Enhance keyboard teleoperation to capture both character and special keys. Clear current pressed keys in KeyboardEndEffectorTeleop for improved state management.

.github/workflows/full_tests.yml

@@ -78,7 +78,7 @@ jobs:
           python-version: ${{ env.PYTHON_VERSION }}
 
       - name: Install lerobot with all extras
-        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
+        run: uv sync --all-extras
 
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv --maxfail=10

.github/workflows/nightly.yml

@@ -119,7 +119,6 @@ jobs:
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
     container:
       image: ${{ needs.build-docker-cpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
-      options: --shm-size "16gb"
       credentials:
         username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
         password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -159,36 +158,3 @@ jobs:
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests
         run: make test-end-to-end
-
-  # This job runs multi-GPU training tests with 4 GPUs
-  nightly-multi-gpu-tests:
-    name: Nightly Multi-GPU Tests
-    needs: [build-docker-gpu-nightly]
-    runs-on:
-      group: aws-g4dn-12xlarge  # Instance with 4 GPUs
-    env:
-      HF_HOME: /home/user_lerobot/.cache/huggingface
-      HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
-      TORCH_HOME: /home/user_lerobot/.cache/torch
-      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
-      CUDA_VISIBLE_DEVICES: "0,1,2,3"
-    container:
-      image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
-      options: --gpus all --shm-size "16gb"
-      credentials:
-        username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: /lerobot
-    steps:
-      - 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
-      # 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/release.yml

@@ -82,14 +82,6 @@ jobs:
             exit 1
           fi
 
-      - name: Remove Tags with Git dependencies
-        # TODO(Steven): Temporary patch to remove libero and 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\[pi\]|lerobot\[libero\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
-          sed -E -i '/@ git\+https|lerobot\[pi\]|lerobot\[libero\]/d' pyproject.toml
-          echo "::info:: Git dependencies removed. Proceeding with build."
-
       - name: Install build dependencies
         run: python -m pip install build
 
@@ -111,7 +103,7 @@ jobs:
       - name: Publish to TestPyPI for pre-releases
         # True for tags like 'v0.2.0-rc1'
         if: startsWith(github.ref, 'refs/tags/v') && contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
+        uses: pypa/gh-action-pypi-publish@v1.12.4 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
         with:
           repository-url: https://test.pypi.org/legacy/
           verbose: true
@@ -119,7 +111,7 @@ jobs:
 
       - name: Publish to PyPI
         if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
+        uses: pypa/gh-action-pypi-publish@v1.12.4 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
         with:
           verbose: true
           print-hash: true
@@ -146,7 +138,7 @@ jobs:
       - name: Setup uv and Python
         uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
         with:
-          enable-cache: true # zizmor: ignore[cache-poisoning]
+          enable-cache: true
           version: ${{ env.UV_VERSION }}
           python-version: ${{ env.PYTHON_VERSION }}
       - name: Create uv virtual environment

.github/workflows/stale.yml

@@ -27,17 +27,15 @@ env:
     This issue was closed because it has been stalled for 14 days with no activity.
     Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
   CLOSE_PR_MESSAGE: >
-    This PR was closed because it has been stalled for 21 days with no activity.
+    This PR was closed because it has been stalled for 14 days with no activity.
     Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
   WARN_ISSUE_MESSAGE: >
     This issue has been automatically marked as stale because it has not had
     recent activity (6 months). It will be closed if no further activity occurs.
-    Any change, comment or update to this issue will reset this count.
     Thank you for your contributions.
   WARN_PR_MESSAGE: >
     This PR has been automatically marked as stale because it has not had
-    recent activity (1 year). It will be closed if no further activity occurs.
-    Any change, comment or update to this PR will reset this count.
+    recent activity (6 months). It will be closed if no further activity occurs.
     Thank you for your contributions.
 
 jobs:
@@ -58,10 +56,10 @@ jobs:
           stale-pr-label: stale
           exempt-issue-labels: never-stale
           exempt-pr-labels: never-stale
-          days-before-issue-stale: 180
+          days-before-issue-stale: 180 # TODO(Steven): Will modify this to 90 after initial cleanup
           days-before-issue-close: 14
-          days-before-pr-stale: 365
-          days-before-pr-close: 21
+          days-before-pr-stale: 180
+          days-before-pr-close: 14
           delete-branch: true
           close-issue-message: ${{ env.CLOSE_ISSUE_MESSAGE }}
           close-pr-message: ${{ env.CLOSE_PR_MESSAGE }}

.github/workflows/unbound_deps_tests.yml

@@ -1,183 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This workflow handles full testing with unboud dependencies versions.
-name: Unbound Dependency Tests
-
-on:
-  # Allows running this workflow manually from the Actions tab
-  workflow_dispatch:
-
-  # Run on the 1st and 15th of every month at 09:00 UTC
-  schedule:
-    - cron: '0 2 1,15 * *'
-
-permissions:
-  contents: read
-
-# Sets up the environment variables
-env:
-  UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
-  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:unbound
-
-# Ensures that only the latest action is built, canceling older runs.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-
-  # This job runs the E2E tests + pytest with all unbound extras
-  full-tests:
-    name: Full Unbound Tests
-    runs-on: ubuntu-latest
-    env:
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          lfs: true
-          persist-credentials: false
-
-      - name: Install apt dependencies
-        run: |
-          sudo apt-get update && sudo apt-get install -y build-essential \
-          git curl libglib2.0-0 libegl1-mesa-dev ffmpeg libusb-1.0-0-dev \
-          speech-dispatcher libgeos-dev portaudio19-dev
-
-      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          enable-cache: true
-          version: ${{ env.UV_VERSION }}
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - name: Unbound dependencies
-        run: |
-          sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml
-          echo "Dependencies unbound:" && cat pyproject.toml
-
-      - name: Install lerobot with all extras
-        run: uv sync --all-extras
-
-      - name: Run pytest (all extras)
-        run: uv run pytest tests -vv
-
-      - name: Run end-to-end tests
-        run: uv run make test-end-to-end
-
-  # This job builds a GPU enabled image for testing
-  build-and-push-docker:
-    name: Build and Push Docker
-    runs-on:
-      group: aws-general-8-plus
-    outputs:
-      image_tag: ${{ env.DOCKER_IMAGE_NAME }}
-    env:
-      GITHUB_REF: ${{ github.ref }}
-    steps:
-      - name: Install Git LFS
-        run: |
-          sudo apt-get update
-          sudo apt-get install git-lfs
-          git lfs install
-      - uses: actions/checkout@v4
-        with:
-          lfs: true
-          persist-credentials: false
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: ./docker/Dockerfile.internal
-          push: true
-          tags: ${{ env.DOCKER_IMAGE_NAME }}
-          build-args: |
-            UNBOUND_DEPS=true
-
-  # This job runs pytest with all unbound extras in a GPU enabled host
-  # It runs everytime a test image is created
-  gpu-tests:
-    name: GPU Unbound Tests
-    needs: [build-and-push-docker]
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_HOME: /home/user_lerobot/.cache/huggingface
-      HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
-      TORCH_HOME: /home/user_lerobot/.cache/torch
-      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
-    container:
-      image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
-      options: --gpus all --shm-size "16gb"
-      credentials:
-        username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: /lerobot
-    steps:
-      - name: Run pytest on GPU
-        run: pytest tests -vv
-      - name: Run end-to-end tests
-        run: make test-end-to-end
-
-  # This job deletes the test image recently created
-  # It runs everytime after the gpu-tests have finished
-  delete-unbound-image:
-    name: Delete Unbound Image
-    needs: [gpu-tests, build-and-push-docker]
-    if: always() && needs.build-and-push-docker.result == 'success'
-    runs-on: ubuntu-latest
-    steps:
-      - name: Get Docker Hub Token and Delete Image
-        # zizmor: ignore[template-injection]
-        run: |
-          IMAGE_NAME=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f1)
-          IMAGE_TAG=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f2)
-
-          echo "Attempting to delete image: $IMAGE_NAME:$IMAGE_TAG"
-
-          TOKEN=$(curl -s -H "Content-Type: application/json" \
-                       -X POST \
-                       -d '{"username": "${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}", "password": "${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}"}' \
-                       https://hub.docker.com/v2/users/login/ | jq -r .token)
-
-          if [ "$TOKEN" == "null" ] || [ -z "$TOKEN" ]; then
-            echo "::error::Failed to get Docker Hub token."
-            exit 1
-          fi
-
-          HTTP_RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" \
-                               -H "Authorization: JWT ${TOKEN}" \
-                               -X DELETE \
-                               https://hub.docker.com/v2/repositories/${IMAGE_NAME}/tags/${IMAGE_TAG}/)
-
-          if [ "$HTTP_RESPONSE" -eq 204 ]; then
-            echo "Successfully deleted Docker image tag: $IMAGE_NAME:$IMAGE_TAG"
-          else
-            echo "::error::Failed to delete Docker image. HTTP status: $HTTP_RESPONSE"
-            exit 1
-          fi

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
 
    ##### General Code Quality & Formatting #####
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v6.0.0
+    rev: v5.0.0
     hooks:
       - id: check-added-large-files
         args: ['--maxkb=1024']
@@ -39,20 +39,20 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.1
+    rev: v0.12.4
     hooks:
       - id: ruff-format
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
 
   - repo: https://github.com/adhtruong/mirrors-typos
-    rev: v1.38.1
+    rev: v1.34.0
     hooks:
       - id: typos
         args: [--force-exclude]
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.21.0
+    rev: v3.20.0
     hooks:
     -   id: pyupgrade
         args: [--py310-plus]
@@ -68,12 +68,12 @@ repos:
 
   ##### Security #####
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.28.0
+    rev: v8.27.2
     hooks:
       - id: gitleaks
 
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.15.2
+    rev: v1.11.0
     hooks:
       - id: zizmor
 
@@ -87,7 +87,7 @@ repos:
   # TODO(Steven): Uncomment when ready to use
   ##### Static Analysis & Typing #####
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.18.2
+    rev: v1.16.0
     hooks:
       - id: mypy
         args: [--config-file=pyproject.toml]

CONTRIBUTING.md

@@ -72,6 +72,7 @@ post it.
 
 Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
 environments ([aloha](https://github.com/huggingface/gym-aloha),
+[xarm](https://github.com/huggingface/gym-xarm),
 [pusht](https://github.com/huggingface/gym-pusht))
 and follow the same api design.
 
@@ -137,7 +138,7 @@ Follow these steps to start contributing:
 4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies.
    Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already.
 
-   Set up a development environment with conda:
+   Set up a development environment with conda or miniconda:
 
    ```bash
    conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev

Makefile

@@ -119,9 +119,10 @@ test-tdmpc-ete-train:
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
 		--policy.push_to_hub=false \
-		--env.type=pusht \
+		--env.type=xarm \
+		--env.task=XarmLift-v0 \
 		--env.episode_length=5 \
-		--dataset.repo_id=lerobot/pusht_image \
+		--dataset.repo_id=lerobot/xarm_lift_medium \
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
@@ -139,10 +140,9 @@ test-tdmpc-ete-eval:
 	lerobot-eval \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
-		--env.type=pusht \
+		--env.type=xarm \
 		--env.episode_length=5 \
-		--env.observation_height=96 \
-        --env.observation_width=96 \
+		--env.task=XarmLift-v0 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1
 

README.md

@@ -104,14 +104,14 @@ LeRobot works with Python 3.10+ and PyTorch 2.2+.
 
 ### Environment Setup
 
-Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniforge`](https://conda-forge.org/download/):
+Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
 
 ```bash
 conda create -y -n lerobot python=3.10
 conda activate lerobot
 ```
 
-When using `conda`, install `ffmpeg` in your environment:
+When using `miniconda`, install `ffmpeg` in your environment:
 
 ```bash
 conda install ffmpeg -c conda-forge
@@ -185,11 +185,6 @@ _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 libero or pi tags, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`.
->
-> This will be solved in the next patch release
-
 ### Weights & Biases
 
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
@@ -202,7 +197,7 @@ wandb login
 
 ### Visualize datasets
 
-Check out [example 1](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
+Check out [example 1](https://github.com/huggingface/lerobot/blob/main/examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
 
 You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
 
@@ -212,13 +207,13 @@ lerobot-dataset-viz \
     --episode-index 0
 ```
 
-or from a dataset in a local folder with the `root` option and the `--mode local` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
+or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
 
 ```bash
 lerobot-dataset-viz \
     --repo-id lerobot/pusht \
     --root ./my_local_data_dir \
-    --mode local \
+    --local-files-only 1 \
     --episode-index 0
 ```
 
@@ -315,7 +310,7 @@ To upload these to the hub, run the following:
 huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model
 ```
 
-See [lerobot_eval.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_eval.py) for an example of how other people may use your policy.
+See [eval.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/eval.py) for an example of how other people may use your policy.
 
 ### Acknowledgment
 
@@ -342,3 +337,7 @@ If you want, you can cite this work with:
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)
+
+```
+
+```

docker/Dockerfile.internal

@@ -75,14 +75,6 @@ RUN uv venv --python python${PYTHON_VERSION}
 # Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot pyproject.toml README.md MANIFEST.in ./
 COPY --chown=user_lerobot:user_lerobot src/ src/
-
-ARG UNBOUND_DEPS=false
-
-RUN if [ "$UNBOUND_DEPS" = "true" ]; then \
-    sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml; \
-    echo "Dependencies unbound:" && cat pyproject.toml; \
-    fi
-
 RUN uv pip install --no-cache ".[all]"
 
 # Copy the rest of the application source code

docker/Dockerfile.user

@@ -61,14 +61,6 @@ RUN uv venv
 # Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot pyproject.toml README.md MANIFEST.in ./
 COPY --chown=user_lerobot:user_lerobot src/ src/
-
-ARG UNBOUND_DEPS=false
-
-RUN if [ "$UNBOUND_DEPS" = "true" ]; then \
-    sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml; \
-    echo "Dependencies unbound:" && cat pyproject.toml; \
-    fi
-
 RUN uv pip install --no-cache ".[all]"
 
 # Copy the rest of the application code

docs/source/_toctree.yml

@@ -7,6 +7,8 @@
 - sections:
   - local: il_robots
     title: Imitation Learning for Robots
+  - local: il_sim
+    title: Imitation Learning in Sim
   - local: cameras
     title: Cameras
   - local: integrate_hardware
@@ -17,37 +19,23 @@
     title: Train RL in Simulation
   - local: async
     title: Use Async Inference
-  - local: multi_gpu_training
-    title: Multi GPU training
   title: "Tutorials"
 - sections:
   - local: lerobot-dataset-v3
     title: Using LeRobotDataset
   - local: porting_datasets_v3
     title: Porting Large Datasets
-  - local: using_dataset_tools
-    title: Using the Dataset Tools
   title: "Datasets"
 - sections:
-  - local: act
-    title: ACT
   - local: smolvla
     title: SmolVLA
   - local: pi0
     title: π₀ (Pi0)
   - local: pi05
     title: π₀.₅ (Pi05)
-  - local: groot
-    title: NVIDIA GR00T N1.5
-  title: "Policies"
-- sections:
-  - local: il_sim
-    title: Imitation Learning in Sim
   - local: libero
     title: Using Libero
-  - local: metaworld
-    title: Using MetaWorld
-  title: "Simulation"
+  title: "Policies"
 - sections:
   - local: introduction_processors
     title: Introduction to Robot Processors

docs/source/act.mdx

@@ -1,92 +0,0 @@
-# ACT (Action Chunking with Transformers)
-
-ACT is a **lightweight and efficient policy for imitation learning**, especially well-suited for fine-grained manipulation tasks. It's the **first model we recommend when you're starting out** with LeRobot due to its fast training time, low computational requirements, and strong performance.
-
-<div class="video-container">
-  <iframe
-    width="100%"
-    height="415"
-    src="https://www.youtube.com/embed/ft73x0LfGpM"
-    title="LeRobot ACT Tutorial"
-    frameborder="0"
-    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-    allowfullscreen
-  ></iframe>
-</div>
-
-_Watch this tutorial from the LeRobot team to learn how ACT works: [LeRobot ACT Tutorial](https://www.youtube.com/watch?v=ft73x0LfGpM)_
-
-## Model Overview
-
-Action Chunking with Transformers (ACT) was introduced in the paper [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://arxiv.org/abs/2304.13705) by Zhao et al. The policy was designed to enable precise, contact-rich manipulation tasks using affordable hardware and minimal demonstration data.
-
-### Why ACT is Great for Beginners
-
-ACT stands out as an excellent starting point for several reasons:
-
-- **Fast Training**: Trains in a few hours on a single GPU
-- **Lightweight**: Only ~80M parameters, making it efficient and easy to work with
-- **Data Efficient**: Often achieves high success rates with just 50 demonstrations
-
-### Architecture
-
-ACT uses a transformer-based architecture with three main components:
-
-1. **Vision Backbone**: ResNet-18 processes images from multiple camera viewpoints
-2. **Transformer Encoder**: Synthesizes information from camera features, joint positions, and a learned latent variable
-3. **Transformer Decoder**: Generates coherent action sequences using cross-attention
-
-The policy takes as input:
-
-- Multiple RGB images (e.g., from wrist cameras, front/top cameras)
-- Current robot joint positions
-- A latent style variable `z` (learned during training, set to zero during inference)
-
-And outputs a chunk of `k` future action sequences.
-
-## Installation Requirements
-
-1. Install LeRobot by following our [Installation Guide](./installation).
-2. ACT is included in the base LeRobot installation, so no additional dependencies are needed!
-
-## Training ACT
-
-ACT works seamlessly with the standard LeRobot training pipeline. Here's a complete example for training ACT on your dataset:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=${HF_USER}/your_dataset \
-  --policy.type=act \
-  --output_dir=outputs/train/act_your_dataset \
-  --job_name=act_your_dataset \
-  --policy.device=cuda \
-  --wandb.enable=true \
-  --policy.repo_id=${HF_USER}/act_policy
-```
-
-### Training Tips
-
-1. **Start with defaults**: ACT's default hyperparameters work well for most tasks
-2. **Training duration**: Expect a few hours for 100k training steps on a single GPU
-3. **Batch size**: Start with batch size 8 and adjust based on your GPU memory
-
-### Train using Google Colab
-
-If your local computer doesn't have a powerful GPU, you can utilize Google Colab to train your model by following the [ACT training notebook](./notebooks#training-act).
-
-## Evaluating ACT
-
-Once training is complete, you can evaluate your ACT policy using the `lerobot-record` command with your trained policy. This will run inference and record evaluation episodes:
-
-```bash
-lerobot-record \
-  --robot.type=so100_follower \
-  --robot.port=/dev/ttyACM0 \
-  --robot.id=my_robot \
-  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-  --display_data=true \
-  --dataset.repo_id=${HF_USER}/eval_act_your_dataset \
-  --dataset.num_episodes=10 \
-  --dataset.single_task="Your task description" \
-  --policy.path=${HF_USER}/act_policy
-```

docs/source/async.mdx

@@ -31,15 +31,15 @@ Then, spin up a policy server (in one terminal, or in a separate machine) specif
 You can spin up a policy server running:
 
 ```shell
-python -m lerobot.async_inference.policy_server \
-     --host=127.0.0.1 \
-     --port=8080
+python src/lerobot/async_inference/policy_server.py \
+    --host=127.0.0.1 \
+    --port=8080 \
 ```
 
 This will start a policy server listening on `127.0.0.1:8080` (`localhost`, port 8080). At this stage, the policy server is empty, as all information related to which policy to run and with which parameters are specified during the first handshake with the client. Spin up a client with:
 
 ```shell
-python -m lerobot.async_inference.robot_client \
+python src/lerobot/async_inference/robot_client.py \
     --server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server
     --robot.type=so100_follower \ # ROBOT: your robot type
     --robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port
@@ -113,9 +113,9 @@ As such, spinning up a policy server is as easy as specifying the host address a
 <hfoptions id="start_policy_server">
 <hfoption id="Command">
 ```bash
-python -m lerobot.async_inference.policy_server \
-     --host=127.0.0.1 \
-     --port=8080
+python -m lerobot.scripts.server.policy_server \
+    --host="localhost" \
+    --port=8080
 ```
 </hfoption>
 <hfoption id="API example">
@@ -148,7 +148,7 @@ The `RobotClient` streams observations to the `PolicyServer`, and receives actio
 <hfoptions id="start_robot_client">
 <hfoption id="Command">
 ```bash
-python -m lerobot.async_inference.robot_client \
+python src/lerobot/async_inference/robot_client.py \
     --server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server
     --robot.type=so100_follower \ # ROBOT: your robot type
     --robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port

docs/source/groot.mdx

@@ -1,122 +0,0 @@
-# GR00T N1.5 Policy
-
-GR00T N1.5 is an open foundation model from NVIDIA designed for generalized humanoid robot reasoning and skills. It is a cross-embodiment model that accepts multimodal input, including language and images, to perform manipulation tasks in diverse environments.
-
-This document outlines the specifics of its integration and usage within the LeRobot framework.
-
-## Model Overview
-
-NVIDIA Isaac GR00T N1.5 is an upgraded version of the GR00T N1 foundation model. It is built to improve generalization and language-following abilities for humanoid robots.
-
-Developers and researchers can post-train GR00T N1.5 with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
-
-GR00T N1.5 (specifically the GR00T-N1.5-3B model) is built using pre-trained vision and language encoders. It utilizes a flow matching action transformer to model a chunk of actions, conditioned on vision, language, and proprioception.
-
-Its strong performance comes from being trained on an expansive and diverse humanoid dataset, which includes:
-
-- Real captured data from robots.
-- Synthetic data generated using NVIDIA Isaac GR00T Blueprint.
-- Internet-scale video data.
-
-This approach allows the model to be highly adaptable through post-training for specific embodiments, tasks, and environments.
-
-## Installation Requirements
-
-As of today, GR00T N1.5 requires flash attention for it's internal working.
-
-We are working on making this optional, but in the meantime that means that we require an extra installation step and it can only be used in CUDA enabled devices.
-
-1. Following the Environment Setup of our [Installation Guide](./installation). **Attention** don't install `lerobot` in this step.
-2. Install [Flash Attention](https://github.com/Dao-AILab/flash-attention) by running:
-
-```bash
-# Check https://pytorch.org/get-started/locally/ for your system
-pip install "torch>=2.2.1,<2.8.0" "torchvision>=0.21.0,<0.23.0" # --index-url https://download.pytorch.org/whl/cu1XX
-pip install ninja "packaging>=24.2,<26.0" # flash attention dependencies
-pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
-python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
-```
-
-3. Install LeRobot by running:
-
-```bash
-pip install lerobot[groot] # consider also installing libero,dev and test tags
-```
-
-## Usage
-
-To use GR00T in your LeRobot configuration, specify the policy type as:
-
-```python
-policy.type=groot
-```
-
-## Training
-
-### Training Command Example
-
-Here's a complete training command for finetuning the base GR00T model on your own dataset:
-
-```bash
-# Using a multi-GPU setup
-accelerate launch \
-  --multi_gpu \
-  --num_processes=$NUM_GPUS \
-  $(which lerobot-train) \
-  --output_dir=$OUTPUT_DIR \
-  --save_checkpoint=true \
-  --batch_size=$BATCH_SIZE \
-  --steps=$NUM_STEPS \
-  --save_freq=$SAVE_FREQ \
-  --log_freq=$LOG_FREQ \
-  --policy.push_to_hub=true \
-  --policy.type=groot \
-  --policy.repo_id=$REPO_ID \
-  --policy.tune_diffusion_model=false \
-  --dataset.repo_id=$DATASET_ID \
-  --wandb.enable=true \
-  --wandb.disable_artifact=true \
-  --job_name=$JOB_NAME
-```
-
-## Performance Results
-
-### Libero Benchmark Results
-
-GR00T has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the GR00T N1.5 model for 30k steps on the Libero dataset and compared the results to the GR00T reference results.
-
-| Benchmark          | LeRobot Implementation | GR00T Reference |
-| ------------------ | ---------------------- | --------------- |
-| **Libero Spatial** | 82.0%                  | 92.0%           |
-| **Libero Object**  | 99.0%                  | 92.0%           |
-| **Libero Long**    | 82.0%                  | 76.0%           |
-| **Average**        | 87.0%                  | 87.0%           |
-
-These results demonstrate GR00T's strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section.
-
-### Evaluate in your hardware setup
-
-Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Imitation Learning for Robots](./il_robots). For example:
-
-```bash
-lerobot-record \
-  --robot.type=bi_so100_follower \
-  --robot.left_arm_port=/dev/ttyACM1 \
-  --robot.right_arm_port=/dev/ttyACM0 \
-  --robot.id=bimanual_follower \
-  --robot.cameras='{ right: {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30},
-    left: {"type": "opencv", "index_or_path": 2, "width": 640, "height": 480, "fps": 30},
-    top: {"type": "opencv", "index_or_path": 4, "width": 640, "height": 480, "fps": 30},
-  }' \
-  --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"
-  --policy.path=<user>/groot-bimanual # your trained model
-  --dataset.episode_time_s=30
-  --dataset.reset_time_s=10
-```
-
-## License
-
-This model follows the **Apache 2.0 License**, consistent with the original [GR00T repository](https://github.com/NVIDIA/Isaac-GR00T).

docs/source/il_robots.mdx

@@ -165,7 +165,7 @@ huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 Then store your Hugging Face repository name in a variable:
 
 ```bash
-HF_USER=$(hf auth whoami | head -n 1)
+HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
 ```
 
@@ -513,14 +513,13 @@ from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import hw_to_dataset_features
 from lerobot.policies.act.modeling_act import ACTPolicy
-from lerobot.policies.factory import make_pre_post_processors
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
 from lerobot.robots.so100_follower.so100_follower import SO100Follower
-from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
-
+from lerobot.record import record_loop
+from lerobot.policies.factory import make_processor
 
 NUM_EPISODES = 5
 FPS = 30
@@ -563,7 +562,7 @@ init_rerun(session_name="recording")
 # Connect the robot
 robot.connect()
 
-preprocessor, postprocessor = make_pre_post_processors(
+preprocessor, postprocessor = make_processor(
     policy_cfg=policy,
     pretrained_path=HF_MODEL_ID,
     dataset_stats=dataset.meta.stats,

docs/source/installation.mdx

@@ -1,15 +1,8 @@
 # Installation
 
-## 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
-```
-
 ## Environment Setup
 
-Create a virtual environment with Python 3.10, using conda:
+Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
 
 ```bash
 conda create -y -n lerobot python=3.10
@@ -21,7 +14,7 @@ Then activate your conda environment, you have to do this each time you open a s
 conda activate lerobot
 ```
 
-When using `conda`, install `ffmpeg` in your environment:
+When using `miniconda`, install `ffmpeg` in your environment:
 
 ```bash
 conda install ffmpeg -c conda-forge
@@ -81,9 +74,6 @@ _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 libero or pi, you will have to do: `pip install "lerobot[pi,libero]@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`.
@@ -101,7 +91,7 @@ LeRobot provides optional extras for specific functionalities. Multiple extras c
 
 ### Simulations
 
-Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
+Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), `xarm` ([gym-xarm](https://github.com/huggingface/gym-xarm)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
 Example:
 
 ```bash

docs/source/integrate_hardware.mdx

@@ -8,7 +8,7 @@ To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blo
 
 - Your own robot which exposes a communication interface (e.g. serial, CAN, TCP)
 - A way to read sensor data and send motor commands programmatically, e.g. manufacturer's SDK or API, or your own protocol implementation.
-- LeRobot installed in your environment. Follow our [Installation Guide](./installation).
+- LeRobot installed in your environment. Follow our [Installation Guide](./installation.mdx).
 
 ## Choose your motors
 
@@ -65,7 +65,7 @@ class MyCoolRobotConfig(RobotConfig):
 ```
 <!-- prettier-ignore-end -->
 
-[Cameras tutorial](./cameras) to understand how to detect and add your camera.
+[Cameras tutorial](./cameras.mdx) to understand how to detect and add your camera.
 
 Next, we'll create our actual robot class which inherits from `Robot`. This abstract class defines a contract you must follow for your robot to be usable with the rest of the LeRobot tools.
 
@@ -208,36 +208,34 @@ LeRobot supports saving and loading calibration data automatically. This is usef
 
 <!-- prettier-ignore-start -->
 ```python
-@property
-def is_calibrated(self) -> bool:
-    return True
-
-def calibrate(self) -> None:
-    pass
-```
-<!-- prettier-ignore-end -->
+> @property
+> def is_calibrated(self) -> bool:
+>    return True
+>
+> def calibrate(self) -> None:
+>    pass
+> ```
 
 ### `is_calibrated`
 
 This should reflect whether your robot has the required calibration loaded.
 
-<!-- prettier-ignore-start -->
-```python
+```
+<!-- prettier-ignore-end -->python
 @property
 def is_calibrated(self) -> bool:
     return self.bus.is_calibrated
 ```
-<!-- prettier-ignore-end -->
 
 ### `calibrate()`
 
 The goal of the calibration is twofold:
-
-- Know the physical range of motion of each motors in order to only send commands within this range.
-- Normalize raw motors positions to sensible continuous values (e.g. percentages, degrees) instead of arbitrary discrete value dependant on the specific motor used that will not replicate elsewhere.
+    - Know the physical range of motion of each motors in order to only send commands within this range.
+    - Normalize raw motors positions to sensible continuous values (e.g. percentages, degrees) instead of arbitrary discrete value dependant on the specific motor used that will not replicate elsewhere.
 
 It should implement the logic for calibration (if relevant) and update the `self.calibration` dictionary. If you are using Feetech or Dynamixel motors, our bus interfaces already include methods to help with this.
 
+
 <!-- prettier-ignore-start -->
 ```python
 def calibrate(self) -> None:
@@ -337,134 +335,6 @@ For implementing teleoperation devices, we also provide a [`Teleoperator`](https
 
 The main differences are in the I/O functions: a teleoperator allows you to produce action via `get_action` and can receive feedback actions via `send_feedback`. Feedback could be anything controllable on the teleoperation device that could help the person controlling it understand the consequences of the actions sent. Think motion/force feedback on a leader arm, vibrations on a gamepad controller for example. To implement a teleoperator, you can follow this same tutorial and adapt it for these two methods.
 
-## Using Your Own `LeRobot` Devices 🔌
-
-You can easily extend `lerobot` with your own custom hardware—be it a camera, robot, or teleoperation device—by creating a separate, installable Python package. If you follow a few simple conventions, the `lerobot` command-line tools (like `lerobot-teleop` and `lerobot-record`) will **automatically discover and integrate your creations** without requiring any changes to the `lerobot` source code.
-
-This guide outlines the conventions your plugin must follow.
-
-### The 4 Core Conventions
-
-To ensure your custom device is discoverable, you must adhere to the following four rules.
-
-#### 1\. Create an Installable Package with a Specific Prefix
-
-Your project must be a standard, installable Python package. Crucially, the name of your package (as defined in `pyproject.toml` or `setup.py`) must begin with one of these prefixes:
-
-- `lerobot_robot_` for a robot.
-- `lerobot_camera_` for a camera.
-- `lerobot_teleoperator_` for a teleoperation device.
-
-This prefix system is how `lerobot` automatically finds your plugin in the Python environment.
-
-#### 2\. Follow the `SomethingConfig`/`Something` Naming Pattern
-
-Your device's implementation class must be named after its configuration class, simply by removing the `Config` suffix.
-
-- **Config Class:** `MyAwesomeTeleopConfig`
-- **Device Class:** `MyAwesomeTeleop`
-
-#### 3\. Place Your Files in a Predictable Structure
-
-The device class (`MyAwesomeTeleop`) must be located in a predictable module relative to its configuration class (`MyAwesomeTeleopConfig`). `lerobot` will automatically search in these locations:
-
-- In the **same module** as the config class.
-- In a **submodule named after the device** (e.g., `my_awesome_teleop.py`).
-
-The recommended and simplest structure is to place them in separate, clearly named files within the same directory.
-
-#### 4\. Expose Classes in `__init__.py`
-
-Your package's `__init__.py` file should import and expose both the configuration and the device classes, making them easily accessible.
-
-### Putting It All Together: A Complete Example
-
-Let's create a new teleoperator called `my_awesome_teleop`.
-
-#### Directory Structure
-
-Here is what the project folder should look like. The package name, `lerobot_teleoperator_my_awesome_teleop`, follows **Convention \#1**.
-
-```
-lerobot_teleoperator_my_awesome_teleop/
-├── pyproject.toml # (or setup.py) lists lerobot as a dependency
-└── lerobot_teleoperator_my_awesome_teleop/
-    ├── __init__.py
-    ├── config_my_awesome_teleop.py
-    └── my_awesome_teleop.py
-```
-
-#### File Contents
-
-- **`config_my_awesome_teleop.py`**: Defines the configuration class. Note the `Config` suffix (**Convention \#2**).
-
-  ```python
-  from dataclasses import dataclass
-
-  from lerobot.teleoperators.config import TeleoperatorConfig
-
-  @TeleoperatorConfig.register_subclass("my_awesome_teleop")
-  @dataclass
-  class MyAwesomeTeleopConfig(TeleoperatorConfig):
-      # Your configuration fields go here
-      port: str = "192.168.1.1"
-  ```
-
-- **`my_awesome_teleop.py`**: Implements the device. The class name `MyAwesomeTeleop` matches its config class name (**Convention \#2**). This file structure adheres to **Convention \#3**.
-
-  ```python
-  from lerobot.teleoperators.teleoperator import Teleoperator
-
-  from .config_my_awesome_teleop import MyAwesomeTeleopConfig
-
-  class MyAwesomeTeleop(Teleoperator):
-      config_class = MyAwesomeTeleopConfig
-      name = "my_awesome_teleop"
-
-      def __init__(self, config: MyAwesomeTeleopConfig):
-          super().__init__(config)
-          self.config = config
-
-      # Your device logic (e.g., connect) goes here
-  ```
-
-- **`__init__.py`**: Exposes the key classes (**Convention \#4**).
-
-  ```python
-  from .config_my_awesome_teleop import MyAwesomeTeleopConfig
-  from .my_awesome_teleop import MyAwesomeTeleop
-  ```
-
-### Installation and Usage
-
-1.  **Install your new plugin in your Python environment.** You can install your local plugin package using `pip`'s editable mode or from PyPi.
-
-    ```bash
-    # Locally
-    # Navigate to your plugin's root directory and install it
-    cd lerobot_teleoperator_my_awesome_teleop
-    pip install -e .
-
-    # From PyPi
-    pip install lerobot_teleoperator_my_awesome_teleop
-    ```
-
-2.  **Use it directly from the command line.** Now, you can use your custom device by referencing its type.
-
-    ```bash
-    lerobot-teleoperate --teleop.type=my_awesome_teleop \
-    # other arguments
-    ```
-
-And that's it\! Your custom device is now fully integrated.
-
-### Looking for an example ?
-
-Check out these two packages from the community:
-
-- https://github.com/SpesRobotics/lerobot-robot-xarm
-- https://github.com/SpesRobotics/lerobot-teleoperator-teleop
-
 ## Wrapping Up
 
 Once your robot class is complete, you can leverage the LeRobot ecosystem:

docs/source/introduction_processors.mdx

@@ -297,9 +297,9 @@ LeRobot provides many registered processor steps. Here are the most commonly use
 
 ### Next Steps
 
-- **[Implement Your Own Processor](./implement_your_own_processor)** - Create custom processor steps
-- **[Debug Your Pipeline](./debug_processor_pipeline)** - Troubleshoot and optimize pipelines
-- **[Processors for Robots and Teleoperators](./processors_robots_teleop)** - Real-world integration patterns
+- **[Implement Your Own Processor](implement_your_own_processor.mdx)** - Create custom processor steps
+- **[Debug Your Pipeline](debug_processor_pipeline.mdx)** - Troubleshoot and optimize pipelines
+- **[Processors for Robots and Teleoperators](processors_robots_teleop.mdx)** - Real-world integration patterns
 
 ## Summary
 

docs/source/lerobot-dataset-v3.mdx

@@ -279,36 +279,3 @@ python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DAT
 - Aggregates parquet files: `episode-0000.parquet`, `episode-0001.parquet`, … → **`file-0000.parquet`**, …
 - Aggregates mp4 files: `episode-0000.mp4`, `episode-0001.mp4`, … → **`file-0000.mp4`**, …
 - Updates `meta/episodes/*` (chunked Parquet) with per‑episode lengths, tasks, and byte/frame offsets.
-
-## Common Issues
-
-### Always call `finalize()` before pushing
-
-When creating or recording datasets, you **must** call `dataset.finalize()` to properly close parquet writers. See the [PR #1903](https://github.com/huggingface/lerobot/pull/1903) for more details.
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-# Create dataset and record episodes
-dataset = LeRobotDataset.create(...)
-
-for episode in range(num_episodes):
-    # Record frames
-    for frame in episode_data:
-        dataset.add_frame(frame)
-    dataset.save_episode()
-
-# Call finalize() when done recording and before push_to_hub()
-dataset.finalize()  # Closes parquet writers, writes metadata footers
-dataset.push_to_hub()
-```
-
-**Why is this necessary?**
-
-Dataset v3.0 uses incremental parquet writing with buffered metadata for efficiency. The `finalize()` method:
-
-- Flushes any buffered episode metadata to disk
-- Closes parquet writers to write footer metadata, otherwise the parquet files will be corrupt
-- Ensures the dataset is valid for loading
-
-Without calling `finalize()`, your parquet files will be incomplete and the dataset won't load properly.

docs/source/libero.mdx

@@ -137,7 +137,7 @@ The finetuned model can be found here:
 We then evaluate the finetuned model using the LeRobot LIBERO implementation, by running the following command:
 
 ```bash
-lerobot-eval \
+python src/lerobot/scripts/eval.py \
   --output_dir=/logs/ \
   --env.type=libero \
   --env.task=libero_spatial,libero_object,libero_goal,libero_10 \

docs/source/metaworld.mdx

@@ -1,80 +0,0 @@
-# Meta-World
-
-Meta-World is a well-designed, open-source simulation benchmark for multi-task and meta reinforcement learning in continuous-control robotic manipulation. It gives researchers a shared, realistic playground to test whether algorithms can _learn many different tasks_ and _generalize quickly to new ones_ — two central challenges for real-world robotics.
-
-- 📄 [MetaWorld paper](https://arxiv.org/pdf/1910.10897)
-- 💻 [Original MetaWorld repo](https://github.com/Farama-Foundation/Metaworld)
-
-![MetaWorld MT10 demo](https://meta-world.github.io/figures/ml45.gif)
-
-## Why Meta-World matters
-
-- **Diverse, realistic tasks.** Meta-World bundles a large suite of simulated manipulation tasks (50 in the MT50 suite) using everyday objects and a common tabletop Sawyer arm. This diversity exposes algorithms to a wide variety of dynamics, contacts and goal specifications while keeping a consistent control and observation structure.
-- **Focus on generalization and multi-task learning.** By evaluating across task distributions that share structure but differ in goals and objects, Meta-World reveals whether an agent truly learns transferable skills rather than overfitting to a narrow task.
-- **Standardized evaluation protocol.** It provides clear evaluation modes and difficulty splits, so different methods can be compared fairly across easy, medium, hard and very-hard regimes.
-- **Empirical insight.** Past evaluations on Meta-World show impressive progress on some fronts, but also highlight that current multi-task and meta-RL methods still struggle with large, diverse task sets. That gap points to important research directions.
-
-## What it enables in LeRobot
-
-In LeRobot, you can evaluate any policy or vision-language-action (VLA) model on Meta-World tasks and get a clear success-rate measure. The integration is designed to be straightforward:
-
-- We provide a LeRobot-ready dataset for Meta-World (MT50) on the HF Hub: `https://huggingface.co/datasets/lerobot/metaworld_mt50`.
-  - This dataset is formatted for the MT50 evaluation that uses all 50 tasks (the most challenging multi-task setting).
-  - MT50 gives the policy a one-hot task vector and uses fixed object/goal positions for consistency.
-
-- Task descriptions and the exact keys required for evaluation are available in the repo/dataset — use these to ensure your policy outputs the right success signals.
-
-## Quick start, train a SmolVLA policy on Meta-World
-
-Example command to train a SmolVLA policy on a subset of tasks:
-
-```bash
-lerobot-train \
-  --policy.type=smolvla \
-  --policy.repo_id=${HF_USER}/metaworld-test \
-  --policy.load_vlm_weights=true \
-  --dataset.repo_id=lerobot/metaworld_mt50 \
-  --env.type=metaworld \
-  --env.task=assembly-v3,dial-turn-v3,handle-press-side-v3 \
-  --output_dir=./outputs/ \
-  --steps=100000 \
-  --batch_size=4 \
-  --eval.batch_size=1 \
-  --eval.n_episodes=1 \
-  --eval_freq=1000
-```
-
-Notes:
-
-- `--env.task` accepts explicit task lists (comma separated) or difficulty groups (e.g., `env.task="hard"`).
-- Adjust `batch_size`, `steps`, and `eval_freq` to match your compute budget.
-- **Gymnasium Assertion Error**: if you encounter an error like
-  `AssertionError: ['human', 'rgb_array', 'depth_array']` when running MetaWorld environments, this comes from a mismatch between MetaWorld and your Gymnasium version.
-  We recommend using:
-
-```bash
-  pip install "gymnasium==1.1.0"
-```
-
-to ensure proper compatibility.
-
-## Quick start — evaluate a trained policy
-
-To evaluate a trained policy on the Meta-World medium difficulty split:
-
-```bash
-lerobot-eval \
-  --policy.path="your-policy-id" \
-  --env.type=metaworld \
-  --env.task=medium \
-  --eval.batch_size=1 \
-  --eval.n_episodes=2
-```
-
-This will run episodes and return per-task success rates using the standard Meta-World evaluation keys.
-
-## Practical tips
-
-- If you care about generalization, run on the full MT50 suite — it’s intentionally challenging and reveals strengths/weaknesses better than a few narrow tasks.
-- Use the one-hot task conditioning for multi-task training (MT10 / MT50 conventions) so policies have explicit task context.
-- Inspect the dataset task descriptions and the `info["is_success"]` keys when writing post-processing or logging so your success metrics line up with the benchmark.

