refactor(utils): enhance task handling in add_envs_task function

- Improved the `add_envs_task` function to validate the output of `task_description` and `task` calls, ensuring they return lists of strings.
- Removed the use of `else` statement for environments without language instructions, simplifying the logic and enhancing readability.
- Streamlined the observation dictionary handling by ensuring consistent data types for task attributes.

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,33 +1,40 @@
 ## What this does
+
 Explain what this PR does. Feel free to tag your PR with the appropriate label(s).
 
 Examples:
-|  Title               | Label           |
+| Title | Label |
 |----------------------|-----------------|
-| Fixes #[issue]       | (🐛 Bug)        |
-| Adds new dataset     | (🗃️ Dataset)    |
-| Optimizes something  | (⚡️ Performance) |
+| Fixes #[issue] | (🐛 Bug) |
+| Adds new dataset | (🗃️ Dataset) |
+| Optimizes something | (⚡️ Performance) |
 
 ## How it was tested
+
 Explain/show how you tested your changes.
 
 Examples:
+
 - Added `test_something` in `tests/test_stuff.py`.
 - Added `new_feature` and checked that training converges with policy X on dataset/environment Y.
 - Optimized `some_function`, it now runs X times faster than previously.
 
 ## How to checkout & try? (for the reviewer)
+
 Provide a simple way for the reviewer to try out your changes.
 
 Examples:
+
 ```bash
 pytest -sx tests/test_stuff.py::test_something
 ```
+
 ```bash
-python -m lerobot.scripts.train --some.option=true
+lerobot-train --some.option=true
 ```
 
 ## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR
+
 **Note**: Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
 members/contributors who may be interested in your PR. Try to avoid tagging more than 3 people.
 

.github/workflows/build-docker-images.yml

@@ -1,135 +0,0 @@
-# Copyright 2024 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.
-
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/build_docker_images.yml
-name: Builds
-
-on:
-  workflow_dispatch:
-  workflow_call:
-  schedule:
-    - cron: "0 1 * * *"
-
-permissions: {}
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  latest-cpu:
-    name: CPU
-    runs-on:
-      group: aws-general-8-plus
-    steps:
-      - name: Install Git LFS
-        run: |
-          sudo apt-get update
-          sudo apt-get install git-lfs
-          git lfs install
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          lfs: true
-          persist-credentials: false
-
-      - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push CPU
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
-        with:
-          context: .
-          file: ./docker/lerobot-cpu/Dockerfile
-          push: true
-          tags: huggingface/lerobot-cpu
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
-
-
-  latest-cuda:
-    name: GPU
-    runs-on:
-      group: aws-general-8-plus
-    steps:
-      - name: Install Git LFS
-        run: |
-          sudo apt-get update
-          sudo apt-get install git-lfs
-          git lfs install
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          lfs: true
-          persist-credentials: false
-
-      - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push GPU
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
-        with:
-          context: .
-          file: ./docker/lerobot-gpu/Dockerfile
-          push: true
-          tags: huggingface/lerobot-gpu
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
-
-
-  latest-cuda-dev:
-    name: GPU Dev
-    runs-on:
-      group: aws-general-8-plus
-    steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          persist-credentials: false
-
-      - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push GPU dev
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
-        with:
-          context: .
-          file: ./docker/lerobot-gpu-dev/Dockerfile
-          push: true
-          tags: huggingface/lerobot-gpu:dev
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}

.github/workflows/build_documentation.yml

@@ -1,23 +0,0 @@
-name: Build documentation
-
-on:
-  workflow_dispatch:
-  push:
-    paths:
-      - "docs/**"
-    branches:
-    - main
-    - doc-builder*
-    - v*-release
-
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
-    with:
-      commit_sha: ${{ github.sha }}
-      package: lerobot
-      additional_args: --not_python_module
-    secrets:
-      token: ${{ secrets.HUGGINGFACE_PUSH }}
-      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -1,19 +0,0 @@
-name: Build PR Documentation
-
-on:
-  pull_request:
-    paths:
-      - "docs/**"
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
-    with:
-      commit_sha: ${{ github.event.pull_request.head.sha }}
-      pr_number: ${{ github.event.number }}
-      package: lerobot
-      additional_args: --not_python_module

.github/workflows/documentation-upload-pr.yml

@@ -0,0 +1,40 @@
+# 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 uploads the documentation preview built for a PR and comments the link on the PR.
+name: Documentation PR Upload
+permissions:
+  contents: read
+  pull-requests: write
+
+on:
+  # Triggered by the completion of the main 'Documentation' workflow.
+  workflow_run: # zizmor: ignore[dangerous-triggers] We follow the same pattern as in Transformers
+    workflows: ["Documentation"]
+    types:
+      - completed
+
+jobs:
+  # This job uploads a preview of the documentation for a pull request.
+  upload_and_comment:
+    name: Upload Preview and Comment
+    if: >
+      github.event.workflow_run.event == 'pull_request' &&
+      github.event.workflow_run.conclusion == 'success'
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
+    with:
+      package_name: lerobot
+    secrets:
+      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
+      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

.github/workflows/documentation.yml

@@ -0,0 +1,70 @@
+# 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 building documentation for both main branches and PRs.
+name: Documentation
+
+on:
+  # Allows running this workflow manually from the Actions tab
+  workflow_dispatch:
+
+  # Triggers the workflow on push events to main for the docs folder
+  push:
+    branches:
+      - main
+    paths:
+      - "docs/**"
+
+  # Triggers the workflow on pull request events targeting main for the docs folder
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "docs/**"
+
+# Ensures that only the latest commit for a PR or branch is built, canceling older runs.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  # This job builds and deploys the official documentation.
+  build_main_docs:
+    name: Build Main Docs
+    if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
+    permissions:
+      contents: read
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
+    with:
+      commit_sha: ${{ github.sha }}
+      package: lerobot
+      additional_args: --not_python_module
+    secrets:
+      token: ${{ secrets.HUGGINGFACE_PUSH }}
+      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
+
+  # This job builds a preview of the documentation for a pull request.
+  # The result of this job triggers the 'Upload PR Documentation' workflow.
+  build_pr_docs:
+    name: Build PR Docs
+    if: github.event_name == 'pull_request'
+    permissions:
+      contents: read
+      pull-requests: write
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
+    with:
+      commit_sha: ${{ github.event.pull_request.head.sha }}
+      pr_number: ${{ github.event.number }}
+      package: lerobot
+      additional_args: --not_python_module

.github/workflows/fast_tests.yml

@@ -0,0 +1,87 @@
+# 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 fast testing.
+name: Fast Tests
+
+on:
+  # Allows running this workflow manually from the Actions tab
+  workflow_dispatch:
+
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/workflows/**"
+      - "pyproject.toml"
+      - "Makefile"
+  push:
+    branches:
+      - main
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/workflows/**"
+      - "pyproject.toml"
+      - "Makefile"
+
+permissions:
+  contents: read
+
+# Sets up the environment variables
+env:
+  UV_VERSION: "0.8.0"
+  PYTHON_VERSION: "3.10"
+  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
+
+# Ensures that only the latest commit for a PR or branch is built, canceling older runs.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  # This job runs pytests with the default dependencies.
+  # It runs everytime we commit to a PR or push to main
+  fast-pytest-tests:
+    name: Fast Pytest Tests
+    runs-on: ubuntu-latest
+    env:
+      MUJOCO_GL: egl
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+          lfs: true
+
+      # TODO(Steven): Evaluate the need of these dependencies
+      - 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: Install lerobot with test extras
+        run: uv sync --extra "test"
+
+      - name: Run pytest
+        run: uv run pytest tests -vv --maxfail=10

.github/workflows/full_tests.yml

@@ -0,0 +1,210 @@
+# 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.
+name: Full Tests
+
+on:
+  # Allows running this workflow manually from the Actions tab
+  workflow_dispatch:
+
+  pull_request_review:
+    types: [submitted]
+  push:
+    branches:
+      - main
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/workflows/**"
+      - "pyproject.toml"
+      - "Makefile"
+
+permissions:
+  contents: read
+
+# Sets up the environment variables
+env:
+  UV_VERSION: "0.8.0"
+  PYTHON_VERSION: "3.10"
+  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
+
+# 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 extras
+  # It runs everytime a PR is approved or a push to main
+  full-tests:
+    name: Full Tests
+    runs-on: ubuntu-latest
+    if: |
+      (github.event_name == 'pull_request_review' && github.event.review.state == 'approved') ||
+      github.event_name == 'push' ||
+      github.event_name == 'workflow_dispatch'
+    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: Install lerobot with all extras
+        run: uv sync --all-extras
+
+      - name: Run pytest (all extras)
+        run: uv run pytest tests -vv --maxfail=10
+
+      - name: Run end-to-end tests
+        run: uv run make test-end-to-end
+
+  # This job builds a GPU enabled image for testing
+  # It runs everytime a PR is approved or a push to main
+  # TODO(Steven): For now we skip this job for community PRs
+  build-and-push-docker:
+    name: Build and Push Docker
+    runs-on:
+      group: aws-general-8-plus
+    if: |
+      (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
+      github.event_name == 'push' ||
+      github.event_name == 'workflow_dispatch'
+    outputs:
+      image_tag: ${{ steps.set_tag.outputs.image_tag }}
+    env:
+      GITHUB_EVENT_NAME: ${{ github.event_name }}
+      GITHUB_REF: ${{ github.ref }}
+      GITHUB_PR_NUMBER: ${{ github.event.pull_request.number }}
+    steps:
+      - name: Set Docker image tag
+        id: set_tag
+        run: |
+          if [[ "${GITHUB_EVENT_NAME}" == "push" ]]; then
+            TAG="${DOCKER_IMAGE_NAME}:latest"
+          elif [[ -n "${GITHUB_PR_NUMBER}" ]]; then
+            TAG="${DOCKER_IMAGE_NAME}:pr-${GITHUB_PR_NUMBER}"
+          else
+            TAG="${DOCKER_IMAGE_NAME}:pr-${GITHUB_REF##*/}"
+          fi
+          echo "image_tag=$TAG" >> $GITHUB_OUTPUT
+      - 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: ${{ steps.set_tag.outputs.image_tag }}
+
+  # This job runs pytest with all extras in a GPU enabled host
+  # It runs everytime a test image is created
+  gpu-tests:
+    name: GPU 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: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+    defaults:
+      run:
+        shell: bash
+        working-directory: /lerobot
+    steps:
+      - name: Run pytest on GPU
+        run: pytest tests -vv --maxfail=10
+      - name: Run end-to-end tests
+        run: make test-end-to-end
+
+  # This job deletes the test image recently created
+  # It runs everytime after the gpu-tests have finished
+  delete-pr-image:
+    name: Delete PR Image
+    needs: [gpu-tests, build-and-push-docker]
+    if: always() && ((github.event.review.state == 'approved') || (github.event_name == 'workflow_dispatch')) && needs.build-and-push-docker.result == 'success'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Get Docker Hub Token and Delete Image
+        # zizmor: ignore[template-injection]
+        run: |
+          IMAGE_NAME=$(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
+
+# TODO(Steven): Check dockerimages pull in ubuntu

.github/workflows/nightly-tests.yml

@@ -1,93 +0,0 @@
-# Copyright 2024 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.
-
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/nightly.yml
-name: Nightly
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: "0 2 * * *"
-
-permissions: {}
-
-# env:
-  # SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }}
-jobs:
-  run_all_tests_cpu:
-    name: CPU
-    strategy:
-      fail-fast: false
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: huggingface/lerobot-cpu:latest  # zizmor: ignore[unpinned-images]
-      options: --shm-size "16gb"
-      credentials:
-        username: ${{ secrets.DOCKERHUB_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_PASSWORD }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: /lerobot
-    steps:
-      - name: Tests
-        run: pytest -v --cov=./src/lerobot --disable-warnings tests
-
-      - name: Tests end-to-end
-        run: make test-end-to-end
-
-
-  run_all_tests_single_gpu:
-    name: GPU
-    strategy:
-      fail-fast: false
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      CUDA_VISIBLE_DEVICES: "0"
-      TEST_TYPE: "single_gpu"
-    container:
-      image: huggingface/lerobot-gpu:latest  # zizmor: ignore[unpinned-images]
-      options: --gpus all --shm-size "16gb"
-      credentials:
-        username: ${{ secrets.DOCKERHUB_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_PASSWORD }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: /lerobot
-    steps:
-      - name: Nvidia-smi
-        run: nvidia-smi
-
-      - name: Test
-        run: pytest -v --cov=./src/lerobot --cov-report=xml --disable-warnings tests
-      #   TODO(aliberts): Link with HF Codecov account
-      # - name: Upload coverage reports to Codecov with GitHub Action
-      #   uses: codecov/codecov-action@v4
-      #   with:
-      #     files: ./coverage.xml
-      #     verbose: true
-      - name: Tests end-to-end
-        env:
-          DEVICE: cuda
-        run: make test-end-to-end
-
-    #   - name: Generate Report
-    #     if: always()
-    #     run: |
-    #       pip install slack_sdk tabulate
-    #       python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY

.github/workflows/nightly.yml

@@ -0,0 +1,160 @@
+# 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 nightly testing & docker images publishing.
+name: Nightly
+permissions:
+  contents: read
+
+on:
+  # Allows running this workflow manually from the Actions tab
+  workflow_dispatch:
+
+  # Runs at 02:00
+  schedule:
+    - cron: "0 2 * * *"
+
+# Sets up the environment variables
+env:
+  UV_VERSION: "0.8.0"
+  PYTHON_VERSION: "3.10"
+  DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
+  DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest
+
+# Ensures that only the latest commit is built, canceling older runs.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  # This job builds a CPU image for testing & distribution
+  build-docker-cpu-nightly:
+    name: Build CPU Docker for Nightly
+    runs-on:
+      group: aws-general-8-plus
+    outputs:
+      image_tag: ${{ env.DOCKER_IMAGE_NAME_CPU }}
+    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 CPU
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: ./docker/Dockerfile.user
+          push: true
+          tags: ${{ env.DOCKER_IMAGE_NAME_CPU }}
+
+  # This job builds a GPU image for testing & distribution
+  build-docker-gpu-nightly:
+    name: Build GPU Docker for Nightly
+    runs-on:
+      group: aws-general-8-plus
+    outputs:
+      image_tag: ${{ env.DOCKER_IMAGE_NAME_GPU }}
+    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 GPU
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: ./docker/Dockerfile.internal
+          push: true
+          tags: ${{ env.DOCKER_IMAGE_NAME_GPU }}
+
+  # This job runs the E2E tests + pytest with all extras in the CPU image
+  nightly-cpu-tests:
+    name: Nightly CPU Tests
+    needs: [build-docker-cpu-nightly]
+    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-docker-cpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
+      credentials:
+        username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+        password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+    defaults:
+      run:
+        shell: bash
+        working-directory: /lerobot
+    steps:
+      - name: Run pytest on CPU
+        run: pytest tests -vv --maxfail=10
+      - name: Run end-to-end tests
+        run: make test-end-to-end
+
+  # This job runs the E2E tests + pytest with all extras in the GPU image
+  nightly-gpu-tests:
+    name: Nightly GPU Tests
+    needs: [build-docker-gpu-nightly]
+    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-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: Run pytest on GPU
+        run: pytest tests -vv --maxfail=10
+      - name: Run end-to-end tests
+        run: make test-end-to-end

.github/workflows/quality.yml

@@ -1,4 +1,4 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+# 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.
@@ -12,61 +12,47 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# This workflow handles linting, formatting, and static analysis checks for the codebase.
 name: Quality
+permissions:
+  contents: read
 
 on:
+  # Allows running this workflow manually from the Actions tab
   workflow_dispatch:
-  workflow_call:
-  pull_request:
+
+  # Triggers the workflow on push events to main
   push:
     branches:
       - main
 
-permissions: {}
+  # Triggers the workflow on pull request events targeting main
+  pull_request:
+    branches:
+      - main
 
-env:
-  PYTHON_VERSION: "3.10"
+# Ensures that only the latest commit for a PR or branch is built, canceling older runs.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
 
 jobs:
-  style:
-    name: Style
+  # This job runs pre-commit hooks to check code style and formatting.
+  pre-commit-checks:
+    name: Run Pre-commit Hooks (Lint, Format & Static Analysis)
     runs-on: ubuntu-latest
     steps:
-      - name: Checkout Repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - name: Checkout code
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1
+        uses: actions/setup-python@v5
         with:
-          python-version: ${{ env.PYTHON_VERSION }}
+          python-version: '3.10'
 
-      - name: Get Ruff Version from pre-commit-config.yaml
-        id: get-ruff-version
-        run: |
-          RUFF_VERSION=$(awk '/repo: https:\/\/github.com\/astral-sh\/ruff-pre-commit/{flag=1;next}/rev:/{if(flag){print $2;exit}}' .pre-commit-config.yaml)
-          echo "ruff_version=${RUFF_VERSION}" >> $GITHUB_OUTPUT
-
-      - name: Install Ruff
-        env:
-          RUFF_VERSION: ${{ steps.get-ruff-version.outputs.ruff_version }}
-        run: python -m pip install "ruff==${RUFF_VERSION}"
-
-      - name: Ruff check
-        run: ruff check --output-format=github
-
-      - name: Ruff format
-        run: ruff format --diff
-
-  typos:
-    name: Typos
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - name: Run pre-commit hooks
+        uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]
         with:
-          persist-credentials: false
-
-      - name: typos-action
-        uses: crate-ci/typos@db35ee91e80fbb447f33b0e5fbddb24d2a1a884f # v1.29.10
+          extra_args: --all-files --show-diff-on-failure --color=always

.github/workflows/release.yml

@@ -0,0 +1,171 @@
+# 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.
+
+name: Create Release and Publish to PyPI
+
+on:
+  push:
+    tags:
+      - 'v*.*.*' # Trigger on tags like v0.1.0, v1.0.0
+
+# Sets up the environment variables
+env:
+  UV_VERSION: "0.8.0"
+  PYTHON_VERSION: "3.10"
+
+jobs:
+  # This job builds the Python package and publishes it to PyPI
+  build-and-publish:
+    name: Build and publish Python distributions
+    runs-on: ubuntu-latest
+    outputs:
+      version: ${{ steps.extract_info.outputs.tag_version }}
+    permissions:
+      contents: write
+      id-token: write
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Extract Version
+        id: extract_info
+        # Extract version from tag (e.g., v0.1.0 -> 0.1.0)
+        # zizmor: ignore[template-injection]
+        run: |
+          VERSION=${{ github.ref_name }}
+          VERSION_NUMBER=${VERSION#v}
+          echo "tag_version=$VERSION_NUMBER" >> $GITHUB_OUTPUT
+      - name: Check if version matches pyproject.toml
+        if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
+        # zizmor: ignore[template-injection]
+        run: |
+          TAG_VERSION=${{ steps.extract_info.outputs.tag_version }}
+
+          PYPROJECT_VERSION=$(grep '^version = ' pyproject.toml | awk -F' = ' '{print $2}' | tr -d '"')
+
+          if [[ "$TAG_VERSION" != "$PYPROJECT_VERSION" ]]; then
+            echo "Error: Tag version ($TAG_VERSION) does not match pyproject.toml version ($PYPROJECT_VERSION)." >&2
+            exit 1
+          else
+            echo "Tag version matches pyproject.toml version: $TAG_VERSION. Proceeding with release."
+          fi
+
+      - name: Check if version exists on PyPI
+      # zizmor: ignore[template-injection]
+        run: |
+          NEW_VERSION=${{ steps.extract_info.outputs.tag_version }}
+
+          response=$(curl -s "https://pypi.org/pypi/lerobot/$NEW_VERSION/json")
+          if echo "$response" | grep -q "message"; then
+            echo "Version $NEW_VERSION is available on PyPI. Proceeding with release."
+          else
+            echo "Error: Version $NEW_VERSION already exists on PyPI. Aborting."
+            exit 1
+          fi
+
+      - name: Install build dependencies
+        run: python -m pip install build
+
+      - name: Build package
+        run: python -m build
+
+      - name: Create GitHub Release
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        # zizmor: ignore[template-injection]
+        run: |
+          gh release create ${{ github.ref_name }} \
+            --title "Release ${{ github.ref_name }}" \
+            --generate-notes \
+            --draft=$([[ "${{ github.ref_name }}" == *-* ]] && echo true || echo false) \
+            --prerelease=$([[ "${{ github.ref_name }}" == *-* ]] && echo true || echo false) \
+            ./dist/*
+
+      - 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@v1.12.4 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
+        with:
+          repository-url: https://test.pypi.org/legacy/
+          verbose: true
+          print-hash: true
+
+      - name: Publish to PyPI
+        if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
+        uses: pypa/gh-action-pypi-publish@v1.12.4 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
+        with:
+          verbose: true
+          print-hash: true
+
+  # This job runs end-to-end tests on the release
+  test-release:
+    name: Test Release
+    needs: [build-and-publish]
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    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: ${{ env.PYTHON_VERSION }}
+      - name: Create uv virtual environment
+        run: uv venv
+      - name: Install lerobot release
+        # zizmor: ignore[template-injection]
+        run: |
+          VERSION="${{ needs.build-and-publish.outputs.version }}"
+          if [[ "$VERSION" == *-* ]]; then
+            BASE_VERSION="${VERSION%%-*}"
+            echo "Installing pre-release version $BASE_VERSION from TestPyPI..."
+            uv pip install \
+              --index-url https://test.pypi.org/simple/ \
+              --extra-index-url https://pypi.org/simple \
+              --index-strategy unsafe-best-match \
+               "lerobot[all]==$BASE_VERSION"
+          else
+            echo "Installing release version $VERSION from PyPI..."
+            uv pip install "lerobot[all]==$VERSION"
+          fi
+      - name: Check lerobot version
+        run: uv run python -c "import lerobot; print(lerobot.__version__)"
+
+      - name: Run end-to-end tests
+        run: uv run make test-end-to-end
+
+
+# TODO(Steven): Publish draft/pre-release and to test pypi weekly
+# TODO(Steven): Separate build and publish job
+# TODO(Steven): Tag documentation with the same version as the package

.github/workflows/security.yml

@@ -0,0 +1,54 @@
+# 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 secret scanning using TruffleHog to detect sensitive information in the codebase.
+name: Security
+permissions:
+  contents: read
+
+on:
+  # Allows running this workflow manually from the Actions tab
+  workflow_dispatch:
+
+  # Triggers the workflow on push events to main
+  push:
+    branches:
+      - main
+
+  # Triggers the workflow on pull request events targeting main
+  pull_request:
+    branches:
+      - main
+
+# Ensures that only the latest commit for a PR or branch is built, canceling older runs.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  # This job runs TruffleHog to scan the full history of the repository for secrets.
+  trufflehog:
+    name: Secret Leaks Scan
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          fetch-depth: 0
+          persist-credentials: false
+
+      - name: Secret Scanning
+        uses: trufflesecurity/trufflehog@v3.90.0  # zizmor: ignore[unpinned-uses]
+        with:
+          extra_args: --only-verified

.github/workflows/test-docker-build.yml

@@ -1,82 +0,0 @@
-# Copyright 2024 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.
-
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/test-docker-build.yml
-name: Test Dockerfiles
-
-on:
-  pull_request:
-    paths:
-      # Run only when DockerFile files are modified
-      - "docker/**"
-
-permissions: {}
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  get_changed_files:
-    name: Detect modified Dockerfiles
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          persist-credentials: false
-
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
-        with:
-          files: docker/**
-          json: "true"
-
-      - name: Run step if only the files listed above change  # zizmor: ignore[template-injection]
-        if: steps.changed-files.outputs.any_changed == 'true'
-        id: set-matrix
-        run: |
-          echo "matrix=${{ steps.changed-files.outputs.all_changed_files}}" >> $GITHUB_OUTPUT
-
-  build_modified_dockerfiles:
-    name: Build modified Docker images
-    needs: get_changed_files
-    runs-on:
-      group: aws-general-8-plus
-    if: needs.get_changed_files.outputs.matrix != ''
-    strategy:
-      fail-fast: false
-      matrix:
-        docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
-    steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
-        with:
-          cache-binary: false
-
-      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          persist-credentials: false
-
-      - name: Build Docker image
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
-        with:
-          file: ${{ matrix.docker-file }}
-          context: .
-          push: False
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}

.github/workflows/test.yml

@@ -1,150 +0,0 @@
-# Copyright 2024 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.
-
-name: Tests
-
-on:
-  pull_request:
-    paths:
-      - "src/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "pyproject.toml"
-      - ".pre-commit-config.yaml"
-      - "Makefile"
-      - ".cache/**"
-  push:
-    branches:
-      - main
-    paths:
-      - "src/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "pyproject.toml"
-      - ".pre-commit-config.yaml"
-      - "Makefile"
-      - ".cache/**"
-
-permissions: {}
-
-env:
-  UV_VERSION: "0.6.0"
-
-jobs:
-  pytest:
-    name: Pytest
-    runs-on: ubuntu-latest
-    env:
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          lfs: true  # Ensure LFS files are pulled
-          persist-credentials: false
-
-      - name: Install apt dependencies
-      # portaudio19-dev is needed to install pyaudio
-        run: |
-          sudo apt-get update && \
-          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
-
-      - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
-        with:
-          enable-cache: true
-          version: ${{ env.UV_VERSION }}
-          python-version: "3.10"
-
-      - name: Install lerobot (all extras)
-        run: uv sync --all-extras
-
-      - name: Test with pytest
-        run: |
-          uv run pytest tests -v --cov=./src/lerobot --durations=0 \
-            -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-            -W ignore::UserWarning:torch.utils.data.dataloader:558 \
-            -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
-            && rm -rf tests/outputs outputs
-
-  pytest-minimal:
-    name: Pytest (minimal install)
-    runs-on: ubuntu-latest
-    env:
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          lfs: true  # Ensure LFS files are pulled
-          persist-credentials: false
-
-      - name: Install apt dependencies
-        run: sudo apt-get update && sudo apt-get install -y ffmpeg
-
-      - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
-        with:
-          enable-cache: true
-          version: ${{ env.UV_VERSION }}
-          python-version: "3.10"
-
-      - name: Install lerobot
-        run: uv sync --extra "test"
-
-      - name: Test with pytest
-        run: |
-          uv run pytest tests -v --cov=./src/lerobot --durations=0 \
-            -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-            -W ignore::UserWarning:torch.utils.data.dataloader:558 \
-            -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
-            && rm -rf tests/outputs outputs
-
-  end-to-end:
-    name: End-to-end
-    runs-on: ubuntu-latest
-    env:
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          lfs: true  # Ensure LFS files are pulled
-          persist-credentials: false
-
-      - name: Install apt dependencies
-      # portaudio19-dev is needed to install pyaudio
-        run: |
-          sudo apt-get update && \
-          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev
-
-      - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
-        with:
-          enable-cache: true
-          version: ${{ env.UV_VERSION }}
-          python-version: "3.10"
-
-      - name: Install lerobot (all extras)
-        run: |
-          uv venv
-          uv sync --all-extras
-
-      - name: venv
-        run: |
-          echo "PYTHON_PATH=${{ github.workspace }}/.venv/bin/python" >> $GITHUB_ENV
-
-      - name: Test end-to-end
-        run: |
-          make test-end-to-end \
-            && rm -rf outputs

.github/workflows/upload_pr_documentation.yml

@@ -1,16 +0,0 @@
-name: Upload PR Documentation
-
-on: # zizmor: ignore[dangerous-triggers] We follow the same pattern as in Transformers
-  workflow_run:
-    workflows: [ "Build PR Documentation" ]
-    types:
-    - completed
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
-    with:
-      package_name: lerobot
-    secrets:
-      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
-      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

.gitignore

@@ -12,164 +12,164 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Dev scripts
-.dev
-
-# Logging
-logs
-tmp
-wandb
-
-# Data
-data
-outputs
-
-# Apple
-.DS_Store
-
-# VS Code
-.vscode
-.devcontainer
-
-# HPC
-nautilus/*.yaml
-*.key
-
-# Slurm
-sbatch*.sh
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# uv/poetry lock files
-poetry.lock
-uv.lock
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-!tests/artifacts
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-
-# Ignore .cache
-.cache/*
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
+### Environments & Dependencies ###
 .env
 .venv
 env/
 venv/
 env.bak/
 venv.bak/
+.python-version
+__pypackages__/
+node_modules/
 
-# Spyder project settings
+# Lock files
+poetry.lock
+uv.lock
+Pipfile.lock
+
+### Build & Distribution ###
+build/
+dist/
+sdist/
+wheels/
+downloads/
+eggs/
+.eggs/
+parts/
+var/
+pip-wheel-metadata/
+share/python-wheels/
+develop-eggs/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+lib/
+lib64/
+
+# PyInstaller
+*.manifest
+*.spec
+
+### Compiled & Cached Files ###
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+*.sage.py
+.cache/
+.ruff_cache/
+.mypy_cache/
+.pyre/
+.pytype/
+cython_debug/
+
+### Testing & Coverage ###
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.pytest_cache/
+.hypothesis/
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+!tests/artifacts
+
+### Logs & Temporary Files ###
+logs/
+tmp/
+*.log
+pip-log.txt
+pip-delete-this-directory.txt
+celerybeat-schedule
+celerybeat.pid
+
+### IDE & Editor Config ###
+# VS Code
+.vscode/
+.devcontainer/
+
+# JetBrains / PyCharm
+.idea/
+
+# Spyder
 .spyderproject
 .spyproject
 
-# Rope project settings
+# Rope
 .ropeproject
 
-# mkdocs documentation
+# Vim
+*.swp
+
+# Other
+*~
+
+### OS Specific ###
+# macOS
+.DS_Store
+
+# Windows
+Thumbs.db
+
+### Framework & Tool Specific ###
+
+.Python
+
+# Django
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask
+instance/
+.webassets-cache
+
+# Scrapy
+.scrapy
+
+# Jupyter
+.ipynb_checkpoints/
+profile_default/
+ipython_config.py
+
+# Sphinx
+docs/_build/
+
+# MkDocs
 /site
 
+# PyBuilder
+.pybuilder/
+target/
+
 # mypy
-.mypy_cache/
 .dmypy.json
 dmypy.json
 
-# Pyre type checker
-.pyre/
+### HPC & Slurm ###
+nautilus/*.yaml
+*.key
+sbatch*.sh
 
-# pytype static type analyzer
-.pytype/
+### Miscellaneous ###
+# W&B
+wandb/
 
-# Cython debug symbols
-cython_debug/
+# Dev scripts
+.dev/
+
+# Data folders
+data/
+outputs/
+
+# Translations
+*.mo
+*.pot
+
+# Dev folders
+.cache/*

.pre-commit-config.yaml

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-exclude: "tests/artifacts/.*\\.safetensors$"
 default_language_version:
     python: python3.10
+
+exclude: "tests/artifacts/.*\\.safetensors$"
+
 repos:
   ##### Meta #####
   - repo: meta
@@ -22,12 +24,12 @@ repos:
       - id: check-useless-excludes
       - id: check-hooks-apply
 
-
-  ##### Style / Misc. #####
+   ##### General Code Quality & Formatting #####
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0
     hooks:
       - id: check-added-large-files
+        args: ['--maxkb=1024']
       - id: debug-statements
       - id: check-merge-conflict
       - id: check-case-conflict
@@ -36,6 +38,13 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
 
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    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: v1.34.0
     hooks:
@@ -46,14 +55,16 @@ repos:
     rev: v3.20.0
     hooks:
     -   id: pyupgrade
+        args: [--py310-plus]
 
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.3
+  ##### Markdown Quality #####
+  - repo: https://github.com/rbubley/mirrors-prettier
+    rev: v3.6.2
     hooks:
-      - id: ruff
-        args: [--fix]
-      - id: ruff-format
-
+      - id: prettier
+        name: Format Markdown with Prettier
+        types_or: [markdown, mdx]
+        args: [--prose-wrap=preserve]
 
   ##### Security #####
   - repo: https://github.com/gitleaks/gitleaks
@@ -72,3 +83,25 @@ repos:
     - id: bandit
       args: ["-c", "pyproject.toml"]
       additional_dependencies: ["bandit[toml]"]
+
+  # TODO(Steven): Uncomment when ready to use
+  ##### Static Analysis & Typing #####
+  # - repo: https://github.com/pre-commit/mirrors-mypy
+  #   rev: v1.16.0
+  #   hooks:
+  #     - id: mypy
+  #       args: [--python-version=3.10]
+
+  ##### Docstring Checks #####
+  # - repo: https://github.com/akaihola/darglint2
+  #   rev: v1.8.2
+  #   hooks:
+  #     - id: darglint2
+  #       args: ["--docstring-style", "google", "-v", "2"]
+  #       exclude: ^tests/.*$
+
+  # - repo: https://github.com/econchick/interrogate
+  #   rev: 1.7.0
+  #   hooks:
+  #     - id: interrogate
+  #       args: ["-vv", "--config=pyproject.toml"]

CODE_OF_CONDUCT.md

@@ -1,4 +1,3 @@
-
 # Contributor Covenant Code of Conduct
 
 ## Our Pledge
@@ -18,23 +17,23 @@ diverse, inclusive, and healthy community.
 Examples of behavior that contributes to a positive environment for our
 community include:
 
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
+- Demonstrating empathy and kindness toward other people
+- Being respectful of differing opinions, viewpoints, and experiences
+- Giving and gracefully accepting constructive feedback
+- Accepting responsibility and apologizing to those affected by our mistakes,
   and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
+- Focusing on what is best not just for us as individuals, but for the overall
   community
 
 Examples of unacceptable behavior include:
 
-* The use of sexualized language or imagery, and sexual attention or advances of
+- The use of sexualized language or imagery, and sexual attention or advances of
   any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email address,
+- Trolling, insulting or derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or email address,
   without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
+- Other conduct which could reasonably be considered inappropriate in a
   professional setting
 
 ## Enforcement Responsibilities

CONTRIBUTING.md

@@ -15,10 +15,11 @@ Whichever way you choose to contribute, please be mindful to respect our
 ## You can contribute in so many ways!
 
 Some of the ways you can contribute to 🤗 LeRobot:
-* Fixing outstanding issues with the existing code.
-* Implementing new models, datasets or simulation environments.
-* Contributing to the examples or to the documentation.
-* Submitting issues related to bugs or desired new features.
+
+- Fixing outstanding issues with the existing code.
+- Implementing new models, datasets or simulation environments.
+- Contributing to the examples or to the documentation.
+- Submitting issues related to bugs or desired new features.
 
 Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](mailto:remi.cadene@huggingface.co).
 
@@ -40,24 +41,26 @@ already reported** (use the search bar on Github under Issues).
 
 Did not find it? :( So we can act quickly on it, please follow these steps:
 
-* Include your **OS type and version**, the versions of **Python** and **PyTorch**.
-* A short, self-contained, code snippet that allows us to reproduce the bug in
+- Include your **OS type and version**, the versions of **Python** and **PyTorch**.
+- A short, self-contained, code snippet that allows us to reproduce the bug in
   less than 30s.
-* The full traceback if an exception is raised.
-* Attach any other additional information, like screenshots, you think may help.
+- The full traceback if an exception is raised.
+- Attach any other additional information, like screenshots, you think may help.
 
 ### Do you want a new feature?
 
 A good feature request addresses the following points:
 
 1. Motivation first:
-* Is it related to a problem/frustration with the library? If so, please explain
+
+- Is it related to a problem/frustration with the library? If so, please explain
   why. Providing a code snippet that demonstrates the problem is best.
-* Is it related to something you would need for a project? We'd love to hear
+- Is it related to something you would need for a project? We'd love to hear
   about it!
-* Is it something you worked on and think could benefit the community?
+- Is it something you worked on and think could benefit the community?
   Awesome! Tell us what problem it solved for you.
-2. Write a *paragraph* describing the feature.
+
+2. Write a _paragraph_ describing the feature.
 3. Provide a **code snippet** that demonstrates its future use.
 4. In case this is related to a paper, please attach a link.
 5. Attach any additional information (drawings, screenshots, etc.) you think may help.
@@ -74,12 +77,15 @@ environments ([aloha](https://github.com/huggingface/gym-aloha),
 and follow the same api design.
 
 When implementing a new dataset loadable with LeRobotDataset follow these steps:
+
 - Update `available_datasets_per_env` in `lerobot/__init__.py`
 
 When implementing a new environment (e.g. `gym_aloha`), follow these steps:
+
 - Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py`
 
 When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
+
 - Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py`
 - Set the required `name` class attribute.
 - Update variables in `tests/test_available.py` by importing your new Policy class
@@ -133,11 +139,13 @@ Follow these steps to start contributing:
    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 or miniconda:
+
    ```bash
    conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev
    ```
 
    If you're using `uv`, it can manage python versions so you can instead do:
+
    ```bash
    uv venv --python 3.10 && source .venv/bin/activate
    ```
@@ -145,11 +153,13 @@ Follow these steps to start contributing:
    To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
 
    using `poetry`
+
    ```bash
    poetry sync --extras "dev test"
    ```
 
    using `uv`
+
    ```bash
    uv sync --extra dev --extra test
    ```
@@ -157,43 +167,48 @@ Follow these steps to start contributing:
    You can also install the project with all its dependencies (including environments):
 
    using `poetry`
+
    ```bash
    poetry sync --all-extras
    ```
 
    using `uv`
+
    ```bash
    uv sync --all-extras
    ```
 
-   > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they *will* be tested in the CI. In general, we advise you to install everything and test locally before pushing.
+   > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they _will_ be tested in the CI. In general, we advise you to install everything and test locally before pushing.
 
    Whichever command you chose to install the project (e.g. `poetry sync --all-extras`), you should run it again when pulling code with an updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the new dependencies.
 
    The equivalent of `pip install some-package`, would just be:
 
    using `poetry`
+
    ```bash
    poetry add some-package
    ```
 
    using `uv`
+
    ```bash
    uv add some-package
    ```
 
    When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
    using `poetry`
+
    ```bash
    poetry lock
    ```
 
    using `uv`
+
    ```bash
    uv lock
    ```
 
-
 5. Develop the features on your branch.
 
    As you work on the features, you should make sure that the test suite
@@ -211,11 +226,13 @@ Follow these steps to start contributing:
    automatically as Git commit hooks.
 
    Install `pre-commit` hooks:
+
    ```bash
    pre-commit install
    ```
 
    You can run these hooks whenever you need on staged files with:
+
    ```bash
    pre-commit
    ```
@@ -229,6 +246,7 @@ Follow these steps to start contributing:
    ```
 
    Note, if you already committed some changes that have a wrong formatting, you can use:
+
    ```bash
    pre-commit run --all-files
    ```
@@ -249,16 +267,15 @@ Follow these steps to start contributing:
    git push -u origin a-descriptive-name-for-my-changes
    ```
 
-6. Once you are satisfied (**and the checklist below is happy too**), go to the
+7. Once you are satisfied (**and the checklist below is happy too**), go to the
    webpage of your fork on GitHub. Click on 'Pull request' to send your changes
    to the project maintainers for review.
 
-7. It's ok if maintainers ask you for changes. It happens to core contributors
+8. It's ok if maintainers ask you for changes. It happens to core contributors
    too! So everyone can see the changes in the Pull request, work in your local
    branch and push the changes to your fork. They will automatically appear in
    the pull request.
 
-
 ### Checklist
 
 1. The title of your pull request should be a summary of its contribution;
@@ -277,18 +294,21 @@ An extensive test suite is included to test the library behavior and several exa
 Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
 
 On Mac:
+
 ```bash
 brew install git-lfs
 git lfs install
 ```
 
 On Ubuntu:
+
 ```bash
 sudo apt-get install git-lfs
 git lfs install
 ```
 
 Pull artifacts if they're not in [tests/artifacts](tests/artifacts)
+
 ```bash
 git lfs pull
 ```
@@ -300,6 +320,5 @@ repository, here's how to run tests with `pytest` for the library:
 python -m pytest -sv ./tests
 ```
 
-
 You can specify a smaller set of tests in order to test only the feature
 you're working on.

Makefile

@@ -26,11 +26,11 @@ export PATH := $(dir $(PYTHON_PATH)):$(PATH)
 
 DEVICE ?= cpu
 
-build-cpu:
-	docker build -t lerobot:latest -f docker/lerobot-cpu/Dockerfile .
+build-user:
+	docker build -f docker/Dockerfile.user -t lerobot-user .
 
-build-gpu:
-	docker build -t lerobot:latest -f docker/lerobot-gpu/Dockerfile .
+build-internal:
+	docker build -f docker/Dockerfile.internal -t lerobot-internal .
 
 test-end-to-end:
 	${MAKE} DEVICE=$(DEVICE) test-act-ete-train
@@ -44,7 +44,7 @@ test-end-to-end:
 	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
 
 test-act-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=act \
 		--policy.dim_model=64 \
 		--policy.n_action_steps=20 \
@@ -68,12 +68,12 @@ test-act-ete-train:
 		--output_dir=tests/outputs/act/
 
 test-act-ete-train-resume:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
 		--resume=true
 
 test-act-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \
@@ -82,7 +82,7 @@ test-act-ete-eval:
 		--eval.batch_size=1
 
 test-diffusion-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=diffusion \
 		--policy.down_dims='[64,128,256]' \
 		--policy.diffusion_step_embed_dim=32 \
@@ -106,7 +106,7 @@ test-diffusion-ete-train:
 		--output_dir=tests/outputs/diffusion/
 
 test-diffusion-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=pusht \
@@ -115,7 +115,7 @@ test-diffusion-ete-eval:
 		--eval.batch_size=1
 
 test-tdmpc-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
 		--policy.push_to_hub=false \
@@ -137,7 +137,7 @@ test-tdmpc-ete-train:
 		--output_dir=tests/outputs/tdmpc/
 
 test-tdmpc-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=xarm \
@@ -148,7 +148,7 @@ test-tdmpc-ete-eval:
 
 
 test-smolvla-ete-train:
-	python -m lerobot.scripts.train \
+	lerobot-train \
 		--policy.type=smolvla \
 		--policy.n_action_steps=20 \
 		--policy.chunk_size=20 \
@@ -171,7 +171,7 @@ test-smolvla-ete-train:
 		--output_dir=tests/outputs/smolvla/
 
 test-smolvla-ete-eval:
-	python -m lerobot.scripts.eval \
+	lerobot-eval \
 		--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \

README.md

@@ -1,25 +1,21 @@
 <p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="media/lerobot-logo-thumbnail.png">
-    <source media="(prefers-color-scheme: light)" srcset="media/lerobot-logo-thumbnail.png">
-    <img alt="LeRobot, Hugging Face Robotics Library" src="media/lerobot-logo-thumbnail.png" style="max-width: 100%;">
-  </picture>
+  <img alt="LeRobot, Hugging Face Robotics Library" src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/lerobot-logo-thumbnail.png" width="100%">
   <br/>
   <br/>
 </p>
 
 <div align="center">
 
-[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly-tests.yml?query=branch%3Amain)
-[![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot)
+[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml?query=branch%3Amain)
 [![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
 [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
 [![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/)
-[![Examples](https://img.shields.io/badge/Examples-green.svg)](https://github.com/huggingface/lerobot/tree/main/examples)
-[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1%20adopted-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md)
+[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md)
 [![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
 
+<!-- [![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot) -->
+
 </div>
 
 <h2 align="center">
@@ -29,10 +25,10 @@
 
 <div align="center">
   <img
-    src="media/hope_jr/hopejr.png?raw=true"
+    src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/hope_jr/hopejr.png"
     alt="HopeJR robot"
     title="HopeJR robot"
-    style="width: 60%;"
+    width="60%"
   />
 
   <p><strong>Meet HopeJR – A humanoid robot arm and hand for dexterous manipulation!</strong></p>
@@ -51,21 +47,12 @@
 </h2>
 
 <div align="center">
-  <div style="display: flex; gap: 1rem; justify-content: center; align-items: center;" >
-    <img
-      src="media/so101/so101.webp?raw=true"
-      alt="SO-101 follower arm"
-      title="SO-101 follower arm"
-      style="width: 40%;"
-    />
-    <img
-      src="media/so101/so101-leader.webp?raw=true"
-      alt="SO-101 leader arm"
-      title="SO-101 leader arm"
-      style="width: 40%;"
-    />
-  </div>
-
+  <table>
+    <tr>
+      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101.webp" alt="SO-101 follower arm" title="SO-101 follower arm" width="90%"/></td>
+      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101-leader.webp" alt="SO-101 leader arm" title="SO-101 leader arm" width="90%"/></td>
+    </tr>
+  </table>
 
   <p><strong>Meet the updated SO100, the SO-101 – Just €114 per arm!</strong></p>
   <p>Train it in minutes with a few simple moves on your laptop.</p>
@@ -77,7 +64,7 @@
   <p>Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!</p>
   <p>Check out the <a href="https://huggingface.co/docs/lerobot/lekiwi">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
 
-  <img src="media/lekiwi/kiwi.webp?raw=true" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
+  <img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/lekiwi/kiwi.webp" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
 </div>
 
 <br/>
@@ -100,9 +87,9 @@
 
 <table>
   <tr>
-    <td><img src="media/gym/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
-    <td><img src="media/gym/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
-    <td><img src="media/gym/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
+    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
+    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
+    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
   </tr>
   <tr>
     <td align="center">ACT policy on ALOHA env</td>
@@ -111,61 +98,97 @@
   </tr>
 </table>
 
-### Acknowledgment
-
-- The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla).
-- Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
-- Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
-- Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
-- Thanks to Antonio Loquercio and Ashish Kumar for their early support.
-- Thanks to [Seungjae (Jay) Lee](https://sjlee.cc/), [Mahi Shafiullah](https://mahis.life/) and colleagues for open sourcing [VQ-BeT](https://sjlee.cc/vq-bet/) policy and helping us adapt the codebase to our repository. The policy is adapted from [VQ-BeT repo](https://github.com/jayLEE0301/vq_bet_official).
-
-
 ## Installation
 
-Download our source code:
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
+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 [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
+
 ```bash
 conda create -y -n lerobot python=3.10
 conda activate lerobot
 ```
 
 When using `miniconda`, install `ffmpeg` in your environment:
+
 ```bash
 conda install ffmpeg -c conda-forge
 ```
 
 > **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->  - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->  ```bash
->  conda install ffmpeg=7.1.1 -c conda-forge
->  ```
->  - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+>
+> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
+>
+> ```bash
+> conda install ffmpeg=7.1.1 -c conda-forge
+> ```
+>
+> - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+
+### Install LeRobot 🤗
+
+#### From Source
+
+First, clone the repository and navigate into the directory:
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+```
+
+Then, install the library in editable mode. This is useful if you plan to contribute to the code.
 
