删除 tests/artifacts/cameras/image_128x128.png

* fix bug in `_copy_data_with_feature_changes`

* Update src/lerobot/datasets/dataset_tools.py

Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co>

* add missing import

---------

Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>

.dockerignore

@@ -1,3 +1,17 @@
+# 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.
+
 # Misc
 .git
 tmp
@@ -59,13 +73,12 @@ pip-log.txt
 pip-delete-this-directory.txt
 
 # Unit test / coverage reports
-!tests/data
+!tests/artifacts
 htmlcov/
 .tox/
 .nox/
 .coverage
 .coverage.*
-.cache
 nosetests.xml
 coverage.xml
 *.cover
@@ -73,6 +86,11 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 
+# Ignore .cache except calibration
+.cache/*
+!.cache/calibration/
+!.cache/calibration/**
+
 # Translations
 *.mo
 *.pot

.gitattributes

@@ -1,6 +1,21 @@
+# 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.
 *.memmap filter=lfs diff=lfs merge=lfs -text
 *.stl filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
-*.json filter=lfs diff=lfs merge=lfs -text
+*.json !text !filter !merge !diff
+tests/artifacts/cameras/*.png filter=lfs diff=lfs merge=lfs -text
+*.bag filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,3 +1,17 @@
+# 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: "\U0001F41B Bug Report"
 description: Submit a bug report to help us improve LeRobot
 body:
@@ -11,7 +25,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: If needed, you can share your lerobot configuration with us by running `python -m lerobot.scripts.display_sys_info` and copy-pasting its outputs below
+      description: Please share your LeRobot configuration by running `lerobot-info` (if installed) or `python -m lerobot.scripts.display_sys_info` (if not installed) and pasting the output below.
       render: Shell
       placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration
     validations:

.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
-DATA_DIR=tests/data pytest -sx tests/test_stuff.py::test_something
+pytest -sx tests/test_stuff.py::test_something
 ```
+
 ```bash
-python lerobot/scripts/train.py --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

@@ -1,131 +0,0 @@
-# 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 * * *"
-
-env:
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  latest-cpu:
-    name: CPU
-    runs-on: ubuntu-latest
-    steps:
-      - name: Cleanup disk
-        run: |
-          sudo df -h
-          # sudo ls -l /usr/local/lib/
-          # sudo ls -l /usr/share/
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/share/dotnet
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo df -h
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          lfs: true
-
-      - name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push CPU
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./docker/lerobot-cpu/Dockerfile
-          push: true
-          tags: huggingface/lerobot-cpu
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
-
-
-  latest-cuda:
-    name: GPU
-    runs-on: ubuntu-latest
-    steps:
-      - name: Cleanup disk
-        run: |
-          sudo df -h
-          # sudo ls -l /usr/local/lib/
-          # sudo ls -l /usr/share/
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/share/dotnet
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo df -h
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          lfs: true
-
-      - name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push GPU
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./docker/lerobot-gpu/Dockerfile
-          push: true
-          tags: huggingface/lerobot-gpu
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
-
-
-  latest-cuda-dev:
-    name: GPU Dev
-    runs-on: ubuntu-latest
-    steps:
-      - name: Cleanup disk
-        run: |
-          sudo df -h
-          # sudo ls -l /usr/local/lib/
-          # sudo ls -l /usr/share/
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/share/dotnet
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo df -h
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Check out code
-        uses: actions/checkout@v4
-
-      - name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-
-      - name: Build and Push GPU dev
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: ./docker/lerobot-gpu-dev/Dockerfile
-          push: true
-          tags: huggingface/lerobot-gpu:dev
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}

.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 --no-extra groot # TODO(Steven): Make flash-attn optional
+
+      - 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

@@ -1,81 +0,0 @@
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/nightly.yml
-name: Nightly
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: "0 2 * * *"
-
-env:
-  DATA_DIR: tests/data
-  # SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }}
-
-jobs:
-  run_all_tests_cpu:
-    name: CPU
-    strategy:
-      fail-fast: false
-    runs-on: ubuntu-latest
-    container:
-      image: huggingface/lerobot-cpu:latest
-      options: --shm-size "16gb"
-      credentials:
-        username: ${{ secrets.DOCKERHUB_USERNAME }}
-        password: ${{ secrets.DOCKERHUB_PASSWORD }}
-    defaults:
-      run:
-        shell: bash
-        working-directory: /lerobot
-    steps:
-      - name: Tests
-        env:
-          DATA_DIR: tests/data
-        run: pytest -v --cov=./lerobot --disable-warnings tests
-
-      - name: Tests end-to-end
-        env:
-          DATA_DIR: tests/data
-        run: make test-end-to-end
-
-
-  run_all_tests_single_gpu:
-    name: GPU
-    strategy:
-      fail-fast: false
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
-    env:
-      CUDA_VISIBLE_DEVICES: "0"
-      TEST_TYPE: "single_gpu"
-    container:
-      image: huggingface/lerobot-gpu:latest
-      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=./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 +1,194 @@
+# 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]
+      options: --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 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
+
+  # This job runs multi-GPU training tests with 4 GPUs
+  nightly-multi-gpu-tests:
+    name: Nightly Multi-GPU Tests
+    needs: [build-docker-gpu-nightly]
+    runs-on:
+      group: aws-g4dn-12xlarge  # Instance with 4 GPUs
+    env:
+      HF_HOME: /home/user_lerobot/.cache/huggingface
+      HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
+      TORCH_HOME: /home/user_lerobot/.cache/torch
+      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
+      CUDA_VISIBLE_DEVICES: "0,1,2,3"
+    container:
+      image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
+      options: --gpus all --shm-size "16gb"
+      credentials:
+        username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+        password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+    defaults:
+      run:
+        shell: bash
+        working-directory: /lerobot
+    steps:
+      - name: Verify GPU availability
+        run: |
+          nvidia-smi
+          python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
+
+      - name: Run multi-GPU training tests
+      # TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
+        run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
+        timeout-minutes: 10

.github/workflows/quality.yml

@@ -1,56 +1,58 @@
+# 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 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:
-    branches:
-      - main
+
+  # Triggers the workflow on push events to main
   push:
     branches:
       - main
 
-env:
-  PYTHON_VERSION: "3.10"
+  # 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:
-  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@v3
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        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}" >> $GITHUB_ENV
-
-      - name: Install Ruff
-        run: python -m pip install "ruff==${{ env.RUFF_VERSION }}"
-
-      - name: Ruff check
-        run: ruff check
-
-      - name: Ruff format
-        run: ruff format --diff
-
-
-  poetry_check:
-    name: Poetry check
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v3
-
-      - name: Install poetry
-        run: pipx install poetry
-
-      - name: Poetry check
-        run: poetry check
+      - name: Run pre-commit hooks
+        uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]
+        with:
+          extra_args: --all-files --show-diff-on-failure --color=always

.github/workflows/release.yml

@@ -0,0 +1,179 @@
+# 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: Remove Tags with Git dependencies
+        # TODO(Steven): Temporary patch to remove libero and pi from PyPi 0.4.0 release due to its reliance on git dependencies.
+        run: |
+          echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
+          grep -E '@ git\+https|lerobot\[pi\]|lerobot\[libero\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
+          sed -E -i '/@ git\+https|lerobot\[pi\]|lerobot\[libero\]/d' pyproject.toml
+          echo "::info:: Git dependencies removed. Proceeding with build."
+
+      - name: Install build dependencies
+        run: python -m pip install build
+
+      - 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@v1.13.0 # 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@v1.13.0 # 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 # zizmor: ignore[cache-poisoning]
+          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/stale.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 closing stale issues and PRs.
+name: Stale
+on:
+  # Allows running this workflow manually from the Actions tab
+  workflow_dispatch:
+
+  # Runs at 02:00
+  schedule:
+    - cron: "0 2 * * *"
+
+env:
+  CLOSE_ISSUE_MESSAGE: >
+    This issue was closed because it has been stalled for 14 days with no activity.
+    Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
+  CLOSE_PR_MESSAGE: >
+    This PR was closed because it has been stalled for 21 days with no activity.
+    Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
+  WARN_ISSUE_MESSAGE: >
+    This issue has been automatically marked as stale because it has not had
+    recent activity (6 months). It will be closed if no further activity occurs.
+    Any change, comment or update to this issue will reset this count.
+    Thank you for your contributions.
+  WARN_PR_MESSAGE: >
+    This PR has been automatically marked as stale because it has not had
+    recent activity (1 year). It will be closed if no further activity occurs.
+    Any change, comment or update to this PR will reset this count.
+    Thank you for your contributions.
+
+jobs:
+  # This job runs the actions/stale action to close stale issues and PRs.
+  stale:
+    name: Close Stale Issues and PRs
+    runs-on: ubuntu-latest
+    permissions:
+      actions: write
+      contents: write # only for delete-branch option
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v10
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-label: stale
+          stale-pr-label: stale
+          exempt-issue-labels: never-stale
+          exempt-pr-labels: never-stale
+          days-before-issue-stale: 180
+          days-before-issue-close: 14
+          days-before-pr-stale: 365
+          days-before-pr-close: 21
+          delete-branch: true
+          close-issue-message: ${{ env.CLOSE_ISSUE_MESSAGE }}
+          close-pr-message: ${{ env.CLOSE_PR_MESSAGE }}
+          stale-issue-message: ${{ env.WARN_ISSUE_MESSAGE }}
+          stale-pr-message: ${{ env.WARN_PR_MESSAGE }}
+          operations-per-run: 500

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

@@ -1,77 +0,0 @@
-# Inspired by
-# https://github.com/huggingface/peft/blob/main/.github/workflows/test-docker-build.yml
-name: Test Dockerfiles
-
-on:
-  pull_request:
-    branches:
-      - main
-    paths:
-      # Run only when DockerFile files are modified
-      - "docker/**"
-
-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@v4
-
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@v44
-        with:
-          files: docker/**
-          json: "true"
-
-      - name: Run step if only the files listed above change
-        if: steps.changed-files.outputs.any_changed == 'true'
-        id: set-matrix
-        env:
-          ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }}
-        run: |
-          echo "matrix=${{ steps.changed-files.outputs.all_changed_files}}" >> $GITHUB_OUTPUT
-
-
-  build_modified_dockerfiles:
-    name: Build modified Docker images
-    needs: get_changed_files
-    runs-on: ubuntu-latest
-    if: ${{ needs.get_changed_files.outputs.matrix }} != ''
-    strategy:
-      fail-fast: false
-      matrix:
-        docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
-    steps:
-      - name: Cleanup disk
-        run: |
-          sudo df -h
-          # sudo ls -l /usr/local/lib/
-          # sudo ls -l /usr/share/
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /usr/share/dotnet
-          sudo du -sh /usr/local/lib/
-          sudo du -sh /usr/share/
-          sudo df -h
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Check out code
-        uses: actions/checkout@v4
-
-      - name: Build Docker image
-        uses: docker/build-push-action@v5
-        with:
-          file: ${{ matrix.docker-file }}
-          context: .
-          push: False
-          build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}

.github/workflows/test.yml

@@ -1,128 +0,0 @@
-name: Tests
-
-on:
-  pull_request:
-    branches:
-      - main
-    paths:
-      - "lerobot/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "poetry.lock"
-  push:
-    branches:
-      - main
-    paths:
-      - "lerobot/**"
-      - "tests/**"
-      - "examples/**"
-      - ".github/**"
-      - "poetry.lock"
-
-jobs:
-  pytest:
-    name: Pytest
-    runs-on: ubuntu-latest
-    env:
-      DATA_DIR: tests/data
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          lfs: true  # Ensure LFS files are pulled
-
-      - name: Install EGL
-        run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
-
-      - name: Install poetry
-        run: |
-          pipx install poetry && poetry config virtualenvs.in-project true
-          echo "${{ github.workspace }}/.venv/bin" >> $GITHUB_PATH
-
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-          cache: "poetry"
-
-      - name: Install poetry dependencies
-        run: |
-          poetry install --all-extras
-
-      - name: Test with pytest
-        run: |
-          pytest tests -v --cov=./lerobot --durations=0 \
-            -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-            -W ignore::UserWarning:torch.utils.data.dataloader:558 \
-            -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
-            && rm -rf tests/outputs outputs
-
-
-  pytest-minimal:
-    name: Pytest (minimal install)
-    runs-on: ubuntu-latest
-    env:
-      DATA_DIR: tests/data
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          lfs: true  # Ensure LFS files are pulled
-
-      - name: Install poetry
-        run: |
-          pipx install poetry && poetry config virtualenvs.in-project true
-          echo "${{ github.workspace }}/.venv/bin" >> $GITHUB_PATH
-
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-
-      - name: Install poetry dependencies
-        run: |
-          poetry install --extras "test"
-
-      - name: Test with pytest
-        run: |
-          pytest tests -v --cov=./lerobot --durations=0 \
-            -W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-            -W ignore::UserWarning:torch.utils.data.dataloader:558 \
-            -W ignore::UserWarning:gymnasium.utils.env_checker:247 \
-            && rm -rf tests/outputs outputs
-
-
-  end-to-end:
-    name: End-to-end
-    runs-on: ubuntu-latest
-    env:
-      DATA_DIR: tests/data
-      MUJOCO_GL: egl
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          lfs: true  # Ensure LFS files are pulled
-
-      - name: Install EGL
-        run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
-
-      - name: Install poetry
-        run: |
-          pipx install poetry && poetry config virtualenvs.in-project true
-          echo "${{ github.workspace }}/.venv/bin" >> $GITHUB_PATH
-
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-          cache: "poetry"
-
-      - name: Install poetry dependencies
-        run: |
-          poetry install --all-extras
-
-      - name: Test end-to-end
-        run: |
-          make test-end-to-end \
-            && rm -rf outputs

.github/workflows/unbound_deps_tests.yml

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

.gitignore

@@ -1,152 +1,179 @@
-# Logging
-logs
-tmp
-wandb
+# 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.
 
-# Data
-data
-outputs
+### Environments & Dependencies ###
+.env
+.venv
+env/
+venv/
+env.bak/
+venv.bak/
+.python-version
+__pypackages__/
+node_modules/
 
-# Apple
-.DS_Store
+# Lock files
+poetry.lock
+uv.lock
+Pipfile.lock
 
-# VS Code
-.vscode
-
-# HPC
-nautilus/*.yaml
-*.key
-
-# Slurm
-sbatch*.sh
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
+### Build & Distribution ###
 build/
-develop-eggs/
 dist/
+sdist/
+wheels/
 downloads/
 eggs/
 .eggs/
-lib/
-lib64/
 parts/
-sdist/
 var/
-wheels/
 pip-wheel-metadata/
 share/python-wheels/
+develop-eggs/
 *.egg-info/
 .installed.cfg
 *.egg
 MANIFEST
+lib/
+lib64/
 
 # 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
+### Compiled & Cached Files ###
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+*.sage.py
+.cache/
+.ruff_cache/
+.mypy_cache/
+.pyre/
+.pytype/
+cython_debug/
 
-# Unit test / coverage reports
-!tests/data
+### Testing & Coverage ###
 htmlcov/
 .tox/
 .nox/
 .coverage
 .coverage.*
-.cache
+.pytest_cache/
+.hypothesis/
 nosetests.xml
 coverage.xml
 *.cover
 *.py,cover
-.hypothesis/
-.pytest_cache/
+!tests/artifacts
 
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
+### 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
+.ropeproject
+
+# 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 stuff:
+# Flask
 instance/
 .webassets-cache
 
-# Scrapy stuff:
+# Scrapy
 .scrapy
 
-# Sphinx documentation
+# Jupyter
+.ipynb_checkpoints/
+profile_default/
+ipython_config.py
+
+# Sphinx
 docs/_build/
 
+# MkDocs
+/site
+
 # 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
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
 # 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/*
+*.stl
+*.urdf
+*.xml
+*.part

.pre-commit-config.yaml

@@ -1,11 +1,35 @@
-exclude: ^(tests/data)
+# 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.
+
 default_language_version:
     python: python3.10
+
+exclude: "tests/artifacts/.*\\.safetensors$"
+
 repos:
+  ##### Meta #####
+  - repo: meta
+    hooks:
+      - id: check-useless-excludes
+      - id: check-hooks-apply
+
+   ##### General Code Quality & Formatting #####
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v6.0.0
     hooks:
       - id: check-added-large-files
+        args: ['--maxkb=1024']
       - id: debug-statements
       - id: check-merge-conflict
       - id: check-case-conflict
@@ -13,21 +37,72 @@ repos:
       - id: check-toml
       - id: end-of-file-fixer
       - id: trailing-whitespace
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.14.1
+    hooks:
+      - id: ruff-format
+      - id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
+
+  - repo: https://github.com/adhtruong/mirrors-typos
+    rev: v1.38.1
+    hooks:
+      - id: typos
+        args: [--force-exclude]
+
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.2
+    rev: v3.21.0
     hooks:
     -   id: pyupgrade
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.3
+        args: [--py310-plus]
+
+  ##### Markdown Quality #####
+  - repo: https://github.com/rbubley/mirrors-prettier
+    rev: v3.6.2
     hooks:
-      - id: ruff
-        args: [--fix]
-      - id: ruff-format
-  - repo: https://github.com/python-poetry/poetry
-    rev: 1.8.0
+      - id: prettier
+        name: Format Markdown with Prettier
+        types_or: [markdown, mdx]
+        args: [--prose-wrap=preserve]
+
+  ##### Security #####
+  - repo: https://github.com/gitleaks/gitleaks
+    rev: v8.28.0
     hooks:
-      - id: poetry-check
-      - id: poetry-lock
-        args:
-          - "--check"
-          - "--no-update"
+      - id: gitleaks
+
+  - repo: https://github.com/woodruffw/zizmor-pre-commit
+    rev: v1.15.2
+    hooks:
+      - id: zizmor
+
+  - repo: https://github.com/PyCQA/bandit
+    rev: 1.8.6
+    hooks:
+    - 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: v1.18.2
+    hooks:
+      - id: mypy
+        args: [--config-file=pyproject.toml]
+        exclude: ^(examples|benchmarks|tests)/
+
+  ##### 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

@@ -15,12 +15,13 @@ 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.
 
-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](remi.cadene@huggingface.co).
+- 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).
 
 If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
 
@@ -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.
@@ -67,19 +70,21 @@ post it.
 
 ## Adding new policies, datasets or environments
 
-Look at our implementations for [datasets](./lerobot/common/datasets/), [policies](./lerobot/common/policies/),
+Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
 environments ([aloha](https://github.com/huggingface/gym-aloha),
-[xarm](https://github.com/huggingface/gym-xarm),
 [pusht](https://github.com/huggingface/gym-pusht))
 and follow the same api design.
 
 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
@@ -129,36 +134,78 @@ Follow these steps to start contributing:
 
    🚨 **Do not** work on the `main` branch.
 
-4. for development, we use `poetry` instead of just `pip` to easily track our dependencies.
-   If you don't have it already, follow the [instructions](https://python-poetry.org/docs/#installation) to install it.
+4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies.
+   Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already.
+
+   Set up a development environment with conda:
 
-   Set up a development environment with conda or miniconda:
    ```bash
    conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev
    ```
 
-   To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
+   If you're using `uv`, it can manage python versions so you can instead do:
+
    ```bash
-   poetry install --sync --extras "dev test"
+   uv venv --python 3.10 && source .venv/bin/activate
+   ```
+
+   To develop on 🤗 LeRobot, you will at least need to install the `dev` and `test` extras dependencies along with the core library:
+
+   using `poetry`
+
+   ```bash
+   poetry sync --extras "dev test"
+   ```
+
+   using `uv`
+
+   ```bash
+   uv sync --extra dev --extra test
    ```
 
    You can also install the project with all its dependencies (including environments):
+
+   using `poetry`
+
    ```bash
-   poetry install --sync --all-extras
+   poetry 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.
+   using `uv`
 
-   Whichever command you chose to install the project (e.g. `poetry install --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.
+   ```bash
+   uv sync --all-extras
+   ```
+
+   > **Note:** If you don't install simulation environments with `--all-extras`, the tests that require them will be skipped when running the pytest suite locally. However, they _will_ be tested in the CI. In general, we advise you to install everything and test locally before pushing.
+
+   Whichever command you chose to install the project (e.g. `poetry sync --all-extras`), you should run it again when pulling code with an updated version of `pyproject.toml` and `poetry.lock` in order to synchronize your virtual environment with the new dependencies.
 
    The equivalent of `pip install some-package`, would just be:
+
+   using `poetry`
+
    ```bash
    poetry add some-package
    ```
 
-   When making changes to the poetry sections of the `pyproject.toml`, you should run the following command to lock dependencies.
+   using `uv`
+
    ```bash
-   poetry lock --no-update
+   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.
@@ -178,11 +225,13 @@ 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
    ```
@@ -195,7 +244,8 @@ Follow these steps to start contributing:
    git commit
    ```
 
-   Note, if you already commited some changes that have a wrong formatting, you can use:
+   Note, if you already committed some changes that have a wrong formatting, you can use:
+
    ```bash
    pre-commit run --all-files
    ```
@@ -216,16 +266,15 @@ 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;
@@ -236,9 +285,6 @@ Follow these steps to start contributing:
    the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
    it from PRs ready to be merged;
 4. Make sure existing tests pass;
-<!-- 5. Add high-coverage tests. No quality testing = no merge.
-
-See an example of a good PR here: https://github.com/huggingface/lerobot/pull/ -->
 
 ### Tests
 
@@ -247,18 +293,21 @@ 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/data](tests/data)
+Pull artifacts if they're not in [tests/artifacts](tests/artifacts)
+
 ```bash
 git lfs pull
 ```
@@ -267,9 +316,8 @@ We use `pytest` in order to run the tests. From the root of the
 repository, here's how to run tests with `pytest` for the library:
 
 ```bash
-DATA_DIR="tests/data" python -m pytest -sv ./tests
+python -m pytest -sv ./tests
 ```
 
-
 You can specify a smaller set of tests in order to test only the feature
 you're working on.

MANIFEST.in

@@ -0,0 +1,2 @@
+include src/lerobot/templates/lerobot_modelcard_template.md
+include src/lerobot/datasets/card_template.md

Makefile

@@ -1,163 +1,180 @@
+# 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.
+
 .PHONY: tests
 
 PYTHON_PATH := $(shell which python)
 
-# If Poetry is installed, redefine PYTHON_PATH to use the Poetry-managed Python
-POETRY_CHECK := $(shell command -v poetry)
-ifneq ($(POETRY_CHECK),)
-    PYTHON_PATH := $(shell poetry run which python)
+# If uv is installed and a virtual environment exists, use it
+UV_CHECK := $(shell command -v uv)
+ifneq ($(UV_CHECK),)
+	PYTHON_PATH := $(shell .venv/bin/python)
 endif
 
 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
+	${MAKE} DEVICE=$(DEVICE) test-act-ete-train-resume
 	${MAKE} DEVICE=$(DEVICE) test-act-ete-eval
-	${MAKE} DEVICE=$(DEVICE) test-act-ete-train-amp
-	${MAKE} DEVICE=$(DEVICE) test-act-ete-eval-amp
 	${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-train
 	${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval
 	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train
 	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval
-	${MAKE} DEVICE=$(DEVICE) test-default-ete-eval
-	${MAKE} DEVICE=$(DEVICE) test-act-pusht-tutorial
+	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-train
+	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
 
 test-act-ete-train:
-	python lerobot/scripts/train.py \
-		policy=act \
-		policy.dim_model=64 \
-		env=aloha \
-		wandb.enable=False \
-		training.offline_steps=2 \
-		training.online_steps=0 \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		device=$(DEVICE) \
-		training.save_checkpoint=true \
-		training.save_freq=2 \
-		policy.n_action_steps=20 \
-		policy.chunk_size=20 \
-		training.batch_size=2 \
-		hydra.run.dir=tests/outputs/act/
+	lerobot-train \
+		--policy.type=act \
+		--policy.dim_model=64 \
+		--policy.n_action_steps=20 \
+		--policy.chunk_size=20 \
+		--policy.device=$(DEVICE) \
+		--policy.push_to_hub=false \
+		--env.type=aloha \
+		--env.episode_length=5 \
+		--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
+		--dataset.image_transforms.enable=true \
+		--dataset.episodes="[0]" \
+		--batch_size=2 \
+		--steps=4 \
+		--eval_freq=2 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1 \
+		--save_freq=2 \
+		--save_checkpoint=true \
+		--log_freq=1 \
+		--wandb.enable=false \
+		--output_dir=tests/outputs/act/
+
+test-act-ete-train-resume:
+	lerobot-train \
+		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
+		--resume=true
 
 test-act-ete-eval:
-	python lerobot/scripts/eval.py \
-		-p tests/outputs/act/checkpoints/000002/pretrained_model \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		env.episode_length=8 \
-		device=$(DEVICE) \
-
-test-act-ete-train-amp:
-	python lerobot/scripts/train.py \
-		policy=act \
-		policy.dim_model=64 \
-		env=aloha \
-		wandb.enable=False \
-		training.offline_steps=2 \
-		training.online_steps=0 \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		device=$(DEVICE) \
-		training.save_checkpoint=true \
-		training.save_freq=2 \
-		policy.n_action_steps=20 \
-		policy.chunk_size=20 \
-		training.batch_size=2 \
-		hydra.run.dir=tests/outputs/act_amp/ \
-		use_amp=true
-
-test-act-ete-eval-amp:
-	python lerobot/scripts/eval.py \
-		-p tests/outputs/act_amp/checkpoints/000002/pretrained_model \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		env.episode_length=8 \
-		device=$(DEVICE) \
-		use_amp=true
+	lerobot-eval \
+		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
+		--policy.device=$(DEVICE) \
+		--env.type=aloha \
+		--env.episode_length=5 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1
 
 test-diffusion-ete-train:
-	python lerobot/scripts/train.py \
-		policy=diffusion \
-		policy.down_dims=\[64,128,256\] \
-		policy.diffusion_step_embed_dim=32 \
-		policy.num_inference_steps=10 \
-		env=pusht \
-		wandb.enable=False \
-		training.offline_steps=2 \
-		training.online_steps=0 \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		device=$(DEVICE) \
-		training.save_checkpoint=true \
-		training.save_freq=2 \
-		training.batch_size=2 \
-		hydra.run.dir=tests/outputs/diffusion/
+	lerobot-train \
+		--policy.type=diffusion \
+		--policy.down_dims='[64,128,256]' \
+		--policy.diffusion_step_embed_dim=32 \
+		--policy.num_inference_steps=10 \
+		--policy.device=$(DEVICE) \
+		--policy.push_to_hub=false \
+		--env.type=pusht \
+		--env.episode_length=5 \
+		--dataset.repo_id=lerobot/pusht \
+		--dataset.image_transforms.enable=true \
+		--dataset.episodes="[0]" \
+		--batch_size=2 \
+		--steps=2 \
+		--eval_freq=2 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1 \
+		--save_checkpoint=true \
+		--save_freq=2 \
+		--log_freq=1 \
+		--wandb.enable=false \
+		--output_dir=tests/outputs/diffusion/
 
 test-diffusion-ete-eval:
-	python lerobot/scripts/eval.py \
-		-p tests/outputs/diffusion/checkpoints/000002/pretrained_model \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		env.episode_length=8 \
-		device=$(DEVICE) \
+	lerobot-eval \
+		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
+		--policy.device=$(DEVICE) \
+		--env.type=pusht \
+		--env.episode_length=5 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1
 
-# TODO(alexander-soare): Restore online_steps to 2 when it is reinstated.
 test-tdmpc-ete-train:
-	python lerobot/scripts/train.py \
-		policy=tdmpc \
-		env=xarm \
-		env.task=XarmLift-v0 \
-		dataset_repo_id=lerobot/xarm_lift_medium \
-		wandb.enable=False \
-		training.offline_steps=2 \
-		training.online_steps=0 \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		env.episode_length=2 \
-		device=$(DEVICE) \
-		training.save_checkpoint=true \
-		training.save_freq=2 \
-		training.batch_size=2 \
-		hydra.run.dir=tests/outputs/tdmpc/
+	lerobot-train \
+		--policy.type=tdmpc \
+		--policy.device=$(DEVICE) \
+		--policy.push_to_hub=false \
+		--env.type=pusht \
+		--env.episode_length=5 \
+		--dataset.repo_id=lerobot/pusht_image \
+		--dataset.image_transforms.enable=true \
+		--dataset.episodes="[0]" \
+		--batch_size=2 \
+		--steps=2 \
+		--eval_freq=2 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1 \
+		--save_checkpoint=true \
+		--save_freq=2 \
+		--log_freq=1 \
+		--wandb.enable=false \
+		--output_dir=tests/outputs/tdmpc/
 
 test-tdmpc-ete-eval:
-	python lerobot/scripts/eval.py \
-		-p tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		env.episode_length=8 \
-		device=$(DEVICE) \
+	lerobot-eval \
+		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
+		--policy.device=$(DEVICE) \
+		--env.type=pusht \
+		--env.episode_length=5 \
+		--env.observation_height=96 \
+        --env.observation_width=96 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1
 
-test-default-ete-eval:
-	python lerobot/scripts/eval.py \
-		--config lerobot/configs/default.yaml \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		env.episode_length=8 \
-		device=$(DEVICE) \
 
-test-act-pusht-tutorial:
-	cp examples/advanced/1_train_act_pusht/act_pusht.yaml lerobot/configs/policy/created_by_Makefile.yaml
-	python lerobot/scripts/train.py \
-		policy=created_by_Makefile.yaml \
-		env=pusht \
-		wandb.enable=False \
-		training.offline_steps=2 \
-		eval.n_episodes=1 \
-		eval.batch_size=1 \
-		env.episode_length=2 \
-		device=$(DEVICE) \
-		training.save_model=true \
-		training.save_freq=2 \
-		training.batch_size=2 \
-		hydra.run.dir=tests/outputs/act_pusht/
-	rm lerobot/configs/policy/created_by_Makefile.yaml
+test-smolvla-ete-train:
+	lerobot-train \
+		--policy.type=smolvla \
+		--policy.n_action_steps=20 \
+		--policy.chunk_size=20 \
+		--policy.device=$(DEVICE) \
+		--policy.push_to_hub=false \
+		--env.type=aloha \
+		--env.episode_length=5 \
+		--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
+		--dataset.image_transforms.enable=true \
+		--dataset.episodes="[0]" \
+		--batch_size=2 \
+		--steps=4 \
+		--eval_freq=2 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1 \
+		--save_freq=2 \
+		--save_checkpoint=true \
+		--log_freq=1 \
+		--wandb.enable=false \
+		--output_dir=tests/outputs/smolvla/
+
+test-smolvla-ete-eval:
+	lerobot-eval \
+		--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
+		--policy.device=$(DEVICE) \
+		--env.type=aloha \
+		--env.episode_length=5 \
+		--eval.n_episodes=1 \
+		--eval.batch_size=1

README.md

@@ -1,34 +1,80 @@
 <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">
+    <p><a href="https://huggingface.co/docs/lerobot/hope_jr">
+        Build Your Own HopeJR Robot!</a></p>
+</h2>
+
+<div align="center">
+  <img
+    src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/hope_jr/hopejr.png"
+    alt="HopeJR robot"
+    title="HopeJR robot"
+    width="60%"
+  />
+
+  <p><strong>Meet HopeJR – A humanoid robot arm and hand for dexterous manipulation!</strong></p>
+  <p>Control it with exoskeletons and gloves for precise hand movements.</p>
+  <p>Perfect for advanced manipulation tasks! 🤖</p>
+
+  <p><a href="https://huggingface.co/docs/lerobot/hope_jr">
+      See the full HopeJR tutorial here.</a></p>
+</div>
+
+<br/>
+
+<h2 align="center">
+    <p><a href="https://huggingface.co/docs/lerobot/so101">
+        Build Your Own SO-101 Robot!</a></p>
+</h2>
+
+<div align="center">
+  <table>
+    <tr>
+      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101.webp" alt="SO-101 follower arm" title="SO-101 follower arm" width="90%"/></td>
+      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101-leader.webp" alt="SO-101 leader arm" title="SO-101 leader arm" width="90%"/></td>
+    </tr>
+  </table>
+
+  <p><strong>Meet the updated SO100, the SO-101 – Just €114 per arm!</strong></p>
+  <p>Train it in minutes with a few simple moves on your laptop.</p>
+  <p>Then sit back and watch your creation act autonomously! 🤯</p>
+
+  <p><a href="https://huggingface.co/docs/lerobot/so101">
+      See the full SO-101 tutorial here.</a></p>
+
+  <p>Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!</p>
+  <p>Check out the <a href="https://huggingface.co/docs/lerobot/lekiwi">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
+
+  <img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/lekiwi/kiwi.webp" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
+</div>
+
+<br/>
+
 <h3 align="center">
-    <p>State-of-the-art Machine Learning for real-world robotics</p>
+    <p>LeRobot: State-of-the-art AI for real-world robotics</p>
 </h3>
 
 ---
 
-
 🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
 
 🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
@@ -41,9 +87,9 @@
 
 <table>
   <tr>
-    <td><img src="http://remicadene.com/assets/gif/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
-    <td><img src="http://remicadene.com/assets/gif/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
-    <td><img src="http://remicadene.com/assets/gif/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>
@@ -52,245 +98,247 @@
   </tr>
 </table>
 
-### Acknowledgment
-
-- Thanks to Tony Zaho, 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.
-
-
 ## Installation
 
-Download our source code:
+LeRobot works with Python 3.10+ and PyTorch 2.2+.
+
+### Environment Setup
+
+Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniforge`](https://conda-forge.org/download/):
+
 ```bash
-git clone https://github.com/huggingface/lerobot.git && cd lerobot
+conda create -y -n lerobot python=3.10
+conda activate lerobot
 ```
 
-Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
+When using `conda`, install `ffmpeg` in your environment:
+
 ```bash
-conda create -y -n lerobot python=3.10 && conda activate lerobot
+conda install ffmpeg -c conda-forge
 ```
 
-Install 🤗 LeRobot:
+> **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
+>
+> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
+>
+> ```bash
+> conda install ffmpeg=7.1.1 -c conda-forge
+> ```
+>
+> - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
+
+### Install LeRobot 🤗
+
+#### From Source
+
+First, clone the repository and navigate into the directory:
+
 ```bash
-pip install .
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
 ```
 
-> **NOTE:** Depending on your platform, If you encounter any build errors during this step
-you may need to install `cmake` and `build-essential` for building some of our dependencies.
-On linux: `sudo apt-get install cmake build-essential`
+Then, install the library in editable mode. This is useful if you plan to contribute to the code.
+
+```bash
+pip install -e .
+```
+
+> **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
+> `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
 
 For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
+
 - [aloha](https://github.com/huggingface/gym-aloha)
 - [xarm](https://github.com/huggingface/gym-xarm)
 - [pusht](https://github.com/huggingface/gym-pusht)
 
 For instance, to install 🤗 LeRobot with aloha and pusht, use:
+
 ```bash
-pip install ".[aloha, pusht]"
+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/
+
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install libero or pi tags, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`.
+>
+> This will be solved in the next patch release
+
+### Weights & Biases
+
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
+
 ```bash
 wandb login
 ```
 
 (note: you will also need to enable WandB in the configuration. See below.)
 
-## Walkthrough
-
-```
-.
-├── examples             # contains demonstration examples, start here to learn about LeRobot
-|   └── advanced         # contains even more examples for those who have mastered the basics
-├── lerobot
-|   ├── configs          # contains hydra yaml files with all options that you can override in the command line
-|   |   ├── default.yaml   # selected by default, it loads pusht environment and diffusion policy
-|   |   ├── env            # various sim environments and their datasets: aloha.yaml, pusht.yaml, xarm.yaml
-|   |   └── policy         # various policies: act.yaml, diffusion.yaml, tdmpc.yaml
-|   ├── common           # contains classes and utilities
-|   |   ├── datasets       # various datasets of human demonstrations: aloha, pusht, xarm
-|   |   ├── envs           # various sim environments: aloha, pusht, xarm
-|   |   ├── policies       # various policies: act, diffusion, tdmpc
-|   |   └── utils          # various utilities
-|   └── scripts          # contains functions to execute via command line
-|       ├── eval.py                 # load policy and evaluate it on an environment
-|       ├── train.py                # train a policy via imitation learning and/or reinforcement learning
-|       ├── push_dataset_to_hub.py  # convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub
-|       └── visualize_dataset.py    # load a dataset and render its demonstrations
-├── outputs               # contains results of scripts execution: logs, videos, model checkpoints
-└── tests                 # contains pytest utilities for continuous integration
-```
-
 ### Visualize datasets
 
-Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically download data from the Hugging Face hub.
+Check out [example 1](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
+
+You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
 
-You can also locally visualize episodes from a dataset by executing our script from the command line:
 ```bash
-python lerobot/scripts/visualize_dataset.py \
+lerobot-dataset-viz \
     --repo-id lerobot/pusht \
     --episode-index 0
 ```
 
+or from a dataset in a local folder with the `root` option and the `--mode local` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
+
+```bash
+lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --root ./my_local_data_dir \
+    --mode local \
+    --episode-index 0
+```
+
 It will open `rerun.io` and display the camera streams, robot states and actions, like this:
 
 https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
 
+Our script can also visualize datasets stored on a distant server. See `lerobot-dataset-viz --help` for more instructions.
 
-Our script can also visualize datasets stored on a distant server. See `python lerobot/scripts/visualize_dataset.py --help` for more instructions.
+### The `LeRobotDataset` format
 
-### Evaluate a pretrained policy
+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.
 
-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.
+A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) for more details on `delta_timestamps`.
 
