add check for cfg.policy in force_cpu line

.github/workflows/documentation.yml

@@ -18,11 +18,6 @@ name: Documentation
 on:
   # Allows running this workflow manually from the Actions tab
   workflow_dispatch:
-    inputs:
-      version:
-        description: 'Version tag (e.g. v0.1.2) - Leave empty for standard main build'
-        required: false
-        type: string
 
   # Triggers the workflow on push events to main for the docs folder
   push:
@@ -59,13 +54,7 @@ jobs:
     with:
       commit_sha: ${{ github.sha }}
       package: lerobot
-      additional_args: >-
-        --not_python_module
-        ${{
-          (github.event_name == 'release' && format('--version {0}', github.event.release.tag_name)) ||
-          (inputs.version != '' && format('--version {0}', inputs.version)) ||
-          ''
-        }}
+      additional_args: --not_python_module ${{ github.event_name == 'release' && format('--version {0}', github.event.release.tag_name) || '' }}
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}
       hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/fast_tests.yml

@@ -44,7 +44,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
 
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:
@@ -61,7 +61,6 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -90,11 +89,5 @@ jobs:
       - name: Install lerobot with test extras
         run: uv sync --extra "test"
 
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          uv run hf auth whoami
-
       - name: Run pytest
         run: uv run pytest tests -vv --maxfail=10

.github/workflows/full_tests.yml

@@ -37,7 +37,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 
 # Ensures that only the latest action is built, canceling older runs.
@@ -60,7 +60,6 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -88,12 +87,6 @@ jobs:
       - name: Install lerobot with all extras
         run: uv sync --extra all # TODO(Steven): Make flash-attn optional
 
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          uv run hf auth whoami
-
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv --maxfail=10
 
@@ -108,11 +101,9 @@ jobs:
     runs-on:
       group: aws-general-8-plus
     if: |
-      github.repository == 'huggingface/lerobot' && (
-        (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
-        github.event_name == 'push' ||
-        github.event_name == 'workflow_dispatch'
-      )
+      (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
+      github.event_name == 'push' ||
+      github.event_name == 'workflow_dispatch'
     outputs:
       image_tag: ${{ steps.set_tag.outputs.image_tag }}
     env:
@@ -169,7 +160,6 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -181,13 +171,6 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
-      - name: Fix ptxas permissions
-        run: chmod +x /lerobot/.venv/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas
       - name: Run pytest on GPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests

.github/workflows/nightly.yml

@@ -28,7 +28,7 @@ on:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
   DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
   DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest
 
@@ -119,7 +119,6 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-docker-cpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --shm-size "16gb"
@@ -131,11 +130,6 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
       - name: Run pytest on CPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests
@@ -152,7 +146,6 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -164,11 +157,6 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
       - name: Run pytest on GPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests
@@ -186,7 +174,6 @@ jobs:
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
       CUDA_VISIBLE_DEVICES: "0,1,2,3"
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -198,15 +185,12 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
       - name: Verify GPU availability
         run: |
           nvidia-smi
           python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
 
       - name: Run multi-GPU training tests
-        run: pytest -vv tests/training/
+      # TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
+        run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
+        timeout-minutes: 10

.github/workflows/quality.yml

@@ -50,7 +50,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
-          python-version: '3.12'
+          python-version: '3.10'
 
       - name: Run pre-commit hooks
         uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]

.github/workflows/release.yml

@@ -22,7 +22,7 @@ on:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
 
 jobs:
   # This job builds the Python package and publishes it to PyPI
@@ -45,7 +45,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
-          python-version: '3.12'
+          python-version: '3.10'
 
       - name: Extract Version
         id: extract_info
@@ -83,6 +83,14 @@ jobs:
             exit 1
           fi
 
+      - name: Remove Tags with Git dependencies
+        # TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
+        run: |
+          echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
+          grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
+          sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
+          echo "::info:: Git dependencies removed. Proceeding with build."
+
       - name: Install build dependencies
         run: python -m pip install build
 

.github/workflows/unbound_deps_tests.yml

@@ -20,8 +20,8 @@ on:
   workflow_dispatch:
 
   # Run on the 1st and 15th of every month at 09:00 UTC
-  # schedule:
-  #  - cron: '0 2 1,15 * *'
+  schedule:
+    - cron: '0 2 1,15 * *'
 
 permissions:
   contents: read
@@ -29,7 +29,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
+  PYTHON_VERSION: "3.10"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:unbound
 
 # Ensures that only the latest action is built, canceling older runs.
@@ -48,7 +48,6 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -80,11 +79,7 @@ jobs:
 
       - name: Install lerobot with all extras
         run: uv sync --extra all # TODO(Steven): Make flash-attn optional
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          uv run hf auth whoami
+
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv
 
@@ -96,7 +91,6 @@ jobs:
     name: Build and Push Docker
     runs-on:
       group: aws-general-8-plus
-    if: github.repository == 'huggingface/lerobot'
     outputs:
       image_tag: ${{ env.DOCKER_IMAGE_NAME }}
     env:
@@ -142,7 +136,6 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -154,11 +147,6 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
-      - name: Login to Hugging Face
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
       - name: Run pytest on GPU
         run: pytest tests -vv
       - name: Run end-to-end tests

.pre-commit-config.yaml

@@ -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

AI_POLICY.md

@@ -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.

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out, and improving the documentation are immensely valuable.
 
-Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md) and our [AI policy](./AI_POLICY.md).
+Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md).
 
 ## Ways to Contribute
 
@@ -14,7 +14,7 @@ You can contribute in many ways:
 - **Documentation:** Improve examples, guides, and docstrings.
 - **Feedback:** Submit tickets related to bugs or desired new features.
 
-If you are unsure where to start, join our [Discord Channel](https://discord.gg/q8Dzzpym3f).
+If you are unsure where to start, join our [Discord Channel](https://discord.gg/JkrYNdmw).
 
 ## Development Setup
 

MANIFEST.in

@@ -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

README.md

@@ -128,14 +128,13 @@ Learn how to implement your own simulation environment or benchmark and distribu
 ## Resources
 
 - **[Documentation](https://huggingface.co/docs/lerobot/index):** The complete guide to tutorials & API.
-- **[Chinese Tutorials: LeRobot+SO-ARM101中文教程-同济子豪兄](https://zihao-ai.feishu.cn/wiki/space/7589642043471924447)** Detailed doc for assembling, teleoperate, dataset, train, deploy. Verified by Seed Studio and 5 global hackathon players.
 - **[Discord](https://discord.gg/q8Dzzpym3f):** Join the `LeRobot` server to discuss with the community.
 - **[X](https://x.com/LeRobotHF):** Follow us on X to stay up-to-date with the latest developments.
 - **[Robot Learning Tutorial](https://huggingface.co/spaces/lerobot/robot-learning-tutorial):** A free, hands-on course to learn robot learning using LeRobot.
 
 ## Citation
 
-If you use LeRobot in your project, please cite the GitHub repository to acknowledge the ongoing development and contributors:
+If you use LeRobot in your research, please cite:
 
 ```bibtex
 @misc{cadene2024lerobot,
@@ -146,23 +145,6 @@ If you use LeRobot in your project, please cite the GitHub repository to acknowl
 }
 ```
 
-If you are referencing our research or the academic paper, please also cite our ICLR publication:
-
-<details>
-<summary><b>ICLR 2026 Paper</b></summary>
-
-```bibtex
-@inproceedings{cadenelerobot,
-  title={LeRobot: An Open-Source Library for End-to-End Robot Learning},
-  author={Cadene, Remi and Alibert, Simon and Capuano, Francesco and Aractingi, Michel and Zouitine, Adil and Kooijmans, Pepijn and Choghari, Jade and Russi, Martino and Pascal, Caroline and Palma, Steven and Shukor, Mustafa and Moss, Jess and Soare, Alexander and Aubakirova, Dana and Lhoest, Quentin and Gallou\'edec, Quentin and Wolf, Thomas},
-  booktitle={The Fourteenth International Conference on Learning Representations},
-  year={2026},
-  url={https://arxiv.org/abs/2602.22818}
-}
-```
-
-</details>
-
 ## Contribute
 
 We welcome contributions from everyone in the community! To get started, please read our [CONTRIBUTING.md](./CONTRIBUTING.md) guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!

SECURITY.md

@@ -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.

benchmarks/video/README.md

@@ -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%**   |

docker/Dockerfile.internal

@@ -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 \
@@ -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 . .

docker/Dockerfile.user

@@ -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

docs/source/_toctree.yml

@@ -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
@@ -27,10 +29,6 @@
     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
@@ -101,19 +99,11 @@
     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
@@ -123,8 +113,6 @@
     title: Notebooks
   - local: feetech
     title: Updating Feetech Firmware
-  - local: damiao
-    title: Damiao Motors and CAN Bus
   title: "Resources"
 - sections:
   - local: contributing

docs/source/act.mdx

@@ -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
 ```

docs/source/async.mdx

@@ -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
@@ -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,

docs/source/bring_your_own_policies.mdx

@@ -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
 
 

docs/source/cameras.mdx

@@ -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`.

docs/source/damiao.mdx

@@ -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
-```

docs/source/dataset_subtask.mdx

@@ -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

docs/source/earthrover_mini_plus.mdx

@@ -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
@@ -13,7 +7,7 @@ The EarthRover Mini Plus is a fully open source mobile robot that connects throu
 ### Hardware
 
 - EarthRover Mini robot
-- Computer with Python 3.12 or newer
+- Computer with Python 3.10 or newer
 - Internet connection
 
 ### Setting Up the Frodobots SDK
@@ -170,13 +164,13 @@ 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 +179,13 @@ 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
 ```
 

docs/source/envhub.mdx

@@ -155,10 +155,10 @@ Upload your repository to Hugging Face:
 pip install huggingface_hub
 
 # Login to Hugging Face
-hf auth login
+huggingface-cli login
 
 # Create a new repository
-hf repo create my-org/my-custom-env
+huggingface-cli repo create my-custom-env --type space --org my-org
 
 # Initialize git and push
 git init

docs/source/groot.mdx

@@ -120,12 +120,9 @@ 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
 ```
 

docs/source/hope_jr.mdx

@@ -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
 ```

docs/source/il_robots.mdx

@@ -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">
@@ -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
@@ -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 \

docs/source/installation.mdx

@@ -1,57 +1,30 @@
 # Installation
 
-This guide uses `conda` (via miniforge) to manage environments (recommended). If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.12 and `ffmpeg` installed with the `libsvtav1` encoder, then skip ahead to [Environment Setup](#step-2-environment-setup).
-
-## Step 1 (`conda` only): Install [`miniforge`](https://conda-forge.org/download/)
+## Install [`miniforge`](https://conda-forge.org/download/)
 
 ```bash
 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
 bash Miniforge3-$(uname)-$(uname -m).sh
 ```
 
-## Step 2: Environment Setup
+## Environment Setup
 
-Create a virtual environment with Python 3.12:
+Create a virtual environment with Python 3.10, using conda:
 
-<!-- prettier-ignore-start -->
-<hfoptions id="create_venv">
-<hfoption id="conda">
 ```bash
-conda create -y -n lerobot python=3.12
+conda create -y -n lerobot python=3.10
 ```
-</hfoption>
-<hfoption id="uv">
+
+Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
+
 ```bash
-uv python install 3.12
-uv venv --python 3.12
-```
-</hfoption>
-</hfoptions>
-<!-- prettier-ignore-end -->
-
-Then activate your virtual environment, you have to do this each time you open a shell to use lerobot:
-
-<!-- prettier-ignore-start -->
-<hfoptions id="activate_venv">
-<hfoption id="conda">```bash
 conda activate lerobot
-```</hfoption>
-<hfoption id="uv">
-```bash
-# Linux/macOSsource
-source .venv/bin/activate
-# Windows PowerShell
-source .venv\Scripts\Activate.ps1
 ```
-</hfoption>
-</hfoptions>
-<!-- prettier-ignore-end -->
 
 When using `conda`, install `ffmpeg` in your environment:
 
 ```bash
 conda install ffmpeg -c conda-forge
-ffmpeg -version  # ffmpeg 8.X is not yet supported !
 ```
 
 > [!TIP]
@@ -65,17 +38,7 @@ ffmpeg -version  # ffmpeg 8.X is not yet supported !
 >
 > - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
 
-> [!NOTE]
-> When installing LeRobot inside WSL (Windows Subsystem for Linux), make sure to install `evdev` with the following command:
->
-> ```bash
-> conda install evdev -c conda-forge
-> ```
-
-> [!IMPORTANT]
-> If you are using `uv` you will have to install `ffmpeg` system-wide (outside of the virtual environment). You rely on `uv` and `torchcodec` ability to dynamically link to the system `ffmpeg`.
-
-## Step 3: Install LeRobot 🤗
+## Install LeRobot 🤗
 
 ### From Source
 
@@ -88,45 +51,23 @@ cd lerobot
 
 Then, install the library in editable mode. This is useful if you plan to contribute to the code.
 
-<!-- prettier-ignore-start -->
-<hfoptions id="install_lerobot_src">
-<hfoption id="conda">
 ```bash
 pip install -e .
 ```
-</hfoption>
-<hfoption id="uv">
-```bash
-uv pip install -e .
-```
-</hfoption>
-</hfoptions>
-<!-- prettier-ignore-end -->
 
 ### Installation from PyPI
 
 **Core Library:**
 Install the base package with:
 
-<!-- prettier-ignore-start -->
-<hfoptions id="install_lerobot_pypi">
-<hfoption id="conda">
 ```bash
 pip install lerobot
 ```
-</hfoption>
-<hfoption id="uv">
-```bash
-uv pip install lerobot
-```
-</hfoption>
-</hfoptions>
-<!-- prettier-ignore-end -->
 
 _This installs only the default dependencies._
 
 **Extra Features:**
-To install additional functionality, use one of the following (If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.):
+To install additional functionality, use one of the following:
 
 ```bash
 pip install 'lerobot[all]'          # All available features
@@ -140,10 +81,13 @@ _Replace `[...]` with your desired features._
 For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
+
 ### Troubleshooting
 
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
-To install these for Linux run:
+To install these for linux run:
 
 ```bash
 sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev
@@ -153,7 +97,7 @@ For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/
 
 ## Optional dependencies
 
-LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`. If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.
+LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
 
 ### Simulations
 

docs/source/lekiwi.mdx

@@ -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
@@ -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
 ```
 

docs/source/lerobot-dataset-v3.mdx

@@ -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.

docs/source/libero.mdx

@@ -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.
 

docs/source/omx.mdx

@@ -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).

docs/source/openarm.mdx

@@ -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)

docs/source/phone_teleop.mdx

@@ -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:
@@ -85,22 +84,19 @@ 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

docs/source/pi0.mdx

@@ -34,6 +34,11 @@ As described by Physical Intelligence, while AI has achieved remarkable success
    pip install -e ".[pi]"
    ```
 
+   > [!NOTE]
+   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+   >
+   > This will be solved in the next patch release
+
 ## Training Data and Capabilities
 
 π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:
@@ -55,7 +60,7 @@ 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 \

docs/source/pi05.mdx

@@ -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 \

docs/source/pi0fast.mdx

@@ -43,11 +43,16 @@ This approach can transform **any existing VLM** into a VLA by training it to pr
    pip install -e ".[pi]"
    ```
 
+   > [!NOTE]
+   > For lerobot 0.4.0, if you want to install the pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+   >
+   > This will be solved in the next patch release
+
 ## Training a Custom FAST Tokenizer
 
 You have two options for the FAST tokenizer:
 
-1. **Use the pre-trained tokenizer**: The `lerobot/fast-action-tokenizer` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
+1. **Use the pre-trained tokenizer**: The `physical-intelligence/fast` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
 
 2. **Train your own tokenizer**: For maximum performance on your specific dataset, you can finetune the tokenizer on your own data.
 
@@ -109,15 +114,15 @@ lerobot-train \
 
 ### Key Training Parameters
 
-| Parameter                              | Description                                        | Default                         |
-| -------------------------------------- | -------------------------------------------------- | ------------------------------- |
-| `--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`                         |
+| 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                              | `physical-intelligence/fast` |
+| `--policy.compile_model=true`          | Enable torch.compile for faster training           | `false`                      |
 
 ## Inference
 

docs/source/reachy2.mdx

@@ -159,9 +159,6 @@ 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
 ```
 
@@ -201,9 +198,6 @@ 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
 ```
 

docs/source/sarm.mdx

@@ -269,7 +269,7 @@ This generates visualizations showing video frames with subtask boundaries overl
 Train with **no annotations** - uses linear progress from 0 to 1:
 
 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=sarm \
   --policy.annotation_mode=single_stage \
@@ -288,7 +288,7 @@ lerobot-train \
 Train with **dense annotations only** (sparse auto-generated):
 
 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=sarm \
   --policy.annotation_mode=dense_only \
@@ -307,7 +307,7 @@ lerobot-train \
 Train with **both sparse and dense annotations**:
 
 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=sarm \
   --policy.annotation_mode=dual \
@@ -468,7 +468,7 @@ This script:
 Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`). Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:
 
 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=pi0 \
   --use_rabc=true \

docs/source/smolvla.mdx

@@ -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 \

docs/source/so101.mdx

@@ -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

docs/source/streaming_video_encoding.mdx

@@ -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.

docs/source/unitree_g1.mdx

@@ -1,49 +1,23 @@
 # Unitree G1
 
-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/unitree_thumbnail.jpg"
-  alt="Unitree G1 locomanipulation demo"
-  style={{ width: "100%" }}
-/>
+This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.
 
-The Unitree G1 humanoid is now supported in LeRobot! You can teleoperate, train locomanipulation policies, test in sim, and more. Both 29 and 23 DoF variants are supported.
+## About
+
+We support both 29 and 23 DOF G1 EDU version. We introduce:
+
+- **`unitree g1` robot class, handling low level read/write from/to the humanoid**
+- **ZMQ socket bridge** for remote communication and camera streaming, allowing for remote policy deployment over wlan, eth or directly on the robot
+- **Locomotion policies** from NVIDIA gr00t and Amazon FAR Holosoma
+- **Simulation mode** for testing policies without the physical robot in mujoco
 
 ---
 
-## Part 1: Getting Started
+## Connection guide
 
-### Install LeRobot on Your Machine
+### Step 1: Configure Ethernet Interface
 
-```bash
-conda create -y -n lerobot python=3.12
-conda activate lerobot
-git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
-cd unitree_sdk2_python && pip install -e .
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e '.[unitree_g1]'
-```
-
-### Test the Installation (Simulation)
-
-```bash
-lerobot-teleoperate \
-  --robot.type=unitree_g1 \
-  --robot.is_simulation=true \
-  --teleop.type=unitree_g1 \
-  --teleop.id=wbc_unitree \
-  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-  --display_data=true
-```
-
-This will launch a [MuJoCo sim instance](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main) for the G1.
-
-- Press `9` to release the robot
-- Press `7` / `8` to increase / decrease waist height
-
-### Connect to the Robot
-
-The G1's Ethernet IP is fixed at `192.168.123.164`. Your machine must have a static IP on the same subnet: `192.168.123.x` where `x ≠ 164`.
+Set a static IP on the same subnet as the robot:
 
 ```bash
 # Replace 'enp131s0' with your ethernet interface name (check with `ip a`)
@@ -52,201 +26,169 @@ sudo ip addr add 192.168.123.200/24 dev enp131s0
 sudo ip link set enp131s0 up
 ```
 
-### SSH into the Robot
+**Note**: The G1's Ethernet IP is fixed at `192.168.123.164`. Your computer must use `192.168.123.x` with x ≠ 164.
+
+### Step 2: SSH into the Robot
 
 ```bash
 ssh unitree@192.168.123.164
 # Password: 123
 ```
 
-### Install LeRobot on the G1
-
-From the robot:
-
-```bash
-conda create -y -n lerobot python=3.12
-conda activate lerobot
-git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
-cd unitree_sdk2_python && pip install -e .
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e '.[unitree_g1]'
-```
-
-> **Note:** The Unitree SDK requires CycloneDDS v0.10.2. See the [Unitree SDK docs](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
+You should now be connected to the G1's Orin.
 
 ---
 
 ## Part 2: Enable WiFi on the Robot
 
-Wi-Fi connectivity is blocked by default on the G1. To activate:
+Wlan0 is disabled by default on the G1. To enable it:
+
+### Step 1: Enable WiFi Hardware
 
 ```bash
+sudo rfkill unblock wifi
 sudo rfkill unblock all
+
+# Bring up wlan0
 sudo ip link set wlan0 up
+
+# Enable NetworkManager control of wlan0
 sudo nmcli radio wifi on
 sudo nmcli device set wlan0 managed yes
 sudo systemctl restart NetworkManager
 ```
 
-**On your laptop** (share internet via Ethernet):
+### Step 2: Enable Internet Forwarding
+
+**On your laptop:**
 
 ```bash
+# Enable IP forwarding
 sudo sysctl -w net.ipv4.ip_forward=1
 
-# Replace wlp132s0f0 with your WiFi interface name
+# Set up NAT (replace wlp132s0f0 with your WiFi interface)
 sudo iptables -t nat -A POSTROUTING -o wlp132s0f0 -s 192.168.123.0/24 -j MASQUERADE
 sudo iptables -A FORWARD -i wlp132s0f0 -o enp131s0 -m state --state RELATED,ESTABLISHED -j ACCEPT
 sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT
 ```
 
-**On the G1** (set default route through your laptop):
+**On the G1:**
 
 ```bash
+# Add laptop as default gateway
 sudo ip route del default 2>/dev/null || true
 sudo ip route add default via 192.168.123.200 dev eth0
 echo "nameserver 8.8.8.8" | sudo tee /etc/resolv.conf
 
-# Verify
+# Test connection
 ping -c 3 8.8.8.8
 ```
 
-**Connect to a WiFi network:**
+### Step 3: Connect to WiFi Network
 
 ```bash
+# List available networks
 nmcli device wifi list
 
+# Connect to your WiFi (example)
 sudo nmcli connection add type wifi ifname wlan0 con-name "YourNetwork" ssid "YourNetwork"
 sudo nmcli connection modify "YourNetwork" wifi-sec.key-mgmt wpa-psk
 sudo nmcli connection modify "YourNetwork" wifi-sec.psk "YourPassword"
 sudo nmcli connection modify "YourNetwork" connection.autoconnect yes
 sudo nmcli connection up "YourNetwork"
 
+# Check WiFi IP address
 ip a show wlan0
 ```
 
-You can now SSH over WiFi:
+### Step 4: SSH Over WiFi
+
+Once connected to WiFi, note the robot's IP address and disconnect the Ethernet cable. You can now SSH over WiFi:
 
 ```bash
-ssh unitree@<ROBOT_WIFI_IP>
+ssh unitree@<YOUR_ROBOT_IP>
 # Password: 123
 ```
 
+Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address.
+
 ---
 
-## Part 3: Teleoperation & Locomotion
+## Part 3: Robot Server Setup
 
-### Run the Robot Server
+### Step 1: Install LeRobot on the Orin
+
+SSH into the robot and install LeRobot:
+
+```bash
+ssh unitree@<YOUR_ROBOT_IP>
+
+conda create -y -n lerobot python=3.10
+conda activate lerobot
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+pip install -e '.[unitree_g1]'
+git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
+cd unitree_sdk2_python  && pip install -e .
+```
+
+**Note**: The Unitree SDK requires CycloneDDS v0.10.2 to be installed. See the [Unitree SDK documentation](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
+
+### Step 2: Run the Robot Server
 
 On the robot:
 
 ```bash
-python src/lerobot/robots/unitree_g1/run_g1_server.py --camera
+python src/lerobot/robots/unitree_g1/run_g1_server.py
 ```
 
-### Run the Locomotion Policy
-
-```bash
-lerobot-teleoperate \
-  --robot.type=unitree_g1 \
-  --robot.is_simulation=false \
-  --robot.robot_ip=<ROBOT_IP> \
-  --teleop.type=unitree_g1 \
-  --teleop.id=wbc_unitree \
-  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-  --display_data=true \
-  --robot.controller=HolosomaLocomotionController
-```
-
-We support both [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) and [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl).
+**Important**: Keep this terminal running. The server must be active for remote control.
 
 ---
 
-## Part 4: Loco-Manipulation with the Homunculus Exoskeleton
+## Part 4: Controlling the robot
 
-We provide a loco-manipulation solution via the Homunculus Exoskeleton — an open-source 7 DoF exoskeleton for whole-body control. Assembly instructions [here](https://github.com/nepyope/hmc_exo).
+With the robot server running, you can now control the robot remotely. Let's launch a locomotion policy
 
-### Calibrate
+### Step 1: Install LeRobot on your machine
 
 ```bash
-lerobot-calibrate \
-  --teleop.type=unitree_g1 \
-  --teleop.left_arm_config.port=/dev/ttyACM1 \
-  --teleop.right_arm_config.port=/dev/ttyACM0 \
-  --teleop.id=exo
+conda create -y -n lerobot python=3.10
+conda activate lerobot
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+pip install -e '.[unitree_g1]'
+git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
+cd unitree_sdk2_python  && pip install -e .
 ```
 
-During calibration move each joint through its entire range. After fitting, move the joint in a neutral position and press `n` to advance.
+### Step 2: Update Robot IP in Config
 
-### Record a Dataset
+Edit the config file to match your robot's WiFi IP:
+
+```python
+# In src/lerobot/robots/unitree_g1/config_unitree_g1.py
+robot_ip: str = "<YOUR_ROBOT_IP>"  # Replace with your robot's WiFi IP.
+```
+
+### Step 3: Run the Locomotion Policy
 
 ```bash
-lerobot-record \
-  --robot.type=unitree_g1 \
-  --robot.is_simulation=true \
-  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-  --teleop.type=unitree_g1 \
-  --teleop.left_arm_config.port=/dev/ttyACM1 \
-  --teleop.right_arm_config.port=/dev/ttyACM0 \
-  --teleop.id=exo \
-  --dataset.repo_id=your-username/dataset-name \
-  --dataset.single_task="Test" \
-  --dataset.num_episodes=2 \
-  --dataset.episode_time_s=5 \
-  --dataset.reset_time_s=5 \
-  --dataset.push_to_hub=true \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2
+# 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
+
 ```
 
-> **Note:** Omit `--teleop.left_arm_config.port` and `--teleop.right_arm_config.port` if you're only using the joystick.
-
-Example dataset: [nepyope/unitree_box_move_blue_full](https://huggingface.co/datasets/nepyope/unitree_box_move_blue_full)
+Press `Ctrl+C` to stop the policy.
 
 ---
 
-## Part 5: Training & Inference
+## Running in Simulation Mode (MuJoCo)
 
-### Train
-
-```bash
-python src/lerobot/scripts/lerobot_train.py \
-  --dataset.repo_id=your-username/dataset-name  \
-  --policy.type=pi05 \
-  --output_dir=./outputs/pi05_training \
-  --job_name=pi05_training \
-  --policy.repo_id=your-username/your-repo-id \
-  --policy.pretrained_path=lerobot/pi05_base \
-  --policy.compile_model=true \
-  --policy.gradient_checkpointing=true \
-  --wandb.enable=true \
-  --policy.dtype=bfloat16 \
-  --policy.freeze_vision_encoder=false \
-  --policy.train_expert_only=false \
-  --steps=3000 \
-  --policy.device=cuda \
-  --batch_size=32
-```
-
-### Inference with RTC
-
-Once trained, we recommend deploying policies using inference-time RTC:
-
-```bash
-python examples/rtc/eval_with_real_robot.py \
-  --policy.path=your-username/your-repo-id \
-  --policy.device=cuda \
-  --robot.type=unitree_g1 \
-  --robot.is_simulation=false \
-  --robot.controller=HolosomaLocomotionController \
-  --robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
-  --task="task_description" \
-  --duration=1000 \
-  --fps=30 \
-  --rtc.enabled=true
-```
-
----
+You can now test policies before unleashing them on the physical robot using MuJoCo. To do so simply set `is_simulation=True` in config.
 
 ## Additional Resources
 
@@ -254,8 +196,8 @@ python examples/rtc/eval_with_real_robot.py \
 - [GR00T-WholeBodyControl](https://github.com/NVlabs/GR00T-WholeBodyControl)
 - [Holosoma](https://github.com/amazon-far/holosoma)
 - [LeRobot Documentation](https://github.com/huggingface/lerobot)
-- [Unitree IL LeRobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
+- [Unitree_IL_Lerobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
 
 ---
 
-_Last updated: March 2026_
+_Last updated: December 2025_

docs/source/using_dataset_tools.mdx

@@ -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:

docs/source/walloss.mdx

@@ -45,7 +45,7 @@ policy.type=wall_x
 For training WallX, you can use the standard LeRobot training script with the appropriate configuration:
 