-Install 🤗 LeRobot:
 ```bash
 pip install -e .
 ```
 
 > **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
-`sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
+> `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
 
 For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
+
 - [aloha](https://github.com/huggingface/gym-aloha)
 - [xarm](https://github.com/huggingface/gym-xarm)
 - [pusht](https://github.com/huggingface/gym-pusht)
 
 For instance, to install 🤗 LeRobot with aloha and pusht, use:
+
 ```bash
 pip install -e ".[aloha, pusht]"
 ```
 
+### Installation from PyPI
+
+**Core Library:**
+Install the base package with:
+
+```bash
+pip install lerobot
+```
+
+_This installs only the default dependencies._
+
+**Extra Features:**
+To install additional functionality, use one of the following:
+
+```bash
+pip install 'lerobot[all]'          # All available features
+pip install 'lerobot[aloha,pusht]'  # Specific features (Aloha & Pusht)
+pip install 'lerobot[feetech]'      # Feetech motor support
+```
+
+_Replace `[...]` with your desired features._
+
+**Available Tags:**
+For a full list of optional dependencies, see:
+https://pypi.org/project/lerobot/
+
+### Weights & Biases
+
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
+
 ```bash
 wandb login
 ```
@@ -174,9 +197,10 @@ wandb login
 
 ### Visualize datasets
 
-Check out [example 1](./examples/1_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:
+
 ```bash
 python -m lerobot.scripts.visualize_dataset \
     --repo-id lerobot/pusht \
@@ -184,6 +208,7 @@ python -m lerobot.scripts.visualize_dataset \
 ```
 
 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
 python -m lerobot.scripts.visualize_dataset \
     --repo-id lerobot/pusht \
@@ -192,19 +217,17 @@ python -m lerobot.scripts.visualize_dataset \
     --episode-index 0
 ```
 
-
 It will open `rerun.io` and display the camera streams, robot states and actions, like this:
 
 https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
 
-
 Our script can also visualize datasets stored on a distant server. See `python -m lerobot.scripts.visualize_dataset --help` for more instructions.
 
 ### The `LeRobotDataset` format
 
 A dataset in `LeRobotDataset` format is very simple to use. It can be loaded from a repository on the Hugging Face hub or a local folder simply with e.g. `dataset = LeRobotDataset("lerobot/aloha_static_coffee")` and can be indexed into like any Hugging Face and PyTorch dataset. For instance `dataset[0]` will retrieve a single temporal frame from the dataset containing observation(s) and an action as PyTorch tensors ready to be fed to a model.
 
-A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}`  one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](examples/1_load_lerobot_dataset.py) for more details on `delta_timestamps`.
+A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](https://github.com/huggingface/lerobot/blob/main/examples/1_load_lerobot_dataset.py) for more details on `delta_timestamps`.
 
 Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor.
 
@@ -239,6 +262,7 @@ dataset attributes:
 ```
 
 A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely:
+
 - hf_dataset stored using Hugging Face datasets library serialization to parquet
 - videos are stored in mp4 format to save space
 - metadata are stored in plain json/jsonl files
@@ -247,11 +271,12 @@ Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work
 
 ### Evaluate a pretrained policy
 
-Check out [example 2](./examples/2_evaluate_pretrained_policy.py) that illustrates how to download a pretrained policy from Hugging Face hub, and run an evaluation on its corresponding environment.
+Check out [example 2](https://github.com/huggingface/lerobot/blob/main/examples/2_evaluate_pretrained_policy.py) that illustrates how to download a pretrained policy from Hugging Face hub, and run an evaluation on its corresponding environment.
 
 We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
+
 ```bash
-python -m lerobot.scripts.eval \
+lerobot-eval \
     --policy.path=lerobot/diffusion_pusht \
     --env.type=pusht \
     --eval.batch_size=10 \
@@ -263,173 +288,78 @@ python -m lerobot.scripts.eval \
 Note: After training your own policy, you can re-evaluate the checkpoints with:
 
 ```bash
-python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
+lerobot-eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
 ```
 
-See `python -m lerobot.scripts.eval --help` for more instructions.
+See `lerobot-eval --help` for more instructions.
 
 ### Train your own policy
 
-Check out [example 3](./examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](./examples/4_train_policy_with_script.md) that shows how to use our training script from command line.
+Check out [example 3](https://github.com/huggingface/lerobot/blob/main/examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](https://github.com/huggingface/lerobot/blob/main/examples/4_train_policy_with_script.md) that shows how to use our training script from command line.
 
 To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding `--wandb.enable=true`.
 
-A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser. Please also check [here](./examples/4_train_policy_with_script.md#typical-logs-and-metrics) for the explanation of some commonly used metrics in logs.
+A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser. Please also check [here](https://github.com/huggingface/lerobot/blob/main/examples/4_train_policy_with_script.md#typical-logs-and-metrics) for the explanation of some commonly used metrics in logs.
 
-![](media/wandb.png)
+\<img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/wandb.png" alt="WandB logs example"\>
 
-Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
+Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `lerobot-eval --help` for more instructions.
 
 #### Reproduce state-of-the-art (SOTA)
 
 We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
 You can reproduce their training by loading the config from their run. Simply running:
+
 ```bash
-python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
+lerobot-train --config_path=lerobot/diffusion_pusht
 ```
+
 reproduces SOTA results for Diffusion Policy on the PushT task.
 
 ## Contribute
 
 If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md).
 
-<!-- ### Add a new dataset
-
-To add a dataset to the hub, you need to login using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Then point to your raw dataset folder (e.g. `data/aloha_static_pingpong_test_raw`), and push your dataset to the hub with:
-```bash
-python lerobot/scripts/push_dataset_to_hub.py \
---raw-dir data/aloha_static_pingpong_test_raw \
---out-dir data \
---repo-id lerobot/aloha_static_pingpong_test \
---raw-format aloha_hdf5
-```
-
-See `python lerobot/scripts/push_dataset_to_hub.py --help` for more instructions.
-
-If your dataset format is not supported, implement your own in `lerobot/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
-
-
 ### Add a pretrained policy
 
 Once you have trained a policy you may upload it to the Hugging Face hub using a hub id that looks like `${hf_user}/${repo_name}` (e.g. [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)).
 
 You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain:
+
 - `config.json`: A serialized version of the policy configuration (following the policy's dataclass config).
 - `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format.
 - `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility.
 
 To upload these to the hub, run the following:
+
 ```bash
 huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model
 ```
 
-See [eval.py](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/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
 
-### Improve your code with profiling
-
-An example of a code snippet to profile the evaluation of a policy:
-```python
-from torch.profiler import profile, record_function, ProfilerActivity
-
-def trace_handler(prof):
-    prof.export_chrome_trace(f"tmp/trace_schedule_{prof.step_num}.json")
-
-with profile(
-    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-    schedule=torch.profiler.schedule(
-        wait=2,
-        warmup=2,
-        active=3,
-    ),
-    on_trace_ready=trace_handler
-) as prof:
-    with record_function("eval_policy"):
-        for i in range(num_episodes):
-            prof.step()
-            # insert code to profile, potentially whole body of eval_policy function
-```
+- The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla).
+- Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
+- Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
+- Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
+- Thanks to Antonio Loquercio and Ashish Kumar for their early support.
+- Thanks to [Seungjae (Jay) Lee](https://sjlee.cc/), [Mahi Shafiullah](https://mahis.life/) and colleagues for open sourcing [VQ-BeT](https://sjlee.cc/vq-bet/) policy and helping us adapt the codebase to our repository. The policy is adapted from [VQ-BeT repo](https://github.com/jayLEE0301/vq_bet_official).
 
 ## Citation
 
 If you want, you can cite this work with:
+
 ```bibtex
 @misc{cadene2024lerobot,
-    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
+    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
     title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
     howpublished = "\url{https://github.com/huggingface/lerobot}",
     year = {2024}
 }
 ```
 
-Additionally, if you are using any of the particular policy architecture, pretrained models, or datasets, it is recommended to cite the original authors of the work as they appear below:
-- [SmolVLA](https://arxiv.org/abs/2506.01844)
-```bibtex
-@article{shukor2025smolvla,
-  title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
-  author={Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and Alibert, Simon and Cord, Matthieu and Wolf, Thomas and Cadene, Remi},
-  journal={arXiv preprint arXiv:2506.01844},
-  year={2025}
-}
-```
-
-- [Diffusion Policy](https://diffusion-policy.cs.columbia.edu)
-```bibtex
-@article{chi2024diffusionpolicy,
-	author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
-	title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
-	journal = {The International Journal of Robotics Research},
-	year = {2024},
-}
-```
-- [ACT or ALOHA](https://tonyzhaozh.github.io/aloha)
-```bibtex
-@article{zhao2023learning,
-  title={Learning fine-grained bimanual manipulation with low-cost hardware},
-  author={Zhao, Tony Z and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
-  journal={arXiv preprint arXiv:2304.13705},
-  year={2023}
-}
-```
-
-- [TDMPC](https://www.nicklashansen.com/td-mpc/)
-
-```bibtex
-@inproceedings{Hansen2022tdmpc,
-	title={Temporal Difference Learning for Model Predictive Control},
-	author={Nicklas Hansen and Xiaolong Wang and Hao Su},
-	booktitle={ICML},
-	year={2022}
-}
-```
-
-- [VQ-BeT](https://sjlee.cc/vq-bet/)
-```bibtex
-@article{lee2024behavior,
-  title={Behavior generation with latent actions},
-  author={Lee, Seungjae and Wang, Yibin and Etukuru, Haritheja and Kim, H Jin and Shafiullah, Nur Muhammad Mahi and Pinto, Lerrel},
-  journal={arXiv preprint arXiv:2403.03181},
-  year={2024}
-}
-```
-
-
-- [HIL-SERL](https://hil-serl.github.io/)
-```bibtex
-@Article{luo2024hilserl,
-title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
-author={Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
-year={2024},
-eprint={2410.21845},
-archivePrefix={arXiv},
-primaryClass={cs.RO}
-}
-```
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)

benchmarks/video/README.md

@@ -1,28 +1,32 @@
 # Video benchmark
 
-
 ## Questions
+
 What is the optimal trade-off between:
+
 - maximizing loading time with random access,
 - minimizing memory space on disk,
 - maximizing success rate of policies,
 - compatibility across devices/platforms for decoding videos (e.g. video players, web browsers).
 
 How to encode videos?
+
 - Which video codec (`-vcodec`) to use? h264, h265, AV1?
 - What pixel format to use (`-pix_fmt`)? `yuv444p` or `yuv420p`?
 - How much compression (`-crf`)? No compression with `0`, intermediate compression with `25` or extreme with `50+`?
 - Which frequency to chose for key frames (`-g`)? A key frame every `10` frames?
 
 How to decode videos?
+
 - Which `decoder`? `torchvision`, `torchaudio`, `ffmpegio`, `decord`, or `nvc`?
 - What scenarios to use for the requesting timestamps during benchmark? (`timestamps_mode`)
 
-
 ## Variables
+
 **Image content & size**
 We don't expect the same optimal settings for a dataset of images from a simulation, or from real-world in an apartment, or in a factory, or outdoor, or with lots of moving objects in the scene, etc. Similarly, loading times might not vary linearly with the image size (resolution).
 For these reasons, we run this benchmark on four representative datasets:
+
 - `lerobot/pusht_image`: (96 x 96 pixels) simulation with simple geometric shapes, fixed camera.
 - `aliberts/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
 - `aliberts/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
@@ -34,8 +38,9 @@ Note: The datasets used for this benchmark need to be image datasets, not video
 We might revisit this benchmark and find better settings if we train our policies with various data augmentations to make them more robust (e.g. robust to color changes, compression, etc.).
 
 ### Encoding parameters
+
 | parameter   | values                                                       |
-|-------------|--------------------------------------------------------------|
+| ----------- | ------------------------------------------------------------ |
 | **vcodec**  | `libx264`, `libx265`, `libsvtav1`                            |
 | **pix_fmt** | `yuv444p`, `yuv420p`                                         |
 | **g**       | `1`, `2`, `3`, `4`, `5`, `6`, `10`, `15`, `20`, `40`, `None` |
@@ -44,19 +49,23 @@ We might revisit this benchmark and find better settings if we train our policie
 Note that `crf` value might be interpreted differently by various video codecs. In other words, the same value used with one codec doesn't necessarily translate into the same compression level with another codec. In fact, the default value (`None`) isn't the same amongst the different video codecs. Importantly, it is also the case for many other ffmpeg arguments like `g` which specifies the frequency of the key frames.
 
 For a comprehensive list and documentation of these parameters, see the ffmpeg documentation depending on the video codec used:
+
 - h264: https://trac.ffmpeg.org/wiki/Encode/H.264
 - h265: https://trac.ffmpeg.org/wiki/Encode/H.265
 - AV1: https://trac.ffmpeg.org/wiki/Encode/AV1
 
 ### Decoding parameters
+
 **Decoder**
 We tested two video decoding backends from torchvision:
+
 - `pyav`
 - `video_reader` (requires to build torchvision from source)
 
 **Requested timestamps**
 Given the way video decoding works, once a keyframe has been loaded, the decoding of subsequent frames is fast.
 This of course is affected by the `-g` parameter during encoding, which specifies the frequency of the keyframes. Given our typical use cases in robotics policies which might request a few timestamps in different random places, we want to replicate these use cases with the following scenarios:
+
 - `1_frame`: 1 frame,
 - `2_frames`: 2 consecutive frames (e.g. `[t, t + 1 / fps]`),
 - `6_frames`: 6 consecutive frames (e.g. `[t + i / fps for i in range(6)]`)
@@ -64,12 +73,13 @@ This of course is affected by the `-g` parameter during encoding, which specifie
 Note that this differs significantly from a typical use case like watching a movie, in which every frame is loaded sequentially from the beginning to the end and it's acceptable to have big values for `-g`.
 
 Additionally, because some policies might request single timestamps that are a few frames apart, we also have the following scenario:
+
 - `2_frames_4_space`: 2 frames with 4 consecutive frames of spacing in between (e.g `[t, t + 5 / fps]`),
 
 However, due to how video decoding is implemented with `pyav`, we don't have access to an accurate seek so in practice this scenario is essentially the same as `6_frames` since all 6 frames between `t` and `t + 5 / fps` will be decoded.
 
-
 ## Metrics
+
 **Data compression ratio (lower is better)**
 `video_images_size_ratio` is the ratio of the memory space on disk taken by the encoded video over the memory space taken by the original images. For instance, `video_images_size_ratio=25%` means that the video takes 4 times less memory space on disk compared to the original images.
 
@@ -87,18 +97,18 @@ However, due to how video decoding is implemented with `pyav`, we don't have acc
 
 One aspect that can't be measured here with those metrics is the compatibility of the encoding across platforms, in particular on web browser, for visualization purposes.
 h264, h265 and AV1 are all commonly used codecs and should not pose an issue. However, the chroma subsampling (`pix_fmt`) format might affect compatibility:
+
 - `yuv420p` is more widely supported across various platforms, including web browsers.
 - `yuv444p` offers higher color fidelity but might not be supported as broadly.
 
-
 <!-- **Loss of a pretrained policy (higher is better)** (not available)
 `loss_pretrained` is the result of evaluating with the selected encoding/decoding settings a policy pretrained on original images. It is easier to understand than `avg_l2_error`.
 
 **Success rate after retraining (higher is better)** (not available)
 `success_rate` is the result of training and evaluating a policy with the selected encoding/decoding settings. It is the most difficult metric to get but also the very best. -->
 
-
 ## How the benchmark works
+
 The benchmark evaluates both encoding and decoding of video frames on the first episode of each dataset.
 
 **Encoding:** for each `vcodec` and `pix_fmt` pair, we use a default value for `g` and `crf` upon which we change a single value (either `g` or `crf`) to one of the specified values (we don't test every combination of those as this would be computationally too heavy).
@@ -110,15 +120,18 @@ Intermediate results saved for each `vcodec` and `pix_fmt` combination in csv ta
 These are then all concatenated to a single table ready for analysis.
 
 ## Caveats
+
 We tried to measure the most impactful parameters for both encoding and decoding. However, for computational reasons we can't test out every combination.
 
 Additional encoding parameters exist that are not included in this benchmark. In particular:
+
 - `-preset` which allows for selecting encoding presets. This represents a collection of options that will provide a certain encoding speed to compression ratio. By leaving this parameter unspecified, it is considered to be `medium` for libx264 and libx265 and `8` for libsvtav1.
 - `-tune` which allows to optimize the encoding for certain aspects (e.g. film quality, fast decoding, etc.).
 
 See the documentation mentioned above for more detailed info on these settings and for a more comprehensive list of other parameters.
 
 Similarly on the decoding side, other decoders exist but are not implemented in our current benchmark. To name a few:
+
 - `torchaudio`
 - `ffmpegio`
 - `decord`
@@ -127,16 +140,17 @@ Similarly on the decoding side, other decoders exist but are not implemented in
 Note as well that since we are mostly interested in the performance at decoding time (also because encoding is done only once before uploading a dataset), we did not measure encoding times nor have any metrics regarding encoding.
 However, besides the necessity to build ffmpeg from source, encoding did not pose any issue and it didn't take a significant amount of time during this benchmark.
 
-
 ## Install
+
 Building ffmpeg from source is required to include libx265 and libaom/libsvtav1 (av1) video codecs ([compilation guide](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu)).
 
 **Note:** While you still need to build torchvision with a conda-installed `ffmpeg<4.3` to use the `video_reader` decoder (as described in [#220](https://github.com/huggingface/lerobot/pull/220)), you also need another version which is custom-built with all the video codecs for encoding. For the script to then use that version, you can prepend the command above with `PATH="$HOME/bin:$PATH"`, which is where ffmpeg should be built.
 
-
 ## Adding a video decoder
+
 Right now, we're only benchmarking the two video decoder available with torchvision: `pyav` and `video_reader`.
 You can easily add a new decoder to benchmark by adding it to this function in the script:
+
 ```diff
 def decode_video_frames(
     video_path: str,
@@ -156,9 +170,10 @@ def decode_video_frames(
         raise NotImplementedError(backend)
 ```
 
-
 ## Example
+
 For a quick run, you can try these parameters:
+
 ```bash
 python benchmark/video/run_video_benchmark.py \
     --output-dir outputs/video_benchmark \
@@ -176,11 +191,12 @@ python benchmark/video/run_video_benchmark.py \
     --save-frames 0
 ```
 
-
 ## Results
 
 ### Reproduce
+
 We ran the benchmark with the following parameters:
+
 ```bash
 # h264 and h265 encodings
 python benchmark/video/run_video_benchmark.py \
@@ -221,9 +237,10 @@ python benchmark/video/run_video_benchmark.py \
 
 The full results are available [here](https://docs.google.com/spreadsheets/d/1OYJB43Qu8fC26k_OyoMFgGBBKfQRCi4BIuYitQnq3sw/edit?usp=sharing)
 
-
 ### Parameters selected for LeRobotDataset
+
 Considering these results, we chose what we think is the best set of encoding parameter:
+
 - vcodec: `libsvtav1`
 - pix-fmt: `yuv420p`
 - g: `2`
@@ -236,7 +253,7 @@ Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_read
 These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_frames` and `backend=pyav`
 
 | video_images_size_ratio            | vcodec     | pix_fmt |           |           |           |
-|------------------------------------|------------|---------|-----------|-----------|-----------|
+| ---------------------------------- | ---------- | ------- | --------- | --------- | --------- |
 |                                    | libx264    |         | libx265   |           | libsvtav1 |
 | repo_id                            | yuv420p    | yuv444p | yuv420p   | yuv444p   | yuv420p   |
 | lerobot/pusht_image                | **16.97%** | 17.58%  | 18.57%    | 18.86%    | 22.06%    |
@@ -245,7 +262,7 @@ These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_
 | aliberts/kitchen                   | 1.40%      | 1.39%   | **1.00%** | **1.00%** | 2.52%     |
 
 | video_images_load_time_ratio       | vcodec  | pix_fmt |          |         |           |
-|------------------------------------|---------|---------|----------|---------|-----------|
+| ---------------------------------- | ------- | ------- | -------- | ------- | --------- |
 |                                    | libx264 |         | libx265  |         | libsvtav1 |
 | repo_id                            | yuv420p | yuv444p | yuv420p  | yuv444p | yuv420p   |
 | lerobot/pusht_image                | 6.45    | 5.19    | **1.90** | 2.12    | 2.47      |
@@ -254,7 +271,7 @@ These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_
 | aliberts/kitchen                   | 1.46    | 1.46    | 0.28     | 0.51    | **0.26**  |
 
 |                                    |          | vcodec   | pix_fmt      |          |           |              |
-|------------------------------------|----------|----------|--------------|----------|-----------|--------------|
+| ---------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
 |                                    |          | libx264  |              | libx265  |           | libsvtav1    |
 | repo_id                            | metric   | yuv420p  | yuv444p      | yuv420p  | yuv444p   | yuv420p      |
 | lerobot/pusht_image                | avg_mse  | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04  | 2.19E-04     |

docker/Dockerfile.internal

@@ -0,0 +1,84 @@
+# 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 Dockerfile is designed for HuggingFace internal CI environments
+# that require GPU access. It starts from an NVIDIA CUDA base image.
+
+# docker build -f docker/Dockerfile.internal -t lerobot-internal .
+
+# Configure the base image for CI with GPU access
+# TODO(Steven): Bump these versions
+ARG CUDA_VERSION=12.4.1
+ARG OS_VERSION=22.04
+FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
+
+# Define Python version argument
+ARG PYTHON_VERSION=3.10
+
+# Configure environment variables
+ENV DEBIAN_FRONTEND=noninteractive \
+    MUJOCO_GL=egl \
+    PATH=/lerobot/.venv/bin:$PATH \
+    CUDA_VISIBLE_DEVICES=0 \
+    TEST_TYPE=single_gpu \
+    DEVICE=cuda
+
+# Install Python, system dependencies, and uv (as root)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common build-essential git curl \
+    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+    libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends \
+       python${PYTHON_VERSION} \
+       python${PYTHON_VERSION}-venv \
+       python${PYTHON_VERSION}-dev \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh \
+    && mv /root/.local/bin/uv /usr/local/bin/uv \
+    && useradd --create-home --shell /bin/bash user_lerobot \
+    && usermod -aG sudo user_lerobot \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# Create application directory and set permissions
+WORKDIR /lerobot
+RUN chown -R user_lerobot:user_lerobot /lerobot
+
+# Switch to the non-root user
+USER user_lerobot
+
+# Environment variables for the testing
+ENV HOME=/home/user_lerobot \
+    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
+
+# Create the virtual environment
+# We use a virtual environment inside the container—even though the container itself \
+# provides isolation—to ensure compatibility with the cluster and to prevent \
+# issues with MuJoCo and OpenGL drivers.
+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/
+RUN uv pip install --no-cache ".[all]"
+
+# Copy the rest of the application source code
+# Make sure to have the git-LFS files for testing
+COPY --chown=user_lerobot:user_lerobot . .
+
+# Set the default command
+CMD ["/bin/bash"]

docker/Dockerfile.user

@@ -0,0 +1,70 @@
+# 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 Dockerfile is designed for a lerobot user who wants to
+# experiment with the project. It starts from an Python Slim base image.
+
+# docker build -f docker/Dockerfile.user -t lerobot-user .
+# docker run -it --rm lerobot-user
+
+# Configure the base image
+ARG PYTHON_VERSION=3.10
+FROM python:${PYTHON_VERSION}-slim
+
+# Configure environment variables
+ENV DEBIAN_FRONTEND=noninteractive \
+    MUJOCO_GL=egl \
+    PATH=/lerobot/.venv/bin:$PATH
+
+# Install system dependencies and uv (as root)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential git curl libglib2.0-0 libegl1-mesa-dev ffmpeg \
+    libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh \
+    && mv /root/.local/bin/uv /usr/local/bin/uv \
+    && useradd --create-home --shell /bin/bash user_lerobot \
+    && usermod -aG sudo user_lerobot \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# Create application directory and set permissions
+WORKDIR /lerobot
+RUN chown -R user_lerobot:user_lerobot /lerobot
+
+# Switch to the non-root user
+USER user_lerobot
+
+# Environment variables for the testing
+ENV HOME=/home/user_lerobot \
+    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
+
+# Create the virtual environment
+# We use a virtual environment inside the container—even though the container itself \
+# provides isolation—to closely resemble local development and allow users to \
+# run other Python projects in the same container without dependency conflicts.
+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/
+RUN uv pip install --no-cache ".[all]"
+
+# Copy the rest of the application code
+# Make sure to have the git-LFS files for testing
+COPY --chown=user_lerobot:user_lerobot . .
+
+# Set the default command
+CMD ["/bin/bash"]

docker/lerobot-cpu/Dockerfile

@@ -1,29 +0,0 @@
-# Configure image
-ARG PYTHON_VERSION=3.10
-FROM python:${PYTHON_VERSION}-slim
-
-# Configure environment variables
-ARG PYTHON_VERSION
-ENV DEBIAN_FRONTEND=noninteractive
-ENV MUJOCO_GL="egl"
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Install dependencies and set up Python in a single layer
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake git \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
-    speech-dispatcher libgeos-dev \
-    && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \
-    && python -m venv /opt/venv \
-    && apt-get clean && rm -rf /var/lib/apt/lists/* \
-    && echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Clone repository and install LeRobot in a single layer
-COPY . /lerobot
-WORKDIR /lerobot
-RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, smolvla]" \
-        --extra-index-url https://download.pytorch.org/whl/cpu
-
-# Execute in bash shell rather than python
-CMD ["/bin/bash"]

docker/lerobot-gpu-dev/Dockerfile

@@ -1,68 +0,0 @@
-FROM nvidia/cuda:12.2.2-devel-ubuntu22.04
-
-# Configure image
-ARG PYTHON_VERSION=3.10
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Install apt dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake \
-    git git-lfs openssh-client \
-    nano vim less util-linux tree \
-    htop atop nvtop \
-    sed gawk grep curl wget zip unzip \
-    tcpdump sysstat screen tmux \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
-    speech-dispatcher portaudio19-dev libgeos-dev \
-    python${PYTHON_VERSION} python${PYTHON_VERSION}-venv python${PYTHON_VERSION}-dev \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# Install ffmpeg build dependencies. See:
-# https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu
-# TODO(aliberts): create image to build dependencies from source instead
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    autoconf automake yasm \
-    libass-dev \
-    libfreetype6-dev \
-    libgnutls28-dev \
-    libunistring-dev \
-    libmp3lame-dev \
-    libtool \
-    libvorbis-dev \
-    meson \
-    ninja-build \
-    pkg-config \
-    texinfo \
-    yasm \
-    zlib1g-dev \
-    nasm \
-    libx264-dev \
-    libx265-dev libnuma-dev \
-    libvpx-dev \
-    libfdk-aac-dev \
-    libopus-dev \
-    libsvtav1-dev libsvtav1enc-dev libsvtav1dec-dev \
-    libdav1d-dev
-
-# Install gh cli tool
-RUN (type -p wget >/dev/null || (apt update && apt-get install wget -y)) \
-    && mkdir -p -m 755 /etc/apt/keyrings \
-    && wget -qO- https://cli.github.com/packages/githubcli-archive-keyring.gpg | tee /etc/apt/keyrings/githubcli-archive-keyring.gpg > /dev/null \
-    && chmod go+r /etc/apt/keyrings/githubcli-archive-keyring.gpg \
-    && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | tee /etc/apt/sources.list.d/github-cli.list > /dev/null \
-    && apt update \
-    && apt install gh -y \
-    && apt clean && rm -rf /var/lib/apt/lists/*
-
-# Setup `python`
-RUN ln -s /usr/bin/python3 /usr/bin/python
-
-# Install poetry
-RUN curl -sSL https://install.python-poetry.org | python -
-ENV PATH="/root/.local/bin:$PATH"
-RUN echo 'if [ "$HOME" != "/root" ]; then ln -sf /root/.local/bin/poetry $HOME/.local/bin/poetry; fi' >> /root/.bashrc
-RUN poetry config virtualenvs.create false
-RUN poetry config virtualenvs.in-project true
-
-# Set EGL as the rendering backend for MuJoCo
-ENV MUJOCO_GL="egl"

docker/lerobot-gpu/Dockerfile

@@ -1,24 +0,0 @@
-FROM nvidia/cuda:12.4.1-base-ubuntu22.04
-
-# Configure environment variables
-ARG PYTHON_VERSION=3.10
-ENV DEBIAN_FRONTEND=noninteractive
-ENV MUJOCO_GL="egl"
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Install dependencies and set up Python in a single layer
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake git \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
-    speech-dispatcher libgeos-dev \
-    python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
-    && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python \
-    && python -m venv /opt/venv \
-    && apt-get clean && rm -rf /var/lib/apt/lists/* \
-    && echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Clone repository and install LeRobot in a single layer
-COPY . /lerobot
-WORKDIR /lerobot
-RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel, smolvla]"

docs-requirements.txt

@@ -0,0 +1,3 @@
+# docs-requirements.txt
+hf-doc-builder @ git+https://github.com/huggingface/doc-builder.git@main
+watchdog>=6.0.0

docs/README.md

@@ -20,12 +20,13 @@ To generate the documentation, you first have to build it. Several packages are
 you can install them with the following command, at the root of the code repository:
 
 ```bash
-pip install -e ".[docs]"
+pip install -e . -r docs-requirements.txt
 ```
 
 You will also need `nodejs`. Please refer to their [installation page](https://nodejs.org/en/download)
 
 ---
+
 **NOTE**
 
 You only need to generate the documentation to inspect it locally (if you're planning changes and want to
@@ -63,6 +64,7 @@ doc-builder preview lerobot docs/source/
 The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
 
 ---
+
 **NOTE**
 
 The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
@@ -89,6 +91,7 @@ Sections that were moved:
 
 [ <a href="#section-b">Section A</a><a id="section-a"></a> ]
 ```
+
 and of course, if you moved it to another file, then:
 
 ```
@@ -119,7 +122,6 @@ and objects like True, None or any strings should usually be put in `code`.
 
 Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
 
-
 ````
 ```
 # first line of code

docs/source/_toctree.yml

@@ -35,10 +35,14 @@
     title: Koch v1.1
   - local: lekiwi
     title: LeKiwi
+  - local: reachy2
+    title: Reachy 2
   title: "Robots"
 - sections:
   - local: notebooks
     title: Notebooks
+  - local: feetech
+    title: Updating Feetech Firmware
   title: "Resources"
 - sections:
   - local: contributing

docs/source/async.mdx

@@ -5,17 +5,18 @@ In this tutorial, we'll show how to use asynchronous inference (_async inference
 **Try async inference with all the policies** supported by LeRobot!
 
 **What you'll learn:**
+
 1. Why asynchronous inference matters and how it compares to, more traditional, sequential inference.
 2. How to spin-up a `PolicyServer` and connect a `RobotClient` from the same machine, and even over the network.
 3. How to tune key parameters (`actions_per_chunk`, `chunk_size_threshold`) for your robot and policy.
 
 If you get stuck, hop into our [Discord community](https://discord.gg/s3KuuzsPFb)!
 
-
-In a nutshell: with *async inference*, your robot keeps acting while the policy server is already busy computing the next chunk of actions---eliminating "wait-for-inference" lags and unlocking smoother, more reactive behaviours.
+In a nutshell: with _async inference_, your robot keeps acting while the policy server is already busy computing the next chunk of actions---eliminating "wait-for-inference" lags and unlocking smoother, more reactive behaviours.
 This is fundamentally different from synchronous inference (sync), where the robot stays idle while the policy computes the next chunk of actions.
 
 ---
+
 ## Getting started with async inference
 
 You can read more information on asynchronous inference in our [blogpost](https://huggingface.co/blog/async-robot-inference). This guide is designed to help you quickly set up and run asynchronous inference in your environment.
@@ -53,40 +54,53 @@ python src/lerobot/scripts/server/robot_client.py \
     --aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions
     --debug_visualize_queue_size=True # CLIENT: whether to visualize the queue size at runtime
 ```
+
 In summary, you need to specify instructions for:
+
 - `SERVER`: the address and port of the policy server
 - `ROBOT`: the type of robot to connect to, the port to connect to, and the local `id` of the robot
 - `POLICY`: the type of policy to run, and the model name/path on server to the checkpoint to run. You also need to specify which device should the sever be using, and how many actions to output at once (capped at the policy max actions value).
 - `CLIENT`: the threshold for the chunk size before sending a new observation to the server, and the function to aggregate actions on overlapping portions. Optionally, you can also visualize the queue size at runtime, to help you tune the `CLIENT` parameters.
 
 Importantly,
+
 - `actions_per_chunk` and `chunk_size_threshold` are key parameters to tune for your setup.
 - `aggregate_fn_name` is the function to aggregate actions on overlapping portions. You can either add a new one to a registry of functions, or add your own in `robot_client.py` (see [here](NOTE:addlinktoLOC))
 - `debug_visualize_queue_size` is a useful tool to tune the `CLIENT` parameters.
 
-Done! You should see your robot moving around by now 😉
----
+## Done! You should see your robot moving around by now 😉
 
 ## Async vs. synchronous inference
 
-Synchronous inference relies on interleaving action chunk prediction and action execution. This inherently results in *idle frames*, frames where the robot awaits idle the policy's output: a new action chunk.
+Synchronous inference relies on interleaving action chunk prediction and action execution. This inherently results in _idle frames_, frames where the robot awaits idle the policy's output: a new action chunk.
 In turn, inference is plagued by evident real-time lags, where the robot simply stops acting due to the lack of available actions.
 With robotics models increasing in size, this problem risks becoming only more severe.
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/sync.png" width="80%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/sync.png"
+    width="80%"
+  ></img>
+</p>
+<p align="center">
+  <i>Synchronous inference</i> makes the robot idle while the policy is
+  computing the next chunk of actions.
 </p>
-<p align="center"><i>Synchronous inference</i> makes the robot idle while the policy is computing the next chunk of actions.</p>
 
 To overcome this, we design async inference, a paradigm where action planning and execution are decoupled, resulting in (1) higher adaptability and, most importantly, (2) no idle frames.
-Crucially, with async inference, the next action chunk is computed *before* the current one is exhausted, resulting in no idleness.
+Crucially, with async inference, the next action chunk is computed _before_ the current one is exhausted, resulting in no idleness.
 Higher adaptability is ensured by aggregating the different action chunks on overlapping portions, obtaining an up-to-date plan and a tighter control loop.
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/async.png" width="80%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/async.png"
+    width="80%"
+  ></img>
+</p>
+<p align="center">
+  <i>Asynchronous inference</i> results in no idleness because the next chunk is
+  computed before the current chunk is exhausted.
 </p>
-<p align="center"><i>Asynchronous inference</i> results in no idleness because the next chunk is computed before the current chunk is exhausted.</p>
-
 
 ---
 
@@ -105,6 +119,8 @@ python -m lerobot.scripts.server.policy_server \
 ```
 </hfoption>
 <hfoption id="API example">
+
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.scripts.server.configs import PolicyServerConfig
 from lerobot.scripts.server.policy_server import serve
@@ -115,6 +131,8 @@ config = PolicyServerConfig(
 )
 serve(config)
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
@@ -147,6 +165,8 @@ python src/lerobot/scripts/server/robot_client.py \
 ```
 </hfoption>
 <hfoption id="API example">
+
+<!-- prettier-ignore-start -->
 ```python
 import threading
 from lerobot.robots.so100_follower import SO100FollowerConfig
@@ -201,6 +221,8 @@ if client.start():
         # (Optionally) plot the action queue size
         visualize_action_queue_size(client.action_queue_size)
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
@@ -216,20 +238,30 @@ The following two parameters are key in every setup:
   </thead>
   <tbody>
     <tr>
-      <td><code>actions_per_chunk</code></td>
+      <td>
+        <code>actions_per_chunk</code>
+      </td>
       <td>50</td>
-      <td>How many actions the policy outputs at once. Typical values: 10-50.</td>
+      <td>
+        How many actions the policy outputs at once. Typical values: 10-50.
+      </td>
     </tr>
     <tr>
-      <td><code>chunk_size_threshold</code></td>
+      <td>
+        <code>chunk_size_threshold</code>
+      </td>
       <td>0.7</td>
-      <td>When the queue is ≤ 50% full, the client sends a fresh observation. Value in [0, 1].</td>
+      <td>
+        When the queue is ≤ 50% full, the client sends a fresh observation.
+        Value in [0, 1].
+      </td>
     </tr>
   </tbody>
 </table>
 
 <Tip>
-Different values of `actions_per_chunk` and `chunk_size_threshold` do result in different behaviours.
+  Different values of `actions_per_chunk` and `chunk_size_threshold` do result
+  in different behaviours.
 </Tip>
 
 On the one hand, increasing the value of `actions_per_chunk` will result in reducing the likelihood of ending up with no actions to execute, as more actions will be available when the new chunk is computed.
@@ -249,10 +281,18 @@ We found the default values of `actions_per_chunk` and `chunk_size_threshold` to
    - We found values around 0.5-0.6 to work well. If you want to tweak this, spin up a `RobotClient` setting the `--debug-visualize-queue-size` to `True`. This will plot the action queue size evolution at runtime, and you can use it to find the value of `chunk_size_threshold` that works best for your setup.
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/queues.png" width="80%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/async-inference/queues.png"
+    width="80%"
+  ></img>
+</p>
+<p align="center">
+  <i>
+    The action queue size is plotted at runtime when the
+    `--debug-visualize-queue-size` flag is passed, for various levels of
+    `chunk_size_threshold` (`g` in the SmolVLA paper).
+  </i>
 </p>
-<p align="center"><i>The action queue size is plotted at runtime when the `--debug-visualize-queue-size` flag is passed, for various levels of `chunk_size_threshold` (`g` in the SmolVLA paper).</i></p>
-
 
 ---
 

docs/source/backwardcomp.mdx

@@ -6,21 +6,22 @@ PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot
 
 ### What changed?
 
-|                                   | Before PR #777                                    | After PR #777                                                               |
-| --------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------- |
-| **Joint range**                   | Degrees `-180...180°`                              | **Normalised range** Joints: `–100...100` Gripper: `0...100` |
-| **Zero position (SO100 / SO101)** | Arm fully extended horizontally                   | **In middle of the range for each joint**                                        |
-| **Boundary handling**             | Software safeguards to detect ±180 ° wrap-arounds | No wrap-around logic needed due to mid-range zero                           |
+|                                   | Before PR #777                                    | After PR #777                                                |
+| --------------------------------- | ------------------------------------------------- | ------------------------------------------------------------ |
+| **Joint range**                   | Degrees `-180...180°`                             | **Normalised range** Joints: `–100...100` Gripper: `0...100` |
+| **Zero position (SO100 / SO101)** | Arm fully extended horizontally                   | **In middle of the range for each joint**                    |
+| **Boundary handling**             | Software safeguards to detect ±180 ° wrap-arounds | No wrap-around logic needed due to mid-range zero            |
 
 ---
 
 ### Impact on existing datasets
 
-* Recorded trajectories created **before** PR #777 will replay incorrectly if loaded directly:
-  * Joint angles are offset and incorrectly normalized.
-* Any models directly finetuned or trained on the old data will need their inputs and outputs converted.
+- Recorded trajectories created **before** PR #777 will replay incorrectly if loaded directly:
+  - Joint angles are offset and incorrectly normalized.
+- Any models directly finetuned or trained on the old data will need their inputs and outputs converted.
 
 ### Using datasets made with the previous calibration system
+
 We provide a migration example script for replaying an episode recorded with the previous calibration here: `examples/backward_compatibility/replay.py`.
 Below we take you through the modifications that are done in the example script to make the previous calibration datasets work.
 
@@ -33,20 +34,31 @@ Below we take you through the modifications that are done in the example script
 
 Let's break this down.
 New codebase uses `.pos` suffix for the position observations and we have removed `main_` prefix:
+
+<!-- prettier-ignore-start -->
 ```python
 key = f"{name.removeprefix('main_')}.pos"
 ```
+<!-- prettier-ignore-end -->
 
 For `"shoulder_lift"` (id = 2), the 0 position is changed by -90 degrees and the direction is reversed compared to old calibration/code.
+
+<!-- prettier-ignore-start -->
 ```python
 action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
 ```
+<!-- prettier-ignore-end -->
+
 For `"elbow_flex"` (id = 3), the 0 position is changed by -90 degrees compared to old calibration/code.
+
+<!-- prettier-ignore-start -->
 ```python
 action["elbow_flex.pos"] -= 90
 ```
+<!-- prettier-ignore-end -->
 
 To use degrees normalization we then set the `--robot.use_degrees` option to `true`.
+
 ```diff
 python examples/backward_compatibility/replay.py \
     --robot.type=so101_follower \
@@ -63,6 +75,7 @@ Policies output actions in the same format as the datasets (`torch.Tensors`). Th
 
 To find these transformations, we recommend to first try and and replay an episode of the dataset your policy was trained on using the section above.
 Then, add these same transformations on your inference script (shown here in the `record.py` script):
+
 ```diff
 action_values = predict_action(
     observation_frame,

docs/source/cameras.mdx

@@ -7,11 +7,13 @@ LeRobot offers multiple options for video capture, including phone cameras, buil
 To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system.
 