-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 lerobot/scripts/eval.py \
-    -p lerobot/diffusion_pusht \
-    eval.n_episodes=10 \
-    eval.batch_size=10
+Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor.
+
+Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects:
+
+```
+dataset attributes:
+  ├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example:
+  │  ├ observation.images.cam_high (VideoFrame):
+  │  │   VideoFrame = {'path': path to a mp4 video, 'timestamp' (float32): timestamp in the video}
+  │  ├ observation.state (list of float32): position of an arm joints (for instance)
+  │  ... (more observations)
+  │  ├ action (list of float32): goal position of an arm joints (for instance)
+  │  ├ episode_index (int64): index of the episode for this sample
+  │  ├ frame_index (int64): index of the frame for this sample in the episode ; starts at 0 for each episode
+  │  ├ timestamp (float32): timestamp in the episode
+  │  ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode
+  │  └ index (int64): general index in the whole dataset
+  ├ meta: a LeRobotDatasetMetadata object containing:
+  │  ├ info: a dictionary of metadata on the dataset
+  │  │  ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with
+  │  │  ├ fps (int): frame per second the dataset is recorded/synchronized to
+  │  │  ├ features (dict): all features contained in the dataset with their shapes and types
+  │  │  ├ total_episodes (int): total number of episodes in the dataset
+  │  │  ├ total_frames (int): total number of frames in the dataset
+  │  │  ├ robot_type (str): robot type used for recording
+  │  │  ├ data_path (str): formattable string for the parquet files
+  │  │  └ video_path (str): formattable string for the video files (if using videos)
+  │  ├ episodes: a DataFrame containing episode metadata with columns:
+  │  │  ├ episode_index (int): index of the episode
+  │  │  ├ tasks (list): list of tasks for this episode
+  │  │  ├ length (int): number of frames in this episode
+  │  │  ├ dataset_from_index (int): start index of this episode in the dataset
+  │  │  └ dataset_to_index (int): end index of this episode in the dataset
+  │  ├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance
+  │  │  ├ observation.images.front_cam: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.}
+  │  │  └ ...
+  │  └ tasks: a DataFrame containing task information with task names as index and task_index as values
+  ├ root (Path): local directory where the dataset is stored
+  ├ image_transforms (Callable): optional image transformations to apply to visual modalities
+  └ delta_timestamps (dict): optional delta timestamps for temporal queries
 ```
 
-Note: After training your own policy, you can re-evaluate the checkpoints with:
+A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely:
 
-```bash
-python lerobot/scripts/eval.py -p {OUTPUT_DIR}/checkpoints/last/pretrained_model
-```
+- 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
 
-See `python lerobot/scripts/eval.py --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.
-
-In general, you can use our training script to easily train any policy. Here is an example of training the ACT policy on trajectories collected by humans on the Aloha simulation environment for the insertion task:
-
-```bash
-python lerobot/scripts/train.py \
-    policy=act \
-    env=aloha \
-    env.task=AlohaInsertion-v0 \
-    dataset_repo_id=lerobot/aloha_sim_insertion_human \
-```
-
-The experiment directory is automatically generated and will show up in yellow in your terminal. It looks like `outputs/train/2024-05-05/20-21-12_aloha_act_default`. You can manually specify an experiment directory by adding this argument to the `train.py` python command:
-```bash
-    hydra.run.dir=your/new/experiment/dir
-```
-
-In the experiment directory there will be a folder called `checkpoints` which will have the following structure:
-
-```bash
-checkpoints
-├── 000250  # checkpoint_dir for training step 250
-│   ├── pretrained_model  # Hugging Face pretrained model dir
-│   │   ├── config.json  # Hugging Face pretrained model config
-│   │   ├── config.yaml  # consolidated Hydra config
-│   │   ├── model.safetensors  # model weights
-│   │   └── README.md  # Hugging Face model card
-│   └── training_state.pth  # optimizer/scheduler/rng state and training step
-```
-
-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:
-
-```bash
-    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:
-
-![](media/wandb.png)
-
-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 lerobot/scripts/eval.py --help` for more instructions.
+Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the `root` argument if it's not in the default `~/.cache/huggingface/lerobot` location.
 
 #### Reproduce state-of-the-art (SOTA)
 
-We have organized our configuration files (found under [`lerobot/configs`](./lerobot/configs)) such that they reproduce SOTA results from a given model variant in their respective original works. Simply running:
+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 lerobot/scripts/train.py policy=diffusion env=pusht
+lerobot-train --config_path=lerobot/diffusion_pusht
 ```
 
 reproduces SOTA results for Diffusion Policy on the PushT task.
 
-Pretrained policies, along with reproduction details, can be found under the "Models" section of https://huggingface.co/lerobot.
-
 ## 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 ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Then move your dataset folder in `data` directory (e.g. `data/aloha_static_pingpong_test`), and push your dataset to the hub with:
-```bash
-python lerobot/scripts/push_dataset_to_hub.py \
---data-dir data \
---dataset-id aloha_static_pingpong_test \
---raw-format aloha_hdf5 \
---community-id lerobot
-```
-
-See `python lerobot/scripts/push_dataset_to_hub.py --help` for more instructions.
-
-If your dataset format is not supported, implement your own in `lerobot/common/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/common/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.
-- `config.yaml`: A consolidated Hydra training configuration containing the policy, environment, and dataset configs. The policy configuration should match `config.json` exactly. The environment config is useful for anyone who wants to evaluate your policy. The dataset config just serves as a paper trail for reproducibility.
+- `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 [lerobot_eval.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_eval.py) for an example of how other people may use your policy.
 