 ```bash
-lerobot-train \
+python src/lerobot/scripts/lerobot_train.py \
     --dataset.repo_id=your_dataset \
     --policy.type=wall_x \
     --output_dir=./outputs/wallx_training \

docs/source/xvla.mdx

@@ -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" \

examples/backward_compatibility/replay.py

@@ -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
 ```
 """
@@ -57,7 +57,7 @@ class DatasetReplayConfig:
     repo_id: str
     # Episode to replay.
     episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | Path | None = None
     # Limit the frames per second. By default, uses the policy fps.
     fps: int = 30
@@ -81,25 +81,24 @@ 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(max(1 / dataset.fps - dt_s, 0.0))
+
+    robot.disconnect()
 
 
 if __name__ == "__main__":

examples/dataset/slurm_compute_rabc.py

@@ -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()

examples/lekiwi/evaluate.py

@@ -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__":

examples/lekiwi/record.py

@@ -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__":

examples/lekiwi/replay.py

@@ -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__":

examples/phone_to_so100/evaluate.py

@@ -142,24 +142,38 @@ 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 +182,24 @@ 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__":

examples/phone_to_so100/record.py

@@ -149,23 +149,38 @@ 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 +188,25 @@ 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__":

examples/phone_to_so100/replay.py

@@ -73,34 +73,32 @@ 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(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+
+    # Clean up
+    robot.disconnect()
 
 
 if __name__ == "__main__":

examples/rtc/eval_dataset.py

@@ -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 \

examples/rtc/eval_with_real_robot.py

@@ -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 \
@@ -78,7 +78,6 @@ from torch import Tensor
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import RTCAttentionSchedule
@@ -98,7 +97,6 @@ from lerobot.robots import (  # noqa: F401
     bi_so_follower,
     koch_follower,
     so_follower,
-    unitree_g1,
 )
 from lerobot.robots.utils import make_robot_from_config
 from lerobot.utils.constants import OBS_IMAGES

examples/so100_to_so100_EE/evaluate.py

@@ -142,24 +142,38 @@ 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 +182,24 @@ 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__":

examples/so100_to_so100_EE/record.py

@@ -146,23 +146,38 @@ 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 +185,25 @@ 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__":

examples/so100_to_so100_EE/replay.py

@@ -74,35 +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))
+        precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
 
-    finally:
-        # Clean up
-        robot.disconnect()
+    # Clean up
+    robot.disconnect()
 
 
 if __name__ == "__main__":

examples/tutorial/async-inf/robot_client.py

@@ -30,7 +30,6 @@ def main():
         robot=robot_cfg,
         server_address=server_address,
         policy_device="mps",
-        client_device="cpu",
         policy_type="act",
         pretrained_name_or_path="<user>/robot_learning_tutorial_act",
         chunk_size_threshold=0.5,  # g

examples/unitree_g1/gr00t_locomotion.py

@@ -14,20 +14,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import argparse
 import logging
+import time
 from collections import deque
 
 import numpy as np
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from lerobot.robots.unitree_g1.g1_utils import (
-    REMOTE_AXES,
-    REMOTE_BUTTONS,
-    G1_29_JointIndex,
-    get_gravity_orientation,
-)
+from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
+from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
 
+logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
@@ -36,13 +36,18 @@ GROOT_DEFAULT_ANGLES[[0, 6]] = -0.1  # Hip pitch
 GROOT_DEFAULT_ANGLES[[3, 9]] = 0.3  # Knee
 GROOT_DEFAULT_ANGLES[[4, 10]] = -0.2  # Ankle pitch
 
+MISSING_JOINTS = []
+G1_MODEL = "g1_23"  # Or "g1_29"
+if G1_MODEL == "g1_23":
+    MISSING_JOINTS = [12, 14, 20, 21, 27, 28]  # Waist yaw/pitch, wrist pitch/yaw
+
 # Control parameters
 ACTION_SCALE = 0.25
 CONTROL_DT = 0.02  # 50Hz
 ANG_VEL_SCALE: float = 0.25
 DOF_POS_SCALE: float = 1.0
 DOF_VEL_SCALE: float = 0.05
-CMD_SCALE: list[float] = [2.0, 2.0, 0.25]
+CMD_SCALE: list = [2.0, 2.0, 0.25]
 
 
 DEFAULT_GROOT_REPO_ID = "nepyope/GR00T-WholeBodyControl_g1"
@@ -80,11 +85,11 @@ def load_groot_policies(
 class GrootLocomotionController:
     """GR00T lower-body locomotion controller for the Unitree G1."""
 
-    control_dt = CONTROL_DT  # Expose for unitree_g1.py
-
-    def __init__(self):
-        # Load policies
-        self.policy_balance, self.policy_walk = load_groot_policies()
+    def __init__(self, policy_balance, policy_walk, robot, config):
+        self.policy_balance = policy_balance
+        self.policy_walk = policy_walk
+        self.robot = robot
+        self.config = config
 
         self.cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)  # vx, vy, theta_dot
 
@@ -104,60 +109,45 @@ class GrootLocomotionController:
 
         logger.info("GrootLocomotionController initialized")
 
-    def reset(self) -> None:
-        """Reset internal state for a new episode."""
-        self.cmd[:] = 0.0
-        self.groot_qj_all[:] = 0.0
-        self.groot_dqj_all[:] = 0.0
-        self.groot_action[:] = 0.0
-        self.groot_obs_single[:] = 0.0
-        self.groot_obs_stacked[:] = 0.0
-        self.groot_height_cmd = 0.74
-        self.groot_orientation_cmd[:] = 0.0
-        self.groot_obs_history.clear()
-        for _ in range(6):
-            self.groot_obs_history.append(np.zeros(86, dtype=np.float32))
+    def run_step(self):
+        # Get current observation
+        obs = self.robot.get_observation()
 
-    def run_step(self, action: dict, lowstate) -> dict:
-        """Run one step of the locomotion controller.
+        if not obs:
+            return
 
-        Args:
-            action: Action dict containing remote.lx/ly/rx/ry and buttons
-            lowstate: Robot lowstate containing motor positions/velocities and IMU
-
-        Returns:
-            Action dict for lower body joints (0-14)
-        """
-        if lowstate is None:
-            return {}
-
-        buttons = [int(action.get(k, 0)) for k in REMOTE_BUTTONS]
-        if buttons[0]:  # R1 - raise waist
+        # Get command from remote controller
+        if obs["remote.buttons"][0]:  # R1 - raise waist
             self.groot_height_cmd += 0.001
             self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
-        if buttons[4]:  # R2 - lower waist
+        if obs["remote.buttons"][4]:  # R2 - lower waist
             self.groot_height_cmd -= 0.001
             self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
 
-        lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
-        self.cmd[0] = ly  # Forward/backward
-        self.cmd[1] = -lx  # Left/right (negated)
-        self.cmd[2] = -rx  # Rotation rate (negated)
+        self.cmd[0] = obs["remote.ly"]  # Forward/backward
+        self.cmd[1] = obs["remote.lx"] * -1  # Left/right
+        self.cmd[2] = obs["remote.rx"] * -1  # Rotation rate
 
-        # Get joint positions and velocities from lowstate
+        # Get joint positions and velocities from flat dict
         for motor in G1_29_JointIndex:
+            name = motor.name
             idx = motor.value
-            self.groot_qj_all[idx] = lowstate.motor_state[idx].q
-            self.groot_dqj_all[idx] = lowstate.motor_state[idx].dq
+            self.groot_qj_all[idx] = obs[f"{name}.q"]
+            self.groot_dqj_all[idx] = obs[f"{name}.dq"]
+
+        # Adapt observation for g1_23dof
+        for idx in MISSING_JOINTS:
+            self.groot_qj_all[idx] = 0.0
+            self.groot_dqj_all[idx] = 0.0
 
         # Scale joint positions and velocities
         qj_obs = self.groot_qj_all.copy()
         dqj_obs = self.groot_dqj_all.copy()
 
         # Express IMU data in gravity frame of reference
-        quat = lowstate.imu_state.quaternion
-        ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
-        gravity_orientation = get_gravity_orientation(quat)
+        quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
+        ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
+        gravity_orientation = self.robot.get_gravity_orientation(quat)
 
         # Scale joint positions and velocities before policy inference
         qj_obs = (qj_obs - GROOT_DEFAULT_ANGLES) * DOF_POS_SCALE
@@ -196,10 +186,73 @@ class GrootLocomotionController:
         # Transform action back to target joint positions
         target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * ACTION_SCALE
 
-        # Build action dict
+        # Build action dict (only first 15 joints for GR00T)
         action_dict = {}
         for i in range(15):
             motor_name = G1_29_JointIndex(i).name
             action_dict[f"{motor_name}.q"] = float(target_dof_pos_15[i])
 
-        return action_dict
+        # Zero out missing joints for g1_23dof
+        for joint_idx in MISSING_JOINTS:
+            motor_name = G1_29_JointIndex(joint_idx).name
+            action_dict[f"{motor_name}.q"] = 0.0
+
+        # Send action to robot
+        self.robot.send_action(action_dict)
+
+
+def run(repo_id: str = DEFAULT_GROOT_REPO_ID) -> None:
+    """Main function to run the GR00T locomotion controller.
+
+    Args:
+        repo_id: Hugging Face Hub repository ID for GR00T policies.
+    """
+    # Load policies
+    policy_balance, policy_walk = load_groot_policies(repo_id=repo_id)
+
+    # Initialize robot
+    config = UnitreeG1Config()
+    robot = UnitreeG1(config)
+
+    robot.connect()
+
+    # Initialize gr00T locomotion controller
+    groot_controller = GrootLocomotionController(
+        policy_balance=policy_balance,
+        policy_walk=policy_walk,
+        robot=robot,
+        config=config,
+    )
+
+    try:
+        robot.reset(CONTROL_DT, GROOT_DEFAULT_ANGLES)
+
+        logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate, R1=raise waist, R2=lower waist")
+        logger.info("Press Ctrl+C to stop")
+
+        # Run step
+        while not robot._shutdown_event.is_set():
+            start_time = time.time()
+            groot_controller.run_step()
+            elapsed = time.time() - start_time
+            sleep_time = max(0, CONTROL_DT - elapsed)
+            time.sleep(sleep_time)
+    except KeyboardInterrupt:
+        logger.info("Stopping locomotion...")
+    finally:
+        if robot.is_connected:
+            robot.disconnect()
+        logger.info("Done!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="GR00T Locomotion Controller for Unitree G1")
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        default=DEFAULT_GROOT_REPO_ID,
+        help=f"Hugging Face Hub repo ID for GR00T policies (default: {DEFAULT_GROOT_REPO_ID})",
+    )
+    args = parser.parse_args()
+
+    run(repo_id=args.repo_id)

examples/unitree_g1/holosoma_locomotion.py

@@ -14,21 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import argparse
 import json
 import logging
+import time
 
 import numpy as np
 import onnx
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from lerobot.robots.unitree_g1.g1_utils import (
-    REMOTE_AXES,
-    G1_29_JointArmIndex,
-    G1_29_JointIndex,
-    get_gravity_orientation,
-)
+from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
+from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
 
+logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 DEFAULT_ANGLES = np.zeros(29, dtype=np.float32)
@@ -40,13 +40,18 @@ DEFAULT_ANGLES[16] = 0.2  # Left shoulder roll
 DEFAULT_ANGLES[23] = -0.2  # Right shoulder roll
 DEFAULT_ANGLES[[18, 25]] = 0.6  # Elbow
 
+MISSING_JOINTS = []
+G1_MODEL = "g1_23"  # Or "g1_29"
+if G1_MODEL == "g1_23":
+    MISSING_JOINTS = [12, 14, 20, 21, 27, 28]  # Waist yaw/pitch, wrist pitch/yaw
+
 # Control parameters
 ACTION_SCALE = 0.25
-CONTROL_DT = 0.005  # 200Hz
+CONTROL_DT = 0.02  # 50Hz
 ANG_VEL_SCALE = 0.25
 DOF_POS_SCALE = 1.0
 DOF_VEL_SCALE = 0.05
-GAIT_PERIOD = 0.5
+GAIT_PERIOD = 1.0
 
 
 DEFAULT_HOLOSOMA_REPO_ID = "nepyope/holosoma_locomotion"
@@ -82,7 +87,7 @@ def load_policy(
     logger.info(f"Policy loaded: {policy.get_inputs()[0].shape} → {policy.get_outputs()[0].shape}")
 
     # Extract KP/KD from ONNX metadata
-    model = onnx.load(policy_path, load_external_data=False)
+    model = onnx.load(policy_path)
     metadata = {prop.key: prop.value for prop in model.metadata_props}
 
     if "kp" not in metadata or "kd" not in metadata:
@@ -96,13 +101,15 @@ def load_policy(
 
 
 class HolosomaLocomotionController:
-    """Holosoma lower-body locomotion controller for Unitree G1."""
+    """Holosoma whole-body locomotion controller for Unitree G1."""
 
-    control_dt = CONTROL_DT  # Expose for unitree_g1.py
+    def __init__(self, policy, robot, kp: np.ndarray, kd: np.ndarray):
+        self.policy = policy
+        self.robot = robot
 
-    def __init__(self):
-        # Load policy and gains
-        self.policy, self.kp, self.kd = load_policy()
+        # Override robot's PD gains with policy gains
+        self.robot.kp = kp
+        self.robot.kd = kd
 
         self.cmd = np.zeros(3, dtype=np.float32)
 
@@ -117,55 +124,35 @@ class HolosomaLocomotionController:
         self.phase_dt = 2 * np.pi / ((1.0 / CONTROL_DT) * GAIT_PERIOD)
         self.is_standing = True
 
-        logger.info("HolosomaLocomotionController initialized")
+    def run_step(self):
+        # Get current observation
+        obs = self.robot.get_observation()
 
-    def reset(self) -> None:
-        """Reset internal state for a new episode."""
-        self.cmd[:] = 0.0
-        self.qj[:] = 0.0
-        self.dqj[:] = 0.0
-        self.obs[:] = 0.0
-        self.last_action[:] = 0.0
-        self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
-        self.is_standing = True
+        if not obs:
+            return
 
-    def run_step(self, action: dict, lowstate) -> dict:
-        """Run one step of the locomotion controller.
-
-        Args:
-            action: Action dict containing remote.lx/ly/rx/ry
-            lowstate: Robot lowstate containing motor positions/velocities and IMU
-
-        Returns:
-            Action dict for lower body joints (0-14)
-        """
-        if lowstate is None:
-            return {}
-
-        lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
-        ly = ly if abs(ly) > 0.1 else 0.0
-        lx = lx if abs(lx) > 0.1 else 0.0
-        rx = rx if abs(rx) > 0.1 else 0.0
-        ly = np.clip(ly, -0.3, 0.3)
-        lx = np.clip(lx, -0.3, 0.3)
+        # Get command from remote controller
+        ly = obs["remote.ly"] if abs(obs["remote.ly"]) > 0.1 else 0.0
+        lx = obs["remote.lx"] if abs(obs["remote.lx"]) > 0.1 else 0.0
+        rx = obs["remote.rx"] if abs(obs["remote.rx"]) > 0.1 else 0.0
         self.cmd[:] = [ly, -lx, -rx]
 
-        # Get joint positions and velocities from lowstate
+        # Get joint positions and velocities
         for motor in G1_29_JointIndex:
+            name = motor.name
             idx = motor.value
-            self.qj[idx] = lowstate.motor_state[idx].q
-            self.dqj[idx] = lowstate.motor_state[idx].dq
+            self.qj[idx] = obs[f"{name}.q"]
+            self.dqj[idx] = obs[f"{name}.dq"]
 
-        # Hide arm positions from policy (show DEFAULT_ANGLES instead)
-        # This prevents policy from reacting to teleop arm movements
-        for arm_joint in G1_29_JointArmIndex:
-            self.qj[arm_joint.value] = DEFAULT_ANGLES[arm_joint.value]
-            self.dqj[arm_joint.value] = 0.0
+        # Adapt observation for g1_23dof
+        for idx in MISSING_JOINTS:
+            self.qj[idx] = 0.0
+            self.dqj[idx] = 0.0
 
         # Express IMU data in gravity frame of reference
-        quat = lowstate.imu_state.quaternion
-        ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
-        gravity = get_gravity_orientation(quat)
+        quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
+        ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
+        gravity = self.robot.get_gravity_orientation(quat)
 
         # Scale joint positions and velocities before policy inference
         qj_obs = (self.qj - DEFAULT_ANGLES) * DOF_POS_SCALE
@@ -199,16 +186,79 @@ class HolosomaLocomotionController:
         # Run policy inference
         ort_in = {self.policy.get_inputs()[0].name: self.obs.reshape(1, -1).astype(np.float32)}
         raw_action = self.policy.run(None, ort_in)[0].squeeze()
-        policy_action = np.clip(raw_action, -100.0, 100.0)
-        self.last_action = policy_action.copy()
+        action = np.clip(raw_action, -100.0, 100.0)
+        self.last_action = action.copy()
 
         # Transform action back to target joint positions
-        target = DEFAULT_ANGLES + policy_action * ACTION_SCALE
+        target = DEFAULT_ANGLES + action * ACTION_SCALE
 
-        # Build action dict (first 15 joints only)
+        # Build action dict
         action_dict = {}
-        for i in range(15):
-            motor_name = G1_29_JointIndex(i).name
-            action_dict[f"{motor_name}.q"] = float(target[i])
+        for motor in G1_29_JointIndex:
+            action_dict[f"{motor.name}.q"] = float(target[motor.value])
 
-        return action_dict
+        # Zero out missing joints for g1_23dof
+        for joint_idx in MISSING_JOINTS:
+            motor_name = G1_29_JointIndex(joint_idx).name
+            action_dict[f"{motor_name}.q"] = 0.0
+
+        # Send action to robot
+        self.robot.send_action(action_dict)
+
+
+def run(repo_id: str = DEFAULT_HOLOSOMA_REPO_ID, policy_type: str = "fastsac") -> None:
+    """Main function to run the Holosoma locomotion controller.
+
+    Args:
+        repo_id: Hugging Face Hub repository ID for Holosoma policies.
+        policy_type: Policy type to use ('fastsac' or 'ppo').
+    """
+    # Load policy and gains
+    policy, kp, kd = load_policy(repo_id=repo_id, policy_type=policy_type)
+
+    # Initialize robot
+    config = UnitreeG1Config()
+    robot = UnitreeG1(config)
+    robot.connect()
+
+    holosoma_controller = HolosomaLocomotionController(policy, robot, kp, kd)
+
+    try:
+        robot.reset(CONTROL_DT, DEFAULT_ANGLES)
+
+        logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate")
+        logger.info("Press Ctrl+C to stop")
+
+        # Run step
+        while not robot._shutdown_event.is_set():
+            start_time = time.time()
+            holosoma_controller.run_step()
+            elapsed = time.time() - start_time
+            sleep_time = max(0, CONTROL_DT - elapsed)
+            time.sleep(sleep_time)
+    except KeyboardInterrupt:
+        logger.info("Stopping locomotion...")
+    finally:
+        if robot.is_connected:
+            robot.disconnect()
+        logger.info("Done!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Holosoma Locomotion Controller for Unitree G1")
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        default=DEFAULT_HOLOSOMA_REPO_ID,
+        help=f"Hugging Face Hub repo ID for Holosoma policies (default: {DEFAULT_HOLOSOMA_REPO_ID})",
+    )
+    parser.add_argument(
+        "--policy",
+        type=str,
+        choices=["fastsac", "ppo"],
+        default="fastsac",
+        help="Policy type to use: 'fastsac' (default) or 'ppo'",
+    )
+    args = parser.parse_args()
+
+    run(repo_id=args.repo_id, policy_type=args.policy)

pyproject.toml

@@ -25,11 +25,11 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.5.0"
+version = "0.4.3"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
-requires-python = ">=3.12"
+requires-python = ">=3.10"
 authors = [
     { name = "Rémi Cadène", email = "re.cadene@gmail.com" },
     { name = "Simon Alibert", email = "alibert.sim@gmail.com" },
@@ -50,8 +50,7 @@ classifiers = [
     "Intended Audience :: Education",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: Apache Software License",
-    "Programming Language :: Python :: 3.12",
-    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.10",
     "Topic :: Software Development :: Build Tools",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
@@ -60,30 +59,28 @@ keywords = ["lerobot", "huggingface", "robotics",  "machine learning", "artifici
 dependencies = [
 
     # Hugging Face dependencies
-    "datasets>=4.0.0,<5.0.0",
+    "datasets>=4.0.0,<4.2.0",
     "diffusers>=0.27.2,<0.36.0",
-    "huggingface-hub>=1.0.0,<2.0.0",
+    "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0",
     "accelerate>=1.10.0,<2.0.0",
 
     # Core dependencies
-    "numpy>=2.0.0,<2.3.0", # NOTE: Explicitly listing numpy helps the resolver converge faster. Upper bound imposed by opencv-python-headless.
     "setuptools>=71.0.0,<81.0.0",
     "cmake>=3.29.0.1,<4.2.0",
-    "packaging>=24.2,<26.0",
-
-    "torch>=2.2.1,<2.11.0",
-    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
-    "torchvision>=0.21.0,<0.26.0",
-
     "einops>=0.8.0,<0.9.0",
     "opencv-python-headless>=4.9.0,<4.13.0",
     "av>=15.0.0,<16.0.0",
     "jsonlines>=4.0.0,<5.0.0",
-    "pynput>=1.7.8,<1.9.0",
+    "packaging>=24.2,<26.0",
+    "pynput>=1.7.7,<1.9.0",
     "pyserial>=3.5,<4.0",
-
     "wandb>=0.24.0,<0.25.0",
-    "draccus==0.10.0", # TODO: Relax version constraint
+
+    "torch>=2.2.1,<2.8.0", # TODO: Bumb dependency
+    "torchcodec>=0.2.1,<0.6.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bumb dependency
+    "torchvision>=0.21.0,<0.23.0", # TODO: Bumb dependency
+
+    "draccus==0.10.0", # TODO: Remove ==
     "gymnasium>=1.1.1,<2.0.0",
     "rerun-sdk>=0.24.0,<0.27.0",
 
@@ -98,57 +95,43 @@ dependencies = [
 
 # Common
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
-placo-dep = ["placo>=0.9.6,<0.9.17"]
-transformers-dep = ["transformers>=5.3.0,<6.0.0"]
+placo-dep = ["placo>=0.9.6,<0.10.0"]
+transformers-dep = ["transformers>=4.57.1,<5.0.0"]
 grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
-can-dep = ["python-can>=4.2.0,<5.0.0"]
-peft-dep = ["peft>=0.18.0,<1.0.0"]
-scipy-dep = ["scipy>=1.14.0,<2.0.0"]
-qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
-matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
 
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
 dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"]
-damiao = ["lerobot[can-dep]"]
-robstride = ["lerobot[can-dep]"]
 
 # Robots
-openarms = ["lerobot[damiao]"]
 gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
 hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
 lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
 unitree_g1 = [
-    "unitree-sdk2==1.0.1",
     "pyzmq>=26.2.1,<28.0.0",
-    "onnxruntime>=1.16.0,<2.0.0",
-    "pin>=3.0.0,<4.0.0",
-    "meshcat>=0.3.0,<0.4.0",
-    "lerobot[matplotlib-dep]",
-    "lerobot[pygame-dep]",
-    "casadi>=3.6.0,<4.0.0",
+    "onnxruntime>=1.16.0,<2.0.0"
 ]
 reachy2 = ["reachy2_sdk>=1.0.15,<1.1.0"]
 kinematics = ["lerobot[placo-dep]"]
 intelrealsense = [
     "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'",
-    "pyrealsense2-macosx>=2.54,<2.57.0 ; sys_platform == 'darwin'",
+    "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'",
 ]
-phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0", "lerobot[scipy-dep]"]
+phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
 
 # Policies
 wallx = [
-    "lerobot[transformers-dep]",
-    "lerobot[peft]",
-    "lerobot[scipy-dep]",
-    "torchdiffeq>=0.2.4,<0.3.0",
-    "lerobot[qwen-vl-utils-dep]",
+    "transformers==4.49.0",
+    "peft==0.17.1",
+    "scipy==1.15.3",
+    "torchdiffeq==0.2.5",
+    "qwen_vl_utils==0.0.11"
 ]
-pi = ["lerobot[transformers-dep]", "lerobot[scipy-dep]"]
+pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi", "scipy>=1.10.1,<1.15"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]
 groot = [
     "lerobot[transformers-dep]",
-    "lerobot[peft]",
+    "peft>=0.13.0,<1.0.0",
     "dm-tree>=0.1.8,<1.0.0",
     "timm>=1.0.0,<1.1.0",
     "safetensors>=0.4.3,<1.0.0",
@@ -157,13 +140,13 @@ groot = [
     "ninja>=1.11.1,<2.0.0",
     "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
+sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "qwen-vl-utils>=0.0.14,<0.1.0"]
 xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
 # Features
-async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
-peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
+async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
+peft = ["lerobot[transformers-dep]", "peft>=0.18.0,<1.0.0"]
 
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
@@ -171,19 +154,13 @@ test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation
-# NOTE: Explicitly listing scipy helps flatten the dependecy tree.
-aloha = ["gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
+aloha = ["gym-aloha>=0.1.2,<0.2.0"]
 pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
-metaworld = ["metaworld==3.0.0", "lerobot[scipy-dep]"]
+libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0"]
+metaworld = ["metaworld==3.0.0"]
 
 # All
 all = [
-    # NOTE(resolver hint): scipy is pulled in transitively via lerobot[scipy-dep] through
-    # multiple extras (aloha, metaworld, pi, wallx, phone). Listing it explicitly
-    # helps pip's resolver converge by constraining scipy early, before it encounters
-    # the loose scipy requirements from transitive deps like dm-control and metaworld.
-    "scipy>=1.14.0,<2.0.0",
     "lerobot[dynamixel]",
     "lerobot[gamepad]",
     "lerobot[hopejr]",
@@ -191,8 +168,8 @@ all = [
     "lerobot[reachy2]",
     "lerobot[kinematics]",
     "lerobot[intelrealsense]",
-    "lerobot[wallx]",
-    "lerobot[pi]",
+    # "lerobot[wallx]",
+    # "lerobot[pi]", TODO(Pepijn): Update pi to transformers v5
     "lerobot[smolvla]",
     # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[xvla]",
@@ -204,11 +181,10 @@ all = [
     "lerobot[aloha]",
     "lerobot[pusht]",
     "lerobot[phone]",
-    "lerobot[libero]; sys_platform == 'linux'",
+    "lerobot[libero]",
     "lerobot[metaworld]",
     "lerobot[sarm]",
     "lerobot[peft]",
-    # "lerobot[unitree_g1]", TODO: Unitree requires specific installation instructions for unitree_sdk2
 ]
 
 [project.scripts]
@@ -227,17 +203,13 @@ lerobot-info="lerobot.scripts.lerobot_info:main"
 lerobot-find-joint-limits="lerobot.scripts.lerobot_find_joint_limits:main"
 lerobot-imgtransform-viz="lerobot.scripts.lerobot_imgtransform_viz:main"
 lerobot-edit-dataset="lerobot.scripts.lerobot_edit_dataset:main"
-lerobot-setup-can="lerobot.scripts.lerobot_setup_can:main"
 
 # ---------------- Tool Configurations ----------------
-[tool.setuptools.package-data]
-lerobot = ["envs/*.json"]
-
 [tool.setuptools.packages.find]
 where = ["src"]
 
 [tool.ruff]
-target-version = "py312"
+target-version = "py310"
 line-length = 110
 exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
 
@@ -306,7 +278,6 @@ default.extend-ignore-identifiers-re = [
     "thw",
     "inpt",
     "ROBOTIS",
-    "OT_VALUE"
 ]
 
 # TODO: Uncomment when ready to use
@@ -329,7 +300,7 @@ default.extend-ignore-identifiers-re = [
 # Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
 
 [tool.mypy]
-python_version = "3.12"
+python_version = "3.10"
 ignore_missing_imports = true
 follow_imports = "skip"
 # warn_return_any = true
@@ -381,9 +352,9 @@ ignore_errors = false
 module = "lerobot.cameras.*"
 ignore_errors = false
 
-[[tool.mypy.overrides]]
-module = "lerobot.motors.*"
-ignore_errors = false
+# [[tool.mypy.overrides]]
+# module = "lerobot.motors.*"
+# ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.robots.*"
@@ -413,3 +384,85 @@ ignore_errors = false
 # [[tool.mypy.overrides]]
 # module = "lerobot.scripts.*"
 # ignore_errors = false
+
+[tool.uv]
+# wallx requires transformers==4.49.0 which conflicts with other extras that need >=4.53.0
+conflicts = [
+    [
+        { extra = "wallx" },
+        { extra = "transformers-dep" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "pi" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "smolvla" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "groot" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "xvla" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "sarm" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "hilserl" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "libero" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "peft" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "all" },
+    ],
+    # pi uses custom branch which conflicts with transformers-dep
+    [
+        { extra = "pi" },
+        { extra = "transformers-dep" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "smolvla" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "groot" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "xvla" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "sarm" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "hilserl" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "libero" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "peft" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "all" },
+    ],
+]

requirements-macos.txt

@@ -1,73 +1,76 @@
 #
-# This file is autogenerated by pip-compile with Python 3.12
+# This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
 #    pip-compile --output-file=requirements-macos.txt requirements.in
 #
 -e .[all]
     # via -[all]
-absl-py==2.4.0
+absl-py==2.3.1
     # via
     #   dm-control
     #   dm-env
     #   dm-tree
     #   labmaze
     #   mujoco
-accelerate==1.13.0
+    #   tensorboard
+accelerate==1.11.0
     # via
     #   lerobot
     #   peft
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.13.3
+aiohttp==3.13.1
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
-annotated-doc==0.0.4
-    # via
-    #   fastapi
-    #   typer
 annotated-types==0.7.0
     # via pydantic
-anyio==4.12.1
+antlr4-python3-runtime==4.9.3
+    # via
+    #   hydra-core
+    #   omegaconf
+anyio==4.11.0
     # via
-    #   httpx
     #   starlette
     #   watchfiles
-asttokens==3.0.1
+asttokens==3.0.0
     # via stack-data
+async-timeout==5.0.1
+    # via aiohttp
 attrs==25.4.0
     # via
     #   aiohttp
     #   dm-tree
     #   jsonlines
+    #   jsonschema
+    #   referencing
     #   rerun-sdk
 av==15.1.0
+    # via lerobot
+bddl==1.0.1
+    # via libero
+certifi==2025.10.5
     # via
-    #   lerobot
-    #   qwen-vl-utils
-certifi==2026.2.25
-    # via
-    #   httpcore
-    #   httpx
     #   requests
     #   sentry-sdk
 cffi==2.0.0
     # via pymunk
-cfgv==3.5.0
+cfgv==3.4.0
     # via pre-commit
-charset-normalizer==3.4.5
+charset-normalizer==3.4.4
     # via requests
-click==8.3.1
+click==8.3.0
     # via
-    #   typer
     #   uvicorn
     #   wandb
-cloudpickle==3.1.2
-    # via gymnasium
-cmake==4.1.3
+cloudpickle==3.1.1
+    # via
+    #   gymnasium
+    #   libero
+cmake==4.1.0
     # via lerobot
-cmeel==0.59.0
+cmeel==0.57.3
     # via
     #   cmeel-assimp
     #   cmeel-boost
@@ -105,17 +108,15 @@ cmeel-zlib==1.3.1
     # via cmeel-assimp
 coal-library==3.0.1
     # via pin
-contourpy==1.3.3
-    # via
-    #   lerobot
-    #   matplotlib
-coverage[toml]==7.13.4
+contourpy==1.3.2
+    # via matplotlib
+coverage[toml]==7.11.0
     # via pytest-cov
 cycler==0.12.1
     # via matplotlib
-datasets==4.6.1
+datasets==4.1.1
     # via lerobot
-debugpy==1.8.20
+debugpy==1.8.17
     # via lerobot
 decorator==5.2.1
     # via ipython
@@ -129,7 +130,7 @@ dill==0.4.0
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.37
+dm-control==1.0.34
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -137,55 +138,69 @@ dm-tree==0.1.9
     # via
     #   dm-control
     #   dm-env
+    #   lerobot
 docopt==0.6.2
     # via num2words
 draccus==0.10.0
     # via lerobot
 dynamixel-sdk==3.8.4
     # via lerobot
+easydict==1.13
+    # via libero
+egl-probe @ git+https://github.com/huggingface/egl_probe.git
+    # via
+    #   libero
+    #   robomimic
 eigenpy==3.10.3
     # via coal-library
-einops==0.8.2
-    # via lerobot
-eiquadprog==1.2.9
-    # via placo
-etils[epath,epy]==1.14.0
-    # via mujoco
-executing==2.2.1
-    # via stack-data
-faker==34.0.2
-    # via lerobot
-farama-notifications==0.0.4
-    # via gymnasium
-fastapi==0.135.1
+einops==0.8.1
     # via
     #   lerobot
-    #   teleop
+    #   libero
+eiquadprog==1.2.9
+    # via placo
+etils[epath,epy]==1.13.0
+    # via mujoco
+exceptiongroup==1.3.0
+    # via
+    #   anyio
+    #   ipython
+    #   pytest
+executing==2.2.1
+    # via stack-data
+farama-notifications==0.0.4
+    # via gymnasium
+fastapi==0.119.1
+    # via teleop
+fastjsonschema==2.21.2
+    # via nbformat
 feetech-servo-sdk==1.0.0
     # via lerobot
-filelock==3.25.0
+filelock==3.20.0
     # via
     #   datasets
     #   diffusers
     #   huggingface-hub
-    #   python-discovery
     #   torch
+    #   transformers
     #   virtualenv
-fonttools==4.61.1
+fonttools==4.60.1
     # via matplotlib
 frozenlist==1.8.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2026.2.0
+fsspec[http]==2025.9.0
     # via
     #   datasets
     #   etils
     #   huggingface-hub
     #   torch
+future==1.0.0
+    # via libero
 gitdb==4.0.12
     # via gitpython
-gitpython==3.1.46
+gitpython==3.1.45
     # via wandb
 glfw==2.10.0
     # via
@@ -197,6 +212,7 @@ grpcio==1.73.1
     #   lerobot
     #   reachy2-sdk
     #   reachy2-sdk-api
+    #   tensorboard
 grpcio-tools==1.73.1
     # via
     #   lerobot
@@ -207,67 +223,71 @@ gym-hil==0.1.13
     # via lerobot
 gym-pusht==0.1.6
     # via lerobot
-gymnasium==1.2.3
+gymnasium==1.2.1
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
     #   lerobot
+    #   libero
     #   metaworld
 h11==0.16.0
-    # via
-    #   httpcore
-    #   uvicorn
+    # via uvicorn
+h5py==3.15.1
+    # via robomimic
 hebi-py==2.11.0
     # via lerobot
-hf-xet==1.3.2
+hf-transfer==0.1.9
+    # via huggingface-hub
+hf-xet==1.1.10
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
-httpcore==1.0.9
-    # via httpx
 httptools==0.7.1
     # via uvicorn
-httpx==0.28.1
-    # via
-    #   datasets
-    #   huggingface-hub
-huggingface-hub==1.6.0
+huggingface-hub[cli,hf-transfer]==0.35.3
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
     #   peft
+    #   timm
     #   tokenizers
     #   transformers
-identify==2.6.17
+hydra-core==1.3.2
+    # via libero
+identify==2.6.15
     # via pre-commit
 idna==3.11
     # via
     #   anyio
-    #   httpx
     #   requests
     #   yarl
-imageio[ffmpeg]==2.37.2
+imageio[ffmpeg]==2.37.0
     # via
     #   gym-aloha
     #   gym-hil
     #   lerobot
     #   metaworld
+    #   robomimic
     #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via imageio
-importlib-metadata==8.7.1
+    # via
+    #   imageio
+    #   robomimic
+importlib-metadata==8.7.0
     # via diffusers
+importlib-resources==6.5.2
+    # via etils
 iniconfig==2.3.0
     # via pytest
-ipython==9.11.0
+inquirerpy==0.3.4
+    # via huggingface-hub
+ipython==8.37.0
