Install ffmpeg for integration tests

Merge branch 'main' into torchcodec-cpu
2026-05-11 22:59:50 +00:00 · 2025-03-13 14:51:34 +01:00 · 2025-03-13 14:00:22 +01:00 · 2025-03-12 21:10:00 +03:00 · 2025-03-12 21:02:16 +03:00 · 2025-03-08 11:12:58 +03:00
499 changed files with 17417 additions and 40981 deletions
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+}
@@ -73,7 +73,7 @@ pip-log.txt
 pip-delete-this-directory.txt

 # Unit test / coverage reports
-!tests/artifacts
+!tests/data
 htmlcov/
 .tox/
 .nox/
@@ -11,11 +11,10 @@
 # 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 !text !filter !merge !diff
-tests/artifacts/cameras/*.png filter=lfs diff=lfs merge=lfs -text
-*.bag filter=lfs diff=lfs merge=lfs -text
@@ -24,7 +24,7 @@ Examples:
 pytest -sx tests/test_stuff.py::test_something
 ```
 ```bash
-python -m lerobot.scripts.train --some.option=true
+python lerobot/scripts/train.py --some.option=true
 ```

 ## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR
@@ -40,24 +40,24 @@ jobs:
          git lfs install

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
        with:
          cache-binary: false

      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false

      - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
+        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_PASSWORD }}

      - name: Build and Push CPU
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
        with:
          context: .
          file: ./docker/lerobot-cpu/Dockerfile
@@ -78,24 +78,24 @@ jobs:
          git lfs install

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
        with:
          cache-binary: false

      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
        with:
          lfs: true
          persist-credentials: false

      - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
+        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_PASSWORD }}

      - name: Build and Push GPU
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
        with:
          context: .
          file: ./docker/lerobot-gpu/Dockerfile
@@ -110,23 +110,23 @@ jobs:
      group: aws-general-8-plus
    steps:
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
        with:
          cache-binary: false

      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
        with:
          persist-credentials: false

      - name: Login to DockerHub
-        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0
+        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@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
        with:
          context: .
          file: ./docker/lerobot-gpu-dev/Dockerfile
@@ -1,23 +0,0 @@
-name: Build documentation
-
-on:
-  workflow_dispatch:
-  push:
-    paths:
-      - "docs/**"
-    branches:
-    - main
-    - doc-builder*
-    - v*-release
-
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
-    with:
-      commit_sha: ${{ github.sha }}
-      package: lerobot
-      additional_args: --not_python_module
-    secrets:
-      token: ${{ secrets.HUGGINGFACE_PUSH }}
-      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
@@ -1,19 +0,0 @@
-name: Build PR Documentation
-
-on:
-  pull_request:
-    paths:
-      - "docs/**"
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
-    with:
-      commit_sha: ${{ github.event.pull_request.head.sha }}
-      pr_number: ${{ github.event.number }}
-      package: lerobot
-      additional_args: --not_python_module
@@ -33,7 +33,7 @@ jobs:
    runs-on:
      group: aws-general-8-plus
    container:
-      image: huggingface/lerobot-cpu:latest  # zizmor: ignore[unpinned-images]
+      image: huggingface/lerobot-cpu:latest
      options: --shm-size "16gb"
      credentials:
        username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -44,7 +44,7 @@ jobs:
        working-directory: /lerobot
    steps:
      - name: Tests
-        run: pytest -v --cov=./src/lerobot --disable-warnings tests
+        run: pytest -v --cov=./lerobot --disable-warnings tests

      - name: Tests end-to-end
        run: make test-end-to-end
@@ -60,7 +60,7 @@ jobs:
      CUDA_VISIBLE_DEVICES: "0"
      TEST_TYPE: "single_gpu"
    container:
-      image: huggingface/lerobot-gpu:latest  # zizmor: ignore[unpinned-images]
+      image: huggingface/lerobot-gpu:latest
      options: --gpus all --shm-size "16gb"
      credentials:
        username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -74,7 +74,7 @@ jobs:
        run: nvidia-smi

      - name: Test
-        run: pytest -v --cov=./src/lerobot --cov-report=xml --disable-warnings tests
+        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
@@ -33,12 +33,12 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
        with:
          persist-credentials: false

      - name: Set up Python
-        uses: actions/setup-python@7f4fc3e22c37d6ff65e88745f38bd3157c663f7c # v4.9.1
+        uses: actions/setup-python@v4
        with:
          python-version: ${{ env.PYTHON_VERSION }}

@@ -64,9 +64,9 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout Repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
        with:
          persist-credentials: false

      - name: typos-action
-        uses: crate-ci/typos@db35ee91e80fbb447f33b0e5fbddb24d2a1a884f # v1.29.10
+        uses: crate-ci/typos@v1.29.10
@@ -35,13 +35,13 @@ jobs:
      matrix: ${{ steps.set-matrix.outputs.matrix }}
    steps:
      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
        with:
          persist-credentials: false

      - name: Get changed files
        id: changed-files
-        uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
+        uses: tj-actions/changed-files@v44
        with:
          files: docker/**
          json: "true"
@@ -64,17 +64,17 @@ jobs:
        docker-file: ${{ fromJson(needs.get_changed_files.outputs.matrix) }}
    steps:
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@b5ca514318bd6ebac0fb2aedd5d36ec1b5c232a2 # v3.10.0
+        uses: docker/setup-buildx-action@v3
        with:
          cache-binary: false

      - name: Check out code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
        with:
          persist-credentials: false

      - name: Build Docker image
-        uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5.4.0
+        uses: docker/build-push-action@v5
        with:
          file: ${{ matrix.docker-file }}
          context: .
@@ -17,7 +17,7 @@ name: Tests
 on:
  pull_request:
    paths:
-      - "src/**"
+      - "lerobot/**"
      - "tests/**"
      - "examples/**"
      - ".github/**"
@@ -29,7 +29,7 @@ on:
    branches:
      - main
    paths:
-      - "src/**"
+      - "lerobot/**"
      - "tests/**"
      - "examples/**"
      - ".github/**"
@@ -50,7 +50,7 @@ jobs:
    env:
      MUJOCO_GL: egl
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@v4
        with:
          lfs: true  # Ensure LFS files are pulled
          persist-credentials: false
@@ -62,7 +62,7 @@ jobs:
          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev

      - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
+        uses: astral-sh/setup-uv@v5
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -73,7 +73,7 @@ jobs:

      - name: Test with pytest
        run: |
-          uv run pytest tests -v --cov=./src/lerobot --durations=0 \
+          uv 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 \
@@ -85,7 +85,7 @@ jobs:
    env:
      MUJOCO_GL: egl
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@v4
        with:
          lfs: true  # Ensure LFS files are pulled
          persist-credentials: false
@@ -94,7 +94,7 @@ jobs:
        run: sudo apt-get update && sudo apt-get install -y ffmpeg

      - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
+        uses: astral-sh/setup-uv@v5
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -105,7 +105,7 @@ jobs:

      - name: Test with pytest
        run: |
-          uv run pytest tests -v --cov=./src/lerobot --durations=0 \
+          uv 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 \
@@ -117,7 +117,7 @@ jobs:
    env:
      MUJOCO_GL: egl
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@v4
        with:
          lfs: true  # Ensure LFS files are pulled
          persist-credentials: false
@@ -129,7 +129,7 @@ jobs:
          sudo apt-get install -y libegl1-mesa-dev ffmpeg portaudio19-dev

      - name: Install uv and python
-        uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5.4.2
+        uses: astral-sh/setup-uv@v5
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -24,12 +24,12 @@ jobs:
    runs-on: ubuntu-latest
    steps:
    - name: Checkout code
-      uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      uses: actions/checkout@v4
      with:
        fetch-depth: 0
        persist-credentials: false

    - name: Secret Scanning
-      uses: trufflesecurity/trufflehog@90694bf9af66e7536abc5824e7a87246dbf933cb # v3.88.35
+      uses: trufflesecurity/trufflehog@main
      with:
        extra_args: --only-verified
@@ -1,16 +0,0 @@
-name: Upload PR Documentation
-
-on: # zizmor: ignore[dangerous-triggers] We follow the same pattern as in Transformers
-  workflow_run:
-    workflows: [ "Build PR Documentation" ]
-    types:
-    - completed
-
-jobs:
-  build:  # zizmor: ignore[excessive-permissions] We follow the same pattern as in Transformers
-    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
-    with:
-      package_name: lerobot
-    secrets:
-      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
-      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}
@@ -12,9 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-# Dev scripts
-.dev
-
 # Logging
 logs
 tmp
@@ -29,7 +26,6 @@ outputs

 # VS Code
 .vscode
-.devcontainer

 # HPC
 nautilus/*.yaml
@@ -82,7 +78,7 @@ pip-log.txt
 pip-delete-this-directory.txt

 # Unit test / coverage reports
-!tests/artifacts
+!tests/data
 htmlcov/
 .tox/
 .nox/
@@ -95,8 +91,10 @@ coverage.xml
 .hypothesis/
 .pytest_cache/

-# Ignore .cache
+# Ignore .cache except calibration
 .cache/*
+!.cache/calibration/
+!.cache/calibration/**

 # Translations
 *.mo
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-exclude: "tests/artifacts/.*\\.safetensors$"
+exclude: ^(tests/data)
 default_language_version:
    python: python3.10
 repos:
@@ -36,19 +36,19 @@ repos:
      - id: end-of-file-fixer
      - id: trailing-whitespace

-  - repo: https://github.com/adhtruong/mirrors-typos
-    rev: v1.33.1
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.30.2
    hooks:
      - id: typos
        args: [--force-exclude]

  - repo: https://github.com/asottile/pyupgrade
-    rev: v3.20.0
+    rev: v3.19.1
    hooks:
    -   id: pyupgrade

  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.13
+    rev: v0.9.10
    hooks:
      - id: ruff
        args: [--fix]
@@ -57,12 +57,12 @@ repos:

  ##### Security #####
  - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.27.2
+    rev: v8.24.0
    hooks:
      - id: gitleaks

  - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.9.0
+    rev: v1.4.1
    hooks:
      - id: zizmor

@@ -67,7 +67,7 @@ post it.

 ## Adding new policies, datasets or environments

-Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
+Look at our implementations for [datasets](./lerobot/common/datasets/), [policies](./lerobot/common/policies/),
 environments ([aloha](https://github.com/huggingface/gym-aloha),
 [xarm](https://github.com/huggingface/gym-xarm),
 [pusht](https://github.com/huggingface/gym-pusht))
@@ -269,6 +269,9 @@ 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

@@ -288,7 +291,7 @@ sudo apt-get install git-lfs
 git lfs install
 ```

-Pull artifacts if they're not in [tests/artifacts](tests/artifacts)
+Pull artifacts if they're not in [tests/data](tests/data)
 ```bash
 git lfs pull
 ```
@@ -1,2 +0,0 @@
-include src/lerobot/templates/lerobot_modelcard_template.md
-include src/lerobot/datasets/card_template.md
@@ -40,17 +40,14 @@ test-end-to-end:
 	${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval
 	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train
 	${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval
-	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-train
-	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval

 test-act-ete-train:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--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 \
@@ -68,12 +65,12 @@ test-act-ete-train:
 		--output_dir=tests/outputs/act/

 test-act-ete-train-resume:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
 		--resume=true

 test-act-ete-eval:
-	python -m lerobot.scripts.eval \
+	python lerobot/scripts/eval.py \
 		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \
@@ -82,13 +79,12 @@ test-act-ete-eval:
 		--eval.batch_size=1

 test-diffusion-ete-train:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--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 \
@@ -106,7 +102,7 @@ test-diffusion-ete-train:
 		--output_dir=tests/outputs/diffusion/

 test-diffusion-ete-eval:
-	python -m lerobot.scripts.eval \
+	python lerobot/scripts/eval.py \
 		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=pusht \
@@ -115,10 +111,9 @@ test-diffusion-ete-eval:
 		--eval.batch_size=1

 test-tdmpc-ete-train:
-	python -m lerobot.scripts.train \
+	python lerobot/scripts/train.py \
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
-		--policy.push_to_hub=false \
 		--env.type=xarm \
 		--env.task=XarmLift-v0 \
 		--env.episode_length=5 \
@@ -137,7 +132,7 @@ test-tdmpc-ete-train:
 		--output_dir=tests/outputs/tdmpc/

 test-tdmpc-ete-eval:
-	python -m lerobot.scripts.eval \
+	python lerobot/scripts/eval.py \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=xarm \
@@ -145,36 +140,3 @@ test-tdmpc-ete-eval:
 		--env.task=XarmLift-v0 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1
-
-
-test-smolvla-ete-train:
-	python -m lerobot.scripts.train \
-		--policy.type=smolvla \
-		--policy.n_action_steps=20 \
-		--policy.chunk_size=20 \
-		--policy.device=$(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:
-	python -m lerobot.scripts.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
@@ -23,36 +23,22 @@
 </div>

 <h2 align="center">
-    <p><a href="https://huggingface.co/docs/lerobot/so101">
-        Build Your Own SO-101 Robot!</a></p>
+    <p><a href="https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md">
+        Build Your Own SO-100 Robot!</a></p>
 </h2>

 <div align="center">
-  <div style="display: flex; gap: 1rem; justify-content: center; align-items: center;" >
-    <img
-      src="media/so101/so101.webp?raw=true"
-      alt="SO-101 follower arm"
-      title="SO-101 follower arm"
-      style="width: 40%;"
-    />
-    <img
-      src="media/so101/so101-leader.webp?raw=true"
-      alt="SO-101 leader arm"
-      title="SO-101 leader arm"
-      style="width: 40%;"
-    />
-  </div>
+  <img src="media/so100/leader_follower.webp?raw=true" alt="SO-100 leader and follower arms" title="SO-100 leader and follower arms" width="50%">

-
-  <p><strong>Meet the updated SO100, the SO-101 – Just €114 per arm!</strong></p>
+  <p><strong>Meet the SO-100 – Just $110 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><a href="https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md">
+      Get the full SO-100 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>
+  <p>Want to take it to the next level? Make your SO-100 mobile by building LeKiwi!</p>
+  <p>Check out the <a href="https://github.com/huggingface/lerobot/blob/main/examples/11_use_lekiwi.md">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>

  <img src="media/lekiwi/kiwi.webp?raw=true" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
 </div>
@@ -65,6 +51,7 @@

 ---

+
 🤗 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.
@@ -90,7 +77,6 @@

 ### Acknowledgment

- The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla).
 - Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
 - Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
 - Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
@@ -112,25 +98,14 @@ conda create -y -n lerobot python=3.10
 conda activate lerobot
 ```

-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-> **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->  - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->  ```bash
->  conda install ffmpeg=7.1.1 -c conda-forge
->  ```
->  - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
-
 Install 🤗 LeRobot:
 ```bash
 pip install -e .
 ```

-> **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
-`sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
+> **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`

 For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
 - [aloha](https://github.com/huggingface/gym-aloha)
@@ -149,20 +124,44 @@ 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 config classes with all options that you can override in the command line
+|   ├── 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
+|   |   ├── robot_devices  # various real devices: dynamixel motors, opencv cameras, koch robots
+|   |   └── 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
+|       ├── control_robot.py        # teleoperate a real robot, record data, run a policy
+|       ├── 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 downloads data from the Hugging Face hub.

 You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
 ```bash
-python -m lerobot.scripts.visualize_dataset \
+python lerobot/scripts/visualize_dataset.py \
    --repo-id lerobot/pusht \
    --episode-index 0
 ```

 or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
 ```bash
-python -m lerobot.scripts.visualize_dataset \
+python lerobot/scripts/visualize_dataset.py \
    --repo-id lerobot/pusht \
    --root ./my_local_data_dir \
    --local-files-only 1 \
@@ -175,7 +174,7 @@ It will open `rerun.io` and display the camera streams, robot states and actions
 https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144


-Our script can also visualize datasets stored on a distant server. See `python -m lerobot.scripts.visualize_dataset --help` for more instructions.
+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

@@ -198,7 +197,7 @@ dataset attributes:
  │  ├ 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
+  │  ├ next.done (bool): indicates the end of en episode ; True for the last frame in each episode
  │  └ index (int64): general index in the whole dataset
  ├ episode_data_index: contains 2 tensors with the start and end indices of each episode
  │  ├ from (1D int64 tensor): first frame index for each episode — shape (num episodes,) starts with 0
@@ -228,7 +227,7 @@ Check out [example 2](./examples/2_evaluate_pretrained_policy.py) that illustrat

 We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
 ```bash
-python -m lerobot.scripts.eval \
+python lerobot/scripts/eval.py \
    --policy.path=lerobot/diffusion_pusht \
    --env.type=pusht \
    --eval.batch_size=10 \
@@ -240,14 +239,14 @@ python -m lerobot.scripts.eval \
 Note: After training your own policy, you can re-evaluate the checkpoints with:

 ```bash
-python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
+python lerobot/scripts/eval.py --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
 ```

-See `python -m lerobot.scripts.eval --help` for more instructions.
+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.
+Check out [example 3](./examples/3_train_policy.py) that illustrate 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.

 To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding `--wandb.enable=true`.

@@ -255,14 +254,14 @@ A link to the wandb logs for the run will also show up in yellow in your termina

 ![](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 -m lerobot.scripts.eval --help` for more instructions.
+Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions.

 #### Reproduce state-of-the-art (SOTA)

 We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
 You can reproduce their training by loading the config from their run. Simply running:
 ```bash
-python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
+python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht
 ```
 reproduces SOTA results for Diffusion Policy on the PushT task.

@@ -288,7 +287,7 @@ python lerobot/scripts/push_dataset_to_hub.py \

 See `python lerobot/scripts/push_dataset_to_hub.py --help` for more instructions.

-If your dataset format is not supported, implement your own in `lerobot/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
+If your dataset format is not supported, implement your own in `lerobot/common/datasets/push_dataset_to_hub/${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
@@ -298,7 +297,7 @@ Once you have trained a policy you may upload it to the Hugging Face hub using a
 You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain:
 - `config.json`: A serialized version of the policy configuration (following the policy's dataclass config).
 - `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format.
- `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility.
+- `train_config.json`: A consolidated configuration containing all parameter userd for training. The policy configuration should match `config.json` exactly. Thisis useful for anyone who wants to evaluate your policy or for reproducibility.

 To upload these to the hub, run the following:
 ```bash
@@ -337,7 +336,7 @@ with profile(
 If you want, you can cite this work with:
 ```bibtex
@misc{cadene2024lerobot,
-    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
+    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and 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}
@@ -345,15 +344,6 @@ If you want, you can cite this work with:
 ```

 Additionally, if you are using any of the particular policy architecture, pretrained models, or datasets, it is recommended to cite the original authors of the work as they appear below:
- [SmolVLA](https://arxiv.org/abs/2506.01844)
-```bibtex
-@article{shukor2025smolvla,
-  title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
-  author={Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and Alibert, Simon and Cord, Matthieu and Wolf, Thomas and Cadene, Remi},
-  journal={arXiv preprint arXiv:2506.01844},
-  year={2025}
-}
-```

 - [Diffusion Policy](https://diffusion-policy.cs.columbia.edu)
 ```bibtex
@@ -394,19 +384,6 @@ Additionally, if you are using any of the particular policy architecture, pretra
  year={2024}
 }
 ```
-
-
- [HIL-SERL](https://hil-serl.github.io/)
-```bibtex
-@Article{luo2024hilserl,
-title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
-author={Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
-year={2024},
-eprint={2410.21845},
-archivePrefix={arXiv},
-primaryClass={cs.RO}
-}
-```
 ## Star History

 [![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)
@@ -51,7 +51,7 @@ For a comprehensive list and documentation of these parameters, see the ffmpeg d
 ### Decoding parameters
 **Decoder**
 We tested two video decoding backends from torchvision:
- `pyav`
+- `pyav` (default)
 - `video_reader` (requires to build torchvision from source)

 **Requested timestamps**
@@ -17,21 +17,12 @@

 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)
-
+def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height: int):
    now = dt.datetime.now()
    capture_dir = output_dir / f"{now:%Y-%m-%d}" / f"{now:%H-%M-%S}"
    if not capture_dir.exists():
@@ -48,21 +39,24 @@ def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

    frame_index = 0
-    start_time = time.time()
-    while time.time() - start_time < duration:
+    while True:
        ret, frame = cap.read()

        if not ret:
            print("Error: Could not read frame.")
            break
-        rr.log("video/stream", rr.Image(frame), static=True)
+
+        cv2.imshow("Video Stream", frame)
        cv2.imwrite(str(capture_dir / f"frame_{frame_index:06d}.png"), frame)
        frame_index += 1

-    # Release the capture
-    cap.release()
+        # Break the loop on 'q' key press
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break

-    # TODO(Steven): Add a graceful shutdown via a close() method for the Viewer context, though not currently supported in the Rerun API.
+    # Release the capture and destroy all windows
+    cap.release()
+    cv2.destroyAllWindows()


 if __name__ == "__main__":
@@ -92,11 +86,5 @@ if __name__ == "__main__":
        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))
@@ -35,12 +35,12 @@ import torch
 from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
 from tqdm import tqdm

-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.video_utils import (
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.video_utils import (
    decode_video_frames_torchvision,
    encode_video_frames,
 )
-from lerobot.utils.benchmark import TimeBenchmark
+from lerobot.common.utils.benchmark import TimeBenchmark

 BASE_ENCODING = OrderedDict(
    [
@@ -416,7 +416,7 @@ if __name__ == "__main__":
        "--vcodec",
        type=str,
        nargs="*",
-        default=["libx264", "hevc", "libsvtav1"],
+        default=["libx264", "libx265", "libsvtav1"],
        help="Video codecs to be tested",
    )
    parser.add_argument(
@@ -446,7 +446,7 @@ if __name__ == "__main__":
    #     nargs="*",
    #     default=[0, 1],
    #     help="Use the fastdecode tuning option. 0 disables it. "
-    #         "For libx264 and libx265/hevc, only 1 is possible. "
+    #         "For libx264 and libx265, 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(
@@ -22,7 +22,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 COPY . /lerobot
 WORKDIR /lerobot
 RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, smolvla]" \
+    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]" \
        --extra-index-url https://download.pytorch.org/whl/cpu