+### Acknowledgment
 
-### 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 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}
 }
 ```
+
+## 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

@@ -0,0 +1,288 @@
+# 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.
+- `aliberts/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
+
+Note: The datasets used for this benchmark need to be image datasets, not video datasets.
+
+**Data augmentations**
+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` |
+| **crf**     | `0`, `5`, `10`, `15`, `20`, `25`, `30`, `40`, `50`, `None`   |
+
+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)]`)
+
+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.
+
+**Loading time ratio (lower is better)**
+`video_images_load_time_ratio` is the ratio of the time it takes to decode frames from the video at a given timestamps over the time it takes to load the exact same original images. Lower is better. For instance, `video_images_load_time_ratio=200%` means that decoding from video is 2 times slower than loading the original images.
+
+**Average Mean Square Error (lower is better)**
+`avg_mse` is the average mean square error between each decoded frame and its corresponding original image over all requested timestamps, and also divided by the number of pixels in the image to be comparable when switching to different image sizes.
+
+**Average Peak Signal to Noise Ratio (higher is better)**
+`avg_psnr` measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR indicates better quality.
+
+**Average Structural Similarity Index Measure (higher is better)**
+`avg_ssim` evaluates the perceived quality of images by comparing luminance, contrast, and structure. SSIM values range from -1 to 1, where 1 indicates perfect similarity.
+
+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).
+This gives a unique set of encoding parameters which is used to encode the episode.
+
+**Decoding:** Then, for each of those unique encodings, we iterate through every combination of the decoding parameters `backend` and `timestamps_mode`. For each of them, we record the metrics of a number of samples (given by `--num-samples`). This is parallelized for efficiency and the number of processes can be controlled with `--num-workers`. Ideally, it's best to have a `--num-samples` that is divisible by `--num-workers`.
+
+Intermediate results saved for each `vcodec` and `pix_fmt` combination in csv tables.
+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`
+- `nvc`
+
+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,
+    timestamps: list[float],
+    tolerance_s: float,
+    backend: str,
+) -> torch.Tensor:
+    if backend in ["pyav", "video_reader"]:
+        return decode_video_frames_torchvision(
+            video_path, timestamps, tolerance_s, backend
+        )
++    elif backend == ["your_decoder"]:
++        return your_decoder_function(
++            video_path, timestamps, tolerance_s, backend
++        )
+    else:
+        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 \
+    --repo-ids \
+        lerobot/pusht_image \
+        aliberts/aloha_mobile_shrimp_image \
+    --vcodec libx264 libx265 \
+    --pix-fmt yuv444p yuv420p \
+    --g 2 20 None \
+    --crf 10 40 None \
+    --timestamps-modes 1_frame 2_frames \
+    --backends pyav video_reader \
+    --num-samples 5 \
+    --num-workers 5 \
+    --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 \
+    --output-dir outputs/video_benchmark \
+    --repo-ids \
+        lerobot/pusht_image \
+        aliberts/aloha_mobile_shrimp_image \
+        aliberts/paris_street \
+        aliberts/kitchen \
+    --vcodec libx264 libx265 \
+    --pix-fmt yuv444p yuv420p \
+    --g 1 2 3 4 5 6 10 15 20 40 None \
+    --crf 0 5 10 15 20 25 30 40 50 None \
+    --timestamps-modes 1_frame 2_frames 6_frames \
+    --backends pyav video_reader \
+    --num-samples 50 \
+    --num-workers 5 \
+    --save-frames 1
+
+# av1 encoding (only compatible with yuv420p and pyav decoder)
+python benchmark/video/run_video_benchmark.py \
+    --output-dir outputs/video_benchmark \
+    --repo-ids \
+        lerobot/pusht_image \
+        aliberts/aloha_mobile_shrimp_image \
+        aliberts/paris_street \
+        aliberts/kitchen \
+    --vcodec libsvtav1 \
+    --pix-fmt yuv420p \
+    --g 1 2 3 4 5 6 10 15 20 40 None \
+    --crf 0 5 10 15 20 25 30 40 50 None \
+    --timestamps-modes 1_frame 2_frames 6_frames \
+    --backends pyav \
+    --num-samples 50 \
+    --num-workers 5 \
+    --save-frames 1
+```
+
+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`
+- crf: `30`
+
+Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_reader` does not support it (and `pyav` doesn't require a custom build of `torchvision`).
+
+### Summary
+
+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%    |
+| aliberts/aloha_mobile_shrimp_image | 2.14%      | 2.11%   | 1.38%     | **1.37%** | 5.59%     |
+| aliberts/paris_street              | 2.12%      | 2.13%   | **1.54%** | **1.54%** | 4.43%     |
+| aliberts/kitchen                   | 1.40%      | 1.39%   | **1.00%** | **1.00%** | 2.52%     |
+
+| video_images_load_time_ratio       | vcodec  | pix_fmt |          |         |           |
+| ---------------------------------- | ------- | ------- | -------- | ------- | --------- |
+|                                    | libx264 |         | libx265  |         | libsvtav1 |
+| repo_id                            | yuv420p | yuv444p | yuv420p  | yuv444p | yuv420p   |
+| lerobot/pusht_image                | 6.45    | 5.19    | **1.90** | 2.12    | 2.47      |
+| aliberts/aloha_mobile_shrimp_image | 11.80   | 7.92    | 0.71     | 0.85    | **0.48**  |
+| aliberts/paris_street              | 2.21    | 2.05    | 0.36     | 0.49    | **0.30**  |
+| aliberts/kitchen                   | 1.46    | 1.46    | 0.28     | 0.51    | **0.26**  |
+
+|                                    |          | vcodec   | pix_fmt      |          |           |              |
+| ---------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
+|                                    |          | libx264  |              | libx265  |           | libsvtav1    |
+| repo_id                            | metric   | yuv420p  | yuv444p      | yuv420p  | yuv444p   | yuv420p      |
+| lerobot/pusht_image                | avg_mse  | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04  | 2.19E-04     |
+|                                    | avg_psnr | 35.44    | 37.07        | 35.49    | **37.30** | 37.20        |
+|                                    | avg_ssim | 98.28%   | **98.85%**   | 98.31%   | 98.84%    | 98.72%       |
+| aliberts/aloha_mobile_shrimp_image | avg_mse  | 2.76E-04 | 2.59E-04     | 3.17E-04 | 3.06E-04  | **1.30E-04** |
+|                                    | avg_psnr | 35.91    | 36.21        | 35.88    | 36.09     | **40.17**    |
+|                                    | avg_ssim | 95.19%   | 95.18%       | 95.00%   | 95.05%    | **97.73%**   |
+| aliberts/paris_street              | avg_mse  | 6.89E-04 | 6.70E-04     | 4.03E-03 | 4.02E-03  | **3.09E-04** |
+|                                    | avg_psnr | 33.48    | 33.68        | 32.05    | 32.15     | **35.40**    |
+|                                    | avg_ssim | 93.76%   | 93.75%       | 89.46%   | 89.46%    | **95.46%**   |
+| aliberts/kitchen                   | avg_mse  | 2.50E-04 | 2.24E-04     | 4.28E-04 | 4.18E-04  | **1.53E-04** |
+|                                    | avg_psnr | 36.73    | 37.33        | 36.56    | 36.75     | **39.12**    |
+|                                    | avg_ssim | 95.47%   | 95.58%       | 95.52%   | 95.53%    | **96.82%**   |

benchmarks/video/benchmark.py

@@ -0,0 +1,94 @@
+#!/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 specific language governing permissions and
+# limitations under the License.
+import threading
+import time
+from contextlib import ContextDecorator
+
+
+class TimeBenchmark(ContextDecorator):
+    """
+    Measures execution time using a context manager or decorator.
+
+    This class supports both context manager and decorator usage, and is thread-safe for multithreaded
+    environments.
+
+    Args:
+        print: If True, prints the elapsed time upon exiting the context or completing the function. Defaults
+        to False.
+
+    Examples:
+
+        Using as a context manager:
+
+        >>> benchmark = TimeBenchmark()
+        >>> with benchmark:
+        ...     time.sleep(1)
+        >>> print(f"Block took {benchmark.result:.4f} seconds")
+        Block took approximately 1.0000 seconds
+
+        Using with multithreading:
+
+        ```python
+        import threading
+
+        benchmark = TimeBenchmark()
+
+
+        def context_manager_example():
+            with benchmark:
+                time.sleep(0.01)
+            print(f"Block took {benchmark.result_ms:.2f} milliseconds")
+
+
+        threads = []
+        for _ in range(3):
+            t1 = threading.Thread(target=context_manager_example)
+            threads.append(t1)
+
+        for t in threads:
+            t.start()
+
+        for t in threads:
+            t.join()
+        ```
+        Expected output:
+        Block took approximately 10.00 milliseconds
+        Block took approximately 10.00 milliseconds
+        Block took approximately 10.00 milliseconds
+    """
+
+    def __init__(self, print=False):
+        self.local = threading.local()
+        self.print_time = print
+
+    def __enter__(self):
+        self.local.start_time = time.perf_counter()
+        return self
+
+    def __exit__(self, *exc):
+        self.local.end_time = time.perf_counter()
+        self.local.elapsed_time = self.local.end_time - self.local.start_time
+        if self.print_time:
+            print(f"Elapsed time: {self.local.elapsed_time:.4f} seconds")
+        return False
+
+    @property
+    def result(self):
+        return getattr(self.local, "elapsed_time", None)
+
+    @property
+    def result_ms(self):
+        return self.result * 1e3

benchmarks/video/capture_camera_feed.py

@@ -0,0 +1,102 @@
+#!/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 specific language governing permissions and
+# limitations under the License.
+"""Capture video feed from a camera as raw images."""
+
+import argparse
+import datetime as dt
+import os
+import time
+from pathlib import Path
+
+import cv2
+import rerun as rr
+
+# see https://rerun.io/docs/howto/visualization/limit-ram
+RERUN_MEMORY_LIMIT = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "5%")
+
+
+def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height: int, duration: int):
+    rr.init("lerobot_capture_camera_feed")
+    rr.spawn(memory_limit=RERUN_MEMORY_LIMIT)
+
+    now = dt.datetime.now()
+    capture_dir = output_dir / f"{now:%Y-%m-%d}" / f"{now:%H-%M-%S}"
+    if not capture_dir.exists():
+        capture_dir.mkdir(parents=True, exist_ok=True)
+
+    # Opens the default webcam
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        print("Error: Could not open video stream.")
+        return
+
+    cap.set(cv2.CAP_PROP_FPS, fps)
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
+
+    frame_index = 0
+    start_time = time.time()
+    while time.time() - start_time < duration:
+        ret, frame = cap.read()
+
+        if not ret:
+            print("Error: Could not read frame.")
+            break
+        rr.log("video/stream", rr.Image(frame), static=True)
+        cv2.imwrite(str(capture_dir / f"frame_{frame_index:06d}.png"), frame)
+        frame_index += 1
+
+    # Release the capture
+    cap.release()
+
+    # TODO(Steven): Add a graceful shutdown via a close() method for the Viewer context, though not currently supported in the Rerun API.
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("outputs/cam_capture/"),
+        help="Directory where the capture images are written. A subfolder named with the current date & time will be created inside it for each capture.",
+    )
+    parser.add_argument(
+        "--fps",
+        type=int,
+        default=30,
+        help="Frames Per Second of the capture.",
+    )
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=1280,
+        help="Width of the captured images.",
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=720,
+        help="Height of the captured images.",
+    )
+    parser.add_argument(
+        "--duration",
+        type=int,
+        default=20,
+        help="Duration in seconds for which the video stream should be captured.",
+    )
+    args = parser.parse_args()
+    display_and_save_video_stream(**vars(args))

benchmarks/video/run_video_benchmark.py

@@ -0,0 +1,493 @@
+#!/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 specific language governing permissions and
+# limitations under the License.
+"""Assess the performance of video decoding in various configurations.
+
+This script will benchmark different video encoding and decoding parameters.
+See the provided README.md or run `python benchmark/video/run_video_benchmark.py --help` for usage info.
+"""
+
+import argparse
+import datetime as dt
+import random
+import shutil
+from collections import OrderedDict
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+import einops
+import numpy as np
+import pandas as pd
+import PIL
+import torch
+from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
+from tqdm import tqdm
+
+from benchmarks.video.benchmark import TimeBenchmark
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.video_utils import (
+    decode_video_frames_torchvision,
+    encode_video_frames,
+)
+from lerobot.utils.constants import OBS_IMAGE
+
+BASE_ENCODING = OrderedDict(
+    [
+        ("vcodec", "libx264"),
+        ("pix_fmt", "yuv444p"),
+        ("g", 2),
+        ("crf", None),
+        # TODO(aliberts): Add fastdecode
+        # ("fastdecode", 0),
+    ]
+)
+
+
+# TODO(rcadene, aliberts): move to `utils.py` folder when we want to refactor
+def parse_int_or_none(value) -> int | None:
+    if value.lower() == "none":
+        return None
+    try:
+        return int(value)
+    except ValueError as e:
+        raise argparse.ArgumentTypeError(f"Invalid int or None: {value}") from e
+
+
+def check_datasets_formats(repo_ids: list) -> None:
+    for repo_id in repo_ids:
+        dataset = LeRobotDataset(repo_id)
+        if len(dataset.meta.video_keys) > 0:
+            raise ValueError(
+                f"Use only image dataset for running this benchmark. Video dataset provided: {repo_id}"
+            )
+
+
+def get_directory_size(directory: Path) -> int:
+    total_size = 0
+    for item in directory.rglob("*"):
+        if item.is_file():
+            total_size += item.stat().st_size
+    return total_size
+
+
+def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> torch.Tensor:
+    frames = []
+    for ts in timestamps:
+        idx = int(ts * fps)
+        frame = PIL.Image.open(imgs_dir / f"frame_{idx:06d}.png")
+        frame = torch.from_numpy(np.array(frame))
+        frame = frame.type(torch.float32) / 255
+        frame = einops.rearrange(frame, "h w c -> c h w")
+        frames.append(frame)
+    return torch.stack(frames)
+
+
+def save_decoded_frames(
+    imgs_dir: Path, save_dir: Path, frames: torch.Tensor, timestamps: list[float], fps: int
+) -> None:
+    if save_dir.exists() and len(list(save_dir.glob("frame_*.png"))) == len(timestamps):
+        return
+
+    save_dir.mkdir(parents=True, exist_ok=True)
+    for i, ts in enumerate(timestamps):
+        idx = int(ts * fps)
+        frame_hwc = (frames[i].permute((1, 2, 0)) * 255).type(torch.uint8).cpu().numpy()
+        PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame_{idx:06d}_decoded.png")
+        shutil.copyfile(imgs_dir / f"frame_{idx:06d}.png", save_dir / f"frame_{idx:06d}_original.png")
+
+
+def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
+    episode_index = 0
+    ep_num_images = dataset.meta.episodes["length"][episode_index]
+    if imgs_dir.exists() and len(list(imgs_dir.glob("frame_*.png"))) == ep_num_images:
+        return
+
+    imgs_dir.mkdir(parents=True, exist_ok=True)
+    hf_dataset = dataset.hf_dataset.with_format(None)
+
+    # We only save images from the first camera
+    img_keys = [key for key in hf_dataset.features if key.startswith(OBS_IMAGE)]
+    imgs_dataset = hf_dataset.select_columns(img_keys[0])
+
+    for i, item in enumerate(
+        tqdm(imgs_dataset, desc=f"saving {dataset.repo_id} first episode images", leave=False)
+    ):
+        img = item[img_keys[0]]
+        img.save(str(imgs_dir / f"frame_{i:06d}.png"), quality=100)
+
+        if i >= ep_num_images - 1:
+            break
+
+
+def sample_timestamps(timestamps_mode: str, ep_num_images: int, fps: int) -> list[float]:
+    # Start at 5 to allow for 2_frames_4_space and 6_frames
+    idx = random.randint(5, ep_num_images - 1)
+    match timestamps_mode:
+        case "1_frame":
+            frame_indexes = [idx]
+        case "2_frames":
+            frame_indexes = [idx - 1, idx]
+        case "2_frames_4_space":
+            frame_indexes = [idx - 5, idx]
+        case "6_frames":
+            frame_indexes = [idx - i for i in range(6)][::-1]
+        case _:
+            raise ValueError(timestamps_mode)
+
+    return [idx / fps for idx in frame_indexes]
+
+
+def decode_video_frames(
+    video_path: str,
+    timestamps: list[float],
+    tolerance_s: float,
+    backend: str,
+) -> torch.Tensor:
+    if backend in ["pyav", "video_reader"]:
+        return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
+    else:
+        raise NotImplementedError(backend)
+
+
+def benchmark_decoding(
+    imgs_dir: Path,
+    video_path: Path,
+    timestamps_mode: str,
+    backend: str,
+    ep_num_images: int,
+    fps: int,
+    num_samples: int = 50,
+    num_workers: int = 4,
+    save_frames: bool = False,
+) -> dict:
+    def process_sample(sample: int):
+        time_benchmark = TimeBenchmark()
+        timestamps = sample_timestamps(timestamps_mode, ep_num_images, fps)
+        num_frames = len(timestamps)
+        result = {
+            "psnr_values": [],
+            "ssim_values": [],
+            "mse_values": [],
+        }
+
+        with time_benchmark:
+            frames = decode_video_frames(video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend)
+        result["load_time_video_ms"] = time_benchmark.result_ms / num_frames
+
+        with time_benchmark:
+            original_frames = load_original_frames(imgs_dir, timestamps, fps)
+        result["load_time_images_ms"] = time_benchmark.result_ms / num_frames
+
+        frames_np, original_frames_np = frames.numpy(), original_frames.numpy()
+        for i in range(num_frames):
+            result["mse_values"].append(mean_squared_error(original_frames_np[i], frames_np[i]))
+            result["psnr_values"].append(
+                peak_signal_noise_ratio(original_frames_np[i], frames_np[i], data_range=1.0)
+            )
+            result["ssim_values"].append(
+                structural_similarity(original_frames_np[i], frames_np[i], data_range=1.0, channel_axis=0)
+            )
+
+        if save_frames and sample == 0:
+            save_dir = video_path.with_suffix("") / f"{timestamps_mode}_{backend}"
+            save_decoded_frames(imgs_dir, save_dir, frames, timestamps, fps)
+
+        return result
+
+    load_times_video_ms = []
+    load_times_images_ms = []
+    mse_values = []
+    psnr_values = []
+    ssim_values = []
+
+    # A sample is a single set of decoded frames specified by timestamps_mode (e.g. a single frame, 2 frames, etc.).
+    # For each sample, we record metrics (loading time and quality metrics) which are then averaged over all samples.
+    # As these samples are independent, we run them in parallel threads to speed up the benchmark.
+    with ThreadPoolExecutor(max_workers=num_workers) as executor:
+        futures = [executor.submit(process_sample, i) for i in range(num_samples)]
+        for future in tqdm(as_completed(futures), total=num_samples, desc="samples", leave=False):
+            result = future.result()
+            load_times_video_ms.append(result["load_time_video_ms"])
+            load_times_images_ms.append(result["load_time_images_ms"])
+            psnr_values.extend(result["psnr_values"])
+            ssim_values.extend(result["ssim_values"])
+            mse_values.extend(result["mse_values"])
+
+    avg_load_time_video_ms = float(np.array(load_times_video_ms).mean())
+    avg_load_time_images_ms = float(np.array(load_times_images_ms).mean())
+    video_images_load_time_ratio = avg_load_time_video_ms / avg_load_time_images_ms
+
+    return {
+        "avg_load_time_video_ms": avg_load_time_video_ms,
+        "avg_load_time_images_ms": avg_load_time_images_ms,
+        "video_images_load_time_ratio": video_images_load_time_ratio,
+        "avg_mse": float(np.mean(mse_values)),
+        "avg_psnr": float(np.mean(psnr_values)),
+        "avg_ssim": float(np.mean(ssim_values)),
+    }
+
+
+def benchmark_encoding_decoding(
+    dataset: LeRobotDataset,
+    video_path: Path,
+    imgs_dir: Path,
+    encoding_cfg: dict,
+    decoding_cfg: dict,
+    num_samples: int,
+    num_workers: int,
+    save_frames: bool,
+    overwrite: bool = False,
+    seed: int = 1337,
+) -> list[dict]:
+    fps = dataset.fps
+
+    if overwrite or not video_path.is_file():
+        tqdm.write(f"encoding {video_path}")
+        encode_video_frames(
+            imgs_dir=imgs_dir,
+            video_path=video_path,
+            fps=fps,
+            vcodec=encoding_cfg["vcodec"],
+            pix_fmt=encoding_cfg["pix_fmt"],
+            g=encoding_cfg.get("g"),
+            crf=encoding_cfg.get("crf"),
+            # fast_decode=encoding_cfg.get("fastdecode"),
+            overwrite=True,
+        )
+
+    episode_index = 0
+    ep_num_images = dataset.meta.episodes["length"][episode_index]
+    width, height = tuple(dataset[0][dataset.meta.camera_keys[0]].shape[-2:])
+    num_pixels = width * height
+    video_size_bytes = video_path.stat().st_size
+    images_size_bytes = get_directory_size(imgs_dir)
+    video_images_size_ratio = video_size_bytes / images_size_bytes
+
+    random.seed(seed)
+    benchmark_table = []
+    for timestamps_mode in tqdm(
+        decoding_cfg["timestamps_modes"], desc="decodings (timestamps_modes)", leave=False
+    ):
+        for backend in tqdm(decoding_cfg["backends"], desc="decodings (backends)", leave=False):
+            benchmark_row = benchmark_decoding(
+                imgs_dir,
+                video_path,
+                timestamps_mode,
+                backend,
+                ep_num_images,
+                fps,
+                num_samples,
+                num_workers,
+                save_frames,
+            )
+            benchmark_row.update(
+                **{
+                    "repo_id": dataset.repo_id,
+                    "resolution": f"{width} x {height}",
+                    "num_pixels": num_pixels,
+                    "video_size_bytes": video_size_bytes,
+                    "images_size_bytes": images_size_bytes,
+                    "video_images_size_ratio": video_images_size_ratio,
+                    "timestamps_mode": timestamps_mode,
+                    "backend": backend,
+                },
+                **encoding_cfg,
+            )
+            benchmark_table.append(benchmark_row)
+
+    return benchmark_table
+
+
+def main(
+    output_dir: Path,
+    repo_ids: list[str],
+    vcodec: list[str],
+    pix_fmt: list[str],
+    g: list[int],
+    crf: list[int],
+    # fastdecode: list[int],
+    timestamps_modes: list[str],
+    backends: list[str],
+    num_samples: int,
+    num_workers: int,
+    save_frames: bool,
+):
+    check_datasets_formats(repo_ids)
+    encoding_benchmarks = {
+        "g": g,
+        "crf": crf,
+        # "fastdecode": fastdecode,
+    }
+    decoding_benchmarks = {
+        "timestamps_modes": timestamps_modes,
+        "backends": backends,
+    }
+    headers = ["repo_id", "resolution", "num_pixels"]
+    headers += list(BASE_ENCODING.keys())
+    headers += [
+        "timestamps_mode",
+        "backend",
+        "video_size_bytes",
+        "images_size_bytes",
+        "video_images_size_ratio",
+        "avg_load_time_video_ms",
+        "avg_load_time_images_ms",
+        "video_images_load_time_ratio",
+        "avg_mse",
+        "avg_psnr",
+        "avg_ssim",
+    ]
+    file_paths = []
+    for video_codec in tqdm(vcodec, desc="encodings (vcodec)"):
+        for pixel_format in tqdm(pix_fmt, desc="encodings (pix_fmt)", leave=False):
+            benchmark_table = []
+            for repo_id in tqdm(repo_ids, desc="encodings (datasets)", leave=False):
+                dataset = LeRobotDataset(repo_id)
+                imgs_dir = output_dir / "images" / dataset.repo_id.replace("/", "_")
+                # We only use the first episode
+                save_first_episode(imgs_dir, dataset)
+                for key, values in tqdm(encoding_benchmarks.items(), desc="encodings (g, crf)", leave=False):
+                    for value in tqdm(values, desc=f"encodings ({key})", leave=False):
+                        encoding_cfg = BASE_ENCODING.copy()
+                        encoding_cfg["vcodec"] = video_codec
+                        encoding_cfg["pix_fmt"] = pixel_format
+                        encoding_cfg[key] = value
+                        args_path = Path("_".join(str(value) for value in encoding_cfg.values()))
+                        video_path = output_dir / "videos" / args_path / f"{repo_id.replace('/', '_')}.mp4"
+                        benchmark_table += benchmark_encoding_decoding(
+                            dataset,
+                            video_path,
+                            imgs_dir,
+                            encoding_cfg,
+                            decoding_benchmarks,
+                            num_samples,
+                            num_workers,
+                            save_frames,
+                        )
+
+            # Save intermediate results
+            benchmark_df = pd.DataFrame(benchmark_table, columns=headers)
+            now = dt.datetime.now()
+            csv_path = (
+                output_dir
+                / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_{video_codec}_{pixel_format}_{num_samples}-samples.csv"
+            )
+            benchmark_df.to_csv(csv_path, header=True, index=False)
+            file_paths.append(csv_path)
+            del benchmark_df
+
+    # Concatenate all results
+    df_list = [pd.read_csv(csv_path) for csv_path in file_paths]
+    concatenated_df = pd.concat(df_list, ignore_index=True)
+    concatenated_path = output_dir / f"{now:%Y-%m-%d}_{now:%H-%M-%S}_all_{num_samples}-samples.csv"
+    concatenated_df.to_csv(concatenated_path, header=True, index=False)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("outputs/video_benchmark"),
+        help="Directory where the video benchmark outputs are written.",
+    )
+    parser.add_argument(
+        "--repo-ids",
+        type=str,
+        nargs="*",
+        default=[
+            "lerobot/pusht_image",
+            "aliberts/aloha_mobile_shrimp_image",
+            "aliberts/paris_street",
+            "aliberts/kitchen",
+        ],
+        help="Datasets repo-ids to test against. First episodes only are used. Must be images.",
+    )
+    parser.add_argument(
+        "--vcodec",
+        type=str,
+        nargs="*",
+        default=["libx264", "hevc", "libsvtav1"],
+        help="Video codecs to be tested",
+    )
+    parser.add_argument(
+        "--pix-fmt",
+        type=str,
+        nargs="*",
+        default=["yuv444p", "yuv420p"],
+        help="Pixel formats (chroma subsampling) to be tested",
+    )
+    parser.add_argument(
+        "--g",
+        type=parse_int_or_none,
+        nargs="*",
+        default=[1, 2, 3, 4, 5, 6, 10, 15, 20, 40, 100, None],
+        help="Group of pictures sizes to be tested.",
+    )
+    parser.add_argument(
+        "--crf",
+        type=parse_int_or_none,
+        nargs="*",
+        default=[0, 5, 10, 15, 20, 25, 30, 40, 50, None],
+        help="Constant rate factors to be tested.",
+    )
+    # parser.add_argument(
+    #     "--fastdecode",
+    #     type=int,
+    #     nargs="*",
+    #     default=[0, 1],
+    #     help="Use the fastdecode tuning option. 0 disables it. "
+    #         "For libx264 and libx265/hevc, only 1 is possible. "
+    #         "For libsvtav1, 1, 2 or 3 are possible values with a higher number meaning a faster decoding optimization",
+    # )
+    parser.add_argument(
+        "--timestamps-modes",
+        type=str,
+        nargs="*",
+        default=[
+            "1_frame",
+            "2_frames",
+            "2_frames_4_space",
+            "6_frames",
+        ],
+        help="Timestamps scenarios to be tested.",
+    )
+    parser.add_argument(
+        "--backends",
+        type=str,
+        nargs="*",
+        default=["pyav", "video_reader"],
+        help="Torchvision decoding backend to be tested.",
+    )
+    parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=50,
+        help="Number of samples for each encoding x decoding config.",
+    )
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=10,
+        help="Number of processes for parallelized sample processing.",
+    )
+    parser.add_argument(
+        "--save-frames",
+        type=int,
+        default=0,
+        help="Whether to save decoded frames or not. Enter a non-zero number for true.",
+    )
+    args = parser.parse_args()
+    main(**vars(args))

docker/Dockerfile.internal

@@ -0,0 +1,93 @@
+# 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:$PATH \
+    CUDA_VISIBLE_DEVICES=0 \
+    TEST_TYPE=single_gpu \
+    DEVICE=cuda
+
+# Install Python, system dependencies, and uv (as root)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common build-essential git curl \
+    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+    libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \
+    cmake pkg-config ninja-build \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends \
+       python${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${PYTHON_VERSION}
+
+# Install Python dependencies for caching
+COPY --chown=user_lerobot:user_lerobot pyproject.toml README.md MANIFEST.in ./
+COPY --chown=user_lerobot:user_lerobot src/ src/
+
+ARG UNBOUND_DEPS=false
+
+RUN if [ "$UNBOUND_DEPS" = "true" ]; then \
+    sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml; \
+    echo "Dependencies unbound:" && cat pyproject.toml; \
+    fi
+
+RUN uv pip install --no-cache ".[all]"
+
+# Copy the rest of the application source code
+# 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 +1,79 @@
+# 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 \
+    cmake pkg-config ninja-build \
+    && 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/
+
+ARG UNBOUND_DEPS=false
+
+RUN if [ "$UNBOUND_DEPS" = "true" ]; then \
+    sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml; \
+    echo "Dependencies unbound:" && cat pyproject.toml; \
+    fi
+
+RUN uv pip install --no-cache ".[all]"
+
+# Copy the rest of the application code
+# 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

@@ -1,31 +0,0 @@
-# Configure image
-ARG PYTHON_VERSION=3.10
-
-FROM python:${PYTHON_VERSION}-slim
-ARG PYTHON_VERSION
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Install apt dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential cmake \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# Create virtual environment
-RUN ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Install LeRobot
-COPY . /lerobot
-WORKDIR /lerobot
-RUN pip install --upgrade --no-cache-dir pip
-RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht]" \
-    --extra-index-url https://download.pytorch.org/whl/cpu
-
-# Set EGL as the rendering backend for MuJoCo
-ENV MUJOCO_GL="egl"
-
-# Execute in bash shell rather than python
-CMD ["/bin/bash"]

docker/lerobot-gpu-dev/Dockerfile

@@ -1,40 +0,0 @@
-FROM nvidia/cuda:12.4.1-base-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 \
-    htop atop nvtop \
-    sed gawk grep curl wget \
-    tcpdump sysstat screen tmux \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
-    python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# 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

@@ -1,29 +0,0 @@
-FROM nvidia/cuda:12.4.1-base-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 \
-    libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
-    python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-
-# Create virtual environment
-RUN ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
-
-# Install LeRobot
-COPY . /lerobot
-WORKDIR /lerobot
-RUN pip install --upgrade --no-cache-dir pip
-RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht]"
-
-# Set EGL as the rendering backend for MuJoCo
-ENV MUJOCO_GL="egl"

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

@@ -0,0 +1,139 @@
+<!---
+Copyright 2020 The HuggingFace 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.
+-->
+
+# Generating the documentation
+
+To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
+you can install them with the following command, at the root of the code repository:
+
+```bash
+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
+check how they look before committing for instance). You don't have to `git commit` the built documentation.
+
+---
+
+## Building the documentation
+
+Once you have setup the `doc-builder` and additional packages, you can generate the documentation by
+typing the following command:
+
+```bash
+doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build
+```
+
+You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
+the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
+Markdown editor.
+
+## Previewing the documentation
+
+To preview the docs, first install the `watchdog` module with:
+
+```bash
+pip install watchdog
+```
+
+Then run the following command:
+
+```bash
+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).
+
+---
+
+## Adding a new element to the navigation bar
+
+Accepted files are Markdown (.md).
+
+Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
+the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/lerobot/blob/main/docs/source/_toctree.yml) file.
+
+## Renaming section headers and moving sections
+
+It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
+
+Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
+
+So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
+
+```
+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:
+
+```
+Sections that were moved:
+
+[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
+```
+
+Use the relative style to link to the new file so that the versioned docs continue to work.
+
+For an example of a rich moved sections set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
+
+### Adding a new tutorial
+
+Adding a new tutorial or section is done in two steps:
+
+- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
+- Link that file in `./source/_toctree.yml` on the correct toc-tree.
+
+Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.
+
+### Writing source documentation
+
+Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
+and objects like True, None or any strings should usually be put in `code`.
+
+#### Writing a multi-line code block
+
+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
+# second line
+# etc
+```
+````
+
+#### Adding an image
+
+Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
+the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
+them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
+If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
+to this dataset.

docs/source/_toctree.yml

@@ -0,0 +1,92 @@
+- sections:
+  - local: index
+    title: LeRobot
+  - local: installation
+    title: Installation
+  title: Get started
+- sections:
+  - local: il_robots
+    title: Imitation Learning for Robots
+  - local: cameras
+    title: Cameras
+  - local: integrate_hardware
+    title: Bring Your Own Hardware
+  - local: hilserl
+    title: Train a Robot with RL
+  - local: hilserl_sim
+    title: Train RL in Simulation
+  - local: async
+    title: Use Async Inference
+  - local: multi_gpu_training
+    title: Multi GPU training
+  title: "Tutorials"
+- sections:
+  - local: lerobot-dataset-v3
+    title: Using LeRobotDataset
+  - local: porting_datasets_v3
+    title: Porting Large Datasets
+  - local: using_dataset_tools
+    title: Using the Dataset Tools
+  title: "Datasets"
+- sections:
+  - local: act
+    title: ACT
+  - local: smolvla
+    title: SmolVLA
+  - local: pi0
+    title: π₀ (Pi0)
+  - local: pi05
+    title: π₀.₅ (Pi05)
+  - local: groot
+    title: NVIDIA GR00T N1.5
+  title: "Policies"
+- sections:
+  - local: envhub
+    title: Environments from the Hub
+  - local: il_sim
+    title: Imitation Learning in Sim
+  - local: libero
+    title: Using Libero
+  - local: metaworld
+    title: Using MetaWorld
+  title: "Simulation"
+- sections:
+  - local: introduction_processors
+    title: Introduction to Robot Processors
+  - local: debug_processor_pipeline
+    title: Debug your processor pipeline
+  - local: implement_your_own_processor
+    title: Implement your own processor
+  - local: processors_robots_teleop
+    title: Processors for Robots and Teleoperators
+  title: "Robot Processors"
+- sections:
+  - local: so101
+    title: SO-101
+  - local: so100
+    title: SO-100
+  - local: koch
+    title: Koch v1.1
+  - local: lekiwi
+    title: LeKiwi
+  - local: hope_jr
+    title: Hope Jr
+  - local: reachy2
+    title: Reachy 2
+  title: "Robots"
+- sections:
+  - local: phone_teleop
+    title: Phone
+  title: "Teleoperators"
+- sections:
+  - local: notebooks
+    title: Notebooks
+  - local: feetech
+    title: Updating Feetech Firmware
+  title: "Resources"
+- sections:
+  - local: contributing
+    title: Contribute to LeRobot
+  - local: backwardcomp
+    title: Backward compatibility
+  title: "About"

docs/source/act.mdx

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

docs/source/async.mdx

@@ -0,0 +1,312 @@
+# Asynchronous Inference
+
+With our [SmolVLA](https://huggingface.co/papers/2506.01844) we introduced a new way to run inference on real-world robots, **decoupling action prediction from action execution**.
+In this tutorial, we'll show how to use asynchronous inference (_async inference_) using a finetuned version of SmolVLA, and all the policies supported by LeRobot.
+**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.
+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.
+
+First, install `lerobot` with the `async` tag, to install the extra dependencies required to run async inference.
+
+```shell
+pip install -e ".[async]"
+```
+
+Then, spin up a policy server (in one terminal, or in a separate machine) specifying the host address and port for the client to connect to.
+You can spin up a policy server running:
+
+```shell
+python -m lerobot.async_inference.policy_server \
+     --host=127.0.0.1 \
+     --port=8080
+```
+
+This will start a policy server listening on `127.0.0.1:8080` (`localhost`, port 8080). At this stage, the policy server is empty, as all information related to which policy to run and with which parameters are specified during the first handshake with the client. Spin up a client with:
+
+```shell
+python -m lerobot.async_inference.robot_client \
+    --server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server
+    --robot.type=so100_follower \ # ROBOT: your robot type
+    --robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port
+    --robot.id=follower_so100 \ # ROBOT: your robot id, to load calibration file
+    --robot.cameras="{ laptop: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}, phone: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ # POLICY: the cameras used to acquire frames, with keys matching the keys expected by the policy
+    --task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
+    --policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
+    --pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
+    --policy_device=mps \ # POLICY: the device to run the policy on, on the server
+    --actions_per_chunk=50 \ # POLICY: the number of actions to output at once
+    --chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
+    --aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions
+    --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 😉
+
+## 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.
+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>
+</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.
+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>
+</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>
+
+---
+
+## Start the Policy Server
+
+Policy servers are wrappers around a `PreTrainedPolicy` interfacing them with observations coming from a robot client.
+Policy servers are initialized as empty containers which are populated with the requested policy specified in the initial handshake between the robot client and the policy server.
+As such, spinning up a policy server is as easy as specifying the host address and port. If you're running the policy server on the same machine as the robot client, you can use `localhost` as the host address.
+
+<hfoptions id="start_policy_server">
+<hfoption id="Command">
+```bash
+python -m lerobot.async_inference.policy_server \
+     --host=127.0.0.1 \
+     --port=8080
+```
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.async_inference.configs import PolicyServerConfig
+from lerobot.async_inference.policy_server import serve
+
+config = PolicyServerConfig(
+    host="localhost",
+    port=8080,
+)
+serve(config)
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+This listens on `localhost:8080` for an incoming connection from the associated`RobotClient`, which will communicate which policy to run during the first client-server handshake.
+
+---
+
+## Launch the Robot Client
+
+`RobotClient` is a wrapper around a `Robot` instance, which `RobotClient` connects to the (possibly remote) `PolicyServer`.
+The `RobotClient` streams observations to the `PolicyServer`, and receives action chunks obtained running inference on the server (which we assume to have better computational resources than the robot controller).
+
+<hfoptions id="start_robot_client">
+<hfoption id="Command">
+```bash
+python -m lerobot.async_inference.robot_client \
+    --server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server
+    --robot.type=so100_follower \ # ROBOT: your robot type
+    --robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port
+    --robot.id=follower_so100 \ # ROBOT: your robot id, to load calibration file
+    --robot.cameras="{ laptop: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}, phone: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ # POLICY: the cameras used to acquire frames, with keys matching the keys expected by the policy
+    --task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
+    --policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
+    --pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
+    --policy_device=mps \ # POLICY: the device to run the policy on, on the server
+    --actions_per_chunk=50 \ # POLICY: the number of actions to output at once
+    --chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
+    --aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions
+    --debug_visualize_queue_size=True # CLIENT: whether to visualize the queue size at runtime
+```
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+import threading
+from lerobot.robots.so100_follower import SO100FollowerConfig
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.async_inference.configs import RobotClientConfig
+from lerobot.async_inference.robot_client import RobotClient
+from lerobot.async_inference.helpers import visualize_action_queue_size
+
+# 1. Create the robot instance
+"""Check out the cameras available in your setup by running `python lerobot/find_cameras.py`"""
+# these cameras must match the ones expected by the policy
+# check the config.json on the Hub for the policy you are using
+camera_cfg = {
+    "top": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30)
+}
+
+robot_cfg = SO100FollowerConfig(
+  port="/dev/tty.usbmodem585A0076841",
+  id="follower_so100",
+  cameras=camera_cfg
+)
+
+# 3. Create client configuration
+client_cfg = RobotClientConfig(
+    robot=robot_cfg,
+    server_address="localhost:8080",
+    policy_device="mps",
+    policy_type="smolvla",
+    pretrained_name_or_path="fracapuano/smolvla_async",
+    chunk_size_threshold=0.5,
+    actions_per_chunk=50,  # make sure this is less than the max actions of the policy
+)
+
+# 4. Create and start client
+client = RobotClient(client_cfg)
+
+# 5. Specify the task
+task = "Don't do anything, stay still"
+
+if client.start():
+    # Start action receiver thread
+    action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
+    action_receiver_thread.start()
+
+    try:
+        # Run the control loop
+        client.control_loop(task)
+    except KeyboardInterrupt:
+        client.stop()
+        action_receiver_thread.join()
+        # (Optionally) plot the action queue size
+        visualize_action_queue_size(client.action_queue_size)
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+The following two parameters are key in every setup:
+
+<table>
+  <thead>
+    <tr>
+      <th>Hyperparameter</th>
+      <th>Default</th>
+      <th>What it does</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>
+        <code>actions_per_chunk</code>
+      </td>
+      <td>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>0.7</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.
+</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.
+However, larger values of `actions_per_chunk` might also result in less precise actions, due to the compounding errors consequent to predicting actions over longer timespans.
+
+On the other hand, increasing the value of `chunk_size_threshold` will result in sending out to the `PolicyServer` observations for inference more often, resulting in a larger number of updates action chunks, overlapping on significant portions. This results in high adaptability, in the limit predicting one action chunk for each observation, which is in turn only marginally consumed while a new one is produced.
+This option does also put more pressure on the inference pipeline, as a consequence of the many requests. Conversely, values of `chunk_size_threshold` close to 0.0 collapse to the synchronous edge case, whereby new observations are only sent out whenever the current chunk is exhausted.
+
+We found the default values of `actions_per_chunk` and `chunk_size_threshold` to work well in the experiments we developed for the [SmolVLA paper](https://huggingface.co/papers/2506.01844), but recommend experimenting with different values to find the best fit for your setup.
+
+### Tuning async inference for your setup
+
+1. **Choose your computational resources carefully.** [PI0](https://huggingface.co/lerobot/pi0) occupies 14GB of memory at inference time, while [SmolVLA](https://huggingface.co/lerobot/smolvla_base) requires only ~2GB. You should identify the best computational resource for your use case keeping in mind smaller policies require less computational resources. The combination of policy and device used (CPU-intensive, using MPS, or the number of CUDA cores on a given NVIDIA GPU) directly impacts the average inference latency you should expect.
+2. **Adjust your `fps` based on inference latency.** While the server generates a new action chunk, the client is not idle and is stepping through its current action queue. If the two processes happen at fundamentally different speeds, the client might end up with an empty queue. As such, you should reduce your fps if you consistently run out of actions in queue.
+3. **Adjust `chunk_size_threshold`**.
+   - Values closer to `0.0` result in almost sequential behavior. Values closer to `1.0` → send observation every step (more bandwidth, relies on good world-model).
+   - 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>
+</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>
+
+---
+
+## Conclusion
+
+Asynchronous inference represents a significant advancement in real-time robotics control, addressing the fundamental challenge of inference latency that has long plagued robotics applications. Through this tutorial, you've learned how to implement a complete async inference pipeline that eliminates idle frames and enables smoother, more reactive robot behaviors.
+
+**Key Takeaways:**
+
+- **Paradigm Shift**: Async inference decouples action prediction from execution, allowing robots to continue acting while new action chunks are computed in parallel
+- **Performance Benefits**: Eliminates "wait-for-inference" lags that are inherent in synchronous approaches, becoming increasingly important as policy models grow larger
+- **Flexible Architecture**: The server-client design enables distributed computing, where inference can run on powerful remote hardware while maintaining real-time robot control
+- **Tunable Parameters**: Success depends on properly configuring `actions_per_chunk` and `chunk_size_threshold` for your specific hardware, policy, and task requirements
+- **Universal Compatibility**: Works with all LeRobot-supported policies, from lightweight ACT models to vision-language models like SmolVLA
+
+Start experimenting with the default parameters, monitor your action queue sizes, and iteratively refine your setup to achieve optimal performance for your specific use case.
+If you want to discuss this further, hop into our [Discord community](https://discord.gg/s3KuuzsPFb), or open an issue on our [GitHub repository](https://github.com/lerobot/lerobot/issues).

docs/source/backwardcomp.mdx

@@ -0,0 +1,151 @@
+# Backward compatibility
+
+## Policy Normalization Migration (PR #1452)
+
+**Breaking Change**: LeRobot policies no longer have built-in normalization layers embedded in their weights. Normalization is now handled by external `PolicyProcessorPipeline` components.
+
+### What changed?
+
+|                            | Before PR #1452                                  | After PR #1452                                               |
+| -------------------------- | ------------------------------------------------ | ------------------------------------------------------------ |
+| **Normalization Location** | Embedded in model weights (`normalize_inputs.*`) | External `PolicyProcessorPipeline` components                |
+| **Model State Dict**       | Contains normalization statistics                | **Clean weights only** - no normalization parameters         |
+| **Usage**                  | `policy(batch)` handles everything               | `preprocessor(batch)` → `policy(...)` → `postprocessor(...)` |
+
+### Impact on existing models
+
+- Models trained **before** PR #1452 have normalization embedded in their weights
+- These models need migration to work with the new `PolicyProcessorPipeline` system
+- The migration extracts normalization statistics and creates separate processor pipelines
+
+### Migrating old models
+
+Use the migration script to convert models with embedded normalization:
+
+```shell
+python src/lerobot/processor/migrate_policy_normalization.py \
+    --pretrained-path lerobot/act_aloha_sim_transfer_cube_human \
+    --push-to-hub \
+    --branch migrated
+```
+
+The script:
+
+1. **Extracts** normalization statistics from model weights
+2. **Creates** external preprocessor and postprocessor pipelines
+3. **Removes** normalization layers from model weights
+4. **Saves** clean model + processor pipelines
+5. **Pushes** to Hub with automatic PR creation
+
+### Using migrated models
+
+```python
+# New usage pattern (after migration)
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+
+# Load model and processors separately
+policy = make_policy(config, ds_meta=dataset.meta)
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=config,
+    dataset_stats=dataset.meta.stats
+)
+
+# Process data through pipeline
+processed_batch = preprocessor(raw_batch)
+action = policy.select_action(processed_batch)
+final_action = postprocessor(action)
+```
+
+## Hardware API redesign
+
+PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is a overview of what changed and how you can continue to work with datasets created before this pull request.
+
+### 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            |
+
+---
+
+### 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.
+
+### 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.
+
+```diff
++   key = f"{name.removeprefix('main_')}.pos"
+    action[key] = action_array[i].item()
++   action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
++   action["elbow_flex.pos"] -= 90
+```
+
+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 \
+    --robot.port=/dev/tty.usbmodem5A460814411 \
+    --robot.id=blue \
++   --robot.use_degrees=true \
+    --dataset.repo_id=my_dataset_id \
+    --dataset.episode=0
+```
+
+### Using policies trained with the previous calibration system
+
+Policies output actions in the same format as the datasets (`torch.Tensors`). Therefore, the same transformations should be applied.
+
+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,
+    policy,
+    get_safe_torch_device(policy.config.device),
+    policy.config.use_amp,
+    task=single_task,
+    robot_type=robot.robot_type,
+    )
+    action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)}
+
++   action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
++   action["elbow_flex.pos"] -= 90
+    robot.send_action(action)
+```
+
+If you have questions or run into migration issues, feel free to ask them on [Discord](https://discord.gg/s3KuuzsPFb)

docs/source/cameras.mdx

@@ -0,0 +1,206 @@
+# Cameras
+
+LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
+
+### Finding your camera
+
+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
+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:
+  Name: OpenCV Camera @ 0
+  Type: OpenCV
+  Id: 0
+  Backend api: AVFOUNDATION
+  Default stream profile:
+    Format: 16.0
+    Width: 1920
+    Height: 1080
+    Fps: 15.0
+--------------------
+(more cameras ...)
+```
+
+> [!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.
+
+- **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
+from lerobot.cameras.configs import ColorMode, Cv2Rotation
+
+# Construct an `OpenCVCameraConfig` with your desired FPS, resolution, color mode, and rotation.
+config = OpenCVCameraConfig(
+    index_or_path=0,
+    fps=15,
+    width=1920,
+    height=1080,
+    color_mode=ColorMode.RGB,
+    rotation=Cv2Rotation.NO_ROTATION
+)
+
+# Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default).
+camera = OpenCVCamera(config)
+camera.connect()
+
+# Read frames asynchronously in a loop via `async_read(timeout_ms)`
+try:
+    for i in range(10):
+        frame = camera.async_read(timeout_ms=200)
+        print(f"Async frame {i} shape:", frame.shape)
+finally:
+    camera.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</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
+from lerobot.cameras.configs import ColorMode, Cv2Rotation
+
+# Create a `RealSenseCameraConfig` specifying your camera’s serial number and enabling depth.
+config = RealSenseCameraConfig(
+    serial_number_or_name="233522074606",
+    fps=15,
+    width=640,
+    height=480,
+    color_mode=ColorMode.RGB,
+    use_depth=True,
+    rotation=Cv2Rotation.NO_ROTATION
+)
+
+# Instantiate and connect a `RealSenseCamera` with warm-up read (default).
+camera = RealSenseCamera(config)
+camera.connect()
+
+# Capture a color frame via `read()` and a depth map via `read_depth()`.
+try:
+    color_frame = camera.read()
+    depth_map = camera.read_depth()
+    print("Color frame shape:", color_frame.shape)
+    print("Depth map shape:", depth_map.shape)
+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.
+
+For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
+
+Your iPhone should be detected automatically when running the camera setup script in the next section.
+
+</hfoption>
+<hfoption id="Linux">
+
+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:
+
+<!-- prettier-ignore-start -->
+```python
+sudo apt install v4l2loopback-dkms v4l-utils
+```
+<!-- 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
+```
+<!-- prettier-ignore-end -->
+
+4. _Install the DroidCam OBS plugin_. This plugin integrates DroidCam with OBS Studio. Install it with:
+
+<!-- prettier-ignore-start -->
+```python
+flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
+```
+<!-- prettier-ignore-end -->
+
+5. _Start OBS Studio_. Launch with:
+
+<!-- prettier-ignore-start -->
+```python
+flatpak run com.obsproject.Studio
+```
+<!-- prettier-ignore-end -->
+
+6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
+7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
+8. _Start virtual camera_. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
+9. _Verify the virtual camera setup_. 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`.
+
+<!-- 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
+>>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
+```
+
+Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
+
+If everything is set up correctly, you can proceed with the rest of the tutorial.
+
+</hfoption>
+</hfoptions>

docs/source/contributing.md

@@ -0,0 +1 @@
+../../CONTRIBUTING.md

docs/source/debug_processor_pipeline.mdx

@@ -0,0 +1,299 @@
+# Debug Your Processor Pipeline
+
+Processor pipelines can be complex, especially when chaining multiple transformation steps.
+Unlike simple function calls, pipelines lack natural observability, you can't easily see what happens
+between each step or where things go wrong.
+This guide provides debugging tools and techniques specifically designed to address these challenges
+and help you understand data flow through your pipelines.
+
+We'll explore three complementary debugging approaches: **hooks** for runtime monitoring, **step-through debugging** for detailed inspection, and **feature validation** for catching structural mismatches. Each serves a different purpose and together they provide complete visibility into your pipeline's behavior.
+
+## Understanding Hooks
+
+Hooks are functions that get called at specific points during pipeline execution.
+They provide a way to inspect, monitor, or modify data without changing your pipeline code.
+Think of them as "event listeners" for your pipeline.
+
+### What is a Hook?
+
+A hook is a callback function that gets automatically invoked at specific moments during pipeline execution.
+The concept comes from event-driven programming, imagine you could "hook into" the pipeline's execution flow to observe or react to what's happening.
+
+Think of hooks like inserting checkpoints into your pipeline. Every time the pipeline reaches one of these checkpoints, it pauses briefly to call your hook function, giving you a chance to inspect the current state, log information, and validate data.
+
+A hook is simply a function that accepts two parameters:
+
+- `step_idx: int` - The index of the current processing step (0, 1, 2, etc.)
+- `transition: EnvTransition` - The data transition at that point in the pipeline
+
+The beauty of hooks is their non-invasive nature: you can add monitoring, validation, or debugging logic without changing a single line of your pipeline code. The pipeline remains clean and focused on its core logic, while hooks handle the cross-cutting concerns like logging, monitoring, and debugging.
+
+### Before vs After Hooks
+
+The pipeline supports two types of hooks:
+
+- **Before hooks** (`register_before_step_hook`) - Called before each step executes
+- **After hooks** (`register_after_step_hook`) - Called after each step completes
+
+```python
+def before_hook(step_idx: int, transition: EnvTransition):
+    """Called before step processes the transition."""
+    print(f"About to execute step {step_idx}")
+    # Useful for: logging, validation, setup
+
+def after_hook(step_idx: int, transition: EnvTransition):
+    """Called after step has processed the transition."""
+    print(f"Completed step {step_idx}")
+    # Useful for: monitoring results, cleanup, debugging
+
+processor.register_before_step_hook(before_hook)
+processor.register_after_step_hook(after_hook)
+```
+
+### Implementing a NaN Detection Hook
+
+Here's a practical example of a hook that detects NaN values:
+
+```python
+def check_nans(step_idx: int, transition: EnvTransition):
+    """Check for NaN values in observations."""
+    obs = transition.get(TransitionKey.OBSERVATION)
+    if obs:
+        for key, value in obs.items():
+            if isinstance(value, torch.Tensor) and torch.isnan(value).any():
+                print(f"NaN detected in {key} at step {step_idx}")
+
+# Register the hook to run after each step
+processor.register_after_step_hook(check_nans)
+
+# Process your data - the hook will be called automatically
+output = processor(input_data)
+
+# Remove the hook when done debugging
+processor.unregister_after_step_hook(check_nans)
+```
+
+### How Hooks Work Internally
+
+Understanding the internal mechanism helps you use hooks more effectively. The pipeline maintains two separate lists: one for before-step hooks and another for after-step hooks. When you register a hook, it's simply appended to the appropriate list.
+
+During execution, the pipeline follows a strict sequence: for each processing step, it first calls all before-hooks in registration order, then executes the actual step transformation, and finally calls all after-hooks in registration order. This creates a predictable, sandwich-like structure around each step.
+
+The key insight is that hooks don't change the core pipeline logic—they're purely additive. The pipeline's `_forward` method orchestrates this dance between hooks and processing steps, ensuring that your debugging or monitoring code runs at exactly the right moments without interfering with the main data flow.
+
+Here's a simplified view of how the pipeline executes hooks:
+
+```python
+class DataProcessorPipeline:
+    def __init__(self):
+        self.steps = [...]
+        self.before_step_hooks = []  # List of before hooks
+        self.after_step_hooks = []   # List of after hooks
+
+    def _forward(self, transition):
+        """Internal method that processes the transition through all steps."""
+        for step_idx, processor_step in enumerate(self.steps):
+            # 1. Call all BEFORE hooks
+            for hook in self.before_step_hooks:
+                hook(step_idx, transition)
+
+            # 2. Execute the actual processing step
+            transition = processor_step(transition)
+
+            # 3. Call all AFTER hooks
+            for hook in self.after_step_hooks:
+                hook(step_idx, transition)
+
+        return transition
+
+    def register_before_step_hook(self, hook_fn):
+        self.before_step_hooks.append(hook_fn)
+
+    def register_after_step_hook(self, hook_fn):
+        self.after_step_hooks.append(hook_fn)
+```
+
+### Execution Flow
+
+The execution flow looks like this:
+
+```
+Input → Before Hook → Step 0 → After Hook → Before Hook → Step 1 → After Hook → ... → Output
+```
+
+For example, with 3 steps and both hook types:
+
+```python
+def timing_before(step_idx, transition):
+    print(f"⏱️  Starting step {step_idx}")
+
+def validation_after(step_idx, transition):
+    print(f"✅ Completed step {step_idx}")
+
+processor.register_before_step_hook(timing_before)
+processor.register_after_step_hook(validation_after)
+
+# This will output:
+# ⏱️  Starting step 0
+# ✅ Completed step 0
+# ⏱️  Starting step 1
+# ✅ Completed step 1
+# ⏱️  Starting step 2
+# ✅ Completed step 2
+```
+
+### Multiple Hooks
+
+You can register multiple hooks of the same type - they execute in the order registered:
+
+```python
+def log_shapes(step_idx: int, transition: EnvTransition):
+    obs = transition.get(TransitionKey.OBSERVATION)
+    if obs:
+        print(f"Step {step_idx} observation shapes:")
+        for key, value in obs.items():
+            if isinstance(value, torch.Tensor):
+                print(f"  {key}: {value.shape}")
+
+processor.register_after_step_hook(check_nans)      # Executes first
+processor.register_after_step_hook(log_shapes)     # Executes second
+
+# Both hooks will be called after each step in registration order
+output = processor(input_data)
+```
+
+While hooks are excellent for monitoring specific issues (like NaN detection) or gathering metrics during normal pipeline execution, sometimes you need to dive deeper. When you want to understand exactly what happens at each step or debug complex transformation logic, step-through debugging provides the detailed inspection you need.
+
+## Step-Through Debugging
+
+Step-through debugging is like having a slow-motion replay for your pipeline. Instead of watching your data get transformed in one quick blur from input to output, you can pause and examine what happens after each individual step.
+
+This approach is particularly valuable when you're trying to understand a complex pipeline, debug unexpected behavior, or verify that each transformation is working as expected. Unlike hooks, which are great for automated monitoring, step-through debugging gives you manual, interactive control over the inspection process.
+
+The `step_through()` method is a generator that yields the transition state after each processing step, allowing you to inspect intermediate results. Think of it as creating a series of snapshots of your data as it flows through the pipeline—each snapshot shows you exactly what your data looks like after one more transformation has been applied.
+
+### How Step-Through Works
+
+The `step_through()` method fundamentally changes how the pipeline executes. Instead of running all steps in sequence and only returning the final result, it transforms the pipeline into an iterator that yields intermediate results.
+
+Here's what happens internally: the method starts by converting your input data into the pipeline's internal transition format, then yields this initial state. Next, it applies the first processing step and yields the result. Then it applies the second step to that result and yields again, and so on. Each `yield` gives you a complete snapshot of the transition at that point.
+
+This generator pattern is powerful because it's lazy—the pipeline only computes the next step when you ask for it. This means you can stop at any point, inspect the current state thoroughly, and decide whether to continue. You're not forced to run the entire pipeline just to debug one problematic step.
+
+Instead of running the entire pipeline and only seeing the final result, `step_through()` pauses after each step and gives you the intermediate transition:
+
+```python
+# This creates a generator that yields intermediate states
+for i, intermediate_result in enumerate(processor.step_through(input_data)):
+    print(f"=== After step {i} ===")
+
+    # Inspect the observation at this stage
+    obs = intermediate_result.get(TransitionKey.OBSERVATION)
+    if obs:
+        for key, value in obs.items():
+            if isinstance(value, torch.Tensor):
+                print(f"{key}: shape={value.shape}, dtype={value.dtype}")
+```
+
+### Interactive Debugging with Breakpoints
+
+You can add breakpoints in the step-through loop to interactively debug:
+
+```python
+# Step through the pipeline with debugging
+for i, intermediate in enumerate(processor.step_through(data)):
+    print(f"Step {i}: {processor.steps[i].__class__.__name__}")
+
+    # Set a breakpoint to inspect the current state
+    breakpoint()  # Debugger will pause here
+
+    # You can now inspect 'intermediate' in the debugger:
+    # - Check tensor shapes and values
+    # - Verify expected transformations
+    # - Look for unexpected changes
+```
+
+During the debugger session, you can:
+
+- Examine `intermediate[TransitionKey.OBSERVATION]` to see observation data
+- Check `intermediate[TransitionKey.ACTION]` for action transformations
+- Inspect any part of the transition to understand what each step does
+
+Step-through debugging is perfect for understanding the _data_ transformations, but what about the _structure_ of that data? While hooks and step-through help you debug runtime behavior, you also need to ensure your pipeline produces data in the format expected by downstream components. This is where feature contract validation comes in.
+
+## Validating Feature Contracts
+
+Feature contracts define what data structure your pipeline expects as input and produces as output.
+Validating these contracts helps catch mismatches early.
+
+### Understanding Feature Contracts
+
+Each processor step has a `transform_features()` method that describes how it changes the data structure:
+
+```python
+# Get the expected output features from your pipeline
+initial_features = {
+    PipelineFeatureType.OBSERVATION: {
+        "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(7,)),
+        "observation.image": PolicyFeature(type=FeatureType.IMAGE, shape=(3, 224, 224))
+    },
+    PipelineFeatureType.ACTION: {
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,))
+    }
+}
+
+# Check what your pipeline will output
+output_features = processor.transform_features(initial_features)
+
+print("Input features:")
+for feature_type, features in initial_features.items():
+    print(f"  {feature_type}:")
+    for key, feature in features.items():
+        print(f"    {key}: {feature.type.value}, shape={feature.shape}")
+
+print("\nOutput features:")
+for feature_type, features in output_features.items():
+    print(f"  {feature_type}:")
+    for key, feature in features.items():
+        print(f"    {key}: {feature.type.value}, shape={feature.shape}")
+```
+
+### Verifying Expected Features
+
+Check that your pipeline produces the features you expect:
+
+```python
+# Define what features you expect the pipeline to produce
+expected_keys = ["observation.state", "observation.image", "action"]
+
+print("Validating feature contract...")
+for expected_key in expected_keys:
+    found = False
+    for feature_type, features in output_features.items():
+        if expected_key in features:
+            feature = features[expected_key]
+            print(f"✅ {expected_key}: {feature.type.value}, shape={feature.shape}")
+            found = True
+            break
+
+    if not found:
+        print(f"❌ Missing expected feature: {expected_key}")
+```
+
+This validation helps ensure your pipeline will work correctly with downstream components that expect specific data structures.
+
+## Summary
+
+Now that you understand the three debugging approaches, you can tackle any pipeline issue systematically:
+
+1. **Hooks** - For runtime monitoring and validation without modifying pipeline code
+2. **Step-through** - For inspecting intermediate states and understanding transformations
+3. **Feature validation** - For ensuring data structure contracts are met
+
+**When to use each approach:**
+
+- Start with **step-through debugging** when you need to understand what your pipeline does or when something unexpected happens
+- Add **hooks** for continuous monitoring during development and production to catch issues automatically
+- Use **feature validation** before deployment to ensure your pipeline works with downstream components
+
+These three tools work together to give you the complete observability that complex pipelines naturally lack. With hooks watching for issues, step-through helping you understand behavior, and feature validation ensuring compatibility, you'll be able to debug any pipeline confidently and efficiently.

docs/source/envhub.mdx

@@ -0,0 +1,424 @@
+# Loading Environments from the Hub
+
+The **EnvHub** feature allows you to load simulation environments directly from the Hugging Face Hub with a single line of code. This unlocks a powerful new model for collaboration: instead of environments being locked away inside monolithic libraries, anyone can publish custom environments and share them with the community.
+
+## Overview
+
+With EnvHub, you can:
+
+- Load environments from the Hub instantly
+- Share your custom simulation tasks with the community
+- Version control your environments using Git
+- Distribute complex physics simulations without packaging hassles
+
+## Quick Start
+
+Loading an environment from the Hub is as simple as:
+
+```python
+from lerobot.envs.factory import make_env
+
+# Load a hub environment (requires explicit consent to run remote code)
+env = make_env("lerobot/cartpole-env", trust_remote_code=True)
+```
+
+<Tip warning={true}>
+  **Security Notice**: Loading environments from the Hub executes Python code
+  from third-party repositories. Only use `trust_remote_code=True` with
+  repositories you trust. We strongly recommend pinning to a specific commit
+  hash for reproducibility and security.
+</Tip>
+
+## What is EnvHub?
+
+EnvHub is a framework that allows researchers and developers to:
+
+1. **Publish environments** to the Hugging Face Hub as Git repositories
+2. **Load environments** dynamically without installing them as packages
+3. **Version and track** environment changes using Git semantics
+4. **Discover** new simulation tasks shared by the community
+
+This design means you can go from discovering an interesting environment on the Hub to running experiments in seconds, without worrying about dependency conflicts or complex installation procedures.
+
+## Repository Structure
+
+To make your environment loadable from the Hub, your repository must contain at minimum:
+
+### Required Files
+
+**`env.py`** (or custom Python file)
+
+- Must expose a `make_env(n_envs: int, use_async_envs: bool)` function
+- This function should return one of:
+  - A `gym.vector.VectorEnv` (most common)
+  - A single `gym.Env` (will be automatically wrapped)
+  - A dict mapping `{suite_name: {task_id: VectorEnv}}` (for multi-task benchmarks)
+
+### Optional Files
+
+**`requirements.txt`**
+
+- List any additional dependencies your environment needs
+- Users will need to install these manually before loading your environment
+
+**`README.md`**
+
+- Document your environment: what task it implements, observation/action spaces, rewards, etc.
+- Include usage examples and any special setup instructions
+
+**`.gitignore`**
+
+- Exclude unnecessary files from your repository
+
+### Example Repository Structure
+
+```
+my-environment-repo/
+├── env.py                 # Main environment definition (required)
+├── requirements.txt       # Dependencies (optional)
+├── README.md             # Documentation (recommended)
+├── assets/               # Images, videos, etc. (optional)
+│   └── demo.gif
+└── configs/              # Config files if needed (optional)
+    └── task_config.yaml
+```
+
+## Creating Your Environment Repository
+
+### Step 1: Define Your Environment
+
+Create an `env.py` file with a `make_env` function:
+
+```python
+# env.py
+import gymnasium as gym
+
+def make_env(n_envs: int = 1, use_async_envs: bool = False):
+    """
+    Create vectorized environments for your custom task.
+
+    Args:
+        n_envs: Number of parallel environments
+        use_async_envs: Whether to use AsyncVectorEnv or SyncVectorEnv
+
+    Returns:
+        gym.vector.VectorEnv or dict mapping suite names to vectorized envs
+    """
+    def _make_single_env():
+        # Create your custom environment
+        return gym.make("CartPole-v1")
+
+    # Choose vector environment type
+    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
+
+    # Create vectorized environment
+    vec_env = env_cls([_make_single_env for _ in range(n_envs)])
+
+    return vec_env
+```
+
+### Step 2: Test Locally
+
+Before uploading, test your environment locally:
+
+```python
+from lerobot.envs.utils import _load_module_from_path, _call_make_env, _normalize_hub_result
+
+# Load your module
+module = _load_module_from_path("./env.py")
+
+# Test the make_env function
+result = _call_make_env(module, n_envs=2, use_async_envs=False)
+normalized = _normalize_hub_result(result)
+
+# Verify it works
+suite_name = next(iter(normalized))
+env = normalized[suite_name][0]
+obs, info = env.reset()
+print(f"Observation shape: {obs.shape if hasattr(obs, 'shape') else type(obs)}")
+env.close()
+```
+
+### Step 3: Upload to the Hub
+
+Upload your repository to Hugging Face:
+
+```bash
+# Install huggingface_hub if needed
+pip install huggingface_hub
+
+# Login to Hugging Face
+huggingface-cli login
+
+# Create a new repository
+huggingface-cli repo create my-custom-env --type space --org my-org
+
+# Initialize git and push
+git init
+git add .
+git commit -m "Initial environment implementation"
+git remote add origin https://huggingface.co/my-org/my-custom-env
+git push -u origin main
+```
+
+Alternatively, use the `huggingface_hub` Python API:
+
+```python
+from huggingface_hub import HfApi
+
+api = HfApi()
+
+# Create repository
+api.create_repo("my-custom-env", repo_type="space")
+
+# Upload files
+api.upload_folder(
+    folder_path="./my-env-folder",
+    repo_id="username/my-custom-env",
+    repo_type="space",
+)
+```
+
+## Loading Environments from the Hub
+
+### Basic Usage
+
+```python
+from lerobot.envs.factory import make_env
+
+# Load from the hub
+envs_dict = make_env(
+    "username/my-custom-env",
+    n_envs=4,
+    trust_remote_code=True
+)
+
+# Access the environment
+suite_name = next(iter(envs_dict))
+env = envs_dict[suite_name][0]
+
+# Use it like any gym environment
+obs, info = env.reset()
+action = env.action_space.sample()
+obs, reward, terminated, truncated, info = env.step(action)
+```
+
+### Advanced: Pinning to Specific Versions
+
+For reproducibility and security, pin to a specific Git revision:
+
+```python
+# Pin to a specific branch
+env = make_env("username/my-env@main", trust_remote_code=True)
+
+# Pin to a specific commit (recommended for papers/experiments)
+env = make_env("username/my-env@abc123def456", trust_remote_code=True)
+
+# Pin to a tag
+env = make_env("username/my-env@v1.0.0", trust_remote_code=True)
+```
+
+### Custom File Paths
+
+If your environment definition is not in `env.py`:
+
+```python
+# Load from a custom file
+env = make_env("username/my-env:custom_env.py", trust_remote_code=True)
+
+# Combine with version pinning
+env = make_env("username/my-env@v1.0:envs/task_a.py", trust_remote_code=True)
+```
+
+### Async Environments
+
+For better performance with multiple environments:
+
+```python
+envs_dict = make_env(
+    "username/my-env",
+    n_envs=8,
+    use_async_envs=True,  # Use AsyncVectorEnv for parallel execution
+    trust_remote_code=True
+)
+```
+
+## URL Format Reference
+
+The hub URL format supports several patterns:
+
+| Pattern              | Description                    | Example                                |
+| -------------------- | ------------------------------ | -------------------------------------- |
+| `user/repo`          | Load `env.py` from main branch | `make_env("lerobot/pusht-env")`        |
+| `user/repo@revision` | Load from specific revision    | `make_env("lerobot/pusht-env@main")`   |
+| `user/repo:path`     | Load custom file               | `make_env("lerobot/envs:pusht.py")`    |
+| `user/repo@rev:path` | Revision + custom file         | `make_env("lerobot/envs@v1:pusht.py")` |
+
+## Multi-Task Environments
+
+For benchmarks with multiple tasks (like LIBERO), return a nested dictionary:
+
+```python
+def make_env(n_envs: int = 1, use_async_envs: bool = False):
+    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
+
+    # Return dict: {suite_name: {task_id: VectorEnv}}
+    return {
+        "suite_1": {
+            0: env_cls([lambda: gym.make("Task1-v0") for _ in range(n_envs)]),
+            1: env_cls([lambda: gym.make("Task2-v0") for _ in range(n_envs)]),
+        },
+        "suite_2": {
+            0: env_cls([lambda: gym.make("Task3-v0") for _ in range(n_envs)]),
+        }
+    }
+```
+
+## Security Considerations
+
+<Tip warning={true}>
+  **Important**: The `trust_remote_code=True` flag is required to execute
+  environment code from the Hub. This is by design for security.
+</Tip>
+
+When loading environments from the Hub:
+
+1. **Review the code first**: Visit the repository and inspect `env.py` before loading
+2. **Pin to commits**: Use specific commit hashes for reproducibility
+3. **Check dependencies**: Review `requirements.txt` for suspicious packages
+4. **Use trusted sources**: Prefer official organizations or well-known researchers
+5. **Sandbox if needed**: Run untrusted code in isolated environments (containers, VMs)
+
+Example of safe usage:
+
+```python
+# ❌ BAD: Loading without inspection
+env = make_env("random-user/untrusted-env", trust_remote_code=True)
+
+# ✅ GOOD: Review code, then pin to specific commit
+# 1. Visit https://huggingface.co/trusted-org/verified-env
+# 2. Review the env.py file
+# 3. Copy the commit hash
+env = make_env("trusted-org/verified-env@a1b2c3d4", trust_remote_code=True)
+```
+
+## Example: CartPole from the Hub
+
+Here's a complete example using the reference CartPole environment:
+
+```python
+from lerobot.envs.factory import make_env
+import numpy as np
+
+# Load the environment
+envs_dict = make_env("lerobot/cartpole-env", n_envs=4, trust_remote_code=True)
+
+# Get the vectorized environment
+suite_name = next(iter(envs_dict))
+env = envs_dict[suite_name][0]
+
+# Run a simple episode
+obs, info = env.reset()
+done = np.zeros(env.num_envs, dtype=bool)
+total_reward = np.zeros(env.num_envs)
+
+while not done.all():
+    # Random policy
+    action = env.action_space.sample()
+    obs, reward, terminated, truncated, info = env.step(action)
+    total_reward += reward
+    done = terminated | truncated
+
+print(f"Average reward: {total_reward.mean():.2f}")
+env.close()
+```
+
+## Benefits of EnvHub
+
+### For Environment Authors
+
+- **Easy distribution**: No PyPI packaging required
+- **Version control**: Use Git for environment versioning
+- **Rapid iteration**: Push updates instantly
+- **Documentation**: Hub README renders beautifully
+- **Community**: Reach LeRobot users directly
+
+### For Researchers
+
+- **Quick experiments**: Load any environment in one line
+- **Reproducibility**: Pin to specific commits
+- **Discovery**: Browse environments on the Hub
+- **No conflicts**: No need to install conflicting packages
+
+### For the Community
+
+- **Growing ecosystem**: More diverse simulation tasks
+- **Standardization**: Common `make_env` API
+- **Collaboration**: Fork and improve existing environments
+- **Accessibility**: Lower barrier to sharing research
+
+## Troubleshooting
+
+### "Refusing to execute remote code"
+
+You must explicitly pass `trust_remote_code=True`:
+
+```python
+env = make_env("user/repo", trust_remote_code=True)
+```
+
+### "Module X not found"
+
+The hub environment has dependencies you need to install:
+
+```bash
+# Check the repo's requirements.txt and install dependencies
+pip install gymnasium numpy
+```
+
+### "make_env not found in module"
+
+Your `env.py` must expose a `make_env` function:
+
+```python
+def make_env(n_envs: int, use_async_envs: bool):
+    # Your implementation
+    pass
+```
+
+### Environment returns wrong type
+
+The `make_env` function must return:
+
+- A `gym.vector.VectorEnv`, or
+- A single `gym.Env`, or
+- A dict `{suite_name: {task_id: VectorEnv}}`
+
+## Best Practices
+
+1. **Document your environment**: Include observation/action space descriptions, reward structure, and termination conditions in your README
+2. **Add requirements.txt**: List all dependencies with versions
+3. **Test thoroughly**: Verify your environment works locally before pushing
+4. **Use semantic versioning**: Tag releases with version numbers
+5. **Add examples**: Include usage examples in your README
+6. **Keep it simple**: Minimize dependencies when possible
+7. **License your work**: Add a LICENSE file to clarify usage terms
+
+## Future Directions
+
+The EnvHub ecosystem enables exciting possibilities:
+
+- **GPU-accelerated physics**: Share Isaac Gym or Brax environments
+- **Photorealistic rendering**: Distribute environments with advanced graphics
+- **Multi-agent scenarios**: Complex interaction tasks
+- **Real-world simulators**: Digital twins of physical setups
+- **Procedural generation**: Infinite task variations
+- **Domain randomization**: Pre-configured DR pipelines
+
+As more researchers and developers contribute, the diversity and quality of available environments will grow, benefiting the entire robotics learning community.
+
+## See Also
+
+- [Hugging Face Hub Documentation](https://huggingface.co/docs/hub/en/index)
+- [Gymnasium Documentation](https://gymnasium.farama.org/index.html)
+- [Example Hub Environment](https://huggingface.co/lerobot/cartpole-env)

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/groot.mdx

@@ -0,0 +1,125 @@
+# GR00T N1.5 Policy
+
+GR00T N1.5 is an open foundation model from NVIDIA designed for generalized humanoid robot reasoning and skills. It is a cross-embodiment model that accepts multimodal input, including language and images, to perform manipulation tasks in diverse environments.
+
+This document outlines the specifics of its integration and usage within the LeRobot framework.
+
+## Model Overview
+
+NVIDIA Isaac GR00T N1.5 is an upgraded version of the GR00T N1 foundation model. It is built to improve generalization and language-following abilities for humanoid robots.
+
+Developers and researchers can post-train GR00T N1.5 with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
+
+GR00T N1.5 (specifically the GR00T-N1.5-3B model) is built using pre-trained vision and language encoders. It utilizes a flow matching action transformer to model a chunk of actions, conditioned on vision, language, and proprioception.
+
+Its strong performance comes from being trained on an expansive and diverse humanoid dataset, which includes:
+
+- Real captured data from robots.
+- Synthetic data generated using NVIDIA Isaac GR00T Blueprint.
+- Internet-scale video data.
+
+This approach allows the model to be highly adaptable through post-training for specific embodiments, tasks, and environments.
+
+## Installation Requirements
+
+As of today, GR00T N1.5 requires flash attention for it's internal working.
+
+We are working on making this optional, but in the meantime that means that we require an extra installation step and it can only be used in CUDA enabled devices.
+
+1. Following the Environment Setup of our [Installation Guide](./installation). **Attention** don't install `lerobot` in this step.
+2. Install [Flash Attention](https://github.com/Dao-AILab/flash-attention) by running:
+
+```bash
+# Check https://pytorch.org/get-started/locally/ for your system
+pip install "torch>=2.2.1,<2.8.0" "torchvision>=0.21.0,<0.23.0" # --index-url https://download.pytorch.org/whl/cu1XX
+pip install ninja "packaging>=24.2,<26.0" # flash attention dependencies
+pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
+python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
+```
+
+3. Install LeRobot by running:
+
+```bash
+pip install lerobot[groot]
+```
+
+## Usage
+
+To use GR00T in your LeRobot configuration, specify the policy type as:
+
+```python
+policy.type=groot
+```
+
+## Training
+
+### Training Command Example
+
+Here's a complete training command for finetuning the base GR00T model on your own dataset:
+
+```bash
+# Using a multi-GPU setup
+accelerate launch \
+  --multi_gpu \
+  --num_processes=$NUM_GPUS \
+  $(which lerobot-train) \
+  --output_dir=$OUTPUT_DIR \
+  --save_checkpoint=true \
+  --batch_size=$BATCH_SIZE \
+  --steps=$NUM_STEPS \
+  --save_freq=$SAVE_FREQ \
+  --log_freq=$LOG_FREQ \
+  --policy.push_to_hub=true \
+  --policy.type=groot \
+  --policy.repo_id=$REPO_ID \
+  --policy.tune_diffusion_model=false \
+  --dataset.repo_id=$DATASET_ID \
+  --wandb.enable=true \
+  --wandb.disable_artifact=true \
+  --job_name=$JOB_NAME
+```
+
+## Performance Results
+
+### Libero Benchmark Results
+
+> [!NOTE]
+> Follow our instructions for Libero usage: [Libero](./libero)
+
+GR00T has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the GR00T N1.5 model for 30k steps on the Libero dataset and compared the results to the GR00T reference results.
+
+| Benchmark          | LeRobot Implementation | GR00T Reference |
+| ------------------ | ---------------------- | --------------- |
+| **Libero Spatial** | 82.0%                  | 92.0%           |
+| **Libero Object**  | 99.0%                  | 92.0%           |
+| **Libero Long**    | 82.0%                  | 76.0%           |
+| **Average**        | 87.0%                  | 87.0%           |
+
+These results demonstrate GR00T's strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section.
+
+### Evaluate in your hardware setup
+
+Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Imitation Learning for Robots](./il_robots). For example:
+
+```bash
+lerobot-record \
+  --robot.type=bi_so100_follower \
+  --robot.left_arm_port=/dev/ttyACM1 \
+  --robot.right_arm_port=/dev/ttyACM0 \
+  --robot.id=bimanual_follower \
+  --robot.cameras='{ right: {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30},
+    left: {"type": "opencv", "index_or_path": 2, "width": 640, "height": 480, "fps": 30},
+    top: {"type": "opencv", "index_or_path": 4, "width": 640, "height": 480, "fps": 30},
+  }' \
+  --display_data=true \
+  --dataset.repo_id=<user>/eval_groot-bimanual  \
+  --dataset.num_episodes=10 \
+  --dataset.single_task="Grab and handover the red cube to the other arm"
+  --policy.path=<user>/groot-bimanual # your trained model
+  --dataset.episode_time_s=30
+  --dataset.reset_time_s=10
+```
+
+## License
+
+This model follows the **Apache 2.0 License**, consistent with the original [GR00T repository](https://github.com/NVIDIA/Isaac-GR00T).

docs/source/hilserl.mdx

@@ -0,0 +1,923 @@
+# HIL-SERL Real Robot Training Workflow Guide
+
+In this tutorial you will go through the full Human-in-the-Loop Sample-Efficient Reinforcement Learning (HIL-SERL) workflow using LeRobot. You will master training a policy with RL on a real robot in just a few hours.
+
+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.
+
+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>
+</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.
+
+## What do I need?
+
+- 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/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
+- Avoid extremely long horizon tasks. Focus on tasks that can be completed in 5-10 seconds.
+- Once you have a good idea of how the system works, you can try more complex tasks and longer horizons.
+  - Pick and place cube
+  - Bimanual tasks to pick objects with two arms
+  - Hand-over tasks to transfer objects from one arm to another
+  - Go crazy!
+
+## Install LeRobot with HIL-SERL
+
+To install LeRobot with HIL-SERL, you need to install the `hilserl` extra.
+
+```bash
+pip install -e ".[hilserl]"
+```
+
+## Real Robot Training Workflow
+
+### Understanding Configuration
+
+The training process begins with proper configuration for the HILSerl environment. The main configuration class is `GymManipulatorConfig` in `lerobot/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
+    processor: HILSerlProcessorConfig    # Processing pipeline configuration (nested)
+    name: str = "real_robot"    # Environment name
+    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
+    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
+
+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. **InterventionActionProcessorStep**: Handles human interventions and episode termination
+4. **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,
+        "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
+
+Before collecting demonstrations, you need to determine the appropriate operational bounds for your robot.
+
+This helps simplify the problem of learning on the real robot in two ways: 1) by limiting the robot's operational space to a specific region that solves the task and avoids unnecessary or unsafe exploration, and 2) by allowing training in end-effector space rather than joint space. Empirically, learning in joint space for reinforcement learning in manipulation is often a harder problem - some tasks are nearly impossible to learn in joint space but become learnable when the action space is transformed to end-effector coordinates.
+
+**Using lerobot-find-joint-limits**
+
+This script helps you find the safe operational bounds for your robot's end-effector. Given that you have a follower and leader arm, you can use the script to find the bounds for the follower arm that will be applied during training.
+Bounding the action space will reduce the redundant exploration of the agent and guarantees safety.
+
+```bash
+lerobot-find-joint-limits \
+  --robot.type=so100_follower \
+  --robot.port=/dev/tty.usbmodem58760431541 \
+  --robot.id=black \
+  --teleop.type=so100_leader \
+  --teleop.port=/dev/tty.usbmodem58760431551 \
+  --teleop.id=blue
+```
+
+**Workflow**
+
+1. Run the script and move the robot through the space that solves the task
+2. The script will record the minimum and maximum end-effector positions and the joint angles and prints them to the console, for example:
+   ```
+   Max ee position [0.2417 0.2012 0.1027]
+   Min ee position [0.1663 -0.0823 0.0336]
+   Max joint positions [-20.0, -20.0, -20.0, -20.0, -20.0, -20.0]
+   Min joint positions [50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+   ```
+3. Use these values in the configuration of your teleoperation device (TeleoperatorConfig) under the `end_effector_bounds` field
+
+**Example Configuration**
+
+```json
+"end_effector_bounds": {
+    "max": [0.24, 0.20, 0.10],
+    "min": [0.16, -0.08, 0.03]
+}
+```
+
+### Collecting Demonstrations
+
+With the bounds defined, you can safely collect demonstrations for training. Training RL with off-policy algorithm allows us to use offline datasets collected in order to improve the efficiency of the learning process.
+
+**Setting Up Record Mode**
+
+Create a configuration file for recording demonstrations (or edit an existing one like [env_config.json](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/env_config.json)):
+
+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
+{
+  "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
+
+Along with your robot, you will need a teleoperation device to control it in order to collect datasets of your task and perform interventions during the online training.
+We support using a gamepad or a keyboard or the leader arm of the robot.
+
+HIL-Serl learns actions in the end-effector space of the robot. Therefore, the teleoperation will control the end-effector's x,y,z displacements.
+
+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."""
+
+    # Default bounds for the end-effector position (in meters)
+    end_effector_bounds: dict[str, list[float]] = field( # bounds for the end-effector in x,y,z direction
+        default_factory=lambda: {
+            "min": [-1.0, -1.0, -1.0],  # min x, y, z
+            "max": [1.0, 1.0, 1.0],  # max x, y, z
+        }
+    )
+
+    max_gripper_pos: float = 50 # maximum gripper position that the gripper will be open at
+
+    end_effector_step_sizes: dict[str, float] = field( # maximum step size for the end-effector in x,y,z direction
+        default_factory=lambda: {
+            "x": 0.02,
+            "y": 0.02,
+            "z": 0.02,
+        }
+    )
+```
+<!-- prettier-ignore-end -->
+
+The `Teleoperator` defines the teleoperation device. You can check the list of available teleoperators in `lerobot/teleoperators`.
+
+**Setting up the Gamepad**
+
+The gamepad provides a very convenient way to control the robot and the episode state.
+
+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
+    },
+    "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>
+</p>
+<p align="center">
+  <i>Gamepad button mapping for robot control and episode management</i>
+</p>
+
+**Setting up the SO101 leader**
+
+The SO101 leader arm has reduced gears that allows it to move and track the follower arm during exploration. Therefore, taking over is much smoother than the gearless SO100.
+
+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",
+      "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.
+During the online training, press `space` to take over the policy and `space` again to give the control back to the policy.
+
+<details>
+<summary><strong>Video: SO101 leader teleoperation</strong></summary>
+
+<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"
+    />
+  </video>
+</div>
+
+<p align="center"><i>SO101 leader teleoperation example, the leader tracks the follower, press `space` to intervene</i></p>
+</details>
+
+**Recording Demonstrations**
+
+Start the recording process, an example of the config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json):
+
+```bash
+python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/env_config_so100.json
+```
+
+During recording:
+
+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
+5. You can rerecord an episode by pressing the "rerecord" button
+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
+
+Note: If you already know the crop parameters, you can skip this step and just set the `crop_params_dict` in the configuration file during recording.
+
+**Determining Crop Parameters**
+
+Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images:
+
+```bash
+python -m lerobot.rl.crop_dataset_roi --repo-id username/pick_lift_cube
+```
+
+1. For each camera view, the script will display the first frame
+2. Draw a rectangle around the relevant workspace area
+3. Press 'c' to confirm the selection
+4. Repeat for all camera views
+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]
+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"
+  />
+</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
+{
+  "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.
+
+### Training a Reward Classifier
+
+The reward classifier plays an important role in the HIL-SERL workflow by automating reward assignment and automatically detecting episode success. Instead of manually defining reward functions or relying on human feedback for every timestep, the reward classifier learns to predict success/failure from visual observations. This enables the RL algorithm to learn efficiently by providing consistent and automated reward signals based on the robot's camera inputs.
+
+This guide explains how to train a reward classifier for human-in-the-loop reinforcement learning implementation of LeRobot. Reward classifiers learn to predict the reward value given a state which can be used in an RL setup to train a policy.
+
+**Note**: Training a reward classifier is optional. You can start the first round of RL experiments by annotating the success manually with your gamepad or keyboard device.
+
+The reward classifier implementation in `modeling_classifier.py` uses a pretrained vision model to process the images. It can output either a single value for binary rewards to predict success/fail cases or multiple values for multi-class settings.
+
+**Collecting a Dataset for the reward classifier**
+
+Before training, you need to collect a dataset with labeled examples. The `record_dataset` function in `gym_manipulator.py` enables the process of collecting a dataset of observations, actions, and rewards.
+
+To collect a dataset, you need to modify some parameters in the environment configuration based on HILSerlRobotEnvConfig.
+
+```bash
+python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/reward_classifier_train_config.json
+```
+
+**Key Parameters for Data Collection**
+
+- **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 `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
+{
+  "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",
+    "task": "reward_classifier_task",
+    "num_episodes_to_record": 20,
+    "replay_episode": null,
+    "push_to_hub": true
+  },
+  "mode": "record",
+  "device": "cpu"
+}
+```
+
+**Reward Classifier Configuration**
+
+The reward classifier is configured using `configuration_classifier.py`. Here are the key parameters:
+
+- **model_name**: Base model architecture (e.g., we mainly use `"helper2424/resnet10"`)
+- **model_type**: `"cnn"` or `"transformer"`
+- **num_cameras**: Number of camera inputs
+- **num_classes**: Number of output classes (typically 2 for binary success/failure)
+- **hidden_dim**: Size of hidden representation
+- **dropout_rate**: Regularization parameter
+- **learning_rate**: Learning rate for optimizer
+
+Example configuration for training the [reward classifier](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/reward_classifier_train_config.json):
+
+```json
+{
+  "policy": {
+    "type": "reward_classifier",
+    "model_name": "helper2424/resnet10",
+    "model_type": "cnn",
+    "num_cameras": 2,
+    "num_classes": 2,
+    "hidden_dim": 256,
+    "dropout_rate": 0.1,
+    "learning_rate": 1e-4,
+    "device": "cuda",
+    "use_amp": true,
+    "input_features": {
+      "observation.images.front": {
+        "type": "VISUAL",
+        "shape": [3, 128, 128]
+      },
+      "observation.images.side": {
+        "type": "VISUAL",
+        "shape": [3, 128, 128]
+      }
+    }
+  }
+}
+```
+
+**Training the Classifier**
+
+To train the classifier, use the `train.py` script with your configuration:
+
+```bash
+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
+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
+{
+  "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.rl.gym_manipulator --config_path path/to/env_config.json
+```
+
+The reward classifier will automatically provide rewards based on the visual input from the robot's cameras.
+
+**Example Workflow for training the reward classifier**
+
+1. **Create the configuration files**:
+   Create the necessary json configuration files for the reward classifier and the environment. Check the examples [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/reward_classifier/config.json).
+
+2. **Collect a dataset**:
+
+   ```bash
+   python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
+   ```
+
+3. **Train the classifier**:
+
+   ```bash
+   lerobot-train --config_path src/lerobot/configs/reward_classifier_train_config.json
+   ```
+
+4. **Test the classifier**:
+   ```bash
+   python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
+   ```
+
+### Training with Actor-Learner
+
+The LeRobot system uses a distributed actor-learner architecture for training. This architecture decouples robot interactions from the learning process, allowing them to run concurrently without blocking each other. The actor server handles robot observations and actions, sending interaction data to the learner server. The learner server performs gradient descent and periodically updates the actor's policy weights. You will need to start two processes: a learner and an actor.
+
+**Configuration Setup**
+
+Create a training configuration file (example available [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/train_config.json)). The training config is based on the main `TrainRLServerPipelineConfig` class in `lerobot/configs/train.py`.
+
+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/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**
+
+First, start the learner server process:
+
+```bash
+python -m lerobot.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json
+```
+
+The learner:
+
+- Initializes the policy network
+- Prepares replay buffers
+- Opens a `gRPC` server to communicate with actors
+- Processes transitions and updates the policy
+
+**Starting the Actor**
+
+In a separate terminal, start the actor process with the same configuration:
+
+```bash
+python -m lerobot.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json
+```
+
+The actor:
+
+- Connects to the learner via `gRPC`
+- Initializes the environment
+- Execute rollouts of the policy to collect experience
+- Sends transitions to the learner
+- Receives updated policy parameters
+
+**Training Flow**
+
+The training proceeds automatically:
+
+1. The actor executes the policy in the environment
+2. Transitions are collected and sent to the learner
+3. The learner updates the policy based on these transitions
+4. Updated policy parameters are sent back to the actor
+5. The process continues until the specified step limit is reached
+
+**Human in the Loop**
+
+- The key to learning efficiently is to have human interventions to provide corrective feedback and completing the task to aide the policy learning and exploration.
+- To perform human interventions, you can press the upper right trigger button on the gamepad (or the `space` key on the keyboard). This will pause the policy actions and allow you to take over.
+- 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>
+</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.
+- We can observe that the number of steps where the policy starts achieving the maximum reward is cut by a quarter when human interventions are present.
+
+**Monitoring and Debugging**
+
+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.
+
+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>
+</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.
+- **`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).
+
+Paper citation:
+
+```
+@article{luo2024precise,
+  title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
+  author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey},
+  journal={arXiv preprint arXiv:2410.21845},
+  year={2024}
+}
+```

