lerobot dataset fix

add evaluate_with_rtc script
integrate delete button openarm UI (#2535 )
2026-05-11 22:59:50 +00:00 · 2025-12-17 16:46:43 +01:00 · 2025-12-17 16:46:43 +01:00 · 2025-12-17 16:46:43 +01:00 · 2025-12-17 16:46:43 +01:00 · 2025-12-17 16:46:43 +01:00
403 changed files with 15808 additions and 36422 deletions
@@ -12,83 +12,57 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-name: "🚀 Issue / Bug / Request"
-description: Report a bug, suggest an improvement, or ask a technical question.
+name: "\U0001F41B Bug Report"
+description: Submit a bug report to help us improve LeRobot
 body:
  - type: markdown
    attributes:
      value: |
-        ### Thanks for contributing to LeRobot! 🙌
-        Please choose the most relevant sections below. If this is a general "how-to" question, consider our [Discord](https://discord.gg/s3KuuzsPFb) for faster community support.
-
-  - type: dropdown
-    id: issue-type
-    attributes:
-      label: Ticket Type
-      description: What kind of ticket are you opening?
-      options:
-        - "🐛 Bug Report (Something isn't working)"
-        - "💡 Feature Request / Improvement"
-        - "❓ Technical Question"
-        - "🧹 Maintenance / Documentation"
-    validations:
-      required: true
+        Thanks for taking the time to submit a bug report! 🐛
+        If this is not a bug related to the LeRobot library directly, but instead a general question about your code or the library specifically please use our [discord](https://discord.gg/s3KuuzsPFb).

  - type: textarea
    id: system-info
    attributes:
-      label: Environment & System Info
-      description: |
-        For bugs or technical questions, please run `lerobot-info` and paste the output.
-        (Optional for feature requests).
+      label: System Info
+      description: Please share your LeRobot configuration by running `lerobot-info` (if installed) or `python -m lerobot.scripts.display_sys_info` (if not installed) and pasting the output below.
      render: Shell
-      placeholder: lerobot version, OS, python version, etc.
+      placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration
+    validations:
+      required: true
+
+  - type: checkboxes
+    id: information-scripts-examples
+    attributes:
+      label: Information
+      description: 'The problem arises when using:'
+      options:
+        - label: "One of the scripts in the examples/ folder of LeRobot"
+        - label: "My own task or dataset (give details below)"

  - type: textarea
-    id: description
+    id: reproduction
    validations:
      required: true
    attributes:
-      label: Description
+      label: Reproduction
      description: |
-        Provide a clear summary of the issue or your proposal.
-        - **Bugs:** What is happening?
-        - **Features:** What is the goal/use case?
-        - **Questions:** What are you trying to achieve?
+        If needed, provide a simple code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
+        Sharing error messages or stack traces could be useful as well!
+        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+        Try to avoid screenshots, as they are hard to read and don't allow copy-and-pasting.
+
      placeholder: |
-        A clear and concise description of the issue or suggestion.
+        Steps to reproduce the behavior:
+
+          1.
+          2.
+          3.

  - type: textarea
-    id: context-repro
+    id: expected-behavior
+    validations:
+      required: true
    attributes:
-      label: Context & Reproduction
-      description: |
-        Provide a code snippet, steps to reproduce a bug, or technical details about your proposal.
-        Please use code blocks for scripts and CLI commands.
-      placeholder: |
-        Steps to reproduce / Usage example:
-        1.
-        2.
-        3.
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant logs or stack trace
-      description: If applicable, paste relevant error logs here.
-      render: Shell
-
-  - type: checkboxes
-    id: extras
-    attributes:
-      label: Checklist
-      options:
-        - label: I have searched existing tickets to ensure this isn't a duplicate.
-        - label: I am using the latest version of the `main` branch.
-        - label: I have verified this is not an environment-specific problem.
-
-  - type: textarea
-    id: workaround
-    attributes:
-      label: Additional Info / Workarounds
-      description: Anything else we should know? If you have a workaround, please share it!
+      label: Expected behavior
+      description: "A clear and concise description of what you would expect to happen."
@@ -1,55 +1,41 @@
-## Title
+## What this does

-Short, imperative summary (e.g., "fix(robots): handle None in sensor parser"). See [CONTRIBUTING.md](../CONTRIBUTING.md) for PR conventions.
+Explain what this PR does. Feel free to tag your PR with the appropriate label(s).

-## Type / Scope
+Examples:
+| Title | Label |
+|----------------------|-----------------|
+| Fixes #[issue] | (🐛 Bug) |
+| Adds new dataset | (🗃️ Dataset) |
+| Optimizes something | (⚡️ Performance) |

- **Type**: (Bug | Feature | Docs | Performance | Test | CI | Chore)
- **Scope**: (optional — name of module or package affected)
+## How it was tested

-## Summary / Motivation
+Explain/show how you tested your changes.

- One-paragraph description of what changes and why.
- Why this change is needed and any trade-offs or design notes.
+Examples:

-## Related issues
+- Added `test_something` in `tests/test_stuff.py`.
+- Added `new_feature` and checked that training converges with policy X on dataset/environment Y.
+- Optimized `some_function`, it now runs X times faster than previously.

- Fixes / Closes: # (if any)
- Related: # (if any)
+## How to checkout & try? (for the reviewer)

-## What changed
+Provide a simple way for the reviewer to try out your changes.

- Short, concrete bullets of the modifications (files/behaviour).
- Short note if this introduces breaking changes and migration steps.
+Examples:

-## How was this tested (or how to run locally)
+```bash
+pytest -sx tests/test_stuff.py::test_something
+```

- Tests added: list new tests or test files.
- Manual checks / dataset runs performed.
- Instructions for the reviewer
+```bash
+lerobot-train --some.option=true
+```

-Example:
+## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR

- Ran the relevant tests:
+**Note**: Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
+members/contributors who may be interested in your PR. Try to avoid tagging more than 3 people.

-  ```bash
-  pytest -q tests/ -k <keyword>
-  ```
-
- Reproduce with a quick example or CLI (if applicable):
-
-  ```bash
-  lerobot-train --some.option=true
-  ```
-
-## Checklist (required before merge)
-
- [ ] Linting/formatting run (`pre-commit run -a`)
- [ ] All tests pass locally (`pytest`)
- [ ] Documentation updated
- [ ] CI is green
-
-## Reviewer notes
-
- Anything the reviewer should focus on (performance, edge-cases, specific files) or general notes.
- Anyone in the community is free to review the PR.
+**Note**: Before submitting this PR, please read the [contributor guideline](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr).
@@ -1,69 +0,0 @@
-# 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.
-
-CI:
-  - changed-files:
-      - any-glob-to-any-file:
-        - '.github/**'
-        - 'docker/**'
-
-github_actions:
-  - changed-files:
-      - any-glob-to-any-file: '.github/**'
-
-documentation:
-  - changed-files:
-      - any-glob-to-any-file:
-          - '**/*.md'
-          - '**/*.mdx'
-          - 'docs/**'
-
-examples:
-  - changed-files:
-      - any-glob-to-any-file: 'examples/**'
-
-tests:
-  - changed-files:
-      - any-glob-to-any-file: 'tests/**'
-
-sensors:
-  - changed-files:
-      - any-glob-to-any-file: 'src/lerobot/cameras/**'
-
-configuration:
-  - changed-files:
-      - any-glob-to-any-file: 'src/lerobot/configs/**'
-
-dataset:
-  - changed-files:
-      - any-glob-to-any-file: 'src/lerobot/datasets/**'
-
-evaluation:
-  - changed-files:
-      - any-glob-to-any-file: 'src/lerobot/envs/**'
-
-robots:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'src/lerobot/teleoperators/**'
-          - 'src/lerobot/robots/**'
-          - 'src/lerobot/motors/**'
-
-policies:
-  - changed-files:
-      - any-glob-to-any-file: 'src/lerobot/policies/**'
-
-processor:
-  - changed-files:
-      - any-glob-to-any-file: 'src/lerobot/processor/**'
@@ -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:
@@ -38,9 +33,6 @@ on:
    paths:
      - "docs/**"

-  release:
-    types: [published]
-
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
@@ -51,7 +43,7 @@ jobs:
  build_main_docs:
    name: Build Main Docs
    if: >
-      (github.event_name == 'push' || github.event_name == 'workflow_dispatch' || github.event_name == 'release') &&
+      (github.event_name == 'push' || github.event_name == 'workflow_dispatch') &&
      github.repository == 'huggingface/lerobot'
    permissions:
      contents: read
@@ -59,13 +51,7 @@ 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
    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:
@@ -61,9 +61,8 @@ 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
+      - uses: actions/checkout@v4
        with:
          persist-credentials: false
          lfs: true
@@ -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.
@@ -60,9 +60,8 @@ 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
+      - uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false
@@ -86,13 +85,7 @@ jobs:
          python-version: ${{ env.PYTHON_VERSION }}

      - 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
+        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional

      - 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:
@@ -136,7 +127,7 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false
@@ -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"
@@ -1,77 +0,0 @@
-# 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.
-
-# This workflow automatically labels issues based on their content.
-name: Issue Labeler
-on:
-  # Trigger on new issues and edits to existing issues
-  issues:
-    types: [opened, edited]
-
-permissions:
-  contents: read
-  issues: write
-
-jobs:
-  label-issue:
-    name: Auto Label Issue
-    runs-on: ubuntu-latest
-    if: github.repository == 'huggingface/lerobot'
-    steps:
-      - uses: actions/github-script@v8
-        with:
-          script: |
-            // Setup Input Text
-            const body = (context.payload.issue.body || '');
-            const title = (context.payload.issue.title || '');
-            const cleanBody = body.replace(/```[\s\S]*?```/g, '');
-            const text = `${title}\n${cleanBody}`.toLowerCase();
-            const labelsToAdd = new Set();
-            const matches = (re) => re.test(text);
-
-            // Keyword Heuristics
-
-            if (matches(/\b(bug|error|crash|exception)\b/i)) labelsToAdd.add('bug');
-            if (matches(/\b(new feature|enhancement|improvement|proposal|feature request)\b/i)) labelsToAdd.add('enhancement');
-            if (matches(/\b(question|how to|clarify|explain|how do i|help me|question about)\b/i)) labelsToAdd.add('question');
-            if (matches(/\b(documentation|docs?|readme|tutorial|wiki|typo|docstring)\b/i)) labelsToAdd.add('documentation');
-            if (matches(/\b(example|sample|demo|notebook)s?\b/i)) labelsToAdd.add('examples');
-            if (matches(/\b(datasets?|data loader|data augmentation|data preprocessing)\b/i)) labelsToAdd.add('dataset');
-            if (matches(/\b(mujoco|isaac|simulation|sim)\b/i)) labelsToAdd.add('simulation');
-            if (matches(/\b(train|training|optimizer|gradient|wandb|sac)\b/i)) labelsToAdd.add('training');
-            if (matches(/\b(rerun|plot|render|rendering|visualizer)/i)) labelsToAdd.add('visualization');
-            if (matches(/\b(cameras?|opencv|realsense|lidars?|sensors?|imus?|microphones?|rgbd|encoders?)\b/i)) labelsToAdd.add('sensors');
-            if (matches(/\b(urdf|actuators?|calibration|end-effector|kinematics)\b/i)) labelsToAdd.add('robots');
-            if (matches(/\b(teleop|teleoperator|controller|leader|follower|joystick|gamepad)\b/i)) labelsToAdd.add('teleoperators');
-            if (matches(/\b(policy|policies|model?)\b/i)) labelsToAdd.add('policies');
-            if (matches(/\b(processor|pipeline|preprocessor|postprocessor)s?\b/i)) labelsToAdd.add('processor');
-            if (matches(/\b(eval|evaluate|evaluation|metrics?|score|benchmarks?)\b/i)) labelsToAdd.add('evaluation');
-            if (matches(/\b(tests?|pytest|unittest|failing test)\b/i)) labelsToAdd.add('tests');
-            if (matches(/\b(ci|github actions?|github workflows?|gha|docker|pypi)\b/i)) labelsToAdd.add('CI');
-            if (matches(/\b(perf|latency|throughput|fps|speed|performance|slow|fast|slower|faster|memory usage)\b/i)) labelsToAdd.add('performance');
-            if (matches(/\b(dependency|dependencies|pip|install error|importerror|package not found|pyproject)\b/i)) labelsToAdd.add('dependencies');
-            if (matches(/\b(configuration|config|arguments?|input feature|dracuss)\b/i)) labelsToAdd.add('configuration');
-
-            // Apply Labels
-            const labels = Array.from(labelsToAdd).filter(Boolean);
-
-            if (labels.length > 0) {
-              console.log(`Adding labels: ${labels.join(', ')}`);
-              await github.rest.issues.addLabels({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: context.issue.number,
-                labels,
-              });
-            }
@@ -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