 # Execute in bash shell rather than python
@@ -14,7 +14,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    tcpdump sysstat screen tmux \
    libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
    speech-dispatcher portaudio19-dev libgeos-dev \
-    python${PYTHON_VERSION} python${PYTHON_VERSION}-venv python${PYTHON_VERSION}-dev \
+    python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
    && apt-get clean && rm -rf /var/lib/apt/lists/*

 # Install ffmpeg build dependencies. See:
@@ -21,4 +21,4 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 COPY . /lerobot
 WORKDIR /lerobot
 RUN /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel, smolvla]"
+    && /opt/venv/bin/pip install --no-cache-dir ".[test, aloha, xarm, pusht, dynamixel]"
@@ -1,137 +0,0 @@
-<!---
-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 ".[docs]"
-```
-
-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.
@@ -1,44 +0,0 @@
- sections:
-  - local: index
-    title: LeRobot
-  - local: installation
-    title: Installation
-  title: Get started
- sections:
-  - local: il_robots
-    title: Imitation Learning for Robots
-  - local: il_sim
-    title: Imitation Learning in Sim
-  - 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
-  title: "Tutorials"
- sections:
-  - local: smolvla
-    title: Finetune SmolVLA
-  title: "Policies"
- sections:
-  - local: so101
-    title: SO-101
-  - local: so100
-    title: SO-100
-  - local: koch
-    title: Koch v1.1
-  - local: lekiwi
-    title: LeKiwi
-  title: "Robots"
- sections:
-  - local: notebooks
-    title: Notebooks
-  title: "Resources"
- sections:
-  - local: contributing
-    title: Contribute to LeRobot
-  - local: backwardcomp
-    title: Backward compatibility
-  title: "About"
@@ -1,82 +0,0 @@
-# Backward compatibility
-
-## 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:
-```python
-key = f"{name.removeprefix('main_')}.pos"
-```
-
-For `"shoulder_lift"` (id = 2), the 0 position is changed by -90 degrees and the direction is reversed compared to old calibration/code.
-```python
-action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
-```
-For `"elbow_flex"` (id = 3), the 0 position is changed by -90 degrees compared to old calibration/code.
-```python
-action["elbow_flex.pos"] -= 90
-```
-
-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)
@@ -1,173 +0,0 @@
-# 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
-python -m 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">
-
-```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()
-```
-
-</hfoption>
-<hfoption id="Intel Realsense Camera">
-
-```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()
-```
-</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:
-```python
-sudo apt install v4l2loopback-dkms v4l-utils
-```
-2. *Install [DroidCam](https://droidcam.app) on your phone*. This app is available for both iOS and Android.
-3. *Install [OBS Studio](https://obsproject.com)*. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
-```python
-flatpak install flathub com.obsproject.Studio
-```
-4. *Install the DroidCam OBS plugin*. This plugin integrates DroidCam with OBS Studio. Install it with:
-```python
-flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
-```
-5. *Start OBS Studio*. Launch with:
-```python
-flatpak run com.obsproject.Studio
-```
-6. *Add your phone as a source*. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
-7. *Adjust resolution settings*. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
-8. *Start virtual camera*. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. *Verify the virtual camera setup*. Use `v4l2-ctl` to list the devices:
-```python
-v4l2-ctl --list-devices
-```
-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`.
-```python
-v4l2-ctl -d /dev/video1 --get-fmt-video
-```
-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>
@@ -1 +0,0 @@
-../../CONTRIBUTING.md
@@ -1,548 +0,0 @@
-# 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/common/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 configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/envs/configs.py`. Which is defined as:
-
-```python
-class HILSerlRobotEnvConfig(EnvConfig):
-    robot: RobotConfig | None = None    # Main robot agent (defined in `lerobot/robots`)
-    teleop: TeleoperatorConfig | None = None    # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`)
-    wrapper: EnvTransformConfig | None = None    # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
-    fps: int = 10    # Control frequency
-    name: str = "real_robot"    # Environment name
-    mode: str = None    # "record", "replay", or None (for training)
-    repo_id: str | None = None    # LeRobot dataset repository ID
-    dataset_root: str | None = None    # Local dataset root (optional)
-    task: str = ""    # Task identifier
-    num_episodes: int = 10    # Number of episodes for recording
-    episode: int = 0    # episode index for replay
-    device: str = "cuda"    # Compute device
-    push_to_hub: bool = True    # Whether to push the recorded datasets to Hub
-    pretrained_policy_name_or_path: str | None = None    # For policy loading
-    reward_classifier_pretrained_path: str | None = None    # For reward model
-    number_of_steps_after_success: int = 0    # For reward classifier, collect more positive examples after a success to train a classifier
-```
-
-
-### 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 find_joint_limits.py**
-
-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
-python -m lerobot.scripts.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_so100.json](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json)):
-
-1. Set `mode` to `"record"`
-2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name")
-3. Set `num_episodes` to the number of demonstrations you want to collect
-4. Set `crop_params_dict` to `null` initially (we'll determine crops later)
-5. Configure `robot`, `cameras`, and other hardware settings
-
-Example configuration section:
-```json
-"mode": "record",
-"repo_id": "username/pick_lift_cube",
-"dataset_root": null,
-"task": "pick_and_lift",
-"num_episodes": 15,
-"episode": 0,
-"push_to_hub": true
-```
-
-### 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.
-
-```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,
-        }
-    )
-```
-
-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
-    "teleop": {
-        "type": "gamepad",
-        "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
-    "teleop": {
-        "type": "so101_leader",
-        "port": "/dev/tty.usbmodem585A0077921", # check your port number
-        "use_degrees": 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.scripts.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 `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.scripts.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
-"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.scripts.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
- **repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub
- **num_episodes**: Number of episodes to record
- **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected
- **fps**: Number of frames per second to record
- **push_to_hub**: Whether to push the dataset to the hub
-
-The `number_of_steps_after_success` parameter is crucial as it allows you to collect more positive examples. When a success is detected, the system will continue recording for the specified number of steps while maintaining the reward=1 label. Otherwise, there won't be enough states in the dataset labeled to 1 to train a good classifier.
-
-Example configuration section for data collection:
-
-```json
-{
-    "mode": "record",
-    "repo_id": "hf_username/dataset_name",
-    "dataset_root": "data/your_dataset",
-    "num_episodes": 20,
-    "push_to_hub": true,
-    "fps": 10,
-    "number_of_steps_after_success": 15
-}
-```
-
-**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
-python -m lerobot.scripts.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:
-
-```python
-env_config = HILSerlRobotEnvConfig(
-    reward_classifier_pretrained_path="path_to_your_pretrained_trained_model",
-    # Other environment parameters
-)
-```
-or set the argument in the json config file.
-
-```json
-{
-    "reward_classifier_pretrained_path": "path_to_your_pretrained_model"
-}
-```
-
-Run `gym_manipulator.py` to test the model.
-```bash
-python -m lerobot.scripts.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/aractingi/lerobot-example-config-files/tree/main).
-
-2. **Collect a dataset**:
-   ```bash
-   python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json
-   ```
-
-3. **Train the classifier**:
-   ```bash
-   python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
-   ```
-
-4. **Test the classifier**:
-   ```bash
-   python -m lerobot.scripts.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/aractingi/lerobot-example-config-files/blob/main/train_config_hilserl_so100.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/19bb621a7d0a31c20cd3cc08b1dbab68d3031454/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.scripts.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.scripts.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}
-}
-```
@@ -1,120 +0,0 @@
-# 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/aractingi/lerobot-example-config-files/blob/main/gym_hil_env.json). Key configuration sections include:
-
-### Environment Type and Task
-
-```json
-{
-    "type": "hil",
-    "name": "franka_sim",
-    "task": "PandaPickCubeGamepad-v0",
-    "device": "cuda"
-}
-```
-
-Available tasks:
- `PandaPickCubeBase-v0`: Basic environment
- `PandaPickCubeGamepad-v0`: With gamepad control
- `PandaPickCubeKeyboard-v0`: With keyboard control
-
-### Gym Wrappers Configuration
-
-```json
-"wrapper": {
-    "gripper_penalty": -0.02,
-    "control_time_s": 15.0,
-    "use_gripper": true,
-    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
-    "end_effector_step_sizes": {
-        "x": 0.025,
-        "y": 0.025,
-        "z": 0.025
-    },
-    "control_mode": "gamepad"
-    }
-```
-
-Important parameters:
- `gripper_penalty`: Penalty for excessive gripper movement
- `use_gripper`: Whether to enable gripper control
- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
- `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:
-
-```python
-python -m lerobot.scripts.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:
-
-```python
-python -m lerobot.scripts.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/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers:
-
-```python
-python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
-```
-
-In a different terminal, run the learner server:
-
-```python
-python -m lerobot.scripts.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}
-}
-```
@@ -1,541 +0,0 @@
-# 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
-python -m 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">
-```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)
-```
-</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
-python -m 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">
-```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)
-```
-</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 ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Then store your Hugging Face repository name in a variable:
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-
-Now you can record a dataset. To record 5 episodes and upload your dataset to the hub, adapt the code below for your robot and execute the command or API example.
-
-<hfoptions id="record">
-<hfoption id="Command">
-```bash
-python -m lerobot.record \
-    --robot.type=so101_follower \
-    --robot.port=/dev/tty.usbmodem585A0076841 \
-    --robot.id=my_awesome_follower_arm \
-    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
-    --teleop.type=so101_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_awesome_leader_arm \
-    --display_data=true \
-    --dataset.repo_id=${HF_USER}/record-test \
-    --dataset.num_episodes=5 \
-    --dataset.single_task="Grab the black cube"
-```
-</hfoption>
-<hfoption id="API example">
-```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()
-```
-</hfoption>
-</hfoptions>
-
-#### Dataset upload
-Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
-```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).
-
-#### 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`.
- 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 ${HF_USER}/so101_test
-```
-
-## Replay an episode
-
-A useful feature is the `replay` function, which allows you to replay any episode that you've recorded or episodes from any dataset out there. This function helps you test the repeatability of your robot's actions and assess transferability across robots of the same model.
-
-You can replay the first episode on your robot with either the command below or with the API example:
-
-<hfoptions id="replay">
-<hfoption id="Command">
-```bash
-python -m lerobot.replay \
-    --robot.type=so101_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=my_awesome_follower_arm \
-    --dataset.repo_id=${HF_USER}/record-test \
-    --dataset.episode=0 # choose the episode you want to replay
-```
-</hfoption>
-<hfoption id="API example">
-```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()
-```
-</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 [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python -m lerobot.scripts.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`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-Training should take several hours. You will find checkpoints in `outputs/train/act_so101_test/checkpoints`.
-
-To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:
-```bash
-python -m lerobot.scripts.train \
-  --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 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}/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/${CKPT}/pretrained_model
-```
-
-## Run inference and evaluate your policy
-
-You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/lerobot/record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
-
-<hfoptions id="eval">
-<hfoption id="Command">
-```bash
-python -m lerobot.record  \
-  --robot.type=so100_follower \
-  --robot.port=/dev/ttyACM1 \
-  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
-  --robot.id=my_awesome_follower_arm \
-  --display_data=false \
-  --dataset.repo_id=${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">
-```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.robots.so100_follower.config_so100_follower import SO100FollowerConfig
-from lerobot.robots.so100_follower.so100_follower import SO100Follower
-from lerobot.utils.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import _init_rerun
-from lerobot.record import record_loop
-
-NUM_EPISODES = 5
-FPS = 30
-EPISODE_TIME_SEC = 60
-TASK_DESCRIPTION = "My task description"
-
-# 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_username>/<my_policy_repo_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_username>/eval_<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
-robot.connect()
-
-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,
-        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()
-```
-</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`).
@@ -1,152 +0,0 @@
-# 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/aractingi/lerobot-example-config-files/blob/main/env_config_gym_hil_il.json).
-
-To teleoperate and collect a dataset, we need to modify this config file and you should add your `repo_id` here: `"repo_id": "il_gym",` and `"num_episodes": 30,` and make sure you set `mode` to `record`, "mode": "record".
-
-If you do not have a Nvidia GPU also change `"device": "cuda"` parameter in the config file (for example to `mps` for MacOS).
-
-By default the config file assumes you use a controller. To use your keyboard please change the envoirment specified at `"task"` in the config file and set it to `"PandaPickCubeKeyboard-v0"`.
-
-Then we can run this command to start:
-
-<hfoptions id="teleop_sim">
-<hfoption id="Linux">
-
-```bash
-python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
-```
-
-</hfoption>
-<hfoption id="MacOS">
-
-```bash
-mjpython -m lerobot.scripts.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 [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-```bash
-python -m lerobot.scripts.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`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-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 the config file that can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json).
-
-Make sure to replace the `repo_id` with the dataset you trained on, for example `pepijn223/il_sim_dataset` and replace the `pretrained_policy_name_or_path` with your model id, for example `pepijn223/il_sim_model`
-
-Then you can run this command to visualize your trained policy
-
-<hfoptions id="eval_policy">
-<hfoption id="Linux">
-
-```bash
-python -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
-```
-
-</hfoption>
-<hfoption id="MacOS">
-
-```bash
-mjpython -m lerobot.scripts.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).
@@ -1,19 +0,0 @@
-<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)
@@ -1,72 +0,0 @@
-# Installation
-
-## Install LeRobot
-
-Currently only available from source.
-
-Download our source code:
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
-
-Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
-```bash
-conda create -y -n lerobot python=3.10
-```
-
-Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
-```bash
-conda activate lerobot
-```
-
-When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-> [!TIP]
-> This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->  - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->  ```bash
->  conda install ffmpeg=7.1.1 -c conda-forge
->  ```
->  - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
-
-Install 🤗 LeRobot:
-```bash
-pip install -e .
-```
-
-### 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)), `xarm` ([gym-xarm](https://github.com/huggingface/gym-xarm)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
-Example:
-```bash
-pip install -e ".[aloha]" # or "[pusht]" for example
-```
-
-### Motor Control
-For Koch v1.1 install the Dynamixel SDK, for SO100/SO101/Moss install the Feetech SDK.
-```bash
-pip install -e ".[feetech]" # or "[dynamixel]" for example
-```
-
-### Experiment Tracking
-To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
-```bash
-wandb login
-```
-
-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.
@@ -1,318 +0,0 @@
-# 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/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/lerobot/motors/feetech/feetech.py) – for controlling Feetech servos
- [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
-
-Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/motors_bus.py) abstract class to learn about its API.
-For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/lerobot/robots/so101_follower/so101_follower.py)
-
-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/lerobot/motors/feetech/tables.py) or [Dynamixel table](https://github.com/huggingface/lerobot/blob/main/lerobot/motors/dynamixel/tables.py). Depending on the model, this will require you to add model-specific information. In most cases though, there shouldn't be a lot of additions to do.
-
-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:
-```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,
-            ),
-        }
-    )
-```
-
-Have a look at our [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!
-
-```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)
-```
-
-## 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:
-```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}
-```
-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.
-```python
-def action_features(self) -> dict:
-    return self._motors_ft
-```
-
-## 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()`.
-
-```python
-@property
-def is_connected(self) -> bool:
-    return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
-```
-
-### `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.
-
-```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()
-```
-
-### `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:
-```python
-def disconnect(self) -> None:
-    self.bus.disconnect()
-    for cam in self.cameras.values():
-        cam.disconnect()
-```
-
-## Step 4: Support Calibration and Configuration
-
-LeRobot supports saving and loading calibration data automatically. This is useful for joint offsets, zero positions, or sensor alignment.
-
-> Note that depending on your hardware, this may not apply. If that's the case, you can simply leave these methods as no-ops:
-> ```python
-> @property
-> def is_calibrated(self) -> bool:
->    return True
->
-> def calibrate(self) -> None:
->    pass
-> ```
-
-### `is_calibrated`
-
-This should reflect whether your robot has the required calibration loaded.
-
-```python
-@property
-def is_calibrated(self) -> bool:
-    return self.bus.is_calibrated
-```
-
-### `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.
-
-```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)
-```
-
-### `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.
-
-```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)
-```
-
-## 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`.
-
-```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
-```
-
-### `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.
-
-```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
-```
-
-## Adding a Teleoperator
-
-For implementing teleoperation devices, we also provide a [`Teleoperator`](https://github.com/huggingface/lerobot/blob/main/lerobot/teleoperators/teleoperator.py) base class. This class is very similar to the `Robot` base class and also doesn't assume anything on form factor.
-
-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.
-
-## 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) 🤗
@@ -1 +0,0 @@
-../../src/lerobot/robots/koch_follower/koch.mdx
@@ -1 +0,0 @@
-../../src/lerobot/robots/lekiwi/lekiwi.mdx
@@ -1,29 +0,0 @@
-# 🤗 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`.
@@ -1,97 +0,0 @@
-# Finetune 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](https://huggingface.co/docs/lerobot/getting_started_real_world_robot#record-a-dataset)
-
-<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 && python -m lerobot.scripts.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
-python -m lerobot.scripts.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](./getting_started_real_world_robot#record-a-dataset).
-Once you are logged in, you can run inference in your setup by doing:
-
-```bash
-python -m lerobot.record \
-  --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.
@@ -1 +0,0 @@
-../../src/lerobot/robots/so100_follower/so100.mdx
@@ -1 +0,0 @@
-../../src/lerobot/robots/so101_follower/so101.mdx
@@ -0,0 +1,614 @@
+# Using the [SO-100](https://github.com/TheRobotStudio/SO-ARM100) with LeRobot
+
+## Table of Contents
+
+  - [A. Source the parts](#a-source-the-parts)
+  - [B. Install LeRobot](#b-install-lerobot)
+  - [C. Configure the Motors](#c-configure-the-motors)
+  - [D. Step-by-Step Assembly Instructions](#d-step-by-step-assembly-instructions)
+  - [E. Calibrate](#e-calibrate)
+  - [F. Teleoperate](#f-teleoperate)
+  - [G. Record a dataset](#g-record-a-dataset)
+  - [H. Visualize a dataset](#h-visualize-a-dataset)
+  - [I. Replay an episode](#i-replay-an-episode)
+  - [J. Train a policy](#j-train-a-policy)
+  - [K. Evaluate your policy](#k-evaluate-your-policy)
+  - [L. More Information](#l-more-information)
+
+## A. 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 advice if it's your first time printing or if you don't own a 3D printer.
+
+Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
+
+## B. Install LeRobot
+
+> [!TIP]
+> We use the Command Prompt (cmd) quite a lot. If you are not comfortable using the cmd or want to brush up using the command line you can have a look here: [Command line crash course](https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Command_line)
+
+On your computer:
+
+#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
+
+#### 2. Restart shell
+Copy paste in your shell: `source ~/.bashrc` or for Mac: `source ~/.bash_profile` or `source ~/.zshrc` if you're using zshell
+
+#### 3. Create and activate a fresh conda environment for lerobot
+
+<details>
+<summary><strong>Video install instructions</strong></summary>
+
+<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
+
+</details>
+
+```bash
+conda create -y -n lerobot python=3.10
+```
+
+Then activate your conda environment (do this each time you open a shell to use lerobot!):
+```bash
+conda activate lerobot
+```
+
+#### 4. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+#### 5. Install LeRobot with dependencies for the feetech motors:
+```bash
+cd ~/lerobot && pip install -e ".[feetech]"
+```
+
+*EXTRA: For Linux only (not Mac)*: install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+Great :hugs:! You are now done installing LeRobot and we can begin assembling the SO100 arms :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.
+
+## C. Configure the motors
+
+> [!NOTE]
+> Throughout this tutorial you will find videos on how to do the steps, the full video tutorial can be found here: [assembly video](https://www.youtube.com/watch?v=FioA2oeFZ5I).
+
+### 1. Find the USB ports associated to each arm
+
+Designate one bus servo adapter and 6 motors for your leader arm, and similarly the other bus servo adapter and 6 motors for the follower arm. It's convenient to label them and write on each motor if it's for the follower `F` or for the leader `L` and it's ID from 1 to 6 (F1...F6 and L1...L6).
+
+#### a. Run the script to find port
+
+<details>
+<summary><strong>Video finding port</strong></summary>
+  <video src="https://github.com/user-attachments/assets/4a21a14d-2046-4805-93c4-ee97a30ba33f"></video>
+  <video src="https://github.com/user-attachments/assets/1cc3aecf-c16d-4ff9-aec7-8c175afbbce2"></video>
+</details>
+
+To find the port for each bus servo adapter, run the utility script:
+```bash
+python lerobot/scripts/find_motors_bus_port.py
+```
+
+#### b. Example outputs
+
+Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
+```
+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 leader arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0031751
+Reconnect the usb cable.
+```
+Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
+```
+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 follower arm and press Enter...]
+
+The port of this MotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the usb cable.
+```
+
+#### c. Troubleshooting
+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
+```
+
+#### d. Update config file
+
+IMPORTANTLY: Now that you have your ports, update the **port** default values of [`SO100RobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
+```python
+@RobotConfig.register_subclass("so100")
+@dataclass
+class So100RobotConfig(ManipulatorRobotConfig):
+    calibration_dir: str = ".cache/calibration/so100"
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    leader_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem58760431091",  <-- UPDATE HERE
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                },
+            ),
+        }
+    )
+
+    follower_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem585A0076891",  <-- UPDATE HERE
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                },
+            ),
+        }
+    )
+```
+
+### 2. Assembling the Base
+Let's begin with assembling the follower arm base
+
+#### a. Set IDs for all 12 motors
+
+<details>
+<summary><strong>Video configuring motor</strong></summary>
+  <video src="https://github.com/user-attachments/assets/ef9b3317-2e11-4858-b9d3-f0a02fb48ecf"></video>
+  <video src="https://github.com/user-attachments/assets/f36b5ed5-c803-4ebe-8947-b39278776a0d"></video>
+</details>
+
+Plug your first motor F1 and run this script to set its ID to 1. It will also set its present position to 2048, so expect your motor to rotate. Replace the text after --port to the corresponding follower control board port and run this command in cmd:
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 1
+```
+
+> [!NOTE]
+> These motors are currently limited. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
+
+Then unplug your motor and plug the second motor and set its ID to 2.
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 2
+```
+
+Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.
+
+
+#### b. Remove the gears of the 6 leader motors
+
+<details>
+<summary><strong>Video removing gears</strong></summary>
+
+<video src="https://github.com/user-attachments/assets/0c95b88c-5b85-413d-ba19-aee2f864f2a7"></video>
+
+</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.
+
+## D. Step-by-Step Assembly Instructions
+
+**Step 1: Clean Parts**
+- Remove all support material from the 3D-printed parts.
+---
+
+### Additional Guidance
+
+<details>
+<summary><strong>Video assembling arms</strong></summary>
+
+<video src="https://github.com/user-attachments/assets/488a39de-0189-4461-9de3-05b015f90cca"></video>
+
+</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="../media/tutorial/img1.jpg" style="height:300px;">
+
+**Step 3: Install in Base**
+- Place the first motor into the base.
+
+  <img src="../media/tutorial/img2.jpg" 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="../media/tutorial/img4.jpg" 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="../media/tutorial/img5.jpg" 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 in photo towards you (see photo).
+- Attach the shoulder part.
+
+  <img src="../media/tutorial/img6.jpg" 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="../media/tutorial/img8.jpg" 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="../media/tutorial/img9.jpg" style="height:250px;">
+    <img src="../media/tutorial/img10.jpg" style="height:250px;">
+    <img src="../media/tutorial/img12.jpg" 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="../media/tutorial/img11.jpg" style="height:300px;">
+
+**Step 14: Attach Upper Arm**
+- Attach the upper arm with 4 screws on each side.
+
+  <img src="../media/tutorial/img13.jpg" 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="../media/tutorial/img14.jpg" style="height:300px;">
+
+**Step 17: Attach Forearm**
+- Connect the forearm to motor 3 using 4 screws on each side.
+
+  <img src="../media/tutorial/img15.jpg" 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="../media/tutorial/img16.jpg" style="height:300px;">
+    <img src="../media/tutorial/img19.jpg" 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="../media/tutorial/img17.jpg" 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="../media/tutorial/img18.jpg" 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="../media/tutorial/img20.jpg" 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="../media/tutorial/img22.jpg" 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="../media/tutorial/img23.jpg" style="height:300px;">
+
+---
+
+### Follower Configuration
+
+**Step 24: Attach Gripper**
+- Attach the gripper to motor 5.
+
+  <img src="../media/tutorial/img24.jpg" 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="../media/tutorial/img25.jpg" 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="../media/tutorial/img26.jpg" style="height:300px;">
+
+**Step 27: Mount Controller**
+- Attach the motor controller on the back.
+
+  <div style="display: flex;">
+    <img src="../media/tutorial/img27.jpg" style="height:300px;">
+    <img src="../media/tutorial/img28.jpg" 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="../media/tutorial/img29.jpg" style="height:300px;">
+
+**Step 25: Attach Handle**
+- Attach the handle to motor 5 using 4 screws.
+
+  <img src="../media/tutorial/img30.jpg" 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="../media/tutorial/img31.jpg" style="height:300px;">
+
+**Step 27: Attach Trigger**
+- Attach the follower trigger with 4 screws.
+
+  <img src="../media/tutorial/img32.jpg" style="height:300px;">
+
+**Step 28: Mount Controller**
+- Attach the motor controller on the back.
+
+  <div style="display: flex;">
+    <img src="../media/tutorial/img27.jpg" style="height:300px;">
+    <img src="../media/tutorial/img28.jpg" style="height:300px;">
+  </div>
+
+*Assembly complete – proceed to calibration.*
+
+
+## E. Calibrate
+
+Next, you'll need to calibrate your SO-100 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one SO-100 robot to work on another.
+
+#### a. Manual calibration of follower arm
+
+> [!IMPORTANT]
+> Contrarily to step 6 of the [assembly video](https://youtu.be/FioA2oeFZ5I?t=724) which illustrates the auto calibration, we will actually do manual calibration of follower for now.
+
+You will need to move the follower arm to these positions sequentially:
+
+| 1. Zero position                                                                                                                                             | 2. Rotated position                                                                                                                                                   | 3. Rest position                                                                                                                                             |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| <img src="../media/so100/follower_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/so100/follower_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/so100/follower_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
+
+Make sure both arms are connected and run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=so100 \
+  --robot.cameras='{}' \
+  --control.type=calibrate \
+  --control.arms='["main_follower"]'
+```
+
+#### b. Manual calibration of leader arm
+Follow step 6 of the [assembly video](https://youtu.be/FioA2oeFZ5I?t=724) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
+
+| 1. Zero position                                                                                                                                       | 2. Rotated position                                                                                                                                             | 3. Rest position                                                                                                                                       |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
+
+Run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=so100 \
+  --robot.cameras='{}' \
+  --control.type=calibrate \
+  --control.arms='["main_leader"]'
+```
+
+## F. Teleoperate
+
+**Simple teleop**
+Then you are ready to teleoperate your robot! Run this simple script (it won't connect and display the cameras):
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=so100 \
+  --robot.cameras='{}' \
+  --control.type=teleoperate
+```
+
+
+#### a. Teleop with displaying cameras
+Follow [this guide to setup your cameras](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). Then you will be able to display the cameras on your computer while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=so100 \
+  --control.type=teleoperate
+```
+
+## G. Record a dataset
