Friction comp | Follower BOTH arms: Teleop")
+    print("\nKeyboard controls:")
+    print("  - Press 'q' to stop recording")
+    print("  - Press 'r' to re-record current episode")
+    print("=" * 70)
+    
+    episode_idx = 0
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+            
+            # Record episode with compensation active
+            record_loop_with_compensation(
+                robot=follower,
+                leader=leader,
+                events=events,
+                fps=FPS,
+                dataset=dataset,
+                dataset_features=dataset_features,
+                control_time_s=EPISODE_TIME_SEC,
+                single_task=TASK_DESCRIPTION,
+                display_data=True,
+            )
+            
+            # Reset the environment if not stopping or re-recording
+            if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+                log_say("Reset the environment")
+                record_loop_with_compensation(
+                    robot=follower,
+                    leader=leader,
+                    events=events,
+                    fps=FPS,
+                    dataset=None,  # Don't save reset period
+                    dataset_features=dataset_features,
+                    control_time_s=RESET_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                )
+            
+            # Handle re-recording
+            if events["rerecord_episode"]:
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            # Only save episode if frames were recorded
+            if dataset.episode_buffer is not None and dataset.episode_buffer["size"] > 0:
+                dataset.save_episode()
+                episode_idx += 1
+            else:
+                log_say("No frames recorded, skipping episode save")
+                # Clear the empty buffer
+                dataset.episode_buffer = None
+    
+    except KeyboardInterrupt:
+        print("\n\nStopping recording...")
+    
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        try:
+            leader.bus_right.disable_torque()
+            leader.bus_left.disable_torque()
+            time.sleep(0.1)
+            leader.disconnect()
+            follower.disconnect()
+            print("✓ Shutdown complete")
+        except Exception as e:
+            print(f"Shutdown error: {e}")
+        
+        # Upload dataset
+        print("\nUploading dataset to Hugging Face Hub...")
+        try:
+            dataset.push_to_hub()
+            print("✓ Dataset uploaded successfully")
+        except Exception as e:
+            print(f"Warning: Failed to upload dataset: {e}")
+            print("You can manually upload later using: dataset.push_to_hub()")
+        
+        print("✓ Recording complete!")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,166 @@
+#!/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.
+
+"""
+OpenArms Dataset Replay Example
+
+Replays position actions from a recorded dataset on an OpenArms follower robot.
+Only position commands (ending with .pos) are replayed, not velocity or torque.
+
+Example usage:
+    python examples/openarms/replay.py
+"""
+
+import time
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.utils.constants import ACTION
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.utils import log_say
+
+# Configuration
+EPISODE_IDX = 0
+DATASET_REPO_ID = "lerobot-data-collection/replay-this-2025-11-02-17-58"  # TODO: Replace with your dataset
+DATASET_ROOT = None  # Use default cache location, or specify custom path
+
+# Robot configuration - adjust these to match your setup
+ROBOT_CONFIG = OpenArmsFollowerConfig(
+    port_left="can2",      # CAN interface for left arm
+    port_right="can3",     # CAN interface for right arm
+    can_interface="socketcan", 
+    id="openarms_follower",
+    disable_torque_on_disconnect=True,
+    max_relative_target=10.0,  # Safety limit: max degrees to move per step
+)
+
+
+def main():
+    """Main replay function."""
+    print("=" * 70)
+    print("OpenArms Dataset Replay")
+    print("=" * 70)
+    print(f"\nDataset: {DATASET_REPO_ID}")
+    print(f"Episode: {EPISODE_IDX}")
+    print(f"Robot: {ROBOT_CONFIG.id}")
+    print(f"  Left arm: {ROBOT_CONFIG.port_left}")
+    print(f"  Right arm: {ROBOT_CONFIG.port_right}")
+    print("\n" + "=" * 70)
+    
+    # Initialize the robot
+    print("\n[1/3] Initializing robot...")
+    robot = OpenArmsFollower(ROBOT_CONFIG)
+    
+    # Load the dataset
+    print(f"\n[2/3] Loading dataset '{DATASET_REPO_ID}'...")
+    dataset = LeRobotDataset(
+        DATASET_REPO_ID,
+        root=DATASET_ROOT,
+        episodes=[EPISODE_IDX]
+    )
+    
+    # Filter dataset to only include frames from the specified episode
+    # (required for dataset V3.0 where episodes are chunked)
+    episode_frames = dataset.hf_dataset.filter(
+        lambda x: x["episode_index"] == EPISODE_IDX
+    )
+    
+    if len(episode_frames) == 0:
+        raise ValueError(
+            f"No frames found for episode {EPISODE_IDX} in dataset {DATASET_REPO_ID}"
+        )
+    
+    print(f"  Found {len(episode_frames)} frames in episode {EPISODE_IDX}")
+    
+    # Extract action features from dataset
+    action_features = dataset.features.get(ACTION, {})
+    action_names = action_features.get("names", [])
+    
+    # Filter to only position actions (ending with .pos)
+    position_action_names = [name for name in action_names if name.endswith(".pos")]
+    
+    if not position_action_names:
+        raise ValueError(
+            f"No position actions found in dataset. Action names: {action_names}"
+        )
+    
+    print(f"  Found {len(position_action_names)} position actions to replay")
+    print(f"  Actions: {', '.join(position_action_names[:5])}{'...' if len(position_action_names) > 5 else ''}")
+    
+    # Select only action columns from dataset
+    actions = episode_frames.select_columns(ACTION)
+    
+    # Connect to the robot
+    print(f"\n[3/3] Connecting to robot...")
+    robot.connect(calibrate=False)  # Skip calibration for replay
+    
+    if not robot.is_connected:
+        raise RuntimeError("Robot failed to connect!")
+    
+    print("\n" + "=" * 70)
+    print("Ready to replay!")
+    print("=" * 70)
+    print("\nThe robot will replay the recorded positions.")
+    print("Press Ctrl+C to stop at any time.\n")
+    
+    input("Press ENTER to start replaying...")
+    
+    # Replay loop
+    log_say(f"Replaying episode {EPISODE_IDX}", blocking=True)
+    
+    try:
+        for idx in range(len(episode_frames)):
+            loop_start = time.perf_counter()
+            
+            # Extract action array from dataset
+            action_array = actions[idx][ACTION]
+            
+            # Build action dictionary, but only include position actions
+            action = {}
+            for i, name in enumerate(action_names):
+                # Only include position actions (ending with .pos)
+                if name.endswith(".pos"):
+                    action[name] = float(action_array[i])
+            
+            # Send action to robot
+            robot.send_action(action)
+            
+            # Maintain replay rate (use dataset fps)
+            loop_duration = time.perf_counter() - loop_start
+            dt_s = 1.0 / dataset.fps - loop_duration
+            busy_wait(dt_s)
+            
+            # Progress indicator every 100 frames
+            if (idx + 1) % 100 == 0:
+                progress = (idx + 1) / len(episode_frames) * 100
+                print(f"Progress: {idx + 1}/{len(episode_frames)} frames ({progress:.1f}%)")
+        
+        print(f"\n✓ Successfully replayed {len(episode_frames)} frames")
+        log_say("Replay complete", blocking=True)
+        
+    except KeyboardInterrupt:
+        print("\n\nReplay interrupted by user")
+    finally:
+        # Disconnect robot
+        print("\nDisconnecting robot...")
+        robot.disconnect()
+        print("✓ Replay complete!")
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,403 @@
+#!/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.
+
+"""
+OpenArms End-Effector Replay Example with Visualization
+
+Replays a dataset recorded with absolute joint positions by:
+1. Converting joint positions to EE poses using FK
+2. Converting EE poses back to joint positions using IK
+3. Sending joint commands to the robot OR visualizing in simulation
+
+Supports three modes:
+- real: Send commands to physical robot
+- sim: Visualize in simulation only (no robot required)
+- both: Real robot + visualization
+
+Example usage:
+    python examples/openarms/replay_ee.py --mode sim
+    python examples/openarms/replay_ee.py --mode real
+    python examples/openarms/replay_ee.py --mode both --visualizer meshcat
+"""
+
+import argparse
+import time
+from os.path import dirname, expanduser
+
+import numpy as np
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
+from lerobot.processor.converters import (
+    robot_action_observation_to_transition,
+    robot_action_to_transition,
+    transition_to_robot_action,
+)
+from lerobot.robots.openarms.robot_kinematic_processor import (
+    BimanualEEBoundsAndSafety,
+    BimanualForwardKinematicsJointsToEE,
+    BimanualInverseKinematicsEEToJoints,
+)
+from lerobot.utils.constants import ACTION
+from lerobot.utils.robot_utils import precise_sleep
+
+
+# Default configuration
+DEFAULT_EPISODE_IDX = 0
+DEFAULT_DATASET = "lerobot-data-collection/rac_blackf0"
+DEFAULT_URDF = "src/lerobot/robots/openarms/urdf/openarm_bimanual_pybullet.urdf"
+DEFAULT_LEFT_EE_FRAME = "openarm_left_hand_tcp"
+DEFAULT_RIGHT_EE_FRAME = "openarm_right_hand_tcp"
+
+# Motor names as used in the dataset actions (e.g., left_joint_1.pos)
+MOTOR_NAMES = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7", "gripper"]
+
+# URDF joint names (no underscore between "joint" and number)
+LEFT_URDF_JOINTS = [f"openarm_left_joint{i}" for i in range(1, 8)]
+RIGHT_URDF_JOINTS = [f"openarm_right_joint{i}" for i in range(1, 8)]
+
+
+class MeshcatVisualizer:
+    """Lightweight URDF visualizer using pinocchio + meshcat."""
+    
+    def __init__(self, urdf_path: str):
+        import pinocchio as pin
+        from pinocchio.visualize import MeshcatVisualizer as PinMeshcat
+        
+        urdf_dir = dirname(urdf_path)
+        self.model, self.collision_model, self.visual_model = pin.buildModelsFromUrdf(
+            urdf_path, urdf_dir, pin.JointModelFreeFlyer()
+        )
+        self.data = self.model.createData()
+        
+        self.viz = PinMeshcat(self.model, self.collision_model, self.visual_model)
+        self.viz.initViewer(open=True)
+        self.viz.loadViewerModel()
+        
+        # Build joint name mapping: dataset name -> pinocchio joint index
+        # Dataset uses: left_joint_1, right_joint_2, etc.
+        # URDF uses: openarm_left_joint1, openarm_right_joint2, etc.
+        self.joint_map = {}
+        for jid in range(1, self.model.njoints):
+            urdf_name = self.model.names[jid]  # e.g., "openarm_left_joint1"
+            # Extract side and number
+            if "left_joint" in urdf_name:
+                num = urdf_name.split("joint")[-1]  # "1"
+                dataset_name = f"left_joint_{num}"
+                self.joint_map[dataset_name] = jid
+            elif "right_joint" in urdf_name:
+                num = urdf_name.split("joint")[-1]
+                dataset_name = f"right_joint_{num}"
+                self.joint_map[dataset_name] = jid
+        
+        print(f"  Meshcat viewer opened (mapped {len(self.joint_map)} joints)")
+        print(f"  Joint map: {list(self.joint_map.keys())[:4]}...")
+        print("  Waiting for meshcat to load...")
+        time.sleep(3)  # Give meshcat time to load meshes
+        self._first_update = True
+    
+    def update(self, joint_positions: dict[str, float]):
+        """Update visualization with new joint positions."""
+        if self._first_update:
+            pos_keys = [k for k in joint_positions.keys() if k.endswith(".pos")]
+            print(f"  First update keys: {pos_keys[:4]}...")
+            # Print sample values
+            for k in pos_keys[:2]:
+                print(f"    {k} = {joint_positions[k]:.2f}")
+        
+        # Build configuration vector (base pose + joints)
+        # Free flyer base: [x, y, z, qx, qy, qz, qw]
+        q = np.zeros(self.model.nq)
+        q[3:7] = [0, 0, 0, 1]  # Identity quaternion
+        
+        matched = 0
+        # Map joint positions using pre-built mapping
+        for name, pos in joint_positions.items():
+            if not name.endswith(".pos"):
+                continue
+            joint_name = name.removesuffix(".pos")  # e.g., "left_joint_1"
+            
+            jid = self.joint_map.get(joint_name)
+            if jid is not None:
+                idx = self.model.idx_qs[jid]
+                if idx < len(q):
+                    q[idx] = np.deg2rad(pos)
+                    matched += 1
+        
+        if self._first_update:
+            print(f"  Matched {matched} joints, q[7:14] = {q[7:14]}")
+            self._first_update = False
+        
+        self.viz.display(q)
+
+
+class RerunVisualizer:
+    """Rerun-based visualizer for plots and EE trajectories."""
+    
+    def __init__(self, urdf_path: str = None, session_name: str = "openarms_replay"):
+        import rerun as rr
+        self.rr = rr
+        rr.init(session_name)
+        rr.spawn(memory_limit="10%")
+        print("  Rerun viewer spawned (plots only, use --visualizer meshcat for 3D robot)")
+    
+    def update(self, joint_positions: dict[str, float], ee_poses: dict[str, float], frame_idx: int):
+        """Log joint positions and EE poses."""
+        self.rr.set_time("frame", sequence=frame_idx)
+        
+        # Log EE positions as colored spheres
+        for prefix, color in [("left", [255, 100, 100]), ("right", [100, 100, 255])]:
+            x, y, z = ee_poses.get(f"{prefix}_ee.x"), ee_poses.get(f"{prefix}_ee.y"), ee_poses.get(f"{prefix}_ee.z")
+            if None not in (x, y, z):
+                self.rr.log(f"ee/{prefix}", self.rr.Points3D([[x, y, z]], colors=[color], radii=[0.02]))
+        
+        # Log joint positions as time series
+        for name, pos in joint_positions.items():
+            if name.endswith(".pos"):
+                self.rr.log(f"joints/{name}", self.rr.Scalars(pos))
+        
+        # Log EE poses as time series
+        for name, val in ee_poses.items():
+            self.rr.log(f"ee_plots/{name}", self.rr.Scalars(val))
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="OpenArms EE Replay with Visualization")
+    parser.add_argument("--mode", choices=["real", "sim", "both"], default="sim",
+                        help="Execution mode: real (robot), sim (visualization), both")
+    parser.add_argument("--visualizer", choices=["meshcat", "rerun", "none"], default="meshcat",
+                        help="Visualization backend (meshcat shows 3D robot, rerun shows plots)")
+    parser.add_argument("--dataset", type=str, default=DEFAULT_DATASET,
+                        help="Dataset repo ID")
+    parser.add_argument("--episode", type=int, default=DEFAULT_EPISODE_IDX,
+                        help="Episode index to replay")
+    parser.add_argument("--urdf", type=str, default=DEFAULT_URDF,
+                        help="Path to URDF file")
+    parser.add_argument("--left-ee-frame", type=str, default=DEFAULT_LEFT_EE_FRAME,
+                        help="Left arm end-effector frame name in URDF")
+    parser.add_argument("--right-ee-frame", type=str, default=DEFAULT_RIGHT_EE_FRAME,
+                        help="Right arm end-effector frame name in URDF")
+    parser.add_argument("--port-left", type=str, default="can2",
+                        help="CAN port for left arm")
+    parser.add_argument("--port-right", type=str, default="can3",
+                        help="CAN port for right arm")
+    parser.add_argument("--speed", type=float, default=1.0,
+                        help="Playback speed multiplier")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    use_robot = args.mode in ["real", "both"]
+    use_viz = args.mode in ["sim", "both"] and args.visualizer != "none"
+    
+    print("=" * 70)
+    print("OpenArms EE Replay (FK -> IK Pipeline)")
+    print("=" * 70)
+    print(f"\nMode: {args.mode}")
+    print(f"Visualizer: {args.visualizer}")
+    print(f"Dataset: {args.dataset}")
+    print(f"Episode: {args.episode}")
+    print(f"Speed: {args.speed}x")
+    print("=" * 70)
+
+    robot = None
+    viz = None
+    
+    # Resolve URDF path (handle relative and ~ paths)
+    from pathlib import Path
+    urdf_path = args.urdf
+    if urdf_path.startswith("~"):
+        urdf_path = expanduser(urdf_path)
+    elif not Path(urdf_path).is_absolute():
+        # Relative to workspace root
+        urdf_path = str(Path(__file__).parent.parent.parent / urdf_path)
+    
+    # Initialize robot if needed
+    if use_robot:
+        from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+        from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+        
+        print("\n[1/5] Initializing robot...")
+        robot_config = OpenArmsFollowerConfig(
+            port_left=args.port_left,
+            port_right=args.port_right,
+            can_interface="socketcan",
+            id="openarms_follower",
+            disable_torque_on_disconnect=True,
+            max_relative_target=10.0,
+        )
+        robot = OpenArmsFollower(robot_config)
+    else:
+        print("\n[1/5] Skipping robot (sim mode)")
+
+    # Initialize visualizer if needed
+    if use_viz:
+        print(f"\n[2/5] Initializing {args.visualizer} visualizer...")
+        if args.visualizer == "meshcat":
+            viz = MeshcatVisualizer(urdf_path)
+        elif args.visualizer == "rerun":
+            viz = RerunVisualizer(urdf_path)
+    else:
+        print("\n[2/5] Skipping visualization")
+
+    # Initialize kinematics with URDF joint names
+    print("\n[3/5] Initializing kinematics solvers...")
+    
+    left_kinematics = RobotKinematics(
+        urdf_path=urdf_path,
+        target_frame_name=args.left_ee_frame,
+        joint_names=LEFT_URDF_JOINTS,
+    )
+    right_kinematics = RobotKinematics(
+        urdf_path=urdf_path,
+        target_frame_name=args.right_ee_frame,
+        joint_names=RIGHT_URDF_JOINTS,
+    )
+
+    # Build pipelines - use motor names without gripper for the processor
+    motor_names_no_gripper = [n for n in MOTOR_NAMES if n != "gripper"]
+    
+    joints_to_ee = RobotProcessorPipeline[RobotAction, RobotAction](
+        steps=[
+            BimanualForwardKinematicsJointsToEE(
+                left_kinematics=left_kinematics,
+                right_kinematics=right_kinematics,
+                motor_names=MOTOR_NAMES,