@@ -52,7 +52,7 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false
@@ -87,7 +87,7 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false
@@ -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
@@ -1,39 +0,0 @@
-# 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.
-
-# This workflow labels pull requests based on the files that were changed.
-name: Pull Request Labeler
-
-on:
-  # Allows labeling pull requests when they are opened or updated
-  # zizmor: ignore[dangerous-triggers] Needed to label PRs from forks
-  pull_request_target:
-    branches:
-      - main
-    types: [opened, synchronize, reopened, ready_for_review]
-
-permissions:
-  contents: read
-  pull-requests: write
-
-jobs:
-  triage:
-    name: Label PR
-    runs-on: ubuntu-latest
-    if: github.repository == 'huggingface/lerobot' && !github.event.pull_request.draft
-    steps:
-      - uses: actions/labeler@v6
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          sync-labels: true # Removes labels if files are removed from the PR
@@ -43,14 +43,14 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          persist-credentials: false

      - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
        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
@@ -38,14 +38,14 @@ jobs:

    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          persist-credentials: false

      - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
        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

@@ -127,7 +135,7 @@ jobs:
    env:
      MUJOCO_GL: egl
    steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false
@@ -169,3 +177,4 @@ jobs:

 # TODO(Steven): Publish draft/pre-release and to test pypi weekly
 # TODO(Steven): Separate build and publish job
+# TODO(Steven): Tag documentation with the same version as the package
@@ -43,7 +43,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6 # zizmor: ignore[unpinned-uses]
+        uses: actions/checkout@v4 # zizmor: ignore[unpinned-uses]
        with:
          fetch-depth: 0
          persist-credentials: false
@@ -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.
@@ -48,9 +48,8 @@ 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
+      - uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false
@@ -79,12 +78,8 @@ jobs:
          echo "Dependencies unbound:" && cat pyproject.toml

      - 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
+        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
+
      - 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:
@@ -107,7 +101,7 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false
@@ -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"
@@ -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
@@ -87,7 +87,7 @@ repos:
  # TODO(Steven): Uncomment when ready to use
  ##### Static Analysis & Typing #####
  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.19.1
+    rev: v1.18.2
    hooks:
      - id: mypy
        args: [--config-file=pyproject.toml]
@@ -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.
@@ -52,7 +52,7 @@ decisions when appropriate.

 This Code of Conduct applies within all community spaces, and also applies when
 an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
+Examples of representing our community include using an official email address,
 posting via an official social media account, or acting as an appointed
 representative at an online or offline event.

@@ -60,7 +60,7 @@ representative at an online or offline event.

 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement at
-feedback@huggingface.co.
+[feedback@huggingface.co](mailto:feedback@huggingface.co).
 All complaints will be reviewed and investigated promptly and fairly.

 All community leaders are obligated to respect the privacy and security of the
@@ -1,83 +1,323 @@
-# How to contribute to 🤗 LeRobot
+# How to contribute to 🤗 LeRobot?

-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.
+Everyone is welcome to contribute, and we value everybody's contribution. Code
+is thus not the only way to help the community. Answering questions, helping
+others, reaching out and improving the documentations are immensely valuable to
+the community.

-Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md) and our [AI policy](./AI_POLICY.md).
+It also helps us if you spread the word: reference the library from blog posts
+on the awesome projects it made possible, shout out on Twitter when it has
+helped you, or simply ⭐️ the repo to say "thank you".

-## Ways to Contribute
+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).

-You can contribute in many ways:
+## You can contribute in so many ways!

- **Fixing issues:** Resolve bugs or improve existing code.
- **New features:** Develop new features.
- **Extend:** Implement new models/policies, robots, or simulation environments and upload datasets to the Hugging Face Hub.
- **Documentation:** Improve examples, guides, and docstrings.
- **Feedback:** Submit tickets related to bugs or desired new features.
+Some of the ways you can contribute to 🤗 LeRobot:

-If you are unsure where to start, join our [Discord Channel](https://discord.gg/q8Dzzpym3f).
+- Fixing outstanding issues with the existing code.
+- Implementing new models, datasets or simulation environments.
+- Contributing to the examples or to the documentation.
+- Submitting issues related to bugs or desired new features.

-## Development Setup
+Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co).

-To contribute code, you need to set up a development environment.
+If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)

-### 1. Fork and Clone
+## Submitting a new issue or feature request

-Fork the repository on GitHub, then clone your fork:
-
-```bash
-git clone https://github.com/<your-handle>/lerobot.git
-cd lerobot
-git remote add upstream https://github.com/huggingface/lerobot.git
-```
-
-### 2. Environment Installation
-
-Please follow our [Installation Guide](./docs/source/installation.mdx) for the environment setup & installation from source.
-
-## Running Tests & Quality Checks
-
-### Code Style (Pre-commit)
-
-Install `pre-commit` hooks to run checks automatically before you commit:
-
-```bash
-pre-commit install
-```
-
-To run checks manually on all files:
-
-```bash
-pre-commit run --all-files
-```
-
-### Running Tests
-
-We use `pytest`. First, ensure you have test artifacts by installing **git-lfs**:
+Do your best to follow these guidelines when submitting an issue or a feature
+request. It will make it easier for us to come back to you quickly and with good
+feedback.
+
+### Did you find a bug?
+
+The 🤗 LeRobot library is robust and reliable thanks to the users who notify us of
+the problems they encounter. So thank you for reporting an issue.
+
+First, we would really appreciate it if you could **make sure the bug was not
+already reported** (use the search bar on Github under Issues).
+
+Did not find it? :( So we can act quickly on it, please follow these steps:
+
+- Include your **OS type and version**, the versions of **Python** and **PyTorch**.
+- A short, self-contained, code snippet that allows us to reproduce the bug in
+  less than 30s.
+- The full traceback if an exception is raised.
+- Attach any other additional information, like screenshots, you think may help.
+
+### Do you want a new feature?
+
+A good feature request addresses the following points:
+
+1. Motivation first:
+
+- Is it related to a problem/frustration with the library? If so, please explain
+  why. Providing a code snippet that demonstrates the problem is best.
+- Is it related to something you would need for a project? We'd love to hear
+  about it!
+- Is it something you worked on and think could benefit the community?
+  Awesome! Tell us what problem it solved for you.
+
+2. Write a _paragraph_ describing the feature.
+3. Provide a **code snippet** that demonstrates its future use.
+4. In case this is related to a paper, please attach a link.
+5. Attach any additional information (drawings, screenshots, etc.) you think may help.
+
+If your issue is well written we're already 80% of the way there by the time you
+post it.
+
+## Adding new policies, datasets or environments
+
+Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
+environments ([aloha](https://github.com/huggingface/gym-aloha),
+[pusht](https://github.com/huggingface/gym-pusht))
+and follow the same api design.
+
+When implementing a new dataset loadable with LeRobotDataset follow these steps:
+
+- Update `available_datasets_per_env` in `lerobot/__init__.py`
+
+When implementing a new environment (e.g. `gym_aloha`), follow these steps:
+
+- Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py`
+
+When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
+
+- Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py`
+- Set the required `name` class attribute.
+- Update variables in `tests/test_available.py` by importing your new Policy class
+
+## Submitting a pull request (PR)
+
+Before writing code, we strongly advise you to search through the existing PRs or
+issues to make sure that nobody is already working on the same thing. If you are
+unsure, it is always a good idea to open an issue to get some feedback.
+
+You will need basic `git` proficiency to be able to contribute to
+🤗 LeRobot. `git` is not the easiest tool to use but it has the greatest
+manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
+Git](https://git-scm.com/book/en/v2) is a very good reference.
+
+Follow these steps to start contributing:
+
+1. Fork the [repository](https://github.com/huggingface/lerobot) by
+   clicking on the 'Fork' button on the repository's page. This creates a copy of the code
+   under your GitHub user account.
+
+2. Clone your fork to your local disk, and add the base repository as a remote. The following command
+   assumes you have your public SSH key uploaded to GitHub. See the following guide for more
+   [information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
+
+   ```bash
+   git clone git@github.com:<your Github handle>/lerobot.git
+   cd lerobot
+   git remote add upstream https://github.com/huggingface/lerobot.git
+   ```
+
+3. Create a new branch to hold your development changes, and do this for every new PR you work on.
+
+   Start by synchronizing your `main` branch with the `upstream/main` branch (more details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)):
+
+   ```bash
+   git checkout main
+   git fetch upstream
+   git rebase upstream/main
+   ```
+
+   Once your `main` branch is synchronized, create a new branch from it:
+
+   ```bash
+   git checkout -b a-descriptive-name-for-my-changes
+   ```
+
+   🚨 **Do not** work on the `main` branch.
+
+4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies.
+   Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already.
+
+   Set up a development environment with conda:
+
+   ```bash
+   conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev
+   ```
+
+   If you're using `uv`, it can manage python versions so you can instead do:
+
+   ```bash
+   uv venv --python 3.10 && source .venv/bin/activate
+   ```
+
+   To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
+
+   using `poetry`
+
+   ```bash
+   poetry sync --extras "dev test"
+   ```
+
+   using `uv`
+
+   ```bash
+   uv sync --extra dev --extra test
+   ```
+
+   You can also install the project with all its dependencies (including environments):
+
+   using `poetry`
+
+   ```bash
+   poetry sync --all-extras
+   ```
+
+   using `uv`
+
+   ```bash
+   uv sync --all-extras
+   ```
+
+   > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they _will_ be tested in the CI. In general, we advise you to install everything and test locally before pushing.
+
+   Whichever command you chose to install the project (e.g. `poetry sync --all-extras`), you should run it again when pulling code with an updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the new dependencies.
+
+   The equivalent of `pip install some-package`, would just be:
+
+   using `poetry`
+
+   ```bash
+   poetry add some-package
+   ```
+
+   using `uv`
+
+   ```bash
+   uv add some-package
+   ```
+
+   When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
+   using `poetry`
+
+   ```bash
+   poetry lock
+   ```
+
+   using `uv`
+
+   ```bash
+   uv lock
+   ```
+
+5. Develop the features on your branch.
+
+   As you work on the features, you should make sure that the test suite
+   passes. You should run the tests impacted by your changes like this (see
+   below an explanation regarding the environment variable):
+
+   ```bash
+   pytest tests/<TEST_TO_RUN>.py
+   ```
+
+6. Follow our style.
+
+   `lerobot` relies on `ruff` to format its source code
+   consistently. Set up [`pre-commit`](https://pre-commit.com/) to run these checks
+   automatically as Git commit hooks.
+
+   Install `pre-commit` hooks:
+
+   ```bash
+   pre-commit install
+   ```
+
+   You can run these hooks whenever you need on staged files with:
+
+   ```bash
+   pre-commit
+   ```
+
+   Once you're happy with your changes, add changed files using `git add` and
+   make a commit with `git commit` to record your changes locally:
+
+   ```bash
+   git add modified_file.py
+   git commit
+   ```
+
+   Note, if you already committed some changes that have a wrong formatting, you can use:
+
+   ```bash
+   pre-commit run --all-files
+   ```
+
+   Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
+
+   It is a good idea to sync your copy of the code with the original
+   repository regularly. This way you can quickly account for changes:
+
+   ```bash
+   git fetch upstream
+   git rebase upstream/main
+   ```
+
+   Push the changes to your account using:
+
+   ```bash
+   git push -u origin a-descriptive-name-for-my-changes
+   ```
+
+7. Once you are satisfied (**and the checklist below is happy too**), go to the
+   webpage of your fork on GitHub. Click on 'Pull request' to send your changes
+   to the project maintainers for review.
+
+8. It's ok if maintainers ask you for changes. It happens to core contributors
+   too! So everyone can see the changes in the Pull request, work in your local
+   branch and push the changes to your fork. They will automatically appear in
+   the pull request.
+
+### Checklist
+
+1. The title of your pull request should be a summary of its contribution;
+2. If your pull request addresses an issue, please mention the issue number in
+   the pull request description to make sure they are linked (and people
+   consulting the issue know you are working on it);
+3. To indicate a work in progress please prefix the title with `[WIP]`, or preferably mark
+   the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
+   it from PRs ready to be merged;
+4. Make sure existing tests pass;
+
+### Tests
+
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/lerobot/tree/main/tests).
+
+Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
+
+On Mac:

 ```bash
+brew install git-lfs
 git lfs install
+```
+
+On Ubuntu:
+
+```bash
+sudo apt-get install git-lfs
+git lfs install
+```
+
+Pull artifacts if they're not in [tests/artifacts](tests/artifacts)
+
+```bash
 git lfs pull
 ```