 To find the camera indices of the cameras plugged into your system, run the following script:
+
 ```bash
-python -m lerobot.find_cameras opencv # or realsense for Intel Realsense cameras
+lerobot-find-cameras opencv # or realsense for Intel Realsense cameras
 ```
 
 The output will look something like this if you have two cameras connected:
+
 ```
 --- Detected Cameras ---
 Camera #0:
@@ -31,7 +33,6 @@ Camera #0:
 > [!WARNING]
 > When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable.
 
-
 ## Use Cameras
 
 Below are two examples, demonstrating how to work with the API.
@@ -39,10 +40,10 @@ Below are two examples, demonstrating how to work with the API.
 - **Asynchronous frame capture** using an OpenCV-based camera
 - **Color and depth capture** using an Intel RealSense camera
 
-
 <hfoptions id="shell_restart">
 <hfoption id="Open CV Camera">
 
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.cameras.opencv.camera_opencv import OpenCVCamera
@@ -70,10 +71,12 @@ try:
 finally:
     camera.disconnect()
 ```
+<!-- prettier-ignore-end -->
 
 </hfoption>
 <hfoption id="Intel Realsense Camera">
 
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig
 from lerobot.cameras.realsense.camera_realsense import RealSenseCamera
@@ -103,15 +106,18 @@ try:
 finally:
     camera.disconnect()
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
-
 ## Use your phone
+
 <hfoptions id="use phone">
 <hfoption id="Mac">
 
 To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
+
 - Ensure your Mac is running macOS 13 or later, and your iPhone is on iOS 16 or later.
 - Sign in both devices with the same Apple ID.
 - Connect your devices with a USB cable or turn on Wi-Fi and Bluetooth for a wireless connection.
@@ -125,40 +131,67 @@ Your iPhone should be detected automatically when running the camera setup scrip
 
 If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
 
-1. *Install `v4l2loopback-dkms` and `v4l-utils`*. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
+1. _Install `v4l2loopback-dkms` and `v4l-utils`_. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
+
+<!-- prettier-ignore-start -->
 ```python
 sudo apt install v4l2loopback-dkms v4l-utils
 ```
-2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
-3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
+<!-- prettier-ignore-end -->
+
+2. _Install [DroidCam](https://droidcam.app) on your phone_. This app is available for both iOS and Android.
+3. _Install [OBS Studio](https://obsproject.com)_. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
+
+<!-- prettier-ignore-start -->
 ```python
 flatpak install flathub com.obsproject.Studio
 ```
-4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
+<!-- prettier-ignore-end -->
+
+4. _Install the DroidCam OBS plugin_. This plugin integrates DroidCam with OBS Studio. Install it with:
+
+<!-- prettier-ignore-start -->
 ```python
 flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
 ```
-5. *Start OBS Studio*. Launch with:
+<!-- prettier-ignore-end -->
+
+5. _Start OBS Studio_. Launch with:
+
+<!-- prettier-ignore-start -->
 ```python
 flatpak run com.obsproject.Studio
 ```
-6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
-7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
-8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
+<!-- prettier-ignore-end -->
+
+6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
+7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
+8. _Start virtual camera_. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
+9. _Verify the virtual camera setup_. Use `v4l2-ctl` to list the devices:
+
+<!-- prettier-ignore-start -->
 ```python
 v4l2-ctl --list-devices
 ```
+<!-- prettier-ignore-end -->
+
 You should see an entry like:
+
 ```
 VirtualCam (platform:v4l2loopback-000):
 /dev/video1
 ```
-10. *Check the camera resolution*. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
+
+10. _Check the camera resolution_. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
+
+<!-- prettier-ignore-start -->
 ```python
 v4l2-ctl -d /dev/video1 --get-fmt-video
 ```
+<!-- prettier-ignore-end -->
+
 You should see an entry like:
+
 ```
 >>> Format Video Capture:
 >>>	Width/Height      : 640/480

docs/source/feetech.mdx

@@ -0,0 +1,71 @@
+# Feetech Motor Firmware Update
+
+This tutorial guides you through updating the firmware of Feetech motors using the official Feetech software.
+
+## Prerequisites
+
+- Windows computer (Feetech software is only available for Windows)
+- Feetech motor control board
+- USB cable to connect the control board to your computer
+- Feetech motors connected to the control board
+
+## Step 1: Download Feetech Software
+
+1. Visit the official Feetech software download page: [https://www.feetechrc.com/software.html](https://www.feetechrc.com/software.html)
+2. Download the latest version of the Feetech debugging software (FD)
+3. Install the software on your Windows computer
+
+## Step 2: Hardware Setup
+
+1. Connect your Feetech motors to the motor control board
+2. Connect the motor control board to your Windows computer via USB cable
+3. Ensure power is supplied to the motors
+
+## Step 3: Configure Connection
+
+1. Launch the Feetech debugging software
+2. Select the correct COM port from the port dropdown menu
+   - If unsure which port to use, check Windows Device Manager under "Ports (COM & LPT)"
+3. Set the appropriate baud rate (typically 1000000 for most Feetech motors)
+4. Click "Open" to establish communication with the control board
+
+## Step 4: Scan for Motors
+
+1. Once connected, click the "Search" button to detect all connected motors
+2. The software will automatically discover and list all motors on the bus
+3. Each motor will appear with its ID number
+
+## Step 5: Update Firmware
+
+For each motor you want to update:
+
+1. **Select the motor** from the list by clicking on it
+2. **Click on Upgrade tab**:
+3. **Click on Online button**:
+   - If an potential firmware update is found, it will be displayed in the box
+4. **Click on Upgrade button**:
+   - The update progress will be displayed
+
+## Step 6: Verify Update
+
+1. After the update completes, the software should automatically refresh the motor information
+2. Verify that the firmware version has been updated to the expected version
+
+## Important Notes
+
+⚠️ **Warning**: Do not disconnect power or USB during firmware updates, it will potentially brick the motor.
+
+## Bonus: Motor Debugging on Linux/macOS
+
+For debugging purposes only, you can use the open-source Feetech Debug Tool:
+
+- **Repository**: [FT_SCServo_Debug_Qt](https://github.com/CarolinePascal/FT_SCServo_Debug_Qt/tree/fix/port-search-timer)
+
+### Installation Instructions
+
+Follow the instructions in the repository to install the tool, for Ubuntu you can directly install it, for MacOS you need to build it from source.
+
+**Limitations:**
+
+- This tool is for debugging and parameter adjustment only
+- Firmware updates must still be done on Windows with official Feetech software

docs/source/hilserl.mdx

@@ -5,17 +5,27 @@ In this tutorial you will go through the full Human-in-the-Loop Sample-Efficient
 HIL-SERL is a sample-efficient reinforcement learning algorithm that combines human demonstrations with online learning and human interventions. The approach starts from a small set of human demonstrations, uses them to train a reward classifier, and then employs an actor-learner architecture where humans can intervene during policy execution to guide exploration and correct unsafe behaviors. In this tutorial, you'll use a gamepad to provide interventions and control the robot during the learning process.
 
 It combines three key ingredients:
-	1.	**Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point.
-	2.	**On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour.
-	3.	**Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe.
+
+1. **Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point.
+
+2. **On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour.
+
+3. **Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe.
 
 Together these elements let HIL-SERL reach near-perfect task success and faster cycle times than imitation-only baselines.
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hilserl-main-figure.png" alt="HIL-SERL workflow" title="HIL-SERL workflow" width="100%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hilserl-main-figure.png"
+    alt="HIL-SERL workflow"
+    title="HIL-SERL workflow"
+    width="100%"
+  ></img>
 </p>
 
-<p align="center"><i>HIL-SERL workflow, Luo et al. 2024</i></p>
+<p align="center">
+  <i>HIL-SERL workflow, Luo et al. 2024</i>
+</p>
 
 This guide provides step-by-step instructions for training a robot policy using LeRobot's HilSerl implementation to train on a real robot.
 
@@ -24,11 +34,12 @@ This guide provides step-by-step instructions for training a robot policy using
 - A gamepad (recommended) or keyboard to control the robot
 - A Nvidia GPU
 - A real robot with a follower and leader arm (optional if you use the keyboard or the gamepad)
-- A URDF file for the robot for the kinematics package (check `lerobot/common/model/kinematics.py`)
+- A URDF file for the robot for the kinematics package (check `lerobot/model/kinematics.py`)
 
 ## What kind of tasks can I train?
 
 One can use HIL-SERL to train on a variety of manipulation tasks. Some recommendations:
+
 - Start with a simple task to understand how the system works.
   - Push cube to a goal region
   - Pick and lift cube with the gripper
@@ -51,28 +62,242 @@ pip install -e ".[hilserl]"
 
 ### Understanding Configuration
 
-The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/envs/configs.py`. Which is defined as:
+The training process begins with proper configuration for the HILSerl environment. The main configuration class is `GymManipulatorConfig` in `lerobot/scripts/rl/gym_manipulator.py`, which contains nested `HILSerlRobotEnvConfig` and `DatasetConfig`. The configuration is organized into focused, nested sub-configs:
 
+<!-- prettier-ignore-start -->
 ```python
+class GymManipulatorConfig:
+    env: HILSerlRobotEnvConfig    # Environment configuration (nested)
+    dataset: DatasetConfig    # Dataset recording/replay configuration (nested)
+    mode: str | None = None    # "record", "replay", or None (for training)
+    device: str = "cpu"    # Compute device
+
 class HILSerlRobotEnvConfig(EnvConfig):
     robot: RobotConfig | None = None    # Main robot agent (defined in `lerobot/robots`)
-    teleop: TeleoperatorConfig | None = None    # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`)
-    wrapper: EnvTransformConfig | None = None    # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
-    fps: int = 10    # Control frequency
+    teleop: TeleoperatorConfig | None = None    # Teleoperator agent, e.g., gamepad or leader arm
+    processor: HILSerlProcessorConfig    # Processing pipeline configuration (nested)
     name: str = "real_robot"    # Environment name
-    mode: str = None    # "record", "replay", or None (for training)
-    repo_id: str | None = None    # LeRobot dataset repository ID
-    dataset_root: str | None = None    # Local dataset root (optional)
-    task: str = ""    # Task identifier
-    num_episodes: int = 10    # Number of episodes for recording
-    episode: int = 0    # episode index for replay
-    device: str = "cuda"    # Compute device
-    push_to_hub: bool = True    # Whether to push the recorded datasets to Hub
-    pretrained_policy_name_or_path: str | None = None    # For policy loading
-    reward_classifier_pretrained_path: str | None = None    # For reward model
-    number_of_steps_after_success: int = 0    # For reward classifier, collect more positive examples after a success to train a classifier
+    task: str | None = None    # Task identifier
+    fps: int = 10    # Control frequency
+
+# Nested processor configuration
+class HILSerlProcessorConfig:
+    control_mode: str = "gamepad"    # Control mode
+    observation: ObservationConfig | None = None    # Observation processing settings
+    image_preprocessing: ImagePreprocessingConfig | None = None    # Image crop/resize settings
+    gripper: GripperConfig | None = None    # Gripper control and penalty settings
+    reset: ResetConfig | None = None    # Environment reset and timing settings
+    inverse_kinematics: InverseKinematicsConfig | None = None    # IK processing settings
+    reward_classifier: RewardClassifierConfig | None = None    # Reward classifier settings
+    max_gripper_pos: float | None = 100.0    # Maximum gripper position
+
+# Sub-configuration classes
+class ObservationConfig:
+    add_joint_velocity_to_observation: bool = False    # Add joint velocities to state
+    add_current_to_observation: bool = False    # Add motor currents to state
+    add_ee_pose_to_observation: bool = False    # Add end-effector pose to state
+    display_cameras: bool = False    # Display camera feeds during execution
+
+class ImagePreprocessingConfig:
+    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None    # Image cropping parameters
+    resize_size: tuple[int, int] | None = None    # Target image size
+
+class GripperConfig:
+    use_gripper: bool = True    # Enable gripper control
+    gripper_penalty: float = 0.0    # Penalty for inappropriate gripper usage
+    gripper_penalty_in_reward: bool = False    # Include gripper penalty in reward
+
+class ResetConfig:
+    fixed_reset_joint_positions: Any | None = None    # Joint positions for reset
+    reset_time_s: float = 5.0    # Time to wait during reset
+    control_time_s: float = 20.0    # Maximum episode duration
+    terminate_on_success: bool = True    # Whether to terminate episodes on success detection
+
+class InverseKinematicsConfig:
+    urdf_path: str | None = None    # Path to robot URDF file
+    target_frame_name: str | None = None    # End-effector frame name
+    end_effector_bounds: dict[str, list[float]] | None = None    # EE workspace bounds
+    end_effector_step_sizes: dict[str, float] | None = None    # EE step sizes per axis
+
+class RewardClassifierConfig:
+    pretrained_path: str | None = None    # Path to pretrained reward classifier
+    success_threshold: float = 0.5    # Success detection threshold
+    success_reward: float = 1.0    # Reward value for successful episodes
+
+# Dataset configuration
+class DatasetConfig:
+    repo_id: str    # LeRobot dataset repository ID
+    task: str    # Task identifier
+    root: str | None = None    # Local dataset root directory
+    num_episodes_to_record: int = 5    # Number of episodes for recording
+    replay_episode: int | None = None    # Episode index for replay
+    push_to_hub: bool = False    # Whether to push datasets to Hub
+```
+<!-- prettier-ignore-end -->
+
+### Processor Pipeline Architecture
+
+HIL-SERL uses a modular processor pipeline architecture that processes robot observations and actions through a series of composable steps. The pipeline is divided into two main components:
+
+#### Environment Processor Pipeline
+
+The environment processor (`env_processor`) handles incoming observations and environment state:
+
+1. **VanillaObservationProcessorStep**: Converts raw robot observations into standardized format
+2. **JointVelocityProcessorStep** (optional): Adds joint velocity information to observations
+3. **MotorCurrentProcessorStep** (optional): Adds motor current readings to observations
+4. **ForwardKinematicsJointsToEE** (optional): Computes end-effector pose from joint positions
+5. **ImageCropResizeProcessorStep** (optional): Crops and resizes camera images
+6. **TimeLimitProcessorStep** (optional): Enforces episode time limits
+7. **GripperPenaltyProcessorStep** (optional): Applies penalties for inappropriate gripper usage
+8. **RewardClassifierProcessorStep** (optional): Automated reward detection using vision models
+9. **AddBatchDimensionProcessorStep**: Converts data to batch format for neural network processing
+10. **DeviceProcessorStep**: Moves data to the specified compute device (CPU/GPU)
+
+#### Action Processor Pipeline
+
+The action processor (`action_processor`) handles outgoing actions and human interventions:
+
+1. **AddTeleopActionAsComplimentaryDataStep**: Captures teleoperator actions for logging
+2. **AddTeleopEventsAsInfoStep**: Records intervention events and episode control signals
+3. **AddRobotObservationAsComplimentaryData**: Stores raw robot state for processing
+4. **InterventionActionProcessorStep**: Handles human interventions and episode termination
+5. **Inverse Kinematics Pipeline** (when enabled):
+   - **MapDeltaActionToRobotActionStep**: Converts delta actions to robot action format
+   - **EEReferenceAndDelta**: Computes end-effector reference and delta movements
+   - **EEBoundsAndSafety**: Enforces workspace safety bounds
+   - **InverseKinematicsEEToJoints**: Converts end-effector actions to joint targets
+   - **GripperVelocityToJoint**: Handles gripper control commands
+
+#### Configuration Examples
+
+**Basic Observation Processing**:
+
+```json
+{
+  "env": {
+    "processor": {
+      "observation": {
+        "add_joint_velocity_to_observation": true,
+        "add_current_to_observation": false,
+        "display_cameras": false
+      }
+    }
+  }
+}
 ```
 
+**Image Processing**:
+
+```json
+{
+  "env": {
+    "processor": {
+      "image_preprocessing": {
+        "crop_params_dict": {
+          "observation.images.front": [180, 250, 120, 150],
+          "observation.images.side": [180, 207, 180, 200]
+        },
+        "resize_size": [128, 128]
+      }
+    }
+  }
+}
+```
+
+**Inverse Kinematics Setup**:
+
+```json
+{
+  "env": {
+    "processor": {
+      "inverse_kinematics": {
+        "urdf_path": "path/to/robot.urdf",
+        "target_frame_name": "end_effector",
+        "end_effector_bounds": {
+          "min": [0.16, -0.08, 0.03],
+          "max": [0.24, 0.2, 0.1]
+        },
+        "end_effector_step_sizes": {
+          "x": 0.02,
+          "y": 0.02,
+          "z": 0.02
+        }
+      }
+    }
+  }
+}
+```
+
+### Advanced Observation Processing
+
+The HIL-SERL framework supports additional observation processing features that can improve policy learning:
+
+#### Joint Velocity Processing
+
+Enable joint velocity estimation to provide the policy with motion information:
+
+```json
+{
+  "env": {
+    "processor": {
+      "observation": {
+        "add_joint_velocity_to_observation": true
+      }
+    }
+  }
+}
+```
+
+This processor:
+
+- Estimates joint velocities using finite differences between consecutive joint position readings
+- Adds velocity information to the observation state vector
+- Useful for policies that need motion awareness for dynamic tasks
+
+#### Motor Current Processing
+
+Monitor motor currents to detect contact forces and load conditions:
+
+```json
+{
+  "env": {
+    "processor": {
+      "observation": {
+        "add_current_to_observation": true
+      }
+    }
+  }
+}
+```
+
+This processor:
+
+- Reads motor current values from the robot's control system
+- Adds current measurements to the observation state vector
+- Helps detect contact events, object weights, and mechanical resistance
+- Useful for contact-rich manipulation tasks
+
+#### Combined Observation Processing
+
+You can enable multiple observation processing features simultaneously:
+
+```json
+{
+  "env": {
+    "processor": {
+      "observation": {
+        "add_joint_velocity_to_observation": true,
+        "add_current_to_observation": true,
+        "add_ee_pose_to_observation": false,
+        "display_cameras": false
+      }
+    }
+  }
+}
+```
+
+**Note**: Enabling additional observation features increases the state space dimensionality, which may require adjusting your policy network architecture and potentially collecting more training data.
 
 ### Finding Robot Workspace Bounds
 
@@ -124,21 +349,56 @@ With the bounds defined, you can safely collect demonstrations for training. Tra
 
 Create a configuration file for recording demonstrations (or edit an existing one like [env_config_so100.json](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json)):
 
-1. Set `mode` to `"record"`
-2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name")
-3. Set `num_episodes` to the number of demonstrations you want to collect
-4. Set `crop_params_dict` to `null` initially (we'll determine crops later)
-5. Configure `robot`, `cameras`, and other hardware settings
+1. Set `mode` to `"record"` at the root level
+2. Specify a unique `repo_id` for your dataset in the `dataset` section (e.g., "username/task_name")
+3. Set `num_episodes_to_record` in the `dataset` section to the number of demonstrations you want to collect
+4. Set `env.processor.image_preprocessing.crop_params_dict` to `{}` initially (we'll determine crops later)
+5. Configure `env.robot`, `env.teleop`, and other hardware settings in the `env` section
 
 Example configuration section:
+
 ```json
-"mode": "record",
-"repo_id": "username/pick_lift_cube",
-"dataset_root": null,
-"task": "pick_and_lift",
-"num_episodes": 15,
-"episode": 0,
-"push_to_hub": true
+{
+  "env": {
+    "type": "gym_manipulator",
+    "name": "real_robot",
+    "fps": 10,
+    "processor": {
+      "control_mode": "gamepad",
+      "observation": {
+        "display_cameras": false
+      },
+      "image_preprocessing": {
+        "crop_params_dict": {},
+        "resize_size": [128, 128]
+      },
+      "gripper": {
+        "use_gripper": true,
+        "gripper_penalty": 0.0
+      },
+      "reset": {
+        "reset_time_s": 5.0,
+        "control_time_s": 20.0
+      }
+    },
+    "robot": {
+      // ... robot configuration ...
+    },
+    "teleop": {
+      // ... teleoperator configuration ...
+    }
+  },
+  "dataset": {
+    "repo_id": "username/pick_lift_cube",
+    "root": null,
+    "task": "pick_and_lift",
+    "num_episodes_to_record": 15,
+    "replay_episode": 0,
+    "push_to_hub": true
+  },
+  "mode": "record",
+  "device": "cpu"
+}
 ```
 
 ### Using a Teleoperation Device
@@ -150,6 +410,7 @@ HIL-Serl learns actions in the end-effector space of the robot. Therefore, the t
 
 For that we need to define a version of the robot that takes actions in the end-effector space. Check the robot class `SO100FollowerEndEffector` and its configuration `SO100FollowerEndEffectorConfig` for the default parameters related to the end-effector space.
 
+<!-- prettier-ignore-start -->
 ```python
 class SO100FollowerEndEffectorConfig(SO100FollowerConfig):
     """Configuration for the SO100FollowerEndEffector robot."""
@@ -172,6 +433,7 @@ class SO100FollowerEndEffectorConfig(SO100FollowerConfig):
         }
     )
 ```
+<!-- prettier-ignore-end -->
 
 The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/teleoperators`.
 
@@ -182,16 +444,33 @@ The gamepad provides a very convenient way to control the robot and the episode
 To setup the gamepad, you need to set the `control_mode` to `"gamepad"` and define the `teleop` section in the configuration file.
 
 ```json
+{
+  "env": {
     "teleop": {
-        "type": "gamepad",
-        "use_gripper": true
+      "type": "gamepad",
+      "use_gripper": true
     },
+    "processor": {
+      "control_mode": "gamepad",
+      "gripper": {
+        "use_gripper": true
+      }
+    }
+  }
+}
 ```
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true"
+    alt="Figure shows the control mappings on a Logitech gamepad."
+    title="Gamepad Control Mapping"
+    width="100%"
+  ></img>
+</p>
+<p align="center">
+  <i>Gamepad button mapping for robot control and episode management</i>
 </p>
-<p align="center"><i>Gamepad button mapping for robot control and episode management</i></p>
 
 **Setting up the SO101 leader**
 
@@ -200,11 +479,21 @@ The SO101 leader arm has reduced gears that allows it to move and track the foll
 To setup the SO101 leader, you need to set the `control_mode` to `"leader"` and define the `teleop` section in the configuration file.
 
 ```json
+{
+  "env": {
     "teleop": {
-        "type": "so101_leader",
-        "port": "/dev/tty.usbmodem585A0077921", # check your port number
-        "use_degrees": true
+      "type": "so101_leader",
+      "port": "/dev/tty.usbmodem585A0077921",
+      "use_degrees": true
     },
+    "processor": {
+      "control_mode": "leader",
+      "gripper": {
+        "use_gripper": true
+      }
+    }
+  }
+}
 ```
 
 In order to annotate the success/failure of the episode, **you will need** to use a keyboard to press `s` for success, `esc` for failure.
@@ -215,7 +504,10 @@ During the online training, press `space` to take over the policy and `space` ag
 
 <div class="video-container">
   <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so101_leader_tutorial.mp4" type="video/mp4" />
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so101_leader_tutorial.mp4"
+      type="video/mp4"
+    />
   </video>
 </div>
 
@@ -231,7 +523,8 @@ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/e
 ```
 
 During recording:
-1. The robot will reset to the initial position defined in the configuration file `fixed_reset_joint_positions`
+
+1. The robot will reset to the initial position defined in the configuration file `env.processor.reset.fixed_reset_joint_positions`
 2. Complete the task successfully
 3. The episode ends with a reward of 1 when you press the "success" button
 4. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0
@@ -239,13 +532,13 @@ During recording:
 6. The process automatically continues to the next episode
 7. After recording all episodes, the dataset is pushed to the Hugging Face Hub (optional) and saved locally
 
-
 ### Processing the Dataset
 
 After collecting demonstrations, process them to determine optimal camera crops.
 Reinforcement learning is sensitive to background distractions, so it is important to crop the images to the relevant workspace area.
 
 Visual RL algorithms learn directly from pixel inputs, making them vulnerable to irrelevant visual information. Background elements like changing lighting, shadows, people moving, or objects outside the workspace can confuse the learning process. Good ROI selection should:
+
 - Include only the essential workspace where the task happens
 - Capture the robot's end-effector and all objects involved in the task
 - Exclude unnecessary background elements and distractions
@@ -267,6 +560,7 @@ python -m lerobot.scripts.rl.crop_dataset_roi --repo-id username/pick_lift_cube
 5. The script outputs cropping parameters and creates a new cropped dataset
 
 Example output:
+
 ```
 Selected Rectangular Regions of Interest (top, left, height, width):
 observation.images.side: [180, 207, 180, 200]
@@ -274,28 +568,39 @@ observation.images.front: [180, 250, 120, 150]
 ```
 
 <p align="center">
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/crop_dataset.gif" width="600"/>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/crop_dataset.gif"
+    width="600"
+  />
 </p>
 
-<p align="center"><i>Interactive cropping tool for selecting regions of interest</i></p>
-
+<p align="center">
+  <i>Interactive cropping tool for selecting regions of interest</i>
+</p>
 
 **Updating Configuration**
 
 Add these crop parameters to your training configuration:
 
 ```json
-"crop_params_dict": {
-    "observation.images.side": [180, 207, 180, 200],
-    "observation.images.front": [180, 250, 120, 150]
-},
-"resize_size": [128, 128]
+{
+  "env": {
+    "processor": {
+      "image_preprocessing": {
+        "crop_params_dict": {
+          "observation.images.side": [180, 207, 180, 200],
+          "observation.images.front": [180, 250, 120, 150]
+        },
+        "resize_size": [128, 128]
+      }
+    }
+  }
+}
 ```
 
 **Recommended image resolution**
 
-Most vision-based policies have been validated on square inputs of either **128×128** (default) or **64×64** pixels.  We therefore advise setting the resize_size parameter to [128, 128] – or [64, 64] if you need to save GPU memory and bandwidth.  Other resolutions are possible but have not been extensively tested.
-
+Most vision-based policies have been validated on square inputs of either **128×128** (default) or **64×64** pixels. We therefore advise setting the resize_size parameter to [128, 128] – or [64, 64] if you need to save GPU memory and bandwidth. Other resolutions are possible but have not been extensively tested.
 
 ### Training a Reward Classifier
 
@@ -319,26 +624,52 @@ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/r
 
 **Key Parameters for Data Collection**
 
-- **mode**: set it to `"record"` to collect a dataset
-- **repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub
-- **num_episodes**: Number of episodes to record
-- **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected
-- **fps**: Number of frames per second to record
-- **push_to_hub**: Whether to push the dataset to the hub
+- **mode**: set it to `"record"` to collect a dataset (at root level)
+- **dataset.repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub
+- **dataset.num_episodes_to_record**: Number of episodes to record
+- **env.processor.reset.terminate_on_success**: Whether to automatically terminate episodes when success is detected (default: `true`)
+- **env.fps**: Number of frames per second to record
+- **dataset.push_to_hub**: Whether to push the dataset to the hub
 
-The `number_of_steps_after_success` parameter is crucial as it allows you to collect more positive examples. When a success is detected, the system will continue recording for the specified number of steps while maintaining the reward=1 label. Otherwise, there won't be enough states in the dataset labeled to 1 to train a good classifier.
+The `env.processor.reset.terminate_on_success` parameter allows you to control episode termination behavior. When set to `false`, episodes will continue even after success is detected, allowing you to collect more positive examples with the reward=1 label. This is crucial for training reward classifiers as it provides more success state examples in your dataset. When set to `true` (default), episodes terminate immediately upon success detection.
+
+**Important**: For reward classifier training, set `terminate_on_success: false` to collect sufficient positive examples. For regular HIL-SERL training, keep it as `true` to enable automatic episode termination when the task is completed successfully.
 
 Example configuration section for data collection:
 
 ```json
 {
-    "mode": "record",
+  "env": {
+    "type": "gym_manipulator",
+    "name": "real_robot",
+    "fps": 10,
+    "processor": {
+      "reset": {
+        "reset_time_s": 5.0,
+        "control_time_s": 20.0,
+        "terminate_on_success": false
+      },
+      "gripper": {
+        "use_gripper": true
+      }
+    },
+    "robot": {
+      // ... robot configuration ...
+    },
+    "teleop": {
+      // ... teleoperator configuration ...
+    }
+  },
+  "dataset": {
     "repo_id": "hf_username/dataset_name",
     "dataset_root": "data/your_dataset",
-    "num_episodes": 20,
-    "push_to_hub": true,
-    "fps": 10,
-    "number_of_steps_after_success": 15
+    "task": "reward_classifier_task",
+    "num_episodes_to_record": 20,
+    "replay_episode": null,
+    "push_to_hub": true
+  },
+  "mode": "record",
+  "device": "cpu"
 }
 ```
 
@@ -388,28 +719,51 @@ Example configuration for training the [reward classifier](https://huggingface.c
 To train the classifier, use the `train.py` script with your configuration:
 
 ```bash
-python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
+lerobot-train --config_path path/to/reward_classifier_train_config.json
 ```
 
 **Deploying and Testing the Model**
 
 To use your trained reward classifier, configure the `HILSerlRobotEnvConfig` to use your model:
 
+<!-- prettier-ignore-start -->
 ```python
-env_config = HILSerlRobotEnvConfig(
-    reward_classifier_pretrained_path="path_to_your_pretrained_trained_model",
-    # Other environment parameters
+config = GymManipulatorConfig(
+    env=HILSerlRobotEnvConfig(
+        processor=HILSerlProcessorConfig(
+            reward_classifier=RewardClassifierConfig(
+                pretrained_path="path_to_your_pretrained_trained_model"
+            )
+        ),
+        # Other environment parameters
+    ),
+    dataset=DatasetConfig(...),
+    mode=None  # For training
 )
 ```
+<!-- prettier-ignore-end -->
+
 or set the argument in the json config file.
 
 ```json
 {
-    "reward_classifier_pretrained_path": "path_to_your_pretrained_model"
+  "env": {
+    "processor": {
+      "reward_classifier": {
+        "pretrained_path": "path_to_your_pretrained_model",
+        "success_threshold": 0.7,
+        "success_reward": 1.0
+      },
+      "reset": {
+        "terminate_on_success": true
+      }
+    }
+  }
 }
 ```
 
 Run `gym_manipulator.py` to test the model.
+
 ```bash
 python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config.json
 ```
@@ -422,13 +776,15 @@ The reward classifier will automatically provide rewards based on the visual inp
    Create the necessary json configuration files for the reward classifier and the environment. Check the examples [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/tree/main).
 
 2. **Collect a dataset**:
+
    ```bash
    python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
    ```
 
 3. **Train the classifier**:
+
    ```bash
-   python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
+   lerobot-train --config_path src/lerobot/configs/reward_classifier_train_config.json
    ```
 
 4. **Test the classifier**:
@@ -447,7 +803,7 @@ Create a training configuration file (example available [here](https://huggingfa
 1. Configure the policy settings (`type="sac"`, `device`, etc.)
 2. Set `dataset` to your cropped dataset
 3. Configure environment settings with crop parameters
-4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/lerobot/policies/sac/configuration_sac.py#L79).
+4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/sac/configuration_sac.py#L79).
 5. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task.
 
 **Starting the Learner**
@@ -459,6 +815,7 @@ python -m lerobot.scripts.rl.learner --config_path src/lerobot/configs/train_con
 ```
 
 The learner:
+
 - Initializes the policy network
 - Prepares replay buffers
 - Opens a `gRPC` server to communicate with actors
@@ -473,6 +830,7 @@ python -m lerobot.scripts.rl.actor --config_path src/lerobot/configs/train_confi
 ```
 
 The actor:
+
 - Connects to the learner via `gRPC`
 - Initializes the environment
 - Execute rollouts of the policy to collect experience
@@ -496,10 +854,19 @@ The training proceeds automatically:
 - A successful experiment is one where the human has to intervene at the start but then reduces the amount of interventions as the policy improves. You can monitor the intervention rate in the `wandb` dashboard.
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hil_effect.png?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/hil_effect.png?raw=true"
+    alt="Figure shows the control mappings on a Logitech gamepad."
+    title="Gamepad Control Mapping"
+    width="100%"
+  ></img>
 </p>
 
-<p align="center"><i>Example showing how human interventions help guide policy learning over time</i></p>
+<p align="center">
+  <i>
+    Example showing how human interventions help guide policy learning over time
+  </i>
+</p>
 
 - The figure shows the plot of the episodic reward over interaction step. The figure shows the effect of human interventions on the policy learning.
 - The orange curve is an experiment without any human interventions. While the pink and blue curves are experiments with human interventions.
@@ -510,7 +877,9 @@ The training proceeds automatically:
 If you have `wandb.enable` set to `true` in your configuration, you can monitor training progress in real-time through the [Weights & Biases](https://wandb.ai/site/) dashboard.
 
 ### Guide to Human Interventions
+
 The learning process is very sensitive to the intervention strategy. It will takes a few runs to understand how to intervene effectively. Some tips and hints:
+
 - Allow the policy to explore for a few episodes at the start of training.
 - Avoid intervening for long periods of time. Try to intervene in situation to correct the robot's behaviour when it goes off track.
 - Once the policy starts achieving the task, even if its not perfect, you can limit your interventions to simple quick actions like a simple grasping commands.
@@ -518,26 +887,36 @@ The learning process is very sensitive to the intervention strategy. It will tak
 The ideal behaviour is that your intervention rate should drop gradually during training as shown in the figure below.
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/intervention_rate_tutorial_rl.png?raw=true" alt="Intervention rate" title="Intervention rate during training" width="100%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/intervention_rate_tutorial_rl.png?raw=true"
+    alt="Intervention rate"
+    title="Intervention rate during training"
+    width="100%"
+  ></img>
 </p>
 
-<p align="center"><i>Plot of the intervention rate during a training run on a pick and lift cube task</i></p>
+<p align="center">
+  <i>
+    Plot of the intervention rate during a training run on a pick and lift cube
+    task
+  </i>
+</p>
 
 ### Key hyperparameters to tune
 
 Some configuration values have a disproportionate impact on training stability and speed:
 
 - **`temperature_init`** (`policy.temperature_init`) – initial entropy temperature in SAC. Higher values encourage more exploration; lower values make the policy more deterministic early on. A good starting point is `1e-2`. We observed that setting it too high can make human interventions ineffective and slow down learning.
-- **`policy_parameters_push_frequency`** (`policy.actor_learner_config.policy_parameters_push_frequency`) – interval in *seconds* between two weight pushes from the learner to the actor. The default is `4 s`. Decrease to **1-2 s** to provide fresher weights (at the cost of more network traffic); increase only if your connection is slow, as this will reduce sample efficiency.
+- **`policy_parameters_push_frequency`** (`policy.actor_learner_config.policy_parameters_push_frequency`) – interval in _seconds_ between two weight pushes from the learner to the actor. The default is `4 s`. Decrease to **1-2 s** to provide fresher weights (at the cost of more network traffic); increase only if your connection is slow, as this will reduce sample efficiency.
 - **`storage_device`** (`policy.storage_device`) – device on which the learner keeps the policy parameters. If you have spare GPU memory, set this to `"cuda"` (instead of the default `"cpu"`). Keeping the weights on-GPU removes CPU→GPU transfer overhead and can significantly increase the number of learner updates per second.
 
-
 Congrats 🎉, you have finished this tutorial!
 
 > [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
 
 Paper citation:
+
 ```
 @article{luo2024precise,
   title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},

docs/source/hilserl_sim.mdx

@@ -11,7 +11,6 @@ This guide explains how to use the `gym_hil` simulation environments as an alter
 
 Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube.
 
-
 ## Installation
 
 First, install the `gym_hil` package within the LeRobot environment:
@@ -25,8 +24,6 @@ pip install -e ".[hilserl]"
 - A gamepad or keyboard to control the robot
 - A Nvidia GPU
 
-
-
 ## Configuration
 
 To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/gym_hil_env.json). Key configuration sections include:
@@ -35,39 +32,56 @@ To use `gym_hil` with LeRobot, you need to create a configuration file. An examp
 
 ```json
 {
-    "type": "hil",
-    "name": "franka_sim",
+  "env": {
+    "type": "gym_manipulator",
+    "name": "gym_hil",
     "task": "PandaPickCubeGamepad-v0",
-    "device": "cuda"
+    "fps": 10
+  },
+  "device": "cuda"
 }
 ```
 
 Available tasks:
+
 - `PandaPickCubeBase-v0`: Basic environment
 - `PandaPickCubeGamepad-v0`: With gamepad control
 - `PandaPickCubeKeyboard-v0`: With keyboard control
 
-### Gym Wrappers Configuration
+### Processor Configuration
 
 ```json
-"wrapper": {
-    "gripper_penalty": -0.02,
-    "control_time_s": 15.0,
-    "use_gripper": true,
-    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
-    "end_effector_step_sizes": {
-        "x": 0.025,
-        "y": 0.025,
-        "z": 0.025
-    },
-    "control_mode": "gamepad"
+{
+  "env": {
+    "processor": {
+      "control_mode": "gamepad",
+      "gripper": {
+        "use_gripper": true,
+        "gripper_penalty": -0.02
+      },
+      "reset": {
+        "control_time_s": 15.0,
+        "fixed_reset_joint_positions": [
+          0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785
+        ]
+      },
+      "inverse_kinematics": {
+        "end_effector_step_sizes": {
+          "x": 0.025,
+          "y": 0.025,
+          "z": 0.025
+        }
+      }
     }
+  }
+}
 ```
 
 Important parameters:
-- `gripper_penalty`: Penalty for excessive gripper movement
-- `use_gripper`: Whether to enable gripper control
-- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
+
+- `gripper.gripper_penalty`: Penalty for excessive gripper movement
+- `gripper.use_gripper`: Whether to enable gripper control
+- `inverse_kinematics.end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
 - `control_mode`: Set to `"gamepad"` to use a gamepad controller
 
 ## Running with HIL RL of LeRobot
@@ -76,7 +90,7 @@ Important parameters:
 
 To run the environment, set mode to null:
 
-```python
+```bash
 python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
 ```
 
@@ -84,7 +98,26 @@ python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.j
 
 To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
 
-```python
+```json
+{
+  "env": {
+    "type": "gym_manipulator",
+    "name": "gym_hil",
+    "task": "PandaPickCubeGamepad-v0"
+  },
+  "dataset": {
+    "repo_id": "username/sim_dataset",
+    "root": null,
+    "task": "pick_cube",
+    "num_episodes_to_record": 10,
+    "replay_episode": null,
+    "push_to_hub": true
+  },
+  "mode": "record"
+}
+```
+
+```bash
 python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
 ```
 
@@ -92,13 +125,13 @@ python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.j
 
 To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers:
 
-```python
+```bash
 python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
 ```
 
 In a different terminal, run the learner server:
 
-```python
+```bash
 python -m lerobot.scripts.rl.learner --config_path path/to/train_gym_hil_env.json
 ```
 
@@ -107,9 +140,10 @@ The simulation environment provides a safe and repeatable way to develop and tes
 Congrats 🎉, you have finished this tutorial!
 
 > [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
 
 Paper citation:
+
 ```
 @article{luo2024precise,
   title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},

docs/source/hope_jr.mdx

@@ -1 +0,0 @@
-../../src/lerobot/robots/hope_jr/hope_jr.mdx

docs/source/hope_jr.mdx

@@ -0,0 +1,277 @@
+# HopeJR
+
+## Prerequisites
+
+- [Hardware Setup](https://github.com/TheRobotStudio/HOPEJr)
+
+## Install LeRobot
+
+Follow the [installation instructions](https://github.com/huggingface/lerobot#installation) to install LeRobot.
+
+Install LeRobot with HopeJR dependencies:
+
+```bash
+pip install -e ".[hopejr]"
+```
+
+## Device Configuration
+
+Before starting calibration and operation, you need to identify the USB ports for each HopeJR component. Run this script to find the USB ports for the arm, hand, glove, and exoskeleton:
+
+```bash
+lerobot-find-port
+```
+
+This will display the available USB ports and their associated devices. Make note of the port paths (e.g., `/dev/tty.usbmodem58760433331`, `/dev/tty.usbmodem11301`) as you'll need to specify them in the `--robot.port` and `--teleop.port` parameters when recording data, replaying episodes, or running teleoperation scripts.
+
+## Step 1: Calibration
+
+Before performing teleoperation, HopeJR's limbs need to be calibrated. Calibration files will be saved in `~/.cache/huggingface/lerobot/calibration`
+
+### 1.1 Calibrate Robot Hand
+
+```bash
+lerobot-calibrate \
+    --robot.type=hope_jr_hand \
+    --robot.port=/dev/tty.usbmodem58760432281 \
+    --robot.id=blue \
+    --robot.side=right
+```
+
+When running the calibration script, a calibration GUI will pop up. Finger joints are named as follows:
+
+**Thumb**:
+
+- **CMC**: base joint connecting thumb to hand
+- **MCP**: knuckle joint
+- **PIP**: first finger joint
+- **DIP** : fingertip joint
+
+**Index, Middle, Ring, and Pinky fingers**:
+
+- **Radial flexor**: Moves base of finger towards the thumb
+- **Ulnar flexor**: Moves base of finger towards the pinky
+- **PIP/DIP**: Flexes the distal and proximal phalanx of the finger
+
+Each one of these will need to be calibrated individually via the GUI.
+Note that ulnar and radial flexors should have ranges of the same size (but with different offsets) in order to get symmetric movement.
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/calibration_gui_1.png"
+    alt="Setting boundaries in the hand calibration GUI"
+    title="Setting boundaries in the hand calibration GUI"
+    width="100%"
+  ></img>
+</p>
+
+Use the calibration interface to set the range boundaries for each joint as shown above.
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/calibration_gui_2.png"
+    alt="Saving calibration values"
+    title="Saving calibration values"
+    width="100%"
+  ></img>
+</p>
+
+Once you have set the appropriate boundaries for all joints, click "Save" to save the calibration values to the motors.
+
+### 1.2 Calibrate Teleoperator Glove
+
+```bash
+lerobot-calibrate \
+    --teleop.type=homunculus_glove \
+    --teleop.port=/dev/tty.usbmodem11201 \
+    --teleop.id=red \
+    --teleop.side=right
+```
+
+Move each finger through its full range of motion, starting from the thumb.
+
+```
+Move thumb through its entire range of motion.
+Recording positions. Press ENTER to stop...
+
+-------------------------------------------
+NAME      |    MIN |    POS |    MAX
+thumb_cmc |   1790 |   1831 |   1853
+thumb_mcp |   1497 |   1514 |   1528
+thumb_pip |   1466 |   1496 |   1515
+thumb_dip |   1463 |   1484 |   1514
+```
+
+Continue with each finger:
+
+```
+Move middle through its entire range of motion.
+Recording positions. Press ENTER to stop...
+
+-------------------------------------------
+NAME                 |    MIN |    POS |    MAX
+middle_mcp_abduction |   1598 |   1718 |   1820
+middle_mcp_flexion   |   1512 |   1658 |   2136
+middle_dip           |   1484 |   1500 |   1547
+```
+
+Once calibration is complete, the system will save the calibration to `/Users/your_username/.cache/huggingface/lerobot/calibration/teleoperators/homunculus_glove/red.json`
+
+### 1.3 Calibrate Robot Arm
+
+```bash
+lerobot-calibrate \
+    --robot.type=hope_jr_arm \
+    --robot.port=/dev/tty.usbserial-1110 \
+    --robot.id=white
+```
+
+This will open a calibration GUI where you can set the range limits for each motor. The arm motions are organized as follows:
+
+- **Shoulder**: pitch, yaw, and roll
+- **Elbow**: flex
+- **Wrist**: pitch, yaw, and roll
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/calibration_gui_2.png"
+    alt="Setting boundaries in the arm calibration GUI"
+    title="Setting boundaries in the arm calibration GUI"
+    width="100%"
+  ></img>
+</p>
+
+Use the calibration interface to set the range boundaries for each joint. Move each joint through its full range of motion and adjust the minimum and maximum values accordingly. Once you have set the appropriate boundaries for all joints, save the calibration.
+
+### 1.4 Calibrate Teleoperator Exoskeleton
+
+```bash
+lerobot-calibrate \
+    --teleop.type=homunculus_arm \
+    --teleop.port=/dev/tty.usbmodem11201 \
+    --teleop.id=black
+```
+
+The exoskeleton allows one to control the robot arm. During calibration, you'll be prompted to move all joints through their full range of motion:
+
+```
+Move all joints through their entire range of motion.
+Recording positions. Press ENTER to stop...
+
+-------------------------------------------
+-------------------------------------------
+NAME            |    MIN |    POS |    MAX
+shoulder_pitch  |    586 |    736 |    895
+shoulder_yaw    |   1257 |   1374 |   1390
+shoulder_roll   |    449 |   1034 |   2564
+elbow_flex      |   3023 |   3117 |   3134
+wrist_roll      |   3073 |   3096 |   3147
+wrist_yaw       |   2143 |   2171 |   2185
+wrist_pitch     |   1975 |   1993 |   2074
+Calibration saved to /Users/your_username/.cache/huggingface/lerobot/calibration/teleoperators/homunculus_arm/black.json
+```
+
+## Step 2: Teleoperation
+
+Due to global variable conflicts in the Feetech middleware, teleoperation for arm and hand must run in separate shell sessions:
+
+### Hand
+
+```bash
+lerobot-teleoperate \
+    --robot.type=hope_jr_hand \
+    --robot.port=/dev/tty.usbmodem58760432281 \
+    --robot.id=blue \
+    --robot.side=right \
+    --teleop.type=homunculus_glove \
+    --teleop.port=/dev/tty.usbmodem11201 \
+    --teleop.id=red \
+    --teleop.side=right \
+    --display_data=true \
+    --fps=30
+```
+
+### Arm
+
+```bash
+lerobot-teleoperate \
+    --robot.type=hope_jr_arm \
+    --robot.port=/dev/tty.usbserial-1110 \
+    --robot.id=white \
+    --teleop.type=homunculus_arm \
+    --teleop.port=/dev/tty.usbmodem11201 \
+    --teleop.id=black \
+    --display_data=true \
+    --fps=30
+```
+
+## Step 3: Record, Replay, Train
+
+Record, Replay and Train with Hope-JR is still experimental.
+
+### Record
+
+This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data/settings).
+
+```bash
+lerobot-record \
+    --robot.type=hope_jr_hand \
+    --robot.port=/dev/tty.usbmodem58760432281 \
+    --robot.id=right \
+    --robot.side=right \
+    --robot.cameras='{"main": {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30}}' \
+    --teleop.type=homunculus_glove \
+    --teleop.port=/dev/tty.usbmodem1201 \
+    --teleop.id=right \
+    --teleop.side=right \
+    --dataset.repo_id=nepyope/hand_record_test_with_video_data \
+    --dataset.single_task="Hand recording test with video data" \
+    --dataset.num_episodes=1 \
+    --dataset.episode_time_s=5 \
+    --dataset.push_to_hub=true \
+    --dataset.private=true \
+    --display_data=true
+```
+
+### Replay
+
+```bash
+lerobot-replay \
+    --robot.type=hope_jr_hand \
+    --robot.port=/dev/tty.usbmodem58760432281 \
+    --robot.id=right \
+    --robot.side=right \
+    --dataset.repo_id=nepyope/hand_record_test_with_camera \
+    --dataset.episode=0
+```
+
+### Train
+
+```bash
+lerobot-train \
+  --dataset.repo_id=nepyope/hand_record_test_with_video_data \
+  --policy.type=act \
+  --output_dir=outputs/train/hopejr_hand \
+  --job_name=hopejr \
+  --policy.device=mps \
+  --wandb.enable=true \
+  --policy.repo_id=nepyope/hand_test_policy
+```
+
+### Evaluate
+
+This training run can be viewed as an example [here](https://wandb.ai/tino/lerobot/runs/rp0k8zvw?nw=nwusertino).
+
+```bash
+lerobot-record \
+  --robot.type=hope_jr_hand \
+  --robot.port=/dev/tty.usbmodem58760432281 \
+  --robot.id=right \
+  --robot.side=right \
+  --robot.cameras='{"main": {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30}}' \
+  --display_data=false \
+  --dataset.repo_id=nepyope/eval_hopejr \
+  --dataset.single_task="Evaluate hopejr hand policy" \
+  --dataset.num_episodes=10 \
+  --policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
+```

docs/source/il_robots.mdx

@@ -3,6 +3,7 @@
 This tutorial will explain how to train a neural network to control a real robot autonomously.
 
 **You'll learn:**
+
 1. How to record and visualize your dataset.
 2. How to train a policy using your data and prepare it for evaluation.
 3. How to evaluate your policy and visualize the results.
@@ -14,7 +15,10 @@ By following these steps, you'll be able to replicate tasks, such as picking up
 
 <div class="video-container">
   <video controls width="600">
-    <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot_task.mp4" type="video/mp4" />
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot_task.mp4"
+      type="video/mp4"
+    />
   </video>
 </div>
 
@@ -41,7 +45,7 @@ Note that the `id` associated with a robot is used to store the calibration file
 <hfoptions id="teleoperate_so101">
 <hfoption id="Command">
 ```bash
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=my_awesome_follower_arm \
@@ -51,6 +55,8 @@ python -m lerobot.teleoperate \
 ```
 </hfoption>
 <hfoption id="API example">
+
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
 from lerobot.robots.so101_follower import SO101FollowerConfig, SO101Follower
@@ -74,10 +80,13 @@ while True:
     action = teleop_device.get_action()
     robot.send_action(action)
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
 The teleoperate command will automatically:
+
 1. Identify any missing calibrations and initiate the calibration procedure.
 2. Connect the robot and teleop device and start teleoperation.
 
@@ -92,7 +101,7 @@ With `rerun`, you can teleoperate again while simultaneously visualizing the cam
 <hfoptions id="teleoperate_koch_camera">
 <hfoption id="Command">
 ```bash
-python -m lerobot.teleoperate \
+lerobot-teleoperate \
     --robot.type=koch_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=my_awesome_follower_arm \
@@ -104,6 +113,8 @@ python -m lerobot.teleoperate \
 ```
 </hfoption>
 <hfoption id="API example">
+
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.teleoperators.koch_leader import KochLeaderConfig, KochLeader
@@ -134,6 +145,8 @@ while True:
     action = teleop_device.get_action()
     robot.send_action(action)
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
@@ -144,11 +157,13 @@ Once you're familiar with teleoperation, you can record your first dataset.
 We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
 
 Add your token to the CLI by running this command:
+
 ```bash
 huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Then store your Hugging Face repository name in a variable:
+
 ```bash
 HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
@@ -159,7 +174,7 @@ Now you can record a dataset. To record 5 episodes and upload your dataset to th
 <hfoptions id="record">
 <hfoption id="Command">
 ```bash
-python -m lerobot.record \
+lerobot-record \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem585A0076841 \
     --robot.id=my_awesome_follower_arm \
@@ -174,6 +189,8 @@ python -m lerobot.record \
 ```
 </hfoption>
 <hfoption id="API example">
+
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
@@ -270,40 +287,49 @@ robot.disconnect()
 teleop.disconnect()
 dataset.push_to_hub()
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
 #### Dataset upload
-Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
+
+Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. `https://huggingface.co/datasets/${HF_USER}/so101_test`) that you can obtain by running:
+
 ```bash
 echo https://huggingface.co/datasets/${HF_USER}/so101_test
 ```
+
 Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
 
 You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot).
 
 You can also push your local dataset to the Hub manually, running:
+
 ```bash
 huggingface-cli upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
 ```
 
-
 #### Record function
 
 The `record` function provides a suite of tools for capturing and managing data during robot operation:
 
 ##### 1. Data Storage
+
 - Data is stored using the `LeRobotDataset` format and is stored on disk during recording.
 - By default, the dataset is pushed to your Hugging Face page after recording.
   - To disable uploading, use `--dataset.push_to_hub=False`.
 
 ##### 2. Checkpointing and Resuming
+
 - Checkpoints are automatically created during recording.
-- If an issue occurs, you can resume by re-running the same command with `--resume=true`.
+- If an issue occurs, you can resume by re-running the same command with `--resume=true`. When resuming a recording, `--dataset.num_episodes` must be set to the **number of additional episodes to be recorded**, and not to the targeted total number of episodes in the dataset !
 - To start recording from scratch, **manually delete** the dataset directory.
 
 ##### 3. Recording Parameters
+
 Set the flow of data recording using command-line arguments:
+
 - `--dataset.episode_time_s=60`
   Duration of each data recording episode (default: **60 seconds**).
 - `--dataset.reset_time_s=60`
@@ -312,7 +338,9 @@ Set the flow of data recording using command-line arguments:
   Total number of episodes to record (default: **50**).
 
 ##### 4. Keyboard Controls During Recording
+
 Control the data recording flow using keyboard shortcuts:
+
 - Press **Right Arrow (`→`)**: Early stop the current episode or reset time and move to the next.
 - Press **Left Arrow (`←`)**: Cancel the current episode and re-record it.
 - Press **Escape (`ESC`)**: Immediately stop the session, encode videos, and upload the dataset.
@@ -327,13 +355,14 @@ Avoid adding too much variation too quickly, as it may hinder your results.
 
 If you want to dive deeper into this important topic, you can check out the [blog post](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset) we wrote on what makes a good dataset.
 