+            ),
+        ],
+        to_transition=robot_action_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    ee_to_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            BimanualEEBoundsAndSafety(
+                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+                max_ee_step_m=0.10,
+            ),
+            BimanualInverseKinematicsEEToJoints(
+                left_kinematics=left_kinematics,
+                right_kinematics=right_kinematics,
+                motor_names=MOTOR_NAMES,
+                initial_guess_current_joints=False,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    # Load dataset
+    print(f"\n[4/5] Loading dataset '{args.dataset}'...")
+    dataset = LeRobotDataset(args.dataset, episodes=[args.episode])
+    episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == args.episode)
+    
+    if len(episode_frames) == 0:
+        raise ValueError(f"No frames found for episode {args.episode}")
+    
+    print(f"  Found {len(episode_frames)} frames at {dataset.fps} FPS")
+    
+    action_features = dataset.features.get(ACTION, {})
+    action_names = action_features.get("names", [])
+    actions = episode_frames.select_columns(ACTION)
+
+    # Connect robot if needed
+    if use_robot:
+        print("\n[5/5] Connecting to robot...")
+        robot.connect(calibrate=False)
+        if not robot.is_connected:
+            raise RuntimeError("Robot failed to connect!")
+    else:
+        print("\n[5/5] Skipping robot connection (sim mode)")
+
+    print("\n" + "=" * 70)
+    print(f"Ready to replay! Mode: {args.mode}")
+    print("=" * 70)
+    
+    if use_robot:
+        input("\nPress ENTER to start...")
+    else:
+        print("\nStarting visualization playback...")
+        time.sleep(1)
+
+    # Simulated observation for sim-only mode
+    sim_obs = {f"{prefix}_{motor}.pos": 0.0 
+               for prefix in ["left", "right"] 
+               for motor in MOTOR_NAMES}
+
+    try:
+        for idx in range(len(episode_frames)):
+            loop_start = time.perf_counter()
+
+            # Get observation
+            if use_robot:
+                robot_obs = robot.get_observation()
+            else:
+                robot_obs = sim_obs.copy()
+
+            # Build joint action from dataset
+            action_array = actions[idx][ACTION]
+            joint_action = {}
+            for i, name in enumerate(action_names):
+                if name.endswith(".pos"):
+                    joint_action[name] = float(action_array[i])
+
+            # Convert: joints -> EE (FK)
+            ee_action = joints_to_ee(joint_action.copy())
+
+            # Convert: EE -> joints (IK)
+            final_joint_action = ee_to_joints((ee_action.copy(), robot_obs))
+
+            # Update simulated observation for next iteration
+            if not use_robot:
+                sim_obs.update(final_joint_action)
+
+            # Send to robot
+            if use_robot:
+                robot.send_action(final_joint_action)
+
+            # Update visualization with ORIGINAL dataset trajectory
+            if viz:
+                if isinstance(viz, MeshcatVisualizer):
+                    viz.update(joint_action)  # Use original, not FK->IK reconstructed
+                elif isinstance(viz, RerunVisualizer):
+                    viz.update(joint_action, ee_action, idx)
+
+            # Maintain replay rate
+            loop_duration = time.perf_counter() - loop_start
+            dt_s = (1.0 / dataset.fps / args.speed) - loop_duration
+            if dt_s > 0:
+                precise_sleep(dt_s)
+
+            if (idx + 1) % 100 == 0:
+                progress = (idx + 1) / len(episode_frames) * 100
+                print(f"Progress: {idx + 1}/{len(episode_frames)} ({progress:.1f}%)")
+
+        print(f"\n✓ Replayed {len(episode_frames)} frames")
+
+    except KeyboardInterrupt:
+        print("\n\nReplay interrupted")
+    finally:
+        if use_robot and robot:
+            print("\nDisconnecting robot...")
+            robot.disconnect()
+        print("✓ Done!")
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,73 @@
+#!/bin/bash
+# Setup all OpenArms CAN interfaces with CAN FD
+
+set -e
+
+echo "=========================================="
+echo "OpenArms CAN FD Interface Setup"
+echo "=========================================="
+echo ""
+echo "Mode: CAN FD"
+echo "  - Nominal bitrate: 1 Mbps"
+echo "  - Data bitrate: 5 Mbps"
+echo ""
+echo "Configuring interfaces can0, can1, can2, can3..."
+echo ""
+
+# Configure each CAN interface with CAN FD
+for i in 0 1 2 3; do
+    interface="can$i"
+    
+    # Check if interface exists
+    if ! ip link show "$interface" &> /dev/null; then
+        echo "⚠ $interface: Not found, skipping"
+        continue
+    fi
+    
+    # Bring down interface
+    sudo ip link set "$interface" down 2>/dev/null
+    
+    # Configure CAN FD mode
+    sudo ip link set "$interface" type can \
+        bitrate 1000000 \
+        dbitrate 5000000 \
+        fd on
+    
+    # Bring up interface
+    sudo ip link set "$interface" up
+    
+    # Verify configuration
+    if ip link show "$interface" | grep -q "UP"; then
+        echo "✓ $interface: Configured and UP"
+    else
+        echo "✗ $interface: Failed to bring UP"
+    fi
+done
+
+echo ""
+echo "=========================================="
+echo "Verification"
+echo "=========================================="
+echo ""
+
+# Show detailed status for each interface
+for i in 0 1 2 3; do
+    interface="can$i"
+    if ip link show "$interface" &> /dev/null; then
+        echo "$interface:"
+        # Show key parameters
+        ip -d link show "$interface" | grep -E "can|state|bitrate|dbitrate" | head -3
+        echo ""
+    fi
+done
+
+echo "=========================================="
+echo "Setup Complete!"
+echo "=========================================="
+echo ""
+echo "All interfaces configured for CAN FD mode"
+echo ""
+echo "Next steps:"
+echo "  1. Test motors: python debug_can_communication.py"
+echo "  2. Run teleoperation: python examples/openarms/teleop.py"
+echo ""
@@ -0,0 +1,148 @@
+"""
+OpenArms Teleoperation Example - Full Dual Arms
+
+This script demonstrates teleoperation of OpenArms follower robot using an OpenArms leader arm.
+It first calibrates both devices, then enters a teleoperation loop for both arms.
+"""
+
+import time
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+
+
+follower_config = OpenArmsFollowerConfig(
+    port_left="can2",   # CAN interface for follower left arm
+    port_right="can3",  # CAN interface for follower right arm
+    can_interface="socketcan",  # Linux SocketCAN
+    id="openarms_follower",
+    disable_torque_on_disconnect=True,
+    max_relative_target=5.0,  # Safety limit
+)
+
+
+leader_config = OpenArmsLeaderConfig(
+    port_left="can0",   # CAN interface for leader left arm
+    port_right="can1",  # CAN interface for leader right arm
+    can_interface="socketcan",  # Linux SocketCAN
+    id="openarms_leader",
+    manual_control=True,  # Enable manual control (torque disabled)
+)
+
+print("=" * 60)
+print("OpenArms Teleoperation - Full Dual Arms")
+print("=" * 60)
+
+# Initialize devices
+print("\n[1/4] Initializing devices...")
+follower = OpenArmsFollower(follower_config)
+leader = OpenArmsLeader(leader_config)
+
+# Connect and calibrate follower
+print("\n[2/4] Connecting and calibrating follower robot...")
+print("Note: If you have existing calibration, just press ENTER to use it.")
+follower.connect(calibrate=True)
+
+# Connect and calibrate leader
+print("\n[3/4] Connecting and calibrating leader arm...")
+print("Note: The leader arm will have torque disabled for manual control.")
+leader.connect(calibrate=True)
+
+# Wait for user to be ready
+print("\n[4/4] Ready for teleoperation!")
+print("\nBoth arms will be controlled (16 motors total):")
+print("  RIGHT ARM: joints 1-7 + gripper")
+print("  LEFT ARM: joints 1-7 + gripper")
+
+print("\nPress ENTER to start teleoperation...")
+input()
+
+print("\nTeleoperation started! Move both leader arms.")
+print("Press Ctrl+C to stop.\n")
+
+# All joints for both arms (16 motors total)
+all_joints = [
+    # Right arm
+    "right_joint_1",
+    "right_joint_2",
+    "right_joint_3",
+    "right_joint_4",
+    "right_joint_5",
+    "right_joint_6",
+    "right_joint_7",
+    "right_gripper",
+    # Left arm
+    "left_joint_1",
+    "left_joint_2",
+    "left_joint_3",
+    "left_joint_4",
+    "left_joint_5",
+    "left_joint_6",
+    "left_joint_7",
+    "left_gripper",
+]
+
+# Performance monitoring
+loop_times = []
+start_time = time.perf_counter()
+last_print_time = start_time
+
+try:
+    while True:
+        loop_start = time.perf_counter()
+        
+        # Get action from leader
+        leader_action = leader.get_action()
+        
+        # Filter to only position data for all joints (both arms)
+        joint_action = {}
+        for joint in all_joints:
+            pos_key = f"{joint}.pos"
+            if pos_key in leader_action:
+                joint_action[pos_key] = leader_action[pos_key]
+        
+        # Send action to follower (both arms)
+        if joint_action:
+            follower.send_action(joint_action)
+        
+        # Measure loop time
+        loop_end = time.perf_counter()
+        loop_time = loop_end - loop_start
+        loop_times.append(loop_time)
+        
+        # Print stats every 2 seconds
+        if loop_end - last_print_time >= 2.0:
+            if loop_times:
+                avg_time = sum(loop_times) / len(loop_times)
+                current_hz = 1.0 / avg_time if avg_time > 0 else 0
+                min_time = min(loop_times)
+                max_time = max(loop_times)
+                max_hz = 1.0 / min_time if min_time > 0 else 0
+                min_hz = 1.0 / max_time if max_time > 0 else 0
+                
+                print(f"[Hz Stats] Avg: {current_hz:.1f} Hz | "
+                      f"Range: {min_hz:.1f}-{max_hz:.1f} Hz | "
+                      f"Avg loop time: {avg_time*1000:.1f} ms")
+                
+                # Reset for next measurement window
+                loop_times = []
+                last_print_time = loop_end
+            
+except KeyboardInterrupt:
+    print("\n\nStopping teleoperation...")
+finally:
+    # Disconnect devices
+    print("Disconnecting devices...")
+    try:
+        follower.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting follower: {e}")
+    
+    try:
+        leader.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting leader: {e}")
+    
+    print("Done!")
@@ -0,0 +1,197 @@
+"""
+OpenArms Mini Teleoperation Example
+
+This script demonstrates teleoperation of an OpenArms follower robot using 
+an OpenArms Mini leader (Feetech-based) with dual arms (16 motors total).
+
+The OpenArms Mini has:
+- Right arm: 8 motors (joint_1 to joint_7 + gripper)
+- Left arm: 8 motors (joint_1 to joint_7 + gripper)
+
+Note on gripper normalization:
+- OpenArms Mini gripper: 0-100 scale (0=closed, 100=open)
+- OpenArms follower gripper: degrees (0=closed, -65=open)
+- This script automatically converts between the two ranges
+"""
+
+import time
+import os
+import sys
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.teleoperators.openarms_mini.openarms_mini import OpenArmsMini
+from lerobot.teleoperators.openarms_mini.config_openarms_mini import OpenArmsMiniConfig
+from lerobot.utils.robot_utils import busy_wait
+
+# Target control frequency
+TARGET_FPS = 30
+
+# Configure the OpenArms follower (Damiao motors on CAN bus)
+follower_config = OpenArmsFollowerConfig(
+    port_left="can0",   # CAN interface for follower left arm
+    port_right="can1",  # CAN interface for follower right arm
+    can_interface="socketcan",  # Linux SocketCAN
+    id="openarms_follower",
+    disable_torque_on_disconnect=True,
+    max_relative_target=10.0,  # Safety limit (degrees per step)
+)
+
+# Configure the OpenArms Mini leader (Feetech motors on serial)
+leader_config = OpenArmsMiniConfig(
+    port_right="/dev/ttyACM0",  # Serial port for right arm
+    port_left="/dev/ttyACM1",   # Serial port for left arm
+    id="openarms_mini",
+    use_degrees=True,
+)
+
+print("OpenArms Mini → OpenArms Follower Teleoperation")
+
+# Initialize devices
+follower = OpenArmsFollower(follower_config)
+leader = OpenArmsMini(leader_config)
+
+# Connect and calibrate follower
+print("Note: If you have existing calibration, just press ENTER to use it.")
+follower.connect(calibrate=True)
+
+# Connect and calibrate leader
+print("Note: The leader arms will have torque disabled for manual control.")
+leader.connect(calibrate=True)
+
+print("\nPress ENTER to start teleoperation...")
+input()
+
+print("Press Ctrl+C to stop.\n")
+
+# All joints for both arms (16 motors total)
+all_joints = [
+    # Right arm
+    "right_joint_1",
+    "right_joint_2",
+    "right_joint_3",
+    "right_joint_4",
+    "right_joint_5",
+    "right_joint_6",
+    "right_joint_7",
+    "right_gripper",
+    # Left arm
+    "left_joint_1",
+    "left_joint_2",
+    "left_joint_3",
+    "left_joint_4",
+    "left_joint_5",
+    "left_joint_6",
+    "left_joint_7",
+    "left_gripper",
+]
+
+# Performance monitoring
+loop_times = []
+avg_loop_time = 0.0
+min_loop_time = float('inf')
+max_loop_time = 0.0
+stats_update_interval = 1.0  # Update stats every 1 second
+last_stats_update = time.perf_counter()
+
+
+SWAPPED_JOINTS = {
+    "right_joint_6": "right_joint_7",
+    "right_joint_7": "right_joint_6",
+    "left_joint_6": "left_joint_7",
+    "left_joint_7": "left_joint_6",
+}
+
+try:
+    while True:
+        loop_start = time.perf_counter()
+
+        # Get actions and observations
+        leader_action = leader.get_action()
+        follower_obs = follower.get_observation()
+
+        joint_action = {}
+        for joint in all_joints:
+            leader_key = f"{joint}.pos"
+
+            # Determine which follower joint this leader joint controls
+            follower_joint = SWAPPED_JOINTS.get(joint, joint)
+            follower_key = f"{follower_joint}.pos"
+
+            # Get leader position (default 0 if missing)
+            pos = leader_action.get(leader_key, 0.0)
+
+            # Convert gripper values: Mini uses 0-100, OpenArms uses 0 to -65 degrees
+            if "gripper" in joint:
+                # Map 0-100 (Mini) to 0 to -65 (OpenArms)
+                # 0 (closed) -> 0°, 100 (open) -> -65°
+                pos = (pos / 100.0) * -65.0
+
+            # Store in action dict for follower
+            joint_action[follower_key] = pos
+
+        follower.send_action(joint_action)
+
+        # Loop timing
+        loop_end = time.perf_counter()
+        loop_time = loop_end - loop_start
+        loop_times.append(loop_time)
+
+        # Update stats periodically
+        current_time = time.perf_counter()
+        if current_time - last_stats_update >= stats_update_interval:
+            if loop_times:
+                avg_loop_time = sum(loop_times) / len(loop_times)
+                min_loop_time = min(loop_times)
+                max_loop_time = max(loop_times)
+                loop_times = []
+                last_stats_update = current_time
+
+        # Display everything
+        sys.stdout.write("\033[H\033[J")  # Clear screen
+        
+        # Show timing stats at the top
+        if avg_loop_time > 0:
+            avg_hz = 1.0 / avg_loop_time
+            min_hz = 1.0 / max_loop_time if max_loop_time > 0 else 0
+            max_hz = 1.0 / min_loop_time if min_loop_time > 0 and min_loop_time < float('inf') else 0
+            print(f"[Performance] Target: {TARGET_FPS} Hz | Avg: {avg_hz:.1f} Hz | Range: {min_hz:.1f}-{max_hz:.1f} Hz | Loop: {avg_loop_time*1000:.1f} ms\n")
+        else:
+            print(f"[Performance] Target: {TARGET_FPS} Hz | Measuring...\n")
+
+        # Show joint positions
+        print(f"{'Joint':<20} {'Leader':>15} {'Follower':>15}")
+        print(f"{'':20} {'(0-100/deg)':>15} {'(deg)':>15}")
+        print("-" * 52)
+
+        for joint in all_joints:
+            leader_key = f"{joint}.pos"
+            follower_joint = SWAPPED_JOINTS.get(joint, joint)
+            follower_key = f"{follower_joint}.pos"
+
+            leader_pos = leader_action.get(leader_key, 0.0)
+            follower_pos = follower_obs.get(follower_key, 0.0)
+
+            print(f"{joint:<20} {leader_pos:>15.2f} {follower_pos:>15.2f}")
+
+        # Smart sleep to maintain target FPS
+        dt_s = time.perf_counter() - loop_start
+        busy_wait(max(0, 1.0 / TARGET_FPS - dt_s))
+            
+except KeyboardInterrupt:
+    print("\n\nStopping teleoperation...")
+finally:
+    # Disconnect devices
+    print("Disconnecting devices...")
+    try:
+        follower.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting follower: {e}")
+    
+    try:
+        leader.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting leader: {e}")
+    
+    print("Done!")
+
@@ -0,0 +1,202 @@
+"""
+OpenArms Teleoperation with Gravity + Friction Compensation
+
+Leader arms (both LEFT and RIGHT): Gravity + Friction compensation (weightless, easy to move)
+Follower arms (both LEFT and RIGHT): Mirror leader movements
+
+Uses the URDF file from the lerobot repository.
+"""
+
+import time
+
+import numpy as np
+
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+
+# Friction compensation scale factor (1.0 = full, 0.3 = 30% for stability)
+FRICTION_SCALE = 1.0
+
+def main():
+    """Main teleoperation loop with gravity compensation"""
+
+    print("=" * 70)
+    print("OpenArms Teleoperation with Gravity Compensation")
+    print("=" * 70)
+
+    # Configuration
+    follower_config = OpenArmsFollowerConfig(
+        port_left="can2",
+        port_right="can3",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+    )
+
+    leader_config = OpenArmsLeaderConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_leader",
+        manual_control=False,  # Enable torque control for gravity compensation
+    )
+
+    # Initialize and connect
+    print("\nInitializing devices...")
+    follower = OpenArmsFollower(follower_config)
+    leader = OpenArmsLeader(leader_config)
+
+    follower.connect()
+    leader.connect()
+
+    # URDF is automatically loaded in the leader constructor
+    if leader.pin_robot is None:
+        raise RuntimeError("URDF model not loaded on leader. Gravity compensation not available.")
+
+    print("\nLeader BOTH arms: Gravity + Friction comp | Follower BOTH arms: Teleop")
+    print("Press ENTER to start...")
+    input()
+
+    # Enable motors on both leader arms for gravity compensation
+    leader.bus_right.enable_torque()
+    leader.bus_left.enable_torque()
+    time.sleep(0.1)
+
+    print("Press Ctrl+C to stop\n")
+
+    # Main control loop
+    loop_times = []
+    last_print_time = time.perf_counter()
+
+    # All joints (both arms)