docs/source/hilserl_sim.mdx

@@ -0,0 +1,154 @@
+# Train RL in Simulation
+
+This guide explains how to use the `gym_hil` simulation environments as an alternative to real robots when working with the LeRobot framework for Human-In-the-Loop (HIL) reinforcement learning.
+
+`gym_hil` is a package that provides Gymnasium-compatible simulation environments specifically designed for Human-In-the-Loop reinforcement learning. These environments allow you to:
+
+- Train policies in simulation to test the RL stack before training on real robots
+
+- Collect demonstrations in sim using external devices like gamepads or keyboards
+- Perform human interventions during policy learning
+
+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:
+
+```bash
+pip install -e ".[hilserl]"
+```
+
+## What do I need?
+
+- 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/lerobot/config_examples/resolve/main/rl/gym_hil/env_config.json). Key configuration sections include:
+
+### Environment Type and Task
+
+```json
+{
+  "env": {
+    "type": "gym_manipulator",
+    "name": "gym_hil",
+    "task": "PandaPickCubeGamepad-v0",
+    "fps": 10
+  },
+  "device": "cuda"
+}
+```
+
+Available tasks:
+
+- `PandaPickCubeBase-v0`: Basic environment
+- `PandaPickCubeGamepad-v0`: With gamepad control
+- `PandaPickCubeKeyboard-v0`: With keyboard control
+
+### Processor Configuration
+
+```json
+{
+  "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.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
+
+### Basic Usage
+
+To run the environment, set mode to null:
+
+```bash
+python -m lerobot.rl.gym_manipulator --config_path path/to/gym_hil_env.json
+```
+
+### Recording a Dataset
+
+To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
+
+```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.rl.gym_manipulator --config_path path/to/gym_hil_env.json
+```
+
+### Training a Policy
+
+To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/gym_hil/train_config.json) and run the actor and learner servers:
+
+```bash
+python -m lerobot.rl.actor --config_path path/to/train_gym_hil_env.json
+```
+
+In a different terminal, run the learner server:
+
+```bash
+python -m lerobot.rl.learner --config_path path/to/train_gym_hil_env.json
+```
+
+The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.
+
+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).
+
+Paper citation:
+
+```
+@article{luo2024precise,
+  title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
+  author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey},
+  journal={arXiv preprint arXiv:2410.21845},
+  year={2024}
+}
+```

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

@@ -0,0 +1,603 @@
+# Imitation Learning on Real-World Robots
+
+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.
+
+By following these steps, you'll be able to replicate tasks, such as picking up a Lego block and placing it in a bin with a high success rate, as shown in the video below.
+
+<details>
+<summary><strong>Video: pickup lego block task</strong></summary>
+
+<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"
+    />
+  </video>
+</div>
+
+</details>
+
+This tutorial isn’t tied to a specific robot: we walk you through the commands and API snippets you can adapt for any supported platform.
+
+During data collection, you’ll use a “teloperation” device, such as a leader arm or keyboard to teleoperate the robot and record its motion trajectories.
+
+Once you’ve gathered enough trajectories, you’ll train a neural network to imitate these trajectories and deploy the trained model so your robot can perform the task autonomously.
+
+If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support.
+
+## Set up and Calibrate
+
+If you haven't yet set up and calibrated your robot and teleop device, please do so by following the robot-specific tutorial.
+
+## Teleoperate
+
+In this example, we’ll demonstrate how to teleoperate the SO101 robot. For each command, we also provide a corresponding API example.
+
+Note that the `id` associated with a robot is used to store the calibration file. It's important to use the same `id` when teleoperating, recording, and evaluating when using the same setup.
+
+<hfoptions id="teleoperate_so101">
+<hfoption id="Command">
+```bash
+lerobot-teleoperate \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.id=my_awesome_follower_arm \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_awesome_leader_arm
+```
+</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
+
+robot_config = SO101FollowerConfig(
+    port="/dev/tty.usbmodem58760431541",
+    id="my_red_robot_arm",
+)
+
+teleop_config = SO101LeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_blue_leader_arm",
+)
+
+robot = SO101Follower(robot_config)
+teleop_device = SO101Leader(teleop_config)
+robot.connect()
+teleop_device.connect()
+
+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.
+
+## Cameras
+
+To add cameras to your setup, follow this [Guide](./cameras#setup-cameras).
+
+## Teleoperate with cameras
+
+With `rerun`, you can teleoperate again while simultaneously visualizing the camera feeds and joint positions. In this example, we’re using the Koch arm.
+
+<hfoptions id="teleoperate_koch_camera">
+<hfoption id="Command">
+```bash
+lerobot-teleoperate \
+    --robot.type=koch_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.id=my_awesome_follower_arm \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
+    --teleop.type=koch_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_awesome_leader_arm \
+    --display_data=true
+```
+</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
+from lerobot.robots.koch_follower import KochFollowerConfig, KochFollower
+
+camera_config = {
+    "front": OpenCVCameraConfig(index_or_path=0, width=1920, height=1080, fps=30)
+}
+
+robot_config = KochFollowerConfig(
+    port="/dev/tty.usbmodem585A0076841",
+    id="my_red_robot_arm",
+    cameras=camera_config
+)
+
+teleop_config = KochLeaderConfig(
+    port="/dev/tty.usbmodem58760431551",
+    id="my_blue_leader_arm",
+)
+
+robot = KochFollower(robot_config)
+teleop_device = KochLeader(teleop_config)
+robot.connect()
+teleop_device.connect()
+
+while True:
+    observation = robot.get_observation()
+    action = teleop_device.get_action()
+    robot.send_action(action)
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+## 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 ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Then store your Hugging Face repository name in a variable:
+
+```bash
+HF_USER=$(hf auth whoami | head -n 1)
+echo $HF_USER
+```
+
+Now you can record a dataset. To record 5 episodes and upload your dataset to the hub, adapt the code below for your robot and execute the command or API example.
+
+<hfoptions id="record">
+<hfoption id="Command">
+```bash
+lerobot-record \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem585A0076841 \
+    --robot.id=my_awesome_follower_arm \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --teleop.id=my_awesome_leader_arm \
+    --display_data=true \
+    --dataset.repo_id=${HF_USER}/record-test \
+    --dataset.num_episodes=5 \
+    --dataset.single_task="Grab the black cube"
+```
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.robots.so100_follower import SO100Follower, SO100FollowerConfig
+from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
+from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun
+from lerobot.record import record_loop
+
+NUM_EPISODES = 5
+FPS = 30
+EPISODE_TIME_SEC = 60
+RESET_TIME_SEC = 10
+TASK_DESCRIPTION = "My task description"
+
+# Create the robot and teleoperator configurations
+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
+)
+teleop_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
+
+# Initialize the robot and teleoperator
+robot = SO100Follower(robot_config)
+teleop = SO100Leader(teleop_config)
+
+# Configure the dataset features
+action_features = hw_to_dataset_features(robot.action_features, "action")
+obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+dataset_features = {**action_features, **obs_features}
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id="<hf_username>/<dataset_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")
+
+# Connect the robot and teleoperator
+robot.connect()
+teleop.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=teleop,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+    )
+
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+        log_say("Reset the environment")
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            teleop=teleop,
+            control_time_s=RESET_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+        )
+
+    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()
+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/${HF_USER}/so101_test`) that you can obtain by running:
+
+```bash
+echo https://huggingface.co/datasets/${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`. 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`
+  Duration for resetting the environment after each episode (default: **60 seconds**).
+- `--dataset.num_episodes=50`
+  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.
+
+#### 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).
+
+## 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 ${HF_USER}/so101_test
+```
+
+## Replay an episode
+
+A useful feature is the `replay` function, which allows you to replay any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
+
+You can replay the first episode on your robot with either the command below or with the API example:
+
+<hfoptions id="replay">
+<hfoption id="Command">
+```bash
+lerobot-replay \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.id=my_awesome_follower_arm \
+    --dataset.repo_id=${HF_USER}/record-test \
+    --dataset.episode=0 # choose the episode you want to replay
+```
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+import time
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.utils import log_say
+
+episode_idx = 0
+
+robot_config = SO100FollowerConfig(port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm")
+
+robot = SO100Follower(robot_config)
+robot.connect()
+
+dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[episode_idx])
+actions = dataset.hf_dataset.select_columns("action")
+
+log_say(f"Replaying episode {episode_idx}")
+for idx in range(dataset.num_frames):
+    t0 = time.perf_counter()
+
+    action = {
+        name: float(actions[idx]["action"][i]) for i, name in enumerate(dataset.features["action"]["names"])
+    }
+    robot.send_action(action)
+
+    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
+
+robot.disconnect()
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+Your robot should replicate movements similar to those you recorded. For example, check out [this video](https://x.com/RemiCadene/status/1793654950905680090) where we use `replay` on a Aloha robot from [Trossen Robotics](https://www.trossenrobotics.com).
+
+## Train a policy
+
+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
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/so101_test \
+  --policy.type=act \
+  --output_dir=outputs/train/act_so101_test \
+  --job_name=act_so101_test \
+  --policy.device=cuda \
+  --wandb.enable=true \
+  --policy.repo_id=${HF_USER}/my_policy
+```
+
+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`](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
+lerobot-train \
+  --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
+  --resume=true
+```
+
+If you do not want to push your model to the hub after training use `--policy.push_to_hub=false`.
+
+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 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} \
+  outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
+```
+
+## Run inference and evaluate your policy
+
+You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
+
+<hfoptions id="eval">
+<hfoption id="Command">
+```bash
+lerobot-record  \
+  --robot.type=so100_follower \
+  --robot.port=/dev/ttyACM1 \
+  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
+  --robot.id=my_awesome_follower_arm \
+  --display_data=false \
+  --dataset.repo_id=${HF_USER}/eval_so100 \
+  --dataset.single_task="Put lego brick into the transparent box" \
+  # <- Teleop optional if you want to teleoperate in between episodes \
+  # --teleop.type=so100_leader \
+  # --teleop.port=/dev/ttyACM0 \
+  # --teleop.id=my_awesome_leader_arm \
+  --policy.path=${HF_USER}/my_policy
+```
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun
+
+
+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)}
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", cameras=camera_config
+)
+
+# Initialize the robot
+robot = SO100Follower(robot_config)
+
+# Initialize the policy
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+
+# Configure the dataset features
+action_features = hw_to_dataset_features(robot.action_features, "action")
+obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+dataset_features = {**action_features, **obs_features}
+
+# 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")
+
+# Connect the robot
+robot.connect()
+
+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}")
+
+    # Run the policy inference loop
+    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,
+    )
+
+    dataset.save_episode()
+
+# Clean up
+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`).
+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

@@ -0,0 +1,220 @@
+# Imitation Learning in Sim
+
+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.
+
+For the simulation environment we use the same [repo](https://github.com/huggingface/gym-hil) that is also being used by the Human-In-the-Loop (HIL) reinforcement learning algorithm.
+This environment is based on [MuJoCo](https://mujoco.org) and allows you to record datasets in LeRobotDataset format.
+Teleoperation is easiest with a controller like the Logitech F710, but you can also use your keyboard if you are up for the challenge.
+
+## Installation
+
+First, install the `gym_hil` package within the LeRobot environment, go to your LeRobot folder and run this command:
+
+```bash
+pip install -e ".[hilserl]"
+```
+
+## Teleoperate and Record a Dataset
+
+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/lerobot/config_examples/resolve/main/sim_il/env_config.json).
+
+To teleoperate and collect a dataset, we need to modify this config file. Here's an example configuration for imitation learning data collection:
+
+```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"
+}
+```
+
+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:
+
+<hfoptions id="teleop_sim">
+<hfoption id="Linux">
+
+```bash
+python -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
+```
+
+</hfoption>
+<hfoption id="MacOS">
+
+```bash
+mjpython -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
+```
+
+</hfoption>
+</hfoptions>
+
+Once rendered you can teleoperate the robot with the gamepad or keyboard, below you can find the gamepad/keyboard controls.
+
+Note that to teleoperate the robot you have to hold the "Human Take Over Pause Policy" Button `RB` to enable control!
+
+**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>
+</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
+  Right Ctrl and Left Ctrl: Open and close gripper
+  ESC: Exit
+```
+
+## Visualize a dataset
+
+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>
+</p>
+<p align="center">
+  <i>Dataset visualizer</i>
+</p>
+
+## Train a policy
+
+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
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/il_gym \
+  --policy.type=act \
+  --output_dir=outputs/train/il_sim_test \
+  --job_name=il_sim_test \
+  --policy.device=cuda \
+  --wandb.enable=true
+```
+
+Let's explain the command:
+
+1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](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} \
+  outputs/train/il_sim_test/checkpoints/${CKPT}/pretrained_model
+```
+
+## Evaluate your policy in Sim
+
+To evaluate your policy we have to use a configuration file. An example can be found [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/sim_il/eval_config.json).
+
+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
+
+<hfoptions id="eval_policy">
+<hfoption id="Linux">
+
+```bash
+python -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
+```
+
+</hfoption>
+<hfoption id="MacOS">
+
+```bash
+mjpython -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
+```
+
+</hfoption>
+</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.
+
+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).