-
 #### Troubleshooting:
+
 - On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
 
 ## Visualize a dataset
 
 If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
+
 ```bash
 echo ${HF_USER}/so101_test
 ```
@@ -347,7 +376,7 @@ You can replay the first episode on your robot with either the command below or
 <hfoptions id="replay">
 <hfoption id="Command">
 ```bash
-python -m lerobot.replay \
+lerobot-replay \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=my_awesome_follower_arm \
@@ -356,6 +385,8 @@ python -m lerobot.replay \
 ```
 </hfoption>
 <hfoption id="API example">
+
+<!-- prettier-ignore-start -->
 ```python
 import time
 
@@ -388,6 +419,8 @@ for idx in range(dataset.num_frames):
 
 robot.disconnect()
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
@@ -395,9 +428,10 @@ Your robot should replicate movements similar to those you recorded. For example
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
   --dataset.repo_id=${HF_USER}/so101_test \
   --policy.type=act \
   --output_dir=outputs/train/act_so101_test \
@@ -408,16 +442,18 @@ python -m lerobot.scripts.train \
 ```
 
 Let's explain the command:
+
 1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+3. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
+4. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
 
 Training should take several hours. You will find checkpoints in `outputs/train/act_so101_test/checkpoints`.
 
 To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
   --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
   --resume=true
 ```
@@ -426,18 +462,21 @@ If you do not want to push your model to the hub after training use `--policy.pu
 
 Additionally you can provide extra `tags` or specify a `license` for your model or make the model repo `private` by adding this: `--policy.private=true --policy.tags=\[ppo,rl\] --policy.license=mit`
 
-#### Train using Collab
-If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act).
+#### Train using Google Colab
+
+If your local computer doesn't have a powerful GPU you could utilize Google Colab to train your model by following the [ACT training notebook](./notebooks#training-act).
 
 #### Upload policy checkpoints
 
 Once training is done, upload the latest checkpoint with:
+
 ```bash
 huggingface-cli upload ${HF_USER}/act_so101_test \
   outputs/train/act_so101_test/checkpoints/last/pretrained_model
 ```
 
 You can also upload intermediate checkpoints with:
+
 ```bash
 CKPT=010000
 huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
@@ -446,12 +485,12 @@ huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
 
 ## Run inference and evaluate your policy
 
-You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
+You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
 
 <hfoptions id="eval">
 <hfoption id="Command">
 ```bash
-python -m lerobot.record  \
+lerobot-record  \
   --robot.type=so100_follower \
   --robot.port=/dev/ttyACM1 \
   --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
@@ -467,6 +506,8 @@ python -m lerobot.record  \
 ```
 </hfoption>
 <hfoption id="API example">
+
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
@@ -478,11 +519,14 @@ 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
 EPISODE_TIME_SEC = 60
 TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
 
 # Create the robot configuration
 camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
@@ -494,7 +538,7 @@ robot_config = SO100FollowerConfig(
 robot = SO100Follower(robot_config)
 
 # Initialize the policy
-policy = ACTPolicy.from_pretrained("<hf_username>/<my_policy_repo_id>")
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
 
 # Configure the dataset features
 action_features = hw_to_dataset_features(robot.action_features, "action")
@@ -503,7 +547,7 @@ dataset_features = {**action_features, **obs_features}
 
 # Create the dataset
 dataset = LeRobotDataset.create(
-    repo_id="<hf_username>/eval_<dataset_repo_id>",
+    repo_id=HF_DATASET_ID,
     fps=FPS,
     features=dataset_features,
     robot_type=robot.name,
@@ -518,6 +562,12 @@ _init_rerun(session_name="recording")
 # Connect the robot
 robot.connect()
 
+preprocessor, postprocessor = make_processor(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+)
+
 for episode_idx in range(NUM_EPISODES):
     log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
@@ -527,6 +577,8 @@ for episode_idx in range(NUM_EPISODES):
         events=events,
         fps=FPS,
         policy=policy,
+        preprocessor=preprocessor,
+        postprocessor=postprocessor,
         dataset=dataset,
         control_time_s=EPISODE_TIME_SEC,
         single_task=TASK_DESCRIPTION,
@@ -539,9 +591,12 @@ for episode_idx in range(NUM_EPISODES):
 robot.disconnect()
 dataset.push_to_hub()
 ```
+<!-- prettier-ignore-end -->
+
 </hfoption>
 </hfoptions>
 
 As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).
+
+1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).
 2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so101_test`).

docs/source/il_sim.mdx

@@ -3,6 +3,7 @@
 This tutorial will explain how to train a neural network to control a robot in simulation with imitation learning.
 
 **You'll learn:**
+
 1. How to record a dataset in simulation with [gym-hil](https://github.com/huggingface/gym-hil) and visualize the dataset.
 2. How to train a policy using your data.
 3. How to evaluate your policy in simulation and visualize the results.
@@ -23,11 +24,36 @@ pip install -e ".[hilserl]"
 
 To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_gym_hil_il.json).
 
-To teleoperate and collect a dataset, we need to modify this config file and you should add your `repo_id` here: `"repo_id": "il_gym",` and `"num_episodes": 30,` and make sure you set `mode` to `record`, "mode": "record".
+To teleoperate and collect a dataset, we need to modify this config file. Here's an example configuration for imitation learning data collection:
 
-If you do not have a Nvidia GPU also change `"device": "cuda"` parameter in the config file (for example to `mps` for MacOS).
+```json
+{
+  "env": {
+    "type": "gym_manipulator",
+    "name": "gym_hil",
+    "task": "PandaPickCubeGamepad-v0",
+    "fps": 10
+  },
+  "dataset": {
+    "repo_id": "your_username/il_gym",
+    "root": null,
+    "task": "pick_cube",
+    "num_episodes_to_record": 30,
+    "replay_episode": null,
+    "push_to_hub": true
+  },
+  "mode": "record",
+  "device": "cuda"
+}
+```
 
-By default the config file assumes you use a controller. To use your keyboard please change the envoirment specified at `"task"` in the config file and set it to `"PandaPickCubeKeyboard-v0"`.
+Key configuration points:
+
+- Set your `repo_id` in the `dataset` section: `"repo_id": "your_username/il_gym"`
+- Set `num_episodes_to_record: 30` to collect 30 demonstration episodes
+- Ensure `mode` is set to `"record"`
+- If you don't have an NVIDIA GPU, change `"device": "cuda"` to `"mps"` for macOS or `"cpu"`
+- To use keyboard instead of gamepad, change `"task"` to `"PandaPickCubeKeyboard-v0"`
 
 Then we can run this command to start:
 
@@ -55,13 +81,21 @@ Note that to teleoperate the robot you have to hold the "Human Take Over Pause P
 **Gamepad Controls**
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true" alt="Figure shows the control mappings on a Logitech gamepad." title="Gamepad Control Mapping" width="100%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true"
+    alt="Figure shows the control mappings on a Logitech gamepad."
+    title="Gamepad Control Mapping"
+    width="100%"
+  ></img>
+</p>
+<p align="center">
+  <i>Gamepad button mapping for robot control and episode management</i>
 </p>
-<p align="center"><i>Gamepad button mapping for robot control and episode management</i></p>
 
 **Keyboard controls**
 
 For keyboard controls use the `spacebar` to enable control and the following keys to move the robot:
+
 ```bash
   Arrow keys: Move in X-Y plane
   Shift and Shift_R: Move in Z axis
@@ -74,16 +108,23 @@ For keyboard controls use the `spacebar` to enable control and the following key
 If you uploaded your dataset to the hub you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id.
 
 <p align="center">
-  <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/dataset_visualizer_sim.png" alt="Figure shows the dataset visualizer" title="Dataset visualization" width="100%"></img>
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/dataset_visualizer_sim.png"
+    alt="Figure shows the dataset visualizer"
+    title="Dataset visualization"
+    width="100%"
+  ></img>
+</p>
+<p align="center">
+  <i>Dataset visualizer</i>
 </p>
-<p align="center"><i>Dataset visualizer</i></p>
-
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
   --dataset.repo_id=${HF_USER}/il_gym \
   --policy.type=act \
   --output_dir=outputs/train/il_sim_test \
@@ -93,25 +134,29 @@ python -m lerobot.scripts.train \
 ```
 
 Let's explain the command:
+
 1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+3. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
+4. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
 
 Training should take several hours, 100k steps (which is the default) will take about 1h on Nvidia A100. You will find checkpoints in `outputs/train/il_sim_test/checkpoints`.
 
 #### Train using Collab
+
 If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act).
 
 #### Upload policy checkpoints
 
 Once training is done, upload the latest checkpoint with:
+
 ```bash
 huggingface-cli upload ${HF_USER}/il_sim_test \
   outputs/train/il_sim_test/checkpoints/last/pretrained_model
 ```
 
 You can also upload intermediate checkpoints with:
+
 ```bash
 CKPT=010000
 huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \
@@ -120,9 +165,32 @@ huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \
 
 ## Evaluate your policy in Sim
 
-To evaluate your policy we have to use the config file that can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json).
+To evaluate your policy we have to use a configuration file. An example can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json).
 
-Make sure to replace the `repo_id` with the dataset you trained on, for example `pepijn223/il_sim_dataset` and replace the `pretrained_policy_name_or_path` with your model id, for example `pepijn223/il_sim_model`
+Here's an example evaluation configuration:
+
+```json
+{
+  "env": {
+    "type": "gym_manipulator",
+    "name": "gym_hil",
+    "task": "PandaPickCubeGamepad-v0",
+    "fps": 10
+  },
+  "dataset": {
+    "repo_id": "your_username/il_sim_dataset",
+    "dataset_root": null,
+    "task": "pick_cube"
+  },
+  "pretrained_policy_name_or_path": "your_username/il_sim_model",
+  "device": "cuda"
+}
+```
+
+Make sure to replace:
+
+- `repo_id` with the dataset you trained on (e.g., `your_username/il_sim_dataset`)
+- `pretrained_policy_name_or_path` with your model ID (e.g., `your_username/il_sim_model`)
 
 Then you can run this command to visualize your trained policy
 
@@ -144,9 +212,9 @@ mjpython -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym
 </hfoptions>
 
 > [!WARNING]
-> While the main workflow of training ACT in simulation is straightforward, there is significant room for exploring  how to set up the task, define the initial state of the environment, and determine the type of data required during collection to learn the most effective policy. If your trained policy doesn't perform well, investigate the quality of the dataset it was trained on using our visualizers, as well as the action values and various hyperparameters related to ACT and the simulation.
+> While the main workflow of training ACT in simulation is straightforward, there is significant room for exploring how to set up the task, define the initial state of the environment, and determine the type of data required during collection to learn the most effective policy. If your trained policy doesn't perform well, investigate the quality of the dataset it was trained on using our visualizers, as well as the action values and various hyperparameters related to ACT and the simulation.
 
 Congrats 🎉, you have finished this tutorial. If you want to continue with using LeRobot in simulation follow this [Tutorial on reinforcement learning in sim with HIL-SERL](https://huggingface.co/docs/lerobot/hilserl_sim)
 
 > [!TIP]
->  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

docs/source/index.mdx

@@ -1,6 +1,10 @@
 <div class="flex justify-center">
   <a target="_blank" href="https://huggingface.co/lerobot">
-      <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-logo-thumbnail.png" style="width: 100%"></img>
+    <img
+      alt="HuggingFace Expert Acceleration Program"
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-logo-thumbnail.png"
+      style="width: 100%"
+    ></img>
   </a>
 </div>
 

docs/source/installation.mdx

@@ -1,49 +1,88 @@
 # Installation
 
-## Install LeRobot
-
-Currently only available from source.
-
-Download our source code:
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
+## Environment Setup
 
 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
 ```
 
 Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
+
 ```bash
 conda activate lerobot
 ```
 
 When using `miniconda`, install `ffmpeg` in your environment:
+
 ```bash
 conda install ffmpeg -c conda-forge
 ```
 
 > [!TIP]
 > This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->  - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->  ```bash
->  conda install ffmpeg=7.1.1 -c conda-forge
->  ```
->  - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+>
+> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
+>
+> ```bash
+> conda install ffmpeg=7.1.1 -c conda-forge
+> ```
+>
+> - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+
+## Install LeRobot 🤗
+
+### From Source
+
+First, clone the repository and navigate into the directory:
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+```
+
+Then, install the library in editable mode. This is useful if you plan to contribute to the code.
 
-Install 🤗 LeRobot:
 ```bash
 pip install -e .
 ```
 
+### Installation from PyPI
+
+**Core Library:**
+Install the base package with:
+
+```bash
+pip install lerobot
+```
+
+_This installs only the default dependencies._
+
+**Extra Features:**
+To install additional functionality, use one of the following:
+
+```bash
+pip install 'lerobot[all]'          # All available features
+pip install 'lerobot[aloha,pusht]'  # Specific features (Aloha & Pusht)
+pip install 'lerobot[feetech]'      # Feetech motor support
+```
+
+_Replace `[...]` with your desired features._
+
+**Available Tags:**
+For a full list of optional dependencies, see:
+https://pypi.org/project/lerobot/
+
 ### Troubleshooting
+
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
 To install these for linux run:
+
 ```bash
 sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config
 ```
+
 For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
 
 ## Optional dependencies
@@ -51,20 +90,26 @@ For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/
 LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
 
 ### Simulations
+
 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
 pip install -e ".[aloha]" # or "[pusht]" for example
 ```
 
 ### Motor Control
+
 For Koch v1.1 install the Dynamixel SDK, for SO100/SO101/Moss install the Feetech SDK.
+
 ```bash
 pip install -e ".[feetech]" # or "[dynamixel]" for example
 ```
 
 ### Experiment Tracking
+
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
+
 ```bash
 wandb login
 ```

docs/source/integrate_hardware.mdx

@@ -2,37 +2,34 @@
 
 This tutorial will explain how to integrate your own robot design into the LeRobot ecosystem and have it access all of our tools (data collection, control pipelines, policy training and inference).
 
-To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blob/main/lerobot/robots/robot.py) base class in the LeRobot which specifies a standard interface for physical robot integration. Let's see how to implement it.
+To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/robot.py) base class in the LeRobot which specifies a standard interface for physical robot integration. Let's see how to implement it.
 
 ## Prerequisites
 
 - 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
 
 If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus interfaces:
 
-- [`FeetechMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/feetech/feetech.py) – for controlling Feetech servos
-- [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
+- [`FeetechMotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/feetech/feetech.py) – for controlling Feetech servos
+- [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
 
-Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/motors_bus.py) abstract class to learn about its API.
-For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/lerobot/robots/so101_follower/so101_follower.py)
+Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/motors_bus.py) abstract class to learn about its API.
+For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so101_follower/so101_follower.py)
 
 Use these if compatible. Otherwise, you'll need to find or write a Python interface (not covered in this tutorial):
+
 - Find an existing SDK in Python (or use bindings to C/C++)
 - Or implement a basic communication wrapper (e.g., via pyserial, socket, or CANopen)
 
 You're not alone—many community contributions use custom boards or firmware!
 
-For Feetech and Dynamixel, we currently support these servos:
-    - Feetech:
-        - STS & SMS series (protocol 0): `sts3215`, `sts3250`, `sm8512bl`
-        - SCS series (protocol 1): `scs0009`
-    - Dynamixel (protocol 2.0 only): `xl330-m077`, `xl330-m288`, `xl430-w250`, `xm430-w350`, `xm540-w270`, `xc430-w150`
+For Feetech and Dynamixel, we currently support these servos: - Feetech: - STS & SMS series (protocol 0): `sts3215`, `sts3250`, `sm8512bl` - SCS series (protocol 1): `scs0009` - Dynamixel (protocol 2.0 only): `xl330-m077`, `xl330-m288`, `xl430-w250`, `xm430-w350`, `xm540-w270`, `xc430-w150`
 
-If you are using Feetech or Dynamixel servos that are not in this list, you can add those in the [Feetech table](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/feetech/tables.py) or [Dynamixel table](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/dynamixel/tables.py). Depending on the model, this will require you to add model-specific information. In most cases though, there shouldn't be a lot of additions to do.
+If you are using Feetech or Dynamixel servos that are not in this list, you can add those in the [Feetech table](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/feetech/tables.py) or [Dynamixel table](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/dynamixel/tables.py). Depending on the model, this will require you to add model-specific information. In most cases though, there shouldn't be a lot of additions to do.
 
 In the next sections, we'll use a `FeetechMotorsBus` as the motors interface for the examples. Replace it and adapt to your motors if necessary.
 
@@ -41,6 +38,8 @@ In the next sections, we'll use a `FeetechMotorsBus` as the motors interface for
 You’ll first need to specify the config class and a string identifier (`name`) for your robot. If your robot has special needs that you'd like to be able to change easily, it should go here (e.g. port/address, baudrate).
 
 Here, we'll add the port name and one camera by default for our robot:
+
+<!-- prettier-ignore-start -->
 ```python
 from dataclasses import dataclass, field
 
@@ -64,13 +63,15 @@ class MyCoolRobotConfig(RobotConfig):
         }
     )
 ```
+<!-- prettier-ignore-end -->
 
-Have a look at our [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.
 
 Here we'll create a simple 5-DoF robot with one camera. It could be a simple arm but notice that the `Robot` abstract class does not assume anything on your robot's form factor. You can let you imagination run wild when designing new robots!
 
+<!-- prettier-ignore-start -->
 ```python
 from lerobot.cameras import make_cameras_from_configs
 from lerobot.motors import Motor, MotorNormMode
@@ -96,10 +97,11 @@ class MyCoolRobot(Robot):
         )
         self.cameras = make_cameras_from_configs(config.cameras)
 ```
+<!-- prettier-ignore-end -->
 
 ## Step 2: Define Observation and Action Features
 
-These two properties define the *interface contract* between your robot and tools that consume it (such as data collection or learning pipelines).
+These two properties define the _interface contract_ between your robot and tools that consume it (such as data collection or learning pipelines).
 
 > [!WARNING]
 > Note that these properties must be callable even if the robot is not yet connected, so avoid relying on runtime hardware state to define them.
@@ -109,6 +111,8 @@ These two properties define the *interface contract* between your robot and tool
 This property should return a dictionary describing the structure of sensor outputs from your robot. The keys match what `get_observation()` returns, and the values describe either the shape (for arrays/images) or the type (for simple values).
 
 Example for our 5-DoF arm with one camera:
+
+<!-- prettier-ignore-start -->
 ```python
 @property
 def _motors_ft(self) -> dict[str, type]:
@@ -130,6 +134,8 @@ def _cameras_ft(self) -> dict[str, tuple]:
 def observation_features(self) -> dict:
     return {**self._motors_ft, **self._cameras_ft}
 ```
+<!-- prettier-ignore-end -->
+
 In this case, observations consist of a simple dict storing each motor's position and a camera image.
 
 ### `action_features`
@@ -137,10 +143,13 @@ In this case, observations consist of a simple dict storing each motor's positio
 This property describes the commands your robot expects via `send_action()`. Again, keys must match the expected input format, and values define the shape/type of each command.
 
 Here, we simply use the same joints proprioceptive features (`self._motors_ft`) as with `observation_features`: the action sent will simply the goal position for each motor.
+
+<!-- prettier-ignore-start -->
 ```python
 def action_features(self) -> dict:
     return self._motors_ft
 ```
+<!-- prettier-ignore-end -->
 
 ## Step 3: Handle Connection and Disconnection
 
@@ -150,16 +159,19 @@ These methods should handle opening and closing communication with your hardware
 
 This property should simply reflect that communication with the robot's hardware is established. When this property is `True`, it should be possible to read and write to the hardware using `get_observation()` and `send_action()`.
 
+<!-- prettier-ignore-start -->
 ```python
 @property
 def is_connected(self) -> bool:
     return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
 ```
+<!-- prettier-ignore-end -->
 
 ### `connect()`
 
 This method should establish communication with the hardware. Moreover, if your robot needs calibration and is not calibrated, it should start a calibration procedure by default. If your robot needs some specific configuration, this should also be called here.
 
+<!-- prettier-ignore-start -->
 ```python
 def connect(self, calibrate: bool = True) -> None:
     self.bus.connect()
@@ -171,25 +183,31 @@ def connect(self, calibrate: bool = True) -> None:
 
     self.configure()
 ```
+<!-- prettier-ignore-end -->
 
 ### `disconnect()`
 
 This method should gracefully terminate communication with the hardware: free any related resources (threads or processes), close ports, etc.
 
 Here, we already handle this in our `MotorsBus` and `Camera` classes so we just need to call their own `disconnect()` methods:
+
+<!-- prettier-ignore-start -->
 ```python
 def disconnect(self) -> None:
     self.bus.disconnect()
     for cam in self.cameras.values():
         cam.disconnect()
 ```
+<!-- prettier-ignore-end -->
 
 ## Step 4: Support Calibration and Configuration
 
 LeRobot supports saving and loading calibration data automatically. This is useful for joint offsets, zero positions, or sensor alignment.
 
 > Note that depending on your hardware, this may not apply. If that's the case, you can simply leave these methods as no-ops:
-> ```python
+
+<!-- prettier-ignore-start -->
+```python
 > @property
 > def is_calibrated(self) -> bool:
 >    return True
@@ -202,7 +220,8 @@ LeRobot supports saving and loading calibration data automatically. This is usef
 
 This should reflect whether your robot has the required calibration loaded.
 
-```python
+```
+<!-- prettier-ignore-end -->python
 @property
 def is_calibrated(self) -> bool:
     return self.bus.is_calibrated
@@ -216,6 +235,8 @@ The goal of the calibration is twofold:
 
 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:
     self.bus.disable_torque()
@@ -245,11 +266,13 @@ def calibrate(self) -> None:
     self._save_calibration()
     print("Calibration saved to", self.calibration_fpath)
 ```
+<!-- prettier-ignore-end -->
 
 ### `configure()`
 
 Use this to set up any configuration for your hardware (servos control modes, controller gains, etc.). This should usually be run at connection time and be idempotent.
 
+<!-- prettier-ignore-start -->
 ```python
 def configure(self) -> None:
     with self.bus.torque_disabled():
@@ -260,6 +283,7 @@ def configure(self) -> None:
             self.bus.write("I_Coefficient", motor, 0)
             self.bus.write("D_Coefficient", motor, 32)
 ```
+<!-- prettier-ignore-end -->
 
 ## Step 5: Implement Sensors Reading and Action Sending
 
@@ -269,6 +293,7 @@ These are the most important runtime functions: the core I/O loop.
 
 Returns a dictionary of sensor values from the robot. These typically include motor states, camera frames, various sensors, etc. In the LeRobot framework, these observations are what will be fed to a policy in order to predict the actions to take. The dictionary keys and structure must match `observation_features`.
 
+<!-- prettier-ignore-start -->
 ```python
 def get_observation(self) -> dict[str, Any]:
     if not self.is_connected:
@@ -284,6 +309,7 @@ def get_observation(self) -> dict[str, Any]:
 
     return obs_dict
 ```
+<!-- prettier-ignore-end -->
 
 ### `send_action()`
 
@@ -291,6 +317,7 @@ Takes a dictionary that matches `action_features`, and sends it to your hardware
 
 For simplicity, we won't be adding any modification of the actions in our example here.
 
+<!-- prettier-ignore-start -->
 ```python
 def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
     goal_pos = {key.removesuffix(".pos"): val for key, val in action.items()}
@@ -300,10 +327,11 @@ def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
 
     return action
 ```
+<!-- prettier-ignore-end -->
 
 ## Adding a Teleoperator
 
-For implementing teleoperation devices, we also provide a [`Teleoperator`](https://github.com/huggingface/lerobot/blob/main/lerobot/teleoperators/teleoperator.py) base class. This class is very similar to the `Robot` base class and also doesn't assume anything on form factor.
+For implementing teleoperation devices, we also provide a [`Teleoperator`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/teleoperators/teleoperator.py) base class. This class is very similar to the `Robot` base class and also doesn't assume anything on form factor.
 
 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.
 

docs/source/koch.mdx

@@ -1 +0,0 @@
-../../src/lerobot/robots/koch_follower/koch.mdx

docs/source/koch.mdx

@@ -0,0 +1,283 @@
+# Koch v1.1
+
+In the steps below, we explain how to assemble the Koch v1.1 robot.
+
+## Order and assemble the parts
+
+Follow the sourcing and assembling instructions provided in this [README](https://github.com/jess-moss/koch-v1-1). This will guide you through setting up both the follower and leader arms, as shown in the image below.
+
+For a visual walkthrough of the assembly process, you can refer to [this video tutorial](https://youtu.be/8nQIg9BwwTk).
+
+> [!WARNING]
+> Since the production of this video, we simplified the configuration phase. Because of this, two things differ from the instructions in that video:
+>
+> - Don't plug in all the motor cables right away and wait to be instructed to do so in [Configure the motors](#configure-the-motors).
+> - Don't screw in the controller board (PCB) to the base right away and wait for being instructed to do so in [Configure the motors](#configure-the-motors).
+
+## Install LeRobot 🤗
+
+To install LeRobot follow, our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Dynamixel SDK:
+
+```bash
+pip install -e ".[dynamixel]"
+```
+
+## Configure the motors
+
+### 1. Find the USB ports associated with each arm
+
+To find the port for each bus servo adapter, run this script:
+
+```bash
+lerobot-find-port
+```
+
+<hfoptions id="example">
+<hfoption id="Mac">
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the USB cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
+
+</hfoption>
+<hfoption id="Linux">
+
+On Linux, you might need to give access to the USB ports by running:
+
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/ttyACM0', '/dev/ttyACM1']
+Remove the usb cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/ttyACM1
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
+
+</hfoption>
+</hfoptions>
+
+### 2. Set the motors ids and baudrates
+
+Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
+
+To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
+
+If you are repurposing motors from another robot, you will probably also need to perform this step, as the ids and baudrate likely won't match.
+
+#### Follower
+
+Connect the usb cable from your computer and the 5V power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
+
+For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+lerobot-setup-motors \
+    --robot.type=koch_follower \
+    --robot.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.robots.koch_follower import KochFollower, KochFollowerConfig
+
+config = KochFollowerConfig(
+    port="/dev/tty.usbmodem575E0031751",
+    id="my_awesome_follower_arm",
+)
+follower = KochFollower(config)
+follower.setup_motors()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+You should see the following instruction.
+
+```
+Connect the controller board to the 'gripper' motor only and press enter.
+```
+
+As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
+
+<details>
+<summary>Troubleshooting</summary>
+
+If you get an error at that point, check your cables and make sure they are plugged in properly:
+
+<ul>
+  <li>Power supply</li>
+  <li>USB cable between your computer and the controller board</li>
+  <li>The 3-pin cable from the controller board to the motor</li>
+</ul>
+
+If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
+
+</details>
+
+You should then see the following message:
+
+```
+'gripper' motor id set to 6
+```
+
+Followed by the next instruction:
+
+```
+Connect the controller board to the 'wrist_roll' motor only and press enter.
+```
+
+You can disconnect the 3-pin cable from the controller board but you can leave it connected to the gripper motor on the other end as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
+
+Repeat the operation for each motor as instructed.
+
+> [!TIP]
+> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
+
+When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
+
+#### Leader
+
+Do the same steps for the leader arm but modify the command or script accordingly.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+lerobot-setup-motors \
+    --teleop.type=koch_leader \
+    --teleop.port=/dev/tty.usbmodem575E0031751 \  # <- paste here the port found at previous step
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.teleoperators.koch_leader import KochLeader, KochLeaderConfig
+
+config = KochLeaderConfig(
+    port="/dev/tty.usbmodem575E0031751",
+    id="my_awesome_leader_arm",
+)
+leader = KochLeader(config)
+leader.setup_motors()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+## Calibrate
+
+Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+#### Follower
+
+Run the following command or API example to calibrate the follower arm:
+
+<hfoptions id="calibrate_follower">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --robot.type=koch_follower \
+    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --robot.id=my_awesome_follower_arm  # <- Give the robot a unique name
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.robots.koch_follower import KochFollowerConfig, KochFollower
+
+config = KochFollowerConfig(
+    port="/dev/tty.usbmodem585A0076891",
+    id="my_awesome_follower_arm",
+)
+
+follower = KochFollower(config)
+follower.connect(calibrate=False)
+follower.calibrate()
+follower.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+We unified the calibration method for most robots. Thus, the calibration steps for this Koch arm are the same as the steps for the SO100 and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
+
+#### Leader
+
+Do the same steps to calibrate the leader arm, run the following command or API example:
+
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --teleop.type=koch_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm  # <- Give the robot a unique name
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.teleoperators.koch_leader import KochLeaderConfig, KochLeader
+
+config = KochLeaderConfig(
+    port="/dev/tty.usbmodem575E0031751",
+    id="my_awesome_leader_arm",
+)
+
+leader = KochLeader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

docs/source/lekiwi.mdx

@@ -1 +0,0 @@
-../../src/lerobot/robots/lekiwi/lekiwi.mdx

docs/source/lekiwi.mdx

@@ -0,0 +1,337 @@
+# LeKiwi
+
+In the steps below, we explain how to assemble the LeKiwi mobile robot.
+
+## Source the parts
+
+Follow this [README](https://github.com/SIGRobotics-UIUC/LeKiwi). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts.
+And advise if it's your first time printing or if you don't own a 3D printer.
+
+### Wired version
+
+If you have the **wired** LeKiwi version, you can skip the installation of the Raspberry Pi and setting up SSH. You can also run all commands directly on your PC for both the LeKiwi scripts and the leader arm scripts for teleoperating.
+
+## Install software on Pi
+
+Now we have to set up the remote PC that will run on the LeKiwi Robot. This is normally a Raspberry Pi, but can be any PC that can run on 5V and has enough usb ports (2 or more) for the cameras and motor control board.
+
+### Install OS
+
+For setting up the Raspberry Pi and its SD-card see: [Setup PI](https://www.raspberrypi.com/documentation/computers/getting-started.html). Here is explained how to download the [Imager](https://www.raspberrypi.com/software/) to install Raspberry Pi OS or Ubuntu.
+
+### Setup SSH
+
+After setting up your Pi, you should enable and set up [SSH](https://www.raspberrypi.com/news/coding-on-raspberry-pi-remotely-with-visual-studio-code/) (Secure Shell Protocol) so you can log in to the Pi from your laptop without requiring a screen, keyboard, and mouse on the Pi. A great tutorial on how to do this can be found [here](https://www.raspberrypi.com/documentation/computers/remote-access.html#ssh). Logging into your Pi can be done in your Command Prompt (cmd) or, if you use VSCode you can use [this](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh) extension.
+
+### Install LeRobot on Pi 🤗
+
+On your Raspberry Pi install LeRobot using our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Feetech SDK & ZeroMQ on your Pi:
+
+```bash
+pip install -e ".[lekiwi]"
+```
+
+## Install LeRobot locally
+
+If you already have installed LeRobot on your laptop/pc you can skip this step; otherwise, please follow along as we do the same steps we did on the Pi.
+
+Follow our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Feetech SDK & ZeroMQ on your laptop/pc:
+
+```bash
+pip install -e ".[lekiwi]"
+```
+
+Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:.
+Every time you now want to use LeRobot, you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
+
+# Step-by-Step Assembly Instructions
+
+First, we will assemble the two SO100/SO101 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base. The instructions for assembling can be found on these two pages:
+
+- [Assemble SO101](./so101#step-by-step-assembly-instructions)
+- [Assemble LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi/blob/main/Assembly.md)
+
+### Find the USB ports associated with motor board
+
+To find the port for each bus servo adapter, run this script:
+
+```bash
+lerobot-find-port
+```
+
+<hfoptions id="example">
+<hfoption id="Mac">
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081']
+Remove the USB cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your board.
+
+</hfoption>
+<hfoption id="Linux">
+
+On Linux, you might need to give access to the USB ports by running:
+
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/ttyACM0']
+Remove the usb cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/ttyACM0
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/ttyACM0` corresponding to your board.
+
+</hfoption>
+</hfoptions>
+
+### Configure motors
+
+The instructions for configuring the motors can be found in the SO101 [docs](./so101#configure-the-motors). Besides the ids for the arm motors, we also need to set the motor ids for the mobile base. These need to be in a specific order to work. Below an image of the motor ids and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ids for the wheels are 7, 8 and 9.
+
+You can run this command to setup motors for LeKiwi. It will first setup the motors for arm (id 6..1) and then setup motors for wheels (9,8,7)
+
+```bash
+lerobot-setup-motors \
+    --robot.type=lekiwi \
+    --robot.port=/dev/tty.usbmodem58760431551 # <- paste here the port found at previous step
+```
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/motor_ids.webp" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
+
+### Troubleshoot communication
+
+If you are having trouble connecting to the Mobile SO100, follow these steps to diagnose and resolve the issue.
+
+#### 1. Verify IP Address Configuration
+
+Make sure that the correct IP for the Pi is used in the commands or in your code. To check the Raspberry Pi's IP address, run (on the Pi command line):
+
+```bash
+hostname -I
+```
+
+#### 2. Check if Pi is reachable from laptop/pc
+
+Try pinging the Raspberry Pi from your laptop:
+
+```bach
+ping <your_pi_ip_address>
+```
+
+If the ping fails:
+
+- Ensure the Pi is powered on and connected to the same network.
+- Check if SSH is enabled on the Pi.
+
+#### 3. Try SSH connection
+
+If you can't SSH into the Pi, it might not be properly connected. Use:
+
+```bash
+ssh <your_pi_user_name>@<your_pi_ip_address>
+```
+
+If you get a connection error:
+
+- Ensure SSH is enabled on the Pi by running:
+  ```bash
+  sudo raspi-config
+  ```
+  Then navigate to: **Interfacing Options -> SSH** and enable it.
+
+### Calibration
+
+Now we have to calibrate the leader arm and the follower arm. The wheel motors don't have to be calibrated.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+### Calibrate follower arm (on mobile base)
+
+Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:
+
+```bash
+lerobot-calibrate \
+    --robot.type=lekiwi \
+    --robot.id=my_awesome_kiwi # <- Give the robot a unique name
+```
+
+We unified the calibration method for most robots, thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video).
+
+### Wired version
+
+If you have the **wired** LeKiwi version, please run all commands on your laptop.
+
+### Calibrate leader arm
+
+Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the following command of API example on your laptop:
+
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
+
+config = SO100LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_awesome_leader_arm",
+)
+
+leader = SO100Leader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+## Teleoperate LeKiwi
+
+> [!TIP]
+> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
+
+To teleoperate, SSH into your Raspberry Pi, and run `conda activate lerobot` and this command:
+
+```bash
+python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi
+```
+
+Then on your laptop, also run `conda activate lerobot` and run the API example, make sure you set the correct `remote_ip` and `port` in `examples/lekiwi/teleoperate.py`.
+
+```bash
+python examples/lekiwi/teleoperate.py
+```
+
+You should see on your laptop something like this: `[INFO] Connected to remote robot at tcp://172.17.133.91:5555 and video stream at tcp://172.17.133.91:5556.` Now you can move the leader arm and use the keyboard (w,a,s,d) to drive forward, left, backwards, right. And use (z,x) to turn left or turn right. You can use (r,f) to increase and decrease the speed of the mobile robot. There are three speed modes, see the table below:
+
+| Speed Mode | Linear Speed (m/s) | Rotation Speed (deg/s) |
+| ---------- | ------------------ | ---------------------- |
+| Fast       | 0.4                | 90                     |
+| Medium     | 0.25               | 60                     |
+| Slow       | 0.1                | 30                     |
+
+| Key | Action         |
+| --- | -------------- |
+| W   | Move forward   |
+| A   | Move left      |
+| S   | Move backward  |
+| D   | Move right     |
+| Z   | Turn left      |
+| X   | Turn right     |
+| R   | Increase speed |
+| F   | Decrease speed |
+
+> [!TIP]
+> If you use a different keyboard, you can change the keys for each command in the [`LeKiwiClientConfig`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/lekiwi/config_lekiwi.py).
+
+### Wired version
+
+If you have the **wired** LeKiwi version, please run all commands on your laptop.
+
+## Record a dataset
+
+Once you're familiar with teleoperation, you can record your first dataset.
+
+We use the Hugging Face hub features for uploading your dataset. If you haven't previously used the Hub, make sure you can login via the cli using a write-access token, this token can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens).
+
+Add your token to the CLI by running this command:
+
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Then store your Hugging Face repository name in a variable:
+
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Now you can record a dataset. To record episodes and upload your dataset to the hub, execute this API example tailored for LeKiwi. Make sure to first adapt the `remote_ip`, `repo_id`, `port` and `task` in the script. If you would like to run the script for longer you can increase `NB_CYCLES_CLIENT_CONNECTION`.
+
+```bash
+python examples/lekiwi/record.py
+```
+
+#### Dataset upload
+
+Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
+
+```bash
+echo https://huggingface.co/datasets/${HF_USER}/so101_test
+```
+
+Your dataset will be automatically tagged with `LeRobot` for the community to find it easily, and you can also add custom tags (in this case `tutorial` for example).
+
+You can look for other LeRobot datasets on the hub by searching for `LeRobot` [tags](https://huggingface.co/datasets?other=LeRobot).
+
+#### Tips for gathering data
+
+Once you're comfortable with data recording, you can create a larger dataset for training. A good starting task is grasping an object at different locations and placing it in a bin. We suggest recording at least 50 episodes, with 10 episodes per location. Keep the cameras fixed and maintain consistent grasping behavior throughout the recordings. Also make sure the object you are manipulating is visible on the camera's. A good rule of thumb is you should be able to do the task yourself by only looking at the camera images.
+
+In the following sections, you’ll train your neural network. After achieving reliable grasping performance, you can start introducing more variations during data collection, such as additional grasp locations, different grasping techniques, and altering camera positions.
+
+Avoid adding too much variation too quickly, as it may hinder your results.
+
+If you want to dive deeper into this important topic, you can check out the [blog post](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset) we wrote on what makes a good dataset.
+
+#### Troubleshooting:
+
+- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
+
+## Replay an episode
+
+To replay an episode run the API example below, make sure to change `remote_ip`, `port`, LeRobotDatasetId and episode index.
+
+```bash
+python examples/lekiwi/replay.py
+```
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by the training part of this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+## Evaluate your policy
+
+To evaluate your policy run the `evaluate.py` API example, make sure to change `remote_ip`, `port`, model..
+
+```bash
+python examples/lekiwi/evaluate.py
+```
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

docs/source/notebooks.mdx

@@ -10,8 +10,8 @@ This repository contains example notebooks for using LeRobot. These notebooks de
 
 We provide a ready-to-run Google Colab notebook to help you train ACT policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases.
 
-| Notebook | Colab |
-|:---------|:------|
+| Notebook                                                                                                | Colab                                                                                                                                                                             |
+| :------------------------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | [Train ACT with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) |
 
 Expected training time for 100k steps: ~1.5 hours on an NVIDIA A100 GPU with batch size of `64`.

docs/source/policy_act_README.md

@@ -0,0 +1,14 @@
+## Paper
+
+https://tonyzhaozh.github.io/aloha
+
+## Citation
+
+```bibtex
+@article{zhao2023learning,
+  title={Learning fine-grained bimanual manipulation with low-cost hardware},
+  author={Zhao, Tony Z and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
+  journal={arXiv preprint arXiv:2304.13705},
+  year={2023}
+}
+```

docs/source/policy_diffusion_README.md

@@ -0,0 +1,14 @@
+## Paper
+
+https://diffusion-policy.cs.columbia.edu
+
+## Citation
+
+```bibtex
+@article{chi2024diffusionpolicy,
+	author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
+	title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
+	journal = {The International Journal of Robotics Research},
+	year = {2024},
+}
+```

docs/source/policy_smolvla_README.md

@@ -0,0 +1,14 @@
+## Paper
+
+https://arxiv.org/abs/2506.01844
+
+## Citation
+
+```bibtex
+@article{shukor2025smolvla,
+  title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
+  author={Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and Alibert, Simon and Cord, Matthieu and Wolf, Thomas and Cadene, Remi},
+  journal={arXiv preprint arXiv:2506.01844},
+  year={2025}
+}
+```

docs/source/policy_tdmpc_README.md

@@ -0,0 +1,14 @@
+## Paper
+
+https://www.nicklashansen.com/td-mpc/
+
+## Citation
+
+```bibtex
+@inproceedings{Hansen2022tdmpc,
+	title={Temporal Difference Learning for Model Predictive Control},
+	author={Nicklas Hansen and Xiaolong Wang and Hao Su},
+	booktitle={ICML},
+	year={2022}
+}
+```

docs/source/policy_vqbet_README.md

@@ -0,0 +1,14 @@
+## Paper
+
+https://sjlee.cc/vq-bet/
+
+## Citation
+
+```bibtex
+@article{lee2024behavior,
+  title={Behavior generation with latent actions},
+  author={Lee, Seungjae and Wang, Yibin and Etukuru, Haritheja and Kim, H Jin and Shafiullah, Nur Muhammad Mahi and Pinto, Lerrel},
+  journal={arXiv preprint arXiv:2403.03181},
+  year={2024}
+}
+```

docs/source/reachy2.mdx

@@ -0,0 +1,288 @@
+# Reachy 2
+
+Reachy 2 is an open-source humanoid robot made by Pollen Robotics, specifically designed for the development of embodied AI and real-world applications.
+Check out [Pollen Robotics website](https://www.pollen-robotics.com/reachy/), or access [Reachy 2 documentation](https://docs.pollen-robotics.com/) for more information on the platform!
+
+## Teleoperate Reachy 2
+
+Currently, there are two ways to teleoperate Reachy 2:
+
+- Pollen Robotics’ VR teleoperation (not included in LeRobot).
+- Robot-to-robot teleoperation (use one Reachy 2 to control another).
+
+## Reachy 2 Simulation
+
+**(Linux only)** You can run Reachy 2 in simulation (Gazebo or MuJoCo) using the provided [Docker image](https://hub.docker.com/r/pollenrobotics/reachy2_core).
+
+1. Install [Docker Engine](https://docs.docker.com/engine/).
+2. Run (for MuJoCo):
+
+```
+docker run --rm -it \
+  --name reachy \
+  --privileged \
+  --network host \
+  --ipc host \
+  --device-cgroup-rule='c 189:* rwm' \
+  --group-add audio \
+  -e ROS_DOMAIN_ID="$ROS_DOMAIN_ID" \
+  -e DISPLAY="$DISPLAY" \
+  -e RCUTILS_CONSOLE_OUTPUT_FORMAT="[{severity}]: {message}" \
+  -e REACHY2_CORE_SERVICE_FAKE="${REACHY2_CORE_SERVICE_FAKE:-true}" \
+  -v /dev:/dev \
+  -v "$HOME/.reachy_config":/home/reachy/.reachy_config_override \
+  -v "$HOME/.reachy.log":/home/reachy/.ros/log \
+  -v /usr/lib/x86_64-linux-gnu:/opt/host-libs \
+  --entrypoint /package/launch.sh \
+  pollenrobotics/reachy2_core:1.7.5.9_deploy \
+  start_rviz:=true start_sdk_server:=true mujoco:=true
+```
+
+> If MuJoCo runs slowly (low simulation frequency), append `-e LD_LIBRARY_PATH="/opt/host-libs:$LD_LIBRARY_PATH" \` to the previous command to improve performance:
+>
+> ```
+> docker run --rm -it \
+>   --name reachy \
+>   --privileged \
+>   --network host \
+>   --ipc host \
+>   --device-cgroup-rule='c 189:* rwm' \
+>   --group-add audio \
+>   -e ROS_DOMAIN_ID="$ROS_DOMAIN_ID" \
+>   -e DISPLAY="$DISPLAY" \
+>   -e RCUTILS_CONSOLE_OUTPUT_FORMAT="[{severity}]: {message}" \
+>   -e REACHY2_CORE_SERVICE_FAKE="${REACHY2_CORE_SERVICE_FAKE:-true}" \
+>   -e LD_LIBRARY_PATH="/opt/host-libs:$LD_LIBRARY_PATH" \
+>   -v /dev:/dev \
+>   -v "$HOME/.reachy_config":/home/reachy/.reachy_config_override \
+>   -v "$HOME/.reachy.log":/home/reachy/.ros/log \
+>   -v /usr/lib/x86_64-linux-gnu:/opt/host-libs \
+>   --entrypoint /package/launch.sh \
+>   pollenrobotics/reachy2_core:1.7.5.9_deploy \
+>   start_rviz:=true start_sdk_server:=true mujoco:=true
+> ```
+
+## Setup
+
+### Prerequisites
+
+- On your robot, check the **service images** meet the minimum versions:
+  - **reachy2-core >= 1.7.5.2**
+  - **webrtc >= 2.0.1.1**
+
+Then, if you want to use VR teleoperation:
+
+- Install the [Reachy 2 teleoperation application](https://docs.pollen-robotics.com/teleoperation/teleoperation-introduction/discover-teleoperation/).
+  Use version **>=v1.2.0**
+
+We recommend using two computers: one for teleoperation (Windows required) and another for recording with LeRobot.
+
+### Install LeRobot
+
+Follow the [installation instructions](https://github.com/huggingface/lerobot#installation) to install LeRobot.
+
+Install LeRobot with Reachy 2 dependencies:
+
+```bash
+pip install -e ".[reachy2]"
+```
+
+### (Optional but recommended) Install pollen_data_acquisition_server
+
+How you manage Reachy 2 recording sessions is up to you, but the **easiest** way is to use this server so you can control sessions directly from the VR teleoperation app.
+
+> **Note:** Currently, only the VR teleoperation application works as a client for this server, so this step primarily targets teleoperation. You’re free to develop custom clients to manage sessions to your needs.
+
+In your LeRobot environment, install the server from source:
+
+```bash
+git clone https://github.com/pollen-robotics/pollen_data_acquisition_server.git
+cd pollen_data_acquisition_server
+pip install -e .
+```
+
+Find the [pollen_data_acquisition_server documentation here](https://github.com/pollen-robotics/pollen_data_acquisition_server).
+
+## Step 1: Recording
+
+### Get Reachy 2 IP address
+
+Before starting teleoperation and data recording, find the [robot's IP address](https://docs.pollen-robotics.com/getting-started/setup-reachy2/connect-reachy2/).
+We strongly recommend connecting all devices (PC and robot) via **Ethernet**.
+
+### Launch recording
+
+There are two ways to manage recording sessions when using the Reachy 2 VR teleoperation application:
+
+- **Using the data acquisition server (recommended for VR teleop)**: The VR app orchestrates sessions (via the server it tells LeRobot when to create datasets, start/stop episodes) while also controlling the robot’s motions.
+- **Using LeRobot’s record script**: LeRobot owns session control and decides when to start/stop episodes. If you also use the VR teleop app, it’s only for motion control.
+
+### Option 1: Using Pollen data acquisition server (recommended for VR teleop)
+
+Make sure you have installed pollen_data_acquisition_server, as explained in the Setup section.
+
+Launch the data acquisition server to be able to manage your session directly from the teleoperation application:
+
+```bash
+python -m pollen_data_acquisition_server.server
+```
+
+Then get into the teleoperation application and choose "Data acquisition session".
+You can finally setup your session by following the screens displayed.
+
+> Even without the VR app, you can use the `pollen_data_acquisition_server` with your own client implementation.
+
+### Option 2: Using lerobot.record
+
+Reachy 2 is fully supported by LeRobot’s recording features.
+If you choose this option but still want to use the VR teleoperation application, select "Standard session" in the app.
+
+**Example: start a recording without the mobile base:**
+First add reachy2 and reachy2_teleoperator to the imports of the record script. Then you can use the following command:
+
+```bash
+python -m lerobot.record \
+    --robot.type=reachy2 \
+    --robot.ip_address=192.168.0.200 \
+    --robot.id=r2-0000 \
+    --robot.use_external_commands=true \
+    --robot.with_mobile_base=false \
+    --teleop.type=reachy2_teleoperator \
+    --teleop.ip_address=192.168.0.200 \
+    --teleop.with_mobile_base=false \
+    --dataset.repo_id=pollen_robotics/record_test \
+    --dataset.single_task="Reachy 2 recording test" \
+    --dataset.num_episodes=1 \
+    --dataset.episode_time_s=5 \
+    --dataset.fps=15 \
+    --dataset.push_to_hub=true \
+    --dataset.private=true \
+    --display_data=true
+```
+
+#### Specific Options
+
+**Extended setup overview (all options included):**
+
+```bash
+python -m lerobot.record \
+    --robot.type=reachy2 \
+    --robot.ip_address=192.168.0.200 \
+    --robot.use_external_commands=true \
+    --robot.with_mobile_base=true \
+    --robot.with_l_arm=true \
+    --robot.with_r_arm=true \
+    --robot.with_neck=true \
+    --robot.with_antennas=true \
+    --robot.with_left_teleop_camera=true \
+    --robot.with_right_teleop_camera=true \
+    --robot.with_torso_camera=false \
+    --robot.disable_torque_on_disconnect=false \
+    --robot.max_relative_target=5.0 \
+    --teleop.type=reachy2_teleoperator \
+    --teleop.ip_address=192.168.0.200 \
+    --teleop.use_present_position=false \
+    --teleop.with_mobile_base=false \
+    --teleop.with_l_arm=true \
+    --teleop.with_r_arm=true \
+    --teleop.with_neck=true \
+    --teleop.with_antennas=true \
+    --dataset.repo_id=pollen_robotics/record_test \
+    --dataset.single_task="Reachy 2 recording test" \
+    --dataset.num_episodes=1 \
+    --dataset.episode_time_s=5 \
+    --dataset.fps=15 \
+    --dataset.push_to_hub=true \
+    --dataset.private=true \
+    --display_data=true
+```
+
+##### `--robot.use_external_commands`
+
+Determine whether LeRobot robot.send_action() sends commands to the robot.
+**Must** be set to false while using the VR teleoperation application, as the app already sends commands.
+
+##### `--teleop.use_present_position`
+
+Determine whether the teleoperator reads the goal or present position of the robot.
+Must be set to true if a compliant Reachy 2 is used to control another one.
+
+##### Use the relevant parts
+
+From our initial tests, recording **all** joints when only some are moving can reduce model quality with certain policies.
+To avoid this, you can exclude specific parts from recording and replay using:
+
+````
+--robot.with_<part>=false
+```,
+with `<part>` being one of : `mobile_base`, `l_arm`, `r_arm", `neck`, `antennas`.
+It determine whether the corresponding part is recorded in the observations. True if not set.
+
+By default, **all parts are recorded**.
+
+The same per-part mechanism is available in `reachy2_teleoperator` as well.
+
+````
+
+--teleop.with\_<part>
+
+```
+with `<part>` being one of : `mobile_base`, `l_arm`, `r_arm", `neck`, `antennas`.
+Determine whether the corresponding part is recorded in the actions. True if not set.
+
+> **Important:** In a given session, the **enabled parts must match** on both the robot and the teleoperator.
+For example, if the robot runs with `--robot.with_mobile_base=false`, the teleoperator must disable the same part `--teleoperator.with_mobile_base=false`.
+
+##### Use the relevant cameras
+
+You can do the same for **cameras**. By default, only the **teleoperation cameras** are recorded (both `left_teleop_camera` and `right_teleop_camera`). Enable or disable each camera with:
+
+```
+
+--robot.with_left_teleop_camera=<true|false>
+--robot.with_right_teleop_camera=<true|false>
+--robot.with_torso_camera=<true|false>
+
+````
+
+
+## Step 2: Replay
+
+Make sure the robot is configured with the same parts as the dataset:
+
+```bash
+python -m lerobot.replay \
+    --robot.type=reachy2 \
+    --robot.ip_address=192.168.0.200 \
+    --robot.use_external_commands=false \
+    --robot.with_mobile_base=false \
+    --dataset.repo_id=pollen_robotics/record_test \
+    --dataset.episode=0
+    --display_data=true
+````
+
+## Step 3: Train
+
+```bash
+python -m lerobot.scripts.train \
+  --dataset.repo_id=pollen_robotics/record_test \
+  --policy.type=act \
+  --output_dir=outputs/train/reachy2_test \
+  --job_name=reachy2 \
+  --policy.device=mps \
+  --wandb.enable=true \
+  --policy.repo_id=pollen_robotics/record_test_policy
+```
+
+## Step 4: Evaluate
+
+```bash
+python -m lerobot.record \
+  --robot.type=reachy2 \
+  --robot.ip_address=192.168.0.200 \
+  --display_data=false \
+  --dataset.repo_id=pollen_robotics/eval_record_test \
+  --dataset.single_task="Evaluate reachy2 policy" \
+  --dataset.num_episodes=10 \
+  --policy.path=outputs/train/reachy2_test/checkpoints/last/pretrained_model
+```

docs/source/smolvla.mdx

@@ -3,9 +3,18 @@
 SmolVLA is Hugging Face’s lightweight foundation model for robotics. Designed for easy fine-tuning on LeRobot datasets, it helps accelerate your development!
 
 <p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/aooU0a3DMtYmy_1IWMaIM.png" alt="SmolVLA architecture." width="500"/>
-  <br/>
-  <em>Figure 1. SmolVLA takes as input (i) multiple cameras views, (ii) the robot’s current sensorimotor state, and (iii) a natural language instruction, encoded into contextual features used to condition the action expert when generating an action chunk.</em>
+  <img
+    src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/aooU0a3DMtYmy_1IWMaIM.png"
+    alt="SmolVLA architecture."
+    width="500"
+  />
+  <br />
+  <em>
+    Figure 1. SmolVLA takes as input (i) multiple cameras views, (ii) the
+    robot’s current sensorimotor state, and (iii) a natural language
+    instruction, encoded into contextual features used to condition the action
+    expert when generating an action chunk.
+  </em>
 </p>
 
 ## Set Up Your Environment
@@ -32,6 +41,7 @@ We recommend checking out the dataset linked below for reference that was used i
 
 In this dataset, we recorded 50 episodes across 5 distinct cube positions. For each position, we collected 10 episodes of pick-and-place interactions. This structure, repeating each variation several times, helped the model generalize better. We tried similar dataset with 25 episodes, and it was not enough leading to a bad performance. So, the data quality and quantity is definitely a key.
 After you have your dataset available on the Hub, you are good to go to use our finetuning script to adapt SmolVLA to your application.
+
 </Tip>
 
 ## Finetune SmolVLA on your data
@@ -44,7 +54,7 @@ If you don't have a gpu device, you can train using our notebook on [![Google Co
 Pass your dataset to the training script using `--dataset.repo_id`. If you want to test your installation, run the following command where we use one of the datasets we collected for the [SmolVLA Paper](https://huggingface.co/papers/2506.01844).
 