-Run the full suite (this may require extras installed):
+We use `pytest` in order to run the tests. From the root of the
+repository, here's how to run tests with `pytest` for the library:

 ```bash
-pytest -sv ./tests
+python -m pytest -sv ./tests
 ```

-Or run a specific test file during development:
-
-```bash
-pytest -sv tests/test_specific_feature.py
-```
-
-## Submitting Issues & Pull Requests
-
-Use the templates for required fields and examples.
-
- **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.
-
-Thank you for contributing to LeRobot!
+You can specify a smaller set of tests in order to test only the feature
+you're working on.
@@ -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
@@ -1,5 +1,7 @@
 <p align="center">
-  <img alt="LeRobot, Hugging Face Robotics Library" src="./media/readme/lerobot-logo-thumbnail.png" width="100%">
+  <img alt="LeRobot, Hugging Face Robotics Library" src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/lerobot-logo-thumbnail.png" width="100%">
+  <br/>
+  <br/>
 </p>

 <div align="center">
@@ -10,132 +12,323 @@
 [![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)
+[![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
+
+<!-- [![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot) -->

 </div>

-**LeRobot** aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry so that everyone can contribute to and benefit from shared datasets and pretrained models.
+<h2 align="center">
+    <p><a href="https://huggingface.co/docs/lerobot/hope_jr">
+        Build Your Own HopeJR Robot!</a></p>
+</h2>

-🤗 A hardware-agnostic, Python-native interface that standardizes control across diverse platforms, from low-cost arms (SO-100) to humanoids.
+<div align="center">
+  <img
+    src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/hope_jr/hopejr.png"
+    alt="HopeJR robot"
+    title="HopeJR robot"
+    width="60%"
+  />

-🤗 A standardized, scalable LeRobotDataset format (Parquet + MP4 or images) hosted on the Hugging Face Hub, enabling efficient storage, streaming and visualization of massive robotic datasets.
+  <p><strong>Meet HopeJR – A humanoid robot arm and hand for dexterous manipulation!</strong></p>
+  <p>Control it with exoskeletons and gloves for precise hand movements.</p>
+  <p>Perfect for advanced manipulation tasks! 🤖</p>

-🤗 State-of-the-art policies that have been shown to transfer to the real-world ready for training and deployment.
+  <p><a href="https://huggingface.co/docs/lerobot/hope_jr">
+      See the full HopeJR tutorial here.</a></p>
+</div>

-🤗 Comprehensive support for the open-source ecosystem to democratize physical AI.
+<br/>

-## Quick Start
+<h2 align="center">
+    <p><a href="https://huggingface.co/docs/lerobot/so101">
+        Build Your Own SO-101 Robot!</a></p>
+</h2>

-LeRobot can be installed directly from PyPI.
+<div align="center">
+  <table>
+    <tr>
+      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101.webp" alt="SO-101 follower arm" title="SO-101 follower arm" width="90%"/></td>
+      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101-leader.webp" alt="SO-101 leader arm" title="SO-101 leader arm" width="90%"/></td>
+    </tr>
+  </table>
+
+  <p><strong>Meet the updated SO100, the SO-101 – Just €114 per arm!</strong></p>
+  <p>Train it in minutes with a few simple moves on your laptop.</p>
+  <p>Then sit back and watch your creation act autonomously! 🤯</p>
+
+  <p><a href="https://huggingface.co/docs/lerobot/so101">
+      See the full SO-101 tutorial here.</a></p>
+
+  <p>Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!</p>
+  <p>Check out the <a href="https://huggingface.co/docs/lerobot/lekiwi">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
+
+  <img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/lekiwi/kiwi.webp" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
+</div>
+
+<br/>
+
+<h3 align="center">
+    <p>LeRobot: State-of-the-art AI for real-world robotics</p>
+</h3>
+
+---
+
+🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
+
+🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
+
+🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulation environments to get started without assembling a robot. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
+
+🤗 LeRobot hosts pretrained models and datasets on this Hugging Face community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
+
+#### Examples of pretrained models on simulation environments
+
+<table>
+  <tr>
+    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
+    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
+    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
+  </tr>
+  <tr>
+    <td align="center">ACT policy on ALOHA env</td>
+    <td align="center">TDMPC policy on SimXArm env</td>
+    <td align="center">Diffusion policy on PushT env</td>
+  </tr>
+</table>
+
+## Installation
+
+LeRobot works with Python 3.10+ and PyTorch 2.2+.
+
+### Environment Setup
+
+Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniforge`](https://conda-forge.org/download/):
+
+```bash
+conda create -y -n lerobot python=3.10
+conda activate lerobot
+```
+
+When using `conda`, install `ffmpeg` in your environment:
+
+```bash
+conda install ffmpeg -c conda-forge
+```
+
+> **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
+>
+> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
+>
+> ```bash
+> conda install ffmpeg=7.1.1 -c conda-forge
+> ```
+>
+> - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+
+### Install LeRobot 🤗
+
+#### From Source
+
+First, clone the repository and navigate into the directory:
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+```
+
+Then, install the library in editable mode. This is useful if you plan to contribute to the code.
+
+```bash
+pip install -e .
+```
+
+> **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
+> `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
+
+For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
+
+- [aloha](https://github.com/huggingface/gym-aloha)
+- [xarm](https://github.com/huggingface/gym-xarm)
+- [pusht](https://github.com/huggingface/gym-pusht)
+
+For instance, to install 🤗 LeRobot with aloha and pusht, use:
+
+```bash
+pip install -e ".[aloha, pusht]"
+```
+
+### Installation from PyPI
+
+**Core Library:**
+Install the base package with:

 ```bash
 pip install lerobot
-lerobot-info
 ```

-> [!IMPORTANT]
-> For detailed installation guide, please see the [Installation Documentation](https://huggingface.co/docs/lerobot/installation).
+_This installs only the default dependencies._

-## Robots & Control
-
-<div align="center">
-  <img src="./media/readme/robots_control_video.webp" width="640px" alt="Reachy 2 Demo">
-</div>
-
-LeRobot provides a unified `Robot` class interface that decouples control logic from hardware specifics. It supports a wide range of robots and teleoperation devices.
-
-```python
-from lerobot.robots.myrobot import MyRobot
-
-# Connect to a robot
-robot = MyRobot(config=...)
-robot.connect()
-
-# Read observation and send action
-obs = robot.get_observation()
-action = model.select_action(obs)
-robot.send_action(action)
-```
-
-**Supported Hardware:** SO100, LeKiwi, Koch, HopeJR, OMX, EarthRover, Reachy2, Gamepads, Keyboards, Phones, OpenARM, Unitree G1.
-
-While these devices are natively integrated into the LeRobot codebase, the library is designed to be extensible. You can easily implement the Robot interface to utilize LeRobot's data collection, training, and visualization tools for your own custom robot.
-
-For detailed hardware setup guides, see the [Hardware Documentation](https://huggingface.co/docs/lerobot/integrate_hardware).
-
-## LeRobot Dataset
-
-To solve the data fragmentation problem in robotics, we utilize the **LeRobotDataset** format.
-
- **Structure:** Synchronized MP4 videos (or images) for vision and Parquet files for state/action data.
- **HF Hub Integration:** Explore thousands of robotics datasets on the [Hugging Face Hub](https://huggingface.co/lerobot).
- **Tools:** Seamlessly delete episodes, split by indices/fractions, add/remove features, and merge multiple datasets.
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-# Load a dataset from the Hub
-dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")
-
-# Access data (automatically handles video decoding)
-episode_index=0
-print(f"{dataset[episode_index]['action'].shape=}\n")
-```
-
-Learn more about it in the [LeRobotDataset Documentation](https://huggingface.co/docs/lerobot/lerobot-dataset-v3)
-
-## SoTA Models
-
-LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, and Vision-Language-Action (VLA) models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.
-
-<p align="center">
-  <img alt="Gr00t Architecture" src="./media/readme/VLA_architecture.jpg" width="640px">
-</p>
-
-Training a policy is as simple as running a script configuration:
+**Extra Features:**
+To install additional functionality, use one of the following:

 ```bash
-lerobot-train \
-  --policy=act \
-  --dataset.repo_id=lerobot/aloha_mobile_cabinet
+pip install 'lerobot[all]'          # All available features
+pip install 'lerobot[aloha,pusht]'  # Specific features (Aloha & Pusht)
+pip install 'lerobot[feetech]'      # Feetech motor support
 ```

-| 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)            |
+_Replace `[...]` with your desired features._

-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
+**Available Tags:**
+For a full list of optional dependencies, see:
+https://pypi.org/project/lerobot/

-For detailed policy setup guides, see the [Policy Documentation](https://huggingface.co/docs/lerobot/bring_your_own_policies).
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install pi tags, 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

-## Inference & Evaluation
+### Weights & Biases

-Evaluate your policies in simulation or on real hardware using the unified evaluation script. LeRobot supports standard benchmarks like **LIBERO**, **MetaWorld** and more to come.
+To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with

 ```bash
-# Evaluate a policy on the LIBERO benchmark
-lerobot-eval \
-  --policy.path=lerobot/pi0_libero_finetuned \
-  --env.type=libero \
-  --env.task=libero_object \
-  --eval.n_episodes=10
+wandb login
 ```

-Learn how to implement your own simulation environment or benchmark and distribute it from the HF Hub by following the [EnvHub Documentation](https://huggingface.co/docs/lerobot/envhub)
+(note: you will also need to enable WandB in the configuration. See below.)

-## Resources
+### Visualize datasets

- **[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.
- **[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.
+Check out [example 1](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
+
+You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
+
+```bash
+lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --episode-index 0
+```
+
+or from a dataset in a local folder with the `root` option and the `--mode local` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
+
+```bash
+lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --root ./my_local_data_dir \
+    --mode local \
+    --episode-index 0
+```
+
+It will open `rerun.io` and display the camera streams, robot states and actions, like this:
+
+https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
+
+Our script can also visualize datasets stored on a distant server. See `lerobot-dataset-viz --help` for more instructions.
+
+### The `LeRobotDataset` format
+
+A dataset in `LeRobotDataset` format is very simple to use. It can be loaded from a repository on the Hugging Face hub or a local folder simply with e.g. `dataset = LeRobotDataset("lerobot/aloha_static_coffee")` and can be indexed into like any Hugging Face and PyTorch dataset. For instance `dataset[0]` will retrieve a single temporal frame from the dataset containing observation(s) and an action as PyTorch tensors ready to be fed to a model.
+
+A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) for more details on `delta_timestamps`.
+
+Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor.
+
+Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects:
+
+```
+dataset attributes:
+  ├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example:
+  │  ├ observation.images.cam_high (VideoFrame):
+  │  │   VideoFrame = {'path': path to a mp4 video, 'timestamp' (float32): timestamp in the video}
+  │  ├ observation.state (list of float32): position of an arm joints (for instance)
+  │  ... (more observations)
+  │  ├ action (list of float32): goal position of an arm joints (for instance)
+  │  ├ episode_index (int64): index of the episode for this sample
+  │  ├ frame_index (int64): index of the frame for this sample in the episode ; starts at 0 for each episode
+  │  ├ timestamp (float32): timestamp in the episode
+  │  ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode
+  │  └ index (int64): general index in the whole dataset
+  ├ meta: a LeRobotDatasetMetadata object containing:
+  │  ├ info: a dictionary of metadata on the dataset
+  │  │  ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with
+  │  │  ├ fps (int): frame per second the dataset is recorded/synchronized to
+  │  │  ├ features (dict): all features contained in the dataset with their shapes and types
+  │  │  ├ total_episodes (int): total number of episodes in the dataset
+  │  │  ├ total_frames (int): total number of frames in the dataset
+  │  │  ├ robot_type (str): robot type used for recording
+  │  │  ├ data_path (str): formattable string for the parquet files
+  │  │  └ video_path (str): formattable string for the video files (if using videos)
+  │  ├ episodes: a DataFrame containing episode metadata with columns:
+  │  │  ├ episode_index (int): index of the episode
+  │  │  ├ tasks (list): list of tasks for this episode
+  │  │  ├ length (int): number of frames in this episode
+  │  │  ├ dataset_from_index (int): start index of this episode in the dataset
+  │  │  └ dataset_to_index (int): end index of this episode in the dataset
+  │  ├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance
+  │  │  ├ observation.images.front_cam: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.}
+  │  │  └ ...
+  │  └ tasks: a DataFrame containing task information with task names as index and task_index as values
+  ├ root (Path): local directory where the dataset is stored
+  ├ image_transforms (Callable): optional image transformations to apply to visual modalities
+  └ delta_timestamps (dict): optional delta timestamps for temporal queries
+```
+
+A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely:
+
+- hf_dataset stored using Hugging Face datasets library serialization to parquet
+- videos are stored in mp4 format to save space
+- metadata are stored in plain json/jsonl files
+
+Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the `root` argument if it's not in the default `~/.cache/huggingface/lerobot` location.
+
+#### Reproduce state-of-the-art (SOTA)
+
+We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
+You can reproduce their training by loading the config from their run. Simply running:
+
+```bash
+lerobot-train --config_path=lerobot/diffusion_pusht
+```
+
+reproduces SOTA results for Diffusion Policy on the PushT task.
+
+## Contribute
+
+If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md).
+
+### Add a pretrained policy
+
+Once you have trained a policy you may upload it to the Hugging Face hub using a hub id that looks like `${hf_user}/${repo_name}` (e.g. [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)).
+
+You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain:
+
+- `config.json`: A serialized version of the policy configuration (following the policy's dataclass config).
+- `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format.
+- `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility.
+
+To upload these to the hub, run the following:
+
+```bash
+huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model
+```
+
+See [lerobot_eval.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_eval.py) for an example of how other people may use your policy.
+
+### Acknowledgment
+
+- The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla).
+- Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
+- Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
+- Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
+- Thanks to Antonio Loquercio and Ashish Kumar for their early support.
+- Thanks to [Seungjae (Jay) Lee](https://sjlee.cc/), [Mahi Shafiullah](https://mahis.life/) and colleagues for open sourcing [VQ-BeT](https://sjlee.cc/vq-bet/) policy and helping us adapt the codebase to our repository. The policy is adapted from [VQ-BeT repo](https://github.com/jayLEE0301/vq_bet_official).