+    all_joints = []
+    for motor in leader.bus_right.motors:
+        all_joints.append(f"right_{motor}")
+    for motor in leader.bus_left.motors:
+        all_joints.append(f"left_{motor}")
+
+    try:
+        while True:
+            loop_start = time.perf_counter()
+
+            # Get leader state
+            leader_action = leader.get_action()
+
+            # Extract positions and velocities in degrees
+            leader_positions_deg = {}
+            leader_velocities_deg_per_sec = {}
+            
+            for motor in leader.bus_right.motors:
+                pos_key = f"right_{motor}.pos"
+                vel_key = f"right_{motor}.vel"
+                if pos_key in leader_action:
+                    leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
+                if vel_key in leader_action:
+                    leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
+
+            for motor in leader.bus_left.motors:
+                pos_key = f"left_{motor}.pos"
+                vel_key = f"left_{motor}.vel"
+                if pos_key in leader_action:
+                    leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
+                if vel_key in leader_action:
+                    leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
+
+            # Calculate gravity torques for leader using built-in method
+            leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
+            leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
+            
+            # Calculate friction torques for leader using built-in method
+            leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
+            leader_friction_torques_nm = leader._friction_from_velocity(
+                leader_velocities_rad_per_sec,
+                friction_scale=FRICTION_SCALE
+            )
+            
+            # Combine gravity + friction torques 
+            leader_total_torques_nm = {}
+            for motor_name in leader_gravity_torques_nm:
+                gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
+                friction = leader_friction_torques_nm.get(motor_name, 0.0)
+                leader_total_torques_nm[motor_name] = gravity + friction
+
+            # Apply gravity + friction compensation to leader RIGHT arm (all joints including gripper)
+            for motor in leader.bus_right.motors:
+                full_name = f"right_{motor}"
+                position = leader_positions_deg.get(full_name, 0.0)
+                torque = leader_total_torques_nm.get(full_name, 0.0)
+                
+                # Get damping gain for stability
+                kd = leader.get_damping_kd(motor)
+
+                leader.bus_right._mit_control(
+                    motor=motor,
+                    kp=0.0,
+                    kd=kd,  # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque,
+                )
+
+            # Apply gravity + friction compensation to leader LEFT arm (all joints including gripper)
+            for motor in leader.bus_left.motors:
+                full_name = f"left_{motor}"
+                position = leader_positions_deg.get(full_name, 0.0)
+                torque = leader_total_torques_nm.get(full_name, 0.0)
+                
+                # Get damping gain for stability
+                kd = leader.get_damping_kd(motor)
+
+                leader.bus_left._mit_control(                    
+                    motor=motor,
+                    kp=0.0,
+                    kd=kd,  # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque,
+                )
+
+            # Send leader positions to follower (both arms)
+            follower_action = {}
+            for joint in all_joints:
+                pos_key = f"{joint}.pos"
+                if pos_key in leader_action:
+                    follower_action[pos_key] = leader_action[pos_key]
+
+            if follower_action:
+                follower.send_action(follower_action)
+
+            # Performance monitoring
+            loop_end = time.perf_counter()
+            loop_time = loop_end - loop_start
+            loop_times.append(loop_time)
+
+            if loop_end - last_print_time >= 2.0:
+                if loop_times:
+                    avg_time = sum(loop_times) / len(loop_times)
+                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
+
+                    print(f"{current_hz:.1f} Hz ({avg_time*1000:.1f} ms)")
+
+                    loop_times = []
+                    last_print_time = loop_end
+
+    except KeyboardInterrupt:
+        print("\n\nStopping...")
+    finally:
+        try:
+            leader.bus_right.disable_torque()
+            leader.bus_left.disable_torque()
+            time.sleep(0.1)
+            leader.disconnect()
+            follower.disconnect()
+            print("✓ Shutdown complete")
+        except Exception as e:
+            print(f"Shutdown error: {e}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,152 @@
+#!/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.
+
+"""
+Unify all tasks in a dataset to a single task (modifies in-place).
+
+This script:
+1. Loads a dataset
+2. Sets all task_index to 0 and task description to "fold"
+3. Updates tasks.parquet and task_index in data files (in-place, no copying)
+
+Usage:
+    python examples/openarms/unify_task.py --repo-id lerobot-data-collection/level1_rac1
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+from pathlib import Path
+
+import pandas as pd
+from tqdm import tqdm
+
+from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
+from lerobot.datasets.utils import (
+    DATA_DIR,
+    write_info,
+    write_tasks,
+)
+from lerobot.utils.constants import HF_LEROBOT_HOME
+
+
+# Single unified task
+UNIFIED_TASK = "fold"
+
+
+def unify_dataset_tasks(
+    repo_id: str,
+    root: Path | None = None,
+    push_to_hub: bool = False,
+) -> None:
+    """Unify all tasks in a dataset to a single task (modifies in-place).
+
+    Args:
+        repo_id: Dataset repository ID.
+        root: Optional root path for dataset.
+        push_to_hub: Whether to push the result to HuggingFace Hub.
+    """
+    input_root = root if root else HF_LEROBOT_HOME / repo_id
+    input_repo_id = repo_id
+
+    logging.info(f"Loading metadata from {repo_id}")
+
+    # Load source metadata
+    src_meta = LeRobotDatasetMetadata(repo_id, root=input_root)
+
+    logging.info(f"Source dataset: {src_meta.total_episodes} episodes, {src_meta.total_frames} frames")
+    logging.info(f"Original tasks: {len(src_meta.tasks)}")
+
+    # Modify in-place (input_root == output_root supported)
+    data_dir = input_root / DATA_DIR
+
+    # Process data files - set all task_index to 0
+    logging.info("Processing data files (in-place)...")
+    for parquet_file in tqdm(sorted(data_dir.rglob("*.parquet")), desc="Processing data"):
+        df = pd.read_parquet(parquet_file)
+        df["task_index"] = 0  # All tasks unified to index 0
+        df.to_parquet(parquet_file)
+
+    # Process episodes metadata - set all tasks to unified task
+    logging.info("Processing episodes metadata (in-place)...")
+    episodes_dir = input_root / "meta" / "episodes"
+    if episodes_dir.exists():
+        for parquet_file in tqdm(sorted(episodes_dir.rglob("*.parquet")), desc="Processing episodes"):
+            df = pd.read_parquet(parquet_file)
+            df["tasks"] = [[UNIFIED_TASK]] * len(df)  # All episodes get the unified task
+            df.to_parquet(parquet_file)
+    else:
+        logging.warning(f"No episodes directory found at {episodes_dir}, skipping")
+
+    # Update tasks.parquet with single task
+    logging.info(f"Creating single task: {UNIFIED_TASK}")
+    new_tasks = pd.DataFrame({"task_index": [0]}, index=[UNIFIED_TASK])
+    write_tasks(new_tasks, input_root)
+
+    # Update info.json
+    new_info = src_meta.info.copy()
+    new_info["total_tasks"] = 1
+    write_info(new_info, input_root)
+
+    logging.info(f"Dataset modified in-place at {input_root}")
+    logging.info(f"Task: {UNIFIED_TASK}")
+
+    if push_to_hub:
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+        logging.info(f"Pushing {input_repo_id} to hub")
+        dataset = LeRobotDataset(input_repo_id, root=input_root)
+        dataset.push_to_hub(private=True)
+        logging.info("Push complete!")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Unify all tasks in a dataset to a single task 'fold' (modifies in-place)."
+    )
+
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="Dataset repository ID",
+    )
+    parser.add_argument(
+        "--root",
+        type=Path,
+        default=None,
+        help="Optional root path (defaults to HF_LEROBOT_HOME/repo_id)",
+    )
+    parser.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Push result to HuggingFace Hub",
+    )
+
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+    unify_dataset_tasks(
+        repo_id=args.repo_id,
+        root=args.root,
+        push_to_hub=args.push_to_hub,
+    )
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,745 @@
+body {
+  margin: 0;
+  padding: 0;
+  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif;
+  background: #f5f5f5;
+}
+
+main {
+  min-height: 100vh;
+  padding: 2rem;
+}
+
+header {
+  text-align: center;
+  margin-bottom: 2rem;
+}
+
+h1 {
+  font-size: 2rem;
+  font-weight: 600;
+  color: #333;
+  margin: 0;
+}
+
+h2 {
+  font-size: 1.25rem;
+  font-weight: 600;
+  color: #333;
+  margin: 0 0 1rem 0;
+}
+
+h3 {
+  font-size: 0.875rem;
+  font-weight: 600;
+  color: #666;
+  margin: 0 0 0.5rem 0;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+}
+
+.container {
+  max-width: 1920px;
+  margin: 0 auto;
+  display: grid;
+  grid-template-columns: minmax(500px, 600px) 1fr;
+  gap: 2rem;
+  align-items: start;
+}
+
+/* Left column container */
+.left-column {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+/* Right column container */
+.right-column {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+/* Responsive: Stack on smaller screens */
+@media (max-width: 1200px) {
+  .container {
+    grid-template-columns: 1fr;
+  }
+}
+
+.panel {
+  background: white;
+  border-radius: 8px;
+  padding: 1.5rem;
+  box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+}
+
+.config-panel {
+  border: 2px solid #e5e7eb;
+}
+
+.config-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  cursor: pointer;
+  user-select: none;
+  padding: 0.5rem 0;
+}
+
+.config-header:hover {
+  opacity: 0.7;
+}
+
+.toggle-icon {
+  font-size: 1rem;
+  color: #6b7280;
+  transition: transform 0.2s;
+}
+
+.config-content {
+  margin-top: 1rem;
+  padding-top: 1rem;
+  border-top: 1px solid #e5e7eb;
+}
+
+.robot-setup {
+  margin-bottom: 0.5rem;
+}
+
+.robot-status {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 1rem;
+  border-radius: 6px;
+  font-weight: 500;
+  gap: 1rem;
+}
+
+.robot-status.ready {
+  background: linear-gradient(135deg, #d1fae5 0%, #a7f3d0 100%);
+  color: #065f46;
+  border: 1px solid #10b981;
+}
+
+.robot-status.not-ready {
+  background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+  color: #92400e;
+  border: 1px solid #f59e0b;
+}
+
+.btn-setup {
+  background: #10b981;
+  color: white;
+  border: none;
+  padding: 0.5rem 1rem;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-setup:hover:not(:disabled) {
+  background: #059669;
+}
+
+.btn-setup:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.btn-zero {
+  background: #8b5cf6;
+  color: white;
+  border: none;
+  padding: 0.5rem 1rem;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-zero:hover:not(:disabled) {
+  background: #7c3aed;
+}
+
+.btn-zero:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.zero-position-section {
+  margin-top: 1rem;
+  padding-top: 1rem;
+  border-top: 1px solid #e5e7eb;
+}
+
+.btn-zero-large {
+  width: 100%;
+  background: #8b5cf6;
+  color: white;
+  border: none;
+  padding: 0.875rem 1.5rem;
+  border-radius: 8px;
+  font-size: 1rem;
+  font-weight: 600;
+  cursor: pointer;
+  transition: all 0.2s;
+  box-shadow: 0 2px 4px rgba(139, 92, 246, 0.2);
+}
+
+.btn-zero-large:hover:not(:disabled) {
+  background: #7c3aed;
+  box-shadow: 0 4px 8px rgba(139, 92, 246, 0.3);
+  transform: translateY(-1px);
+}
+
+.btn-zero-large:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+  box-shadow: none;
+  transform: none;
+}
+
+.delete-episode-section {
+  margin-top: 1rem;
+  padding-top: 1rem;
+  border-top: 1px solid #e5e7eb;
+}
+
+.btn-delete {
+  width: 100%;
+  background: #ef4444;
+  color: white;
+  border: none;
+  padding: 0.875rem 1.5rem;
+  border-radius: 8px;
+  font-size: 1rem;
+  font-weight: 600;
+  cursor: pointer;
+  transition: all 0.2s;
+  box-shadow: 0 2px 4px rgba(239, 68, 68, 0.2);
+}
+
+.btn-delete:hover:not(:disabled) {
+  background: #dc2626;
+  box-shadow: 0 4px 8px rgba(239, 68, 68, 0.3);
+  transform: translateY(-1px);
+}
+
+.btn-delete:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+  box-shadow: none;
+  transform: none;
+}
+
+.delete-info {
+  margin-top: 0.5rem;
+  font-size: 0.875rem;
+  color: #666;
+  text-align: center;
+  font-style: italic;
+}
+
+.btn-disconnect {
+  background: #ef4444;
+  color: white;
+  border: none;
+  padding: 0.5rem 1rem;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-disconnect:hover {
+  background: #dc2626;
+}
+
+.btn-refresh {
+  background: #3b82f6;
+  color: white;
+  border: none;
+  padding: 0.4rem 0.8rem;
+  border-radius: 4px;
+  font-size: 0.75rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-refresh:hover:not(:disabled) {
+  background: #2563eb;
+}
+
+.btn-refresh:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.control-panel {
+  border: 2px solid #10b981;
+}
+
+.status-banner {
+  display: flex;
+  align-items: center;
+  gap: 1rem;
+  padding: 1rem 1.5rem;
+  border-radius: 6px;
+  margin-bottom: 1.5rem;
+  font-weight: 500;
+  font-size: 0.95rem;
+}
+
+.status-banner.initializing {
+  background: linear-gradient(135deg, #dbeafe 0%, #bfdbfe 100%);
+  color: #1e40af;
+  border-left: 4px solid #3b82f6;
+}
+
+.status-banner.encoding {
+  background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+  color: #92400e;
+  border-left: 4px solid #f59e0b;
+}
+
+.status-banner.uploading {
+  background: linear-gradient(135deg, #e0e7ff 0%, #c7d2fe 100%);
+  color: #3730a3;
+  border-left: 4px solid #6366f1;
+}
+
+.status-banner.success {
+  background: linear-gradient(135deg, #d1fae5 0%, #a7f3d0 100%);
+  color: #065f46;
+  border-left: 4px solid #10b981;
+}
+
+.status-banner.warning {
+  background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%);
+  color: #991b1b;
+  border-left: 4px solid #ef4444;
+}
+
+.spinner {
+  width: 20px;
+  height: 20px;
+  border: 3px solid rgba(0, 0, 0, 0.1);
+  border-top-color: currentColor;
+  border-radius: 50%;
+  animation: spin 0.8s linear infinite;
+}
+
+@keyframes spin {
+  to { transform: rotate(360deg); }
+}
+
+.control-horizontal {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+.control-left {
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+}
+
+.control-right {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.input-group {
+  display: flex;
+  gap: 0.5rem;
+  margin-bottom: 0;
+}
+
+input[type="text"] {
+  flex: 1;
+  padding: 0.75rem;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  font-size: 1rem;
+}
+
+input[type="text"]:disabled {
+  background: #f5f5f5;
+  cursor: not-allowed;
+}
+
+input[type="text"]:focus {
+  outline: none;
+  border-color: #10b981;
+}
+
+button {
+  padding: 0.75rem 1.5rem;
+  border: none;
+  border-radius: 4px;
+  font-size: 1rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: all 0.2s;
+}
+
+.btn-set-task {
+  background: #3b82f6;
+  color: white;
+  min-width: 120px;
+}
+
+.btn-set-task:hover:not(:disabled) {
+  background: #2563eb;
+}
+
+.btn-set-task:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.btn-start {
+  background: #10b981;
+  color: white;
+}
+
+.btn-start:hover:not(:disabled) {
+  background: #059669;
+}
+
+.btn-start:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.btn-stop {
+  background: #ef4444;
+  color: white;
+}
+
+.btn-stop:hover {
+  background: #dc2626;
+}
+
+.btn-reset {
+  padding: 0.5rem 1rem;
+  background: #6b7280;
+  color: white;
+  font-size: 0.875rem;
+}
+
+.btn-reset:hover {
+  background: #4b5563;
+}
+
+.status {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  padding: 1rem;
+  border-radius: 4px;
+  margin-bottom: 1rem;
+}
+
+.status.recording {
+  background: #fee2e2;
+  color: #991b1b;
+}
+
+.status.recording.recording-active {
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+  background: #dc2626;
+  color: white;
+  padding: 1.5rem;
+  border: 4px solid #991b1b;
+  box-shadow: 0 4px 12px rgba(220, 38, 38, 0.4);
+  font-weight: 700;
+  font-size: 1rem;
+}
+
+.status.recording.recording-active .indicator {
+  width: 20px;
+  height: 20px;
+  background: #fef2f2;
+  animation: pulse-strong 1s ease-in-out infinite;
+}
+
+@keyframes pulse-strong {
+  0%, 100% { 
+    opacity: 1;
+    transform: scale(1);
+  }
+  50% { 
+    opacity: 0.7;
+    transform: scale(1.1);
+  }
+}
+
+.status.recording.recording-active .time-display {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+  font-size: 1.5rem;
+  font-weight: 700;
+  color: white;
+}
+
+.fps-display {
+  font-size: 1rem;
+  font-weight: 500;
+  opacity: 0.95;
+}
+
+.fps-warning {
+  color: #fef2f2;
+  animation: pulse-warning 1s ease-in-out infinite;
+}
+
+@keyframes pulse-warning {
+  0%, 100% { opacity: 1; }
+  50% { opacity: 0.5; }
+}
+
+.status.recording.recording-active .btn-stop {
+  align-self: stretch;
+}
+
+.ramp-up-countdown {
+  display: flex;
+  justify-content: center;
+  margin-bottom: 1rem;
+}
+
+.countdown-box {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  padding: 2rem 3rem;
+  background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+  border: 4px solid #f59e0b;
+  border-radius: 16px;
+  box-shadow: 0 6px 20px rgba(245, 158, 11, 0.4);
+  min-width: 280px;
+  animation: pulse-warm 1.5s ease-in-out infinite;
+}
+
+@keyframes pulse-warm {
+  0%, 100% {
+    box-shadow: 0 6px 20px rgba(245, 158, 11, 0.4);
+  }
+  50% {
+    box-shadow: 0 6px 25px rgba(245, 158, 11, 0.6);
+  }
+}
+
+.countdown-label {
+  font-size: 1rem;
+  color: #92400e;
+  text-transform: uppercase;
+  letter-spacing: 1.5px;
+  font-weight: 800;
+  margin-bottom: 1rem;
+  text-align: center;
+}
+
+.countdown-value {
+  font-size: 4.5rem;
+  font-weight: 900;
+  color: #d97706;
+  font-family: 'Courier New', monospace;
+  line-height: 1;
+  text-shadow: 2px 2px 6px rgba(0, 0, 0, 0.15);
+  margin-bottom: 0.5rem;
+}
+
+.countdown-subtitle {
+  font-size: 0.875rem;
+  color: #78350f;
+  font-weight: 600;
+  font-style: italic;
+  text-align: center;
+  margin-top: 0.5rem;
+}
+
+.status.idle {
+  background: #f3f4f6;
+  color: #374151;
+}
+
+.indicator {