docs/source/implement_your_own_processor.mdx

@@ -0,0 +1,273 @@
+# Implement your own Robot Processor
+
+In this tutorial, you'll learn how to implement your own Robot Processor.
+It begins by exploring the need for a custom processor, then uses the `NormalizerProcessorStep` as the running example to explain how to implement, configure, and serialize a processor. Finally, it lists all helper processors that ship with LeRobot.
+
+## Why would you need a custom processor?
+
+In most cases, when reading raw data from sensors or when models output actions, you need to process this data to make it compatible with your target system. For example, a common need is normalizing data ranges to make them suitable for neural networks.
+
+LeRobot's `NormalizerProcessorStep` handles this crucial task:
+
+```python
+# Input: raw joint positions in [0, 180] degrees
+raw_action = torch.tensor([90.0, 45.0, 135.0])
+
+# After processing: normalized to [-1, 1] range for model training
+normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=dataset_stats)
+normalized_result = normalizer(transition)
+# ...
+```
+
+Other common processing needs include:
+
+- **Device placement**: Moving tensors between CPU/GPU and converting data types
+- **Format conversion**: Transforming between different data structures
+- **Batching**: Adding/removing batch dimensions for model compatibility
+- **Safety constraints**: Applying limits to robot commands
+
+```python
+# Example pipeline combining multiple processors
+pipeline = PolicyProcessorPipeline([
+    RenameObservationsProcessorStep(rename_map={}),
+    AddBatchDimensionProcessorStep(),
+    NormalizerProcessorStep(features=features, stats=stats),
+    DeviceProcessorStep(device="cuda"),
+    # ...
+])
+```
+
+LeRobot provides a pipeline mechanism to implement sequences of processing steps for both input data and output actions, making it easy to compose these transformations in the right order for optimal performance.
+
+## How to implement your own processor?
+
+We'll use the `NormalizerProcessorStep` as our main example because it demonstrates essential processor patterns including state management, configuration serialization, and tensor handling that you'll commonly need.
+
+Prepare the sequence of processing steps necessary for your problem. A processor step is a class that implements the following methods:
+
+- `__call__`: implements the processing step for the input transition.
+- `get_config`: gets the configuration of the processor step.
+- `state_dict`: gets the state of the processor step.
+- `load_state_dict`: loads the state of the processor step.
+- `reset`: resets the state of the processor step.
+- `feature_contract`: displays the modification to the feature space during the processor step.
+
+### Implement the `__call__` method
+
+The `__call__` method is the core of your processor step. It takes an `EnvTransition` and returns a modified `EnvTransition`. Here's how the `NormalizerProcessorStep` works:
+
+```python
+@dataclass
+@ProcessorStepRegistry.register("normalizer_processor")
+class NormalizerProcessorStep(ProcessorStep):
+    """Normalize observations/actions using dataset statistics."""
+
+    features: dict[str, PolicyFeature]
+    norm_map: dict[FeatureType, NormalizationMode]
+    stats: dict[str, dict[str, Any]] | None = None
+    eps: float = 1e-8
+    _tensor_stats: dict = field(default_factory=dict, init=False, repr=False)
+
+    def __post_init__(self):
+        """Convert stats to tensors for efficient computation."""
+        self.stats = self.stats or {}
+        self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=torch.float32)
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        new_transition = transition.copy()
+        # Normalize observations
+        # ...
+        # Normalize action
+        # ...
+        return new_transition
+
+```
+
+See the full implementation in `src/lerobot/processor/normalize_processor.py` for complete details.
+
+**Key principles:**
+
+- **Always use `transition.copy()`** to avoid side effects
+- **Handle both observations and actions** consistently
+- **Separate config from state**: `get_config()` returns JSON-serializable params, `state_dict()` returns tensors
+- **Convert stats to tensors** in `__post_init__()` for efficient computation
+
+### Configuration and State Management
+
+Processors support serialization through three methods that separate configuration from tensor state. The `NormalizerProcessorStep` demonstrates this perfectly - it carries dataset statistics (tensors) in its state, and hyperparameters in its config:
+
+```python
+# Continuing the NormalizerProcessorStep example...
+
+def get_config(self) -> dict[str, Any]:
+    """JSON-serializable configuration (no tensors)."""
+    return {
+        "eps": self.eps,
+        "features": {k: {"type": v.type.value, "shape": v.shape} for k, v in self.features.items()},
+        "norm_map": {ft.value: nm.value for ft, nm in self.norm_map.items()},
+        # ...
+    }
+
+def state_dict(self) -> dict[str, torch.Tensor]:
+    """Tensor state only (e.g., dataset statistics)."""
+    flat: dict[str, torch.Tensor] = {}
+    for key, sub in self._tensor_stats.items():
+        for stat_name, tensor in sub.items():
+            flat[f"{key}.{stat_name}"] = tensor.cpu()  # Always save to CPU
+    return flat
+
+def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+    """Restore tensor state at runtime."""
+    self._tensor_stats.clear()
+    for flat_key, tensor in state.items():
+        key, stat_name = flat_key.rsplit(".", 1)
+        # Load to processor's configured device
+        self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to(
+            dtype=torch.float32, device=self.device
+        )
+        # ...
+```
+
+**Usage:**
+
+```python
+# Save (e.g., inside a policy)
+config = normalizer.get_config()
+tensors = normalizer.state_dict()
+
+# Restore (e.g., loading a pretrained policy)
+new_normalizer = NormalizerProcessorStep(**config)
+new_normalizer.load_state_dict(tensors)
+# Now new_normalizer has the same stats and configuration
+```
+
+### Transform features
+
+The `transform_features` method defines how your processor transforms feature names and shapes. This is crucial for policy configuration and debugging.
+
+For `NormalizerProcessorStep`, features are typically preserved unchanged since normalization doesn't alter keys or shapes:
+
+```python
+def transform_features(self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+    """Normalization preserves all feature definitions."""
+    return features  # No changes to feature structure
+    # ...
+```
+
+When your processor renames or reshapes data, implement this method to reflect the mapping for downstream components. For example, a simple rename processor:
+
+```python
+def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+    # Simple renaming
+    if "pixels" in features:
+        features["observation.image"] = features.pop("pixels")
+
+    # Pattern-based renaming
+    for key in list(features.keys()):
+        if key.startswith("env_state."):
+            suffix = key[len("env_state."):]
+            features[f"observation.{suffix}"] = features.pop(key)
+            # ...
+
+    return features
+```
+
+**Key principles:**
+
+- Use `features.pop(old_key)` to remove and get the old feature
+- Use `features[new_key] = old_feature` to add the renamed feature
+- Always return the modified features dictionary
+- Document transformations clearly in the docstring
+
+### Using overrides
+
+You can override step parameters at load-time using `overrides`. This is handy for non-serializable objects or site-specific settings. It works both in policy factories and with `DataProcessorPipeline.from_pretrained(...)`.
+
+**Foundational model adaptation**: This is particularly useful when working with foundational pretrained policies where you rarely have access to the original training statistics. You can inject your own dataset statistics to adapt the normalizer to your specific robot or environment data.
+
+Example: during policy evaluation on the robot, override the device and rename map.
+Use this to run a policy trained on CUDA on a CPU-only robot, or to remap camera keys when the robot uses different names than the dataset.
+
+Direct usage with `from_pretrained`:
+
+```python
+from lerobot.processor import RobotProcessorPipeline
+
+# Load a foundational policy trained on diverse robot data
+# but adapt normalization to your specific robot/environment
+new_stats = LeRobotDataset(repo_id="username/my-dataset").meta.stats
+processor = RobotProcessorPipeline.from_pretrained(
+    "huggingface/foundational-robot-policy",  # Pretrained foundation model
+    overrides={
+        "normalizer_processor": {"stats": new_stats},     # Inject your robot's statistics
+        "device_processor": {"device": "cuda:0"},         # registry name for registered steps
+        "rename_processor": {"rename_map": robot_key_map}, # Map your robot's observation keys
+        # ...
+    },
+)
+```
+
+## Best Practices
+
+Based on analysis of all LeRobot processor implementations, here are the key patterns and practices:
+
+### 1. **Safe Data Handling**
+
+Always create copies of input data to avoid unintended side effects. Use `transition.copy()` and `observation.copy()` rather than modifying data in-place. This prevents your processor from accidentally affecting other components in the pipeline.
+
+Check for required data before processing and handle missing data gracefully. If your processor expects certain keys (like `"pixels"` for image processing), validate their presence first. For optional data, use safe access patterns like `transition.get()` and handle `None` values appropriately.
+
+When data validation fails, provide clear, actionable error messages that help users understand what went wrong and how to fix it.
+
+### 2. **Choose Appropriate Base Classes**
+
+LeRobot provides specialized base classes that reduce boilerplate code and ensure consistency. Use `ObservationProcessorStep` when you only need to modify observations, `ActionProcessorStep` for action-only processing, and `RobotActionProcessorStep` specifically for dictionary-based robot actions.
+
+Only inherit directly from `ProcessorStep` when you need full control over the entire transition or when processing multiple transition components simultaneously. The specialized base classes handle the transition management for you and provide type safety.
+
+### 3. **Registration and Naming**
+
+Register your processors with descriptive, namespaced names using `@ProcessorStepRegistry.register()`. Use organization prefixes like `"robotics_lab/safety_clipper"` or `"acme_corp/vision_enhancer"` to avoid naming conflicts. Avoid generic names like `"processor"` or `"step"` that could clash with other implementations.
+
+Good registration makes your processors discoverable and enables clean serialization/deserialization when saving and loading pipelines.
+
+### 4. **State Management Patterns**
+
+Distinguish between configuration parameters (JSON-serializable values) and internal state (tensors, buffers). Use dataclass fields with `init=False, repr=False` for internal state that shouldn't appear in the constructor or string representation.
+
+Implement the `reset()` method to clear internal state between episodes. This is crucial for stateful processors that accumulate data over time, like moving averages or temporal filters.
+
+Remember that `get_config()` should only return JSON-serializable configuration, while `state_dict()` handles tensor state separately.
+
+### 5. **Input Validation and Error Handling**
+
+Validate input types and shapes before processing. Check tensor properties like `dtype` and dimensions to ensure compatibility with your algorithms. For robot actions, verify that required pose components or joint values are present and within expected ranges.
+
+Use early returns for edge cases where no processing is needed. Provide clear, descriptive error messages that include the expected vs. actual data types or shapes. This makes debugging much easier for users.
+
+### 6. **Device and Dtype Awareness**
+
+Design your processors to automatically adapt to the device and dtype of input tensors. Internal tensors (like normalization statistics) should match the input tensor's device and dtype to ensure compatibility with multi-GPU training, mixed precision, and distributed setups.
+
+Implement a `to()` method that moves your processor's internal state to the specified device. Check device/dtype compatibility at runtime and automatically migrate internal state when needed. This pattern enables seamless operation across different hardware configurations without manual intervention.
+
+## Conclusion
+
+You now have all the tools to implement custom processors in LeRobot! The key steps are:
+
+1. **Define your processor** as a dataclass with the required methods (`__call__`, `get_config`, `state_dict`, `load_state_dict`, `reset`, `transform_features`)
+2. **Register it** using `@ProcessorStepRegistry.register("name")` for discoverability
+3. **Integrate it** into a `DataProcessorPipeline` with other processing steps
+4. **Use base classes** like `ObservationProcessorStep` when possible to reduce boilerplate
+5. **Implement device/dtype awareness** to support multi-GPU and mixed precision setups
+
+The processor system is designed to be modular and composable, allowing you to build complex data processing pipelines from simple, focused components. Whether you're preprocessing sensor data for training or post-processing model outputs for robot execution, custom processors give you the flexibility to handle any data transformation your robotics application requires.
+
+Key principles for robust processors:
+
+- **Device/dtype adaptation**: Internal tensors should match input tensors
+- **Clear error messages**: Help users understand what went wrong
+- **Base class usage**: Leverage specialized base classes to reduce boilerplate
+- **Feature contracts**: Declare data structure changes with `transform_features()`
+
+Start simple, test thoroughly, and ensure your processors work seamlessly across different hardware configurations!

docs/source/index.mdx

@@ -0,0 +1,23 @@
+<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>
+  </a>
+</div>
+
+# LeRobot
+
+**State-of-the-art machine learning for real-world robotics**
+
+🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
+
+🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
+
+🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started.
+
+🤗 LeRobot hosts pretrained models and datasets on the LeRobot HuggingFace page.
+
+Join the LeRobot community on [Discord](https://discord.gg/s3KuuzsPFb)

docs/source/installation.mdx

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+# Installation
+
+## Install [`miniforge`](https://conda-forge.org/download/)
+
+```bash
+wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+bash Miniforge3-$(uname)-$(uname -m).sh
+```
+
+## Environment Setup
+
+Create a virtual environment with Python 3.10, using conda:
+
+```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 `conda`, 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`.
+
+## Install LeRobot 🤗
+
+### From Source
+
+First, clone the repository and navigate into the directory:
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+```
+
+Then, install the library in editable mode. This is useful if you plan to contribute to the code.
+
+```bash
+pip install -e .
+```
+
+### 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/
+
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install libero or pi, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`
+
+### Troubleshooting
+
+If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
+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
+
+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)), 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
+```
+
+You can now assemble your robot if it's not ready yet, look for your robot type on the left. Then follow the link below to use Lerobot with your robot.

docs/source/integrate_hardware.mdx

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+# Bring Your Own Hardware
+
+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/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).
+
+## 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/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/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`
+
+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.
+
+## Step 1: Subclass the `Robot` Interface
+
+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
+
+from lerobot.cameras import CameraConfig
+from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.robots import RobotConfig
+
+
+@RobotConfig.register_subclass("my_cool_robot")
+@dataclass
+class MyCoolRobotConfig(RobotConfig):
+    port: str
+    cameras: dict[str, CameraConfig] = field(
+        default_factory={
+            "cam_1": OpenCVCameraConfig(
+                index_or_path=2,
+                fps=30,
+                width=480,
+                height=640,
+            ),
+        }
+    )
+```
+<!-- prettier-ignore-end -->
+
+[Cameras tutorial](./cameras) 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
+from lerobot.motors.feetech import FeetechMotorsBus
+from lerobot.robots import Robot
+
+class MyCoolRobot(Robot):
+    config_class = MyCoolRobotConfig
+    name = "my_cool_robot"
+
+    def __init__(self, config: MyCoolRobotConfig):
+        super().__init__(config)
+        self.bus = FeetechMotorsBus(
+            port=self.config.port,
+            motors={
+                "joint_1": Motor(1, "sts3250", MotorNormMode.RANGE_M100_100),
+                "joint_2": Motor(2, "sts3215", MotorNormMode.RANGE_M100_100),
+                "joint_3": Motor(3, "sts3215", MotorNormMode.RANGE_M100_100),
+                "joint_4": Motor(4, "sts3215", MotorNormMode.RANGE_M100_100),
+                "joint_5": Motor(5, "sts3215", MotorNormMode.RANGE_M100_100),
+            },
+            calibration=self.calibration,
+        )
+        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).
+
+> [!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.
+
+### `observation_features`
+
+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]:
+    return {
+        "joint_1.pos": float,
+        "joint_2.pos": float,
+        "joint_3.pos": float,
+        "joint_4.pos": float,
+        "joint_5.pos": float,
+    }
+
+@property
+def _cameras_ft(self) -> dict[str, tuple]:
+    return {
+        cam: (self.cameras[cam].height, self.cameras[cam].width, 3) for cam in self.cameras
+    }
+
+@property
+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`
+
+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
+
+These methods should handle opening and closing communication with your hardware (e.g. serial ports, CAN interfaces, USB devices, cameras).
+
+### `is_connected`
+
+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()
+    if not self.is_calibrated and calibrate:
+        self.calibrate()
+
+    for cam in self.cameras.values():
+        cam.connect()
+
+    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:
+
+<!-- prettier-ignore-start -->
+```python
+@property
+def is_calibrated(self) -> bool:
+    return True
+
+def calibrate(self) -> None:
+    pass
+```
+<!-- prettier-ignore-end -->
+
+### `is_calibrated`
+
+This should reflect whether your robot has the required calibration loaded.
+
+<!-- prettier-ignore-start -->
+```python
+@property
+def is_calibrated(self) -> bool:
+    return self.bus.is_calibrated
+```
+<!-- prettier-ignore-end -->
+
+### `calibrate()`
+
+The goal of the calibration is twofold:
+
+- Know the physical range of motion of each motors in order to only send commands within this range.
+- Normalize raw motors positions to sensible continuous values (e.g. percentages, degrees) instead of arbitrary discrete value dependant on the specific motor used that will not replicate elsewhere.
+
+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()
+    for motor in self.bus.motors:
+        self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+
+    input(f"Move {self} to the middle of its range of motion and press ENTER....")
+    homing_offsets = self.bus.set_half_turn_homings()
+
+    print(
+        "Move all joints sequentially through their entire ranges "
+        "of motion.\nRecording positions. Press ENTER to stop..."
+    )
+    range_mins, range_maxes = self.bus.record_ranges_of_motion()
+
+    self.calibration = {}
+    for motor, m in self.bus.motors.items():
+        self.calibration[motor] = MotorCalibration(
+            id=m.id,
+            drive_mode=0,
+            homing_offset=homing_offsets[motor],
+            range_min=range_mins[motor],
+            range_max=range_maxes[motor],
+        )
+
+    self.bus.write_calibration(self.calibration)
+    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():
+        self.bus.configure_motors()
+        for motor in self.bus.motors:
+            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+            self.bus.write("P_Coefficient", motor, 16)
+            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
+
+These are the most important runtime functions: the core I/O loop.
+
+### `get_observation()`
+
+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:
+        raise ConnectionError(f"{self} is not connected.")
+
+    # Read arm position
+    obs_dict = self.bus.sync_read("Present_Position")
+    obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
+
+    # Capture images from cameras
+    for cam_key, cam in self.cameras.items():
+        obs_dict[cam_key] = cam.async_read()
+
+    return obs_dict
+```
+<!-- prettier-ignore-end -->
+
+### `send_action()`
+
+Takes a dictionary that matches `action_features`, and sends it to your hardware. You can add safety limits (clipping, smoothing) and return what was actually sent.
+
+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()}
+
+    # Send goal position to the arm
+    self.bus.sync_write("Goal_Position", goal_pos)
+
+    return action
+```
+<!-- prettier-ignore-end -->
+
+## Adding a Teleoperator
+
+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.
+
+## Using Your Own `LeRobot` Devices 🔌
+
+You can easily extend `lerobot` with your own custom hardware—be it a camera, robot, or teleoperation device—by creating a separate, installable Python package. If you follow a few simple conventions, the `lerobot` command-line tools (like `lerobot-teleop` and `lerobot-record`) will **automatically discover and integrate your creations** without requiring any changes to the `lerobot` source code.
+
+This guide outlines the conventions your plugin must follow.
+
+### The 4 Core Conventions
+
+To ensure your custom device is discoverable, you must adhere to the following four rules.
+
+#### 1\. Create an Installable Package with a Specific Prefix
+
+Your project must be a standard, installable Python package. Crucially, the name of your package (as defined in `pyproject.toml` or `setup.py`) must begin with one of these prefixes:
+
+- `lerobot_robot_` for a robot.
+- `lerobot_camera_` for a camera.
+- `lerobot_teleoperator_` for a teleoperation device.
+
+This prefix system is how `lerobot` automatically finds your plugin in the Python environment.
+
+#### 2\. Follow the `SomethingConfig`/`Something` Naming Pattern
+
+Your device's implementation class must be named after its configuration class, simply by removing the `Config` suffix.
+
+- **Config Class:** `MyAwesomeTeleopConfig`
+- **Device Class:** `MyAwesomeTeleop`
+
+#### 3\. Place Your Files in a Predictable Structure
+
+The device class (`MyAwesomeTeleop`) must be located in a predictable module relative to its configuration class (`MyAwesomeTeleopConfig`). `lerobot` will automatically search in these locations:
+
+- In the **same module** as the config class.
+- In a **submodule named after the device** (e.g., `my_awesome_teleop.py`).
+
+The recommended and simplest structure is to place them in separate, clearly named files within the same directory.
+
+#### 4\. Expose Classes in `__init__.py`
+
+Your package's `__init__.py` file should import and expose both the configuration and the device classes, making them easily accessible.
+
+### Putting It All Together: A Complete Example
+
+Let's create a new teleoperator called `my_awesome_teleop`.
+
+#### Directory Structure
+
+Here is what the project folder should look like. The package name, `lerobot_teleoperator_my_awesome_teleop`, follows **Convention \#1**.
+
+```
+lerobot_teleoperator_my_awesome_teleop/
+├── pyproject.toml # (or setup.py) lists lerobot as a dependency
+└── lerobot_teleoperator_my_awesome_teleop/
+    ├── __init__.py
+    ├── config_my_awesome_teleop.py
+    └── my_awesome_teleop.py
+```
+
+#### File Contents
+
+- **`config_my_awesome_teleop.py`**: Defines the configuration class. Note the `Config` suffix (**Convention \#2**).
+
+  ```python
+  from dataclasses import dataclass
+
+  from lerobot.teleoperators.config import TeleoperatorConfig
+
+  @TeleoperatorConfig.register_subclass("my_awesome_teleop")
+  @dataclass
+  class MyAwesomeTeleopConfig(TeleoperatorConfig):
+      # Your configuration fields go here
+      port: str = "192.168.1.1"
+  ```
+
+- **`my_awesome_teleop.py`**: Implements the device. The class name `MyAwesomeTeleop` matches its config class name (**Convention \#2**). This file structure adheres to **Convention \#3**.
+
+  ```python
+  from lerobot.teleoperators.teleoperator import Teleoperator
+
+  from .config_my_awesome_teleop import MyAwesomeTeleopConfig
+
+  class MyAwesomeTeleop(Teleoperator):
+      config_class = MyAwesomeTeleopConfig
+      name = "my_awesome_teleop"
+
+      def __init__(self, config: MyAwesomeTeleopConfig):
+          super().__init__(config)
+          self.config = config
+
+      # Your device logic (e.g., connect) goes here
+  ```
+
+- **`__init__.py`**: Exposes the key classes (**Convention \#4**).
+
+  ```python
+  from .config_my_awesome_teleop import MyAwesomeTeleopConfig
+  from .my_awesome_teleop import MyAwesomeTeleop
+  ```
+
+### Installation and Usage
+
+1.  **Install your new plugin in your Python environment.** You can install your local plugin package using `pip`'s editable mode or from PyPi.
+
+    ```bash
+    # Locally
+    # Navigate to your plugin's root directory and install it
+    cd lerobot_teleoperator_my_awesome_teleop
+    pip install -e .
+
+    # From PyPi
+    pip install lerobot_teleoperator_my_awesome_teleop
+    ```
+
+2.  **Use it directly from the command line.** Now, you can use your custom device by referencing its type.
+
+    ```bash
+    lerobot-teleoperate --teleop.type=my_awesome_teleop \
+    # other arguments
+    ```
+
+And that's it\! Your custom device is now fully integrated.
+
+### Looking for an example ?
+
+Check out these two packages from the community:
+
+- https://github.com/SpesRobotics/lerobot-robot-xarm
+- https://github.com/SpesRobotics/lerobot-teleoperator-teleop
+
+## Wrapping Up
+
+Once your robot class is complete, you can leverage the LeRobot ecosystem:
+
+- Control your robot with available teleoperators or integrate directly your teleoperating device
+- Record training data and visualize it
+- Integrate it into RL or imitation learning pipelines
+
+Don't hesitate to reach out to the community for help on our [Discord](https://discord.gg/s3KuuzsPFb) 🤗

docs/source/introduction_processors.mdx

@@ -0,0 +1,314 @@
+# Introduction to Processors
+
+In robotics, there's a fundamental mismatch between the data that robots and humans produce and what machine learning models expect.
+Robots output raw sensor data like camera images and joint positions that need normalization, batching, and device placement before models can process them.
+Language instructions from humans must be tokenized into numerical representations, and different robots use different coordinate systems that need standardization.
+
+The challenge extends to model outputs as well.
+Models might output end-effector positions while robots need joint-space commands, or teleoperators produce relative movements while robots expect absolute commands.
+Model predictions are often normalized and need conversion back to real-world scales.
+
+Cross-domain translation adds another layer of complexity.
+Training data from one robot setup needs adaptation for deployment on different hardware, models trained with specific camera configurations must work with new arrangements, and datasets with different naming conventions need harmonization.
+
+**That's where processors come in.** They serve as universal translators that bridge these gaps, ensuring seamless data flow from sensors to models to actuators.
+Processors handle all the preprocessing and postprocessing steps needed to convert raw environment data into model-ready inputs and vice versa.
+
+This means that your favorite policy can be used like this:
+
+```python
+import torch
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.your_policy import YourPolicy
+from lerobot.processor.pipeline import RobotProcessorPipeline, PolicyProcessorPipeline
+dataset = LeRobotDataset("hf_user/dataset", episodes=[0])
+sample = dataset[10]
+
+model = YourPolicy.from_pretrained(
+    "hf_user/model",
+)
+model.eval()
+model.to("cuda")
+preprocessor, postprocessor = make_pre_post_processors(model.config, pretrained_path="hf_user/model", dataset_stats=dataset.meta.stats)
+
+preprocessed_sample = preprocessor(sample)
+action = model.select_action(preprocessed_sample)
+postprocessed_action = postprocessor(action)
+```
+
+## What are Processors?
+
+In robotics, data comes in many forms: images from cameras, joint positions from sensors, text instructions from users, and more. Each type of data requires specific transformations before a model can use it effectively. Models need this data to be:
+
+- **Normalized**: Scaled to appropriate ranges for neural network processing
+- **Batched**: Organized with proper dimensions for batch processing
+- **Tokenized**: Text converted to numerical representations
+- **Device-placed**: Moved to the right hardware (CPU/GPU)
+- **Type-converted**: Cast to appropriate data types
+
+Processors handle these transformations through composable, reusable steps that can be chained together into pipelines. Think of them as a modular assembly line where each station performs a specific transformation on your data.
+
+## Core Concepts
+
+### EnvTransition: The Universal Data Container
+
+The `EnvTransition` is the fundamental data structure that flows through all processors.
+It's a typed dictionary that represents a complete robot-environment interaction:
+
+- **OBSERVATION**: All sensor data (images, states, proprioception)
+- **ACTION**: The action to execute or that was executed
+- **REWARD**: Reinforcement learning signal
+- **DONE/TRUNCATED**: Episode boundary indicators
+- **INFO**: Arbitrary metadata
+- **COMPLEMENTARY_DATA**: Task descriptions, indices, padding flags, inter-step data
+
+### ProcessorStep: The Building Block
+
+A `ProcessorStep` is a single transformation unit that processes transitions. It's an abstract base class with two required methods:
+
+```python
+from lerobot.processor import ProcessorStep, EnvTransition
+
+class MyProcessorStep(ProcessorStep):
+    """Example processor step - inherit and implement abstract methods."""
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        """Transform the transition - REQUIRED abstract method."""
+        # Your processing logic here
+        return transition
+
+    def transform_features(self, features):
+        """Declare how this step transforms feature shapes/types - REQUIRED abstract method."""
+        return features  # Most processors return features unchanged
+```
+
+`__call__` is the core of your processor step. It takes an `EnvTransition` and returns a modified `EnvTransition`.
+
+`transform_features` is used to declare how this step transforms feature shapes/types.
+
+### DataProcessorPipeline: The Generic Orchestrator
+
+The `DataProcessorPipeline[TInput, TOutput]` chains multiple `ProcessorStep` instances together:
+
+```python
+from lerobot.processor import RobotProcessorPipeline, PolicyProcessorPipeline
+
+# For robot hardware (unbatched data)
+robot_processor = RobotProcessorPipeline[RobotAction, RobotAction](
+    steps=[step1, step2, step3],
+    name="robot_pipeline"
+)
+
+# For model training/inference (batched data)
+policy_processor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+    steps=[step1, step2, step3],
+    name="policy_pipeline"
+)
+```
+
+## RobotProcessorPipeline vs PolicyProcessorPipeline
+
+The key distinction is in the data structures they handle:
+
+| Aspect          | RobotProcessorPipeline                       | PolicyProcessorPipeline                  |
+| --------------- | -------------------------------------------- | ---------------------------------------- |
+| **Input**       | `dict[str, Any]` - Individual robot values   | `dict[str, Any]` - Batched tensors       |
+| **Output**      | `dict[str, Any]` - Individual robot commands | `torch.Tensor` - Policy predictions      |
+| **Use Case**    | Real-time robot control                      | Model training/inference                 |
+| **Data Format** | Unbatched, heterogeneous                     | Batched, homogeneous                     |
+| **Examples**    | `{"joint_1": 0.5}`                           | `{"observation.state": tensor([[0.5]])}` |
+
+**Use `RobotProcessorPipeline`** for robot hardware interfaces:
+
+```python
+# Robot data structures: dict[str, Any] for observations and actions
+robot_obs: dict[str, Any] = {
+    "joint_1": 0.5,           # Individual joint values
+    "joint_2": -0.3,
+    "camera_0": image_array   # Raw camera data
+}
+
+robot_action: dict[str, Any] = {
+    "joint_1": 0.2,          # Target joint positions
+    "joint_2": 0.1,
+    "gripper": 0.8
+}
+```
+
+**Use `PolicyProcessorPipeline`** for model training and batch processing:
+
+```python
+# Policy data structures: batch dicts and tensors
+policy_batch: dict[str, Any] = {
+    "observation.state": torch.tensor([[0.5, -0.3]]),      # Batched states
+    "observation.images.camera0": torch.tensor(...),        # Batched images
+    "action": torch.tensor([[0.2, 0.1, 0.8]])              # Batched actions
+}
+
+policy_action: torch.Tensor = torch.tensor([[0.2, 0.1, 0.8]])  # Model output tensor
+```
+
+## Converter Functions
+
+LeRobot provides converter functions to bridge different data formats in `lerobot.processor.converters`. These functions handle the crucial translations between robot hardware data structures, policy model formats, and the internal `EnvTransition` representation that flows through processor pipelines.
+
+| Category                       | Function                      | Description                     |
+| ------------------------------ | ----------------------------- | ------------------------------- |
+| **Robot Hardware Converters**  | `robot_action_to_transition`  | Robot dict → EnvTransition      |
+|                                | `observation_to_transition`   | Robot obs → EnvTransition       |
+|                                | `transition_to_robot_action`  | EnvTransition → Robot dict      |
+| **Policy/Training Converters** | `batch_to_transition`         | Batch dict → EnvTransition      |
+|                                | `transition_to_batch`         | EnvTransition → Batch dict      |
+|                                | `policy_action_to_transition` | Policy tensor → EnvTransition   |
+|                                | `transition_to_policy_action` | EnvTransition → Policy tensor   |
+| **Utilities**                  | `create_transition`           | Build transitions with defaults |
+|                                | `identity_transition`         | Pass-through converter          |
+
+The key insight is that **robot hardware converters** work with individual values and dictionaries, while **policy/training converters** work with batched tensors and model outputs. The converter functions automatically handle the structural differences, so your processor steps can focus on the core transformations without worrying about data format compatibility.
+
+## Processor Examples
+
+The following examples demonstrate real-world processor configurations for policy training and inference.
+
+Here is an example processor for policy training and inference:
+
+```python
+# Training data preprocessing (optimized order for GPU performance)
+training_preprocessor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+    steps=[
+        RenameObservationsProcessorStep(rename_map={}),     # Standardize keys
+        AddBatchDimensionProcessorStep(),                   # Add batch dims
+        TokenizerProcessorStep(tokenizer_name="...", ...),  # Tokenize language
+        DeviceProcessorStep(device="cuda"),                 # Move to GPU first
+        NormalizerProcessorStep(features=..., stats=...),   # Normalize on GPU
+    ]
+)
+
+# Model output postprocessing
+training_postprocessor = PolicyProcessorPipeline[torch.Tensor, torch.Tensor](
+    steps=[
+        DeviceProcessorStep(device="cpu"),                  # Move to CPU
+        UnnormalizerProcessorStep(features=..., stats=...), # Denormalize
+    ]
+    to_transition=policy_action_to_transition,
+    to_output=transition_to_policy_action,
+)
+```
+
+### An interaction between a robot and a policy with processors
+
+The most common real-world scenario combines both pipeline types robot hardware generates observations that need policy processing, and policy outputs need robot-compatible postprocessing:
+
+```python
+# Real deployment: Robot sensors → Model → Robot commands
+with torch.no_grad():
+    while not done:
+        raw_obs = robot.get_observation()  # dict[str, Any]
+
+        # Add your robot observation to policy observation processor
+
+        policy_input = policy_preprocessor(raw_obs)  # Batched dict
+
+        policy_output = policy.select_action(policy_input)  # Policy tensor
+
+        policy_action = policy_postprocessor(policy_output)
+
+        # Add your robot action to policy action processor
+
+        robot.send_action(policy_action)
+```
+
+## Feature Contracts: Shape and Type Transformation
+
+Processors don't just transform data - they can also **change the data structure itself**. The `transform_features()` method declares these changes, which is crucial for dataset recording and policy creation.
+
+### Why Feature Contracts Matter
+
+When building datasets or policies, LeRobot needs to know:
+
+- **What data fields will exist** after processing
+- **What shapes and types** each field will have
+- **How to configure models** for the expected data structure
+
+```python
+# Example: A processor that adds velocity to observations
+class VelocityProcessor(ObservationProcessorStep):
+    def observation(self, obs):
+        new_obs = obs.copy()
+        if "observation.state" in obs:
+            # concatenate computed velocity field to the state
+            new_obs["observation.state"] = self._compute_velocity(obs["observation.state"])
+        return new_obs
+
+    def transform_features(self, features):
+        """Declare the new velocity field we're adding."""
+        state_feature = features[PipelineFeatureType.OBSERVATION].get("observation.state")
+        if state_feature:
+            double_shape = (state_feature.shape[0] * 2,) if state_feature.shape else (2,)
+            features[PipelineFeatureType.OBSERVATION]["observation.state"] = PolicyFeature(
+                type=FeatureType.STATE, shape=double_shape
+            )
+        return features
+```
+
+### Feature Specification Functions
+
+`create_initial_features()` and `aggregate_pipeline_dataset_features()` solve a critical dataset creation problem: determining the exact final data structure before any data is processed.
+Since processor pipelines can add new features (like velocity fields), change tensor shapes (like cropping images), or rename keys, datasets need to know the complete output specification upfront to allocate proper storage and define schemas.
+These functions work together by starting with robot hardware specifications (`create_initial_features()`) then simulating the entire pipeline transformation (`aggregate_pipeline_dataset_features()`) to compute the final feature dictionary that gets passed to `LeRobotDataset.create()`, ensuring perfect alignment between what processors output and what datasets expect to store.
+
+```python
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features
+
+# Start with robot's raw features
+initial_features = create_initial_features(
+    observation=robot.observation_features,  # {"joint_1.pos": float, "camera_0": (480,640,3)}
+    action=robot.action_features            # {"joint_1.pos": float, "gripper.pos": float}
+)
+
+# Apply processor pipeline to compute final features
+final_features = aggregate_pipeline_dataset_features(
+    pipeline=my_processor_pipeline,
+    initial_features=initial_features,
+    use_videos=True
+)
+
+# Use for dataset creation
+dataset = LeRobotDataset.create(
+    repo_id="my_dataset",
+    features=final_features,  # Knows exactly what data to expect
+    ...
+)
+```
+
+## Common Processor Steps
+
+LeRobot provides many registered processor steps. Here are the most commonly used core processors:
+
+### Essential Processors
+
+- **`normalizer_processor`**: Normalize observations/actions using dataset statistics (mean/std or min/max)
+- **`device_processor`**: Move tensors to CPU/GPU with optional dtype conversion
+- **`to_batch_processor`**: Add batch dimensions to transitions for model compatibility
+- **`rename_observations_processor`**: Rename observation keys using mapping dictionaries
+- **`tokenizer_processor`**: Tokenize natural language task descriptions into tokens and attention masks
+
+### Next Steps
+
+- **[Implement Your Own Processor](./implement_your_own_processor)** - Create custom processor steps
+- **[Debug Your Pipeline](./debug_processor_pipeline)** - Troubleshoot and optimize pipelines
+- **[Processors for Robots and Teleoperators](./processors_robots_teleop)** - Real-world integration patterns
+
+## Summary
+
+Processors solve the data translation problem in robotics by providing:
+
+- **Modular transformations**: Composable, reusable processing steps
+- **Type safety**: Generic pipelines with compile-time checking
+- **Performance optimization**: GPU-accelerated operations
+- **Robot/Policy distinction**: Separate pipelines for different data structures
+- **Comprehensive ecosystem**: 30+ registered processors for common tasks
+
+The key insight: `RobotProcessorPipeline` handles unbatched robot hardware data, while `PolicyProcessorPipeline` handles batched model data. Choose the right tool for your data structure!