 ## Citation

-If you use LeRobot in your research, please cite:
+If you want, you can cite this work with:

 ```bibtex
@misc{cadene2024lerobot,
@@ -146,14 +339,6 @@ If you use LeRobot in your research, please cite:
 }
 ```

-## Contribute
+## Star History

-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">
-</p>
-
-<div align="center">
-<sub>Built by the <a href="https://huggingface.co/lerobot">LeRobot</a> team at <a href="https://huggingface.co">Hugging Face</a> with ❤️</sub>
-</div>
+[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)
@@ -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 . .
@@ -19,7 +19,7 @@
 # docker run -it --rm lerobot-user

 # Configure the base image
-ARG PYTHON_VERSION=3.12
+ARG PYTHON_VERSION=3.10
 FROM python:${PYTHON_VERSION}-slim

 # Configure environment variables
@@ -59,7 +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
@@ -17,8 +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)
  title: "Tutorials"
 - sections:
  - local: lerobot-dataset-v3
@@ -27,10 +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
@@ -39,23 +35,13 @@
    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"
- sections:
-  - local: sarm
-    title: SARM
-  title: "Reward Models"
 - sections:
  - local: async
    title: Use Async Inference
@@ -67,8 +53,6 @@
    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
@@ -103,19 +87,11 @@
    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
@@ -125,8 +101,6 @@
    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
 ```
@@ -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,
@@ -32,7 +32,7 @@ version = "0.1.0"
 dependencies = [
    # your policy-specific dependencies
 ]
-requires-python = ">= 3.12"
+requires-python = ">= 3.11"

 [build-system]
 build-backend = # your-build-backend
@@ -82,7 +82,7 @@ Create your policy implementation by inheriting from LeRobot's base `PreTrainedP
 # modeling_my_custom_policy.py
 import torch
 import torch.nn as nn
-from typing import Any
+from typing import Dict, Any

 from lerobot.policies.pretrained import PreTrainedPolicy
 from .configuration_my_custom_policy import MyCustomPolicyConfig
@@ -91,7 +91,7 @@ class MyCustomPolicy(PreTrainedPolicy):
    config_class = MyCustomPolicyConfig
    name = "my_custom_policy"

-    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: dict[str, Any] = None):
+    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: Dict[str, Any] = None):
        super().__init__(config, dataset_stats)
        ...
 ```
@@ -102,7 +102,7 @@ Create processor functions:

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


@@ -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
 ```

@@ -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=$(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

@@ -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
@@ -1,20 +1,18 @@
 # Installation

-This guide uses conda (via miniforge) to manage environments. 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 [Install LeRobot](#step-3-install-lerobot-).
-
-## Step 1: 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, using conda:
+Create a virtual environment with Python 3.10, using conda:

 ```bash
-conda create -y -n lerobot python=3.12
+conda create -y -n lerobot python=3.10
 ```

 Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
@@ -40,14 +38,7 @@ conda install ffmpeg -c conda-forge
 >
 > - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.

-> [!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
-> ```
-
-## Step 3: Install LeRobot 🤗
+## Install LeRobot 🤗

 ### From Source

@@ -90,6 +81,9 @@ _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`.
@@ -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
@@ -82,15 +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.
-
 ## 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
@@ -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}
-}
-```
@@ -1,45 +0,0 @@
-# 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.
-
---
-
-## 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 |
-| 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:
-
-```bibtex
-@article{zhai2025igniting,
-    title   = {Igniting VLMs Toward the Embodied Space},
-    author  = {Zhai, Andy and Liu, Brae and Fang, Bruno and Cai, Chalse and Ma, Ellie and Yin, Ethan and Wang, Hao and Zhou, Hugo and Wang, James and Shi, Lights and Liang, Lucy and Wang, Make and Wang, Qian and Gan, Roy and Yu, Ryan and Li, Shalfun and Liu, Starrick and Chen, Sylas and Chen, Vincent and Xu, Zach},
-    journal = {arXiv preprint arXiv:2509.11766},
-    year    = {2025}
-}
-```
-
---
-
-## License
-
-This model follows the **Apache 2.0 License**, consistent with the original [WallX repository](https://github.com/X-Square-Robot/wall-x).
@@ -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
@@ -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 \
@@ -1,592 +0,0 @@
-# SARM: Stage-Aware Reward Modeling
-
-SARM (Stage-Aware Reward Modeling) is a video-based reward modeling framework for long-horizon robot manipulation tasks. This guide covers how to train SARM reward models and optionally use them with Reward-Aligned Behavior Cloning (RA-BC).
-
-**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.
-
-## Overview
-
-SARM has following features:
-
-1. **Stage-aware architecture**: Jointly predicts the high-level task stage and fine-grained progress within each stage
-2. **Subtask annotations**: Uses natural language subtask annotations to derive consistent progress labels
-3. **Temporal proportions**: Computes dataset-level priors (α̅\_k) for each subtask to normalize progress across variable-length demonstrations
-
-SARM trains on a compact **stage+tau** target for each frame:
-
- **stage**: integer stage index `k ∈ {0, ..., K-1}`
- **τ (tau)**: within-stage progress `τ ∈ [0, 1]`
- **target encoding**: `y = k + τ` (this is what the dataset processor produces)
-
-At inference time (and in downstream RA-BC), SARM converts the raw `k + τ` value into a **normalized progress** in `[0, 1]` using dataset-level **temporal proportions** `α̅_k` (stored in `meta/temporal_proportions_*.json`).
-
-This matches **Formula (2)** from the paper:
-
-```
-progress_t = P_{k-1} + α̅_k × τ_t
-```
-
-Where:
-
- `τ_t = (t - s_k) / (e_k - s_k)` is within-subtask normalized time
- `P_{k-1}` is cumulative prior (sum of previous subtask proportions)
- `α̅_k` is the temporal proportion for subtask k
-
-This ensures identical task states map to consistent progress values, even across demonstrations of different lengths.
-
-## Inputs and Targets (What the new code expects)
-
-SARM is trained through its processor (`src/lerobot/policies/sarm/processor_sarm.py`), which:
-
- **Encodes** images and task text with CLIP (ViT-B/32) into `video_features` and `text_features`
- **Pads/truncates** robot state into `state_features` (up to `max_state_dim`)
- **Builds targets** as `sparse_targets` (and `dense_targets` in `dense_only`/`dual`) using the stage+tau encoding `y = k + τ`
- **Masks rewind frames** using a per-sample `lengths` tensor (rewind is a training-time augmentation)
-
-At minimum, each training sample needs:
-
- `task` (string): task description
- `policy.image_key` images and `policy.state_key` states from the dataset
-
---
-
-## Annotation Modes
-
-You can choose from **3 annotation modes** that determine how progress labels are computed:
-
-| Mode           | Annotations Required | Heads                        | Use Case                                                     |
-| -------------- | -------------------- | ---------------------------- | ------------------------------------------------------------ |
-| `single_stage` | None                 | Sparse only                  | Simple tasks, quick experiments, no VLM needed               |
-| `dense_only`   | Dense (VLM)          | Dual (sparse auto-generated) | Detailed subtask tracking without defining high-level stages |
-| `dual`         | Sparse + Dense (VLM) | Dual                         | Full SARM paper setup with both granularities                |
-
-### Mode Details
-
-<hfoptions id="mode_explanation">
-<hfoption id="single_stage">
-
-**No annotations required.** The entire episode is treated as a single stage called `"task"`, and progress is linear from 0 to 1 over the episode duration.
-
- **Sparse head**: 1 stage ("task"), linear progress
- **Dense head**: Not used
- **Best for**: Simple tasks, quick experiments, or when VLM annotation is not available
-
-## Set Up Your Environment
-
-1. Install LeRobot by following our [Installation Guide](./installation).
-2. Install SARM dependencies by running:
-
-```bash
-pip install -e ".[sarm]"
-```
-
-Workflow:
-
-```
-1. Train SARM → 2. Visualize predictions → 3. (Optional) Train policy with RA-BC
-```
-
-</hfoption>
-<hfoption id="dense_only">
-
-**Only dense (fine-grained) annotations from a VLM.** The sparse head automatically uses a single `"task"` stage covering the full episode, while the dense head learns detailed subtask progression.
-
- **Sparse head**: 1 stage ("task"), linear progress (auto-generated)
- **Dense head**: Multiple fine-grained stages from VLM annotations
- **Best for**: When you want detailed subtask tracking but don't need to define high-level stages
-
-Workflow:
-
-```
-1. Annotate (dense) → 2. Verify → 3. Train SARM → 4. Visualize → 5. (Optional) Train policy with RA-BC
-```
-
-</hfoption>
-<hfoption id="dual">
-
-**Both sparse and dense annotations from VLM.** Full dual-head mode as described in the SARM paper, with both high-level (sparse) and fine-grained (dense) stage predictions.
-
- **Sparse head**: High-level stages from VLM annotations
- **Dense head**: Fine-grained stages from VLM annotations
- **Best for**: Complex multi-stage tasks where both granularities are useful
-
-Workflow:
-
-```
-1. Annotate (sparse+dense) → 2. Verify → 3. Train SARM → 4. Visualize → 5. (Optional) Train policy with RA-BC
-```
-
-</hfoption>
-</hfoptions>
-
---
-
-## Step 1: Subtask Annotation
-
-<hfoptions id="annotation_mode">
-<hfoption id="single_stage">
-
-**No annotation required!** Skip this step entirely. The model will use the episode's task description and compute linear progress automatically.
-
-</hfoption>
-<hfoption id="dense_only">
-
-Generate **dense (fine-grained) annotations only** using a VLM. The sparse stage will be auto-generated.
-
-```bash
-python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
-  --repo-id your-username/your-dataset \
-  --dense-only \
-  --dense-subtasks "Bring robot arms up from starting position,Grab near side and do 1st fold,Grab side and do 2nd fold,Grab side and do 3rd fold to finish folding" \
-  --video-key observation.images.base \
-  --num-workers 4 \
-  --push-to-hub
-```
-
-**What gets saved:**
-
- `meta/temporal_proportions_sparse.json` - Auto-generated sparse proportions (`{"task": 1.0}`)
- `meta/temporal_proportions_dense.json` - Dense temporal proportions
- Per-episode columns in `episodes/*.parquet`:
-  - `dense_subtask_names`, `dense_subtask_start_frames`, `dense_subtask_end_frames`
-  - (also time-based columns: `dense_subtask_start_times`, `dense_subtask_end_times`)
-
-</hfoption>
-<hfoption id="dual">
-
-Generate **both sparse (high-level) and dense (fine-grained) annotations** using a VLM.
-
-```bash
-python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
-  --repo-id your-username/your-dataset \
-  --sparse-subtasks "Bring arms up from starting position,Fold the towel (3 folds in total)" \
-  --dense-subtasks "Bring robot arms up from starting position,Grab near side and do 1st fold,Grab side and do 2nd fold,Grab side and do 3rd fold to finish folding" \
-  --video-key observation.images.base \
-  --num-workers 4 \
-  --push-to-hub
-```
-
-**What gets saved:**
-
- `meta/temporal_proportions_sparse.json` - Sparse temporal proportions
- `meta/temporal_proportions_dense.json` - Dense temporal proportions
- Per-episode columns in `episodes/*.parquet`:
-  - `sparse_subtask_names`, `sparse_subtask_start_frames`, `sparse_subtask_end_frames`
-  - `dense_subtask_names`, `dense_subtask_start_frames`, `dense_subtask_end_frames`
-  - (also time-based columns: `*_subtask_start_times`, `*_subtask_end_times`)
-
-</hfoption>
-</hfoptions>
-
-### Annotation Arguments
-
-| Argument               | Description                                                                     |
-| ---------------------- | ------------------------------------------------------------------------------- |
-| `--repo-id`            | HuggingFace dataset repository ID                                               |
-| `--sparse-subtasks`    | Comma-separated list of high-level subtask names                                |
-| `--dense-subtasks`     | Comma-separated list of fine-grained subtask names                              |
-| `--dense-only`         | Generate only dense annotations (auto-creates sparse "task" stage)              |
-| `--video-key`          | Camera/video key to use (e.g., `observation.images.top`)                        |
-| `--num-workers`        | Number of parallel GPU workers (default: 1)                                     |
-| `--episodes`           | Specific episode indices to annotate (default: all)                             |
-| `--skip-existing`      | Skip episodes that already have annotations                                     |
-| `--model`              | VLM model (default: `Qwen/Qwen3-VL-30B-A3B-Instruct`)                           |
-| `--num-visualizations` | Number of episodes to visualize after annotation (default: 5, set to 0 to skip) |
-
-> **Note**: After annotation completes, 5 episodes are automatically visualized by default. Use `--num-visualizations 0` to skip this step.
-
---
-
-## Step 2: Verify Annotations
-
-<hfoptions id="verify_mode">
-<hfoption id="single_stage">
-
-**No verification needed!** Skip this step.
-
-</hfoption>
-<hfoption id="dense_only">
-
-Visualize annotations using the `--visualize-only` flag:
-
-```bash
-python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
-  --repo-id your-username/your-dataset \
-  --visualize-only \
-  --visualize-type dense \
-  --num-visualizations 5 \
-  --video-key observation.images.base \
-  --output-dir ./subtask_viz
-```
-
-</hfoption>
-<hfoption id="dual">
-
-Visualize annotations using the `--visualize-only` flag:
-
-```bash
-python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
-  --repo-id your-username/your-dataset \
-  --visualize-only \
-  --visualize-type both \
-  --num-visualizations 5 \
-  --video-key observation.images.base \
-  --output-dir ./subtask_viz
-```
-
-</hfoption>
-</hfoptions>
-
-This generates visualizations showing video frames with subtask boundaries overlaid and timeline of subtasks.
-
-### Visualization Arguments
-
-| Argument               | Description                                                    |
-| ---------------------- | -------------------------------------------------------------- |
-| `--visualize-only`     | Only visualize existing annotations (no generation)            |
-| `--num-visualizations` | Number of episodes to visualize (default: 5)                   |
-| `--visualize-type`     | Type of annotations to visualize: `sparse`, `dense`, or `both` |
-
-**Tip**: If annotations are inaccurate, adjust your subtask descriptions to be more specific and re-run.
-
---
-
-## Step 3: Train SARM
-
-<hfoptions id="train_mode">
-<hfoption id="single_stage">
-
-Train with **no annotations** - uses linear progress from 0 to 1:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=your-username/your-dataset \
-  --policy.type=sarm \
-  --policy.annotation_mode=single_stage \
-  --policy.image_key=observation.images.base \
-  --output_dir=outputs/train/sarm_single \
-  --batch_size=32 \
-  --steps=5000 \
-  --wandb.enable=true \
-  --wandb.project=sarm \
-  --policy.repo_id=your-username/your-model-name
-```
-
-</hfoption>
-<hfoption id="dense_only">
-
-Train with **dense annotations only** (sparse auto-generated):
-
-```bash
-lerobot-train \
-  --dataset.repo_id=your-username/your-dataset \
-  --policy.type=sarm \
-  --policy.annotation_mode=dense_only \
-  --policy.image_key=observation.images.base \
-  --output_dir=outputs/train/sarm_dense \
-  --batch_size=32 \
-  --steps=5000 \
-  --wandb.enable=true \
-  --wandb.project=sarm \
-  --policy.repo_id=your-username/your-model-name
-```
-
-</hfoption>
-<hfoption id="dual">
-
-Train with **both sparse and dense annotations**:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=your-username/your-dataset \
-  --policy.type=sarm \
-  --policy.annotation_mode=dual \
-  --policy.image_key=observation.images.base \
-  --output_dir=outputs/train/sarm_dual \
-  --batch_size=32 \
-  --steps=5000 \
-  --wandb.enable=true \
-  --wandb.project=sarm \
-  --policy.repo_id=your-username/your-model-name
-```
-
-</hfoption>
-</hfoptions>
-
-### Multi-GPU Training
-
-Add `accelerate launch --multi_gpu --num_processes=4` to use multiple GPUs for training.
-
-### Training Arguments
-
-| Argument                   | Description                                                       | Default                  |
-| -------------------------- | ----------------------------------------------------------------- | ------------------------ |
-| `--policy.annotation_mode` | `single_stage`, `dense_only`, or `dual`                           | `single_stage`           |
-| `--policy.image_key`       | Camera key for images                                             | `observation.images.top` |
-| `--policy.state_key`       | Key for joint states                                              | `observation.state`      |
-| `--policy.n_obs_steps`     | Observation history steps (total obs frames = `n_obs_steps + 1`)  | `8`                      |
-| `--policy.frame_gap`       | Gap (in frames) between sampled observations (at 30 fps: 30 ≈ 1s) | `30`                     |
-
---
-
-## Step 4: Visualize Predictions
-
-Use `compute_rabc_weights.py` with `--visualize-only` to visualize model predictions (and, if available, annotation-derived targets) without writing a parquet file.
-
-<hfoptions id="viz_mode">
-<hfoption id="single_stage">
-
-```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
-  --dataset-repo-id your-username/your-dataset \
-  --reward-model-path your-username/sarm-model \
-  --visualize-only \
-  --num-visualizations 5 \
-  --head-mode sparse \
-  --output-dir ./sarm_viz
-```
-
-</hfoption>
-<hfoption id="dense_only">
-
-```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
-  --dataset-repo-id your-username/your-dataset \
-  --reward-model-path your-username/sarm-model \
-  --visualize-only \
-  --num-visualizations 5 \
-  --head-mode dense \
-  --output-dir ./sarm_viz
-```
-
-</hfoption>
-<hfoption id="dual">
-
-```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
-  --dataset-repo-id your-username/your-dataset \
-  --reward-model-path your-username/sarm-model \
-  --visualize-only \
-  --num-visualizations 5 \
-  --head-mode both \
-  --output-dir ./sarm_viz
-```
-
-</hfoption>
-</hfoptions>
-
-The visualization shows:
-
- **Progress plot**: Predicted progress (and optional annotation-derived “GT” when available and `--stride 1`)
- **Stage probabilities**: Stacked area plot of predicted stage probabilities
- **Sample frames**: Key frames from the episode with progress/stage labels
-
-### Visualization Arguments
-
-| Argument               | Description                                               |
-| ---------------------- | --------------------------------------------------------- |
-| `--visualize-only`     | Only visualize predictions (no RABC computation)          |
-| `--num-visualizations` | Number of episodes to visualize (default: 5)              |
-| `--head-mode`          | SARM head to use: `sparse`, `dense`, or `both`            |
-| `--stride`             | Compute every N frames, interpolate the rest (default: 1) |
-
---
-
-## Step 5 (Optional): Train Policy with RA-BC
-
-Reward-Aligned Behavior Cloning (RA-BC) uses the trained SARM model to weight training samples based on predicted progress improvement. This requires two steps:
-
-1. **Precompute progress values** for all frames using the trained SARM model
-2. **Train policy** with RA-BC weighting using the precomputed values
-
-### How RA-BC Works
-
-For each training sample, RA-BC computes the progress delta:
-
-```
-r_i = φ(o_{t+Δ}) - φ(o_t)
-```
-
-Where `φ` is the SARM progress prediction and `Δ` is the policy's `chunk_size`. Samples with positive progress (good demonstrations) get higher weights, while samples with negative or zero progress get down-weighted.
-
-The weighting follows **Equations 8-9** from the paper:
-
- **Soft weight**: `w̃_i = clip((r_i − (μ − 2σ)) / (4σ + ε), 0, 1)`
- **Final weight**: `w_i = 𝟙{r_i > κ} + 𝟙{0 ≤ r_i ≤ κ} × w̃_i`
-
-### Step 5a: Compute SARM Progress Values
-
-First, run the SARM model on all frames in your dataset to compute progress values:
-
-```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
-  --dataset-repo-id your-username/your-dataset \
-  --reward-model-path your-username/sarm-model \
-  --head-mode sparse \
-  --num-visualizations 5 \
-  --push-to-hub
-```
-
-This script:
-
- Processes all frames and computes progress values
- Saves progress values to a parquet file next to the dataset on disk (defaults to `<dataset_root>/sarm_progress.parquet`)
- Generates visualizations of the first N episodes (default: 5)
-
-**Arguments:**
-
-| Argument               | Description                                                    | Default    |
-| ---------------------- | -------------------------------------------------------------- | ---------- |
-| `--reward-model-path`  | Path to trained SARM model                                     | (required) |
-| `--head-mode`          | SARM head to use: `sparse`, `dense`, or `both`                 | `sparse`   |
-| `--device`             | Device for inference                                           | `cuda`     |
-| `--visualize-only`     | Only visualize predictions (no RA-BC computation)              | `false`    |
-| `--num-visualizations` | Number of episodes to visualize (default: 5, set to 0 to skip) | `5`        |
-
-**Output format** (`sarm_progress.parquet`):
-
-| Column            | Description                                    |
-| ----------------- | ---------------------------------------------- |
-| `index`           | Global frame index in dataset                  |
-| `episode_index`   | Episode number                                 |
-| `frame_index`     | Local frame index within episode               |
-| `progress_sparse` | Sparse head progress value [0, 1]              |
-| `progress_dense`  | Dense head progress value [0, 1] (if computed) |
-
-### Step 5b: Train Policy with RA-BC
-
-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 \
-  --dataset.repo_id=your-username/your-dataset \
-  --policy.type=pi0 \
-  --use_rabc=true \
-  --rabc_head_mode=sparse \
-  --rabc_kappa=0.01 \
-  --output_dir=outputs/train/policy_rabc \
-  --batch_size=32 \
-  --steps=40000
-```
-
-The training script automatically:
-
- Loads the precomputed progress values from the parquet file
- Uses the policy's `chunk_size` to compute progress deltas (Δ)
- Computes sample weights based on progress improvement
- Applies weighted loss during training
-
-**RA-BC Arguments:**
-
-| Argument               | Description                                                | Default                            |
-| ---------------------- | ---------------------------------------------------------- | ---------------------------------- |
-| `--use_rabc`           | Enable RA-BC sample weighting                              | `false`                            |
-| `--rabc_progress_path` | Path to progress parquet file (auto-detected from dataset) | `sarm_progress.parquet` in dataset |
-| `--rabc_head_mode`     | Which SARM head's progress to use: `sparse` or `dense`     | `sparse`                           |
-| `--rabc_kappa`         | Threshold κ for high-quality samples                       | `0.01`                             |
-
-### Tuning RA-BC Kappa
-
-The `kappa` parameter is the threshold that determines which samples get full weight (w=1). Understanding how to tune it is critical for RA-BC to work effectively.
-
-**How the weighting works:**
-
-| Condition           | Weight                  |
-| ------------------- | ----------------------- |
-| `delta > kappa`     | 1.0 (hard threshold)    |
-| `0 ≤ delta ≤ kappa` | Soft weight from Eq. 8  |
-| `delta < 0`         | 0.0 (negative progress) |
-
-**Diagnosing kappa issues:**
-
-Monitor these WandB metrics during training:
-
-| Metric             | Healthy Range | Problem Indicator         |
-| ------------------ | ------------- | ------------------------- |
-| `rabc_mean_weight` | 0.3 - 0.8     | ≈ 1.0 means kappa too low |
-| `rabc_delta_mean`  | > 0           | Should be positive        |
-| `rabc_delta_std`   | > 0           | Variance in data quality  |
-
-**If `rabc_mean_weight ≈ 1.0`:** Your kappa is too low. Most samples have `delta > kappa` and bypass the soft-weighting entirely. RA-BC becomes equivalent to vanilla BC.
-
-**Setting kappa based on your data:**
-
-The default `kappa=0.01` was tuned for the paper's T-shirt folding task (~90s episodes at 30fps). For your dataset, check the logged `rabc_delta_mean` and `rabc_delta_std`:
-
-```
-# If delta_mean ≈ 0.03 and delta_std ≈ 0.02:
-# Most deltas fall in range [0.01, 0.05]
-
-# Option 1: Set kappa = delta_mean (medium selectivity)
--rabc_kappa=0.03
-
-# Option 2: Set kappa = delta_mean + delta_std (high selectivity)
--rabc_kappa=0.05
-
-# Option 3: Set kappa = delta_mean + 2*delta_std (very selective)
--rabc_kappa=0.07
-```
-
-**When RA-BC may not help:**
-
-If your dataset is already high quality (consistent progress across all demonstrations), RA-BC won't provide much benefit since there's nothing to filter.
-
-### Multi-GPU Training with RA-BC
-
-```bash
-accelerate launch \
-  --multi_gpu \
-  --num_processes=4 \
-  src/lerobot/scripts/lerobot_train.py \
-  --dataset.repo_id=your-username/your-dataset \
-  --policy.type=pi0 \
-  --use_rabc=true \
-  --rabc_kappa=0.01 \
-  --output_dir=outputs/train/policy_rabc \
-  --batch_size=32 \
-  --steps=40000
-```
-
---
-
-## Tips & Best Practices
-
-### Choosing a Mode
-
- **Start with `single_stage`** for quick experiments - no annotation overhead
- Use **`dense_only`** when you want detailed progress tracking but tasks don't have clear high-level stages
- Use **`dual`** for complex tasks where both coarse and fine-grained progress is meaningful
-
-### Annotation Quality
-
-1. **Be specific with subtask names**: Instead of "fold", use "grab near side and fold toward center"
-2. **Verify with visualization**: Always check a few episodes before training
-3. **Consistent naming**: Use the same subtask names across all episodes
-
-### RA-BC
-
-1. **Train SARM first**: RA-BC quality depends entirely on SARM quality
-2. **Monitor `rabc_mean_weight`**: If it's ≈ 1.0, increase kappa (see [Tuning RA-BC Kappa](#tuning-ra-bc-kappa))
-
---
-
-## Citation
-
-```bibtex
-@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}
-}
-```
@@ -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",
@@ -377,7 +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",
@@ -426,7 +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.
@@ -1,21 +1,21 @@
-# Unitree G1
+# Unitree G1 Robot Setup and Control

 This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.

-## About
+## About the Unitree G1

-We support both 29 and 23 DOF G1 EDU version. We introduce:
+We offer support for both 29 and 23 DOF G1. We introduce:

- **`unitree g1` robot class, handling low level read/write from/to the humanoid**
- **ZMQ socket bridge** for remote communication and camera streaming, allowing for remote policy deployment over wlan, eth or directly on the robot
- **Locomotion policies** from NVIDIA gr00t and Amazon FAR Holosoma
- **Simulation mode** for testing policies without the physical robot in mujoco
+- **`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

 ---

-## Connection guide
+## Part 1: Connect to Robot over Ethernet

-### Step 1: Configure Ethernet Interface
+### Step 1: Configure Your Computer's Ethernet Interface

 Set a static IP on the same subnet as the robot:

@@ -26,7 +26,7 @@ sudo ip addr add 192.168.123.200/24 dev enp131s0
 sudo ip link set enp131s0 up
 ```