     # via meshcat
-ipython-pygments-lexers==1.1.1
-    # via ipython
 ischedule==1.2.7
     # via placo
 jedi==0.19.2
@@ -276,24 +296,44 @@ jinja2==3.1.6
     # via torch
 jsonlines==4.0.0
     # via lerobot
+jsonschema==4.25.1
+    # via nbformat
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+jupyter-core==5.9.1
+    # via nbformat
+jupytext==1.18.1
+    # via bddl
 kiwisolver==1.4.9
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
-lazy-loader==0.5
+lazy-loader==0.4
     # via scikit-image
-librt==0.8.1
-    # via mypy
+libero @ git+https://github.com/huggingface/lerobot-libero.git@main
+    # via lerobot
+llvmlite==0.45.1
+    # via numba
 lxml==6.0.2
     # via dm-control
+markdown==3.9
+    # via tensorboard
 markdown-it-py==4.0.0
-    # via rich
+    # via
+    #   jupytext
+    #   mdit-py-plugins
 markupsafe==3.0.3
-    # via jinja2
-matplotlib==3.10.8
-    # via lerobot
+    # via
+    #   jinja2
+    #   werkzeug
+matplotlib==3.10.7
+    # via
+    #   lerobot
+    #   libero
 matplotlib-inline==0.2.1
     # via ipython
+mdit-py-plugins==0.5.0
+    # via jupytext
 mdurl==0.1.2
     # via markdown-it-py
 mergedeep==1.3.4
@@ -306,35 +346,41 @@ mock-serial==0.0.1
     # via lerobot
 mpmath==1.3.0
     # via sympy
-mujoco==3.5.0
+mujoco==3.3.7
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
+    #   libero
     #   metaworld
-multidict==6.7.1
+    #   robosuite
+multidict==6.7.0
     # via
     #   aiohttp
     #   yarl
-multiprocess==0.70.18
+multiprocess==0.70.16
     # via datasets
-mypy==1.19.1
-    # via lerobot
 mypy-extensions==1.1.0
+    # via typing-inspect
+nbformat==5.10.4
+    # via jupytext
+networkx==3.4.2
     # via
-    #   mypy
-    #   typing-inspect
-networkx==3.6.1
-    # via
+    #   bddl
     #   scikit-image
     #   torch
-nodeenv==1.10.0
+ninja==1.13.0
+    # via lerobot
+nodeenv==1.9.1
     # via pre-commit
 num2words==0.5.14
     # via lerobot
+numba==0.62.1
+    # via robosuite
 numpy==2.2.6
     # via
     #   accelerate
+    #   bddl
     #   cmeel-boost
     #   contourpy
     #   datasets
@@ -343,14 +389,16 @@ numpy==2.2.6
     #   dm-env
     #   dm-tree
     #   gymnasium
+    #   h5py
     #   hebi-py
     #   imageio
     #   labmaze
-    #   lerobot
+    #   libero
     #   matplotlib
     #   meshcat
     #   metaworld
     #   mujoco
+    #   numba
     #   opencv-python
     #   opencv-python-headless
     #   pandas
@@ -358,18 +406,26 @@ numpy==2.2.6
     #   pyquaternion
     #   reachy2-sdk
     #   rerun-sdk
+    #   robomimic
+    #   robosuite
     #   scikit-image
     #   scipy
     #   shapely
     #   teleop
+    #   tensorboard
+    #   tensorboardx
     #   tifffile
     #   torchvision
     #   transformers
     #   transforms3d
-opencv-python==4.13.0.92
+omegaconf==2.3.0
+    # via hydra-core
+opencv-python==4.12.0.88
     # via
     #   gym-pusht
+    #   libero
     #   reachy2-sdk
+    #   robosuite
 opencv-python-headless==4.12.0.88
     # via lerobot
 orderly-set==5.5.0
@@ -379,87 +435,97 @@ packaging==25.0
     #   accelerate
     #   datasets
     #   huggingface-hub
+    #   hydra-core
+    #   jupytext
     #   lazy-loader
     #   lerobot
     #   matplotlib
     #   peft
     #   pytest
-    #   qwen-vl-utils
     #   reachy2-sdk
     #   scikit-image
+    #   tensorboard
+    #   tensorboardx
     #   transformers
     #   wandb
 pandas==2.3.3
     # via
     #   datasets
     #   lerobot
-parso==0.8.6
+parso==0.8.5
     # via jedi
-pathspec==1.0.4
-    # via mypy
-peft==0.18.1
+peft==0.17.1
     # via lerobot
 pexpect==4.9.0
     # via ipython
-pillow==12.1.1
+pfzy==0.3.4
+    # via inquirerpy
+pillow==12.0.0
     # via
     #   diffusers
     #   imageio
+    #   lerobot
     #   matplotlib
     #   meshcat
-    #   qwen-vl-utils
     #   rerun-sdk
+    #   robosuite
     #   scikit-image
+    #   tensorboard
     #   torchvision
 pin==3.4.0
     # via placo
-placo==0.9.16
+placo==0.9.14
     # via lerobot
-platformdirs==4.9.4
+platformdirs==4.5.0
     # via
-    #   python-discovery
+    #   jupyter-core
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.5.1
+pre-commit==4.3.0
     # via lerobot
 prompt-toolkit==3.0.52
-    # via ipython
+    # via
+    #   inquirerpy
+    #   ipython
 propcache==0.4.1
     # via
     #   aiohttp
     #   yarl
-protobuf==6.31.1
+protobuf==6.31.0
     # via
     #   dm-control
     #   grpcio-tools
     #   lerobot
     #   reachy2-sdk
     #   reachy2-sdk-api
+    #   tensorboard
+    #   tensorboardx
     #   wandb
-psutil==7.2.2
+psutil==7.1.1
     # via
     #   accelerate
     #   imageio
     #   peft
+    #   robomimic
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
     # via stack-data
-pyarrow==23.0.1
+pyarrow==21.0.0
     # via
     #   datasets
     #   rerun-sdk
-pycparser==3.0
+pycparser==2.23
     # via cffi
-pydantic==2.12.5
+pydantic==2.12.3
     # via
     #   fastapi
     #   wandb
-pydantic-core==2.41.5
+pydantic-core==2.41.4
     # via pydantic
 pygame==2.6.1
     # via
@@ -469,35 +535,33 @@ pygame==2.6.1
 pygments==2.19.2
     # via
     #   ipython
-    #   ipython-pygments-lexers
     #   pytest
-    #   rich
 pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.5.1
+pyngrok==7.4.1
     # via meshcat
 pynput==1.8.1
     # via
     #   gym-hil
     #   lerobot
-pyobjc-core==12.1
+pyobjc-core==12.0
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-cocoa
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-applicationservices==12.1
+pyobjc-framework-applicationservices==12.0
     # via pynput
-pyobjc-framework-cocoa==12.1
+pyobjc-framework-cocoa==12.0
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-coretext==12.1
+pyobjc-framework-coretext==12.0
     # via pyobjc-framework-applicationservices
-pyobjc-framework-quartz==12.1
+pyobjc-framework-quartz==12.0
     # via
     #   pynput
     #   pyobjc-framework-applicationservices
@@ -506,13 +570,13 @@ pyopengl==3.1.10
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.3.2
+pyparsing==3.2.5
     # via
     #   dm-control
     #   matplotlib
 pyquaternion==0.9.9
     # via reachy2-sdk
-pyrealsense2-macosx==2.56.5
+pyrealsense2-macosx==2.54.2
     # via lerobot
 pyserial==3.5
     # via
@@ -521,6 +585,7 @@ pyserial==3.5
     #   lerobot
 pytest==8.4.2
     # via
+    #   bddl
     #   lerobot
     #   pytest-cov
     #   pytest-timeout
@@ -531,14 +596,11 @@ pytest-timeout==2.4.0
     # via lerobot
 python-dateutil==2.9.0.post0
     # via
-    #   faker
     #   matplotlib
     #   pandas
-python-discovery==1.1.1
-    # via virtualenv
-python-dotenv==1.2.2
+python-dotenv==1.1.1
     # via uvicorn
-pytz==2026.1.post1
+pytz==2025.2
     # via pandas
 pyyaml==6.0.3
     # via
@@ -547,10 +609,13 @@ pyyaml==6.0.3
     #   draccus
     #   hebi-py
     #   huggingface-hub
+    #   jupytext
+    #   omegaconf
     #   peft
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
+    #   timm
     #   transformers
     #   uvicorn
     #   wandb
@@ -560,13 +625,15 @@ pyzmq==27.1.0
     # via
     #   lerobot
     #   meshcat
-qwen-vl-utils==0.0.14
-    # via lerobot
-reachy2-sdk==1.0.15
+reachy2-sdk==1.0.14
     # via lerobot
 reachy2-sdk-api==1.0.21
     # via reachy2-sdk
-regex==2026.2.28
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+regex==2025.10.23
     # via
     #   diffusers
     #   transformers
@@ -575,150 +642,184 @@ requests==2.32.5
     #   datasets
     #   diffusers
     #   dm-control
-    #   qwen-vl-utils
+    #   huggingface-hub
     #   teleop
+    #   transformers
     #   wandb
-rerun-sdk==0.26.2
+rerun-sdk==0.26.1
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
-rich==14.3.3
-    # via typer
-safetensors==0.7.0
+robomimic==0.2.0
+    # via libero
+robosuite==1.4.0
+    # via libero
+rpds-py==0.28.0
+    # via
+    #   jsonschema
+    #   referencing
+safetensors==0.6.2
     # via
     #   accelerate
     #   diffusers
     #   lerobot
     #   peft
+    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
     #   gym-pusht
     #   lerobot
-scipy==1.17.1
+scipy==1.15.3
     # via
     #   dm-control
-    #   lerobot
     #   metaworld
+    #   robosuite
     #   scikit-image
-    #   torchdiffeq
-sentry-sdk==2.54.0
+sentry-sdk==2.42.1
     # via wandb
 shapely==2.1.2
     # via gym-pusht
-shellingham==1.5.4
-    # via typer
 six==1.17.0
     # via
     #   pynput
     #   python-dateutil
-smmap==5.0.3
+smmap==5.0.2
     # via gitdb
+sniffio==1.3.1
+    # via anyio
 stack-data==0.6.3
     # via ipython
-starlette==0.52.1
+starlette==0.48.0
     # via fastapi
 sympy==1.14.0
     # via torch
-teleop==0.1.4
+teleop==0.1.2
     # via lerobot
-termcolor==3.3.0
-    # via lerobot
-tifffile==2026.3.3
+tensorboard==2.20.0
+    # via robomimic
+tensorboard-data-server==0.7.2
+    # via tensorboard
+tensorboardx==2.6.4
+    # via robomimic
+termcolor==3.1.0
+    # via
+    #   lerobot
+    #   robomimic
+thop==0.1.1.post2209072238
+    # via libero
+tifffile==2025.5.10
     # via scikit-image
-tokenizers==0.22.2
+timm==1.0.20
+    # via lerobot
+tokenizers==0.22.1
     # via transformers
 toml==0.10.2
     # via draccus
-torch==2.10.0
+tomli==2.3.0
+    # via
+    #   cmeel
+    #   coverage
+    #   jupytext
+    #   pytest
+torch==2.7.1
     # via
     #   accelerate
     #   lerobot
     #   peft
-    #   torchdiffeq
+    #   robomimic
+    #   thop
+    #   timm
     #   torchvision
-torchcodec==0.10.0
+torchcodec==0.5
     # via lerobot
-torchdiffeq==0.2.5
-    # via lerobot
-torchvision==0.25.0
-    # via lerobot
-tornado==6.5.4
+torchvision==0.22.1
+    # via
+    #   lerobot
+    #   robomimic
+    #   timm
+tornado==6.5.2
     # via meshcat
-tqdm==4.67.3
+tqdm==4.67.1
     # via
     #   datasets
     #   dm-control
     #   huggingface-hub
     #   peft
+    #   robomimic
     #   transformers
 traitlets==5.14.3
     # via
     #   ipython
+    #   jupyter-core
     #   matplotlib-inline
-transformers==5.3.0
+    #   nbformat
+transformers==4.57.1
     # via
     #   lerobot
+    #   libero
     #   peft
 transforms3d==0.4.2
     # via teleop
-typer==0.24.1
-    # via
-    #   huggingface-hub
-    #   transformers
 typing-extensions==4.15.0
     # via
     #   aiosignal
     #   anyio
     #   etils
-    #   faker
+    #   exceptiongroup
     #   fastapi
     #   gymnasium
     #   huggingface-hub
-    #   mypy
+    #   ipython
+    #   multidict
     #   pydantic
     #   pydantic-core
+    #   referencing
     #   rerun-sdk
     #   starlette
     #   torch
     #   typing-inspect
     #   typing-inspection
+    #   uvicorn
+    #   virtualenv
     #   wandb
 typing-inspect==0.9.0
     # via draccus
 typing-inspection==0.4.2
-    # via
-    #   fastapi
-    #   pydantic
-tzdata==2025.3
+    # via pydantic
+tzdata==2025.2
     # via pandas
 u-msgpack-python==2.8.0
     # via meshcat
-urllib3==2.6.3
+urllib3==2.5.0
     # via
     #   requests
     #   sentry-sdk
-uvicorn[standard]==0.41.0
+uvicorn[standard]==0.38.0
     # via teleop
 uvloop==0.22.1
     # via uvicorn
-virtualenv==21.1.0
+virtualenv==20.35.3
     # via pre-commit
-wandb==0.24.2
-    # via lerobot
+wandb==0.21.4
+    # via
+    #   lerobot
+    #   libero
 watchfiles==1.1.1
     # via uvicorn
-wcwidth==0.6.0
+wcwidth==0.2.14
     # via prompt-toolkit
 websocket-client==1.9.0
     # via teleop
-websockets==16.0
+websockets==15.0.1
     # via uvicorn
-wrapt==2.1.2
+werkzeug==3.1.3
+    # via tensorboard
+wrapt==2.0.0
     # via dm-tree
 xxhash==3.6.0
     # via datasets
-yarl==1.23.0
+yarl==1.22.0
     # via aiohttp
 zipp==3.23.0
     # via

requirements-ubuntu.txt

@@ -1,12 +1,12 @@
 #
-# This file is autogenerated by pip-compile with Python 3.12
+# This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
 #    pip-compile --output-file=requirements-ubuntu.txt requirements.in
 #
 -e .[all]
     # via -[all]
-absl-py==2.4.0
+absl-py==2.3.1
     # via
     #   dm-control
     #   dm-env
@@ -14,33 +14,30 @@ absl-py==2.4.0
     #   labmaze
     #   mujoco
     #   tensorboard
-accelerate==1.13.0
+accelerate==1.11.0
     # via
     #   lerobot
     #   peft
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.13.3
+aiohttp==3.13.1
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
-annotated-doc==0.0.4
-    # via
-    #   fastapi
-    #   typer
 annotated-types==0.7.0
     # via pydantic
 antlr4-python3-runtime==4.9.3
     # via
     #   hydra-core
     #   omegaconf
-anyio==4.12.1
+anyio==4.11.0
     # via
-    #   httpx
     #   starlette
     #   watchfiles
-asttokens==3.0.1
+asttokens==3.0.0
     # via stack-data
+async-timeout==5.0.1
+    # via aiohttp
 attrs==25.4.0
     # via
     #   aiohttp
@@ -50,35 +47,30 @@ attrs==25.4.0
     #   referencing
     #   rerun-sdk
 av==15.1.0
-    # via
-    #   lerobot
-    #   qwen-vl-utils
+    # via lerobot
 bddl==1.0.1
-    # via hf-libero
-certifi==2026.2.25
+    # via libero
+certifi==2025.10.5
     # via
-    #   httpcore
-    #   httpx
     #   requests
     #   sentry-sdk
 cffi==2.0.0
     # via pymunk
-cfgv==3.5.0
+cfgv==3.4.0
     # via pre-commit
-charset-normalizer==3.4.5
+charset-normalizer==3.4.4
     # via requests
-click==8.3.1
+click==8.3.0
     # via
-    #   typer
     #   uvicorn
     #   wandb
-cloudpickle==3.1.2
+cloudpickle==3.1.1
     # via
     #   gymnasium
-    #   hf-libero
-cmake==4.1.3
+    #   libero
+cmake==4.1.0
     # via lerobot
-cmeel==0.59.0
+cmeel==0.57.3
     # via
     #   cmeel-assimp
     #   cmeel-boost
@@ -116,24 +108,20 @@ cmeel-zlib==1.3.1
     # via cmeel-assimp
 coal-library==3.0.1
     # via pin
-contourpy==1.3.3
-    # via
-    #   lerobot
-    #   matplotlib
-coverage[toml]==7.13.4
+contourpy==1.3.2
+    # via matplotlib
+coverage[toml]==7.11.0
     # via pytest-cov
-cuda-bindings==12.9.4
-    # via torch
-cuda-pathfinder==1.4.1
-    # via cuda-bindings
 cycler==0.12.1
     # via matplotlib
-datasets==4.6.1
+datasets==4.1.1
     # via lerobot
-debugpy==1.8.20
+debugpy==1.8.17
     # via lerobot
 decorator==5.2.1
     # via ipython
+decord==0.6.0
+    # via lerobot
 deepdiff==8.6.1
     # via lerobot
 diffusers==0.35.2
@@ -144,7 +132,7 @@ dill==0.4.0
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.37
+dm-control==1.0.34
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -152,6 +140,7 @@ dm-tree==0.1.9
     # via
     #   dm-control
     #   dm-env
+    #   lerobot
 docopt==0.6.2
     # via num2words
 draccus==0.10.0
@@ -159,60 +148,66 @@ draccus==0.10.0
 dynamixel-sdk==3.8.4
     # via lerobot
 easydict==1.13
-    # via hf-libero
-egl-probe==1.0.2
-    # via robomimic
+    # via libero
+egl-probe @ git+https://github.com/huggingface/egl_probe.git
+    # via
+    #   libero
+    #   robomimic
 eigenpy==3.10.3
     # via coal-library
-einops==0.8.2
+einops==0.8.1
     # via
-    #   hf-libero
+    #   flash-attn
     #   lerobot
+    #   libero
 eiquadprog==1.2.9
     # via placo
-etils[epath,epy]==1.14.0
+etils[epath,epy]==1.13.0
     # via mujoco
-evdev==1.9.3
+evdev==1.9.2
     # via pynput
+exceptiongroup==1.3.0
+    # via
+    #   anyio
+    #   ipython
+    #   pytest
 executing==2.2.1
     # via stack-data
-faker==34.0.2
-    # via lerobot
 farama-notifications==0.0.4
     # via gymnasium
-fastapi==0.135.1
-    # via
-    #   lerobot
-    #   teleop
+fastapi==0.119.1
+    # via teleop
 fastjsonschema==2.21.2
     # via nbformat
 feetech-servo-sdk==1.0.0
     # via lerobot
-filelock==3.25.0
+filelock==3.20.0
     # via
     #   datasets
     #   diffusers
     #   huggingface-hub
-    #   python-discovery
     #   torch
+    #   transformers
     #   virtualenv
-fonttools==4.61.1
+flash-attn==2.8.3
+    # via lerobot
+fonttools==4.60.1
     # via matplotlib
 frozenlist==1.8.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2026.2.0
+fsspec[http]==2025.9.0
     # via
     #   datasets
     #   etils
     #   huggingface-hub
     #   torch
 future==1.0.0
-    # via hf-libero
+    # via libero
 gitdb==4.0.12
     # via gitpython
-gitpython==3.1.46
+gitpython==3.1.45
     # via wandb
 glfw==2.10.0
     # via
@@ -235,60 +230,50 @@ gym-hil==0.1.13
     # via lerobot
 gym-pusht==0.1.6
     # via lerobot
-gymnasium==1.2.3
+gymnasium==1.2.1
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
-    #   hf-libero
     #   lerobot
+    #   libero
     #   metaworld
 h11==0.16.0
-    # via
-    #   httpcore
-    #   uvicorn
-h5py==3.16.0
+    # via uvicorn
+h5py==3.15.1
     # via robomimic
 hebi-py==2.11.0
     # via lerobot
-hf-egl-probe==1.0.2
-    # via hf-libero
-hf-libero==0.1.3
-    # via lerobot
-hf-xet==1.3.2
+hf-transfer==0.1.9
+    # via huggingface-hub
+hf-xet==1.1.10
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
-httpcore==1.0.9
-    # via httpx
 httptools==0.7.1
     # via uvicorn
-httpx==0.28.1
-    # via
-    #   datasets
-    #   huggingface-hub
-huggingface-hub==1.6.0
+huggingface-hub[cli,hf-transfer]==0.35.3
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
     #   peft
+    #   timm
     #   tokenizers
     #   transformers
 hydra-core==1.3.2
-    # via hf-libero
-identify==2.6.17
+    # via libero
+identify==2.6.15
     # via pre-commit
 idna==3.11
     # via
     #   anyio
-    #   httpx
     #   requests
     #   yarl
-imageio[ffmpeg]==2.37.2
+imageio[ffmpeg]==2.37.0
     # via
     #   gym-aloha
     #   gym-hil
@@ -300,14 +285,16 @@ imageio-ffmpeg==0.6.0
     # via
     #   imageio
     #   robomimic
-importlib-metadata==8.7.1
+importlib-metadata==8.7.0
     # via diffusers
+importlib-resources==6.5.2
+    # via etils
 iniconfig==2.3.0
     # via pytest
-ipython==9.11.0
+inquirerpy==0.3.4
+    # via huggingface-hub
+ipython==8.37.0
     # via meshcat
-ipython-pygments-lexers==1.1.1
-    # via ipython
 ischedule==1.2.7
     # via placo
 jedi==0.19.2
@@ -316,41 +303,40 @@ jinja2==3.1.6
     # via torch
 jsonlines==4.0.0
     # via lerobot
-jsonschema==4.26.0
+jsonschema==4.25.1
     # via nbformat
 jsonschema-specifications==2025.9.1
     # via jsonschema
 jupyter-core==5.9.1
     # via nbformat
-jupytext==1.19.1
+jupytext==1.18.1
     # via bddl
 kiwisolver==1.4.9
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
-lazy-loader==0.5
+lazy-loader==0.4
     # via scikit-image
-librt==0.8.1
-    # via mypy
-llvmlite==0.46.0
+libero @ git+https://github.com/huggingface/lerobot-libero.git@main
+    # via lerobot
+llvmlite==0.45.1
     # via numba
 lxml==6.0.2
     # via dm-control
-markdown==3.10.2
+markdown==3.9
     # via tensorboard
 markdown-it-py==4.0.0
     # via
     #   jupytext
     #   mdit-py-plugins
-    #   rich
 markupsafe==3.0.3
     # via
     #   jinja2
     #   werkzeug
-matplotlib==3.10.8
+matplotlib==3.10.7
     # via
-    #   hf-libero
     #   lerobot
+    #   libero
 matplotlib-inline==0.2.1
     # via ipython
 mdit-py-plugins==0.5.0
@@ -367,38 +353,36 @@ mock-serial==0.0.1
     # via lerobot
 mpmath==1.3.0
     # via sympy
-mujoco==3.5.0
+mujoco==3.3.7
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
-    #   hf-libero
+    #   libero
     #   metaworld
     #   robosuite
-multidict==6.7.1
+multidict==6.7.0
     # via
     #   aiohttp
     #   yarl
-multiprocess==0.70.18
+multiprocess==0.70.16
     # via datasets
-mypy==1.19.1
-    # via lerobot
 mypy-extensions==1.1.0
-    # via
-    #   mypy
-    #   typing-inspect
+    # via typing-inspect
 nbformat==5.10.4
     # via jupytext
-networkx==3.6.1
+networkx==3.4.2
     # via
     #   bddl
     #   scikit-image
     #   torch
-nodeenv==1.10.0
+ninja==1.13.0
+    # via lerobot
+nodeenv==1.9.1
     # via pre-commit
 num2words==0.5.14
     # via lerobot
-numba==0.64.0
+numba==0.62.1
     # via robosuite
 numpy==2.2.6
     # via
@@ -407,6 +391,7 @@ numpy==2.2.6
     #   cmeel-boost
     #   contourpy
     #   datasets
+    #   decord
     #   diffusers
     #   dm-control
     #   dm-env
@@ -414,10 +399,9 @@ numpy==2.2.6
     #   gymnasium
     #   h5py
     #   hebi-py
-    #   hf-libero
     #   imageio
     #   labmaze
-    #   lerobot
+    #   libero
     #   matplotlib
     #   meshcat
     #   metaworld
@@ -442,51 +426,49 @@ numpy==2.2.6
     #   torchvision
     #   transformers
     #   transforms3d
-nvidia-cublas-cu12==12.8.4.1
+nvidia-cublas-cu12==12.6.4.1
     # via
     #   nvidia-cudnn-cu12
     #   nvidia-cusolver-cu12
     #   torch
-nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-cupti-cu12==12.6.80
     # via torch
-nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-nvrtc-cu12==12.6.77
     # via torch
-nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cuda-runtime-cu12==12.6.77
     # via torch
-nvidia-cudnn-cu12==9.10.2.21
+nvidia-cudnn-cu12==9.5.1.17
     # via torch
-nvidia-cufft-cu12==11.3.3.83
+nvidia-cufft-cu12==11.3.0.4
     # via torch
-nvidia-cufile-cu12==1.13.1.3
+nvidia-cufile-cu12==1.11.1.6
     # via torch
-nvidia-curand-cu12==10.3.9.90
+nvidia-curand-cu12==10.3.7.77
     # via torch
-nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusolver-cu12==11.7.1.2
     # via torch
-nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparse-cu12==12.5.4.2
     # via
     #   nvidia-cusolver-cu12
     #   torch
-nvidia-cusparselt-cu12==0.7.1
+nvidia-cusparselt-cu12==0.6.3
     # via torch
-nvidia-nccl-cu12==2.27.5
+nvidia-nccl-cu12==2.26.2
     # via torch
-nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvjitlink-cu12==12.6.85
     # via
     #   nvidia-cufft-cu12
     #   nvidia-cusolver-cu12
     #   nvidia-cusparse-cu12
     #   torch
-nvidia-nvshmem-cu12==3.4.5
-    # via torch
-nvidia-nvtx-cu12==12.8.90
+nvidia-nvtx-cu12==12.6.77
     # via torch
 omegaconf==2.3.0
     # via hydra-core
-opencv-python==4.13.0.92
+opencv-python==4.12.0.88
     # via
     #   gym-pusht
-    #   hf-libero
+    #   libero
     #   reachy2-sdk
     #   robosuite
 opencv-python-headless==4.12.0.88
@@ -505,7 +487,6 @@ packaging==25.0
     #   matplotlib
     #   peft
     #   pytest
-    #   qwen-vl-utils
     #   reachy2-sdk
     #   scikit-image
     #   tensorboard
@@ -516,21 +497,21 @@ pandas==2.3.3
     # via
     #   datasets
     #   lerobot
-parso==0.8.6
+parso==0.8.5
     # via jedi
-pathspec==1.0.4
-    # via mypy
-peft==0.18.1
+peft==0.17.1
     # via lerobot
 pexpect==4.9.0
     # via ipython
-pillow==12.1.1
+pfzy==0.3.4
+    # via inquirerpy
+pillow==12.0.0
     # via
     #   diffusers
     #   imageio
+    #   lerobot
     #   matplotlib
     #   meshcat
-    #   qwen-vl-utils
     #   rerun-sdk
     #   robosuite
     #   scikit-image
@@ -538,27 +519,28 @@ pillow==12.1.1
     #   torchvision
 pin==3.4.0
     # via placo
-placo==0.9.16
+placo==0.9.14
     # via lerobot
-platformdirs==4.9.4
+platformdirs==4.5.0
     # via
     #   jupyter-core
-    #   python-discovery
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.5.1
+pre-commit==4.3.0
     # via lerobot
 prompt-toolkit==3.0.52
-    # via ipython
+    # via
+    #   inquirerpy
+    #   ipython
 propcache==0.4.1
     # via
     #   aiohttp
     #   yarl
-protobuf==6.31.1
+protobuf==6.31.0
     # via
     #   dm-control
     #   grpcio-tools
@@ -568,7 +550,7 @@ protobuf==6.31.1
     #   tensorboard
     #   tensorboardx
     #   wandb
-psutil==7.2.2
+psutil==7.1.1
     # via
     #   accelerate
     #   imageio
@@ -578,17 +560,17 @@ ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
     # via stack-data
-pyarrow==23.0.1
+pyarrow==21.0.0
     # via
     #   datasets
     #   rerun-sdk
-pycparser==3.0
+pycparser==2.23
     # via cffi
-pydantic==2.12.5
+pydantic==2.12.3
     # via
     #   fastapi
     #   wandb
-pydantic-core==2.41.5
+pydantic-core==2.41.4
     # via pydantic
 pygame==2.6.1
     # via
@@ -598,14 +580,12 @@ pygame==2.6.1
 pygments==2.19.2
     # via
     #   ipython
-    #   ipython-pygments-lexers
     #   pytest
-    #   rich
 pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.5.1
+pyngrok==7.4.1
     # via meshcat
 pynput==1.8.1
     # via
@@ -615,7 +595,7 @@ pyopengl==3.1.10
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.3.2
+pyparsing==3.2.5
     # via
     #   dm-control
     #   matplotlib
@@ -641,16 +621,13 @@ pytest-timeout==2.4.0
     # via lerobot
 python-dateutil==2.9.0.post0
     # via
-    #   faker
     #   matplotlib
     #   pandas
-python-discovery==1.1.1
-    # via virtualenv
-python-dotenv==1.2.2
+python-dotenv==1.1.1
     # via uvicorn
 python-xlib==0.33
     # via pynput
-pytz==2026.1.post1
+pytz==2025.2
     # via pandas
 pyyaml==6.0.3
     # via
@@ -665,6 +642,7 @@ pyyaml==6.0.3
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
+    #   timm
     #   transformers
     #   uvicorn
     #   wandb
@@ -674,9 +652,7 @@ pyzmq==27.1.0
     # via
     #   lerobot
     #   meshcat
-qwen-vl-utils==0.0.14
-    # via lerobot
-reachy2-sdk==1.0.15
+reachy2-sdk==1.0.14
     # via lerobot
 reachy2-sdk-api==1.0.21
     # via reachy2-sdk
@@ -684,7 +660,7 @@ referencing==0.37.0
     # via
     #   jsonschema
     #   jsonschema-specifications
-regex==2026.2.28
+regex==2025.10.23
     # via
     #   diffusers
     #   transformers
@@ -693,62 +669,60 @@ requests==2.32.5
     #   datasets
     #   diffusers
     #   dm-control
-    #   qwen-vl-utils
+    #   huggingface-hub
     #   teleop
+    #   transformers
     #   wandb
-rerun-sdk==0.26.2
+rerun-sdk==0.26.1
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
-rich==14.3.3
-    # via typer
 robomimic==0.2.0
-    # via hf-libero
+    # via libero
 robosuite==1.4.0
-    # via hf-libero
-rpds-py==0.30.0
+    # via libero
+rpds-py==0.28.0
     # via
     #   jsonschema
     #   referencing
-safetensors==0.7.0
+safetensors==0.6.2
     # via
     #   accelerate
     #   diffusers
     #   lerobot
     #   peft
+    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
     #   gym-pusht
     #   lerobot
-scipy==1.17.1
+scipy==1.15.3
     # via
     #   dm-control
-    #   lerobot
     #   metaworld
     #   robosuite
     #   scikit-image
-    #   torchdiffeq
-sentry-sdk==2.54.0
+sentry-sdk==2.42.1
     # via wandb
 shapely==2.1.2
     # via gym-pusht
-shellingham==1.5.4
-    # via typer
 six==1.17.0
     # via
     #   pynput
     #   python-dateutil
     #   python-xlib
-smmap==5.0.3
+smmap==5.0.2
     # via gitdb
+sniffio==1.3.1
+    # via anyio
 stack-data==0.6.3
     # via ipython
-starlette==0.52.1
+starlette==0.48.0
     # via fastapi
 sympy==1.14.0
     # via torch
-teleop==0.1.4
+teleop==0.1.2
     # via lerobot
 tensorboard==2.20.0
     # via robomimic
@@ -756,38 +730,46 @@ tensorboard-data-server==0.7.2
     # via tensorboard
 tensorboardx==2.6.4
     # via robomimic
-termcolor==3.3.0
+termcolor==3.1.0
     # via
     #   lerobot
     #   robomimic
 thop==0.1.1.post2209072238
-    # via hf-libero
-tifffile==2026.3.3
+    # via libero
+tifffile==2025.5.10
     # via scikit-image
-tokenizers==0.22.2
+timm==1.0.20
+    # via lerobot
+tokenizers==0.22.1
     # via transformers
 toml==0.10.2
     # via draccus
-torch==2.10.0
+tomli==2.3.0
+    # via
+    #   cmeel
+    #   coverage
+    #   jupytext
+    #   pytest
+torch==2.7.1
     # via
     #   accelerate
+    #   flash-attn
     #   lerobot
     #   peft
     #   robomimic
     #   thop
-    #   torchdiffeq
+    #   timm
     #   torchvision
-torchcodec==0.10.0
+torchcodec==0.5
     # via lerobot
-torchdiffeq==0.2.5
-    # via lerobot
-torchvision==0.25.0
+torchvision==0.22.1
     # via
     #   lerobot
     #   robomimic
-tornado==6.5.4
+    #   timm
+tornado==6.5.2
     # via meshcat
-tqdm==4.67.3
+tqdm==4.67.1
     # via
     #   datasets
     #   dm-control
@@ -801,29 +783,26 @@ traitlets==5.14.3
     #   jupyter-core
     #   matplotlib-inline
     #   nbformat
-transformers==5.3.0
+transformers==4.57.1
     # via
-    #   hf-libero
     #   lerobot
+    #   libero
     #   peft
 transforms3d==0.4.2
     # via teleop
-triton==3.6.0
+triton==3.3.1
     # via torch
-typer==0.24.1
-    # via
-    #   huggingface-hub
-    #   transformers
 typing-extensions==4.15.0
     # via
     #   aiosignal
     #   anyio
     #   etils
-    #   faker
+    #   exceptiongroup
     #   fastapi
     #   gymnasium
     #   huggingface-hub
-    #   mypy
+    #   ipython
+    #   multidict
     #   pydantic
     #   pydantic-core
     #   referencing
@@ -832,46 +811,46 @@ typing-extensions==4.15.0
     #   torch
     #   typing-inspect
     #   typing-inspection
+    #   uvicorn
+    #   virtualenv
     #   wandb
 typing-inspect==0.9.0
     # via draccus
 typing-inspection==0.4.2
-    # via
-    #   fastapi
-    #   pydantic
-tzdata==2025.3
+    # via pydantic
+tzdata==2025.2
     # via pandas
 u-msgpack-python==2.8.0
     # via meshcat
-urllib3==2.6.3
+urllib3==2.5.0
     # via
     #   requests
     #   sentry-sdk
-uvicorn[standard]==0.41.0
+uvicorn[standard]==0.38.0
     # via teleop
 uvloop==0.22.1
     # via uvicorn
-virtualenv==21.1.0
+virtualenv==20.35.3
     # via pre-commit
-wandb==0.24.2
+wandb==0.21.4
     # via
-    #   hf-libero
     #   lerobot
+    #   libero
 watchfiles==1.1.1
     # via uvicorn
-wcwidth==0.6.0
+wcwidth==0.2.14
     # via prompt-toolkit
 websocket-client==1.9.0
     # via teleop
-websockets==16.0
+websockets==15.0.1
     # via uvicorn
-werkzeug==3.1.6
+werkzeug==3.1.3
     # via tensorboard
-wrapt==2.1.2
+wrapt==2.0.0
     # via dm-tree
 xxhash==3.6.0
     # via datasets
-yarl==1.23.0
+yarl==1.22.0
     # via aiohttp
 zipp==3.23.0
     # via

requirements.in

@@ -1,9 +1,9 @@
 # requirements.in
 
-# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.3.1 25D2128 arm64).
-# Darwin MacBook-Pro.local 25.3.0 Darwin Kernel Version 25.3.0: Wed Jan 28 20:54:55 PST 2026; root:xnu-12377.91.3~2/RELEASE_ARM64_T8132 arm64
+# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.0.1 25A362 arm64).
+# Darwin MacBook-Pro.local 25.0.0 Darwin Kernel Version 25.0.0: Wed Sep 17 21:42:08 PDT 2025; root:xnu-12377.1.9~141/RELEASE_ARM64_T8132 arm64
 
-# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.4 LTS x86_64).
-# Linux lerobot-linux 6.17.0-14-generic #14~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jan 15 15:52:10 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
+# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.3 LTS x86_64).
+# Linux mlerobot-linux 6.14.0-33-generic #33~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Sep 19 17:02:30 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
 
 -e .[all]

src/lerobot/async_inference/configs.py

@@ -126,12 +126,6 @@ class RobotClientConfig:
 
     # Device configuration
     policy_device: str = field(default="cpu", metadata={"help": "Device for policy inference"})
-    client_device: str = field(
-        default="cpu",
-        metadata={
-            "help": "Device to move actions to after receiving from server (e.g., for downstream planners)"
-        },
-    )
 
     # Control behavior configuration
     chunk_size_threshold: float = field(default=0.5, metadata={"help": "Threshold for chunk size control"})
@@ -167,9 +161,6 @@ class RobotClientConfig:
         if not self.policy_device:
             raise ValueError("policy_device cannot be empty")
 
-        if not self.client_device:
-            raise ValueError("client_device cannot be empty")
-
         if self.chunk_size_threshold < 0 or self.chunk_size_threshold > 1:
             raise ValueError(f"chunk_size_threshold must be between 0 and 1, got {self.chunk_size_threshold}")
 
@@ -193,7 +184,6 @@ class RobotClientConfig:
             "policy_type": self.policy_type,
             "pretrained_name_or_path": self.pretrained_name_or_path,
             "policy_device": self.policy_device,
-            "client_device": self.client_device,
             "chunk_size_threshold": self.chunk_size_threshold,
             "fps": self.fps,
             "actions_per_chunk": self.actions_per_chunk,

src/lerobot/async_inference/constants.py

@@ -23,7 +23,7 @@ DEFAULT_INFERENCE_LATENCY = 1 / DEFAULT_FPS
 DEFAULT_OBS_QUEUE_TIMEOUT = 2
 
 # All action chunking policies
-SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05", "groot"]
+SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05"]
 
 # TODO: Add all other robots
 SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so_follower", "omx_follower"]

src/lerobot/async_inference/helpers.py

@@ -18,7 +18,6 @@ import os
 import time
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any
 
 import torch
 
@@ -40,8 +39,8 @@ from lerobot.utils.utils import init_logging
 
 Action = torch.Tensor
 
-# observation as received from the robot (can be numpy arrays, floats, etc.)
-RawObservation = dict[str, Any]
+# observation as received from the robot
+RawObservation = dict[str, torch.Tensor]
 
 # observation as those recorded in LeRobot dataset (keys are different)
 LeRobotObservation = dict[str, torch.Tensor]

src/lerobot/async_inference/policy_server.py

@@ -381,8 +381,6 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         action_tensor = torch.stack(processed_actions, dim=1).squeeze(0)
         self.logger.debug(f"Postprocessed action shape: {action_tensor.shape}")
 
-        action_tensor = action_tensor.detach().cpu()
-
         """5. Convert to TimedAction list"""
         action_chunk = self._time_action_chunk(
             observation_t.get_timestamp(), list(action_tensor), observation_t.get_timestep()

src/lerobot/async_inference/robot_client.py

@@ -25,7 +25,6 @@ python src/lerobot/async_inference/robot_client.py \
     --policy_type=act \
     --pretrained_name_or_path=user/model \
     --policy_device=mps \
-    --client_device=cpu \
     --actions_per_chunk=50 \
     --chunk_size_threshold=0.5 \
     --aggregate_fn_name=weighted_average \
@@ -41,7 +40,6 @@ from collections.abc import Callable
 from dataclasses import asdict
 from pprint import pformat
 from queue import Queue
-from typing import Any
 
 import draccus
 import grpc
@@ -49,6 +47,7 @@ import torch
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.processor import RobotAction
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
@@ -63,9 +62,9 @@ from lerobot.transport import (
     services_pb2_grpc,  # type: ignore
 )
 from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks
-from lerobot.utils.import_utils import register_third_party_plugins
 
 from .configs import RobotClientConfig
+from .constants import SUPPORTED_ROBOTS
 from .helpers import (
     Action,
     FPSTracker,
@@ -286,21 +285,6 @@ class RobotClient:
                 timed_actions = pickle.loads(actions_chunk.data)  # nosec
                 deserialize_time = time.perf_counter() - deserialize_start
 
-                # Log device type of received actions
-                if len(timed_actions) > 0:
-                    received_device = timed_actions[0].get_action().device.type
-                    self.logger.debug(f"Received actions on device: {received_device}")
-
-                # Move actions to client_device (e.g., for downstream planners that need GPU)
-                client_device = self.config.client_device
-                if client_device != "cpu":
-                    for timed_action in timed_actions:
-                        if timed_action.get_action().device.type != client_device:
-                            timed_action.action = timed_action.get_action().to(client_device)
-                    self.logger.debug(f"Converted actions to device: {client_device}")
-                else:
-                    self.logger.debug(f"Actions kept on device: {client_device}")