 ```bash
-cd lerobot && python -m lerobot.scripts.train \
+cd lerobot && lerobot-train \
   --policy.path=lerobot/smolvla_base \
   --dataset.repo_id=${HF_USER}/mydataset \
   --batch_size=64 \
@@ -56,29 +66,38 @@ cd lerobot && python -m lerobot.scripts.train \
 ```
 
 <Tip>
-You can start with a small batch size and increase it incrementally, if the GPU allows it, as long as loading times remain short.
+  You can start with a small batch size and increase it incrementally, if the
+  GPU allows it, as long as loading times remain short.
 </Tip>
 
 Fine-tuning is an art. For a complete overview of the options for finetuning, run
 
 ```bash
-python -m lerobot.scripts.train --help
+lerobot-train --help
 ```
 
 <p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/S-3vvVCulChREwHDkquoc.gif" alt="Comparison of SmolVLA across task variations." width="500"/>
-  <br/>
-  <em>Figure 2: Comparison of SmolVLA across task variations. From left to right: (1) pick-place cube counting, (2) pick-place cube counting, (3) pick-place cube counting under perturbations, and (4) generalization on pick-and-place of the lego block with real-world SO101.</em>
+  <img
+    src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/S-3vvVCulChREwHDkquoc.gif"
+    alt="Comparison of SmolVLA across task variations."
+    width="500"
+  />
+  <br />
+  <em>
+    Figure 2: Comparison of SmolVLA across task variations. From left to right:
+    (1) pick-place cube counting, (2) pick-place cube counting, (3) pick-place
+    cube counting under perturbations, and (4) generalization on pick-and-place
+    of the lego block with real-world SO101.
+  </em>
 </p>
 
-
 ## Evaluate the finetuned model and run it in real-time
 
 Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./getting_started_real_world_robot#record-a-dataset).
 Once you are logged in, you can run inference in your setup by doing:
 
 ```bash
-python -m lerobot.record \
+lerobot-record \
   --robot.type=so101_follower \
   --robot.port=/dev/ttyACM0 \ # <- Use your port
   --robot.id=my_blue_follower_arm \ # <- Use your robot id

docs/source/so100.mdx

@@ -1 +0,0 @@
-../../src/lerobot/robots/so100_follower/so100.mdx

docs/source/so100.mdx

@@ -0,0 +1,640 @@
+# SO-100
+
+In the steps below, we explain how to assemble the SO-100 robot.
+
+## Source the parts
+
+Follow this [README](https://github.com/TheRobotStudio/SO-ARM100/blob/main/SO100.md). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts. And advise if it's your first time printing or if you don't own a 3D printer.
+
+## Install LeRobot 🤗
+
+To install LeRobot, follow our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Feetech SDK:
+
+```bash
+pip install -e ".[feetech]"
+```
+
+## Configure the motors
+
+**Note:**
+Unlike the SO-101, the motor connectors are not easily accessible once the arm is assembled, so the configuration step must be done beforehand.
+
+### 1. Find the USB ports associated with each arm
+
+To find the port for each bus servo adapter, run this script:
+
+```bash
+lerobot-find-port
+```
+
+<hfoptions id="example">
+<hfoption id="Mac">
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the USB cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
+
+</hfoption>
+<hfoption id="Linux">
+
+On Linux, you might need to give access to the USB ports by running:
+
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/ttyACM0', '/dev/ttyACM1']
+Remove the usb cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/ttyACM1
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
+
+</hfoption>
+</hfoptions>
+
+### 2. Set the motors ids and baudrates
+
+Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
+
+To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
+
+If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match.
+
+#### Follower
+
+Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
+
+For a visual reference on how to set the motor ids please refer to [this video](https://huggingface.co/docs/lerobot/en/so101#setup-motors-video) where we follow the process for the SO101 arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+lerobot-setup-motors \
+    --robot.type=so100_follower \
+    --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.robots.so100_follower import SO100Follower, SO100FollowerConfig
+
+config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_follower_arm",
+)
+follower = SO100Follower(config)
+follower.setup_motors()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+You should see the following instruction
+
+```
+Connect the controller board to the 'gripper' motor only and press enter.
+```
+
+As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
+
+<details>
+<summary>Troubleshooting</summary>
+
+If you get an error at that point, check your cables and make sure they are plugged in properly:
+
+<ul>
+  <li>Power supply</li>
+  <li>USB cable between your computer and the controller board</li>
+  <li>The 3-pin cable from the controller board to the motor</li>
+</ul>
+
+If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
+
+</details>
+
+You should then see the following message:
+
+```
+'gripper' motor id set to 6
+```
+
+Followed by the next instruction:
+
+```
+Connect the controller board to the 'wrist_roll' motor only and press enter.
+```
+
+You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
+
+Repeat the operation for each motor as instructed.
+
+> [!TIP]
+> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
+
+When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
+
+#### Leader
+
+Do the same steps for the leader arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+```bash
+lerobot-setup-motors \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
+```
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+
+config = SO100LeaderConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_leader_arm",
+)
+leader = SO100Leader(config)
+leader.setup_motors()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+## Step-by-Step Assembly Instructions
+
+## Remove the gears of the 6 leader motors
+
+<details>
+<summary><strong>Video removing gears</strong></summary>
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+</details>
+
+Follow the video for removing gears. You need to remove the gear for the motors of the leader arm. As a result, you will only use the position encoding of the motor and reduce friction to more easily operate the leader arm.
+
+### Clean Parts
+
+Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
+
+### Additional Guidance
+
+<details>
+<summary><strong>Video assembling arms</strong></summary>
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+</details>
+
+**Note:**
+This video provides visual guidance for assembling the arms, but it doesn't specify when or how to do the wiring. Inserting the cables beforehand is much easier than doing it afterward. The first arm may take a bit more than 1 hour to assemble, but once you get used to it, you can assemble the second arm in under 1 hour.
+
+---
+
+### First Motor
+
+**Step 2: Insert Wires**
+
+- Insert two wires into the first motor.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_1.webp"
+  style="height:300px;"
+/>
+
+**Step 3: Install in Base**
+
+- Place the first motor into the base.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_2.webp"
+  style="height:300px;"
+/>
+
+**Step 4: Secure Motor**
+
+- Fasten the motor with 4 screws. Two from the bottom and two from top.
+
+**Step 5: Attach Motor Holder**
+
+- Slide over the first motor holder and fasten it using two screws (one on each side).
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_4.webp"
+  style="height:300px;"
+/>
+
+**Step 6: Attach Motor Horns**
+
+- Install both motor horns, securing the top horn with a screw. Try not to move the motor position when attaching the motor horn, especially for the leader arms, where we removed the gears.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_5.webp"
+  style="height:300px;"
+/>
+
+<details>
+  <summary>
+    <strong>Video adding motor horn</strong>
+  </summary>
+  <video src="https://github.com/user-attachments/assets/ef3391a4-ad05-4100-b2bd-1699bf86c969"></video>
+</details>
+
+**Step 7: Attach Shoulder Part**
+
+- Route one wire to the back of the robot and the other to the left or towards you (see photo).
+- Attach the shoulder part.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_6.webp"
+  style="height:300px;"
+/>
+
+**Step 8: Secure Shoulder**
+
+- Tighten the shoulder part with 4 screws on top and 4 on the bottom
+  _(access bottom holes by turning the shoulder)._
+
+---
+
+### Second Motor Assembly
+
+**Step 9: Install Motor 2**
+
+- Slide the second motor in from the top and link the wire from motor 1 to motor 2.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_8.webp"
+  style="height:300px;"
+/>
+
+**Step 10: Attach Shoulder Holder**
+
+- Add the shoulder motor holder.
+- Ensure the wire from motor 1 to motor 2 goes behind the holder while the other wire is routed upward (see photo).
+- This part can be tight to assemble, you can use a workbench like the image or a similar setup to push the part around the motor.
+
+<div style="display: flex;">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_9.webp"
+    style="height:250px;"
+  />
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_10.webp"
+    style="height:250px;"
+  />
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_12.webp"
+    style="height:250px;"
+  />
+</div>
+
+**Step 11: Secure Motor 2**
+
+- Fasten the second motor with 4 screws.
+
+**Step 12: Attach Motor Horn**
+
+- Attach both motor horns to motor 2, again use the horn screw.
+
+**Step 13: Attach Base**
+
+- Install the base attachment using 2 screws.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_11.webp" style="height:300px;">
+
+**Step 14: Attach Upper Arm**
+
+- Attach the upper arm with 4 screws on each side.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_13.webp" style="height:300px;">
+
+---
+
+### Third Motor Assembly
+
+**Step 15: Install Motor 3**
+
+- Route the motor cable from motor 2 through the cable holder to motor 3, then secure motor 3 with 4 screws.
+
+**Step 16: Attach Motor Horn**
+
+- Attach both motor horns to motor 3 and secure one again with a horn screw.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_14.webp"
+  style="height:300px;"
+/>
+
+**Step 17: Attach Forearm**
+
+- Connect the forearm to motor 3 using 4 screws on each side.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_15.webp"
+  style="height:300px;"
+/>
+
+---
+
+### Fourth Motor Assembly
+
+**Step 18: Install Motor 4**
+
+- Slide in motor 4, attach the cable from motor 3, and secure the cable in its holder with a screw.
+
+<div style="display: flex;">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_16.webp"
+    style="height:300px;"
+  />
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_19.webp"
+    style="height:300px;"
+  />
+</div>
+
+**Step 19: Attach Motor Holder 4**
+
+- Install the fourth motor holder (a tight fit). Ensure one wire is routed upward and the wire from motor 3 is routed downward (see photo).
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_17.webp"
+  style="height:300px;"
+/>
+
+**Step 20: Secure Motor 4 & Attach Horn**
+
+- Fasten motor 4 with 4 screws and attach its motor horns, use for one a horn screw.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_18.webp"
+  style="height:300px;"
+/>
+
+---
+
+### Wrist Assembly
+
+**Step 21: Install Motor 5**
+
+- Insert motor 5 into the wrist holder and secure it with 2 front screws.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_20.webp"
+  style="height:300px;"
+/>
+
+**Step 22: Attach Wrist**
+
+- Connect the wire from motor 4 to motor 5. And already insert the other wire for the gripper.
+- Secure the wrist to motor 4 using 4 screws on both sides.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_22.webp"
+  style="height:300px;"
+/>
+
+**Step 23: Attach Wrist Horn**
+
+- Install only one motor horn on the wrist motor and secure it with a horn screw.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_23.webp"
+  style="height:300px;"
+/>
+
+---
+
+### Follower Configuration
+
+**Step 24: Attach Gripper**
+
+- Attach the gripper to motor 5.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_24.webp"
+  style="height:300px;"
+/>
+
+**Step 25: Install Gripper Motor**
+
+- Insert the gripper motor, connect the motor wire from motor 5 to motor 6, and secure it with 3 screws on each side.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_25.webp"
+  style="height:300px;"
+/>
+
+**Step 26: Attach Gripper Horn & Claw**
+
+- Attach the motor horns and again use a horn screw.
+- Install the gripper claw and secure it with 4 screws on both sides.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_26.webp"
+  style="height:300px;"
+/>
+
+**Step 27: Mount Controller**
+
+- Attach the motor controller to the back of the robot.
+
+<div style="display: flex;">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp"
+    style="height:300px;"
+  />
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp"
+    style="height:300px;"
+  />
+</div>
+
+_Assembly complete – proceed to Leader arm assembly._
+
+---
+
+### Leader Configuration
+
+For the leader configuration, perform **Steps 1–23**. Make sure that you removed the motor gears from the motors.
+
+**Step 24: Attach Leader Holder**
+
+- Mount the leader holder onto the wrist and secure it with a screw.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_29.webp"
+  style="height:300px;"
+/>
+
+**Step 25: Attach Handle**
+
+- Attach the handle to motor 5 using 4 screws.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_30.webp"
+  style="height:300px;"
+/>
+
+**Step 26: Install Gripper Motor**
+
+- Insert the gripper motor, secure it with 3 screws on each side, attach a motor horn using a horn screw, and connect the motor wire.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_31.webp"
+  style="height:300px;"
+/>
+
+**Step 27: Attach Trigger**
+
+- Attach the follower trigger with 4 screws.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_32.webp"
+  style="height:300px;"
+/>
+
+**Step 28: Mount Controller**
+
+- Attach the motor controller to the back of the robot.
+
+<div style="display: flex;">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_27.webp"
+    style="height:300px;"
+  />
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/so100_assembly_28.webp"
+    style="height:300px;"
+  />
+</div>
+
+## Calibrate
+
+Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+#### Follower
+
+Run the following command or API example to calibrate the follower arm:
+
+<hfoptions id="calibrate_follower">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --robot.type=so100_follower \
+    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.robots.so100_follower import SO100FollowerConfig, SO100Follower
+
+config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem585A0076891",
+    id="my_awesome_follower_arm",
+)
+
+follower = SO100Follower(config)
+follower.connect(calibrate=False)
+follower.calibrate()
+follower.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+We unified the calibration method for most robots. Thus, the calibration steps for this SO100 arm are the same as the steps for the Koch and SO101. First, we have to move the robot to the position where each joint is in the middle of its range, then we press `Enter`. Secondly, we move all joints through their full range of motion. A video of this same process for the SO101 as reference can be found [here](https://huggingface.co/docs/lerobot/en/so101#calibration-video)
+
+#### Leader
+
+Do the same steps to calibrate the leader arm, run the following command or API example:
+
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.teleoperators.so100_leader import SO100LeaderConfig, SO100Leader
+
+config = SO100LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_awesome_leader_arm",
+)
+
+leader = SO100Leader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

docs/source/so101.mdx

@@ -1 +0,0 @@
-../../src/lerobot/robots/so101_follower/so101.mdx

docs/source/so101.mdx

@@ -0,0 +1,436 @@
+# SO-101
+
+In the steps below, we explain how to assemble our flagship robot, the SO-101.
+
+## Source the parts
+
+Follow this [README](https://github.com/TheRobotStudio/SO-ARM100). It contains the bill of materials, with a link to source the parts, as well as the instructions to 3D print the parts.
+And advise if it's your first time printing or if you don't own a 3D printer.
+
+## Install LeRobot 🤗
+
+To install LeRobot, follow our [Installation Guide](./installation)
+
+In addition to these instructions, you need to install the Feetech SDK:
+
+```bash
+pip install -e ".[feetech]"
+```
+
+## Step-by-Step Assembly Instructions
+
+The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader, however, uses three differently geared motors to make sure it can both sustain its own weight and it can be moved without requiring much force. Which motor is needed for which joint is shown in the table below.
+
+| Leader-Arm Axis     | Motor | Gear Ratio |
+| ------------------- | :---: | :--------: |
+| Base / Shoulder Pan |   1   |  1 / 191   |
+| Shoulder Lift       |   2   |  1 / 345   |
+| Elbow Flex          |   3   |  1 / 191   |
+| Wrist Flex          |   4   |  1 / 147   |
+| Wrist Roll          |   5   |  1 / 147   |
+| Gripper             |   6   |  1 / 147   |
+
+### Clean Parts
+
+Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
+
+It is advisable to install one 3-pin cable in the motor after placing them before continuing assembly.
+
+### Joint 1
+
+- Place the first motor into the base.
+- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from the bottom.
+- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
+- Install both motor horns, securing the top horn with a M3x6mm screw.
+- Attach the shoulder part.
+- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
+- Add the shoulder motor holder.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 2
+
+- Slide the second motor in from the top.
+- Fasten the second motor with 4 M2x6mm screws.
+- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
+- Attach the upper arm with 4 M3x6mm screws on each side.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 3
+
+- Insert motor 3 and fasten using 4 M2x6mm screws
+- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
+- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 4
+
+- Slide over motor holder 4.
+- Slide in motor 4.
+- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 5
+
+- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
+- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
+- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Gripper / Handle
+
+<hfoptions id="assembly">
+<hfoption id="Follower">
+
+- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
+- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
+- Attach the motor horns and again use a M3x6mm horn screw.
+- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+</hfoption>
+<hfoption id="Leader">
+
+- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
+- Attach the handle to motor 5 using 1 M2x6mm screw.
+- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
+- Attach the follower trigger with 4 M3x6mm screws.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+</hfoption>
+</hfoptions>
+
+## Configure the motors
+
+### 1. Find the USB ports associated with each arm
+
+To find the port for each bus servo adapter, connect MotorBus to your computer via USB and power. Run the following script and disconnect the MotorBus when prompted:
+
+```bash
+lerobot-find-port
+```
+
+<hfoptions id="example">
+<hfoption id="Mac">
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the USB cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/tty.usbmodem575E0032081` corresponding to your leader or follower arm.
+
+</hfoption>
+<hfoption id="Linux">
+
+On Linux, you might need to give access to the USB ports by running:
+
+```bash
+sudo chmod 666 /dev/ttyACM0
+sudo chmod 666 /dev/ttyACM1
+```
+
+Example output:
+
+```
+Finding all available ports for the MotorBus.
+['/dev/ttyACM0', '/dev/ttyACM1']
+Remove the usb cable from your MotorsBus and press Enter when done.
+
+[...Disconnect corresponding leader or follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/ttyACM1
+Reconnect the USB cable.
+```
+
+Where the found port is: `/dev/ttyACM1` corresponding to your leader or follower arm.
+
+</hfoption>
+</hfoptions>
+
+### 2. Set the motors ids and baudrates
+
+Each motor is identified by a unique id on the bus. When brand new, motors usually come with a default id of `1`. For the communication to work properly between the motors and the controller, we first need to set a unique, different id to each motor. Additionally, the speed at which data is transmitted on the bus is determined by the baudrate. In order to talk to each other, the controller and all the motors need to be configured with the same baudrate.
+
+To that end, we first need to connect to each motor individually with the controller in order to set these. Since we will write these parameters in the non-volatile section of the motors' internal memory (EEPROM), we'll only need to do this once.
+
+If you are repurposing motors from another robot, you will probably also need to perform this step as the ids and baudrate likely won't match.
+
+The video below shows the sequence of steps for setting the motor ids.
+
+##### Setup motors video
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/setup_motors_so101_2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+#### Follower
+
+Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+lerobot-setup-motors \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.robots.so101_follower import SO101Follower, SO101FollowerConfig
+
+config = SO101FollowerConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_follower_arm",
+)
+follower = SO101Follower(config)
+follower.setup_motors()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+You should see the following instruction
+
+```bash
+Connect the controller board to the 'gripper' motor only and press enter.
+```
+
+As instructed, plug the gripper's motor. Make sure it's the only motor connected to the board, and that the motor itself is not yet daisy-chained to any other motor. As you press `[Enter]`, the script will automatically set the id and baudrate for that motor.
+
+<details>
+<summary>Troubleshooting</summary>
+
+If you get an error at that point, check your cables and make sure they are plugged in properly:
+
+<ul>
+  <li>Power supply</li>
+  <li>USB cable between your computer and the controller board</li>
+  <li>The 3-pin cable from the controller board to the motor</li>
+</ul>
+
+If you are using a Waveshare controller board, make sure that the two jumpers are set on the `B` channel (USB).
+
+</details>
+
+You should then see the following message:
+
+```bash
+'gripper' motor id set to 6
+```
+
+Followed by the next instruction:
+
+```bash
+Connect the controller board to the 'wrist_roll' motor only and press enter.
+```
+
+You can disconnect the 3-pin cable from the controller board, but you can leave it connected to the gripper motor on the other end, as it will already be in the right place. Now, plug in another 3-pin cable to the wrist roll motor and connect it to the controller board. As with the previous motor, make sure it is the only motor connected to the board and that the motor itself isn't connected to any other one.
+
+Repeat the operation for each motor as instructed.
+
+> [!TIP]
+> Check your cabling at each step before pressing Enter. For instance, the power supply cable might disconnect as you manipulate the board.
+
+When you are done, the script will simply finish, at which point the motors are ready to be used. You can now plug the 3-pin cable from each motor to the next one, and the cable from the first motor (the 'shoulder pan' with id=1) to the controller board, which can now be attached to the base of the arm.
+
+#### Leader
+
+Do the same steps for the leader arm.
+
+<hfoptions id="setup_motors">
+<hfoption id="Command">
+
+```bash
+lerobot-setup-motors \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.teleoperators.so101_leader import SO101Leader, SO101LeaderConfig
+
+config = SO101LeaderConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_awesome_leader_arm",
+)
+leader = SO101Leader(config)
+leader.setup_motors()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+## Calibrate
+
+Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.
+The calibration process is very important because it allows a neural network trained on one robot to work on another.
+
+#### Follower
+
+Run the following command or API example to calibrate the follower arm:
+
+<hfoptions id="calibrate_follower">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.robots.so101_follower import SO101FollowerConfig, SO101Follower
+
+config = SO101FollowerConfig(
+    port="/dev/tty.usbmodem585A0076891",
+    id="my_awesome_follower_arm",
+)
+
+follower = SO101Follower(config)
+follower.connect(calibrate=False)
+follower.calibrate()
+follower.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+The video below shows how to perform the calibration. First you need to move the robot to the position where all joints are in the middle of their ranges. Then after pressing enter you have to move each joint through its full range of motion.
+
+##### Calibration video
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/calibrate_so101_2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+#### Leader
+
+Do the same steps to calibrate the leader arm, run the following command or API example:
+
+<hfoptions id="calibrate_leader">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
+    --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name
+```
+
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.teleoperators.so101_leader import SO101LeaderConfig, SO101Leader
+
+config = SO101LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_awesome_leader_arm",
+)
+
+leader = SO101Leader(config)
+leader.connect(calibrate=False)
+leader.calibrate()
+leader.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot)
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

examples/4_train_policy_with_script.md

@@ -1,6 +1,6 @@
 This tutorial will explain the training script, how to use it, and particularly how to configure everything needed for the training run.
-> **Note:** The following assumes you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
 
+> **Note:** The following assumes you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
 
 ## The training script
 
@@ -15,17 +15,22 @@ LeRobot offers a training script at [`lerobot/scripts/train.py`](../src/lerobot/
 ## Overview of the configuration system
 
 In the training script, the main function `train` expects a `TrainPipelineConfig` object:
+
+<!-- prettier-ignore-start -->
 ```python
 # train.py
 @parser.wrap()
 def train(cfg: TrainPipelineConfig):
 ```
+<!-- prettier-ignore-end -->
 
 You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../src/lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option)
 
 When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated to this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)
 
 Let's have a look at a simplified example. Amongst other attributes, the training config has the following attributes:
+
+<!-- prettier-ignore-start -->
 ```python
 @dataclass
 class TrainPipelineConfig:
@@ -33,7 +38,11 @@ class TrainPipelineConfig:
     env: envs.EnvConfig | None = None
     policy: PreTrainedConfig | None = None
 ```
+<!-- prettier-ignore-end -->
+
 in which `DatasetConfig` for example is defined as such:
+
+<!-- prettier-ignore-start -->
 ```python
 @dataclass
 class DatasetConfig:
@@ -41,42 +50,47 @@ class DatasetConfig:
     episodes: list[int] | None = None
     video_backend: str = "pyav"
 ```
+<!-- prettier-ignore-end -->
 
 This creates a hierarchical relationship where, for example assuming we have a `cfg` instance of `TrainPipelineConfig`, we can access the `repo_id` value with `cfg.dataset.repo_id`.
 From the command line, we can specify this value by using a very similar syntax `--dataset.repo_id=repo/id`.
 
 By default, every field takes its default value specified in the dataclass. If a field doesn't have a default value, it needs to be specified either from the command line or from a config file – which path is also given in the command line (more in this below). In the example above, the `dataset` field doesn't have a default value which means it must be specified.
 
-
 ## Specifying values from the CLI
 
 Let's say that we want to train [Diffusion Policy](../src/lerobot/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --dataset.repo_id=lerobot/pusht \
     --policy.type=diffusion \
     --env.type=pusht
 ```
 
 Let's break this down:
+
 - To specify the dataset, we just need to specify its `repo_id` on the hub which is the only required argument in the `DatasetConfig`. The rest of the fields have default values and in this case we are fine with those so we can just add the option `--dataset.repo_id=lerobot/pusht`.
 - To specify the policy, we can just select diffusion policy using `--policy` appended with `.type`. Here, `.type` is a special argument which allows us to select config classes inheriting from `draccus.ChoiceRegistry` and that have been decorated with the `register_subclass()` method. To have a better explanation of this feature, have a look at this [Draccus demo](https://github.com/dlwh/draccus?tab=readme-ov-file#more-flexible-configuration-with-choice-types). In our code, we use this mechanism mainly to select policies, environments, robots, and some other components like optimizers. The policies available to select are located in [lerobot/policies](../src/lerobot/policies)
 - Similarly, we select the environment with `--env.type=pusht`. The different environment configs are available in [`lerobot/envs/configs.py`](../src/lerobot/envs/configs.py)
 
 Let's see another example. Let's say you've been training [ACT](../src/lerobot/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \
     --output_dir=outputs/train/act_aloha_insertion
 ```
+
 > Notice we added `--output_dir` to explicitly tell where to write outputs from this run (checkpoints, training state, configs etc.). This is not mandatory and if you don't specify it, a default directory will be created from the current date and time, env.type and policy.type. This will typically look like `outputs/train/2025-01-24/16-10-05_aloha_act`.
 
 We now want to train a different policy for aloha on another task. We'll change the dataset and use [lerobot/aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human) instead. Of course, we also need to change the task of the environment as well to match this other task.
 Looking at the [`AlohaEnv`](../src/lerobot/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
     --env.type=aloha \
@@ -87,6 +101,7 @@ python -m lerobot.scripts.train \
 ## Loading from a config file
 
 Now, let's assume that we want to reproduce the run just above. That run has produced a `train_config.json` file in its checkpoints, which serializes the `TrainPipelineConfig` instance it used:
+
 ```json
 {
     "dataset": {
@@ -110,36 +125,42 @@ Now, let's assume that we want to reproduce the run just above. That run has pro
 ```
 
 We can then simply load the config values from this file using:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
     --output_dir=outputs/train/act_aloha_transfer_2
 ```
+
 `--config_path` is also a special argument which allows to initialize the config from a local config file. It can point to a directory that contains `train_config.json` or to the config file itself directly.
 
 Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
     --output_dir=outputs/train/act_aloha_transfer_2
     --policy.n_action_steps=80
 ```
+
 > Note: While `--output_dir` is not required in general, in this case we need to specify it since it will otherwise take the value from the `train_config.json` (which is `outputs/train/act_aloha_transfer`). In order to prevent accidental deletion of previous run checkpoints, we raise an error if you're trying to write in an existing directory. This is not the case when resuming a run, which is what you'll learn next.
 
 `--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running:
-```bash
-python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
-```
-will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)
 
+```bash
+lerobot-train --config_path=lerobot/diffusion_pusht
+```
+
+will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)
 
 ## Resume training
 
 Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to do that here.
 
 Let's reuse the command from the previous run and add a few more options:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
     --env.type=aloha \
@@ -150,28 +171,35 @@ python -m lerobot.scripts.train \
 ```
 
 Here we've taken care to set up the log frequency and checkpointing frequency to low numbers so we can showcase resumption. You should be able to see some logging and have a first checkpoint within 1 minute (depending on hardware). Wait for the first checkpoint to happen, you should see a line that looks like this in your terminal:
+
 ```
 INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100
 ```
+
 Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true
 ```
+
 You should see from the logging that your training picks up from where it left off.
 
 Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--steps`, which is 100 000 by default.
 You could double the number of steps of the previous run with:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true \
     --steps=200000
 ```
 
 ## Outputs of a run
+
 In the output directory, there will be a folder called `checkpoints` with the following structure:
+
 ```bash
 outputs/train/run_resumption/checkpoints
 ├── 000100  # checkpoint_dir for training step 100
@@ -194,8 +222,9 @@ outputs/train/run_resumption/checkpoints
 In addition to the features currently in Draccus, we've added a special `.path` argument for the policy, which allows to load a policy as you would with `PreTrainedPolicy.from_pretrained()`. In that case, `path` can be a local directory that contains a checkpoint or a repo_id pointing to a pretrained policy on the hub.
 
 For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with:
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --policy.path=lerobot/act_aloha_sim_transfer_cube_human \
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \
@@ -209,15 +238,19 @@ When doing so, keep in mind that the features of the fine-tuning dataset would h
 When you start the training process, you will first see your full configuration being printed in the terminal. You can check it to make sure that you configured your run correctly. The final configuration will also be saved with the checkpoint.
 
 After that, you will see training log like this one:
+
 ```
 INFO 2024-08-14 13:35:12 ts/train.py:192 step:0 smpl:64 ep:1 epch:0.00 loss:1.112 grdn:15.387 lr:2.0e-07 updt_s:1.738 data_s:4.774
 ```
+
 or evaluation log:
+
 ```
 INFO 2024-08-14 13:38:45 ts/train.py:226 step:100 smpl:6K ep:52 epch:0.25 ∑rwrd:20.693 success:0.0% eval_s:120.266
 ```
 
 These logs will also be saved in wandb if `wandb.enable` is set to `true`. Here are the meaning of some abbreviations:
+
 - `smpl`: number of samples seen during training.
 - `ep`: number of episodes seen during training. An episode contains multiple samples in a complete manipulation task.
 - `epch`: number of time all unique samples are seen (epoch).
@@ -235,31 +268,35 @@ Some metrics are useful for initial performance profiling. For example, if you f
 We'll summarize here the main use cases to remember from this tutorial.
 
 #### Train a policy from scratch – CLI
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --policy.type=act \  # <- select 'act' policy
     --env.type=pusht \  # <- select 'pusht' environment
     --dataset.repo_id=lerobot/pusht  # <- train on this dataset
 ```
 
 #### Train a policy from scratch - config file + CLI
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --config_path=path/to/pretrained_model \  # <- can also be a repo_id
     --policy.n_action_steps=80  # <- you may still override values
 ```
 
 #### Resume/continue a training run
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --config_path=checkpoint/pretrained_model/ \
     --resume=true \
     --steps=200000  # <- you can change some training parameters
 ```
 
 #### Fine-tuning
+
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
     --policy.path=lerobot/act_aloha_sim_transfer_cube_human \  # <- can also be a local path to a checkpoint
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \

examples/advanced/1_add_image_transforms.py

@@ -1,67 +0,0 @@
-# Copyright 2024 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 script demonstrates how to use torchvision's image transformation with LeRobotDataset for data
-augmentation purposes. The transformations are passed to the dataset as an argument upon creation, and
-transforms are applied to the observation images before they are returned in the dataset's __getitem__.
-"""
-
-from pathlib import Path
-
-from torchvision.transforms import ToPILImage, v2
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-dataset_repo_id = "lerobot/aloha_static_screw_driver"
-
-# Create a LeRobotDataset with no transformations
-dataset = LeRobotDataset(dataset_repo_id, episodes=[0])
-# This is equivalent to `dataset = LeRobotDataset(dataset_repo_id, image_transforms=None)`
-
-# Get the index of the first observation in the first episode
-first_idx = dataset.episode_data_index["from"][0].item()
-
-# Get the frame corresponding to the first camera
-frame = dataset[first_idx][dataset.meta.camera_keys[0]]
-
-
-# Define the transformations
-transforms = v2.Compose(
-    [
-        v2.ColorJitter(brightness=(0.5, 1.5)),
-        v2.ColorJitter(contrast=(0.5, 1.5)),
-        v2.ColorJitter(hue=(-0.1, 0.1)),
-        v2.RandomAdjustSharpness(sharpness_factor=2, p=1),
-    ]
-)
-
-# Create another LeRobotDataset with the defined transformations
-transformed_dataset = LeRobotDataset(dataset_repo_id, episodes=[0], image_transforms=transforms)
-
-# Get a frame from the transformed dataset
-transformed_frame = transformed_dataset[first_idx][transformed_dataset.meta.camera_keys[0]]
-
-# Create a directory to store output images
-output_dir = Path("outputs/image_transforms")
-output_dir.mkdir(parents=True, exist_ok=True)
-
-# Save the original frame
-to_pil = ToPILImage()
-to_pil(frame).save(output_dir / "original_frame.png", quality=100)
-print(f"Original frame saved to {output_dir / 'original_frame.png'}.")
-
-# Save the transformed frame
-to_pil(transformed_frame).save(output_dir / "transformed_frame.png", quality=100)
-print(f"Transformed frame saved to {output_dir / 'transformed_frame.png'}.")

examples/advanced/2_calculate_validation_loss.py

@@ -1,104 +0,0 @@
-# Copyright 2024 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 script demonstrates how to slice a dataset and calculate the loss on a subset of the data.
-
-This technique can be useful for debugging and testing purposes, as well as identifying whether a policy
-is learning effectively.
-
-Furthermore, relying on validation loss to evaluate performance is generally not considered a good practice,
-especially in the context of imitation learning. The most reliable approach is to evaluate the policy directly
-on the target environment, whether that be in simulation or the real world.
-"""
-
-import math
-
-import torch
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
-
-
-def main():
-    device = torch.device("cuda")
-
-    # Download the diffusion policy for pusht environment
-    pretrained_policy_path = "lerobot/diffusion_pusht"
-    # OR uncomment the following to evaluate a policy from the local outputs/train folder.
-    # pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")
-
-    policy = DiffusionPolicy.from_pretrained(pretrained_policy_path)
-    policy.eval()
-    policy.to(device)
-
-    # Set up the dataset.
-    delta_timestamps = {
-        # Load the previous image and state at -0.1 seconds before current frame,
-        # then load current image and state corresponding to 0.0 second.
-        "observation.image": [-0.1, 0.0],
-        "observation.state": [-0.1, 0.0],
-        # Load the previous action (-0.1), the next action to be executed (0.0),
-        # and 14 future actions with a 0.1 seconds spacing. All these actions will be
-        # used to calculate the loss.
-        "action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],
-    }
-
-    # Load the last 10% of episodes of the dataset as a validation set.
-    # - Load dataset metadata
-    dataset_metadata = LeRobotDatasetMetadata("lerobot/pusht")
-    # - Calculate train and val episodes
-    total_episodes = dataset_metadata.total_episodes
-    episodes = list(range(dataset_metadata.total_episodes))
-    num_train_episodes = math.floor(total_episodes * 90 / 100)
-    train_episodes = episodes[:num_train_episodes]
-    val_episodes = episodes[num_train_episodes:]
-    print(f"Number of episodes in full dataset: {total_episodes}")
-    print(f"Number of episodes in training dataset (90% subset): {len(train_episodes)}")
-    print(f"Number of episodes in validation dataset (10% subset): {len(val_episodes)}")
-    # - Load train and val datasets
-    train_dataset = LeRobotDataset(
-        "lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps
-    )
-    val_dataset = LeRobotDataset("lerobot/pusht", episodes=val_episodes, delta_timestamps=delta_timestamps)
-    print(f"Number of frames in training dataset (90% subset): {len(train_dataset)}")
-    print(f"Number of frames in validation dataset (10% subset): {len(val_dataset)}")
-
-    # Create dataloader for evaluation.
-    val_dataloader = torch.utils.data.DataLoader(
-        val_dataset,
-        num_workers=4,
-        batch_size=64,
-        shuffle=False,
-        pin_memory=device != torch.device("cpu"),
-        drop_last=False,
-    )
-
-    # Run validation loop.
-    loss_cumsum = 0
-    n_examples_evaluated = 0
-    for batch in val_dataloader:
-        batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
-        loss, _ = policy.forward(batch)
-
-        loss_cumsum += loss.item()
-        n_examples_evaluated += batch["index"].shape[0]
-
-    # Calculate the average loss over the validation set.
-    average_loss = loss_cumsum / n_examples_evaluated
-
-    print(f"Average loss on validation set: {average_loss:.4f}")
-
-
-if __name__ == "__main__":
-    main()

examples/backward_compatibility/replay.py

@@ -18,7 +18,7 @@ Replays the actions of an episode from a dataset on a robot.
 Example:
 
 ```shell
-python -m lerobot.replay \
+lerobot-replay \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=black \

examples/lekiwi/evaluate.py

@@ -1,6 +1,7 @@
 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.record import record_loop
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
 from lerobot.utils.control_utils import init_keyboard_listener
@@ -11,12 +12,14 @@ NUM_EPISODES = 2
 FPS = 30
 EPISODE_TIME_SEC = 60
 TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
 
 # Create the robot and teleoperator configurations
 robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 robot = LeKiwiClient(robot_config)
 
-policy = ACTPolicy.from_pretrained("<hf_username>/<policy_repo_id>")
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
 
 # Configure the dataset features
 action_features = hw_to_dataset_features(robot.action_features, "action")
@@ -25,7 +28,7 @@ dataset_features = {**action_features, **obs_features}
 
 # Create the dataset
 dataset = LeRobotDataset.create(
-    repo_id="<hf_username>/<eval_dataset_repo_id>",
+    repo_id=HF_DATASET_ID,
     fps=FPS,
     features=dataset_features,
     robot_type=robot.name,
@@ -43,6 +46,12 @@ listener, events = init_keyboard_listener()
 if not robot.is_connected:
     raise ValueError("Robot is not connected!")
 