-**Note**: The G1's Ethernet IP is fixed at `192.168.123.164`. Your computer must use `192.168.123.x` with x ≠ 164.
+**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

@@ -35,24 +35,25 @@ ssh unitree@192.168.123.164
 # Password: 123
 ```

-You should now be connected to the G1's Orin.
+You should now be connected to the robot's onboard computer.

 ---

 ## Part 2: Enable WiFi on the Robot

-Wlan0 is disabled by default on the G1. To enable it:
+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 wlan0
+# Bring up WiFi interface
 sudo ip link set wlan0 up

-# Enable NetworkManager control of wlan0
+# Enable NetworkManager control
 sudo nmcli radio wifi on
 sudo nmcli device set wlan0 managed yes
 sudo systemctl restart NetworkManager
@@ -72,7 +73,7 @@ sudo iptables -A FORWARD -i wlp132s0f0 -o enp131s0 -m state --state RELATED,ESTA
 sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT
 ```

-**On the G1:**
+**On the robot:**

 ```bash
 # Add laptop as default gateway
@@ -110,7 +111,7 @@ ssh unitree@<YOUR_ROBOT_IP>
 # Password: 123
 ```

-Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address.
+Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address (e.g., `172.18.129.215`).

 ---

@@ -123,7 +124,7 @@ SSH into the robot and install LeRobot:
 ```bash
 ssh unitree@<YOUR_ROBOT_IP>

-conda create -y -n lerobot python=3.12
+conda create -y -n lerobot python=3.10
 conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
@@ -146,14 +147,14 @@ python src/lerobot/robots/unitree_g1/run_g1_server.py

 ---

-## Part 4: Controlling the robot
+## Part 4: Running GR00T Locomotion

-With the robot server running, you can now control the robot remotely. Let's launch a locomotion policy
+With the robot server running, you can now control the robot from your laptop.

 ### Step 1: Install LeRobot on your machine