-
                 self.action_chunk_size = max(self.action_chunk_size, len(timed_actions))
 
                 # Calculate network latency if we have matching observations
@@ -367,7 +351,7 @@ class RobotClient:
         action = {key: action_tensor[i].item() for i, key in enumerate(self.robot.action_features)}
         return action
 
-    def control_loop_action(self, verbose: bool = False) -> dict[str, Any]:
+    def control_loop_action(self, verbose: bool = False) -> RobotAction:
         """Reading and performing actions in local queue"""
 
         # Lock only for queue operations
@@ -485,9 +469,8 @@ class RobotClient:
 def async_client(cfg: RobotClientConfig):
     logging.info(pformat(asdict(cfg)))
 
-    # TODO: Assert if checking robot support is still needed with the plugin system
-    # if cfg.robot.type not in SUPPORTED_ROBOTS:
-    #     raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
+    if cfg.robot.type not in SUPPORTED_ROBOTS:
+        raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
 
     client = RobotClient(cfg)
 
@@ -513,5 +496,4 @@ def async_client(cfg: RobotClientConfig):
 
 
 if __name__ == "__main__":
-    register_third_party_plugins()
     async_client()  # run the client

src/lerobot/cameras/__init__.py

@@ -13,5 +13,5 @@
 # limitations under the License.
 
 from .camera import Camera
-from .configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
+from .configs import CameraConfig, ColorMode, Cv2Rotation
 from .utils import make_cameras_from_configs

src/lerobot/cameras/camera.py

@@ -15,12 +15,11 @@
 # limitations under the License.
 
 import abc
-import warnings
 from typing import Any
 
 from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
 
-from .configs import CameraConfig
+from .configs import CameraConfig, ColorMode
 
 
 class Camera(abc.ABC):
@@ -31,12 +30,20 @@ class Camera(abc.ABC):
 
     Manages basic camera properties (FPS, resolution) and core operations:
     - Connection/disconnection
-    - Frame capture (sync/async/latest)
+    - Frame capture (sync/async)
 
     Attributes:
         fps (int | None): Configured frames per second
         width (int | None): Frame width in pixels
         height (int | None): Frame height in pixels
+
+    Example:
+        class MyCamera(Camera):
+            def __init__(self, config): ...
+            @property
+            def is_connected(self) -> bool: ...
+            def connect(self, warmup=True): ...
+            # Plus other required methods
     """
 
     def __init__(self, config: CameraConfig):
@@ -49,32 +56,6 @@ class Camera(abc.ABC):
         self.width: int | None = config.width
         self.height: int | None = config.height
 
-    def __enter__(self):
-        """
-        Context manager entry.
-        Automatically connects to the camera.
-        """
-        self.connect()
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback) -> None:
-        """
-        Context manager exit.
-        Automatically disconnects, ensuring resources are released even on error.
-        """
-        self.disconnect()
-
-    def __del__(self) -> None:
-        """
-        Destructor safety net.
-        Attempts to disconnect if the object is garbage collected without cleanup.
-        """
-        try:
-            if self.is_connected:
-                self.disconnect()
-        except Exception:  # nosec B110
-            pass
-
     @property
     @abc.abstractmethod
     def is_connected(self) -> bool:
@@ -108,10 +89,12 @@ class Camera(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def read(self) -> NDArray[Any]:
-        """Capture and return a single frame from the camera synchronously.
+    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
+        """Capture and return a single frame from the camera.
 
-        This is a blocking call that will wait for the hardware and its SDK.
+        Args:
+            color_mode: Desired color mode for the output frame. If None,
+                        uses the camera's default color mode.
 
         Returns:
             np.ndarray: Captured frame as a numpy array.
@@ -120,64 +103,17 @@ class Camera(abc.ABC):
 
     @abc.abstractmethod
     def async_read(self, timeout_ms: float = ...) -> NDArray[Any]:
-        """Return the most recent new frame.
-
-        This method retrieves the latest frame captured by the background thread.
-        If a new frame is already available in the buffer (captured since the last call),
-        it returns it immediately.
-
-        It blocks up to `timeout_ms` only if the buffer is empty or if the latest frame
-        was already consumed by a previous `async_read` call.
-
-        Essentially, this method return the latest unconsumed frame, waiting if necessary
-        for a new one to arrive within the specified timeout.
-
-        Usage:
-            - Ideal for control loops where you want to ensure every processed frame
-            is fresh, effectively synchronizing your loop to the camera's FPS.
-            - Causes of a timeout usually include: very low camera FPS, heavy processing load,
-            or if the camera is disconnected.
+        """Asynchronously capture and return a single frame from the camera.
 
         Args:
-            timeout_ms: Maximum time to wait for a new frame in milliseconds.
-                        Defaults to 200ms (0.2s).
+            timeout_ms: Maximum time to wait for a frame in milliseconds.
+                        Defaults to implementation-specific timeout.
 
         Returns:
             np.ndarray: Captured frame as a numpy array.
-
-        Raises:
-            TimeoutError: If no new frame arrives within `timeout_ms`.
         """
         pass
 
-    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
-        """Return the most recent frame captured immediately (Peeking).
-
-        This method is non-blocking and returns whatever is currently in the
-        memory buffer. The frame may be stale,
-        meaning it could have been captured a while ago (hanging camera scenario e.g.).
-
-        Usage:
-            Ideal for scenarios requiring zero latency or decoupled frequencies & when
-            we want a guaranteed frame, such as UI visualization, logging, or
-            non-critical monitoring.
-
-        Returns:
-            NDArray[Any]: The frame image (numpy array).
-
-        Raises:
-            TimeoutError: If the latest frame is older than `max_age_ms`.
-            NotConnectedError: If the camera is not connected.
-            RuntimeError: If the camera is connected but has not captured any frames yet.
-        """
-        warnings.warn(
-            f"{self.__class__.__name__}.read_latest() is not implemented. "
-            "Please override read_latest(); it will be required in future releases.",
-            FutureWarning,
-            stacklevel=2,
-        )
-        return self.async_read()
-
     @abc.abstractmethod
     def disconnect(self) -> None:
         """Disconnect from the camera and release resources."""

src/lerobot/cameras/configs.py

@@ -25,10 +25,6 @@ class ColorMode(str, Enum):
     RGB = "rgb"
     BGR = "bgr"
 
-    @classmethod
-    def _missing_(cls, value: object) -> None:
-        raise ValueError(f"`color_mode` is expected to be in {list(cls)}, but {value} is provided.")
-
 
 class Cv2Rotation(int, Enum):
     NO_ROTATION = 0
@@ -36,25 +32,6 @@ class Cv2Rotation(int, Enum):
     ROTATE_180 = 180
     ROTATE_270 = -90
 
-    @classmethod
-    def _missing_(cls, value: object) -> None:
-        raise ValueError(f"`rotation` is expected to be in {list(cls)}, but {value} is provided.")
-
-
-# Subset from https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html
-class Cv2Backends(int, Enum):
-    ANY = 0
-    V4L2 = 200
-    DSHOW = 700
-    PVAPI = 800
-    ANDROID = 1000
-    AVFOUNDATION = 1200
-    MSMF = 1400
-
-    @classmethod
-    def _missing_(cls, value: object) -> None:
-        raise ValueError(f"`backend` is expected to be in {list(cls)}, but {value} is provided.")
-
 
 @dataclass(kw_only=True)
 class CameraConfig(draccus.ChoiceRegistry, abc.ABC):  # type: ignore  # TODO: add type stubs for draccus

src/lerobot/cameras/opencv/camera_opencv.py

@@ -32,11 +32,10 @@ if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"
     os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
 import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
-from lerobot.utils.errors import DeviceNotConnectedError
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
-from ..utils import get_cv2_rotation
+from ..utils import get_cv2_backend, get_cv2_rotation
 from .configuration_opencv import ColorMode, OpenCVCameraConfig
 
 # NOTE(Steven): The maximum opencv device index depends on your operating system. For instance,
@@ -71,24 +70,34 @@ class OpenCVCamera(Camera):
     Example:
         ```python
         from lerobot.cameras.opencv import OpenCVCamera
-        from lerobot.cameras.configuration_opencv import OpenCVCameraConfig
+        from lerobot.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation
 
         # Basic usage with camera index 0
         config = OpenCVCameraConfig(index_or_path=0)
         camera = OpenCVCamera(config)
         camera.connect()
 
-        # Read 1 frame synchronously (blocking)
+        # Read 1 frame synchronously
         color_image = camera.read()
+        print(color_image.shape)
 
-        # Read 1 frame asynchronously (waits for new frame with a timeout)
+        # Read 1 frame asynchronously
         async_image = camera.async_read()
 
-        # Get the latest frame immediately (no wait, returns timestamp)
-        latest_image, timestamp = camera.read_latest()
-
         # When done, properly disconnect the camera using
         camera.disconnect()
+
+        # Example with custom settings
+        custom_config = OpenCVCameraConfig(
+            index_or_path='/dev/video0', # Or use an index
+            fps=30,
+            width=1280,
+            height=720,
+            color_mode=ColorMode.RGB,
+            rotation=Cv2Rotation.ROTATE_90
+        )
+        custom_camera = OpenCVCamera(custom_config)
+        # ... connect, read, disconnect ...
         ```
     """
 
@@ -114,11 +123,10 @@ class OpenCVCamera(Camera):
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
         self.latest_frame: NDArray[Any] | None = None
-        self.latest_timestamp: float | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
-        self.backend: int = config.backend
+        self.backend: int = get_cv2_backend()
 
         if self.height and self.width:
             self.capture_width, self.capture_height = self.width, self.height
@@ -133,23 +141,20 @@ class OpenCVCamera(Camera):
         """Checks if the camera is currently connected and opened."""
         return isinstance(self.videocapture, cv2.VideoCapture) and self.videocapture.isOpened()
 
-    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
         """
         Connects to the OpenCV camera specified in the configuration.
 
         Initializes the OpenCV VideoCapture object, sets desired camera properties
-        (FPS, width, height), starts the background reading thread and performs initial checks.
-
-        Args:
-            warmup (bool): If True, waits at connect() time until at least one valid frame
-                           has been captured by the background thread. Defaults to True.
+        (FPS, width, height), and performs initial checks.
 
         Raises:
             DeviceAlreadyConnectedError: If the camera is already connected.
-            ConnectionError: If the specified camera index/path is not found or fails to open.
-            RuntimeError: If the camera opens but fails to apply requested settings.
+            ConnectionError: If the specified camera index/path is not found or the camera is found but fails to open.
+            RuntimeError: If the camera opens but fails to apply requested FPS/resolution settings.
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         # Use 1 thread for OpenCV operations to avoid potential conflicts or
         # blocking in multi-threaded applications, especially during data collection.
@@ -165,20 +170,15 @@ class OpenCVCamera(Camera):
             )
 
         self._configure_capture_settings()
-        self._start_read_thread()
 
-        if warmup and self.warmup_s > 0:
+        if warmup:
             start_time = time.time()
             while time.time() - start_time < self.warmup_s:
-                self.async_read(timeout_ms=self.warmup_s * 1000)
+                self.read()
                 time.sleep(0.1)
-            with self.frame_lock:
-                if self.latest_frame is None:
-                    raise ConnectionError(f"{self} failed to capture frames during warmup.")
 
         logger.info(f"{self} connected.")
 
-    @check_if_not_connected
     def _configure_capture_settings(self) -> None:
         """
         Applies the specified FOURCC, FPS, width, and height settings to the connected camera.
@@ -196,8 +196,11 @@ class OpenCVCamera(Camera):
         Raises:
             RuntimeError: If the camera fails to set any of the specified properties
                           to the requested value.
-            DeviceNotConnectedError: If the camera is not connected.
+            DeviceNotConnectedError: If the camera is not connected when attempting
+                                     to configure settings.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.")
 
         # Set FOURCC first (if specified) as it can affect available FPS/resolution options
         if self.config.fourcc is not None:
@@ -336,18 +339,6 @@ class OpenCVCamera(Camera):
 
         return found_cameras_info
 
-    def _read_from_hardware(self) -> NDArray[Any]:
-        if self.videocapture is None:
-            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
-
-        ret, frame = self.videocapture.read()
-
-        if not ret:
-            raise RuntimeError(f"{self} read failed (status={ret}).")
-
-        return frame
-
-    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
@@ -355,6 +346,11 @@ class OpenCVCamera(Camera):
         This is a blocking call. It waits for the next available frame from the
         camera hardware via OpenCV.
 
+        Args:
+            color_mode (Optional[ColorMode]): If specified, overrides the default
+                color mode (`self.color_mode`) for this read operation (e.g.,
+                request RGB even if default is BGR).
+
         Returns:
             np.ndarray: The captured frame as a NumPy array in the format
                        (height, width, channels), using the specified or default
@@ -366,31 +362,34 @@ class OpenCVCamera(Camera):
                           received frame dimensions don't match expectations before rotation.
             ValueError: If an invalid `color_mode` is requested.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         start_time = time.perf_counter()
 
-        if color_mode is not None:
-            logger.warning(
-                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
-            )
+        if self.videocapture is None:
+            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
 
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
+        ret, frame = self.videocapture.read()
 
-        self.new_frame_event.clear()
-        frame = self.async_read(timeout_ms=10000)
+        if not ret or frame is None:
+            raise RuntimeError(f"{self} read failed (status={ret}).")
+
+        processed_frame = self._postprocess_image(frame, color_mode)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
         logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
 
-        return frame
+        return processed_frame
 
-    def _postprocess_image(self, image: NDArray[Any]) -> NDArray[Any]:
+    def _postprocess_image(self, image: NDArray[Any], color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Applies color conversion, dimension validation, and rotation to a raw frame.
 
         Args:
             image (np.ndarray): The raw image frame (expected BGR format from OpenCV).
+            color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None,
+                                             uses the instance's default `self.color_mode`.
 
         Returns:
             np.ndarray: The processed image frame.
@@ -400,10 +399,11 @@ class OpenCVCamera(Camera):
             RuntimeError: If the raw frame dimensions do not match the configured
                           `width` and `height`.
         """
+        requested_color_mode = self.color_mode if color_mode is None else color_mode
 
-        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+        if requested_color_mode not in (ColorMode.RGB, ColorMode.BGR):
             raise ValueError(
-                f"Invalid color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+                f"Invalid color mode '{requested_color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
             )
 
         h, w, c = image.shape
@@ -417,7 +417,7 @@ class OpenCVCamera(Camera):
             raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).")
 
         processed_image = image
-        if self.color_mode == ColorMode.RGB:
+        if requested_color_mode == ColorMode.RGB:
             processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
         if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE, cv2.ROTATE_180]:
@@ -431,7 +431,7 @@ class OpenCVCamera(Camera):
 
         On each iteration:
         1. Reads a color frame
-        2. Stores result in latest_frame and updates timestamp (thread-safe)
+        2. Stores result in latest_frame (thread-safe)
         3. Sets new_frame_event to notify listeners
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
@@ -439,37 +439,30 @@ class OpenCVCamera(Camera):
         if self.stop_event is None:
             raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
 
-        failure_count = 0
         while not self.stop_event.is_set():
             try:
-                raw_frame = self._read_from_hardware()
-                processed_frame = self._postprocess_image(raw_frame)
-                capture_time = time.perf_counter()
+                color_image = self.read()
 
                 with self.frame_lock:
-                    self.latest_frame = processed_frame
-                    self.latest_timestamp = capture_time
+                    self.latest_frame = color_image
                 self.new_frame_event.set()
-                failure_count = 0
 
             except DeviceNotConnectedError:
                 break
             except Exception as e:
-                if failure_count <= 10:
-                    failure_count += 1
-                    logger.warning(f"Error reading frame in background thread for {self}: {e}")
-                else:
-                    raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e
+                logger.warning(f"Error reading frame in background thread for {self}: {e}")
 
     def _start_read_thread(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
-        self._stop_read_thread()
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=0.1)
+        if self.stop_event is not None:
+            self.stop_event.set()
 
         self.stop_event = Event()
         self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
         self.thread.daemon = True
         self.thread.start()
-        time.sleep(0.1)
 
     def _stop_read_thread(self) -> None:
         """Signals the background read thread to stop and waits for it to join."""
@@ -482,12 +475,6 @@ class OpenCVCamera(Camera):
         self.thread = None
         self.stop_event = None
 
-        with self.frame_lock:
-            self.latest_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
-
-    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame asynchronously.
@@ -495,7 +482,6 @@ class OpenCVCamera(Camera):
         This method retrieves the most recent frame captured by the background
         read thread. It does not block waiting for the camera hardware directly,
         but may wait up to timeout_ms for the background thread to provide a frame.
-        It is “best effort” under high FPS.
 
         Args:
             timeout_ms (float): Maximum time in milliseconds to wait for a frame
@@ -510,14 +496,17 @@ class OpenCVCamera(Camera):
             TimeoutError: If no frame becomes available within the specified timeout.
             RuntimeError: If an unexpected error occurs.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
+            self._start_read_thread()
 
         if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+            thread_alive = self.thread is not None and self.thread.is_alive()
             raise TimeoutError(
                 f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
-                f"Read thread alive: {self.thread.is_alive()}."
+                f"Read thread alive: {thread_alive}."
             )
 
         with self.frame_lock:
@@ -529,41 +518,6 @@ class OpenCVCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
-    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
-        """Return the most recent frame captured immediately (Peeking).
-
-        This method is non-blocking and returns whatever is currently in the
-        memory buffer. The frame may be stale,
-        meaning it could have been captured a while ago (hanging camera scenario e.g.).
-
-        Returns:
-            NDArray[Any]: The frame image (numpy array).
-
-        Raises:
-            TimeoutError: If the latest frame is older than `max_age_ms`.
-            DeviceNotConnectedError: If the camera is not connected.
-            RuntimeError: If the camera is connected but has not captured any frames yet.
-        """
-
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
-
-        with self.frame_lock:
-            frame = self.latest_frame
-            timestamp = self.latest_timestamp
-
-        if frame is None or timestamp is None:
-            raise RuntimeError(f"{self} has not captured any frames yet.")
-
-        age_ms = (time.perf_counter() - timestamp) * 1e3
-        if age_ms > max_age_ms:
-            raise TimeoutError(
-                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
-            )
-
-        return frame
-
     def disconnect(self) -> None:
         """
         Disconnects from the camera and cleans up resources.
@@ -584,9 +538,4 @@ class OpenCVCamera(Camera):
             self.videocapture.release()
             self.videocapture = None
 
-        with self.frame_lock:
-            self.latest_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
-
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/opencv/configuration_opencv.py

@@ -15,9 +15,9 @@
 from dataclasses import dataclass
 from pathlib import Path
 
-from ..configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
+from ..configs import CameraConfig, ColorMode, Cv2Rotation
 
-__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation", "Cv2Backends"]
+__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation"]
 
 
 @CameraConfig.register_subclass("opencv")
@@ -50,7 +50,6 @@ class OpenCVCameraConfig(CameraConfig):
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
         warmup_s: Time reading frames before returning from connect (in seconds)
         fourcc: FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults to None (auto-detect).
-        backend: OpenCV backend identifier (https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html). Defaults to ANY.
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.
@@ -63,12 +62,22 @@ class OpenCVCameraConfig(CameraConfig):
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
     fourcc: str | None = None
-    backend: Cv2Backends = Cv2Backends.ANY
 
     def __post_init__(self) -> None:
-        self.color_mode = ColorMode(self.color_mode)
-        self.rotation = Cv2Rotation(self.rotation)
-        self.backend = Cv2Backends(self.backend)
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
+
+        if self.rotation not in (
+            Cv2Rotation.NO_ROTATION,
+            Cv2Rotation.ROTATE_90,
+            Cv2Rotation.ROTATE_180,
+            Cv2Rotation.ROTATE_270,
+        ):
+            raise ValueError(
+                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
+            )
 
         if self.fourcc is not None and (not isinstance(self.fourcc, str) or len(self.fourcc) != 4):
             raise ValueError(

src/lerobot/cameras/reachy2_camera/configuration_reachy2_camera.py

@@ -74,4 +74,7 @@ class Reachy2CameraConfig(CameraConfig):
                 f"`image_type` is expected to be 'left' or 'right' for teleop camera, and 'rgb' or 'depth' for depth camera, but {self.image_type} is provided."
             )
 
-        self.color_mode = ColorMode(self.color_mode)
+        if self.color_mode not in ["rgb", "bgr"]:
+            raise ValueError(
+                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
+            )

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -32,7 +32,6 @@ if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"
 import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 import numpy as np  # type: ignore  # TODO: add type stubs for numpy
 
-from lerobot.utils.decorators import check_if_not_connected
 from lerobot.utils.import_utils import _reachy2_sdk_available
 
 if TYPE_CHECKING or _reachy2_sdk_available:
@@ -81,8 +80,6 @@ class Reachy2Camera(Camera):
         self.config = config
 
         self.color_mode = config.color_mode
-        self.latest_frame: NDArray[Any] | None = None
-        self.latest_timestamp: float | None = None
 
         self.cam_manager: CameraManager | None = None
 
@@ -124,12 +121,16 @@ class Reachy2Camera(Camera):
         """
         raise NotImplementedError("Camera detection is not implemented for Reachy2 cameras.")
 
-    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
 
-        This method retrieves the most recent frame available in Reachy 2's low-level software.
+        This is a blocking call.
+
+        Args:
+            color_mode (Optional[ColorMode]): If specified, overrides the default
+                color mode (`self.color_mode`) for this read operation (e.g.,
+                request RGB even if default is BGR).
 
         Returns:
             np.ndarray: The captured frame as a NumPy array in the format
@@ -138,13 +139,11 @@ class Reachy2Camera(Camera):
         """
         start_time = time.perf_counter()
 
-        if self.cam_manager is None:
+        if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
-        if color_mode is not None:
-            logger.warning(
-                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
-            )
+        if self.cam_manager is None:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         frame: NDArray[Any] = np.empty((0, 0, 3), dtype=np.uint8)
 
@@ -166,27 +165,25 @@ class Reachy2Camera(Camera):
             raise ValueError(f"Invalid camera name '{self.config.name}'. Expected 'teleop' or 'depth'.")
 
         if frame is None:
-            raise RuntimeError(f"Internal error: No frame available for {self}.")
+            return np.empty((0, 0, 3), dtype=np.uint8)
 
-        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
-            raise ValueError(
-                f"Invalid color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
-            )
-        if self.color_mode == ColorMode.RGB:
+        if self.config.color_mode == "rgb":
             frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
-        self.latest_frame = frame
-        self.latest_timestamp = time.perf_counter()
-
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
         logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
 
         return frame
 
-    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
-        Same as read()
+        Reads the latest available frame.
+
+        This method retrieves the most recent frame available in Reachy 2's low-level software.
+
+        Args:
+            timeout_ms (float): Maximum time in milliseconds to wait for a frame
+                to become available. Defaults to 200ms (0.2 seconds).
 
         Returns:
             np.ndarray: The latest captured frame as a NumPy array in the format
@@ -197,40 +194,16 @@ class Reachy2Camera(Camera):
             TimeoutError: If no frame becomes available within the specified timeout.
             RuntimeError: If an unexpected error occurs.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
-        return self.read()
+        frame = self.read()
 
-    @check_if_not_connected
-    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
-        """Return the most recent frame captured immediately (Peeking).
+        if frame is None:
+            raise RuntimeError(f"Internal error: No frame available for {self}.")
 
-        This method is non-blocking and returns whatever is currently in the
-        memory buffer. The frame may be stale,
-        meaning it could have been captured a while ago (hanging camera scenario e.g.).
+        return frame
 
-        Returns:
-            tuple[NDArray, float]:
-                - The frame image (numpy array).
-                - The timestamp (time.perf_counter) when this frame was captured.
-
-        Raises:
-            TimeoutError: If the latest frame is older than `max_age_ms`.
-            DeviceNotConnectedError: If the camera is not connected.
-            RuntimeError: If the camera is connected but has not captured any frames yet.
-        """
-
-        if self.latest_frame is None or self.latest_timestamp is None:
-            raise RuntimeError(f"{self} has not captured any frames yet.")
-
-        age_ms = (time.perf_counter() - self.latest_timestamp) * 1e3
-        if age_ms > max_age_ms:
-            raise TimeoutError(
-                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
-            )
-
-        return self.latest_frame
-
-    @check_if_not_connected
     def disconnect(self) -> None:
         """
         Stops the background read thread (if running).
@@ -238,6 +211,8 @@ class Reachy2Camera(Camera):
         Raises:
             DeviceNotConnectedError: If the camera is already disconnected.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} not connected.")
 
         if self.cam_manager is not None:
             self.cam_manager.disconnect()

src/lerobot/cameras/realsense/camera_realsense.py

@@ -30,8 +30,7 @@ try:
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
-from lerobot.utils.errors import DeviceNotConnectedError
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -73,14 +72,15 @@ class RealSenseCamera(Camera):
         camera = RealSenseCamera(config)
         camera.connect()
 
-        # Read 1 frame synchronously (blocking)
+        # Read 1 frame synchronously
         color_image = camera.read()
+        print(color_image.shape)
 
-        # Read 1 frame asynchronously (waits for new frame with a timeout)
+        # Read 1 frame asynchronously
         async_image = camera.async_read()
 
-        # Get the latest frame immediately (no wait, returns timestamp)
-        latest_image, timestamp = camera.read_latest()
+        # When done, properly disconnect the camera using
+        camera.disconnect()
 
         # Example with depth capture and custom settings
         custom_config = RealSenseCameraConfig(
@@ -133,9 +133,7 @@ class RealSenseCamera(Camera):
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
-        self.latest_color_frame: NDArray[Any] | None = None
-        self.latest_depth_frame: NDArray[Any] | None = None
-        self.latest_timestamp: float | None = None
+        self.latest_frame: NDArray[Any] | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
@@ -153,7 +151,6 @@ class RealSenseCamera(Camera):
         """Checks if the camera pipeline is started and streams are active."""
         return self.rs_pipeline is not None and self.rs_profile is not None
 
-    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
         """
         Connects to the RealSense camera specified in the configuration.
@@ -161,16 +158,14 @@ class RealSenseCamera(Camera):
         Initializes the RealSense pipeline, configures the required streams (color
         and optionally depth), starts the pipeline, and validates the actual stream settings.
 
-        Args:
-            warmup (bool): If True, waits at connect() time until at least one valid frame
-                           has been captured by the background thread. Defaults to True.
-
         Raises:
             DeviceAlreadyConnectedError: If the camera is already connected.
             ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique).
             ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all.
             RuntimeError: If the pipeline starts but fails to apply requested settings.
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         self.rs_pipeline = rs.pipeline()
         rs_config = rs.config()
@@ -186,18 +181,15 @@ class RealSenseCamera(Camera):
             ) from e
 
         self._configure_capture_settings()
-        self._start_read_thread()
 
-        # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
-        self.warmup_s = max(self.warmup_s, 1)
-
-        start_time = time.time()
-        while time.time() - start_time < self.warmup_s:
-            self.async_read(timeout_ms=self.warmup_s * 1000)
-            time.sleep(0.1)
-        with self.frame_lock:
-            if self.latest_color_frame is None or self.use_depth and self.latest_depth_frame is None:
-                raise ConnectionError(f"{self} failed to capture frames during warmup.")
+        if warmup:
+            time.sleep(
+                1
+            )  # NOTE(Steven): RS cameras need a bit of time to warm up before the first read. If we don't wait, the first read from the warmup will raise.
+            start_time = time.time()
+            while time.time() - start_time < self.warmup_s:
+                self.read()
+                time.sleep(0.1)
 
         logger.info(f"{self} connected.")
 
@@ -290,7 +282,6 @@ class RealSenseCamera(Camera):
             if self.use_depth:
                 rs_config.enable_stream(rs.stream.depth)
 
-    @check_if_not_connected
     def _configure_capture_settings(self) -> None:
         """Sets fps, width, and height from device stream if not already configured.
 
@@ -300,6 +291,8 @@ class RealSenseCamera(Camera):
         Raises:
             DeviceNotConnectedError: If device is not connected.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Cannot validate settings for {self} as it is not connected.")
 
         if self.rs_profile is None:
             raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
@@ -319,7 +312,6 @@ class RealSenseCamera(Camera):
                 self.width, self.height = actual_width, actual_height
                 self.capture_width, self.capture_height = actual_width, actual_height
 