+  width: 12px;
+  height: 12px;
+  border-radius: 50%;
+  background: #ef4444;
+  animation: pulse 1.5s ease-in-out infinite;
+}
+
+@keyframes pulse {
+  0%, 100% { opacity: 1; }
+  50% { opacity: 0.5; }
+}
+
+.counter {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 0.75rem;
+  padding: 1.5rem;
+  background: linear-gradient(135deg, #f9fafb 0%, #f3f4f6 100%);
+  border-radius: 8px;
+  border: 2px solid #e5e7eb;
+  min-width: 200px;
+}
+
+.counter-label {
+  font-size: 0.75rem;
+  color: #6b7280;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+  font-weight: 600;
+}
+
+.counter-value {
+  font-size: 3rem;
+  font-weight: 700;
+  color: #10b981;
+  line-height: 1;
+}
+
+.time-display {
+  font-size: 1.5rem;
+  font-weight: 600;
+  font-family: 'Courier New', monospace;
+}
+
+.error-box {
+  padding: 1rem;
+  background: #fee2e2;
+  color: #991b1b;
+  border-radius: 4px;
+  border-left: 4px solid #ef4444;
+  font-size: 0.875rem;
+}
+
+.config-section {
+  margin-bottom: 1.5rem;
+}
+
+.config-section:last-child {
+  margin-bottom: 0;
+}
+
+.config-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+  gap: 1rem;
+}
+
+label {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+  font-size: 0.875rem;
+  color: #374151;
+  font-weight: 500;
+}
+
+select {
+  padding: 0.5rem;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  background: white;
+}
+
+select:disabled {
+  background: #f5f5f5;
+  cursor: not-allowed;
+}
+
+/* Camera Layout */
+.camera-layout {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+.camera-base {
+  width: 100%;
+}
+
+.camera-wrist-container {
+  display: grid;
+  grid-template-columns: repeat(2, 1fr);
+  gap: 1.5rem;
+}
+
+.camera-wrist {
+  width: 100%;
+}
+
+.camera {
+  border: 1px solid #e5e7eb;
+  border-radius: 4px;
+  overflow: hidden;
+}
+
+.camera h3 {
+  padding: 0.75rem;
+  background: #f9fafb;
+  border-bottom: 1px solid #e5e7eb;
+  margin: 0;
+}
+
+.camera img {
+  width: 100%;
+  height: auto;
+  display: block;
+  background: #000;
+  min-height: 300px;
+  object-fit: cover;
+}
+
+.camera-placeholder {
+  text-align: center;
+  padding: 4rem 2rem;
+  background: #f9fafb;
+  border-radius: 4px;
+  border: 2px dashed #d1d5db;
+}
+
+.camera-placeholder p {
+  margin: 0.5rem 0;
+  font-size: 1rem;
+  color: #6b7280;
+}
+
+.camera-placeholder p:first-child {
+  font-size: 1.25rem;
+  font-weight: 500;
+  color: #374151;
+}
+
+.hint {
+  margin-top: 0.5rem;
+  font-size: 0.75rem;
+  color: #6b7280;
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  flex-wrap: wrap;
+}
+
@@ -0,0 +1,857 @@
+import { useState, useEffect, useCallback, useRef } from 'react';
+import './App.css';
+
+const API_BASE = 'http://localhost:8000/api';
+
+function App() {
+  // State
+  const [task, setTask] = useState('');
+  const [isRecording, setIsRecording] = useState(false);
+  const [isInitializing, setIsInitializing] = useState(false);
+  const [isEncoding, setIsEncoding] = useState(false);
+  const [isUploading, setIsUploading] = useState(false);
+  const [robotsReady, setRobotsReady] = useState(false);
+  const [elapsedTime, setElapsedTime] = useState(0);
+  const [currentFps, setCurrentFps] = useState(0);
+  const [loopFps, setLoopFps] = useState(0);
+  const [episodeCount, setEpisodeCount] = useState(0);
+  const [error, setError] = useState(null);
+  const [statusMessage, setStatusMessage] = useState('Ready');
+  const [uploadStatus, setUploadStatus] = useState(null);
+  const [rampUpRemaining, setRampUpRemaining] = useState(0);
+  const [movingToZero, setMovingToZero] = useState(false);
+  const [configExpanded, setConfigExpanded] = useState(false);
+  const [latestRepoId, setLatestRepoId] = useState(null);
+
+  // Configuration
+  const [config, setConfig] = useState({
+    leader_type: 'openarms',  // 'openarms' or 'openarms_mini'
+    leader_left: 'can0',
+    leader_right: 'can1',
+    follower_left: 'can2',
+    follower_right: 'can3',
+    left_wrist: '/dev/video0',
+    right_wrist: '/dev/video1',
+    base: '/dev/video4'
+  });
+
+  // Available options
+  const [availableCameras, setAvailableCameras] = useState([]);
+  const [availableUsbPorts, setAvailableUsbPorts] = useState([]);
+  const canInterfaces = ['can0', 'can1', 'can2', 'can3'];
+
+  const statusIntervalRef = useRef(null);
+  const hasInitializedRef = useRef(false);
+
+  const loadConfig = () => {
+    try {
+      const saved = localStorage.getItem('openarms_config');
+      if (saved) {
+        const loadedConfig = JSON.parse(saved);
+        setConfig(prev => ({ ...prev, ...loadedConfig }));
+      }
+    } catch (e) {
+      console.error('Load config error:', e);
+    }
+  };
+
+  const saveConfig = (newConfig) => {
+    try {
+      localStorage.setItem('openarms_config', JSON.stringify(newConfig || config));
+    } catch (e) {
+      console.error('Save config error:', e);
+    }
+  };
+
+  // Fetch status periodically
+  const fetchStatus = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/status`);
+      const data = await response.json();
+
+      setIsRecording(data.is_recording);
+      setIsInitializing(data.is_initializing);
+      setIsEncoding(data.is_encoding);
+      setIsUploading(data.is_uploading);
+      setRobotsReady(data.robots_ready);
+      setElapsedTime(data.elapsed_time);
+      setCurrentFps(data.current_fps || 0);
+      setLoopFps(data.loop_fps || 0);
+      setEpisodeCount(data.episode_count);
+      setError(data.error);
+      setStatusMessage(data.status_message || 'Ready');
+      setUploadStatus(data.upload_status);
+      setRampUpRemaining(data.ramp_up_remaining || 0);
+      setMovingToZero(data.moving_to_zero || false);
+      
+      // Track the latest repo_id from the backend
+      if (data.latest_repo_id) {
+        setLatestRepoId(data.latest_repo_id);
+      }
+
+      if (data.config) {
+        // Only merge server config if we don't have a saved config (first load)
+        if (!localStorage.getItem('openarms_config')) {
+          setConfig(prev => {
+            const merged = { ...data.config, ...prev };
+            localStorage.setItem('openarms_config', JSON.stringify(merged));
+            return merged;
+          });
+        }
+      }
+    } catch (e) {
+      console.error('Failed to fetch status:', e);
+    }
+  };
+
+  const setupRobots = async () => {
+    // Show warning to verify camera positions
+    const confirmed = window.confirm(
+      '⚠️ IMPORTANT: Before connecting robots, please verify:\n\n' +
+      '📹 Check that cameras are correctly positioned:\n' +
+      '   • LEFT wrist camera is actually on the LEFT arm\n' +
+      '   • RIGHT wrist camera is actually on the RIGHT arm\n' +
+      '   • BASE camera is actually the BASE/overhead camera\n\n' +
+      'Incorrect camera positioning will result in invalid training data!\n\n' +
+      'Click OK to continue with robot setup, or Cancel to review configuration.'
+    );
+    
+    if (!confirmed) {
+      return; // User cancelled, don't proceed
+    }
+    
+    setError(null);
+    try {
+      const response = await fetch(`${API_BASE}/robots/setup`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(config)
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to setup robots');
+      }
+
+      await response.json();
+      saveConfig(config);
+    } catch (e) {
+      setError(`Robot setup failed: ${e.message}`);
+    }
+  };
+
+  // Disconnect robots
+  const disconnectRobots = async () => {
+    try {
+      await fetch(`${API_BASE}/robots/disconnect`, { method: 'POST' });
+      setRobotsReady(false);
+    } catch (e) {
+      console.error('Failed to disconnect robots:', e);
+    }
+  };
+
+  // Discover cameras
+  const discoverCameras = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/cameras/discover`);
+      const data = await response.json();
+      const cameras = data.cameras || [];
+      setAvailableCameras(cameras);
+
+      // Get list of valid camera IDs
+      const validCameraIds = cameras.map(cam => String(cam.id));
+
+      // Auto-fix config if current values are invalid or not set
+      const updated = { ...config };
+      let changed = false;
+
+      // Auto-fix invalid camera config
+      if (!config.left_wrist || !validCameraIds.includes(config.left_wrist)) {
+        if (cameras.length >= 1) {
+          updated.left_wrist = String(cameras[0].id);
+          changed = true;
+        }
+      }
+
+      if (!config.right_wrist || !validCameraIds.includes(config.right_wrist)) {
+        if (cameras.length >= 2) {
+          updated.right_wrist = String(cameras[1].id);
+          changed = true;
+        }
+      }
+
+      if (!config.base || !validCameraIds.includes(config.base)) {
+        if (cameras.length >= 3) {
+          updated.base = String(cameras[2].id);
+          changed = true;
+        }
+      }
+
+      if (changed) {
+        setConfig(updated);
+        saveConfig(updated);
+      }
+
+      if (cameras.length === 0) {
+        setError('No cameras detected! Please connect cameras and refresh.');
+      }
+    } catch (e) {
+      console.error('Failed to discover cameras:', e);
+      setError(`Camera discovery failed: ${e.message}`);
+    }
+  };
+
+  // Discover USB ports
+  const discoverUsbPorts = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/usb/discover`);
+      const data = await response.json();
+      const ports = data.ports || [];
+      setAvailableUsbPorts(ports);
+
+      // Auto-fix config if OpenArms Mini is selected and ports are invalid
+      if (config.leader_type === 'openarms_mini') {
+        const updated = { ...config };
+        let changed = false;
+
+        if (ports.length >= 1 && !ports.includes(config.leader_left)) {
+          updated.leader_left = ports[0];
+          changed = true;
+        }
+
+        if (ports.length >= 2 && !ports.includes(config.leader_right)) {
+          updated.leader_right = ports[1];
+          changed = true;
+        }
+
+        if (changed) {
+          setConfig(updated);
+          saveConfig(updated);
+        }
+      }
+
+      if (ports.length === 0) {
+        console.warn('No USB ports detected for OpenArms Mini');
+      }
+    } catch (e) {
+      console.error('Failed to discover USB ports:', e);
+    }
+  };
+
+  // Set task only (for pedal use)
+  const setTaskOnly = async () => {
+    if (!task.trim()) {
+      setError('Please enter a task description');
+      return;
+    }
+
+    setError(null);
+
+    try {
+      const response = await fetch(`${API_BASE}/recording/set-task`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ task, ...config })
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to set task');
+      }
+
+      const result = await response.json();
+      setStatusMessage(result.message || `Task set: ${task}`);
+      saveConfig(config);
+      
+      // Clear success message after 3 seconds
+      setTimeout(() => {
+        if (!isRecording && !isInitializing) {
+          setStatusMessage('Ready');
+        }
+      }, 3000);
+    } catch (e) {
+      setError(e.message);
+    }
+  };
+
+  // Start recording
+  const startRecording = async () => {
+    if (!task.trim()) {
+      setError('Please enter a task description');
+      return;
+    }
+
+    setError(null);
+
+    try {
+      const response = await fetch(`${API_BASE}/recording/start`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ task, ...config })
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to start recording');
+      }
+
+      await response.json();
+      saveConfig(config);
+    } catch (e) {
+      setError(e.message);
+    }
+  };
+
+  // Stop recording
+  const stopRecording = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/recording/stop`, {
+        method: 'POST'
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to stop recording');
+      }
+
+      const data = await response.json();
+      setError(null);
+      // Update latest repo_id after recording
+      if (data.dataset_name) {
+        setLatestRepoId(`lerobot-data-collection/${data.dataset_name}`);
+      }
+    } catch (e) {
+      setError(e.message);
+    }
+  };
+
+  const deleteLatestEpisode = async () => {
+    if (!latestRepoId) {
+      setError('No episode to delete');
+      return;
+    }
+    
+    const confirmed = window.confirm(
+      `WARNING: This will permanently delete the repository:\n\n${latestRepoId}\n\nThis action cannot be undone. Continue?`
+    );
+    
+    if (!confirmed) {
+      return;
+    }
+    
+    try {
+      const response = await fetch(`${API_BASE}/recording/delete-latest`, { method: 'POST' });
+      
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to delete episode');
+      }
+
+      const data = await response.json();
+      setLatestRepoId(null);
+      setEpisodeCount(Math.max(0, episodeCount - 1));
+      setStatusMessage(`Deleted: ${data.deleted_repo}`);
+      
+      setTimeout(() => {
+        if (!isRecording && !isInitializing) {
+          setStatusMessage('Ready');
+        }
+      }, 3000);
+    } catch (e) {
+      setError(`Delete failed: ${e.message}`);
+    }
+  };
+
+  // Reset counter
+  const resetCounter = async () => {
+    try {
+      await fetch(`${API_BASE}/counter/reset`, { method: 'POST' });
+      setEpisodeCount(0);
+    } catch (e) {
+      console.error('Failed to reset counter:', e);
+    }
+  };
+
+  // Move robot to zero position
+  const moveToZero = async () => {
+    setError(null);
+    try {
+      const response = await fetch(`${API_BASE}/robots/move-to-zero`, { method: 'POST' });
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to move to zero position');
+      }
+      await response.json();
+    } catch (e) {
+      setError(`Move to zero failed: ${e.message}`);
+    }
+  };
+
+  // Format time as MM:SS
+  const formatTime = (seconds) => {
+    const mins = Math.floor(seconds / 60);
+    const secs = Math.floor(seconds % 60);
+    return `${mins.toString().padStart(2, '0')}:${secs.toString().padStart(2, '0')}`;
+  };
+
+  // Update config and save
+  const updateConfig = (key, value) => {
+    const updated = { ...config, [key]: value };
+    setConfig(updated);
+    saveConfig(updated);
+  };
+
+  // Initialize on mount only
+  useEffect(() => {
+    // Prevent double-initialization in development
+    if (hasInitializedRef.current) {
+      return;
+    }
+    hasInitializedRef.current = true;
+
+    loadConfig();
+    discoverCameras();
+    discoverUsbPorts();
+    fetchStatus();
+    statusIntervalRef.current = setInterval(fetchStatus, 1000);
+
+    return () => {
+      if (statusIntervalRef.current) {
+        clearInterval(statusIntervalRef.current);
+      }
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []); // Run only once on mount
+
+  // Discover USB ports when leader type changes to Mini
+  useEffect(() => {
+    if (config.leader_type === 'openarms_mini') {
+      discoverUsbPorts();
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [config.leader_type]);
+
+  return (
+    <main>
+      <header>
+        <h1>OpenArms Recording</h1>
+      </header>
+
+      <div className="container">
+        {/* Left Column: Configuration and Recording Control */}
+        <div className="left-column">
+          {/* Configuration Panel */}
+          <section className="panel config-panel">
+          <div
+            className="config-header"
+            onClick={() => setConfigExpanded(!configExpanded)}
+            role="button"
+            tabIndex={0}
+            onKeyDown={(e) => e.key === 'Enter' && setConfigExpanded(!configExpanded)}
+          >
+            <h2>⚙️ Configuration</h2>
+            <span className="toggle-icon">{configExpanded ? '▼' : '▶'}</span>
+          </div>
+
+          {configExpanded && (
+            <div className="config-content">
+              {/* Robot Setup */}
+              <div className="config-section">
+                <h3>🤖 Robot Setup</h3>
+                <div className="robot-setup">
+                  {robotsReady ? (
+                    <div className="robot-status ready">
+                      <span>✅ Robots Ready - Recording will start instantly</span>
+                      <button onClick={disconnectRobots} className="btn-disconnect">
+                        Disconnect Robots
+                      </button>
+                    </div>
+                  ) : (
+                    <div className="robot-status not-ready">
+                      <span>⚠️ Robots not initialized - Recording will take ~10 seconds</span>
+                      <button
+                        onClick={setupRobots}
+                        disabled={isRecording || isInitializing}
+                        className="btn-setup"
+                      >
+                        🚀 Setup Robots
+                      </button>
+                    </div>
+                  )}
+                </div>
+              </div>
+
+              {/* Leader Type Selection */}
+              <div className="config-section">
+                <h3>🎮 Leader Type</h3>
+                <div className="config-grid">
+                  <label style={{gridColumn: '1 / -1'}}>
+                    Leader Arm Type
+                    <select
+                      value={config.leader_type}
+                      onChange={(e) => updateConfig('leader_type', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      <option value="openarms">OpenArms (CAN Bus - Damiao Motors)</option>
+                      <option value="openarms_mini">OpenArms Mini (USB - Feetech Motors)</option>
+                    </select>
+                  </label>
+                </div>
+              </div>
+
+              {/* Leader Interfaces (CAN or USB based on type) */}
+              <div className="config-section">
+                <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '0.5rem' }}>
+                  <h3>
+                    {config.leader_type === 'openarms_mini' 
+                      ? `Leader Ports (USB/Serial) ${availableUsbPorts.length > 0 ? `(${availableUsbPorts.length} detected)` : ''}` 
+                      : 'Leader Interfaces (CAN)'}
+                  </h3>
+                  {config.leader_type === 'openarms_mini' && (
+                    <button
+                      onClick={discoverUsbPorts}
+                      className="btn-refresh"
+                      disabled={isRecording || robotsReady}
+                    >
+                      🔄 Refresh
+                    </button>
+                  )}
+                </div>
+                
+                <div className="config-grid">
+                  <label>
+                    Leader Left
+                    <select
+                      value={config.leader_left}
+                      onChange={(e) => updateConfig('leader_left', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {config.leader_type === 'openarms_mini' ? (