 ```bash
-conda create -y -n lerobot python=3.12
+conda create -y -n lerobot python=3.10
 conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
@@ -171,134 +172,34 @@ Edit the config file to match your robot's WiFi IP:
 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
 # Run GR00T locomotion controller
 python examples/unitree_g1/gr00t_locomotion.py --repo-id "nepyope/GR00T-WholeBodyControl_g1"
-
-# Run Holosoma locomotion controller
-python examples/unitree_g1/holosoma_locomotion.py
-
 ```

+### 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.

 ---

-## Running in Simulation Mode (MuJoCo)
+## Extra: Running in Simulation Mode (MuJoCo)

-You can test policies before deploying on the physical robot using MuJoCo simulation. Set `is_simulation=True` in config or pass `--robot.is_simulation=true` via CLI.
-
-### Calibrate Exoskeleton Teleoperator
-
-```bash
-lerobot-calibrate \
-    --teleop.type=unitree_g1 \
-    --teleop.left_arm_config.port=/dev/ttyACM1 \
-    --teleop.right_arm_config.port=/dev/ttyACM0 \
-    --teleop.id=exo
-```
-
-### Teleoperate in Simulation
-
-```bash
-lerobot-teleoperate \
-    --robot.type=unitree_g1 \
-    --robot.is_simulation=true \
-    --teleop.type=unitree_g1 \
-    --teleop.left_arm_config.port=/dev/ttyACM1 \
-    --teleop.right_arm_config.port=/dev/ttyACM0 \
-    --teleop.id=exo \
-    --fps=100
-```
-
-### Record Dataset in Simulation
-
-```bash
-lerobot-record \
-    --robot.type=unitree_g1 \
-    --robot.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.vcodec=auto \
-    --dataset.encoder_threads=2
-```
-
-Example simulation dataset: [nepyope/teleop_test_sim](https://huggingface.co/datasets/nepyope/teleop_test_sim)
-
---
-
-## Running on Real Robot
-
-Once the robot server is running on the G1 (see Part 3), you can teleoperate and record on the real robot.
-
-### Start the Camera Server
-
-On the robot, start the ZMQ image server:
-
-```bash
-python src/lerobot/cameras/zmq/image_server.py
-```
-
-Keep this running in a separate terminal for camera streaming during recording.
-
-### Teleoperate Real Robot
-
-```bash
-lerobot-teleoperate \
-    --robot.type=unitree_g1 \
-    --robot.is_simulation=false \
-    --teleop.type=unitree_g1 \
-    --teleop.left_arm_config.port=/dev/ttyACM1 \
-    --teleop.right_arm_config.port=/dev/ttyACM0 \
-    --teleop.id=exo \
-    --fps=100
-```
-
-### Record Dataset on Real Robot
-
-```bash
-lerobot-record \
-    --robot.type=unitree_g1 \
-    --robot.is_simulation=false \
-    --robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-    --teleop.type=unitree_g1 \
-    --teleop.left_arm_config.port=/dev/ttyACM1 \
-    --teleop.right_arm_config.port=/dev/ttyACM0 \
-    --teleop.id=exo \
-    --dataset.repo_id=your-username/dataset-name \
-    --dataset.single_task="Test" \
-    --dataset.num_episodes=2 \
-    --dataset.episode_time_s=5 \
-    --dataset.reset_time_s=5 \
-    --dataset.push_to_hub=true \
-    --dataset.streaming_encoding=true \
-    # --dataset.vcodec=auto \
-    --dataset.encoder_threads=2
-```
-
-**Note**: Update `server_address` to match your robot's camera server IP.
-
-Example real robot dataset: [nepyope/teleop_test_real](https://huggingface.co/datasets/nepyope/teleop_test_real)
-
---
+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)

@@ -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:
@@ -1,80 +0,0 @@
-# WALL-OSS
-
-WALL-OSS is an open-source foundation model for embodied intelligence, proposed by the [XSquare Robot](https://x2robot.com/en/research/68bc2cde8497d7f238dde690) team in 2025. The LeRobot implementation is adapted from their open-source [WallX](https://github.com/X-Square-Robot/wall-x) repository.
-
-X Square Robot’s WALL-OSS is now integrated into Hugging Face’s LeRobot ecosystem. This is an exciting collaborative project between the LeRobot and X Square Robot teams. You can now post-train, evaluate, and deploy WALL-OSS directly through LeRobot. With this, we’re aiming to make it easier for the open-source robotics community to customize and deploy WALL-OSS foundation models. Read and explore WALL-OSS [paper](https://arxiv.org/pdf/2509.11766) and [code](https://github.com/X-Square-Robot/wall-x).
-
-## Model Overview
-
-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.
- **Unified Cross-Level Chain-of-Thought (Uni-CoT)**: A single differentiable framework that unifies high-level instruction reasoning, sub-task decomposition, and fine-grained action synthesis, forming a continuous chain from “understanding” to “execution.”
- **Mixture-of-Experts (MoE) action heads**: Dynamically activating experts depending on the task phase and modeling actions in discrete or continuous space to maintain stable VLM priors.
- **Two-stage training paradigm**:
-  - **Inspiration stage**: Injecting discrete action priors to strengthen spatial understanding and semantic-action alignment.
-  - **Integration stage**: Using flow matching to achieve high-frequency continuous control.
-
-## Installation Requirements
-
-1. Install LeRobot by following our [Installation Guide](./installation).
-2. Install WallX dependencies by running:
-
-   ```bash
-   pip install -e ".[wallx]"
-   ```
-
-## Usage
-
-To use WallX in LeRobot, specify the policy type as:
-
-```python
-policy.type=wall_x
-```
-
-## Training
-
-For training WallX, you can use the standard LeRobot training script with the appropriate configuration:
-
-```bash
-lerobot-train \
-    --dataset.repo_id=your_dataset \
-    --policy.type=wall_x \
-    --output_dir=./outputs/wallx_training \
-    --job_name=wallx_training \
-    --policy.repo_id=your_repo_id \
-    --policy.pretrained_name_or_path=x-square-robot/wall-oss-flow \
-    --policy.prediction_mode=diffusion \
-    --policy.attn_implementation=eager \
-    --steps=3000 \
-    --policy.device=cuda \
-    --batch_size=32
-```
-
-### Training Arguments
-
-| Argument                       | Description                                                                                                                                                   |
-| ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `--dataset.repo_id`            | The Hugging Face Hub repository ID for your training dataset (e.g., `lerobot/aloha_sim_insertion_human`)                                                      |
-| `--policy.type`                | Specifies using the WallX policy architecture                                                                                                                 |
-| `--output_dir`                 | Local directory where training checkpoints and logs will be saved                                                                                             |
-| `--job_name`                   | A name identifier for this training run (used in logging/tracking)                                                                                            |
-| `--policy.repo_id`             | Your Hugging Face Hub repo ID where the trained model will be pushed                                                                                          |
-| `--policy.pretrained_path`     | Path to pretrained WallX weights to initialize from (the official WALL-OSS checkpoint)                                                                        |
-| `--policy.prediction_mode`     | The action prediction strategy: `diffusion` or `fast` - `diffusion` uses iterative denoising for action generation, `fast` uses next token prediction instead |
-| `--policy.attn_implementation` | Attention implementation backend - `eager` uses standard PyTorch attention (alternatives include `flash_attention_2` or `sdpa`)                               |
-| `--steps`                      | Total number of training steps to run                                                                                                                         |
-| `--policy.device`              | Device to train on (`cuda` for GPU, `cpu` for CPU)                                                                                                            |
-| `--batch_size`                 | Number of samples per training batch                                                                                                                          |
-
-## License
-
-This model follows the **Apache 2.0 License**, consistent with the original [WallX repository](https://github.com/X-Square-Robot/wall-x).
@@ -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
@@ -81,25 +82,24 @@ def replay(cfg: ReplayConfig):
    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__":
@@ -1,490 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-SLURM-distributed SARM RA-BC annotation pipeline.
-
-Computes SARM progress values for all frames in a dataset, distributed across
-SLURM workers, then merges the shards into a single sarm_progress.parquet.
-
-Two subcommands, each a separate SLURM submission:
-
-  compute    – N workers, each computes progress for a subset of episodes
-  aggregate  – 1 worker, merges N shards into sarm_progress.parquet, pushes to hub
-
-Usage:
-    python slurm_compute_rabc.py compute \\
-        --repo-id user/dataset --reward-model-path user/sarm_model \\
-        --stride 10 --device cpu --workers 50 --partition cpu
-
-    python slurm_compute_rabc.py aggregate \\
-        --repo-id user/dataset --reward-model-path user/sarm_model \\
-        --partition cpu --push-to-hub
-"""
-
-import argparse
-from pathlib import Path
-
-from datatrove.executor import LocalPipelineExecutor
-from datatrove.executor.slurm import SlurmPipelineExecutor
-from datatrove.pipeline.base import PipelineStep
-
-
-class ComputeProgressShards(PipelineStep):
-    """Each worker computes SARM progress for its assigned episodes."""
-
-    def __init__(
-        self, repo_id, reward_model_path, stride=1, head_mode="sparse", device="cpu", shard_dir="rabc_shards"
-    ):
-        super().__init__()
-        if stride < 1:
-            raise ValueError(f"stride must be >= 1, got {stride}")
-        self.repo_id = repo_id
-        self.reward_model_path = reward_model_path
-        self.stride = stride
-        self.head_mode = head_mode
-        self.device = device
-        self.shard_dir = shard_dir
-
-    def run(self, data=None, rank: int = 0, world_size: int = 1):
-        import logging
-        from pathlib import Path
-
-        import numpy as np
-        import pyarrow as pa
-        import pyarrow.parquet as pq
-        import torch
-        from tqdm import tqdm
-
-        from lerobot.policies.sarm.compute_rabc_weights import (
-            generate_all_frame_indices,
-            interpolate_progress,
-            load_sarm_resources,
-        )
-        from lerobot.utils.utils import init_logging
-
-        init_logging()
-
-        dataset, reward_model, preprocess = load_sarm_resources(
-            self.repo_id,
-            self.reward_model_path,
-            self.device,
-        )
-
-        if hasattr(preprocess, "eval"):
-            preprocess.eval()
-        for step in preprocess.steps:
-            if hasattr(step, "eval"):
-                step.eval()
-
-        image_key = reward_model.config.image_key
-        state_key = reward_model.config.state_key
-        frame_gap = reward_model.config.frame_gap
-        center_idx = reward_model.config.n_obs_steps // 2
-
-        dual_mode = reward_model.config.uses_dual_heads
-        compute_sparse = self.head_mode in ("sparse", "both") or not dual_mode
-        compute_dense = self.head_mode in ("dense", "both") and dual_mode
-
-        my_episodes = list(range(dataset.num_episodes))[rank::world_size]
-        if not my_episodes:
-            logging.info(f"Rank {rank}: no episodes assigned")
-            return
-        logging.info(f"Rank {rank}: {len(my_episodes)} / {dataset.num_episodes} episodes")
-
-        all_rows = []
-
-        for ep_idx in tqdm(my_episodes, desc=f"Rank {rank}"):
-            ep = dataset.meta.episodes[ep_idx]
-            ep_start, ep_end = ep["dataset_from_index"], ep["dataset_to_index"]
-            task = dataset[ep_start].get("task", "perform the task")
-
-            all_ep_indices = generate_all_frame_indices(ep_start, ep_end, frame_gap)
-            if self.stride > 1:
-                compute_indices = [i for i in all_ep_indices if (i - ep_start) % self.stride == 0]
-                if (ep_end - 1) not in compute_indices:
-                    compute_indices.append(ep_end - 1)
-                compute_indices = sorted(set(compute_indices))
-            else:
-                compute_indices = all_ep_indices
-
-            frame_results = {}
-            for qi in tqdm(compute_indices, desc=f"  Ep {ep_idx}", leave=False):
-                try:
-                    sample = dataset[qi]
-                    batch = {
-                        image_key: sample[image_key],
-                        "task": task,
-                        "index": qi,
-                        "episode_index": ep_idx,
-                    }
-                    if state_key in sample:
-                        batch[state_key] = sample[state_key]
-
-                    with torch.no_grad():
-                        processed = preprocess(batch)
-                        vf = processed["video_features"].to(self.device)
-                        tf = processed["text_features"].to(self.device)
-                        sf = processed.get("state_features")
-                        if sf is not None:
-                            sf = sf.to(self.device)
-                        lengths = processed.get("lengths")
-
-                        sparse_val = dense_val = np.nan
-                        if compute_sparse:
-                            r = reward_model.calculate_rewards(
-                                text_embeddings=tf,
-                                video_embeddings=vf,
-                                state_features=sf,
-                                lengths=lengths,
-                                return_all_frames=True,
-                                head_mode="sparse",
-                            )
-                            sparse_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
-                        if compute_dense:
-                            r = reward_model.calculate_rewards(
-                                text_embeddings=tf,
-                                video_embeddings=vf,
-                                state_features=sf,
-                                lengths=lengths,
-                                return_all_frames=True,
-                                head_mode="dense",
-                            )
-                            dense_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
-
-                        frame_results[qi] = (sparse_val, dense_val)
-                except Exception as e:
-                    logging.warning(f"Failed frame {qi}: {e}")
-
-            if not frame_results:
-                logging.warning(f"Episode {ep_idx}: all frames failed, skipping")
-                continue
-
-            # Interpolate to all frames in this episode
-            computed_idx = np.array(sorted(frame_results.keys()))
-            all_frame_arr = np.arange(ep_start, ep_end)
-
-            sparse_vals = np.array([frame_results[i][0] for i in computed_idx]) if compute_sparse else None
-            dense_vals = np.array([frame_results[i][1] for i in computed_idx]) if compute_dense else None
-
-            if self.stride > 1 and len(computed_idx) > 1:
-                if compute_sparse:
-                    sparse_vals = interpolate_progress(computed_idx, sparse_vals, all_frame_arr)
-                if compute_dense:
-                    dense_vals = interpolate_progress(computed_idx, dense_vals, all_frame_arr)
-                output_frames = all_frame_arr
-            else:
-                # Use only successfully computed frames to avoid indexing mismatch on failures
-                output_frames = computed_idx
-
-            for i, fi in enumerate(output_frames):
-                row = {"index": int(fi), "episode_index": ep_idx, "frame_index": int(fi - ep_start)}
-                if compute_sparse:
-                    row["progress_sparse"] = float(sparse_vals[i])
-                if compute_dense:
-                    row["progress_dense"] = float(dense_vals[i])
-                all_rows.append(row)
-
-        if all_rows:
-            import pandas as pd
-
-            df = pd.DataFrame(all_rows).sort_values("index").reset_index(drop=True)
-            table = pa.Table.from_pandas(df, preserve_index=False)
-            table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
-            shard_dir = Path(self.shard_dir)
-            shard_dir.mkdir(parents=True, exist_ok=True)
-            out = shard_dir / f"shard_{rank:05d}.parquet"
-            pq.write_table(table, out)
-            logging.info(f"Rank {rank}: saved {len(df)} rows to {out}")
-
-
-class AggregateProgress(PipelineStep):
-    """Merge all shard parquets into final sarm_progress.parquet."""
-
-    def __init__(self, repo_id, reward_model_path, shard_dir="rabc_shards", push_to_hub=False):
-        super().__init__()
-        self.repo_id = repo_id
-        self.reward_model_path = reward_model_path
-        self.shard_dir = shard_dir
-        self.push_to_hub = push_to_hub
-
-    def run(self, data=None, rank: int = 0, world_size: int = 1):
-        import datetime
-        import logging
-        import os
-        from pathlib import Path
-
-        import pandas as pd
-        import pyarrow as pa
-        import pyarrow.parquet as pq
-
-        from lerobot.datasets.lerobot_dataset import LeRobotDataset
-        from lerobot.utils.utils import init_logging
-
-        init_logging()
-        if rank != 0:
-            return
-
-        shard_dir = Path(self.shard_dir)
-        shards = sorted(shard_dir.glob("shard_*.parquet"))
-        if not shards:
-            raise FileNotFoundError(f"No shards found in {shard_dir}")
-
-        # Log shard modification time range to help detect stale files
-        mtimes = [os.path.getmtime(s) for s in shards]