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+)
+
 recorded_episodes = 0
 while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
     log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
@@ -53,6 +62,8 @@ while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
         events=events,
         fps=FPS,
         policy=policy,
+        preprocessor=preprocessor,
+        postprocessor=postprocessor,
         dataset=dataset,
         control_time_s=EPISODE_TIME_SEC,
         single_task=TASK_DESCRIPTION,

examples/lekiwi/teleoperate.py

@@ -38,7 +38,7 @@ while True:
     keyboard_keys = keyboard.get_action()
     base_action = robot._from_keyboard_to_base_action(keyboard_keys)
 
-    log_rerun_data(observation, {**arm_action, **base_action})
+    log_rerun_data(observation=observation, action={**arm_action, **base_action})
 
     action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
 

examples/phone_to_so100/evaluate.py

@@ -0,0 +1,159 @@
+# !/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.
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
+    identity_transition,
+    observation_to_transition,
+    transition_to_action,
+)
+from lerobot.record import record_loop
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    AddRobotObservationAsComplimentaryData,
+    ForwardKinematicsJointsToEE,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import _init_rerun
+
+NUM_EPISODES = 5
+FPS = 30
+EPISODE_TIME_SEC = 60
+TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
+
+# Initialize the robot with degrees
+camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem58760434471",
+    id="my_awesome_follower_arm",
+    cameras=camera_config,
+    use_degrees=True,
+)
+
+# Initialize the robot
+robot = SO100Follower(robot_config)
+
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./src/lerobot/teleoperators/sim/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
+
+# Build pipeline to convert ee pose action to joint action
+robot_ee_to_joints_processor = RobotProcessorPipeline(
+    steps=[
+        AddRobotObservationAsComplimentaryData(robot=robot),
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=identity_transition,
+    to_output=transition_to_action,
+)
+
+# Build pipeline to convert joint observation to ee pose observation
+robot_joints_to_ee_pose_processor = RobotProcessorPipeline(
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=identity_transition,
+)
+
+# Build dataset action and gripper features
+action_ee_and_gripper = aggregate_pipeline_dataset_features(
+    pipeline=robot_ee_to_joints_processor,
+    initial_features=create_initial_features(),
+    use_videos=True,
+    patterns=["action.ee", "action.gripper.pos", "observation.state.gripper.pos"],
+)  # Get all ee action features + gripper pos action features
+
+# Build dataset observation features
+obs_ee = aggregate_pipeline_dataset_features(
+    pipeline=robot_joints_to_ee_pose_processor,
+    initial_features=create_initial_features(observation=robot.observation_features),
+    use_videos=True,
+    patterns=["observation.state.ee"],
+)  # Get all ee observation features
+
+dataset_features = combine_feature_dicts(obs_ee, action_ee_and_gripper)
+
+print("All dataset features: ", dataset_features)
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_DATASET_ID,
+    fps=FPS,
+    features=dataset_features,
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+# Initialize the keyboard listener and rerun visualization
+_, events = init_keyboard_listener()
+_init_rerun(session_name="recording_phone")
+
+# Connect the robot and teleoperator
+robot.connect()
+
+episode_idx = 0
+
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+)
+
+for episode_idx in range(NUM_EPISODES):
+    log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        policy=policy,
+        preprocessor=preprocessor,
+        postprocessor=postprocessor,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
+    )
+    dataset.save_episode()
+
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+dataset.push_to_hub()

examples/phone_to_so100/record.py

@@ -0,0 +1,216 @@
+# !/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.
+
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
+    action_to_transition,
+    identity_transition,
+    observation_to_transition,
+    transition_to_action,
+)
+from lerobot.record import record_loop
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    AddRobotObservationAsComplimentaryData,
+    EEBoundsAndSafety,
+    EEReferenceAndDelta,
+    ForwardKinematicsJointsToEE,
+    GripperVelocityToJoint,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
+from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.teleoperators.phone.teleop_phone import Phone
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import _init_rerun
+
+NUM_EPISODES = 10
+FPS = 30
+EPISODE_TIME_SEC = 60
+RESET_TIME_SEC = 30
+TASK_DESCRIPTION = "My task description"
+HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
+
+# Initialize the robot and teleoperator
+camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem58760434471",
+    id="my_awesome_follower_arm",
+    cameras=camera_config,
+    use_degrees=True,
+)
+teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
+
+# Initialize the robot and teleoperator
+robot = SO100Follower(robot_config)
+phone = Phone(teleop_config)
+
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./src/lerobot/teleoperators/sim/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
+
+# Build pipeline to convert phone action to ee pose action
+phone_to_robot_ee_pose_processor = RobotProcessorPipeline(
+    steps=[
+        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+        AddRobotObservationAsComplimentaryData(robot=robot),
+        EEReferenceAndDelta(
+            kinematics=kinematics_solver,
+            end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+            motor_names=list(robot.bus.motors.keys()),
+        ),
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.20,
+            max_ee_twist_step_rad=0.50,
+        ),
+    ],
+    to_transition=action_to_transition,
+    to_output=identity_transition,
+)
+
+# Build pipeline to convert ee pose action to joint action
+robot_ee_to_joints_processor = RobotProcessorPipeline(
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+        GripperVelocityToJoint(
+            motor_names=list(robot.bus.motors.keys()),
+            speed_factor=20.0,
+        ),
+    ],
+    to_transition=identity_transition,
+    to_output=transition_to_action,
+)
+
+# Build pipeline to convert joint observation to ee pose observation
+robot_joints_to_ee_pose = RobotProcessorPipeline(
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=identity_transition,
+)
+
+# Build dataset ee action features
+action_ee = aggregate_pipeline_dataset_features(
+    pipeline=phone_to_robot_ee_pose_processor,
+    initial_features=create_initial_features(action=phone.action_features),
+    use_videos=True,
+    patterns=["action.ee"],
+)
+
+# Get gripper pos action features
+gripper = aggregate_pipeline_dataset_features(
+    pipeline=robot_ee_to_joints_processor,
+    initial_features=create_initial_features(),
+    use_videos=True,
+    patterns=["action.gripper.pos", "observation.state.gripper.pos"],
+)
+
+# Build dataset ee observation features
+observation_ee = aggregate_pipeline_dataset_features(
+    pipeline=robot_joints_to_ee_pose,
+    initial_features=create_initial_features(observation=robot.observation_features),
+    use_videos=True,
+    patterns=["observation.state.ee"],
+)
+
+dataset_features = combine_feature_dicts(action_ee, gripper, observation_ee)
+
+print("All dataset features: ", dataset_features)
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_REPO_ID,
+    fps=FPS,
+    features=dataset_features,
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+# Initialize the keyboard listener and rerun visualization
+_, events = init_keyboard_listener()
+_init_rerun(session_name="recording_phone")
+
+# Connect the robot and teleoperator
+robot.connect()
+phone.connect()
+
+episode_idx = 0
+while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        teleop=phone,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=phone_to_robot_ee_pose_processor,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose,
+    )
+
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+        log_say("Reset the environment")
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            teleop=phone,
+            control_time_s=RESET_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=phone_to_robot_ee_pose_processor,
+            robot_action_processor=robot_ee_to_joints_processor,
+            robot_observation_processor=robot_joints_to_ee_pose,
+        )
+
+    if events["rerecord_episode"]:
+        log_say("Re-recording episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
+
+    dataset.save_episode()
+    episode_idx += 1
+
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+phone.disconnect()
+dataset.push_to_hub()

examples/phone_to_so100/replay.py

@@ -0,0 +1,81 @@
+# !/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.
+
+
+import time
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import action_to_transition, transition_to_action
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    AddRobotObservationAsComplimentaryData,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.utils import log_say
+
+EPISODE_IDX = 0
+HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
+
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", use_degrees=True
+)
+robot = SO100Follower(robot_config)
+robot.connect()
+
+dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
+actions = dataset.hf_dataset.select_columns("action")
+
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./src/lerobot/teleoperators/sim/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
+
+# Build pipeline to convert ee pose action to joint action
+robot_ee_to_joints_processor = RobotProcessorPipeline(
+    steps=[
+        AddRobotObservationAsComplimentaryData(robot=robot),
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=False,  # Because replay is open loop
+        ),
+    ],
+    to_transition=action_to_transition,
+    to_output=transition_to_action,
+)
+
+robot_ee_to_joints_processor.reset()
+
+log_say(f"Replaying episode {EPISODE_IDX}")
+for idx in range(dataset.num_frames):
+    t0 = time.perf_counter()
+
+    ee_action = {
+        name: float(actions[idx]["action"][i]) for i, name in enumerate(dataset.features["action"]["names"])
+    }
+
+    joint_action = robot_ee_to_joints_processor(ee_action)
+    action_sent = robot.send_action(joint_action)
+
+    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
+
+robot.disconnect()

examples/phone_to_so100/teleoperate.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+
+# Copyright 2024 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 specif
+
+import time
+
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import action_to_transition, transition_to_action
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    AddRobotObservationAsComplimentaryData,
+    EEBoundsAndSafety,
+    EEReferenceAndDelta,
+    GripperVelocityToJoint,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
+from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.teleoperators.phone.teleop_phone import Phone
+
+# Initialize the robot and teleoperator
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", use_degrees=True
+)
+teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
+
+# Initialize the robot and teleoperator
+robot = SO100Follower(robot_config)
+teleop_device = Phone(teleop_config)
+
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./src/lerobot/teleoperators/sim/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
+
+# Build pipeline to convert phone action to ee pose action to joint action
+phone_to_robot_joints_processor = RobotProcessorPipeline(
+    steps=[
+        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+        AddRobotObservationAsComplimentaryData(robot=robot),
+        EEReferenceAndDelta(
+            kinematics=kinematics_solver,
+            end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+            motor_names=list(robot.bus.motors.keys()),
+        ),
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.10,
+            max_ee_twist_step_rad=0.50,
+        ),
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+        ),
+        GripperVelocityToJoint(
+            motor_names=list(robot.bus.motors.keys()),
+            speed_factor=20.0,
+        ),
+    ],
+    to_transition=action_to_transition,
+    to_output=transition_to_action,
+)
+
+robot.connect()
+teleop_device.connect()
+
+print("Starting teleop loop. Move your phone to teleoperate the robot.")
+while True:
+    # Get teleop observation
+    phone_obs = teleop_device.get_action()
+
+    # Phone -> EE pose -> Joints transition
+    joint_action = phone_to_robot_joints_processor(phone_obs)
+
+    if joint_action:
+        robot.send_action(joint_action)
+
+    time.sleep(0.01)

pyproject.toml

@@ -12,118 +12,215 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
 [project.urls]
-homepage = "https://github.com/huggingface/lerobot"
+homepage = "https://huggingface.co/lerobot"
+documentation = "https://huggingface.co/docs/lerobot/index"
+source = "https://github.com/huggingface/lerobot"
 issues = "https://github.com/huggingface/lerobot/issues"
 discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.1.0"
+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" }
+requires-python = ">=3.10"
 authors = [
     { name = "Rémi Cadène", email = "re.cadene@gmail.com" },
     { name = "Simon Alibert", email = "alibert.sim@gmail.com" },
     { name = "Alexander Soare", email = "alexander.soare159@gmail.com" },
     { name = "Quentin Gallouédec", email = "quentin.gallouedec@ec-lyon.fr" },
-    { name = "Adil Zouitine", email = "adilzouitinegm@gmail.com" },
-    { name = "Thomas Wolf", email = "thomaswolfcontact@gmail.com" },
     { name = "Steven Palma", email = "imstevenpmwork@ieee.org" },
+    { name = "Pepijn Kooijmans", email = "pepijnkooijmans@outlook.com"},
+    { name = "Michel Aractingi", email = "michel.aractingi@gmail.com"},
+    { name = "Adil Zouitine", email = "adilzouitinegm@gmail.com" },
+    { name = "Dana Aubakirova", email = "danaaubakirova17@gmail.com"},
+    { name = "Caroline Pascal", email = "caroline8.pascal@gmail.com"},
+    { name = "Martino Russi", email = "nopyeps@gmail.com"},
+    { name = "Thomas Wolf", email = "thomaswolfcontact@gmail.com" },
 ]
-readme = "README.md"
-license = { text = "Apache-2.0" }
-requires-python = ">=3.10"
-keywords = ["robotics", "deep learning", "pytorch"]
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
     "Intended Audience :: Education",
     "Intended Audience :: Science/Research",
-    "Topic :: Software Development :: Build Tools",
-    "Topic :: Scientific/Engineering :: Artificial Intelligence",
     "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python :: 3.10",
+    "Topic :: Software Development :: Build Tools",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
+keywords = ["lerobot", "huggingface", "robotics",  "machine learning", "artificial intelligence"]
+
 dependencies = [
-    "cmake>=3.29.0.1",
-    "datasets>=2.19.0,<=3.6.0",
-    "deepdiff>=7.0.1",
+
+    # Hugging Face dependencies
+    "datasets>=2.19.0,<=3.6.0", # TODO: Bumb dependency
     "diffusers>=0.27.2",
-    "draccus==0.10.0",
+    "huggingface-hub[hf-transfer,cli]>=0.34.2",
+
+    # Core dependencies
+    "cmake>=3.29.0.1",
     "einops>=0.8.0",
-    "flask>=3.0.3",
-    "gdown>=5.1.0",
-    "gymnasium==0.29.1", # TODO(rcadene, aliberts): Make gym 1.0.0 work
-    "h5py>=3.10.0",
-    "huggingface-hub[hf-transfer,cli]>=0.27.1 ; python_version < '4.0'",
-    "imageio[ffmpeg]>=2.34.0",
-    "jsonlines>=4.0.0",
-    "numba>=0.59.0",
-    "omegaconf>=2.3.0",
     "opencv-python-headless>=4.9.0",
-    "packaging>=24.2",
     "av>=14.2.0",
-    "pymunk>=6.6.0,<7.0.0",
+    "jsonlines>=4.0.0",
+    "packaging>=24.2",
     "pynput>=1.7.7",
     "pyserial>=3.5",
-    "pyzmq>=26.2.1",
-    "rerun-sdk>=0.21.0",
-    "termcolor>=2.4.0",
-    "torch>=2.2.1",
-    "torchcodec>=0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
-    "torchvision>=0.21.0",
-    "wandb>=0.16.3",
-    "zarr>=2.17.0",
+    "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>=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",
+    "flask>=3.0.3,<4.0.0",
+    "imageio[ffmpeg]>=2.34.0,<3.0.0",
+    "termcolor>=2.4.0,<4.0.0",
 ]
 
+# Optional dependencies
 [project.optional-dependencies]
-aloha = ["gym-aloha>=0.1.1 ; python_version < '4.0'"]
-docs = ["hf-doc-builder @ git+https://github.com/huggingface/doc-builder.git@main", "watchdog >= 6.0.0"]
-dev = ["pre-commit>=3.7.0", "debugpy>=1.8.1", "grpcio-tools==1.71.0"]
-dora = [
-    "gym-dora @ git+https://github.com/dora-rs/dora-lerobot.git#subdirectory=gym_dora ; python_version < '4.0'",
-]
-dynamixel = ["dynamixel-sdk>=3.7.31"]
+
+# Common
+pygame-dep = ["pygame>=2.5.1"]
+placo-dep = ["placo>=0.9.6"]
+transformers-dep = ["transformers<=4.52.0"]
+grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"]
+
+# Motors
 feetech = ["feetech-servo-sdk>=1.0.0"]
-gamepad = ["pygame>=2.5.1", "hidapi>=0.14.0"]
-hopejr = ["feetech-servo-sdk>=1.0.0", "pygame>=2.5.1"]
-kinematics = ["placo>=0.9.6"]
+dynamixel = ["dynamixel-sdk>=3.7.31"]
+
+# Robots
+gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0"]
+hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
+lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1"]
+reachy2 = ["reachy2_sdk>=1.0.14"]
+kinematics = ["lerobot[placo-dep]"]
 intelrealsense = [
     "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'",
     "pyrealsense2-macosx>=2.54 ; sys_platform == 'darwin'",
 ]
-pi0 = ["transformers>=4.50.3"]
-smolvla = ["transformers>=4.50.3", "num2words>=0.5.14", "accelerate>=1.7.0", "safetensors>=0.4.3"]
-pusht = ["gym-pusht>=0.1.5 ; python_version < '4.0'"]
-stretch = [
-    "hello-robot-stretch-body>=0.7.27 ; python_version < '4.0' and sys_platform == 'linux'",
-    "pyrender @ git+https://github.com/mmatl/pyrender.git ; sys_platform == 'linux'",
-    "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'"
-]
-test = ["pytest>=8.1.0", "pytest-timeout>=2.4.0", "pytest-cov>=5.0.0", "pyserial>=3.5", "mock-serial>=0.0.1 ; sys_platform != 'win32'"]
-hilserl = ["transformers>=4.50.3", "gym-hil>=0.1.9", "protobuf>=5.29.3", "grpcio==1.71.0", "placo>=0.9.6"]
-umi = ["imagecodecs>=2024.1.1"]
-video_benchmark = ["scikit-image>=0.23.2", "pandas>=2.2.2"]
-xarm = ["gym-xarm>=0.1.1 ; python_version < '4.0'"]
-async = ["grpcio==1.71.0", "matplotlib>=3.10.3"]
+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
 
-[tool.poetry]
-requires-poetry = ">=2.1"
-packages = [
-    { include = "lerobot", from = "src" }
+# Policies
+pi0 = ["lerobot[transformers-dep]"]
+smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14", "accelerate>=1.7.0", "safetensors>=0.4.3"]
+hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.9", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
+
+# Features
+async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3"]
+
+# Development
+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>=0.1.1"]
+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"]
+
+# All
+all = [
+    "lerobot[dynamixel]",
+    "lerobot[gamepad]",
+    "lerobot[hopejr]",
+    "lerobot[lekiwi]",
+    "lerobot[reachy2]",
+    "lerobot[kinematics]",
+    "lerobot[intelrealsense]",
+    "lerobot[pi0]",
+    "lerobot[smolvla]",
+    "lerobot[hilserl]",
+    "lerobot[async]",
+    "lerobot[dev]",
+    "lerobot[test]",
+    "lerobot[video_benchmark]",
+    "lerobot[aloha]",
+    "lerobot[pusht]",
+    "lerobot[xarm]",
+    "lerobot[phone]",
 ]
 
+[project.scripts]
+lerobot-calibrate="lerobot.calibrate:main"
+lerobot-find-cameras="lerobot.find_cameras:main"
+lerobot-find-port="lerobot.find_port:main"
+lerobot-record="lerobot.record:main"
+lerobot-replay="lerobot.replay:main"
+lerobot-setup-motors="lerobot.setup_motors:main"
+lerobot-teleoperate="lerobot.teleoperate:main"
+lerobot-eval="lerobot.scripts.eval:main"
+lerobot-train="lerobot.scripts.train:main"
+
+# ---------------- Tool Configurations ----------------
+[tool.setuptools.packages.find]
+where = ["src"]
+
 [tool.ruff]
-line-length = 110
 target-version = "py310"
-exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py", "*.part", "*.stl"]
+line-length = 110
+exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
 
 [tool.ruff.lint]
-select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
+# E, W: pycodestyle errors and warnings
+# F: PyFlakes
+# I: isort
+# UP: pyupgrade
+# B: flake8-bugbear (good practices, potential bugs)
+# C4: flake8-comprehensions (more concise comprehensions)
+# A: flake8-builtins (shadowing builtins)
+# SIM: flake8-simplify
+# RUF: Ruff-specific rules
+# D: pydocstyle (for docstring style/formatting)
+# S: flake8-bandit (some security checks, complements Bandit)
+# T20: flake8-print (discourage print statements in production code)
+# N: pep8-naming
+# TODO: Uncomment rules when ready to use
+select = [
+    "E", "W", "F", "I", "B", "C4", "T20", "N" # "SIM", "A", "S", "D", "RUF", "UP"
+]
+ignore = [
+    "E501", # Line too long
+    "T201", # Print statement found
+    "T203", # Pprint statement found
+    "B008", # Perform function call in argument defaults
+]
 
 [tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["F401", "F403"]
 
+[tool.ruff.lint.isort]
+combine-as-imports = true
+known-first-party = ["lerobot"]
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
+docstring-code-format = true
+
 [tool.bandit]
 exclude_dirs = [
     "tests",
@@ -148,12 +245,24 @@ default.extend-ignore-identifiers-re = [
     "ein",
 ]
 
-[tool.typos.files]
-extend-exclude = [
-  "*.stl",
-  "*.part",
-]
+# TODO: Uncomment when ready to use
+# [tool.interrogate]
+# ignore-init-module = true
+# ignore-init-method = true
+# ignore-nested-functions = false
+# ignore-magic = false
+# ignore-semiprivate = false
+# ignore-private = false
+# ignore-property-decorators = false
+# ignore-module = false
+# ignore-setters = false
+# fail-under = 80
+# output-format = "term-missing"
+# color = true
+# paths = ["src/lerobot"]
 
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+# [tool.mypy]
+# python_version = "3.10"
+# warn_return_any = true
+# warn_unused_configs = true
+# ignore_missing_imports = false

requirements-macos.txt

@@ -0,0 +1,625 @@
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
+#
+#    pip-compile --output-file=requirements-macos.txt requirements.in
+#
+-e .[all]
+    # via -[all]
+absl-py==2.3.1
+    # via
+    #   dm-control
+    #   dm-env
+    #   dm-tree
+    #   labmaze
+    #   mujoco
+accelerate==1.9.0
+    # via lerobot
+aiohappyeyeballs==2.6.1
+    # via aiohttp
+aiohttp==3.12.15
+    # via fsspec
+aiosignal==1.4.0
+    # via aiohttp
+annotated-types==0.7.0
+    # via pydantic
+asttokens==3.0.0
+    # via stack-data
+async-timeout==5.0.1
+    # via aiohttp
+attrs==25.3.0
+    # via
+    #   aiohttp
+    #   dm-tree
+    #   jsonlines
+    #   rerun-sdk
+av==15.0.0
+    # via lerobot
+blinker==1.9.0
+    # via flask
+certifi==2025.7.14
+    # via
+    #   requests
+    #   sentry-sdk
+cffi==1.17.1
+    # via pymunk
+cfgv==3.4.0
+    # via pre-commit
+charset-normalizer==3.4.2
+    # via requests
+click==8.2.1
+    # via
+    #   flask
+    #   wandb
+cloudpickle==3.1.1
+    # via gymnasium
+cmake==4.0.3
+    # via lerobot
+cmeel==0.57.3
+    # via
+    #   cmeel-assimp
+    #   cmeel-boost
+    #   cmeel-console-bridge
+    #   cmeel-octomap
+    #   cmeel-qhull
+    #   cmeel-tinyxml2
+    #   cmeel-urdfdom
+    #   cmeel-zlib
+    #   coal-library
+    #   eigenpy
+    #   eiquadprog
+    #   pin
+    #   placo
+    #   rhoban-cmeel-jsoncpp
+cmeel-assimp==5.4.3.1
+    # via coal-library
+cmeel-boost==1.87.0.1
+    # via
+    #   coal-library
+    #   eigenpy
+    #   eiquadprog
+    #   pin
+cmeel-console-bridge==1.0.2.3
+    # via cmeel-urdfdom
+cmeel-octomap==1.10.0
+    # via coal-library
+cmeel-qhull==8.0.2.1
+    # via coal-library
+cmeel-tinyxml2==10.0.0
+    # via cmeel-urdfdom
+cmeel-urdfdom==4.0.1
+    # via pin
+cmeel-zlib==1.3.1
+    # via cmeel-assimp
+coal-library==3.0.1
+    # via pin
+contourpy==1.3.2
+    # via matplotlib
+coverage[toml]==7.10.1
+    # via pytest-cov
+cycler==0.12.1
+    # via matplotlib
+datasets==3.6.0
+    # via lerobot
+debugpy==1.8.15
+    # via lerobot
+decorator==5.2.1
+    # via ipython
+deepdiff==8.5.0
+    # via lerobot
+diffusers==0.34.0
+    # via lerobot
+dill==0.3.8
+    # via
+    #   datasets
+    #   multiprocess
+distlib==0.4.0
+    # via virtualenv
+dm-control==1.0.14
+    # via gym-aloha
+dm-env==1.6
+    # via dm-control
+dm-tree==0.1.9
+    # via
+    #   dm-control
+    #   dm-env
+docopt==0.6.2
+    # via num2words
+draccus==0.10.0
+    # via lerobot
+dynamixel-sdk==3.7.31
+    # via lerobot
+eigenpy==3.10.3
+    # via coal-library
+einops==0.8.1
+    # via lerobot
+eiquadprog==1.2.9
+    # via placo
+exceptiongroup==1.3.0
+    # via
+    #   ipython
+    #   pytest
+executing==2.2.0
+    # via stack-data
+farama-notifications==0.0.4
+    # via gymnasium
+feetech-servo-sdk==1.0.0
+    # via lerobot
+filelock==3.18.0
+    # via
+    #   datasets
+    #   diffusers
+    #   huggingface-hub
+    #   torch
+    #   transformers
+    #   virtualenv
+flask==3.1.1
+    # via lerobot
+fonttools==4.59.0
+    # via matplotlib
+frozenlist==1.7.0
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec[http]==2025.3.0
+    # via
+    #   datasets
+    #   huggingface-hub
+    #   torch
+gitdb==4.0.12
+    # via gitpython
+gitpython==3.1.45
+    # via wandb
+glfw==2.9.0
+    # via
+    #   dm-control
+    #   mujoco
+grpcio==1.73.1
+    # via
+    #   grpcio-tools
+    #   lerobot
+grpcio-tools==1.73.1
+    # via lerobot
+gym-aloha==0.1.1
+    # via lerobot
+gym-hil==0.1.10
+    # via lerobot
+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
+    #   pettingzoo
+gymnasium-robotics==1.2.4
+    # via gym-xarm
+hf-transfer==0.1.9
+    # via huggingface-hub
+hf-xet==1.1.5
+    # via huggingface-hub
+hidapi==0.14.0.post4
+    # via
+    #   gym-hil
+    #   lerobot
+huggingface-hub[cli,hf-transfer]==0.34.3
+    # via
+    #   accelerate
+    #   datasets
+    #   diffusers
+    #   lerobot
+    #   tokenizers
+    #   transformers
+identify==2.6.12
+    # via pre-commit
+idna==3.10
+    # via
+    #   requests
+    #   yarl
+imageio[ffmpeg]==2.37.0
+    # via
+    #   gym-aloha
+    #   gym-hil
+    #   gymnasium-robotics
+    #   lerobot
+    #   scikit-image
+imageio-ffmpeg==0.6.0
+    # via imageio
+importlib-metadata==8.7.0
+    # via diffusers
+iniconfig==2.1.0
+    # via pytest
+inquirerpy==0.3.4
+    # via huggingface-hub
+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
+    #   flask
+    #   gymnasium-robotics
+    #   torch
+jsonlines==4.0.0
+    # via lerobot
+kiwisolver==1.4.8
+    # via matplotlib
+labmaze==1.0.6
+    # via dm-control
+lazy-loader==0.4
+    # via scikit-image
+lxml==6.0.0
+    # via dm-control
+markupsafe==3.0.2
+    # via
+    #   flask
+    #   jinja2
+    #   werkzeug
+matplotlib==3.10.5
+    # via lerobot
+matplotlib-inline==0.1.7
+    # via ipython
+mergedeep==1.3.4
+    # via draccus
+meshcat==0.3.2
+    # via placo
+mock-serial==0.0.1
+    # via lerobot
+mpmath==1.3.0
+    # via sympy
+mujoco==2.3.7
+    # via
+    #   dm-control
+    #   gym-aloha
+    #   gym-hil
+    #   gym-xarm
+    #   gymnasium-robotics
+multidict==6.6.3
+    # via
+    #   aiohttp
+    #   yarl
+multiprocess==0.70.16
+    # via datasets
+mypy-extensions==1.1.0
+    # via typing-inspect
+networkx==3.4.2
+    # via
+    #   scikit-image
+    #   torch
+nodeenv==1.9.1
+    # via pre-commit
+num2words==0.5.14
+    # via lerobot
+numpy==2.2.6
+    # via
+    #   accelerate
+    #   cmeel-boost
+    #   contourpy
+    #   datasets
+    #   diffusers
+    #   dm-control
+    #   dm-env
+    #   dm-tree
+    #   gymnasium
+    #   gymnasium-robotics
+    #   imageio
+    #   labmaze
+    #   matplotlib
+    #   meshcat
+    #   mujoco
+    #   opencv-python
+    #   opencv-python-headless
+    #   pandas
+    #   pettingzoo
+    #   rerun-sdk
+    #   scikit-image
+    #   scipy
+    #   shapely
+    #   tifffile
+    #   torchvision
+    #   transformers
+opencv-python==4.12.0.88
+    # via gym-pusht
+opencv-python-headless==4.12.0.88
+    # via lerobot
+orderly-set==5.5.0
+    # via deepdiff
+packaging==25.0
+    # via
+    #   accelerate
+    #   datasets
+    #   huggingface-hub
+    #   lazy-loader
+    #   lerobot
+    #   matplotlib
+    #   pytest
+    #   scikit-image
+    #   transformers
+    #   wandb
+pandas==2.3.1
+    # via
+    #   datasets
+    #   lerobot
+parso==0.8.4
+    # via jedi
+pettingzoo==1.24.3
+    # via gymnasium-robotics
+pexpect==4.9.0
+    # via ipython
+pfzy==0.3.4
+    # via inquirerpy
+pillow==11.3.0
+    # via
+    #   diffusers
+    #   imageio
+    #   matplotlib
+    #   meshcat
+    #   rerun-sdk
+    #   scikit-image
+    #   torchvision
+pin==3.4.0
+    # via placo
+placo==0.9.14
+    # via lerobot
+platformdirs==4.3.8
+    # via
+    #   virtualenv
+    #   wandb
+pluggy==1.6.0
+    # via
+    #   pytest
+    #   pytest-cov
+pre-commit==4.2.0
+    # via lerobot
+prompt-toolkit==3.0.51
+    # via
+    #   inquirerpy
+    #   ipython
+propcache==0.3.2
+    # via
+    #   aiohttp
+    #   yarl
+protobuf==6.31.0
+    # via
+    #   dm-control
+    #   grpcio-tools
+    #   lerobot
+    #   wandb
+psutil==7.0.0
+    # via
+    #   accelerate
+    #   imageio
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.3
+    # via stack-data
+pyarrow==21.0.0
+    # via
+    #   datasets
+    #   rerun-sdk
+pycparser==2.22
+    # via cffi
+pydantic==2.11.7
+    # via wandb
+pydantic-core==2.33.2
+    # via pydantic
+pygame==2.6.1
+    # via
+    #   gym-hil
+    #   gym-pusht
+    #   lerobot
+pygments==2.19.2
+    # via
+    #   ipython
+    #   pytest
+pymunk==6.11.1
+    # via
+    #   gym-pusht
+    #   lerobot
+pyngrok==7.2.12
+    # via meshcat
+pynput==1.8.1
+    # via
+    #   gym-hil
+    #   lerobot
+pyobjc-core==11.1
+    # via
+    #   pyobjc-framework-applicationservices
+    #   pyobjc-framework-cocoa
+    #   pyobjc-framework-coretext
+    #   pyobjc-framework-quartz
+pyobjc-framework-applicationservices==11.1
+    # via pynput
+pyobjc-framework-cocoa==11.1
+    # via
+    #   pyobjc-framework-applicationservices
+    #   pyobjc-framework-coretext
+    #   pyobjc-framework-quartz
+pyobjc-framework-coretext==11.1
+    # via pyobjc-framework-applicationservices
+pyobjc-framework-quartz==11.1
+    # via
+    #   pynput
+    #   pyobjc-framework-applicationservices
+    #   pyobjc-framework-coretext
+pyopengl==3.1.9
+    # via
+    #   dm-control
+    #   mujoco
+pyparsing==3.2.3
+    # via
+    #   dm-control
+    #   matplotlib
+pyrealsense2-macosx==2.54.2
+    # via lerobot
+pyserial==3.5
+    # via
+    #   dynamixel-sdk
+    #   feetech-servo-sdk
+    #   lerobot
+pytest==8.4.1
+    # via
+    #   lerobot
+    #   pytest-cov
+    #   pytest-timeout
+pytest-cov==6.2.1
+    # via lerobot
+pytest-timeout==2.4.0
+    # via lerobot
+python-dateutil==2.9.0.post0
+    # via
+    #   matplotlib
+    #   pandas
+pytz==2025.2
+    # via pandas
+pyyaml==6.0.2
+    # via
+    #   accelerate
+    #   datasets
+    #   draccus
+    #   huggingface-hub
+    #   pre-commit
+    #   pyngrok
+    #   pyyaml-include
+    #   transformers
+    #   wandb
+pyyaml-include==1.4.1
+    # via draccus
+pyzmq==27.0.0
+    # via
+    #   lerobot
+    #   meshcat
+regex==2025.7.34
+    # via
+    #   diffusers
+    #   transformers
+requests==2.32.4
+    # via
+    #   datasets
+    #   diffusers
+    #   dm-control
+    #   huggingface-hub
+    #   transformers
+    #   wandb
+rerun-sdk==0.22.1
+    # via lerobot
+rhoban-cmeel-jsoncpp==1.9.4.9
+    # via placo
+safetensors==0.5.3
+    # via
+    #   accelerate
+    #   diffusers
+    #   lerobot
+    #   transformers
+scikit-image==0.25.2
+    # via
+    #   gym-pusht
+    #   lerobot
+scipy==1.15.3
+    # via
+    #   dm-control
+    #   scikit-image
+sentry-sdk==2.34.1
+    # via wandb
+shapely==2.1.1
+    # via gym-pusht
+six==1.17.0
+    # via
+    #   pynput
+    #   python-dateutil
+smmap==5.0.2
+    # via gitdb
+stack-data==0.6.3
+    # via ipython
+sympy==1.14.0
+    # via torch
+termcolor==3.1.0
+    # via lerobot
+tifffile==2025.5.10
+    # via scikit-image
+tokenizers==0.21.4
+    # via transformers
+toml==0.10.2
+    # via draccus
+tomli==2.2.1
+    # via
+    #   cmeel
+    #   coverage
+    #   pytest
+torch==2.7.1
+    # via
+    #   accelerate
+    #   lerobot
+    #   torchvision
+torchcodec==0.5
+    # via lerobot
+torchvision==0.22.1
+    # via lerobot
+tornado==6.5.1
+    # via meshcat
+tqdm==4.67.1
+    # via
+    #   datasets
+    #   dm-control
+    #   huggingface-hub
+    #   transformers
+traitlets==5.14.3
+    # via
+    #   ipython
+    #   matplotlib-inline
+transformers==4.51.3
+    # via lerobot
+typing-extensions==4.14.1
+    # via
+    #   aiosignal
+    #   exceptiongroup
+    #   gymnasium
+    #   huggingface-hub
+    #   ipython
+    #   multidict
+    #   pydantic
+    #   pydantic-core
+    #   rerun-sdk
+    #   torch
+    #   typing-inspect
+    #   typing-inspection
+    #   wandb
+typing-inspect==0.9.0
+    # via draccus
+typing-inspection==0.4.1
+    # via pydantic
+tzdata==2025.2
+    # via pandas
+u-msgpack-python==2.8.0
+    # via meshcat
+urllib3==2.5.0
+    # via
+    #   requests
+    #   sentry-sdk
+virtualenv==20.32.0
+    # via pre-commit
+wandb==0.21.0
+    # via lerobot
+wcwidth==0.2.13
+    # via prompt-toolkit
+werkzeug==3.1.3
+    # via flask
+wrapt==1.17.2
+    # via dm-tree
+xxhash==3.5.0
+    # via datasets
+yarl==1.20.1
+    # via aiohttp
+zipp==3.23.0
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools

requirements-ubuntu.txt

@@ -0,0 +1,650 @@
+#
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
+#
+#    pip-compile --output-file=requirements-ubuntu.txt requirements.in
+#
+-e .[all]
+    # via -[all]
+absl-py==2.3.1
+    # via
+    #   dm-control
+    #   dm-env
+    #   dm-tree
+    #   labmaze
+    #   mujoco
+accelerate==1.9.0
+    # via lerobot
+aiohappyeyeballs==2.6.1
+    # via aiohttp
+aiohttp==3.12.15
+    # via fsspec
+aiosignal==1.4.0
+    # via aiohttp
+annotated-types==0.7.0
+    # via pydantic
+asttokens==3.0.0
+    # via stack-data
+async-timeout==5.0.1
+    # via aiohttp
+attrs==25.3.0
+    # via
+    #   aiohttp
+    #   dm-tree
+    #   jsonlines
+    #   rerun-sdk
+av==15.0.0
+    # via lerobot
+blinker==1.9.0
+    # via flask
+certifi==2025.7.14
+    # via
+    #   requests
+    #   sentry-sdk
+cffi==1.17.1
+    # via pymunk
+cfgv==3.4.0
+    # via pre-commit
+charset-normalizer==3.4.2
+    # via requests
+click==8.2.1
+    # via
+    #   flask
+    #   wandb
+cloudpickle==3.1.1
+    # via gymnasium
+cmake==4.0.3
+    # via lerobot
+cmeel==0.57.3
+    # via
+    #   cmeel-assimp
+    #   cmeel-boost
+    #   cmeel-console-bridge
+    #   cmeel-octomap
+    #   cmeel-qhull
+    #   cmeel-tinyxml2
+    #   cmeel-urdfdom
+    #   cmeel-zlib
+    #   coal-library
+    #   eigenpy
+    #   eiquadprog
+    #   pin
+    #   placo
+    #   rhoban-cmeel-jsoncpp
+cmeel-assimp==5.4.3.1
+    # via coal-library
+cmeel-boost==1.87.0.1
+    # via
+    #   coal-library
+    #   eigenpy
+    #   eiquadprog
+    #   pin
+cmeel-console-bridge==1.0.2.3
+    # via cmeel-urdfdom
+cmeel-octomap==1.10.0
+    # via coal-library
+cmeel-qhull==8.0.2.1
+    # via coal-library
+cmeel-tinyxml2==10.0.0
+    # via cmeel-urdfdom
+cmeel-urdfdom==4.0.1
+    # via pin
+cmeel-zlib==1.3.1
+    # via cmeel-assimp
+coal-library==3.0.1
+    # via pin
+contourpy==1.3.2
+    # via matplotlib
+coverage[toml]==7.10.1
+    # via pytest-cov
+cycler==0.12.1
+    # via matplotlib
+datasets==3.6.0
+    # via lerobot
+debugpy==1.8.15
+    # via lerobot
+decorator==5.2.1
+    # via ipython
+deepdiff==8.5.0
+    # via lerobot
+diffusers==0.34.0
+    # via lerobot
+dill==0.3.8
+    # via
+    #   datasets
+    #   multiprocess
+distlib==0.4.0
+    # via virtualenv
+dm-control==1.0.14
+    # via gym-aloha
+dm-env==1.6
+    # via dm-control
+dm-tree==0.1.9
+    # via
+    #   dm-control
+    #   dm-env
+docopt==0.6.2
+    # via num2words
+draccus==0.10.0
+    # via lerobot
+dynamixel-sdk==3.7.31
+    # via lerobot
+eigenpy==3.10.3
+    # via coal-library
+einops==0.8.1
+    # via lerobot
+eiquadprog==1.2.9
+    # via placo
+evdev==1.9.2
+    # via pynput
+exceptiongroup==1.3.0
+    # via
+    #   ipython
+    #   pytest
+executing==2.2.0
+    # via stack-data
+farama-notifications==0.0.4
+    # via gymnasium
+feetech-servo-sdk==1.0.0
+    # via lerobot
+filelock==3.18.0
+    # via
+    #   datasets
+    #   diffusers
+    #   huggingface-hub
+    #   torch
+    #   transformers
+    #   virtualenv
+flask==3.1.1
+    # via lerobot
+fonttools==4.59.0
+    # via matplotlib
+frozenlist==1.7.0
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec[http]==2025.3.0
+    # via
+    #   datasets
+    #   huggingface-hub
+    #   torch
+gitdb==4.0.12
+    # via gitpython
+gitpython==3.1.45
+    # via wandb
+glfw==2.9.0
+    # via
+    #   dm-control
+    #   mujoco
+grpcio==1.73.1
+    # via
+    #   grpcio-tools
+    #   lerobot
+grpcio-tools==1.73.1
+    # via lerobot
+gym-aloha==0.1.1
+    # via lerobot
+gym-hil==0.1.10
+    # via lerobot
+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
+    #   pettingzoo
+gymnasium-robotics==1.2.4
+    # via gym-xarm
+hf-transfer==0.1.9
+    # via huggingface-hub
+hf-xet==1.1.5
+    # via huggingface-hub
+hidapi==0.14.0.post4
+    # via
+    #   gym-hil
+    #   lerobot
+huggingface-hub[cli,hf-transfer]==0.34.3
+    # via
+    #   accelerate
+    #   datasets
+    #   diffusers
+    #   lerobot
+    #   tokenizers
+    #   transformers
+identify==2.6.12
+    # via pre-commit
+idna==3.10
+    # via
+    #   requests
+    #   yarl
+imageio[ffmpeg]==2.37.0
+    # via
+    #   gym-aloha
+    #   gym-hil
+    #   gymnasium-robotics
+    #   lerobot
+    #   scikit-image
+imageio-ffmpeg==0.6.0
+    # via imageio
+importlib-metadata==8.7.0
+    # via diffusers
+iniconfig==2.1.0
+    # via pytest
+inquirerpy==0.3.4
+    # via huggingface-hub
+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
+    #   flask
+    #   gymnasium-robotics
+    #   torch
+jsonlines==4.0.0
+    # via lerobot
+kiwisolver==1.4.8
+    # via matplotlib
+labmaze==1.0.6
+    # via dm-control
+lazy-loader==0.4
+    # via scikit-image
+lxml==6.0.0
+    # via dm-control
+markupsafe==3.0.2
+    # via
+    #   flask
+    #   jinja2
+    #   werkzeug
+matplotlib==3.10.5
+    # via lerobot
+matplotlib-inline==0.1.7
+    # via ipython
+mergedeep==1.3.4
+    # via draccus
+meshcat==0.3.2
+    # via placo
+mock-serial==0.0.1
+    # via lerobot
+mpmath==1.3.0
+    # via sympy
+mujoco==2.3.7
+    # via
+    #   dm-control
+    #   gym-aloha
+    #   gym-hil
+    #   gym-xarm
+    #   gymnasium-robotics
+multidict==6.6.3
+    # via
+    #   aiohttp
+    #   yarl
+multiprocess==0.70.16
+    # via datasets
+mypy-extensions==1.1.0
+    # via typing-inspect
+networkx==3.4.2
+    # via
+    #   scikit-image
+    #   torch
+nodeenv==1.9.1
+    # via pre-commit
+num2words==0.5.14
+    # via lerobot
+numpy==2.2.6
+    # via
+    #   accelerate
+    #   cmeel-boost
+    #   contourpy
+    #   datasets
+    #   diffusers
+    #   dm-control
+    #   dm-env
+    #   dm-tree
+    #   gymnasium
+    #   gymnasium-robotics
+    #   imageio
+    #   labmaze
+    #   matplotlib
+    #   meshcat
+    #   mujoco
+    #   opencv-python
+    #   opencv-python-headless
+    #   pandas
+    #   pettingzoo
+    #   rerun-sdk
+    #   scikit-image
+    #   scipy
+    #   shapely
+    #   tifffile
+    #   torchvision
+    #   transformers
+nvidia-cublas-cu12==12.6.4.1
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.6.80
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.6.77
+    # via torch
+nvidia-cuda-runtime-cu12==12.6.77
+    # via torch
+nvidia-cudnn-cu12==9.5.1.17
+    # via torch
+nvidia-cufft-cu12==11.3.0.4
+    # via torch
+nvidia-cufile-cu12==1.11.1.6
+    # via torch
+nvidia-curand-cu12==10.3.7.77
+    # via torch
+nvidia-cusolver-cu12==11.7.1.2
+    # via torch
+nvidia-cusparse-cu12==12.5.4.2
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cusparselt-cu12==0.6.3
+    # via torch
+nvidia-nccl-cu12==2.26.2
+    # via torch
+nvidia-nvjitlink-cu12==12.6.85
+    # via
+    #   nvidia-cufft-cu12
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvtx-cu12==12.6.77
+    # via torch
+opencv-python==4.12.0.88
+    # via gym-pusht
+opencv-python-headless==4.12.0.88
+    # via lerobot
+orderly-set==5.5.0
+    # via deepdiff
+packaging==25.0
+    # via
+    #   accelerate
+    #   datasets
+    #   huggingface-hub
+    #   lazy-loader
+    #   lerobot
+    #   matplotlib
+    #   pytest
+    #   scikit-image
+    #   transformers
+    #   wandb
+pandas==2.3.1
+    # via
+    #   datasets
+    #   lerobot
+parso==0.8.4
+    # via jedi
+pettingzoo==1.24.3
+    # via gymnasium-robotics
+pexpect==4.9.0
+    # via ipython
+pfzy==0.3.4
+    # via inquirerpy
+pillow==11.3.0
+    # via
+    #   diffusers
+    #   imageio
+    #   matplotlib
+    #   meshcat
+    #   rerun-sdk
+    #   scikit-image
+    #   torchvision
+pin==3.4.0
+    # via placo
+placo==0.9.14
+    # via lerobot
+platformdirs==4.3.8
+    # via
+    #   virtualenv
+    #   wandb
+pluggy==1.6.0
+    # via
+    #   pytest
+    #   pytest-cov
+pre-commit==4.2.0
+    # via lerobot
+prompt-toolkit==3.0.51
+    # via
+    #   inquirerpy
+    #   ipython
+propcache==0.3.2
+    # via
+    #   aiohttp
+    #   yarl
+protobuf==6.31.0
+    # via
+    #   dm-control
+    #   grpcio-tools
+    #   lerobot
+    #   wandb
+psutil==7.0.0
+    # via
+    #   accelerate
+    #   imageio
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.3
+    # via stack-data
+pyarrow==21.0.0
+    # via
+    #   datasets
+    #   rerun-sdk
+pycparser==2.22
+    # via cffi
+pydantic==2.11.7
+    # via wandb
+pydantic-core==2.33.2
+    # via pydantic
+pygame==2.6.1
+    # via
+    #   gym-hil
+    #   gym-pusht
+    #   lerobot
+pygments==2.19.2
+    # via
+    #   ipython
+    #   pytest
+pymunk==6.11.1
+    # via
+    #   gym-pusht
+    #   lerobot
+pyngrok==7.2.12
+    # via meshcat
+pynput==1.8.1
+    # via
+    #   gym-hil
+    #   lerobot
+pyopengl==3.1.9
+    # via
+    #   dm-control
+    #   mujoco
+pyparsing==3.2.3
+    # via
+    #   dm-control
+    #   matplotlib
+pyrealsense2==2.56.5.9235
+    # via lerobot
+pyserial==3.5
+    # via
+    #   dynamixel-sdk
+    #   feetech-servo-sdk
+    #   lerobot
+pytest==8.4.1
+    # via
+    #   lerobot
+    #   pytest-cov
+    #   pytest-timeout
+pytest-cov==6.2.1
+    # via lerobot
+pytest-timeout==2.4.0
+    # via lerobot
+python-dateutil==2.9.0.post0
+    # via
+    #   matplotlib
+    #   pandas
+python-xlib==0.33
+    # via pynput
+pytz==2025.2
+    # via pandas
+pyyaml==6.0.2
+    # via
+    #   accelerate
+    #   datasets
+    #   draccus
+    #   huggingface-hub
+    #   pre-commit
+    #   pyngrok
+    #   pyyaml-include
+    #   transformers
+    #   wandb
+pyyaml-include==1.4.1
+    # via draccus
+pyzmq==27.0.0
+    # via
+    #   lerobot
+    #   meshcat
+regex==2025.7.34
+    # via
+    #   diffusers
+    #   transformers
+requests==2.32.4
+    # via
+    #   datasets
+    #   diffusers
+    #   dm-control
+    #   huggingface-hub
+    #   transformers
+    #   wandb
+rerun-sdk==0.22.1
+    # via lerobot
+rhoban-cmeel-jsoncpp==1.9.4.9
+    # via placo
+safetensors==0.5.3
+    # via
+    #   accelerate
+    #   diffusers
+    #   lerobot
+    #   transformers
+scikit-image==0.25.2
+    # via
+    #   gym-pusht
+    #   lerobot
+scipy==1.15.3
+    # via
+    #   dm-control
+    #   scikit-image
+sentry-sdk==2.34.1
+    # via wandb
+shapely==2.1.1
+    # via gym-pusht
+six==1.17.0
+    # via
+    #   pynput
+    #   python-dateutil
+    #   python-xlib
+smmap==5.0.2
+    # via gitdb
+stack-data==0.6.3
+    # via ipython
+sympy==1.14.0
+    # via torch
+termcolor==3.1.0
+    # via lerobot
+tifffile==2025.5.10
+    # via scikit-image
+tokenizers==0.21.4
+    # via transformers
+toml==0.10.2
+    # via draccus
+tomli==2.2.1
+    # via
+    #   cmeel
+    #   coverage
+    #   pytest
+torch==2.7.1
+    # via
+    #   accelerate
+    #   lerobot
+    #   torchvision
+torchcodec==0.5
+    # via lerobot
+torchvision==0.22.1
+    # via lerobot
+tornado==6.5.1
+    # via meshcat
+tqdm==4.67.1
+    # via
+    #   datasets
+    #   dm-control
+    #   huggingface-hub
+    #   transformers
+traitlets==5.14.3
+    # via
+    #   ipython
+    #   matplotlib-inline
+transformers==4.51.3
+    # via lerobot
+triton==3.3.1
+    # via torch
+typing-extensions==4.14.1
+    # via
+    #   aiosignal
+    #   exceptiongroup
+    #   gymnasium
+    #   huggingface-hub
+    #   ipython
+    #   multidict
+    #   pydantic
+    #   pydantic-core
+    #   rerun-sdk
+    #   torch
+    #   typing-inspect
+    #   typing-inspection
+    #   wandb
+typing-inspect==0.9.0
+    # via draccus
+typing-inspection==0.4.1
+    # via pydantic
+tzdata==2025.2
+    # via pandas
+u-msgpack-python==2.8.0
+    # via meshcat
+urllib3==2.5.0
+    # via
+    #   requests
+    #   sentry-sdk
+virtualenv==20.32.0
+    # via pre-commit
+wandb==0.21.0
+    # via lerobot
+wcwidth==0.2.13
+    # via prompt-toolkit
+werkzeug==3.1.3
+    # via flask
+wrapt==1.17.2
+    # via dm-tree
+xxhash==3.5.0
+    # via datasets
+yarl==1.20.1
+    # via aiohttp
+zipp==3.23.0
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools

requirements.in

@@ -0,0 +1,9 @@
+# requirements.in
+
+# 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 24.04.2 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