+                        availableUsbPorts.length > 0 ? (
+                          availableUsbPorts.map((port) => (
+                            <option key={port} value={port}>{port}</option>
+                          ))
+                        ) : (
+                          <option value="">No USB ports detected</option>
+                        )
+                      ) : (
+                        canInterfaces.map((iface) => (
+                          <option key={iface} value={iface}>{iface}</option>
+                        ))
+                      )}
+                    </select>
+                  </label>
+
+                  <label>
+                    Leader Right
+                    <select
+                      value={config.leader_right}
+                      onChange={(e) => updateConfig('leader_right', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {config.leader_type === 'openarms_mini' ? (
+                        availableUsbPorts.length > 0 ? (
+                          availableUsbPorts.map((port) => (
+                            <option key={port} value={port}>{port}</option>
+                          ))
+                        ) : (
+                          <option value="">No USB ports detected</option>
+                        )
+                      ) : (
+                        canInterfaces.map((iface) => (
+                          <option key={iface} value={iface}>{iface}</option>
+                        ))
+                      )}
+                    </select>
+                  </label>
+                </div>
+              </div>
+
+              {/* Follower CAN Interfaces */}
+              <div className="config-section">
+                <h3>Follower Interfaces (CAN)</h3>
+                
+                <div className="config-grid">
+                  <label>
+                    Follower Left
+                    <select
+                      value={config.follower_left}
+                      onChange={(e) => updateConfig('follower_left', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {canInterfaces.map((iface) => (
+                        <option key={iface} value={iface}>{iface}</option>
+                      ))}
+                    </select>
+                  </label>
+
+                  <label>
+                    Follower Right
+                    <select
+                      value={config.follower_right}
+                      onChange={(e) => updateConfig('follower_right', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {canInterfaces.map((iface) => (
+                        <option key={iface} value={iface}>{iface}</option>
+                      ))}
+                    </select>
+                  </label>
+                </div>
+              </div>
+
+              {/* Camera Configuration */}
+              <div className="config-section">
+                <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '0.5rem' }}>
+                  <h3>Cameras {availableCameras.length > 0 && `(${availableCameras.length} detected)`}</h3>
+                  <button
+                    onClick={discoverCameras}
+                    className="btn-refresh"
+                    disabled={isRecording || robotsReady}
+                  >
+                    🔄 Refresh
+                  </button>
+                </div>
+                <div className="config-grid">
+                  <label>
+                    Left Wrist
+                    <select
+                      value={config.left_wrist}
+                      onChange={(e) => updateConfig('left_wrist', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {availableCameras.map((cam) => (
+                        <option key={cam.id} value={String(cam.id)}>
+                          {cam.name || `Camera @ ${cam.id}`}
+                        </option>
+                      ))}
+                    </select>
+                  </label>
+
+                  <label>
+                    Right Wrist
+                    <select
+                      value={config.right_wrist}
+                      onChange={(e) => updateConfig('right_wrist', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {availableCameras.map((cam) => (
+                        <option key={cam.id} value={String(cam.id)}>
+                          {cam.name || `Camera @ ${cam.id}`}
+                        </option>
+                      ))}
+                    </select>
+                  </label>
+
+                  <label>
+                    Base Camera
+                    <select
+                      value={config.base}
+                      onChange={(e) => updateConfig('base', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {availableCameras.map((cam) => (
+                        <option key={cam.id} value={String(cam.id)}>
+                          {cam.name || `Camera @ ${cam.id}`}
+                        </option>
+                      ))}
+                    </select>
+                  </label>
+                </div>
+              </div>
+            </div>
+          )}
+        </section>
+
+        {/* Control Panel */}
+        <section className="panel control-panel">
+          <h2>🎬 Recording Control</h2>
+
+          {/* Status Banner - Always show important statuses */}
+          {isInitializing && (
+            <div className="status-banner initializing">
+              <div className="spinner"></div>
+              <span>{statusMessage}</span>
+            </div>
+          )}
+
+          {isEncoding && (
+            <div className="status-banner encoding">
+              <div className="spinner"></div>
+              <span>📹 {statusMessage}</span>
+            </div>
+          )}
+
+          {isUploading && (
+            <div className="status-banner uploading">
+              <div className="spinner"></div>
+              <span>☁️ {statusMessage}</span>
+            </div>
+          )}
+
+          {uploadStatus && !isRecording && !isEncoding && !isUploading && (
+            <div className={`status-banner ${uploadStatus.startsWith('✓') ? 'success' : 'warning'}`}>
+              <span>{uploadStatus}</span>
+            </div>
+          )}
+
+          <div className="control-horizontal">
+            {/* Task Input and Status */}
+            <div className="control-left">
+              <div className="input-group">
+                <input
+                  type="text"
+                  value={task}
+                  onChange={(e) => setTask(e.target.value)}
+                  placeholder="Task description (e.g., 'pick and place')"
+                  disabled={isRecording || isInitializing || isEncoding || isUploading}
+                  onKeyPress={(e) => {
+                    if (e.key === 'Enter' && robotsReady) {
+                      setTaskOnly();
+                    }
+                  }}
+                />
+                <button
+                  onClick={setTaskOnly}
+                  disabled={isRecording || isInitializing || isEncoding || isUploading || !robotsReady}
+                  className="btn-set-task"
+                  title={!robotsReady ? 'Please setup robots first' : 'Store task for pedal use (Enter key)'}
+                >
+                  💾 Set Task
+                </button>
+                <button
+                  onClick={startRecording}
+                  disabled={isRecording || isInitializing || isEncoding || isUploading || !robotsReady}
+                  className="btn-start"
+                  title={!robotsReady ? 'Please setup robots first' : ''}
+                >
+                  {isInitializing
+                    ? '⏳ Initializing...'
+                    : isRecording
+                    ? '⏺ Recording...'
+                    : robotsReady
+                    ? '⏺ Start Recording'
+                    : '⏺ Setup Robots First'}
+                </button>
+              </div>
+
+              {/* Ramp-up Countdown */}
+              {isRecording && rampUpRemaining > 0 && (
+                <div className="ramp-up-countdown">
+                  <div className="countdown-box">
+                    <div className="countdown-label">⚡ WARMING UP - PID RAMP-UP</div>
+                    <div className="countdown-value">{rampUpRemaining.toFixed(1)}s</div>
+                    <div className="countdown-subtitle">Recording will start automatically...</div>
+                  </div>
+                </div>
+              )}
+
+              {/* Recording Status - Only show after ramp-up */}
+              {isRecording && rampUpRemaining <= 0 && (
+                <div className="status recording recording-active">
+                  <div className="indicator"></div>
+                  <div className="time-display">
+                    <span>{formatTime(elapsedTime)}</span>
+                    <span className="fps-display">
+                      Loop: {loopFps.toFixed(1)} Hz
+                      {loopFps > 0 && loopFps < 29 && <span className="fps-warning"> ⚠️</span>}
+                    </span>
+                    <span className="fps-display">Recording: {currentFps.toFixed(1)} FPS</span>
+                  </div>
+                  <button onClick={stopRecording} className="btn-stop">
+                    ⏹ Stop
+                  </button>
+                </div>
+              )}
+            </div>
+
+            {/* Episode Counter */}
+            <div className="control-right">
+              <div className="counter">
+                <div className="counter-label">Episodes Recorded</div>
+                <div className="counter-value">{episodeCount}</div>
+                <button onClick={resetCounter} className="btn-reset">
+                  Reset
+                </button>
+              </div>
+            </div>
+          </div>
+
+          {/* Delete Latest Episode Button */}
+          {!isRecording && !isInitializing && latestRepoId && (
+            <div className="delete-episode-section">
+              <button 
+                onClick={deleteLatestEpisode}
+                className="btn-delete"
+                title="Delete the latest recorded episode from HuggingFace Hub"
+              >
+                Delete Latest Episode
+              </button>
+              <div className="delete-info">Will delete: {latestRepoId}</div>
+            </div>
+          )}
+
+          {/* Move to Zero Button */}
+          {robotsReady && !isRecording && !isInitializing && (
+            <div className="zero-position-section">
+              <button 
+                onClick={moveToZero} 
+                disabled={movingToZero}
+                className="btn-zero-large"
+                title="Move both leader and follower robots to zero position (2s)"
+              >
+                {movingToZero ? '⏳ Moving to Zero Position...' : '🎯 Move to Zero Position (Leader + Follower)'}
+              </button>
+            </div>
+          )}
+
+          {/* Error Display */}
+          {error && (
+            <div className="error-box">
+              ⚠️ {error}
+            </div>
+          )}
+        </section>
+        </div>
+
+        {/* Right Column: Camera Feeds */}
+        <div className="right-column">
+          <section className="panel cameras">
+          <h2>📹 Camera Views</h2>
+          {robotsReady || isRecording || isInitializing ? (
+            <div className="camera-layout">
+              {/* Base camera - full width */}
+              <div className="camera camera-base">
+                <h3>Base Camera</h3>
+                <img src={`${API_BASE}/camera/stream/base`} alt="Base Camera" />
+              </div>
+
+              {/* Wrist cameras - side by side */}
+              <div className="camera-wrist-container">
+                <div className="camera camera-wrist">
+                  <h3>Left Wrist</h3>
+                  <img src={`${API_BASE}/camera/stream/left_wrist`} alt="Left Wrist Camera" />
+                </div>
+
+                <div className="camera camera-wrist">
+                  <h3>Right Wrist</h3>
+                  <img src={`${API_BASE}/camera/stream/right_wrist`} alt="Right Wrist Camera" />
+                </div>
+              </div>
+            </div>
+          ) : (
+            <div className="camera-placeholder">
+              <p>📷 Camera feeds will appear when robots are set up</p>
+              <p className="hint">Click "Setup Robots" above to preview camera feeds</p>
+            </div>
+          )}
+        </section>
+        </div>
+
+      </div>
+    </main>
+  );
+}
+
+export default App;
+
@@ -0,0 +1,41 @@
+# OpenArms Web Recording Interface
+
+A web interface for recording OpenArms datasets.
+
+## Installation
+
+```bash
+cd examples/openarms_web_interface
+npm install
+```
+
+## Usage
+
+**Start everything with one command:**
+
+```bash
+./launch.sh
+```
+
+This will:
+- Start the FastAPI backend on port 8000
+- Start the React frontend on port 5173
+- Show live logs from both services
+
+Then open your browser to: **http://localhost:5173**
+
+**Stop with:** `Ctrl+C`
+
+---
+
+## Workflow
+
+1. **Configure CAN interfaces** and **camera paths** in the dropdowns
+2. Click **"Setup Robots"** to initialize (once at start)
+3. Enter a **task description**
+4. Click **"Start Recording"** to begin an episode
+5. Click **"Stop Recording"** when done
+6. Dataset is automatically encoded and uploaded to HuggingFace Hub as **private**
+7. Repeat steps 3-6 for more episodes (no need to re-setup robots!)
+
+---
@@ -0,0 +1,12 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>OpenArms Recording Interface</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/main.jsx"></script>
+  </body>
+</html>
@@ -0,0 +1,142 @@
+#!/bin/bash
+
+# OpenArms Web Interface Launcher
+# Starts Rerun viewer, FastAPI backend, and React frontend
+
+set -e
+
+# Colors for output
+GREEN='\033[0;32m'
+BLUE='\033[0;34m'
+YELLOW='\033[1;33m'
+RED='\033[0;31m'
+NC='\033[0m' # No Color
+
+# Get script directory
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+cd "$SCRIPT_DIR"
+
+echo -e "${BLUE}╔════════════════════════════════════════╗${NC}"
+echo -e "${BLUE}║   OpenArms Web Recording Interface    ║${NC}"
+echo -e "${BLUE}╚════════════════════════════════════════╝${NC}"
+echo ""
+
+# Function to cleanup on exit
+cleanup() {
+    echo ""
+    echo -e "${YELLOW}Shutting down services...${NC}"
+    
+    # Kill all child processes
+    pkill -P $$ 2>/dev/null || true
+    
+    # Kill specific services by port
+    lsof -ti:8000 | xargs kill -9 2>/dev/null || true  # Backend
+    lsof -ti:5173 | xargs kill -9 2>/dev/null || true  # Frontend
+    lsof -ti:9876 | xargs kill -9 2>/dev/null || true  # Rerun (if spawned)
+    
+    echo -e "${GREEN}✓ Services stopped${NC}"
+    exit 0
+}
+
+# Register cleanup on script exit
+trap cleanup EXIT INT TERM
+
+# Check if required commands exist
+command -v rerun >/dev/null 2>&1 || { 
+    echo -e "${RED}✗ Error: 'rerun' not found. Please install: pip install rerun-sdk${NC}"
+    exit 1
+}
+
+command -v python >/dev/null 2>&1 || { 
+    echo -e "${RED}✗ Error: 'python' not found${NC}"
+    exit 1
+}
+
+command -v npm >/dev/null 2>&1 || { 
+    echo -e "${RED}✗ Error: 'npm' not found${NC}"
+    exit 1
+}
+
+# Check if node_modules exists
+if [ ! -d "node_modules" ]; then
+    echo -e "${YELLOW}⚠ node_modules not found. Running npm install...${NC}"
+    npm install
+    echo -e "${GREEN}✓ Dependencies installed${NC}"
+    echo ""
+fi
+
+echo -e "${GREEN}Starting services...${NC}"
+echo ""
+
+# 1. Start FastAPI backend (Rerun will start when recording begins)
+echo -e "${BLUE}[1/2]${NC} Starting FastAPI backend on port 8000..."
+cd "$SCRIPT_DIR"
+
+# Use Python from current environment (if lerobot env is active, it will use that)
+# Otherwise, check if we need to use conda run
+if [[ "$CONDA_DEFAULT_ENV" == "lerobot" ]]; then
+    # Already in lerobot environment
+    echo -e "${GREEN}✓ Using active lerobot environment${NC}"
+    PYTHON_CMD="python"
+elif command -v conda >/dev/null 2>&1 && conda env list | grep -q "^lerobot "; then
+    # lerobot env exists but not active - use conda run
+    echo -e "${YELLOW}Using conda run with lerobot environment...${NC}"
+    PYTHON_CMD="conda run -n lerobot --no-capture-output python"
+else
+    # Fall back to system python
+    echo -e "${YELLOW}⚠ Warning: lerobot environment not found, using system python${NC}"
+    PYTHON_CMD="python"
+fi
+
+$PYTHON_CMD web_record_server.py > /tmp/openarms_backend.log 2>&1 &
+BACKEND_PID=$!
+sleep 3
+
+if ps -p $BACKEND_PID > /dev/null; then
+    echo -e "${GREEN}✓ Backend started${NC} (PID: $BACKEND_PID)"
+    echo -e "      URL: ${BLUE}http://localhost:8000${NC}"
+else
+    echo -e "${RED}✗ Failed to start backend${NC}"
+    echo -e "${YELLOW}Check logs: tail -f /tmp/openarms_backend.log${NC}"
+    exit 1
+fi
+echo ""
+
+# 2. Start React frontend
+echo -e "${BLUE}[2/2]${NC} Starting React frontend on port 5173..."
+cd "$SCRIPT_DIR"
+npm run dev > /tmp/openarms_frontend.log 2>&1 &
+FRONTEND_PID=$!
+sleep 3
+
+if ps -p $FRONTEND_PID > /dev/null; then
+    echo -e "${GREEN}✓ Frontend started${NC} (PID: $FRONTEND_PID)"
+    echo -e "      URL: ${BLUE}http://localhost:5173${NC}"
+else
+    echo -e "${RED}✗ Failed to start frontend${NC}"
+    echo -e "${YELLOW}Check logs: tail -f /tmp/openarms_frontend.log${NC}"
+    exit 1
+fi
+echo ""
+
+# Display status
+echo -e "${GREEN}╔════════════════════════════════════════╗${NC}"
+echo -e "${GREEN}║     All services running! 🚀           ║${NC}"
+echo -e "${GREEN}╚════════════════════════════════════════╝${NC}"
+echo ""
+echo -e "🔧 ${BLUE}Backend:${NC}  http://localhost:8000"
+echo -e "🌐 ${BLUE}Frontend:${NC} http://localhost:5173"
+echo -e "📊 ${BLUE}Rerun:${NC}    Will spawn automatically when recording starts"
+echo ""
+echo -e "${YELLOW}Open your browser to:${NC} ${BLUE}http://localhost:5173${NC}"
+echo ""
+echo -e "${YELLOW}Logs:${NC}"
+echo -e "  • Backend:  tail -f /tmp/openarms_backend.log"
+echo -e "  • Frontend: tail -f /tmp/openarms_frontend.log"
+echo ""
+echo -e "${RED}Press Ctrl+C to stop all services${NC}"
+echo ""
+
+# Keep script running and wait for any service to exit
+wait
+
@@ -0,0 +1,7 @@
+import { createRoot } from 'react-dom/client'
+import App from './App.jsx'
+
+createRoot(document.getElementById('root')).render(
+  <App />
+)
+
@@ -0,0 +1,21 @@
+{
+  "name": "openarms-web-interface",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1"
+  },
+  "devDependencies": {
+    "@types/react": "^18.3.12",
+    "@types/react-dom": "^18.3.1",
+    "@vitejs/plugin-react": "^4.3.4",
+    "vite": "^6.0.1"
+  }
+}
@@ -0,0 +1,17 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+  server: {
+    port: 5173,
+    strictPort: false,
+    host: true,
+    open: false
+  },
+  build: {
+    outDir: 'dist',
+    sourcemap: true
+  }
+})
@@ -16,13 +16,15 @@