docs/source/multi_gpu_training.mdx

@@ -1,125 +0,0 @@
-# Multi-GPU Training
-
-This guide shows you how to train policies on multiple GPUs using [Hugging Face Accelerate](https://huggingface.co/docs/accelerate).
-
-## Installation
-
-First, ensure you have accelerate installed:
-
-```bash
-pip install accelerate
-```
-
-## Training with Multiple GPUs
-
-You can launch training in two ways:
-
-### Option 1: Without config (specify parameters directly)
-
-You can specify all parameters directly in the command without running `accelerate config`:
-
-```bash
-accelerate launch \
-  --multi_gpu \
-  --num_processes=2 \
-  $(which lerobot-train) \
-  --dataset.repo_id=${HF_USER}/my_dataset \
-  --policy.type=act \
-  --policy.repo_id=${HF_USER}/my_trained_policy \
-  --output_dir=outputs/train/act_multi_gpu \
-  --job_name=act_multi_gpu \
-  --wandb.enable=true
-```
-
-**Key accelerate parameters:**
-
-- `--multi_gpu`: Enable multi-GPU training
-- `--num_processes=2`: Number of GPUs to use
-- `--mixed_precision=fp16`: Use fp16 mixed precision (or `bf16` if supported)
-
-### Option 2: Using accelerate config
-
-If you prefer to save your configuration, you can optionally configure accelerate for your hardware setup by running:
-
-```bash
-accelerate config
-```
-
-This interactive setup will ask you questions about your training environment (number of GPUs, mixed precision settings, etc.) and saves the configuration for future use. For a simple multi-GPU setup on a single machine, you can use these recommended settings:
-
-- Compute environment: This machine
-- Number of machines: 1
-- Number of processes: (number of GPUs you want to use)
-- GPU ids to use: (leave empty to use all)
-- Mixed precision: fp16 or bf16 (recommended for faster training)
-
-Then launch training with:
-
-```bash
-accelerate launch $(which lerobot-train) \
-  --dataset.repo_id=${HF_USER}/my_dataset \
-  --policy.type=act \
-  --policy.repo_id=${HF_USER}/my_trained_policy \
-  --output_dir=outputs/train/act_multi_gpu \
-  --job_name=act_multi_gpu \
-  --wandb.enable=true
-```
-
-## How It Works
-
-When you launch training with accelerate:
-
-1. **Automatic detection**: LeRobot automatically detects if it's running under accelerate
-2. **Data distribution**: Your batch is automatically split across GPUs
-3. **Gradient synchronization**: Gradients are synchronized across GPUs during backpropagation
-4. **Single process logging**: Only the main process logs to wandb and saves checkpoints
-
-## Learning Rate and Training Steps Scaling
-
-**Important:** LeRobot does **NOT** automatically scale learning rates or training steps based on the number of GPUs. This gives you full control over your training hyperparameters.
-
-### Why No Automatic Scaling?
-
-Many distributed training frameworks automatically scale the learning rate by the number of GPUs (e.g., `lr = base_lr × num_gpus`).
-However, LeRobot keeps the learning rate exactly as you specify it.
-
-### When and How to Scale
-
-If you want to scale your hyperparameters when using multiple GPUs, you should do it manually:
-
-**Learning Rate Scaling:**
-
-```bash
-# Example: 2 GPUs with linear LR scaling
-# Base LR: 1e-4, with 2 GPUs -> 2e-4
-accelerate launch --num_processes=2 $(which lerobot-train) \
-  --optimizer.lr=2e-4 \
-  --dataset.repo_id=lerobot/pusht \
-  --policy=act
-```
-
-**Training Steps Scaling:**
-
-Since the effective batch size `bs` increases with multiple GPUs (batch_size × num_gpus), you may want to reduce the number of training steps proportionally:
-
-```bash
-# Example: 2 GPUs with effective batch size 2x larger
-# Original: batch_size=8, steps=100000
-# With 2 GPUs: batch_size=8 (16 in total), steps=50000
-accelerate launch --num_processes=2 $(which lerobot-train) \
-  --batch_size=8 \
-  --steps=50000 \
-  --dataset.repo_id=lerobot/pusht \
-  --policy=act
-```
-
-## Notes
-
-- The `--policy.use_amp` flag in `lerobot-train` is only used when **not** running with accelerate. When using accelerate, mixed precision is controlled by accelerate's configuration.
-- Training logs, checkpoints, and hub uploads are only done by the main process to avoid conflicts. Non-main processes have console logging disabled to prevent duplicate output.
-- The effective batch size is `batch_size × num_gpus`. If you use 4 GPUs with `--batch_size=8`, your effective batch size is 32.
-- Learning rate scheduling is handled correctly across multiple processes—LeRobot sets `step_scheduler_with_optimizer=False` to prevent accelerate from adjusting scheduler steps based on the number of processes.
-- When saving or pushing models, LeRobot automatically unwraps the model from accelerate's distributed wrapper to ensure compatibility.
-- WandB integration automatically initializes only on the main process, preventing multiple runs from being created.
-
-For more advanced configurations and troubleshooting, see the [Accelerate documentation](https://huggingface.co/docs/accelerate). If you want to learn more about how to train on a large number of GPUs, checkout this awesome guide: [Ultrascale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook).

docs/source/openarms.mdx

@@ -1,328 +0,0 @@
-# OpenArms Robot
-
-OpenArms is a 7 DOF robotic arm with a gripper, designed by [Enactic, Inc.](https://www.enactic.com/) It uses Damiao motors controlled via CAN bus communication and MIT control mode for smooth, precise motion.
-
-## Hardware Overview
-
-- **7 DOF per arm** (14 DOF total for dual arm setup)
-- **1 gripper per arm** (2 grippers total)
-- **Damiao motors** with 4 different types:
-  - **DM8009** (DM-J8009P-2EC) for shoulders (J1, J2) - high torque
-  - **DM4340** for shoulder rotation and elbow (J3, J4)
-  - **DM4310** (DM-J4310-2EC V1.1) for wrist (J5, J6, J7) and gripper (J8)
-- **24V power supply** required
-- **CAN interface device**:
-  - **Linux**: Any SocketCAN-compatible adapter
-  - **macOS**: CANable, PEAK PCAN-USB, or Kvaser USBcan
-- Proper CAN wiring (CANH, CANL, 120Ω termination)
-
-
-## Motor Configuration
-
-Each arm has the following motor configuration based on the [OpenArm setup guide](https://docs.openarm.dev/software/setup/):
-
-| Joint | Motor | Motor Type | Sender CAN ID | Receiver ID | Description |
-|-------|-------|------------|---------------|-------------|-------------|
-| J1 | joint_1 | DM8009 | 0x01 | 0x11 | Shoulder pan |
-| J2 | joint_2 | DM8009 | 0x02 | 0x12 | Shoulder lift |
-| J3 | joint_3 | DM4340 | 0x03 | 0x13 | Shoulder rotation |
-| J4 | joint_4 | DM4340 | 0x04 | 0x14 | Elbow flex |
-| J5 | joint_5 | DM4310 | 0x05 | 0x15 | Wrist roll |
-| J6 | joint_6 | DM4310 | 0x06 | 0x16 | Wrist pitch |
-| J7 | joint_7 | DM4310 | 0x07 | 0x17 | Wrist rotation |
-| J8 | gripper | DM4310 | 0x08 | 0x18 | Gripper |
-
-For dual arm setups, the left arm uses IDs 0x09-0x10 for joints 1-8 with the same motor types.
-
-## Quick Start 
-
-```bash
-# Install system dependencies
-sudo apt install can-utils iproute2
-
-# Install LeRobot with OpenArms support
-pip install -e ".[openarms]"
-```
-
-## Setup Guide
-
-### Step 1: Motor ID Configuration
-
-**IMPORTANT**: Before using the robot, motors must be configured with the correct CAN IDs.
-
-Refer to the [OpenArm Motor ID Configuration Guide](https://docs.openarm.dev/software/setup/motor-id) for detailed instructions using the Damiao Debugging Tools on Windows.
-
-Key points:
-- Each motor needs a unique **Sender CAN ID** (0x01-0x08)
-- Each motor needs a unique **Receiver/Master ID** (0x11-0x18)
-- Use the Damiao Debugging Tools to set these IDs
-
-### Step 2: Setup CAN Interface
-
-Configure your CAN interface as described in the [OpenArm CAN Setup Guide](https://docs.openarm.dev/software/setup/can-setup):
-
-#### Linux (SocketCAN)
-
-```bash
-# Find your CAN interface
-ip link show
-
-# Configure can0, 1, 2, 3
-sudo ip link set can0 down
-sudo ip link set can0 type can bitrate 1000000
-sudo ip link set can0 up
-
-sudo ip link set can1 down
-sudo ip link set can1 type can bitrate 1000000
-sudo ip link set can1 up
-
-sudo ip link set can2 down
-sudo ip link set can2 type can bitrate 1000000
-sudo ip link set can2 up
-
-sudo ip link set can3 down
-sudo ip link set can3 type can bitrate 1000000
-sudo ip link set can3 up
-
-# Verify configuration
-ip link show can0
-```
-
-or run:
-
-`examples/openarms/setup_can.sh`
-
-### Testing canbus and motor connection
-
-Please run this script to check if all motors can be found and to find your can-fd speed: `python examples/openarms/debug_can_communication.py`
-
-## Usage
-
-### Basic Setup
-
-
-```python
-from lerobot.robots.openarms import OpenArmsFollower
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-
-# Configure for dual arm setup
-config = OpenArmsFollowerConfig(
-    port="can0",
-    can_interface="socketcan",  # Or "auto" for auto-detection
-    id="openarms_dual",
-    is_dual_arm=True,
-)
-
-robot = OpenArmsFollower(config)
-robot.connect()
-```
-
-### Calibration
-
-On first use, you'll need to calibrate the robot:
-
-```python
-robot.calibrate()
-```
-
-The calibration process will:
-1. Disable torque on all motors
-2. Ask you to position arms in **hanging position with grippers closed**
-3. Set this as the zero position
-4. Ask you to move each joint through its full range
-5. Record min/max positions for each joint
-6. Save calibration to file
-
-### Reading Observations
-
-The robot provides comprehensive state information:
-
-```python
-observation = robot.get_observation()
-
-# Observation includes for each motor:
-# - {motor_name}.pos: Position in degrees
-# - {motor_name}.vel: Velocity in degrees/second  
-# - {motor_name}.torque: Motor torque
-# - {camera_name}: Camera images (if configured)
-
-print(f"Right arm joint 1 position: {observation['right_joint_1.pos']:.1f}°")
-print(f"Right arm joint 1 velocity: {observation['right_joint_1.vel']:.1f}°/s")
-print(f"Right arm joint 1 torque: {observation['right_joint_1.torque']:.3f} N·m")
-```
-
-### Sending Actions
-
-```python
-# Send target positions (in degrees)
-action = {
-    "right_joint_1.pos": 45.0,
-    "right_joint_2.pos": -30.0,
-    # ... all joints
-    "right_gripper.pos": 45.0,  # Half-closed
-}
-
-actual_action = robot.send_action(action)
-```
-
-### Gripper Control
-
-```python
-# Open gripper
-robot.open_gripper(arm="right")
-
-# Close gripper  
-robot.close_gripper(arm="right")
-```
-
-## Safety Features
-
-### 1. Maximum Relative Target
-
-Limits how far a joint can move in a single command to prevent sudden movements:
-
-```python
-config = OpenArmsFollowerConfig(
-    port="can0",
-    # Limit all joints to 10 degrees per command
-    max_relative_target=10.0,
-    
-    # Or set per-motor limits
-    max_relative_target={
-        "right_joint_1": 15.0,  # Slower moving joint
-        "right_joint_2": 10.0,
-        "right_gripper": 5.0,   # Very slow gripper
-    }
-)
-```
-
-**How it works**: If current position is 50° and you command 80°, with `max_relative_target=10.0`, the robot will only move to 60° in that step.
-
-### 2. Torque Limits
-
-Control maximum torque output, especially important for grippers and teleoperation:
-
-```python
-config = OpenArmsFollowerConfig(
-    port="can0",
-    # Gripper torque limit (fraction of motor's max torque)
-    gripper_torque_limit=0.5,  # 50% of max torque
-)
-```
-
-Lower torque limits prevent damage when gripping delicate objects.
-
-### 3. MIT Control Gains
-
-Control responsiveness and stability via PID-like gains:
-
-```python
-config = OpenArmsFollowerConfig(
-    port="can0",
-    position_kp=10.0,  # Position gain (higher = more responsive)
-    position_kd=0.5,   # Velocity damping (higher = more damped)
-)
-```
-
-**Guidelines**:
-- **For following (robot)**: Higher gains for responsiveness
-  - `position_kp=10.0`, `position_kd=0.5`
-- **For teleoperation (leader)**: Lower gains or disable torque for manual movement
-  - `manual_control=True` (torque disabled)
-
-### 4. Velocity Limits
-
-Velocity limits are enforced by the Damiao motors based on motor type. For DM4310:
-- Max velocity: 30 rad/s ≈ 1718°/s
-
-The motors will automatically limit velocity to safe values.
-
-## Teleoperation
-
-### Leader Arm Setup
-
-The leader arm is moved manually (torque disabled) to generate commands:
-
-```python
-from lerobot.teleoperators.openarms import OpenArmsLeader
-from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
-
-config = OpenArmsLeaderConfig(
-    port="can1",  # Separate CAN interface for leader
-    id="openarms_leader",
-    manual_control=True,  # Torque disabled for manual movement
-    is_dual_arm=True,
-)
-
-leader = OpenArmsLeader(config)
-leader.connect()
-
-# Read current position as action
-action = leader.get_action()
-# action contains positions for all joints in degrees
-```
-
-### Safety Considerations for Teleoperation
-
-1. **Use separate CAN interfaces** for leader and follower to avoid conflicts
-2. **Enable max_relative_target** on follower to smooth abrupt movements
-3. **Lower torque limits** on follower to prevent damage from tracking errors
-4. **Test with one arm** before enabling dual arm teleoperation
-5. **Have emergency stop** ready (power switch or CAN disable)
-
-```python
-# Recommended follower config for teleoperation
-follower_config = OpenArmsFollowerConfig(
-    port="can0",
-    max_relative_target=5.0,  # Small steps for smooth following
-    gripper_torque_limit=0.3, # Low torque for safety
-    position_kp=5.0,  # Lower gains for gentler following
-    position_kd=0.3,
-)
-```
-
-## Troubleshooting
-
-### Motor Shaking/Unstable
-
-- **Lower control gains**: Reduce `position_kp` and `position_kd`
-- **Check calibration**: Re-run calibration procedure
-- **Verify power**: Insufficient current can cause instability
-- **Check mechanical**: Loose connections, binding, or damaged components
-
-### CAN Bus Errors
-
-```bash
-# Check for errors
-ip -s link show can0
-
-# Reset CAN interface
-sudo ip link set can0 down
-sudo ip link set can0 up
-```
-
-### Control Mode
-
-OpenArms uses **MIT control mode** which allows simultaneous control of:
-- Position (degrees)
-- Velocity (degrees/second)
-- Torque (N·m)
-- Position gain (Kp)
-- Velocity damping (Kd)
-
-### Communication
-
-- **Protocol**: CAN 2.0 at 1 Mbps (or CAN-FD at 5 Mbps)
-- **Frame format**: Standard 11-bit IDs
-- **Update rate**: Typically 50-100 Hz depending on motor count
-- **Latency**: ~10-20ms per motor command
-
-## References
-
-- [OpenArm Official Documentation](https://docs.openarm.dev/)
-- [OpenArm Setup Guide](https://docs.openarm.dev/software/setup/)
-- [Motor ID Configuration](https://docs.openarm.dev/software/setup/motor-id)
-- [CAN Interface Setup](https://docs.openarm.dev/software/setup/can-setup)
-- [Motor Communication Test](https://docs.openarm.dev/software/setup/configure-test)
-- [Damiao Motor Documentation](https://wiki.seeedstudio.com/damiao_series/)
-- [Enactic GitHub](https://github.com/enactic/openarm_can)

docs/source/phone_teleop.mdx

@@ -79,7 +79,7 @@ After running the example:
 - Android: after starting the script, open the printed local URL on your phone, tap Start, then press and hold Move.
 - iOS: open HEBI Mobile I/O first; B1 enables motion. A3 controls the gripper.
 
-Additionally you can customize mapping or safety limits by editing the processor steps shown in the examples. You can also remap inputs (e.g., use a different analog input) or adapt the pipeline to other robots (e.g., LeKiwi) by modifying the input and kinematics steps. More about this in the [Processors for Robots and Teleoperators](./processors_robots_teleop) guide.
+Additionally you can customize mapping or safety limits by editing the processor steps shown in the examples. You can also remap inputs (e.g., use a different analog input) or adapt the pipeline to other robots (e.g., LeKiwi) by modifying the input and kinematics steps. More about this in the [Processors for Robots and Teleoperators](./processors_robots_teleop.mdx) guide.
 
 - Run this example to record a dataset, which saves absolute end effector observations and actions:
 

docs/source/policy_groot_README.md

@@ -1,27 +0,0 @@
-## Research Paper
-
-Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
-
-## Repository
-
-Code: https://github.com/NVIDIA/Isaac-GR00T
-
-## Citation
-
-```bibtex
-@inproceedings{gr00tn1_2025,
-  archivePrefix = {arxiv},
-  eprint     = {2503.14734},
-  title      = {{GR00T} {N1}: An Open Foundation Model for Generalist Humanoid Robots},
-  author     = {NVIDIA and Johan Bjorck andFernando Castañeda, Nikita Cherniadev and Xingye Da and Runyu Ding and Linxi "Jim" Fan and Yu Fang and Dieter Fox and Fengyuan Hu and Spencer Huang and Joel Jang and Zhenyu Jiang and Jan Kautz and Kaushil Kundalia and Lawrence Lao and Zhiqi Li and Zongyu Lin and Kevin Lin and Guilin Liu and Edith Llontop and Loic Magne and Ajay Mandlekar and Avnish Narayan and Soroush Nasiriany and Scott Reed and You Liang Tan and Guanzhi Wang and Zu Wang and Jing Wang and Qi Wang and Jiannan Xiang and Yuqi Xie and Yinzhen Xu and Zhenjia Xu and Seonghyeon Ye and Zhiding Yu and Ao Zhang and Hao Zhang and Yizhou Zhao and Ruijie Zheng and Yuke Zhu},
-  month      = {March},
-  year       = {2025},
-  booktitle  = {ArXiv Preprint},
-}
-```
-
-## Additional Resources
-
-Blog: https://developer.nvidia.com/isaac/gr00t
-
-Hugging Face Model: https://huggingface.co/nvidia/GR00T-N1.5-3B

docs/source/using_dataset_tools.mdx

@@ -1,102 +0,0 @@
-# Using Dataset Tools
-
-This guide covers the dataset tools utilities available in LeRobot for modifying and editing existing datasets.
-
-## Overview
-
-LeRobot provides several utilities for manipulating datasets:
-
-1. **Delete Episodes** - Remove specific episodes from a dataset
-2. **Split Dataset** - Divide a dataset into multiple smaller datasets
-3. **Merge Datasets** - Combine multiple datasets into one. The datasets must have identical features, and episodes are concatenated in the order specified in `repo_ids`
-4. **Add Features** - Add new features to a dataset
-5. **Remove Features** - Remove features from a dataset
-
-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`.
-
-## Command-Line Tool: lerobot-edit-dataset
-
-`lerobot-edit-dataset` is a command-line script for editing datasets. It can be used to delete episodes, split datasets, merge datasets, add features, and remove features.
-
-Run `lerobot-edit-dataset --help` for more information on the configuration of each operation.
-
-### Usage Examples
-
-#### Delete Episodes
-
-Remove specific episodes from a dataset. This is useful for filtering out undesired data.
-
-```bash
-# Delete episodes 0, 2, and 5 (modifies original dataset)
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht \
-    --operation.type delete_episodes \
-    --operation.episode_indices "[0, 2, 5]"
-
-# Delete episodes and save to a new dataset (preserves original dataset)
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht \
-    --new_repo_id lerobot/pusht_after_deletion \
-    --operation.type delete_episodes \
-    --operation.episode_indices "[0, 2, 5]"
-```
-
-#### Split Dataset
-
-Divide a dataset into multiple subsets.
-
-```bash
-# Split by fractions (e.g. 80% train, 20% test, 20% val)
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht \
-    --operation.type split \
-    --operation.splits '{"train": 0.8, "test": 0.2, "val": 0.2}'
-
-# Split by specific episode indices
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht \
-    --operation.type split \
-    --operation.splits '{"task1": [0, 1, 2, 3], "task2": [4, 5]}'
-```
-
-There are no constraints on the split names, they can be determined by the user. Resulting datasets are saved under the repo id with the split name appended, e.g. `lerobot/pusht_train`, `lerobot/pusht_task1`, `lerobot/pusht_task2`.
-
-#### Merge Datasets
-
-Combine multiple datasets into a single dataset.
-
-```bash
-# Merge train and validation splits back into one dataset
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_merged \
-    --operation.type merge \
-    --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
-```
-
-#### Remove Features
-
-Remove features from a dataset.
-
-```bash
-# Remove a camera feature
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht \
-    --operation.type remove_feature \
-    --operation.feature_names "['observation.images.top']"
-```
-
-### Push to Hub
-
-Add the `--push_to_hub` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:
-
-```bash
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht \
-    --new_repo_id lerobot/pusht_after_deletion \
-    --operation.type delete_episodes \
-    --operation.episode_indices "[0, 2, 5]" \
-    --push_to_hub
-```
-
-There is also a tool for adding features to a dataset that is not yet covered in `lerobot-edit-dataset`.

examples/dataset/load_lerobot_dataset.py

@@ -132,15 +132,17 @@ print(f"\n{dataset[0][camera_key].shape=}")  # (4, c, h, w)
 print(f"{dataset[0]['observation.state'].shape=}")  # (6, c)
 print(f"{dataset[0]['action'].shape=}\n")  # (64, c)
 
-if __name__ == "__main__":
-    dataloader = torch.utils.data.DataLoader(
-        dataset,
-        num_workers=4,
-        batch_size=32,
-        shuffle=True,
-    )
-    for batch in dataloader:
-        print(f"{batch[camera_key].shape=}")  # (32, 4, c, h, w)
-        print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
-        print(f"{batch['action'].shape=}")  # (32, 64, c)
-        break
+# Finally, our datasets are fully compatible with PyTorch dataloaders and samplers because they are just
+# PyTorch datasets.
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    num_workers=4,
+    batch_size=32,
+    shuffle=True,
+)
+
+for batch in dataloader:
+    print(f"{batch[camera_key].shape=}")  # (32, 4, c, h, w)
+    print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
+    print(f"{batch['action'].shape=}")  # (32, 64, c)
+    break

examples/dataset/use_dataset_tools.py

@@ -1,124 +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.
-
-"""
-Example script demonstrating dataset tools utilities.
-
-This script shows how to:
-1. Delete episodes from a dataset
-2. Split a dataset into train/val sets
-3. Add/remove features
-4. Merge datasets
-
-Usage:
-    python examples/dataset/use_dataset_tools.py
-"""
-
-import numpy as np
-
-from lerobot.datasets.dataset_tools import (
-    add_features,
-    delete_episodes,
-    merge_datasets,
-    modify_features,
-    remove_feature,
-    split_dataset,
-)
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-
-def main():
-    dataset = LeRobotDataset("lerobot/pusht")
-
-    print(f"Original dataset: {dataset.meta.total_episodes} episodes, {dataset.meta.total_frames} frames")
-    print(f"Features: {list(dataset.meta.features.keys())}")
-
-    print("\n1. Deleting episodes 0 and 2...")
-    filtered_dataset = delete_episodes(dataset, episode_indices=[0, 2], repo_id="lerobot/pusht_filtered")
-    print(f"Filtered dataset: {filtered_dataset.meta.total_episodes} episodes")
-
-    print("\n2. Splitting dataset into train/val...")
-    splits = split_dataset(
-        dataset,
-        splits={"train": 0.8, "val": 0.2},
-    )
-    print(f"Train split: {splits['train'].meta.total_episodes} episodes")
-    print(f"Val split: {splits['val'].meta.total_episodes} episodes")
-
-    print("\n3. Adding features...")
-
-    reward_values = np.random.randn(dataset.meta.total_frames).astype(np.float32)
-
-    def compute_success(row_dict, episode_index, frame_index):
-        episode_length = 10
-        return float(frame_index >= episode_length - 10)
-
-    dataset_with_features = add_features(
-        dataset,
-        features={
-            "reward": (
-                reward_values,
-                {"dtype": "float32", "shape": (1,), "names": None},
-            ),
-            "success": (
-                compute_success,
-                {"dtype": "float32", "shape": (1,), "names": None},
-            ),
-        },
-        repo_id="lerobot/pusht_with_features",
-    )
-
-    print(f"New features: {list(dataset_with_features.meta.features.keys())}")
-
-    print("\n4. Removing the success feature...")
-    dataset_cleaned = remove_feature(
-        dataset_with_features, feature_names="success", repo_id="lerobot/pusht_cleaned"
-    )
-    print(f"Features after removal: {list(dataset_cleaned.meta.features.keys())}")
-
-    print("\n5. Using modify_features to add and remove features simultaneously...")
-    dataset_modified = modify_features(
-        dataset_with_features,
-        add_features={
-            "discount": (
-                np.ones(dataset.meta.total_frames, dtype=np.float32) * 0.99,
-                {"dtype": "float32", "shape": (1,), "names": None},
-            ),
-        },
-        remove_features="reward",
-        repo_id="lerobot/pusht_modified",
-    )
-    print(f"Modified features: {list(dataset_modified.meta.features.keys())}")
-
-    print("\n6. Merging train and val splits back together...")
-    merged = merge_datasets([splits["train"], splits["val"]], output_repo_id="lerobot/pusht_merged")
-    print(f"Merged dataset: {merged.meta.total_episodes} episodes")
-
-    print("\n7. Complex workflow example...")
-
-    if len(dataset.meta.camera_keys) > 1:
-        camera_to_remove = dataset.meta.camera_keys[0]
-        print(f"Removing camera: {camera_to_remove}")
-        dataset_no_cam = remove_feature(
-            dataset, feature_names=camera_to_remove, repo_id="pusht_no_first_camera"
-        )
-        print(f"Remaining cameras: {dataset_no_cam.meta.camera_keys}")
-
-    print("\nDone! Check ~/.cache/huggingface/lerobot/ for the created datasets.")
-
-
-if __name__ == "__main__":
-    main()

examples/lekiwi/evaluate.py

@@ -133,6 +133,4 @@ while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
 log_say("Stop recording")
 robot.disconnect()
 listener.stop()
-
-dataset.finalize()
 dataset.push_to_hub()

examples/lekiwi/record.py

@@ -130,6 +130,4 @@ robot.disconnect()
 leader_arm.disconnect()
 keyboard.disconnect()
 listener.stop()
-
-dataset.finalize()
 dataset.push_to_hub()

examples/openarms/debug_can_communication.py

@@ -1,416 +0,0 @@
-#!/usr/bin/env python3
-"""
-Comprehensive debug script for OpenArms CAN FD communication.
-Tests all 4 CAN interfaces with CAN FD support.
-"""
-
-import can
-import time
-import sys
-import subprocess
-
-def check_can_interface(port):
-    """Check if CAN interface is UP and configured."""
-    try:
-        result = subprocess.run(['ip', 'link', 'show', port], 
-                              capture_output=True, text=True)
-        if result.returncode != 0:
-            return False, "Interface not found", None
-        
-        output = result.stdout
-        if 'UP' not in output:
-            return False, "Interface is DOWN", None
-        
-        # Check if CAN FD is enabled
-        is_fd = 'fd on' in output.lower() or 'canfd' in output.lower()
-        
-        return True, "Interface is UP", is_fd
-    except FileNotFoundError:
-        return None, "Cannot check (ip command not found)", None
-
-
-def test_motor_on_interface(bus, motor_id, timeout=2.0, use_fd=False):
-    """
-    Test a single motor and return all responses.
-    
-    Returns:
-        list of (arbitration_id, data) tuples for all responses received
-    """
-    # Send enable command
-    enable_msg = can.Message(
-        arbitration_id=motor_id,
-        data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC],
-        is_extended_id=False,
-        is_fd=use_fd
-    )
-    
-    try:
-        bus.send(enable_msg)
-    except Exception as e:
-        return None, f"Send error: {e}"
-    
-    # Listen for responses
-    responses = []
-    start_time = time.time()
-    
-    while time.time() - start_time < timeout:
-        msg = bus.recv(timeout=0.1)
-        if msg:
-            responses.append((msg.arbitration_id, msg.data, msg.is_fd if hasattr(msg, 'is_fd') else False))
-    
-    # Send disable command
-    disable_msg = can.Message(
-        arbitration_id=motor_id,
-        data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD],
-        is_extended_id=False,
-        is_fd=use_fd
-    )
-    try:
-        bus.send(disable_msg)
-    except:
-        pass
-    
-    return responses, None
-
-
-def test_interface(port, interface_type="socketcan", use_can_fd=True):
-    """Test all 8 motors on a single CAN interface."""
-    
-    results = {
-        'interface': port,
-        'status': None,
-        'is_fd': use_can_fd,
-        'motors': {}
-    }
-    
-    # Check interface status
-    status_ok, status_msg, interface_has_fd = check_can_interface(port)
-    
-    if interface_has_fd is not None:
-        results['interface_fd_enabled'] = interface_has_fd
-        if use_can_fd and not interface_has_fd:
-            status_msg += " (CAN FD NOT enabled on interface!)"
-        elif interface_has_fd:
-            status_msg += " (CAN FD enabled)"
-    
-    results['status'] = status_msg
-    
-    if status_ok is False:
-        return results
-    
-    # Try to connect
-    try:
-        if use_can_fd:
-            print(f"  Connecting to {port} with CAN FD (1 Mbps / 5 Mbps)...")
-            bus = can.interface.Bus(
-                channel=port,
-                interface=interface_type,
-                bitrate=1000000,
-                data_bitrate=5000000,
-                fd=True
-            )
-        else:
-            print(f"  Connecting to {port} with CAN 2.0 (1 Mbps)...")
-            bus = can.interface.Bus(
-                channel=port,
-                interface=interface_type,
-                bitrate=1000000
-            )
-    except Exception as e:
-        results['status'] = f"Connection failed: {e}"
-        return results
-    
-    try:
-        # Clear any pending messages
-        while bus.recv(timeout=0.01):
-            pass
-        
-        # Test each motor (0x01 to 0x08)
-        for motor_id in range(0x01, 0x09):
-            responses, error = test_motor_on_interface(bus, motor_id, timeout=1.0, use_fd=use_can_fd)
-            
-            if error:
-                results['motors'][motor_id] = {'error': error}
-            elif responses:
-                results['motors'][motor_id] = {
-                    'found': True,
-                    'responses': responses
-                }
-            else:
-                results['motors'][motor_id] = {
-                    'found': False,
-                    'responses': []
-                }
-            
-            time.sleep(0.05)  # Small delay between motors
-        
-    finally:
-        bus.shutdown()
-    
-    return results
-
-
-def print_results(all_results):
-    """Print formatted results for all interfaces."""
-    
-    print("SUMMARY - Motors Found on Each Interface")
-    
-    motor_names = {
-        0x01: "joint_1 (Shoulder pan)",
-        0x02: "joint_2 (Shoulder lift)",
-        0x03: "joint_3 (Shoulder rotation)",
-        0x04: "joint_4 (Elbow flex)",
-        0x05: "joint_5 (Wrist roll)",
-        0x06: "joint_6 (Wrist pitch)",
-        0x07: "joint_7 (Wrist rotation)",
-        0x08: "gripper",
-    }
-    
-    total_found = 0
-    
-    for result in all_results:
-        interface = result['interface']
-        status = result['status']
-        
-        print(f"{interface}: {status}")
-        if result.get('is_fd'):
-            print(f"  Mode: CAN FD")
-        else:
-            print(f"  Mode: CAN 2.0")
-        
-        if 'Connection failed' in status or 'DOWN' in status:
-            print(f"  ⚠ Cannot test {interface}")
-            continue
-        
-        motors_found = 0
-        
-        for motor_id in range(0x01, 0x09):
-            motor_data = result['motors'].get(motor_id, {})
-            motor_name = motor_names.get(motor_id, "Unknown")
-            
-            if motor_data.get('error'):
-                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✗ {motor_data['error']}")
-            elif motor_data.get('found'):
-                motors_found += 1
-                total_found += 1
-                responses = motor_data['responses']
-                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✓ FOUND")
-                
-                for resp_id, data, is_fd in responses:
-                    data_hex = data.hex()
-                    fd_flag = " [FD]" if is_fd else " [2.0]"
-                    print(f"    → Response from 0x{resp_id:02X}{fd_flag}: {data_hex}")
-            else:
-                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✗ No response")
-        
-        print(f"\n  Summary: {motors_found}/8 motors found on {interface}")
-    
-    # Overall summary
-    print("OVERALL SUMMARY")
-    print(f"Total motors found across all interfaces: {total_found}")
-    
-    # Analyze configuration
-    print("DIAGNOSIS")
-    
-    for result in all_results:
-        interface = result['interface']
-        motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
-        
-        if motors_found == 0:
-            print(f"\n⚠ {interface}: NO MOTORS FOUND")
-            print("  Possible issues:")
-            print("    1. CAN FD mode mismatch (interface vs motor configuration)")
-            print("    2. Missing 120Ω termination resistors at BOTH cable ends")
-            print("    3. Motor timeout parameter set incorrectly (should NOT be 0)")
-            print("    4. CANH/CANL wiring issue")
-            print("    5. Cable too long (>40m for CAN FD at 5Mbps)")
-            
-            # Check FD mismatch
-            if result.get('is_fd') and not result.get('interface_fd_enabled'):
-                print("    ⚠️ CRITICAL: Trying CAN FD but interface NOT configured for FD!")
-                print(f"       Fix: sudo ip link set {interface} type can bitrate 1000000 dbitrate 5000000 fd on")
-                
-        elif motors_found < 8:
-            print(f"\n⚠ {interface}: Only {motors_found}/8 motors responding")
-            print("  Check power and connections for missing motors")
-        else:
-            print(f"\n✓ {interface}: All 8 motors responding correctly!")
-    
-    # Check for unexpected response IDs
-    print("RESPONSE ID ANALYSIS")
-    
-    for result in all_results:
-        interface = result['interface']
-        unexpected = []
-        
-        for motor_id, motor_data in result['motors'].items():
-            if motor_data.get('found'):
-                expected_id = motor_id + 0x10
-                actual_ids = [resp[0] for resp in motor_data['responses']]
-                
-                if expected_id not in actual_ids:
-                    unexpected.append((motor_id, actual_ids))
-        
-        if unexpected:
-            print(f"\n⚠ {interface}: Unexpected response IDs detected")
-            for motor_id, actual_ids in unexpected:
-                expected_id = motor_id + 0x10
-                print(f"  Motor 0x{motor_id:02X}: Expected 0x{expected_id:02X}, "
-                      f"got {[f'0x{id:02X}' for id in actual_ids]}")
-            print("  → Motor Master IDs need reconfiguration")
-        else:
-            motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
-            if motors_found > 0:
-                print(f"\n✓ {interface}: All responding motors use correct IDs")
-
-
-def test_communication_speed(interface, motor_id, num_iterations=100):
-    """
-    Test communication speed with a motor.
-    
-    Returns:
-        tuple: (hz, avg_latency_ms) or (None, None) if test failed
-    """
-    try:
-        # Connect to interface
-        bus = can.interface.Bus(
-            channel=interface,
-            interface="socketcan",
-            bitrate=1000000,
-            data_bitrate=5000000,
-            fd=True
-        )
-        
-        # Send refresh commands and measure round-trip time
-        latencies = []
-        successful = 0
-        
-        for _ in range(num_iterations):
-            start = time.perf_counter()
-            
-            # Send enable command (lightweight operation)
-            enable_msg = can.Message(
-                arbitration_id=motor_id,
-                data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC],
-                is_extended_id=False,
-                is_fd=True
-            )
-            bus.send(enable_msg)
-            
-            # Wait for response
-            msg = bus.recv(timeout=0.1)
-            
-            if msg:
-                latency = (time.perf_counter() - start) * 1000  # Convert to ms
-                latencies.append(latency)
-                successful += 1
-        
-        bus.shutdown()
-        
-        if successful > 0:
-            avg_latency = sum(latencies) / len(latencies)
-            hz = 1000.0 / avg_latency if avg_latency > 0 else 0
-            return hz, avg_latency
-        
-        return None, None
-        
-    except Exception as e:
-        print(f"    Speed test error: {e}")
-        return None, None
-
-
-def main():
-    """Main function to test all CAN interfaces with CAN FD."""
-    
-    print("\nThis will test all 4 CAN interfaces (can0-can3) with CAN FD")
-    print("Testing motors 0x01-0x08 on each interface")
-    print()
-    print("Make sure:")
-    print("  ✓ Motors are powered (24V)")
-    print("  ✓ CAN interfaces configured with FD mode:")
-    print("    ./examples/openarms/setup_can.sh")
-    print("  ✓ Motor 'timeout' parameter NOT set to 0 (use Damiao tools)")
-    print("  ✓ CAN wiring includes 120Ω termination at BOTH ends")
-    print()
-    
-    input("Press ENTER to start testing...")
-    
-    # Test all 4 interfaces with CAN FD
-    all_results = []
-    
-    for i in range(4):
-        interface = f"can{i}"
-        print(f"Testing {interface}...")
-        
-        result = test_interface(interface, use_can_fd=True)
-        all_results.append(result)
-        
-        # Quick status
-        if 'Connection failed' in result['status'] or 'DOWN' in result['status']:
-            print(f"  ⚠ {interface}: {result['status']}")
-        else:
-            motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
-            print(f"  {interface}: {motors_found}/8 motors found")
-        
-        time.sleep(0.2)
-    
-    # Print detailed results
-    print_results(all_results)
-    
-    print("Testing Complete!")
-    
-    all_found = sum(sum(1 for m in r['motors'].values() if m.get('found')) for r in all_results)
-    
-    if all_found == 0:
-        print("\n⚠️ CRITICAL: No motors found on any interface!")
-        print("\nTop issues to check:")
-        print("  1. Motor 'timeout' parameter (use Damiao tools to set > 0)")
-        print("  2. CAN FD not enabled (run ./examples/openarms/setup_can.sh)")
-        print("  3. Missing termination resistors")
-        print("\nTry:")
-        print("  a) Check motor parameters with Damiao Debugging Tools")
-        print("  b) Verify CAN FD is enabled: ip -d link show can0 | grep fd")
-        print("  c) Run setup script: ./examples/openarms/setup_can.sh")
-    else:
-        # Run speed test on interfaces with motors
-        print("COMMUNICATION SPEED TEST")
-        print("\nTesting maximum communication frequency...")
-        
-        for result in all_results:
-            interface = result['interface']
-            
-            # Find first responding motor
-            responding_motor = None
-            for motor_id, motor_data in result['motors'].items():
-                if motor_data.get('found'):
-                    responding_motor = motor_id
-                    break
-            
-            if responding_motor:
-                print(f"\n{interface}: Testing with motor 0x{responding_motor:02X}...")
-                hz, latency = test_communication_speed(interface, responding_motor, num_iterations=100)
-                
-                if hz:
-                    print(f"  ✓ Max frequency: {hz:.1f} Hz")
-                    print(f"  ✓ Avg latency: {latency:.2f} ms")
-                    print(f"  ✓ Commands per second: ~{int(hz)}")
-                else:
-                    print(f"  ✗ Speed test failed")
-            else:
-                print(f"\n{interface}: No motors found, skipping speed test")
-
-        print()
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except KeyboardInterrupt:
-        print("\n\nTesting interrupted by user.")
-        sys.exit(1)
-    except Exception as e:
-        print(f"\nUnexpected error: {e}")
-        import traceback
-        traceback.print_exc()
-        sys.exit(1)
-