@@ -170,7 +170,7 @@ available_datasets = sorted(
 # lists all available policies from `lerobot/policies`
 available_policies = ["act", "diffusion", "tdmpc", "vqbet"]
 
-# lists all available robots from `lerobot/robot_devices/robots`
+# lists all available robots from `lerobot/robots`
 available_robots = [
     "koch",
     "koch_bimanual",
@@ -179,13 +179,13 @@ available_robots = [
     "so101",
 ]
 
-# lists all available cameras from `lerobot/robot_devices/cameras`
+# lists all available cameras from `lerobot/cameras`
 available_cameras = [
     "opencv",
     "intelrealsense",
 ]
 
-# lists all available motors from `lerobot/robot_devices/motors`
+# lists all available motors from `lerobot/motors`
 available_motors = [
     "dynamixel",
     "feetech",

src/lerobot/bi_teleoperate.py

@@ -1,175 +0,0 @@
-import math
-import sys
-import time
-
-from lerobot.robots.so101_follower_torque.config_so101_follower_t import SO101FollowerTConfig
-from lerobot.robots.so101_follower_torque.so101_follower_t import SO101FollowerT
-from lerobot.utils.robot_utils import busy_wait
-from lerobot.utils.visualization_utils import _init_rerun, log_rerun_data
-
-FRQ = 100
-PRINT_HZ = 10
-RERUN_HZ = 100
-ESC_CLR_EOL = "\x1b[K"
-CURSOR_UP = "\x1b[F"
-
-follower_cfg = SO101FollowerTConfig(
-    port="/dev/tty.usbmodem58760432961",
-    id="follower_arm_torque",
-)
-
-leader_cfg = SO101FollowerTConfig(
-    port="/dev/tty.usbmodem58760432571",
-    id="leader_arm_torque",
-)
-
-follower = SO101FollowerT(follower_cfg)
-leader = SO101FollowerT(leader_cfg)
-follower.connect()
-leader.connect()
-
-_init_rerun("bilateral_teleoperation")
-
-print("Starting 4-channel bilateral teleoperation")
-first_print = True
-loop_count = 0
-tic_prev = time.perf_counter()
-
-while True:
-    tic = time.perf_counter()
-
-    obs_l, obs_f = leader.get_observation(), follower.get_observation()
-
-    dt = tic - tic_prev
-    tic_prev = tic
-    if dt <= 0.0:
-        dt = 0.01  # avoid div-by-zero
-
-    tau_cmd_f, tau_cmd_l = [], []
-    debug_info_f, debug_info_l = {}, {}
-
-    pos_f = {j: obs_f[f"{j}.pos"] for j in follower.bus.motors}
-    vel_f = {j: obs_f[f"{j}.vel"] for j in follower.bus.motors}
-    tau_reaction_f = {j: obs_f[f"{j}.effort"] for j in follower.bus.motors}
-
-    pos_l = {j: obs_l[f"{j}.pos"] for j in leader.bus.motors}
-    vel_l = {j: obs_l[f"{j}.vel"] for j in leader.bus.motors}
-    tau_reaction_l = {j: obs_l[f"{j}.effort"] for j in leader.bus.motors}
-
-    # Joint-specific control gains
-    kp_gains = follower.kp_gains
-    kd_gains = follower.kd_gains
-    kf_gains = follower.kf_gains
-
-    # Compute torque commands
-    tau_cmd_f = [
-        kp_gains[j] * (pos_l[j] - pos_f[j])  # Position tracking
-        + kd_gains[j] * (vel_l[j] - vel_f[j])  # Velocity damping
-        + kf_gains[j] * (-tau_reaction_l[j] - tau_reaction_f[j])  # Force reflection
-        for j in follower.bus.motors
-    ]
-
-    tau_cmd_l = [
-        kp_gains[j] * (pos_f[j] - pos_l[j])  # Position tracking
-        + kd_gains[j] * (vel_f[j] - vel_l[j])  # Velocity damping
-        + kf_gains[j] * (-tau_reaction_f[j] - tau_reaction_l[j])  # Force reflection
-        for j in leader.bus.motors
-    ]
-
-    # Store debug info
-    for i, j in enumerate(follower.bus.motors):
-        debug_info_f[j] = {
-            "τ_reaction": tau_reaction_f[j],
-            "τ_ref": tau_cmd_f[i],
-            "θ_err": pos_l[j] - pos_f[j],
-            "ω_err": vel_l[j] - vel_f[j],
-            "τ_err": -tau_reaction_l[j] - tau_reaction_f[j],
-        }
-        debug_info_l[j] = {
-            "τ_reaction": tau_reaction_l[j],
-            "τ_ref": tau_cmd_l[i],
-            "θ_err": pos_f[j] - pos_l[j],
-            "ω_err": vel_f[j] - vel_l[j],
-            "τ_err": -tau_reaction_f[j] - tau_reaction_l[j],
-        }
-
-    # Send torques to both arms
-    follower.send_action({f"{m}.effort": tau_cmd_f[i] for i, m in enumerate(follower.bus.motors)})
-    leader.send_action({f"{m}.effort": tau_cmd_l[i] for i, m in enumerate(leader.bus.motors)})
-
-    observation = {
-        "follower_joint_angles": pos_f,  # θ_f: current angles
-        "follower_angular_velocities": vel_f,  # ω_f: current velocities
-        "follower_external_torques": tau_reaction_f,  # τ_ext: measured minus deterministic components
-    }
-
-    action = {
-        "leader_target_angles": pos_l,  # θ_leader[τ]: absolute target angles
-        "leader_target_velocities": vel_l,  # ω_leader[τ]: absolute target velocities
-        "leader_interaction_torques": tau_reaction_l,  # τ_leader[τ]: cmd minus deterministic components
-    }
-
-    if loop_count % (FRQ // RERUN_HZ) == 0:
-        log_rerun_data(observation, action)
-
-    loop_count += 1
-    if loop_count % (FRQ // PRINT_HZ) == 0:
-        hz = 1.0 / dt
-
-        lines = [f"Loop {hz:6.1f} Hz    Δt {dt * 1e3:5.2f} ms"]
-        lines.append("=" * 106)
-        lines.append("LEADER ARM TORQUE ANALYSIS:")
-        lines.append(f"{'Joint':<13}{'Pos':>8}{'React':>6}{'Cmd':>6}")
-        lines.append(f"{'':13}{'(deg)':>8}{'(Nm)':>6}{'(Nm)':>6}")
-        lines.append("-" * 86)
-
-        for i, j in enumerate(leader.bus.motors):
-            debug_l = debug_info_l[j]
-
-            lines.append(
-                f"{j:<13s}{math.degrees(pos_l[j]):+8.1f}{debug_l['τ_reaction']:+6.2f}{tau_cmd_l[i]:+6.2f}"
-            )
-
-        lines.append("")
-        lines.append("FOLLOWER ARM TORQUE ANALYSIS:")
-        lines.append(f"{'Joint':<13}{'Pos':>8}{'React':>6}{'Cmd':>6}")
-        lines.append(f"{'':13}{'(deg)':>8}{'(Nm)':>6}{'(Nm)':>6}")
-        lines.append("-" * 86)
-
-        for i, j in enumerate(follower.bus.motors):
-            debug_f = debug_info_f[j]
-
-            lines.append(
-                f"{j:<13s}{math.degrees(pos_f[j]):+8.1f}{debug_f['τ_reaction']:+6.2f}{tau_cmd_f[i]:+6.2f}"
-            )
-
-        lines.append("")
-        lines.append("=" * 86)
-        lines.append("TORQUE COMPONENT EXPLANATIONS:")
-        lines.append("• Pos   (joint pos)  = Joint position in degrees")
-        lines.append("• React (reaction)  = External forces (human interaction, contact)")
-        lines.append("• Meas  (measured)  = Raw torque from motor current sensor")
-        lines.append("• Cmd   (command)   = Final torque sent to motor")
-        lines.append("-" * 86)
-        lines.append(
-            "Cmd = Track + Vel + Force + (Added as feedforward in send_action: Grav + Inert + Frict)"
-        )
-        lines.append("React = Meas - Grav - Inert - Frict  (external forces)")
-        lines.append("Force = Kf × (reflect_other_robot - React)  (telepresence)")
-        lines.append("Frict = b_visc×ω + f_coulomb×sign(ω)  (transparency)")
-        lines.append(
-            f"Joint Gains: shoulder_pan Kp={kp_gains['shoulder_pan']:.1f} | shoulder_pan Kd={kd_gains['shoulder_pan']:.1f} | shoulder_pan Kf={kf_gains['shoulder_pan']:.1f}"
-        )
-        lines.append(
-            f"Friction Comp, Viscous: {follower.friction_viscous['shoulder_pan']:.3f} | Coulomb: {follower.friction_coulomb['shoulder_pan']:.3f} (robot-class)"
-        )
-
-        block = "\n".join(lines)
-        if first_print:
-            sys.stdout.write(block + "\n")
-            first_print = False
-        else:
-            sys.stdout.write(CURSOR_UP * len(lines) + ESC_CLR_EOL + block + "\n")
-        sys.stdout.flush()
-
-    busy_wait(max(0.0, 1.0 / FRQ - (time.perf_counter() - tic)))

src/lerobot/calibrate.py

@@ -18,7 +18,7 @@ Helper to recalibrate your device (robot or teleoperator).
 Example:
 
 ```shell
-python -m lerobot.calibrate \
+lerobot-calibrate \
     --teleop.type=so100_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \
     --teleop.id=blue
@@ -82,5 +82,9 @@ def calibrate(cfg: CalibrateConfig):
     device.disconnect()
 
 
-if __name__ == "__main__":
+def main():
     calibrate()
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/cameras/camera.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 
 import abc
-from typing import Any, Dict, List
+from typing import Any
 
 import numpy as np
 
@@ -69,7 +69,7 @@ class Camera(abc.ABC):
 
     @staticmethod
     @abc.abstractmethod
-    def find_cameras() -> List[Dict[str, Any]]:
+    def find_cameras() -> list[dict[str, Any]]:
         """Detects available cameras connected to the system.
         Returns:
             List[Dict[str, Any]]: A list of dictionaries,

src/lerobot/cameras/opencv/camera_opencv.py

@@ -23,7 +23,7 @@ import platform
 import time
 from pathlib import Path
 from threading import Event, Lock, Thread
-from typing import Any, Dict, List
+from typing import Any
 
 # 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:
@@ -60,7 +60,7 @@ class OpenCVCamera(Camera):
     or port changes, especially on Linux. Use the provided utility script to find
     available camera indices or paths:
     ```bash
-    python -m lerobot.find_cameras opencv
+    lerobot-find-cameras opencv
     ```
 
     The camera's default settings (FPS, resolution, color mode) are used unless
@@ -112,8 +112,7 @@ class OpenCVCamera(Camera):
         self.config = config
         self.index_or_path = config.index_or_path
 
-        self.wanted_fps = config.fps
-        self.camera_fps = None
+        self.fps = config.fps
         self.color_mode = config.color_mode
         self.warmup_s = config.warmup_s
 
@@ -166,8 +165,7 @@ class OpenCVCamera(Camera):
             self.videocapture.release()
             self.videocapture = None
             raise ConnectionError(
-                f"Failed to open {self}."
-                f"Run `python -m lerobot.find_cameras opencv` to find available cameras."
+                f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
             )
 
         self._configure_capture_settings()
@@ -201,9 +199,10 @@ class OpenCVCamera(Camera):
         if not self.is_connected:
             raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.")
 
-        # We don't set the FPS. We GET the actual (max) FPS from the camera.
-        self.camera_fps = self.videocapture.get(cv2.CAP_PROP_FPS)
-        logger.info(f"{self} is running at its default/max FPS: {self.camera_fps:.2f}")
+        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)))
@@ -245,7 +244,7 @@ class OpenCVCamera(Camera):
             )
 
     @staticmethod
-    def find_cameras() -> List[Dict[str, Any]]:
+    def find_cameras() -> list[dict[str, Any]]:
         """
         Detects available OpenCV cameras connected to the system.
 
@@ -316,23 +315,19 @@ class OpenCVCamera(Camera):
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
-        # Start the background capture thread if it's not running
-        if self.thread is None or not self.thread.is_alive():
-            # Perform an initial blocking read to populate the first frame
-            ret, frame = self.videocapture.read()
-            if not ret or frame is None:
-                raise RuntimeError(f"{self} failed to read initial frame.")
+        start_time = time.perf_counter()
 
-            self.latest_frame = self._postprocess_image(frame)
-            self._start_read_thread()
+        ret, frame = self.videocapture.read()
 
-        with self.frame_lock:
-            frame = self.latest_frame
+        if not ret or frame is None:
+            raise RuntimeError(f"{self} read failed (status={ret}).")
 
-        if frame is None:
-            raise RuntimeError(f"Internal error: Read thread started but no frame is available for {self}.")
+        processed_frame = self._postprocess_image(frame, color_mode)
 
-        return frame.copy()
+        read_duration_ms = (time.perf_counter() - start_time) * 1e3
+        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+
+        return processed_frame
 
     def _postprocess_image(self, image: np.ndarray, color_mode: ColorMode | None = None) -> np.ndarray:
         """
@@ -372,7 +367,7 @@ class OpenCVCamera(Camera):
         if requested_color_mode == ColorMode.RGB:
             processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE, cv2.ROTATE_180]:
             processed_image = cv2.rotate(processed_image, self.rotation)
 
         return processed_image
@@ -390,23 +385,16 @@ class OpenCVCamera(Camera):
         """
         while not self.stop_event.is_set():
             try:
-                ret, frame = self.videocapture.read()
-                if not ret or frame is None:
-                    logger.warning(f"Failed to read frame in background for {self}.")
-                    time.sleep(0.01)
-                    continue
-
-                processed_frame = self._postprocess_image(frame)
+                color_image = self.read()
 
                 with self.frame_lock:
-                    self.latest_frame = processed_frame
-
+                    self.latest_frame = color_image
                 self.new_frame_event.set()
 
+            except DeviceNotConnectedError:
+                break
             except Exception as e:
                 logger.warning(f"Error reading frame in background thread for {self}: {e}")
-                if not self.is_connected:
-                    break
 
     def _start_read_thread(self) -> None:
         """Starts or restarts the background read thread if it's not running."""

src/lerobot/cameras/reachy2_camera/__init__.py

@@ -12,24 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-on:
-  push:
-
-name: Secret Leaks
-
-permissions: {}
-
-jobs:
-  trufflehog:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-      with:
-        fetch-depth: 0
-        persist-credentials: false
-
-    - name: Secret Scanning
-      uses: trufflesecurity/trufflehog@90694bf9af66e7536abc5824e7a87246dbf933cb # v3.88.35
-      with:
-        extra_args: --only-verified
+from .configuration_reachy2_camera import Reachy2CameraConfig
+from .reachy2_camera import Reachy2Camera

src/lerobot/cameras/reachy2_camera/configuration_reachy2_camera.py

@@ -0,0 +1,78 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+from ..configs import CameraConfig, ColorMode
+
+
+@CameraConfig.register_subclass("reachy2_camera")
+@dataclass
+class Reachy2CameraConfig(CameraConfig):
+    """Configuration class for Reachy 2 camera devices.
+
+    This class provides configuration options for Reachy 2 cameras,
+    supporting both the teleop and depth cameras. It includes settings
+    for resolution, frame rate, color mode, and the selection of the cameras.
+
+    Example configurations:
+    ```python
+    # Basic configurations
+    Reachy2CameraConfig(
+        name="teleop",
+        image_type="left",
+        ip_address="192.168.0.200",  # IP address of the robot
+        fps=15,
+        width=640,
+        height=480,
+        color_mode=ColorMode.RGB,
+    )  # Left teleop camera, 640x480 @ 15FPS
+    ```
+
+    Attributes:
+        name: Name of the camera device. Can be "teleop" or "depth".
+        image_type: Type of image stream. For "teleop" camera, can be "left" or "right".
+                    For "depth" camera, can be "rgb" or "depth". (depth is not supported yet)
+        fps: Requested frames per second for the color stream.
+        width: Requested frame width in pixels for the color stream.
+        height: Requested frame height in pixels for the color stream.
+        color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
+        ip_address: IP address of the robot. Defaults to "localhost".
+        port: Port number for the camera server. Defaults to 50065.
+
+    Note:
+        - Only 3-channel color output (RGB/BGR) is currently supported.
+    """
+
+    name: str
+    image_type: str
+    color_mode: ColorMode = ColorMode.RGB
+    ip_address: str | None = "localhost"
+    port: int = 50065
+    # use_depth: bool = False
+
+    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 (
+            self.name == "depth" and self.image_type not in ["rgb", "depth"]
+        ):
+            raise ValueError(
+                f"`image_type` is expected to be 'left' or 'right' for teleop camera, and 'rgb' or 'depth' for depth camera, but {self.image_type} is provided."
+            )
+
+        if self.color_mode not in ["rgb", "bgr"]:
+            raise ValueError(
+                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
+            )

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -0,0 +1,288 @@
+# Copyright 2024 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.
+
+"""
+Provides the Reachy2Camera class for capturing frames from Reachy 2 cameras using Reachy 2's CameraManager.
+"""
+
+import logging
+import os
+import platform
+import time
+from threading import Event, Lock, Thread
+from typing import Any
+
+# 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
+import numpy as np
+from reachy2_sdk.media.camera import CameraView
+from reachy2_sdk.media.camera_manager import CameraManager
+
+from lerobot.errors import DeviceNotConnectedError
+
+from ..camera import Camera
+from .configuration_reachy2_camera import ColorMode, Reachy2CameraConfig
+
+logger = logging.getLogger(__name__)
+
+
+class Reachy2Camera(Camera):
+    """
+    Manages Reachy 2 camera using Reachy 2 CameraManager.
+
+    This class provides a high-level interface to connect to, configure, and read
+    frames from Reachy 2 cameras. It supports both synchronous and asynchronous
+    frame reading.
+
+    An Reachy2Camera instance requires a camera name (e.g., "teleop") and an image
+    type (e.g., "left") to be specified in the configuration.
+
+    The camera's default settings (FPS, resolution, color mode) are used unless
+    overridden in the configuration.
+    """
+
+    def __init__(self, config: Reachy2CameraConfig):
+        """
+        Initializes the Reachy2Camera instance.
+
+        Args:
+            config: The configuration settings for the camera.
+        """
+        super().__init__(config)
+
+        self.config = config
+
+        self.fps = config.fps
+        self.color_mode = config.color_mode
+
+        self.cam_manager: CameraManager | None = None
+
+        self.thread: Thread | None = None
+        self.stop_event: Event | None = None
+        self.frame_lock: Lock = Lock()
+        self.latest_frame: np.ndarray | None = None
+        self.new_frame_event: Event = Event()
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}({self.config.name}, {self.config.image_type})"
+
+    @property
+    def is_connected(self) -> bool:
+        """Checks if the camera is currently connected and opened."""
+        if self.config.name == "teleop":
+            return self.cam_manager._grpc_connected and self.cam_manager.teleop if self.cam_manager else False
+        elif self.config.name == "depth":
+            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):
+        """
+        Connects to the Reachy2 CameraManager as specified in the configuration.
+        """
+        self.cam_manager = CameraManager(host=self.config.ip_address, port=self.config.port)
+        self.cam_manager.initialize_cameras()
+
+        logger.info(f"{self} connected.")
+
+    @staticmethod
+    def find_cameras(ip_address: str = "localhost", port: int = 50065) -> list[dict[str, Any]]:
+        """
+        Detects available Reachy 2 cameras.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries,
+            where each dictionary contains 'name', 'stereo',
+            and the default profile properties (width, height, fps).
+        """
+        initialized_cameras = []
+        camera_manager = CameraManager(host=ip_address, port=port)
+
+        for camera in [camera_manager.teleop, camera_manager.depth]:
+            if camera is None:
+                continue
+
+            height, width, _, _, _, _, _ = camera.get_parameters()
+
+            camera_info = {
+                "name": camera._cam_info.name,
+                "stereo": camera._cam_info.stereo,
+                "default_profile": {
+                    "width": width,
+                    "height": height,
+                    "fps": 30,
+                },
+            }
+            initialized_cameras.append(camera_info)
+
+        camera_manager.disconnect()
+        return initialized_cameras
+
+    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
+        """
+        Reads a single frame synchronously from the camera.
+
+        This is a blocking call.
+
+        Args:
+            color_mode (Optional[ColorMode]): If specified, overrides the default
+                color mode (`self.color_mode`) for this read operation (e.g.,
+                request RGB even if default is BGR).
+
+        Returns:
+            np.ndarray: The captured frame as a NumPy array in the format
+                       (height, width, channels), using the specified or default
+                       color mode and applying any configured rotation.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        start_time = time.perf_counter()
+
+        frame = None
+
+        if self.cam_manager is None:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+        else:
+            if self.config.name == "teleop" and hasattr(self.cam_manager, "teleop"):
+                if self.config.image_type == "left":
+                    frame = self.cam_manager.teleop.get_frame(CameraView.LEFT, size=(640, 480))[0]
+                elif self.config.image_type == "right":
+                    frame = self.cam_manager.teleop.get_frame(CameraView.RIGHT, size=(640, 480))[0]
+            elif self.config.name == "depth" and hasattr(self.cam_manager, "depth"):
+                if self.config.image_type == "depth":
+                    frame = self.cam_manager.depth.get_depth_frame()[0]
+                elif self.config.image_type == "rgb":
+                    frame = self.cam_manager.depth.get_frame(size=(640, 480))[0]
+
+            if frame is None:
+                return np.empty((0, 0, 3), dtype=np.uint8)
+
+            if self.config.color_mode == "rgb":
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        read_duration_ms = (time.perf_counter() - start_time) * 1e3
+        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+
+        return frame
+
+    def _read_loop(self):
+        """
+        Internal loop run by the background thread for asynchronous reading.
+
+        On each iteration:
+        1. Reads a color frame
+        2. Stores result in latest_frame (thread-safe)
+        3. Sets new_frame_event to notify listeners
+
+        Stops on DeviceNotConnectedError, logs other errors and continues.
+        """
+        while not self.stop_event.is_set():
+            try:
+                color_image = self.read()
+
+                with self.frame_lock:
+                    self.latest_frame = color_image
+                self.new_frame_event.set()
+
+            except DeviceNotConnectedError:
+                break
+            except Exception as e:
+                logger.warning(f"Error reading frame in background thread for {self}: {e}")
+
+    def _start_read_thread(self) -> None:
+        """Starts or restarts the background read thread if it's not running."""
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=0.1)
+        if self.stop_event is not None:
+            self.stop_event.set()
+
+        self.stop_event = Event()
+        self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
+        self.thread.daemon = True
+        self.thread.start()
+
+    def _stop_read_thread(self) -> None:
+        """Signals the background read thread to stop and waits for it to join."""
+        if self.stop_event is not None:
+            self.stop_event.set()
+
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=2.0)
+
+        self.thread = None
+        self.stop_event = None
+
+    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+        """
+        Reads the latest available frame asynchronously.
+
+        This method retrieves the most recent frame captured by the background
+        read thread. It does not block waiting for the camera hardware directly,
+        but may wait up to timeout_ms for the background thread to provide a frame.
+
+        Args:
+            timeout_ms (float): Maximum time in milliseconds to wait for a frame
+                to become available. Defaults to 200ms (0.2 seconds).
+
+        Returns:
+            np.ndarray: The latest captured frame as a NumPy array in the format
+                       (height, width, channels), processed according to configuration.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            TimeoutError: If no frame becomes available within the specified timeout.
+            RuntimeError: If an unexpected error occurs.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        if self.thread is None or not self.thread.is_alive():
+            self._start_read_thread()
+
+        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+            thread_alive = self.thread is not None and self.thread.is_alive()
+            raise TimeoutError(
+                f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
+                f"Read thread alive: {thread_alive}."
+            )
+
+        with self.frame_lock:
+            frame = self.latest_frame
+            self.new_frame_event.clear()
+
+        if frame is None:
+            raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
+
+        return frame
+
+    def disconnect(self):
+        """
+        Stops the background read thread (if running).
+
+        Raises:
+            DeviceNotConnectedError: If the camera is already disconnected.
+        """
+        if not self.is_connected and self.thread is None:
+            raise DeviceNotConnectedError(f"{self} not connected.")
+
+        if self.thread is not None:
+            self._stop_read_thread()
+
+        if self.cam_manager is not None:
+            self.cam_manager.disconnect()
+
+        logger.info(f"{self} disconnected.")

src/lerobot/cameras/realsense/camera_realsense.py

@@ -19,7 +19,7 @@ Provides the RealSenseCamera class for capturing frames from Intel RealSense cam
 import logging
 import time
 from threading import Event, Lock, Thread
-from typing import Any, Dict, List
+from typing import Any
 
 import cv2
 import numpy as np
@@ -51,7 +51,7 @@ class RealSenseCamera(Camera):
 
     Use the provided utility script to find available camera indices and default profiles:
     ```bash
-    python -m lerobot.find_cameras realsense
+    lerobot-find-cameras realsense
     ```
 
     A `RealSenseCamera` instance requires a configuration object specifying the
@@ -176,8 +176,7 @@ class RealSenseCamera(Camera):
             self.rs_profile = None
             self.rs_pipeline = None
             raise ConnectionError(
-                f"Failed to open {self}."
-                "Run `python -m lerobot.find_cameras realsense` to find available cameras."
+                f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
             ) from e
 
         self._configure_capture_settings()
@@ -194,7 +193,7 @@ class RealSenseCamera(Camera):
         logger.info(f"{self} connected.")
 
     @staticmethod
-    def find_cameras() -> List[Dict[str, Any]]:
+    def find_cameras() -> list[dict[str, Any]]:
         """
         Detects available Intel RealSense cameras connected to the system.
 
@@ -434,7 +433,7 @@ class RealSenseCamera(Camera):
         if self.color_mode == ColorMode.BGR:
             processed_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
-        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE, cv2.ROTATE_180]:
             processed_image = cv2.rotate(processed_image, self.rotation)
 
         return processed_image

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -28,12 +28,12 @@ class RealSenseCameraConfig(CameraConfig):
     Example configurations for Intel RealSense D405:
     ```python
     # Basic configurations
-    RealSenseCameraConfig("0123456789", 30, 1280, 720)   # 1280x720 @ 30FPS
-    RealSenseCameraConfig("0123456789", 60, 640, 480)   # 640x480 @ 60FPS
+    RealSenseCameraConfig("0123456789", 30, 1280, 720)  # 1280x720 @ 30FPS
+    RealSenseCameraConfig("0123456789", 60, 640, 480)  # 640x480 @ 60FPS
 
     # Advanced configurations
     RealSenseCameraConfig("0123456789", 30, 640, 480, use_depth=True)  # With depth sensing
-    RealSenseCameraConfig("0123456789", 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)     # With 90° rotation
+    RealSenseCameraConfig("0123456789", 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)  # With 90° rotation
     ```
 
     Attributes:

src/lerobot/cameras/utils.py

@@ -37,8 +37,14 @@ def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[s
             from .realsense.camera_realsense import RealSenseCamera
 
             cameras[key] = RealSenseCamera(cfg)
+
+        elif cfg.type == "reachy2_camera":
+            from .reachy2_camera.reachy2_camera import Reachy2Camera
+
+            cameras[key] = Reachy2Camera(cfg)
+
         else:
-            raise ValueError(f"The motor type '{cfg.type}' is not valid.")
+            raise ValueError(f"The camera type '{cfg.type}' is not valid.")
 
     return cameras
 

src/lerobot/configs/parser.py

@@ -16,9 +16,9 @@ import inspect
 import pkgutil
 import sys
 from argparse import ArgumentError
+from collections.abc import Sequence
 from functools import wraps
 from pathlib import Path
-from typing import Sequence
 
 import draccus
 
@@ -76,9 +76,8 @@ def parse_plugin_args(plugin_arg_suffix: str, args: Sequence[str]) -> dict:
             - Values are the corresponding argument values
 
     Example:
-        >>> args = ['--env.discover_packages_path=my_package',
-        ...         '--other_arg=value']
-        >>> parse_plugin_args('discover_packages_path', args)
+        >>> args = ["--env.discover_packages_path=my_package", "--other_arg=value"]
+        >>> parse_plugin_args("discover_packages_path", args)
         {'env.discover_packages_path': 'my_package'}
     """
     plugin_args = {}
@@ -111,7 +110,7 @@ def load_plugin(plugin_path: str) -> None:
         PluginLoadError: If the plugin cannot be loaded due to import errors or if the package path is invalid.
 
     Examples:
-        >>> load_plugin("external_plugin.core")       # Loads plugin from external package
+        >>> load_plugin("external_plugin.core")  # Loads plugin from external package
 
     Notes:
         - The plugin package should handle its own registration during import

src/lerobot/configs/policies.py

@@ -12,26 +12,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import abc
+import builtins
 import json
 import logging
 import os
 import tempfile
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Type, TypeVar
+from typing import TypeVar
 
 import draccus
 from huggingface_hub import hf_hub_download
 from huggingface_hub.constants import CONFIG_NAME
 from huggingface_hub.errors import HfHubHTTPError
 
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.constants import ACTION, OBS_STATE
 from lerobot.optim.optimizers import OptimizerConfig
 from lerobot.optim.schedulers import LRSchedulerConfig
 from lerobot.utils.hub import HubMixin
 from lerobot.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
 
-# Generic variable that is either PreTrainedConfig or a subclass thereof
 T = TypeVar("T", bound="PreTrainedConfig")
 
 
@@ -52,7 +53,6 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
     """
 
     n_obs_steps: int = 1
-    normalization_mapping: dict[str, NormalizationMode] = field(default_factory=dict)
 
     input_features: dict[str, PolicyFeature] = field(default_factory=dict)
     output_features: dict[str, PolicyFeature] = field(default_factory=dict)
@@ -119,8 +119,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
 
     @property
     def robot_state_feature(self) -> PolicyFeature | None:
-        for _, ft in self.input_features.items():
-            if ft.type is FeatureType.STATE:
+        for ft_name, ft in self.input_features.items():
+            if ft.type is FeatureType.STATE and ft_name == OBS_STATE:
                 return ft
         return None
 
@@ -137,8 +137,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
 
     @property
     def action_feature(self) -> PolicyFeature | None:
-        for _, ft in self.output_features.items():
-            if ft.type is FeatureType.ACTION:
+        for ft_name, ft in self.output_features.items():
+            if ft.type is FeatureType.ACTION and ft_name == ACTION:
                 return ft
         return None
 
@@ -148,7 +148,7 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
 
     @classmethod
     def from_pretrained(
-        cls: Type[T],
+        cls: builtins.type[T],
         pretrained_name_or_path: str | Path,
         *,
         force_download: bool = False,

src/lerobot/configs/train.py

@@ -11,11 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import builtins
 import datetime as dt
 import os
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Type
 
 import draccus
 from huggingface_hub import hf_hub_download
@@ -135,7 +135,7 @@ class TrainPipelineConfig(HubMixin):
 
     @classmethod
     def from_pretrained(
-        cls: Type["TrainPipelineConfig"],
+        cls: builtins.type["TrainPipelineConfig"],
         pretrained_name_or_path: str | Path,
         *,
         force_download: bool = False,

src/lerobot/configs/types.py

@@ -24,6 +24,12 @@ class FeatureType(str, Enum):
     ENV = "ENV"
     ACTION = "ACTION"
     REWARD = "REWARD"
+    LANGUAGE = "LANGUAGE"
+
+
+class PipelineFeatureType(str, Enum):
+    ACTION = "ACTION"
+    OBSERVATION = "OBSERVATION"
 
 
 class NormalizationMode(str, Enum):

src/lerobot/constants.py

@@ -21,16 +21,19 @@ OBS_ENV_STATE = "observation.environment_state"
 OBS_STATE = "observation.state"
 OBS_IMAGE = "observation.image"
 OBS_IMAGES = "observation.images"
+OBS_LANGUAGE = "observation.language"
 ACTION = "action"
 REWARD = "next.reward"
+TRUNCATED = "next.truncated"
+DONE = "next.done"
+
+OBS_LANGUAGE_TOKENS = "observation.language.tokens"
+OBS_LANGUAGE_ATTENTION_MASK = "observation.language.attention_mask"
 
 ROBOTS = "robots"
 ROBOT_TYPE = "robot_type"
 TELEOPERATORS = "teleoperators"
 
-ROBOTS = "robots"
-TELEOPERATORS = "teleoperators"
-
 # files & directories
 CHECKPOINTS_DIR = "checkpoints"
 LAST_CHECKPOINT_LINK = "last"
@@ -42,6 +45,9 @@ OPTIMIZER_STATE = "optimizer_state.safetensors"
 OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
 SCHEDULER_STATE = "scheduler_state.json"
 
+POLICY_PREPROCESSOR_DEFAULT_NAME = "policy_preprocessor"
+POLICY_POSTPROCESSOR_DEFAULT_NAME = "policy_postprocessor"
+
 if "LEROBOT_HOME" in os.environ:
     raise ValueError(
         f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n"

src/lerobot/datasets/card_template.md

@@ -1,7 +1,8 @@
 ---
 # For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards
-{{ card_data }}
+# prettier-ignore
+{{card_data}}
 ---
 
 This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

src/lerobot/datasets/lerobot_dataset.py

@@ -16,8 +16,8 @@
 import contextlib
 import logging
 import shutil
+from collections.abc import Callable
 from pathlib import Path
-from typing import Callable
 
 import datasets
 import numpy as np
@@ -260,8 +260,6 @@ class LeRobotDatasetMetadata:
 
         self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"}
         self.info["total_videos"] += len(self.video_keys)
-        if len(self.video_keys) > 0:
-            self.update_video_info()
 
         write_info(self.info, self.root)
 
@@ -342,6 +340,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         force_cache_sync: bool = False,
         download_videos: bool = True,
         video_backend: str | None = None,
+        batch_encoding_size: int = 1,
     ):
         """
         2 modes are available for instantiating this class, depending on 2 different use cases:
@@ -434,7 +433,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 multiples of 1/fps. Defaults to 1e-4.
             revision (str, optional): An optional Git revision id which can be a branch name, a tag, or a
                 commit hash. Defaults to current codebase version tag.
-            sync_cache_first (bool, optional): Flag to sync and refresh local files first. If True and files
+            force_cache_sync (bool, optional): Flag to sync and refresh local files first. If True and files
                 are already present in the local cache, this will be faster. However, files loaded might not
                 be in sync with the version on the hub, especially if you specified 'revision'. Defaults to
                 False.
@@ -443,6 +442,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 True.
             video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec when available int the platform; otherwise, defaults to 'pyav'.
                 You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
+            batch_encoding_size (int, optional): Number of episodes to accumulate before batch encoding videos.
+                Set to 1 for immediate encoding (default), or higher for batched encoding. Defaults to 1.
         """
         super().__init__()
         self.repo_id = repo_id
@@ -454,6 +455,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.revision = revision if revision else CODEBASE_VERSION
         self.video_backend = video_backend if video_backend else get_safe_default_codec()
         self.delta_indices = None
+        self.batch_encoding_size = batch_encoding_size
+        self.episodes_since_last_encoding = 0
 
         # Unused attributes
         self.image_writer = None
@@ -811,6 +814,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
         """
         This will save to disk the current episode in self.episode_buffer.
 
+        Video encoding is handled automatically based on batch_encoding_size:
+        - If batch_encoding_size == 1: Videos are encoded immediately after each episode
+        - If batch_encoding_size > 1: Videos are encoded in batches.
+
         Args:
             episode_data (dict | None, optional): Dict containing the episode data to save. If None, this will
                 save the current episode in self.episode_buffer, which is filled with 'add_frame'. Defaults to
@@ -818,6 +825,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         """
         if not episode_data:
             episode_buffer = self.episode_buffer
+        else:
+            episode_buffer = episode_data
 
         validate_episode_buffer(episode_buffer, self.meta.total_episodes, self.features)
 
@@ -850,14 +859,28 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self._save_episode_table(episode_buffer, episode_index)
         ep_stats = compute_episode_stats(episode_buffer, self.features)
 
-        if len(self.meta.video_keys) > 0:
-            video_paths = self.encode_episode_videos(episode_index)
-            for key in self.meta.video_keys:
-                episode_buffer[key] = video_paths[key]
+        has_video_keys = len(self.meta.video_keys) > 0
+        use_batched_encoding = self.batch_encoding_size > 1
 
-        # `meta.save_episode` be executed after encoding the videos
+        if has_video_keys and not use_batched_encoding:
+            self.encode_episode_videos(episode_index)
+
+        # `meta.save_episode` should be executed after encoding the videos
         self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats)
 
+        # Check if we should trigger batch encoding
+        if has_video_keys and use_batched_encoding:
+            self.episodes_since_last_encoding += 1
+            if self.episodes_since_last_encoding == self.batch_encoding_size:
+                start_ep = self.num_episodes - self.batch_encoding_size
+                end_ep = self.num_episodes
+                logging.info(
+                    f"Batch encoding {self.batch_encoding_size} videos for episodes {start_ep} to {end_ep - 1}"
+                )
+                self.batch_encode_videos(start_ep, end_ep)
+                self.episodes_since_last_encoding = 0
+
+        # Episode data index and timestamp checking
         ep_data_index = get_episode_data_index(self.meta.episodes, [episode_index])
         ep_data_index_np = {k: t.numpy() for k, t in ep_data_index.items()}
         check_timestamps_sync(
@@ -868,16 +891,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
             self.tolerance_s,
         )
 
-        video_files = list(self.root.rglob("*.mp4"))
-        assert len(video_files) == self.num_episodes * len(self.meta.video_keys)
-
+        # Verify that we have one parquet file per episode and the number of video files matches the number of encoded episodes
         parquet_files = list(self.root.rglob("*.parquet"))
         assert len(parquet_files) == self.num_episodes
-
-        # delete images
-        img_dir = self.root / "images"
-        if img_dir.is_dir():
-            shutil.rmtree(self.root / "images")
+        video_files = list(self.root.rglob("*.mp4"))
+        assert len(video_files) == (self.num_episodes - self.episodes_since_last_encoding) * len(
+            self.meta.video_keys
+        )
 
         if not episode_data:  # Reset the buffer
             self.episode_buffer = self.create_episode_buffer()
@@ -894,6 +914,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
     def clear_episode_buffer(self) -> None:
         episode_index = self.episode_buffer["episode_index"]
+
+        # Clean up image files for the current episode buffer
         if self.image_writer is not None:
             for cam_key in self.meta.camera_keys:
                 img_dir = self._get_image_file_path(
@@ -930,25 +952,22 @@ class LeRobotDataset(torch.utils.data.Dataset):
         if self.image_writer is not None:
             self.image_writer.wait_until_done()
 
-    def encode_videos(self) -> None:
+    def encode_episode_videos(self, episode_index: int) -> None:
         """
         Use ffmpeg to convert frames stored as png into mp4 videos.
         Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
         since video encoding with ffmpeg is already using multithreading.
-        """
-        for ep_idx in range(self.meta.total_episodes):
-            self.encode_episode_videos(ep_idx)
 
-    def encode_episode_videos(self, episode_index: int) -> dict:
+        This method handles video encoding steps:
+        - Video encoding via ffmpeg
+        - Video info updating in metadata
+        - Raw image cleanup
+
+        Args:
+            episode_index (int): Index of the episode to encode.
         """
-        Use ffmpeg to convert frames stored as png into mp4 videos.
-        Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
-        since video encoding with ffmpeg is already using multithreading.
-        """
-        video_paths = {}
         for key in self.meta.video_keys:
             video_path = self.root / self.meta.get_video_file_path(episode_index, key)
-            video_paths[key] = str(video_path)
             if video_path.is_file():
                 # Skip if video is already encoded. Could be the case when resuming data recording.
                 continue
@@ -956,8 +975,32 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 episode_index=episode_index, image_key=key, frame_index=0
             ).parent
             encode_video_frames(img_dir, video_path, self.fps, overwrite=True)
+            shutil.rmtree(img_dir)
 
-        return video_paths
+        # Update video info (only needed when first episode is encoded since it reads from episode 0)
+        if len(self.meta.video_keys) > 0 and episode_index == 0:
+            self.meta.update_video_info()
+            write_info(self.meta.info, self.meta.root)  # ensure video info always written properly
+
+    def batch_encode_videos(self, start_episode: int = 0, end_episode: int | None = None) -> None:
+        """
+        Batch encode videos for multiple episodes.
+
+        Args:
+            start_episode: Starting episode index (inclusive)
+            end_episode: Ending episode index (exclusive). If None, encodes all episodes from start_episode
+        """
+        if end_episode is None:
+            end_episode = self.meta.total_episodes
+
+        logging.info(f"Starting batch video encoding for episodes {start_episode} to {end_episode - 1}")
+
+        # Encode all episodes with cleanup enabled for individual episodes
+        for ep_idx in range(start_episode, end_episode):
+            logging.info(f"Encoding videos for episode {ep_idx}")
+            self.encode_episode_videos(ep_idx)
+
+        logging.info("Batch video encoding completed")
 
     @classmethod
     def create(
@@ -972,6 +1015,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         image_writer_processes: int = 0,
         image_writer_threads: int = 0,
         video_backend: str | None = None,
+        batch_encoding_size: int = 1,
     ) -> "LeRobotDataset":
         """Create a LeRobot Dataset from scratch in order to record data."""
         obj = cls.__new__(cls)
@@ -988,6 +1032,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.revision = None
         obj.tolerance_s = tolerance_s
         obj.image_writer = None
+        obj.batch_encoding_size = batch_encoding_size
+        obj.episodes_since_last_encoding = 0
 
         if image_writer_processes or image_writer_threads:
             obj.start_image_writer(image_writer_processes, image_writer_threads)

src/lerobot/datasets/pipeline_features.py

@@ -0,0 +1,141 @@
+# 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.
+
+import re
+from collections.abc import Sequence
+from typing import Any
+
+from lerobot.configs.types import PipelineFeatureType
+from lerobot.constants import ACTION, OBS_IMAGES, OBS_STATE
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.processor import DataProcessorPipeline
+
+
+def create_initial_features(
+    action: dict[str, Any] | None, observation: dict[str, Any] | None
+) -> dict[PipelineFeatureType, dict[str, Any]]:
+    """
+    Creates the initial features dict for the dataset from action and observation specs.
+
+    Args:
+        action: A dictionary of action feature names to their types/shapes.
+        observation: A dictionary of observation feature names to their types/shapes.
+
+    Returns:
+        The initial features dictionary structured by PipelineFeatureType.
+    """
+    features = {PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: {}}
+    if action:
+        features[PipelineFeatureType.ACTION] = action
+    if observation:
+        features[PipelineFeatureType.OBSERVATION] = observation
+    return features
+
+
+# Helper to filter state/action keys based on regex patterns.
+def should_keep(key: str, patterns: tuple[str]) -> bool:
+    if patterns is None:
+        return True
+    return any(re.search(pat, key) for pat in patterns)
+
+
+def strip_prefix(key: str, prefixes_to_strip: tuple[str]) -> str:
+    for prefix in prefixes_to_strip:
+        if key.startswith(prefix):
+            return key[len(prefix) :]
+    return key
+
+
+# Define prefixes to strip from feature keys for clean names.
+# Handles both fully qualified (e.g., "action.state") and short (e.g., "state") forms.
+PREFIXES_TO_STRIP = tuple(
+    f"{token}." for const in (ACTION, OBS_STATE, OBS_IMAGES) for token in (const, const.split(".")[-1])
+)
+
+
+def aggregate_pipeline_dataset_features(
+    pipeline: DataProcessorPipeline,
+    initial_features: dict[PipelineFeatureType, dict[str, Any]],
+    *,
+    use_videos: bool = True,
+    patterns: Sequence[str] | None = None,
+) -> dict[str, dict]:
+    """
+    Aggregates and filters pipeline features to create a dataset-ready features dictionary.
+
+    This function transforms initial features using the pipeline, categorizes them as action or observations
+    (image or state), filters them based on `use_videos` and `patterns`, and finally
+    formats them for use with a Hugging Face LeRobot Dataset.
+
+    Args:
+        pipeline: The DataProcessorPipeline to apply.
+        initial_features: A dictionary of raw feature specs for actions and observations.
+        use_videos: If False, image features are excluded.
+        patterns: A sequence of regex patterns to filter action and state features.
+                  Image features are not affected by this filter.
+
+    Returns:
+        A dictionary of features formatted for a Hugging Face LeRobot Dataset.
+    """
+    all_features = pipeline.transform_features(initial_features)
+
+    # Intermediate storage for categorized and filtered features.
+    processed_features: dict[str, dict[str, Any]] = {
+        "action": {},
+        "observation": {},
+    }
+    images_token = OBS_IMAGES.split(".")[-1]
+
+    # Iterate through all features transformed by the pipeline.
+    for ptype, feats in all_features.items():
+        if ptype not in [PipelineFeatureType.ACTION, PipelineFeatureType.OBSERVATION]:
+            continue
+
+        for key, value in feats.items():
+            # 1. Categorize the feature.
+            is_action = ptype == PipelineFeatureType.ACTION
+            # Observations are classified as images if their key matches image-related tokens or if the shape of the feature is 3.
+            # All other observations are treated as state.
+            is_image = not is_action and (
+                (isinstance(value, tuple) and len(value) == 3)
+                or (
+                    key.startswith(f"{OBS_IMAGES}.")
+                    or key.startswith(f"{images_token}.")
+                    or f".{images_token}." in key
+                )
+            )
+
+            # 2. Apply filtering rules.
+            if is_image and not use_videos:
+                continue
+            if not is_image and not should_keep(key, patterns):
+                continue
+
+            # 3. Add the feature to the appropriate group with a clean name.
+            name = strip_prefix(key, PREFIXES_TO_STRIP)
+            if is_action:
+                processed_features["action"][name] = value
+            else:
+                processed_features["observation"][name] = value
+
+    # Convert the processed features into the final dataset format.
+    dataset_features = {}
+    if processed_features["action"]:
+        dataset_features.update(hw_to_dataset_features(processed_features["action"], ACTION, use_videos))
+    if processed_features["observation"]:
+        dataset_features.update(
+            hw_to_dataset_features(processed_features["observation"], "observation", use_videos)
+        )
+
+    return dataset_features

src/lerobot/datasets/push_dataset_to_hub/utils.py

@@ -16,7 +16,6 @@
 import inspect
 from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
-from typing import Dict
 
 import datasets
 import numpy
@@ -77,7 +76,7 @@ def check_repo_id(repo_id: str) -> None:
 
 
 # TODO(aliberts): remove
-def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]:
+def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> dict[str, torch.Tensor]:
     """
     Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset.
 

src/lerobot/datasets/sampler.py

@@ -13,7 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Iterator, Union
+from collections.abc import Iterator
 
 import torch
 
@@ -22,7 +22,7 @@ class EpisodeAwareSampler:
     def __init__(
         self,
         episode_data_index: dict,
-        episode_indices_to_use: Union[list, None] = None,
+        episode_indices_to_use: list | None = None,
         drop_n_first_frames: int = 0,
         drop_n_last_frames: int = 0,
         shuffle: bool = False,

src/lerobot/datasets/transforms.py

@@ -14,13 +14,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import collections
+from collections.abc import Callable, Sequence
 from dataclasses import dataclass, field
-from typing import Any, Callable, Sequence
+from typing import Any
 
 import torch
 from torchvision.transforms import v2
-from torchvision.transforms.v2 import Transform
-from torchvision.transforms.v2 import functional as F  # noqa: N812
+from torchvision.transforms.v2 import (
+    Transform,
+    functional as F,  # noqa: N812
+)
 
 
 class RandomSubsetApply(Transform):

src/lerobot/datasets/utils.py

@@ -41,7 +41,6 @@ from lerobot.datasets.backward_compatibility import (
     BackwardCompatibilityError,
     ForwardCompatibilityError,
 )
-from lerobot.robots import Robot
 from lerobot.utils.utils import is_valid_numpy_dtype_string
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of episodes per chunk
@@ -76,13 +75,20 @@ DEFAULT_FEATURES = {
 
 
 def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict:
-    """Flatten a nested dictionary structure by collapsing nested keys into one key with a separator.
+    """Flatten a nested dictionary by joining keys with a separator.
 
-    For example:
-    ```
-    >>> dct = {"a": {"b": 1, "c": {"d": 2}}, "e": 3}`
-    >>> print(flatten_dict(dct))
-    {"a/b": 1, "a/c/d": 2, "e": 3}
+    Example:
+        >>> dct = {"a": {"b": 1, "c": {"d": 2}}, "e": 3}
+        >>> print(flatten_dict(dct))
+        {'a/b': 1, 'a/c/d': 2, 'e': 3}
+
+    Args:
+        d (dict): The dictionary to flatten.
+        parent_key (str): The base key to prepend to the keys in this level.
+        sep (str): The separator to use between keys.
+
+    Returns:
+        dict: A flattened dictionary.
     """
     items = []
     for k, v in d.items():
@@ -95,6 +101,20 @@ def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict:
 