 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.configs.types import FeatureType, PolicyFeature
-from lerobot.datasets.feature_utils import combine_feature_dicts
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies.act.modeling_act import ACTPolicy
 from lerobot.policies.factory import make_pre_post_processors
 from lerobot.processor import (
+    RobotAction,
+    RobotObservation,
    RobotProcessorPipeline,
    make_default_teleop_action_processor,
 )
@@ -32,13 +34,13 @@ from lerobot.processor.converters import (
    transition_to_observation,
    transition_to_robot_action,
 )
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.robots.so_follower.robot_kinematic_processor import (
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
    ForwardKinematicsJointsToEE,
    InverseKinematicsEEToJoints,
 )
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.scripts.lerobot_record import record_loop
-from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
@@ -141,24 +143,38 @@ def main():
    listener, events = init_keyboard_listener()
    init_rerun(session_name="phone_so100_evaluate")

-    try:
-        if not robot.is_connected:
-            raise ValueError("Robot is not connected!")
+    if not robot.is_connected:
+        raise ValueError("Robot is not connected!")

-        print("Starting evaluate loop...")
-        episode_idx = 0
-        for episode_idx in range(NUM_EPISODES):
-            log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
+    print("Starting evaluate loop...")
+    episode_idx = 0
+    for episode_idx in range(NUM_EPISODES):
+        log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")