examples/openarms/evaluate.py

@@ -1,360 +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.
-
-"""
-OpenArms Policy Evaluation
-
-Evaluates a trained policy on the OpenArms robot by running inference and recording
-the evaluation episodes to a dataset. Supports optional leader arm for manual resets.
-
-Example usage:
-    python examples/openarms/evaluate.py
-"""
-
-import time
-from pathlib import Path
-
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.datasets.utils import combine_feature_dicts
-from lerobot.policies.factory import make_policy, make_pre_post_processors
-from lerobot.processor import make_default_processors
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.scripts.lerobot_record import record_loop
-from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
-from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
-from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
-
-
-HF_MODEL_ID = "lerobot-data-collection/three-folds-pi0"  # TODO: Replace with your trained model
-HF_EVAL_DATASET_ID = "lerobot-data-collection/three-folds-pi0_eval7"  # TODO: Replace with your eval dataset name
-TASK_DESCRIPTION = "three-folds-dataset"  # TODO: Replace with your task, this should match!!
-
-NUM_EPISODES = 1
-FPS = 30
-EPISODE_TIME_SEC = 300
-RESET_TIME_SEC = 60
-
-# Robot CAN interfaces
-FOLLOWER_LEFT_PORT = "can0"
-FOLLOWER_RIGHT_PORT = "can1"
-
-# If enabled, you can manually reset the environment between evaluation episodes
-USE_LEADER_FOR_RESETS = True  # Set to False if you don't want to use leader
-LEADER_LEFT_PORT = "can2"
-LEADER_RIGHT_PORT = "can3"
-
-# Camera configuration
-CAMERA_CONFIG = {
-    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=FPS),
-    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
-    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=FPS),
-}
-
-def main():
-    """Main evaluation function."""
-    print("OpenArms Policy Evaluation")
-    print(f"\nModel: {HF_MODEL_ID}")
-    print(f"Evaluation Dataset: {HF_EVAL_DATASET_ID}")
-    print(f"Task: {TASK_DESCRIPTION}")
-    print(f"Episodes: {NUM_EPISODES}")
-    print(f"Episode Duration: {EPISODE_TIME_SEC}s")
-    print(f"Reset Duration: {RESET_TIME_SEC}s")
-    print(f"Use Leader for Resets: {USE_LEADER_FOR_RESETS}")
-    
-    follower_config = OpenArmsFollowerConfig(
-        port_left=FOLLOWER_LEFT_PORT,
-        port_right=FOLLOWER_RIGHT_PORT,
-        can_interface="socketcan",
-        id="openarms_follower",
-        disable_torque_on_disconnect=True,
-        max_relative_target=10.0,
-        cameras=CAMERA_CONFIG,
-    )
-    
-    follower = OpenArmsFollower(follower_config)
-    follower.connect(calibrate=False)
-    
-    if not follower.is_connected:
-        raise RuntimeError("Follower robot failed to connect!")
-
-    
-    leader = None
-    if USE_LEADER_FOR_RESETS:
-        leader_config = OpenArmsLeaderConfig(
-            port_left=LEADER_LEFT_PORT,
-            port_right=LEADER_RIGHT_PORT,
-            can_interface="socketcan",
-            id="openarms_leader",
-            manual_control=False,  # Enable torque control for gravity compensation
-        )
-        
-        leader = OpenArmsLeader(leader_config)
-        leader.connect(calibrate=False)
-        
-        if not leader.is_connected:
-            raise RuntimeError("Leader robot failed to connect!")
-        
-        # Enable gravity compensation
-        if leader.pin_robot is not None:
-            leader.bus_right.enable_torque()
-            leader.bus_left.enable_torque()
-            time.sleep(0.1)
-            print(f"Leader connected with gravity compensation ({LEADER_LEFT_PORT}, {LEADER_RIGHT_PORT})")
-        else:
-            print(f"Leader connected but gravity compensation unavailable (no URDF)")
-
-    # Build default processors for action and observation
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-    
-    # Build dataset features from robot features and processors
-    # For actions, only include positions (no velocity or torque)
-    action_features_hw = {}
-    for key, value in follower.action_features.items():
-        if key.endswith(".pos"):
-            action_features_hw[key] = value
-    
-    dataset_features = combine_feature_dicts(
-        aggregate_pipeline_dataset_features(
-            pipeline=teleop_action_processor,
-            initial_features=create_initial_features(action=action_features_hw),
-            use_videos=True,
-        ),
-        aggregate_pipeline_dataset_features(
-            pipeline=robot_observation_processor,
-            initial_features=create_initial_features(observation=follower.observation_features),
-            use_videos=True,
-        ),
-    )
-    
-    # Check if dataset already exists
-    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
-    if dataset_path.exists():
-        print(f"Evaluation dataset already exists at: {dataset_path}")
-        print("This will append new episodes to the existing dataset.")
-        choice = input("  Continue? (y/n): ").strip().lower()
-        if choice != 'y':
-            print("  Aborting evaluation.")
-            follower.disconnect()
-            if leader:
-                leader.disconnect()
-            return
-    
-    # Create dataset
-    dataset = LeRobotDataset.create(
-        repo_id=HF_EVAL_DATASET_ID,
-        fps=FPS,
-        features=dataset_features,
-        robot_type=follower.name,
-        use_videos=True,
-        image_writer_processes=0,
-        image_writer_threads=12, 
-    )
-    
-    # Load policy config from pretrained model and create policy using factory
-    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
-    policy_config.pretrained_path = HF_MODEL_ID
-    policy = make_policy(policy_config, ds_meta=dataset.meta)
-    
-    preprocessor, postprocessor = make_pre_post_processors(
-        policy_cfg=policy.config,
-        pretrained_path=HF_MODEL_ID,
-        dataset_stats=dataset.meta.stats,
-        preprocessor_overrides={
-            "device_processor": {"device": str(policy.config.device)}
-        },
-    )
-
-    print(f"\nRunning evaluation...")
-    # Initialize keyboard listener and visualization
-    listener, events = init_keyboard_listener()
-    init_rerun(session_name="openarms_evaluation")
-    episode_idx = 0
-    
-    try:
-        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-            log_say(f"Evaluating episode {episode_idx + 1} of {NUM_EPISODES}")
-            print(f"\nRunning inference for episode {episode_idx + 1}...")
-            
-            # Run inference with policy
-            record_loop(
-                robot=follower,
-                events=events,
-                fps=FPS,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
-                policy=policy,
-                preprocessor=preprocessor,
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-            )
-            
-            # Handle re-recording
-            if events["rerecord_episode"]:
-                log_say("Re-recording episode")
-                events["rerecord_episode"] = False
-                events["exit_early"] = False
-                dataset.clear_episode_buffer()
-                continue
-            
-            # Save episode
-            if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
-                print(f"Saving episode {episode_idx + 1} ({dataset.episode_buffer['size']} frames)...")
-                dataset.save_episode()
-                episode_idx += 1
-            
-            # Reset environment between episodes (if not last episode)
-            if not events["stop_recording"] and episode_idx < NUM_EPISODES:
-                if USE_LEADER_FOR_RESETS and leader:
-                    log_say("Reset the environment using leader arms")
-                    print(f"\nManual reset period ({RESET_TIME_SEC}s)...")
-                    
-                    # Use leader for manual reset with gravity compensation
-                    import numpy as np
-                    
-                    dt = 1 / FPS
-                    reset_start_time = time.perf_counter()
-                    
-                    while time.perf_counter() - reset_start_time < RESET_TIME_SEC:
-                        if events["exit_early"] or events["stop_recording"]:
-                            break
-                        
-                        loop_start = time.perf_counter()
-                        
-                        # Get leader state
-                        leader_action = leader.get_action()
-                        
-                        # Extract positions and velocities
-                        leader_positions_deg = {}
-                        leader_velocities_deg_per_sec = {}
-                        
-                        for motor in leader.bus_right.motors:
-                            pos_key = f"right_{motor}.pos"
-                            vel_key = f"right_{motor}.vel"
-                            if pos_key in leader_action:
-                                leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
-                            if vel_key in leader_action:
-                                leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
-                        
-                        for motor in leader.bus_left.motors:
-                            pos_key = f"left_{motor}.pos"
-                            vel_key = f"left_{motor}.vel"
-                            if pos_key in leader_action:
-                                leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
-                            if vel_key in leader_action:
-                                leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
-                        
-                        # Calculate gravity and friction torques
-                        leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
-                        leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
-                        
-                        leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
-                        leader_friction_torques_nm = leader._friction_from_velocity(
-                            leader_velocities_rad_per_sec,
-                            friction_scale=1.0
-                        )
-                        
-                        # Combine torques
-                        leader_total_torques_nm = {}
-                        for motor_name in leader_gravity_torques_nm:
-                            gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
-                            friction = leader_friction_torques_nm.get(motor_name, 0.0)
-                            leader_total_torques_nm[motor_name] = gravity + friction
-                        
-                        # Apply compensation
-                        for motor in leader.bus_right.motors:
-                            full_name = f"right_{motor}"
-                            position = leader_positions_deg.get(full_name, 0.0)
-                            torque = leader_total_torques_nm.get(full_name, 0.0)
-                            kd = leader.get_damping_kd(motor)
-                            
-                            leader.bus_right._mit_control(
-                                motor=motor, kp=0.0, kd=kd,
-                                position_degrees=position,
-                                velocity_deg_per_sec=0.0,
-                                torque=torque,
-                            )
-                        
-                        for motor in leader.bus_left.motors:
-                            full_name = f"left_{motor}"
-                            position = leader_positions_deg.get(full_name, 0.0)
-                            torque = leader_total_torques_nm.get(full_name, 0.0)
-                            kd = leader.get_damping_kd(motor)
-                            
-                            leader.bus_left._mit_control(
-                                motor=motor, kp=0.0, kd=kd,
-                                position_degrees=position,
-                                velocity_deg_per_sec=0.0,
-                                torque=torque,
-                            )
-                        
-                        # Send leader positions to follower
-                        follower_action = {}
-                        for joint in leader_positions_deg.keys():
-                            pos_key = f"{joint}.pos"
-                            if pos_key in leader_action:
-                                follower_action[pos_key] = leader_action[pos_key]
-                        
-                        if follower_action:
-                            follower.send_action(follower_action)
-                        
-                        # Maintain loop rate
-                        loop_duration = time.perf_counter() - loop_start
-                        sleep_time = dt - loop_duration
-                        if sleep_time > 0:
-                            time.sleep(sleep_time)
-                    
-                    print("Reset complete")
-                else:
-                    log_say("Waiting for manual reset")
-                    print(f"Manually reset the environment and press ENTER to continue")
-                    input("Press ENTER when ready...")
-        
-        print(f"Evaluation complete! {episode_idx} episodes recorded")
-        log_say("Evaluation complete", blocking=True)
-    
-    except KeyboardInterrupt:
-        print("\n\nEvaluation interrupted by user")
-    
-    finally:
-        if leader:
-            leader.bus_right.disable_torque()
-            leader.bus_left.disable_torque()
-            time.sleep(0.1)
-            leader.disconnect()
-
-        follower.disconnect()
-        
-        if listener is not None:
-            listener.stop()
-        
-        dataset.finalize()
-        print("\nUploading to Hugging Face Hub...")
-        dataset.push_to_hub(private=True)
-
-
-if __name__ == "__main__":
-    main()
-

examples/openarms/friction_compensation.py

@@ -1,216 +0,0 @@
-import time
-import numpy as np
-
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-
-
-# Friction model parameters from OpenArms config/follower.yaml
-# τ_fric(ω) = Fo + Fv·ω + Fc·tanh(k·ω)
-# For 8 motors: [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7, gripper]
-FRICTION_PARAMS = {
-    "Fc": [0.306, 0.306, 0.40, 0.166, 0.050, 0.093, 0.172, 0.0512],  # Coulomb friction [Nm]
-    "k": [28.417, 28.417, 29.065, 130.038, 151.771, 242.287, 7.888, 4.000],  # tanh steepness
-    "Fv": [0.063, 0.0630, 0.604, 0.813, 0.029, 0.072, 0.084, 0.084],  # Viscous friction [Nm·s/rad]
-    "Fo": [0.088, 0.088, 0.008, -0.058, 0.005, 0.009, -0.059, -0.050],  # Offset torque [Nm]
-}
-
-# Constants from OpenArms C++ implementation
-AMP_TMP = 1.0
-COEF_TMP = 0.1
-
-FRICTION_SCALE = 1.0  # OpenArms C++ uses 0.3 factor in unilateral mode
-DAMPING_KD = [0.5, 0.5, 0.5, 0.5, 0.1, 0.1, 0.1, 0.1]  # Damping gains for stability
-
-def compute_friction_torque(velocity_rad_per_sec: float, motor_index: int) -> float:
-    """
-    Compute friction torque for a single motor using the tanh friction model.
-    
-    Args:
-        velocity_rad_per_sec: Angular velocity in rad/s
-        motor_index: Index of the motor (0-7)
-        
-    Returns:
-        Friction torque in N·m (scaled for stability)
-    """
-    
-    Fc = FRICTION_PARAMS["Fc"][motor_index]
-    k = FRICTION_PARAMS["k"][motor_index]
-    Fv = FRICTION_PARAMS["Fv"][motor_index]
-    Fo = FRICTION_PARAMS["Fo"][motor_index]
-    
-    # Friction model: τ_fric = amp * Fc * tanh(coef * k * ω) + Fv * ω + Fo
-    friction_torque = (
-        AMP_TMP * Fc * np.tanh(COEF_TMP * k * velocity_rad_per_sec) +
-        Fv * velocity_rad_per_sec +
-        Fo
-    )
-    
-    # Scale down friction compensation for stability at lower control rates
-    # (OpenArms C++ uses 0.3 factor in unilateral mode)!!
-    friction_torque *= FRICTION_SCALE
-    
-    return friction_torque
-
-
-def main() -> None:
-    config = OpenArmsFollowerConfig(
-        port_left="can0",
-        port_right="can1",
-        can_interface="socketcan",
-        id="openarms_follower",
-        disable_torque_on_disconnect=True,
-        max_relative_target=5.0,
-    )
-    
-    print("Initializing robot...")
-    follower = OpenArmsFollower(config)
-    follower.connect(calibrate=True)
-    
-    print(f"Applying friction compensation")
-    print("  1. Support the arm before starting")
-    print("  2. The arm will be held in place by friction compensation")
-    print("  3. You should be able to move it with gentle force")
-    print("\nPress ENTER when ready to start...")
-    input()
-    
-    print(f"✓ Motors enabled")
-    print("\nStarting friction compensation loop...")
-    print("Press Ctrl+C to stop\n")
-    
-    loop_times = []
-    last_print_time = time.perf_counter()
-    
-    # Motor name to index mapping
-    motor_name_to_index = {
-        "joint_1": 0,
-        "joint_2": 1,
-        "joint_3": 2,
-        "joint_4": 3,
-        "joint_5": 4,
-        "joint_6": 5,
-        "joint_7": 6,
-        "gripper": 7,
-    }
-    
-    try:
-        while True:
-            loop_start = time.perf_counter()
-            
-            # Get current joint positions and velocities from robot
-            obs = follower.get_observation()
-            
-            # Extract velocities in degrees per second
-            velocities_deg_per_sec = {}
-            positions_deg = {}
-            
-            for motor in follower.bus_right.motors:
-                vel_key = f"right_{motor}.vel"
-                pos_key = f"right_{motor}.pos"
-                if vel_key in obs:
-                    velocities_deg_per_sec[f"right_{motor}"] = obs[vel_key]
-                if pos_key in obs:
-                    positions_deg[f"right_{motor}"] = obs[pos_key]
-            
-            for motor in follower.bus_left.motors:
-                vel_key = f"left_{motor}.vel"
-                pos_key = f"left_{motor}.pos"
-                if vel_key in obs:
-                    velocities_deg_per_sec[f"left_{motor}"] = obs[vel_key]
-                if pos_key in obs:
-                    positions_deg[f"left_{motor}"] = obs[pos_key]
-            
-            # Convert velocities to rad/s and compute friction torques
-            friction_torques_nm = {}
-            for motor_full_name, velocity_deg_per_sec in velocities_deg_per_sec.items():
-                # Extract motor name without arm prefix
-                if motor_full_name.startswith("right_"):
-                    motor_name = motor_full_name.removeprefix("right_")
-                elif motor_full_name.startswith("left_"):
-                    motor_name = motor_full_name.removeprefix("left_")
-                else:
-                    continue
-                
-                # Get motor index for friction parameters
-                motor_index = motor_name_to_index.get(motor_name, 0)
-                
-                # Convert velocity to rad/s
-                velocity_rad_per_sec = np.deg2rad(velocity_deg_per_sec)
-                
-                # Compute friction torque
-                friction_torque = compute_friction_torque(velocity_rad_per_sec, motor_index)
-                friction_torques_nm[motor_full_name] = friction_torque
-            
-            # Apply friction compensation to right arm (all joints INCLUDING gripper)
-            for motor in follower.bus_right.motors:
-                full_name = f"right_{motor}"
-                position = positions_deg.get(full_name, 0.0)
-                torque = friction_torques_nm.get(full_name, 0.0)
-                
-                # Get motor index for damping gain
-                motor_index = motor_name_to_index.get(motor, 0)
-                kd = DAMPING_KD[motor_index]
-                
-                # Send MIT control command with friction compensation + damping
-                follower.bus_right._mit_control(
-                    motor=motor,
-                    kp=0.0,  # No position control
-                    kd=kd,   # Add damping for stability
-                    position_degrees=position,
-                    velocity_deg_per_sec=0.0,
-                    torque=torque
-                )
-
-            # Apply friction compensation to left arm (all joints INCLUDING gripper)
-            for motor in follower.bus_left.motors:
-                full_name = f"left_{motor}"
-                position = positions_deg.get(full_name, 0.0)
-                torque = friction_torques_nm.get(full_name, 0.0)
-                
-                # Get motor index for damping gain
-                motor_index = motor_name_to_index.get(motor, 0)
-                kd = DAMPING_KD[motor_index]
-                
-                # Send MIT control command with friction compensation + damping
-                follower.bus_left._mit_control(
-                    motor=motor,
-                    kp=0.0,  # No position control
-                    kd=kd,   # Add damping for stability
-                    position_degrees=position,
-                    velocity_deg_per_sec=0.0,
-                    torque=torque
-                )
-            
-            # Measure loop time
-            loop_end = time.perf_counter()
-            loop_time = loop_end - loop_start
-            loop_times.append(loop_time)
-            
-            # Print status every 2 seconds
-            if loop_end - last_print_time >= 2.0:
-                if loop_times:
-                    avg_time = sum(loop_times) / len(loop_times)
-                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
-                    
-                    print(f"{current_hz:.1f} Hz")
-
-                    loop_times = []
-                    last_print_time = loop_end
-            
-            time.sleep(0.001)
-            
-    except KeyboardInterrupt:
-        print("\n\nStopping friction compensation...")
-    
-    finally:
-        print("\nDisabling all motors and disconnecting...")
-        follower.bus_right.disable_torque()
-        follower.bus_left.disable_torque()
-        time.sleep(0.1)
-        follower.disconnect()
-        print("✓ Safe shutdown complete")
-
-
-if __name__ == "__main__":
-    main()
-

examples/openarms/gravity_compensation.py

@@ -1,142 +0,0 @@
-import time
-import numpy as np
-import pinocchio as pin
-from os.path import join, dirname, exists, expanduser
-
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-
-
-def main() -> None:
-    config = OpenArmsFollowerConfig(
-        port_left="can0",
-        port_right="can1",
-        can_interface="socketcan",
-        id="openarms_follower",
-        disable_torque_on_disconnect=True,
-        max_relative_target=5.0,
-    )
-    
-    
-    print("Initializing robot...")
-    follower = OpenArmsFollower(config)
-    follower.connect(calibrate=True)
-    
-    # Load URDF for Pinocchio dynamics
-    urdf_path = "/home/croissant/Documents/openarm_description/openarm_bimanual_pybullet.urdf"
-    
-    pin_robot = pin.RobotWrapper.BuildFromURDF(urdf_path, dirname(urdf_path))
-    pin_robot.data = pin_robot.model.createData()
-    print(f"✓ Loaded Pinocchio model with {pin_robot.nq} DoFs")
-    
-    follower.pin_robot = pin_robot
-    
-    print(f"Applying gravity compensation")
-    print("  1. Support the arm before starting")
-    print("  2. The arm will be held in place by gravity compensation")
-    print("  3. You should be able to move it with gentle force")
-    print("\nPress ENTER when ready to start...")
-    input()
-    
-    print(f"✓ Motors enabled")
-    print("\nStarting gravity compensation loop...")
-    print("Press Ctrl+C to stop\n")
-    
-    loop_times = []
-    last_print_time = time.perf_counter()
-    
-    try:
-        while True:
-            loop_start = time.perf_counter()
-            
-            # Get current joint positions from robot
-            obs = follower.get_observation()
-            
-            # Extract positions in degrees
-            positions_deg = {}
-            for motor in follower.bus_right.motors:
-                key = f"right_{motor}.pos"
-                if key in obs:
-                    positions_deg[f"right_{motor}"] = obs[key]
-            
-            for motor in follower.bus_left.motors:
-                key = f"left_{motor}.pos"
-                if key in obs:
-                    positions_deg[f"left_{motor}"] = obs[key]
-            
-            # Convert to radians and calculate gravity torques
-            # Use the built-in method from OpenArmsFollower
-            positions_rad = {k: np.deg2rad(v) for k, v in positions_deg.items()}
-            torques_nm = follower._gravity_from_q(positions_rad)
-            
-            # Apply gravity compensation to right arm (all joints except gripper)
-            for motor in follower.bus_right.motors:
-                if motor == "gripper":
-                    continue  # Skip gripper
-                    
-                full_name = f"right_{motor}"
-                position = positions_deg.get(full_name, 0.0)
-                torque = torques_nm.get(full_name, 0.0)
-                
-                # Send MIT control command with gravity compensation torque
-                follower.bus_right._mit_control(
-                    motor=motor,
-                    kp=0.0,  # No position control
-                    kd=0.0,  # No velocity damping
-                    position_degrees=position,
-                    velocity_deg_per_sec=0.0,
-                    torque=torque
-                )
-
-            # Apply gravity compensation to left arm (all joints except gripper)
-            for motor in follower.bus_left.motors:
-                if motor == "gripper":
-                    continue  # Skip gripper
-                    
-                full_name = f"left_{motor}"
-                position = positions_deg.get(full_name, 0.0)
-                torque = torques_nm.get(full_name, 0.0)
-                
-                # Send MIT control command with gravity compensation torque
-                follower.bus_left._mit_control(
-                    motor=motor,
-                    kp=0.0,  # No position control
-                    kd=0.0,  # No velocity damping
-                    position_degrees=position,
-                    velocity_deg_per_sec=0.0,
-                    torque=torque
-                )
-            
-            # Measure loop time
-            loop_end = time.perf_counter()
-            loop_time = loop_end - loop_start
-            loop_times.append(loop_time)
-            
-            # Print status every 2 seconds
-            if loop_end - last_print_time >= 2.0:
-                if loop_times:
-                    avg_time = sum(loop_times) / len(loop_times)
-                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
-                    
-                    print(f"{current_hz:.1f} Hz ({avg_time*1000:.1f} ms)")
-
-                    loop_times = []
-                    last_print_time = loop_end
-            
-            time.sleep(0.005)
-            
-    except KeyboardInterrupt:
-        print("\n\nStopping gravity compensation...")
-    
-    finally:
-        print("\nDisabling all motors and disconnecting...")
-        follower.bus_right.disable_torque()
-        follower.bus_left.disable_torque()
-        time.sleep(0.1)
-        follower.disconnect()
-        print("✓ Safe shutdown complete")
-
-
-if __name__ == "__main__":
-    main()
-

examples/openarms/record_with_compensation.py

@@ -1,395 +0,0 @@
-"""
-OpenArms Dataset Recording with Gravity + Friction Compensation
-
-Records a dataset using OpenArms follower robot with leader teleoperator.
-Leader arms have gravity and friction compensation for weightless, easy movement.
-Includes 3 cameras: left wrist, right wrist, and base camera.
-
-Uses the same compensation approach as teleop_with_compensation.py
-"""
-
-import shutil
-import time
-from pathlib import Path
-
-import numpy as np
-
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
-from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
-from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
-
-# Recording parameters
-NUM_EPISODES = 1
-FPS = 30
-EPISODE_TIME_SEC = 600
-RESET_TIME_SEC = 120
-TASK_DESCRIPTION = "OpenArms task description"
-
-# Friction compensation scale factor (1.0 = full, 0.3 = 30% for stability)
-FRICTION_SCALE = 1.0
-
-def record_loop_with_compensation(
-    robot,
-    leader,
-    events,
-    fps,
-    dataset,
-    dataset_features,
-    control_time_s,
-    single_task,
-    display_data=True,
-):
-    """
-    Custom record loop that applies gravity + friction compensation to leader.
-    Based on record_loop but with integrated compensation.
-    """
-    dt = 1 / fps
-    episode_start_time = time.perf_counter()
-    
-    # All joints (both arms)
-    all_joints = []
-    for motor in leader.bus_right.motors:
-        all_joints.append(f"right_{motor}")
-    for motor in leader.bus_left.motors:
-        all_joints.append(f"left_{motor}")
-    
-    while True:
-        loop_start = time.perf_counter()
-        elapsed = loop_start - episode_start_time
-        
-        # Check if we should exit
-        if elapsed >= control_time_s or events["exit_early"] or events["stop_recording"]:
-            break
-        
-        # Get leader state
-        leader_action = leader.get_action()
-        
-        # Extract positions and velocities in degrees
-        leader_positions_deg = {}
-        leader_velocities_deg_per_sec = {}
-        
-        for motor in leader.bus_right.motors:
-            pos_key = f"right_{motor}.pos"
-            vel_key = f"right_{motor}.vel"
-            if pos_key in leader_action:
-                leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
-            if vel_key in leader_action:
-                leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
-        
-        for motor in leader.bus_left.motors:
-            pos_key = f"left_{motor}.pos"
-            vel_key = f"left_{motor}.vel"
-            if pos_key in leader_action:
-                leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
-            if vel_key in leader_action:
-                leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
-        
-        # Calculate gravity torques for leader using built-in method
-        leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
-        leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
-        
-        # Calculate friction torques for leader using built-in method
-        leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
-        leader_friction_torques_nm = leader._friction_from_velocity(
-            leader_velocities_rad_per_sec,
-            friction_scale=FRICTION_SCALE
-        )
-        
-        # Combine gravity + friction torques
-        leader_total_torques_nm = {}
-        for motor_name in leader_gravity_torques_nm:
-            gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
-            friction = leader_friction_torques_nm.get(motor_name, 0.0)
-            leader_total_torques_nm[motor_name] = gravity + friction
-        
-        # Apply gravity + friction compensation to leader RIGHT arm (all joints including gripper)
-        for motor in leader.bus_right.motors:
-            full_name = f"right_{motor}"
-            position = leader_positions_deg.get(full_name, 0.0)
-            torque = leader_total_torques_nm.get(full_name, 0.0)
-            
-            # Get damping gain for stability
-            kd = leader.get_damping_kd(motor)
-            
-            leader.bus_right._mit_control(
-                motor=motor,
-                kp=0.0,
-                kd=kd,  # Add damping for stability
-                position_degrees=position,
-                velocity_deg_per_sec=0.0,
-                torque=torque,
-            )
-        
-        # Apply gravity + friction compensation to leader LEFT arm (all joints including gripper)
-        for motor in leader.bus_left.motors:
-            full_name = f"left_{motor}"
-            position = leader_positions_deg.get(full_name, 0.0)
-            torque = leader_total_torques_nm.get(full_name, 0.0)
-            
-            # Get damping gain for stability
-            kd = leader.get_damping_kd(motor)
-            
-            leader.bus_left._mit_control(
-                motor=motor,
-                kp=0.0,
-                kd=kd,  # Add damping for stability
-                position_degrees=position,
-                velocity_deg_per_sec=0.0,
-                torque=torque,
-            )
-        
-        # Send leader positions to follower (both arms)
-        follower_action = {}
-        for joint in all_joints:
-            pos_key = f"{joint}.pos"
-            if pos_key in leader_action:
-                follower_action[pos_key] = leader_action[pos_key]
-        
-        # Send action to robot
-        if follower_action:
-            robot.send_action(follower_action)
-        
-        # Get observation from robot (includes camera images)
-        observation = robot.get_observation()
-        
-        # Add to dataset if we have a dataset
-        if dataset is not None:
-            # Build properly formatted observation frame
-            obs_frame = build_dataset_frame(dataset_features, observation, prefix="observation")
-            
-            # Build properly formatted action frame (keep .pos suffix - it matches the feature names)
-            action_frame = build_dataset_frame(dataset_features, follower_action, prefix="action")
-            
-            # Combine into single frame
-            frame = {**obs_frame, **action_frame}
-            
-            # Add metadata (task is required, timestamp will be auto-calculated by add_frame)
-            frame["task"] = single_task
-            
-            dataset.add_frame(frame)
-        
-        # Display data if requested
-        if display_data:
-            log_rerun_data(observation=observation, action=follower_action)
-        
-        # Maintain loop rate
-        loop_duration = time.perf_counter() - loop_start
-        sleep_time = dt - loop_duration
-        if sleep_time > 0:
-            time.sleep(sleep_time)
-
-
-def main():
-    """Main recording loop with gravity compensation."""
-    
-    print("=" * 70)
-    print("OpenArms Dataset Recording with Compensation")
-    print("=" * 70)
-    
-    # Create camera configurations (3 cameras: left wrist, right wrist, base)
-    # Using actual device paths found by lerobot-find-cameras opencv
-    camera_config = {
-        "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video0", width=640, height=480, fps=FPS),
-        "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
-        "base": OpenCVCameraConfig(index_or_path="/dev/video7", width=640, height=480, fps=FPS),
-    }
-    
-    # Configure follower robot with cameras
-    follower_config = OpenArmsFollowerConfig(
-        port_left="can2",
-        port_right="can3",
-        can_interface="socketcan",
-        id="openarms_follower",
-        disable_torque_on_disconnect=True,
-        max_relative_target=10.0,
-        cameras=camera_config,
-    )
-    
-    # Configure leader teleoperator (no cameras needed)
-    leader_config = OpenArmsLeaderConfig(
-        port_left="can0",
-        port_right="can1",
-        can_interface="socketcan",
-        id="openarms_leader",
-        manual_control=False,  # Enable torque control for gravity compensation
-    )
-    
-    # Initialize robot and teleoperator
-    print("\nInitializing devices...")
-    follower = OpenArmsFollower(follower_config)
-    leader = OpenArmsLeader(leader_config)
-    
-    # Connect devices
-    print("Connecting and calibrating...")
-    follower.connect(calibrate=True)
-    leader.connect(calibrate=True)
-    
-    # Verify URDF is loaded for gravity compensation
-    if leader.pin_robot is None:
-        raise RuntimeError("URDF model not loaded on leader. Gravity compensation not available.")
-    
-    # Configure the dataset features
-    # For actions, we only want to record positions (not velocity or torque)
-    action_features_hw = {}
-    for key, value in follower.action_features.items():
-        if key.endswith(".pos"):
-            action_features_hw[key] = value
-    
-    action_features = hw_to_dataset_features(action_features_hw, "action")
-    obs_features = hw_to_dataset_features(follower.observation_features, "observation")
-    dataset_features = {**action_features, **obs_features}
-    
-    # Create the dataset
-    print("\nCreating dataset...")
-    repo_id = "<hf_username>/<dataset_repo_id>"  # TODO: Replace with your Hugging Face repo
-    
-    # Check if dataset already exists and prompt user
-    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / repo_id
-    while dataset_path.exists():
-        print(f"\nDataset already exists at: {dataset_path}")
-        print("\nOptions:")
-        print("  1. Overwrite existing dataset")
-        print("  2. Use a different name")
-        print("  3. Abort")
-        
-        choice = input("\nEnter your choice (1/2/3): ").strip()
-        
-        if choice == '1':
-            print(f"Removing existing dataset...")
-            shutil.rmtree(dataset_path)
-            print("✓ Existing dataset removed")
-            break
-        elif choice == '2':
-            print("\nCurrent repo_id:", repo_id)
-            new_repo_id = input("Enter new repo_id (format: <username>/<dataset_name>): ").strip()
-            if new_repo_id and '/' in new_repo_id:
-                repo_id = new_repo_id
-                dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / repo_id
-                print(f"✓ Using new repo_id: {repo_id}")
-                # Loop will continue if this new path also exists
-            else:
-                print("Invalid repo_id format. Please use format: <username>/<dataset_name>")
-        elif choice == '3':
-            print("Aborting. Please remove the existing dataset manually or restart with a different repo_id.")
-            follower.disconnect()
-            leader.disconnect()
-            return
-        else:
-            print("Invalid choice. Please enter 1, 2, or 3.")
-    
-    dataset = LeRobotDataset.create(
-        repo_id=repo_id,
-        fps=FPS,
-        features=dataset_features,
-        robot_type=follower.name,
-        use_videos=True,
-        image_writer_threads=4,
-    )
-    
-    # Initialize keyboard listener and visualization
-    _, events = init_keyboard_listener()
-    init_rerun(session_name="openarms_recording")
-    
-    # Enable motors on both leader arms for gravity compensation
-    leader.bus_right.enable_torque()
-    leader.bus_left.enable_torque()
-    time.sleep(0.1)
-    
-    print("\n" + "=" * 70)
-    print(f"Recording {NUM_EPISODES} episodes")
-    print(f"Task: {TASK_DESCRIPTION}")
-    print("=" * 70)
-    print("\nLeader BOTH arms: Gravity + Friction comp | Follower BOTH arms: Teleop")
-    print("\nKeyboard controls:")
-    print("  - Press 'q' to stop recording")
-    print("  - Press 'r' to re-record current episode")
-    print("=" * 70)
-    
-    episode_idx = 0
-    
-    try:
-        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-            log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
-            
-            # Record episode with compensation active
-            record_loop_with_compensation(
-                robot=follower,
-                leader=leader,
-                events=events,
-                fps=FPS,
-                dataset=dataset,
-                dataset_features=dataset_features,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-            )
-            
-            # Reset the environment if not stopping or re-recording
-            if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
-                log_say("Reset the environment")
-                record_loop_with_compensation(
-                    robot=follower,
-                    leader=leader,
-                    events=events,
-                    fps=FPS,
-                    dataset=None,  # Don't save reset period
-                    dataset_features=dataset_features,
-                    control_time_s=RESET_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                )
-            
-            # Handle re-recording
-            if events["rerecord_episode"]:
-                log_say("Re-recording episode")
-                events["rerecord_episode"] = False
-                events["exit_early"] = False
-                dataset.clear_episode_buffer()
-                continue
-            
-            # Only save episode if frames were recorded
-            if dataset.episode_buffer is not None and dataset.episode_buffer["size"] > 0:
-                dataset.save_episode()
-                episode_idx += 1
-            else:
-                log_say("No frames recorded, skipping episode save")
-                # Clear the empty buffer
-                dataset.episode_buffer = None
-    
-    except KeyboardInterrupt:
-        print("\n\nStopping recording...")
-    
-    finally:
-        # Clean up
-        log_say("Stop recording")
-        try:
-            leader.bus_right.disable_torque()
-            leader.bus_left.disable_torque()
-            time.sleep(0.1)
-            leader.disconnect()
-            follower.disconnect()
-            print("✓ Shutdown complete")
-        except Exception as e:
-            print(f"Shutdown error: {e}")
-        
-        # Upload dataset
-        print("\nUploading dataset to Hugging Face Hub...")
-        try:
-            dataset.push_to_hub()
-            print("✓ Dataset uploaded successfully")
-        except Exception as e:
-            print(f"Warning: Failed to upload dataset: {e}")
-            print("You can manually upload later using: dataset.push_to_hub()")
-        
-        print("✓ Recording complete!")
-
-
-if __name__ == "__main__":
-    main()

examples/openarms/replay.py

@@ -1,166 +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.
-
-"""
-OpenArms Dataset Replay Example
-
-Replays position actions from a recorded dataset on an OpenArms follower robot.
-Only position commands (ending with .pos) are replayed, not velocity or torque.
-
-Example usage:
-    python examples/openarms/replay.py
-"""
-
-import time
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.utils.constants import ACTION
-from lerobot.utils.robot_utils import busy_wait
-from lerobot.utils.utils import log_say
-
-# Configuration
-EPISODE_IDX = 0
-DATASET_REPO_ID = "lerobot-data-collection/replay-this-2025-11-02-17-58"  # TODO: Replace with your dataset
-DATASET_ROOT = None  # Use default cache location, or specify custom path
-
-# Robot configuration - adjust these to match your setup
-ROBOT_CONFIG = OpenArmsFollowerConfig(
-    port_left="can2",      # CAN interface for left arm
-    port_right="can3",     # CAN interface for right arm
-    can_interface="socketcan", 
-    id="openarms_follower",
-    disable_torque_on_disconnect=True,
-    max_relative_target=10.0,  # Safety limit: max degrees to move per step
-)
-
-
-def main():
-    """Main replay function."""
-    print("=" * 70)
-    print("OpenArms Dataset Replay")
-    print("=" * 70)
-    print(f"\nDataset: {DATASET_REPO_ID}")
-    print(f"Episode: {EPISODE_IDX}")
-    print(f"Robot: {ROBOT_CONFIG.id}")
-    print(f"  Left arm: {ROBOT_CONFIG.port_left}")
-    print(f"  Right arm: {ROBOT_CONFIG.port_right}")
-    print("\n" + "=" * 70)
-    
-    # Initialize the robot
-    print("\n[1/3] Initializing robot...")
-    robot = OpenArmsFollower(ROBOT_CONFIG)
-    
-    # Load the dataset
-    print(f"\n[2/3] Loading dataset '{DATASET_REPO_ID}'...")
-    dataset = LeRobotDataset(
-        DATASET_REPO_ID,
-        root=DATASET_ROOT,
-        episodes=[EPISODE_IDX]
-    )
-    
-    # Filter dataset to only include frames from the specified episode
-    # (required for dataset V3.0 where episodes are chunked)
-    episode_frames = dataset.hf_dataset.filter(
-        lambda x: x["episode_index"] == EPISODE_IDX
-    )
-    
-    if len(episode_frames) == 0:
-        raise ValueError(
-            f"No frames found for episode {EPISODE_IDX} in dataset {DATASET_REPO_ID}"
-        )
-    
-    print(f"  Found {len(episode_frames)} frames in episode {EPISODE_IDX}")
-    
-    # Extract action features from dataset
-    action_features = dataset.features.get(ACTION, {})
-    action_names = action_features.get("names", [])
-    
-    # Filter to only position actions (ending with .pos)
-    position_action_names = [name for name in action_names if name.endswith(".pos")]
-    
-    if not position_action_names:
-        raise ValueError(
-            f"No position actions found in dataset. Action names: {action_names}"
-        )
-    
-    print(f"  Found {len(position_action_names)} position actions to replay")
-    print(f"  Actions: {', '.join(position_action_names[:5])}{'...' if len(position_action_names) > 5 else ''}")
-    
-    # Select only action columns from dataset
-    actions = episode_frames.select_columns(ACTION)
-    
-    # Connect to the robot
-    print(f"\n[3/3] Connecting to robot...")
-    robot.connect(calibrate=False)  # Skip calibration for replay
-    
-    if not robot.is_connected:
-        raise RuntimeError("Robot failed to connect!")
-    
-    print("\n" + "=" * 70)
-    print("Ready to replay!")
-    print("=" * 70)
-    print("\nThe robot will replay the recorded positions.")
-    print("Press Ctrl+C to stop at any time.\n")
-    
-    input("Press ENTER to start replaying...")
-    
-    # Replay loop
-    log_say(f"Replaying episode {EPISODE_IDX}", blocking=True)
-    
-    try:
-        for idx in range(len(episode_frames)):
-            loop_start = time.perf_counter()
-            
-            # Extract action array from dataset
-            action_array = actions[idx][ACTION]
-            
-            # Build action dictionary, but only include position actions
-            action = {}
-            for i, name in enumerate(action_names):
-                # Only include position actions (ending with .pos)
-                if name.endswith(".pos"):
-                    action[name] = float(action_array[i])
-            
-            # Send action to robot
-            robot.send_action(action)
-            
-            # Maintain replay rate (use dataset fps)
-            loop_duration = time.perf_counter() - loop_start
-            dt_s = 1.0 / dataset.fps - loop_duration
-            busy_wait(dt_s)
-            
-            # Progress indicator every 100 frames
-            if (idx + 1) % 100 == 0:
-                progress = (idx + 1) / len(episode_frames) * 100
-                print(f"Progress: {idx + 1}/{len(episode_frames)} frames ({progress:.1f}%)")
-        
-        print(f"\n✓ Successfully replayed {len(episode_frames)} frames")
-        log_say("Replay complete", blocking=True)
-        
-    except KeyboardInterrupt:
-        print("\n\nReplay interrupted by user")
-    finally:
-        # Disconnect robot
-        print("\nDisconnecting robot...")
-        robot.disconnect()
-        print("✓ Replay complete!")
-
-
-if __name__ == "__main__":
-    main()
-

examples/openarms/setup_can.sh

@@ -1,73 +0,0 @@
-#!/bin/bash
-# Setup all OpenArms CAN interfaces with CAN FD
-
-set -e
-
-echo "=========================================="
-echo "OpenArms CAN FD Interface Setup"
-echo "=========================================="
-echo ""
-echo "Mode: CAN FD"
-echo "  - Nominal bitrate: 1 Mbps"
-echo "  - Data bitrate: 5 Mbps"
-echo ""
-echo "Configuring interfaces can0, can1, can2, can3..."
-echo ""
-
-# Configure each CAN interface with CAN FD
-for i in 0 1 2 3; do
-    interface="can$i"
-    
-    # Check if interface exists
-    if ! ip link show "$interface" &> /dev/null; then
-        echo "⚠ $interface: Not found, skipping"
-        continue
-    fi
-    
-    # Bring down interface
-    sudo ip link set "$interface" down 2>/dev/null
-    
-    # Configure CAN FD mode
-    sudo ip link set "$interface" type can \
-        bitrate 1000000 \
-        dbitrate 5000000 \
-        fd on
-    
-    # Bring up interface
-    sudo ip link set "$interface" up
-    
-    # Verify configuration
-    if ip link show "$interface" | grep -q "UP"; then
-        echo "✓ $interface: Configured and UP"
-    else
-        echo "✗ $interface: Failed to bring UP"
-    fi
-done
-
-echo ""
-echo "=========================================="
-echo "Verification"
-echo "=========================================="
-echo ""
-
-# Show detailed status for each interface
-for i in 0 1 2 3; do
-    interface="can$i"
-    if ip link show "$interface" &> /dev/null; then
-        echo "$interface:"
-        # Show key parameters
-        ip -d link show "$interface" | grep -E "can|state|bitrate|dbitrate" | head -3
-        echo ""
-    fi
-done
-
-echo "=========================================="
-echo "Setup Complete!"
-echo "=========================================="
-echo ""
-echo "All interfaces configured for CAN FD mode"
-echo ""
-echo "Next steps:"
-echo "  1. Test motors: python debug_can_communication.py"
-echo "  2. Run teleoperation: python examples/openarms/teleop.py"
-echo ""

examples/openarms/teleop.py

@@ -1,148 +0,0 @@
-"""
-OpenArms Teleoperation Example - Full Dual Arms
-
-This script demonstrates teleoperation of OpenArms follower robot using an OpenArms leader arm.
-It first calibrates both devices, then enters a teleoperation loop for both arms.
-"""
-
-import time
-
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
-from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
-
-
-follower_config = OpenArmsFollowerConfig(
-    port_left="can2",   # CAN interface for follower left arm
-    port_right="can3",  # CAN interface for follower right arm
-    can_interface="socketcan",  # Linux SocketCAN
-    id="openarms_follower",
-    disable_torque_on_disconnect=True,
-    max_relative_target=5.0,  # Safety limit
-)
-
-
-leader_config = OpenArmsLeaderConfig(
-    port_left="can0",   # CAN interface for leader left arm
-    port_right="can1",  # CAN interface for leader right arm
-    can_interface="socketcan",  # Linux SocketCAN
-    id="openarms_leader",
-    manual_control=True,  # Enable manual control (torque disabled)
-)
-
-print("=" * 60)
-print("OpenArms Teleoperation - Full Dual Arms")
-print("=" * 60)
-
-# Initialize devices
-print("\n[1/4] Initializing devices...")
-follower = OpenArmsFollower(follower_config)
-leader = OpenArmsLeader(leader_config)
-
-# Connect and calibrate follower
-print("\n[2/4] Connecting and calibrating follower robot...")
-print("Note: If you have existing calibration, just press ENTER to use it.")
-follower.connect(calibrate=True)
-
-# Connect and calibrate leader
-print("\n[3/4] Connecting and calibrating leader arm...")
-print("Note: The leader arm will have torque disabled for manual control.")
-leader.connect(calibrate=True)
-
-# Wait for user to be ready
-print("\n[4/4] Ready for teleoperation!")
-print("\nBoth arms will be controlled (16 motors total):")
-print("  RIGHT ARM: joints 1-7 + gripper")
-print("  LEFT ARM: joints 1-7 + gripper")
-
-print("\nPress ENTER to start teleoperation...")
-input()
-
-print("\nTeleoperation started! Move both leader arms.")
-print("Press Ctrl+C to stop.\n")
-
-# All joints for both arms (16 motors total)
-all_joints = [
-    # Right arm
-    "right_joint_1",
-    "right_joint_2",
-    "right_joint_3",
-    "right_joint_4",
-    "right_joint_5",
-    "right_joint_6",
-    "right_joint_7",
-    "right_gripper",
-    # Left arm
-    "left_joint_1",
-    "left_joint_2",
-    "left_joint_3",
-    "left_joint_4",
-    "left_joint_5",
-    "left_joint_6",
-    "left_joint_7",
-    "left_gripper",
-]
-
-# Performance monitoring
-loop_times = []
-start_time = time.perf_counter()
-last_print_time = start_time
-
-try:
-    while True:
-        loop_start = time.perf_counter()
-        
-        # Get action from leader
-        leader_action = leader.get_action()
-        
-        # Filter to only position data for all joints (both arms)
-        joint_action = {}
-        for joint in all_joints:
-            pos_key = f"{joint}.pos"
-            if pos_key in leader_action:
-                joint_action[pos_key] = leader_action[pos_key]
-        
-        # Send action to follower (both arms)
-        if joint_action:
-            follower.send_action(joint_action)
-        
-        # Measure loop time
-        loop_end = time.perf_counter()
-        loop_time = loop_end - loop_start
-        loop_times.append(loop_time)
-        
-        # Print stats every 2 seconds
-        if loop_end - last_print_time >= 2.0:
-            if loop_times:
-                avg_time = sum(loop_times) / len(loop_times)
-                current_hz = 1.0 / avg_time if avg_time > 0 else 0
-                min_time = min(loop_times)
-                max_time = max(loop_times)
-                max_hz = 1.0 / min_time if min_time > 0 else 0
-                min_hz = 1.0 / max_time if max_time > 0 else 0
-                
-                print(f"[Hz Stats] Avg: {current_hz:.1f} Hz | "
-                      f"Range: {min_hz:.1f}-{max_hz:.1f} Hz | "
-                      f"Avg loop time: {avg_time*1000:.1f} ms")
-                
-                # Reset for next measurement window
-                loop_times = []
-                last_print_time = loop_end
-            
-except KeyboardInterrupt:
-    print("\n\nStopping teleoperation...")
-finally:
-    # Disconnect devices
-    print("Disconnecting devices...")
-    try:
-        follower.disconnect()
-    except Exception as e:
-        print(f"Error disconnecting follower: {e}")
-    
-    try:
-        leader.disconnect()
-    except Exception as e:
-        print(f"Error disconnecting leader: {e}")
-    
-    print("Done!")