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](./il_robots)
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

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](./il_robots)
+
+## 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/lerobot-dataset-v3.mdx

@@ -0,0 +1,314 @@
+# LeRobotDataset v3.0
+
+`LeRobotDataset v3.0` is a standardized format for robot learning data. It provides unified access to multi-modal time-series data, sensorimotor signals and multi‑camera video, as well as rich metadata for indexing, search, and visualization on the Hugging Face Hub.
+
+This docs will guide you to:
+
+- Understand the v3.0 design and directory layout
+- Record a dataset and push it to the Hub
+- Load datasets for training with `LeRobotDataset`
+- Stream datasets without downloading using `StreamingLeRobotDataset`
+- Apply image transforms for data augmentation during training
+- Migrate existing `v2.1` datasets to `v3.0`
+
+## What’s new in `v3`
+
+- **File-based storage**: Many episodes per Parquet/MP4 file (v2 used one file per episode).
+- **Relational metadata**: Episode boundaries and lookups are resolved through metadata, not filenames.
+- **Hub-native streaming**: Consume datasets directly from the Hub with `StreamingLeRobotDataset`.
+- **Lower file-system pressure**: Fewer, larger files ⇒ faster initialization and fewer issues at scale.
+- **Unified organization**: Clean directory layout with consistent path templates across data and videos.
+
+## Installation
+
+`LeRobotDataset v3.0` will be included in `lerobot >= 0.4.0`.
+
+Until that stable release, you can use the main branch by following the [build from source instructions](./installation#from-source).
+
+## Record a dataset
+
+Run the command below to record a dataset with the SO-101 and push to the Hub:
+
+```bash
+lerobot-record \
+  --robot.type=so101_follower \
+  --robot.port=/dev/tty.usbmodem585A0076841 \
+  --robot.id=my_awesome_follower_arm \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
+  --teleop.type=so101_leader \
+  --teleop.port=/dev/tty.usbmodem58760431551 \
+  --teleop.id=my_awesome_leader_arm \
+  --display_data=true \
+  --dataset.repo_id=${HF_USER}/record-test \
+  --dataset.num_episodes=5 \
+  --dataset.single_task="Grab the black cube"
+```
+
+See the [recording guide](./il_robots#record-a-dataset) for more details.
+
+## Format design
+
+A core v3 principle is **decoupling storage from the user API**: data is stored efficiently (few large files), while the public API exposes intuitive episode-level access.
+
+`v3` has three pillars:
+
+1. **Tabular data**: Low‑dimensional, high‑frequency signals (states, actions, timestamps) stored in **Apache Parquet**. Access is memory‑mapped or streamed via the `datasets` stack.
+2. **Visual data**: Camera frames concatenated and encoded into **MP4**. Frames from the same episode are grouped; videos are sharded per camera for practical sizes.
+3. **Metadata**: JSON/Parquet records describing schema (feature names, dtypes, shapes), frame rates, normalization stats, and **episode segmentation** (start/end offsets into shared Parquet/MP4 files).
+
+> To scale to millions of episodes, tabular rows and video frames from multiple episodes are **concatenated** into larger files. Episode‑specific views are reconstructed **via metadata**, not file boundaries.
+
+<div style="display:flex; justify-content:center; gap:12px; flex-wrap:wrap;">
+  <figure style="margin:0; text-align:center;">
+    <img
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobotdataset-v3/asset1datasetv3.png"
+      alt="LeRobotDataset v3 diagram"
+      width="220"
+    />
+    <figcaption style="font-size:0.9em; color:#666;">
+      From episode‑based to file‑based datasets
+    </figcaption>
+  </figure>
+</div>
+
+### Directory layout (simplified)
+
+- **`meta/info.json`**: canonical schema (features, shapes/dtypes), FPS, codebase version, and **path templates** to locate data/video shards.
+- **`meta/stats.json`**: global feature statistics (mean/std/min/max) used for normalization; exposed as `dataset.meta.stats`.
+- **`meta/tasks.jsonl`**: natural‑language task descriptions mapped to integer IDs for task‑conditioned policies.
+- **`meta/episodes/`**: per‑episode records (lengths, tasks, offsets) stored as **chunked Parquet** for scalability.
+- **`data/`**: frame‑by‑frame **Parquet** shards; each file typically contains **many episodes**.
+- **`videos/`**: **MP4** shards per camera; each file typically contains **many episodes**.
+
+## Load a dataset for training
+
+`LeRobotDataset` returns Python dictionaries of PyTorch tensors and integrates with `torch.utils.data.DataLoader`. Here is a code example showing its use:
+
+```python
+import torch
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+repo_id = "yaak-ai/L2D-v3"
+
+# 1) Load from the Hub (cached locally)
+dataset = LeRobotDataset(repo_id)
+
+# 2) Random access by index
+sample = dataset[100]
+print(sample)
+# {
+#   'observation.state': tensor([...]),
+#   'action': tensor([...]),
+#   'observation.images.front_left': tensor([C, H, W]),
+#   'timestamp': tensor(1.234),
+#   ...
+# }
+
+# 3) Temporal windows via delta_timestamps (seconds relative to t)
+delta_timestamps = {
+    "observation.images.front_left": [-0.2, -0.1, 0.0]  # 0.2s and 0.1s before current frame
+}
+
+dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
+
+# Accessing an index now returns a stack for the specified key(s)
+sample = dataset[100]
+print(sample["observation.images.front_left"].shape)  # [T, C, H, W], where T=3
+
+# 4) Wrap with a DataLoader for training
+batch_size = 16
+data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+for batch in data_loader:
+    observations = batch["observation.state"].to(device)
+    actions = batch["action"].to(device)
+    images = batch["observation.images.front_left"].to(device)
+    # model.forward(batch)
+```
+
+## Stream a dataset (no downloads)
+
+Use `StreamingLeRobotDataset` to iterate directly from the Hub without local copies. This allows to stream large datasets without the need to downloading them onto disk or loading them onto memory, and is a key feature of the new dataset format.
+
+```python
+from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
+
+repo_id = "yaak-ai/L2D-v3"
+dataset = StreamingLeRobotDataset(repo_id)  # streams directly from the Hub
+```
+
+<div style="display:flex; justify-content:center; gap:12px; flex-wrap:wrap;">
+  <figure style="margin:0; text-align:center;">
+    <img
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobotdataset-v3/streaming-lerobot.png"
+      alt="StreamingLeRobotDataset"
+      width="520"
+    />
+    <figcaption style="font-size:0.9em; color:#666;">
+      Stream directly from the Hub for on‑the‑fly training.
+    </figcaption>
+  </figure>
+</div>
+
+## Image transforms
+
+Image transforms are data augmentations applied to camera frames during training to improve model robustness and generalization. LeRobot supports various transforms including brightness, contrast, saturation, hue, and sharpness adjustments.
+
+### Using transforms during dataset creation/recording
+
+Currently, transforms are applied during **training time only**, not during recording. When you create or record a dataset, the raw images are stored without transforms. This allows you to experiment with different augmentations later without re-recording data.
+
+### Adding transforms to existing datasets (API)
+
+Use the `image_transforms` parameter when loading a dataset for training:
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig, ImageTransformConfig
+
+# Option 1: Use default transform configuration (disabled by default)
+transforms_config = ImageTransformsConfig(
+    enable=True,  # Enable transforms
+    max_num_transforms=3,  # Apply up to 3 transforms per frame
+    random_order=False,  # Apply in standard order
+)
+transforms = ImageTransforms(transforms_config)
+
+dataset = LeRobotDataset(
+    repo_id="your-username/your-dataset",
+    image_transforms=transforms
+)
+
+# Option 2: Create custom transform configuration
+custom_transforms_config = ImageTransformsConfig(
+    enable=True,
+    max_num_transforms=2,
+    random_order=True,
+    tfs={
+        "brightness": ImageTransformConfig(
+            weight=1.0,
+            type="ColorJitter",
+            kwargs={"brightness": (0.7, 1.3)}  # Adjust brightness range
+        ),
+        "contrast": ImageTransformConfig(
+            weight=2.0,  # Higher weight = more likely to be selected
+            type="ColorJitter",
+            kwargs={"contrast": (0.8, 1.2)}
+        ),
+        "sharpness": ImageTransformConfig(
+            weight=0.5,  # Lower weight = less likely to be selected
+            type="SharpnessJitter",
+            kwargs={"sharpness": (0.3, 2.0)}
+        ),
+    }
+)
+
+dataset = LeRobotDataset(
+    repo_id="your-username/your-dataset",
+    image_transforms=ImageTransforms(custom_transforms_config)
+)
+
+# Option 3: Use pure torchvision transforms
+from torchvision.transforms import v2
+
+torchvision_transforms = v2.Compose([
+    v2.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+    v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),
+])
+
+dataset = LeRobotDataset(
+    repo_id="your-username/your-dataset",
+    image_transforms=torchvision_transforms
+)
+```
+
+### Available transform types
+
+LeRobot provides several transform types:
+
+- **`ColorJitter`**: Adjusts brightness, contrast, saturation, and hue
+- **`SharpnessJitter`**: Randomly adjusts image sharpness
+- **`Identity`**: No transformation (useful for testing)
+
+You can also use any `torchvision.transforms.v2` transform by passing it directly to the `image_transforms` parameter.
+
+### Configuration options
+
+- **`enable`**: Enable/disable transforms (default: `False`)
+- **`max_num_transforms`**: Maximum number of transforms applied per frame (default: `3`)
+- **`random_order`**: Apply transforms in random order vs. standard order (default: `False`)
+- **`weight`**: Sampling probability for each transform (higher = more likely, if sum of weights is not 1, they will be normalized)
+- **`kwargs`**: Transform-specific parameters (e.g., brightness range)
+
+### Visualizing transforms
+
+Use the visualization script to preview how transforms affect your data:
+
+```bash
+lerobot-imgtransform-viz \
+  --repo-id=your-username/your-dataset \
+  --output-dir=./transform_examples \
+  --n-examples=5
+```
+
+This saves example images showing the effect of each transform, helping you tune parameters.
+
+### Best practices
+
+- **Start conservative**: Begin with small ranges (e.g., brightness 0.9-1.1) and increase gradually
+- **Test first**: Use the visualization script to ensure transforms look reasonable
+- **Monitor training**: Strong augmentations can hurt performance if too aggressive
+- **Match your domain**: If your robot operates in varying lighting, use brightness/contrast transforms
+- **Combine wisely**: Using too many transforms simultaneously can make training unstable
+
+## Migrate `v2.1` → `v3.0`
+
+A converter aggregates per‑episode files into larger shards and writes episode offsets/metadata. Convert your dataset using the instructions below.
+
+```bash
+# Pre-release build with v3 support:
+pip install "https://github.com/huggingface/lerobot/archive/33cad37054c2b594ceba57463e8f11ee374fa93c.zip"
+
+# Convert an existing v2.1 dataset hosted on the Hub:
+python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DATASET_ID>
+```
+
+**What it does**
+
+- Aggregates parquet files: `episode-0000.parquet`, `episode-0001.parquet`, … → **`file-0000.parquet`**, …
+- Aggregates mp4 files: `episode-0000.mp4`, `episode-0001.mp4`, … → **`file-0000.mp4`**, …
+- Updates `meta/episodes/*` (chunked Parquet) with per‑episode lengths, tasks, and byte/frame offsets.
+
+## Common Issues
+
+### Always call `finalize()` before pushing
+
+When creating or recording datasets, you **must** call `dataset.finalize()` to properly close parquet writers. See the [PR #1903](https://github.com/huggingface/lerobot/pull/1903) for more details.
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# Create dataset and record episodes
+dataset = LeRobotDataset.create(...)
+
+for episode in range(num_episodes):
+    # Record frames
+    for frame in episode_data:
+        dataset.add_frame(frame)
+    dataset.save_episode()
+
+# Call finalize() when done recording and before push_to_hub()
+dataset.finalize()  # Closes parquet writers, writes metadata footers
+dataset.push_to_hub()
+```
+
+**Why is this necessary?**
+
+Dataset v3.0 uses incremental parquet writing with buffered metadata for efficiency. The `finalize()` method:
+
+- Flushes any buffered episode metadata to disk
+- Closes parquet writers to write footer metadata, otherwise the parquet files will be corrupt
+- Ensures the dataset is valid for loading
+
+Without calling `finalize()`, your parquet files will be incomplete and the dataset won't load properly.

docs/source/libero.mdx

@@ -0,0 +1,171 @@
+# LIBERO
+
+**LIBERO** is a benchmark designed to study **lifelong robot learning**. The idea is that robots won’t just be pretrained once in a factory, they’ll need to keep learning and adapting with their human users over time. This ongoing adaptation is called **lifelong learning in decision making (LLDM)**, and it’s a key step toward building robots that become truly personalized helpers.
+
+- 📄 [LIBERO paper](https://arxiv.org/abs/2306.03310)
+- 💻 [Original LIBERO repo](https://github.com/Lifelong-Robot-Learning/LIBERO)
+
+To make progress on this challenge, LIBERO provides a set of standardized tasks that focus on **knowledge transfer**: how well a robot can apply what it has already learned to new situations. By evaluating on LIBERO, different algorithms can be compared fairly and researchers can build on each other’s work.
+
+LIBERO includes **five task suites**:
+
+- **LIBERO-Spatial (`libero_spatial`)** – tasks that require reasoning about spatial relations.
+- **LIBERO-Object (`libero_object`)** – tasks centered on manipulating different objects.
+- **LIBERO-Goal (`libero_goal`)** – goal-conditioned tasks where the robot must adapt to changing targets.
+- **LIBERO-90 (`libero_90`)** – 90 short-horizon tasks from the LIBERO-100 collection.
+- **LIBERO-Long (`libero_10`)** – 10 long-horizon tasks from the LIBERO-100 collection.
+
+Together, these suites cover **130 tasks**, ranging from simple object manipulations to complex multi-step scenarios. LIBERO is meant to grow over time, and to serve as a shared benchmark where the community can test and improve lifelong learning algorithms.
+
+![An overview of the LIBERO benchmark](https://libero-project.github.io/assets/img/libero/fig1.png)
+
+## Evaluating with LIBERO
+
+At **LeRobot**, we ported [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) into our framework and used it mainly to **evaluate [SmolVLA](https://huggingface.co/docs/lerobot/en/smolvla)**, our lightweight Vision-Language-Action model.
+
+LIBERO is now part of our **multi-eval supported simulation**, meaning you can benchmark your policies either on a **single suite of tasks** or across **multiple suites at once** with just a flag.
+
+To Install LIBERO, after following LeRobot official instructions, just do:
+`pip install -e ".[libero]"`
+
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install libero tag, you will have to do: `pip install "lerobot[libero]@git+https://github.com/huggingface/lerobot.git"`.
+>
+> This will be solved in the next patch release
+
+### Single-suite evaluation
+
+Evaluate a policy on one LIBERO suite:
+
+```bash
+lerobot-eval \
+  --policy.path="your-policy-id" \
+  --env.type=libero \
+  --env.task=libero_object \
+  --eval.batch_size=2 \
+  --eval.n_episodes=3
+```
+
+- `--env.task` picks the suite (`libero_object`, `libero_spatial`, etc.).
+- `--eval.batch_size` controls how many environments run in parallel.
+- `--eval.n_episodes` sets how many episodes to run in total.
+
+---
+
+### Multi-suite evaluation
+
+Benchmark a policy across multiple suites at once:
+
+```bash
+lerobot-eval \
+  --policy.path="your-policy-id" \
+  --env.type=libero \
+  --env.task=libero_object,libero_spatial \
+  --eval.batch_size=1 \
+  --eval.n_episodes=2
+```
+
+- Pass a comma-separated list to `--env.task` for multi-suite evaluation.
+
+### Policy inputs and outputs
+
+When using LIBERO through LeRobot, policies interact with the environment via **observations** and **actions**:
+
+- **Observations**
+  - `observation.state` – proprioceptive features (agent state).
+  - `observation.images.image` – main camera view (`agentview_image`).
+  - `observation.images.image2` – wrist camera view (`robot0_eye_in_hand_image`).
+
+  ⚠️ **Note:** LeRobot enforces the `.images.*` prefix for any multi-modal visual features. Always ensure that your policy config `input_features` use the same naming keys, and that your dataset metadata keys follow this convention during evaluation.
+  If your data contains different keys, you must rename the observations to match what the policy expects, since naming keys are encoded inside the normalization statistics layer.
+  This will be fixed with the upcoming Pipeline PR.
+
+- **Actions**
+  - Continuous control values in a `Box(-1, 1, shape=(7,))` space.
+
+We also provide a notebook for quick testing:
+Training with LIBERO
+
+## Training with LIBERO
+
+When training on LIBERO tasks, make sure your dataset parquet and metadata keys follow the LeRobot convention.
+
+The environment expects:
+
+- `observation.state` → 8-dim agent state
+- `observation.images.image` → main camera (`agentview_image`)
+- `observation.images.image2` → wrist camera (`robot0_eye_in_hand_image`)
+
+⚠️ Cleaning the dataset upfront is **cleaner and more efficient** than remapping keys inside the code.
+To avoid potential mismatches and key errors, we provide a **preprocessed LIBERO dataset** that is fully compatible with the current LeRobot codebase and requires no additional manipulation:
+👉 [HuggingFaceVLA/libero](https://huggingface.co/datasets/HuggingFaceVLA/libero)
+
+For reference, here is the **original dataset** published by Physical Intelligence:
+👉 [physical-intelligence/libero](https://huggingface.co/datasets/physical-intelligence/libero)
+
+---
+
+### Example training command
+
+```bash
+lerobot-train \
+  --policy.type=smolvla \
+  --policy.repo_id=${HF_USER}/libero-test \
+  --policy.load_vlm_weights=true \
+  --dataset.repo_id=HuggingFaceVLA/libero \
+  --env.type=libero \
+  --env.task=libero_10 \
+  --output_dir=./outputs/ \
+  --steps=100000 \
+  --batch_size=4 \
+  --eval.batch_size=1 \
+  --eval.n_episodes=1 \
+  --eval_freq=1000 \
+```
+
+---
+
+### Note on rendering
+
+LeRobot uses MuJoCo for simulation. You need to set the rendering backend before training or evaluation:
+
+- `export MUJOCO_GL=egl` → for headless servers (e.g. HPC, cloud)
+
+## Reproducing π₀.₅ results
+
+We reproduce the results of π₀.₅ on the LIBERO benchmark using the LeRobot implementation. We take the Physical Intelligence LIBERO base model (`pi05_libero`) and finetune for an additional 6k steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
+
+The finetuned model can be found here:
+
+- **π₀.₅ LIBERO**: [lerobot/pi05_libero_finetuned](https://huggingface.co/lerobot/pi05_libero_finetuned)
+
+We then evaluate the finetuned model using the LeRobot LIBERO implementation, by running the following command:
+
+```bash
+lerobot-eval \
+  --output_dir=/logs/ \
+  --env.type=libero \
+  --env.task=libero_spatial,libero_object,libero_goal,libero_10 \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10 \
+  --policy.path=pi05_libero_finetuned \
+  --policy.n_action_steps=10 \
+  --output_dir=./eval_logs/ \
+  --env.max_parallel_tasks=1
+```
+
+**Note:** We set `n_action_steps=10`, similar to the original OpenPI implementation.
+
+### Results
+
+We obtain the following results on the LIBERO benchmark:
+
+| Model    | LIBERO Spatial | LIBERO Object | LIBERO Goal | LIBERO 10 | Average  |
+| -------- | -------------- | ------------- | ----------- | --------- | -------- |
+| **π₀.₅** | 97.0           | 99.0          | 98.0        | 96.0      | **97.5** |
+
+These results are consistent with the original [results](https://github.com/Physical-Intelligence/openpi/tree/main/examples/libero#results) reported by Physical Intelligence:
+
+| Model    | LIBERO Spatial | LIBERO Object | LIBERO Goal | LIBERO 10 | Average   |
+| -------- | -------------- | ------------- | ----------- | --------- | --------- |
+| **π₀.₅** | 98.8           | 98.2          | 98.0        | 92.4      | **96.85** |

docs/source/metaworld.mdx

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

docs/source/multi_gpu_training.mdx

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

docs/source/notebooks.mdx

@@ -0,0 +1,29 @@
+# 🤗 LeRobot Notebooks
+
+This repository contains example notebooks for using LeRobot. These notebooks demonstrate how to train policies on real or simulation datasets using standardized policies.
+
+---
+
+### Training ACT
+
+[ACT](https://huggingface.co/papers/2304.13705) (Action Chunking Transformer) is a transformer-based policy architecture for imitation learning that processes robot states and camera inputs to generate smooth, chunked action sequences.
+
+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                                                                                                                                                                             |
+| :------------------------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [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`.
+
+### Training SmolVLA
+
+[SmolVLA](https://huggingface.co/papers/2506.01844) is a small but efficient Vision-Language-Action model. It is compact in size with 450 M-parameter and is developed by Hugging Face.
+
+We provide a ready-to-run Google Colab notebook to help you train SmolVLA policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases.
+
+| Notebook                                                                                                        | Colab                                                                                                                                                                                 |
+| :-------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [Train SmolVLA with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-smolvla.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-smolvla.ipynb) |
+
+Expected training time for 20k steps: ~5 hours on an NVIDIA A100 GPU with batch size of `64`.

docs/source/phone_teleop.mdx

@@ -0,0 +1,191 @@
+# Phone
+
+Use your phone (iOS or Android) to control your robot.
+
+**In this guide you'll learn:**
+
+- How to connect an iOS/Android phone
+- How phone pose is mapped to robot end‑effector (EE) targets
+- How to tweak safety limits, gripper control, and IK settings
+
+To use phone to control your robot, install the relevant dependencies with:
+
+```bash
+pip install lerobot[phone]
+```
+
+## Get started
+
+### Supported platforms
+
+- iOS: Uses the HEBI Mobile I/O app (ARKit pose + buttons). Download the app first, open it and the examples will discover it on your network and stream the phone pose and inputs.
+- Android: Uses the `teleop` package (WebXR). When you start the Python process, it prints a local URL. Open the link on your phone, tap Start, then use Move to stream pose.
+
+Links:
+
+- Android WebXR library: [`teleop` on PyPI](https://pypi.org/project/teleop/)
+- iOS app: [HEBI Mobile I/O](https://docs.hebi.us/tools.html#mobile-io)
+
+### Phone orientation and controls
+
+- Orientation: hold the phone with the screen facing up and the top edge pointing in the same direction as the robot gripper. This ensures calibration aligns the phone’s frame with the robot frame so motion feels natural, see the image below for reference.
+- Enable/disable:
+  - iOS: Hold `B1` to enable teleoperation, release to stop. The first press captures a reference pose.
+  - Android: Press and hold the `Move` button, release to stop. The first press captures a reference pose.
+- Gripper control:
+  - iOS: Analog input `A3` controls the gripper as velocity input.
+  - Android: Buttons `A` and `B` act like increment/decrement (A opens, B closes). You can tune velocity in the `GripperVelocityToJoint` step.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/phone_teleop.webp" alt="Phone teleop orientation" title="Phone teleop orientation" width="40%">
+
+### Step 1: Choose the platform
+
+Modify the examples to use `PhoneOS.IOS` or `PhoneOS.ANDROID` in `PhoneConfig`. The API is identical across platforms, only the input source differs. All examples are under `examples/` and have `phone_so100_*.py` variants.
+
+Teleoperation example:
+
+```36:43:examples/phone_so100_teleop.py
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
+
+teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
+teleop_device = Phone(teleop_config)
+```
+
+### Step 2: Connect and calibrate
+
+When `Phone(teleop_config)` is created and `connect()` is called, calibration is prompted automatically. Hold the phone in the orientation described above, then:
+
+- iOS: press and hold `B1` to capture the reference pose.
+- Android: press `Move` button on the WebXR page to capture the reference pose.
+
+Why calibrate? We capture the current pose so subsequent poses are expressed in a robot aligned frame. When you again press the button to enable control, the position is recaptured to avoid drift when your phone is repositioned while it was disabled.
+
+### Step 3: Run an example
+
+Run on of the examples scripts to teleoperate, record a dataset, replay a dataset or evaluate a policy.
+
+All scripts assume you configured your robot (e.g., SO-100 follower) and set the correct serial port.
+
+Additionally you need to **copy the urdf of the robot to the examples folder**. For the examples in this tutorial (Using SO100/SO101) it is highly recommended to use the urdf in the [SO-ARM100 repo](https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf)
+
+- Run this example to teleoperate:
+
+  ```bash
+  python examples/phone_to_so100/teleoperate.py
+  ```
+
+After running the example:
+
+- Android: after starting the script, open the printed local URL on your phone, tap Start, then press and hold Move.
+- iOS: open HEBI Mobile I/O first; B1 enables motion. A3 controls the gripper.
+
+Additionally you can customize mapping or safety limits by editing the processor steps shown in the examples. You can also remap inputs (e.g., use a different analog input) or adapt the pipeline to other robots (e.g., LeKiwi) by modifying the input and kinematics steps. More about this in the [Processors for Robots and Teleoperators](./processors_robots_teleop) guide.
+
+- Run this example to record a dataset, which saves absolute end effector observations and actions:
+
+  ```bash
+  python examples/phone_to_so100/record.py
+  ```
+
+- Run this example to replay recorded episodes:
+
+  ```bash
+  python examples/phone_to_so100/replay.py
+  ```
+
+- Run this example to evaluate a pretrained policy:
+
+  ```bash
+  python examples/phone_to_so100/evaluate.py
+  ```
+
+### Important pipeline steps and options
+
+- Kinematics are used in multiple steps. We use [Placo](https://github.com/Rhoban/placo) which is a wrapper around Pinocchio for handling our kinematics. We construct the kinematics object by passing the robot's URDF and target frame. We set `target_frame_name` to the gripper frame.
+
+  ```examples/phone_to_so100/teleoperate.py
+  kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+  )
+
+  ```
+
+- The `MapPhoneActionToRobotAction` step converts the calibrated phone pose and inputs into target deltas and gripper commands, below is shown what the step outputs.
+
+  ```src/lerobot/teleoperators/phone/phone_processor.py
+  action["enabled"] = enabled
+        action["target_x"] = -pos[1] if enabled else 0.0
+        action["target_y"] = pos[0] if enabled else 0.0
+        action["target_z"] = pos[2] if enabled else 0.0
+        action["target_wx"] = rotvec[1] if enabled else 0.0
+        action["target_wy"] = rotvec[0] if enabled else 0.0
+        action["target_wz"] = -rotvec[2] if enabled else 0.0
+        action["gripper_vel"] = gripper_vel  # Still send gripper action when disabled
+  ```
+
+- The `EEReferenceAndDelta` step converts target deltas to an absolute desired EE pose, storing a reference on enable, the `end_effector_step_sizes` are the step sizes for the EE pose and can be modified to change the motion speed.
+
+  ```examples/phone_to_so100/teleoperate.py
+  EEReferenceAndDelta(
+      kinematics=kinematics_solver,
+      end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+      motor_names=list(robot.bus.motors.keys()),
+      use_latched_reference=True,
+  ),
+  ```
+
+- The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` are the step limits for the EE pose and can be modified to change the safety limits.
+
+  ```examples/phone_to_so100/teleoperate.py
+  EEBoundsAndSafety(
+      end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+      max_ee_step_m=0.10,
+  )
+  ```
+
+- The `GripperVelocityToJoint` step turns a velocity‑like gripper input into absolute gripper position using the current measured state. The `speed_factor` is the factor by which the velocity is multiplied.
+
+  ```examples/phone_to_so100/teleoperate.py
+  GripperVelocityToJoint(speed_factor=20.0)
+  ```
+
+#### Different IK initial guesses
+
+We use different IK initial guesses in the kinematic steps. As initial guess either the current measured joints or the previous IK solution is used.
+
+- Closed loop (used in record/eval): sets `initial_guess_current_joints=True` so IK starts from the measured joints each frame.
+
+  ```examples/phone_to_so100/record.py
+  InverseKinematicsEEToJoints(
+      kinematics=kinematics_solver,
+      motor_names=list(robot.bus.motors.keys()),
+      initial_guess_current_joints=True,  # closed loop
+  )
+  ```
+
+- Open loop (used in replay): sets `initial_guess_current_joints=False` so IK continues from the previous IK solution rather than the measured state. This preserves action stability when we replay without feedback.
+
+  ```examples/phone_to_so100/replay.py
+  InverseKinematicsEEToJoints(
+      kinematics=kinematics_solver,
+      motor_names=list(robot.bus.motors.keys()),
+      initial_guess_current_joints=False,  # open loop
+  )
+  ```
+
+### Pipeline steps explained
+
+- MapPhoneActionToRobotAction: converts calibrated phone pose and inputs into target deltas and a gripper command. Motion is gated by an enable signal (B1 on iOS, Move on Android).
+- EEReferenceAndDelta: latches a reference EE pose on enable and combines it with target deltas to produce an absolute desired EE pose each frame. When disabled, it keeps sending the last commanded pose.
+- EEBoundsAndSafety: clamps the EE pose to a workspace and rate‑limits jumps for safety. Also declares `action.ee.*` features.
+- InverseKinematicsEEToJoints: turns an EE pose into joint positions with IK. `initial_guess_current_joints=True` is recommended for closed‑loop control; set `False` for open‑loop replay for stability.
+- GripperVelocityToJoint: integrates a velocity‑like gripper input into an absolute gripper position using the current measured state.
+- ForwardKinematicsJointsToEE: computes `observation.state.ee.*` from observed joints for logging and training on EE state.
+
+### Troubleshooting
+
+- iOS not discovered: ensure HEBI Mobile I/O is open and your laptop/phone are on the same network.
+- Android URL not reachable: check local you used `https` instead of `http`, use the exact IP printed by the script and allow your browser to enter and ignore the certificate issue.
+- Motion feels inverted: adjust the sign flips in `MapPhoneActionToRobotAction` or swap axes to match your setup.

docs/source/pi0.mdx

@@ -0,0 +1,84 @@
+# π₀ (Pi0)
+
+π₀ is a **Vision-Language-Action model for general robot control**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository.
+
+## Model Overview
+
+π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi0). Unlike traditional robot programs that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.
+
+### The Vision for Physical Intelligence
+
+As described by Physical Intelligence, while AI has achieved remarkable success in digital domains, from chess-playing to drug discovery, human intelligence still dramatically outpaces AI in the physical world. To paraphrase Moravec's paradox, winning a game of chess represents an "easy" problem for AI, but folding a shirt or cleaning up a table requires solving some of the most difficult engineering problems ever conceived. π₀ represents a first step toward developing artificial physical intelligence that enables users to simply ask robots to perform any task they want, just like they can with large language models.
+
+### Architecture and Approach
+
+π₀ combines several key innovations:
+
+- **Flow Matching**: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models)
+- **Cross-Embodiment Training**: Trained on data from 8 distinct robot platforms including UR5e, Bimanual UR5e, Franka, Bimanual Trossen, Bimanual ARX, Mobile Trossen, and Mobile Fibocom
+- **Internet-Scale Pre-training**: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model
+- **High-Frequency Control**: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation
+
+## Installation Requirements
+
+1. Install LeRobot by following our [Installation Guide](./installation).
+2. Install Pi0 dependencies by running:
+
+   ```bash
+   pip install -e ".[pi]"
+   ```
+
+   > [!NOTE]
+   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+   >
+   > This will be solved in the next patch release
+
+## Training Data and Capabilities
+
+π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:
+
+1. **Internet-Scale Pre-training**: Vision-language data from the web for semantic understanding
+2. **Open X-Embodiment Dataset**: Open-source robot manipulation datasets
+3. **Physical Intelligence Dataset**: Large and diverse dataset of dexterous tasks across 8 distinct robots
+
+## Usage
+
+To use π₀ in LeRobot, specify the policy type as:
+
+```python
+policy.type=pi0
+```
+
+## Training
+
+For training π₀, you can use the standard LeRobot training script with the appropriate configuration:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+    --dataset.repo_id=your_dataset \
+    --policy.type=pi0 \
+    --output_dir=./outputs/pi0_training \
+    --job_name=pi0_training \
+    --policy.pretrained_path=lerobot/pi0_base \
+    --policy.repo_id=your_repo_id \
+    --policy.compile_model=true \
+    --policy.gradient_checkpointing=true \
+    --policy.dtype=bfloat16 \
+    --steps=3000 \
+    --policy.device=cuda \
+    --batch_size=32
+```
+
+### Key Training Parameters
+
+- **`--policy.compile_model=true`**: Enables model compilation for faster training
+- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training
+- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency
+- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory
+- **`--policy.pretrained_path=lerobot/pi0_base`**: The base π₀ model you want to finetune, options are:
+  - [lerobot/pi0_base](https://huggingface.co/lerobot/pi0_base)
+  - [lerobot/pi0_libero](https://huggingface.co/lerobot/pi0_libero) (specifically trained on the Libero dataset)
+
+## License
+
+This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).

docs/source/pi05.mdx

@@ -0,0 +1,112 @@
+# π₀.₅ (Pi05) Policy
+
+π₀.₅ is a **Vision-Language-Action model with open-world generalization**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository.
+
+## Model Overview
+
+π₀.₅ represents a significant evolution from π₀, developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi05) to address a big challenge in robotics: **open-world generalization**. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training.
+
+### The Generalization Challenge
+
+As Physical Intelligence explains, the fundamental challenge isn't performing tasks of agility or dexterity, but generalization, the ability to correctly perform tasks in new settings with new objects. Consider a robot cleaning different homes: each home has different objects in different places. Generalization must occur at multiple levels:
+
+- **Physical Level**: Understanding how to pick up a spoon (by the handle) or plate (by the edge), even with unseen objects in cluttered environments
+- **Semantic Level**: Understanding task semantics, where to put clothes and shoes (laundry hamper, not on the bed), and what tools are appropriate for cleaning spills
+- **Environmental Level**: Adapting to "messy" real-world environments like homes, grocery stores, offices, and hospitals
+
+### Co-Training on Heterogeneous Data
+
+The breakthrough innovation in π₀.₅ is **co-training on heterogeneous data sources**. The model learns from:
+
+1. **Multimodal Web Data**: Image captioning, visual question answering, object detection
+2. **Verbal Instructions**: Humans coaching robots through complex tasks step-by-step
+3. **Subtask Commands**: High-level semantic behavior labels (e.g., "pick up the pillow" for an unmade bed)
+4. **Cross-Embodiment Robot Data**: Data from various robot platforms with different capabilities
+5. **Multi-Environment Data**: Static robots deployed across many different homes
+6. **Mobile Manipulation Data**: ~400 hours of mobile robot demonstrations
+
+This diverse training mixture creates a "curriculum" that enables generalization across physical, visual, and semantic levels simultaneously.
+
+## Installation Requirements
+
+1. Install LeRobot by following our [Installation Guide](./installation).
+2. Install Pi0.5 dependencies by running:
+
+   ```bash
+   pip install -e ".[pi]"
+   ```
+
+   > [!NOTE]
+   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+   >
+   > This will be solved in the next patch release
+
+## Usage
+
+To use π₀.₅ in your LeRobot configuration, specify the policy type as:
+
+```python
+policy.type=pi05
+```
+
+## Training
+
+### Training Command Example
+
+Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py\
+    --dataset.repo_id=your_dataset \
+    --policy.type=pi05 \
+    --output_dir=./outputs/pi05_training \
+    --job_name=pi05_training \
+    --policy.repo_id=your_repo_id \
+    --policy.pretrained_path=lerobot/pi05_base \
+    --policy.compile_model=true \
+    --policy.gradient_checkpointing=true \
+    --wandb.enable=true \
+    --policy.dtype=bfloat16 \
+    --steps=3000 \
+    --policy.device=cuda \
+    --batch_size=32
+```
+
+### Key Training Parameters
+
+- **`--policy.compile_model=true`**: Enables model compilation for faster training
+- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training
+- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency
+- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory
+- **`--policy.pretrained_path=lerobot/pi05_base`**: The base π₀.₅ model you want to finetune, options are:
+  - [lerobot/pi05_base](https://huggingface.co/lerobot/pi05_base)
+  - [lerobot/pi05_libero](https://huggingface.co/lerobot/pi05_libero) (specifically trained on the Libero dataset)
+
+If your dataset is not converted with `quantiles`, you can convert it with the following command:
+
+```bash
+python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \
+    --repo-id=your_dataset \
+```
+
+Or train pi05 with this normalization mapping: `--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'`
+
+## Performance Results
+
+### Libero Benchmark Results
+
+π₀.₅ has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the libero base model for an additional 6k steps on the Libero dataset and compared the results to the OpenPI reference results.
+
+| Benchmark          | LeRobot Implementation | OpenPI Reference |
+| ------------------ | ---------------------- | ---------------- |
+| **Libero Spatial** | 97.0%                  | 98.8%            |
+| **Libero Object**  | 99.0%                  | 98.2%            |
+| **Libero Goal**    | 98.0%                  | 98.0%            |
+| **Libero 10**      | 96.0%                  | 92.4%            |
+| **Average**        | 97.5%                  | 96.85%           |
+
+These results demonstrate π₀.₅'s strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section.
+
+## License
+
+This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).