-            # Main record loop
+        # Main record loop
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            policy=policy,
+            preprocessor=preprocessor,  # Pass the pre and post policy processors
+            postprocessor=postprocessor,
+            dataset=dataset,
+            control_time_s=EPISODE_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=make_default_teleop_action_processor(),
+            robot_action_processor=robot_ee_to_joints_processor,
+            robot_observation_processor=robot_joints_to_ee_pose_processor,
+        )
+
+        # Reset the environment if not stopping or re-recording
+        if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
+            log_say("Reset the environment")
            record_loop(
                robot=robot,
                events=events,
                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
                control_time_s=EPISODE_TIME_SEC,
                single_task=TASK_DESCRIPTION,
                display_data=True,
@@ -167,41 +183,24 @@ def main():
                robot_observation_processor=robot_joints_to_ee_pose_processor,
            )

-            # Reset the environment if not stopping or re-recording
-            if not events["stop_recording"] and (
-                (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
-            ):
-                log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=make_default_teleop_action_processor(),
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose_processor,
-                )
+        if events["rerecord_episode"]:
+            log_say("Re-record episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue

-            if events["rerecord_episode"]:
-                log_say("Re-record episode")
-                events["rerecord_episode"] = False
-                events["exit_early"] = False
-                dataset.clear_episode_buffer()
-                continue
+        # Save episode
+        dataset.save_episode()
+        episode_idx += 1

-            # Save episode
-            dataset.save_episode()
-            episode_idx += 1
-    finally:
-        # Clean up
-        log_say("Stop recording")
-        robot.disconnect()
-        listener.stop()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    listener.stop()

-        dataset.finalize()
-        dataset.push_to_hub()
+    dataset.finalize()
+    dataset.push_to_hub()


 if __name__ == "__main__":
@@ -15,30 +15,30 @@
 # limitations under the License.

 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.feature_utils import combine_feature_dicts
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
 from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
 from lerobot.processor.converters import (
    observation_to_transition,
    robot_action_observation_to_transition,
    transition_to_observation,
    transition_to_robot_action,
 )
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.robots.so_follower.robot_kinematic_processor import (
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
    EEBoundsAndSafety,
    EEReferenceAndDelta,
    ForwardKinematicsJointsToEE,
    GripperVelocityToJoint,
    InverseKinematicsEEToJoints,
 )
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
 from lerobot.teleoperators.phone.teleop_phone import Phone
-from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
@@ -150,23 +150,38 @@ def main():
    listener, events = init_keyboard_listener()
    init_rerun(session_name="phone_so100_record")

-    try:
-        if not robot.is_connected or not phone.is_connected:
-            raise ValueError("Robot or teleop is not connected!")
+    if not robot.is_connected or not phone.is_connected:
+        raise ValueError("Robot or teleop is not connected!")