examples/openarms/teleop_openarms_mini.py

@@ -1,197 +0,0 @@
-"""
-OpenArms Mini Teleoperation Example
-
-This script demonstrates teleoperation of an OpenArms follower robot using 
-an OpenArms Mini leader (Feetech-based) with dual arms (16 motors total).
-
-The OpenArms Mini has:
-- Right arm: 8 motors (joint_1 to joint_7 + gripper)
-- Left arm: 8 motors (joint_1 to joint_7 + gripper)
-
-Note on gripper normalization:
-- OpenArms Mini gripper: 0-100 scale (0=closed, 100=open)
-- OpenArms follower gripper: degrees (0=closed, -65=open)
-- This script automatically converts between the two ranges
-"""
-
-import time
-import os
-import sys
-
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-from lerobot.teleoperators.openarms_mini.openarms_mini import OpenArmsMini
-from lerobot.teleoperators.openarms_mini.config_openarms_mini import OpenArmsMiniConfig
-from lerobot.utils.robot_utils import busy_wait
-
-# Target control frequency
-TARGET_FPS = 30
-
-# Configure the OpenArms follower (Damiao motors on CAN bus)
-follower_config = OpenArmsFollowerConfig(
-    port_left="can0",   # CAN interface for follower left arm
-    port_right="can1",  # CAN interface for follower right arm
-    can_interface="socketcan",  # Linux SocketCAN
-    id="openarms_follower",
-    disable_torque_on_disconnect=True,
-    max_relative_target=10.0,  # Safety limit (degrees per step)
-)
-
-# Configure the OpenArms Mini leader (Feetech motors on serial)
-leader_config = OpenArmsMiniConfig(
-    port_right="/dev/ttyACM0",  # Serial port for right arm
-    port_left="/dev/ttyACM1",   # Serial port for left arm
-    id="openarms_mini",
-    use_degrees=True,
-)
-
-print("OpenArms Mini → OpenArms Follower Teleoperation")
-
-# Initialize devices
-follower = OpenArmsFollower(follower_config)
-leader = OpenArmsMini(leader_config)
-
-# Connect and calibrate follower
-print("Note: If you have existing calibration, just press ENTER to use it.")
-follower.connect(calibrate=True)
-
-# Connect and calibrate leader
-print("Note: The leader arms will have torque disabled for manual control.")
-leader.connect(calibrate=True)
-
-print("\nPress ENTER to start teleoperation...")
-input()
-
-print("Press Ctrl+C to stop.\n")
-
-# All joints for both arms (16 motors total)
-all_joints = [
-    # Right arm
-    "right_joint_1",
-    "right_joint_2",
-    "right_joint_3",
-    "right_joint_4",
-    "right_joint_5",
-    "right_joint_6",
-    "right_joint_7",
-    "right_gripper",
-    # Left arm
-    "left_joint_1",
-    "left_joint_2",
-    "left_joint_3",
-    "left_joint_4",
-    "left_joint_5",
-    "left_joint_6",
-    "left_joint_7",
-    "left_gripper",
-]
-
-# Performance monitoring
-loop_times = []
-avg_loop_time = 0.0
-min_loop_time = float('inf')
-max_loop_time = 0.0
-stats_update_interval = 1.0  # Update stats every 1 second
-last_stats_update = time.perf_counter()
-
-
-SWAPPED_JOINTS = {
-    "right_joint_6": "right_joint_7",
-    "right_joint_7": "right_joint_6",
-    "left_joint_6": "left_joint_7",
-    "left_joint_7": "left_joint_6",
-}
-
-try:
-    while True:
-        loop_start = time.perf_counter()
-
-        # Get actions and observations
-        leader_action = leader.get_action()
-        follower_obs = follower.get_observation()
-
-        joint_action = {}
-        for joint in all_joints:
-            leader_key = f"{joint}.pos"
-
-            # Determine which follower joint this leader joint controls
-            follower_joint = SWAPPED_JOINTS.get(joint, joint)
-            follower_key = f"{follower_joint}.pos"
-
-            # Get leader position (default 0 if missing)
-            pos = leader_action.get(leader_key, 0.0)
-
-            # Convert gripper values: Mini uses 0-100, OpenArms uses 0 to -65 degrees
-            if "gripper" in joint:
-                # Map 0-100 (Mini) to 0 to -65 (OpenArms)
-                # 0 (closed) -> 0°, 100 (open) -> -65°
-                pos = (pos / 100.0) * -65.0
-
-            # Store in action dict for follower
-            joint_action[follower_key] = pos
-
-        follower.send_action(joint_action)
-
-        # Loop timing
-        loop_end = time.perf_counter()
-        loop_time = loop_end - loop_start
-        loop_times.append(loop_time)
-
-        # Update stats periodically
-        current_time = time.perf_counter()
-        if current_time - last_stats_update >= stats_update_interval:
-            if loop_times:
-                avg_loop_time = sum(loop_times) / len(loop_times)
-                min_loop_time = min(loop_times)
-                max_loop_time = max(loop_times)
-                loop_times = []
-                last_stats_update = current_time
-
-        # Display everything
-        sys.stdout.write("\033[H\033[J")  # Clear screen
-        
-        # Show timing stats at the top
-        if avg_loop_time > 0:
-            avg_hz = 1.0 / avg_loop_time
-            min_hz = 1.0 / max_loop_time if max_loop_time > 0 else 0
-            max_hz = 1.0 / min_loop_time if min_loop_time > 0 and min_loop_time < float('inf') else 0
-            print(f"[Performance] Target: {TARGET_FPS} Hz | Avg: {avg_hz:.1f} Hz | Range: {min_hz:.1f}-{max_hz:.1f} Hz | Loop: {avg_loop_time*1000:.1f} ms\n")
-        else:
-            print(f"[Performance] Target: {TARGET_FPS} Hz | Measuring...\n")
-
-        # Show joint positions
-        print(f"{'Joint':<20} {'Leader':>15} {'Follower':>15}")
-        print(f"{'':20} {'(0-100/deg)':>15} {'(deg)':>15}")
-        print("-" * 52)
-
-        for joint in all_joints:
-            leader_key = f"{joint}.pos"
-            follower_joint = SWAPPED_JOINTS.get(joint, joint)
-            follower_key = f"{follower_joint}.pos"
-
-            leader_pos = leader_action.get(leader_key, 0.0)
-            follower_pos = follower_obs.get(follower_key, 0.0)
-
-            print(f"{joint:<20} {leader_pos:>15.2f} {follower_pos:>15.2f}")
-
-        # Smart sleep to maintain target FPS
-        dt_s = time.perf_counter() - loop_start
-        busy_wait(max(0, 1.0 / TARGET_FPS - dt_s))
-            
-except KeyboardInterrupt:
-    print("\n\nStopping teleoperation...")
-finally:
-    # Disconnect devices
-    print("Disconnecting devices...")
-    try:
-        follower.disconnect()
-    except Exception as e:
-        print(f"Error disconnecting follower: {e}")
-    
-    try:
-        leader.disconnect()
-    except Exception as e:
-        print(f"Error disconnecting leader: {e}")
-    
-    print("Done!")
-

examples/openarms/teleop_with_compensation.py

@@ -1,202 +0,0 @@
-"""
-OpenArms Teleoperation with Gravity + Friction Compensation
-
-Leader arms (both LEFT and RIGHT): Gravity + Friction compensation (weightless, easy to move)
-Follower arms (both LEFT and RIGHT): Mirror leader movements
-
-Uses the URDF file from the lerobot repository.
-"""
-
-import time
-
-import numpy as np
-
-from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
-from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
-from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
-from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
-
-# Friction compensation scale factor (1.0 = full, 0.3 = 30% for stability)
-FRICTION_SCALE = 1.0
-
-def main():
-    """Main teleoperation loop with gravity compensation"""
-
-    print("=" * 70)
-    print("OpenArms Teleoperation with Gravity Compensation")
-    print("=" * 70)
-
-    # Configuration
-    follower_config = OpenArmsFollowerConfig(
-        port_left="can2",
-        port_right="can3",
-        can_interface="socketcan",
-        id="openarms_follower",
-        disable_torque_on_disconnect=True,
-        max_relative_target=10.0,
-    )
-
-    leader_config = OpenArmsLeaderConfig(
-        port_left="can0",
-        port_right="can1",
-        can_interface="socketcan",
-        id="openarms_leader",
-        manual_control=False,  # Enable torque control for gravity compensation
-    )
-
-    # Initialize and connect
-    print("\nInitializing devices...")
-    follower = OpenArmsFollower(follower_config)
-    leader = OpenArmsLeader(leader_config)
-
-    follower.connect()
-    leader.connect()
-
-    # URDF is automatically loaded in the leader constructor
-    if leader.pin_robot is None:
-        raise RuntimeError("URDF model not loaded on leader. Gravity compensation not available.")
-
-    print("\nLeader BOTH arms: Gravity + Friction comp | Follower BOTH arms: Teleop")
-    print("Press ENTER to start...")
-    input()
-
-    # Enable motors on both leader arms for gravity compensation
-    leader.bus_right.enable_torque()
-    leader.bus_left.enable_torque()
-    time.sleep(0.1)
-
-    print("Press Ctrl+C to stop\n")
-
-    # Main control loop
-    loop_times = []
-    last_print_time = time.perf_counter()
-
-    # All joints (both arms)
-    all_joints = []
-    for motor in leader.bus_right.motors:
-        all_joints.append(f"right_{motor}")
-    for motor in leader.bus_left.motors:
-        all_joints.append(f"left_{motor}")
-
-    try:
-        while True:
-            loop_start = time.perf_counter()
-
-            # Get leader state
-            leader_action = leader.get_action()
-
-            # Extract positions and velocities in degrees
-            leader_positions_deg = {}
-            leader_velocities_deg_per_sec = {}
-            
-            for motor in leader.bus_right.motors:
-                pos_key = f"right_{motor}.pos"
-                vel_key = f"right_{motor}.vel"
-                if pos_key in leader_action:
-                    leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
-                if vel_key in leader_action:
-                    leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
-
-            for motor in leader.bus_left.motors:
-                pos_key = f"left_{motor}.pos"
-                vel_key = f"left_{motor}.vel"
-                if pos_key in leader_action:
-                    leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
-                if vel_key in leader_action:
-                    leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
-
-            # Calculate gravity torques for leader using built-in method
-            leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
-            leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
-            
-            # Calculate friction torques for leader using built-in method
-            leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
-            leader_friction_torques_nm = leader._friction_from_velocity(
-                leader_velocities_rad_per_sec,
-                friction_scale=FRICTION_SCALE
-            )
-            
-            # Combine gravity + friction torques 
-            leader_total_torques_nm = {}
-            for motor_name in leader_gravity_torques_nm:
-                gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
-                friction = leader_friction_torques_nm.get(motor_name, 0.0)
-                leader_total_torques_nm[motor_name] = gravity + friction
-
-            # Apply gravity + friction compensation to leader RIGHT arm (all joints including gripper)
-            for motor in leader.bus_right.motors:
-                full_name = f"right_{motor}"
-                position = leader_positions_deg.get(full_name, 0.0)
-                torque = leader_total_torques_nm.get(full_name, 0.0)
-                
-                # Get damping gain for stability
-                kd = leader.get_damping_kd(motor)
-
-                leader.bus_right._mit_control(
-                    motor=motor,
-                    kp=0.0,
-                    kd=kd,  # Add damping for stability
-                    position_degrees=position,
-                    velocity_deg_per_sec=0.0,
-                    torque=torque,
-                )
-
-            # Apply gravity + friction compensation to leader LEFT arm (all joints including gripper)
-            for motor in leader.bus_left.motors:
-                full_name = f"left_{motor}"
-                position = leader_positions_deg.get(full_name, 0.0)
-                torque = leader_total_torques_nm.get(full_name, 0.0)
-                
-                # Get damping gain for stability
-                kd = leader.get_damping_kd(motor)
-
-                leader.bus_left._mit_control(                    
-                    motor=motor,
-                    kp=0.0,
-                    kd=kd,  # Add damping for stability
-                    position_degrees=position,
-                    velocity_deg_per_sec=0.0,
-                    torque=torque,
-                )
-
-            # Send leader positions to follower (both arms)
-            follower_action = {}
-            for joint in all_joints:
-                pos_key = f"{joint}.pos"
-                if pos_key in leader_action:
-                    follower_action[pos_key] = leader_action[pos_key]
-
-            if follower_action:
-                follower.send_action(follower_action)
-
-            # Performance monitoring
-            loop_end = time.perf_counter()
-            loop_time = loop_end - loop_start
-            loop_times.append(loop_time)
-
-            if loop_end - last_print_time >= 2.0:
-                if loop_times:
-                    avg_time = sum(loop_times) / len(loop_times)
-                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
-
-                    print(f"{current_hz:.1f} Hz ({avg_time*1000:.1f} ms)")
-
-                    loop_times = []
-                    last_print_time = loop_end
-
-    except KeyboardInterrupt:
-        print("\n\nStopping...")
-    finally:
-        try:
-            leader.bus_right.disable_torque()
-            leader.bus_left.disable_torque()
-            time.sleep(0.1)
-            leader.disconnect()
-            follower.disconnect()
-            print("✓ Shutdown complete")
-        except Exception as e:
-            print(f"Shutdown error: {e}")
-
-
-if __name__ == "__main__":
-    main()

examples/openarms_web_interface/App.css

@@ -1,745 +0,0 @@
-body {
-  margin: 0;
-  padding: 0;
-  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif;
-  background: #f5f5f5;
-}
-
-main {
-  min-height: 100vh;
-  padding: 2rem;
-}
-
-header {
-  text-align: center;
-  margin-bottom: 2rem;
-}
-
-h1 {
-  font-size: 2rem;
-  font-weight: 600;
-  color: #333;
-  margin: 0;
-}
-
-h2 {
-  font-size: 1.25rem;
-  font-weight: 600;
-  color: #333;
-  margin: 0 0 1rem 0;
-}
-
-h3 {
-  font-size: 0.875rem;
-  font-weight: 600;
-  color: #666;
-  margin: 0 0 0.5rem 0;
-  text-transform: uppercase;
-  letter-spacing: 0.5px;
-}
-
-.container {
-  max-width: 1920px;
-  margin: 0 auto;
-  display: grid;
-  grid-template-columns: minmax(500px, 600px) 1fr;
-  gap: 2rem;
-  align-items: start;
-}
-
-/* Left column container */
-.left-column {
-  display: flex;
-  flex-direction: column;
-  gap: 1.5rem;
-}
-
-/* Right column container */
-.right-column {
-  display: flex;
-  flex-direction: column;
-  gap: 1.5rem;
-}
-
-/* Responsive: Stack on smaller screens */
-@media (max-width: 1200px) {
-  .container {
-    grid-template-columns: 1fr;
-  }
-}
-
-.panel {
-  background: white;
-  border-radius: 8px;
-  padding: 1.5rem;
-  box-shadow: 0 1px 3px rgba(0,0,0,0.1);
-}
-
-.config-panel {
-  border: 2px solid #e5e7eb;
-}
-
-.config-header {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  cursor: pointer;
-  user-select: none;
-  padding: 0.5rem 0;
-}
-
-.config-header:hover {
-  opacity: 0.7;
-}
-
-.toggle-icon {
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-.spinner {
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-  to { transform: rotate(360deg); }
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-.status.recording.recording-active {
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-.status.recording.recording-active .indicator {
-  width: 20px;
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-  animation: pulse-strong 1s ease-in-out infinite;
-}
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-@keyframes pulse-strong {
-  0%, 100% { 
-    opacity: 1;
-    transform: scale(1);
-  }
-  50% { 
-    opacity: 0.7;
-    transform: scale(1.1);
-  }
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-.status.recording.recording-active .time-display {
-  display: flex;
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-  0%, 100% { opacity: 1; }
-  50% { opacity: 0.5; }
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-  align-self: stretch;
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-.ramp-up-countdown {
-  display: flex;
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-  display: flex;
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-  padding: 2rem 3rem;
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-@keyframes pulse-warm {
-  0%, 100% {
-    box-shadow: 0 6px 20px rgba(245, 158, 11, 0.4);
-  }
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-}
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-}
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-}
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-  font-size: 0.875rem;
-  background: white;
-}
-
-select:disabled {
-  background: #f5f5f5;
-  cursor: not-allowed;
-}
-
-/* Camera Layout */
-.camera-layout {
-  display: flex;
-  flex-direction: column;
-  gap: 1.5rem;
-}
-
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-  width: 100%;
-}
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-  display: grid;
-  grid-template-columns: repeat(2, 1fr);
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-}
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-  width: 100%;
-}
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-}
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-  text-align: center;
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-  background: #f9fafb;
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-}
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-  margin: 0.5rem 0;
-  font-size: 1rem;
-  color: #6b7280;
-}
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-  font-size: 1.25rem;
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-}
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-  flex-wrap: wrap;
-}
-

examples/openarms_web_interface/App.jsx

@@ -1,857 +0,0 @@
-import { useState, useEffect, useCallback, useRef } from 'react';
-import './App.css';
-
-const API_BASE = 'http://localhost:8000/api';
-
-function App() {
-  // State
-  const [task, setTask] = useState('');
-  const [isRecording, setIsRecording] = useState(false);
-  const [isInitializing, setIsInitializing] = useState(false);
-  const [isEncoding, setIsEncoding] = useState(false);
-  const [isUploading, setIsUploading] = useState(false);
-  const [robotsReady, setRobotsReady] = useState(false);
-  const [elapsedTime, setElapsedTime] = useState(0);
-  const [currentFps, setCurrentFps] = useState(0);
-  const [loopFps, setLoopFps] = useState(0);
-  const [episodeCount, setEpisodeCount] = useState(0);
-  const [error, setError] = useState(null);
-  const [statusMessage, setStatusMessage] = useState('Ready');
-  const [uploadStatus, setUploadStatus] = useState(null);
-  const [rampUpRemaining, setRampUpRemaining] = useState(0);
-  const [movingToZero, setMovingToZero] = useState(false);
-  const [configExpanded, setConfigExpanded] = useState(false);
-  const [latestRepoId, setLatestRepoId] = useState(null);
-
-  // Configuration
-  const [config, setConfig] = useState({
-    leader_type: 'openarms',  // 'openarms' or 'openarms_mini'
-    leader_left: 'can0',
-    leader_right: 'can1',
-    follower_left: 'can2',
-    follower_right: 'can3',
-    left_wrist: '/dev/video0',
-    right_wrist: '/dev/video1',
-    base: '/dev/video4'
-  });
-
-  // Available options
-  const [availableCameras, setAvailableCameras] = useState([]);
-  const [availableUsbPorts, setAvailableUsbPorts] = useState([]);
-  const canInterfaces = ['can0', 'can1', 'can2', 'can3'];
-
-  const statusIntervalRef = useRef(null);
-  const hasInitializedRef = useRef(false);
-
-  const loadConfig = () => {
-    try {
-      const saved = localStorage.getItem('openarms_config');
-      if (saved) {
-        const loadedConfig = JSON.parse(saved);
-        setConfig(prev => ({ ...prev, ...loadedConfig }));
-      }
-    } catch (e) {
-      console.error('Load config error:', e);
-    }
-  };
-
-  const saveConfig = (newConfig) => {
-    try {
-      localStorage.setItem('openarms_config', JSON.stringify(newConfig || config));
-    } catch (e) {
-      console.error('Save config error:', e);
-    }
-  };
-
-  // Fetch status periodically
-  const fetchStatus = async () => {
-    try {
-      const response = await fetch(`${API_BASE}/status`);
-      const data = await response.json();
-
-      setIsRecording(data.is_recording);
-      setIsInitializing(data.is_initializing);
-      setIsEncoding(data.is_encoding);
-      setIsUploading(data.is_uploading);
-      setRobotsReady(data.robots_ready);
-      setElapsedTime(data.elapsed_time);
-      setCurrentFps(data.current_fps || 0);
-      setLoopFps(data.loop_fps || 0);
-      setEpisodeCount(data.episode_count);
-      setError(data.error);
-      setStatusMessage(data.status_message || 'Ready');
-      setUploadStatus(data.upload_status);
-      setRampUpRemaining(data.ramp_up_remaining || 0);
-      setMovingToZero(data.moving_to_zero || false);
-      
-      // Track the latest repo_id from the backend
-      if (data.latest_repo_id) {
-        setLatestRepoId(data.latest_repo_id);
-      }
-
-      if (data.config) {
-        // Only merge server config if we don't have a saved config (first load)
-        if (!localStorage.getItem('openarms_config')) {
-          setConfig(prev => {
-            const merged = { ...data.config, ...prev };
-            localStorage.setItem('openarms_config', JSON.stringify(merged));
-            return merged;
-          });
-        }
-      }
-    } catch (e) {
-      console.error('Failed to fetch status:', e);
-    }
-  };
-
-  const setupRobots = async () => {
-    // Show warning to verify camera positions
-    const confirmed = window.confirm(
-      '⚠️ IMPORTANT: Before connecting robots, please verify:\n\n' +
-      '📹 Check that cameras are correctly positioned:\n' +
-      '   • LEFT wrist camera is actually on the LEFT arm\n' +
-      '   • RIGHT wrist camera is actually on the RIGHT arm\n' +
-      '   • BASE camera is actually the BASE/overhead camera\n\n' +
-      'Incorrect camera positioning will result in invalid training data!\n\n' +
-      'Click OK to continue with robot setup, or Cancel to review configuration.'
-    );
-    
-    if (!confirmed) {
-      return; // User cancelled, don't proceed
-    }
-    
-    setError(null);
-    try {
-      const response = await fetch(`${API_BASE}/robots/setup`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify(config)
-      });
-
-      if (!response.ok) {
-        const data = await response.json();
-        throw new Error(data.detail || 'Failed to setup robots');
-      }
-
-      await response.json();
-      saveConfig(config);
-    } catch (e) {
-      setError(`Robot setup failed: ${e.message}`);
-    }
-  };
-
-  // Disconnect robots
-  const disconnectRobots = async () => {
-    try {
-      await fetch(`${API_BASE}/robots/disconnect`, { method: 'POST' });
-      setRobotsReady(false);
-    } catch (e) {
-      console.error('Failed to disconnect robots:', e);
-    }
-  };
-
-  // Discover cameras
-  const discoverCameras = async () => {
-    try {
-      const response = await fetch(`${API_BASE}/cameras/discover`);
-      const data = await response.json();
-      const cameras = data.cameras || [];
-      setAvailableCameras(cameras);
-
-      // Get list of valid camera IDs
-      const validCameraIds = cameras.map(cam => String(cam.id));
-
-      // Auto-fix config if current values are invalid or not set
-      const updated = { ...config };
-      let changed = false;
-
-      // Auto-fix invalid camera config
-      if (!config.left_wrist || !validCameraIds.includes(config.left_wrist)) {
-        if (cameras.length >= 1) {
-          updated.left_wrist = String(cameras[0].id);
-          changed = true;
-        }
-      }
-
-      if (!config.right_wrist || !validCameraIds.includes(config.right_wrist)) {
-        if (cameras.length >= 2) {
-          updated.right_wrist = String(cameras[1].id);
-          changed = true;
-        }
-      }
-
-      if (!config.base || !validCameraIds.includes(config.base)) {
-        if (cameras.length >= 3) {
-          updated.base = String(cameras[2].id);
-          changed = true;
-        }
-      }
-
-      if (changed) {
-        setConfig(updated);
-        saveConfig(updated);
-      }
-
-      if (cameras.length === 0) {
-        setError('No cameras detected! Please connect cameras and refresh.');
-      }
-    } catch (e) {
-      console.error('Failed to discover cameras:', e);
-      setError(`Camera discovery failed: ${e.message}`);
-    }
-  };
-
-  // Discover USB ports
-  const discoverUsbPorts = async () => {
-    try {
-      const response = await fetch(`${API_BASE}/usb/discover`);
-      const data = await response.json();
-      const ports = data.ports || [];
-      setAvailableUsbPorts(ports);
-
-      // Auto-fix config if OpenArms Mini is selected and ports are invalid
-      if (config.leader_type === 'openarms_mini') {
-        const updated = { ...config };
-        let changed = false;
-
-        if (ports.length >= 1 && !ports.includes(config.leader_left)) {
-          updated.leader_left = ports[0];
-          changed = true;
-        }
-
-        if (ports.length >= 2 && !ports.includes(config.leader_right)) {
-          updated.leader_right = ports[1];
-          changed = true;
-        }
-
-        if (changed) {
-          setConfig(updated);
-          saveConfig(updated);
-        }
-      }
-
-      if (ports.length === 0) {
-        console.warn('No USB ports detected for OpenArms Mini');
-      }
-    } catch (e) {
-      console.error('Failed to discover USB ports:', e);
-    }
-  };
-
-  // Set task only (for pedal use)
-  const setTaskOnly = async () => {
-    if (!task.trim()) {
-      setError('Please enter a task description');
-      return;
-    }
-
-    setError(null);
-
-    try {
-      const response = await fetch(`${API_BASE}/recording/set-task`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ task, ...config })
-      });
-
-      if (!response.ok) {
-        const data = await response.json();
-        throw new Error(data.detail || 'Failed to set task');
-      }
-
-      const result = await response.json();
-      setStatusMessage(result.message || `Task set: ${task}`);
-      saveConfig(config);
-      
-      // Clear success message after 3 seconds
-      setTimeout(() => {
-        if (!isRecording && !isInitializing) {
-          setStatusMessage('Ready');
-        }
-      }, 3000);
-    } catch (e) {
-      setError(e.message);
-    }
-  };
-
-  // Start recording
-  const startRecording = async () => {
-    if (!task.trim()) {
-      setError('Please enter a task description');
-      return;
-    }
-
-    setError(null);
-
-    try {
-      const response = await fetch(`${API_BASE}/recording/start`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ task, ...config })
-      });
-
-      if (!response.ok) {
-        const data = await response.json();
-        throw new Error(data.detail || 'Failed to start recording');
-      }
-
-      await response.json();
-      saveConfig(config);
-    } catch (e) {
-      setError(e.message);
-    }
-  };
-
-  // Stop recording
-  const stopRecording = async () => {
-    try {
-      const response = await fetch(`${API_BASE}/recording/stop`, {
-        method: 'POST'
-      });
-
-      if (!response.ok) {
-        const data = await response.json();
-        throw new Error(data.detail || 'Failed to stop recording');
-      }
-
-      const data = await response.json();
-      setError(null);
-      // Update latest repo_id after recording
-      if (data.dataset_name) {
-        setLatestRepoId(`lerobot-data-collection/${data.dataset_name}`);
-      }
-    } catch (e) {
-      setError(e.message);
-    }
-  };
-
-  const deleteLatestEpisode = async () => {
-    if (!latestRepoId) {
-      setError('No episode to delete');
-      return;
-    }
-    
-    const confirmed = window.confirm(
-      `WARNING: This will permanently delete the repository:\n\n${latestRepoId}\n\nThis action cannot be undone. Continue?`
-    );
-    
-    if (!confirmed) {
-      return;
-    }
-    
-    try {
-      const response = await fetch(`${API_BASE}/recording/delete-latest`, { method: 'POST' });
-      
-      if (!response.ok) {
-        const data = await response.json();
-        throw new Error(data.detail || 'Failed to delete episode');
-      }
-
-      const data = await response.json();
-      setLatestRepoId(null);
-      setEpisodeCount(Math.max(0, episodeCount - 1));
-      setStatusMessage(`Deleted: ${data.deleted_repo}`);
-      
-      setTimeout(() => {
-        if (!isRecording && !isInitializing) {
-          setStatusMessage('Ready');
-        }
-      }, 3000);
-    } catch (e) {
-      setError(`Delete failed: ${e.message}`);
-    }
-  };
-
-  // Reset counter
-  const resetCounter = async () => {
-    try {
-      await fetch(`${API_BASE}/counter/reset`, { method: 'POST' });
-      setEpisodeCount(0);
-    } catch (e) {
-      console.error('Failed to reset counter:', e);
-    }
-  };
-
-  // Move robot to zero position
-  const moveToZero = async () => {
-    setError(null);
-    try {
-      const response = await fetch(`${API_BASE}/robots/move-to-zero`, { method: 'POST' });
-      if (!response.ok) {
-        const data = await response.json();
-        throw new Error(data.detail || 'Failed to move to zero position');
-      }
-      await response.json();
-    } catch (e) {
-      setError(`Move to zero failed: ${e.message}`);
-    }
-  };
-
-  // Format time as MM:SS
-  const formatTime = (seconds) => {
-    const mins = Math.floor(seconds / 60);
-    const secs = Math.floor(seconds % 60);
-    return `${mins.toString().padStart(2, '0')}:${secs.toString().padStart(2, '0')}`;
-  };
-
-  // Update config and save
-  const updateConfig = (key, value) => {
-    const updated = { ...config, [key]: value };
-    setConfig(updated);
-    saveConfig(updated);
-  };
-
-  // Initialize on mount only
-  useEffect(() => {
-    // Prevent double-initialization in development
-    if (hasInitializedRef.current) {
-      return;
-    }
-    hasInitializedRef.current = true;
-
-    loadConfig();
-    discoverCameras();
-    discoverUsbPorts();
-    fetchStatus();
-    statusIntervalRef.current = setInterval(fetchStatus, 1000);
-
-    return () => {
-      if (statusIntervalRef.current) {
-        clearInterval(statusIntervalRef.current);
-      }
-    };
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, []); // Run only once on mount
-
-  // Discover USB ports when leader type changes to Mini
-  useEffect(() => {
-    if (config.leader_type === 'openarms_mini') {
-      discoverUsbPorts();
-    }
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [config.leader_type]);
-
-  return (
-    <main>
-      <header>
-        <h1>OpenArms Recording</h1>
-      </header>
-
-      <div className="container">
-        {/* Left Column: Configuration and Recording Control */}
-        <div className="left-column">
-          {/* Configuration Panel */}
-          <section className="panel config-panel">
-          <div
-            className="config-header"
-            onClick={() => setConfigExpanded(!configExpanded)}
-            role="button"
-            tabIndex={0}
-            onKeyDown={(e) => e.key === 'Enter' && setConfigExpanded(!configExpanded)}
-          >
-            <h2>⚙️ Configuration</h2>
-            <span className="toggle-icon">{configExpanded ? '▼' : '▶'}</span>
-          </div>
-
-          {configExpanded && (
-            <div className="config-content">
-              {/* Robot Setup */}
-              <div className="config-section">
-                <h3>🤖 Robot Setup</h3>
-                <div className="robot-setup">
-                  {robotsReady ? (
-                    <div className="robot-status ready">
-                      <span>✅ Robots Ready - Recording will start instantly</span>
-                      <button onClick={disconnectRobots} className="btn-disconnect">
-                        Disconnect Robots
-                      </button>
-                    </div>
-                  ) : (
-                    <div className="robot-status not-ready">
-                      <span>⚠️ Robots not initialized - Recording will take ~10 seconds</span>
-                      <button
-                        onClick={setupRobots}
-                        disabled={isRecording || isInitializing}
-                        className="btn-setup"
-                      >
-                        🚀 Setup Robots
-                      </button>
-                    </div>
-                  )}
-                </div>
-              </div>
-
-              {/* Leader Type Selection */}
-              <div className="config-section">
-                <h3>🎮 Leader Type</h3>
-                <div className="config-grid">
-                  <label style={{gridColumn: '1 / -1'}}>
-                    Leader Arm Type
-                    <select
-                      value={config.leader_type}
-                      onChange={(e) => updateConfig('leader_type', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      <option value="openarms">OpenArms (CAN Bus - Damiao Motors)</option>
-                      <option value="openarms_mini">OpenArms Mini (USB - Feetech Motors)</option>
-                    </select>
-                  </label>
-                </div>
-              </div>
-
-              {/* Leader Interfaces (CAN or USB based on type) */}
-              <div className="config-section">
-                <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '0.5rem' }}>
-                  <h3>
-                    {config.leader_type === 'openarms_mini' 
-                      ? `Leader Ports (USB/Serial) ${availableUsbPorts.length > 0 ? `(${availableUsbPorts.length} detected)` : ''}` 
-                      : 'Leader Interfaces (CAN)'}
-                  </h3>
-                  {config.leader_type === 'openarms_mini' && (
-                    <button
-                      onClick={discoverUsbPorts}
-                      className="btn-refresh"
-                      disabled={isRecording || robotsReady}
-                    >
-                      🔄 Refresh
-                    </button>
-                  )}
-                </div>
-                
-                <div className="config-grid">
-                  <label>
-                    Leader Left
-                    <select
-                      value={config.leader_left}
-                      onChange={(e) => updateConfig('leader_left', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      {config.leader_type === 'openarms_mini' ? (
-                        availableUsbPorts.length > 0 ? (
-                          availableUsbPorts.map((port) => (
-                            <option key={port} value={port}>{port}</option>
-                          ))
-                        ) : (
-                          <option value="">No USB ports detected</option>
-                        )
-                      ) : (
-                        canInterfaces.map((iface) => (
-                          <option key={iface} value={iface}>{iface}</option>
-                        ))
-                      )}
-                    </select>
-                  </label>
-
-                  <label>
-                    Leader Right
-                    <select
-                      value={config.leader_right}
-                      onChange={(e) => updateConfig('leader_right', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      {config.leader_type === 'openarms_mini' ? (
-                        availableUsbPorts.length > 0 ? (
-                          availableUsbPorts.map((port) => (
-                            <option key={port} value={port}>{port}</option>
-                          ))
-                        ) : (
-                          <option value="">No USB ports detected</option>
-                        )
-                      ) : (
-                        canInterfaces.map((iface) => (
-                          <option key={iface} value={iface}>{iface}</option>
-                        ))
-                      )}
-                    </select>
-                  </label>
-                </div>
-              </div>
-
-              {/* Follower CAN Interfaces */}
-              <div className="config-section">
-                <h3>Follower Interfaces (CAN)</h3>
-                
-                <div className="config-grid">
-                  <label>
-                    Follower Left
-                    <select
-                      value={config.follower_left}
-                      onChange={(e) => updateConfig('follower_left', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      {canInterfaces.map((iface) => (
-                        <option key={iface} value={iface}>{iface}</option>
-                      ))}
-                    </select>
-                  </label>
-
-                  <label>
-                    Follower Right
-                    <select
-                      value={config.follower_right}
-                      onChange={(e) => updateConfig('follower_right', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      {canInterfaces.map((iface) => (
-                        <option key={iface} value={iface}>{iface}</option>
-                      ))}
-                    </select>
-                  </label>
-                </div>
-              </div>
-
-              {/* Camera Configuration */}
-              <div className="config-section">
-                <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '0.5rem' }}>
-                  <h3>Cameras {availableCameras.length > 0 && `(${availableCameras.length} detected)`}</h3>
-                  <button
-                    onClick={discoverCameras}
-                    className="btn-refresh"
-                    disabled={isRecording || robotsReady}
-                  >
-                    🔄 Refresh
-                  </button>
-                </div>
-                <div className="config-grid">
-                  <label>
-                    Left Wrist
-                    <select
-                      value={config.left_wrist}
-                      onChange={(e) => updateConfig('left_wrist', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      {availableCameras.map((cam) => (
-                        <option key={cam.id} value={String(cam.id)}>
-                          {cam.name || `Camera @ ${cam.id}`}
-                        </option>
-                      ))}
-                    </select>
-                  </label>
-
-                  <label>
-                    Right Wrist
-                    <select
-                      value={config.right_wrist}
-                      onChange={(e) => updateConfig('right_wrist', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      {availableCameras.map((cam) => (
-                        <option key={cam.id} value={String(cam.id)}>
-                          {cam.name || `Camera @ ${cam.id}`}
-                        </option>
-                      ))}
-                    </select>
-                  </label>
-
-                  <label>
-                    Base Camera
-                    <select
-                      value={config.base}
-                      onChange={(e) => updateConfig('base', e.target.value)}
-                      disabled={isRecording || robotsReady}
-                    >
-                      {availableCameras.map((cam) => (
-                        <option key={cam.id} value={String(cam.id)}>
-                          {cam.name || `Camera @ ${cam.id}`}
-                        </option>
-                      ))}
-                    </select>
-                  </label>
-                </div>
-              </div>
-            </div>
-          )}
-        </section>
-
-        {/* Control Panel */}
-        <section className="panel control-panel">
-          <h2>🎬 Recording Control</h2>
-
-          {/* Status Banner - Always show important statuses */}
-          {isInitializing && (
-            <div className="status-banner initializing">
-              <div className="spinner"></div>
-              <span>{statusMessage}</span>
-            </div>
-          )}
-
-          {isEncoding && (
-            <div className="status-banner encoding">
-              <div className="spinner"></div>
-              <span>📹 {statusMessage}</span>
-            </div>
-          )}
-
-          {isUploading && (
-            <div className="status-banner uploading">
-              <div className="spinner"></div>
-              <span>☁️ {statusMessage}</span>
-            </div>
-          )}
-
-          {uploadStatus && !isRecording && !isEncoding && !isUploading && (
-            <div className={`status-banner ${uploadStatus.startsWith('✓') ? 'success' : 'warning'}`}>
-              <span>{uploadStatus}</span>
-            </div>
-          )}
-
-          <div className="control-horizontal">
-            {/* Task Input and Status */}
-            <div className="control-left">
-              <div className="input-group">
-                <input
-                  type="text"
-                  value={task}
-                  onChange={(e) => setTask(e.target.value)}
-                  placeholder="Task description (e.g., 'pick and place')"
-                  disabled={isRecording || isInitializing || isEncoding || isUploading}
-                  onKeyPress={(e) => {
-                    if (e.key === 'Enter' && robotsReady) {
-                      setTaskOnly();
-                    }
-                  }}
-                />
-                <button
-                  onClick={setTaskOnly}
-                  disabled={isRecording || isInitializing || isEncoding || isUploading || !robotsReady}
-                  className="btn-set-task"
-                  title={!robotsReady ? 'Please setup robots first' : 'Store task for pedal use (Enter key)'}
-                >
-                  💾 Set Task
-                </button>
-                <button
-                  onClick={startRecording}
-                  disabled={isRecording || isInitializing || isEncoding || isUploading || !robotsReady}
-                  className="btn-start"
-                  title={!robotsReady ? 'Please setup robots first' : ''}
-                >
-                  {isInitializing
-                    ? '⏳ Initializing...'
-                    : isRecording
-                    ? '⏺ Recording...'
-                    : robotsReady
-                    ? '⏺ Start Recording'
-                    : '⏺ Setup Robots First'}
-                </button>
-              </div>
-
-              {/* Ramp-up Countdown */}
-              {isRecording && rampUpRemaining > 0 && (
-                <div className="ramp-up-countdown">
-                  <div className="countdown-box">
-                    <div className="countdown-label">⚡ WARMING UP - PID RAMP-UP</div>
-                    <div className="countdown-value">{rampUpRemaining.toFixed(1)}s</div>
-                    <div className="countdown-subtitle">Recording will start automatically...</div>
-                  </div>
-                </div>
-              )}
-
-              {/* Recording Status - Only show after ramp-up */}
-              {isRecording && rampUpRemaining <= 0 && (
-                <div className="status recording recording-active">
-                  <div className="indicator"></div>
-                  <div className="time-display">
-                    <span>{formatTime(elapsedTime)}</span>
-                    <span className="fps-display">
-                      Loop: {loopFps.toFixed(1)} Hz
-                      {loopFps > 0 && loopFps < 29 && <span className="fps-warning"> ⚠️</span>}
-                    </span>
-                    <span className="fps-display">Recording: {currentFps.toFixed(1)} FPS</span>
-                  </div>
-                  <button onClick={stopRecording} className="btn-stop">
-                    ⏹ Stop
-                  </button>
-                </div>
-              )}
-            </div>
-
-            {/* Episode Counter */}
-            <div className="control-right">
-              <div className="counter">
-                <div className="counter-label">Episodes Recorded</div>
-                <div className="counter-value">{episodeCount}</div>
-                <button onClick={resetCounter} className="btn-reset">
-                  Reset
-                </button>
-              </div>
-            </div>
-          </div>
-
-          {/* Delete Latest Episode Button */}
-          {!isRecording && !isInitializing && latestRepoId && (
-            <div className="delete-episode-section">
-              <button 
-                onClick={deleteLatestEpisode}
-                className="btn-delete"
-                title="Delete the latest recorded episode from HuggingFace Hub"
-              >
-                Delete Latest Episode
-              </button>
-              <div className="delete-info">Will delete: {latestRepoId}</div>
-            </div>
-          )}
-
-          {/* Move to Zero Button */}
-          {robotsReady && !isRecording && !isInitializing && (
-            <div className="zero-position-section">
-              <button 
-                onClick={moveToZero} 
-                disabled={movingToZero}
-                className="btn-zero-large"
-                title="Move both leader and follower robots to zero position (2s)"
-              >
-                {movingToZero ? '⏳ Moving to Zero Position...' : '🎯 Move to Zero Position (Leader + Follower)'}
-              </button>
-            </div>
-          )}
-
-          {/* Error Display */}
-          {error && (
-            <div className="error-box">
-              ⚠️ {error}
-            </div>
-          )}
-        </section>
-        </div>
-
-        {/* Right Column: Camera Feeds */}
-        <div className="right-column">
-          <section className="panel cameras">
-          <h2>📹 Camera Views</h2>
-          {robotsReady || isRecording || isInitializing ? (
-            <div className="camera-layout">
-              {/* Base camera - full width */}
-              <div className="camera camera-base">
-                <h3>Base Camera</h3>
-                <img src={`${API_BASE}/camera/stream/base`} alt="Base Camera" />
-              </div>
-
-              {/* Wrist cameras - side by side */}
-              <div className="camera-wrist-container">
-                <div className="camera camera-wrist">
-                  <h3>Left Wrist</h3>
-                  <img src={`${API_BASE}/camera/stream/left_wrist`} alt="Left Wrist Camera" />
-                </div>
-
-                <div className="camera camera-wrist">
-                  <h3>Right Wrist</h3>
-                  <img src={`${API_BASE}/camera/stream/right_wrist`} alt="Right Wrist Camera" />
-                </div>
-              </div>
-            </div>
-          ) : (
-            <div className="camera-placeholder">
-              <p>📷 Camera feeds will appear when robots are set up</p>
-              <p className="hint">Click "Setup Robots" above to preview camera feeds</p>
-            </div>
-          )}
-        </section>
-        </div>
-
-      </div>
-    </main>
-  );
-}
-
-export default App;
-