 
 def unflatten_dict(d: dict, sep: str = "/") -> dict:
+    """Unflatten a dictionary with delimited keys into a nested dictionary.
+
+    Example:
+        >>> flat_dct = {"a/b": 1, "a/c/d": 2, "e": 3}
+        >>> print(unflatten_dict(flat_dct))
+        {'a': {'b': 1, 'c': {'d': 2}}, 'e': 3}
+
+    Args:
+        d (dict): A dictionary with flattened keys.
+        sep (str): The separator used in the keys.
+
+    Returns:
+        dict: A nested dictionary.
+    """
     outdict = {}
     for key, value in d.items():
         parts = key.split(sep)
@@ -108,6 +128,16 @@ def unflatten_dict(d: dict, sep: str = "/") -> dict:
 
 
 def get_nested_item(obj: DictLike, flattened_key: str, sep: str = "/") -> Any:
+    """Access an item in a nested dictionary using a flattened key.
+
+    Args:
+        obj (DictLike): The nested dictionary-like object.
+        flattened_key (str): A key with parts separated by `sep`.
+        sep (str): The separator used in the flattened key.
+
+    Returns:
+        Any: The value from the nested dictionary.
+    """
     split_keys = flattened_key.split(sep)
     getter = obj[split_keys[0]]
     if len(split_keys) == 1:
@@ -120,6 +150,19 @@ def get_nested_item(obj: DictLike, flattened_key: str, sep: str = "/") -> Any:
 
 
 def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict:
+    """Serialize a dictionary containing tensors or numpy arrays to be JSON-compatible.
+
+    Converts torch.Tensor, np.ndarray, and np.generic types to lists or native Python types.
+
+    Args:
+        stats (dict): A dictionary that may contain non-serializable numeric types.
+
+    Returns:
+        dict: A dictionary with all values converted to JSON-serializable types.
+
+    Raises:
+        NotImplementedError: If a value has an unsupported type.
+    """
     serialized_dict = {}
     for key, value in flatten_dict(stats).items():
         if isinstance(value, (torch.Tensor, np.ndarray)):
@@ -134,6 +177,17 @@ def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict:
 
 
 def embed_images(dataset: datasets.Dataset) -> datasets.Dataset:
+    """Embed image bytes into the dataset table before saving to Parquet.
+
+    This function prepares a Hugging Face dataset for serialization by converting
+    image objects into an embedded format that can be stored in Arrow/Parquet.
+
+    Args:
+        dataset (datasets.Dataset): The input dataset, possibly containing image features.
+
+    Returns:
+        datasets.Dataset: The dataset with images embedded in the table storage.
+    """
     # Embed image bytes into the table before saving to parquet
     format = dataset.format
     dataset = dataset.with_format("arrow")
@@ -143,38 +197,94 @@ def embed_images(dataset: datasets.Dataset) -> datasets.Dataset:
 
 
 def load_json(fpath: Path) -> Any:
+    """Load data from a JSON file.
+
+    Args:
+        fpath (Path): Path to the JSON file.
+
+    Returns:
+        Any: The data loaded from the JSON file.
+    """
     with open(fpath) as f:
         return json.load(f)
 
 
 def write_json(data: dict, fpath: Path) -> None:
+    """Write data to a JSON file.
+
+    Creates parent directories if they don't exist.
+
+    Args:
+        data (dict): The dictionary to write.
+        fpath (Path): The path to the output JSON file.
+    """
     fpath.parent.mkdir(exist_ok=True, parents=True)
     with open(fpath, "w") as f:
         json.dump(data, f, indent=4, ensure_ascii=False)
 
 
 def load_jsonlines(fpath: Path) -> list[Any]:
+    """Load data from a JSON Lines file.
+
+    Args:
+        fpath (Path): Path to the JSON Lines file.
+
+    Returns:
+        list[Any]: A list of objects loaded from the file.
+    """
     with jsonlines.open(fpath, "r") as reader:
         return list(reader)
 
 
 def write_jsonlines(data: dict, fpath: Path) -> None:
+    """Write a list of dictionaries to a JSON Lines file.
+
+    Creates parent directories if they don't exist.
+
+    Args:
+        data (dict): The list of dictionaries to write.
+        fpath (Path): The path to the output JSON Lines file.
+    """
     fpath.parent.mkdir(exist_ok=True, parents=True)
     with jsonlines.open(fpath, "w") as writer:
         writer.write_all(data)
 
 
 def append_jsonlines(data: dict, fpath: Path) -> None:
+    """Append a dictionary to a JSON Lines file.
+
+    Creates parent directories if they don't exist.
+
+    Args:
+        data (dict): The dictionary to append.
+        fpath (Path): The path to the JSON Lines file.
+    """
     fpath.parent.mkdir(exist_ok=True, parents=True)
     with jsonlines.open(fpath, "a") as writer:
         writer.write(data)
 
 
 def write_info(info: dict, local_dir: Path):
+    """Write dataset info metadata to its standard file path.
+
+    Args:
+        info (dict): The dataset information dictionary.
+        local_dir (Path): The root directory of the dataset.
+    """
     write_json(info, local_dir / INFO_PATH)
 
 
 def load_info(local_dir: Path) -> dict:
+    """Load dataset info metadata from its standard file path.
+
+    Also converts shape lists to tuples for consistency.
+
+    Args:
+        local_dir (Path): The root directory of the dataset.
+
+    Returns:
+        dict: The dataset information dictionary.
+    """
     info = load_json(local_dir / INFO_PATH)
     for ft in info["features"].values():
         ft["shape"] = tuple(ft["shape"])
@@ -182,16 +292,40 @@ def load_info(local_dir: Path) -> dict:
 
 
 def write_stats(stats: dict, local_dir: Path):
+    """Serialize and write dataset statistics to their standard file path.
+
+    Args:
+        stats (dict): The statistics dictionary (can contain tensors/numpy arrays).
+        local_dir (Path): The root directory of the dataset.
+    """
     serialized_stats = serialize_dict(stats)
     write_json(serialized_stats, local_dir / STATS_PATH)
 
 
 def cast_stats_to_numpy(stats) -> dict[str, dict[str, np.ndarray]]:
+    """Recursively cast numerical values in a stats dictionary to numpy arrays.
+
+    Args:
+        stats (dict): The statistics dictionary.
+
+    Returns:
+        dict: The statistics dictionary with values cast to numpy arrays.
+    """
     stats = {key: np.array(value) for key, value in flatten_dict(stats).items()}
     return unflatten_dict(stats)
 
 
 def load_stats(local_dir: Path) -> dict[str, dict[str, np.ndarray]]:
+    """Load dataset statistics and cast numerical values to numpy arrays.
+
+    Returns None if the stats file doesn't exist.
+
+    Args:
+        local_dir (Path): The root directory of the dataset.
+
+    Returns:
+        A dictionary of statistics or None if the file is not found.
+    """
     if not (local_dir / STATS_PATH).exists():
         return None
     stats = load_json(local_dir / STATS_PATH)
@@ -199,6 +333,13 @@ def load_stats(local_dir: Path) -> dict[str, dict[str, np.ndarray]]:
 
 
 def write_task(task_index: int, task: dict, local_dir: Path):
+    """Write a single task to the tasks metadata file.
+
+    Args:
+        task_index (int): The index of the task.
+        task (dict): The task description dictionary.
+        local_dir (Path): The root directory of the dataset.
+    """
     task_dict = {
         "task_index": task_index,
         "task": task,
@@ -207,6 +348,16 @@ def write_task(task_index: int, task: dict, local_dir: Path):
 
 
 def load_tasks(local_dir: Path) -> tuple[dict, dict]:
+    """Load tasks from the tasks metadata file.
+
+    Args:
+        local_dir (Path): The root directory of the dataset.
+
+    Returns:
+        A tuple containing:
+        - A dictionary mapping task index to task description.
+        - A dictionary mapping task description to task index.
+    """
     tasks = load_jsonlines(local_dir / TASKS_PATH)
     tasks = {item["task_index"]: item["task"] for item in sorted(tasks, key=lambda x: x["task_index"])}
     task_to_task_index = {task: task_index for task_index, task in tasks.items()}
@@ -214,15 +365,36 @@ def load_tasks(local_dir: Path) -> tuple[dict, dict]:
 
 
 def write_episode(episode: dict, local_dir: Path):
+    """Write a single episode's metadata to the episodes metadata file.
+
+    Args:
+        episode (dict): The episode metadata dictionary.
+        local_dir (Path): The root directory of the dataset.
+    """
     append_jsonlines(episode, local_dir / EPISODES_PATH)
 
 
 def load_episodes(local_dir: Path) -> dict:
+    """Load episode metadata from the episodes metadata file.
+
+    Args:
+        local_dir (Path): The root directory of the dataset.
+
+    Returns:
+        dict: A dictionary mapping episode index to episode metadata.
+    """
     episodes = load_jsonlines(local_dir / EPISODES_PATH)
     return {item["episode_index"]: item for item in sorted(episodes, key=lambda x: x["episode_index"])}
 
 
 def write_episode_stats(episode_index: int, episode_stats: dict, local_dir: Path):
+    """Write statistics for a single episode to the episode stats file.
+
+    Args:
+        episode_index (int): The index of the episode.
+        episode_stats (dict): The statistics for the episode.
+        local_dir (Path): The root directory of the dataset.
+    """
     # We wrap episode_stats in a dictionary since `episode_stats["episode_index"]`
     # is a dictionary of stats and not an integer.
     episode_stats = {"episode_index": episode_index, "stats": serialize_dict(episode_stats)}
@@ -230,6 +402,14 @@ def write_episode_stats(episode_index: int, episode_stats: dict, local_dir: Path
 
 
 def load_episodes_stats(local_dir: Path) -> dict:
+    """Load per-episode statistics from the episode stats file.
+
+    Args:
+        local_dir (Path): The root directory of the dataset.
+
+    Returns:
+        dict: A dictionary mapping episode index to its statistics dictionary.
+    """
     episodes_stats = load_jsonlines(local_dir / EPISODES_STATS_PATH)
     return {
         item["episode_index"]: cast_stats_to_numpy(item["stats"])
@@ -240,12 +420,35 @@ def load_episodes_stats(local_dir: Path) -> dict:
 def backward_compatible_episodes_stats(
     stats: dict[str, dict[str, np.ndarray]], episodes: list[int]
 ) -> dict[str, dict[str, np.ndarray]]:
+    """Create a per-episode stats dictionary from a global stats dictionary.
+
+    This is used for backward compatibility with older datasets that only had global stats.
+
+    Args:
+        stats (dict): The global dataset statistics.
+        episodes (list[int]): A list of episode indices.
+
+    Returns:
+        dict: A dictionary mapping each episode index to the global stats.
+    """
     return dict.fromkeys(episodes, stats)
 
 
 def load_image_as_numpy(
     fpath: str | Path, dtype: np.dtype = np.float32, channel_first: bool = True
 ) -> np.ndarray:
+    """Load an image from a file into a numpy array.
+
+    Args:
+        fpath (str | Path): Path to the image file.
+        dtype (np.dtype): The desired data type of the output array. If floating,
+            pixels are scaled to [0, 1].
+        channel_first (bool): If True, converts the image to (C, H, W) format.
+            Otherwise, it remains in (H, W, C) format.
+
+    Returns:
+        np.ndarray: The image as a numpy array.
+    """
     img = PILImage.open(fpath).convert("RGB")
     img_array = np.array(img, dtype=dtype)
     if channel_first:  # (H, W, C) -> (C, H, W)
@@ -256,10 +459,19 @@ def load_image_as_numpy(
 
 
 def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
-    """Get a transform function that convert items from Hugging Face dataset (pyarrow)
-    to torch tensors. Importantly, images are converted from PIL, which corresponds to
-    a channel last representation (h w c) of uint8 type, to a torch image representation
-    with channel first (c h w) of float32 type in range [0,1].
+    """Convert a batch from a Hugging Face dataset to torch tensors.
+
+    This transform function converts items from Hugging Face dataset format (pyarrow)
+    to torch tensors. Importantly, images are converted from PIL objects (H, W, C, uint8)
+    to a torch image representation (C, H, W, float32) in the range [0, 1]. Other
+    types are converted to torch.tensor.
+
+    Args:
+        items_dict (dict): A dictionary representing a batch of data from a
+            Hugging Face dataset.
+
+    Returns:
+        dict: The batch with items converted to torch tensors.
     """
     for key in items_dict:
         first_item = items_dict[key][0]
@@ -274,6 +486,14 @@ def hf_transform_to_torch(items_dict: dict[torch.Tensor | None]):
 
 
 def is_valid_version(version: str) -> bool:
+    """Check if a string is a valid PEP 440 version.
+
+    Args:
+        version (str): The version string to check.
+
+    Returns:
+        bool: True if the version string is valid, False otherwise.
+    """
     try:
         packaging.version.parse(version)
         return True
@@ -287,6 +507,18 @@ def check_version_compatibility(
     current_version: str | packaging.version.Version,
     enforce_breaking_major: bool = True,
 ) -> None:
+    """Check for version compatibility between a dataset and the current codebase.
+
+    Args:
+        repo_id (str): The repository ID for logging purposes.
+        version_to_check (str | packaging.version.Version): The version of the dataset.
+        current_version (str | packaging.version.Version): The current version of the codebase.
+        enforce_breaking_major (bool): If True, raise an error on major version mismatch.
+
+    Raises:
+        BackwardCompatibilityError: If the dataset version is from a newer, incompatible
+            major version of the codebase.
+    """
     v_check = (
         packaging.version.parse(version_to_check)
         if not isinstance(version_to_check, packaging.version.Version)
@@ -304,7 +536,14 @@ def check_version_compatibility(
 
 
 def get_repo_versions(repo_id: str) -> list[packaging.version.Version]:
-    """Returns available valid versions (branches and tags) on given repo."""
+    """Return available valid versions (branches and tags) on a given Hub repo.
+
+    Args:
+        repo_id (str): The repository ID on the Hugging Face Hub.
+
+    Returns:
+        list[packaging.version.Version]: A list of valid versions found.
+    """
     api = HfApi()
     repo_refs = api.list_repo_refs(repo_id, repo_type="dataset")
     repo_refs = [b.name for b in repo_refs.branches + repo_refs.tags]
@@ -317,9 +556,22 @@ def get_repo_versions(repo_id: str) -> list[packaging.version.Version]:
 
 
 def get_safe_version(repo_id: str, version: str | packaging.version.Version) -> str:
-    """
-    Returns the version if available on repo or the latest compatible one.
-    Otherwise, will throw a `CompatibilityError`.
+    """Return the specified version if available on repo, or the latest compatible one.
+
+    If the exact version is not found, it looks for the latest version with the
+    same major version number that is less than or equal to the target minor version.
+
+    Args:
+        repo_id (str): The repository ID on the Hugging Face Hub.
+        version (str | packaging.version.Version): The target version.
+
+    Returns:
+        str: The safe version string (e.g., "v1.2.3") to use as a revision.
+
+    Raises:
+        RevisionNotFoundError: If the repo has no version tags.
+        BackwardCompatibilityError: If only older major versions are available.
+        ForwardCompatibilityError: If only newer major versions are available.
     """
     target_version = (
         packaging.version.parse(version) if not isinstance(version, packaging.version.Version) else version
@@ -361,6 +613,17 @@ def get_safe_version(repo_id: str, version: str | packaging.version.Version) ->
 
 
 def get_hf_features_from_features(features: dict) -> datasets.Features:
+    """Convert a LeRobot features dictionary to a `datasets.Features` object.
+
+    Args:
+        features (dict): A LeRobot-style feature dictionary.
+
+    Returns:
+        datasets.Features: The corresponding Hugging Face `datasets.Features` object.
+
+    Raises:
+        ValueError: If a feature has an unsupported shape.
+    """
     hf_features = {}
     for key, ft in features.items():
         if ft["dtype"] == "video":
@@ -388,6 +651,14 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
 
 
 def _validate_feature_names(features: dict[str, dict]) -> None:
+    """Validate that feature names do not contain invalid characters.
+
+    Args:
+        features (dict): The LeRobot features dictionary.
+
+    Raises:
+        ValueError: If any feature name contains '/'.
+    """
     invalid_features = {name: ft for name, ft in features.items() if "/" in name}
     if invalid_features:
         raise ValueError(f"Feature names should not contain '/'. Found '/' in '{invalid_features}'.")
@@ -396,6 +667,22 @@ def _validate_feature_names(features: dict[str, dict]) -> None:
 def hw_to_dataset_features(
     hw_features: dict[str, type | tuple], prefix: str, use_video: bool = True
 ) -> dict[str, dict]:
+    """Convert hardware-specific features to a LeRobot dataset feature dictionary.
+
+    This function takes a dictionary describing hardware outputs (like joint states
+    or camera image shapes) and formats it into the standard LeRobot feature
+    specification.
+
+    Args:
+        hw_features (dict): Dictionary mapping feature names to their type (float for
+            joints) or shape (tuple for images).
+        prefix (str): The prefix to add to the feature keys (e.g., "observation"
+            or "action").
+        use_video (bool): If True, image features are marked as "video", otherwise "image".
+
+    Returns:
+        dict: A LeRobot features dictionary.
+    """
     features = {}
     joint_fts = {key: ftype for key, ftype in hw_features.items() if ftype is float}
     cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
@@ -428,6 +715,20 @@ def hw_to_dataset_features(
 def build_dataset_frame(
     ds_features: dict[str, dict], values: dict[str, Any], prefix: str
 ) -> dict[str, np.ndarray]:
+    """Construct a single data frame from raw values based on dataset features.
+
+    A "frame" is a dictionary containing all the data for a single timestep,
+    formatted as numpy arrays according to the feature specification.
+
+    Args:
+        ds_features (dict): The LeRobot dataset features dictionary.
+        values (dict): A dictionary of raw values from the hardware/environment.
+        prefix (str): The prefix to filter features by (e.g., "observation"
+            or "action").
+
+    Returns:
+        dict: A dictionary representing a single frame of data.
+    """
     frame = {}
     for key, ft in ds_features.items():
         if key in DEFAULT_FEATURES or not key.startswith(prefix):
@@ -440,17 +741,22 @@ def build_dataset_frame(
     return frame
 
 
-def get_features_from_robot(robot: Robot, use_videos: bool = True) -> dict:
-    camera_ft = {}
-    if robot.cameras:
-        camera_ft = {
-            key: {"dtype": "video" if use_videos else "image", **ft}
-            for key, ft in robot.camera_features.items()
-        }
-    return {**robot.motor_features, **camera_ft, **DEFAULT_FEATURES}
-
-
 def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFeature]:
+    """Convert dataset features to policy features.
+
+    This function transforms the dataset's feature specification into a format
+    that a policy can use, classifying features by type (e.g., visual, state,
+    action) and ensuring correct shapes (e.g., channel-first for images).
+
+    Args:
+        features (dict): The LeRobot dataset features dictionary.
+
+    Returns:
+        dict: A dictionary mapping feature keys to `PolicyFeature` objects.
+
+    Raises:
+        ValueError: If an image feature does not have a 3D shape.
+    """
     # TODO(aliberts): Implement "type" in dataset features and simplify this
     policy_features = {}
     for key, ft in features.items():
@@ -481,6 +787,58 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
     return policy_features
 
 
+def combine_feature_dicts(*dicts: dict) -> dict:
+    """Merge LeRobot grouped feature dicts.
+
+    - For 1D numeric specs (dtype not image/video/string) with "names": we merge the names and recompute the shape.
+    - For others (e.g. `observation.images.*`), the last one wins (if they are identical).
+
+    Args:
+        *dicts: A variable number of LeRobot feature dictionaries to merge.
+
+    Returns:
+        dict: A single merged feature dictionary.
+
+    Raises:
+        ValueError: If there's a dtype mismatch for a feature being merged.
+    """
+    out: dict = {}
+    for d in dicts:
+        for key, value in d.items():
+            if not isinstance(value, dict):
+                out[key] = value
+                continue
+
+            dtype = value.get("dtype")
+            shape = value.get("shape")
+            is_vector = (
+                dtype not in ("image", "video", "string")
+                and isinstance(shape, tuple)
+                and len(shape) == 1
+                and "names" in value
+            )
+
+            if is_vector:
+                # Initialize or retrieve the accumulating dict for this feature key
+                target = out.setdefault(key, {"dtype": dtype, "names": [], "shape": (0,)})
+                # Ensure consistent data types across merged entries
+                if "dtype" in target and dtype != target["dtype"]:
+                    raise ValueError(f"dtype mismatch for '{key}': {target['dtype']} vs {dtype}")
+
+                # Merge feature names: append only new ones to preserve order without duplicates
+                seen = set(target["names"])
+                for n in value["names"]:
+                    if n not in seen:
+                        target["names"].append(n)
+                        seen.add(n)
+                # Recompute the shape to reflect the updated number of features
+                target["shape"] = (len(target["names"]),)
+            else:
+                # For images/videos and non-1D entries: override with the latest definition
+                out[key] = value
+    return out
+
+
 def create_empty_dataset_info(
     codebase_version: str,
     fps: int,
@@ -488,6 +846,18 @@ def create_empty_dataset_info(
     use_videos: bool,
     robot_type: str | None = None,
 ) -> dict:
+    """Create a template dictionary for a new dataset's `info.json`.
+
+    Args:
+        codebase_version (str): The version of the LeRobot codebase.
+        fps (int): The frames per second of the data.
+        features (dict): The LeRobot features dictionary for the dataset.
+        use_videos (bool): Whether the dataset will store videos.
+        robot_type (str | None): The type of robot used, if any.
+
+    Returns:
+        dict: A dictionary with the initial dataset metadata.
+    """
     return {
         "codebase_version": codebase_version,
         "robot_type": robot_type,
@@ -508,6 +878,18 @@ def create_empty_dataset_info(
 def get_episode_data_index(
     episode_dicts: dict[dict], episodes: list[int] | None = None
 ) -> dict[str, torch.Tensor]:
+    """Calculate the start and end indices for each episode in a flattened dataset.
+
+    Args:
+        episode_dicts (dict): A dictionary mapping episode index to episode metadata,
+            which must contain a "length" key.
+        episodes (list[int] | None): An optional list of episode indices to consider.
+            If None, all episodes are used.
+
+    Returns:
+        dict: A dictionary with "from" and "to" keys, containing torch tensors
+            with the start and end indices for each episode.
+    """
     episode_lengths = {ep_idx: ep_dict["length"] for ep_idx, ep_dict in episode_dicts.items()}
     if episodes is not None:
         episode_lengths = {ep_idx: episode_lengths[ep_idx] for ep_idx in episodes}
@@ -527,16 +909,19 @@ def check_timestamps_sync(
     tolerance_s: float,
     raise_value_error: bool = True,
 ) -> bool:
-    """
-    This check is to make sure that each timestamp is separated from the next by (1/fps) +/- tolerance
-    to account for possible numerical error.
+    """Check if timestamps are separated by (1/fps) +/- tolerance.
+
+    This check ensures that consecutive timestamps within an episode are spaced
+    correctly, accounting for possible numerical errors. It ignores the boundaries
+    between episodes.
 
     Args:
         timestamps (np.ndarray): Array of timestamps in seconds.
         episode_indices (np.ndarray): Array indicating the episode index for each timestamp.
-        episode_data_index (dict[str, np.ndarray]): A dictionary that includes 'to',
+        episode_data_index (dict): A dictionary that includes 'to',
             which identifies indices for the end of each episode.
-        fps (int): Frames per second. Used to check the expected difference between consecutive timestamps.
+        fps (int): Frames per second. Used to check the expected difference between
+            consecutive timestamps.
         tolerance_s (float): Allowed deviation from the expected (1/fps) difference.
         raise_value_error (bool): Whether to raise a ValueError if the check fails.
 
@@ -544,7 +929,8 @@ def check_timestamps_sync(
         bool: True if all checked timestamp differences lie within tolerance, False otherwise.
 
     Raises:
-        ValueError: If the check fails and `raise_value_error` is True.
+        ValueError: If `timestamps` and `episode_indices` shapes do not match, or if
+            the check fails and `raise_value_error` is True.
     """
     if timestamps.shape != episode_indices.shape:
         raise ValueError(
@@ -595,9 +981,23 @@ def check_timestamps_sync(
 def check_delta_timestamps(
     delta_timestamps: dict[str, list[float]], fps: int, tolerance_s: float, raise_value_error: bool = True
 ) -> bool:
-    """This will check if all the values in delta_timestamps are multiples of 1/fps +/- tolerance.
-    This is to ensure that these delta_timestamps added to any timestamp from a dataset will themselves be
-    actual timestamps from the dataset.
+    """Check if delta timestamps are multiples of 1/fps +/- tolerance.
+
+    This ensures that adding these delta timestamps to any existing timestamp in
+    the dataset will result in a value that aligns with the dataset's frame rate.
+
+    Args:
+        delta_timestamps (dict): A dictionary where values are lists of time
+            deltas in seconds.
+        fps (int): The frames per second of the dataset.
+        tolerance_s (float): The allowed tolerance in seconds.
+        raise_value_error (bool): If True, raises an error on failure.
+
+    Returns:
+        bool: True if all deltas are valid, False otherwise.
+
+    Raises:
+        ValueError: If any delta is outside the tolerance and `raise_value_error` is True.
     """
     outside_tolerance = {}
     for key, delta_ts in delta_timestamps.items():
@@ -623,6 +1023,15 @@ def check_delta_timestamps(
 
 
 def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dict[str, list[int]]:
+    """Convert delta timestamps in seconds to delta indices in frames.
+
+    Args:
+        delta_timestamps (dict): A dictionary of time deltas in seconds.
+        fps (int): The frames per second of the dataset.
+
+    Returns:
+        dict: A dictionary of frame delta indices.
+    """
     delta_indices = {}
     for key, delta_ts in delta_timestamps.items():
         delta_indices[key] = [round(d * fps) for d in delta_ts]
@@ -631,9 +1040,17 @@ def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dic
 
 
 def cycle(iterable):
-    """The equivalent of itertools.cycle, but safe for Pytorch dataloaders.
+    """Create a dataloader-safe cyclical iterator.
 
-    See https://github.com/pytorch/pytorch/issues/23900 for information on why itertools.cycle is not safe.
+    This is an equivalent of `itertools.cycle` but is safe for use with
+    PyTorch DataLoaders with multiple workers.
+    See https://github.com/pytorch/pytorch/issues/23900 for details.
+
+    Args:
+        iterable: The iterable to cycle over.
+
+    Yields:
+        Items from the iterable, restarting from the beginning when exhausted.
     """
     iterator = iter(iterable)
     while True:
@@ -644,8 +1061,14 @@ def cycle(iterable):
 
 
 def create_branch(repo_id, *, branch: str, repo_type: str | None = None) -> None:
-    """Create a branch on a existing Hugging Face repo. Delete the branch if it already
-    exists before creating it.
+    """Create a branch on an existing Hugging Face repo.
+
+    Deletes the branch if it already exists before creating it.
+
+    Args:
+        repo_id (str): The ID of the repository.
+        branch (str): The name of the branch to create.
+        repo_type (str | None): The type of the repository (e.g., "dataset").
     """
     api = HfApi()
 
@@ -663,9 +1086,20 @@ def create_lerobot_dataset_card(
     dataset_info: dict | None = None,
     **kwargs,
 ) -> DatasetCard:
-    """
-    Keyword arguments will be used to replace values in src/lerobot/datasets/card_template.md.
-    Note: If specified, license must be one of https://huggingface.co/docs/hub/repositories-licenses.
+    """Create a `DatasetCard` for a LeRobot dataset.
+
+    Keyword arguments are used to replace values in the card template.
+    Note: If specified, `license` must be a valid license identifier from
+    https://huggingface.co/docs/hub/repositories-licenses.
+
+    Args:
+        tags (list | None): A list of tags to add to the dataset card.
+        dataset_info (dict | None): The dataset's info dictionary, which will
+            be displayed on the card.
+        **kwargs: Additional keyword arguments to populate the card template.
+
+    Returns:
+        DatasetCard: The generated dataset card object.
     """
     card_tags = ["LeRobot"]
 
@@ -697,19 +1131,16 @@ def create_lerobot_dataset_card(
 
 
 class IterableNamespace(SimpleNamespace):
-    """
-    A namespace object that supports both dictionary-like iteration and dot notation access.
-    Automatically converts nested dictionaries into IterableNamespaces.
+    """A namespace object that supports both dictionary-like iteration and dot notation.
 
-    This class extends SimpleNamespace to provide:
-    - Dictionary-style iteration over keys
-    - Access to items via both dot notation (obj.key) and brackets (obj["key"])
-    - Dictionary-like methods: items(), keys(), values()
-    - Recursive conversion of nested dictionaries
+    This class extends `SimpleNamespace` to provide dictionary-style iteration,
+    access to items via brackets (`obj["key"]`), and dictionary-like methods
+    (`items()`, `keys()`, `values()`). Nested dictionaries are recursively
+    converted to `IterableNamespace` objects.
 
     Args:
-        dictionary: Optional dictionary to initialize the namespace
-        **kwargs: Additional keyword arguments passed to SimpleNamespace
+        dictionary (dict, optional): A dictionary to initialize the namespace with.
+        **kwargs: Additional keyword arguments to initialize the namespace.
 
     Examples:
         >>> data = {"name": "Alice", "details": {"age": 25}}
@@ -723,10 +1154,16 @@ class IterableNamespace(SimpleNamespace):
         >>> for key, value in ns.items():
         ...     print(f"{key}: {value}")
         name: Alice
-        details: IterableNamespace(age=25)
+        details: <__main__.IterableNamespace object at ...>
     """
 
     def __init__(self, dictionary: dict[str, Any] = None, **kwargs):
+        """Initialize the IterableNamespace.
+
+        Args:
+            dictionary (dict, optional): Dictionary to populate the namespace.
+            **kwargs: Keyword arguments to populate the namespace.
+        """
         super().__init__(**kwargs)
         if dictionary is not None:
             for key, value in dictionary.items():
@@ -736,22 +1173,46 @@ class IterableNamespace(SimpleNamespace):
                     setattr(self, key, value)
 
     def __iter__(self) -> Iterator[str]:
+        """Return an iterator over the keys of the namespace."""
         return iter(vars(self))
 
     def __getitem__(self, key: str) -> Any:
+        """Allow bracket-style access to attributes.
+
+        Args:
+            key (str): The name of the attribute.
+
+        Returns:
+            Any: The value of the attribute.
+        """
         return vars(self)[key]
 
     def items(self):
+        """Return a view of the namespace's (key, value) pairs."""
         return vars(self).items()
 
     def values(self):
+        """Return a view of the namespace's values."""
         return vars(self).values()
 
     def keys(self):
+        """Return a view of the namespace's keys."""
         return vars(self).keys()
 
 
 def validate_frame(frame: dict, features: dict):
+    """Validate a single data frame against the dataset's feature specification.
+
+    Checks for missing/extra features, and validates the dtype and shape of each
+    provided feature.
+
+    Args:
+        frame (dict): The data frame to validate.
+        features (dict): The LeRobot features dictionary for the dataset.
+
+    Raises:
+        ValueError: If the frame does not match the feature specification.
+    """
     expected_features = set(features) - set(DEFAULT_FEATURES)
     actual_features = set(frame)
 
@@ -766,6 +1227,15 @@ def validate_frame(frame: dict, features: dict):
 
 
 def validate_features_presence(actual_features: set[str], expected_features: set[str]):
+    """Check for missing or extra features in a frame.
+
+    Args:
+        actual_features (set[str]): The set of feature names present in the frame.
+        expected_features (set[str]): The set of feature names expected in the frame.
+
+    Returns:
+        str: An error message string if there's a mismatch, otherwise an empty string.
+    """
     error_message = ""
     missing_features = expected_features - actual_features
     extra_features = actual_features - expected_features
@@ -781,6 +1251,19 @@ def validate_features_presence(actual_features: set[str], expected_features: set
 
 
 def validate_feature_dtype_and_shape(name: str, feature: dict, value: np.ndarray | PILImage.Image | str):
+    """Validate the dtype and shape of a single feature's value.
+
+    Args:
+        name (str): The name of the feature.
+        feature (dict): The feature specification from the LeRobot features dictionary.
+        value: The value of the feature to validate.
+
+    Returns:
+        str: An error message if validation fails, otherwise an empty string.
+
+    Raises:
+        NotImplementedError: If the feature dtype is not supported for validation.
+    """
     expected_dtype = feature["dtype"]
     expected_shape = feature["shape"]
     if is_valid_numpy_dtype_string(expected_dtype):
@@ -796,6 +1279,17 @@ def validate_feature_dtype_and_shape(name: str, feature: dict, value: np.ndarray
 def validate_feature_numpy_array(
     name: str, expected_dtype: str, expected_shape: list[int], value: np.ndarray
 ):
+    """Validate a feature that is expected to be a numpy array.
+
+    Args:
+        name (str): The name of the feature.
+        expected_dtype (str): The expected numpy dtype as a string.
+        expected_shape (list[int]): The expected shape.
+        value (np.ndarray): The numpy array to validate.
+
+    Returns:
+        str: An error message if validation fails, otherwise an empty string.
+    """
     error_message = ""
     if isinstance(value, np.ndarray):
         actual_dtype = value.dtype
@@ -813,6 +1307,18 @@ def validate_feature_numpy_array(
 
 
 def validate_feature_image_or_video(name: str, expected_shape: list[str], value: np.ndarray | PILImage.Image):
+    """Validate a feature that is expected to be an image or video frame.
+
+    Accepts `np.ndarray` (channel-first or channel-last) or `PIL.Image.Image`.
+
+    Args:
+        name (str): The name of the feature.
+        expected_shape (list[str]): The expected shape (C, H, W).
+        value: The image data to validate.
+
+    Returns:
+        str: An error message if validation fails, otherwise an empty string.
+    """
     # Note: The check of pixels range ([0,1] for float and [0,255] for uint8) is done by the image writer threads.
     error_message = ""
     if isinstance(value, np.ndarray):
@@ -829,12 +1335,35 @@ def validate_feature_image_or_video(name: str, expected_shape: list[str], value:
 
 
 def validate_feature_string(name: str, value: str):
+    """Validate a feature that is expected to be a string.
+
+    Args:
+        name (str): The name of the feature.
+        value (str): The value to validate.
+
+    Returns:
+        str: An error message if validation fails, otherwise an empty string.
+    """
     if not isinstance(value, str):
         return f"The feature '{name}' is expected to be of type 'str', but type '{type(value)}' provided instead.\n"
     return ""
 
 
 def validate_episode_buffer(episode_buffer: dict, total_episodes: int, features: dict):
+    """Validate the episode buffer before it's written to disk.
+
+    Ensures the buffer has the required keys, contains at least one frame, and
+    has features consistent with the dataset's specification.
+
+    Args:
+        episode_buffer (dict): The buffer containing data for a single episode.
+        total_episodes (int): The current total number of episodes in the dataset.
+        features (dict): The LeRobot features dictionary for the dataset.
+
+    Raises:
+        ValueError: If the buffer is invalid.
+        NotImplementedError: If the episode index is manually set and doesn't match.
+    """
     if "size" not in episode_buffer:
         raise ValueError("size key not found in episode_buffer")
 

src/lerobot/datasets/video_utils.py

@@ -16,6 +16,7 @@
 import glob
 import importlib
 import logging
+import shutil
 import warnings
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -451,3 +452,66 @@ def get_image_pixel_channels(image: Image):
         return 4  # RGBA
     else:
         raise ValueError("Unknown format")
+
+
+class VideoEncodingManager:
+    """
+    Context manager that ensures proper video encoding and data cleanup even if exceptions occur.
+
+    This manager handles:
+    - Batch encoding for any remaining episodes when recording interrupted
+    - Cleaning up temporary image files from interrupted episodes
+    - Removing empty image directories
+
+    Args:
+        dataset: The LeRobotDataset instance
+    """
+
+    def __init__(self, dataset):
+        self.dataset = dataset
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        # Handle any remaining episodes that haven't been batch encoded
+        if self.dataset.episodes_since_last_encoding > 0:
+            if exc_type is not None:
+                logging.info("Exception occurred. Encoding remaining episodes before exit...")
+            else:
+                logging.info("Recording stopped. Encoding remaining episodes...")
+
+            start_ep = self.dataset.num_episodes - self.dataset.episodes_since_last_encoding
+            end_ep = self.dataset.num_episodes
+            logging.info(
+                f"Encoding remaining {self.dataset.episodes_since_last_encoding} episodes, "
+                f"from episode {start_ep} to {end_ep - 1}"
+            )
+            self.dataset.batch_encode_videos(start_ep, end_ep)
+
+        # Clean up episode images if recording was interrupted
+        if exc_type is not None:
+            interrupted_episode_index = self.dataset.num_episodes
+            for key in self.dataset.meta.video_keys:
+                img_dir = self.dataset._get_image_file_path(
+                    episode_index=interrupted_episode_index, image_key=key, frame_index=0
+                ).parent
+                if img_dir.exists():
+                    logging.debug(
+                        f"Cleaning up interrupted episode images for episode {interrupted_episode_index}, camera {key}"
+                    )
+                    shutil.rmtree(img_dir)
+
+        # Clean up any remaining images directory if it's empty
+        img_dir = self.dataset.root / "images"
+        # Check for any remaining PNG files
+        png_files = list(img_dir.rglob("*.png"))
+        if len(png_files) == 0:
+            # Only remove the images directory if no PNG files remain
+            if img_dir.exists():
+                shutil.rmtree(img_dir)
+                logging.debug("Cleaned up empty images directory")
+        else:
+            logging.debug(f"Images directory is not empty, containing {len(png_files)} PNG files")
+
+        return False  # Don't suppress the original exception

src/lerobot/envs/configs.py

@@ -14,7 +14,7 @@
 
 import abc
 from dataclasses import dataclass, field
-from typing import Any, Optional
+from typing import Any
 
 import draccus
 
@@ -44,7 +44,7 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
 @EnvConfig.register_subclass("aloha")
 @dataclass
 class AlohaEnv(EnvConfig):
-    task: str = "AlohaInsertion-v0"
+    task: str | None = "AlohaInsertion-v0"
     fps: int = 50
     episode_length: int = 400
     obs_type: str = "pixels_agent_pos"
@@ -82,7 +82,7 @@ class AlohaEnv(EnvConfig):
 @EnvConfig.register_subclass("pusht")
 @dataclass
 class PushtEnv(EnvConfig):
-    task: str = "PushT-v0"
+    task: str | None = "PushT-v0"
     fps: int = 10
     episode_length: int = 300
     obs_type: str = "pixels_agent_pos"
@@ -124,7 +124,7 @@ class PushtEnv(EnvConfig):
 @EnvConfig.register_subclass("xarm")
 @dataclass
 class XarmEnv(EnvConfig):
-    task: str = "XarmLift-v0"
+    task: str | None = "XarmLift-v0"
     fps: int = 15
     episode_length: int = 200
     obs_type: str = "pixels_agent_pos"
@@ -161,113 +161,84 @@ class XarmEnv(EnvConfig):
 
 
 @dataclass
-class VideoRecordConfig:
-    """Configuration for video recording in ManiSkill environments."""
-
-    enabled: bool = False
-    record_dir: str = "videos"
-    trajectory_name: str = "trajectory"
+class ImagePreprocessingConfig:
+    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
+    resize_size: tuple[int, int] | None = None
 
 
 @dataclass
-class EnvTransformConfig:
-    """Configuration for environment wrappers."""
+class RewardClassifierConfig:
+    """Configuration for reward classification."""
+
+    pretrained_path: str | None = None
+    success_threshold: float = 0.5
+    success_reward: float = 1.0
+
+
+@dataclass
+class InverseKinematicsConfig:
+    """Configuration for inverse kinematics processing."""
+
+    urdf_path: str | None = None
+    target_frame_name: str | None = None
+    end_effector_bounds: dict[str, list[float]] | None = None
+    end_effector_step_sizes: dict[str, float] | None = None
+
+
+@dataclass
+class ObservationConfig:
+    """Configuration for observation processing."""
 
-    # ee_action_space_params: EEActionSpaceConfig = field(default_factory=EEActionSpaceConfig)
-    control_mode: str = "gamepad"
-    display_cameras: bool = False
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
     add_ee_pose_to_observation: bool = False
-    crop_params_dict: Optional[dict[str, tuple[int, int, int, int]]] = None
-    resize_size: Optional[tuple[int, int]] = None
-    control_time_s: float = 20.0
-    fixed_reset_joint_positions: Optional[Any] = None
-    reset_time_s: float = 5.0
+    display_cameras: bool = False
+
+
+@dataclass
+class GripperConfig:
+    """Configuration for gripper control and penalties."""
+
     use_gripper: bool = True
-    gripper_quantization_threshold: float | None = 0.8
     gripper_penalty: float = 0.0
     gripper_penalty_in_reward: bool = False
 
 
+@dataclass
+class ResetConfig:
+    """Configuration for environment reset behavior."""
+
+    fixed_reset_joint_positions: Any | None = None
+    reset_time_s: float = 5.0
+    control_time_s: float = 20.0
+    terminate_on_success: bool = True
+
+
+@dataclass
+class HILSerlProcessorConfig:
+    """Configuration for environment processing pipeline."""
+
+    control_mode: str = "gamepad"
+    observation: ObservationConfig | None = None
+    image_preprocessing: ImagePreprocessingConfig | None = None
+    gripper: GripperConfig | None = None
+    reset: ResetConfig | None = None
+    inverse_kinematics: InverseKinematicsConfig | None = None
+    reward_classifier: RewardClassifierConfig | None = None
+    max_gripper_pos: float | None = 100.0
+
+
 @EnvConfig.register_subclass(name="gym_manipulator")
 @dataclass
 class HILSerlRobotEnvConfig(EnvConfig):
     """Configuration for the HILSerlRobotEnv environment."""
 
-    robot: Optional[RobotConfig] = None
-    teleop: Optional[TeleoperatorConfig] = None
-    wrapper: Optional[EnvTransformConfig] = None
-    fps: int = 10
+    robot: RobotConfig | None = None
+    teleop: TeleoperatorConfig | None = None
+    processor: HILSerlProcessorConfig = field(default_factory=HILSerlProcessorConfig)
+
     name: str = "real_robot"
-    mode: str = None  # Either "record", "replay", None
-    repo_id: Optional[str] = None
-    dataset_root: Optional[str] = None
-    task: str = ""
-    num_episodes: int = 10  # only for record mode
-    episode: int = 0
-    device: str = "cuda"
-    push_to_hub: bool = True
-    pretrained_policy_name_or_path: Optional[str] = None
-    reward_classifier_pretrained_path: Optional[str] = None
-    # For the reward classifier, to record more positive examples after a success
-    number_of_steps_after_success: int = 0
-
-    def gym_kwargs(self) -> dict:
-        return {}
-
-
-@EnvConfig.register_subclass("hil")
-@dataclass
-class HILEnvConfig(EnvConfig):
-    """Configuration for the HIL environment."""
-
-    type: str = "hil"
-    name: str = "PandaPickCube"
-    task: str = "PandaPickCubeKeyboard-v0"
-    use_viewer: bool = True
-    gripper_penalty: float = 0.0
-    use_gamepad: bool = True
-    state_dim: int = 18
-    action_dim: int = 4
-    fps: int = 100
-    episode_length: int = 100
-    video_record: VideoRecordConfig = field(default_factory=VideoRecordConfig)
-    features: dict[str, PolicyFeature] = field(
-        default_factory=lambda: {
-            "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)),
-            "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
-            "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(18,)),
-        }
-    )
-    features_map: dict[str, str] = field(
-        default_factory=lambda: {
-            "action": ACTION,
-            "observation.image": OBS_IMAGE,
-            "observation.state": OBS_STATE,
-        }
-    )
-    ################# args from hilserlrobotenv
-    reward_classifier_pretrained_path: Optional[str] = None
-    robot_config: Optional[RobotConfig] = None
-    teleop_config: Optional[TeleoperatorConfig] = None
-    wrapper: Optional[EnvTransformConfig] = None
-    mode: str = None  # Either "record", "replay", None
-    repo_id: Optional[str] = None
-    dataset_root: Optional[str] = None
-    num_episodes: int = 10  # only for record mode
-    episode: int = 0
-    device: str = "cuda"
-    push_to_hub: bool = True
-    pretrained_policy_name_or_path: Optional[str] = None
-    # For the reward classifier, to record more positive examples after a success
-    number_of_steps_after_success: int = 0
-    ############################
 
     @property
     def gym_kwargs(self) -> dict:
-        return {
-            "use_viewer": self.use_viewer,
-            "use_gamepad": self.use_gamepad,
-            "gripper_penalty": self.gripper_penalty,
-        }
+        return {}

src/lerobot/envs/factory.py

@@ -17,7 +17,7 @@ import importlib
 
 import gymnasium as gym
 
-from lerobot.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv
+from lerobot.envs.configs import AlohaEnv, EnvConfig, PushtEnv, XarmEnv
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
@@ -27,8 +27,6 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
         return PushtEnv(**kwargs)
     elif env_type == "xarm":
         return XarmEnv(**kwargs)
-    elif env_type == "hil":
-        return HILEnvConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{env_type}' is not available.")