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_groot_README.md

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

docs/source/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/porting_datasets_v3.mdx

@@ -0,0 +1,321 @@
+# Porting Large Datasets to LeRobot Dataset v3.0
+
+This tutorial explains how to port large-scale robotic datasets to the LeRobot Dataset v3.0 format. We'll use the **DROID 1.0.1** dataset as our primary example, which demonstrates handling multi-terabyte datasets with thousands of shards across SLURM clusters.
+
+## File Organization: v2.1 vs v3.0
+
+Dataset v3.0 fundamentally changes how data is organized and stored:
+
+**v2.1 Structure (Episode-based)**:
+
+```
+dataset/
+├── data/chunk-000/episode_000000.parquet
+├── data/chunk-000/episode_000001.parquet
+├── videos/chunk-000/camera/episode_000000.mp4
+└── meta/episodes.jsonl
+```
+
+**v3.0 Structure (File-based)**:
+
+```
+dataset/
+├── data/chunk-000/file-000.parquet        # Multiple episodes per file
+├── videos/camera/chunk-000/file-000.mp4   # Consolidated video chunks
+└── meta/episodes/chunk-000/file-000.parquet  # Structured metadata
+```
+
+This transition from individual episode files to file-based chunks dramatically improves performance and reduces storage overhead.
+
+## What's New in Dataset v3.0
+
+Dataset v3.0 introduces significant improvements for handling large datasets:
+
+### 🏗️ **Enhanced File Organization**
+
+- **File-based structure**: Episodes are now grouped into chunked files rather than individual episode files
+- **Configurable file sizes**: for data and video files
+- **Improved storage efficiency**: Better compression and reduced overhead
+
+### 📊 **Modern Metadata Management**
+
+- **Parquet-based metadata**: Replaced JSON Lines with efficient parquet format
+- **Structured episode access**: Direct pandas DataFrame access via `dataset.meta.episodes`
+- **Per-episode statistics**: Enhanced statistics tracking at episode level
+
+### 🚀 **Performance Enhancements**
+
+- **Memory-mapped access**: Improved RAM usage through PyArrow memory mapping
+- **Faster loading**: Significantly reduced dataset initialization time
+- **Better scalability**: Designed for datasets with millions of episodes
+
+## Prerequisites
+
+Before porting large datasets, ensure you have:
+
+- **LeRobot installed** with v3.0 support. Follow our [Installation Guide](./installation).
+- **Sufficient storage**: Raw datasets can be very large (e.g., DROID requires 2TB)
+- **Cluster access** (recommended for large datasets): SLURM or similar job scheduler
+- **Dataset-specific dependencies**: For DROID, you'll need TensorFlow Dataset utilities
+
+## Understanding the DROID Dataset
+
+[DROID 1.0.1](https://droid-dataset.github.io/droid/the-droid-dataset) is an excellent example of a large-scale robotic dataset:
+
+- **Size**: 1.7TB (RLDS format), 8.7TB (raw data)
+- **Structure**: 2048 pre-defined TensorFlow dataset shards
+- **Content**: 76,000+ robot manipulation trajectories from Franka Emika Panda robots
+- **Scope**: Real-world manipulation tasks across multiple environments and objects
+- **Format**: Originally in TensorFlow Records/RLDS format, requiring conversion to LeRobot format
+- **Hosting**: Google Cloud Storage with public access via `gsutil`
+
+The dataset contains diverse manipulation demonstrations with:
+
+- Multiple camera views (wrist camera, exterior cameras)
+- Natural language task descriptions
+- Robot proprioceptive state and actions
+- Success/failure annotations
+
+### DROID Features Schema
+
+```python
+DROID_FEATURES = {
+    # Episode markers
+    "is_first": {"dtype": "bool", "shape": (1,)},
+    "is_last": {"dtype": "bool", "shape": (1,)},
+    "is_terminal": {"dtype": "bool", "shape": (1,)},
+
+    # Language instructions
+    "language_instruction": {"dtype": "string", "shape": (1,)},
+    "language_instruction_2": {"dtype": "string", "shape": (1,)},
+    "language_instruction_3": {"dtype": "string", "shape": (1,)},
+
+    # Robot state
+    "observation.state.gripper_position": {"dtype": "float32", "shape": (1,)},
+    "observation.state.cartesian_position": {"dtype": "float32", "shape": (6,)},
+    "observation.state.joint_position": {"dtype": "float32", "shape": (7,)},
+
+    # Camera observations
+    "observation.images.wrist_left": {"dtype": "image"},
+    "observation.images.exterior_1_left": {"dtype": "image"},
+    "observation.images.exterior_2_left": {"dtype": "image"},
+
+    # Actions
+    "action.gripper_position": {"dtype": "float32", "shape": (1,)},
+    "action.cartesian_position": {"dtype": "float32", "shape": (6,)},
+    "action.joint_position": {"dtype": "float32", "shape": (7,)},
+
+    # Standard LeRobot format
+    "observation.state": {"dtype": "float32", "shape": (8,)},  # joints + gripper
+    "action": {"dtype": "float32", "shape": (8,)},  # joints + gripper
+}
+```
+
+## Approach 1: Single Computer Porting
+
+### Step 1: Install Dependencies
+
+For DROID specifically:
+
+```bash
+pip install tensorflow
+pip install tensorflow_datasets
+```
+
+For other datasets, install the appropriate readers for your source format.
+
+### Step 2: Download Raw Data
+
+Download DROID from Google Cloud Storage using `gsutil`:
+
+```bash
+# Install Google Cloud SDK if not already installed
+# https://cloud.google.com/sdk/docs/install
+
+# Download the full RLDS dataset (1.7TB)
+gsutil -m cp -r gs://gresearch/robotics/droid/1.0.1 /your/data/
+
+# Or download just the 100-episode sample (2GB) for testing
+gsutil -m cp -r gs://gresearch/robotics/droid_100 /your/data/
+```
+
+> [!WARNING]
+> Large datasets require substantial time and storage:
+>
+> - **Full DROID (1.7TB)**: Several days to download depending on bandwidth
+> - **Processing time**: 7+ days for local porting of full dataset
+> - **Upload time**: 3+ days to push to Hugging Face Hub
+> - **Local storage**: ~400GB for processed LeRobot format
+
+### Step 3: Port the Dataset
+
+```bash
+python examples/port_datasets/port_droid.py \
+    --raw-dir /your/data/droid/1.0.1 \
+    --repo-id your_id/droid_1.0.1 \
+    --push-to-hub
+```
+
+### Development and Testing
+
+For development, you can port a single shard:
+
+```bash
+python examples/port_datasets/port_droid.py \
+    --raw-dir /your/data/droid/1.0.1 \
+    --repo-id your_id/droid_1.0.1_test \
+    --num-shards 2048 \
+    --shard-index 0
+```
+
+This approach works for smaller datasets or testing, but large datasets require cluster computing.
+
+## Approach 2: SLURM Cluster Porting (Recommended)
+
+For large datasets like DROID, parallel processing across multiple nodes dramatically reduces processing time.
+
+### Step 1: Install Cluster Dependencies
+
+```bash
+pip install datatrove  # Hugging Face's distributed processing library
+```
+
+### Step 2: Configure Your SLURM Environment
+
+Find your partition information:
+
+```bash
+sinfo --format="%R"  # List available partitions
+sinfo -N -p your_partition -h -o "%N cpus=%c mem=%m"  # Check resources
+```
+
+Choose a **CPU partition** - no GPU needed for dataset porting.
+
+### Step 3: Launch Parallel Porting Jobs
+
+```bash
+python examples/port_datasets/slurm_port_shards.py \
+    --raw-dir /your/data/droid/1.0.1 \
+    --repo-id your_id/droid_1.0.1 \
+    --logs-dir /your/logs \
+    --job-name port_droid \
+    --partition your_partition \
+    --workers 2048 \
+    --cpus-per-task 8 \
+    --mem-per-cpu 1950M
+```
+
+#### Parameter Guidelines
+
+- **`--workers`**: Number of parallel jobs (max 2048 for DROID's shard count)
+- **`--cpus-per-task`**: 8 CPUs recommended for frame encoding parallelization
+- **`--mem-per-cpu`**: ~16GB total RAM (8×1950M) for loading raw frames
+
+> [!TIP]
+> Start with fewer workers (e.g., 100) to test your cluster configuration before launching thousands of jobs.
+
+### Step 4: Monitor Progress
+
+Check running jobs:
+
+```bash
+squeue -u $USER
+```
+
+Monitor overall progress:
+
+```bash
+jobs_status /your/logs
+```
+
+Inspect individual job logs:
+
+```bash
+less /your/logs/port_droid/slurm_jobs/JOB_ID_WORKER_ID.out
+```
+
+Debug failed jobs:
+
+```bash
+failed_logs /your/logs/port_droid
+```
+
+### Step 5: Aggregate Shards
+
+Once all porting jobs complete:
+
+```bash
+python examples/port_datasets/slurm_aggregate_shards.py \
+    --repo-id your_id/droid_1.0.1 \
+    --logs-dir /your/logs \
+    --job-name aggr_droid \
+    --partition your_partition \
+    --workers 2048 \
+    --cpus-per-task 8 \
+    --mem-per-cpu 1950M
+```
+
+### Step 6: Upload to Hub
+
+```bash
+python examples/port_datasets/slurm_upload.py \
+    --repo-id your_id/droid_1.0.1 \
+    --logs-dir /your/logs \
+    --job-name upload_droid \
+    --partition your_partition \
+    --workers 50 \
+    --cpus-per-task 4 \
+    --mem-per-cpu 1950M
+```
+
+> [!NOTE]
+> Upload uses fewer workers (50) since it's network-bound rather than compute-bound.
+
+## Dataset v3.0 File Structure
+
+Your completed dataset will have this modern structure:
+
+```
+dataset/
+├── meta/
+│   ├── episodes/
+│   │   └── chunk-000/
+│   │       └── file-000.parquet    # Episode metadata
+│   ├── tasks.parquet               # Task definitions
+│   ├── stats.json                  # Aggregated statistics
+│   └── info.json                   # Dataset information
+├── data/
+│   └── chunk-000/
+│       └── file-000.parquet        # Consolidated episode data
+└── videos/
+    └── camera_key/
+        └── chunk-000/
+            └── file-000.mp4        # Consolidated video files
+```
+
+This replaces the old episode-per-file structure with efficient, optimally-sized chunks.
+
+## Migrating from Dataset v2.1
+
+If you have existing datasets in v2.1 format, use the migration tool:
+
+```bash
+python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
+    --repo-id your_id/existing_dataset
+```
+
+This automatically:
+
+- Converts file structure to v3.0 format
+- Migrates metadata from JSON Lines to parquet
+- Aggregates statistics and creates per-episode stats
+- Updates version information
+
+## Performance Benefits
+
+Dataset v3.0 provides significant improvements for large datasets:
+
+- **Faster loading**: 3-5x reduction in initialization time
+- **Memory efficiency**: Better RAM usage through memory mapping
+- **Scalable processing**: Handles millions of episodes efficiently
+- **Storage optimization**: Reduced file count and improved compression

docs/source/processors_robots_teleop.mdx

@@ -0,0 +1,151 @@
+# Processors for Robots and Teleoperators
+
+This guide shows how to build and modify processing pipelines that connect teleoperators (e.g., phone) to robots and datasets. Pipelines standardize conversions between different action/observation spaces so you can swap teleops and robots without rewriting glue code.
+
+We use the Phone to SO‑100 follower examples for concreteness, but the same patterns apply to other robots.
+
+**What you'll learn**
+
+- Absolute vs. relative EE control: What each means, trade‑offs, and how to choose for your task.
+- Three-pipeline pattern: How to map teleop actions → dataset actions → robot commands, and robot observations → dataset observations.
+- Adapters (`to_transition` / `to_output`): How these convert raw dicts to `EnvTransition` and back to reduce boilerplate.
+- Dataset feature contracts: How steps declare features via `transform_features(...)`, and how to aggregate/merge them for recording.
+- Choosing a representation: When to store joints, absolute EE poses, or relative EE deltas—and how that affects training.
+- Pipeline customization guidance: How to swap robots/URDFs safely and tune bounds, step sizes, and options like IK initialization.
+
+### Absolute vs relative EE control
+
+The examples in this guide use absolute end effector (EE) poses because they are easy to reason about. In practice, relative EE deltas or joint position are often preferred as learning features.
+
+With processors, you choose the learning features you want to use for your policy. This could be joints positions/velocities, absolute EE, or relative EE positions. You can also choose to store other features, such as joint torques, motor currents, etc.
+
+## Three pipelines
+
+We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match.
+Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
+
+1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets)
+2. Pipeline 2: Dataset action space → robot command space (EE targets → joints)
+3. Pipeline 3: Robot observation space → dataset observation space (joints → EE pose)
+
+Below is an example of the three pipelines that we use in the phone to SO-100 follower examples:
+
+```69:90:examples/phone_so100_record.py
+phone_to_robot_ee_pose_processor = RobotProcessorPipeline[RobotAction, RobotAction]( # teleop -> dataset action
+    steps=[
+        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+        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,
+        ),
+        GripperVelocityToJoint(),
+    ],
+    to_transition=robot_action_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+robot_ee_to_joints_processor = RobotProcessorPipeline[RobotAction, RobotAction]( # dataset action -> robot
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()), initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=robot_action_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation]( # robot obs -> dataset obs
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=transition_to_observation,
+)
+```
+
+## Why to_transition / to_output
+
+To convert from robot/teleoperator to pipeline and back, we use the `to_transition` and `to_output` pipeline adapters.
+They standardize conversions to reduce boilerplate code, and form the bridge between the robot and teleoperators raw dictionaries and the pipeline’s `EnvTransition` format.
+In the phone to SO-100 follower examples we use the following adapters:
+
+- `robot_action_to_transition`: transforms the teleop action dict to a pipeline transition.
+- `transition_to_robot_action`: transforms the pipeline transition to a robot action dict.
+- `observation_to_transition`: transforms the robot observation dict to a pipeline transition.
+- `transition_to_observation`: transforms the pipeline transition to a observation dict.
+
+Checkout [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
+
+## Dataset feature contracts
+
+Dataset features are determined by the keys saved in the dataset. Each step can declare what features it modifies in a contract called `transform_features(...)`. Once you build a processor, the processor can then aggregate all of these features with `aggregate_pipeline_dataset_features()` and merge multiple feature dicts with `combine_feature_dicts(...)`.
+
+Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
+
+```src/lerobot/robots/so100_follower/robot_kinematic_processor.py
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        # We only use the ee pose in the dataset, so we don't need the joint positions
+        for n in self.motor_names:
+            features[PipelineFeatureType.ACTION].pop(f"{n}.pos", None)
+        # We specify the dataset features of this step that we want to be stored in the dataset
+        for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]:
+            features[PipelineFeatureType.ACTION][f"ee.{k}"] = PolicyFeature(
+                type=FeatureType.STATE, shape=(1,)
+            )
+        return features
+```
+
+Here we declare what PolicyFeatures we modify in this step, so we know what features we can expect when we run the processor. These features can then be aggregated and used to create the dataset features.
+
+Below is an example of how we aggregate and merge features in the phone to SO-100 record example:
+
+```121:145:examples/phone_so100_record.py
+features=combine_feature_dicts(
+        # Run the feature contract of the pipelines
+        # This tells you how the features would look like after the pipeline steps
+        aggregate_pipeline_dataset_features(
+            pipeline=phone_to_robot_ee_pose_processor,
+            initial_features=create_initial_features(action=phone.action_features), # <- Action features we can expect, these come from our teleop device (phone) and action processor
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_joints_to_ee_pose,
+            initial_features=create_initial_features(observation=robot.observation_features), # <- Observation features we can expect, these come from our robot and observation processor
+            use_videos=True,
+            patterns=["observation.state.ee"], # <- Here you could optionally filter the features we want to store in the dataset, with a specific pattern
+
+        ),
+    ),
+```
+
+How it works:
+
+- `aggregate_pipeline_dataset_features(...)`: applies `transform_features` across the pipeline and filters by patterns (images included when `use_videos=True`, and state features included when `patterns` is specified).
+- `combine_feature_dicts(...)`: combine multiple feature dicts.
+- Recording with `record_loop(...)` uses `build_dataset_frame(...)` to build frames consistent with `dataset.features` before we call `add_frame(...)` to add the frame to the dataset.
+
+## Guidance when customizing robot pipelines
+
+You can store any of the following features as your action/observation space:
+
+- Joint positions
+- Absolute EE poses
+- Relative EE deltas
+- Other features: joint velocity, torques, etc.
+
+Pick what you want to use for your policy action and observation space and configure/modify the pipelines and steps accordingly.
+
+### Different robots
+
+- You can easily reuse pipelines, for example to use another robot with phone teleop, modify the examples and swap the robot `RobotKinematics` (URDF) and `motor_names` to use your own robot with Phone teleop. Additionally you should ensure `target_frame_name` points to your gripper/wrist.
+
+### Safety first
+
+- When changing pipelines, start with tight bounds, implement safety steps when working with real robots.
+- Its advised to start with simulation first and then move to real robots.
+
+Thats it! We hope this guide helps you get started with customizing your robot pipelines, If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support.

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

@@ -0,0 +1,116 @@
+# SmolVLA
+
+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>
+</p>
+
+## Set Up Your Environment
+
+1. Install LeRobot by following our [Installation Guide](./installation).
+2. Install SmolVLA dependencies by running:
+
+   ```bash
+   pip install -e ".[smolvla]"
+   ```
+
+## Collect a dataset
+
+SmolVLA is a base model, so fine-tuning on your own data is required for optimal performance in your setup.
+We recommend recording ~50 episodes of your task as a starting point. Follow our guide to get started: [Recording a Dataset](./il_robots)
+
+<Tip>
+
+In your dataset, make sure to have enough demonstrations per each variation (e.g. the cube position on the table if it is cube pick-place task) you are introducing.
+
+We recommend checking out the dataset linked below for reference that was used in the [SmolVLA paper](https://huggingface.co/papers/2506.01844):
+
+🔗 [SVLA SO100 PickPlace](https://huggingface.co/spaces/lerobot/visualize_dataset?path=%2Flerobot%2Fsvla_so100_pickplace%2Fepisode_0)
+
+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
+
+Use [`smolvla_base`](https://hf.co/lerobot/smolvla_base), our pretrained 450M model, and fine-tune it on your data.
+Training the model for 20k steps will roughly take ~4 hrs on a single A100 GPU. You should tune the number of steps based on performance and your use-case.
+
+If you don't have a gpu device, you can train using our notebook on [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb)
+
+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 && lerobot-train \
+  --policy.path=lerobot/smolvla_base \
+  --dataset.repo_id=${HF_USER}/mydataset \
+  --batch_size=64 \
+  --steps=20000 \
+  --output_dir=outputs/train/my_smolvla \
+  --job_name=my_smolvla_training \
+  --policy.device=cuda \
+  --wandb.enable=true
+```
+
+<Tip>
+  You can start with a small batch size and increase it incrementally, if the
+  GPU allows it, as long as loading times remain short.
+</Tip>
+
+Fine-tuning is an art. For a complete overview of the options for finetuning, run
+
+```bash
+lerobot-train --help
+```
+
+<p align="center">
+  <img
+    src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/S-3vvVCulChREwHDkquoc.gif"
+    alt="Comparison of SmolVLA across task variations."
+    width="500"
+  />
+  <br />
+  <em>
+    Figure 2: Comparison of SmolVLA across task variations. From left to right:
+    (1) pick-place cube counting, (2) pick-place cube counting, (3) pick-place
+    cube counting under perturbations, and (4) generalization on pick-and-place
+    of the lego block with real-world SO101.
+  </em>
+</p>
+
+## Evaluate the finetuned model and run it in real-time
+
+Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./il_robots).
+Once you are logged in, you can run inference in your setup by doing:
+
+```bash
+lerobot-record \
+  --robot.type=so101_follower \
+  --robot.port=/dev/ttyACM0 \ # <- Use your port
+  --robot.id=my_blue_follower_arm \ # <- Use your robot id
+  --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras
+  --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording
+  --dataset.repo_id=${HF_USER}/eval_DATASET_NAME_test \  # <- This will be the dataset name on HF Hub
+  --dataset.episode_time_s=50 \
+  --dataset.num_episodes=10 \
+  # <- Teleop optional if you want to teleoperate in between episodes \
+  # --teleop.type=so100_leader \
+  # --teleop.port=/dev/ttyACM0 \
+  # --teleop.id=my_red_leader_arm \
+  --policy.path=HF_USER/FINETUNE_MODEL_NAME # <- Use your fine-tuned model
+```
+
+Depending on your evaluation setup, you can configure the duration and the number of episodes to record for your evaluation suite.

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](./il_robots)
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

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](./il_robots)
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

docs/source/using_dataset_tools.mdx

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

examples/1_load_lerobot_dataset.py

@@ -1,91 +0,0 @@
-"""
-This script demonstrates the use of `LeRobotDataset` class for handling and processing robotic datasets from Hugging Face.
-It illustrates how to load datasets, manipulate them, and apply transformations suitable for machine learning tasks in PyTorch.
-
-Features included in this script:
-- Loading a dataset and accessing its properties.
-- Filtering data by episode number.
-- Converting tensor data for visualization.
-- Saving video files from dataset frames.
-- Using advanced dataset features like timestamp-based frame selection.
-- Demonstrating compatibility with PyTorch DataLoader for batch processing.
-
-The script ends with examples of how to batch process data using PyTorch's DataLoader.
-"""
-
-from pathlib import Path
-from pprint import pprint
-
-import imageio
-import torch
-
-import lerobot
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-
-print("List of available datasets:")
-pprint(lerobot.available_datasets)
-
-# Let's take one for this example
-repo_id = "lerobot/pusht"
-
-# You can easily load a dataset from a Hugging Face repository
-dataset = LeRobotDataset(repo_id)
-
-# LeRobotDataset is actually a thin wrapper around an underlying Hugging Face dataset
-# (see https://huggingface.co/docs/datasets/index for more information).
-print(dataset)
-print(dataset.hf_dataset)
-
-# And provides additional utilities for robotics and compatibility with Pytorch
-print(f"\naverage number of frames per episode: {dataset.num_samples / dataset.num_episodes:.3f}")
-print(f"frames per second used during data collection: {dataset.fps=}")
-print(f"keys to access images from cameras: {dataset.camera_keys=}\n")
-
-# Access frame indexes associated to first episode
-episode_index = 0
-from_idx = dataset.episode_data_index["from"][episode_index].item()
-to_idx = dataset.episode_data_index["to"][episode_index].item()
-
-# LeRobot datasets actually subclass PyTorch datasets so you can do everything you know and love from working
-# with the latter, like iterating through the dataset. Here we grab all the image frames.
-frames = [dataset[idx]["observation.image"] for idx in range(from_idx, to_idx)]
-
-# Video frames are now float32 in range [0,1] channel first (c,h,w) to follow pytorch convention. To visualize
-# them, we convert to uint8 in range [0,255]
-frames = [(frame * 255).type(torch.uint8) for frame in frames]
-# and to channel last (h,w,c).
-frames = [frame.permute((1, 2, 0)).numpy() for frame in frames]
-
-# Finally, we save the frames to a mp4 video for visualization.
-Path("outputs/examples/1_load_lerobot_dataset").mkdir(parents=True, exist_ok=True)
-imageio.mimsave("outputs/examples/1_load_lerobot_dataset/episode_0.mp4", frames, fps=dataset.fps)
-
-# For many machine learning applications we need to load the history of past observations or trajectories of
-# future actions. Our datasets can load previous and future frames for each key/modality, using timestamps
-# differences with the current loaded frame. For instance:
-delta_timestamps = {
-    # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
-    "observation.image": [-1, -0.5, -0.20, 0],
-    # loads 8 state vectors: 1.5 seconds before, 1 second before, ... 20 ms, 10 ms, and current frame
-    "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, -0.02, -0.01, 0],
-    # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
-    "action": [t / dataset.fps for t in range(64)],
-}
-dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
-print(f"\n{dataset[0]['observation.image'].shape=}")  # (4,c,h,w)
-print(f"{dataset[0]['observation.state'].shape=}")  # (8,c)
-print(f"{dataset[0]['action'].shape=}\n")  # (64,c)
-
-# Finally, our datasets are fully compatible with PyTorch dataloaders and samplers because they are just
-# PyTorch datasets.
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    num_workers=0,
-    batch_size=32,
-    shuffle=True,
-)
-for batch in dataloader:
-    print(f"{batch['observation.image'].shape=}")  # (32,4,c,h,w)
-    print(f"{batch['observation.state'].shape=}")  # (32,8,c)
-    print(f"{batch['action'].shape=}")  # (32,64,c)
-    break

examples/2_evaluate_pretrained_policy.py

@@ -1,112 +0,0 @@
-"""
-This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
-training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
-"""
-
-from pathlib import Path
-
-import gym_pusht  # noqa: F401
-import gymnasium as gym
-import imageio
-import numpy
-import torch
-from huggingface_hub import snapshot_download
-
-from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
-
-# Create a directory to store the video of the evaluation
-output_directory = Path("outputs/eval/example_pusht_diffusion")
-output_directory.mkdir(parents=True, exist_ok=True)
-
-device = torch.device("cuda")
-
-# Download the diffusion policy for pusht environment
-pretrained_policy_path = Path(snapshot_download("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)
-
-# Initialize evaluation environment to render two observation types:
-# an image of the scene and state/position of the agent. The environment
-# also automatically stops running after 300 interactions/steps.
-env = gym.make(
-    "gym_pusht/PushT-v0",
-    obs_type="pixels_agent_pos",
-    max_episode_steps=300,
-)
-
-# Reset the policy and environmens to prepare for rollout
-policy.reset()
-numpy_observation, info = env.reset(seed=42)
-
-# Prepare to collect every rewards and all the frames of the episode,
-# from initial state to final state.
-rewards = []
-frames = []
-
-# Render frame of the initial state
-frames.append(env.render())
-
-step = 0
-done = False
-while not done:
-    # Prepare observation for the policy running in Pytorch
-    state = torch.from_numpy(numpy_observation["agent_pos"])
-    image = torch.from_numpy(numpy_observation["pixels"])
-
-    # Convert to float32 with image from channel first in [0,255]
-    # to channel last in [0,1]
-    state = state.to(torch.float32)
-    image = image.to(torch.float32) / 255
-    image = image.permute(2, 0, 1)
-
-    # Send data tensors from CPU to GPU
-    state = state.to(device, non_blocking=True)
-    image = image.to(device, non_blocking=True)
-
-    # Add extra (empty) batch dimension, required to forward the policy
-    state = state.unsqueeze(0)
-    image = image.unsqueeze(0)
-
-    # Create the policy input dictionary
-    observation = {
-        "observation.state": state,
-        "observation.image": image,
-    }
-
-    # Predict the next action with respect to the current observation
-    with torch.inference_mode():
-        action = policy.select_action(observation)
-
-    # Prepare the action for the environment
-    numpy_action = action.squeeze(0).to("cpu").numpy()
-
-    # Step through the environment and receive a new observation
-    numpy_observation, reward, terminated, truncated, info = env.step(numpy_action)
-    print(f"{step=} {reward=} {terminated=}")
-
-    # Keep track of all the rewards and frames
-    rewards.append(reward)
-    frames.append(env.render())
-
-    # The rollout is considered done when the success state is reach (i.e. terminated is True),
-    # or the maximum number of iterations is reached (i.e. truncated is True)
-    done = terminated | truncated | done
-    step += 1
-
-if terminated:
-    print("Success!")
-else:
-    print("Failure!")
-
-# Get the speed of environment (i.e. its number of frames per second).
-fps = env.metadata["render_fps"]
-
-# Encode all frames into a mp4 video.
-video_path = output_directory / "rollout.mp4"
-imageio.mimsave(str(video_path), numpy.stack(frames), fps=fps)
-
-print(f"Video of the evaluation is available in '{video_path}'.")

examples/3_train_policy.py

@@ -1,79 +0,0 @@
-"""This scripts demonstrates how to train Diffusion Policy on the PushT environment.
-
-Once you have trained a model with this script, you can try to evaluate it on
-examples/2_evaluate_pretrained_policy.py
-"""
-
-from pathlib import Path
-
-import torch
-
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
-
-# Create a directory to store the training checkpoint.
-output_directory = Path("outputs/train/example_pusht_diffusion")
-output_directory.mkdir(parents=True, exist_ok=True)
-
-# Number of offline training steps (we'll only do offline training for this example.)
-# Adjust as you prefer. 5000 steps are needed to get something worth evaluating.
-training_steps = 5000
-device = torch.device("cuda")
-log_freq = 250
-
-# 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 supervise the policy.
-    "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],
-}
-dataset = LeRobotDataset("lerobot/pusht", delta_timestamps=delta_timestamps)
-
-# Set up the the policy.
-# Policies are initialized with a configuration class, in this case `DiffusionConfig`.
-# For this example, no arguments need to be passed because the defaults are set up for PushT.
-# If you're doing something different, you will likely need to change at least some of the defaults.
-cfg = DiffusionConfig()
-policy = DiffusionPolicy(cfg, dataset_stats=dataset.stats)
-policy.train()
-policy.to(device)
-
-optimizer = torch.optim.Adam(policy.parameters(), lr=1e-4)
-
-# Create dataloader for offline training.
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    num_workers=4,
-    batch_size=64,
-    shuffle=True,
-    pin_memory=device != torch.device("cpu"),
-    drop_last=True,
-)
-
-# Run training loop.
-step = 0
-done = False
-while not done:
-    for batch in dataloader:
-        batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
-        output_dict = policy.forward(batch)
-        loss = output_dict["loss"]
-        loss.backward()
-        optimizer.step()
-        optimizer.zero_grad()
-
-        if step % log_freq == 0:
-            print(f"step: {step} loss: {loss.item():.3f}")
-        step += 1
-        if step >= training_steps:
-            done = True
-            break
-
-# Save a policy checkpoint.
-policy.save_pretrained(output_directory)

examples/4_train_policy_with_script.md

@@ -1,183 +0,0 @@
-This tutorial will explain the training script, how to use it, and particularly the use of Hydra to configure everything needed for the training run.
-
-## The training script
-
-LeRobot offers a training script at [`lerobot/scripts/train.py`](../../lerobot/scripts/train.py). At a high level it does the following:
-
-- Loads a Hydra configuration file for the following steps (more on Hydra in a moment).
-- Makes a simulation environment.
-- Makes a dataset corresponding to that simulation environment.
-- Makes a policy.
-- Runs a standard training loop with forward pass, backward pass, optimization step, and occasional logging, evaluation (of the policy on the environment), and checkpointing.
-
-## Basics of how we use Hydra
-
-Explaining the ins and outs of [Hydra](https://hydra.cc/docs/intro/) is beyond the scope of this document, but here we'll share the main points you need to know.
-
-First, `lerobot/configs` has a directory structure like this:
-
-```
-.
-├── default.yaml
-├── env
-│   ├── aloha.yaml
-│   ├── pusht.yaml
-│   └── xarm.yaml
-└── policy
-    ├── act.yaml
-    ├── diffusion.yaml
-    └── tdmpc.yaml
-```
-
-**_For brevity, in the rest of this document we'll drop the leading `lerobot/configs` path. So `default.yaml` really refers to `lerobot/configs/default.yaml`._**
-
-When you run the training script with
-
-```python
-python lerobot/scripts/train.py
-```
-
-Hydra is set up to read `default.yaml` (via the `@hydra.main` decorator). If you take a look at the `@hydra.main`'s arguments you will see `config_path="../configs", config_name="default"`. At the top of `default.yaml`, is a `defaults` section which looks likes this:
-
-```yaml
-defaults:
-  - _self_
-  - env: pusht
-  - policy: diffusion
-```
-
-This logic tells Hydra to incorporate configuration parameters from `env/pusht.yaml` and `policy/diffusion.yaml`. _Note: Be aware of the order as any configuration parameters with the same name will be overidden. Thus, `default.yaml` is overriden by `env/pusht.yaml`  which is overidden by `policy/diffusion.yaml`_.
-
-Then, `default.yaml` also contains common configuration parameters such as `device: cuda` or `use_amp: false` (for enabling fp16 training). Some other parameters are set to `???` which indicates that they are expected to be set in additional yaml files. For instance, `training.offline_steps: ???` in `default.yaml` is set to `200000` in `diffusion.yaml`.
-
-Thanks to this `defaults` section in `default.yaml`, if you want to train Diffusion Policy with PushT, you really only need to run:
-
-```bash
-python lerobot/scripts/train.py
-```
-
-However, you can be more explicit and launch the exact same Diffusion Policy training on PushT with:
-
-```bash
-python lerobot/scripts/train.py policy=diffusion env=pusht
-```
-
-This way of overriding defaults via the CLI is especially useful when you want to change the policy and/or environment. For instance, you can train ACT on the default Aloha environment with:
-
-```bash
-python lerobot/scripts/train.py policy=act env=aloha
-```
-
-There are two things to note here:
-- Config overrides are passed as `param_name=param_value`.
-- Here we have overridden the defaults section. `policy=act` tells Hydra to use `policy/act.yaml`, and `env=aloha` tells Hydra to use `env/pusht.yaml`.
-
-_As an aside: we've set up all of our configurations so that they reproduce state-of-the-art results from papers in the literature._
-
-## Overriding configuration parameters in the CLI
-
-Now let's say that we want to train on a different task in the Aloha environment. If you look in `env/aloha.yaml` you will see something like:
-
-```yaml
-# lerobot/configs/env/aloha.yaml
-env:
-  task: AlohaInsertion-v0
-```
-
-And if you look in `policy/act.yaml` you will see something like:
-
-```yaml
-# lerobot/configs/policy/act.yaml
-dataset_repo_id: lerobot/aloha_sim_insertion_human
-```
-
-But our Aloha environment actually supports a cube transfer task as well. To train for this task, you could manually modify the two yaml configuration files respectively.
-
-First, we'd need to switch to using the cube transfer task for the ALOHA environment.
-
-```diff
-# lerobot/configs/env/aloha.yaml
-env:
--  task: AlohaInsertion-v0
-+  task: AlohaTransferCube-v0
-```
-
-Then, we'd also need to switch to using the cube transfer dataset.
-
-```diff
-# lerobot/configs/policy/act.yaml
--dataset_repo_id: lerobot/aloha_sim_insertion_human
-+dataset_repo_id: lerobot/aloha_sim_transfer_cube_human
-```
-
-Then, you'd be able to run:
-
-```bash
-python lerobot/scripts/train.py policy=act env=aloha
-```
-
-and you'd be training and evaluating on the cube transfer task.
-
-An alternative approach to editing the yaml configuration files, would be to override the defaults via the command line:
-
-```bash
-python lerobot/scripts/train.py \
-    policy=act \
-    dataset_repo_id=lerobot/aloha_sim_transfer_cube_human \
-    env=aloha \
-    env.task=AlohaTransferCube-v0
-```
-
-There's something new here. Notice the `.` delimiter used to traverse the configuration hierarchy. _But be aware that the `defaults` section is an exception. As you saw above, we didn't need to write `defaults.policy=act` in the CLI. `policy=act` was enough._
-
-Putting all that knowledge together, here's the command that was used to train https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human.
-
-```bash
-python lerobot/scripts/train.py \
-    hydra.run.dir=outputs/train/act_aloha_sim_transfer_cube_human \
-    device=cuda
-    env=aloha \
-    env.task=AlohaTransferCube-v0 \
-    dataset_repo_id=lerobot/aloha_sim_transfer_cube_human \
-    policy=act \
-    training.eval_freq=10000 \
-    training.log_freq=250 \
-    training.offline_steps=100000 \
-    training.save_model=true \
-    training.save_freq=25000 \
-    eval.n_episodes=50 \
-    eval.batch_size=50 \
-    wandb.enable=false \
-```
-
-There's one new thing here: `hydra.run.dir=outputs/train/act_aloha_sim_transfer_cube_human`, which specifies where to save the training output.
-
-## Using a configuration file not in `lerobot/configs`
-
-Above we discusses the our training script is set up such that Hydra looks for `default.yaml` in `lerobot/configs`. But, if you have a configuration file elsewhere in your filesystem you may use:
-
-```bash
-python lerobot/scripts/train.py --config-dir PARENT/PATH --config-name FILE_NAME_WITHOUT_EXTENSION
-```
-
-Note: here we use regular syntax for providing CLI arguments to a Python script, not Hydra's `param_name=param_value` syntax.
-
-As a concrete example, this becomes particularly handy when you have a folder with training outputs, and would like to re-run the training. For example, say you previously ran the training script with one of the earlier commands and have `outputs/train/my_experiment/checkpoints/pretrained_model/config.yaml`. This `config.yaml` file will have the full set of configuration parameters within it. To run the training with the same configuration again, do:
-
-```bash
-python lerobot/scripts/train.py --config-dir outputs/train/my_experiment/checkpoints/last/pretrained_model --config-name config
-```
-
-Note that you may still use the regular syntax for config parameter overrides (eg: by adding `training.offline_steps=200000`).
-
----
-
-So far we've seen how to train Diffusion Policy for PushT and ACT for ALOHA. Now, what if we want to train ACT for PushT? Well, there are aspects of the ACT configuration that are specific to the ALOHA environments, and these happen to be incompatible with PushT. Therefore, trying to run the following will almost certainly raise an exception of sorts (eg: feature dimension mismatch):
-
-```bash
-python lerobot/scripts/train.py policy=act env=pusht dataset_repo_id=lerobot/pusht
-```
-
-Please, head on over to our [advanced tutorial on adapting policy configuration to various environments](./advanced/train_act_pusht/train_act_pusht.md) to learn more.
-
-Or in the meantime, happy coding! 🤗

examples/5_resume_training.md

@@ -1,37 +0,0 @@
-This tutorial explains how to resume a training run that you've started with the training script. If you don't know how our training script and configuration system works, please read [4_train_policy_with_script.md](./4_train_policy_with_script.md) first.
-
-## Basic training resumption
-
-Let's consider the example of training ACT for one of the ALOHA tasks. Here's a command that can achieve that:
-
-```bash
-python lerobot/scripts/train.py \
-    hydra.run.dir=outputs/train/run_resumption \
-    policy=act \
-    dataset_repo_id=lerobot/aloha_sim_transfer_cube_human \
-    env=aloha \
-    env.task=AlohaTransferCube-v0 \
-    training.log_freq=25 \
-    training.save_checkpoint=true \
-    training.save_freq=100
-```
-
-Here we're using the default dataset and environment for ACT, and we've taken care to set up the log frequency and checkpointing frequency to low numbers so we can test resumption. You should be able to see some logging and have a first checkpoint within 1 minute. Please interrupt the training after the first checkpoint.
-
-To resume, all that we have to do is run the training script, providing the run directory, and the resume option:
-
-```bash
-python lerobot/scripts/train.py \
-    hydra.run.dir=outputs/train/run_resumption \
-    resume=true
-```
-
-You should see from the logging that your training picks up from where it left off.
-
-Note that with `resume=true`, the configuration file from the last checkpoint in the training output directory is loaded. So it doesn't matter that we haven't provided all the other configuration parameters from our previous command (although there may be warnings to notify you that your command has a different configuration than than the checkpoint).
-
----
-
-Now you should know how to resume your training run in case it gets interrupted or you want to extend a finished training run.
-
-Happy coding! 🤗