examples/openarms_web_interface/README.md

@@ -1,41 +0,0 @@
-# OpenArms Web Recording Interface
-
-A web interface for recording OpenArms datasets.
-
-## Installation
-
-```bash
-cd examples/openarms_web_interface
-npm install
-```
-
-## Usage
-
-**Start everything with one command:**
-
-```bash
-./launch.sh
-```
-
-This will:
-- Start the FastAPI backend on port 8000
-- Start the React frontend on port 5173
-- Show live logs from both services
-
-Then open your browser to: **http://localhost:5173**
-
-**Stop with:** `Ctrl+C`
-
----
-
-## Workflow
-
-1. **Configure CAN interfaces** and **camera paths** in the dropdowns
-2. Click **"Setup Robots"** to initialize (once at start)
-3. Enter a **task description**
-4. Click **"Start Recording"** to begin an episode
-5. Click **"Stop Recording"** when done
-6. Dataset is automatically encoded and uploaded to HuggingFace Hub as **private**
-7. Repeat steps 3-6 for more episodes (no need to re-setup robots!)
-
----

examples/openarms_web_interface/index.html

@@ -1,12 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>OpenArms Recording Interface</title>
-  </head>
-  <body>
-    <div id="root"></div>
-    <script type="module" src="/main.jsx"></script>
-  </body>
-</html>

examples/openarms_web_interface/launch.sh

@@ -1,142 +0,0 @@
-#!/bin/bash
-
-# OpenArms Web Interface Launcher
-# Starts Rerun viewer, FastAPI backend, and React frontend
-
-set -e
-
-# Colors for output
-GREEN='\033[0;32m'
-BLUE='\033[0;34m'
-YELLOW='\033[1;33m'
-RED='\033[0;31m'
-NC='\033[0m' # No Color
-
-# Get script directory
-SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
-cd "$SCRIPT_DIR"
-
-echo -e "${BLUE}╔════════════════════════════════════════╗${NC}"
-echo -e "${BLUE}║   OpenArms Web Recording Interface    ║${NC}"
-echo -e "${BLUE}╚════════════════════════════════════════╝${NC}"
-echo ""
-
-# Function to cleanup on exit
-cleanup() {
-    echo ""
-    echo -e "${YELLOW}Shutting down services...${NC}"
-    
-    # Kill all child processes
-    pkill -P $$ 2>/dev/null || true
-    
-    # Kill specific services by port
-    lsof -ti:8000 | xargs kill -9 2>/dev/null || true  # Backend
-    lsof -ti:5173 | xargs kill -9 2>/dev/null || true  # Frontend
-    lsof -ti:9876 | xargs kill -9 2>/dev/null || true  # Rerun (if spawned)
-    
-    echo -e "${GREEN}✓ Services stopped${NC}"
-    exit 0
-}
-
-# Register cleanup on script exit
-trap cleanup EXIT INT TERM
-
-# Check if required commands exist
-command -v rerun >/dev/null 2>&1 || { 
-    echo -e "${RED}✗ Error: 'rerun' not found. Please install: pip install rerun-sdk${NC}"
-    exit 1
-}
-
-command -v python >/dev/null 2>&1 || { 
-    echo -e "${RED}✗ Error: 'python' not found${NC}"
-    exit 1
-}
-
-command -v npm >/dev/null 2>&1 || { 
-    echo -e "${RED}✗ Error: 'npm' not found${NC}"
-    exit 1
-}
-
-# Check if node_modules exists
-if [ ! -d "node_modules" ]; then
-    echo -e "${YELLOW}⚠ node_modules not found. Running npm install...${NC}"
-    npm install
-    echo -e "${GREEN}✓ Dependencies installed${NC}"
-    echo ""
-fi
-
-echo -e "${GREEN}Starting services...${NC}"
-echo ""
-
-# 1. Start FastAPI backend (Rerun will start when recording begins)
-echo -e "${BLUE}[1/2]${NC} Starting FastAPI backend on port 8000..."
-cd "$SCRIPT_DIR"
-
-# Use Python from current environment (if lerobot env is active, it will use that)
-# Otherwise, check if we need to use conda run
-if [[ "$CONDA_DEFAULT_ENV" == "lerobot" ]]; then
-    # Already in lerobot environment
-    echo -e "${GREEN}✓ Using active lerobot environment${NC}"
-    PYTHON_CMD="python"
-elif command -v conda >/dev/null 2>&1 && conda env list | grep -q "^lerobot "; then
-    # lerobot env exists but not active - use conda run
-    echo -e "${YELLOW}Using conda run with lerobot environment...${NC}"
-    PYTHON_CMD="conda run -n lerobot --no-capture-output python"
-else
-    # Fall back to system python
-    echo -e "${YELLOW}⚠ Warning: lerobot environment not found, using system python${NC}"
-    PYTHON_CMD="python"
-fi
-
-$PYTHON_CMD web_record_server.py > /tmp/openarms_backend.log 2>&1 &
-BACKEND_PID=$!
-sleep 3
-
-if ps -p $BACKEND_PID > /dev/null; then
-    echo -e "${GREEN}✓ Backend started${NC} (PID: $BACKEND_PID)"
-    echo -e "      URL: ${BLUE}http://localhost:8000${NC}"
-else
-    echo -e "${RED}✗ Failed to start backend${NC}"
-    echo -e "${YELLOW}Check logs: tail -f /tmp/openarms_backend.log${NC}"
-    exit 1
-fi
-echo ""
-
-# 2. Start React frontend
-echo -e "${BLUE}[2/2]${NC} Starting React frontend on port 5173..."
-cd "$SCRIPT_DIR"
-npm run dev > /tmp/openarms_frontend.log 2>&1 &
-FRONTEND_PID=$!
-sleep 3
-
-if ps -p $FRONTEND_PID > /dev/null; then
-    echo -e "${GREEN}✓ Frontend started${NC} (PID: $FRONTEND_PID)"
-    echo -e "      URL: ${BLUE}http://localhost:5173${NC}"
-else
-    echo -e "${RED}✗ Failed to start frontend${NC}"
-    echo -e "${YELLOW}Check logs: tail -f /tmp/openarms_frontend.log${NC}"
-    exit 1
-fi
-echo ""
-
-# Display status
-echo -e "${GREEN}╔════════════════════════════════════════╗${NC}"
-echo -e "${GREEN}║     All services running! 🚀           ║${NC}"
-echo -e "${GREEN}╚════════════════════════════════════════╝${NC}"
-echo ""
-echo -e "🔧 ${BLUE}Backend:${NC}  http://localhost:8000"
-echo -e "🌐 ${BLUE}Frontend:${NC} http://localhost:5173"
-echo -e "📊 ${BLUE}Rerun:${NC}    Will spawn automatically when recording starts"
-echo ""
-echo -e "${YELLOW}Open your browser to:${NC} ${BLUE}http://localhost:5173${NC}"
-echo ""
-echo -e "${YELLOW}Logs:${NC}"
-echo -e "  • Backend:  tail -f /tmp/openarms_backend.log"
-echo -e "  • Frontend: tail -f /tmp/openarms_frontend.log"
-echo ""
-echo -e "${RED}Press Ctrl+C to stop all services${NC}"
-echo ""
-
-# Keep script running and wait for any service to exit
-wait
-

examples/openarms_web_interface/main.jsx

@@ -1,7 +0,0 @@
-import { createRoot } from 'react-dom/client'
-import App from './App.jsx'
-
-createRoot(document.getElementById('root')).render(
-  <App />
-)
-

examples/openarms_web_interface/package.json

@@ -1,21 +0,0 @@
-{
-  "name": "openarms-web-interface",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "vite build",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "react": "^18.3.1",
-    "react-dom": "^18.3.1"
-  },
-  "devDependencies": {
-    "@types/react": "^18.3.12",
-    "@types/react-dom": "^18.3.1",
-    "@vitejs/plugin-react": "^4.3.4",
-    "vite": "^6.0.1"
-  }
-}

examples/openarms_web_interface/vite.config.js

@@ -1,17 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vite.dev/config/
-export default defineConfig({
-  plugins: [react()],
-  server: {
-    port: 5173,
-    strictPort: false,
-    host: true,
-    open: false
-  },
-  build: {
-    outDir: 'dist',
-    sourcemap: true
-  }
-})

examples/phone_to_so100/evaluate.py

@@ -194,6 +194,4 @@ for episode_idx in range(NUM_EPISODES):
 log_say("Stop recording")
 robot.disconnect()
 listener.stop()
-
-dataset.finalize()
 dataset.push_to_hub()

examples/phone_to_so100/record.py

@@ -200,6 +200,4 @@ log_say("Stop recording")
 robot.disconnect()
 phone.disconnect()
 listener.stop()
-
-dataset.finalize()
 dataset.push_to_hub()

examples/port_datasets/port_droid.py

@@ -362,8 +362,6 @@ def port_droid(
         lerobot_dataset.save_episode()
         logging.info("Save_episode")
 
-    lerobot_dataset.finalize()
-
     if push_to_hub:
         lerobot_dataset.push_to_hub(
             # Add openx tag, since it belongs to the openx collection of datasets

examples/so100_to_so100_EE/evaluate.py

@@ -195,6 +195,4 @@ for episode_idx in range(NUM_EPISODES):
 log_say("Stop recording")
 robot.disconnect()
 listener.stop()
-
-dataset.finalize()
 dataset.push_to_hub()

examples/so100_to_so100_EE/record.py

@@ -199,6 +199,4 @@ log_say("Stop recording")
 leader.disconnect()
 follower.disconnect()
 listener.stop()
-
-dataset.finalize()
 dataset.push_to_hub()

examples/tutorial/act/act_training_example.py

@@ -1,98 +0,0 @@
-"""This script demonstrates how to train ACT Policy on a real-world dataset."""
-
-from pathlib import Path
-
-import torch
-
-from lerobot.configs.types import FeatureType
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.datasets.utils import dataset_to_policy_features
-from lerobot.policies.act.configuration_act import ACTConfig
-from lerobot.policies.act.modeling_act import ACTPolicy
-from lerobot.policies.factory import make_pre_post_processors
-
-
-def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[float]:
-    if delta_indices is None:
-        return [0]
-
-    return [i / fps for i in delta_indices]
-
-
-output_directory = Path("outputs/robot_learning_tutorial/act")
-output_directory.mkdir(parents=True, exist_ok=True)
-
-# Select your device
-device = torch.device("mps")  # or "cuda" or "cpu"
-
-dataset_id = "lerobot/svla_so101_pickplace"
-
-# This specifies the inputs the model will be expecting and the outputs it will produce
-dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-features = dataset_to_policy_features(dataset_metadata.features)
-
-output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
-input_features = {key: ft for key, ft in features.items() if key not in output_features}
-
-cfg = ACTConfig(input_features=input_features, output_features=output_features)
-policy = ACTPolicy(cfg)
-preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
-
-policy.train()
-policy.to(device)
-
-# To perform action chunking, ACT expects a given number of actions as targets
-delta_timestamps = {
-    "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
-}
-
-# add image features if they are present
-delta_timestamps |= {
-    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
-}
-
-# Instantiate the dataset
-dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
-
-# Create the optimizer and dataloader for offline training
-optimizer = cfg.get_optimizer_preset().build(policy.parameters())
-batch_size = 32
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    batch_size=batch_size,
-    shuffle=True,
-    pin_memory=device.type != "cpu",
-    drop_last=True,
-)
-
-# Number of training steps and logging frequency
-training_steps = 1
-log_freq = 1
-
-# Run training loop
-step = 0
-done = False
-while not done:
-    for batch in dataloader:
-        batch = preprocessor(batch)
-        loss, _ = policy.forward(batch)
-        loss.backward()
-        optimizer.step()
-        optimizer.zero_grad()
-
-        if step % log_freq == 0:
-            print(f"step: {step} loss: {loss.item():.3f}")
-        step += 1
-        if step >= training_steps:
-            done = True
-            break
-
-# Save the policy checkpoint, alongside the pre/post processors
-policy.save_pretrained(output_directory)
-preprocessor.save_pretrained(output_directory)
-postprocessor.save_pretrained(output_directory)
-
-# Save all assets to the Hub
-policy.push_to_hub("fracapuano/robot_learning_tutorial_act")
-preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")
-postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")

examples/tutorial/act/act_using_example.py

@@ -1,57 +0,0 @@
-import torch
-
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
-from lerobot.policies.act.modeling_act import ACTPolicy
-from lerobot.policies.factory import make_pre_post_processors
-from lerobot.policies.utils import build_inference_frame, make_robot_action
-from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so100_follower.so100_follower import SO100Follower
-
-device = torch.device("mps")  # or "cuda" or "cpu"
-model_id = "fracapuano/robot_learning_tutorial_act"
-model = ACTPolicy.from_pretrained(model_id)
-
-dataset_id = "lerobot/svla_so101_pickplace"
-# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
-dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-preprocess, postprocess = make_pre_post_processors(model.config, dataset_stats=dataset_metadata.stats)
-
-# # find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
-
-# # the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
-
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
-
-# Robot and environment configuration
-# Camera keys must match the name and resolutions of the ones used for training!
-# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-camera_config = {
-    "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-    "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-}
-
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
-
-for _ in range(MAX_EPISODES):
-    for _ in range(MAX_STEPS_PER_EPISODE):
-        obs = robot.get_observation()
-        obs_frame = build_inference_frame(
-            observation=obs, ds_features=dataset_metadata.features, device=device
-        )
-
-        obs = preprocess(obs_frame)
-
-        action = model.select_action(obs)
-        action = postprocess(action)
-
-        action = make_robot_action(action, dataset_metadata.features)
-
-        robot.send_action(action)
-
-    print("Episode finished! Starting new episode...")

examples/tutorial/async-inf/policy_server.py

@@ -1,11 +0,0 @@
-from lerobot.async_inference.configs import PolicyServerConfig
-from lerobot.async_inference.policy_server import serve
-
-host = ...  # something like "127.0.0.1" if you're exposing to localhost
-port = ...  # something like 8080
-
-config = PolicyServerConfig(
-    host=host,
-    port=port,
-)
-serve(config)

examples/tutorial/async-inf/robot_client.py

@@ -1,55 +0,0 @@
-import threading
-
-from lerobot.async_inference.configs import RobotClientConfig
-from lerobot.async_inference.helpers import visualize_action_queue_size
-from lerobot.async_inference.robot_client import RobotClient
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.robots.so100_follower import SO100FollowerConfig
-
-# these cameras must match the ones expected by the policy - find your cameras with lerobot-find-cameras
-# check the config.json on the Hub for the policy you are using to see the expected camera specs
-camera_cfg = {
-    "up": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-    "side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-}
-
-# # find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
-
-# # the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
-
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
-
-server_address = ...  # something like "127.0.0.1:8080" if using localhost
-
-# 3. Create client configuration
-client_cfg = RobotClientConfig(
-    robot=robot_cfg,
-    server_address=server_address,
-    policy_device="mps",
-    policy_type="act",
-    pretrained_name_or_path="fracapuano/robot_learning_tutorial_act",
-    chunk_size_threshold=0.5,  # g
-    actions_per_chunk=50,  # make sure this is less than the max actions of the policy
-)
-
-# 4. Create and start client
-client = RobotClient(client_cfg)
-
-# 5. Provide a textual description of the task
-task = ...
-
-if client.start():
-    # Start action receiver thread
-    action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
-    action_receiver_thread.start()
-
-    try:
-        # Run the control loop
-        client.control_loop(task)
-    except KeyboardInterrupt:
-        client.stop()
-        action_receiver_thread.join()
-        # (Optionally) plot the action queue size
-        visualize_action_queue_size(client.action_queue_size)

examples/tutorial/diffusion/diffusion_training_example.py

@@ -1,99 +0,0 @@
-"""This script demonstrates how to train Diffusion Policy on a real-world dataset."""
-
-from pathlib import Path
-
-import torch
-
-from lerobot.configs.types import FeatureType
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.datasets.utils import dataset_to_policy_features
-from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
-from lerobot.policies.factory import make_pre_post_processors
-
-
-def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[float]:
-    if delta_indices is None:
-        return [0]
-
-    return [i / fps for i in delta_indices]
-
-
-output_directory = Path("outputs/robot_learning_tutorial/diffusion")
-output_directory.mkdir(parents=True, exist_ok=True)
-
-# Select your device
-device = torch.device("mps")  # or "cuda" or "cpu"
-
-dataset_id = "lerobot/svla_so101_pickplace"
-
-# This specifies the inputs the model will be expecting and the outputs it will produce
-dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-features = dataset_to_policy_features(dataset_metadata.features)
-
-output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
-input_features = {key: ft for key, ft in features.items() if key not in output_features}
-
-cfg = DiffusionConfig(input_features=input_features, output_features=output_features)
-policy = DiffusionPolicy(cfg)
-preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
-
-policy.train()
-policy.to(device)
-
-# To perform action chunking, ACT expects a given number of actions as targets
-delta_timestamps = {
-    "observation.state": make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps),
-    "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
-}
-
-# add image features if they are present
-delta_timestamps |= {
-    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
-}
-
-# Instantiate the dataset
-dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
-
-# Create the optimizer and dataloader for offline training
-optimizer = cfg.get_optimizer_preset().build(policy.parameters())
-batch_size = 32
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    batch_size=batch_size,
-    shuffle=True,
-    pin_memory=device.type != "cpu",
-    drop_last=True,
-)
-
-# Number of training steps and logging frequency
-training_steps = 1
-log_freq = 1
-
-# Run training loop
-step = 0
-done = False
-while not done:
-    for batch in dataloader:
-        batch = preprocessor(batch)
-        loss, _ = policy.forward(batch)
-        loss.backward()
-        optimizer.step()
-        optimizer.zero_grad()
-
-        if step % log_freq == 0:
-            print(f"step: {step} loss: {loss.item():.3f}")
-        step += 1
-        if step >= training_steps:
-            done = True
-            break
-
-# Save the policy checkpoint, alongside the pre/post processors
-policy.save_pretrained(output_directory)
-preprocessor.save_pretrained(output_directory)
-postprocessor.save_pretrained(output_directory)
-
-# Save all assets to the Hub
-policy.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
-preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
-postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")

examples/tutorial/diffusion/diffusion_using_example.py

@@ -1,60 +0,0 @@
-import torch
-
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
-from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
-from lerobot.policies.factory import make_pre_post_processors
-from lerobot.policies.utils import build_inference_frame, make_robot_action
-from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so100_follower.so100_follower import SO100Follower
-
-device = torch.device("mps")  # or "cuda" or "cpu"
-model_id = "fracapuano/robot_learning_tutorial_diffusion"
-
-model = DiffusionPolicy.from_pretrained(model_id)
-
-dataset_id = "lerobot/svla_so101_pickplace"
-# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
-dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-preprocess, postprocess = make_pre_post_processors(
-    model.config, model_id, dataset_stats=dataset_metadata.stats
-)
-
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
-
-
-# # find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
-
-# # the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
-
-# Robot and environment configuration
-# Camera keys must match the name and resolutions of the ones used for training!
-# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-camera_config = {
-    "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-    "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-}
-
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
-
-
-for _ in range(MAX_EPISODES):
-    for _ in range(MAX_STEPS_PER_EPISODE):
-        obs = robot.get_observation()
-        obs_frame = build_inference_frame(
-            observation=obs, ds_features=dataset_metadata.features, device=device
-        )
-
-        obs = preprocess(obs_frame)
-
-        action = model.select_action(obs)
-        action = postprocess(action)
-        action = make_robot_action(action, dataset_metadata.features)
-        robot.send_action(action)
-
-    print("Episode finished! Starting new episode...")

examples/tutorial/pi0/using_pi0_example.py

@@ -1,67 +0,0 @@
-import torch
-
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.policies.factory import make_pre_post_processors
-from lerobot.policies.pi0.modeling_pi0 import PI0Policy
-from lerobot.policies.utils import build_inference_frame, make_robot_action
-from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so100_follower.so100_follower import SO100Follower
-
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
-
-device = torch.device("mps")  # or "cuda" or "cpu"
-model_id = "lerobot/pi0_base"
-
-model = PI0Policy.from_pretrained(model_id)
-
-preprocess, postprocess = make_pre_post_processors(
-    model.config,
-    model_id,
-    # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
-    preprocessor_overrides={"device_processor": {"device": str(device)}},
-)
-
-# find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
-
-# the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
-
-# Robot and environment configuration
-# Camera keys must match the name and resolutions of the ones used for training!
-# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-camera_config = {
-    "base_0_rgb": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-    "left_wrist_0_rgb": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-    "right_wrist_0_rgb": OpenCVCameraConfig(index_or_path=2, width=640, height=480, fps=30),
-}
-
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
-
-task = ""  # something like "pick the red block"
-robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
-
-# This is used to match the raw observation keys to the keys expected by the policy
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-for _ in range(MAX_EPISODES):
-    for _ in range(MAX_STEPS_PER_EPISODE):
-        obs = robot.get_observation()
-        obs_frame = build_inference_frame(
-            observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
-        )
-
-        obs = preprocess(obs_frame)
-
-        action = model.select_action(obs)
-        action = postprocess(action)
-        action = make_robot_action(action, dataset_features)
-        robot.send_action(action)
-
-    print("Episode finished! Starting new episode...")

examples/tutorial/rl/hilserl_example.py

@@ -1,345 +0,0 @@
-import multiprocessing as mp
-import signal
-from pathlib import Path
-from queue import Empty, Full
-
-import torch
-import torch.optim as optim
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.envs.configs import HILSerlProcessorConfig, HILSerlRobotEnvConfig
-from lerobot.policies.sac.configuration_sac import SACConfig
-from lerobot.policies.sac.modeling_sac import SACPolicy
-from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
-from lerobot.rl.buffer import ReplayBuffer
-from lerobot.rl.gym_manipulator import make_robot_env
-from lerobot.robots.so100_follower import SO100FollowerConfig
-from lerobot.teleoperators.so100_leader import SO100LeaderConfig
-from lerobot.teleoperators.utils import TeleopEvents
-
-LOG_EVERY = 10
-SEND_EVERY = 10
-
-
-def run_learner(
-    transitions_queue: mp.Queue,
-    parameters_queue: mp.Queue,
-    shutdown_event: mp.Event,
-    policy_learner: SACPolicy,
-    online_buffer: ReplayBuffer,
-    offline_buffer: ReplayBuffer,
-    lr: float = 3e-4,
-    batch_size: int = 32,
-    device: torch.device = "mps",
-):
-    """The learner process - trains SAC policy on transitions streamed from the actor, updating parameters
-    for the actor to adopt."""
-    policy_learner.train()
-    policy_learner.to(device)
-
-    # Create Adam optimizer from scratch - simple and clean
-    optimizer = optim.Adam(policy_learner.parameters(), lr=lr)
-
-    print(f"[LEARNER] Online buffer capacity: {online_buffer.capacity}")
-    print(f"[LEARNER] Offline buffer capacity: {offline_buffer.capacity}")
-
-    training_step = 0
-
-    while not shutdown_event.is_set():
-        # retrieve incoming transitions from the actor process
-        try:
-            transitions = transitions_queue.get(timeout=0.1)
-            for transition in transitions:
-                # HIL-SERL: Add ALL transitions to online buffer
-                online_buffer.add(**transition)
-
-                # HIL-SERL: Add ONLY human intervention transitions to offline buffer
-                is_intervention = transition.get("complementary_info", {}).get("is_intervention", False)
-                if is_intervention:
-                    offline_buffer.add(**transition)
-                    print(
-                        f"[LEARNER] Human intervention detected! Added to offline buffer (now {len(offline_buffer)} transitions)"
-                    )
-
-        except Empty:
-            pass  # No transitions available, continue
-
-        # Train if we have enough data
-        if len(online_buffer) >= policy_learner.config.online_step_before_learning:
-            # Sample from online buffer (autonomous + human data)
-            online_batch = online_buffer.sample(batch_size // 2)
-
-            # Sample from offline buffer (human demonstrations only, either precollected or at runtime)
-            offline_batch = offline_buffer.sample(batch_size // 2)
-
-            # Combine batches - this is the key HIL-SERL mechanism!
-            batch = {}
-            for key in online_batch:
-                if key in offline_batch:
-                    batch[key] = torch.cat([online_batch[key], offline_batch[key]], dim=0)
-                else:
-                    batch[key] = online_batch[key]
-
-            loss, _ = policy_learner.forward(batch)
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-            training_step += 1
-
-            if training_step % LOG_EVERY == 0:
-                print(
-                    f"[LEARNER] Training step {training_step}, Loss: {loss.item():.4f}, "
-                    f"Buffers: Online={len(online_buffer)}, Offline={len(offline_buffer)}"
-                )
-
-            # Send updated parameters to actor every 10 training steps
-            if training_step % SEND_EVERY == 0:
-                try:
-                    state_dict = {k: v.cpu() for k, v in policy_learner.state_dict().items()}
-                    parameters_queue.put_nowait(state_dict)
-                    print("[LEARNER] Sent updated parameters to actor")
-                except Full:
-                    # Missing write due to queue not being consumed (should happen rarely)
-                    pass
-
-    print("[LEARNER] Learner process finished")
-
-
-def run_actor(
-    transitions_queue: mp.Queue,
-    parameters_queue: mp.Queue,
-    shutdown_event: mp.Event,
-    policy_actor: SACPolicy,
-    reward_classifier: Classifier,
-    env_cfg: HILSerlRobotEnvConfig,
-    device: torch.device = "mps",
-    output_directory: Path | None = None,
-):
-    """The actor process - interacts with environment and collects data.
-    The policy is frozen and only the parameters are updated, popping the most recent ones from a queue."""
-    policy_actor.eval()
-    policy_actor.to(device)
-
-    reward_classifier.eval()
-    reward_classifier.to(device)
-
-    # Create robot environment inside the actor process
-    env, teleop_device = make_robot_env(env_cfg)
-
-    try:
-        for episode in range(MAX_EPISODES):
-            if shutdown_event.is_set():
-                break
-
-            obs, _info = env.reset()
-            episode_reward = 0.0
-            step = 0
-            episode_transitions = []
-
-            print(f"[ACTOR] Starting episode {episode + 1}")
-
-            while step < MAX_STEPS_PER_EPISODE and not shutdown_event.is_set():
-                try:
-                    new_params = parameters_queue.get_nowait()
-                    policy_actor.load_state_dict(new_params)
-                    print("[ACTOR] Updated policy parameters from learner")
-                except Empty:  # No new updated parameters available from learner, waiting
-                    pass
-
-                # Get action from policy
-                policy_obs = make_policy_obs(obs, device=device)
-                action_tensor = policy_actor.select_action(policy_obs)  # predicts a single action
-                action = action_tensor.squeeze(0).cpu().numpy()
-
-                # Step environment
-                next_obs, _env_reward, terminated, truncated, _info = env.step(action)
-                done = terminated or truncated
-
-                # Predict reward
-                policy_next_obs = make_policy_obs(next_obs, device=device)
-                reward = reward_classifier.predict_reward(policy_next_obs)
-
-                if reward >= 1.0 and not done:  # success detected! halt episode
-                    terminated = True
-                    done = True
-
-                # In HIL-SERL, human interventions come from the teleop device
-                is_intervention = False
-                if hasattr(teleop_device, "get_teleop_events"):
-                    # Real intervention detection from teleop device
-                    teleop_events = teleop_device.get_teleop_events()
-                    is_intervention = teleop_events.get(TeleopEvents.IS_INTERVENTION, False)
-
-                # Store transition with intervention metadata
-                transition = {
-                    "state": policy_obs,
-                    "action": action,
-                    "reward": float(reward) if hasattr(reward, "item") else reward,
-                    "next_state": policy_next_obs,
-                    "done": done,
-                    "truncated": truncated,
-                    "complementary_info": {
-                        "is_intervention": is_intervention,
-                    },
-                }
-
-                episode_transitions.append(transition)
-
-                episode_reward += reward
-                step += 1
-
-                obs = next_obs
-
-                if done:
-                    break
-
-            # Send episode transitions to learner
-            transitions_queue.put_nowait(episode_transitions)
-
-    except KeyboardInterrupt:
-        print("[ACTOR] Interrupted by user")
-    finally:
-        # Clean up
-        if hasattr(env, "robot") and env.robot.is_connected:
-            env.robot.disconnect()
-        if teleop_device and hasattr(teleop_device, "disconnect"):
-            teleop_device.disconnect()
-        if output_directory is not None:
-            policy_actor.save_pretrained(output_directory)
-            print(f"[ACTOR] Latest actor policy saved at: {output_directory}")
-
-        print("[ACTOR] Actor process finished")
-
-
-def make_policy_obs(obs, device: torch.device = "cpu"):
-    return {
-        "observation.state": torch.from_numpy(obs["agent_pos"]).float().unsqueeze(0).to(device),
-        **{
-            f"observation.image.{k}": torch.from_numpy(obs["pixels"][k]).float().unsqueeze(0).to(device)
-            for k in obs["pixels"]
-        },
-    }
-
-
-"""Main function - coordinates actor and learner processes."""
-
-device = "mps"  # or "cuda" or "cpu"
-output_directory = Path("outputs/robot_learning_tutorial/hil_serl")
-output_directory.mkdir(parents=True, exist_ok=True)
-
-# find ports using lerobot-find-port
-follower_port = ...
-leader_port = ...
-
-# the robot ids are used the load the right calibration files
-follower_id = ...
-leader_id = ...
-
-# A pretrained model (to be used in-distribution!)
-reward_classifier_id = "fracapuano/reward_classifier_hil_serl_example"
-reward_classifier = Classifier.from_pretrained(reward_classifier_id)
-
-reward_classifier.to(device)
-reward_classifier.eval()
-
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
-
-# Robot and environment configuration
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id)
-teleop_cfg = SO100LeaderConfig(port=leader_port, id=leader_id)
-processor_cfg = HILSerlProcessorConfig(control_mode="leader")
-
-env_cfg = HILSerlRobotEnvConfig(robot=robot_cfg, teleop=teleop_cfg, processor=processor_cfg)
-
-# Create robot environment
-env, teleop_device = make_robot_env(env_cfg)
-
-obs_features = hw_to_dataset_features(env.robot.observation_features, "observation")
-action_features = hw_to_dataset_features(env.robot.action_features, "action")
-
-# Create SAC policy for action selection
-policy_cfg = SACConfig(
-    device=device,
-    input_features=obs_features,
-    output_features=action_features,
-)
-
-policy_actor = SACPolicy(policy_cfg)
-policy_learner = SACPolicy(policy_cfg)
-
-demonstrations_repo_id = "lerobot/example_hil_serl_dataset"
-offline_dataset = LeRobotDataset(repo_id=demonstrations_repo_id)
-
-# Online buffer: initialized from scratch
-online_replay_buffer = ReplayBuffer(device=device, state_keys=list(obs_features.keys()))
-# Offline buffer: Created from dataset (pre-populated it with demonstrations)
-offline_replay_buffer = ReplayBuffer.from_lerobot_dataset(
-    lerobot_dataset=offline_dataset, device=device, state_keys=list(obs_features.keys())
-)
-
-# Create communication channels between learner and actor processes
-transitions_queue = mp.Queue(maxsize=10)
-parameters_queue = mp.Queue(maxsize=2)
-shutdown_event = mp.Event()
-
-
-# Signal handler for graceful shutdown
-def signal_handler(sig):
-    print(f"\nSignal {sig} received, shutting down...")
-    shutdown_event.set()
-
-
-signal.signal(signal.SIGINT, signal_handler)
-signal.signal(signal.SIGTERM, signal_handler)
-
-# Create processes
-learner_process = mp.Process(
-    target=run_learner,
-    args=(
-        transitions_queue,
-        parameters_queue,
-        shutdown_event,
-        policy_learner,
-        online_replay_buffer,
-        offline_replay_buffer,
-    ),
-    kwargs={"device": device},  # can run on accelerated hardware for training
-)
-
-actor_process = mp.Process(
-    target=run_actor,
-    args=(
-        transitions_queue,
-        parameters_queue,
-        shutdown_event,
-        policy_actor,
-        reward_classifier,
-        env_cfg,
-        output_directory,
-    ),
-    kwargs={"device": "cpu"},  # actor is frozen, can run on CPU or accelerate for inference
-)
-
-learner_process.start()
-actor_process.start()
-
-try:
-    # Wait for actor to finish (it controls the episode loop)
-    actor_process.join()
-    shutdown_event.set()
-    learner_process.join(timeout=10)
-
-except KeyboardInterrupt:
-    print("Main process interrupted")
-    shutdown_event.set()
-    actor_process.join(timeout=5)
-    learner_process.join(timeout=10)
-
-finally:
-    if learner_process.is_alive():
-        learner_process.terminate()
-    if actor_process.is_alive():
-        actor_process.terminate()

examples/tutorial/rl/reward_classifier_example.py

@@ -1,62 +0,0 @@
-import torch
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.policies.factory import make_policy, make_pre_post_processors
-from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
-
-# Device to use for training
-device = "mps"  # or "cuda", or "cpu"
-
-# Load the dataset used for training
-repo_id = "lerobot/example_hil_serl_dataset"
-dataset = LeRobotDataset(repo_id)
-
-# Configure the policy to extract features from the image frames
-camera_keys = dataset.meta.camera_keys
-
-config = RewardClassifierConfig(
-    num_cameras=len(camera_keys),
-    device=device,
-    # backbone model to extract features from the image frames
-    model_name="microsoft/resnet-18",
-)
-
-# Make policy, preprocessor, and optimizer
-policy = make_policy(config, ds_meta=dataset.meta)
-optimizer = config.get_optimizer_preset().build(policy.parameters())
-preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
-
-
-classifier_id = "fracapuano/reward_classifier_hil_serl_example"
-
-# Instantiate a dataloader
-dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
-
-# Training loop
-num_epochs = 5
-for epoch in range(num_epochs):
-    total_loss = 0
-    total_accuracy = 0
-    for batch in dataloader:
-        # Preprocess the batch and move it to the correct device.
-        batch = preprocessor(batch)
-
-        # Forward pass
-        loss, output_dict = policy.forward(batch)
-
-        # Backward pass and optimization
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-        total_loss += loss.item()
-        total_accuracy += output_dict["accuracy"]
-
-    avg_loss = total_loss / len(dataloader)
-    avg_accuracy = total_accuracy / len(dataloader)
-    print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}, Accuracy: {avg_accuracy:.2f}%")
-
-print("Training finished!")
-
-# You can now save the trained policy.
-policy.push_to_hub(classifier_id)

examples/tutorial/smolvla/using_smolvla_example.py

@@ -1,66 +0,0 @@
-import torch
-
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.policies.factory import make_pre_post_processors
-from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
-from lerobot.policies.utils import build_inference_frame, make_robot_action
-from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so100_follower.so100_follower import SO100Follower
-
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
-
-device = torch.device("mps")  # or "cuda" or "cpu"
-model_id = "lerobot/smolvla_base"
-
-model = SmolVLAPolicy.from_pretrained(model_id)
-
-preprocess, postprocess = make_pre_post_processors(
-    model.config,
-    model_id,
-    # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
-    preprocessor_overrides={"device_processor": {"device": str(device)}},
-)
-
-# find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
-
-# the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
-
-# Robot and environment configuration
-# Camera keys must match the name and resolutions of the ones used for training!
-# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-camera_config = {
-    "camera1": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-    "camera2": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-}
-
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
-
-task = ""  # something like "pick the red block"
-robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
-
-# This is used to match the raw observation keys to the keys expected by the policy
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-for _ in range(MAX_EPISODES):
-    for _ in range(MAX_STEPS_PER_EPISODE):
-        obs = robot.get_observation()
-        obs_frame = build_inference_frame(
-            observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
-        )
-
-        obs = preprocess(obs_frame)
-
-        action = model.select_action(obs)
-        action = postprocess(action)
-        action = make_robot_action(action, dataset_features)
-        robot.send_action(action)
-
-    print("Episode finished! Starting new episode...")

loop_datasets.py

@@ -1,10 +0,0 @@
-from huggingface_hub import HfApi, list_datasets
-
-api = HfApi()
-datasets = list_datasets(author="lerobot-data-collection")
-print('"[', end="")
-i=0
-for dataset in datasets:
-    if "three-folds-dataset" in dataset.id:
-        print("'" + dataset.id + "',", end="")
-print(']"',)

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.4.1"
+version = "0.3.4"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 readme = "README.md"
 license = { text = "Apache-2.0" }
@@ -59,30 +59,28 @@ keywords = ["lerobot", "huggingface", "robotics",  "machine learning", "artifici
 dependencies = [
 
     # Hugging Face dependencies
-    "datasets>=4.0.0,<4.2.0",
-    "diffusers>=0.27.2,<0.36.0",
-    "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0",
-    "accelerate>=1.10.0,<2.0.0",
+    "datasets>=4.0.0",
+    "diffusers>=0.27.2",
+    "huggingface-hub[hf-transfer,cli]>=0.34.2",
 
     # Core dependencies
-    "setuptools>=71.0.0,<81.0.0",
-    "cmake>=3.29.0.1,<4.2.0",
-    "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",
-    "packaging>=24.2,<26.0",
-    "pynput>=1.7.7,<1.9.0",
-    "pyserial>=3.5,<4.0",
-    "wandb>=0.20.0,<0.22.0", # TODO: Bumb dependency (compatible with protobuf)
+    "cmake>=3.29.0.1",
+    "einops>=0.8.0",
+    "opencv-python-headless>=4.9.0",
+    "av>=14.2.0",
+    "jsonlines>=4.0.0",
+    "packaging>=24.2",
+    "pynput>=1.7.7",
+    "pyserial>=3.5",
+    "wandb>=0.20.0",
 
     "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",
+    "gymnasium>=0.29.1,<1.0.0", # TODO: Bumb dependency
+    "rerun-sdk>=0.21.0,<0.23.0", # TODO: Bumb dependency
 
     # Support dependencies
     "deepdiff>=7.0.1,<9.0.0",
@@ -94,63 +92,55 @@ dependencies = [
 [project.optional-dependencies]
 
 # Common
-pygame-dep = ["pygame>=2.5.1,<2.7.0"]
-placo-dep = ["placo>=0.9.6,<0.10.0"]
-transformers-dep = ["transformers>=4.53.0,<5.0.0"]
-grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"] # TODO: Bumb dependency (compatible with wandb)
+pygame-dep = ["pygame>=2.5.1"]
+placo-dep = ["placo>=0.9.6"]
+transformers-dep = ["transformers>=4.53.0"]
+grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"]
 
 # Motors
-feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
-dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"]
-damiao = ["python-can>=4.2.0,<5.0.0"]
+feetech = ["feetech-servo-sdk>=1.0.0"]
+dynamixel = ["dynamixel-sdk>=3.7.31"]
 
 # Robots
-openarms = ["lerobot[damiao]"]
-gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
+gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0"]
 hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
-lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
-reachy2 = ["reachy2_sdk>=1.0.14,<1.1.0"]
+lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1"]
+reachy2 = ["reachy2_sdk>=1.0.14"]
 kinematics = ["lerobot[placo-dep]"]
 intelrealsense = [
-    "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'",
-    "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'",
+    "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'",
+    "pyrealsense2-macosx>=2.54 ; sys_platform == 'darwin'",
 ]
-phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
+phone = ["hebi-py>=2.8.0", "teleop>=0.1.0"]
+# stretch = [
+#     "hello-robot-stretch-body>=0.7.27 ; sys_platform == 'linux'",
+#     "pyrender @ git+https://github.com/mmatl/pyrender.git ; sys_platform == 'linux'",
+#     "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'"
+# ] # TODO: Currently not supported
 
 # Policies
 pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi"]
-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]",
-    "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",
-    "Pillow>=10.0.0,<13.0.0",
-    "decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
-    "ninja>=1.11.1,<2.0.0",
-    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
-]
-hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
+smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14", "accelerate>=1.7.0", "safetensors>=0.4.3"]
+hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.11", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
 # Features
-async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
+async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3"]
 