-        oldest = datetime.datetime.fromtimestamp(min(mtimes)).isoformat(timespec="seconds")
-        newest = datetime.datetime.fromtimestamp(max(mtimes)).isoformat(timespec="seconds")
-        logging.info(f"Aggregating {len(shards)} shards (oldest: {oldest}, newest: {newest})")
-
-        df = pd.concat([pd.read_parquet(s) for s in shards], ignore_index=True)
-        df = df.sort_values("index").reset_index(drop=True)
-
-        table = pa.Table.from_pandas(df, preserve_index=False)
-        table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
-
-        temp_ds = LeRobotDataset(self.repo_id, download_videos=False)
-        out_path = Path(temp_ds.root) / "sarm_progress.parquet"
-        out_path.parent.mkdir(parents=True, exist_ok=True)
-        pq.write_table(table, out_path)
-        logging.info(f"Saved {len(df)} rows to {out_path}")
-
-        for col in ["progress_sparse", "progress_dense"]:
-            if col in df.columns:
-                v = df[col].dropna()
-                logging.info(
-                    f"{col}: mean={v.mean():.4f} std={v.std():.4f} min={v.min():.4f} max={v.max():.4f}"
-                )
-
-        if self.push_to_hub:
-            from huggingface_hub import HfApi
-
-            api = HfApi()
-            hub_path = "sarm_progress.parquet"
-            logging.info(f"Uploading to {self.repo_id}/{hub_path}")
-            api.upload_file(
-                path_or_fileobj=str(out_path),
-                path_in_repo=hub_path,
-                repo_id=self.repo_id,
-                repo_type="dataset",
-            )
-            logging.info(f"Uploaded: https://huggingface.co/datasets/{self.repo_id}/blob/main/{hub_path}")
-
-
-def make_compute_executor(
-    repo_id,
-    reward_model_path,
-    stride,
-    head_mode,
-    device,
-    shard_dir,
-    logs_dir,
-    job_name,
-    slurm,
-    workers,
-    partition,
-    cpus_per_task,
-    mem_per_cpu,
-):
-    kwargs = {
-        "pipeline": [
-            ComputeProgressShards(repo_id, reward_model_path, stride, head_mode, device, str(shard_dir)),
-        ],
-        "logging_dir": str(logs_dir / job_name),
-    }
-
-    if slurm:
-        kwargs.update(
-            {
-                "job_name": job_name,
-                "tasks": workers,
-                "workers": workers,
-                "time": "24:00:00",
-                "partition": partition,
-                "cpus_per_task": cpus_per_task,
-                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
-            }
-        )
-        return SlurmPipelineExecutor(**kwargs)
-
-    kwargs.update({"tasks": workers, "workers": 1})
-    return LocalPipelineExecutor(**kwargs)
-
-
-def make_aggregate_executor(
-    repo_id,
-    reward_model_path,
-    shard_dir,
-    logs_dir,
-    job_name,
-    slurm,
-    partition,
-    cpus_per_task,
-    mem_per_cpu,
-    push_to_hub,
-):
-    kwargs = {
-        "pipeline": [
-            AggregateProgress(repo_id, reward_model_path, str(shard_dir), push_to_hub),
-        ],
-        "logging_dir": str(logs_dir / job_name),
-    }
-
-    if slurm:
-        kwargs.update(
-            {
-                "job_name": job_name,
-                "tasks": 1,
-                "workers": 1,
-                "time": "02:00:00",
-                "partition": partition,
-                "cpus_per_task": cpus_per_task,
-                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
-            }
-        )
-        return SlurmPipelineExecutor(**kwargs)
-
-    kwargs.update({"tasks": 1, "workers": 1})
-    return LocalPipelineExecutor(**kwargs)
-
-
-def _add_shared_args(p):
-    p.add_argument(
-        "--repo-id",
-        type=str,
-        required=True,
-        help="Hugging Face repository identifier, e.g. 'user/dataset'.",
-    )
-    p.add_argument(
-        "--shard-dir",
-        type=Path,
-        default=Path("rabc_shards"),
-        help="Directory to read/write per-rank parquet shards.",
-    )
-    p.add_argument(
-        "--logs-dir",
-        type=Path,
-        default=Path("logs"),
-        help="Directory for datatrove logs.",
-    )
-    p.add_argument(
-        "--job-name",
-        type=str,
-        default=None,
-        help="SLURM job name (defaults to rabc_<subcommand>).",
-    )
-    p.add_argument(
-        "--slurm",
-        type=int,
-        default=1,
-        help="1 = submit via SLURM; 0 = run locally (useful for debugging).",
-    )
-    p.add_argument(
-        "--partition",
-        type=str,
-        default=None,
-        help="SLURM partition to submit to.",
-    )
-    p.add_argument(
-        "--cpus-per-task",
-        type=int,
-        default=4,
-        help="Number of CPUs per SLURM task.",
-    )
-    p.add_argument(
-        "--mem-per-cpu",
-        type=str,
-        default="4G",
-        help="Memory per CPU, e.g. '4G' or '1950M'.",
-    )
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="SLURM-distributed SARM RA-BC annotation pipeline",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    sub = parser.add_subparsers(dest="command", required=True)
-
-    # compute subcommand
-    cp = sub.add_parser(
-        "compute",
-        help="Distribute progress computation across SLURM workers.",
-    )
-    _add_shared_args(cp)
-    cp.add_argument(
-        "--reward-model-path",
-        type=str,
-        required=True,
-        help="Path or HF repo id of the SARM reward model.",
-    )
-    cp.add_argument(
-        "--stride",
-        type=int,
-        default=1,
-        help="Compute every Nth frame; intermediate frames are interpolated (must be >= 1).",
-    )
-    cp.add_argument(
-        "--head-mode",
-        type=str,
-        default="sparse",
-        choices=["sparse", "dense", "both"],
-        help="Which reward head(s) to compute.",
-    )
-    cp.add_argument(
-        "--device",
-        type=str,
-        default="cpu",
-        help="Device for reward model inference, e.g. 'cpu' or 'cuda'.",
-    )
-    cp.add_argument(
-        "--workers",
-        type=int,
-        default=50,
-        help="Number of parallel SLURM tasks (one shard per worker).",
-    )
-
-    # aggregate subcommand
-    ap = sub.add_parser(
-        "aggregate",
-        help="Merge per-rank shards into a single sarm_progress.parquet.",
-    )
-    _add_shared_args(ap)
-    ap.add_argument(
-        "--reward-model-path",
-        type=str,
-        required=True,
-        help="Path or HF repo id of the SARM reward model (stored in parquet metadata).",
-    )
-    ap.add_argument(
-        "--push-to-hub",
-        action="store_true",
-        help="Upload sarm_progress.parquet to the Hugging Face Hub after aggregation.",
-    )
-
-    args = parser.parse_args()
-    job_name = args.job_name or f"rabc_{args.command}"
-    kwargs = vars(args)
-    kwargs["slurm"] = kwargs.pop("slurm") == 1
-    kwargs["job_name"] = job_name
-    command = kwargs.pop("command")
-
-    executor = make_compute_executor(**kwargs) if command == "compute" else make_aggregate_executor(**kwargs)
-
-    executor.run()
-
-
-if __name__ == "__main__":
-    main()
@@ -78,24 +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__":
@@ -21,7 +21,7 @@ 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__":
@@ -42,27 +42,25 @@ def main():
    # 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(len(episode_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 +1,416 @@
+#!/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 +1,653 @@
+#!/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 +1,216 @@
+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 +1,73 @@
+#!/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 +1,745 @@
+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
+  }
+})
@@ -34,11 +34,12 @@ 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.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
@@ -142,24 +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,
@@ -168,41 +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__":
@@ -26,14 +26,15 @@ 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 (
    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
@@ -149,23 +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,
@@ -173,43 +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__":
@@ -23,10 +23,11 @@ from lerobot.processor.converters import (
    robot_action_observation_to_transition,
    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 (
    InverseKinematicsEEToJoints,
 )
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
@@ -73,34 +74,32 @@ def main():
    # 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(len(episode_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
-            ee_action = {
-                name: float(actions[idx][ACTION][i])
-                for i, name in enumerate(dataset.features[ACTION]["names"])
-            }
+        # Get recorded action from dataset
+        ee_action = {
+            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+        }

-            # Get robot observation
-            robot_obs = robot.get_observation()
+        # Get robot observation
+        robot_obs = robot.get_observation()

-            # Dataset EE -> robot joints
-            joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+        # Dataset EE -> robot joints
+        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))

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

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


 if __name__ == "__main__":
@@ -21,13 +21,14 @@ from lerobot.processor.converters import (
    robot_action_observation_to_transition,
    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,
    GripperVelocityToJoint,
    InverseKinematicsEEToJoints,
 )
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 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
@@ -27,8 +27,8 @@ measuring consistency and ground truth alignment.
 Usage:
    # Basic usage with smolvla policy
    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --dataset.repo_id=<USER>/check_rtc \
+        --policy.path=helper2424/smolvla_check_rtc_last3 \
+        --dataset.repo_id=helper2424/check_rtc \
        --rtc.execution_horizon=8 \
        --device=mps \
        --rtc.max_guidance_weight=10.0 \
@@ -58,16 +58,16 @@ Usage:
        --device=cuda

    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=<USER>/reuben_pi0 \
-        --dataset.repo_id=<USER>/so101_cube_in_cup \
+        --policy.path=lipsop/reuben_pi0 \
+        --dataset.repo_id=ReubenLim/so101_cube_in_cup \
        --rtc.execution_horizon=8 \
        --device=cuda

    # With torch.compile for faster inference (PyTorch 2.0+)
    # Note: CUDA graphs disabled by default due to in-place ops in denoising loop
    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --dataset.repo_id=<USER>/check_rtc \
+        --policy.path=helper2424/smolvla_check_rtc_last3 \
+        --dataset.repo_id=helper2424/check_rtc \
        --rtc.execution_horizon=8 \
        --device=mps \
        --use_torch_compile=true \
@@ -75,8 +75,8 @@ Usage:

    # With torch.compile on CUDA (CUDA graphs disabled by default)
    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --dataset.repo_id=<USER>/check_rtc \
+        --policy.path=helper2424/smolvla_check_rtc_last3 \
+        --dataset.repo_id=helper2424/check_rtc \
        --rtc.execution_horizon=8 \
        --device=cuda \
        --use_torch_compile=true \
@@ -84,8 +84,8 @@ Usage:

    # Enable CUDA graphs (advanced - may cause tensor aliasing errors)
    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --dataset.repo_id=<USER>/check_rtc \
+        --policy.path=helper2424/smolvla_check_rtc_last3 \
+        --dataset.repo_id=helper2424/check_rtc \
        --use_torch_compile=true \
        --torch_compile_backend=inductor \
        --torch_compile_mode=max-autotune \
@@ -28,7 +28,7 @@ For simulation environments, see eval_with_simulation.py
 Usage:
    # Run RTC with Real robot with RTC
    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
+        --policy.path=helper2424/smolvla_check_rtc_last3 \
        --policy.device=mps \
        --rtc.enabled=true \
        --rtc.execution_horizon=20 \
@@ -41,7 +41,7 @@ Usage:

    # Run RTC with Real robot without RTC
    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
+        --policy.path=helper2424/smolvla_check_rtc_last3 \
        --policy.device=mps \
        --rtc.enabled=false \
        --robot.type=so100_follower \
@@ -53,7 +53,7 @@ Usage:

    # Run RTC with Real robot with pi0.5 policy
    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/pi05_check_rtc \
+        --policy.path=helper2424/pi05_check_rtc \
        --policy.device=mps \
        --rtc.enabled=true \
        --rtc.execution_horizon=20 \
@@ -94,9 +94,9 @@ from lerobot.rl.process import ProcessSignalHandler
 from lerobot.robots import (  # noqa: F401
    Robot,
    RobotConfig,
-    bi_so_follower,
    koch_follower,
-    so_follower,
+    so100_follower,
+    so101_follower,
 )
 from lerobot.robots.utils import make_robot_from_config
 from lerobot.utils.constants import OBS_IMAGES
@@ -455,18 +455,7 @@ def demo_cli(cfg: RTCDemoConfig):
    if cfg.policy.type == "pi05" or cfg.policy.type == "pi0":
        config.compile_model = cfg.use_torch_compile

-    if config.use_peft:
-        from peft import PeftConfig, PeftModel
-
-        peft_pretrained_path = cfg.policy.pretrained_path
-        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
-
-        policy = policy_class.from_pretrained(
-            pretrained_name_or_path=peft_config.base_model_name_or_path, config=config
-        )
-        policy = PeftModel.from_pretrained(policy, peft_pretrained_path, config=peft_config)
-    else:
-        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
+    policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)

    # Turn on RTC
    policy.config.rtc_config = cfg.rtc
@@ -34,11 +34,12 @@ 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.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
@@ -142,24 +143,38 @@ def main():
    listener, events = init_keyboard_listener()
    init_rerun(session_name="so100_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,
@@ -168,41 +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__":
@@ -27,14 +27,16 @@ 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 (
    EEBoundsAndSafety,
    ForwardKinematicsJointsToEE,
    InverseKinematicsEEToJoints,
 )
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.scripts.lerobot_record import record_loop
-from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+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
@@ -146,23 +148,38 @@ def main():
    listener, events = init_keyboard_listener()
    init_rerun(session_name="recording_phone")

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

-        print("Starting record loop...")
-        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...")
+    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=follower,
+            events=events,
+            fps=FPS,
+            teleop=leader,
+            dataset=dataset,
+            control_time_s=EPISODE_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=leader_joints_to_ee,
+            robot_action_processor=ee_to_follower_joints,
+            robot_observation_processor=follower_joints_to_ee,
+        )
+
+        # 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=follower,
                events=events,
                fps=FPS,
                teleop=leader,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
+                control_time_s=RESET_TIME_SEC,
                single_task=TASK_DESCRIPTION,
                display_data=True,
                teleop_action_processor=leader_joints_to_ee,
@@ -170,44 +187,25 @@ def main():
                robot_observation_processor=follower_joints_to_ee,
            )

-            # 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=follower,
-                    events=events,
-                    fps=FPS,
-                    teleop=leader,
-                    control_time_s=RESET_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=leader_joints_to_ee,
-                    robot_action_processor=ee_to_follower_joints,
-                    robot_observation_processor=follower_joints_to_ee,
-                )
+        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
+    # Clean up
+    log_say("Stop recording")
+    leader.disconnect()
+    follower.disconnect()
+    listener.stop()

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


 if __name__ == "__main__":
@@ -24,10 +24,11 @@ from lerobot.processor.converters import (
    robot_action_observation_to_transition,
    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 (
    InverseKinematicsEEToJoints,
 )
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
@@ -74,35 +75,32 @@ def main():
    # 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(len(episode_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
-            ee_action = {
-                name: float(actions[idx][ACTION][i])
-                for i, name in enumerate(dataset.features[ACTION]["names"])
-            }
+        # Get recorded action from dataset
+        ee_action = {
+            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+        }

-            # Get robot observation
-            robot_obs = robot.get_observation()
+        # Get robot observation
+        robot_obs = robot.get_observation()

-            # Dataset EE -> robot joints
-            joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+        # Dataset EE -> robot joints
+        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))

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

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

-    finally:
-        # Clean up
-        robot.disconnect()
+    # Clean up
+    robot.disconnect()


 if __name__ == "__main__":
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
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