-    @check_if_not_connected
     def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
         """
         Reads a single frame (depth) synchronously from the camera.
@@ -327,6 +319,9 @@ class RealSenseCamera(Camera):
         This is a blocking call. It waits for a coherent set of frames (depth)
         from the camera hardware via the RealSense pipeline.
 
+        Args:
+            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
+
         Returns:
             np.ndarray: The depth map as a NumPy array (height, width)
                   of type `np.uint16` (raw depth values in millimeters) and rotation.
@@ -335,50 +330,44 @@ class RealSenseCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If reading frames from the pipeline fails or frames are invalid.
         """
-        if timeout_ms:
-            logger.warning(
-                f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions."
-            )
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
         if not self.use_depth:
             raise RuntimeError(
                 f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}."
             )
 
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
+        start_time = time.perf_counter()
 
-        self.new_frame_event.clear()
-
-        _ = self.async_read(timeout_ms=10000)
-
-        with self.frame_lock:
-            depth_map = self.latest_depth_frame
-
-        if depth_map is None:
-            raise RuntimeError("No depth frame available. Ensure camera is streaming.")
-
-        return depth_map
-
-    def _read_from_hardware(self):
         if self.rs_pipeline is None:
             raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.")
 
-        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=10000)
+        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
 
         if not ret or frame is None:
-            raise RuntimeError(f"{self} read failed (status={ret}).")
+            raise RuntimeError(f"{self} read_depth failed (status={ret}).")
 
-        return frame
+        depth_frame = frame.get_depth_frame()
+        depth_map = np.asanyarray(depth_frame.get_data())
 
-    @check_if_not_connected
-    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 0) -> NDArray[Any]:
+        depth_map_processed = self._postprocess_image(depth_map, depth_frame=True)
+
+        read_duration_ms = (time.perf_counter() - start_time) * 1e3
+        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+
+        return depth_map_processed
+
+    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> NDArray[Any]:
         """
         Reads a single frame (color) synchronously from the camera.
 
         This is a blocking call. It waits for a coherent set of frames (color)
         from the camera hardware via the RealSense pipeline.
 
+        Args:
+            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
+
         Returns:
             np.ndarray: The captured color frame as a NumPy array
               (height, width, channels), processed according to `color_mode` and rotation.
@@ -389,36 +378,39 @@ class RealSenseCamera(Camera):
             ValueError: If an invalid `color_mode` is requested.
         """
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
         start_time = time.perf_counter()
 
-        if color_mode is not None:
-            logger.warning(
-                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
-            )
+        if self.rs_pipeline is None:
+            raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.")
 
-        if timeout_ms:
-            logger.warning(
-                f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions."
-            )
+        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
 
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
+        if not ret or frame is None:
+            raise RuntimeError(f"{self} read failed (status={ret}).")
 
-        self.new_frame_event.clear()
+        color_frame = frame.get_color_frame()
+        color_image_raw = np.asanyarray(color_frame.get_data())
 
-        frame = self.async_read(timeout_ms=10000)
+        color_image_processed = self._postprocess_image(color_image_raw, color_mode)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
         logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
 
-        return frame
+        return color_image_processed
 
-    def _postprocess_image(self, image: NDArray[Any], depth_frame: bool = False) -> NDArray[Any]:
+    def _postprocess_image(
+        self, image: NDArray[Any], color_mode: ColorMode | None = None, depth_frame: bool = False
+    ) -> NDArray[Any]:
         """
         Applies color conversion, dimension validation, and rotation to a raw color frame.
 
         Args:
             image (np.ndarray): The raw image frame (expected RGB format from RealSense).
+            color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None,
+                                             uses the instance's default `self.color_mode`.
 
         Returns:
             np.ndarray: The processed image frame according to `self.color_mode` and `self.rotation`.
@@ -429,9 +421,9 @@ class RealSenseCamera(Camera):
                           `width` and `height`.
         """
 
-        if self.color_mode and self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+        if color_mode and color_mode not in (ColorMode.RGB, ColorMode.BGR):
             raise ValueError(
-                f"Invalid requested color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+                f"Invalid requested color mode '{color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
             )
 
         if depth_frame:
@@ -462,7 +454,7 @@ class RealSenseCamera(Camera):
 
         On each iteration:
         1. Reads a color frame with 500ms timeout
-        2. Stores result in latest_frame and updates timestamp (thread-safe)
+        2. Stores result in latest_frame (thread-safe)
         3. Sets new_frame_event to notify listeners
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
@@ -470,41 +462,25 @@ class RealSenseCamera(Camera):
         if self.stop_event is None:
             raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
 
-        failure_count = 0
         while not self.stop_event.is_set():
             try:
-                frame = self._read_from_hardware()
-                color_frame_raw = frame.get_color_frame()
-                color_frame = np.asanyarray(color_frame_raw.get_data())
-                processed_color_frame = self._postprocess_image(color_frame)
-
-                if self.use_depth:
-                    depth_frame_raw = frame.get_depth_frame()
-                    depth_frame = np.asanyarray(depth_frame_raw.get_data())
-                    processed_depth_frame = self._postprocess_image(depth_frame, depth_frame=True)
-
-                capture_time = time.perf_counter()
+                color_image = self.read(timeout_ms=500)
 
                 with self.frame_lock:
-                    self.latest_color_frame = processed_color_frame
-                    if self.use_depth:
-                        self.latest_depth_frame = processed_depth_frame
-                    self.latest_timestamp = capture_time
+                    self.latest_frame = color_image
                 self.new_frame_event.set()
-                failure_count = 0
 
             except DeviceNotConnectedError:
                 break
             except Exception as e:
-                if failure_count <= 10:
-                    failure_count += 1
-                    logger.warning(f"Error reading frame in background thread for {self}: {e}")
-                else:
-                    raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e
+                logger.warning(f"Error reading frame in background thread for {self}: {e}")
 
     def _start_read_thread(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
-        self._stop_read_thread()
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=0.1)
+        if self.stop_event is not None:
+            self.stop_event.set()
 
         self.stop_event = Event()
         self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
@@ -522,14 +498,7 @@ class RealSenseCamera(Camera):
         self.thread = None
         self.stop_event = None
 
-        with self.frame_lock:
-            self.latest_color_frame = None
-            self.latest_depth_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
-
     # NOTE(Steven): Missing implementation for depth for now
-    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame data (color) asynchronously.
@@ -537,7 +506,6 @@ class RealSenseCamera(Camera):
         This method retrieves the most recent color frame captured by the background
         read thread. It does not block waiting for the camera hardware directly,
         but may wait up to timeout_ms for the background thread to provide a frame.
-        It is “best effort” under high FPS.
 
         Args:
             timeout_ms (float): Maximum time in milliseconds to wait for a frame
@@ -552,18 +520,21 @@ class RealSenseCamera(Camera):
             TimeoutError: If no frame data becomes available within the specified timeout.
             RuntimeError: If the background thread died unexpectedly or another error occurs.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
+            self._start_read_thread()
 
         if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+            thread_alive = self.thread is not None and self.thread.is_alive()
             raise TimeoutError(
                 f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
-                f"Read thread alive: {self.thread.is_alive()}."
+                f"Read thread alive: {thread_alive}."
             )
 
         with self.frame_lock:
-            frame = self.latest_color_frame
+            frame = self.latest_frame
             self.new_frame_event.clear()
 
         if frame is None:
@@ -571,42 +542,6 @@ class RealSenseCamera(Camera):
 
         return frame
 
-    # NOTE(Steven): Missing implementation for depth for now
-    @check_if_not_connected
-    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
-        """Return the most recent (color) frame captured immediately (Peeking).
-
-        This method is non-blocking and returns whatever is currently in the
-        memory buffer. The frame may be stale,
-        meaning it could have been captured a while ago (hanging camera scenario e.g.).
-
-        Returns:
-            NDArray[Any]: The frame image (numpy array).
-
-        Raises:
-            TimeoutError: If the latest frame is older than `max_age_ms`.
-            DeviceNotConnectedError: If the camera is not connected.
-            RuntimeError: If the camera is connected but has not captured any frames yet.
-        """
-
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
-
-        with self.frame_lock:
-            frame = self.latest_color_frame
-            timestamp = self.latest_timestamp
-
-        if frame is None or timestamp is None:
-            raise RuntimeError(f"{self} has not captured any frames yet.")
-
-        age_ms = (time.perf_counter() - timestamp) * 1e3
-        if age_ms > max_age_ms:
-            raise TimeoutError(
-                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
-            )
-
-        return frame
-
     def disconnect(self) -> None:
         """
         Disconnects from the camera, stops the pipeline, and cleans up resources.
@@ -630,10 +565,4 @@ class RealSenseCamera(Camera):
             self.rs_pipeline = None
             self.rs_profile = None
 
-        with self.frame_lock:
-            self.latest_color_frame = None
-            self.latest_depth_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
-
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -60,8 +60,20 @@ class RealSenseCameraConfig(CameraConfig):
     warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        self.color_mode = ColorMode(self.color_mode)
-        self.rotation = Cv2Rotation(self.rotation)
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
+
+        if self.rotation not in (
+            Cv2Rotation.NO_ROTATION,
+            Cv2Rotation.ROTATE_90,
+            Cv2Rotation.ROTATE_180,
+            Cv2Rotation.ROTATE_270,
+        ):
+            raise ValueError(
+                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
+            )
 
         values = (self.fps, self.width, self.height)
         if any(v is not None for v in values) and any(v is None for v in values):

src/lerobot/cameras/utils.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import platform
 from typing import cast
 
 from lerobot.utils.import_utils import make_device_from_device_class
@@ -67,3 +68,14 @@ def get_cv2_rotation(rotation: Cv2Rotation) -> int | None:
         return int(cv2.ROTATE_90_COUNTERCLOCKWISE)
     else:
         return None
+
+
+def get_cv2_backend() -> int:
+    import cv2
+
+    if platform.system() == "Windows":
+        return int(cv2.CAP_MSMF)  # Use MSMF for Windows instead of AVFOUNDATION
+    # elif platform.system() == "Darwin":  # macOS
+    #     return cv2.CAP_AVFOUNDATION
+    else:  # Linux and others
+        return int(cv2.CAP_ANY)

src/lerobot/cameras/zmq/camera_zmq.py

@@ -34,8 +34,7 @@ import cv2
 import numpy as np
 from numpy.typing import NDArray
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
-from lerobot.utils.errors import DeviceNotConnectedError
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -46,12 +45,6 @@ logger = logging.getLogger(__name__)
 
 class ZMQCamera(Camera):
     """
-    Manages camera interactions via ZeroMQ for receiving frames from a remote server.
-
-    This class connects to a ZMQ Publisher, subscribes to frame topics, and decodes
-    incoming JSON messages containing Base64 encoded images. It supports both
-    synchronous and asynchronous frame reading patterns.
-
     Example usage:
         ```python
         from lerobot.cameras.zmq import ZMQCamera, ZMQCameraConfig
@@ -59,16 +52,7 @@ class ZMQCamera(Camera):
         config = ZMQCameraConfig(server_address="192.168.123.164", port=5555, camera_name="head_camera")
         camera = ZMQCamera(config)
         camera.connect()
-
-        # Read 1 frame synchronously (blocking)
-        color_image = camera.read()
-
-        # Read 1 frame asynchronously (waits for new frame with a timeout)
-        async_image = camera.async_read()
-
-        # Get the latest frame immediately (no wait, returns timestamp)
-        latest_image, timestamp = camera.read_latest()
-
+        frame = camera.read()
         camera.disconnect()
         ```
     """
@@ -84,17 +68,14 @@ class ZMQCamera(Camera):
         self.color_mode = config.color_mode
         self.timeout_ms = config.timeout_ms
 
-        # ZMQ Context and Socket
         self.context: zmq.Context | None = None
         self.socket: zmq.Socket | None = None
         self._connected = False
 
-        # Threading resources
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
         self.latest_frame: NDArray[Any] | None = None
-        self.latest_timestamp: float | None = None
         self.new_frame_event: Event = Event()
 
     def __str__(self) -> str:
@@ -102,17 +83,12 @@ class ZMQCamera(Camera):
 
     @property
     def is_connected(self) -> bool:
-        """Checks if the ZMQ socket is initialized and connected."""
         return self._connected and self.context is not None and self.socket is not None
 
-    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
-        """Connect to ZMQ camera server.
-
-        Args:
-            warmup (bool): If True, waits for the camera to provide at least one
-                           valid frame before returning. Defaults to True.
-        """
+        """Connect to ZMQ camera server."""
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         logger.info(f"Connecting to {self}...")
 
@@ -127,28 +103,17 @@ class ZMQCamera(Camera):
             self.socket.connect(f"tcp://{self.server_address}:{self.port}")
             self._connected = True
 
-            # Auto-detect resolution if not provided
+            # Auto-detect resolution
             if self.width is None or self.height is None:
-                # Read directly from hardware because the thread isn't running yet
-                temp_frame = self._read_from_hardware()
-                h, w = temp_frame.shape[:2]
+                h, w = self.read().shape[:2]
                 self.height = h
                 self.width = w
-                logger.info(f"{self} resolution detected: {w}x{h}")
+                logger.info(f"{self} resolution: {w}x{h}")
 
-            self._start_read_thread()
             logger.info(f"{self} connected.")
 
             if warmup:
-                # Ensure we have captured at least one frame via the thread
-                start_time = time.time()
-                while time.time() - start_time < (self.config.warmup_s):  # Wait a bit more than timeout
-                    self.async_read(timeout_ms=self.config.warmup_s * 1000)
-                    time.sleep(0.1)
-
-                with self.frame_lock:
-                    if self.latest_frame is None:
-                        raise ConnectionError(f"{self} failed to capture frames during warmup.")
+                time.sleep(0.1)
 
         except Exception as e:
             self._cleanup()
@@ -166,14 +131,15 @@ class ZMQCamera(Camera):
 
     @staticmethod
     def find_cameras() -> list[dict[str, Any]]:
-        """
-        Detection not implemented for ZMQ cameras. These cameras require manual configuration (server address/port).
-        """
-        raise NotImplementedError("Camera detection is not implemented for ZMQ cameras.")
+        """ZMQ cameras require manual configuration (server address/port)."""
+        return []
 
-    def _read_from_hardware(self) -> NDArray[Any]:
+    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
-        Reads a single frame directly from the ZMQ socket.
+        Read a single frame from the ZMQ camera.
+
+        Returns:
+            np.ndarray: Decoded frame (height, width, 3)
         """
         if not self.is_connected or self.socket is None:
             raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -181,7 +147,6 @@ class ZMQCamera(Camera):
         try:
             message = self.socket.recv_string()
         except Exception as e:
-            # zmq is lazy-imported in connect(), so check by name to avoid a top-level import
             if type(e).__name__ == "Again":
                 raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e
             raise
@@ -211,114 +176,42 @@ class ZMQCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
-    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
-        """
-        Reads a single frame synchronously from the camera.
-
-        This is a blocking call. It waits for the next available frame from the
-        camera background thread.
-
-        Returns:
-            np.ndarray: Decoded frame (height, width, 3)
-        """
-        start_time = time.perf_counter()
-
-        if color_mode is not None:
-            logger.warning(
-                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
-            )
-
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
-
-        self.new_frame_event.clear()
-        frame = self.async_read(timeout_ms=10000)
-
-        read_duration_ms = (time.perf_counter() - start_time) * 1e3
-        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
-
-        return frame
-
     def _read_loop(self) -> None:
-        """
-        Internal loop run by the background thread for asynchronous reading.
-        """
-        if self.stop_event is None:
-            raise RuntimeError(f"{self}: stop_event is not initialized.")
-
-        failure_count = 0
-        while not self.stop_event.is_set():
+        while self.stop_event and not self.stop_event.is_set():
             try:
-                frame = self._read_from_hardware()
-                capture_time = time.perf_counter()
-
+                frame = self.read()
                 with self.frame_lock:
                     self.latest_frame = frame
-                    self.latest_timestamp = capture_time
                 self.new_frame_event.set()
-                failure_count = 0
-
             except DeviceNotConnectedError:
                 break
-            except (TimeoutError, Exception) as e:
-                if failure_count <= 10:
-                    failure_count += 1
-                    logger.warning(f"Read error: {e}")
-                else:
-                    raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e
+            except TimeoutError:
+                pass
+            except Exception as e:
+                logger.warning(f"Read error: {e}")
 
     def _start_read_thread(self) -> None:
-        if self.stop_event is not None:
-            self.stop_event.set()
-        if self.thread is not None and self.thread.is_alive():
-            self.thread.join(timeout=2.0)
-
-        with self.frame_lock:
-            self.latest_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
-
+        if self.thread and self.thread.is_alive():
+            return
         self.stop_event = Event()
-        self.thread = Thread(target=self._read_loop, daemon=True, name=f"{self}_read_loop")
+        self.thread = Thread(target=self._read_loop, daemon=True)
         self.thread.start()
-        time.sleep(0.1)
 
     def _stop_read_thread(self) -> None:
-        if self.stop_event is not None:
+        if self.stop_event:
             self.stop_event.set()
-
-        if self.thread is not None and self.thread.is_alive():
+        if self.thread and self.thread.is_alive():
             self.thread.join(timeout=2.0)
-
         self.thread = None
         self.stop_event = None
 
-        with self.frame_lock:
-            self.latest_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
+    def async_read(self, timeout_ms: float = 10000) -> NDArray[Any]:
+        """Read latest frame asynchronously (non-blocking)."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
-    @check_if_not_connected
-    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
-        """
-        Reads the latest available frame asynchronously.
-
-        Args:
-            timeout_ms (float): Maximum time in milliseconds to wait for a frame
-                to become available. Defaults to 200ms.
-
-        Returns:
-            np.ndarray: The latest captured frame.
-
-        Raises:
-            DeviceNotConnectedError: If the camera is not connected.
-            TimeoutError: If no frame data becomes available within the specified timeout.
-            RuntimeError: If the background thread is not running.
-        """
-
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
+        if not self.thread or not self.thread.is_alive():
+            self._start_read_thread()
 
         if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
             raise TimeoutError(f"{self} async_read timeout after {timeout_ms}ms")
@@ -332,54 +225,11 @@ class ZMQCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
-    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
-        """Return the most recent frame captured immediately (Peeking).
-
-        This method is non-blocking and returns whatever is currently in the
-        memory buffer. The frame may be stale,
-        meaning it could have been captured a while ago (hanging camera scenario e.g.).
-
-        Returns:
-            NDArray[Any]: The frame image (numpy array).
-
-        Raises:
-            TimeoutError: If the latest frame is older than `max_age_ms`.
-            DeviceNotConnectedError: If the camera is not connected.
-            RuntimeError: If the camera is connected but has not captured any frames yet.
-        """
-
-        if self.thread is None or not self.thread.is_alive():
-            raise RuntimeError(f"{self} read thread is not running.")
-
-        with self.frame_lock:
-            frame = self.latest_frame
-            timestamp = self.latest_timestamp
-
-        if frame is None or timestamp is None:
-            raise RuntimeError(f"{self} has not captured any frames yet.")
-
-        age_ms = (time.perf_counter() - timestamp) * 1e3
-        if age_ms > max_age_ms:
-            raise TimeoutError(
-                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
-            )
-
-        return frame
-
     def disconnect(self) -> None:
         """Disconnect from ZMQ camera."""
-        if not self.is_connected and self.thread is None:
+        if not self.is_connected and not self.thread:
             raise DeviceNotConnectedError(f"{self} not connected.")
 
-        if self.thread is not None:
-            self._stop_read_thread()
-
+        self._stop_read_thread()
         self._cleanup()
-
-        with self.frame_lock:
-            self.latest_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
-
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/zmq/configuration_zmq.py

@@ -29,10 +29,12 @@ class ZMQCameraConfig(CameraConfig):
     camera_name: str = "zmq_camera"
     color_mode: ColorMode = ColorMode.RGB
     timeout_ms: int = 5000
-    warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        self.color_mode = ColorMode(self.color_mode)
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
 
         if self.timeout_ms <= 0:
             raise ValueError(f"`timeout_ms` must be positive, but {self.timeout_ms} is provided.")

src/lerobot/cameras/zmq/image_server.py

@@ -23,7 +23,6 @@ import base64
 import contextlib
 import json
 import logging
-import threading
 import time
 from collections import deque
 
@@ -43,57 +42,10 @@ def encode_image(image: np.ndarray, quality: int = 80) -> str:
     return base64.b64encode(buffer).decode("utf-8")
 
 
-class CameraCaptureThread:
-    """Background thread that continuously captures and encodes frames from a camera."""
-
-    def __init__(self, camera: OpenCVCamera, name: str):
-        self.camera = camera
-        self.name = name
-        self.latest_encoded: str | None = None  # Pre-encoded JPEG as base64
-        self.latest_timestamp: float = 0.0
-        self.frame_lock = threading.Lock()
-        self.running = False
-        self.thread: threading.Thread | None = None
-
-    def start(self):
-        """Start the capture thread."""
-        self.running = True
-        self.thread = threading.Thread(target=self._capture_loop, daemon=True)
-        self.thread.start()
-
-    def stop(self):
-        """Stop the capture thread."""
-        self.running = False
-        if self.thread:
-            self.thread.join(timeout=1.0)
-
-    def _capture_loop(self):
-        """Continuously capture and encode frames at the camera's native rate."""
-        while self.running:
-            try:
-                frame = self.camera.read()  # Blocks at camera's native rate
-                timestamp = time.time()
-                # Encode immediately in capture thread (this is the slow part)
-                encoded = encode_image(frame)
-                with self.frame_lock:
-                    self.latest_encoded = encoded
-                    self.latest_timestamp = timestamp
-            except Exception as e:
-                logger.warning(f"Camera {self.name} capture error: {e}")
-                time.sleep(0.01)
-
-    def get_latest(self) -> tuple[str | None, float]:
-        """Get the latest encoded frame and its timestamp."""
-        with self.frame_lock:
-            return self.latest_encoded, self.latest_timestamp
-
-
 class ImageServer:
     def __init__(self, config: dict, port: int = 5555):
-        # fps controls the publish loop rate (how often frames are sent over ZMQ), not the camera capture rate
         self.fps = config.get("fps", 30)
         self.cameras: dict[str, OpenCVCamera] = {}
-        self.capture_threads: dict[str, CameraCaptureThread] = {}
 
         for name, cfg in config.get("cameras", {}).items():
             shape = cfg.get("shape", [480, 640])
@@ -109,10 +61,6 @@ class ImageServer:
             self.cameras[name] = camera
             logger.info(f"Camera {name}: {shape[1]}x{shape[0]}")
 
-            # Create capture thread for this camera
-            capture_thread = CameraCaptureThread(camera, name)
-            self.capture_threads[name] = capture_thread
-
         # ZMQ PUB socket
         self.context = zmq.Context()
         self.socket = self.context.socket(zmq.PUB)
@@ -125,18 +73,6 @@ class ImageServer:
     def run(self):
         frame_count = 0
         frame_times = deque(maxlen=60)
-        last_published_ts: dict[str, float] = {}
-
-        # Start all capture threads
-        for capture_thread in self.capture_threads.values():
-            capture_thread.start()
-
-        # Wait for first frames to be captured and encoded
-        logger.info("Waiting for cameras to start capturing...")
-        for name, capture_thread in self.capture_threads.items():
-            while capture_thread.get_latest()[0] is None:
-                time.sleep(0.01)
-            logger.info(f"Camera {name} ready (capture + encode in background)")
 
         try:
             while True:
@@ -144,12 +80,10 @@ class ImageServer:
 
                 # Build message
                 message = {"timestamps": {}, "images": {}}
-                for name, capture_thread in self.capture_threads.items():
-                    encoded, timestamp = capture_thread.get_latest()
-                    if encoded is not None and timestamp > last_published_ts.get(name, 0.0):
-                        message["timestamps"][name] = timestamp
-                        message["images"][name] = encoded
-                        last_published_ts[name] = timestamp
+                for name, cam in self.cameras.items():
+                    frame = cam.read()  # Returns RGB
+                    message["timestamps"][name] = time.time()
+                    message["images"][name] = encode_image(frame)
 
                 # Send as JSON string (suppress if buffer full)
                 with contextlib.suppress(zmq.Again):
@@ -168,8 +102,6 @@ class ImageServer:
         except KeyboardInterrupt:
             pass
         finally:
-            for capture_thread in self.capture_threads.values():
-                capture_thread.stop()
             for cam in self.cameras.values():
                 cam.disconnect()
             self.socket.close()

src/lerobot/configs/default.py

@@ -27,7 +27,7 @@ class DatasetConfig:
     # "dataset_index" into the returned item. The index mapping is made according to the order in which the
     # datasets are provided.
     repo_id: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | None = None
     episodes: list[int] | None = None
     image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)

src/lerobot/configs/policies.py

@@ -45,12 +45,12 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
     Args:
         n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
             current step and additional steps going back).
-        input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents
-            the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
-        output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents
-            the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
-        normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
-            a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
+        input_shapes: A dictionary defining the shapes of the input data for the policy.
+        output_shapes: A dictionary defining the shapes of the output data for the policy.
+        input_normalization_modes: A dictionary with key representing the modality and the value specifies the
+            normalization mode to apply.
+        output_normalization_modes: Similar dictionary as `input_normalization_modes`, but to unnormalize to
+            the original scale.
     """
 
     n_obs_steps: int = 1

src/lerobot/configs/train.py

@@ -50,9 +50,6 @@ class TrainPipelineConfig(HubMixin):
     # `seed` is used for training (eg: model initialization, dataset shuffling)
     # AND for the evaluation environments.
     seed: int | None = 1000
-    # Set to True to use deterministic cuDNN algorithms for reproducibility.
-    # This disables cudnn.benchmark and may reduce training speed by ~10-20%.
-    cudnn_deterministic: bool = False
     # Number of workers for the dataloader.
     num_workers: int = 4
     batch_size: int = 8

src/lerobot/datasets/aggregate.py

@@ -19,7 +19,6 @@ import logging
 import shutil
 from pathlib import Path
 
-import datasets
 import pandas as pd
 import tqdm
 
@@ -33,7 +32,6 @@ from lerobot.datasets.utils import (
     DEFAULT_VIDEO_FILE_SIZE_IN_MB,
     DEFAULT_VIDEO_PATH,
     get_file_size_in_mb,
-    get_hf_features_from_features,
     get_parquet_file_size_in_mb,
     to_parquet_with_hf_images,
     update_chunk_file_indices,
@@ -116,9 +114,6 @@ def update_meta_data(
     Adjusts all indices and timestamps to account for previously aggregated
     data and videos in the destination dataset.
 
-    For data file indices, uses the 'src_to_dst' mapping from aggregate_data()
-    to correctly map source file indices to their destination locations.
-
     Args:
         df: DataFrame containing the metadata to be updated.
         dst_meta: Destination dataset metadata.
@@ -132,50 +127,8 @@ def update_meta_data(
 
     df["meta/episodes/chunk_index"] = df["meta/episodes/chunk_index"] + meta_idx["chunk"]
     df["meta/episodes/file_index"] = df["meta/episodes/file_index"] + meta_idx["file"]
-
-    # Update data file indices using source-to-destination mapping
-    # This is critical for handling datasets that are already results of a merge
-    data_src_to_dst = data_idx.get("src_to_dst", {})
-    if data_src_to_dst:
-        # Store original indices for lookup
-        df["_orig_data_chunk"] = df["data/chunk_index"].copy()
-        df["_orig_data_file"] = df["data/file_index"].copy()
-
-        # Vectorized mapping from (src_chunk, src_file) to (dst_chunk, dst_file)
-        # This is much faster than per-row iteration for large metadata tables
-        mapping_index = pd.MultiIndex.from_tuples(
-            list(data_src_to_dst.keys()),
-            names=["chunk_index", "file_index"],
-        )
-        mapping_values = list(data_src_to_dst.values())
-        mapping_df = pd.DataFrame(
-            mapping_values,
-            index=mapping_index,
-            columns=["dst_chunk", "dst_file"],
-        )
-
-        # Construct a MultiIndex for each row based on original data indices
-        row_index = pd.MultiIndex.from_arrays(
-            [df["_orig_data_chunk"], df["_orig_data_file"]],
-            names=["chunk_index", "file_index"],
-        )
-
-        # Align mapping to rows; missing keys fall back to the default destination
-        reindexed = mapping_df.reindex(row_index)
-        reindexed[["dst_chunk", "dst_file"]] = reindexed[["dst_chunk", "dst_file"]].fillna(