+
+Once you're familiar with teleoperation, you can record your first dataset with SO-100.
+
+If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Store your Hugging Face repository name in a variable to run these commands:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Record 2 episodes and upload your dataset to the hub:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=so100 \
+  --control.type=record \
+  --control.fps=30 \
+  --control.single_task="Grasp a lego block and put it in the bin." \
+  --control.repo_id=${HF_USER}/so100_test \
+  --control.tags='["so100","tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=2 \
+  --control.push_to_hub=true
+```
+
+Note: You can resume recording by adding `--control.resume=true`.
+
+## H. 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}/so100_test
+```
+
+If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with (a window can be opened in the browser `http://127.0.0.1:9090` with the visualization tool):
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --repo-id ${HF_USER}/so100_test \
+  --local-files-only 1
+```
+
+## I. Replay an episode
+
+Now try to replay the first episode on your robot:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=so100 \
+  --control.type=replay \
+  --control.fps=30 \
+  --control.repo_id=${HF_USER}/so100_test \
+  --control.episode=0
+```
+
+## J. Train a policy
+
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+```bash
+python lerobot/scripts/train.py \
+  --dataset.repo_id=${HF_USER}/so100_test \
+  --policy.type=act \
+  --output_dir=outputs/train/act_so100_test \
+  --job_name=act_so100_test \
+  --policy.device=cuda \
+  --wandb.enable=true
+```
+
+Let's explain it:
+1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so100_test`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
+5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+
+Training should take several hours. You will find checkpoints in `outputs/train/act_so100_test/checkpoints`.
+
+## K. Evaluate your policy
+
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=so100 \
+  --control.type=record \
+  --control.fps=30 \
+  --control.single_task="Grasp a lego block and put it in the bin." \
+  --control.repo_id=${HF_USER}/eval_act_so100_test \
+  --control.tags='["tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=10 \
+  --control.push_to_hub=true \
+  --control.policy.path=outputs/train/act_so100_test/checkpoints/last/pretrained_model
+```
+
+As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_so100_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so100_test`).
+2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so100_test`).
+
+## L. More Information
+
+Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth tutorial on controlling real robots with LeRobot.
+
+> [!TIP]
+>  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb) in the channel [`#so100-arm`](https://discord.com/channels/1216765309076115607/1237741463832363039).
@@ -0,0 +1,585 @@
+# Using the [LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi) Robot with LeRobot
+
+## Table of Contents
+
+  - [A. Source the parts](#a-source-the-parts)
+  - [B. Install software Pi](#b-install-software-on-pi)
+  - [C. Setup LeRobot laptop/pc](#c-install-lerobot-on-laptop)
+  - [D. Assemble the arms](#d-assembly)
+  - [E. Calibrate](#e-calibration)
+  - [F. Teleoperate](#f-teleoperate)
+  - [G. Record a dataset](#g-record-a-dataset)
+  - [H. Visualize a dataset](#h-visualize-a-dataset)
+  - [I. Replay an episode](#i-replay-an-episode)
+  - [J. Train a policy](#j-train-a-policy)
+  - [K. Evaluate your policy](#k-evaluate-your-policy)
+
+> [!TIP]
+>  If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb) in the channel [`#mobile-so-100-arm`](https://discord.com/channels/1216765309076115607/1318390825528332371).
+
+## A. 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 advice if it's your first time printing or if you don't own a 3D printer.
+
+Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
+
+### 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.
+
+## B. Install software on Pi
+Now we have to setup 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 setup [SSH](https://www.raspberrypi.com/news/coding-on-raspberry-pi-remotely-with-visual-studio-code/) (Secure Shell Protocol) so you can login into the Pi from your laptop without requiring a screen, keyboard and mouse in 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 your Raspberry Pi:
+
+#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
+
+#### 2. Restart shell
+Copy paste in your shell: `source ~/.bashrc` or for Mac: `source ~/.bash_profile` or `source ~/.zshrc` if you're using zshell
+
+#### 3. Create and activate a fresh conda environment for lerobot
+
+<details>
+<summary><strong>Video install instructions</strong></summary>
+
+<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
+
+</details>
+
+```bash
+conda create -y -n lerobot python=3.10
+```
+
+Then activate your conda environment (do this each time you open a shell to use lerobot!):
+```bash
+conda activate lerobot
+```
+
+#### 4. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+#### 5. Install LeRobot with dependencies for the feetech motors:
+```bash
+cd ~/lerobot && pip install -e ".[feetech]"
+```
+
+## C. Install LeRobot on laptop
+If you already have install LeRobot on your laptop you can skip this step, otherwise please follow along as we do the same steps we did on the Pi.
+
+> [!TIP]
+> We use the Command Prompt (cmd) quite a lot. If you are not comfortable using the cmd or want to brush up using the command line you can have a look here: [Command line crash course](https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Command_line)
+
+On your computer:
+
+#### 1. [Install Miniconda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install):
+
+#### 2. Restart shell
+Copy paste in your shell: `source ~/.bashrc` or for Mac: `source ~/.bash_profile` or `source ~/.zshrc` if you're using zshell
+
+#### 3. Create and activate a fresh conda environment for lerobot
+
+<details>
+<summary><strong>Video install instructions</strong></summary>
+
+<video src="https://github.com/user-attachments/assets/17172d3b-3b64-4b80-9cf1-b2b7c5cbd236"></video>
+
+</details>
+
+```bash
+conda create -y -n lerobot python=3.10
+```
+
+Then activate your conda environment (do this each time you open a shell to use lerobot!):
+```bash
+conda activate lerobot
+```
+
+#### 4. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+#### 5. Install LeRobot with dependencies for the feetech motors:
+```bash
+cd ~/lerobot && pip install -e ".[feetech]"
+```
+
+*EXTRA: For Linux only (not Mac)*: install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+Great :hugs:! You are now done installing LeRobot and we can begin assembling the SO100 arms and 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.
+
+# D. Assembly
+
+First we will assemble the two SO100 arms. One to attach to the mobile base and one for teleoperation. Then we will assemble the mobile base.
+
+## SO100 Arms
+### Configure motors
+The instructions for configuring the motors can be found [Here](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#c-configure-the-motors) in step C of the SO100 tutorial. Besides the ID's for the arm motors we also need to set the motor ID's for the mobile base. These needs to be in a specific order to work. Below an image of the motor ID's and motor mounting positions for the mobile base. Note that we only use one Motor Control board on LeKiwi. This means the motor ID's for the wheels are 7, 8 and 9.
+
+<img src="../media/lekiwi/motor_ids.webp?raw=true" alt="Motor ID's for mobile robot" title="Motor ID's for mobile robot" width="60%">
+
+### Assemble arms
+[Assemble arms instruction](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md#d-assemble-the-arms)
+
+## Mobile base (LeKiwi)
+[Assemble LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi)
+
+### Update config
+Both config files on the LeKiwi LeRobot and on the laptop should be the same. First we should find the Ip address of the Raspberry Pi of the mobile manipulator. This is the same Ip address used in SSH. We also need the usb port of the control board of the leader arm on the laptop and the port of the control board on LeKiwi. We can find these ports with the following script.
+
+#### a. Run the script to find port
+
+<details>
+<summary><strong>Video finding port</strong></summary>
+  <video src="https://github.com/user-attachments/assets/4a21a14d-2046-4805-93c4-ee97a30ba33f"></video>
+  <video src="https://github.com/user-attachments/assets/1cc3aecf-c16d-4ff9-aec7-8c175afbbce2"></video>
+</details>
+
+To find the port for each bus servo adapter, run the utility script:
+```bash
+python lerobot/scripts/find_motors_bus_port.py
+```
+
+#### b. Example outputs
+
+Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect leader arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
+Reconnect the usb cable.
+```
+Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect follower arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the usb cable.
+```
+
+#### c. Troubleshooting
+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
+```
+
+#### d. Update config file
+
+IMPORTANTLY: Now that you have your ports of leader and follower arm and ip address of the mobile-so100, update the **ip** in Network configuration, **port** in leader_arms and **port** in lekiwi. In the [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py) file. Where you will find something like:
+```python
+@RobotConfig.register_subclass("lekiwi")
+@dataclass
+class LeKiwiRobotConfig(RobotConfig):
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    # Network Configuration
+    ip: str = "172.17.133.91"
+    port: int = 5555
+    video_port: int = 5556
+
+    cameras: dict[str, CameraConfig] = field(
+        default_factory=lambda: {
+            "mobile": OpenCVCameraConfig(camera_index="/dev/video0", fps=30, width=640, height=480),
+            "mobile2": OpenCVCameraConfig(camera_index="/dev/video2", fps=30, width=640, height=480),
+        }
+    )
+
+    calibration_dir: str = ".cache/calibration/lekiwi"
+
+    leader_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem585A0077581",
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                },
+            ),
+        }
+    )
+
+    follower_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/ttyACM0",
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                    "left_wheel": (7, "sts3215"),
+                    "back_wheel": (8, "sts3215"),
+                    "right_wheel": (9, "sts3215"),
+                },
+            ),
+        }
+    )
+
+    teleop_keys: dict[str, str] = field(
+        default_factory=lambda: {
+            # Movement
+            "forward": "w",
+            "backward": "s",
+            "left": "a",
+            "right": "d",
+            "rotate_left": "z",
+            "rotate_right": "x",
+            # Speed control
+            "speed_up": "r",
+            "speed_down": "f",
+            # quit teleop
+            "quit": "q",
+        }
+    )
+
+    mock: bool = False
+```
+
+## Wired version
+
+For the wired LeKiwi version your configured IP address should refer to your own laptop (127.0.0.1), because leader arm and LeKiwi are in this case connected to own laptop. Below and example configuration for this wired setup:
+```python
+@RobotConfig.register_subclass("lekiwi")
+@dataclass
+class LeKiwiRobotConfig(RobotConfig):
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    # Network Configuration
+    ip: str = "127.0.0.1"
+    port: int = 5555
+    video_port: int = 5556
+
+    cameras: dict[str, CameraConfig] = field(
+        default_factory=lambda: {
+            "front": OpenCVCameraConfig(
+                camera_index=0, fps=30, width=640, height=480, rotation=90
+            ),
+            "wrist": OpenCVCameraConfig(
+                camera_index=1, fps=30, width=640, height=480, rotation=180
+            ),
+        }
+    )
+
+    calibration_dir: str = ".cache/calibration/lekiwi"
+
+    leader_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem585A0077581",
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                },
+            ),
+        }
+    )
+
+    follower_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem58760431061",
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                    "left_wheel": (7, "sts3215"),
+                    "back_wheel": (8, "sts3215"),
+                    "right_wheel": (9, "sts3215"),
+                },
+            ),
+        }
+    )
+
+    teleop_keys: dict[str, str] = field(
+        default_factory=lambda: {
+            # Movement
+            "forward": "w",
+            "backward": "s",
+            "left": "a",
+            "right": "d",
+            "rotate_left": "z",
+            "rotate_right": "x",
+            # Speed control
+            "speed_up": "r",
+            "speed_down": "f",
+            # quit teleop
+            "quit": "q",
+        }
+    )
+
+    mock: bool = False
+```
+
+# E. Calibration
+Now we have to calibrate the leader arm and the follower arm. The wheel motors don't have to be calibrated.
+
+
+### Calibrate follower arm (on mobile base)
+> [!IMPORTANT]
+> Contrarily to step 6 of the [assembly video](https://youtu.be/FioA2oeFZ5I?t=724) which illustrates the auto calibration, we will actually do manual calibration of follower for now.
+
+You will need to move the follower arm to these positions sequentially:
+
+| 1. Zero position                                                                                                                                                  | 2. Rotated position                                                                                                                                                        | 3. Rest position                                                                                                                                                  |
+| ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| <img src="../media/lekiwi/mobile_calib_zero.webp?raw=true" alt="SO-100 follower arm zero position" title="SO-100 follower arm zero position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rotated.webp?raw=true" alt="SO-100 follower arm rotated position" title="SO-100 follower arm rotated position" style="width:100%;"> | <img src="../media/lekiwi/mobile_calib_rest.webp?raw=true" alt="SO-100 follower arm rest position" title="SO-100 follower arm rest position" style="width:100%;"> |
+
+Make sure the arm is connected to the Raspberry Pi and run this script (on the Raspberry Pi) to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --robot.cameras='{}' \
+  --control.type=calibrate \
+  --control.arms='["main_follower"]'
+```
+
+### Wired version
+If you have the **wired** LeKiwi version please run all commands including this calibration command on your laptop.
+
+### Calibrate leader arm
+Then to calibrate the leader arm (which is attached to the laptop/pc). You will need to move the leader arm to these positions sequentially:
+
+| 1. Zero position                                                                                                                                       | 2. Rotated position                                                                                                                                             | 3. Rest position                                                                                                                                       |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| <img src="../media/so100/leader_zero.webp?raw=true" alt="SO-100 leader arm zero position" title="SO-100 leader arm zero position" style="width:100%;"> | <img src="../media/so100/leader_rotated.webp?raw=true" alt="SO-100 leader arm rotated position" title="SO-100 leader arm rotated position" style="width:100%;"> | <img src="../media/so100/leader_rest.webp?raw=true" alt="SO-100 leader arm rest position" title="SO-100 leader arm rest position" style="width:100%;"> |
+
+Run this script (on your laptop/pc) to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --robot.cameras='{}' \
+  --control.type=calibrate \
+  --control.arms='["main_leader"]'
+```
+
+# F. Teleoperate
+To teleoperate SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --control.type=remote_robot
+```
+
+Then on your laptop, also run `conda activate lerobot` and this script:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --control.type=teleoperate \
+  --control.fps=30
+```
+
+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 [`LeKiwiRobotConfig`](../lerobot/common/robot_devices/robots/configs.py).
+
+### Wired version
+If you have the **wired** LeKiwi version please run all commands including both these teleoperation commands on your laptop.
+
+## 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 set in the configuration file. 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.
+
+### 4. Same config file
+Make sure the configuration file on both your laptop/pc and the Raspberry Pi is the same.
+
+# G. Record a dataset
+Once you're familiar with teleoperation, you can record your first dataset with LeKiwi.
+
+To start the program on LeKiwi, SSH into your Raspberry Pi, and run `conda activate lerobot` and this script:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --control.type=remote_robot
+```
+
+If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Store your Hugging Face repository name in a variable to run these commands:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+On your laptop then run this command to record 2 episodes and upload your dataset to the hub:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --control.type=record \
+  --control.fps=30 \
+  --control.single_task="Grasp a lego block and put it in the bin." \
+  --control.repo_id=${HF_USER}/lekiwi_test \
+  --control.tags='["tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=2 \
+  --control.push_to_hub=true
+```
+
+Note: You can resume recording by adding `--control.resume=true`.
+
+### Wired version
+If you have the **wired** LeKiwi version please run all commands including both these record dataset commands on your laptop.
+
+# H. 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}/lekiwi_test
+```
+
+If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with (a window can be opened in the browser `http://127.0.0.1:9090` with the visualization tool):
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --repo-id ${HF_USER}/lekiwi_test \
+  --local-files-only 1
+```
+
+# I. Replay an episode
+Now try to replay the first episode on your robot:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --control.type=replay \
+  --control.fps=30 \
+  --control.repo_id=${HF_USER}/lekiwi_test \
+  --control.episode=0
+```
+
+## J. Train a policy
+
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+```bash
+python lerobot/scripts/train.py \
+  --dataset.repo_id=${HF_USER}/lekiwi_test \
+  --policy.type=act \
+  --output_dir=outputs/train/act_lekiwi_test \
+  --job_name=act_lekiwi_test \
+  --policy.device=cuda \
+  --wandb.enable=true
+```
+
+Let's explain it:
+1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/lekiwi_test`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
+5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+
+Training should take several hours. You will find checkpoints in `outputs/train/act_lekiwi_test/checkpoints`.
+
+## K. Evaluate your policy
+
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=lekiwi \
+  --control.type=record \
+  --control.fps=30 \
+  --control.single_task="Drive to the red block and pick it up" \
+  --control.repo_id=${HF_USER}/eval_act_lekiwi_test \
+  --control.tags='["tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=10 \
+  --control.push_to_hub=true \
+  --control.policy.path=outputs/train/act_lekiwi_test/checkpoints/last/pretrained_model
+```
+
+As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_lekiwi_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_lekiwi_test`).
+2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_lekiwi_test`).
@@ -0,0 +1,335 @@
+This tutorial explains how to use [Moss v1](https://github.com/jess-moss/moss-robot-arms) with LeRobot.
+
+## Source the parts
+
+Follow this [README](https://github.com/jess-moss/moss-robot-arms). It contains the bill of materials with link to source the parts, as well as the instructions to 3D print the parts and advice if it's your first time printing or if you don't own a 3D printer already.
+
+**Important**: Before assembling, you will first need to configure your motors. To this end, we provide a nice script, so let's first install LeRobot. After configuration, we will also guide you through assembly.
+
+## Install LeRobot
+
+On your computer:
+
+1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
+```bash
+mkdir -p ~/miniconda3
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
+bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
+rm ~/miniconda3/miniconda.sh
+~/miniconda3/bin/conda init bash
+```
+
+2. Restart shell or `source ~/.bashrc`
+
+3. Create and activate a fresh conda environment for lerobot
+```bash
+conda create -y -n lerobot python=3.10 && conda activate lerobot
+```
+
+4. Clone LeRobot:
+```bash
+git clone https://github.com/huggingface/lerobot.git ~/lerobot
+```
+
+5. Install LeRobot with dependencies for the feetech motors:
+```bash
+cd ~/lerobot && pip install -e ".[feetech]"
+```
+
+For Linux only (not Mac), install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+
+## Configure the motors
+
+Follow steps 1 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the use of our scripts below.
+
+**Find USB ports associated to your arms**
+To find the correct ports for each arm, run the utility script twice:
+```bash
+python lerobot/scripts/find_motors_bus_port.py
+```
+
+Example output when identifying the leader arm's port (e.g., `/dev/tty.usbmodem575E0031751` on Mac, or possibly `/dev/ttyACM0` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect leader arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0031751
+Reconnect the usb cable.
+```
+
+Example output when identifying the follower arm's port (e.g., `/dev/tty.usbmodem575E0032081`, or possibly `/dev/ttyACM1` on Linux):
+```
+Finding all available ports for the MotorBus.
+['/dev/tty.usbmodem575E0032081', '/dev/tty.usbmodem575E0031751']
+Remove the usb cable from your DynamixelMotorsBus and press Enter when done.
+
+[...Disconnect follower arm and press Enter...]
+
+The port of this DynamixelMotorsBus is /dev/tty.usbmodem575E0032081
+Reconnect the usb cable.
+```
+
+Troubleshooting: 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
+```
+
+#### Update config file
+
+IMPORTANTLY: Now that you have your ports, update the **port** default values of [`MossRobotConfig`](../lerobot/common/robot_devices/robots/configs.py). You will find something like:
+```python
+@RobotConfig.register_subclass("moss")
+@dataclass
+class MossRobotConfig(ManipulatorRobotConfig):
+    calibration_dir: str = ".cache/calibration/moss"
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
+    # the number of motors in your follower arms.
+    max_relative_target: int | None = None
+
+    leader_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem58760431091",  <-- UPDATE HERE
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                },
+            ),
+        }
+    )
+
+    follower_arms: dict[str, MotorsBusConfig] = field(
+        default_factory=lambda: {
+            "main": FeetechMotorsBusConfig(
+                port="/dev/tty.usbmodem585A0076891",  <-- UPDATE HERE
+                motors={
+                    # name: (index, model)
+                    "shoulder_pan": [1, "sts3215"],
+                    "shoulder_lift": [2, "sts3215"],
+                    "elbow_flex": [3, "sts3215"],
+                    "wrist_flex": [4, "sts3215"],
+                    "wrist_roll": [5, "sts3215"],
+                    "gripper": [6, "sts3215"],
+                },
+            ),
+        }
+    )
+```
+
+**Configure your motors**
+Plug your first motor and run this script to set its ID to 1. It will also set its present position to 2048, so expect your motor to rotate:
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 1
+```
+
+Note: These motors are currently limitated. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
+
+Then unplug your motor and plug the second motor and set its ID to 2.
+```bash
+python lerobot/scripts/configure_motor.py \
+  --port /dev/tty.usbmodem58760432961 \
+  --brand feetech \
+  --model sts3215 \
+  --baudrate 1000000 \
+  --ID 2
+```
+
+Redo the process for all your motors until ID 6. Do the same for the 6 motors of the leader arm.
+
+**Remove the gears of the 6 leader motors**
+Follow step 2 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). 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.
+
+**Add motor horn to the motors**
+Follow step 3 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). For Moss v1, you need to align the holes on the motor horn to the motor spline to be approximately 3, 6, 9 and 12 o'clock.
+Try to avoid rotating the motor while doing so to keep position 2048 set during configuration. It is especially tricky for the leader motors as it is more sensible without the gears, but it's ok if it's a bit rotated.
+
+## Assemble the arms
+
+Follow step 4 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic). The first arm should take a bit more than 1 hour to assemble, but once you get use to it, you can do it under 1 hour for the second arm.
+
+## Calibrate
+
+Next, you'll need to calibrate your Moss v1 robot to ensure that the leader and follower arms have the same position values when they are in the same physical position. This calibration is essential because it allows a neural network trained on one Moss v1 robot to work on another.
+
+**Manual calibration of follower arm**
+/!\ Contrarily to step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the auto calibration, we will actually do manual calibration of follower for now.
+
+You will need to move the follower arm to these positions sequentially:
+
+| 1. Zero position                                                                                                                                              | 2. Rotated position                                                                                                                                                    | 3. Rest position                                                                                                                                              |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| <img src="../media/moss/follower_zero.webp?raw=true" alt="Moss v1 follower arm zero position" title="Moss v1 follower arm zero position" style="width:100%;"> | <img src="../media/moss/follower_rotated.webp?raw=true" alt="Moss v1 follower arm rotated position" title="Moss v1 follower arm rotated position" style="width:100%;"> | <img src="../media/moss/follower_rest.webp?raw=true" alt="Moss v1 follower arm rest position" title="Moss v1 follower arm rest position" style="width:100%;"> |
+
+Make sure both arms are connected and run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=moss \
+  --robot.cameras='{}' \
+  --control.type=calibrate \
+  --control.arms='["main_follower"]'
+```
+
+**Manual calibration of leader arm**
+Follow step 6 of the [assembly video](https://www.youtube.com/watch?v=DA91NJOtMic) which illustrates the manual calibration. You will need to move the leader arm to these positions sequentially:
+
+| 1. Zero position                                                                                                                                        | 2. Rotated position                                                                                                                                              | 3. Rest position                                                                                                                                        |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| <img src="../media/moss/leader_zero.webp?raw=true" alt="Moss v1 leader arm zero position" title="Moss v1 leader arm zero position" style="width:100%;"> | <img src="../media/moss/leader_rotated.webp?raw=true" alt="Moss v1 leader arm rotated position" title="Moss v1 leader arm rotated position" style="width:100%;"> | <img src="../media/moss/leader_rest.webp?raw=true" alt="Moss v1 leader arm rest position" title="Moss v1 leader arm rest position" style="width:100%;"> |
+
+Run this script to launch manual calibration:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=moss \
+  --robot.cameras='{}' \
+  --control.type=calibrate \
+  --control.arms='["main_leader"]'
+```
+
+## Teleoperate
+
+**Simple teleop**
+Then you are ready to teleoperate your robot! Run this simple script (it won't connect and display the cameras):
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=moss \
+  --robot.cameras='{}' \
+  --control.type=teleoperate
+```
+
+
+**Teleop with displaying cameras**
+Follow [this guide to setup your cameras](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#c-add-your-cameras-with-opencvcamera). Then you will be able to display the cameras on your computer while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=moss \
+  --control.type=teleoperate
+```
+
+## Record a dataset
+
+Once you're familiar with teleoperation, you can record your first dataset with Moss v1.
+
+If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
+```bash
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+```
+
+Store your Hugging Face repository name in a variable to run these commands:
+```bash
+HF_USER=$(huggingface-cli whoami | head -n 1)
+echo $HF_USER
+```
+
+Record 2 episodes and upload your dataset to the hub:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=moss \
+  --control.type=record \
+  --control.fps=30 \
+  --control.single_task="Grasp a lego block and put it in the bin." \
+  --control.repo_id=${HF_USER}/moss_test \
+  --control.tags='["moss","tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=2 \
+  --control.push_to_hub=true
+```
+
+Note: You can resume recording by adding `--control.resume=true`.
+
+## 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}/moss_test
+```
+
+If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with:
+```bash
+python lerobot/scripts/visualize_dataset_html.py \
+  --repo-id ${HF_USER}/moss_test \
+  --local-files-only 1
+```
+
+## Replay an episode
+
+Now try to replay the first episode on your robot:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=moss \
+  --control.type=replay \
+  --control.fps=30 \
+  --control.repo_id=${HF_USER}/moss_test \
+  --control.episode=0
+```
+
+## Train a policy
+
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+```bash
+python lerobot/scripts/train.py \
+  --dataset.repo_id=${HF_USER}/moss_test \
+  --policy.type=act \
+  --output_dir=outputs/train/act_moss_test \
+  --job_name=act_moss_test \
+  --policy.device=cuda \
+  --wandb.enable=true
+```
+
+Let's explain it:
+1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/moss_test`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
+5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+
+Training should take several hours. You will find checkpoints in `outputs/train/act_moss_test/checkpoints`.
+
+## Evaluate your policy
+
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+```bash
+python lerobot/scripts/control_robot.py \
+  --robot.type=moss \
+  --control.type=record \
+  --control.fps=30 \
+  --control.single_task="Grasp a lego block and put it in the bin." \
+  --control.repo_id=${HF_USER}/eval_act_moss_test \
+  --control.tags='["tutorial"]' \
+  --control.warmup_time_s=5 \
+  --control.episode_time_s=30 \
+  --control.reset_time_s=30 \
+  --control.num_episodes=10 \
+  --control.push_to_hub=true \
+  --control.policy.path=outputs/train/act_moss_test/checkpoints/last/pretrained_model
+```
+
+As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with  (e.g. `outputs/train/eval_act_moss_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_moss_test`).
+2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_moss_test`).
+
+## More
+
+Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth tutorial on controlling real robots with LeRobot.
+
+If you have any question or need help, please reach out on Discord in the channel [`#moss-arm`](https://discord.com/channels/1216765309076115607/1275374638985252925).
@@ -32,7 +32,7 @@ import torch
 from huggingface_hub import HfApi

 import lerobot
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.common.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:")
@@ -119,7 +119,7 @@ print(dataset.features[camera_key]["shape"])
 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
+    # loads 8 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)],
@@ -143,6 +143,6 @@ dataloader = torch.utils.data.DataLoader(

 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['observation.state'].shape=}")  # (32, 5, c)
    print(f"{batch['action'].shape=}")  # (32, 64, c)
    break
@@ -13,12 +13,12 @@
 # limitations under the License.

 """
-This script demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
+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.

 It requires the installation of the 'gym_pusht' simulation environment. Install it by running:
 ```bash
-pip install -e ".[pusht]"
+pip install -e ".[pusht]"`
 ```
 """

@@ -30,7 +30,7 @@ import imageio
 import numpy
 import torch

-from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+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")
@@ -119,7 +119,7 @@ while not done:
    rewards.append(reward)
    frames.append(env.render())

-    # The rollout is considered done when the success state is reached (i.e. terminated is True),
+    # 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
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-"""This script demonstrates how to train Diffusion Policy on the PushT environment.
+"""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
@@ -22,11 +22,11 @@ from pathlib import Path

 import torch

+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.common.datasets.utils import dataset_to_policy_features
+from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 from lerobot.configs.types import FeatureType
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.datasets.utils import dataset_to_policy_features
-from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy


 def main():
@@ -1,10 +1,10 @@
 This tutorial will explain the training script, how to use it, and particularly how to configure everything needed for the training run.
-> **Note:** The following assumes you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.
+> **Note:** The following assume you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu.


 ## The training script

-LeRobot offers a training script at [`lerobot/scripts/train.py`](../src/lerobot/scripts/train.py). At a high level it does the following:
+LeRobot offers a training script at [`lerobot/scripts/train.py`](../../lerobot/scripts/train.py). At a high level it does the following:

 - Initialize/load a configuration for the following steps using.
 - Instantiates a dataset.
@@ -21,9 +21,9 @@ In the training script, the main function `train` expects a `TrainPipelineConfig
 def train(cfg: TrainPipelineConfig):
 ```