-        print("Starting record loop. Move your phone to teleoperate the robot...")
-        episode_idx = 0
-        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-            log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+    print("Starting record loop. Move your phone to teleoperate the robot...")
+    episode_idx = 0
+    while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+        log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")

-            # Main record loop
+        # Main record loop
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            teleop=phone,
+            dataset=dataset,
+            control_time_s=EPISODE_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=phone_to_robot_ee_pose_processor,
+            robot_action_processor=robot_ee_to_joints_processor,
+            robot_observation_processor=robot_joints_to_ee_pose,
+        )
+
+        # Reset the environment if not stopping or re-recording
+        if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+            log_say("Reset the environment")
            record_loop(
                robot=robot,
                events=events,
                fps=FPS,
                teleop=phone,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
+                control_time_s=RESET_TIME_SEC,
                single_task=TASK_DESCRIPTION,
                display_data=True,
                teleop_action_processor=phone_to_robot_ee_pose_processor,
@@ -174,43 +189,25 @@ def main():
                robot_observation_processor=robot_joints_to_ee_pose,
            )

-            # Reset the environment if not stopping or re-recording
-            if not events["stop_recording"] and (
-                episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]
-            ):
-                log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    teleop=phone,
-                    control_time_s=RESET_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=phone_to_robot_ee_pose_processor,
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose,
-                )
+        if events["rerecord_episode"]:
+            log_say("Re-recording episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue

-            if events["rerecord_episode"]:
-                log_say("Re-recording episode")
-                events["rerecord_episode"] = False
-                events["exit_early"] = False
-                dataset.clear_episode_buffer()
-                continue
+        # Save episode
+        dataset.save_episode()
+        episode_idx += 1

-            # Save episode
-            dataset.save_episode()
-            episode_idx += 1
-    finally:
-        # Clean up
-        log_say("Stop recording")
-        robot.disconnect()
-        phone.disconnect()
-        listener.stop()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    phone.disconnect()
+    listener.stop()

-        dataset.finalize()
-        dataset.push_to_hub()
+    dataset.finalize()
+    dataset.push_to_hub()


 if __name__ == "__main__":
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Pepijn	7fcd24427d	print update hz	2026-01-09 11:35:51 +01:00
Pepijn	e92d6fa067	fix build frame	2026-01-09 11:31:29 +01:00
Pepijn	bbaadc49c2	do evaluate with rtc	2026-01-09 11:20:20 +01:00
Pepijn	c4953a57c0	debug	2026-01-09 11:08:02 +01:00
Pepijn	d63b83ff40	add debug	2026-01-09 10:21:02 +01:00
Pepijn	c3f4aa2d98	fix build obs	2026-01-09 10:13:09 +01:00
Pepijn	33f84fe0ec	with rtc	2026-01-09 09:56:14 +01:00
Pepijn	8e430f323f	revert	2026-01-09 09:41:04 +01:00
Pepijn	6d12740c24	try async	2026-01-09 09:36:02 +01:00
Pepijn	9b4d63358a	Add code for interpolation, pid tuning, raw action scaling and vel FF	2026-01-09 00:53:37 +01:00
Pepijn	99cdb07dda	match state and exoected obs dimensions	2026-01-08 17:52:19 +01:00
Pepijn	cbeb9ce00a	Update evaluate_ee.py	2026-01-08 17:36:53 +01:00
Pepijn	09904e7797	build obs with policy names	2026-01-08 17:31:48 +01:00
Pepijn	8025ab0594	build obs frame with name from training	2026-01-08 17:28:39 +01:00
Pepijn	8039a76e77	build camera dict properly	2026-01-08 17:22:03 +01:00
Pepijn	3ebeb59cdc	make flow similar to evaluate.py	2026-01-08 17:15:14 +01:00
Pepijn	a9cf770b99	fix abolute	2026-01-08 17:02:00 +01:00
Pepijn	037747da82	add task to processor	2026-01-08 16:56:59 +01:00
Pepijn	71f3cf30cd	add eval dataset	2026-01-08 16:41:52 +01:00
Pepijn	cf75b75474	auto detect mode and stats	2026-01-08 16:34:46 +01:00
Pepijn	c720a4a347	.	2026-01-08 14:49:26 +01:00
Pepijn	ddfdf9aa76	unsqueeze after unnromalize	2026-01-08 14:44:09 +01:00
Pepijn	84f06a86af	fix return action	2026-01-08 14:39:23 +01:00
Pepijn	cafb956e15	fix import	2026-01-08 13:32:54 +01:00
Pepijn	0f19308152	fix import	2026-01-08 11:58:43 +01:00
Pepijn	6697ae789d	add eval	2026-01-08 11:20:24 +01:00
Pepijn	0e5278f6b8	Merge branch 'feat/relative_umi' into feat/openarm_relative_ee	2026-01-08 10:21:36 +01:00
Pepijn	5a15a6a911	use seperate process for stats computation	2026-01-07 16:52:15 +01:00
Pepijn	c23472e376	only main saves stat file	2026-01-07 15:30:25 +01:00
Pepijn	63619619bf	fix data loader issue	2026-01-07 10:03:56 +01:00
Pepijn	ecfc8af9dd	add stl	2026-01-07 09:27:16 +01:00
Pepijn	c6c74b3093	extend arm 5 cm	2026-01-06 22:38:56 +01:00
Pepijn	a5d3702927	Add relative code	2026-01-06 21:47:18 +01:00
Pepijn	c85f1692d6	in place	2026-01-03 22:12:22 +01:00
Pepijn	9fd329713a	modift in place	2026-01-03 22:11:11 +01:00
Pepijn	97d068e5a2	rename to fold	2026-01-03 21:59:11 +01:00
Pepijn	e5bea36387	add unify task	2026-01-03 21:52:19 +01:00
Pepijn	574081ac02	fix mem bug	2026-01-03 11:34:31 +01:00
Pepijn	c5f66edff9	shuffle false	2026-01-02 22:34:57 +01:00
Pepijn	7f16e8cb09	fix	2026-01-02 19:56:42 +01:00
Pepijn	0367955590	add code for relative actions and state and unifing tasks	2026-01-02 18:58:47 +01:00
Pepijn	01c7c74070	Add relative position UMI style	2026-01-02 15:57:39 +01:00
Pepijn	cf1d8c3d5b	stop policy when we dont teleop yet	2026-01-02 13:12:22 +01:00
Pepijn	464b65cfb0	wait for start button before teleop	2026-01-02 13:05:00 +01:00
Pepijn	90145426b4	add gripper in send feedback	2026-01-02 11:22:45 +01:00
Pepijn	c76bc4cdea	Move robot to zero before begin episode	2026-01-02 10:52:48 +01:00
Pepijn	20f0381f81	wait for takeover press	2026-01-02 10:18:59 +01:00
Pepijn	a447c652cb	change pedal flow	2026-01-02 09:53:40 +01:00
Pepijn	8277dbf0dc	add foot pedal support	2026-01-02 09:36:36 +01:00
Pepijn	eb0918249d	keep teleop active in reset	2026-01-02 09:21:15 +01:00
Pepijn	640a7889fc	use same flip for send_feedback	2026-01-01 16:49:04 +01:00
Pepijn	03c6ee5f9a	fix grippers	2026-01-01 16:40:53 +01:00
Pepijn	dfd229ae4f	fix direction and encoding	2026-01-01 16:37:11 +01:00
Pepijn	aba42c805f	some changes to smooth	2025-12-31 15:16:23 +01:00
Pepijn	8b6b41f8dc	reverse	2025-12-31 15:11:00 +01:00
Pepijn	1771da222b	openarms mini swap joints 6 and 7	2025-12-31 15:08:06 +01:00
Pepijn	0514616c87	dont move teleop when not pause pressed	2025-12-31 12:33:40 +01:00
Pepijn	f15872293d	Only move teleop after space press	2025-12-31 12:24:43 +01:00
Pepijn	a97255e3d1	use robot_action	2025-12-30 12:04:30 +01:00
Pepijn	1716d599c1	only use position in dataset	2025-12-30 12:01:26 +01:00
Pepijn	c07ab7e1fa	policy path can be none	2025-12-30 11:14:21 +01:00
Pepijn	5ba9fbd9ca	fix processor step	2025-12-30 11:09:16 +01:00
Pepijn	38b814f3d4	add feedback to openarms mini	2025-12-30 10:48:55 +01:00
Pepijn	48a963793b	Add rac openarms	2025-12-30 10:41:32 +01:00
Pepijn	9833b84bf8	merge rac	2025-12-30 10:37:48 +01:00
Pepijn	27eeff7535	Add RaC doc and example	2025-12-30 09:57:40 +01:00
Michel Aractingi	202a493c14	missing imports processor wallx	2025-12-17 18:25:21 +01:00
Pepijn	eadd4c0856	only export WallXConfig from wall_x package to avoid peft import in CI	2025-12-17 18:06:42 +01:00
Pepijn	3434a5d5df	pre-commit	2025-12-17 18:06:42 +01:00
Pepijn	1ba51a6d02	fix: peft test import	2025-12-17 18:06:41 +01:00
Pepijn	c62ca6c5d2	fix: add uv conflicts for wallx transformers version	2025-12-17 18:06:41 +01:00
Pepijn	4831195310	fix: exclude wallx extra properly in CI workflows	2025-12-17 18:06:41 +01:00
Pepijn	c514d9ffe2	fix precommit issues	2025-12-17 18:06:40 +01:00
Pepijn	9ae4477356	fix ci	2025-12-17 18:06:40 +01:00
Geoffrey19	0e545e5177	remove lerobot[wallx]	2025-12-17 18:06:40 +01:00
Geoffrey19	a0c9a7d85d	fix pre-commit errors	2025-12-17 18:06:39 +01:00
Geoffrey19	9ce6dd9e25	add some small modifications	2025-12-17 18:06:39 +01:00
Geoffrey19	51bd288f1a	fix bug for inference	2025-12-17 18:06:39 +01:00
Geoffrey19	fc6262e23d	remove flash-attn requirement && fix bug in inference and fast mode	2025-12-17 18:06:38 +01:00
Geoffrey19	d2b16afb12	update	2025-12-17 18:06:38 +01:00
Geoffrey19	a754c86f64	add wallx dependencies	2025-12-17 18:06:37 +01:00
Geoffrey19	76e6dc1ba1	fixed dtype bugs	2025-12-17 18:06:37 +01:00
Geoffrey19	d10d3ef251	reduce to least config and params & pass lerobot basic test	2025-12-17 18:06:37 +01:00
Geoffrey19	feebca050a	update the policy methods	2025-12-17 18:06:36 +01:00
Geoffrey19	a8e7a2967c	incorporate wallx model into lerobot	2025-12-17 18:06:36 +01:00
Geoffrey19	2cf509795e	fix bugs in flow	2025-12-17 18:06:36 +01:00
vincentchen	d3846b0beb	support wallx	2025-12-17 18:06:35 +01:00
Michel Aractingi	08d2ed8015	lerobot dataset fix	2025-12-17 16:46:43 +01:00
Michel Aractingi	4bcd14b8de	add evaluate_with_rtc script	2025-12-17 16:46:43 +01:00
Michel Aractingi	c34935090d	integrate delete button openarm UI (#2535 ) * add visualize_dataset call from `lerobot_dataset_viz` in web record server * add delete button * fixes * remove viz * unused import	2025-12-17 16:46:43 +01:00
CarolinePascal	9cfd56587e	fix(num processes)	2025-12-17 16:46:43 +01:00
Caroline Pascal	ff8584a025	fix(os version) Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>	2025-12-17 16:46:43 +01:00
Caroline Pascal	6bc1e5186a	fix(import os) Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>	2025-12-17 16:46:43 +01:00
Caroline Pascal	69dc8165ae	fix(max workers) Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>	2025-12-17 16:46:42 +01:00
CarolinePascal	021bca2ad9	feat(multi-processes): adding support for multiprocess encoding	2025-12-17 16:46:42 +01:00
CarolinePascal	4e0ee0d643	feat(preset): adding encoding preset	2025-12-17 16:46:42 +01:00
croissant	0a8aa85871	ruse video datasets	2025-12-17 16:46:42 +01:00
croissant	76ddd8b948	use image datasets and change ui	2025-12-17 16:46:42 +01:00
croissant	bf08733068	frontend set correct port openarms mini	2025-12-17 16:46:42 +01:00
croissant	e38f56c071	add default mini arms	2025-12-17 16:46:41 +01:00
croissant	19fe69dac0	add improv openarm mini	2025-12-17 16:46:41 +01:00
pepijn kooijmans	14319ee608	add openarms mini	2025-12-17 16:46:41 +01:00
croissant	9b04fd25b6	cam res	2025-12-17 16:46:41 +01:00
Pepijn	40e98ba690	fix calibration of gripper and add max clip positions for openarm for safety	2025-12-17 16:46:41 +01:00
pepijn kooijmans	894d65d58a	add openarms to setup motors	2025-12-17 16:46:41 +01:00
Pepijn	f58d508df2	cleanuo	2025-12-17 16:46:40 +01:00
Pepijn	e22b909e7c	Add mini openarms to test	2025-12-17 16:46:40 +01:00
croissant	09f1673cbf	add longer timeout	2025-12-17 16:46:40 +01:00
croissant	4744f99990	add timing debugging, foot pedal and eval script	2025-12-17 16:46:40 +01:00
croissant	01c1735739	add disable torque	2025-12-17 16:46:40 +01:00
croissant	6808a42455	add pid ramp	2025-12-17 16:46:40 +01:00
croissant	fff719cb4f	add web interface example	2025-12-17 16:46:39 +01:00
croissant	e2c00f6ed8	speedup	2025-12-17 16:46:39 +01:00
croissant	0f90db23c5	add full bimanual gravity comp	2025-12-17 16:46:39 +01:00
Michel Aractingi	96b192f2ae	Add gravity compensation to the openarms teleoperation (#2352 ) * adding first attempt at gcompensation to open arms * add teleop with gravity compensation script	2025-12-17 16:46:39 +01:00
Pepijn	ecdc34a699	faster canbus	2025-12-17 16:46:39 +01:00
croissant	fa6a2fb9b7	pos teleop	2025-12-17 16:46:39 +01:00
Pepijn	b011643dc9	add tests and debug	2025-12-17 16:46:38 +01:00
Pepijn	30c10c1c6e	Add damiao motors and open arm robot	2025-12-17 16:46:38 +01:00
Pepijn	56e2360072	add damiao	2025-12-17 16:46:38 +01:00