examples/advanced/1_train_act_pusht/act_pusht.yaml

@@ -1,87 +0,0 @@
-# @package _global_
-
-# Change the seed to match what PushT eval uses
-# (to avoid evaluating on seeds used for generating the training data).
-seed: 100000
-# Change the dataset repository to the PushT one.
-dataset_repo_id: lerobot/pusht
-
-override_dataset_stats:
-  observation.image:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-
-training:
-  offline_steps: 80000
-  online_steps: 0
-  eval_freq: 10000
-  save_freq: 100000
-  log_freq: 250
-  save_model: true
-
-  batch_size: 8
-  lr: 1e-5
-  lr_backbone: 1e-5
-  weight_decay: 1e-4
-  grad_clip_norm: 10
-  online_steps_between_rollouts: 1
-
-  delta_timestamps:
-    action: "[i / ${fps} for i in range(${policy.chunk_size})]"
-
-eval:
-  n_episodes: 50
-  batch_size: 50
-
-# See `configuration_act.py` for more details.
-policy:
-  name: act
-
-  # Input / output structure.
-  n_obs_steps: 1
-  chunk_size: 100 # chunk_size
-  n_action_steps: 100
-
-  input_shapes:
-    observation.image: [3, 96, 96]
-    observation.state: ["${env.state_dim}"]
-  output_shapes:
-    action: ["${env.action_dim}"]
-
-  # Normalization / Unnormalization
-  input_normalization_modes:
-    observation.image: mean_std
-    # Use min_max normalization just because it's more standard.
-    observation.state: min_max
-  output_normalization_modes:
-    # Use min_max normalization just because it's more standard.
-    action: min_max
-
-  # Architecture.
-  # Vision backbone.
-  vision_backbone: resnet18
-  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
-  replace_final_stride_with_dilation: false
-  # Transformer layers.
-  pre_norm: false
-  dim_model: 512
-  n_heads: 8
-  dim_feedforward: 3200
-  feedforward_activation: relu
-  n_encoder_layers: 4
-    # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
-  # that means only the first layer is used. Here we match the original implementation by setting this to 1.
-  # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
-  n_decoder_layers: 1
-  # VAE.
-  use_vae: true
-  latent_dim: 32
-  n_vae_encoder_layers: 4
-
-  # Inference.
-  temporal_ensemble_momentum: null
-
-  # Training and loss computation.
-  dropout: 0.1
-  kl_weight: 10.0

examples/advanced/1_train_act_pusht/train_act_pusht.md

@@ -1,70 +0,0 @@
-In this tutorial we will learn how to adapt a policy configuration to be compatible with a new environment and dataset. As a concrete example, we will adapt the default configuration for ACT to be compatible with the PushT environment and dataset.
-
-If you haven't already read our tutorial on the [training script and configuration tooling](../4_train_policy_with_script.md) please do so prior to tackling this tutorial.
-
-Let's get started!
-
-Suppose we want to train ACT for PushT. Well, there are aspects of the ACT configuration that are specific to the ALOHA environments, and these happen to be incompatible with PushT. Therefore, trying to run the following will almost certainly raise an exception of sorts (eg: feature dimension mismatch):
-
-```bash
-python lerobot/scripts/train.py policy=act env=pusht dataset_repo_id=lerobot/pusht
-```
-
-We need to adapt the parameters of the ACT policy configuration to the PushT environment. The most important ones are the image keys.
-
-ALOHA's datasets and environments typically use a variable number of cameras. In `lerobot/configs/policy/act.yaml` you may notice two relevant sections. Here we show you the minimal diff needed to adjust to PushT:
-
-```diff
-override_dataset_stats:
--  observation.images.top:
-+  observation.image:
-    # stats from imagenet, since we use a pretrained vision model
-    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
-    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
-
-policy:
-  input_shapes:
--    observation.images.top: [3, 480, 640]
-+    observation.image: [3, 96, 96]
-    observation.state: ["${env.state_dim}"]
-  output_shapes:
-    action: ["${env.action_dim}"]
-
-  input_normalization_modes:
--    observation.images.top: mean_std
-+    observation.image: mean_std
-     observation.state: min_max
-  output_normalization_modes:
-    action: min_max
-```
-
-Here we've accounted for the following:
-- PushT uses "observation.image" for its image key.
-- PushT provides smaller images.
-
-_Side note: technically we could override these via the CLI, but with many changes it gets a bit messy, and we also have a bit of a challenge in that we're using `.` in our observation keys which is treated by Hydra as a hierarchical separator_.
-
-For your convenience, we provide [`act_pusht.yaml`](./act_pusht.yaml) in this directory. It contains the diff above, plus some other (optional) ones that are explained within. Please copy it into `lerobot/configs/policy` with:
-
-```bash
-cp examples/advanced/1_train_act_pusht/act_pusht.yaml lerobot/configs/policy/act_pusht.yaml
-```
-
-(remember from a [previous tutorial](../4_train_policy_with_script.md) that Hydra will look in the `lerobot/configs` directory). Now try running the following.
-
-<!-- Note to contributor: are you changing this command? Note that it's tested in `Makefile`, so change it there too! -->
-```bash
-python lerobot/scripts/train.py policy=act_pusht env=pusht
-```
-
-Notice that this is much the same as the command that failed at the start of the tutorial, only:
-- Now we are using `policy=act_pusht` to point to our new configuration file.
-- We can drop `dataset_repo_id=lerobot/pusht` as the change is incorporated in our new configuration file.
-
-Hurrah! You're now training ACT for the PushT environment.
-
----
-
-The bottom line of this tutorial is that when training policies for different environments and datasets you will need to understand what parts of the policy configuration are specific to those and make changes accordingly.
-
-Happy coding! 🤗

examples/advanced/2_calculate_validation_loss.py

@@ -1,90 +0,0 @@
-"""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
-from pathlib import Path
-
-import torch
-from huggingface_hub import snapshot_download
-
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
-
-device = torch.device("cuda")
-
-# Download the diffusion policy for pusht environment
-pretrained_policy_path = Path(snapshot_download("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 full dataset
-full_dataset = LeRobotDataset("lerobot/pusht", split="train")
-# - Calculate train and val subsets
-num_train_episodes = math.floor(full_dataset.num_episodes * 90 / 100)
-num_val_episodes = full_dataset.num_episodes - num_train_episodes
-print(f"Number of episodes in full dataset: {full_dataset.num_episodes}")
-print(f"Number of episodes in training dataset (90% subset): {num_train_episodes}")
-print(f"Number of episodes in validation dataset (10% subset): {num_val_episodes}")
-# - Get first frame index of the validation set
-first_val_frame_index = full_dataset.episode_data_index["from"][num_train_episodes].item()
-# - Load frames subset belonging to validation set using the `split` argument.
-#   It utilizes the `datasets` library's syntax for slicing datasets.
-#   For more information on the Slice API, please see:
-#   https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits
-train_dataset = LeRobotDataset(
-    "lerobot/pusht", split=f"train[:{first_val_frame_index}]", delta_timestamps=delta_timestamps
-)
-val_dataset = LeRobotDataset(
-    "lerobot/pusht", split=f"train[{first_val_frame_index}:]", 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()}
-    output_dict = policy.forward(batch)
-
-    loss_cumsum += output_dict["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}")

examples/backward_compatibility/replay.py

@@ -0,0 +1,106 @@
+# 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.
+
+"""
+Replays the actions of an episode from a dataset on a robot.
+
+Example:
+
+```shell
+lerobot-replay \
+    --robot.type=so100_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.id=black \
+    --dataset.repo_id=aliberts/record-test \
+    --dataset.episode=2
+```
+"""
+
+import logging
+import time
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from pprint import pformat
+
+import draccus
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.robots import (  # noqa: F401
+    Robot,
+    RobotConfig,
+    koch_follower,
+    make_robot_from_config,
+    so100_follower,
+    so101_follower,
+)
+from lerobot.utils.constants import ACTION
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.utils import (
+    init_logging,
+    log_say,
+)
+
+
+@dataclass
+class DatasetReplayConfig:
+    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
+    repo_id: str
+    # Episode to replay.
+    episode: int
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
+    root: str | Path | None = None
+    # Limit the frames per second. By default, uses the policy fps.
+    fps: int = 30
+
+
+@dataclass
+class ReplayConfig:
+    robot: RobotConfig
+    dataset: DatasetReplayConfig
+    # Use vocal synthesis to read events.
+    play_sounds: bool = True
+
+
+@draccus.wrap()
+def replay(cfg: ReplayConfig):
+    init_logging()
+    logging.info(pformat(asdict(cfg)))
+
+    robot = make_robot_from_config(cfg.robot)
+    dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode])
+    actions = dataset.hf_dataset.select_columns(ACTION)
+    robot.connect()
+
+    log_say("Replaying episode", cfg.play_sounds, blocking=True)
+    for idx in range(dataset.num_frames):
+        start_episode_t = time.perf_counter()
+
+        action_array = actions[idx][ACTION]
+        action = {}
+        for i, name in enumerate(dataset.features[ACTION]["names"]):
+            key = f"{name.removeprefix('main_')}.pos"
+            action[key] = action_array[i].item()
+
+        action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
+        action["elbow_flex.pos"] -= 90
+        robot.send_action(action)
+
+        dt_s = time.perf_counter() - start_episode_t
+        busy_wait(1 / dataset.fps - dt_s)
+
+    robot.disconnect()
+
+
+if __name__ == "__main__":
+    replay()

examples/dataset/load_lerobot_dataset.py

@@ -0,0 +1,146 @@
+# 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 the use of `LeRobotDataset` class for handling and processing robotic datasets from Hugging Face.
+It illustrates how to load datasets, manipulate them, and apply transformations suitable for machine learning tasks in PyTorch.
+
+Features included in this script:
+- Viewing a dataset's metadata and exploring its properties.
+- Loading an existing dataset from the hub or a subset of it.
+- Accessing frames by episode number.
+- Using advanced dataset features like timestamp-based frame selection.
+- Demonstrating compatibility with PyTorch DataLoader for batch processing.
+
+The script ends with examples of how to batch process data using PyTorch's DataLoader.
+"""
+
+from pprint import pprint
+
+import torch
+from huggingface_hub import HfApi
+
+import lerobot
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+
+# We ported a number of existing datasets ourselves, use this to see the list:
+print("List of available datasets:")
+pprint(lerobot.available_datasets)
+
+# You can also browse through the datasets created/ported by the community on the hub using the hub api:
+hub_api = HfApi()
+repo_ids = [info.id for info in hub_api.list_datasets(task_categories="robotics", tags=["LeRobot"])]
+pprint(repo_ids)
+
+# Or simply explore them in your web browser directly at:
+# https://huggingface.co/datasets?other=LeRobot
+
+# Let's take this one for this example
+repo_id = "lerobot/aloha_mobile_cabinet"
+# We can have a look and fetch its metadata to know more about it:
+ds_meta = LeRobotDatasetMetadata(repo_id)
+
+# By instantiating just this class, you can quickly access useful information about the content and the
+# structure of the dataset without downloading the actual data yet (only metadata files — which are
+# lightweight).
+print(f"Total number of episodes: {ds_meta.total_episodes}")
+print(f"Average number of frames per episode: {ds_meta.total_frames / ds_meta.total_episodes:.3f}")
+print(f"Frames per second used during data collection: {ds_meta.fps}")
+print(f"Robot type: {ds_meta.robot_type}")
+print(f"keys to access images from cameras: {ds_meta.camera_keys=}\n")
+
+print("Tasks:")
+print(ds_meta.tasks)
+print("Features:")
+pprint(ds_meta.features)
+
+# You can also get a short summary by simply printing the object:
+print(ds_meta)
+
+# You can then load the actual dataset from the hub.
+# Either load any subset of episodes:
+dataset = LeRobotDataset(repo_id, episodes=[0, 10, 11, 23])
+
+# And see how many frames you have:
+print(f"Selected episodes: {dataset.episodes}")
+print(f"Number of episodes selected: {dataset.num_episodes}")
+print(f"Number of frames selected: {dataset.num_frames}")
+
+# Or simply load the entire dataset:
+dataset = LeRobotDataset(repo_id)
+print(f"Number of episodes selected: {dataset.num_episodes}")
+print(f"Number of frames selected: {dataset.num_frames}")
+
+# The previous metadata class is contained in the 'meta' attribute of the dataset:
+print(dataset.meta)
+
+# LeRobotDataset actually wraps an underlying Hugging Face dataset
+# (see https://huggingface.co/docs/datasets for more information).
+print(dataset.hf_dataset)
+
+# LeRobot datasets also subclasses PyTorch datasets so you can do everything you know and love from working
+# with the latter, like iterating through the dataset.
+# The __getitem__ iterates over the frames of the dataset. Since our datasets are also structured by
+# episodes, you can access the frame indices of any episode using dataset.meta.episodes. Here, we access
+# frame indices associated to the first episode:
+episode_index = 0
+from_idx = dataset.meta.episodes["dataset_from_index"][episode_index]
+to_idx = dataset.meta.episodes["dataset_to_index"][episode_index]
+
+# Then we grab all the image frames from the first camera:
+camera_key = dataset.meta.camera_keys[0]
+frames = [dataset[idx][camera_key] for idx in range(from_idx, to_idx)]
+
+# The objects returned by the dataset are all torch.Tensors
+print(type(frames[0]))
+print(frames[0].shape)
+
+# Since we're using pytorch, the shape is in pytorch, channel-first convention (c, h, w).
+# We can compare this shape with the information available for that feature
+pprint(dataset.features[camera_key])
+# In particular:
+print(dataset.features[camera_key]["shape"])
+# The shape is in (h, w, c) which is a more universal format.
+
+# For many machine learning applications we need to load the history of past observations or trajectories of
+# future actions. Our datasets can load previous and future frames for each key/modality, using timestamps
+# differences with the current loaded frame. For instance:
+delta_timestamps = {
+    # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
+    camera_key: [-1, -0.5, -0.20, 0],
+    # loads 6 state vectors: 1.5 seconds before, 1 second before, ... 200 ms, 100 ms, and current frame
+    "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, 0],
+    # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
+    "action": [t / dataset.fps for t in range(64)],
+}
+# Note that in any case, these delta_timestamps values need to be multiples of (1/fps) so that added to any
+# timestamp, you still get a valid timestamp.
+
+dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
+print(f"\n{dataset[0][camera_key].shape=}")  # (4, c, h, w)
+print(f"{dataset[0]['observation.state'].shape=}")  # (6, c)
+print(f"{dataset[0]['action'].shape=}\n")  # (64, c)
+
+if __name__ == "__main__":
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=4,
+        batch_size=32,
+        shuffle=True,
+    )
+    for batch in dataloader:
+        print(f"{batch[camera_key].shape=}")  # (32, 4, c, h, w)
+        print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
+        print(f"{batch['action'].shape=}")  # (32, 64, c)
+        break

examples/dataset/use_dataset_image_transforms.py

@@ -0,0 +1,177 @@
+#!/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 specific language governing permissions and
+# limitations under the License.
+
+"""
+This example demonstrates how to use image transforms with LeRobot datasets for data augmentation during training.
+
+Image transforms are applied to camera frames to improve model robustness and generalization. They are applied
+at training time only, not during dataset recording, allowing you to experiment with different augmentations
+without re-recording data.
+"""
+
+import torch
+from torchvision.transforms import v2
+from torchvision.transforms.functional import to_pil_image
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.transforms import ImageTransformConfig, ImageTransforms, ImageTransformsConfig
+
+
+def save_image(tensor, filename):
+    """Helper function to save a tensor as an image file."""
+    if tensor.dim() == 3:  # [C, H, W]
+        if tensor.max() > 1.0:
+            tensor = tensor / 255.0
+        tensor = torch.clamp(tensor, 0.0, 1.0)
+        pil_image = to_pil_image(tensor)
+        pil_image.save(filename)
+        print(f"Saved: {filename}")
+    else:
+        print(f"Skipped {filename}: unexpected tensor shape {tensor.shape}")
+
+
+def example_1_default_transforms():
+    """Example 1: Use default transform configuration and save original vs transformed images"""
+    print("\n Example 1: Default Transform Configuration with Image Saving")
+
+    repo_id = "pepijn223/record_main_0"  # Example dataset
+
+    try:
+        # Load dataset without transforms (original)
+        dataset_original = LeRobotDataset(repo_id=repo_id)
+
+        # Load dataset with transforms enabled
+        transforms_config = ImageTransformsConfig(
+            enable=True,  # Enable transforms (disabled by default)
+            max_num_transforms=2,  # Apply up to 2 transforms per frame
+            random_order=False,  # Apply in standard order
+        )
+        dataset_with_transforms = LeRobotDataset(
+            repo_id=repo_id, image_transforms=ImageTransforms(transforms_config)
+        )
+
+        # Save original and transformed images for comparison
+        if len(dataset_original) > 0:
+            frame_idx = 0  # Use first frame
+            original_sample = dataset_original[frame_idx]
+            transformed_sample = dataset_with_transforms[frame_idx]
+
+            print(f"Saving comparison images (frame {frame_idx}):")
+
+            for cam_key in dataset_original.meta.camera_keys:
+                if cam_key in original_sample and cam_key in transformed_sample:
+                    cam_name = cam_key.replace(".", "_").replace("/", "_")
+
+                    # Save original and transformed images
+                    save_image(original_sample[cam_key], f"{cam_name}_original.png")
+                    save_image(transformed_sample[cam_key], f"{cam_name}_transformed.png")
+
+    except Exception as e:
+        print(f"Could not load dataset '{repo_id}': {e}")
+
+
+def example_2_custom_transforms():
+    """Example 2: Create custom transform configuration and save examples"""
+    print("\n Example 2: Custom Transform Configuration")
+
+    repo_id = "pepijn223/record_main_0"  # Example dataset
+
+    try:
+        # Create custom transform configuration with strong effects
+        custom_transforms_config = ImageTransformsConfig(
+            enable=True,
+            max_num_transforms=2,  # Apply up to 2 transforms per frame
+            random_order=True,  # Apply transforms in random order
+            tfs={
+                "brightness": ImageTransformConfig(
+                    weight=1.0,
+                    type="ColorJitter",
+                    kwargs={"brightness": (0.5, 1.5)},  # Strong brightness range
+                ),
+                "contrast": ImageTransformConfig(
+                    weight=1.0,  # Higher weight = more likely to be selected
+                    type="ColorJitter",
+                    kwargs={"contrast": (0.6, 1.4)},  # Strong contrast
+                ),
+                "sharpness": ImageTransformConfig(
+                    weight=0.5,  # Lower weight = less likely to be selected
+                    type="SharpnessJitter",
+                    kwargs={"sharpness": (0.2, 2.0)},  # Strong sharpness variation
+                ),
+            },
+        )
+
+        dataset_with_custom_transforms = LeRobotDataset(
+            repo_id=repo_id, image_transforms=ImageTransforms(custom_transforms_config)
+        )
+
+        # Save examples with strong transforms
+        if len(dataset_with_custom_transforms) > 0:
+            sample = dataset_with_custom_transforms[0]
+            print("Saving custom transform examples:")
+
+            for cam_key in dataset_with_custom_transforms.meta.camera_keys:
+                if cam_key in sample:
+                    cam_name = cam_key.replace(".", "_").replace("/", "_")
+                    save_image(sample[cam_key], f"{cam_name}_custom_transforms.png")
+
+    except Exception as e:
+        print(f"Could not load dataset '{repo_id}': {e}")
+
+
+def example_3_torchvision_transforms():
+    """Example 3: Use pure torchvision transforms and save examples"""
+    print("\n Example 3: Pure Torchvision Transforms")
+
+    repo_id = "pepijn223/record_main_0"  # Example dataset
+
+    try:
+        # Create torchvision transform pipeline
+        torchvision_transforms = v2.Compose(
+            [
+                v2.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.1),
+                v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),
+                v2.RandomRotation(degrees=10),  # Small rotation
+            ]
+        )
+
+        dataset_with_torchvision = LeRobotDataset(repo_id=repo_id, image_transforms=torchvision_transforms)
+
+        # Save examples with torchvision transforms
+        if len(dataset_with_torchvision) > 0:
+            sample = dataset_with_torchvision[0]
+            print("Saving torchvision transform examples:")
+
+            for cam_key in dataset_with_torchvision.meta.camera_keys:
+                if cam_key in sample:
+                    cam_name = cam_key.replace(".", "_").replace("/", "_")
+                    save_image(sample[cam_key], f"{cam_name}_torchvision.png")
+
+    except Exception as e:
+        print(f"Could not load dataset '{repo_id}': {e}")
+
+
+def main():
+    """Run all examples"""
+    print("LeRobot Dataset Image Transforms Examples")
+
+    example_1_default_transforms()
+    example_2_custom_transforms()
+    example_3_torchvision_transforms()
+
+
+if __name__ == "__main__":
+    main()

examples/dataset/use_dataset_tools.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Example script demonstrating dataset tools utilities.
+
+This script shows how to:
+1. Delete episodes from a dataset
+2. Split a dataset into train/val sets
+3. Add/remove features
+4. Merge datasets
+
+Usage:
+    python examples/dataset/use_dataset_tools.py
+"""
+
+import numpy as np
+
+from lerobot.datasets.dataset_tools import (
+    add_features,
+    delete_episodes,
+    merge_datasets,
+    modify_features,
+    remove_feature,
+    split_dataset,
+)
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+
+def main():
+    dataset = LeRobotDataset("lerobot/pusht")
+
+    print(f"Original dataset: {dataset.meta.total_episodes} episodes, {dataset.meta.total_frames} frames")
+    print(f"Features: {list(dataset.meta.features.keys())}")
+
+    print("\n1. Deleting episodes 0 and 2...")
+    filtered_dataset = delete_episodes(dataset, episode_indices=[0, 2], repo_id="lerobot/pusht_filtered")
+    print(f"Filtered dataset: {filtered_dataset.meta.total_episodes} episodes")
+
+    print("\n2. Splitting dataset into train/val...")
+    splits = split_dataset(
+        dataset,
+        splits={"train": 0.8, "val": 0.2},
+    )
+    print(f"Train split: {splits['train'].meta.total_episodes} episodes")
+    print(f"Val split: {splits['val'].meta.total_episodes} episodes")
+
+    print("\n3. Adding features...")
+
+    reward_values = np.random.randn(dataset.meta.total_frames).astype(np.float32)
+
+    def compute_success(row_dict, episode_index, frame_index):
+        episode_length = 10
+        return float(frame_index >= episode_length - 10)
+
+    dataset_with_features = add_features(
+        dataset,
+        features={
+            "reward": (
+                reward_values,
+                {"dtype": "float32", "shape": (1,), "names": None},
+            ),
+            "success": (
+                compute_success,
+                {"dtype": "float32", "shape": (1,), "names": None},
+            ),
+        },
+        repo_id="lerobot/pusht_with_features",
+    )
+
+    print(f"New features: {list(dataset_with_features.meta.features.keys())}")
+
+    print("\n4. Removing the success feature...")
+    dataset_cleaned = remove_feature(
+        dataset_with_features, feature_names="success", repo_id="lerobot/pusht_cleaned"
+    )
+    print(f"Features after removal: {list(dataset_cleaned.meta.features.keys())}")
+
+    print("\n5. Using modify_features to add and remove features simultaneously...")
+    dataset_modified = modify_features(
+        dataset_with_features,
+        add_features={
+            "discount": (
+                np.ones(dataset.meta.total_frames, dtype=np.float32) * 0.99,
+                {"dtype": "float32", "shape": (1,), "names": None},
+            ),
+        },
+        remove_features="reward",
+        repo_id="lerobot/pusht_modified",
+    )
+    print(f"Modified features: {list(dataset_modified.meta.features.keys())}")
+
+    print("\n6. Merging train and val splits back together...")
+    merged = merge_datasets([splits["train"], splits["val"]], output_repo_id="lerobot/pusht_merged")
+    print(f"Merged dataset: {merged.meta.total_episodes} episodes")
+
+    print("\n7. Complex workflow example...")
+
+    if len(dataset.meta.camera_keys) > 1:
+        camera_to_remove = dataset.meta.camera_keys[0]
+        print(f"Removing camera: {camera_to_remove}")
+        dataset_no_cam = remove_feature(
+            dataset, feature_names=camera_to_remove, repo_id="pusht_no_first_camera"
+        )
+        print(f"Remaining cameras: {dataset_no_cam.meta.camera_keys}")
+
+    print("\nDone! Check ~/.cache/huggingface/lerobot/ for the created datasets.")
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/evaluate.py

@@ -0,0 +1,138 @@
+# !/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.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.processor import make_default_processors
+from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.utils.constants import ACTION, OBS_STR
+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 = 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 configuration & robot
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+
+robot = LeKiwiClient(robot_config)
+
+# Create policy
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+
+# Configure the dataset features
+action_features = hw_to_dataset_features(robot.action_features, ACTION)
+obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
+dataset_features = {**action_features, **obs_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,
+)
+
+# Build Policy Processors
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+    # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
+    preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+)
+
+# Connect the robot
+# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
+robot.connect()
+
+# TODO(Steven): Update this example to use pipelines
+teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="lekiwi_evaluate")
+
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
+
+print("Starting evaluate loop...")
+recorded_episodes = 0
+while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        policy=policy,
+        preprocessor=preprocessor,  # Pass the pre and post policy processors
+        postprocessor=postprocessor,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=teleop_action_processor,
+        robot_action_processor=robot_action_processor,
+        robot_observation_processor=robot_observation_processor,
+    )
+
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (
+        (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+    ):
+        log_say("Reset the environment")
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            control_time_s=EPISODE_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
+        )
+
+    if events["rerecord_episode"]:
+        log_say("Re-record episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
+
+    # Save episode
+    dataset.save_episode()
+    recorded_episodes += 1
+
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+listener.stop()
+
+dataset.finalize()
+dataset.push_to_hub()

examples/lekiwi/record.py

@@ -0,0 +1,135 @@
+# !/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.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.processor import make_default_processors
+from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun
+
+NUM_EPISODES = 2
+FPS = 30
+EPISODE_TIME_SEC = 30
+RESET_TIME_SEC = 10
+TASK_DESCRIPTION = "My task description"
+HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
+
+# Create the robot and teleoperator configurations
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
+keyboard_config = KeyboardTeleopConfig()
+
+# Initialize the robot and teleoperator
+robot = LeKiwiClient(robot_config)
+leader_arm = SO100Leader(leader_arm_config)
+keyboard = KeyboardTeleop(keyboard_config)
+
+# TODO(Steven): Update this example to use pipelines
+teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
+# Configure the dataset features
+action_features = hw_to_dataset_features(robot.action_features, ACTION)
+obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
+dataset_features = {**action_features, **obs_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,
+)
+
+# Connect the robot and teleoperator
+# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
+robot.connect()
+leader_arm.connect()
+keyboard.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="lekiwi_record")
+
+if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
+
+print("Starting record loop...")
+recorded_episodes = 0
+while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Recording episode {recorded_episodes}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        dataset=dataset,
+        teleop=[leader_arm, keyboard],
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=teleop_action_processor,
+        robot_action_processor=robot_action_processor,
+        robot_observation_processor=robot_observation_processor,
+    )
+
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (
+        (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+    ):
+        log_say("Reset the environment")
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            teleop=[leader_arm, keyboard],
+            control_time_s=RESET_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
+        )
+
+    if events["rerecord_episode"]:
+        log_say("Re-record episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
+
+    # Save episode
+    dataset.save_episode()
+    recorded_episodes += 1
+
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+leader_arm.disconnect()
+keyboard.disconnect()
+listener.stop()
+
+dataset.finalize()
+dataset.push_to_hub()

examples/lekiwi/replay.py

@@ -0,0 +1,61 @@
+# !/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.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
+from lerobot.utils.constants import ACTION
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.utils import log_say
+
+EPISODE_IDX = 0
+
+# Initialize the robot config
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+
+# Initialize the robot
+robot = LeKiwiClient(robot_config)
+
+# Fetch the dataset to replay
+dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[EPISODE_IDX])
+# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
+episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
+actions = episode_frames.select_columns(ACTION)
+
+# Connect to the robot
+robot.connect()
+
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
+
+print("Starting replay loop...")
+log_say(f"Replaying episode {EPISODE_IDX}")
+for idx in range(len(episode_frames)):
+    t0 = time.perf_counter()
+
+    # Get recorded action from dataset
+    action = {
+        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+    }
+
+    # Send action to robot
+    _ = robot.send_action(action)
+
+    busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+
+robot.disconnect()

examples/lekiwi/teleoperate.py

@@ -0,0 +1,72 @@
+# !/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.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
+from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig
+from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+FPS = 30
+
+# Create the robot and teleoperator configurations
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi")
+teleop_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
+keyboard_config = KeyboardTeleopConfig(id="my_laptop_keyboard")
+
+# Initialize the robot and teleoperator
+robot = LeKiwiClient(robot_config)
+leader_arm = SO100Leader(teleop_arm_config)
+keyboard = KeyboardTeleop(keyboard_config)
+
+# Connect to the robot and teleoperator
+# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
+robot.connect()
+leader_arm.connect()
+keyboard.connect()
+
+# Init rerun viewer
+init_rerun(session_name="lekiwi_teleop")
+
+if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
+
+print("Starting teleop loop...")
+while True:
+    t0 = time.perf_counter()
+
+    # Get robot observation
+    observation = robot.get_observation()
+
+    # Get teleop action
+    # Arm
+    arm_action = leader_arm.get_action()
+    arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
+    # Keyboard
+    keyboard_keys = keyboard.get_action()
+    base_action = robot._from_keyboard_to_base_action(keyboard_keys)
+
+    action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
+
+    # Send action to robot
+    _ = robot.send_action(action)
+
+    # Visualize
+    log_rerun_data(observation=observation, action=action)
+
+    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))

examples/phone_to_so100/evaluate.py

@@ -0,0 +1,199 @@
+# !/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.configs.types import FeatureType, PolicyFeature
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.processor import (
+    RobotAction,
+    RobotObservation,
+    RobotProcessorPipeline,
+    make_default_teleop_action_processor,
+)
+from lerobot.processor.converters import (
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    ForwardKinematicsJointsToEE,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+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>"
+
+# Create the robot configuration & robot
+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,
+)
+
+robot = SO100Follower(robot_config)
+
+# Create policy
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+
+# 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="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
+
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert joints observation to EE observation
+robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=transition_to_observation,
+)
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_DATASET_ID,
+    fps=FPS,
+    features=combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_joints_to_ee_pose_processor,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=True,
+        ),
+        # User for now should be explicit on the feature keys that were used for record
+        # Alternatively, the user can pass the processor step that has the right features
+        aggregate_pipeline_dataset_features(
+            pipeline=make_default_teleop_action_processor(),
+            initial_features=create_initial_features(
+                action={
+                    f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+                    for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
+                }
+            ),
+            use_videos=True,
+        ),
+    ),
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+# Build Policy Processors
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+    # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
+    preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+)
+
+# Connect the robot
+robot.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="phone_so100_evaluate")
+
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
+
+print("Starting evaluate loop...")
+episode_idx = 0
+for episode_idx in range(NUM_EPISODES):
+    log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        policy=policy,
+        preprocessor=preprocessor,  # Pass the pre and post policy processors
+        postprocessor=postprocessor,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=make_default_teleop_action_processor(),
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
+    )
+
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
+        log_say("Reset the environment")
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            control_time_s=EPISODE_TIME_SEC,
+            single_task=TASK_DESCRIPTION,
+            display_data=True,
+            teleop_action_processor=make_default_teleop_action_processor(),
+            robot_action_processor=robot_ee_to_joints_processor,
+            robot_observation_processor=robot_joints_to_ee_pose_processor,
+        )
+
+    if events["rerecord_episode"]:
+        log_say("Re-record episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
+
+    # Save episode
+    dataset.save_episode()
+    episode_idx += 1
+
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+listener.stop()
+
+dataset.finalize()
+dataset.push_to_hub()

examples/phone_to_so100/record.py

@@ -0,0 +1,205 @@
+# !/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 RobotAction, RobotObservation, RobotProcessorPipeline
+from lerobot.processor.converters import (
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    EEBoundsAndSafety,
+    EEReferenceAndDelta,
+    ForwardKinematicsJointsToEE,
+    GripperVelocityToJoint,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
+from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.teleoperators.phone.teleop_phone import Phone
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun
+
+NUM_EPISODES = 2
+FPS = 30
+EPISODE_TIME_SEC = 60
+RESET_TIME_SEC = 30
+TASK_DESCRIPTION = "My task description"
+HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
+
+# Create the robot and teleoperator configurations
+camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411",
+    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="./SO101/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 action
+phone_to_robot_ee_pose_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+        EEReferenceAndDelta(
+            kinematics=kinematics_solver,
+            end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+            motor_names=list(robot.bus.motors.keys()),
+            use_latched_reference=True,
+        ),
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.20,
+        ),
+        GripperVelocityToJoint(speed_factor=20.0),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert joint observation to EE observation
+robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=transition_to_observation,
+)
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_REPO_ID,
+    fps=FPS,
+    features=combine_feature_dicts(
+        # Run the feature contract of the pipelines
+        # This tells you how the features would look like after the pipeline steps
+        aggregate_pipeline_dataset_features(
+            pipeline=phone_to_robot_ee_pose_processor,
+            initial_features=create_initial_features(action=phone.action_features),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_joints_to_ee_pose,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=True,
+        ),
+    ),
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+# Connect the robot and teleoperator
+robot.connect()
+phone.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="phone_so100_record")
+
+if not robot.is_connected or not phone.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
+
+
+print("Starting record loop. Move your phone to teleoperate the robot...")
+episode_idx = 0
+while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        teleop=phone,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=phone_to_robot_ee_pose_processor,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose,
+    )
+
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+        log_say("Reset the environment")
+        record_loop(
+            robot=robot,
+            events=events,
+            fps=FPS,
+            teleop=phone,
+            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
+
+    # Save episode
+    dataset.save_episode()
+    episode_idx += 1
+
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+phone.disconnect()
+listener.stop()
+
+dataset.finalize()
+dataset.push_to_hub()

examples/phone_to_so100/replay.py

@@ -0,0 +1,100 @@
+# !/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 RobotAction, RobotObservation, RobotProcessorPipeline
+from lerobot.processor.converters import (
+    robot_action_observation_to_transition,
+    transition_to_robot_action,
+)
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.utils.constants import ACTION
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.utils import log_say
+
+EPISODE_IDX = 0
+HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
+
+# Initialize the robot config
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", 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="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
+
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=False,  # Because replay is open loop
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Fetch the dataset to replay
+dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
+# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
+episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
+actions = episode_frames.select_columns(ACTION)
+
+# Connect to the robot
+robot.connect()
+
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
+
+print("Starting replay loop...")
+log_say(f"Replaying episode {EPISODE_IDX}")
+for idx in range(len(episode_frames)):
+    t0 = time.perf_counter()
+
+    # Get recorded action from dataset
+    ee_action = {
+        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+    }
+
+    # Get robot observation
+    robot_obs = robot.get_observation()
+
+    # Dataset EE -> robot joints
+    joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+
+    # Send action to robot
+    _ = robot.send_action(joint_action)
+
+    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
+
+# Clean up
+robot.disconnect()

examples/phone_to_so100/teleoperate.py

@@ -0,0 +1,113 @@
+# !/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 specif
+
+import time
+
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
+from lerobot.processor.converters import (
+    robot_action_observation_to_transition,
+    transition_to_robot_action,
+)
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.robot_kinematic_processor import (
+    EEBoundsAndSafety,
+    EEReferenceAndDelta,
+    GripperVelocityToJoint,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
+from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.teleoperators.phone.teleop_phone import Phone
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+FPS = 30
+
+# Initialize the robot and teleoperator
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411", 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="./SO101/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[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+        EEReferenceAndDelta(
+            kinematics=kinematics_solver,
+            end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+            motor_names=list(robot.bus.motors.keys()),
+            use_latched_reference=True,
+        ),
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.10,
+        ),
+        GripperVelocityToJoint(
+            speed_factor=20.0,
+        ),
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Connect to the robot and teleoperator
+robot.connect()
+teleop_device.connect()
+
+# Init rerun viewer
+init_rerun(session_name="phone_so100_teleop")
+
+if not robot.is_connected or not teleop_device.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
+
+print("Starting teleop loop. Move your phone to teleoperate the robot...")
+while True:
+    t0 = time.perf_counter()
+
+    # Get robot observation
+    robot_obs = robot.get_observation()
+
+    # Get teleop action
+    phone_obs = teleop_device.get_action()
+
+    # Phone -> EE pose -> Joints transition
+    joint_action = phone_to_robot_joints_processor((phone_obs, robot_obs))
+
+    # Send action to robot
+    _ = robot.send_action(joint_action)
+
+    # Visualize
+    log_rerun_data(observation=phone_obs, action=joint_action)
+
+    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))

examples/port_datasets/display_error_files.py

@@ -0,0 +1,85 @@
+#!/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 argparse
+import json
+from pathlib import Path
+
+
+def find_missing_workers(completions_dir, world_size):
+    """Find workers that are not completed and returns their indices."""
+    full = list(range(world_size))
+
+    completed = []
+    for path in completions_dir.glob("*"):
+        if path.name in [".", ".."]:
+            continue
+        index = path.name.lstrip("0")
+        index = 0 if index == "" else int(index)
+        completed.append(index)
+
+    missing_workers = set(full) - set(completed)
+    return missing_workers
+
+
+def find_output_files(slurm_dir, worker_indices):
+    """Find output files associated to worker indices, and return tuples
+    of (worker index, output file path)
+    """
+    out_files = []
+    for path in slurm_dir.glob("*.out"):
+        _, worker_id = path.name.replace(".out", "").split("_")
+        worker_id = int(worker_id)
+        if worker_id in worker_indices:
+            out_files.append((worker_id, path))
+    return out_files
+
+
+def display_error_files(logs_dir, job_name):
+    executor_path = Path(logs_dir) / job_name / "executor.json"
+    completions_dir = Path(logs_dir) / job_name / "completions"
+
+    with open(executor_path) as f:
+        executor = json.load(f)
+
+    missing_workers = find_missing_workers(completions_dir, executor["world_size"])
+
+    for missing in sorted(missing_workers)[::-1]:
+        print(missing)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--logs-dir",
+        type=str,
+        help="Path to logs directory for `datatrove`.",
+    )
+    parser.add_argument(
+        "--job-name",
+        type=str,
+        default="port_droid",
+        help="Job name used in slurm, and name of the directory created inside the provided logs directory.",
+    )
+
+    args = parser.parse_args()
+
+    display_error_files(**vars(args))
+
+
+if __name__ == "__main__":
+    main()