-You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../src/lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option)
+You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../../lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option)

-When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated to this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)
+When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated for this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.)

 Let's have a look at a simplified example. Amongst other attributes, the training config has the following attributes:
 ```python
@@ -43,16 +43,16 @@ class DatasetConfig:
 ```

 This creates a hierarchical relationship where, for example assuming we have a `cfg` instance of `TrainPipelineConfig`, we can access the `repo_id` value with `cfg.dataset.repo_id`.
-From the command line, we can specify this value by using a very similar syntax `--dataset.repo_id=repo/id`.
+From the command line, we can specify this value with using a very similar syntax `--dataset.repo_id=repo/id`.

 By default, every field takes its default value specified in the dataclass. If a field doesn't have a default value, it needs to be specified either from the command line or from a config file – which path is also given in the command line (more in this below). In the example above, the `dataset` field doesn't have a default value which means it must be specified.


 ## Specifying values from the CLI

-Let's say that we want to train [Diffusion Policy](../src/lerobot/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:
+Let's say that we want to train [Diffusion Policy](../../lerobot/common/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --dataset.repo_id=lerobot/pusht \
    --policy.type=diffusion \
    --env.type=pusht
@@ -60,12 +60,12 @@ python -m lerobot.scripts.train \

 Let's break this down:
 - To specify the dataset, we just need to specify its `repo_id` on the hub which is the only required argument in the `DatasetConfig`. The rest of the fields have default values and in this case we are fine with those so we can just add the option `--dataset.repo_id=lerobot/pusht`.
- To specify the policy, we can just select diffusion policy using `--policy` appended with `.type`. Here, `.type` is a special argument which allows us to select config classes inheriting from `draccus.ChoiceRegistry` and that have been decorated with the `register_subclass()` method. To have a better explanation of this feature, have a look at this [Draccus demo](https://github.com/dlwh/draccus?tab=readme-ov-file#more-flexible-configuration-with-choice-types). In our code, we use this mechanism mainly to select policies, environments, robots, and some other components like optimizers. The policies available to select are located in [lerobot/policies](../src/lerobot/policies)
- Similarly, we select the environment with `--env.type=pusht`. The different environment configs are available in [`lerobot/envs/configs.py`](../src/lerobot/envs/configs.py)
+- To specify the policy, we can just select diffusion policy using `--policy` appended with `.type`. Here, `.type` is a special argument which allows us to select config classes inheriting from `draccus.ChoiceRegistry` and that have been decorated with the `register_subclass()` method. To have a better explanation of this feature, have a look at this [Draccus demo](https://github.com/dlwh/draccus?tab=readme-ov-file#more-flexible-configuration-with-choice-types). In our code, we use this mechanism mainly to select policies, environments, robots, and some other components like optimizers. The policies available to select are located in [lerobot/common/policies](../../lerobot/common/policies)
+- Similarly, we select the environment with `--env.type=pusht`. The different environment configs are available in [`lerobot/common/envs/configs.py`](../../lerobot/common/envs/configs.py)

-Let's see another example. Let's say you've been training [ACT](../src/lerobot/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:
+Let's see another example. Let's say you've been training [ACT](../../lerobot/common/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --policy.type=act \
    --dataset.repo_id=lerobot/aloha_sim_insertion_human \
    --env.type=aloha \
@@ -74,9 +74,9 @@ python -m lerobot.scripts.train \
 > Notice we added `--output_dir` to explicitly tell where to write outputs from this run (checkpoints, training state, configs etc.). This is not mandatory and if you don't specify it, a default directory will be created from the current date and time, env.type and policy.type. This will typically look like `outputs/train/2025-01-24/16-10-05_aloha_act`.

 We now want to train a different policy for aloha on another task. We'll change the dataset and use [lerobot/aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human) instead. Of course, we also need to change the task of the environment as well to match this other task.
-Looking at the [`AlohaEnv`](../src/lerobot/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:
+Looking at the [`AlohaEnv`](../../lerobot/common/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --policy.type=act \
    --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
    --env.type=aloha \
@@ -111,7 +111,7 @@ Now, let's assume that we want to reproduce the run just above. That run has pro

 We can then simply load the config values from this file using:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
    --output_dir=outputs/train/act_aloha_transfer_2
 ```
@@ -119,7 +119,7 @@ python -m lerobot.scripts.train \

 Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
    --output_dir=outputs/train/act_aloha_transfer_2
    --policy.n_action_steps=80
@@ -128,18 +128,18 @@ python -m lerobot.scripts.train \

 `--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running:
 ```bash
-python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
+python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht
 ```
 will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)


 ## Resume training

-Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to do that here.
+Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to that here.

 Let's reuse the command from the previous run and add a few more options:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --policy.type=act \
    --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
    --env.type=aloha \
@@ -155,7 +155,7 @@ INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100
 ```
 Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
    --resume=true
 ```
@@ -164,7 +164,7 @@ You should see from the logging that your training picks up from where it left o
 Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--steps`, which is 100 000 by default.
 You could double the number of steps of the previous run with:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
    --resume=true \
    --steps=200000
@@ -195,7 +195,7 @@ In addition to the features currently in Draccus, we've added a special `.path`

 For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --policy.path=lerobot/act_aloha_sim_transfer_cube_human \
    --dataset.repo_id=lerobot/aloha_sim_insertion_human \
    --env.type=aloha \
@@ -236,7 +236,7 @@ We'll summarize here the main use cases to remember from this tutorial.

 #### Train a policy from scratch – CLI
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --policy.type=act \  # <- select 'act' policy
    --env.type=pusht \  # <- select 'pusht' environment
    --dataset.repo_id=lerobot/pusht  # <- train on this dataset
@@ -244,14 +244,14 @@ python -m lerobot.scripts.train \

 #### Train a policy from scratch - config file + CLI
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --config_path=path/to/pretrained_model \  # <- can also be a repo_id
    --policy.n_action_steps=80  # <- you may still override values
 ```

 #### Resume/continue a training run
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --config_path=checkpoint/pretrained_model/ \
    --resume=true \
    --steps=200000  # <- you can change some training parameters
@@ -259,7 +259,7 @@ python -m lerobot.scripts.train \

 #### Fine-tuning
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
    --policy.path=lerobot/act_aloha_sim_transfer_cube_human \  # <- can also be a local path to a checkpoint
    --dataset.repo_id=lerobot/aloha_sim_insertion_human \
    --env.type=aloha \
@@ -43,19 +43,21 @@ conda create -y -n lerobot python=3.10 && conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git ~/lerobot
 ```

-6. When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-7. Install LeRobot with stretch dependencies:
+6. Install LeRobot with stretch dependencies:
 ```bash
 cd ~/lerobot && pip install -e ".[stretch]"
 ```

 > **Note:** If you get this message, you can ignore it: `ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.`

-8. Run a [system check](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#system-check) to make sure your robot is ready:
+For Linux only (not Mac), install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+
+7. Run a [system check](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#system-check) to make sure your robot is ready:
 ```bash
 stretch_system_check.py
 ```
@@ -99,11 +101,9 @@ This is equivalent to running `stretch_robot_home.py`
 > **Note:** If you run any of the LeRobot scripts below and Stretch is not properly homed, it will automatically home/calibrate first.

 **Teleoperate**
-Before trying teleoperation, you need to activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation).
+Before trying teleoperation, you need activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation).

 Now try out teleoperation (see above documentation to learn about the gamepad controls):
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
 ```bash
 python lerobot/scripts/control_robot.py \
    --robot.type=stretch \
@@ -30,16 +30,18 @@ conda create -y -n lerobot python=3.10 && conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git ~/lerobot
 ```

-5. When using `miniconda`, install `ffmpeg` in your environment:
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-6. Install LeRobot with dependencies for the Aloha motors (dynamixel) and cameras (intelrealsense):
+5. Install LeRobot with dependencies for the Aloha motors (dynamixel) and cameras (intelrealsense):
 ```bash
 cd ~/lerobot && pip install -e ".[dynamixel, intelrealsense]"
 ```

+For Linux only (not Mac), install extra dependencies for recording datasets:
+```bash
+conda install -y -c conda-forge ffmpeg
+pip uninstall -y opencv-python
+conda install -y -c conda-forge "opencv>=4.10.0"
+```
+
 ## Teleoperate

 **/!\ FOR SAFETY, READ THIS /!\**
@@ -48,9 +50,6 @@ Teleoperation consists in manually operating the leader arms to move the followe
 2. Our code assumes that your robot has been assembled following Trossen Robotics instructions. This allows us to skip calibration, as we use the pre-defined calibration files in `.cache/calibration/aloha_default`. If you replace a motor, make sure you follow the exact instructions from Trossen Robotics.

 By running the following code, you can start your first **SAFE** teleoperation:
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
-
 ```bash
 python lerobot/scripts/control_robot.py \
  --robot.type=aloha \
@@ -58,7 +57,7 @@ python lerobot/scripts/control_robot.py \
  --control.type=teleoperate
 ```

-By adding `--robot.max_relative_target=5`, we override the default value for `max_relative_target` defined in [`AlohaRobotConfig`](lerobot/robot_devices/robots/configs.py). It is expected to be `5` to limit the magnitude of the movement for more safety, but the teleoperation won't be smooth. When you feel confident, you can disable this limit by adding `--robot.max_relative_target=null` to the command line:
+By adding `--robot.max_relative_target=5`, we override the default value for `max_relative_target` defined in [`AlohaRobotConfig`](lerobot/common/robot_devices/robots/configs.py). It is expected to be `5` to limit the magnitude of the movement for more safety, but the teleoperation won't be smooth. When you feel confident, you can disable this limit by adding `--robot.max_relative_target=null` to the command line:
 ```bash
 python lerobot/scripts/control_robot.py \
  --robot.type=aloha \
@@ -107,7 +106,7 @@ echo ${HF_USER}/aloha_test

 If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with:
 ```bash
-python -m lerobot.scripts.visualize_dataset_html \
+python lerobot/scripts/visualize_dataset_html.py \
  --repo-id ${HF_USER}/aloha_test
 ```

@@ -129,9 +128,9 @@ python lerobot/scripts/control_robot.py \

 ## Train a policy

-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 ```bash
-python -m lerobot.scripts.train \
+python lerobot/scripts/train.py \
  --dataset.repo_id=${HF_USER}/aloha_test \
  --policy.type=act \
  --output_dir=outputs/train/act_aloha_test \
@@ -142,7 +141,7 @@ python -m lerobot.scripts.train \

 Let's explain it:
 1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/aloha_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
 4. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
 5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.