-            {"dst_chunk": data_idx["chunk"], "dst_file": data_idx["file"]}
-        )
-
-        # Assign mapped destination indices back to the DataFrame
-        df["data/chunk_index"] = reindexed["dst_chunk"].to_numpy()
-        df["data/file_index"] = reindexed["dst_file"].to_numpy()
-
-        # Clean up temporary columns
-        df = df.drop(columns=["_orig_data_chunk", "_orig_data_file"])
-    else:
-        # Fallback to simple offset (backward compatibility for single-file sources)
-        df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"]
-        df["data/file_index"] = df["data/file_index"] + data_idx["file"]
+    df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"]
+    df["data/file_index"] = df["data/file_index"] + data_idx["file"]
     for key, video_idx in videos_idx.items():
         # Store original video file indices before updating
         orig_chunk_col = f"videos/{key}/chunk_index"
@@ -191,7 +144,8 @@ def update_meta_data(
         if src_to_dst:
             # Map each episode to its correct destination file and apply offset
             for idx in df.index:
-                src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"])
+                # Convert to Python int to avoid numpy type mismatch in dict lookup
+                src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"]))
 
                 # Get destination chunk/file for this source file
                 dst_chunk, dst_file = src_to_dst.get(src_key, (video_idx["chunk"], video_idx["file"]))
@@ -207,7 +161,8 @@ def update_meta_data(
             df[orig_chunk_col] = video_idx["chunk"]
             df[orig_file_col] = video_idx["file"]
             for idx in df.index:
-                src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"])
+                # Convert to Python int to avoid numpy type mismatch in dict lookup
+                src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"]))
                 offset = src_to_offset.get(src_key, 0)
                 df.at[idx, f"videos/{key}/from_timestamp"] += offset
                 df.at[idx, f"videos/{key}/to_timestamp"] += offset
@@ -289,9 +244,7 @@ def aggregate_datasets(
 
     logging.info("Find all tasks")
     unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
-    dst_meta.tasks = pd.DataFrame(
-        {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
-    )
+    dst_meta.tasks = pd.DataFrame({"task_index": range(len(unique_tasks))}, index=unique_tasks)
 
     meta_idx = {"chunk": 0, "file": 0}
     data_idx = {"chunk": 0, "file": 0}
@@ -307,10 +260,6 @@ def aggregate_datasets(
 
         meta_idx = aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx)
 
-        # Clear the src_to_dst mapping after processing each source dataset
-        # to avoid interference between different source datasets
-        data_idx.pop("src_to_dst", None)
-
         dst_meta.info["total_episodes"] += src_meta.total_episodes
         dst_meta.info["total_frames"] += src_meta.total_frames
 
@@ -361,6 +310,10 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
         dst_file_durations = video_idx["dst_file_durations"]
 
         for src_chunk_idx, src_file_idx in unique_chunk_file_pairs:
+            # Convert to Python int to ensure consistent dict keys
+            src_chunk_idx = int(src_chunk_idx)
+            src_file_idx = int(src_file_idx)
+
             src_path = src_meta.root / DEFAULT_VIDEO_PATH.format(
                 video_key=key,
                 chunk_index=src_chunk_idx,
@@ -433,16 +386,10 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
     Reads source data files, updates indices to match the aggregated dataset,
     and writes them to the destination with proper file rotation.
 
-    Tracks a `src_to_dst` mapping from source (chunk, file) to destination (chunk, file)
-    which is critical for correctly updating episode metadata when source datasets
-    have multiple data files (e.g., from a previous merge operation).
-
     Args:
         src_meta: Source dataset metadata.
         dst_meta: Destination dataset metadata.
         data_idx: Dictionary tracking data chunk and file indices.
-        data_files_size_in_mb: Maximum size for data files in MB.
-        chunk_size: Maximum number of files per chunk.
 
     Returns:
         dict: Updated data_idx with current chunk and file indices.
@@ -455,47 +402,25 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
     }
 
     unique_chunk_file_ids = sorted(unique_chunk_file_ids)
-    contains_images = len(dst_meta.image_keys) > 0
-
-    # retrieve features schema for proper image typing in parquet
-    hf_features = get_hf_features_from_features(dst_meta.features) if contains_images else None
-
-    # Track source to destination file mapping for metadata update
-    # This is critical for handling datasets that are already results of a merge
-    src_to_dst: dict[tuple[int, int], tuple[int, int]] = {}
 
     for src_chunk_idx, src_file_idx in unique_chunk_file_ids:
         src_path = src_meta.root / DEFAULT_DATA_PATH.format(
             chunk_index=src_chunk_idx, file_index=src_file_idx
         )
-        if contains_images:
-            # Use HuggingFace datasets to read source data to preserve image format
-            src_ds = datasets.Dataset.from_parquet(str(src_path))
-            df = src_ds.to_pandas()
-        else:
-            df = pd.read_parquet(src_path)
+        df = pd.read_parquet(src_path)
         df = update_data_df(df, src_meta, dst_meta)
 
-        # Write data and get the actual destination file it was written to
-        # This avoids duplicating the rotation logic here
-        data_idx, (dst_chunk, dst_file) = append_or_create_parquet_file(
+        data_idx = append_or_create_parquet_file(
             df,
             src_path,
             data_idx,
             data_files_size_in_mb,
             chunk_size,
             DEFAULT_DATA_PATH,
-            contains_images=contains_images,
+            contains_images=len(dst_meta.image_keys) > 0,
             aggr_root=dst_meta.root,
-            hf_features=hf_features,
         )
 
-        # Record the mapping from source to actual destination
-        src_to_dst[(src_chunk_idx, src_file_idx)] = (dst_chunk, dst_file)
-
-    # Add the mapping to data_idx for use in metadata update
-    data_idx["src_to_dst"] = src_to_dst
-
     return data_idx
 
 
@@ -536,7 +461,7 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
             videos_idx,
         )
 
-        meta_idx, _ = append_or_create_parquet_file(
+        meta_idx = append_or_create_parquet_file(
             df,
             src_path,
             meta_idx,
@@ -563,8 +488,7 @@ def append_or_create_parquet_file(
     default_path: str,
     contains_images: bool = False,
     aggr_root: Path = None,
-    hf_features: datasets.Features | None = None,
-) -> tuple[dict[str, int], tuple[int, int]]:
+):
     """Appends data to an existing parquet file or creates a new one based on size constraints.
 
     Manages file rotation when size limits are exceeded to prevent individual files
@@ -579,49 +503,40 @@ def append_or_create_parquet_file(
         default_path: Format string for generating file paths.
         contains_images: Whether the data contains images requiring special handling.
         aggr_root: Root path for the aggregated dataset.
-        hf_features: Optional HuggingFace Features schema for proper image typing.
 
     Returns:
-        tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
-               and (dst_chunk, dst_file) is the actual destination file the data was written to.
+        dict: Updated index dictionary with current chunk and file indices.
     """
-    dst_chunk, dst_file = idx["chunk"], idx["file"]
-    dst_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
+    dst_path = aggr_root / default_path.format(chunk_index=idx["chunk"], file_index=idx["file"])
 
     if not dst_path.exists():
         dst_path.parent.mkdir(parents=True, exist_ok=True)
         if contains_images:
-            to_parquet_with_hf_images(df, dst_path, features=hf_features)
+            to_parquet_with_hf_images(df, dst_path)
         else:
             df.to_parquet(dst_path)
-        return idx, (dst_chunk, dst_file)
+        return idx
 
     src_size = get_parquet_file_size_in_mb(src_path)
     dst_size = get_parquet_file_size_in_mb(dst_path)
 
     if dst_size + src_size >= max_mb:
         idx["chunk"], idx["file"] = update_chunk_file_indices(idx["chunk"], idx["file"], chunk_size)
-        dst_chunk, dst_file = idx["chunk"], idx["file"]
-        new_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
+        new_path = aggr_root / default_path.format(chunk_index=idx["chunk"], file_index=idx["file"])
         new_path.parent.mkdir(parents=True, exist_ok=True)
         final_df = df
         target_path = new_path
     else:
-        if contains_images:
-            # Use HuggingFace datasets to read existing data to preserve image format
-            existing_ds = datasets.Dataset.from_parquet(str(dst_path))
-            existing_df = existing_ds.to_pandas()
-        else:
-            existing_df = pd.read_parquet(dst_path)
+        existing_df = pd.read_parquet(dst_path)
         final_df = pd.concat([existing_df, df], ignore_index=True)
         target_path = dst_path
 
     if contains_images:
-        to_parquet_with_hf_images(final_df, target_path, features=hf_features)
+        to_parquet_with_hf_images(final_df, target_path)
     else:
         final_df.to_parquet(target_path)
 
-    return idx, (dst_chunk, dst_file)
+    return idx
 
 
 def finalize_aggregation(aggr_meta, all_metadata):

src/lerobot/datasets/card_template.md

@@ -7,13 +7,6 @@
 
 This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
 
-{% if repo_id is defined and repo_id %}
-<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path={{ repo_id }}">
-<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
-<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
-</a>
-{% endif %}
-
 ## Dataset Description
 
 {{ dataset_description | default("", true) }}

src/lerobot/datasets/dataset_tools.py

@@ -26,7 +26,6 @@ This module provides utilities for:
 import logging
 import shutil
 from collections.abc import Callable
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
 
 import datasets
@@ -52,8 +51,7 @@ from lerobot.datasets.utils import (
     write_stats,
     write_tasks,
 )
-from lerobot.datasets.video_utils import encode_video_frames, get_video_info
-from lerobot.utils.constants import HF_LEROBOT_HOME, OBS_IMAGE
+from lerobot.utils.constants import HF_LEROBOT_HOME
 
 
 def _load_episode_with_stats(src_dataset: LeRobotDataset, episode_idx: int) -> dict:
@@ -89,8 +87,8 @@ def delete_episodes(
     Args:
         dataset: The source LeRobotDataset.
         episode_indices: List of episode indices to delete.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
     """
     if not episode_indices:
         raise ValueError("No episodes to delete")
@@ -152,7 +150,7 @@ def split_dataset(
         dataset: The source LeRobotDataset to split.
         splits: Either a dict mapping split names to episode indices, or a dict mapping
                 split names to fractions (must sum to <= 1.0).
-        output_dir: Root directory where the split datasets will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
+        output_dir: Base directory for output datasets. If None, uses default location.
 
     Examples:
       Split by specific episodes
@@ -243,8 +241,8 @@ def merge_datasets(
 
     Args:
         datasets: List of LeRobotDatasets to merge.
-        output_repo_id: Merged dataset identifier.
-        output_dir: Root directory where the merged dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/output_repo_id.
+        output_repo_id: Repository ID for the merged dataset.
+        output_dir: Directory to save the merged dataset. If None, uses default location.
     """
     if not datasets:
         raise ValueError("No datasets to merge")
@@ -288,8 +286,8 @@ def modify_features(
         dataset: The source LeRobotDataset.
         add_features: Optional dict mapping feature names to (feature_values, feature_info) tuples.
         remove_features: Optional feature name(s) to remove. Can be a single string or list.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
 
     Returns:
         New dataset with features modified.
@@ -390,8 +388,8 @@ def add_features(
     Args:
         dataset: The source LeRobotDataset.
         features: Dictionary mapping feature names to (feature_values, feature_info) tuples.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
 
     Returns:
         New dataset with all features added.
@@ -427,8 +425,8 @@ def remove_feature(
     Args:
         dataset: The source LeRobotDataset.
         feature_names: Name(s) of features to remove. Can be a single string or list.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
 
     Returns:
         New dataset with features removed.
@@ -567,22 +565,20 @@ def _copy_and_reindex_data(
 def _keep_episodes_from_video_with_av(
     input_path: Path,
     output_path: Path,
-    episodes_to_keep: list[tuple[int, int]],
+    episodes_to_keep: list[tuple[float, float]],
     fps: float,
     vcodec: str = "libsvtav1",
     pix_fmt: str = "yuv420p",
 ) -> None:
     """Keep only specified episodes from a video file using PyAV.
 
-    This function decodes frames from specified frame ranges and re-encodes them with
+    This function decodes frames from specified time ranges and re-encodes them with
     properly reset timestamps to ensure monotonic progression.
 
     Args:
         input_path: Source video file path.
         output_path: Destination video file path.
-        episodes_to_keep: List of (start_frame, end_frame) tuples for episodes to keep.
-            Ranges are half-open intervals: [start_frame, end_frame), where start_frame
-            is inclusive and end_frame is exclusive.
+        episodes_to_keep: List of (start_time, end_time) tuples for episodes to keep.
         fps: Frame rate of the video.
         vcodec: Video codec to use for encoding.
         pix_fmt: Pixel format for output video.
@@ -624,10 +620,9 @@ def _keep_episodes_from_video_with_av(
 
     # Create set of (start, end) ranges for fast lookup.
     # Convert to a sorted list for efficient checking.
-    frame_ranges = sorted(episodes_to_keep)
+    time_ranges = sorted(episodes_to_keep)
 
     # Track frame index for setting PTS and current range being processed.
-    src_frame_count = 0
     frame_count = 0
     range_idx = 0
 
@@ -637,20 +632,21 @@ def _keep_episodes_from_video_with_av(
             if frame is None:
                 continue
 
-            # Check if frame is in any of our desired frame ranges.
+            # Get frame timestamp.
+            frame_time = float(frame.pts * frame.time_base) if frame.pts is not None else 0.0
+
+            # Check if frame is in any of our desired time ranges.
             # Skip ranges that have already passed.
-            while range_idx < len(frame_ranges) and src_frame_count >= frame_ranges[range_idx][1]:
+            while range_idx < len(time_ranges) and frame_time >= time_ranges[range_idx][1]:
                 range_idx += 1
 
             # If we've passed all ranges, stop processing.
-            if range_idx >= len(frame_ranges):
+            if range_idx >= len(time_ranges):
                 break
 
             # Check if frame is in current range.
-            start_frame = frame_ranges[range_idx][0]
-
-            if src_frame_count < start_frame:
-                src_frame_count += 1
+            start_ts, end_ts = time_ranges[range_idx]
+            if frame_time < start_ts:
                 continue
 
             # Frame is in range - create a new frame with reset timestamps.
@@ -663,7 +659,6 @@ def _keep_episodes_from_video_with_av(
             for pkt in v_out.encode(new_frame):
                 out.mux(pkt)
 
-            src_frame_count += 1
             frame_count += 1
 
     # Flush encoder.
@@ -752,17 +747,15 @@ def _copy_and_reindex_videos(
                         f"videos/{video_key}/to_timestamp"
                     ]
             else:
-                # Build list of frame ranges to keep, in sorted order.
+                # Build list of time ranges to keep, in sorted order.
                 sorted_keep_episodes = sorted(episodes_in_file, key=lambda x: episode_mapping[x])
-                episodes_to_keep_ranges: list[tuple[int, int]] = []
+                episodes_to_keep_ranges: list[tuple[float, float]] = []
+
                 for old_idx in sorted_keep_episodes:
                     src_ep = src_dataset.meta.episodes[old_idx]
-                    from_frame = round(src_ep[f"videos/{video_key}/from_timestamp"] * src_dataset.meta.fps)
-                    to_frame = round(src_ep[f"videos/{video_key}/to_timestamp"] * src_dataset.meta.fps)
-                    assert src_ep["length"] == to_frame - from_frame, (
-                        f"Episode length mismatch: {src_ep['length']} vs {to_frame - from_frame}"
-                    )
-                    episodes_to_keep_ranges.append((from_frame, to_frame))
+                    from_ts = src_ep[f"videos/{video_key}/from_timestamp"]
+                    to_ts = src_ep[f"videos/{video_key}/to_timestamp"]
+                    episodes_to_keep_ranges.append((from_ts, to_ts))
 
                 # Use PyAV filters to efficiently re-encode only the desired segments.
                 assert src_dataset.meta.video_path is not None
@@ -1090,690 +1083,3 @@ def _copy_episodes_metadata_and_stats(
     else:
         if src_dataset.meta.stats:
             write_stats(src_dataset.meta.stats, dst_meta.root)
-
-
-def _save_episode_images_for_video(
-    dataset: LeRobotDataset,
-    imgs_dir: Path,
-    img_key: str,
-    episode_index: int,
-    num_workers: int = 4,
-) -> None:
-    """Save images from a specific episode and camera to disk for video encoding.
-
-    Args:
-        dataset: The LeRobot dataset to extract images from
-        imgs_dir: Directory to save images to
-        img_key: The image key (camera) to extract
-        episode_index: Index of the episode to save
-        num_workers: Number of threads for parallel image saving
-    """
-    # Create directory
-    imgs_dir.mkdir(parents=True, exist_ok=True)
-
-    # Get dataset without torch format for PIL image access
-    hf_dataset = dataset.hf_dataset.with_format(None)
-
-    # Select only this camera's images
-    imgs_dataset = hf_dataset.select_columns(img_key)
-
-    # Get episode start and end indices
-    from_idx = dataset.meta.episodes["dataset_from_index"][episode_index]
-    to_idx = dataset.meta.episodes["dataset_to_index"][episode_index]
-
-    # Get all items for this episode
-    episode_dataset = imgs_dataset.select(range(from_idx, to_idx))
-
-    # Define function to save a single image
-    def save_single_image(i_item_tuple):
-        i, item = i_item_tuple
-        img = item[img_key]
-        # Use frame-XXXXXX.png format to match encode_video_frames expectations
-        img.save(str(imgs_dir / f"frame-{i:06d}.png"), quality=100)
-        return i
-
-    # Save images with proper naming convention for encode_video_frames (frame-XXXXXX.png)
-    items = list(enumerate(episode_dataset))
-
-    with ThreadPoolExecutor(max_workers=num_workers) as executor:
-        futures = [executor.submit(save_single_image, item) for item in items]
-        for future in as_completed(futures):
-            future.result()  # This will raise any exceptions that occurred
-
-
-def _save_batch_episodes_images(
-    dataset: LeRobotDataset,
-    imgs_dir: Path,
-    img_key: str,
-    episode_indices: list[int],
-    num_workers: int = 4,
-) -> list[float]:
-    """Save images from multiple episodes to disk for batch video encoding.
-
-    Args:
-        dataset: The LeRobot dataset to extract images from
-        imgs_dir: Directory to save images to
-        img_key: The image key (camera) to extract
-        episode_indices: List of episode indices to save
-        num_workers: Number of threads for parallel image saving
-
-    Returns:
-        List of episode durations in seconds
-    """
-    imgs_dir.mkdir(parents=True, exist_ok=True)
-    hf_dataset = dataset.hf_dataset.with_format(None)
-    imgs_dataset = hf_dataset.select_columns(img_key)
-
-    # Define function to save a single image with global frame index
-    # Defined once outside the loop to avoid repeated closure creation
-    def save_single_image(i_item_tuple, base_frame_idx, img_key_param):
-        i, item = i_item_tuple
-        img = item[img_key_param]
-        # Use global frame index for naming
-        img.save(str(imgs_dir / f"frame-{base_frame_idx + i:06d}.png"), quality=100)
-        return i
-
-    episode_durations = []
-    frame_idx = 0
-
-    for ep_idx in episode_indices:
-        # Get episode range
-        from_idx = dataset.meta.episodes["dataset_from_index"][ep_idx]
-        to_idx = dataset.meta.episodes["dataset_to_index"][ep_idx]
-        episode_length = to_idx - from_idx
-        episode_durations.append(episode_length / dataset.fps)
-
-        # Get episode images
-        episode_dataset = imgs_dataset.select(range(from_idx, to_idx))
-
-        # Save images
-        items = list(enumerate(episode_dataset))
-        with ThreadPoolExecutor(max_workers=num_workers) as executor:
-            futures = [executor.submit(save_single_image, item, frame_idx, img_key) for item in items]
-            for future in as_completed(futures):
-                future.result()
-
-        frame_idx += episode_length
-
-    return episode_durations
-
-
-def _iter_episode_batches(
-    episode_indices: list[int],
-    episode_lengths: dict[int, int],
-    size_per_frame_mb: float,
-    video_file_size_limit: float,
-    max_episodes: int | None,
-    max_frames: int | None,
-):
-    """Generator that yields batches of episode indices for video encoding.
-
-    Groups episodes into batches that respect size and memory constraints:
-    - Stays under video file size limit
-    - Respects maximum episodes per batch (if specified)
-    - Respects maximum frames per batch (if specified)
-
-    Args:
-        episode_indices: List of episode indices to batch
-        episode_lengths: Dictionary mapping episode index to episode length
-        size_per_frame_mb: Estimated size per frame in MB
-        video_file_size_limit: Maximum video file size in MB
-        max_episodes: Maximum number of episodes per batch (None = no limit)
-        max_frames: Maximum number of frames per batch (None = no limit)
-
-    Yields:
-        List of episode indices for each batch
-    """
-    batch_episodes = []
-    estimated_size = 0.0
-    total_frames = 0
-
-    for ep_idx in episode_indices:
-        ep_length = episode_lengths[ep_idx]
-        ep_estimated_size = ep_length * size_per_frame_mb
-
-        # we check if adding this episode would exceed any constraint
-        would_exceed_size = estimated_size > 0 and estimated_size + ep_estimated_size >= video_file_size_limit
-        would_exceed_episodes = max_episodes is not None and len(batch_episodes) >= max_episodes
-        would_exceed_frames = max_frames is not None and total_frames + ep_length > max_frames
-
-        if batch_episodes and (would_exceed_size or would_exceed_episodes or would_exceed_frames):
-            # yield current batch before adding this episode
-            yield batch_episodes
-            # start a new batch with current episode
-            batch_episodes = [ep_idx]
-            estimated_size = ep_estimated_size
-            total_frames = ep_length
-        else:
-            # add to current batch
-            batch_episodes.append(ep_idx)
-            estimated_size += ep_estimated_size
-            total_frames += ep_length
-
-    # yield final batch if not empty
-    if batch_episodes:
-        yield batch_episodes
-
-
-def _estimate_frame_size_via_calibration(
-    dataset: LeRobotDataset,
-    img_key: str,
-    episode_indices: list[int],
-    temp_dir: Path,
-    fps: int,
-    vcodec: str,
-    pix_fmt: str,
-    g: int,
-    crf: int,
-    fast_decode: int,
-    num_calibration_frames: int = 30,
-) -> float:
-    """Estimate MB per frame by encoding a small calibration sample.
-
-    Encodes a representative sample of frames using the exact codec parameters
-    to measure actual compression ratio, which is more accurate than heuristics.
-
-    Args:
-        dataset: Source dataset with images.
-        img_key: Image key to calibrate (e.g., "observation.images.top").
-        episode_indices: List of episode indices being processed.
-        temp_dir: Temporary directory for calibration files.
-        fps: Frames per second for video encoding.
-        vcodec: Video codec (libsvtav1, h264, hevc).
-        pix_fmt: Pixel format (yuv420p, etc.).
-        g: GOP size (group of pictures).
-        crf: Constant Rate Factor (quality).
-        fast_decode: Fast decode tuning parameter.
-        num_calibration_frames: Number of frames to use for calibration (default: 30).
-
-    Returns:
-        Estimated size in MB per frame based on actual encoding.
-    """
-    calibration_dir = temp_dir / "calibration" / img_key
-    calibration_dir.mkdir(parents=True, exist_ok=True)
-
-    try:
-        # Select a representative episode (prefer middle episode if available)
-        calibration_ep_idx = episode_indices[len(episode_indices) // 2]
-
-        # Get episode range
-        from_idx = dataset.meta.episodes["dataset_from_index"][calibration_ep_idx]
-        to_idx = dataset.meta.episodes["dataset_to_index"][calibration_ep_idx]
-        episode_length = to_idx - from_idx
-
-        # Use up to num_calibration_frames from this episode
-        num_frames = min(num_calibration_frames, episode_length)
-
-        # Get frames from dataset
-        hf_dataset = dataset.hf_dataset.with_format(None)
-        sample_indices = range(from_idx, from_idx + num_frames)
-
-        # Save calibration frames
-        for i, idx in enumerate(sample_indices):
-            img = hf_dataset[idx][img_key]
-            img.save(str(calibration_dir / f"frame-{i:06d}.png"), quality=100)
-
-        # Encode calibration video
-        calibration_video_path = calibration_dir / "calibration.mp4"
-        encode_video_frames(
-            imgs_dir=calibration_dir,
-            video_path=calibration_video_path,
-            fps=fps,
-            vcodec=vcodec,
-            pix_fmt=pix_fmt,
-            g=g,
-            crf=crf,
-            fast_decode=fast_decode,
-            overwrite=True,
-        )
-
-        # Measure actual compressed size
-        video_size_bytes = calibration_video_path.stat().st_size
-        video_size_mb = video_size_bytes / BYTES_PER_MIB
-        size_per_frame_mb = video_size_mb / num_frames
-
-        logging.info(
-            f"  Calibration: {num_frames} frames -> {video_size_mb:.2f} MB "
-            f"= {size_per_frame_mb:.4f} MB/frame for {img_key}"
-        )
-
-        return size_per_frame_mb
-
-    finally:
-        # Clean up calibration files
-        if calibration_dir.exists():
-            shutil.rmtree(calibration_dir)
-
-
-def _copy_data_without_images(
-    src_dataset: LeRobotDataset,
-    dst_meta: LeRobotDatasetMetadata,
-    episode_indices: list[int],
-    img_keys: list[str],
-) -> None:
-    """Copy data files without image columns.
-
-    Args:
-        src_dataset: Source dataset
-        dst_meta: Destination metadata
-        episode_indices: Episodes to include
-        img_keys: Image keys to remove
-    """
-    from lerobot.datasets.utils import DATA_DIR
-
-    data_dir = src_dataset.root / DATA_DIR
-    parquet_files = sorted(data_dir.glob("*/*.parquet"))
-
-    if not parquet_files:
-        raise ValueError(f"No parquet files found in {data_dir}")
-
-    episode_set = set(episode_indices)
-
-    for src_path in tqdm(parquet_files, desc="Processing data files"):
-        df = pd.read_parquet(src_path).reset_index(drop=True)
-
-        # Filter to only include selected episodes
-        df = df[df["episode_index"].isin(episode_set)].copy()
-
-        if len(df) == 0:
-            continue
-
-        # Remove image columns
-        columns_to_drop = [col for col in img_keys if col in df.columns]
-        if columns_to_drop:
-            df = df.drop(columns=columns_to_drop)
-
-        # Get chunk and file indices from path
-        relative_path = src_path.relative_to(src_dataset.root)
-        chunk_dir = relative_path.parts[1]
-        file_name = relative_path.parts[2]
-        chunk_idx = int(chunk_dir.split("-")[1])
-        file_idx = int(file_name.split("-")[1].split(".")[0])
-
-        # Write to destination without pandas index
-        dst_path = dst_meta.root / f"data/chunk-{chunk_idx:03d}/file-{file_idx:03d}.parquet"
-        dst_path.parent.mkdir(parents=True, exist_ok=True)
-        df.to_parquet(dst_path, index=False)
-
-
-# Video conversion constants
-BYTES_PER_KIB = 1024
-BYTES_PER_MIB = BYTES_PER_KIB * BYTES_PER_KIB
-
-
-def modify_tasks(
-    dataset: LeRobotDataset,
-    new_task: str | None = None,
-    episode_tasks: dict[int, str] | None = None,
-) -> LeRobotDataset:
-    """Modify tasks in a LeRobotDataset.
-
-    This function allows you to either:
-    1. Set a single task for the entire dataset (using `new_task`)
-    2. Set specific tasks for specific episodes (using `episode_tasks`)
-
-    You can combine both: `new_task` sets the default, and `episode_tasks` overrides
-    specific episodes.
-
-    The dataset is modified in-place, updating only the task-related files:
-    - meta/tasks.parquet
-    - data/**/*.parquet (task_index column)
-    - meta/episodes/**/*.parquet (tasks column)
-    - meta/info.json (total_tasks)
-
-    Args:
-        dataset: The source LeRobotDataset to modify.
-        new_task: A single task string to apply to all episodes. If None and episode_tasks
-            is also None, raises an error.
-        episode_tasks: Optional dict mapping episode indices to their task strings.
-            Overrides `new_task` for specific episodes.
-
-
-    Examples:
-        Set a single task for all episodes:
-            dataset = modify_tasks(dataset, new_task="Pick up the cube")
-
-        Set different tasks for specific episodes:
-            dataset = modify_tasks(
-                dataset,
-                episode_tasks={0: "Task A", 1: "Task B", 2: "Task A"}
-            )
-
-        Set a default task with overrides:
-            dataset = modify_tasks(
-                dataset,
-                new_task="Default task",
-                episode_tasks={5: "Special task for episode 5"}
-            )
-    """
-    if new_task is None and episode_tasks is None:
-        raise ValueError("Must specify at least one of new_task or episode_tasks")
-
-    if episode_tasks is not None:
-        valid_indices = set(range(dataset.meta.total_episodes))
-        invalid = set(episode_tasks.keys()) - valid_indices
-        if invalid:
-            raise ValueError(f"Invalid episode indices: {invalid}")
-
-    # Ensure episodes metadata is loaded
-    if dataset.meta.episodes is None:
-        dataset.meta.episodes = load_episodes(dataset.root)
-
-    # Build the mapping from episode index to task string
-    episode_to_task: dict[int, str] = {}
-    for ep_idx in range(dataset.meta.total_episodes):
-        if episode_tasks and ep_idx in episode_tasks:
-            episode_to_task[ep_idx] = episode_tasks[ep_idx]
-        elif new_task is not None:
-            episode_to_task[ep_idx] = new_task
-        else:
-            # Keep original task if not overridden and no default provided
-            original_tasks = dataset.meta.episodes[ep_idx]["tasks"]
-            if not original_tasks:
-                raise ValueError(f"Episode {ep_idx} has no tasks and no default task was provided")
-            episode_to_task[ep_idx] = original_tasks[0]
-
-    # Collect all unique tasks and create new task mapping
-    unique_tasks = sorted(set(episode_to_task.values()))
-    new_task_df = pd.DataFrame(
-        {"task_index": list(range(len(unique_tasks)))}, index=pd.Index(unique_tasks, name="task")
-    )
-    task_to_index = {task: idx for idx, task in enumerate(unique_tasks)}
-
-    logging.info(f"Modifying tasks in {dataset.repo_id}")
-    logging.info(f"New tasks: {unique_tasks}")
-
-    root = dataset.root
-
-    # Update data files - modify task_index column
-    logging.info("Updating data files...")
-    data_dir = root / DATA_DIR
-
-    for parquet_path in tqdm(sorted(data_dir.rglob("*.parquet")), desc="Updating data"):
-        df = pd.read_parquet(parquet_path)
-
-        # Build a mapping from episode_index to new task_index for rows in this file
-        episode_indices_in_file = df["episode_index"].unique()
-        ep_to_new_task_idx = {
-            ep_idx: task_to_index[episode_to_task[ep_idx]] for ep_idx in episode_indices_in_file
-        }
-
-        # Update task_index column
-        df["task_index"] = df["episode_index"].map(ep_to_new_task_idx)
-        df.to_parquet(parquet_path, index=False)
-
-    # Update episodes metadata - modify tasks column
-    logging.info("Updating episodes metadata...")
-    episodes_dir = root / "meta" / "episodes"
-
-    for parquet_path in tqdm(sorted(episodes_dir.rglob("*.parquet")), desc="Updating episodes"):
-        df = pd.read_parquet(parquet_path)
-
-        # Update tasks column
-        df["tasks"] = df["episode_index"].apply(lambda ep_idx: [episode_to_task[ep_idx]])
-        df.to_parquet(parquet_path, index=False)
-
-    # Write new tasks.parquet
-    write_tasks(new_task_df, root)
-
-    # Update info.json
-    dataset.meta.info["total_tasks"] = len(unique_tasks)
-    write_info(dataset.meta.info, root)
-
-    # Reload metadata to reflect changes
-    dataset.meta.tasks = new_task_df
-    dataset.meta.episodes = load_episodes(root)
-
-    logging.info(f"Tasks: {unique_tasks}")
-
-    return dataset
-
-
-def convert_image_to_video_dataset(
-    dataset: LeRobotDataset,
-    output_dir: Path | None = None,
-    repo_id: str | None = None,
-    vcodec: str = "libsvtav1",
-    pix_fmt: str = "yuv420p",
-    g: int = 2,
-    crf: int = 30,
-    fast_decode: int = 0,
-    episode_indices: list[int] | None = None,
-    num_workers: int = 4,
-    max_episodes_per_batch: int | None = None,
-    max_frames_per_batch: int | None = None,
-) -> LeRobotDataset:
-    """Convert image-to-video dataset.
-
-    Creates a new LeRobotDataset with images encoded as videos, following the proper
-    LeRobot dataset structure with videos stored in chunked MP4 files.
-
-    Args:
-        dataset: The source LeRobot dataset with images
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
-        vcodec: Video codec (default: libsvtav1)
-        pix_fmt: Pixel format (default: yuv420p)
-        g: Group of pictures size (default: 2)
-        crf: Constant rate factor (default: 30)
-        fast_decode: Fast decode tuning (default: 0)