 # 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"]
-test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
-video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
+dev = ["pre-commit>=3.7.0", "debugpy>=1.8.1", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1"]
+test = ["pytest>=8.1.0", "pytest-timeout>=2.4.0", "pytest-cov>=5.0.0", "mock-serial>=0.0.1 ; sys_platform != 'win32'"]
+video_benchmark = ["scikit-image>=0.23.2", "pandas>=2.2.2"]
 
 # Simulation
-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
+aloha = ["gym-aloha>=0.1.1"]
+pusht = ["gym-pusht>=0.1.5", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
+xarm = ["gym-xarm>=0.1.1"]
 libero = ["lerobot[transformers-dep]", "libero @ git+https://github.com/huggingface/lerobot-libero.git@main#egg=libero"]
-metaworld = ["metaworld==3.0.0"]
+
 
 # All
 all = [
     "lerobot[dynamixel]",
-    "lerobot[openarms]",
     "lerobot[gamepad]",
     "lerobot[hopejr]",
     "lerobot[lekiwi]",
@@ -159,7 +149,6 @@ all = [
     "lerobot[intelrealsense]",
     "lerobot[pi]",
     "lerobot[smolvla]",
-    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[hilserl]",
     "lerobot[async]",
     "lerobot[dev]",
@@ -167,9 +156,9 @@ all = [
     "lerobot[video_benchmark]",
     "lerobot[aloha]",
     "lerobot[pusht]",
+    "lerobot[xarm]",
     "lerobot[phone]",
     "lerobot[libero]",
-    "lerobot[metaworld]",
 ]
 
 [project.scripts]
@@ -186,7 +175,6 @@ lerobot-dataset-viz="lerobot.scripts.lerobot_dataset_viz:main"
 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"
 
 # ---------------- Tool Configurations ----------------
 [tool.setuptools.packages.find]
@@ -244,6 +232,9 @@ exclude_dirs = [
     "tests",
     "benchmarks",
     "src/lerobot/datasets/push_dataset_to_hub",
+    "src/lerobot/datasets/v2/convert_dataset_v1_to_v2",
+    "src/lerobot/policies/pi0/conversion_scripts",
+    "src/lerobot/scripts/push_dataset_to_hub.py",
 ]
 skips = ["B101", "B311", "B404", "B603", "B615"]
 
@@ -258,8 +249,6 @@ default.extend-ignore-identifiers-re = [
     "pn",
     "ser",
     "ein",
-    "thw",
-    "inpt",
 ]
 
 # TODO: Uncomment when ready to use
@@ -298,6 +287,7 @@ ignore_errors = true
 
 [[tool.mypy.overrides]]
 module = "lerobot.envs.*"
+# Enable type checking only for the envs module
 ignore_errors = false
 
 
@@ -305,22 +295,17 @@ ignore_errors = false
 # module = "lerobot.utils.*"
 # ignore_errors = false
 
-[[tool.mypy.overrides]]
-module = "lerobot.configs.*"
-ignore_errors = false
-
-# extra strictness for configs
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-check_untyped_defs = true
+# [[tool.mypy.overrides]]
+# module = "lerobot.configs.*"
+# ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.optim.*"
 # ignore_errors = false
 
-[[tool.mypy.overrides]]
-module = "lerobot.model.*"
-ignore_errors = false
+# [[tool.mypy.overrides]]
+# module = "lerobot.model.*"
+# ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.processor.*"
@@ -330,9 +315,9 @@ ignore_errors = false
 # module = "lerobot.datasets.*"
 # ignore_errors = false
 
-[[tool.mypy.overrides]]
-module = "lerobot.cameras.*"
-ignore_errors = false
+# [[tool.mypy.overrides]]
+# module = "lerobot.cameras.*"
+# ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.motors.*"

requirements-macos.txt

@@ -1,4 +1,3 @@
-#
 # This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
@@ -13,62 +12,47 @@ absl-py==2.3.1
     #   dm-tree
     #   labmaze
     #   mujoco
-    #   tensorboard
-accelerate==1.11.0
-    # via
-    #   lerobot
-    #   peft
+accelerate==1.9.0
+    # via lerobot
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.13.1
+aiohttp==3.12.15
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
 annotated-types==0.7.0
     # via pydantic
-antlr4-python3-runtime==4.9.3
-    # via
-    #   hydra-core
-    #   omegaconf
-anyio==4.11.0
-    # via
-    #   starlette
-    #   watchfiles
 asttokens==3.0.0
     # via stack-data
 async-timeout==5.0.1
     # via aiohttp
-attrs==25.4.0
+attrs==25.3.0
     # via
     #   aiohttp
     #   dm-tree
     #   jsonlines
-    #   jsonschema
-    #   referencing
     #   rerun-sdk
-av==15.1.0
+av==15.0.0
     # via lerobot
-bddl==1.0.1
-    # via libero
-certifi==2025.10.5
+blinker==1.9.0
+    # via flask
+certifi==2025.7.14
     # via
     #   requests
     #   sentry-sdk
-cffi==2.0.0
+cffi==1.17.1
     # via pymunk
 cfgv==3.4.0
     # via pre-commit
-charset-normalizer==3.4.4
+charset-normalizer==3.4.2
     # via requests
-click==8.3.0
+click==8.2.1
     # via
-    #   uvicorn
+    #   flask
     #   wandb
 cloudpickle==3.1.1
-    # via
-    #   gymnasium
-    #   libero
-cmake==4.1.0
+    # via gymnasium
+cmake==4.0.3
     # via lerobot
 cmeel==0.57.3
     # via
@@ -110,27 +94,27 @@ coal-library==3.0.1
     # via pin
 contourpy==1.3.2
     # via matplotlib
-coverage[toml]==7.11.0
+coverage[toml]==7.10.1
     # via pytest-cov
 cycler==0.12.1
     # via matplotlib
-datasets==4.1.1
+datasets==3.6.0
     # via lerobot
-debugpy==1.8.17
+debugpy==1.8.15
     # via lerobot
 decorator==5.2.1
     # via ipython
-deepdiff==8.6.1
+deepdiff==8.5.0
     # via lerobot
-diffusers==0.35.2
+diffusers==0.34.0
     # via lerobot
-dill==0.4.0
+dill==0.3.8
     # via
     #   datasets
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.34
+dm-control==1.0.14
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -138,45 +122,29 @@ 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
+dynamixel-sdk==3.7.31
     # 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==0.8.1
-    # via
-    #   lerobot
-    #   libero
+    # via lerobot
 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
+executing==2.2.0
     # 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==3.20.0
+filelock==3.18.0
     # via
     #   datasets
     #   diffusers
@@ -184,25 +152,24 @@ filelock==3.20.0
     #   torch
     #   transformers
     #   virtualenv
-fonttools==4.60.1
+flask==3.1.1
+    # via lerobot
+fonttools==4.59.0
     # via matplotlib
-frozenlist==1.8.0
+frozenlist==1.7.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2025.9.0
+fsspec[http]==2025.3.0
     # via
     #   datasets
-    #   etils
     #   huggingface-hub
     #   torch
-future==1.0.0
-    # via libero
 gitdb==4.0.12
     # via gitpython
 gitpython==3.1.45
     # via wandb
-glfw==2.10.0
+glfw==2.9.0
     # via
     #   dm-control
     #   mujoco
@@ -210,79 +177,61 @@ grpcio==1.73.1
     # via
     #   grpcio-tools
     #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-    #   tensorboard
 grpcio-tools==1.73.1
-    # via
-    #   lerobot
-    #   reachy2-sdk-api
-gym-aloha==0.1.3
     # via lerobot
-gym-hil==0.1.13
+gym-aloha==0.1.1
     # via lerobot
-gym-pusht==0.1.6
+gym-hil==0.1.10
     # via lerobot
-gymnasium==1.2.1
+gym-pusht==0.1.5
+    # via lerobot
+gym-xarm==0.1.1
+    # via lerobot
+gymnasium==0.29.1
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
+    #   gym-xarm
+    #   gymnasium-robotics
     #   lerobot
-    #   libero
-    #   metaworld
-h11==0.16.0
-    # via uvicorn
-h5py==3.15.1
-    # via robomimic
-hebi-py==2.11.0
-    # via lerobot
+    #   pettingzoo
+gymnasium-robotics==1.2.4
+    # via gym-xarm
 hf-transfer==0.1.9
     # via huggingface-hub
-hf-xet==1.1.10
+hf-xet==1.1.5
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
-httptools==0.7.1
-    # via uvicorn
-huggingface-hub[cli,hf-transfer]==0.35.3
+huggingface-hub[cli,hf-transfer]==0.34.3
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
-    #   peft
-    #   timm
     #   tokenizers
     #   transformers
-hydra-core==1.3.2
-    # via libero
-identify==2.6.15
+identify==2.6.12
     # via pre-commit
-idna==3.11
+idna==3.10
     # via
-    #   anyio
     #   requests
     #   yarl
 imageio[ffmpeg]==2.37.0
     # via
     #   gym-aloha
     #   gym-hil
+    #   gymnasium-robotics
     #   lerobot
-    #   metaworld
-    #   robomimic
     #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via
-    #   imageio
-    #   robomimic
+    # via imageio
 importlib-metadata==8.7.0
     # via diffusers
-importlib-resources==6.5.2
-    # via etils
-iniconfig==2.3.0
+iniconfig==2.1.0
     # via pytest
 inquirerpy==0.3.4
     # via huggingface-hub
@@ -290,71 +239,50 @@ ipython==8.37.0
     # via meshcat
 ischedule==1.2.7
     # via placo
+itsdangerous==2.2.0
+    # via flask
 jedi==0.19.2
     # via ipython
 jinja2==3.1.6
-    # via torch
+    # via
+    #   flask
+    #   gymnasium-robotics
+    #   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
+kiwisolver==1.4.8
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
 lazy-loader==0.4
     # via scikit-image
-libero @ git+https://github.com/huggingface/lerobot-libero.git@main
-    # via lerobot
-llvmlite==0.45.1
-    # via numba
-lxml==6.0.2
+lxml==6.0.0
     # via dm-control
-markdown==3.9
-    # via tensorboard
-markdown-it-py==4.0.0
-    # via
-    #   jupytext
-    #   mdit-py-plugins
-markupsafe==3.0.3
+markupsafe==3.0.2
     # via
+    #   flask
     #   jinja2
     #   werkzeug
-matplotlib==3.10.7
-    # via
-    #   lerobot
-    #   libero
-matplotlib-inline==0.2.1
+matplotlib==3.10.5
+    # via lerobot
+matplotlib-inline==0.1.7
     # via ipython
-mdit-py-plugins==0.5.0
-    # via jupytext
-mdurl==0.1.2
-    # via markdown-it-py
 mergedeep==1.3.4
     # via draccus
 meshcat==0.3.2
     # via placo
-metaworld==3.0.0
-    # via lerobot
 mock-serial==0.0.1
     # via lerobot
 mpmath==1.3.0
     # via sympy
-mujoco==3.3.7
+mujoco==2.3.7
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
-    #   libero
-    #   metaworld
-    #   robosuite
-multidict==6.7.0
+    #   gym-xarm
+    #   gymnasium-robotics
+multidict==6.6.3
     # via
     #   aiohttp
     #   yarl
@@ -362,25 +290,17 @@ multiprocess==0.70.16
     # via datasets
 mypy-extensions==1.1.0
     # via typing-inspect
-nbformat==5.10.4
-    # via jupytext
 networkx==3.4.2
     # via
-    #   bddl
     #   scikit-image
     #   torch
-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
@@ -389,43 +309,25 @@ numpy==2.2.6
     #   dm-env
     #   dm-tree
     #   gymnasium
-    #   h5py
-    #   hebi-py
+    #   gymnasium-robotics
     #   imageio
     #   labmaze
-    #   libero
     #   matplotlib
     #   meshcat
-    #   metaworld
     #   mujoco
-    #   numba
     #   opencv-python
     #   opencv-python-headless
     #   pandas
-    #   peft
-    #   pyquaternion
-    #   reachy2-sdk
+    #   pettingzoo
     #   rerun-sdk
-    #   robomimic
-    #   robosuite
     #   scikit-image
     #   scipy
     #   shapely
-    #   teleop
-    #   tensorboard
-    #   tensorboardx
     #   tifffile
     #   torchvision
     #   transformers
-    #   transforms3d
-omegaconf==2.3.0
-    # via hydra-core
 opencv-python==4.12.0.88
-    # via
-    #   gym-pusht
-    #   libero
-    #   reachy2-sdk
-    #   robosuite
+    # via gym-pusht
 opencv-python-headless==4.12.0.88
     # via lerobot
 orderly-set==5.5.0
@@ -435,63 +337,53 @@ packaging==25.0
     #   accelerate
     #   datasets
     #   huggingface-hub
-    #   hydra-core
-    #   jupytext
     #   lazy-loader
     #   lerobot
     #   matplotlib
-    #   peft
     #   pytest
-    #   reachy2-sdk
     #   scikit-image
-    #   tensorboard
-    #   tensorboardx
     #   transformers
     #   wandb
-pandas==2.3.3
+pandas==2.3.1
     # via
     #   datasets
     #   lerobot
-parso==0.8.5
+parso==0.8.4
     # via jedi
-peft==0.17.1
-    # via lerobot
+pettingzoo==1.24.3
+    # via gymnasium-robotics
 pexpect==4.9.0
     # via ipython
 pfzy==0.3.4
     # via inquirerpy
-pillow==12.0.0
+pillow==11.3.0
     # via
     #   diffusers
     #   imageio
-    #   lerobot
     #   matplotlib
     #   meshcat
     #   rerun-sdk
-    #   robosuite
     #   scikit-image
-    #   tensorboard
     #   torchvision
 pin==3.4.0
     # via placo
 placo==0.9.14
     # via lerobot
-platformdirs==4.5.0
+platformdirs==4.3.8
     # via
-    #   jupyter-core
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.3.0
+pre-commit==4.2.0
     # via lerobot
-prompt-toolkit==3.0.52
+prompt-toolkit==3.0.51
     # via
     #   inquirerpy
     #   ipython
-propcache==0.4.1
+propcache==0.3.2
     # via
     #   aiohttp
     #   yarl
@@ -500,17 +392,11 @@ protobuf==6.31.0
     #   dm-control
     #   grpcio-tools
     #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-    #   tensorboard
-    #   tensorboardx
     #   wandb
-psutil==7.1.1
+psutil==7.0.0
     # via
     #   accelerate
     #   imageio
-    #   peft
-    #   robomimic
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
@@ -519,13 +405,11 @@ pyarrow==21.0.0
     # via
     #   datasets
     #   rerun-sdk
-pycparser==2.23
+pycparser==2.22
     # via cffi
-pydantic==2.12.3
-    # via
-    #   fastapi
-    #   wandb
-pydantic-core==2.41.4
+pydantic==2.11.7
+    # via wandb
+pydantic-core==2.33.2
     # via pydantic
 pygame==2.6.1
     # via
@@ -540,42 +424,40 @@ pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.4.1
+pyngrok==7.2.12
     # via meshcat
 pynput==1.8.1
     # via
     #   gym-hil
     #   lerobot
-pyobjc-core==12.0
+pyobjc-core==11.1
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-cocoa
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-applicationservices==12.0
+pyobjc-framework-applicationservices==11.1
     # via pynput
-pyobjc-framework-cocoa==12.0
+pyobjc-framework-cocoa==11.1
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-coretext==12.0
+pyobjc-framework-coretext==11.1
     # via pyobjc-framework-applicationservices
-pyobjc-framework-quartz==12.0
+pyobjc-framework-quartz==11.1
     # via
     #   pynput
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-coretext
-pyopengl==3.1.10
+pyopengl==3.1.9
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.2.5
+pyparsing==3.2.3
     # via
     #   dm-control
     #   matplotlib
-pyquaternion==0.9.9
-    # via reachy2-sdk
 pyrealsense2-macosx==2.54.2
     # via lerobot
 pyserial==3.5
@@ -583,14 +465,12 @@ pyserial==3.5
     #   dynamixel-sdk
     #   feetech-servo-sdk
     #   lerobot
-pytest==8.4.2
+pytest==8.4.1
     # via
-    #   bddl
     #   lerobot
     #   pytest-cov
     #   pytest-timeout
-    #   teleop
-pytest-cov==7.0.0
+pytest-cov==6.2.1
     # via lerobot
 pytest-timeout==2.4.0
     # via lerobot
@@ -598,73 +478,46 @@ python-dateutil==2.9.0.post0
     # via
     #   matplotlib
     #   pandas
-python-dotenv==1.1.1
-    # via uvicorn
 pytz==2025.2
     # via pandas
-pyyaml==6.0.3
+pyyaml==6.0.2
     # via
     #   accelerate
     #   datasets
     #   draccus
-    #   hebi-py
     #   huggingface-hub
-    #   jupytext
-    #   omegaconf
-    #   peft
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
-    #   timm
     #   transformers
-    #   uvicorn
     #   wandb
 pyyaml-include==1.4.1
     # via draccus
-pyzmq==27.1.0
+pyzmq==27.0.0
     # via
     #   lerobot
     #   meshcat
-reachy2-sdk==1.0.14
-    # via lerobot
-reachy2-sdk-api==1.0.21
-    # via reachy2-sdk
-referencing==0.37.0
-    # via
-    #   jsonschema
-    #   jsonschema-specifications
-regex==2025.10.23
+regex==2025.7.34
     # via
     #   diffusers
     #   transformers
-requests==2.32.5
+requests==2.32.4
     # via
     #   datasets
     #   diffusers
     #   dm-control
     #   huggingface-hub
-    #   teleop
     #   transformers
     #   wandb
-rerun-sdk==0.26.1
+rerun-sdk==0.22.1
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
-robomimic==0.2.0
-    # via libero
-robosuite==1.4.0
-    # via libero
-rpds-py==0.28.0
-    # via
-    #   jsonschema
-    #   referencing
-safetensors==0.6.2
+safetensors==0.5.3
     # via
     #   accelerate
     #   diffusers
     #   lerobot
-    #   peft
-    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
@@ -673,12 +526,10 @@ scikit-image==0.25.2
 scipy==1.15.3
     # via
     #   dm-control
-    #   metaworld
-    #   robosuite
     #   scikit-image
-sentry-sdk==2.42.1
+sentry-sdk==2.34.1
     # via wandb
-shapely==2.1.2
+shapely==2.1.1
     # via gym-pusht
 six==1.17.0
     # via
@@ -686,106 +537,64 @@ six==1.17.0
     #   python-dateutil
 smmap==5.0.2
     # via gitdb
-sniffio==1.3.1
-    # via anyio
 stack-data==0.6.3
     # via ipython
-starlette==0.48.0
-    # via fastapi
 sympy==1.14.0
     # via torch
-teleop==0.1.2
-    # via lerobot
-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
+    # via lerobot
 tifffile==2025.5.10
     # via scikit-image
-timm==1.0.20
-    # via lerobot
-tokenizers==0.22.1
+tokenizers==0.21.4
     # via transformers
 toml==0.10.2
     # via draccus
-tomli==2.3.0
+tomli==2.2.1
     # via
     #   cmeel
     #   coverage
-    #   jupytext
     #   pytest
 torch==2.7.1
     # via
     #   accelerate
     #   lerobot
-    #   peft
-    #   robomimic
-    #   thop
-    #   timm
     #   torchvision
 torchcodec==0.5
     # via lerobot
 torchvision==0.22.1
-    # via
-    #   lerobot
-    #   robomimic
-    #   timm
-tornado==6.5.2
+    # via lerobot
+tornado==6.5.1
     # via meshcat
 tqdm==4.67.1
     # via
     #   datasets
     #   dm-control
     #   huggingface-hub
-    #   peft
-    #   robomimic
     #   transformers
 traitlets==5.14.3
     # via
     #   ipython
-    #   jupyter-core
     #   matplotlib-inline
-    #   nbformat
-transformers==4.57.1
-    # via
-    #   lerobot
-    #   libero
-    #   peft
-transforms3d==0.4.2
-    # via teleop
-typing-extensions==4.15.0
+transformers==4.51.3
+    # via lerobot
+typing-extensions==4.14.1
     # via
     #   aiosignal
-    #   anyio
-    #   etils
     #   exceptiongroup
-    #   fastapi
     #   gymnasium
     #   huggingface-hub
     #   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
+typing-inspection==0.4.1
     # via pydantic
 tzdata==2025.2
     # via pandas
@@ -795,36 +604,22 @@ urllib3==2.5.0
     # via
     #   requests
     #   sentry-sdk
-uvicorn[standard]==0.38.0
-    # via teleop
-uvloop==0.22.1
-    # via uvicorn
-virtualenv==20.35.3
+virtualenv==20.32.0
     # via pre-commit
-wandb==0.21.4
-    # via
-    #   lerobot
-    #   libero
-watchfiles==1.1.1
-    # via uvicorn
-wcwidth==0.2.14
+wandb==0.21.0
+    # via lerobot
+wcwidth==0.2.13
     # via prompt-toolkit
-websocket-client==1.9.0
-    # via teleop
-websockets==15.0.1
-    # via uvicorn
 werkzeug==3.1.3
-    # via tensorboard
-wrapt==2.0.0
+    # via flask
+wrapt==1.17.2
     # via dm-tree
-xxhash==3.6.0
+xxhash==3.5.0
     # via datasets
-yarl==1.22.0
+yarl==1.20.1
     # via aiohttp
 zipp==3.23.0
-    # via
-    #   etils
-    #   importlib-metadata
+    # via importlib-metadata
 
 # The following packages are considered to be unsafe in a requirements file:
 # setuptools

requirements-ubuntu.txt

@@ -13,62 +13,47 @@ absl-py==2.3.1
     #   dm-tree
     #   labmaze
     #   mujoco
-    #   tensorboard
-accelerate==1.11.0
-    # via
-    #   lerobot
-    #   peft
+accelerate==1.9.0
+    # via lerobot
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.13.1
+aiohttp==3.12.15
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
 annotated-types==0.7.0
     # via pydantic
-antlr4-python3-runtime==4.9.3
-    # via
-    #   hydra-core
-    #   omegaconf
-anyio==4.11.0
-    # via
-    #   starlette
-    #   watchfiles
 asttokens==3.0.0
     # via stack-data
 async-timeout==5.0.1
     # via aiohttp
-attrs==25.4.0
+attrs==25.3.0
     # via
     #   aiohttp
     #   dm-tree
     #   jsonlines
-    #   jsonschema
-    #   referencing
     #   rerun-sdk
-av==15.1.0
+av==15.0.0
     # via lerobot
-bddl==1.0.1
-    # via libero
-certifi==2025.10.5
+blinker==1.9.0
+    # via flask
+certifi==2025.7.14
     # via
     #   requests
     #   sentry-sdk
-cffi==2.0.0
+cffi==1.17.1
     # via pymunk
 cfgv==3.4.0
     # via pre-commit
-charset-normalizer==3.4.4
+charset-normalizer==3.4.2
     # via requests
-click==8.3.0
+click==8.2.1
     # via
-    #   uvicorn
+    #   flask
     #   wandb
 cloudpickle==3.1.1
-    # via
-    #   gymnasium
-    #   libero
-cmake==4.1.0
+    # via gymnasium
+cmake==4.0.3
     # via lerobot
 cmeel==0.57.3
     # via
@@ -110,29 +95,27 @@ coal-library==3.0.1
     # via pin
 contourpy==1.3.2
     # via matplotlib
-coverage[toml]==7.11.0
+coverage[toml]==7.10.1
     # via pytest-cov
 cycler==0.12.1
     # via matplotlib
-datasets==4.1.1
+datasets==3.6.0
     # via lerobot
-debugpy==1.8.17
+debugpy==1.8.15
     # via lerobot
 decorator==5.2.1
     # via ipython
-decord==0.6.0
+deepdiff==8.5.0
     # via lerobot
-deepdiff==8.6.1
+diffusers==0.34.0
     # via lerobot
-diffusers==0.35.2
-    # via lerobot
-dill==0.4.0
+dill==0.3.8
     # via
     #   datasets
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.34
+dm-control==1.0.14
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -140,48 +123,31 @@ 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
+dynamixel-sdk==3.7.31
     # 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==0.8.1
-    # via
-    #   flash-attn
-    #   lerobot
-    #   libero
+    # via lerobot
 eiquadprog==1.2.9
     # via placo
-etils[epath,epy]==1.13.0
-    # via mujoco
 evdev==1.9.2
     # via pynput
 exceptiongroup==1.3.0
     # via
-    #   anyio
     #   ipython
     #   pytest
-executing==2.2.1
+executing==2.2.0
     # 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==3.20.0
+filelock==3.18.0
     # via
     #   datasets
     #   diffusers
@@ -189,27 +155,24 @@ filelock==3.20.0
     #   torch
     #   transformers
     #   virtualenv
-flash-attn==2.8.3
+flask==3.1.1
     # via lerobot
-fonttools==4.60.1
+fonttools==4.59.0
     # via matplotlib
-frozenlist==1.8.0
+frozenlist==1.7.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2025.9.0
+fsspec[http]==2025.3.0
     # via
     #   datasets
-    #   etils
     #   huggingface-hub
     #   torch
-future==1.0.0
-    # via libero
 gitdb==4.0.12
     # via gitpython
 gitpython==3.1.45
     # via wandb
-glfw==2.10.0
+glfw==2.9.0
     # via
     #   dm-control
     #   mujoco
@@ -217,79 +180,61 @@ grpcio==1.73.1
     # via
     #   grpcio-tools
     #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-    #   tensorboard
 grpcio-tools==1.73.1
-    # via
-    #   lerobot
-    #   reachy2-sdk-api
-gym-aloha==0.1.3
     # via lerobot
-gym-hil==0.1.13
+gym-aloha==0.1.1
     # via lerobot
-gym-pusht==0.1.6
+gym-hil==0.1.10
     # via lerobot
-gymnasium==1.2.1
+gym-pusht==0.1.5
+    # via lerobot
+gym-xarm==0.1.1
+    # via lerobot
+gymnasium==0.29.1
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
+    #   gym-xarm
+    #   gymnasium-robotics
     #   lerobot
-    #   libero
-    #   metaworld
-h11==0.16.0
-    # via uvicorn
-h5py==3.15.1
-    # via robomimic
-hebi-py==2.11.0
-    # via lerobot
+    #   pettingzoo
+gymnasium-robotics==1.2.4
+    # via gym-xarm
 hf-transfer==0.1.9
     # via huggingface-hub
-hf-xet==1.1.10
+hf-xet==1.1.5
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
-httptools==0.7.1
-    # via uvicorn
-huggingface-hub[cli,hf-transfer]==0.35.3
+huggingface-hub[cli,hf-transfer]==0.34.3
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
-    #   peft
-    #   timm
     #   tokenizers
     #   transformers
-hydra-core==1.3.2
-    # via libero
-identify==2.6.15
+identify==2.6.12
     # via pre-commit
-idna==3.11
+idna==3.10
     # via
-    #   anyio
     #   requests
     #   yarl
 imageio[ffmpeg]==2.37.0
     # via
     #   gym-aloha
     #   gym-hil
+    #   gymnasium-robotics
     #   lerobot
-    #   metaworld
-    #   robomimic
     #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via
-    #   imageio
-    #   robomimic
+    # via imageio
 importlib-metadata==8.7.0
     # via diffusers
-importlib-resources==6.5.2
-    # via etils
-iniconfig==2.3.0
+iniconfig==2.1.0
     # via pytest
 inquirerpy==0.3.4
     # via huggingface-hub
@@ -297,71 +242,50 @@ ipython==8.37.0
     # via meshcat
 ischedule==1.2.7
     # via placo
+itsdangerous==2.2.0
+    # via flask
 jedi==0.19.2
     # via ipython
 jinja2==3.1.6
-    # via torch
+    # via
+    #   flask
+    #   gymnasium-robotics
+    #   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
+kiwisolver==1.4.8
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
 lazy-loader==0.4
     # via scikit-image
-libero @ git+https://github.com/huggingface/lerobot-libero.git@main
-    # via lerobot
-llvmlite==0.45.1
-    # via numba
-lxml==6.0.2
+lxml==6.0.0
     # via dm-control
-markdown==3.9
-    # via tensorboard
-markdown-it-py==4.0.0
-    # via
-    #   jupytext
-    #   mdit-py-plugins
-markupsafe==3.0.3
+markupsafe==3.0.2
     # via
+    #   flask
     #   jinja2
     #   werkzeug
-matplotlib==3.10.7
-    # via
-    #   lerobot
-    #   libero
-matplotlib-inline==0.2.1
+matplotlib==3.10.5
+    # via lerobot
+matplotlib-inline==0.1.7
     # via ipython
-mdit-py-plugins==0.5.0
-    # via jupytext
-mdurl==0.1.2
-    # via markdown-it-py
 mergedeep==1.3.4
     # via draccus
 meshcat==0.3.2
     # via placo
-metaworld==3.0.0
-    # via lerobot
 mock-serial==0.0.1
     # via lerobot
 mpmath==1.3.0
     # via sympy
-mujoco==3.3.7
+mujoco==2.3.7
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
-    #   libero
-    #   metaworld
-    #   robosuite
-multidict==6.7.0
+    #   gym-xarm
+    #   gymnasium-robotics
+multidict==6.6.3
     # via
     #   aiohttp
     #   yarl
@@ -369,63 +293,42 @@ multiprocess==0.70.16
     # via datasets
 mypy-extensions==1.1.0
     # via typing-inspect
-nbformat==5.10.4
-    # via jupytext
 networkx==3.4.2
     # via
-    #   bddl
     #   scikit-image
     #   torch
-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
-    #   decord
     #   diffusers
     #   dm-control
     #   dm-env
     #   dm-tree
     #   gymnasium
-    #   h5py
-    #   hebi-py
+    #   gymnasium-robotics
     #   imageio
     #   labmaze
-    #   libero
     #   matplotlib
     #   meshcat
-    #   metaworld
     #   mujoco
-    #   numba
     #   opencv-python
     #   opencv-python-headless
     #   pandas
-    #   peft
-    #   pyquaternion
-    #   reachy2-sdk
+    #   pettingzoo
     #   rerun-sdk
-    #   robomimic
-    #   robosuite
     #   scikit-image
     #   scipy
     #   shapely
-    #   teleop
-    #   tensorboard
-    #   tensorboardx
     #   tifffile
     #   torchvision
     #   transformers
-    #   transforms3d
 nvidia-cublas-cu12==12.6.4.1
     # via
     #   nvidia-cudnn-cu12
@@ -463,14 +366,8 @@ nvidia-nvjitlink-cu12==12.6.85
     #   torch
 nvidia-nvtx-cu12==12.6.77
     # via torch
-omegaconf==2.3.0
-    # via hydra-core
 opencv-python==4.12.0.88
-    # via
-    #   gym-pusht
-    #   libero
-    #   reachy2-sdk
-    #   robosuite
+    # via gym-pusht
 opencv-python-headless==4.12.0.88
     # via lerobot
 orderly-set==5.5.0
@@ -480,63 +377,53 @@ packaging==25.0
     #   accelerate
     #   datasets
     #   huggingface-hub
-    #   hydra-core
-    #   jupytext
     #   lazy-loader
     #   lerobot
     #   matplotlib
-    #   peft
     #   pytest
-    #   reachy2-sdk
     #   scikit-image
-    #   tensorboard
-    #   tensorboardx
     #   transformers
     #   wandb
-pandas==2.3.3
+pandas==2.3.1
     # via
     #   datasets
     #   lerobot
-parso==0.8.5
+parso==0.8.4
     # via jedi
-peft==0.17.1
-    # via lerobot
+pettingzoo==1.24.3
+    # via gymnasium-robotics
 pexpect==4.9.0
     # via ipython
 pfzy==0.3.4
     # via inquirerpy
-pillow==12.0.0
+pillow==11.3.0
     # via
     #   diffusers
     #   imageio
-    #   lerobot
     #   matplotlib
     #   meshcat
     #   rerun-sdk
-    #   robosuite
     #   scikit-image
-    #   tensorboard
     #   torchvision
 pin==3.4.0
     # via placo
 placo==0.9.14
     # via lerobot
-platformdirs==4.5.0
+platformdirs==4.3.8
     # via
-    #   jupyter-core
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.3.0
+pre-commit==4.2.0
     # via lerobot
-prompt-toolkit==3.0.52
+prompt-toolkit==3.0.51
     # via
     #   inquirerpy
     #   ipython
-propcache==0.4.1
+propcache==0.3.2
     # via
     #   aiohttp
     #   yarl
@@ -545,17 +432,11 @@ protobuf==6.31.0
     #   dm-control
     #   grpcio-tools
     #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-    #   tensorboard
-    #   tensorboardx
     #   wandb
-psutil==7.1.1
+psutil==7.0.0
     # via
     #   accelerate
     #   imageio
-    #   peft
-    #   robomimic
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
@@ -564,13 +445,11 @@ pyarrow==21.0.0
     # via
     #   datasets
     #   rerun-sdk
-pycparser==2.23
+pycparser==2.22
     # via cffi
-pydantic==2.12.3
-    # via
-    #   fastapi
-    #   wandb
-pydantic-core==2.41.4
+pydantic==2.11.7
+    # via wandb
+pydantic-core==2.33.2
     # via pydantic
 pygame==2.6.1
     # via
@@ -585,22 +464,20 @@ pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.4.1
+pyngrok==7.2.12
     # via meshcat
 pynput==1.8.1
     # via
     #   gym-hil
     #   lerobot
-pyopengl==3.1.10
+pyopengl==3.1.9
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.2.5
+pyparsing==3.2.3
     # via
     #   dm-control
     #   matplotlib
-pyquaternion==0.9.9
-    # via reachy2-sdk
 pyrealsense2==2.56.5.9235
     # via lerobot
 pyserial==3.5
@@ -608,14 +485,12 @@ pyserial==3.5
     #   dynamixel-sdk
     #   feetech-servo-sdk
     #   lerobot
-pytest==8.4.2
+pytest==8.4.1
     # via
-    #   bddl
     #   lerobot
     #   pytest-cov
     #   pytest-timeout
-    #   teleop
-pytest-cov==7.0.0
+pytest-cov==6.2.1
     # via lerobot
 pytest-timeout==2.4.0
     # via lerobot
@@ -623,75 +498,48 @@ python-dateutil==2.9.0.post0
     # via
     #   matplotlib
     #   pandas
-python-dotenv==1.1.1
-    # via uvicorn
 python-xlib==0.33
     # via pynput
 pytz==2025.2
     # via pandas
-pyyaml==6.0.3
+pyyaml==6.0.2
     # via
     #   accelerate
     #   datasets
     #   draccus
-    #   hebi-py
     #   huggingface-hub
-    #   jupytext
-    #   omegaconf
-    #   peft
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
-    #   timm
     #   transformers
-    #   uvicorn
     #   wandb
 pyyaml-include==1.4.1
     # via draccus
-pyzmq==27.1.0
+pyzmq==27.0.0
     # via
     #   lerobot
     #   meshcat
-reachy2-sdk==1.0.14
-    # via lerobot
-reachy2-sdk-api==1.0.21
-    # via reachy2-sdk
-referencing==0.37.0
-    # via
-    #   jsonschema
-    #   jsonschema-specifications
-regex==2025.10.23
+regex==2025.7.34
     # via
     #   diffusers
     #   transformers
-requests==2.32.5
+requests==2.32.4
     # via
     #   datasets
     #   diffusers
     #   dm-control
     #   huggingface-hub
-    #   teleop
     #   transformers
     #   wandb
-rerun-sdk==0.26.1
+rerun-sdk==0.22.1
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
-robomimic==0.2.0
-    # via libero
-robosuite==1.4.0
-    # via libero
-rpds-py==0.28.0
-    # via
-    #   jsonschema
-    #   referencing
-safetensors==0.6.2
+safetensors==0.5.3
     # via
     #   accelerate
     #   diffusers
     #   lerobot
-    #   peft
-    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
@@ -700,12 +548,10 @@ scikit-image==0.25.2
 scipy==1.15.3
     # via
     #   dm-control
-    #   metaworld
-    #   robosuite
     #   scikit-image
-sentry-sdk==2.42.1
+sentry-sdk==2.34.1
     # via wandb
-shapely==2.1.2
+shapely==2.1.1
     # via gym-pusht
 six==1.17.0
     # via
@@ -714,109 +560,66 @@ six==1.17.0
     #   python-xlib
 smmap==5.0.2
     # via gitdb
-sniffio==1.3.1
-    # via anyio
 stack-data==0.6.3
     # via ipython
-starlette==0.48.0
-    # via fastapi
 sympy==1.14.0
     # via torch
-teleop==0.1.2
-    # via lerobot
-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
+    # via lerobot
 tifffile==2025.5.10
     # via scikit-image
-timm==1.0.20
-    # via lerobot
-tokenizers==0.22.1
+tokenizers==0.21.4
     # via transformers
 toml==0.10.2
     # via draccus
-tomli==2.3.0
+tomli==2.2.1
     # via
     #   cmeel
     #   coverage
-    #   jupytext
     #   pytest
 torch==2.7.1
     # via
     #   accelerate
-    #   flash-attn
     #   lerobot
-    #   peft
-    #   robomimic
-    #   thop
-    #   timm
     #   torchvision
 torchcodec==0.5
     # via lerobot
 torchvision==0.22.1
-    # via
-    #   lerobot
-    #   robomimic
-    #   timm
-tornado==6.5.2
+    # via lerobot
+tornado==6.5.1
     # via meshcat
 tqdm==4.67.1
     # via
     #   datasets
     #   dm-control
     #   huggingface-hub
-    #   peft
-    #   robomimic
     #   transformers
 traitlets==5.14.3
     # via
     #   ipython
-    #   jupyter-core
     #   matplotlib-inline
-    #   nbformat
-transformers==4.57.1
-    # via
-    #   lerobot
-    #   libero
-    #   peft
-transforms3d==0.4.2
-    # via teleop
+transformers==4.51.3
+    # via lerobot
 triton==3.3.1
     # via torch
-typing-extensions==4.15.0
+typing-extensions==4.14.1
     # via
     #   aiosignal
-    #   anyio
-    #   etils
     #   exceptiongroup
-    #   fastapi
     #   gymnasium
     #   huggingface-hub
     #   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
+typing-inspection==0.4.1
     # via pydantic
 tzdata==2025.2
     # via pandas
@@ -826,36 +629,22 @@ urllib3==2.5.0
     # via
     #   requests
     #   sentry-sdk
-uvicorn[standard]==0.38.0
-    # via teleop
-uvloop==0.22.1
-    # via uvicorn
-virtualenv==20.35.3
+virtualenv==20.32.0
     # via pre-commit
-wandb==0.21.4
-    # via
-    #   lerobot
-    #   libero
-watchfiles==1.1.1
-    # via uvicorn
-wcwidth==0.2.14
+wandb==0.21.0
+    # via lerobot
+wcwidth==0.2.13
     # via prompt-toolkit
-websocket-client==1.9.0
-    # via teleop
-websockets==15.0.1
-    # via uvicorn
 werkzeug==3.1.3
-    # via tensorboard
-wrapt==2.0.0
+    # via flask
+wrapt==1.17.2
     # via dm-tree
-xxhash==3.6.0
+xxhash==3.5.0
     # via datasets
-yarl==1.22.0
+yarl==1.20.1
     # via aiohttp
 zipp==3.23.0
-    # via
-    #   etils
-    #   importlib-metadata
+    # via importlib-metadata
 
 # The following packages are considered to be unsafe in a requirements file:
 # setuptools

requirements.in

@@ -1,9 +1,9 @@
 # requirements.in
 
-# 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-macos.txt was generated on macOS and is platform-specific (macOS 15.5 24F74 arm64).
+# Darwin MacBook-Pro.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132 arm64
 
-# 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
+# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.2 LTS x86_64).
+# Linux mlerobot-linux 6.14.0-27-generic #27~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Jul 22 17:38:49 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
 
 -e .[all]

src/lerobot/__init__.py

@@ -57,6 +57,7 @@ available_tasks_per_env = {
         "AlohaTransferCube-v0",
     ],
     "pusht": ["PushT-v0"],
+    "xarm": ["XarmLift-v0"],
 }
 available_envs = list(available_tasks_per_env.keys())
 
@@ -74,6 +75,16 @@ available_datasets_per_env = {
     # TODO(alexander-soare): Add "lerobot/pusht_keypoints". Right now we can't because this is too tightly
     # coupled with tests.
     "pusht": ["lerobot/pusht", "lerobot/pusht_image"],
+    "xarm": [
+        "lerobot/xarm_lift_medium",
+        "lerobot/xarm_lift_medium_replay",
+        "lerobot/xarm_push_medium",
+        "lerobot/xarm_push_medium_replay",
+        "lerobot/xarm_lift_medium_image",
+        "lerobot/xarm_lift_medium_replay_image",
+        "lerobot/xarm_push_medium_image",
+        "lerobot/xarm_push_medium_replay_image",
+    ],
 }
 
 available_real_world_datasets = [
@@ -184,6 +195,7 @@ available_motors = [
 available_policies_per_env = {
     "aloha": ["act"],
     "pusht": ["diffusion", "vqbet"],
+    "xarm": ["tdmpc"],
     "koch_real": ["act_koch_real"],
     "aloha_real": ["act_aloha_real"],
 }

src/lerobot/async_inference/configs.py

@@ -142,6 +142,11 @@ class RobotClientConfig:
         default=False, metadata={"help": "Visualize the action queue size"}
     )
 
+    # Verification configuration
+    verify_robot_cameras: bool = field(
+        default=True, metadata={"help": "Verify that the robot cameras match the policy cameras"}
+    )
+
     @property
     def environment_dt(self) -> float:
         """Environment time step, in seconds"""

src/lerobot/async_inference/constants.py

@@ -26,4 +26,4 @@ DEFAULT_OBS_QUEUE_TIMEOUT = 2
 SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05"]
 
 # TODO: Add all other robots
-SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so100_follower"]
+SUPPORTED_ROBOTS = ["so100_follower", "so101_follower"]

src/lerobot/async_inference/helpers.py

@@ -16,7 +16,7 @@ import logging
 import logging.handlers
 import os
 import time
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from pathlib import Path
 
 import torch
@@ -62,6 +62,15 @@ def visualize_action_queue_size(action_queue_size: list[int]) -> None:
     plt.show()
 