@@ -152,7 +151,7 @@ Training should take several hours. You will find checkpoints in `outputs/train/

 ## Evaluate your policy

-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../src/lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
+You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
 ```bash
 python lerobot/scripts/control_robot.py \
  --robot.type=aloha \
@@ -22,7 +22,7 @@ from pathlib import Path

 from torchvision.transforms import ToPILImage, v2

-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

 dataset_repo_id = "lerobot/aloha_static_screw_driver"

@@ -26,8 +26,8 @@ import math

 import torch

-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy


 def main():
@@ -66,7 +66,7 @@ def main():
    print(f"Number of episodes in full dataset: {total_episodes}")
    print(f"Number of episodes in training dataset (90% subset): {len(train_episodes)}")
    print(f"Number of episodes in validation dataset (10% subset): {len(val_episodes)}")
-    # - Load train and val datasets
+    # - Load train an val datasets
    train_dataset = LeRobotDataset(
        "lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps
    )
@@ -1,105 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Replays the actions of an episode from a dataset on a robot.
-
-Example:
-
-```shell
-python -m 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.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()
@@ -1,90 +0,0 @@
-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.record import record_loop
-from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-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"
-
-# Create the robot and teleoperator configurations
-robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
-robot = LeKiwiClient(robot_config)
-
-policy = ACTPolicy.from_pretrained("<hf_username>/<policy_repo_id>")
-
-# 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>/<eval_dataset_repo_id>",
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# 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()
-
-_init_rerun(session_name="recording")
-
-listener, events = init_keyboard_listener()
-
-if not robot.is_connected:
-    raise ValueError("Robot is not connected!")
-
-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}")
-
-    # Run the policy inference loop
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        policy=policy,
-        dataset=dataset,
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
-    )
-
-    # Logic for reset env
-    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,
-        )
-
-    if events["rerecord_episode"]:
-        log_say("Re-record episode")
-        events["rerecord_episode"] = False
-        events["exit_early"] = False
-        dataset.clear_episode_buffer()
-        continue
-
-    dataset.save_episode()
-    recorded_episodes += 1
-
-# Upload to hub and clean up
-dataset.push_to_hub()
-
-robot.disconnect()
-listener.stop()
@@ -1,101 +0,0 @@
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.record import record_loop
-from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
-from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
-from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
-from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
-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 = 3
-FPS = 30
-EPISODE_TIME_SEC = 30
-RESET_TIME_SEC = 10
-TASK_DESCRIPTION = "My task description"
-
-# 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()
-
-robot = LeKiwiClient(robot_config)
-leader_arm = SO100Leader(leader_arm_config)
-keyboard = KeyboardTeleop(keyboard_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,
-)
-
-# 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(session_name="lekiwi_record")
-
-listener, events = init_keyboard_listener()
-
-if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-    raise ValueError("Robot, leader arm of keyboard is not connected!")
-
-recorded_episodes = 0
-while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-    log_say(f"Recording episode {recorded_episodes}")
-
-    # Run the 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,
-    )
-
-    # Logic for reset env
-    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,
-        )
-
-    if events["rerecord_episode"]:
-        log_say("Re-record episode")
-        events["rerecord_episode"] = False
-        events["exit_early"] = False
-        dataset.clear_episode_buffer()
-        continue
-
-    dataset.save_episode()
-    recorded_episodes += 1
-
-# Upload to hub and clean up
-dataset.push_to_hub()
-
-robot.disconnect()
-leader_arm.disconnect()
-keyboard.disconnect()
-listener.stop()
@@ -1,33 +0,0 @@
-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.robot_utils import busy_wait
-from lerobot.utils.utils import log_say
-
-EPISODE_IDX = 0
-
-robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
-robot = LeKiwiClient(robot_config)
-
-dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[EPISODE_IDX])
-actions = dataset.hf_dataset.select_columns("action")
-
-robot.connect()
-
-if not robot.is_connected:
-    raise ValueError("Robot is not connected!")
-
-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(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
-
-robot.disconnect()
@@ -1,47 +0,0 @@
-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")
-
-robot = LeKiwiClient(robot_config)
-leader_arm = SO100Leader(teleop_arm_config)
-keyboard = KeyboardTeleop(keyboard_config)
-
-# 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(session_name="lekiwi_teleop")
-
-if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-    raise ValueError("Robot, leader arm of keyboard is not connected!")
-
-while True:
-    t0 = time.perf_counter()
-
-    observation = robot.get_observation()
-
-    arm_action = leader_arm.get_action()
-    arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
-
-    keyboard_keys = keyboard.get_action()
-    base_action = robot._from_keyboard_to_base_action(keyboard_keys)
-
-    log_rerun_data(observation, {**arm_action, **base_action})
-
-    action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
-
-    robot.send_action(action)
-
-    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
@@ -0,0 +1,243 @@
+# 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 shutil
+from pathlib import Path
+
+import numpy as np
+from huggingface_hub import HfApi
+
+from lerobot.common.constants import HF_LEROBOT_HOME
+from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
+from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
+
+PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface."
+PUSHT_FEATURES = {
+    "observation.state": {
+        "dtype": "float32",
+        "shape": (2,),
+        "names": {
+            "axes": ["x", "y"],
+        },
+    },
+    "action": {
+        "dtype": "float32",
+        "shape": (2,),
+        "names": {
+            "axes": ["x", "y"],
+        },
+    },
+    "next.reward": {
+        "dtype": "float32",
+        "shape": (1,),
+        "names": None,
+    },
+    "next.success": {
+        "dtype": "bool",
+        "shape": (1,),
+        "names": None,
+    },
+    "observation.environment_state": {
+        "dtype": "float32",
+        "shape": (16,),
+        "names": [
+            "keypoints",
+        ],
+    },
+    "observation.image": {
+        "dtype": None,
+        "shape": (3, 96, 96),
+        "names": [
+            "channels",
+            "height",
+            "width",
+        ],
+    },
+}
+
+
+def build_features(mode: str) -> dict:
+    features = PUSHT_FEATURES
+    if mode == "keypoints":
+        features.pop("observation.image")
+    else:
+        features.pop("observation.environment_state")
+        features["observation.image"]["dtype"] = mode
+
+    return features
+
+
+def load_raw_dataset(zarr_path: Path):
+    try:
+        from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import (
+            ReplayBuffer as DiffusionPolicyReplayBuffer,
+        )
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+
+    zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)
+    return zarr_data
+
+
+def calculate_coverage(zarr_data):
+    try:
+        import pymunk
+        from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+
+    block_pos = zarr_data["state"][:, 2:4]
+    block_angle = zarr_data["state"][:, 4]
+
+    num_frames = len(block_pos)
+
+    coverage = np.zeros((num_frames,), dtype=np.float32)
+    # 8 keypoints with 2 coords each
+    keypoints = np.zeros((num_frames, 16), dtype=np.float32)
+
+    # Set x, y, theta (in radians)
+    goal_pos_angle = np.array([256, 256, np.pi / 4])
+    goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
+
+    for i in range(num_frames):
+        space = pymunk.Space()
+        space.gravity = 0, 0
+        space.damping = 0
+
+        # Add walls.
+        walls = [
+            PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
+            PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
+            PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
+            PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
+        ]
+        space.add(*walls)
+
+        block_body, block_shapes = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
+        goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
+        block_geom = pymunk_to_shapely(block_body, block_body.shapes)
+        intersection_area = goal_geom.intersection(block_geom).area
+        goal_area = goal_geom.area
+        coverage[i] = intersection_area / goal_area
+        keypoints[i] = PushTEnv.get_keypoints(block_shapes).flatten()
+
+    return coverage, keypoints
+
+
+def calculate_success(coverage: float, success_threshold: float):
+    return coverage > success_threshold
+
+
+def calculate_reward(coverage: float, success_threshold: float):
+    return np.clip(coverage / success_threshold, 0, 1)
+
+
+def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = True):
+    if mode not in ["video", "image", "keypoints"]:
+        raise ValueError(mode)
+
+    if (HF_LEROBOT_HOME / repo_id).exists():
+        shutil.rmtree(HF_LEROBOT_HOME / repo_id)
+
+    if not raw_dir.exists():
+        download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw")
+
+    zarr_data = load_raw_dataset(zarr_path=raw_dir / "pusht_cchi_v7_replay.zarr")
+
+    env_state = zarr_data["state"][:]
+    agent_pos = env_state[:, :2]
+
+    action = zarr_data["action"][:]
+    image = zarr_data["img"]  # (b, h, w, c)
+
+    if image.dtype == np.float32 and image.max() == np.float32(255):
+        # HACK: images are loaded as float32 but they actually encode uint8 data
+        image = image.astype(np.uint8)
+
+    episode_data_index = {
+        "from": np.concatenate(([0], zarr_data.meta["episode_ends"][:-1])),
+        "to": zarr_data.meta["episode_ends"],
+    }
+
+    # Calculate success and reward based on the overlapping area
+    # of the T-object and the T-area.
+    coverage, keypoints = calculate_coverage(zarr_data)
+    success = calculate_success(coverage, success_threshold=0.95)
+    reward = calculate_reward(coverage, success_threshold=0.95)
+
+    features = build_features(mode)
+    dataset = LeRobotDataset.create(
+        repo_id=repo_id,
+        fps=10,
+        robot_type="2d pointer",
+        features=features,
+        image_writer_threads=4,
+    )
+    episodes = range(len(episode_data_index["from"]))
+    for ep_idx in episodes:
+        from_idx = episode_data_index["from"][ep_idx]
+        to_idx = episode_data_index["to"][ep_idx]
+        num_frames = to_idx - from_idx
+
+        for frame_idx in range(num_frames):
+            i = from_idx + frame_idx
+            idx = i + (frame_idx < num_frames - 1)
+            frame = {
+                "action": action[i],
+                # Shift reward and success by +1 until the last item of the episode
+                "next.reward": reward[idx : idx + 1],
+                "next.success": success[idx : idx + 1],
+                "task": PUSHT_TASK,
+            }
+
+            frame["observation.state"] = agent_pos[i]
+
+            if mode == "keypoints":
+                frame["observation.environment_state"] = keypoints[i]
+            else:
+                frame["observation.image"] = image[i]
+
+            dataset.add_frame(frame)
+
+        dataset.save_episode()
+
+    if push_to_hub:
+        dataset.push_to_hub()
+        hub_api = HfApi()
+        hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
+
+
+if __name__ == "__main__":
+    # To try this script, modify the repo id with your own HuggingFace user (e.g cadene/pusht)
+    repo_id = "lerobot/pusht"
+
+    modes = ["video", "image", "keypoints"]
+    # Uncomment if you want to try with a specific mode
+    # modes = ["video"]
+    # modes = ["image"]
+    # modes = ["keypoints"]
+
+    raw_dir = Path("data/lerobot-raw/pusht_raw")
+    for mode in modes:
+        if mode in ["image", "keypoints"]:
+            repo_id += f"_{mode}"
+
+        # download and load raw dataset, create LeRobotDataset, populate it, push to hub
+        main(raw_dir, repo_id=repo_id, mode=mode)
+
+        # Uncomment if you want to load the local dataset and explore it
+        # dataset = LeRobotDataset(repo_id=repo_id)
+        # breakpoint()
@@ -167,25 +167,30 @@ available_datasets = sorted(
    set(itertools.chain(*available_datasets_per_env.values(), available_real_world_datasets))
 )

-# lists all available policies from `lerobot/policies`
-available_policies = ["act", "diffusion", "tdmpc", "vqbet"]
+# lists all available policies from `lerobot/common/policies`
+available_policies = [
+    "act",
+    "diffusion",
+    "tdmpc",
+    "vqbet",
+]

-# lists all available robots from `lerobot/robot_devices/robots`
+# lists all available robots from `lerobot/common/robot_devices/robots`
 available_robots = [
    "koch",
    "koch_bimanual",
    "aloha",
    "so100",
-    "so101",
+    "moss",
 ]

-# lists all available cameras from `lerobot/robot_devices/cameras`
+# lists all available cameras from `lerobot/common/robot_devices/cameras`
 available_cameras = [
    "opencv",
    "intelrealsense",
 ]

-# lists all available motors from `lerobot/robot_devices/motors`
+# lists all available motors from `lerobot/common/robot_devices/motors`
 available_motors = [
    "dynamixel",
    "feetech",
@@ -17,20 +17,11 @@ from pathlib import Path

 from huggingface_hub.constants import HF_HOME

-OBS_ENV_STATE = "observation.environment_state"
-OBS_STATE = "observation.state"
+OBS_ENV = "observation.environment_state"
+OBS_ROBOT = "observation.state"
 OBS_IMAGE = "observation.image"
 OBS_IMAGES = "observation.images"
 ACTION = "action"
-OBS_IMAGE_2 = "observation.image2"
-OBS_IMAGE_3 = "observation.image3"
-OBS_IMAGE_4 = "observation.image4"
-REWARD = "next.reward"
-
-ROBOTS = "robots"
-TASK = "task"
-ROBOT_TYPE = "robot_type"
-TELEOPERATORS = "teleoperators"

 # files & directories
 CHECKPOINTS_DIR = "checkpoints"
@@ -43,16 +34,12 @@ OPTIMIZER_STATE = "optimizer_state.safetensors"
 OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
 SCHEDULER_STATE = "scheduler_state.json"

+# cache dir
+default_cache_path = Path(HF_HOME) / "lerobot"
+HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser()
+
 if "LEROBOT_HOME" in os.environ:
    raise ValueError(
        f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n"
        "'LEROBOT_HOME' is deprecated, please use 'HF_LEROBOT_HOME' instead."
    )
-
-# cache dir
-default_cache_path = Path(HF_HOME) / "lerobot"
-HF_LEROBOT_HOME = Path(os.getenv("HF_LEROBOT_HOME", default_cache_path)).expanduser()
-
-# calibration dir
-default_calibration_path = HF_LEROBOT_HOME / "calibration"
-HF_LEROBOT_CALIBRATION = Path(os.getenv("HF_LEROBOT_CALIBRATION", default_calibration_path)).expanduser()
@@ -20,7 +20,7 @@ The dataset you requested ({repo_id}) is in {version} format.
 We introduced a new format since v2.0 which is not backward compatible with v1.x.
 Please, use our conversion script. Modify the following command with your own task description:
 ```
-python -m lerobot.datasets.v2.convert_dataset_v1_to_v2 \\
+python lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py \\
    --repo-id {repo_id} \\
    --single-task "TASK DESCRIPTION."  # <---- /!\\ Replace TASK DESCRIPTION /!\\
 ```
@@ -40,7 +40,7 @@ The dataset you requested ({repo_id}) is in {version} format.
 While current version of LeRobot is backward-compatible with it, the version of your dataset still uses global
 stats instead of per-episode stats. Update your dataset stats to the new format using this command:
 ```
-python -m lerobot.datasets.v21.convert_dataset_v20_to_v21 --repo-id={repo_id}
+python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py --repo-id={repo_id}
 ```

 If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)
@@ -15,7 +15,7 @@
 # limitations under the License.
 import numpy as np

-from lerobot.datasets.utils import load_image_as_numpy
+from lerobot.common.datasets.utils import load_image_as_numpy


 def estimate_num_samples(
@@ -125,30 +125,9 @@ def _assert_type_and_shape(stats_list: list[dict[str, dict]]):

 def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]:
    """Aggregates stats for a single feature."""
-    # Filter out stats that don't have required keys
-    valid_stats = []
-    for s in stats_ft_list:
-        if all(key in s for key in ["mean", "std", "count", "min", "max"]):
-            valid_stats.append(s)
-        else:
-            # If count is missing, add it with a default value
-            if "count" not in s:
-                s["count"] = np.array([1])  # Default count
-            valid_stats.append(s)
-    
-    if not valid_stats:
-        # If no valid stats, return empty stats
-        return {
-            "min": np.array([0]),
-            "max": np.array([0]),
-            "mean": np.array([0]),
-            "std": np.array([0]),
-            "count": np.array([0]),
-        }
-    
-    means = np.stack([s["mean"] for s in valid_stats])
-    variances = np.stack([s["std"] ** 2 for s in valid_stats])
-    counts = np.stack([s["count"] for s in valid_stats])
+    means = np.stack([s["mean"] for s in stats_ft_list])
+    variances = np.stack([s["std"] ** 2 for s in stats_ft_list])
+    counts = np.stack([s["count"] for s in stats_ft_list])
    total_count = counts.sum(axis=0)

    # Prepare weighted mean by matching number of dimensions
@@ -163,13 +142,12 @@ def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, d
    delta_means = means - total_mean
    weighted_variances = (variances + delta_means**2) * counts
    total_variance = weighted_variances.sum(axis=0) / total_count
-    total_std = np.sqrt(total_variance)

    return {
-        "min": np.min(np.stack([s["min"] for s in valid_stats]), axis=0),
-        "max": np.max(np.stack([s["max"] for s in valid_stats]), axis=0),
+        "min": np.min(np.stack([s["min"] for s in stats_ft_list]), axis=0),
+        "max": np.max(np.stack([s["max"] for s in stats_ft_list]), axis=0),
        "mean": total_mean,
-        "std": total_std,
+        "std": np.sqrt(total_variance),
        "count": total_count,
    }

@@ -18,22 +18,20 @@ from pprint import pformat

 import torch

-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.train import TrainPipelineConfig
-from lerobot.datasets.lerobot_dataset import (
+from lerobot.common.datasets.lerobot_dataset import (
    LeRobotDataset,
    LeRobotDatasetMetadata,
    MultiLeRobotDataset,
 )
-from lerobot.datasets.transforms import ImageTransforms
+from lerobot.common.datasets.transforms import ImageTransforms
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig

 IMAGENET_STATS = {
    "mean": [[[0.485]], [[0.456]], [[0.406]]],  # (c,1,1)
    "std": [[[0.229]], [[0.224]], [[0.225]]],  # (c,1,1)
 }

-from lerobot.datasets.utils_must import EPISODES_DATASET_MAPPING, FEATURE_KEYS_MAPPING
-

 def resolve_delta_timestamps(
    cfg: PreTrainedConfig, ds_meta: LeRobotDatasetMetadata
@@ -51,7 +49,7 @@ def resolve_delta_timestamps(
                "observation.state": [-0.04, -0.02, 0]
                "observation.action": [-0.02, 0, 0.02]
            }
-            returns `None` if the resulting dict is empty.
+            returns `None` if the the resulting dict is empty.
    """
    delta_timestamps = {}
    for key in ds_meta.features:
@@ -83,87 +81,37 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
    image_transforms = (
        ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
    )
-    if "," in cfg.dataset.repo_id:
-        repo_id = cfg.dataset.repo_id.split(",")
-        repo_id = [r for r in repo_id if r]
-    else:
-        repo_id = cfg.dataset.repo_id
-    sampling_weights = cfg.dataset.sampling_weights.split(",") if cfg.dataset.sampling_weights else None
-    feature_keys_mapping = FEATURE_KEYS_MAPPING
-    if isinstance(repo_id, str):
-        revision = getattr(cfg.dataset, "revision", None)
+
+    if isinstance(cfg.dataset.repo_id, str):
        ds_meta = LeRobotDatasetMetadata(
-            cfg.dataset.repo_id,
-            feature_keys_mapping=feature_keys_mapping,
-            revision=revision,
+            cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision
        )
        delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
        dataset = LeRobotDataset(
            cfg.dataset.repo_id,
-            root=getattr(cfg.dataset, "root", None),
+            root=cfg.dataset.root,
            episodes=cfg.dataset.episodes,
            delta_timestamps=delta_timestamps,
            image_transforms=image_transforms,
-            revision=revision,
+            revision=cfg.dataset.revision,
            video_backend=cfg.dataset.video_backend,
-            download_videos=True,
-            feature_keys_mapping=feature_keys_mapping,
-            max_action_dim=cfg.dataset.max_action_dim,
-            max_state_dim=cfg.dataset.max_state_dim,
-            max_num_images=cfg.dataset.max_num_images,
-            max_image_dim=cfg.dataset.max_image_dim,
        )
    else:
-        delta_timestamps = {}
-        episodes = {}
-        for i in range(len(repo_id)):
-            ds_meta = LeRobotDatasetMetadata(
-                repo_id[i],
-                feature_keys_mapping=feature_keys_mapping,
-            )  # FIXME(mshukor): ?
-            delta_timestamps[repo_id[i]] = resolve_delta_timestamps(cfg.policy, ds_meta)
-            episodes[repo_id[i]] = EPISODES_DATASET_MAPPING.get(repo_id[i], cfg.dataset.episodes)
-        # training_features = TRAINING_FEATURES.get(cfg.dataset.features_version, None)
-        # FIXME: (jadechoghari): check support for training features