-        episode_indices: List of episode indices to convert (None = all episodes)
-        num_workers: Number of threads for parallel processing (default: 4)
-        max_episodes_per_batch: Maximum episodes per video batch to avoid memory issues (None = no limit)
-        max_frames_per_batch: Maximum frames per video batch to avoid memory issues (None = no limit)
-
-    Returns:
-        New LeRobotDataset with images encoded as videos
-    """
-    # Check that it's an image dataset
-    if len(dataset.meta.video_keys) > 0:
-        raise ValueError(
-            f"This operation is for image datasets only. Video dataset provided: {dataset.repo_id}"
-        )
-
-    # Get all image keys
-    hf_dataset = dataset.hf_dataset.with_format(None)
-    img_keys = [key for key in hf_dataset.features if key.startswith(OBS_IMAGE)]
-
-    if len(img_keys) == 0:
-        raise ValueError(f"No image keys found in dataset {dataset.repo_id}")
-
-    # Determine which episodes to process
-    if episode_indices is None:
-        episode_indices = list(range(dataset.meta.total_episodes))
-
-    if repo_id is None:
-        repo_id = f"{dataset.repo_id}_video"
-
-    logging.info(
-        f"Converting {len(episode_indices)} episodes with {len(img_keys)} cameras from {dataset.repo_id}"
-    )
-    logging.info(f"Video codec: {vcodec}, pixel format: {pix_fmt}, GOP: {g}, CRF: {crf}")
-
-    # Create new features dict, converting image features to video features
-    new_features = {}
-    for key, value in dataset.meta.features.items():
-        if key not in img_keys:
-            new_features[key] = value
-        else:
-            # Convert image key to video format
-            new_features[key] = value.copy()
-            new_features[key]["dtype"] = "video"  # Change dtype from "image" to "video"
-            # Video info will be updated after episodes are encoded
-
-    # Create new metadata for video dataset
-    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
-    new_meta = LeRobotDatasetMetadata.create(
-        repo_id=repo_id,
-        fps=dataset.meta.fps,
-        features=new_features,
-        robot_type=dataset.meta.robot_type,
-        root=output_dir,
-        use_videos=True,
-        chunks_size=dataset.meta.chunks_size,
-        data_files_size_in_mb=dataset.meta.data_files_size_in_mb,
-        video_files_size_in_mb=dataset.meta.video_files_size_in_mb,
-    )
-
-    # Create temporary directory for image extraction
-    temp_dir = output_dir / "temp_images"
-    temp_dir.mkdir(parents=True, exist_ok=True)
-
-    # Process all episodes and batch encode videos
-    # Use dictionary for O(1) episode metadata lookups instead of O(n) linear search
-    all_episode_metadata = {}
-    fps = int(dataset.fps)
-
-    try:
-        # Build episode metadata entries first
-        logging.info("Building episode metadata...")
-        cumulative_frame_idx = 0
-        for ep_idx in episode_indices:
-            src_episode = dataset.meta.episodes[ep_idx]
-            ep_length = src_episode["length"]
-            ep_meta = {
-                "episode_index": ep_idx,
-                "length": ep_length,
-                "dataset_from_index": cumulative_frame_idx,
-                "dataset_to_index": cumulative_frame_idx + ep_length,
-            }
-            if "data/chunk_index" in src_episode:
-                ep_meta["data/chunk_index"] = src_episode["data/chunk_index"]
-                ep_meta["data/file_index"] = src_episode["data/file_index"]
-            all_episode_metadata[ep_idx] = ep_meta
-            cumulative_frame_idx += ep_length
-
-        # Process each camera and batch encode multiple episodes together
-        video_file_size_limit = new_meta.video_files_size_in_mb
-
-        # Pre-compute episode lengths for batching
-        episode_lengths = {ep_idx: dataset.meta.episodes["length"][ep_idx] for ep_idx in episode_indices}
-
-        for img_key in tqdm(img_keys, desc="Processing cameras"):
-            # Estimate size per frame by encoding a small calibration sample
-            # This provides accurate compression ratio for the specific codec parameters
-            size_per_frame_mb = _estimate_frame_size_via_calibration(
-                dataset=dataset,
-                img_key=img_key,
-                episode_indices=episode_indices,
-                temp_dir=temp_dir,
-                fps=fps,
-                vcodec=vcodec,
-                pix_fmt=pix_fmt,
-                g=g,
-                crf=crf,
-                fast_decode=fast_decode,
-            )
-
-            logging.info(f"Processing camera: {img_key}")
-            chunk_idx, file_idx = 0, 0
-            cumulative_timestamp = 0.0
-
-            # Process episodes in batches to stay under size limit
-            for batch_episodes in _iter_episode_batches(
-                episode_indices=episode_indices,
-                episode_lengths=episode_lengths,
-                size_per_frame_mb=size_per_frame_mb,
-                video_file_size_limit=video_file_size_limit,
-                max_episodes=max_episodes_per_batch,
-                max_frames=max_frames_per_batch,
-            ):
-                total_frames_in_batch = sum(episode_lengths[idx] for idx in batch_episodes)
-                logging.info(
-                    f"  Encoding batch of {len(batch_episodes)} episodes "
-                    f"({batch_episodes[0]}-{batch_episodes[-1]}) = {total_frames_in_batch} frames"
-                )
-
-                # Save images for all episodes in this batch
-                imgs_dir = temp_dir / f"batch_{chunk_idx}_{file_idx}" / img_key
-                episode_durations = _save_batch_episodes_images(
-                    dataset=dataset,
-                    imgs_dir=imgs_dir,
-                    img_key=img_key,
-                    episode_indices=batch_episodes,
-                    num_workers=num_workers,
-                )
-
-                # Encode all batched episodes into single video
-                video_path = new_meta.root / new_meta.video_path.format(
-                    video_key=img_key, chunk_index=chunk_idx, file_index=file_idx
-                )
-                video_path.parent.mkdir(parents=True, exist_ok=True)
-
-                encode_video_frames(
-                    imgs_dir=imgs_dir,
-                    video_path=video_path,
-                    fps=fps,
-                    vcodec=vcodec,
-                    pix_fmt=pix_fmt,
-                    g=g,
-                    crf=crf,
-                    fast_decode=fast_decode,
-                    overwrite=True,
-                )
-
-                # Clean up temporary images
-                shutil.rmtree(imgs_dir)
-
-                # Update metadata for each episode in the batch
-                for ep_idx, duration in zip(batch_episodes, episode_durations, strict=True):
-                    from_timestamp = cumulative_timestamp
-                    to_timestamp = cumulative_timestamp + duration
-                    cumulative_timestamp = to_timestamp
-
-                    # Find episode metadata entry and add video metadata (O(1) dictionary lookup)
-                    ep_meta = all_episode_metadata[ep_idx]
-                    ep_meta[f"videos/{img_key}/chunk_index"] = chunk_idx
-                    ep_meta[f"videos/{img_key}/file_index"] = file_idx
-                    ep_meta[f"videos/{img_key}/from_timestamp"] = from_timestamp
-                    ep_meta[f"videos/{img_key}/to_timestamp"] = to_timestamp
-
-                # Move to next video file for next batch
-                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, new_meta.chunks_size)
-                cumulative_timestamp = 0.0
-
-        # Copy and transform data files (removing image columns)
-        _copy_data_without_images(dataset, new_meta, episode_indices, img_keys)
-
-        # Save episode metadata
-        episodes_df = pd.DataFrame(list(all_episode_metadata.values()))
-        episodes_path = new_meta.root / "meta" / "episodes" / "chunk-000" / "file-000.parquet"
-        episodes_path.parent.mkdir(parents=True, exist_ok=True)
-        episodes_df.to_parquet(episodes_path, index=False)
-
-        # Update metadata info
-        new_meta.info["total_episodes"] = len(episode_indices)
-        new_meta.info["total_frames"] = sum(ep["length"] for ep in all_episode_metadata.values())
-        new_meta.info["total_tasks"] = dataset.meta.total_tasks
-        new_meta.info["splits"] = {"train": f"0:{len(episode_indices)}"}
-
-        # Update video info for all image keys (now videos)
-        # We need to manually set video info since update_video_info() checks video_keys first
-        for img_key in img_keys:
-            if not new_meta.features[img_key].get("info", None):
-                video_path = new_meta.root / new_meta.video_path.format(
-                    video_key=img_key, chunk_index=0, file_index=0
-                )
-                new_meta.info["features"][img_key]["info"] = get_video_info(video_path)
-
-        write_info(new_meta.info, new_meta.root)
-
-        # Copy stats and tasks
-        if dataset.meta.stats is not None:
-            # Remove image stats
-            new_stats = {k: v for k, v in dataset.meta.stats.items() if k not in img_keys}
-            write_stats(new_stats, new_meta.root)
-
-        if dataset.meta.tasks is not None:
-            write_tasks(dataset.meta.tasks, new_meta.root)
-
-    finally:
-        # Clean up temporary directory
-        if temp_dir.exists():
-            shutil.rmtree(temp_dir)
-
-    logging.info(f"Completed converting {dataset.repo_id} to video format")
-    logging.info(f"New dataset saved to: {output_dir}")
-
-    # Return new dataset
-    return LeRobotDataset(repo_id=repo_id, root=output_dir)

src/lerobot/datasets/lerobot_dataset.py

@@ -57,7 +57,6 @@ from lerobot.datasets.utils import (
     load_info,
     load_nested_dataset,
     load_stats,
-    load_subtasks,
     load_tasks,
     update_chunk_file_indices,
     validate_episode_buffer,
@@ -68,7 +67,6 @@ from lerobot.datasets.utils import (
     write_tasks,
 )
 from lerobot.datasets.video_utils import (
-    StreamingVideoEncoder,
     VideoFrame,
     concatenate_video_files,
     decode_video_frames,
@@ -76,11 +74,11 @@ from lerobot.datasets.video_utils import (
     get_safe_default_codec,
     get_video_duration_in_s,
     get_video_info,
-    resolve_vcodec,
 )
 from lerobot.utils.constants import HF_LEROBOT_HOME
 
 CODEBASE_VERSION = "v3.0"
+VALID_VIDEO_CODECS = {"h264", "hevc", "libsvtav1"}
 
 
 class LeRobotDatasetMetadata:
@@ -164,7 +162,6 @@ class LeRobotDatasetMetadata:
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
         self.tasks = load_tasks(self.root)
-        self.subtasks = load_subtasks(self.root)
         self.episodes = load_episodes(self.root)
         self.stats = load_stats(self.root)
 
@@ -314,7 +311,7 @@ class LeRobotDatasetMetadata:
         if self.tasks is None:
             new_tasks = tasks
             task_indices = range(len(tasks))
-            self.tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(tasks, name="task"))
+            self.tasks = pd.DataFrame({"task_index": task_indices}, index=tasks)
         else:
             new_tasks = [task for task in tasks if task not in self.tasks.index]
             new_task_indices = range(len(self.tasks), len(self.tasks) + len(new_tasks))
@@ -521,7 +518,6 @@ class LeRobotDatasetMetadata:
         _validate_feature_names(features)
 
         obj.tasks = None
-        obj.subtasks = None
         obj.episodes = None
         obj.stats = None
         obj.info = create_empty_dataset_info(
@@ -546,19 +542,12 @@ class LeRobotDatasetMetadata:
 
 
 def _encode_video_worker(
-    video_key: str,
-    episode_index: int,
-    root: Path,
-    fps: int,
-    vcodec: str = "libsvtav1",
-    encoder_threads: int | None = None,
+    video_key: str, episode_index: int, root: Path, fps: int, vcodec: str = "libsvtav1"
 ) -> Path:
     temp_path = Path(tempfile.mkdtemp(dir=root)) / f"{video_key}_{episode_index:03d}.mp4"
     fpath = DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=episode_index, frame_index=0)
     img_dir = (root / fpath).parent
-    encode_video_frames(
-        img_dir, temp_path, fps, vcodec=vcodec, overwrite=True, encoder_threads=encoder_threads
-    )
+    encode_video_frames(img_dir, temp_path, fps, vcodec=vcodec, overwrite=True)
     shutil.rmtree(img_dir)
     return temp_path
 
@@ -578,9 +567,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
         vcodec: str = "libsvtav1",
-        streaming_encoding: bool = False,
-        encoder_queue_maxsize: int = 30,
-        encoder_threads: int | None = None,
     ):
         """
         2 modes are available for instantiating this class, depending on 2 different use cases:
@@ -664,11 +650,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         for the README).
 
         Args:
-            repo_id (str): This is the repo id that will be used to fetch the dataset.
-            root (Path | None, optional): Local directory where the dataset will be downloaded and
-                stored. If set, all dataset files will be stored directly under this path. If not set, the
-                dataset files will be stored under $HF_LEROBOT_HOME/repo_id (configurable via the
-                HF_LEROBOT_HOME environment variable).
+            repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset
+                will be stored under root/repo_id.
+            root (Path | None, optional): Local directory to use for downloading/writing files. You can also
+                set the LEROBOT_HOME environment variable to point to a different location. Defaults to
+                '~/.cache/huggingface/lerobot'.
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
             image_transforms (Callable | None, optional): You can pass standard v2 image transforms from
@@ -694,17 +680,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
             batch_encoding_size (int, optional): Number of episodes to accumulate before batch encoding videos.
                 Set to 1 for immediate encoding (default), or higher for batched encoding. Defaults to 1.
             vcodec (str, optional): Video codec for encoding videos during recording. Options: 'h264', 'hevc',
-                'libsvtav1', 'auto', or hardware-specific codecs like 'h264_videotoolbox', 'h264_nvenc'.
-                Defaults to 'libsvtav1'. Use 'auto' to auto-detect the best available hardware encoder.
-            streaming_encoding (bool, optional): If True, encode video frames in real-time during capture
-                instead of writing PNG images first. This makes save_episode() near-instant. Defaults to False.
-            encoder_queue_maxsize (int, optional): Maximum number of frames to buffer per camera when using
-                streaming encoding. Defaults to 30 (~1s at 30fps).
-            encoder_threads (int | None, optional): Number of threads per encoder instance. None lets the
-                codec auto-detect (default). Lower values reduce CPU usage per encoder. Maps to 'lp' (via svtav1-params) for
-                libsvtav1 and 'threads' for h264/hevc.
+                'libsvtav1'. Defaults to 'libsvtav1'. Use 'h264' for faster encoding on systems where AV1
+                encoding is CPU-heavy.
         """
         super().__init__()
+        if vcodec not in VALID_VIDEO_CODECS:
+            raise ValueError(f"Invalid vcodec '{vcodec}'. Must be one of: {sorted(VALID_VIDEO_CODECS)}")
         self.repo_id = repo_id
         self.root = Path(root) if root else HF_LEROBOT_HOME / repo_id
         self.image_transforms = image_transforms
@@ -716,8 +697,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.delta_indices = None
         self.batch_encoding_size = batch_encoding_size
         self.episodes_since_last_encoding = 0
-        self.vcodec = resolve_vcodec(vcodec)
-        self._encoder_threads = encoder_threads
+        self.vcodec = vcodec
 
         # Unused attributes
         self.image_writer = None
@@ -725,7 +705,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.writer = None
         self.latest_episode = None
         self._current_file_start_frame = None  # Track the starting frame index of the current parquet file
-        self._streaming_encoder = None
 
         self.root.mkdir(exist_ok=True, parents=True)
 
@@ -747,7 +726,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             # Check if cached dataset contains all requested episodes
             if not self._check_cached_episodes_sufficient():
                 raise FileNotFoundError("Cached dataset doesn't contain all requested episodes")
-        except (FileNotFoundError, NotADirectoryError):
+        except (AssertionError, FileNotFoundError, NotADirectoryError):
             if is_valid_version(self.revision):
                 self.revision = get_safe_version(self.repo_id, self.revision)
             self.download(download_videos)
@@ -767,19 +746,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s)
             self.delta_indices = get_delta_indices(self.delta_timestamps, self.fps)
 
-        # Initialize streaming encoder for resumed recording
-        if streaming_encoding and len(self.meta.video_keys) > 0:
-            self._streaming_encoder = StreamingVideoEncoder(
-                fps=self.meta.fps,
-                vcodec=self.vcodec,
-                pix_fmt="yuv420p",
-                g=2,
-                crf=30,
-                preset=None,
-                queue_maxsize=encoder_queue_maxsize,
-                encoder_threads=encoder_threads,
-            )
-
     def _close_writer(self) -> None:
         """Close and cleanup the parquet writer if it exists."""
         writer = getattr(self, "writer", None)
@@ -839,7 +805,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             hub_api.upload_folder(**upload_kwargs)
 
         card = create_lerobot_dataset_card(
-            tags=tags, dataset_info=self.meta.info, license=license, repo_id=self.repo_id, **card_kwargs
+            tags=tags, dataset_info=self.meta.info, license=license, **card_kwargs
         )
         card.push_to_hub(repo_id=self.repo_id, repo_type="dataset", revision=branch)
 
@@ -969,30 +935,17 @@ class LeRobotDataset(torch.utils.data.Dataset):
         else:
             return get_hf_features_from_features(self.features)
 
-    def _get_query_indices(
-        self, abs_idx: int, ep_idx: int
-    ) -> tuple[dict[str, list[int]], dict[str, torch.Tensor]]:
-        """Compute query indices for delta timestamps.
-
-        Args:
-            abs_idx: The absolute index in the full dataset (not the relative index in filtered episodes).
-            ep_idx: The episode index.
-
-        Returns:
-            A tuple of (query_indices, padding) where:
-            - query_indices: Dict mapping keys to lists of absolute indices to query
-            - padding: Dict mapping "{key}_is_pad" to boolean tensors indicating padded positions
-        """
+    def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]:
         ep = self.meta.episodes[ep_idx]
         ep_start = ep["dataset_from_index"]
         ep_end = ep["dataset_to_index"]
         query_indices = {
-            key: [max(ep_start, min(ep_end - 1, abs_idx + delta)) for delta in delta_idx]
+            key: [max(ep_start, min(ep_end - 1, idx + delta)) for delta in delta_idx]
             for key, delta_idx in self.delta_indices.items()
         }
         padding = {  # Pad values outside of current episode range
             f"{key}_is_pad": torch.BoolTensor(
-                [(abs_idx + delta < ep_start) | (abs_idx + delta >= ep_end) for delta in delta_idx]
+                [(idx + delta < ep_start) | (idx + delta >= ep_end) for delta in delta_idx]
             )
             for key, delta_idx in self.delta_indices.items()
         }
@@ -1084,12 +1037,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self._ensure_hf_dataset_loaded()
         item = self.hf_dataset[idx]
         ep_idx = item["episode_index"].item()
-        # Use the absolute index from the dataset for delta timestamp calculations
-        abs_idx = item["index"].item()
 
         query_indices = None
         if self.delta_indices is not None:
-            query_indices, padding = self._get_query_indices(abs_idx, ep_idx)
+            query_indices, padding = self._get_query_indices(idx, ep_idx)
             query_result = self._query_hf_dataset(query_indices)
             item = {**item, **padding}
             for key, val in query_result.items():
@@ -1109,12 +1060,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         # Add task as a string
         task_idx = item["task_index"].item()
         item["task"] = self.meta.tasks.iloc[task_idx].name
-
-        # add subtask information if available
-        if "subtask_index" in self.features and self.meta.subtasks is not None:
-            subtask_idx = item["subtask_index"].item()
-            item["subtask"] = self.meta.subtasks.iloc[subtask_idx].name
-
         return item
 
     def __repr__(self):
@@ -1135,8 +1080,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         """
         self._close_writer()
         self.meta._close_writer()
-        if self._streaming_encoder is not None:
-            self._streaming_encoder.close()
 
     def create_episode_buffer(self, episode_index: int | None = None) -> dict:
         current_ep_idx = self.meta.total_episodes if episode_index is None else episode_index
@@ -1191,13 +1134,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.episode_buffer["timestamp"].append(timestamp)
         self.episode_buffer["task"].append(frame.pop("task"))  # Remove task from frame after processing
 
-        # Start streaming encoder on first frame of episode (once, before iterating keys)
-        if frame_index == 0 and self._streaming_encoder is not None:
-            self._streaming_encoder.start_episode(
-                video_keys=list(self.meta.video_keys),
-                temp_dir=self.root,
-            )
-
         # Add frame features to episode_buffer
         for key in frame:
             if key not in self.features:
@@ -1205,10 +1141,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                     f"An element of the frame is not in the features. '{key}' not in '{self.features.keys()}'."
                 )
 
-            if self.features[key]["dtype"] == "video" and self._streaming_encoder is not None:
-                self._streaming_encoder.feed_frame(key, frame[key])
-                self.episode_buffer[key].append(None)  # Placeholder (video keys are skipped in parquet)
-            elif self.features[key]["dtype"] in ["image", "video"]:
+            if self.features[key]["dtype"] in ["image", "video"]:
                 img_path = self._get_image_file_path(
                     episode_index=self.episode_buffer["episode_index"], image_key=key, frame_index=frame_index
                 )
@@ -1269,38 +1202,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         # Wait for image writer to end, so that episode stats over images can be computed
         self._wait_image_writer()
-
-        has_video_keys = len(self.meta.video_keys) > 0
-        use_streaming = self._streaming_encoder is not None and has_video_keys
-        use_batched_encoding = self.batch_encoding_size > 1
-
-        if use_streaming:
-            # Compute stats for non-video features only (video stats come from encoder)
-            non_video_buffer = {
-                k: v
-                for k, v in episode_buffer.items()
-                if self.features.get(k, {}).get("dtype") not in ("video",)
-            }
-            non_video_features = {k: v for k, v in self.features.items() if v["dtype"] != "video"}
-            ep_stats = compute_episode_stats(non_video_buffer, non_video_features)
-        else:
-            ep_stats = compute_episode_stats(episode_buffer, self.features)
+        ep_stats = compute_episode_stats(episode_buffer, self.features)
 
         ep_metadata = self._save_episode_data(episode_buffer)
+        has_video_keys = len(self.meta.video_keys) > 0
+        use_batched_encoding = self.batch_encoding_size > 1
 
-        if use_streaming:
-            # Finish streaming encoding and collect results
-            streaming_results = self._streaming_encoder.finish_episode()
-            for video_key in self.meta.video_keys:
-                temp_path, video_stats = streaming_results[video_key]
-                if video_stats is not None:
-                    # Format stats same as compute_episode_stats: normalize to [0,1], reshape to (C,1,1)
-                    ep_stats[video_key] = {
-                        k: v if k == "count" else np.squeeze(v.reshape(1, -1, 1, 1) / 255.0, axis=0)
-                        for k, v in video_stats.items()
-                    }
-                ep_metadata.update(self._save_episode_video(video_key, episode_index, temp_path=temp_path))
-        elif has_video_keys and not use_batched_encoding:
+        if has_video_keys and not use_batched_encoding:
             num_cameras = len(self.meta.video_keys)
             if parallel_encoding and num_cameras > 1:
                 # TODO(Steven): Ideally we would like to control the number of threads per encoding such that:
@@ -1314,7 +1222,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
                             self.root,
                             self.fps,
                             self.vcodec,
-                            self._encoder_threads,
                         ): video_key
                         for video_key in self.meta.video_keys
                     }
@@ -1583,10 +1490,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return metadata
 
     def clear_episode_buffer(self, delete_images: bool = True) -> None:
-        # Cancel streaming encoder if active
-        if self._streaming_encoder is not None:
-            self._streaming_encoder.cancel_episode()
-
         # Clean up image files for the current episode buffer
         if delete_images:
             # Wait for the async image writer to finish
@@ -1595,7 +1498,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             episode_index = self.episode_buffer["episode_index"]
             if isinstance(episode_index, np.ndarray):
                 episode_index = episode_index.item() if episode_index.size == 1 else episode_index[0]
-            for cam_key in self.meta.image_keys:
+            for cam_key in self.meta.camera_keys:
                 img_dir = self._get_image_file_dir(episode_index, cam_key)
                 if img_dir.is_dir():
                     shutil.rmtree(img_dir)
@@ -1634,9 +1537,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
         since video encoding with ffmpeg is already using multithreading.
         """
-        return _encode_video_worker(
-            video_key, episode_index, self.root, self.fps, self.vcodec, self._encoder_threads
-        )
+        return _encode_video_worker(video_key, episode_index, self.root, self.fps, self.vcodec)
 
     @classmethod
     def create(
@@ -1653,13 +1554,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
         vcodec: str = "libsvtav1",
-        metadata_buffer_size: int = 10,
-        streaming_encoding: bool = False,
-        encoder_queue_maxsize: int = 30,
-        encoder_threads: int | None = None,
     ) -> "LeRobotDataset":
         """Create a LeRobot Dataset from scratch in order to record data."""
-        vcodec = resolve_vcodec(vcodec)
+        if vcodec not in VALID_VIDEO_CODECS:
+            raise ValueError(f"Invalid vcodec '{vcodec}'. Must be one of: {sorted(VALID_VIDEO_CODECS)}")
         obj = cls.__new__(cls)
         obj.meta = LeRobotDatasetMetadata.create(
             repo_id=repo_id,
@@ -1668,7 +1566,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             features=features,
             root=root,
             use_videos=use_videos,
-            metadata_buffer_size=metadata_buffer_size,
         )
         obj.repo_id = obj.meta.repo_id
         obj.root = obj.meta.root
@@ -1678,7 +1575,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.batch_encoding_size = batch_encoding_size
         obj.episodes_since_last_encoding = 0
         obj.vcodec = vcodec
-        obj._encoder_threads = encoder_threads
 
         if image_writer_processes or image_writer_threads:
             obj.start_image_writer(image_writer_processes, image_writer_threads)
@@ -1700,22 +1596,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj._lazy_loading = False
         obj._recorded_frames = 0
         obj._writer_closed_for_reading = False
-
-        # Initialize streaming encoder
-        if streaming_encoding and len(obj.meta.video_keys) > 0:
-            obj._streaming_encoder = StreamingVideoEncoder(
-                fps=fps,
-                vcodec=vcodec,
-                pix_fmt="yuv420p",
-                g=2,
-                crf=30,
-                preset=None,
-                queue_maxsize=encoder_queue_maxsize,
-                encoder_threads=encoder_threads,
-            )
-        else:
-            obj._streaming_encoder = None
-
         return obj
 
 
@@ -1771,12 +1651,11 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
             )
         for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
             extra_keys = set(ds.features).difference(intersection_features)
-            if extra_keys:
-                logging.warning(
-                    f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
-                    "other datasets."
-                )
-                self.disabled_features.update(extra_keys)
+            logging.warning(
+                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                "other datasets."
+            )
+            self.disabled_features.update(extra_keys)
 
         self.image_transforms = image_transforms
         self.delta_timestamps = delta_timestamps

src/lerobot/datasets/transforms.py

@@ -216,17 +216,16 @@ class ImageTransformsConfig:
 
 
 def make_transform_from_config(cfg: ImageTransformConfig):
-    if cfg.type == "SharpnessJitter":
+    if cfg.type == "Identity":
+        return v2.Identity(**cfg.kwargs)
+    elif cfg.type == "ColorJitter":
+        return v2.ColorJitter(**cfg.kwargs)
+    elif cfg.type == "SharpnessJitter":
         return SharpnessJitter(**cfg.kwargs)
-
-    transform_cls = getattr(v2, cfg.type, None)
-    if isinstance(transform_cls, type) and issubclass(transform_cls, Transform):
-        return transform_cls(**cfg.kwargs)
-
-    raise ValueError(
-        f"Transform '{cfg.type}' is not valid. It must be a class in "
-        f"torchvision.transforms.v2 or 'SharpnessJitter'."
-    )
+    elif cfg.type == "RandomAffine":
+        return v2.RandomAffine(**cfg.kwargs)
+    else:
+        raise ValueError(f"Transform '{cfg.type}' is not valid.")
 
 
 class ImageTransforms(Transform):

src/lerobot/datasets/utils.py

@@ -21,7 +21,7 @@ from collections import deque
 from collections.abc import Iterable, Iterator
 from pathlib import Path
 from pprint import pformat
-from typing import Any
+from typing import Any, Generic, TypeVar
 
 import datasets
 import numpy as np
@@ -60,7 +60,6 @@ VIDEO_DIR = "videos"
 
 CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
-DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
@@ -78,6 +77,8 @@ DEFAULT_FEATURES = {
     "task_index": {"dtype": "int64", "shape": (1,), "names": None},
 }
 
+T = TypeVar("T")
+
 
 def get_parquet_file_size_in_mb(parquet_path: str | Path) -> float:
     metadata = pq.read_metadata(parquet_path)
@@ -120,9 +121,19 @@ def load_nested_dataset(
         raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}")
 
     with SuppressProgressBars():
-        # We use .from_parquet() memory-mapped loading for efficiency
-        filters = pa_ds.field("episode_index").isin(episodes) if episodes is not None else None
-        return Dataset.from_parquet([str(path) for path in paths], filters=filters, features=features)
+        # When no filtering needed, Dataset uses memory-mapped loading for efficiency
+        # PyArrow loads the entire dataset into memory
+        if episodes is None:
+            return Dataset.from_parquet([str(path) for path in paths], features=features)
+
+        arrow_dataset = pa_ds.dataset(paths, format="parquet")
+        filter_expr = pa_ds.field("episode_index").isin(episodes)
+        table = arrow_dataset.to_table(filter=filter_expr)
+
+        if features is not None:
+            table = table.cast(features.arrow_schema)
+
+        return Dataset(table)
 
 
 def get_parquet_num_frames(parquet_path: str | Path) -> int:
@@ -339,18 +350,9 @@ def write_tasks(tasks: pandas.DataFrame, local_dir: Path) -> None:
 
 def load_tasks(local_dir: Path) -> pandas.DataFrame:
     tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
-    tasks.index.name = "task"
     return tasks
 
 
-def load_subtasks(local_dir: Path) -> pandas.DataFrame | None:
-    """Load subtasks from subtasks.parquet if it exists."""
-    subtasks_path = local_dir / DEFAULT_SUBTASKS_PATH
-    if subtasks_path.exists():
-        return pd.read_parquet(subtasks_path)
-    return None
-
-
 def write_episodes(episodes: Dataset, local_dir: Path) -> None:
     """Write episode metadata to a parquet file in the LeRobot v3.0 format.
     This function writes episode-level metadata to a single parquet file.
@@ -1170,21 +1172,12 @@ def validate_episode_buffer(episode_buffer: dict, total_episodes: int, features:
         )
 
 
-def to_parquet_with_hf_images(
-    df: pandas.DataFrame, path: Path, features: datasets.Features | None = None
-) -> None:
+def to_parquet_with_hf_images(df: pandas.DataFrame, path: Path) -> None:
     """This function correctly writes to parquet a panda DataFrame that contains images encoded by HF dataset.
     This way, it can be loaded by HF dataset and correctly formatted images are returned.
-
-    Args:
-        df: DataFrame to write to parquet.
-        path: Path to write the parquet file.
-        features: Optional HuggingFace Features schema. If provided, ensures image columns
-                  are properly typed as Image() in the parquet schema.
     """
     # TODO(qlhoest): replace this weird synthax by `df.to_parquet(path)` only
-    ds = datasets.Dataset.from_dict(df.to_dict(orient="list"), features=features)
-    ds.to_parquet(path)
+    datasets.Dataset.from_dict(df.to_dict(orient="list")).to_parquet(path)
 
 
 def item_to_torch(item: dict) -> dict:
@@ -1232,7 +1225,7 @@ class LookAheadError(Exception):
     pass
 
 
-class Backtrackable[T]:
+class Backtrackable(Generic[T]):
     """
     Wrap any iterator/iterable so you can step back up to `history` items
     and look ahead up to `lookahead` items.

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -36,11 +36,8 @@ Convert a local dataset (works in place):
 ```bash
 python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
     --repo-id=lerobot/pusht \
-    --root=/path/to/local/dataset/directory \
+    --root=/path/to/local/dataset/directory
     --push-to-hub=false