 
+def validate_robot_cameras_for_policy(
+    lerobot_observation_features: dict[str, dict], policy_image_features: dict[str, PolicyFeature]
+) -> None:
+    image_keys = list(filter(is_image_key, lerobot_observation_features))
+    assert set(image_keys) == set(policy_image_features.keys()), (
+        f"Policy image features must match robot cameras! Received {list(policy_image_features.keys())} != {image_keys}"
+    )
+
+
 def map_robot_keys_to_lerobot_features(robot: Robot) -> dict[str, dict]:
     return hw_to_dataset_features(robot.observation_features, OBS_STR, use_video=False)
 
@@ -83,11 +92,11 @@ def resize_robot_observation_image(image: torch.tensor, resize_dims: tuple[int,
     return resized.squeeze(0)
 
 
-# TODO(Steven): Consider implementing a pipeline step for this
 def raw_observation_to_observation(
     raw_observation: RawObservation,
     lerobot_features: dict[str, dict],
     policy_image_features: dict[str, PolicyFeature],
+    device: str,
 ) -> Observation:
     observation = {}
 
@@ -96,7 +105,9 @@ def raw_observation_to_observation(
         if isinstance(v, torch.Tensor):  # VLAs present natural-language instructions in observations
             if "image" in k:
                 # Policy expects images in shape (B, C, H, W)
-                observation[k] = prepare_image(v).unsqueeze(0)
+                observation[k] = prepare_image(v).unsqueeze(0).to(device)
+            else:
+                observation[k] = v.to(device)
         else:
             observation[k] = v
 
@@ -268,7 +279,6 @@ class RemotePolicyConfig:
     lerobot_features: dict[str, PolicyFeature]
     actions_per_chunk: int
     device: str = "cpu"
-    rename_map: dict[str, str] = field(default_factory=dict)
 
 
 def _compare_observation_states(obs1_state: torch.Tensor, obs2_state: torch.Tensor, atol: float) -> bool:

src/lerobot/async_inference/policy_server.py

@@ -15,7 +15,7 @@
 """
 Example:
 ```shell
-python -m lerobot.async_inference.policy_server \
+python src/lerobot/async_inference/policy_server.py \
      --host=127.0.0.1 \
      --port=8080 \
      --fps=30 \
@@ -32,17 +32,12 @@ from concurrent import futures
 from dataclasses import asdict
 from pprint import pformat
 from queue import Empty, Queue
-from typing import Any
 
 import draccus
 import grpc
 import torch
 
-from lerobot.policies.factory import get_policy_class, make_pre_post_processors
-from lerobot.processor import (
-    PolicyAction,
-    PolicyProcessorPipeline,
-)
+from lerobot.policies.factory import get_policy_class
 from lerobot.transport import (
     services_pb2,  # type: ignore
     services_pb2_grpc,  # type: ignore
@@ -87,8 +82,6 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         self.lerobot_features = None
         self.actions_per_chunk = None
         self.policy = None
-        self.preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]] | None = None
-        self.postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction] | None = None
 
     @property
     def running(self):
@@ -153,19 +146,6 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         start = time.perf_counter()
         self.policy = policy_class.from_pretrained(policy_specs.pretrained_name_or_path)
         self.policy.to(self.device)
-
-        # Load preprocessor and postprocessor, overriding device to match requested device
-        device_override = {"device": self.device}
-        self.preprocessor, self.postprocessor = make_pre_post_processors(
-            self.policy.config,
-            pretrained_path=policy_specs.pretrained_name_or_path,
-            preprocessor_overrides={
-                "device_processor": device_override,
-                "rename_observations_processor": {"rename_map": policy_specs.rename_map},
-            },
-            postprocessor_overrides={"device_processor": device_override},
-        )
-
         end = time.perf_counter()
 
         self.logger.info(f"Time taken to put policy on {self.device}: {end - start:.4f} seconds")
@@ -193,7 +173,7 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         # Calculate FPS metrics
         fps_metrics = self.fps_tracker.calculate_fps_metrics(obs_timestamp)
 
-        self.logger.debug(
+        self.logger.info(
             f"Received observation #{obs_timestep} | "
             f"Avg FPS: {fps_metrics['avg_fps']:.2f} | "  # fps at which observations are received from client
             f"Target: {fps_metrics['target_fps']:.2f} | "
@@ -209,7 +189,7 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         if not self._enqueue_observation(
             timed_observation  # wrapping a RawObservation
         ):
-            self.logger.debug(f"Observation #{obs_timestep} has been filtered out")
+            self.logger.info(f"Observation #{obs_timestep} has been filtered out")
 
         return services_pb2.Empty()
 
@@ -321,6 +301,23 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
             for i, action in enumerate(action_chunk)
         ]
 
+    def _prepare_observation(self, observation_t: TimedObservation) -> Observation:
+        """
+        Prepare observation, ready for policy inference.
+        E.g.: To keep observation sampling rate high (and network packet tiny) we send int8 [0,255] images from the
+        client and then convert them to float32 [0,1] images here, before running inference.
+        """
+        # RawObservation from robot.get_observation() - wrong keys, wrong dtype, wrong image shape
+        observation: Observation = raw_observation_to_observation(
+            observation_t.get_observation(),
+            self.lerobot_features,
+            self.policy_image_features,
+            self.device,
+        )
+        # processed Observation - right keys, right dtype, right image shape
+
+        return observation
+
     def _get_action_chunk(self, observation: dict[str, torch.Tensor]) -> torch.Tensor:
         """Get an action chunk from the policy. The chunk contains only"""
         chunk = self.policy.predict_action_chunk(observation)
@@ -330,76 +327,44 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         return chunk[:, : self.actions_per_chunk, :]
 
     def _predict_action_chunk(self, observation_t: TimedObservation) -> list[TimedAction]:
-        """Predict an action chunk based on an observation.
+        """Predict an action chunk based on an observation"""
+        inference_starts = time.perf_counter()
 
-        Pipeline:
-        1. Convert raw observation to LeRobot format
-        2. Apply preprocessor (tokenization, normalization, batching, device placement)
-        3. Run policy inference to get action chunk
-        4. Apply postprocessor (unnormalization, device movement)
-        5. Convert to TimedAction list
-        """
         """1. Prepare observation"""
-        start_prepare = time.perf_counter()
-        observation: Observation = raw_observation_to_observation(
-            observation_t.get_observation(),
-            self.lerobot_features,
-            self.policy_image_features,
-        )
-        prepare_time = time.perf_counter() - start_prepare
+        start_time = time.perf_counter()
+        observation = self._prepare_observation(observation_t)
+        preprocessing_time = time.perf_counter() - start_time
 
-        """2. Apply preprocessor"""
-        start_preprocess = time.perf_counter()
-        observation = self.preprocessor(observation)
         self.last_processed_obs: TimedObservation = observation_t
-        preprocessing_time = time.perf_counter() - start_preprocess
 
-        """3. Get action chunk"""
-        start_inference = time.perf_counter()
+        """2. Get action chunk"""
+        start_time = time.perf_counter()
         action_tensor = self._get_action_chunk(observation)
-        inference_time = time.perf_counter() - start_inference
-        self.logger.info(
-            f"Preprocessing and inference took {inference_time:.4f}s, action shape: {action_tensor.shape}"
-        )
+        inference_time = time.perf_counter() - start_time
 
-        """4. Apply postprocessor"""
-        # Apply postprocessor (handles unnormalization and device movement)
-        # Postprocessor expects (B, action_dim) per action, but we have (B, chunk_size, action_dim)
-        # So we process each action in the chunk individually
-        start_postprocess = time.perf_counter()
-        _, chunk_size, _ = action_tensor.shape
+        """3. Post-inference processing"""
+        start_time = time.perf_counter()
+        # Move to CPU before serializing
+        action_tensor = action_tensor.cpu().squeeze(0)
 
-        # Process each action in the chunk
-        processed_actions = []
-        for i in range(chunk_size):
-            # Extract action at timestep i: (B, action_dim)
-            single_action = action_tensor[:, i, :]
-            processed_action = self.postprocessor(single_action)
-            processed_actions.append(processed_action)
-
-        # Stack back to (B, chunk_size, action_dim), then remove batch dim
-        action_tensor = torch.stack(processed_actions, dim=1).squeeze(0)
-        self.logger.debug(f"Postprocessed action shape: {action_tensor.shape}")
-
-        """5. Convert to TimedAction list"""
         action_chunk = self._time_action_chunk(
             observation_t.get_timestamp(), list(action_tensor), observation_t.get_timestep()
         )
-        postprocess_stops = time.perf_counter()
-        postprocessing_time = postprocess_stops - start_postprocess
+        postprocessing_time = time.perf_counter() - start_time
+        inference_stops = time.perf_counter()
 
         self.logger.info(
-            f"Observation {observation_t.get_timestep()} | "
-            f"Total time: {1000 * (postprocess_stops - start_prepare):.2f}ms"
+            f"Observation {observation_t.get_timestep()} |"
+            f"Inference time: {1000 * (inference_stops - inference_starts):.2f}ms"
         )
 
+        # full-process latency breakdown for debugging purposes
         self.logger.debug(
             f"Observation {observation_t.get_timestep()} | "
-            f"Prepare time: {1000 * prepare_time:.2f}ms | "
-            f"Preprocessing time: {1000 * preprocessing_time:.2f}ms | "
-            f"Inference time: {1000 * inference_time:.2f}ms | "
-            f"Postprocessing time: {1000 * postprocessing_time:.2f}ms | "
-            f"Total time: {1000 * (postprocess_stops - start_prepare):.2f}ms"
+            f"Preprocessing time: {1000 * (preprocessing_time - inference_starts):.2f}ms | "
+            f"Inference time: {1000 * (inference_time - preprocessing_time):.2f}ms | "
+            f"Postprocessing time: {1000 * (postprocessing_time - inference_time):.2f}ms | "
+            f"Total time: {1000 * (postprocessing_time - inference_starts):.2f}ms"
         )
 
         return action_chunk

src/lerobot/async_inference/robot_client.py

@@ -48,10 +48,10 @@ import torch
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.configs.policies import PreTrainedConfig
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
-    bi_so100_follower,
     koch_follower,
     make_robot_from_config,
     so100_follower,
@@ -75,6 +75,7 @@ from .helpers import (
     TimedObservation,
     get_logger,
     map_robot_keys_to_lerobot_features,
+    validate_robot_cameras_for_policy,
     visualize_action_queue_size,
 )
 
@@ -96,6 +97,14 @@ class RobotClient:
 
         lerobot_features = map_robot_keys_to_lerobot_features(self.robot)
 
+        if config.verify_robot_cameras:
+            # Load policy config for validation
+            policy_config = PreTrainedConfig.from_pretrained(config.pretrained_name_or_path)
+            policy_image_features = policy_config.image_features
+
+            # The cameras specified for inference must match the one supported by the policy chosen
+            validate_robot_cameras_for_policy(lerobot_features, policy_image_features)
+
         # Use environment variable if server_address is not provided in config
         self.server_address = config.server_address
 
@@ -205,7 +214,7 @@ class RobotClient:
             )
             _ = self.stub.SendObservations(observation_iterator)
             obs_timestep = obs.get_timestep()
-            self.logger.debug(f"Sent observation #{obs_timestep} | ")
+            self.logger.info(f"Sent observation #{obs_timestep} | ")
 
             return True
 
@@ -458,7 +467,7 @@ class RobotClient:
             if self._ready_to_send_observation():
                 _captured_observation = self.control_loop_observation(task, verbose)
 
-            self.logger.debug(f"Control loop (ms): {(time.perf_counter() - control_loop_start) * 1000:.2f}")
+            self.logger.info(f"Control loop (ms): {(time.perf_counter() - control_loop_start) * 1000:.2f}")
             # Dynamically adjust sleep time to maintain the desired control frequency
             time.sleep(max(0, self.config.environment_dt - (time.perf_counter() - control_loop_start)))
 

src/lerobot/cameras/camera.py

@@ -17,7 +17,7 @@
 import abc
 from typing import Any
 
-from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
+import numpy as np
 
 from .configs import CameraConfig, ColorMode
 
@@ -89,7 +89,7 @@ class Camera(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
+    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
         """Capture and return a single frame from the camera.
 
         Args:
@@ -102,7 +102,7 @@ class Camera(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def async_read(self, timeout_ms: float = ...) -> NDArray[Any]:
+    def async_read(self, timeout_ms: float = ...) -> np.ndarray:
         """Asynchronously capture and return a single frame from the camera.
 
         Args:

src/lerobot/cameras/configs.py

@@ -18,7 +18,7 @@ import abc
 from dataclasses import dataclass
 from enum import Enum
 
-import draccus  # type: ignore  # TODO: add type stubs for draccus
+import draccus
 
 
 class ColorMode(str, Enum):
@@ -34,11 +34,11 @@ class Cv2Rotation(int, Enum):
 
 
 @dataclass(kw_only=True)
-class CameraConfig(draccus.ChoiceRegistry, abc.ABC):  # type: ignore  # TODO: add type stubs for draccus
+class CameraConfig(draccus.ChoiceRegistry, abc.ABC):
     fps: int | None = None
     width: int | None = None
     height: int | None = None
 
     @property
     def type(self) -> str:
-        return str(self.get_choice_name(self.__class__))
+        return self.get_choice_name(self.__class__)

src/lerobot/cameras/opencv/__init__.py

@@ -14,5 +14,3 @@
 
 from .camera_opencv import OpenCVCamera
 from .configuration_opencv import OpenCVCameraConfig
-
-__all__ = ["OpenCVCamera", "OpenCVCameraConfig"]

src/lerobot/cameras/opencv/camera_opencv.py

@@ -25,12 +25,11 @@ from pathlib import Path
 from threading import Event, Lock, Thread
 from typing import Any
 
-from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
-
 # Fix MSMF hardware transform compatibility for Windows before importing cv2
 if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS" not in os.environ:
     os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
-import cv2  # type: ignore  # TODO: add type stubs for OpenCV
+import cv2
+import numpy as np
 
 from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
@@ -122,7 +121,7 @@ class OpenCVCamera(Camera):
         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_frame: np.ndarray | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
@@ -141,7 +140,7 @@ class OpenCVCamera(Camera):
         """Checks if the camera is currently connected and opened."""
         return isinstance(self.videocapture, cv2.VideoCapture) and self.videocapture.isOpened()
 
-    def connect(self, warmup: bool = True) -> None:
+    def connect(self, warmup: bool = True):
         """
         Connects to the OpenCV camera specified in the configuration.
 
@@ -181,14 +180,12 @@ class OpenCVCamera(Camera):
 
     def _configure_capture_settings(self) -> None:
         """
-        Applies the specified FOURCC, FPS, width, and height settings to the connected camera.
+        Applies the specified FPS, width, and height settings to the connected camera.
 
         This method attempts to set the camera properties via OpenCV. It checks if
         the camera successfully applied the settings and raises an error if not.
-        FOURCC is set first (if specified) as it can affect the available FPS and resolution options.
 
         Args:
-            fourcc: The desired FOURCC code (e.g., "MJPG", "YUYV"). If None, auto-detect.
             fps: The desired frames per second. If None, the setting is skipped.
             width: The desired capture width. If None, the setting is skipped.
             height: The desired capture height. If None, the setting is skipped.
@@ -202,11 +199,10 @@ class OpenCVCamera(Camera):
         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:
-            self._validate_fourcc()
-        if self.videocapture is None:
-            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
+        if self.fps is None:
+            self.fps = self.videocapture.get(cv2.CAP_PROP_FPS)
+        else:
+            self._validate_fps()
 
         default_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH)))
         default_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
@@ -220,56 +216,18 @@ class OpenCVCamera(Camera):
         else:
             self._validate_width_and_height()
 
-        if self.fps is None:
-            self.fps = self.videocapture.get(cv2.CAP_PROP_FPS)
-        else:
-            self._validate_fps()
-
     def _validate_fps(self) -> None:
         """Validates and sets the camera's frames per second (FPS)."""
 
-        if self.videocapture is None:
-            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
-
-        if self.fps is None:
-            raise ValueError(f"{self} FPS is not set")
-
         success = self.videocapture.set(cv2.CAP_PROP_FPS, float(self.fps))
         actual_fps = self.videocapture.get(cv2.CAP_PROP_FPS)
         # Use math.isclose for robust float comparison
         if not success or not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
             raise RuntimeError(f"{self} failed to set fps={self.fps} ({actual_fps=}).")
 
-    def _validate_fourcc(self) -> None:
-        """Validates and sets the camera's FOURCC code."""
-
-        fourcc_code = cv2.VideoWriter_fourcc(*self.config.fourcc)
-
-        if self.videocapture is None:
-            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
-
-        success = self.videocapture.set(cv2.CAP_PROP_FOURCC, fourcc_code)
-        actual_fourcc_code = self.videocapture.get(cv2.CAP_PROP_FOURCC)
-
-        # Convert actual FOURCC code back to string for comparison
-        actual_fourcc_code_int = int(actual_fourcc_code)
-        actual_fourcc = "".join([chr((actual_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])
-
-        if not success or actual_fourcc != self.config.fourcc:
-            logger.warning(
-                f"{self} failed to set fourcc={self.config.fourcc} (actual={actual_fourcc}, success={success}). "
-                f"Continuing with default format."
-            )
-
     def _validate_width_and_height(self) -> None:
         """Validates and sets the camera's frame capture width and height."""
 
-        if self.videocapture is None:
-            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
-
-        if self.capture_width is None or self.capture_height is None:
-            raise ValueError(f"{self} capture_width or capture_height is not set")
-
         width_success = self.videocapture.set(cv2.CAP_PROP_FRAME_WIDTH, float(self.capture_width))
         height_success = self.videocapture.set(cv2.CAP_PROP_FRAME_HEIGHT, float(self.capture_height))
 
@@ -300,12 +258,11 @@ class OpenCVCamera(Camera):
         """
         found_cameras_info = []
 
-        targets_to_scan: list[str | int]
         if platform.system() == "Linux":
             possible_paths = sorted(Path("/dev").glob("video*"), key=lambda p: p.name)
             targets_to_scan = [str(p) for p in possible_paths]
         else:
-            targets_to_scan = [int(i) for i in range(MAX_OPENCV_INDEX)]
+            targets_to_scan = list(range(MAX_OPENCV_INDEX))
 
         for target in targets_to_scan:
             camera = cv2.VideoCapture(target)
@@ -314,12 +271,6 @@ class OpenCVCamera(Camera):
                 default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
                 default_fps = camera.get(cv2.CAP_PROP_FPS)
                 default_format = camera.get(cv2.CAP_PROP_FORMAT)
-
-                # Get FOURCC code and convert to string
-                default_fourcc_code = camera.get(cv2.CAP_PROP_FOURCC)
-                default_fourcc_code_int = int(default_fourcc_code)
-                default_fourcc = "".join([chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])
-
                 camera_info = {
                     "name": f"OpenCV Camera @ {target}",
                     "type": "OpenCV",
@@ -327,7 +278,6 @@ class OpenCVCamera(Camera):
                     "backend_api": camera.getBackendName(),
                     "default_stream_profile": {
                         "format": default_format,
-                        "fourcc": default_fourcc,
                         "width": default_width,
                         "height": default_height,
                         "fps": default_fps,
@@ -339,7 +289,7 @@ class OpenCVCamera(Camera):
 
         return found_cameras_info
 
-    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
+    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
         """
         Reads a single frame synchronously from the camera.
 
@@ -367,9 +317,6 @@ class OpenCVCamera(Camera):
 
         start_time = time.perf_counter()
 
-        if self.videocapture is None:
-            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
-
         ret, frame = self.videocapture.read()
 
         if not ret or frame is None:
@@ -382,7 +329,7 @@ class OpenCVCamera(Camera):
 
         return processed_frame
 
-    def _postprocess_image(self, image: NDArray[Any], color_mode: ColorMode | None = None) -> NDArray[Any]:
+    def _postprocess_image(self, image: np.ndarray, color_mode: ColorMode | None = None) -> np.ndarray:
         """
         Applies color conversion, dimension validation, and rotation to a raw frame.
 
@@ -425,7 +372,7 @@ class OpenCVCamera(Camera):
 
         return processed_image
 
-    def _read_loop(self) -> None:
+    def _read_loop(self):
         """
         Internal loop run by the background thread for asynchronous reading.
 
@@ -436,9 +383,6 @@ class OpenCVCamera(Camera):
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
         """
-        if self.stop_event is None:
-            raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
-
         while not self.stop_event.is_set():
             try:
                 color_image = self.read()
@@ -475,7 +419,7 @@ class OpenCVCamera(Camera):
         self.thread = None
         self.stop_event = None
 
-    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
+    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
         """
         Reads the latest available frame asynchronously.
 
@@ -518,7 +462,7 @@ class OpenCVCamera(Camera):
 
         return frame
 
-    def disconnect(self) -> None:
+    def disconnect(self):
         """
         Disconnects from the camera and cleans up resources.
 

src/lerobot/cameras/opencv/configuration_opencv.py

@@ -17,8 +17,6 @@ from pathlib import Path
 
 from ..configs import CameraConfig, ColorMode, Cv2Rotation
 
-__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation"]
-
 
 @CameraConfig.register_subclass("opencv")
 @dataclass
@@ -35,9 +33,8 @@ class OpenCVCameraConfig(CameraConfig):
     OpenCVCameraConfig(0, 30, 1280, 720)   # 1280x720 @ 30FPS
     OpenCVCameraConfig(/dev/video4, 60, 640, 480)   # 640x480 @ 60FPS
 
-    # Advanced configurations with FOURCC format
-    OpenCVCameraConfig(128422271347, 30, 640, 480, rotation=Cv2Rotation.ROTATE_90, fourcc="MJPG")     # With 90° rotation and MJPG format
-    OpenCVCameraConfig(0, 30, 1280, 720, fourcc="YUYV")     # With YUYV format
+    # Advanced configurations
+    OpenCVCameraConfig(128422271347, 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)     # With 90° rotation
     ```
 
     Attributes:
@@ -49,21 +46,17 @@ class OpenCVCameraConfig(CameraConfig):
         color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
         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).
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.
-        - FOURCC codes must be 4-character strings (e.g., "MJPG", "YUYV"). Some common FOUCC codes: https://learn.microsoft.com/en-us/windows/win32/medfound/video-fourccs#fourcc-constants
-        - Setting FOURCC can help achieve higher frame rates on some cameras.
     """
 
     index_or_path: int | Path
     color_mode: ColorMode = ColorMode.RGB
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
-    fourcc: str | None = None
 
-    def __post_init__(self) -> None:
+    def __post_init__(self):
         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."
@@ -78,8 +71,3 @@ class OpenCVCameraConfig(CameraConfig):
             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(
-                f"`fourcc` must be a 4-character string (e.g., 'MJPG', 'YUYV'), but '{self.fourcc}' is provided."
-            )

src/lerobot/cameras/reachy2_camera/configuration_reachy2_camera.py

@@ -16,8 +16,6 @@ from dataclasses import dataclass
 
 from ..configs import CameraConfig, ColorMode
 
-__all__ = ["CameraConfig", "ColorMode", "Reachy2CameraConfig"]
-
 
 @CameraConfig.register_subclass("reachy2_camera")
 @dataclass
@@ -64,7 +62,7 @@ class Reachy2CameraConfig(CameraConfig):
     port: int = 50065
     # use_depth: bool = False
 
-    def __post_init__(self) -> None:
+    def __post_init__(self):
         if self.name not in ["teleop", "depth"]:
             raise ValueError(f"`name` is expected to be 'teleop' or 'depth', but {self.name} is provided.")
         if (self.name == "teleop" and self.image_type not in ["left", "right"]) or (

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -23,17 +23,13 @@ import time
 from threading import Event, Lock, Thread
 from typing import Any
 
-from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
-
 # Fix MSMF hardware transform compatibility for Windows before importing cv2
 if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS" not in os.environ:
     os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
-import cv2  # type: ignore  # TODO: add type stubs for OpenCV
-import numpy as np  # type: ignore  # TODO: add type stubs for numpy
-from reachy2_sdk.media.camera import CameraView  # type: ignore  # TODO: add type stubs for reachy2_sdk
-from reachy2_sdk.media.camera_manager import (  # type: ignore  # TODO: add type stubs for reachy2_sdk
-    CameraManager,
-)
+import cv2
+import numpy as np
+from reachy2_sdk.media.camera import CameraView
+from reachy2_sdk.media.camera_manager import CameraManager
 
 from lerobot.utils.errors import DeviceNotConnectedError
 
@@ -77,7 +73,7 @@ class Reachy2Camera(Camera):
         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_frame: np.ndarray | None = None
         self.new_frame_event: Event = Event()
 
     def __str__(self) -> str:
@@ -87,17 +83,13 @@ class Reachy2Camera(Camera):
     def is_connected(self) -> bool:
         """Checks if the camera is currently connected and opened."""
         if self.config.name == "teleop":
-            return bool(
-                self.cam_manager._grpc_connected and self.cam_manager.teleop if self.cam_manager else False
-            )
+            return self.cam_manager._grpc_connected and self.cam_manager.teleop if self.cam_manager else False
         elif self.config.name == "depth":
-            return bool(
-                self.cam_manager._grpc_connected and self.cam_manager.depth if self.cam_manager else False
-            )
+            return self.cam_manager._grpc_connected and self.cam_manager.depth if self.cam_manager else False
         else:
             raise ValueError(f"Invalid camera name '{self.config.name}'. Expected 'teleop' or 'depth'.")
 
-    def connect(self, warmup: bool = True) -> None:
+    def connect(self, warmup: bool = True):
         """
         Connects to the Reachy2 CameraManager as specified in the configuration.
         """
@@ -139,7 +131,7 @@ class Reachy2Camera(Camera):
         camera_manager.disconnect()
         return initialized_cameras
 
-    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
+    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
         """
         Reads a single frame synchronously from the camera.
 
@@ -160,7 +152,7 @@ class Reachy2Camera(Camera):
 
         start_time = time.perf_counter()
 
-        frame: NDArray[Any] = np.empty((0, 0, 3), dtype=np.uint8)
+        frame = None
 
         if self.cam_manager is None:
             raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -187,7 +179,7 @@ class Reachy2Camera(Camera):
 
         return frame
 
-    def _read_loop(self) -> None:
+    def _read_loop(self):
         """
         Internal loop run by the background thread for asynchronous reading.
 
@@ -198,9 +190,6 @@ class Reachy2Camera(Camera):
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
         """
-        if self.stop_event is None:
-            raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
-
         while not self.stop_event.is_set():
             try:
                 color_image = self.read()
@@ -237,7 +226,7 @@ class Reachy2Camera(Camera):
         self.thread = None
         self.stop_event = None
 
-    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
+    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
         """
         Reads the latest available frame asynchronously.
 
@@ -280,7 +269,7 @@ class Reachy2Camera(Camera):
 
         return frame
 
-    def disconnect(self) -> None:
+    def disconnect(self):
         """
         Stops the background read thread (if running).
 

src/lerobot/cameras/realsense/camera_realsense.py

@@ -21,12 +21,11 @@ import time
 from threading import Event, Lock, Thread
 from typing import Any
 
-import cv2  # type: ignore  # TODO: add type stubs for OpenCV
-import numpy as np  # type: ignore  # TODO: add type stubs for numpy
-from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
+import cv2
+import numpy as np
 
 try:
-    import pyrealsense2 as rs  # type: ignore  # TODO: add type stubs for pyrealsense2
+    import pyrealsense2 as rs
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
@@ -133,7 +132,7 @@ class RealSenseCamera(Camera):
         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_frame: np.ndarray | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
@@ -151,7 +150,7 @@ 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
 
-    def connect(self, warmup: bool = True) -> None:
+    def connect(self, warmup: bool = True):
         """
         Connects to the RealSense camera specified in the configuration.
 
@@ -265,7 +264,7 @@ class RealSenseCamera(Camera):
         serial_number = str(found_devices[0]["serial_number"])
         return serial_number
 
-    def _configure_rs_pipeline_config(self, rs_config: Any) -> None:
+    def _configure_rs_pipeline_config(self, rs_config):
         """Creates and configures the RealSense pipeline configuration object."""
         rs.config.enable_device(rs_config, self.serial_number)
 
@@ -294,9 +293,6 @@ class RealSenseCamera(Camera):
         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.")
-
         stream = self.rs_profile.get_stream(rs.stream.color).as_video_stream_profile()
 
         if self.fps is None:
@@ -312,7 +308,7 @@ class RealSenseCamera(Camera):
                 self.width, self.height = actual_width, actual_height
                 self.capture_width, self.capture_height = actual_width, actual_height
 
-    def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
+    def read_depth(self, timeout_ms: int = 200) -> np.ndarray:
         """
         Reads a single frame (depth) synchronously from the camera.
 
@@ -340,9 +336,6 @@ class RealSenseCamera(Camera):
 
         start_time = time.perf_counter()
 
-        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=timeout_ms)
 
         if not ret or frame is None:
@@ -358,7 +351,7 @@ class RealSenseCamera(Camera):
 
         return depth_map_processed
 
-    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> NDArray[Any]:
+    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> np.ndarray:
         """
         Reads a single frame (color) synchronously from the camera.
 
@@ -383,9 +376,6 @@ class RealSenseCamera(Camera):
 
         start_time = time.perf_counter()
 
-        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=timeout_ms)
 
         if not ret or frame is None:
@@ -402,8 +392,8 @@ class RealSenseCamera(Camera):
         return color_image_processed
 
     def _postprocess_image(
-        self, image: NDArray[Any], color_mode: ColorMode | None = None, depth_frame: bool = False
-    ) -> NDArray[Any]:
+        self, image: np.ndarray, color_mode: ColorMode | None = None, depth_frame: bool = False
+    ) -> np.ndarray:
         """
         Applies color conversion, dimension validation, and rotation to a raw color frame.
 
@@ -448,7 +438,7 @@ class RealSenseCamera(Camera):
 
         return processed_image
 
-    def _read_loop(self) -> None:
+    def _read_loop(self):
         """
         Internal loop run by the background thread for asynchronous reading.
 
@@ -459,9 +449,6 @@ class RealSenseCamera(Camera):
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
         """
-        if self.stop_event is None:
-            raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
-
         while not self.stop_event.is_set():
             try:
                 color_image = self.read(timeout_ms=500)
@@ -487,7 +474,7 @@ class RealSenseCamera(Camera):
         self.thread.daemon = True
         self.thread.start()
 
-    def _stop_read_thread(self) -> None:
+    def _stop_read_thread(self):
         """Signals the background read thread to stop and waits for it to join."""
         if self.stop_event is not None:
             self.stop_event.set()
@@ -499,7 +486,7 @@ class RealSenseCamera(Camera):
         self.stop_event = None
 
     # NOTE(Steven): Missing implementation for depth for now
-    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
+    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
         """
         Reads the latest available frame data (color) asynchronously.
 
@@ -542,7 +529,7 @@ class RealSenseCamera(Camera):
 
         return frame
 
-    def disconnect(self) -> None:
+    def disconnect(self):
         """
         Disconnects from the camera, stops the pipeline, and cleans up resources.
 

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -59,7 +59,7 @@ class RealSenseCameraConfig(CameraConfig):
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
 
-    def __post_init__(self) -> None:
+    def __post_init__(self):
         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."

src/lerobot/cameras/utils.py

@@ -15,19 +15,15 @@
 # limitations under the License.
 
 import platform
-from typing import cast
-
-from lerobot.utils.import_utils import make_device_from_device_class
 
 from .camera import Camera
 from .configs import CameraConfig, Cv2Rotation
 
 
 def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[str, Camera]:
-    cameras: dict[str, Camera] = {}
+    cameras = {}
 
     for key, cfg in camera_configs.items():
-        # TODO(Steven): Consider just using the make_device_from_device_class for all types
         if cfg.type == "opencv":
             from .opencv import OpenCVCamera
 
@@ -44,23 +40,20 @@ def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[s
             cameras[key] = Reachy2Camera(cfg)
 
         else:
-            try:
-                cameras[key] = cast(Camera, make_device_from_device_class(cfg))
-            except Exception as e:
-                raise ValueError(f"Error creating camera {key} with config {cfg}: {e}") from e
+            raise ValueError(f"The camera type '{cfg.type}' is not valid.")
 
     return cameras
 
 
 def get_cv2_rotation(rotation: Cv2Rotation) -> int | None:
-    import cv2  # type: ignore  # TODO: add type stubs for OpenCV
+    import cv2
 
     if rotation == Cv2Rotation.ROTATE_90:
-        return int(cv2.ROTATE_90_CLOCKWISE)
+        return cv2.ROTATE_90_CLOCKWISE
     elif rotation == Cv2Rotation.ROTATE_180:
-        return int(cv2.ROTATE_180)
+        return cv2.ROTATE_180
     elif rotation == Cv2Rotation.ROTATE_270:
-        return int(cv2.ROTATE_90_COUNTERCLOCKWISE)
+        return cv2.ROTATE_90_COUNTERCLOCKWISE
     else:
         return None
 
@@ -69,8 +62,8 @@ def get_cv2_backend() -> int:
     import cv2
 
     if platform.system() == "Windows":
-        return int(cv2.CAP_MSMF)  # Use MSMF for Windows instead of AVFOUNDATION
+        return 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)
+        return cv2.CAP_ANY

src/lerobot/configs/default.py

@@ -57,7 +57,7 @@ class EvalConfig:
     # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
     use_async_envs: bool = False
 
-    def __post_init__(self) -> None:
+    def __post_init__(self):
         if self.batch_size > self.n_episodes:
             raise ValueError(
                 "The eval batch size is greater than the number of eval episodes "

src/lerobot/configs/eval.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 import datetime as dt
+import logging
 from dataclasses import dataclass, field
-from logging import getLogger
 from pathlib import Path
 
 from lerobot import envs, policies  # noqa: F401
@@ -22,8 +22,6 @@ from lerobot.configs import parser
 from lerobot.configs.default import EvalConfig
 from lerobot.configs.policies import PreTrainedConfig
 
-logger = getLogger(__name__)
-
 
 @dataclass
 class EvalPipelineConfig:
@@ -36,31 +34,25 @@ class EvalPipelineConfig:
     output_dir: Path | None = None
     job_name: str | None = None
     seed: int | None = 1000
-    # Rename map for the observation to override the image and state keys
-    rename_map: dict[str, str] = field(default_factory=dict)
 
-    def __post_init__(self) -> None:
+    def __post_init__(self):
         # HACK: We parse again the cli args here to get the pretrained path if there was one.
         policy_path = parser.get_path_arg("policy")
         if policy_path:
             cli_overrides = parser.get_cli_overrides("policy")
             self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
-            self.policy.pretrained_path = Path(policy_path)
+            self.policy.pretrained_path = policy_path
 
         else:
-            logger.warning(
+            logging.warning(
                 "No pretrained path was provided, evaluated policy will be built from scratch (random weights)."
             )
 
         if not self.job_name:
             if self.env is None:
-                self.job_name = f"{self.policy.type if self.policy is not None else 'scratch'}"
+                self.job_name = f"{self.policy.type}"
             else:
-                self.job_name = (
-                    f"{self.env.type}_{self.policy.type if self.policy is not None else 'scratch'}"
-                )
-
-            logger.warning(f"No job name provided, using '{self.job_name}' as job name.")
+                self.job_name = f"{self.env.type}_{self.policy.type}"
 
         if not self.output_dir:
             now = dt.datetime.now()

src/lerobot/configs/parser.py

@@ -16,19 +16,14 @@ import inspect
 import pkgutil
 import sys
 from argparse import ArgumentError
-from collections.abc import Callable, Iterable, Sequence
+from collections.abc import Sequence
 from functools import wraps
 from pathlib import Path
-from pkgutil import ModuleInfo
-from types import ModuleType
-from typing import Any, TypeVar, cast
 
 import draccus
 
 from lerobot.utils.utils import has_method
 
-F = TypeVar("F", bound=Callable[..., object])
-
 PATH_KEY = "path"
 PLUGIN_DISCOVERY_SUFFIX = "discover_packages_path"
 
@@ -65,7 +60,7 @@ def parse_arg(arg_name: str, args: Sequence[str] | None = None) -> str | None:
     return None
 
 
-def parse_plugin_args(plugin_arg_suffix: str, args: Sequence[str]) -> dict[str, str]:
+def parse_plugin_args(plugin_arg_suffix: str, args: Sequence[str]) -> dict:
     """Parse plugin-related arguments from command-line arguments.
 
     This function extracts arguments from command-line arguments that match a specified suffix pattern.
@@ -132,7 +127,7 @@ def load_plugin(plugin_path: str) -> None:
             f"Failed to load plugin '{plugin_path}'. Verify the path and installation: {str(e)}"
         ) from e
 
-    def iter_namespace(ns_pkg: ModuleType) -> Iterable[ModuleInfo]:
+    def iter_namespace(ns_pkg):
         return pkgutil.iter_modules(ns_pkg.__path__, ns_pkg.__name__ + ".")
 
     try:
@@ -153,8 +148,6 @@ def get_type_arg(field_name: str, args: Sequence[str] | None = None) -> str | No
 
 
 def filter_arg(field_to_filter: str, args: Sequence[str] | None = None) -> list[str]:
-    if args is None:
-        return []
     return [arg for arg in args if not arg.startswith(f"--{field_to_filter}=")]
 
 
@@ -178,8 +171,7 @@ def filter_path_args(fields_to_filter: str | list[str], args: Sequence[str] | No
     if isinstance(fields_to_filter, str):
         fields_to_filter = [fields_to_filter]
 
-    filtered_args = [] if args is None else list(args)
-
+    filtered_args = args
     for field in fields_to_filter:
         if get_path_arg(field, args):
             if get_type_arg(field, args):
@@ -192,7 +184,7 @@ def filter_path_args(fields_to_filter: str | list[str], args: Sequence[str] | No
     return filtered_args
 
 
-def wrap(config_path: Path | None = None) -> Callable[[F], F]:
+def wrap(config_path: Path | None = None):
     """
     HACK: Similar to draccus.wrap but does three additional things:
         - Will remove '.path' arguments from CLI in order to process them later on.
@@ -203,9 +195,9 @@ def wrap(config_path: Path | None = None) -> Callable[[F], F]:
             from the CLI '.type' arguments
     """
 
-    def wrapper_outer(fn: F) -> F:
+    def wrapper_outer(fn):
         @wraps(fn)
-        def wrapper_inner(*args: Any, **kwargs: Any) -> Any:
+        def wrapper_inner(*args, **kwargs):
             argspec = inspect.getfullargspec(fn)
             argtype = argspec.annotations[argspec.args[0]]
             if len(args) > 0 and type(args[0]) is argtype:
@@ -233,6 +225,6 @@ def wrap(config_path: Path | None = None) -> Callable[[F], F]:
             response = fn(cfg, *args, **kwargs)
             return response
 
-        return cast(F, wrapper_inner)
+        return wrapper_inner
 
-    return cast(Callable[[F], F], wrapper_outer)
+    return wrapper_outer

src/lerobot/configs/policies.py

@@ -14,12 +14,12 @@
 import abc
 import builtins
 import json
+import logging
 import os
 import tempfile
 from dataclasses import dataclass, field
-from logging import getLogger
 from pathlib import Path
-from typing import Any, TypeVar
+from typing import TypeVar
 
 import draccus
 from huggingface_hub import hf_hub_download
@@ -34,11 +34,10 @@ from lerobot.utils.hub import HubMixin
 from lerobot.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
 
 T = TypeVar("T", bound="PreTrainedConfig")
-logger = getLogger(__name__)
 
 
 @dataclass
-class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: ignore[misc,name-defined] #TODO: draccus issue
+class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
     """
     Base configuration class for policy models.
 
@@ -58,12 +57,12 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
     input_features: dict[str, PolicyFeature] = field(default_factory=dict)
     output_features: dict[str, PolicyFeature] = field(default_factory=dict)
 
-    device: str | None = None  # e.g. "cuda", "cuda:0", "cpu", or "mps"
+    device: str | None = None  # cuda | cpu | mp
     # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
     # automatic gradient scaling is used.
     use_amp: bool = False
 
-    push_to_hub: bool = True  # type: ignore[assignment] # TODO: use a different name to avoid override
+    push_to_hub: bool = True
     repo_id: str | None = None
 
     # Upload on private repository on the Hugging Face hub.
@@ -74,41 +73,38 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
     license: str | None = None
     # Either the repo ID of a model hosted on the Hub or a path to a directory containing weights
     # saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch.
-    pretrained_path: Path | None = None
+    pretrained_path: str | None = None
 
-    def __post_init__(self) -> None:
+    def __post_init__(self):
         if not self.device or not is_torch_device_available(self.device):
             auto_device = auto_select_torch_device()
-            logger.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.")
+            logging.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.")
             self.device = auto_device.type
 
         # Automatically deactivate AMP if necessary
         if self.use_amp and not is_amp_available(self.device):
-            logger.warning(
+            logging.warning(
                 f"Automatic Mixed Precision (amp) is not available on device '{self.device}'. Deactivating AMP."
             )
             self.use_amp = False
 
     @property
     def type(self) -> str:
-        choice_name = self.get_choice_name(self.__class__)
-        if not isinstance(choice_name, str):
-            raise TypeError(f"Expected string from get_choice_name, got {type(choice_name)}")
-        return choice_name
+        return self.get_choice_name(self.__class__)
 
     @property
     @abc.abstractmethod
-    def observation_delta_indices(self) -> list | None:  # type: ignore[type-arg] #TODO: No implementation
+    def observation_delta_indices(self) -> list | None:
         raise NotImplementedError
 
     @property
     @abc.abstractmethod
-    def action_delta_indices(self) -> list | None:  # type: ignore[type-arg]    #TODO: No implementation
+    def action_delta_indices(self) -> list | None:
         raise NotImplementedError
 
     @property
     @abc.abstractmethod
-    def reward_delta_indices(self) -> list | None:  # type: ignore[type-arg]    #TODO: No implementation
+    def reward_delta_indices(self) -> list | None:
         raise NotImplementedError
 
     @abc.abstractmethod
@@ -158,13 +154,13 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
         pretrained_name_or_path: str | Path,
         *,
         force_download: bool = False,
-        resume_download: bool | None = None,
-        proxies: dict[Any, Any] | None = None,
+        resume_download: bool = None,
+        proxies: dict | None = None,
         token: str | bool | None = None,
         cache_dir: str | Path | None = None,
         local_files_only: bool = False,
         revision: str | None = None,
-        **policy_kwargs: Any,
+        **policy_kwargs,
     ) -> T:
         model_id = str(pretrained_name_or_path)
         config_file: str | None = None
@@ -172,7 +168,7 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
             if CONFIG_NAME in os.listdir(model_id):
                 config_file = os.path.join(model_id, CONFIG_NAME)
             else:
-                logger.error(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}")
+                print(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}")
         else:
             try:
                 config_file = hf_hub_download(
@@ -198,9 +194,6 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
         with draccus.config_type("json"):
             orig_config = draccus.parse(cls, config_file, args=[])
 
-        if config_file is None:
-            raise FileNotFoundError(f"{CONFIG_NAME} not found in {model_id}")
-
         with open(config_file) as f:
             config = json.load(f)
 

src/lerobot/configs/train.py

@@ -16,7 +16,6 @@ import datetime as dt
 import os
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any
 
 import draccus
 from huggingface_hub import hf_hub_download
@@ -64,18 +63,18 @@ class TrainPipelineConfig(HubMixin):
     scheduler: LRSchedulerConfig | None = None
     eval: EvalConfig = field(default_factory=EvalConfig)
     wandb: WandBConfig = field(default_factory=WandBConfig)
-    checkpoint_path: Path | None = field(init=False, default=None)
-    # Rename map for the observation to override the image and state keys
-    rename_map: dict[str, str] = field(default_factory=dict)
 
-    def validate(self) -> None:
+    def __post_init__(self):
+        self.checkpoint_path = None
+
+    def validate(self):
         # HACK: We parse again the cli args here to get the pretrained paths if there was some.
         policy_path = parser.get_path_arg("policy")
         if policy_path:
             # Only load the policy config
             cli_overrides = parser.get_cli_overrides("policy")
             self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
-            self.policy.pretrained_path = Path(policy_path)
+            self.policy.pretrained_path = policy_path
         elif self.resume:
             # The entire train config is already loaded, we just need to get the checkpoint dir
             config_path = parser.parse_arg("config_path")
@@ -83,22 +82,14 @@ class TrainPipelineConfig(HubMixin):
                 raise ValueError(
                     f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
                 )
-
             if not Path(config_path).resolve().exists():
                 raise NotADirectoryError(
                     f"{config_path=} is expected to be a local path. "
                     "Resuming from the hub is not supported for now."
                 )
-
-            policy_dir = Path(config_path).parent
-            if self.policy is not None:
-                self.policy.pretrained_path = policy_dir
-            self.checkpoint_path = policy_dir.parent
-
-        if self.policy is None:
-            raise ValueError(
-                "Policy is not configured. Please specify a pretrained policy with `--policy.path`."
-            )
+            policy_path = Path(config_path).parent
+            self.policy.pretrained_path = policy_path
+            self.checkpoint_path = policy_path.parent
 
         if not self.job_name:
             if self.env is None:
@@ -135,8 +126,8 @@ class TrainPipelineConfig(HubMixin):
         """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
         return ["policy"]
 
-    def to_dict(self) -> dict[str, Any]:
-        return draccus.encode(self)  # type: ignore[no-any-return]  # because of the third-party library draccus uses Any as the return type
+    def to_dict(self) -> dict:
+        return draccus.encode(self)
 
     def _save_pretrained(self, save_directory: Path) -> None:
         with open(save_directory / TRAIN_CONFIG_NAME, "w") as f, draccus.config_type("json"):
@@ -148,13 +139,13 @@ class TrainPipelineConfig(HubMixin):
         pretrained_name_or_path: str | Path,
         *,
         force_download: bool = False,
-        resume_download: bool | None = None,
-        proxies: dict[Any, Any] | None = None,
+        resume_download: bool = None,
+        proxies: dict | None = None,
         token: str | bool | None = None,
         cache_dir: str | Path | None = None,
         local_files_only: bool = False,
         revision: str | None = None,
-        **kwargs: Any,
+        **kwargs,
     ) -> "TrainPipelineConfig":
         model_id = str(pretrained_name_or_path)
         config_file: str | None = None
@@ -190,6 +181,4 @@ class TrainPipelineConfig(HubMixin):
 
 @dataclass(kw_only=True)
 class TrainRLServerPipelineConfig(TrainPipelineConfig):
-    # NOTE: In RL, we don't need an offline dataset
-    # TODO: Make `TrainPipelineConfig.dataset` optional
-    dataset: DatasetConfig | None = None  # type: ignore[assignment] # because the parent class has made it's type non-optional
+    dataset: DatasetConfig | None = None  # NOTE: In RL, we don't need an offline dataset

src/lerobot/configs/types.py

@@ -42,4 +42,4 @@ class NormalizationMode(str, Enum):
 @dataclass
 class PolicyFeature:
     type: FeatureType
-    shape: tuple[int, ...]
+    shape: tuple

src/lerobot/datasets/aggregate.py

@@ -31,15 +31,15 @@ from lerobot.datasets.utils import (
     DEFAULT_EPISODES_PATH,
     DEFAULT_VIDEO_FILE_SIZE_IN_MB,
     DEFAULT_VIDEO_PATH,
-    get_file_size_in_mb,
     get_parquet_file_size_in_mb,
+    get_video_size_in_mb,
     to_parquet_with_hf_images,
     update_chunk_file_indices,
     write_info,
     write_stats,
     write_tasks,
 )
-from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s
+from lerobot.datasets.video_utils import concatenate_video_files
 
 
 def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
@@ -130,34 +130,10 @@ def update_meta_data(
     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"
-        orig_file_col = f"videos/{key}/file_index"
-        df["_orig_chunk"] = df[orig_chunk_col].copy()
-        df["_orig_file"] = df[orig_file_col].copy()
-
-        # Update chunk and file indices to point to destination
-        df[orig_chunk_col] = video_idx["chunk"]
-        df[orig_file_col] = video_idx["file"]
-
-        # Apply per-source-file timestamp offsets
-        src_to_offset = video_idx.get("src_to_offset", {})
-        if src_to_offset:
-            # Apply offset based on original source file
-            for idx in df.index:
-                src_key = (df.at[idx, "_orig_chunk"], 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
-        else:
-            # Fallback to simple offset (for backward compatibility)
-            df[f"videos/{key}/from_timestamp"] = (
-                df[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"]
-            )
-            df[f"videos/{key}/to_timestamp"] = df[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"]
-
-        # Clean up temporary columns
-        df = df.drop(columns=["_orig_chunk", "_orig_file"])
+        df[f"videos/{key}/chunk_index"] = df[f"videos/{key}/chunk_index"] + video_idx["chunk"]
+        df[f"videos/{key}/file_index"] = df[f"videos/{key}/file_index"] + video_idx["file"]
+        df[f"videos/{key}/from_timestamp"] = df[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"]
+        df[f"videos/{key}/to_timestamp"] = df[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"]
 
     df["dataset_from_index"] = df["dataset_from_index"] + dst_meta.info["total_frames"]
     df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info["total_frames"]
@@ -217,10 +193,6 @@ def aggregate_datasets(
         robot_type=robot_type,
         features=features,
         root=aggr_root,
-        use_videos=len(video_keys) > 0,
-        chunks_size=chunk_size,
-        data_files_size_in_mb=data_files_size_in_mb,
-        video_files_size_in_mb=video_files_size_in_mb,
     )
 
     logging.info("Find all tasks")
@@ -264,11 +236,6 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
     Returns:
         dict: Updated videos_idx with current chunk and file indices.
     """
-    for key in videos_idx:
-        videos_idx[key]["episode_duration"] = 0
-        # Track offset for each source (chunk, file) pair
-        videos_idx[key]["src_to_offset"] = {}
-
     for key, video_idx in videos_idx.items():
         unique_chunk_file_pairs = {
             (chunk, file)
@@ -282,7 +249,6 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
 
         chunk_idx = video_idx["chunk"]
         file_idx = video_idx["file"]
-        current_offset = video_idx["latest_duration"]
 
         for src_chunk_idx, src_file_idx in unique_chunk_file_pairs:
             src_path = src_meta.root / DEFAULT_VIDEO_PATH.format(
@@ -297,25 +263,21 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
                 file_index=file_idx,
             )
 
-            src_duration = get_video_duration_in_s(src_path)
+            # If a new file is created, we don't want to increment the latest_duration
+            update_latest_duration = False
 
             if not dst_path.exists():
-                # Store offset before incrementing
-                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
+                # First write to this destination file
                 dst_path.parent.mkdir(parents=True, exist_ok=True)
                 shutil.copy(str(src_path), str(dst_path))
-                videos_idx[key]["episode_duration"] += src_duration
-                current_offset += src_duration
-                continue
+                continue  # not accumulating further, already copied the file in place
 
             # Check file sizes before appending
-            src_size = get_file_size_in_mb(src_path)
-            dst_size = get_file_size_in_mb(dst_path)
+            src_size = get_video_size_in_mb(src_path)
+            dst_size = get_video_size_in_mb(dst_path)
 
             if dst_size + src_size >= video_files_size_in_mb:
-                # Rotate to a new file, this source becomes start of new destination
-                # So its offset should be 0
-                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = 0
+                # Rotate to a new chunk/file
                 chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, chunk_size)
                 dst_path = dst_meta.root / DEFAULT_VIDEO_PATH.format(
                     video_key=key,
@@ -324,22 +286,25 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
                 )
                 dst_path.parent.mkdir(parents=True, exist_ok=True)
                 shutil.copy(str(src_path), str(dst_path))
-                # Reset offset for next file
-                current_offset = src_duration
             else:
-                # Append to existing video file - use current accumulated offset
-                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
+                # Get the timestamps shift for this video
+                timestamps_shift_s = dst_meta.info["total_frames"] / dst_meta.info["fps"]
+
+                # Append to existing video file
                 concatenate_video_files(
                     [dst_path, src_path],
                     dst_path,
                 )
-                current_offset += src_duration
-
-            videos_idx[key]["episode_duration"] += src_duration
+                # Update the latest_duration when appending (shifts timestamps!)
+                update_latest_duration = not update_latest_duration
 
+        # Update the videos_idx with the final chunk and file indices for this key
         videos_idx[key]["chunk"] = chunk_idx
         videos_idx[key]["file"] = file_idx
 
+        if update_latest_duration:
+            videos_idx[key]["latest_duration"] += timestamps_shift_s
+
     return videos_idx
 
 
@@ -424,6 +389,9 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
             videos_idx,
         )
 
+        for k in videos_idx:
+            videos_idx[k]["latest_duration"] += videos_idx[k]["episode_duration"]
+
         meta_idx = append_or_create_parquet_file(
             df,
             src_path,
@@ -435,10 +403,6 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
             aggr_root=dst_meta.root,
         )
 
-    # Increment latest_duration by the total duration added from this source dataset
-    for k in videos_idx:
-        videos_idx[k]["latest_duration"] += videos_idx[k]["episode_duration"]
-
     return meta_idx
 
 

src/lerobot/datasets/image_writer.py

@@ -68,30 +68,7 @@ def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True)
     return PIL.Image.fromarray(image_array)
 
 
-def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1):
-    """
-    Saves a NumPy array or PIL Image to a file.
-
-    This function handles both NumPy arrays and PIL Image objects, converting
-    the former to a PIL Image before saving. It includes error handling for
-    the save operation.
-
-    Args:
-        image (np.ndarray | PIL.Image.Image): The image data to save.
-        fpath (Path): The destination file path for the image.
-        compress_level (int, optional): The compression level for the saved
-            image, as used by PIL.Image.save(). Defaults to 1.
-            Refer to: https://github.com/huggingface/lerobot/pull/2135
-            for more details on the default value rationale.
-
-    Raises:
-        TypeError: If the input 'image' is not a NumPy array or a
-            PIL.Image.Image object.
-
-    Side Effects:
-        Prints an error message to the console if the image writing process
-        fails for any reason.
-    """
+def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path):
     try:
         if isinstance(image, np.ndarray):
             img = image_array_to_pil_image(image)
@@ -99,7 +76,7 @@ def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level
             img = image
         else:
             raise TypeError(f"Unsupported image type: {type(image)}")
-        img.save(fpath, compress_level=compress_level)
+        img.save(fpath)
     except Exception as e:
         print(f"Error writing image {fpath}: {e}")
 

src/lerobot/datasets/lerobot_dataset.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import contextlib
+import gc
 import logging
 import shutil
 import tempfile
@@ -22,13 +23,9 @@ from pathlib import Path
 
 import datasets
 import numpy as np
-import os
 import packaging.version
 import pandas as pd
 import PIL.Image
-import pyarrow as pa
-import pyarrow.parquet as pq
-from concurrent.futures import ProcessPoolExecutor
 import torch
 import torch.utils
 from huggingface_hub import HfApi, snapshot_download
@@ -49,9 +46,13 @@ from lerobot.datasets.utils import (
     embed_images,
     flatten_dict,
     get_delta_indices,
-    get_file_size_in_mb,
+    get_hf_dataset_cache_dir,
+    get_hf_dataset_size_in_mb,
     get_hf_features_from_features,
+    get_parquet_file_size_in_mb,
+    get_parquet_num_frames,
     get_safe_version,
+    get_video_size_in_mb,
     hf_transform_to_torch,
     is_valid_version,
     load_episodes,
@@ -59,6 +60,7 @@ from lerobot.datasets.utils import (
     load_nested_dataset,
     load_stats,
     load_tasks,
+    to_parquet_with_hf_images,
     update_chunk_file_indices,
     validate_episode_buffer,
     validate_frame,
@@ -88,15 +90,10 @@ class LeRobotDatasetMetadata:
         root: str | Path | None = None,
         revision: str | None = None,
         force_cache_sync: bool = False,
-        metadata_buffer_size: int = 10,
     ):
         self.repo_id = repo_id
         self.revision = revision if revision else CODEBASE_VERSION
         self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
-        self.writer = None
-        self.latest_episode = None
-        self.metadata_buffer: list[dict] = []
-        self.metadata_buffer_size = metadata_buffer_size
 
         try:
             if force_cache_sync:
@@ -110,54 +107,6 @@ class LeRobotDatasetMetadata:
             self.pull_from_repo(allow_patterns="meta/")
             self.load_metadata()
 
-    def _flush_metadata_buffer(self) -> None:
-        """Write all buffered episode metadata to parquet file."""
-        if not hasattr(self, "metadata_buffer") or len(self.metadata_buffer) == 0:
-            return
-
-        combined_dict = {}
-        for episode_dict in self.metadata_buffer:
-            for key, value in episode_dict.items():
-                if key not in combined_dict:
-                    combined_dict[key] = []
-                # Extract value and serialize numpy arrays
-                # because PyArrow's from_pydict function doesn't support numpy arrays
-                val = value[0] if isinstance(value, list) else value
-                combined_dict[key].append(val.tolist() if isinstance(val, np.ndarray) else val)
-
-        first_ep = self.metadata_buffer[0]
-        chunk_idx = first_ep["meta/episodes/chunk_index"][0]
-        file_idx = first_ep["meta/episodes/file_index"][0]
-
-        table = pa.Table.from_pydict(combined_dict)
-
-        if not self.writer:
-            path = Path(self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx))
-            path.parent.mkdir(parents=True, exist_ok=True)
-            self.writer = pq.ParquetWriter(
-                path, schema=table.schema, compression="snappy", use_dictionary=True
-            )
-
-        self.writer.write_table(table)
-
-        self.latest_episode = self.metadata_buffer[-1]
-        self.metadata_buffer.clear()
-
-    def _close_writer(self) -> None:
-        """Close and cleanup the parquet writer if it exists."""
-        self._flush_metadata_buffer()
-
-        writer = getattr(self, "writer", None)
-        if writer is not None:
-            writer.close()
-            self.writer = None
-
-    def __del__(self):
-        """
-        Trust the user to call .finalize() but as an added safety check call the parquet writer to stop when calling the destructor
-        """
-        self._close_writer()
-
     def load_metadata(self):
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
@@ -189,12 +138,6 @@ class LeRobotDatasetMetadata:
         return packaging.version.parse(self.info["codebase_version"])
 
     def get_data_file_path(self, ep_index: int) -> Path:
-        if self.episodes is None:
-            self.episodes = load_episodes(self.root)
-        if ep_index >= len(self.episodes):
-            raise IndexError(
-                f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}"
-            )
         ep = self.episodes[ep_index]
         chunk_idx = ep["data/chunk_index"]
         file_idx = ep["data/file_index"]
@@ -202,12 +145,6 @@ class LeRobotDatasetMetadata:
         return Path(fpath)
 
     def get_video_file_path(self, ep_index: int, vid_key: str) -> Path:
-        if self.episodes is None:
-            self.episodes = load_episodes(self.root)
-        if ep_index >= len(self.episodes):
-            raise IndexError(
-                f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}"
-            )
         ep = self.episodes[ep_index]
         chunk_idx = ep[f"videos/{vid_key}/chunk_index"]
         file_idx = ep[f"videos/{vid_key}/file_index"]
@@ -323,75 +260,72 @@ class LeRobotDatasetMetadata:
             write_tasks(self.tasks, self.root)
 
     def _save_episode_metadata(self, episode_dict: dict) -> None:
-        """Buffer episode metadata and write to parquet in batches for efficiency.
+        """Save episode metadata to a parquet file and update the Hugging Face dataset of episodes metadata.
 
-        This function accumulates episode metadata in a buffer and flushes it when the buffer
-        reaches the configured size. This reduces I/O overhead by writing multiple episodes
-        at once instead of one row at a time.
+        This function processes episodes metadata from a dictionary, converts it into a Hugging Face dataset,
+        and saves it as a parquet file. It handles both the creation of new parquet files and the
+        updating of existing ones based on size constraints. After saving the metadata, it reloads
+        the Hugging Face dataset to ensure it is up-to-date.
 
         Notes: We both need to update parquet files and HF dataset:
         - `pandas` loads parquet file in RAM
         - `datasets` relies on a memory mapping from pyarrow (no RAM). It either converts parquet files to a pyarrow cache on disk,
           or loads directly from pyarrow cache.
         """
-        # Convert to list format for each value
+        # Convert buffer into HF Dataset
         episode_dict = {key: [value] for key, value in episode_dict.items()}
+        ep_dataset = datasets.Dataset.from_dict(episode_dict)
+        ep_size_in_mb = get_hf_dataset_size_in_mb(ep_dataset)
+        df = pd.DataFrame(ep_dataset)
         num_frames = episode_dict["length"][0]
 
-        if self.latest_episode is None:
+        if self.episodes is None:
             # Initialize indices and frame count for a new dataset made of the first episode data
             chunk_idx, file_idx = 0, 0
-            if self.episodes is not None and len(self.episodes) > 0:
-                # It means we are resuming recording, so we need to load the latest episode
-                # Update the indices to avoid overwriting the latest episode
-                chunk_idx = self.episodes[-1]["meta/episodes/chunk_index"]
-                file_idx = self.episodes[-1]["meta/episodes/file_index"]
-                latest_num_frames = self.episodes[-1]["dataset_to_index"]
-                episode_dict["dataset_from_index"] = [latest_num_frames]
-                episode_dict["dataset_to_index"] = [latest_num_frames + num_frames]
-
-                # When resuming, move to the next file
-                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
-            else:
-                episode_dict["dataset_from_index"] = [0]
-                episode_dict["dataset_to_index"] = [num_frames]
-
-            episode_dict["meta/episodes/chunk_index"] = [chunk_idx]
-            episode_dict["meta/episodes/file_index"] = [file_idx]
+            df["meta/episodes/chunk_index"] = [chunk_idx]
+            df["meta/episodes/file_index"] = [file_idx]
+            df["dataset_from_index"] = [0]
+            df["dataset_to_index"] = [num_frames]
         else:
-            chunk_idx = self.latest_episode["meta/episodes/chunk_index"][0]
-            file_idx = self.latest_episode["meta/episodes/file_index"][0]
+            # Retrieve information from the latest parquet file
+            latest_ep = self.episodes[-1]
+            chunk_idx = latest_ep["meta/episodes/chunk_index"]
+            file_idx = latest_ep["meta/episodes/file_index"]
 
-            latest_path = (
-                self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
-                if self.writer is None
-                else self.writer.where
-            )
+            latest_path = self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
+            latest_size_in_mb = get_parquet_file_size_in_mb(latest_path)
 
-            if Path(latest_path).exists():
-                latest_size_in_mb = get_file_size_in_mb(Path(latest_path))
-                latest_num_frames = self.latest_episode["episode_index"][0]
-
-                av_size_per_frame = latest_size_in_mb / latest_num_frames if latest_num_frames > 0 else 0.0
-
-                if latest_size_in_mb + av_size_per_frame * num_frames >= self.data_files_size_in_mb:
-                    # Size limit is reached, flush buffer and prepare new parquet file
-                    self._flush_metadata_buffer()
-                    chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
-                    self._close_writer()
+            if latest_size_in_mb + ep_size_in_mb >= self.data_files_size_in_mb:
+                # Size limit is reached, prepare new parquet file
+                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
 
             # Update the existing pandas dataframe with new row
-            episode_dict["meta/episodes/chunk_index"] = [chunk_idx]
-            episode_dict["meta/episodes/file_index"] = [file_idx]
-            episode_dict["dataset_from_index"] = [self.latest_episode["dataset_to_index"][0]]
-            episode_dict["dataset_to_index"] = [self.latest_episode["dataset_to_index"][0] + num_frames]
+            df["meta/episodes/chunk_index"] = [chunk_idx]
+            df["meta/episodes/file_index"] = [file_idx]
+            df["dataset_from_index"] = [latest_ep["dataset_to_index"]]
+            df["dataset_to_index"] = [latest_ep["dataset_to_index"] + num_frames]
 
-        # Add to buffer
-        self.metadata_buffer.append(episode_dict)
-        self.latest_episode = episode_dict
+            if latest_size_in_mb + ep_size_in_mb < self.data_files_size_in_mb:
+                # Size limit wasnt reached, concatenate latest dataframe with new one
+                latest_df = pd.read_parquet(latest_path)
+                df = pd.concat([latest_df, df], ignore_index=True)
 
-        if len(self.metadata_buffer) >= self.metadata_buffer_size:
-            self._flush_metadata_buffer()
+                # Memort optimization
+                del latest_df
+                gc.collect()
+
+        # Write the resulting dataframe from RAM to disk
+        path = self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_parquet(path, index=False)
+
+        if self.episodes is not None:
+            # Remove the episodes cache directory, necessary to avoid cache bloat
+            cached_dir = get_hf_dataset_cache_dir(self.episodes)
+            if cached_dir is not None:
+                shutil.rmtree(cached_dir)
+
+        self.episodes = load_episodes(self.root)
 
     def save_episode(
         self,
@@ -504,10 +438,6 @@ class LeRobotDatasetMetadata:
         robot_type: str | None = None,
         root: str | Path | None = None,
         use_videos: bool = True,
-        metadata_buffer_size: int = 10,
-        chunks_size: int | None = None,
-        data_files_size_in_mb: int | None = None,
-        video_files_size_in_mb: int | None = None,
     ) -> "LeRobotDatasetMetadata":
         """Creates metadata for a LeRobotDataset."""
         obj = cls.__new__(cls)
@@ -522,24 +452,11 @@ class LeRobotDatasetMetadata:
         obj.tasks = None
         obj.episodes = None
         obj.stats = None
-        obj.info = create_empty_dataset_info(
-            CODEBASE_VERSION,
-            fps,
-            features,
-            use_videos,
-            robot_type,
-            chunks_size,
-            data_files_size_in_mb,
-            video_files_size_in_mb,
-        )
+        obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, features, use_videos, robot_type)
         if len(obj.video_keys) > 0 and not use_videos:
             raise ValueError()
         write_json(obj.info, obj.root / INFO_PATH)
         obj.revision = None
-        obj.writer = None
-        obj.latest_episode = None
-        obj.metadata_buffer = []
-        obj.metadata_buffer_size = metadata_buffer_size
         return obj
 
 
@@ -686,9 +603,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         # Unused attributes
         self.image_writer = None
         self.episode_buffer = None
-        self.writer = None
-        self.latest_episode = None
-        self._current_file_start_frame = None  # Track the starting frame index of the current parquet file
 
         self.root.mkdir(exist_ok=True, parents=True)
 
@@ -697,11 +611,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             self.repo_id, self.root, self.revision, force_cache_sync=force_cache_sync
         )
 
-        # Track dataset state for efficient incremental writing
-        self._lazy_loading = False
-        self._recorded_frames = self.meta.total_frames
-        self._writer_closed_for_reading = False
-
         # Load actual data
         try:
             if force_cache_sync:
@@ -711,8 +620,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             if not self._check_cached_episodes_sufficient():
                 raise FileNotFoundError("Cached dataset doesn't contain all requested episodes")
         except (AssertionError, FileNotFoundError, NotADirectoryError):
-            if is_valid_version(self.revision):
-                self.revision = get_safe_version(self.repo_id, self.revision)
+            self.revision = get_safe_version(self.repo_id, self.revision)
             self.download(download_videos)
             self.hf_dataset = self.load_hf_dataset()
 
@@ -721,19 +629,6 @@ 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)
 
-    def _close_writer(self) -> None:
-        """Close and cleanup the parquet writer if it exists."""
-        writer = getattr(self, "writer", None)
-        if writer is not None:
-            writer.close()
-            self.writer = None
-
-    def __del__(self):
-        """
-        Trust the user to call .finalize() but as an added safety check call the parquet writer to stop when calling the destructor
-        """
-        self._close_writer()
-
     def push_to_hub(
         self,
         branch: str | None = None,
@@ -839,14 +734,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return hf_dataset
 
     def _check_cached_episodes_sufficient(self) -> bool:
-        """Check if the cached dataset contains all requested episodes and their video files."""
+        """Check if the cached dataset contains all requested episodes."""
         if self.hf_dataset is None or len(self.hf_dataset) == 0:
             return False
 
         # Get available episode indices from cached dataset
         available_episodes = {
             ep_idx.item() if isinstance(ep_idx, torch.Tensor) else ep_idx
-            for ep_idx in self.hf_dataset.unique("episode_index")
+            for ep_idx in self.hf_dataset["episode_index"]
         }
 
         # Determine requested episodes
@@ -858,18 +753,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             requested_episodes = set(self.episodes)
 
         # Check if all requested episodes are available in cached data
-        if not requested_episodes.issubset(available_episodes):
-            return False
-
-        # Check if all required video files exist
-        if len(self.meta.video_keys) > 0:
-            for ep_idx in requested_episodes:
-                for vid_key in self.meta.video_keys:
-                    video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key)
-                    if not video_path.exists():
-                        return False
-
-        return True
+        return requested_episodes.issubset(available_episodes)
 
     def create_hf_dataset(self) -> datasets.Dataset:
         features = get_hf_features_from_features(self.features)
@@ -885,15 +769,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
     @property
     def num_frames(self) -> int:
-        """Number of frames in selected episodes.
-
-        Note: When episodes a subset of the full dataset is requested, we must return the
-        actual loaded data length (len(self.hf_dataset)) rather than metadata total_frames.
-        self.meta.total_frames is the total number of frames in the full dataset.
-        """
-        if self.episodes is not None and self.hf_dataset is not None:
-            return len(self.hf_dataset)
-        return self.meta.total_frames
+        """Number of frames in selected episodes."""
+        return len(self.hf_dataset) if self.hf_dataset is not None else self.meta.total_frames
 
     @property
     def num_episodes(self) -> int:
@@ -971,22 +848,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         return item
 
-    def _ensure_hf_dataset_loaded(self):
-        """Lazy load the HF dataset only when needed for reading."""
-        if self._lazy_loading or self.hf_dataset is None:
-            # Close the writer before loading to ensure parquet file is properly finalized
-            if self.writer is not None:
-                self._close_writer()
-                self._writer_closed_for_reading = True
-            self.hf_dataset = self.load_hf_dataset()
-            self._lazy_loading = False
-
     def __len__(self):
         return self.num_frames
 
     def __getitem__(self, idx) -> dict:
-        # Ensure dataset is loaded when we actually need to read from it
-        self._ensure_hf_dataset_loaded()
         item = self.hf_dataset[idx]
         ep_idx = item["episode_index"].item()
 
@@ -1025,14 +890,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             "})',\n"
         )
 
-    def finalize(self):
-        """
-        Close the parquet writers. This function needs to be called after data collection/conversion, else footer metadata won't be written to the parquet files.
-        The dataset won't be valid and can't be loaded as ds = LeRobotDataset(repo_id=repo, root=HF_LEROBOT_HOME.joinpath(repo))
-        """
-        self._close_writer()
-        self.meta._close_writer()
-
     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
         ep_buffer = {}
@@ -1151,9 +1008,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         use_batched_encoding = self.batch_encoding_size > 1
 
         if has_video_keys and not use_batched_encoding:
-            video_paths = self._encode_multiple_temporary_episode_videos(self.meta.video_keys, episode_index)
-            for (video_key, video_path) in zip(self.meta.video_keys, video_paths):
-                ep_metadata.update(self._save_episode_video(video_key, episode_index, video_path))
+            for video_key in self.meta.video_keys:
+                ep_metadata.update(self._save_episode_video(video_key, episode_index))
 
         # `meta.save_episode` need to be executed after encoding the videos
         self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats, ep_metadata)
@@ -1241,107 +1097,74 @@ class LeRobotDataset(torch.utils.data.Dataset):
         ep_dict = {key: episode_buffer[key] for key in self.hf_features}
         ep_dataset = datasets.Dataset.from_dict(ep_dict, features=self.hf_features, split="train")
         ep_dataset = embed_images(ep_dataset)
+        ep_size_in_mb = get_hf_dataset_size_in_mb(ep_dataset)
         ep_num_frames = len(ep_dataset)
+        df = pd.DataFrame(ep_dataset)
 
-        if self.latest_episode is None:
+        if self.meta.episodes is None:
             # Initialize indices and frame count for a new dataset made of the first episode data
             chunk_idx, file_idx = 0, 0
-            global_frame_index = 0
-            self._current_file_start_frame = 0
-            # However, if the episodes already exists
-            # It means we are resuming recording, so we need to load the latest episode
-            # Update the indices to avoid overwriting the latest episode
-            if self.meta.episodes is not None and len(self.meta.episodes) > 0:
-                latest_ep = self.meta.episodes[-1]
-                global_frame_index = latest_ep["dataset_to_index"]
-                chunk_idx = latest_ep["data/chunk_index"]
-                file_idx = latest_ep["data/file_index"]
-
-                # When resuming, move to the next file
-                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size)
-                self._current_file_start_frame = global_frame_index
+            latest_num_frames = 0
         else:
             # Retrieve information from the latest parquet file
-            latest_ep = self.latest_episode
+            latest_ep = self.meta.episodes[-1]
             chunk_idx = latest_ep["data/chunk_index"]
             file_idx = latest_ep["data/file_index"]
-            global_frame_index = latest_ep["index"][-1] + 1
 
             latest_path = self.root / self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
-            latest_size_in_mb = get_file_size_in_mb(latest_path)
-
-            frames_in_current_file = global_frame_index - self._current_file_start_frame
-            av_size_per_frame = (
-                latest_size_in_mb / frames_in_current_file if frames_in_current_file > 0 else 0
-            )
+            latest_size_in_mb = get_parquet_file_size_in_mb(latest_path)
+            latest_num_frames = get_parquet_num_frames(latest_path)
 
             # Determine if a new parquet file is needed
-            if (
-                latest_size_in_mb + av_size_per_frame * ep_num_frames >= self.meta.data_files_size_in_mb
-                or self._writer_closed_for_reading
-            ):
-                # Size limit is reached or writer was closed for reading, prepare new parquet file
+            if latest_size_in_mb + ep_size_in_mb >= self.meta.data_files_size_in_mb:
+                # Size limit is reached, prepare new parquet file
                 chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size)
-                self._close_writer()
-                self._writer_closed_for_reading = False
-                self._current_file_start_frame = global_frame_index
+                latest_num_frames = 0
+            else:
+                # Update the existing parquet file with new rows
+                latest_df = pd.read_parquet(latest_path)
+                df = pd.concat([latest_df, df], ignore_index=True)
 
-        ep_dict["data/chunk_index"] = chunk_idx
-        ep_dict["data/file_index"] = file_idx
+                # Memort optimization
+                del latest_df
+                gc.collect()
 
         # Write the resulting dataframe from RAM to disk
         path = self.root / self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
         path.parent.mkdir(parents=True, exist_ok=True)
+        if len(self.meta.image_keys) > 0:
+            to_parquet_with_hf_images(df, path)
+        else:
+            df.to_parquet(path)
 
-        table = ep_dataset.with_format("arrow")[:]
-        if not self.writer:
-            self.writer = pq.ParquetWriter(
-                path, schema=table.schema, compression="snappy", use_dictionary=True
-            )
-        self.writer.write_table(table)
+        if self.hf_dataset is not None:
+            # Remove hf dataset cache directory, necessary to avoid cache bloat
+            cached_dir = get_hf_dataset_cache_dir(self.hf_dataset)
+            if cached_dir is not None:
+                shutil.rmtree(cached_dir)
+
+        self.hf_dataset = self.load_hf_dataset()
 
         metadata = {
             "data/chunk_index": chunk_idx,
             "data/file_index": file_idx,
-            "dataset_from_index": global_frame_index,
-            "dataset_to_index": global_frame_index + ep_num_frames,
+            "dataset_from_index": latest_num_frames,
+            "dataset_to_index": latest_num_frames + ep_num_frames,
         }
-
-        # Store metadata with episode data for next episode
-        self.latest_episode = {**ep_dict, **metadata}
-
-        # Mark that the HF dataset needs reloading (lazy loading approach)
-        # This avoids expensive reloading during sequential recording
-        self._lazy_loading = True
-        # Update recorded frames count for efficient length tracking
-        self._recorded_frames += ep_num_frames
-
         return metadata
 
-    def _save_episode_video(self, video_key: str, episode_index: int, video_path: str | Path | None = None) -> dict:
+    def _save_episode_video(self, video_key: str, episode_index: int) -> dict:
         # Encode episode frames into a temporary video
-        if video_path is None:
-            ep_path = self._encode_temporary_episode_video(video_key, episode_index)
-        else:
-            ep_path = video_path
-        ep_size_in_mb = get_file_size_in_mb(ep_path)
+        ep_path = self._encode_temporary_episode_video(video_key, episode_index)
+        ep_size_in_mb = get_video_size_in_mb(ep_path)
         ep_duration_in_s = get_video_duration_in_s(ep_path)
 
-        if (
-            episode_index == 0
-            or self.meta.latest_episode is None
-            or f"videos/{video_key}/chunk_index" not in self.meta.latest_episode
+        if self.meta.episodes is None or (
+            f"videos/{video_key}/chunk_index" not in self.meta.episodes.column_names
+            or f"videos/{video_key}/file_index" not in self.meta.episodes.column_names
         ):
             # Initialize indices for a new dataset made of the first episode data
             chunk_idx, file_idx = 0, 0
-            if self.meta.episodes is not None and len(self.meta.episodes) > 0:
-                # It means we are resuming recording, so we need to load the latest episode
-                # Update the indices to avoid overwriting the latest episode
-                old_chunk_idx = self.meta.episodes[-1][f"videos/{video_key}/chunk_index"]
-                old_file_idx = self.meta.episodes[-1][f"videos/{video_key}/file_index"]
-                chunk_idx, file_idx = update_chunk_file_indices(
-                    old_chunk_idx, old_file_idx, self.meta.chunks_size
-                )
             latest_duration_in_s = 0.0
             new_path = self.root / self.meta.video_path.format(
                 video_key=video_key, chunk_index=chunk_idx, file_index=file_idx
@@ -1349,16 +1172,16 @@ class LeRobotDataset(torch.utils.data.Dataset):
             new_path.parent.mkdir(parents=True, exist_ok=True)
             shutil.move(str(ep_path), str(new_path))
         else:
-            # Retrieve information from the latest updated video file using latest_episode
-            latest_ep = self.meta.latest_episode
-            chunk_idx = latest_ep[f"videos/{video_key}/chunk_index"][0]
-            file_idx = latest_ep[f"videos/{video_key}/file_index"][0]
+            # Retrieve information from the latest updated video file (possibly several episodes ago)
+            latest_ep = self.meta.episodes[episode_index - 1]
+            chunk_idx = latest_ep[f"videos/{video_key}/chunk_index"]
+            file_idx = latest_ep[f"videos/{video_key}/file_index"]
 
             latest_path = self.root / self.meta.video_path.format(
                 video_key=video_key, chunk_index=chunk_idx, file_index=file_idx
             )
-            latest_size_in_mb = get_file_size_in_mb(latest_path)
-            latest_duration_in_s = latest_ep[f"videos/{video_key}/to_timestamp"][0]
+            latest_size_in_mb = get_video_size_in_mb(latest_path)
+            latest_duration_in_s = get_video_duration_in_s(latest_path)
 
             if latest_size_in_mb + ep_size_in_mb >= self.meta.video_files_size_in_mb:
                 # Move temporary episode video to a new video file in the dataset
@@ -1447,22 +1270,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         shutil.rmtree(img_dir)
         return temp_path
 
-    def _encode_multiple_temporary_episode_videos(self, video_keys, episode_index):
-        temp_paths = []
-        img_dirs = []
-        for video_key in video_keys:
-            temp_paths.append(Path(tempfile.mkdtemp(dir=self.root)) / f"{video_key}_{episode_index:03d}.mp4")
-            img_dirs.append(self._get_image_file_dir(episode_index, video_key))
-        fps = [self.fps]*len(video_keys)
-
-        with ProcessPoolExecutor(max_workers=len(video_keys)) as executor:
-            executor.map(encode_video_frames,img_dirs,temp_paths,fps)
-
-        for img_dir in img_dirs:
-            shutil.rmtree(img_dir)
-
-        return temp_paths
-
     @classmethod
     def create(
         cls,
@@ -1508,13 +1315,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.delta_timestamps = None
         obj.delta_indices = None
         obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec()
-        obj.writer = None
-        obj.latest_episode = None
-        obj._current_file_start_frame = None
-        # Initialize tracking for incremental recording
-        obj._lazy_loading = False
-        obj._recorded_frames = 0
-        obj._writer_closed_for_reading = False
         return obj
 
 

src/lerobot/datasets/transforms.py

@@ -206,11 +206,6 @@ class ImageTransformsConfig:
                 type="SharpnessJitter",
                 kwargs={"sharpness": (0.5, 1.5)},
             ),
-            "affine": ImageTransformConfig(
-                weight=1.0,
-                type="RandomAffine",
-                kwargs={"degrees": (-5.0, 5.0), "translate": (0.05, 0.05)},
-            ),
         }
     )
 
@@ -222,8 +217,6 @@ def make_transform_from_config(cfg: ImageTransformConfig):
         return v2.ColorJitter(**cfg.kwargs)
     elif cfg.type == "SharpnessJitter":
         return SharpnessJitter(**cfg.kwargs)
-    elif cfg.type == "RandomAffine":
-        return v2.RandomAffine(**cfg.kwargs)
     else:
         raise ValueError(f"Transform '{cfg.type}' is not valid.")
 

src/lerobot/datasets/utils.py

@@ -30,7 +30,7 @@ import pandas
 import pandas as pd
 import pyarrow.parquet as pq
 import torch
-from datasets import Dataset
+from datasets import Dataset, concatenate_datasets
 from datasets.table import embed_table_storage
 from huggingface_hub import DatasetCard, DatasetCardData, HfApi
 from huggingface_hub.errors import RevisionNotFoundError
@@ -44,7 +44,7 @@ from lerobot.datasets.backward_compatibility import (
     ForwardCompatibilityError,
 )
 from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_STR
-from lerobot.utils.utils import SuppressProgressBars, is_valid_numpy_dtype_string
+from lerobot.utils.utils import is_valid_numpy_dtype_string
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of files per chunk
 DEFAULT_DATA_FILE_SIZE_IN_MB = 100  # Max size per file
@@ -94,6 +94,12 @@ def get_hf_dataset_size_in_mb(hf_ds: Dataset) -> int:
     return hf_ds.data.nbytes // (1024**2)
 
 
+def get_hf_dataset_cache_dir(hf_ds: Dataset) -> Path | None:
+    if hf_ds.cache_files is None or len(hf_ds.cache_files) == 0:
+        return None
+    return Path(hf_ds.cache_files[0]["filename"]).parents[2]
+
+
 def update_chunk_file_indices(chunk_idx: int, file_idx: int, chunks_size: int) -> tuple[int, int]:
     if file_idx == chunks_size - 1:
         file_idx = 0
@@ -117,9 +123,8 @@ def load_nested_dataset(pq_dir: Path, features: datasets.Features | None = None)
         raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}")
 
     # TODO(rcadene): set num_proc to accelerate conversion to pyarrow
-    with SuppressProgressBars():
-        datasets = Dataset.from_parquet([str(path) for path in paths], features=features)
-    return datasets
+    datasets = [Dataset.from_parquet(str(path), features=features) for path in paths]
+    return concatenate_datasets(datasets)
 
 
 def get_parquet_num_frames(parquet_path: str | Path) -> int:
@@ -127,14 +132,10 @@ def get_parquet_num_frames(parquet_path: str | Path) -> int:
     return metadata.num_rows
 
 
-def get_file_size_in_mb(file_path: Path) -> float:
-    """Get file size on disk in megabytes.
-
-    Args:
-        file_path (Path): Path to the file.
-    """
-    file_size_bytes = file_path.stat().st_size
-    return file_size_bytes / (1024**2)
+def get_video_size_in_mb(mp4_path: Path) -> float:
+    file_size_bytes = mp4_path.stat().st_size
+    file_size_mb = file_size_bytes / (1024**2)
+    return file_size_mb
 
 
 def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict:

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -69,9 +69,9 @@ from lerobot.datasets.utils import (
     LEGACY_TASKS_PATH,
     cast_stats_to_numpy,
     flatten_dict,
-    get_file_size_in_mb,
     get_parquet_file_size_in_mb,
     get_parquet_num_frames,
+    get_video_size_in_mb,
     load_info,
     update_chunk_file_indices,
     write_episodes,
@@ -98,7 +98,7 @@ OLD
 videos/chunk-000/CAMERA/episode_000000.mp4
 
 NEW
-videos/CAMERA/chunk-000/file_000.mp4
+videos/chunk-000/file_000.mp4
 -------------------------
 OLD
 episodes.jsonl
@@ -310,7 +310,7 @@ def convert_videos_of_camera(root: Path, new_root: Path, video_key: str, video_f
     episodes_metadata = []
 
     for ep_path in tqdm.tqdm(ep_paths, desc=f"convert videos of {video_key}"):
-        ep_size_in_mb = get_file_size_in_mb(ep_path)
+        ep_size_in_mb = get_video_size_in_mb(ep_path)
         ep_duration_in_s = get_video_duration_in_s(ep_path)
 
         # Check if adding this episode would exceed the limit