-        training_features = None
+        raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")
        dataset = MultiLeRobotDataset(
-            repo_id,
+            cfg.dataset.repo_id,
            # TODO(aliberts): add proper support for multi dataset
-            episodes=episodes,
-            delta_timestamps=delta_timestamps,
+            # delta_timestamps=delta_timestamps,
            image_transforms=image_transforms,
            video_backend=cfg.dataset.video_backend,
-            download_videos=True,
-            sampling_weights=sampling_weights,
-            feature_keys_mapping=feature_keys_mapping,
-            max_action_dim=cfg.policy.max_action_dim,
-            max_state_dim=cfg.policy.max_state_dim,
-            max_num_images=cfg.dataset.max_num_images,
-            max_image_dim=cfg.dataset.max_image_dim,
-            train_on_all_features=cfg.dataset.train_on_all_features,
-            training_features=training_features,
-            discard_first_n_frames=cfg.dataset.discard_first_n_frames,
-            min_fps=cfg.dataset.min_fps,
-            max_fps=cfg.dataset.max_fps,
-            discard_first_idle_frames=cfg.dataset.discard_first_idle_frames,
-            motion_threshold=cfg.dataset.motion_threshold,
-            motion_window_size=cfg.dataset.motion_window_size,
-            motion_buffer=cfg.dataset.motion_buffer,
        )
        logging.info(
            "Multiple datasets were provided. Applied the following index mapping to the provided datasets: "
            f"{pformat(dataset.repo_id_to_index, indent=2)}"
        )
+
    if cfg.dataset.use_imagenet_stats:
-        # Initialize stats structure if it doesn't exist
-        if dataset.meta.stats is None:
-            dataset.meta.stats = {}
-        
        for key in dataset.meta.camera_keys:
-            # Initialize stats for this camera key if it doesn't exist
-            if key not in dataset.meta.stats or dataset.meta.stats[key] is None:
-                dataset.meta.stats[key] = {}
-                
            for stats_type, stats in IMAGENET_STATS.items():
                dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)

@@ -106,7 +106,7 @@ def worker_process(queue: queue.Queue, num_threads: int):
 class AsyncImageWriter:
    """
    This class abstract away the initialisation of processes or/and threads to
-    save images on disk asynchronously, which is critical to control a robot and record data
+    save images on disk asynchrounously, which is critical to control a robot and record data
    at a high frame rate.

    When `num_processes=0`, it creates a threads pool of size `num_threads`.
@@ -15,7 +15,6 @@
 # limitations under the License.
 import contextlib
 import logging
-import os
 import shutil
 from pathlib import Path
 from typing import Callable
@@ -31,25 +30,17 @@ from huggingface_hub import HfApi, snapshot_download
 from huggingface_hub.constants import REPOCARD_NAME
 from huggingface_hub.errors import RevisionNotFoundError

-from lerobot.constants import (
-    ACTION,
-    HF_LEROBOT_HOME,
-    OBS_ENV_STATE,
-    OBS_STATE,
-)
-from lerobot.datasets.compute_stats import (  # aggregate_stats_per_robot_type,
-    aggregate_stats,
-    compute_episode_stats,
-)
-from lerobot.datasets.image_writer import AsyncImageWriter, write_image
-from lerobot.datasets.utils import (
+from lerobot.common.constants import HF_LEROBOT_HOME
+from lerobot.common.datasets.compute_stats import aggregate_stats, compute_episode_stats
+from lerobot.common.datasets.image_writer import AsyncImageWriter, write_image
+from lerobot.common.datasets.utils import (
    DEFAULT_FEATURES,
    DEFAULT_IMAGE_PATH,
    INFO_PATH,
    TASKS_PATH,
-    _validate_feature_names,
    append_jsonlines,
    backward_compatible_episodes_stats,
+    check_delta_timestamps,
    check_timestamps_sync,
    check_version_compatibility,
    create_empty_dataset_info,
@@ -57,6 +48,7 @@ from lerobot.datasets.utils import (
    embed_images,
    get_delta_indices,
    get_episode_data_index,
+    get_features_from_robot,
    get_hf_features_from_features,
    get_safe_version,
    hf_transform_to_torch,
@@ -66,53 +58,22 @@ from lerobot.datasets.utils import (
    load_info,
    load_stats,
    load_tasks,
-    map_dict_keys,
    validate_episode_buffer,
    validate_frame,
    write_episode,
    write_episode_stats,
    write_info,
    write_json,
-    # keep_datasets_with_the_same_features_per_robot_type,
-    # map_dict_pad_keys,
-    # keep_datasets_with_valid_fps,
-    # find_start_of_motion,
 )
-
-# mustafa stuff here
-from lerobot.datasets.utils_must import (
-    OBS_IMAGE,
-    OBS_IMAGE_2,
-    OBS_IMAGE_3,
-    ROBOT_TYPE_KEYS_MAPPING,
-    TASKS_KEYS_MAPPING,
-    aggregate_stats_per_robot_type,
-    create_padded_features,
-    find_start_of_motion,
-    keep_datasets_with_the_same_features_per_robot_type,
-    keep_datasets_with_valid_fps,
-    map_dict_keys,
-    pad_tensor,
-    reshape_features_to_max_dim,
-)
-from lerobot.datasets.video_utils import (
+from lerobot.common.datasets.video_utils import (
    VideoFrame,
    decode_video_frames,
    encode_video_frames,
-    get_safe_default_codec,
    get_video_info,
 )
+from lerobot.common.robot_devices.robots.utils import Robot

 CODEBASE_VERSION = "v2.1"
-LEROBOT_HOME = Path(os.getenv("LEROBOT_HOME", "~/.cache/huggingface/lerobot")).expanduser()
-
-
-def find_start_of_motion(velocities, window_size, threshold, motion_buffer):
-    for t in range(len(velocities) - window_size):
-        window_mean = velocities[t : t + window_size].mean()
-        if window_mean > threshold:
-            return max(0, t - motion_buffer)  # include slight context before motion
-    return 0


 class LeRobotDatasetMetadata:
@@ -120,13 +81,10 @@ class LeRobotDatasetMetadata:
        self,
        repo_id: str,
        root: str | Path | None = None,
-        local_files_only: bool = False,
-        feature_keys_mapping: dict[str, str] | None = None,
        revision: str | None = None,
        force_cache_sync: bool = False,
    ):
        self.repo_id = repo_id
-        self.local_files_only = local_files_only
        self.revision = revision if revision else CODEBASE_VERSION
        self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id

@@ -141,27 +99,18 @@ class LeRobotDatasetMetadata:
            (self.root / "meta").mkdir(exist_ok=True, parents=True)
            self.pull_from_repo(allow_patterns="meta/")
            self.load_metadata()
-        # added by mshukor
-        self.feature_keys_mapping = feature_keys_mapping.get(repo_id, None) if feature_keys_mapping else None
-        self.inverse_feature_keys_mapping = (
-            {v: k for k, v in self.feature_keys_mapping.items() if v} if self.feature_keys_mapping else {}
-        )
-        self.info["features"] = map_dict_keys(
-            self.info["features"], feature_keys_mapping=self.feature_keys_mapping
-        )

    def load_metadata(self):
        self.info = load_info(self.root)
        check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
        self.tasks, self.task_to_task_index = load_tasks(self.root)
        self.episodes = load_episodes(self.root)
-        # Force all datasets to use v2.1 format (episodes_stats.jsonl) to avoid missing stats.json issues, because I converted all the datasets to v2.1 format. 
-        # if self._version < packaging.version.parse("v2.1"):
-        #     self.stats = load_stats(self.root)
-        #     self.episodes_stats = backward_compatible_episodes_stats(self.stats, self.episodes)
-        # else:
-        self.episodes_stats = load_episodes_stats(self.root)
-        self.stats = aggregate_stats(list(self.episodes_stats.values()))
+        if self._version < packaging.version.parse("v2.1"):
+            self.stats = load_stats(self.root)
+            self.episodes_stats = backward_compatible_episodes_stats(self.stats, self.episodes)
+        else:
+            self.episodes_stats = load_episodes_stats(self.root)
+            self.stats = aggregate_stats(list(self.episodes_stats.values()))

    def pull_from_repo(
        self,
@@ -228,15 +177,7 @@ class LeRobotDatasetMetadata:
    @property
    def video_keys(self) -> list[str]:
        """Keys to access visual modalities stored as videos."""
-        # changed
-        keys = []
-        for key, ft in self.features.items():
-            key_ = (
-                self.inverse_feature_keys_mapping.get(key, key) if self.inverse_feature_keys_mapping else key
-            )
-            if ft["dtype"] == "video":
-                keys.append(key_)
-        return keys
+        return [key for key, ft in self.features.items() if ft["dtype"] == "video"]

    @property
    def camera_keys(self) -> list[str]:
@@ -362,9 +303,10 @@ class LeRobotDatasetMetadata:
        cls,
        repo_id: str,
        fps: int,
-        features: dict,
-        robot_type: str | None = None,
        root: str | Path | None = None,
+        robot: Robot | None = None,
+        robot_type: str | None = None,
+        features: dict | None = None,
        use_videos: bool = True,
    ) -> "LeRobotDatasetMetadata":
        """Creates metadata for a LeRobotDataset."""
@@ -374,13 +316,33 @@ class LeRobotDatasetMetadata:

        obj.root.mkdir(parents=True, exist_ok=False)

-        # TODO(aliberts, rcadene): implement sanity check for features
-        features = {**features, **DEFAULT_FEATURES}
-        _validate_feature_names(features)
+        if robot is not None:
+            features = get_features_from_robot(robot, use_videos)
+            robot_type = robot.robot_type
+            if not all(cam.fps == fps for cam in robot.cameras.values()):
+                logging.warning(
+                    f"Some cameras in your {robot.robot_type} robot don't have an fps matching the fps of your dataset."
+                    "In this case, frames from lower fps cameras will be repeated to fill in the blanks."
+                )
+        elif features is None:
+            raise ValueError(
+                "Dataset features must either come from a Robot or explicitly passed upon creation."
+            )
+        else:
+            # TODO(aliberts, rcadene): implement sanity check for features
+            features = {**features, **DEFAULT_FEATURES}
+
+            # check if none of the features contains a "/" in their names,
+            # as this would break the dict flattening in the stats computation, which uses '/' as separator
+            for key in features:
+                if "/" in key:
+                    raise ValueError(f"Feature names should not contain '/'. Found '/' in feature '{key}'.")
+
+            features = {**features, **DEFAULT_FEATURES}

        obj.tasks, obj.task_to_task_index = {}, {}
        obj.episodes_stats, obj.stats, obj.episodes = {}, {}, {}
-        obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, features, use_videos, robot_type)
+        obj.info = create_empty_dataset_info(CODEBASE_VERSION, fps, robot_type, features, use_videos)
        if len(obj.video_keys) > 0 and not use_videos:
            raise ValueError()
        write_json(obj.info, obj.root / INFO_PATH)
@@ -401,19 +363,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
        force_cache_sync: bool = False,
        download_videos: bool = True,
        video_backend: str | None = None,
-        local_files_only: bool = False,
-        # new thing by M
-        feature_keys_mapping: dict[str, str] | None = None,
-        max_action_dim: int = None,
-        max_state_dim: int = None,
-        max_num_images: int = None,
-        max_image_dim: int = None,
-        training_features: list | None = None,
-        discard_first_n_frames: int = 0,
-        discard_first_idle_frames: bool = False,
-        motion_threshold: float = 5e-2,
-        motion_window_size: int = 10,
-        motion_buffer: int = 3,
    ):
        """
        2 modes are available for instantiating this class, depending on 2 different use cases:
@@ -429,7 +378,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
              the dataset from that address and load it, pending your dataset is compliant with
              codebase_version v2.0. If your dataset has been created before this new format, you will be
              prompted to convert it using our conversion script from v1.6 to v2.0, which you can find at
-              lerobot/datasets/v2/convert_dataset_v1_to_v2.py.
+              lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py.


        2. Your dataset doesn't already exists (either on local disk or on the Hub): you can create an empty
@@ -513,7 +462,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
            download_videos (bool, optional): Flag to download the videos. Note that when set to True but the
                video files are already present on local disk, they won't be downloaded again. Defaults to
                True.
-            video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec when available int the platform; otherwise, defaults to 'pyav'.
+            video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec.
                You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
        """
        super().__init__()
@@ -524,38 +473,18 @@ class LeRobotDataset(torch.utils.data.Dataset):
        self.episodes = episodes
        self.tolerance_s = tolerance_s
        self.revision = revision if revision else CODEBASE_VERSION
-        self.video_backend = video_backend if video_backend else get_safe_default_codec()
+        self.video_backend = video_backend if video_backend else "torchcodec"
        self.delta_indices = None

-        # by mshukor
-        self.training_features = training_features
-        self.discard_first_n_frames = discard_first_n_frames
-        self.discard_first_idle_frames = discard_first_idle_frames
-        self.motion_threshold = motion_threshold
-        self.motion_window_size = motion_window_size
-        self.motion_buffer = motion_buffer
-
        # Unused attributes
        self.image_writer = None
        self.episode_buffer = None

        self.root.mkdir(exist_ok=True, parents=True)

-        # more mshukor
-        self.feature_keys_mapping = feature_keys_mapping.get(repo_id, None) if feature_keys_mapping else None
-        self.inverse_feature_keys_mapping = (
-            {v: k for k, v in self.feature_keys_mapping.items() if v} if self.feature_keys_mapping else {}
-        )
-
        # Load metadata
-        # TODO: change
        self.meta = LeRobotDatasetMetadata(
-            self.repo_id,
-            self.root,
-            local_files_only=local_files_only,
-            revision=self.revision,
-            force_cache_sync=force_cache_sync,
-            feature_keys_mapping=feature_keys_mapping,
+            self.repo_id, self.root, self.revision, force_cache_sync=force_cache_sync
        )
        if self.episodes is not None and self.meta._version >= packaging.version.parse("v2.1"):
            episodes_stats = [self.meta.episodes_stats[ep_idx] for ep_idx in self.episodes]
@@ -574,74 +503,17 @@ class LeRobotDataset(torch.utils.data.Dataset):

        self.episode_data_index = get_episode_data_index(self.meta.episodes, self.episodes)

-        # mustafa code
-        if self.discard_first_n_frames > 0:
-            print("Discarding first n frames:", self.discard_first_n_frames)
-            self.subset_frame_ids = []
-            for ep_idx in range(self.num_episodes):
-                from_ = self.episode_data_index["from"][ep_idx]
-                to_ = self.episode_data_index["to"][ep_idx]
-                # TODO implement advanced strategy
-                self.subset_frame_ids += [
-                    frame_idx for frame_idx in range(from_ + int(self.fps * self.discard_first_n_frames), to_)
-                ]
-        elif self.discard_first_idle_frames:
-            print(
-                f"Discarding first idle frames: motion_threshold={self.motion_threshold}, motion_window_size={self.motion_window_size}, motion_buffer={self.motion_buffer}"
-            )
-            self.robot_states = torch.stack(self.hf_dataset[OBS_STATE]).numpy()  # shape: [T, D]
-            self.subset_frame_ids = []
-            for ep_idx in range(self.num_episodes):
-                from_ = self.episode_data_index["from"][ep_idx]
-                to_ = self.episode_data_index["to"][ep_idx]
-                ep_states = self.robot_states[from_:to_]
-                velocities = np.linalg.norm(np.diff(ep_states, axis=0), axis=1)
-                velocities = np.concatenate([[0.0], velocities])
-                start_idx = find_start_of_motion(
-                    velocities, self.motion_window_size, self.motion_threshold, self.motion_buffer
-                )
-                self.subset_frame_ids += list(range(from_ + start_idx, to_))
-
        # Check timestamps
-        # commented TODO: check why
-        # timestamps = torch.stack(self.hf_dataset["timestamp"]).numpy()
-        # episode_indices = torch.stack(self.hf_dataset["episode_index"]).numpy()
-        # ep_data_index_np = {k: t.numpy() for k, t in self.episode_data_index.items()}
-        # check_timestamps_sync(timestamps, episode_indices, ep_data_index_np, self.fps, self.tolerance_s)
+        timestamps = torch.stack(self.hf_dataset["timestamp"]).numpy()
+        episode_indices = torch.stack(self.hf_dataset["episode_index"]).numpy()
+        ep_data_index_np = {k: t.numpy() for k, t in self.episode_data_index.items()}
+        check_timestamps_sync(timestamps, episode_indices, ep_data_index_np, self.fps, self.tolerance_s)

        # Setup delta_indices
        if self.delta_timestamps is not None:
-            # TODO: check why commented
-            # check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s)
+            check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s)
            self.delta_indices = get_delta_indices(self.delta_timestamps, self.fps)

-        # Mustafa
-        self.meta.info["features"] = map_dict_keys(
-            self.meta.info["features"],
-            feature_keys_mapping=self.feature_keys_mapping,
-            training_features=self.training_features,
-        )
-        self.keys_to_max_dim = {
-            ACTION: max_action_dim,
-            OBS_ENV_STATE: max_state_dim,
-            OBS_STATE: max_state_dim,
-            OBS_IMAGE: max_image_dim,
-            OBS_IMAGE_2: max_image_dim,
-            OBS_IMAGE_3: max_image_dim,
-        }
-        self.meta.info["features"] = reshape_features_to_max_dim(
-            self.meta.info["features"], reshape_dim=-1, keys_to_max_dim=self.keys_to_max_dim
-        )
-        self.meta.stats = map_dict_keys(
-            self.meta.stats,
-            feature_keys_mapping=self.feature_keys_mapping,
-            training_features=self.training_features,
-        )
-        self.robot_type = self.meta.info.get("robot_type", "")
-        # Override tasks
-        print(TASKS_KEYS_MAPPING.get(self.repo_id, self.meta.tasks), "previous", self.meta.tasks)
-        self.meta.tasks = TASKS_KEYS_MAPPING.get(self.repo_id, self.meta.tasks)
-
    def push_to_hub(
        self,
        branch: str | None = None,
@@ -790,18 +662,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
            return get_hf_features_from_features(self.features)

    def _get_query_indices(self, idx: int, ep_idx: int) -> tuple[dict[str, list[int | bool]]]:
-        # Bounds check to prevent IndexError when episode_index is out of range
-        if ep_idx >= len(self.episode_data_index["from"]):
-            # Fall back to the last valid episode
-            ep_idx = len(self.episode_data_index["from"]) - 1
-            
        ep_start = self.episode_data_index["from"][ep_idx]
        ep_end = self.episode_data_index["to"][ep_idx]
        query_indices = {
            key: [max(ep_start.item(), min(ep_end.item() - 1, idx + delta)) for delta in delta_idx]
            for key, delta_idx in self.delta_indices.items()
        }
-        # FIXME(mshukor): what if we train on multiple datasets with different features
        padding = {  # Pad values outside of current episode range
            f"{key}_is_pad": torch.BoolTensor(
                [(idx + delta < ep_start.item()) | (idx + delta >= ep_end.item()) for delta in delta_idx]
@@ -825,21 +691,12 @@ class LeRobotDataset(torch.utils.data.Dataset):

        return query_timestamps

-    # TODO: changed by mustafa
    def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        queries = {}
-        for key, q_idx in query_indices.items():
-            if (
-                key not in self.meta.video_keys
-                and self.inverse_feature_keys_mapping.get(key, key) not in self.meta.video_keys
-            ):
-                key_ = (
-                    self.inverse_feature_keys_mapping.get(key, key)
-                    if self.inverse_feature_keys_mapping
-                    else key
-                )
-                queries[key] = torch.stack(self.hf_dataset.select(q_idx)[key_])
-        return queries
+        return {
+            key: torch.stack(self.hf_dataset.select(q_idx)[key])
+            for key, q_idx in query_indices.items()
+            if key not in self.meta.video_keys
+        }

    def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
        """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function
@@ -863,12 +720,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
    def __len__(self):
        return self.num_frames

-    # changed by mshukor
    def __getitem__(self, idx) -> dict:
-        if self.discard_first_n_frames > 0 or self.discard_first_idle_frames:
-            idx = self.subset_frame_ids[idx]
        item = self.hf_dataset[idx]
-        item = map_dict_keys(item, feature_keys_mapping=self.feature_keys_mapping)
        ep_idx = item["episode_index"].item()

        query_indices = None
@@ -885,27 +738,15 @@ class LeRobotDataset(torch.utils.data.Dataset):
            video_frames = self._query_videos(query_timestamps, ep_idx)
            item = {**video_frames, **item}

+        if self.image_transforms is not None:
+            image_keys = self.meta.camera_keys
+            for cam in image_keys:
+                item[cam] = self.image_transforms(item[cam])
+
        # Add task as a string
        task_idx = item["task_index"].item()
-        try:
-            item["task"] = self.meta.tasks[task_idx]
-        except:
-            print(self.meta.tasks, task_idx, self.repo_id)
-        if "robot_type" not in item:
-            item["robot_type"] = self.robot_type
-        item = map_dict_keys(