-
-N.B. Path semantics (v2): --root is the exact dataset folder containing
-meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
 ```
 
 """
@@ -108,7 +105,7 @@ episodes.jsonl
 {"episode_index": 1, "tasks": ["Put the blue block in the green bowl"], "length": 266}
 
 NEW
-meta/episodes/chunk-000/file_000.parquet
+meta/episodes/chunk-000/episodes_000.parquet
 episode_index | video_chunk_index | video_file_index | data_chunk_index | data_file_index | tasks | length
 -------------------------
 OLD
@@ -116,16 +113,15 @@ tasks.jsonl
 {"task_index": 1, "task": "Put the blue block in the green bowl"}
 
 NEW
-meta/tasks.parquet
+meta/tasks/chunk-000/file_000.parquet
 task_index | task
 -------------------------
 OLD
 episodes_stats.jsonl
-{"episode_index": 1, "stats": {"feature_name": {"min": ..., "max": ..., "mean": ..., "std": ..., "count": ...}}}
 
 NEW
-meta/episodes/chunk-000/file_000.parquet
-episode_index | feature_name/min | feature_name/max | feature_name/mean | feature_name/std | feature_name/count
+meta/episodes_stats/chunk-000/file_000.parquet
+episode_index | mean | std | min | max
 -------------------------
 UPDATE
 meta/info.json
@@ -174,7 +170,7 @@ def convert_tasks(root, new_root):
     tasks, _ = legacy_load_tasks(root)
     task_indices = tasks.keys()
     task_strings = tasks.values()
-    df_tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(task_strings, name="task"))
+    df_tasks = pd.DataFrame({"task_index": task_indices}, index=task_strings)
     write_tasks(df_tasks, new_root)
 
 
@@ -205,6 +201,7 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
 
     image_keys = get_image_keys(root)
 
+    ep_idx = 0
     chunk_idx = 0
     file_idx = 0
     size_in_mb = 0
@@ -214,23 +211,9 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
 
     logging.info(f"Converting data files from {len(ep_paths)} episodes")
 
-    for ep_idx, ep_path in enumerate(tqdm.tqdm(ep_paths, desc="convert data files")):
+    for ep_path in tqdm.tqdm(ep_paths, desc="convert data files"):
         ep_size_in_mb = get_parquet_file_size_in_mb(ep_path)
         ep_num_frames = get_parquet_num_frames(ep_path)
-
-        # Check if we need to start a new file BEFORE creating metadata
-        if size_in_mb + ep_size_in_mb >= data_file_size_in_mb and len(paths_to_cat) > 0:
-            # Write the accumulated data files
-            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
-
-            # Move to next file
-            chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
-
-            # Reset for the next file
-            size_in_mb = 0
-            paths_to_cat = []
-
-        # Now create metadata with correct chunk/file indices
         ep_metadata = {
             "episode_index": ep_idx,
             "data/chunk_index": chunk_idx,
@@ -241,7 +224,20 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
         size_in_mb += ep_size_in_mb
         num_frames += ep_num_frames
         episodes_metadata.append(ep_metadata)
-        paths_to_cat.append(ep_path)
+        ep_idx += 1
+
+        if size_in_mb < data_file_size_in_mb:
+            paths_to_cat.append(ep_path)
+            continue
+
+        if paths_to_cat:
+            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
+
+        # Reset for the next file
+        size_in_mb = ep_size_in_mb
+        paths_to_cat = [ep_path]
+
+        chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
 
     # Write remaining data if any
     if paths_to_cat:
@@ -473,7 +469,7 @@ def convert_dataset(
 
     # Set root based on whether local dataset path is provided
     use_local_dataset = False
-    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root)
+    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root) / repo_id
     if root.exists():
         validate_local_dataset_version(root)
         use_local_dataset = True
@@ -533,7 +529,7 @@ if __name__ == "__main__":
         type=str,
         required=True,
         help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset "
-        "(e.g. `lerobot/pusht`, `<USER>/aloha_sim_insertion_human`).",
+        "(e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).",
     )
     parser.add_argument(
         "--branch",
@@ -557,7 +553,7 @@ if __name__ == "__main__":
         "--root",
         type=str,
         default=None,
-        help="Local directory to use for downloading/writing the dataset. Defaults to $HF_LEROBOT_HOME/repo_id.",
+        help="Local directory to use for downloading/writing the dataset.",
     )
     parser.add_argument(
         "--push-to-hub",

src/lerobot/datasets/video_utils.py

@@ -13,106 +13,25 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import contextlib
 import glob
 import importlib
 import logging
-import queue
 import shutil
 import tempfile
-import threading
 import warnings
 from dataclasses import dataclass, field
-from fractions import Fraction
 from pathlib import Path
 from threading import Lock
 from typing import Any, ClassVar
 
 import av
 import fsspec
-import numpy as np
 import pyarrow as pa
 import torch
 import torchvision
 from datasets.features.features import register_feature
 from PIL import Image
 
-# List of hardware encoders to probe for auto-selection. Availability depends on the platform and FFmpeg build.
-# Determines the order of preference for auto-selection when vcodec="auto" is used.
-HW_ENCODERS = [
-    "h264_videotoolbox",  # macOS
-    "hevc_videotoolbox",  # macOS
-    "h264_nvenc",  # NVIDIA GPU
-    "hevc_nvenc",  # NVIDIA GPU
-    "h264_vaapi",  # Linux Intel/AMD
-    "h264_qsv",  # Intel Quick Sync
-]
-
-VALID_VIDEO_CODECS = {"h264", "hevc", "libsvtav1", "auto"} | set(HW_ENCODERS)
-
-
-def _get_codec_options(
-    vcodec: str,
-    g: int | None = 2,
-    crf: int | None = 30,
-    preset: int | None = None,
-) -> dict:
-    """Build codec-specific options dict for video encoding."""
-    options = {}
-
-    # GOP size (keyframe interval) - supported by VideoToolbox and software encoders
-    if g is not None and (vcodec in ("h264_videotoolbox", "hevc_videotoolbox") or vcodec not in HW_ENCODERS):
-        options["g"] = str(g)
-
-    # Quality control (codec-specific parameter names)
-    if crf is not None:
-        if vcodec in ("h264", "hevc", "libsvtav1"):
-            options["crf"] = str(crf)
-        elif vcodec in ("h264_videotoolbox", "hevc_videotoolbox"):
-            quality = max(1, min(100, int(100 - crf * 2)))
-            options["q:v"] = str(quality)
-        elif vcodec in ("h264_nvenc", "hevc_nvenc"):
-            options["rc"] = "constqp"
-            options["qp"] = str(crf)
-        elif vcodec in ("h264_vaapi",):
-            options["qp"] = str(crf)
-        elif vcodec in ("h264_qsv",):
-            options["global_quality"] = str(crf)
-
-    # Preset (only for libsvtav1)
-    if vcodec == "libsvtav1":
-        options["preset"] = str(preset) if preset is not None else "12"
-
-    return options
-
-
-def detect_available_hw_encoders() -> list[str]:
-    """Probe PyAV/FFmpeg for available hardware video encoders."""
-    available = []
-    for codec_name in HW_ENCODERS:
-        try:
-            av.codec.Codec(codec_name, "w")
-            available.append(codec_name)
-        except Exception:  # nosec B110
-            pass  # nosec B110
-    return available
-
-
-def resolve_vcodec(vcodec: str) -> str:
-    """Validate vcodec and resolve 'auto' to best available HW encoder, fallback to libsvtav1."""
-    if vcodec not in VALID_VIDEO_CODECS:
-        raise ValueError(f"Invalid vcodec '{vcodec}'. Must be one of: {sorted(VALID_VIDEO_CODECS)}")
-    if vcodec != "auto":
-        logging.info(f"Using video codec: {vcodec}")
-        return vcodec
-    available = detect_available_hw_encoders()
-    for encoder in HW_ENCODERS:
-        if encoder in available:
-            logging.info(f"Auto-selected video codec: {encoder}")
-            return encoder
-    logging.info("No hardware encoder available, falling back to software encoder 'libsvtav1'")
-    return "libsvtav1"
-
 
 def get_safe_default_codec():
     if importlib.util.find_spec("torchcodec"):
@@ -227,17 +146,16 @@ def decode_video_frames_torchvision(
     min_, argmin_ = dist.min(1)
 
     is_within_tol = min_ < tolerance_s
-    if not is_within_tol.all():
-        raise FrameTimestampError(
-            f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
-            " It means that the closest frame that can be loaded from the video is too far away in time."
-            " This might be due to synchronization issues with timestamps during data collection."
-            " To be safe, we advise to ignore this item during training."
-            f"\nqueried timestamps: {query_ts}"
-            f"\nloaded timestamps: {loaded_ts}"
-            f"\nvideo: {video_path}"
-            f"\nbackend: {backend}"
-        )
+    assert is_within_tol.all(), (
+        f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
+        "It means that the closest frame that can be loaded from the video is too far away in time."
+        "This might be due to synchronization issues with timestamps during data collection."
+        "To be safe, we advise to ignore this item during training."
+        f"\nqueried timestamps: {query_ts}"
+        f"\nloaded timestamps: {loaded_ts}"
+        f"\nvideo: {video_path}"
+        f"\nbackend: {backend}"
+    )
 
     # get closest frames to the query timestamps
     closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
@@ -249,11 +167,7 @@ def decode_video_frames_torchvision(
     # convert to the pytorch format which is float32 in [0,1] range (and channel first)
     closest_frames = closest_frames.type(torch.float32) / 255
 
-    if len(timestamps) != len(closest_frames):
-        raise FrameTimestampError(
-            f"Number of retrieved frames ({len(closest_frames)}) does not match "
-            f"number of queried timestamps ({len(timestamps)})"
-        )
+    assert len(timestamps) == len(closest_frames)
     return closest_frames
 
 
@@ -358,16 +272,15 @@ def decode_video_frames_torchcodec(
     min_, argmin_ = dist.min(1)
 
     is_within_tol = min_ < tolerance_s
-    if not is_within_tol.all():
-        raise FrameTimestampError(
-            f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
-            " It means that the closest frame that can be loaded from the video is too far away in time."
-            " This might be due to synchronization issues with timestamps during data collection."
-            " To be safe, we advise to ignore this item during training."
-            f"\nqueried timestamps: {query_ts}"
-            f"\nloaded timestamps: {loaded_ts}"
-            f"\nvideo: {video_path}"
-        )
+    assert is_within_tol.all(), (
+        f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
+        "It means that the closest frame that can be loaded from the video is too far away in time."
+        "This might be due to synchronization issues with timestamps during data collection."
+        "To be safe, we advise to ignore this item during training."
+        f"\nqueried timestamps: {query_ts}"
+        f"\nloaded timestamps: {loaded_ts}"
+        f"\nvideo: {video_path}"
+    )
 
     # get closest frames to the query timestamps
     closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
@@ -396,13 +309,14 @@ def encode_video_frames(
     g: int | None = 2,
     crf: int | None = 30,
     fast_decode: int = 0,
-    log_level: int | None = av.logging.WARNING,
+    log_level: int | None = av.logging.ERROR,
     overwrite: bool = False,
     preset: int | None = None,
-    encoder_threads: int | None = None,
 ) -> None:
     """More info on ffmpeg arguments tuning on `benchmark/video/README.md`"""
-    vcodec = resolve_vcodec(vcodec)
+    # Check encoder availability
+    if vcodec not in ["h264", "hevc", "libsvtav1"]:
+        raise ValueError(f"Unsupported video codec: {vcodec}. Supported codecs are: h264, hevc, libsvtav1.")
 
     video_path = Path(video_path)
     imgs_dir = Path(imgs_dir)
@@ -433,22 +347,21 @@ def encode_video_frames(
         width, height = dummy_image.size
 
     # Define video codec options
-    video_options = _get_codec_options(vcodec, g, crf, preset)
+    video_options = {}
+
+    if g is not None:
+        video_options["g"] = str(g)
+
+    if crf is not None:
+        video_options["crf"] = str(crf)
 
     if fast_decode:
         key = "svtav1-params" if vcodec == "libsvtav1" else "tune"
         value = f"fast-decode={fast_decode}" if vcodec == "libsvtav1" else "fastdecode"
         video_options[key] = value
 
-    if encoder_threads is not None:
-        if vcodec == "libsvtav1":
-            lp_param = f"lp={encoder_threads}"
-            if "svtav1-params" in video_options:
-                video_options["svtav1-params"] += f":{lp_param}"
-            else:
-                video_options["svtav1-params"] = lp_param
-        else:
-            video_options["threads"] = str(encoder_threads)
+    if vcodec == "libsvtav1":
+        video_options["preset"] = str(preset) if preset is not None else "12"
 
     # Set logging level
     if log_level is not None:
@@ -567,348 +480,6 @@ def concatenate_video_files(
     Path(tmp_concatenate_path).unlink()
 
 
-class _CameraEncoderThread(threading.Thread):
-    """A thread that encodes video frames streamed via a queue into an MP4 file.
-
-    One instance is created per camera per episode. Frames are received as numpy arrays
-    from the main thread, encoded in real-time using PyAV (which releases the GIL during
-    encoding), and written to disk. Stats are computed incrementally using
-    RunningQuantileStats and returned via result_queue.
-    """
-
-    def __init__(
-        self,
-        video_path: Path,
-        fps: int,
-        vcodec: str,
-        pix_fmt: str,
-        g: int | None,
-        crf: int | None,
-        preset: int | None,
-        frame_queue: queue.Queue,
-        result_queue: queue.Queue,
-        stop_event: threading.Event,
-        encoder_threads: int | None = None,
-    ):
-        super().__init__(daemon=True)
-        self.video_path = video_path
-        self.fps = fps
-        self.vcodec = vcodec
-        self.pix_fmt = pix_fmt
-        self.g = g
-        self.crf = crf
-        self.preset = preset
-        self.frame_queue = frame_queue
-        self.result_queue = result_queue
-        self.stop_event = stop_event
-        self.encoder_threads = encoder_threads
-
-    def run(self) -> None:
-        from lerobot.datasets.compute_stats import RunningQuantileStats, auto_downsample_height_width
-
-        container = None
-        output_stream = None
-        stats_tracker = RunningQuantileStats()
-        frame_count = 0
-
-        try:
-            logging.getLogger("libav").setLevel(av.logging.WARNING)
-
-            while True:
-                try:
-                    frame_data = self.frame_queue.get(timeout=1)
-                except queue.Empty:
-                    if self.stop_event.is_set():
-                        break
-                    continue
-
-                if frame_data is None:
-                    # Sentinel: flush and close
-                    break
-
-                # Ensure HWC uint8 numpy array
-                if isinstance(frame_data, np.ndarray):
-                    if frame_data.ndim == 3 and frame_data.shape[0] == 3:
-                        # CHW -> HWC
-                        frame_data = frame_data.transpose(1, 2, 0)
-                    if frame_data.dtype != np.uint8:
-                        frame_data = (frame_data * 255).astype(np.uint8)
-
-                # Open container on first frame (to get width/height)
-                if container is None:
-                    height, width = frame_data.shape[:2]
-                    video_options = _get_codec_options(self.vcodec, self.g, self.crf, self.preset)
-                    if self.encoder_threads is not None:
-                        if self.vcodec == "libsvtav1":
-                            lp_param = f"lp={self.encoder_threads}"
-                            if "svtav1-params" in video_options:
-                                video_options["svtav1-params"] += f":{lp_param}"
-                            else:
-                                video_options["svtav1-params"] = lp_param
-                        else:
-                            video_options["threads"] = str(self.encoder_threads)
-                    Path(self.video_path).parent.mkdir(parents=True, exist_ok=True)
-                    container = av.open(str(self.video_path), "w")
-                    output_stream = container.add_stream(self.vcodec, self.fps, options=video_options)
-                    output_stream.pix_fmt = self.pix_fmt
-                    output_stream.width = width
-                    output_stream.height = height
-                    output_stream.time_base = Fraction(1, self.fps)
-
-                # Encode frame with explicit timestamps
-                pil_img = Image.fromarray(frame_data)
-                video_frame = av.VideoFrame.from_image(pil_img)
-                video_frame.pts = frame_count
-                video_frame.time_base = Fraction(1, self.fps)
-                packet = output_stream.encode(video_frame)
-                if packet:
-                    container.mux(packet)
-
-                # Update stats with downsampled frame (per-channel stats like compute_episode_stats)
-                img_chw = frame_data.transpose(2, 0, 1)  # HWC -> CHW
-                img_downsampled = auto_downsample_height_width(img_chw)
-                # Reshape CHW to (H*W, C) for per-channel stats
-                channels = img_downsampled.shape[0]
-                img_for_stats = img_downsampled.transpose(1, 2, 0).reshape(-1, channels)
-                stats_tracker.update(img_for_stats)
-
-                frame_count += 1
-
-            # Flush encoder
-            if output_stream is not None:
-                packet = output_stream.encode()
-                if packet:
-                    container.mux(packet)
-
-            if container is not None:
-                container.close()
-
-            av.logging.restore_default_callback()
-
-            # Get stats and put on result queue
-            if frame_count >= 2:
-                stats = stats_tracker.get_statistics()
-                self.result_queue.put(("ok", stats))
-            else:
-                self.result_queue.put(("ok", None))
-
-        except Exception as e:
-            logging.error(f"Encoder thread error: {e}")
-            if container is not None:
-                with contextlib.suppress(Exception):
-                    container.close()
-            self.result_queue.put(("error", str(e)))
-
-
-class StreamingVideoEncoder:
-    """Manages per-camera encoder threads for real-time video encoding during recording.
-
-    Instead of writing frames as PNG images and then encoding to MP4 at episode end,
-    this class streams frames directly to encoder threads, eliminating the
-    PNG round-trip and making save_episode() near-instant.
-
-    Uses threading instead of multiprocessing to avoid the overhead of pickling large
-    numpy arrays through multiprocessing.Queue. PyAV's encode() releases the GIL,
-    so encoding runs in parallel with the main recording loop.
-    """
-
-    def __init__(
-        self,
-        fps: int,
-        vcodec: str = "libsvtav1",
-        pix_fmt: str = "yuv420p",
-        g: int | None = 2,
-        crf: int | None = 30,
-        preset: int | None = None,
-        queue_maxsize: int = 30,
-        encoder_threads: int | None = None,
-    ):
-        self.fps = fps
-        self.vcodec = resolve_vcodec(vcodec)
-        self.pix_fmt = pix_fmt
-        self.g = g
-        self.crf = crf
-        self.preset = preset
-        self.queue_maxsize = queue_maxsize
-        self.encoder_threads = encoder_threads
-
-        self._frame_queues: dict[str, queue.Queue] = {}
-        self._result_queues: dict[str, queue.Queue] = {}
-        self._threads: dict[str, _CameraEncoderThread] = {}
-        self._stop_events: dict[str, threading.Event] = {}
-        self._video_paths: dict[str, Path] = {}
-        self._dropped_frames: dict[str, int] = {}
-        self._episode_active = False
-
-    def start_episode(self, video_keys: list[str], temp_dir: Path) -> None:
-        """Start encoder threads for a new episode.
-
-        Args:
-            video_keys: List of video feature keys (e.g. ["observation.images.laptop"])
-            temp_dir: Base directory for temporary MP4 files
-        """
-        if self._episode_active:
-            self.cancel_episode()
-
-        self._dropped_frames.clear()
-
-        for video_key in video_keys:
-            frame_queue: queue.Queue = queue.Queue(maxsize=self.queue_maxsize)
-            result_queue: queue.Queue = queue.Queue(maxsize=1)
-            stop_event = threading.Event()
-
-            temp_video_dir = Path(tempfile.mkdtemp(dir=temp_dir))
-            video_path = temp_video_dir / f"{video_key.replace('/', '_')}_streaming.mp4"
-
-            encoder_thread = _CameraEncoderThread(
-                video_path=video_path,
-                fps=self.fps,
-                vcodec=self.vcodec,
-                pix_fmt=self.pix_fmt,
-                g=self.g,
-                crf=self.crf,
-                preset=self.preset,
-                frame_queue=frame_queue,
-                result_queue=result_queue,
-                stop_event=stop_event,
-                encoder_threads=self.encoder_threads,
-            )
-            encoder_thread.start()
-
-            self._frame_queues[video_key] = frame_queue
-            self._result_queues[video_key] = result_queue
-            self._threads[video_key] = encoder_thread
-            self._stop_events[video_key] = stop_event
-            self._video_paths[video_key] = video_path
-
-        self._episode_active = True
-
-    def feed_frame(self, video_key: str, image: np.ndarray) -> None:
-        """Feed a frame to the encoder for a specific camera.
-
-        A copy of the image is made before enqueueing to prevent race conditions
-        with camera drivers that may reuse buffers. If the encoder queue is full
-        (encoder can't keep up), the frame is dropped with a warning instead of
-        crashing the recording session.
-
-        Args:
-            video_key: The video feature key
-            image: numpy array in (H,W,C) or (C,H,W) format, uint8 or float
-
-        Raises:
-            RuntimeError: If the encoder thread has crashed
-        """
-        if not self._episode_active:
-            raise RuntimeError("No active episode. Call start_episode() first.")
-
-        thread = self._threads[video_key]
-        if not thread.is_alive():
-            # Check for error
-            try:
-                status, msg = self._result_queues[video_key].get_nowait()
-                if status == "error":
-                    raise RuntimeError(f"Encoder thread for {video_key} crashed: {msg}")
-            except queue.Empty:
-                pass
-            raise RuntimeError(f"Encoder thread for {video_key} is not alive")
-
-        try:
-            self._frame_queues[video_key].put(image.copy(), timeout=0.1)
-        except queue.Full:
-            self._dropped_frames[video_key] = self._dropped_frames.get(video_key, 0) + 1
-            count = self._dropped_frames[video_key]
-            # Log periodically to avoid spam (1st, then every 10th)
-            if count == 1 or count % 10 == 0:
-                logging.warning(
-                    f"Encoder queue full for {video_key}, dropped {count} frame(s). "
-                    f"Consider using vcodec='auto' for hardware encoding or increasing encoder_queue_maxsize."
-                )
-
-    def finish_episode(self) -> dict[str, tuple[Path, dict | None]]:
-        """Finish encoding the current episode.
-
-        Sends sentinel values, waits for encoder threads to complete,
-        and collects results.
-
-        Returns:
-            Dict mapping video_key to (mp4_path, stats_dict_or_None)
-        """
-        if not self._episode_active:
-            raise RuntimeError("No active episode to finish.")
-
-        results = {}
-
-        # Report dropped frames
-        for video_key, count in self._dropped_frames.items():
-            if count > 0:
-                logging.warning(f"Episode finished with {count} dropped frame(s) for {video_key}.")
-
-        # Send sentinel to all queues
-        for video_key in self._frame_queues:
-            self._frame_queues[video_key].put(None)
-
-        # Wait for all threads and collect results
-        for video_key in self._threads:
-            self._threads[video_key].join(timeout=120)
-            if self._threads[video_key].is_alive():
-                logging.error(f"Encoder thread for {video_key} did not finish in time")
-                self._stop_events[video_key].set()
-                self._threads[video_key].join(timeout=5)
-                results[video_key] = (self._video_paths[video_key], None)
-                continue
-
-            try:
-                status, data = self._result_queues[video_key].get(timeout=5)
-                if status == "error":
-                    raise RuntimeError(f"Encoder thread for {video_key} failed: {data}")
-                results[video_key] = (self._video_paths[video_key], data)
-            except queue.Empty:
-                logging.error(f"No result from encoder thread for {video_key}")
-                results[video_key] = (self._video_paths[video_key], None)
-
-        self._cleanup()
-        self._episode_active = False
-        return results
-
-    def cancel_episode(self) -> None:
-        """Cancel the current episode, stopping encoder threads and cleaning up."""
-        if not self._episode_active:
-            return
-
-        # Signal all threads to stop
-        for video_key in self._stop_events:
-            self._stop_events[video_key].set()
-
-        # Wait for threads to finish
-        for video_key in self._threads:
-            self._threads[video_key].join(timeout=5)
-
-            # Clean up temp MP4 files
-            video_path = self._video_paths.get(video_key)
-            if video_path is not None and video_path.exists():
-                shutil.rmtree(str(video_path.parent), ignore_errors=True)
-
-        self._cleanup()
-        self._episode_active = False
-
-    def close(self) -> None:
-        """Close the encoder, canceling any in-progress episode."""
-        if self._episode_active:
-            self.cancel_episode()
-
-    def _cleanup(self) -> None:
-        """Clean up queues and thread tracking dicts."""
-        for q in self._frame_queues.values():
-            with contextlib.suppress(Exception):
-                while not q.empty():
-                    q.get_nowait()
-        self._frame_queues.clear()
-        self._result_queues.clear()
-        self._threads.clear()
-        self._stop_events.clear()
-        self._video_paths.clear()
-
-
 @dataclass
 class VideoFrame:
     # TODO(rcadene, lhoestq): move to Hugging Face `datasets` repo
@@ -943,7 +514,7 @@ with warnings.catch_warnings():
 
 def get_audio_info(video_path: Path | str) -> dict:
     # Set logging level
-    logging.getLogger("libav").setLevel(av.logging.WARNING)
+    logging.getLogger("libav").setLevel(av.logging.ERROR)
 
     # Getting audio stream information
     audio_info = {}
@@ -975,7 +546,7 @@ def get_audio_info(video_path: Path | str) -> dict:
 
 def get_video_info(video_path: Path | str) -> dict:
     # Set logging level
-    logging.getLogger("libav").setLevel(av.logging.WARNING)
+    logging.getLogger("libav").setLevel(av.logging.ERROR)
 
     # Getting video stream information
     video_info = {}
@@ -1061,15 +632,8 @@ class VideoEncodingManager:
         return self
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        streaming_encoder = getattr(self.dataset, "_streaming_encoder", None)
-
-        if streaming_encoder is not None:
-            # Handle streaming encoder cleanup
-            if exc_type is not None:
-                streaming_encoder.cancel_episode()
-            streaming_encoder.close()
-        elif self.dataset.episodes_since_last_encoding > 0:
-            # Handle any remaining episodes that haven't been batch encoded
+        # Handle any remaining episodes that haven't been batch encoded
+        if self.dataset.episodes_since_last_encoding > 0:
             if exc_type is not None:
                 logging.info("Exception occurred. Encoding remaining episodes before exit...")
             else:
@@ -1086,8 +650,8 @@ class VideoEncodingManager:
         # Finalize the dataset to properly close all writers
         self.dataset.finalize()
 
-        # Clean up episode images if recording was interrupted (only for non-streaming mode)
-        if exc_type is not None and streaming_encoder is None:
+        # Clean up episode images if recording was interrupted
+        if exc_type is not None:
             interrupted_episode_index = self.dataset.num_episodes
             for key in self.dataset.meta.video_keys:
                 img_dir = self.dataset._get_image_file_path(
@@ -1101,12 +665,14 @@ class VideoEncodingManager:
 
         # Clean up any remaining images directory if it's empty
         img_dir = self.dataset.root / "images"
-        if img_dir.exists():
-            png_files = list(img_dir.rglob("*.png"))
-            if len(png_files) == 0:
+        # Check for any remaining PNG files
+        png_files = list(img_dir.rglob("*.png"))
+        if len(png_files) == 0:
+            # Only remove the images directory if no PNG files remain
+            if img_dir.exists():
                 shutil.rmtree(img_dir)
                 logging.debug("Cleaned up empty images directory")
-            else:
-                logging.debug(f"Images directory is not empty, containing {len(png_files)} PNG files")
+        else:
+            logging.debug(f"Images directory is not empty, containing {len(png_files)} PNG files")
 
         return False  # Don't suppress the original exception

src/lerobot/envs/configs.py

@@ -205,7 +205,6 @@ class ObservationConfig:
 
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
-    add_ee_pose_to_observation: bool = False
     display_cameras: bool = False
 
 
@@ -261,7 +260,6 @@ class HILSerlRobotEnvConfig(EnvConfig):
 @dataclass
 class LiberoEnv(EnvConfig):
     task: str = "libero_10"  # can also choose libero_spatial, libero_object, etc.
-    task_ids: list[int] | None = None
     fps: int = 30
     episode_length: int | None = None
     obs_type: str = "pixels_agent_pos"
@@ -340,10 +338,10 @@ class LiberoEnv(EnvConfig):
 
     @property
     def gym_kwargs(self) -> dict:
-        kwargs: dict[str, Any] = {"obs_type": self.obs_type, "render_mode": self.render_mode}
-        if self.task_ids is not None:
-            kwargs["task_ids"] = self.task_ids
-        return kwargs
+        return {
+            "obs_type": self.obs_type,
+            "render_mode": self.render_mode,
+        }
 
 
 @EnvConfig.register_subclass("metaworld")

src/lerobot/envs/libero.py

@@ -112,7 +112,6 @@ class LiberoEnv(gym.Env):
         visualization_height: int = 480,
         init_states: bool = True,
         episode_index: int = 0,
-        n_envs: int = 1,
         camera_name_mapping: dict[str, str] | None = None,
         num_steps_wait: int = 10,
         control_mode: str = "relative",
@@ -146,9 +145,7 @@ class LiberoEnv(gym.Env):
         self.episode_length = episode_length
         # Load once and keep
         self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
-        self._reset_stride = n_envs  # when performing a reset, append `_reset_stride` to `init_state_id`.
-
-        self.init_state_id = self.episode_index  # tie each sub-env to a fixed init state
+        self._init_state_id = self.episode_index  # tie each sub-env to a fixed init state
 
         self._env = self._make_envs_task(task_suite, self.task_id)
         default_steps = 500
@@ -296,10 +293,9 @@ class LiberoEnv(gym.Env):
     def reset(self, seed=None, **kwargs):
         super().reset(seed=seed)
         self._env.seed(seed)
-        raw_obs = self._env.reset()
         if self.init_states and self._init_states is not None:
-            raw_obs = self._env.set_init_state(self._init_states[self.init_state_id % len(self._init_states)])
-            self.init_state_id += self._reset_stride  # Change init_state_id when reset
+            self._env.set_init_state(self._init_states[self._init_state_id])
+        raw_obs = self._env.reset()
 
         # After reset, objects may be unstable (slightly floating, intersecting, etc.).
         # Step the simulator with a no-op action for a few frames so everything settles.
@@ -377,7 +373,6 @@ def _make_env_fns(
             init_states=init_states,
             episode_length=episode_length,
             episode_index=episode_index,
-            n_envs=n_envs,
             control_mode=control_mode,
             **local_kwargs,
         )

src/lerobot/motors/__init__.py

@@ -14,8 +14,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .motors_bus import (
-    Motor,
-    MotorCalibration,
-    MotorNormMode,
-)
+from .motors_bus import Motor, MotorCalibration, MotorNormMode, MotorsBus

src/lerobot/motors/calibration_gui.py

@@ -18,7 +18,7 @@ from dataclasses import dataclass
 
 os.environ["PYGAME_HIDE_SUPPORT_PROMPT"] = "1"
 
-from .motors_bus import MotorCalibration, MotorsBus
+from lerobot.motors import MotorCalibration, MotorsBus
 
 BAR_LEN, BAR_THICKNESS = 450, 8
 HANDLE_R = 10
@@ -221,7 +221,7 @@ class RangeFinderGUI:
 
         self.bus = bus
         self.groups = groups if groups is not None else {"all": list(bus.motors)}
-        self.group_names = list(self.groups)
+        self.group_names = list(groups)
         self.current_group = self.group_names[0]
 
         if not bus.is_connected:
@@ -230,20 +230,18 @@ class RangeFinderGUI:
         self.calibration = bus.read_calibration()
         self.res_table = bus.model_resolution_table
         self.present_cache = {
-            m: bus.read("Present_Position", m, normalize=False)
-            for motors in self.groups.values()
-            for m in motors
+            m: bus.read("Present_Position", m, normalize=False) for motors in groups.values() for m in motors
         }
 
         pygame.init()
         self.font = pygame.font.Font(None, FONT_SIZE)
 
-        label_pad = max(self.font.size(m)[0] for ms in self.groups.values() for m in ms)
+        label_pad = max(self.font.size(m)[0] for ms in groups.values() for m in ms)
         self.label_pad = label_pad
         width = 40 + label_pad + BAR_LEN + 6 + BTN_W + 10 + SAVE_W + 10
         self.controls_bottom = 10 + SAVE_H
         self.base_y = self.controls_bottom + TOP_GAP
-        height = self.base_y + PADDING_Y * len(self.groups[self.current_group]) + 40
+        height = self.base_y + PADDING_Y * len(groups[self.current_group]) + 40
 
         self.screen = pygame.display.set_mode((width, height))
         pygame.display.set_caption("Motors range finder")