-            item, feature_keys_mapping=self.feature_keys_mapping, training_features=self.training_features
-        )
-        # Add padded features
-        # item = self._add_padded_features(item, self.training_features)
-        if self.image_transforms is not None:
-            for cam in item:
-                if cam in self.meta.camera_keys or ("image" in cam and "is_pad" not in cam):
-                    item[cam] = self.image_transforms(item[cam])
-        # Map pad keys
-        # print(item.keys(), "before")
-        # item = map_dict_pad_keys(item, feature_keys_mapping=self.feature_keys_mapping, training_features=self.training_features)
-        # print(item.keys())
+        item["task"] = self.meta.tasks[task_idx]
+
        return item

    def __repr__(self):
@@ -943,7 +784,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
        else:
            self.image_writer.save_image(image=image, fpath=fpath)

-    def add_frame(self, frame: dict, task: str, timestamp: float | None = None) -> None:
+    def add_frame(self, frame: dict) -> None:
        """
        This function only adds the frame to the episode_buffer. Apart from images — which are written in a
        temporary directory — nothing is written to disk. To save those frames, the 'save_episode()' method
@@ -961,14 +802,17 @@ class LeRobotDataset(torch.utils.data.Dataset):

        # Automatically add frame_index and timestamp to episode buffer
        frame_index = self.episode_buffer["size"]
-        if timestamp is None:
-            timestamp = frame_index / self.fps
+        timestamp = frame.pop("timestamp") if "timestamp" in frame else frame_index / self.fps
        self.episode_buffer["frame_index"].append(frame_index)
        self.episode_buffer["timestamp"].append(timestamp)
-        self.episode_buffer["task"].append(task)

        # Add frame features to episode_buffer
        for key in frame:
+            if key == "task":
+                # Note: we associate the task in natural language to its task index during `save_episode`
+                self.episode_buffer["task"].append(frame["task"])
+                continue
+
            if key not in self.features:
                raise ValueError(
                    f"An element of the frame is not in the features. '{key}' not in '{self.features.keys()}'."
@@ -1099,7 +943,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
    def stop_image_writer(self) -> None:
        """
        Whenever wrapping this dataset inside a parallelized DataLoader, this needs to be called first to
-        remove the image_writer in order for the LeRobotDataset object to be picklable and parallelized.
+        remove the image_writer in order for the LeRobotDataset object to be pickleable and parallelized.
        """
        if self.image_writer is not None:
            self.image_writer.stop()
@@ -1144,9 +988,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
        cls,
        repo_id: str,
        fps: int,
-        features: dict,
        root: str | Path | None = None,
+        robot: Robot | None = None,
        robot_type: str | None = None,
+        features: dict | None = None,
        use_videos: bool = True,
        tolerance_s: float = 1e-4,
        image_writer_processes: int = 0,
@@ -1158,14 +1003,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
        obj.meta = LeRobotDatasetMetadata.create(
            repo_id=repo_id,
            fps=fps,
+            root=root,
+            robot=robot,
            robot_type=robot_type,
            features=features,
-            root=root,
            use_videos=use_videos,
        )
        obj.repo_id = obj.meta.repo_id
        obj.root = obj.meta.root
-        obj.local_files_only = obj.meta.local_files_only
        obj.revision = None
        obj.tolerance_s = tolerance_s
        obj.image_writer = None
@@ -1182,110 +1027,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
        obj.delta_timestamps = None
        obj.delta_indices = None
        obj.episode_data_index = None
-        obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec()
+        obj.video_backend = video_backend if video_backend is not None else "torchcodec"
        return obj


-class MultiLeRobotDatasetMeta:
-    def __init__(
-        self,
-        datasets: list[LeRobotDataset],
-        repo_ids: list[str],
-        keys_to_max_dim: dict[str, int],
-        train_on_all_features: bool = False,
-    ):
-        self.repo_ids = repo_ids
-        self.keys_to_max_dim = keys_to_max_dim
-        self.train_on_all_features = train_on_all_features
-        self.robot_types = [ds.meta.info["robot_type"] for ds in datasets]
-
-        # assign robot_type if missing
-        for ds in datasets:
-            ds.meta.info["robot_type"] = ROBOT_TYPE_KEYS_MAPPING.get(ds.repo_id, ds.meta.info["robot_type"])
-            ds.robot_type = ds.meta.info["robot_type"]
-
-        # step 1: compute disabled features
-        self.disabled_features = set()
-        if not self.train_on_all_features:
-            intersection = set(datasets[0].features)
-            for ds in datasets:
-                intersection.intersection_update(ds.features)
-            if not intersection:
-                raise RuntimeError("No common features across datasets.")
-            for repo_id, ds in zip(repo_ids, datasets, strict=False):
-                extra = set(ds.features) - intersection
-                logging.warning(f"Disabling {extra} for repo {repo_id}")
-                self.disabled_features.update(extra)
-
-        # step 2: build union_features excluding disabled
-        self.union_features = {}
-        for ds in datasets:
-            for k, v in ds.features.items():
-                if k not in self.disabled_features:
-                    self.union_features[k] = v
-
-        # step 3: reshape feature schema
-        self.features = reshape_features_to_max_dim(
-            self.union_features, reshape_dim=-1, keys_to_max_dim=self.keys_to_max_dim
-        )
-
-        # step 4: aggregate stats
-        self.stats = aggregate_stats_per_robot_type(datasets)
-        for robot_type_, stats_ in self.stats.items():
-            for feat_key, feat_stats in stats_.items():
-                if feat_key in [ACTION, OBS_ENV_STATE, OBS_STATE]:
-                    for k, v in feat_stats.items():
-                        pad_value = 0 if k in ["min", "mean"] else 1
-                        self.stats[robot_type_][feat_key][k] = pad_tensor(
-                            v,
-                            max_size=self.keys_to_max_dim.get(feat_key, -1),
-                            pad_dim=-1,
-                            pad_value=pad_value,
-                        )
-
-        # step 5: episodes & tasks
-        self.episodes = {repo_id: ds.meta.episodes for repo_id, ds in zip(repo_ids, datasets, strict=False)}
-        self.tasks = {repo_id: ds.meta.tasks for repo_id, ds in zip(repo_ids, datasets, strict=False)}
-        self.info = {repo_id: ds.meta.info for repo_id, ds in zip(repo_ids, datasets, strict=False)}
-
-
-class MultiLeRobotDatasetCleaner:
-    def __init__(
-        self,
-        datasets: list[LeRobotDataset],
-        repo_ids: list[str],
-        sampling_weights: list[float],
-        datasets_repo_ids: list[str],
-        min_fps: int = 1,
-        max_fps: int = 100,
-    ):
-        self.original_datasets = datasets
-        self.original_repo_ids = repo_ids
-        self.original_weights = sampling_weights
-        self.original_datasets_repo_ids = datasets_repo_ids
-
-        # step 1: remove datasets with invalid fps
-        valid_fps_datasets = keep_datasets_with_valid_fps(datasets, min_fps=min_fps, max_fps=max_fps)
-
-        # step 2: keep datasets with same features per robot type
-        consistent_datasets, keep_mask = keep_datasets_with_the_same_features_per_robot_type(
-            valid_fps_datasets
-        )
-
-        self.cleaned_datasets = consistent_datasets
-        self.keep_mask = keep_mask
-        self.cleaned_weights = [sampling_weights[i] for i in range(len(valid_fps_datasets)) if keep_mask[i]]
-        self.cleaned_repo_ids = [repo_ids[i] for i in range(len(valid_fps_datasets)) if keep_mask[i]]
-        self.cleaned_datasets_repo_ids = [
-            datasets_repo_ids[i] for i in range(len(valid_fps_datasets)) if keep_mask[i]
-        ]
-
-        self.cumulative_sizes = np.array(
-            [0] + list(torch.cumsum(torch.tensor([len(d) for d in consistent_datasets]), dim=0))
-        )
-        self.cleaned_weights = np.array(self.cleaned_weights, dtype=np.float32)
-
-
 class MultiLeRobotDataset(torch.utils.data.Dataset):
    """A dataset consisting of multiple underlying `LeRobotDataset`s.

@@ -1302,114 +1047,54 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
        delta_timestamps: dict[list[float]] | None = None,
        tolerances_s: dict | None = None,
        download_videos: bool = True,
-        local_files_only: bool = False,
        video_backend: str | None = None,
-        # add
-        sampling_weights: list[float] | None = None,
-        feature_keys_mapping: dict[str, dict[str, str]] | None = None,
-        max_action_dim: int = None,
-        max_state_dim: int = None,
-        max_num_images: int = None,
-        max_image_dim: int = None,
-        train_on_all_features: bool = False,
-        training_features: list | None = None,
-        discard_first_n_frames: int = 0,
-        min_fps: int = 1,
-        max_fps: int = 100,
-        discard_first_idle_frames: bool = False,
-        motion_threshold: float = 0.05,
-        motion_window_size: int = 10,
-        motion_buffer: int = 3,
    ):
        super().__init__()
        self.repo_ids = repo_ids
        self.root = Path(root) if root else HF_LEROBOT_HOME
-        self.tolerances_s = tolerances_s if tolerances_s else dict.fromkeys(repo_ids, 0.0001)
+        self.tolerances_s = tolerances_s if tolerances_s else {repo_id: 1e-4 for repo_id in repo_ids}
        # Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
        # are handled by this class.
-        _datasets = []
-        datasets_repo_ids = []
-        self.sampling_weights = []
-        self.training_features = training_features
-
-        sampling_weights = sampling_weights if sampling_weights is not None else [1] * len(repo_ids)
-        assert len(sampling_weights) == len(repo_ids), (
-            "The number of sampling weights must match the number of datasets. "
-            f"Got {len(sampling_weights)} weights for {len(repo_ids)} datasets."
-        )
-        for i, repo_id in enumerate(repo_ids):
-            try:
-                # delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
-                _datasets.append(
-                    LeRobotDataset(
-                        repo_id,
-                        root=self.root / repo_id,
-                        episodes=episodes.get(repo_id, None) if episodes else None,
-                        image_transforms=image_transforms,
-                        delta_timestamps=delta_timestamps.get(repo_id, None) if delta_timestamps else None,
-                        tolerance_s=self.tolerances_s[repo_id],
-                        download_videos=download_videos,
-                        video_backend=video_backend,
-                        feature_keys_mapping=feature_keys_mapping,
-                        training_features=training_features,
-                        discard_first_n_frames=discard_first_n_frames,
-                        discard_first_idle_frames=discard_first_idle_frames,
-                        motion_threshold=motion_threshold,
-                        motion_window_size=motion_window_size,
-                        motion_buffer=motion_buffer,
-                    )
-                )
-                datasets_repo_ids.append(repo_id)
-                self.sampling_weights.append(float(sampling_weights[i]))
-            except Exception as e:
-                print(f"Failed to load dataset: {repo_id} due to Exception: {e}")
-        print(
-            f"Finish loading {len(_datasets)} datasets, with sampling weights: {self.sampling_weights} corresponding to: {datasets_repo_ids}"
-        )
+        self._datasets = [
+            LeRobotDataset(
+                repo_id,
+                root=self.root / repo_id,
+                episodes=episodes[repo_id] if episodes else None,
+                image_transforms=image_transforms,
+                delta_timestamps=delta_timestamps,
+                tolerance_s=self.tolerances_s[repo_id],
+                download_videos=download_videos,
+                video_backend=video_backend,
+            )
+            for repo_id in repo_ids
+        ]

        # Disable any data keys that are not common across all of the datasets. Note: we may relax this
        # restriction in future iterations of this class. For now, this is necessary at least for being able
        # to use PyTorch's default DataLoader collate function.
-        # FIXME(mshukor): apply mapping to unify used keys
-        # FIXME(mshukor): pad based on types in case we have more than one state?
+        self.disabled_features = set()
+        intersection_features = set(self._datasets[0].features)
+        for ds in self._datasets:
+            intersection_features.intersection_update(ds.features)
+        if len(intersection_features) == 0:
+            raise RuntimeError(
+                "Multiple datasets were provided but they had no keys common to all of them. "
+                "The multi-dataset functionality currently only keeps common keys."
+            )
+        for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
+            extra_keys = set(ds.features).difference(intersection_features)
+            logging.warning(
+                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                "other datasets."
+            )
+            self.disabled_features.update(extra_keys)
+
        self.image_transforms = image_transforms
-        self.delta_timestamps = (
-            delta_timestamps.get(repo_id, None) if delta_timestamps else None
-        )  # delta_timestamps # FIXME(mshukor): last repo?
-        # In case datasets with the same robot_type have different features
-        cleaner = MultiLeRobotDatasetCleaner(
-            datasets=_datasets,
-            repo_ids=repo_ids,
-            sampling_weights=self.sampling_weights,
-            datasets_repo_ids=datasets_repo_ids,
-            min_fps=min_fps,
-            max_fps=max_fps,
-        )
-        self._datasets = cleaner.cleaned_datasets
-        self.sampling_weights = cleaner.cleaned_weights
-        self.repo_ids = cleaner.cleaned_repo_ids
-        self.datasets_repo_ids = cleaner.cleaned_datasets_repo_ids
-        self.cumulative_sizes = cleaner.cumulative_sizes
-        # self.meta = copy.deepcopy(self._datasets[0].meta)  # FIXME(mshukor): aggregate meta from all datasets
-        # self.meta.info = {
-        #     repo_id: ds.meta.info for repo_id, ds in zip(self.repo_ids, self._datasets, strict=False)
-        # }
-        # self.meta.info["features"] = self._datasets[0].meta.info["features"] # Assume all datasets have the same features
-        self.meta = MultiLeRobotDatasetMeta(
-            datasets=self._datasets,
-            repo_ids=self.repo_ids,
-            keys_to_max_dim={
-                ACTION: max_action_dim,
-                OBS_ENV_STATE: max_state_dim,
-                OBS_STATE: max_state_dim,
-                OBS_IMAGE: max_image_dim,
-                OBS_IMAGE_2: max_image_dim,
-                OBS_IMAGE_3: max_image_dim,
-            },
-            train_on_all_features=train_on_all_features,
-        )
-        self.disabled_features = self.meta.disabled_features
-        self.stats = self.meta.stats
+        self.delta_timestamps = delta_timestamps
+        # TODO(rcadene, aliberts): We should not perform this aggregation for datasets
+        # with multiple robots of different ranges. Instead we should have one normalization
+        # per robot.
+        self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets])

    @property
    def repo_id_to_index(self):
@@ -1497,14 +1182,23 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
    def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
        if idx >= len(self):
            raise IndexError(f"Index {idx} out of bounds.")
-        dataset_idx = np.searchsorted(self.cumulative_sizes, idx, side="right").item() - 1
-        local_idx = (idx - self.cumulative_sizes[dataset_idx]).item()
-        item = self._datasets[dataset_idx][local_idx]
+        # Determine which dataset to get an item from based on the index.
+        start_idx = 0
+        dataset_idx = 0
+        for dataset in self._datasets:
+            if idx >= start_idx + dataset.num_frames:
+                start_idx += dataset.num_frames
+                dataset_idx += 1
+                continue
+            break
+        else:
+            raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
+        item = self._datasets[dataset_idx][idx - start_idx]
        item["dataset_index"] = torch.tensor(dataset_idx)
-        item = create_padded_features(item, self.meta.features)
-        for data_key in self.disabled_features:  # FIXME(mshukor): not in getitem?
+        for data_key in self.disabled_features:
            if data_key in item:
                del item[data_key]
+
        return item

    def __repr__(self):
@@ -28,7 +28,7 @@ from typing import Any
 import numpy as np
 import torch

-from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset


 def _make_memmap_safe(**kwargs) -> np.memmap:
@@ -0,0 +1,85 @@
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+https://drive.google.com/file/d/1rmgN8UUzph1qwJnzG1d-uOafodn-gLvb/view?usp=drive_link
+https://drive.google.com/file/d/1NYQ-XxsBVinB6dUoZmVWweT83367P3i2/view?usp=drive_link
+https://drive.google.com/file/d/1oAv_j74zxxCJieMG7r5Vl2BeHK1__3s3/view?usp=drive_link
+https://drive.google.com/file/d/1wFUJQROsrTJt64YRuIeExhFjr2wnK5uu/view?usp=drive_link
+https://drive.google.com/file/d/1KzL3Tt0Le7jVl58XVRUcmigmXjyiuhbK/view?usp=drive_link
+https://drive.google.com/file/d/1qy_YBladeHtianSSGtgAPSHtMin7msvf/view?usp=drive_link
+https://drive.google.com/file/d/1rA_F0V_qL_nyuC_0aBKCisF4-0TIkF2Y/view?usp=drive_link
+https://drive.google.com/file/d/1hw-8qMpz9VgSt62XoASqNRuPECpCwJQP/view?usp=drive_link
+https://drive.google.com/file/d/1BpHOl9rKMzdvNGka6js7C0s40hH6vnDA/view?usp=drive_link
+https://drive.google.com/file/d/1PazhkhiDnJ-OUMyDVDFxEZNKQQqHiNWS/view?usp=drive_link
+https://drive.google.com/file/d/1lZ665R6ATl57dypxH4dGJ2NSt6XYnbuz/view?usp=drive_link
+https://drive.google.com/file/d/1V9HzLaf-tlG15wUzT7KrTDCS_z1vi5NV/view?usp=drive_link
+https://drive.google.com/file/d/1aKauWiXoKqbNwn_2xs4MrmLlaNYlVNmO/view?usp=drive_link
+https://drive.google.com/file/d/1WVD5DFhriO1YmmOgiVHhacR6HWoTPxav/view?usp=drive_link
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+https://drive.google.com/file/d/15C5K6v_lkjnMSmUvVyqHQKwh2N166e7K/view?usp=drive_link
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+https://drive.google.com/file/d/1NpzWJeK6CqjxzjIMYe6aYdX8xGsQwD4o/view?usp=drive_link
+https://drive.google.com/file/d/1NMLezwufKJ9_8xTc9KQThSzVVD71B9Ui/view?usp=drive_link
+https://drive.google.com/file/d/1aa71DCUqs6oXlIxX35jgsmsgm-NlDxPV/view?usp=drive_link
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+https://drive.google.com/file/d/1JVnIoR7EIQp70T4eAf9RX65JcTrzsjQc/view?usp=drive_link
+https://drive.google.com/file/d/1W2xr4h23ucjPrc-mBEeqnACsfaImpc0p/view?usp=drive_link
+https://drive.google.com/file/d/10xj_0V7A07o3uCa7v5omUrTC0YlPW8H3/view?usp=drive_link
+https://drive.google.com/file/d/1FOc3EMaCy8Mb0_a7PuXLAwKwvxkbKmwU/view?usp=drive_link
+https://drive.google.com/file/d/143PgDXBcf2GQ0Q07ZPMVMfBgZDd5sLJG/view?usp=drive_link
+https://drive.google.com/file/d/1pE5Tyj0LlGbGWvUzuhixp86Ibu55Ez3I/view?usp=drive_link
+https://drive.google.com/file/d/141668b1VzX80ncrVJPzhkoAeIFB4MEK9/view?usp=drive_link
+https://drive.google.com/file/d/1bw12lo37p1ZvRvErHsll7cEYi2OxscvZ/view?usp=drive_link
+https://drive.google.com/file/d/1zfnMFvbgBjl6SzYhksbaOzfbwLrCN6tb/view?usp=drive_link
+https://drive.google.com/file/d/1-GIszA6mUJMaNB-tdh9r9skc77SWA0VX/view?usp=drive_link
+https://drive.google.com/file/d/1fTB0zWFYU6zh4IIUFT2zX_OkwYqmElwY/view?usp=drive_link
+https://drive.google.com/file/d/1gPIPNKGmrO9c7gKF7SP0SuUYbIBBq8z1/view?usp=drive_link
+https://drive.google.com/file/d/12JeJ-dQd5lYyn6PlDOGdE-ChVeiZ-Uv0/view?usp=drive_link
+https://drive.google.com/file/d/100_20cgCqerU6qoh3TfTbwLy9mlDAFEG/view?usp=drive_link
+https://drive.google.com/file/d/111oAGJ76ku_pYgbBoIdZAC1_XEQcPI__/view?usp=drive_link
+https://drive.google.com/file/d/1UhC8L-354ZQ2gblPFGI35EMsVwfpuKa0/view?usp=drive_link
+https://drive.google.com/file/d/1sIXQSgUR_xdrNtGrL6QGBnkLMKErsIp1/view?usp=drive_link
+https://drive.google.com/file/d/16Ax77bDSIXnsn4GFL8XYKKT1P6bPpfMd/view?usp=drive_link
+https://drive.google.com/file/d/1pgRVYwwVIsWq_qsWqZpe1UBzZfF5Fa9D/view?usp=drive_link
+https://drive.google.com/file/d/1jtimaZkWsY1P5gC2bbS64H_WCUU7HXN2/view?usp=drive_link
+https://drive.google.com/file/d/1N6Bh02P-RiTEgtx1YH1Db_X3TGpP-X_r/view?usp=drive_link
+https://drive.google.com/file/d/14Fy8EwJ8d9Vh97Yt1VOvUChSCrfIjBij/view?usp=drive_link
+https://drive.google.com/file/d/1IRuv42dvIMPuKhcMZmuXaBjJ-lPFOmQd/view?usp=drive_link
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+https://drive.google.com/file/d/1CyTVkdrNGGpouCXr4CfhKbMzE6Ah3oo3/view?usp=drive_link
+https://drive.google.com/file/d/1hDRyeM-XEDpHXpptbT8LvNnlQUR3PWOh/view?usp=drive_link
+https://drive.google.com/file/d/1XhHWxbra8Iy5irQZ83IvxwaJqHq9x4s1/view?usp=drive_link
+https://drive.google.com/file/d/1haZcn6aM1o4JlmP9tJj3x2enrxiPaDSD/view?usp=drive_link
+https://drive.google.com/file/d/1ypDyuUTbljaBZ34f-t7lj3O_0bRmyX2n/view?usp=drive_link
+https://drive.google.com/file/d/1ILEEZo_tA9_ChIAprr2mPaNVKZi5vXsO/view?usp=drive_link
+https://drive.google.com/file/d/1U7nVYFaGE8vVTfLCW33D74xOjDcqfgyJ/view?usp=drive_link
+https://drive.google.com/file/d/1rZ93_rmCov5SMDxPkfM3qthcRELZrQX6/view?usp=drive_link
+https://drive.google.com/file/d/1mYO1b_csddtyE3qT6cwLiw-m2w2_1Lxh/view?usp=drive_link
+https://drive.google.com/file/d/1xz7Q5x2jikY8wJQjMRQpRws6AnfWlHm5/view?usp=drive_link
+https://drive.google.com/file/d/1OO8GaO-0FrSZRd1kxMYwBmubyiLOWnbl/view?usp=drive_link
+https://drive.google.com/file/d/1EXn4NVDmf-4_HCy34mYwT-vwK2CFI9ev/view?usp=drive_link
+https://drive.google.com/file/d/10hH70XhXRL9C5SnAG4toHtfHqfJUJo4H/view?usp=drive_link
+https://drive.google.com/file/d/18tiBcxea0guUai4lwsXQvt0q2LZ8ZnnJ/view?usp=drive_link
+https://drive.google.com/file/d/1Q8R8qv37vk5PQ5kQ2ibx6BFLOySD0VpX/view?usp=drive_link
+https://drive.google.com/file/d/17aNriHzjhdibCyuUjQoMFZqjybJZtggG/view?usp=drive_link
+https://drive.google.com/file/d/1LVjEYHSdeKm6CotU1QguIeNEPaIaFl_1/view?usp=drive_link
+https://drive.google.com/file/d/1ufAhE_EkgJ85slg2EW8aW_grOzE_Lmxd/view?usp=drive_link
+https://drive.google.com/file/d/1wtzLtXrkw9eXRGESTPIOlpl1tInu-b2m/view?usp=drive_link
+https://drive.google.com/file/d/1Mk5qvVtD_QHwGOUApRq76TUw2T5THu6f/view?usp=drive_link
+https://drive.google.com/file/d/1y1WQ3hboWVJ68KEYQQ3OhreGuaUpSgwc/view?usp=drive_link
@@ -0,0 +1,52 @@
+https://drive.google.com/drive/folders/1dxWh6YFZUDt6qXIoxgD9bla3CiFjZ11C
+https://drive.google.com/file/d/1hNBJN00SCAlOl0ZEgm7RRGbAGDjyBs0p/view?usp=drive_link
+https://drive.google.com/file/d/17He0CVwXGeoMmXg4SHKo-osNn7YPKVL7/view?usp=drive_link
+https://drive.google.com/file/d/1laNKUVID1x2CV6a2O2WQjwFewKu4lidL/view?usp=drive_link
+https://drive.google.com/file/d/1pNf36xbZJGRArYLmNAvRj5y6CoqdC6kB/view?usp=drive_link
+https://drive.google.com/file/d/1_4E1-y3JXk5I0ebycLYM70YDPK9g52gZ/view?usp=drive_link
+https://drive.google.com/file/d/1PHfzhGPdbolKyOpS3FnR2w7Q8zUlJXSk/view?usp=drive_link
+https://drive.google.com/file/d/17ls2PPN-Pi3tEuK059cwV2_iDT8aGhOO/view?usp=drive_link
+https://drive.google.com/file/d/1LWsg6PmCT00Kv_N_slrmcwKmQPGoBT3k/view?usp=drive_link
+https://drive.google.com/file/d/12LckrchoHTUVH7rxi8J7zD9dA19GXvoW/view?usp=drive_link
+https://drive.google.com/file/d/1VqrJKjAIkj5gtFXL69grdSeu9CyaqnSw/view?usp=drive_link
+https://drive.google.com/file/d/1g5rQYDBZvW-kUtYPeyF3qmd53v6k7kXu/view?usp=drive_link
+https://drive.google.com/file/d/10kUgaSJ0TS7teaG83G3Rf_DG4XGrBt6A/view?usp=drive_link
+https://drive.google.com/file/d/1je9XmneZQZvTma5adMJICUPDovW3ppei/view?usp=drive_link
+https://drive.google.com/file/d/1v28r6bedwZGbUPVVTVImXhK-42XdtGfj/view?usp=drive_link
+https://drive.google.com/file/d/1-TEEx9sGVvzMMaNXYfQMtY2JJ6cvl0dT/view?usp=drive_link
+https://drive.google.com/file/d/1YdBKdJFP9rJWBUX7qrOYL_gfUA8o6J9M/view?usp=drive_link
+https://drive.google.com/file/d/1X9vffwQHNUSKLXr2RlYNtbWDIFCIDfdF/view?usp=drive_link
+https://drive.google.com/file/d/11hqesqa5kvEe5FABUnZRcvmOhR373cYM/view?usp=drive_link
+https://drive.google.com/file/d/1ltTTECjEcbQPgS3UPRgMzaE2x9n6H7dC/view?usp=drive_link
+https://drive.google.com/file/d/1Zxqfa29JdwT-bfMpivi6IG2vz34d21dD/view?usp=drive_link
+https://drive.google.com/file/d/11LQlVxS5hz494dYUJ_PNRPx2NHIJbQns/view?usp=drive_link
+https://drive.google.com/file/d/1i1JhNtnZpO_E8rAv8gxBP3ZTZRvcvsZi/view?usp=drive_link
+https://drive.google.com/file/d/11jOXAr2EULUO4Qkm748634lg4UUFho5U/view?usp=drive_link
+https://drive.google.com/file/d/1rj67wur8DdB_Pipwx24bY43xu4X1eQ5e/view?usp=drive_link
+https://drive.google.com/file/d/15ZTm6lO6f_JQy_4SNfrOu3iPYn1Ro8mh/view?usp=drive_link
+https://drive.google.com/file/d/1q4gBtqWPJtCwXEvknGgN0WHGp7Vfn1b9/view?usp=drive_link
+https://drive.google.com/file/d/1t17keyre47AYqm8GgXiQ7EcvcUkeSiDQ/view?usp=drive_link
+https://drive.google.com/file/d/1OYUPGxtZgOF86Ng_BEOTXm_XOYpuQPsO/view?usp=drive_link
+https://drive.google.com/file/d/1cBjbGHi3dwWHtx6r9EQJi0JT_CE3LuHt/view?usp=drive_link
+https://drive.google.com/file/d/14qaMyF0mcbCB-fCYKNyo5_2NahSC6D5u/view?usp=drive_link
+https://drive.google.com/file/d/12FgX86eA7Y5co9ULBVK80XMsiKQSs-Ri/view?usp=drive_link
+https://drive.google.com/file/d/1yvoHWidf-jdBVw6qCCXOFfkVwKj_2hPk/view?usp=drive_link
+https://drive.google.com/file/d/1a2SugsSDlC8UtUrFzp-_KAwyZckQOvdQ/view?usp=drive_link
+https://drive.google.com/file/d/1l8pILBFSAosypWJMza2K09Vm7rug9axm/view?usp=drive_link
+https://drive.google.com/file/d/1hfPQ8dBCk97PnOhq6_MIISm3IEzcOxJG/view?usp=drive_link
+https://drive.google.com/file/d/1PPAUwlJCFKpms8cqF_k1v2_fCgDBOc3S/view?usp=drive_link
+https://drive.google.com/file/d/1lVKQZeqFfK3amEmLuFhYLUFQ2eyE8rOW/view?usp=drive_link
+https://drive.google.com/file/d/1K9iPMLfDowcIFoyzpvgn88dQ6x6kVwNG/view?usp=drive_link
+https://drive.google.com/file/d/1PNvMqG9tL7QxeLaYBGHiWYR6SYb5iIct/view?usp=drive_link
+https://drive.google.com/file/d/1xkRtzbvIkUsylx9hrFLGQsJn0h1EYu-5/view?usp=drive_link
+https://drive.google.com/file/d/1nxMRrJlSayjDIfr5CmHO1NzAw3COhsLi/view?usp=drive_link
+https://drive.google.com/file/d/1Qs3WEyMGrmagiHIkkFEueWNnJhkUeR1s/view?usp=drive_link
+https://drive.google.com/file/d/1D-G2_Q0SS3M8zyJbg_XzkF2ANPw1HTuX/view?usp=drive_link
+https://drive.google.com/file/d/1mdmJsDGO-YtJAOF_yPKl6lq4PJOIbQhT/view?usp=drive_link
+https://drive.google.com/file/d/11m9bwfop_sPmnQr_8amB6EEsrbAeG_z5/view?usp=drive_link
+https://drive.google.com/file/d/19tyYt5FMn5kru0g9o2nMJhKPnsDqkIZv/view?usp=drive_link
+https://drive.google.com/file/d/1XvTpUdsVTZ-vydvdYYmynbma--HfUGSl/view?usp=drive_link
+https://drive.google.com/file/d/1MO3hFu68J6NohTzr9aB_fY02VA6QSOqj/view?usp=drive_link
+https://drive.google.com/file/d/1Lh-UjwAk__04YOTWINF_QGVU8SjetVaY/view?usp=drive_link
+https://drive.google.com/file/d/1jkSOUwZV5GJ7rZlVeErjcu0DBQs8Np0d/view?usp=drive_link
+https://drive.google.com/file/d/1VIN1eLI-93WrVQwCjsv6XQr353DqqBYA/view?usp=drive_link
@@ -0,0 +1,8 @@
+https://drive.google.com/drive/folders/1EgKar7rWBmTIRmeJYZciSwjZx3uP2mHO
+https://drive.google.com/file/d/12eYWQO15atK2hBjXhynPJd9MKAj_42pz/view?usp=drive_link
+https://drive.google.com/file/d/1Ul4oEeICJDjgfYTl4H1uaisTzVYIM6wd/view?usp=drive_link
+https://drive.google.com/file/d/1WSF-OG8lKSe2wVYCv5D1aJNipxpgddk-/view?usp=drive_link
+https://drive.google.com/file/d/1_ppD5j5sFh26aWW0JmhLzJMeNB-lCArk/view?usp=drive_link
+https://drive.google.com/file/d/1WUp846dgWXYhu4oJfhHxiU6YL_7N6s4W/view?usp=drive_link
+https://drive.google.com/file/d/1HRZNAIoAQw_uYiPwnBvtBioQoqiqoXdA/view?usp=drive_link
+https://drive.google.com/file/d/1hedGq-QDMnIn8GlXXBC3GiEJ_Y-LTxyt/view?usp=drive_link
@@ -0,0 +1,634 @@
+#!/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.
+"""Helper code for loading PushT dataset from Diffusion Policy (https://diffusion-policy.cs.columbia.edu/)
+
+Copied from the original Diffusion Policy repository and used in our `download_and_upload_dataset.py` script.
+"""
+
+from __future__ import annotations
+
+import math
+import numbers
+import os
+from functools import cached_property
+
+import numcodecs
+import numpy as np
+import zarr
+
+
+def check_chunks_compatible(chunks: tuple, shape: tuple):
+    assert len(shape) == len(chunks)
+    for c in chunks:
+        assert isinstance(c, numbers.Integral)
+        assert c > 0
+
+
+def rechunk_recompress_array(group, name, chunks=None, chunk_length=None, compressor=None, tmp_key="_temp"):
+    old_arr = group[name]
+    if chunks is None:
+        chunks = (chunk_length,) + old_arr.chunks[1:] if chunk_length is not None else old_arr.chunks
+    check_chunks_compatible(chunks, old_arr.shape)
+
+    if compressor is None:
+        compressor = old_arr.compressor
+
+    if (chunks == old_arr.chunks) and (compressor == old_arr.compressor):
+        # no change
+        return old_arr
+
+    # rechunk recompress
+    group.move(name, tmp_key)
+    old_arr = group[tmp_key]
+    n_copied, n_skipped, n_bytes_copied = zarr.copy(
+        source=old_arr,
+        dest=group,
+        name=name,
+        chunks=chunks,
+        compressor=compressor,
+    )
+    del group[tmp_key]
+    arr = group[name]
+    return arr
+
+
+def get_optimal_chunks(shape, dtype, target_chunk_bytes=2e6, max_chunk_length=None):
+    """
+    Common shapes
+    T,D
+    T,N,D
+    T,H,W,C
+    T,N,H,W,C
+    """
+    itemsize = np.dtype(dtype).itemsize
+    # reversed
+    rshape = list(shape[::-1])
+    if max_chunk_length is not None:
+        rshape[-1] = int(max_chunk_length)
+    split_idx = len(shape) - 1
+    for i in range(len(shape) - 1):
+        this_chunk_bytes = itemsize * np.prod(rshape[:i])
+        next_chunk_bytes = itemsize * np.prod(rshape[: i + 1])
+        if this_chunk_bytes <= target_chunk_bytes and next_chunk_bytes > target_chunk_bytes:
+            split_idx = i
+
+    rchunks = rshape[:split_idx]
+    item_chunk_bytes = itemsize * np.prod(rshape[:split_idx])
+    this_max_chunk_length = rshape[split_idx]
+    next_chunk_length = min(this_max_chunk_length, math.ceil(target_chunk_bytes / item_chunk_bytes))
+    rchunks.append(next_chunk_length)
+    len_diff = len(shape) - len(rchunks)
+    rchunks.extend([1] * len_diff)
+    chunks = tuple(rchunks[::-1])
+    # print(np.prod(chunks) * itemsize / target_chunk_bytes)
+    return chunks
+
+
+class ReplayBuffer:
+    """
+    Zarr-based temporal datastructure.
+    Assumes first dimension to be time. Only chunk in time dimension.
+    """
+
+    def __init__(self, root: zarr.Group | dict[str, dict]):
+        """
+        Dummy constructor. Use copy_from* and create_from* class methods instead.
+        """
+        assert "data" in root
+        assert "meta" in root
+        assert "episode_ends" in root["meta"]
+        for value in root["data"].values():
+            assert value.shape[0] == root["meta"]["episode_ends"][-1]
+        self.root = root
+
+    # ============= create constructors ===============
+    @classmethod
+    def create_empty_zarr(cls, storage=None, root=None):
+        if root is None:
+            if storage is None:
+                storage = zarr.MemoryStore()
+            root = zarr.group(store=storage)
+        root.require_group("data", overwrite=False)
+        meta = root.require_group("meta", overwrite=False)
+        if "episode_ends" not in meta:
+            meta.zeros("episode_ends", shape=(0,), dtype=np.int64, compressor=None, overwrite=False)
+        return cls(root=root)
+
+    @classmethod
+    def create_empty_numpy(cls):
+        root = {"data": {}, "meta": {"episode_ends": np.zeros((0,), dtype=np.int64)}}
+        return cls(root=root)
+
+    @classmethod
+    def create_from_group(cls, group, **kwargs):
+        if "data" not in group:
+            # create from stratch
+            buffer = cls.create_empty_zarr(root=group, **kwargs)
+        else:
+            # already exist
+            buffer = cls(root=group, **kwargs)
+        return buffer
+
+    @classmethod
+    def create_from_path(cls, zarr_path, mode="r", **kwargs):
+        """
+        Open a on-disk zarr directly (for dataset larger than memory).
+        Slower.
+        """
+        group = zarr.open(os.path.expanduser(zarr_path), mode)
+        return cls.create_from_group(group, **kwargs)
+
+    # ============= copy constructors ===============
+    @classmethod
+    def copy_from_store(
+        cls,
+        src_store,
+        store=None,
+        keys=None,
+        chunks: dict[str, tuple] | None = None,
+        compressors: dict | str | numcodecs.abc.Codec | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        """
+        Load to memory.
+        """
+        src_root = zarr.group(src_store)
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        root = None
+        if store is None:
+            # numpy backend
+            meta = {}
+            for key, value in src_root["meta"].items():
+                if len(value.shape) == 0:
+                    meta[key] = np.array(value)
+                else:
+                    meta[key] = value[:]
+
+            if keys is None:
+                keys = src_root["data"].keys()
+            data = {}
+            for key in keys:
+                arr = src_root["data"][key]
+                data[key] = arr[:]
+
+            root = {"meta": meta, "data": data}
+        else:
+            root = zarr.group(store=store)
+            # copy without recompression
+            n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                source=src_store, dest=store, source_path="/meta", dest_path="/meta", if_exists=if_exists
+            )
+            data_group = root.create_group("data", overwrite=True)
+            if keys is None:
+                keys = src_root["data"].keys()
+            for key in keys:
+                value = src_root["data"][key]
+                cks = cls._resolve_array_chunks(chunks=chunks, key=key, array=value)
+                cpr = cls._resolve_array_compressor(compressors=compressors, key=key, array=value)
+                if cks == value.chunks and cpr == value.compressor:
+                    # copy without recompression
+                    this_path = "/data/" + key
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                        source=src_store,
+                        dest=store,
+                        source_path=this_path,
+                        dest_path=this_path,
+                        if_exists=if_exists,
+                    )
+                else:
+                    # copy with recompression
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy(
+                        source=value,
+                        dest=data_group,
+                        name=key,
+                        chunks=cks,
+                        compressor=cpr,
+                        if_exists=if_exists,
+                    )
+        buffer = cls(root=root)
+        return buffer
+
+    @classmethod
+    def copy_from_path(
+        cls,
+        zarr_path,
+        backend=None,
+        store=None,
+        keys=None,
+        chunks: dict[str, tuple] | None = None,
+        compressors: dict | str | numcodecs.abc.Codec | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        """
+        Copy a on-disk zarr to in-memory compressed.
+        Recommended
+        """
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        if backend == "numpy":
+            print("backend argument is deprecated!")
+            store = None
+        group = zarr.open(os.path.expanduser(zarr_path), "r")
+        return cls.copy_from_store(
+            src_store=group.store,
+            store=store,
+            keys=keys,
+            chunks=chunks,
+            compressors=compressors,
+            if_exists=if_exists,
+            **kwargs,
+        )
+
+    # ============= save methods ===============
+    def save_to_store(
+        self,
+        store,
+        chunks: dict[str, tuple] | None = None,
+        compressors: str | numcodecs.abc.Codec | dict | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        root = zarr.group(store)
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        if self.backend == "zarr":
+            # recompression free copy
+            n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                source=self.root.store,
+                dest=store,
+                source_path="/meta",
+                dest_path="/meta",
+                if_exists=if_exists,
+            )
+        else:
+            meta_group = root.create_group("meta", overwrite=True)
+            # save meta, no chunking
+            for key, value in self.root["meta"].items():
+                _ = meta_group.array(name=key, data=value, shape=value.shape, chunks=value.shape)
+
+        # save data, chunk
+        data_group = root.create_group("data", overwrite=True)
+        for key, value in self.root["data"].items():
+            cks = self._resolve_array_chunks(chunks=chunks, key=key, array=value)
+            cpr = self._resolve_array_compressor(compressors=compressors, key=key, array=value)
+            if isinstance(value, zarr.Array):
+                if cks == value.chunks and cpr == value.compressor:
+                    # copy without recompression
+                    this_path = "/data/" + key
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
+                        source=self.root.store,
+                        dest=store,
+                        source_path=this_path,
+                        dest_path=this_path,
+                        if_exists=if_exists,
+                    )
+                else:
+                    # copy with recompression
+                    n_copied, n_skipped, n_bytes_copied = zarr.copy(
+                        source=value,
+                        dest=data_group,
+                        name=key,
+                        chunks=cks,
+                        compressor=cpr,
+                        if_exists=if_exists,
+                    )
+            else:
+                # numpy
+                _ = data_group.array(name=key, data=value, chunks=cks, compressor=cpr)
+        return store
+
+    def save_to_path(
+        self,
+        zarr_path,
+        chunks: dict[str, tuple] | None = None,
+        compressors: str | numcodecs.abc.Codec | dict | None = None,
+        if_exists="replace",
+        **kwargs,
+    ):
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        store = zarr.DirectoryStore(os.path.expanduser(zarr_path))
+        return self.save_to_store(
+            store, chunks=chunks, compressors=compressors, if_exists=if_exists, **kwargs
+        )
+
+    @staticmethod
+    def resolve_compressor(compressor="default"):
+        if compressor == "default":
+            compressor = numcodecs.Blosc(cname="lz4", clevel=5, shuffle=numcodecs.Blosc.NOSHUFFLE)
+        elif compressor == "disk":
+            compressor = numcodecs.Blosc("zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
+        return compressor
+
+    @classmethod
+    def _resolve_array_compressor(cls, compressors: dict | str | numcodecs.abc.Codec, key, array):
+        # allows compressor to be explicitly set to None
+        cpr = "nil"
+        if isinstance(compressors, dict):
+            if key in compressors:
+                cpr = cls.resolve_compressor(compressors[key])
+            elif isinstance(array, zarr.Array):
+                cpr = array.compressor
+        else:
+            cpr = cls.resolve_compressor(compressors)
+        # backup default
+        if cpr == "nil":
+            cpr = cls.resolve_compressor("default")
+        return cpr
+
+    @classmethod
+    def _resolve_array_chunks(cls, chunks: dict | tuple, key, array):
+        cks = None
+        if isinstance(chunks, dict):
+            if key in chunks:
+                cks = chunks[key]
+            elif isinstance(array, zarr.Array):
+                cks = array.chunks
+        elif isinstance(chunks, tuple):
+            cks = chunks
+        else:
+            raise TypeError(f"Unsupported chunks type {type(chunks)}")
+        # backup default
+        if cks is None:
+            cks = get_optimal_chunks(shape=array.shape, dtype=array.dtype)
+        # check
+        check_chunks_compatible(chunks=cks, shape=array.shape)
+        return cks
+
+    # ============= properties =================
+    @cached_property
+    def data(self):
+        return self.root["data"]
+
+    @cached_property
+    def meta(self):
+        return self.root["meta"]
+
+    def update_meta(self, data):
+        # sanitize data
+        np_data = {}
+        for key, value in data.items():
+            if isinstance(value, np.ndarray):
+                np_data[key] = value
+            else:
+                arr = np.array(value)
+                if arr.dtype == object:
+                    raise TypeError(f"Invalid value type {type(value)}")
+                np_data[key] = arr
+
+        meta_group = self.meta
+        if self.backend == "zarr":
+            for key, value in np_data.items():
+                _ = meta_group.array(
+                    name=key, data=value, shape=value.shape, chunks=value.shape, overwrite=True
+                )
+        else:
+            meta_group.update(np_data)
+
+        return meta_group
+
+    @property
+    def episode_ends(self):
+        return self.meta["episode_ends"]
+
+    def get_episode_idxs(self):
+        import numba
+
+        numba.jit(nopython=True)
+
+        def _get_episode_idxs(episode_ends):
+            result = np.zeros((episode_ends[-1],), dtype=np.int64)
+            for i in range(len(episode_ends)):
+                start = 0
+                if i > 0:
+                    start = episode_ends[i - 1]
+                end = episode_ends[i]
+                for idx in range(start, end):
+                    result[idx] = i
+            return result
+
+        return _get_episode_idxs(self.episode_ends)
+
+    @property
+    def backend(self):
+        backend = "numpy"
+        if isinstance(self.root, zarr.Group):
+            backend = "zarr"
+        return backend
+
+    # =========== dict-like API ==============
+    def __repr__(self) -> str:
+        if self.backend == "zarr":
+            return str(self.root.tree())
+        else:
+            return super().__repr__()
+
+    def keys(self):
+        return self.data.keys()
+
+    def values(self):
+        return self.data.values()
+
+    def items(self):
+        return self.data.items()
+
+    def __getitem__(self, key):
+        return self.data[key]
+
+    def __contains__(self, key):
+        return key in self.data
+
+    # =========== our API ==============
+    @property
+    def n_steps(self):
+        if len(self.episode_ends) == 0:
+            return 0
+        return self.episode_ends[-1]
+
+    @property
+    def n_episodes(self):
+        return len(self.episode_ends)
+
+    @property
+    def chunk_size(self):
+        if self.backend == "zarr":
+            return next(iter(self.data.arrays()))[-1].chunks[0]
+        return None
+
+    @property
+    def episode_lengths(self):
+        ends = self.episode_ends[:]
+        ends = np.insert(ends, 0, 0)
+        lengths = np.diff(ends)
+        return lengths
+
+    def add_episode(
+        self,
+        data: dict[str, np.ndarray],
+        chunks: dict[str, tuple] | None = None,
+        compressors: str | numcodecs.abc.Codec | dict | None = None,
+    ):
+        if chunks is None:
+            chunks = {}
+        if compressors is None:
+            compressors = {}
+        assert len(data) > 0
+        is_zarr = self.backend == "zarr"
+
+        curr_len = self.n_steps
+        episode_length = None
+        for value in data.values():
+            assert len(value.shape) >= 1
+            if episode_length is None:
+                episode_length = len(value)
+            else:
+                assert episode_length == len(value)
+        new_len = curr_len + episode_length
+
+        for key, value in data.items():
+            new_shape = (new_len,) + value.shape[1:]
+            # create array
+            if key not in self.data:
+                if is_zarr:
+                    cks = self._resolve_array_chunks(chunks=chunks, key=key, array=value)
+                    cpr = self._resolve_array_compressor(compressors=compressors, key=key, array=value)
+                    arr = self.data.zeros(
+                        name=key, shape=new_shape, chunks=cks, dtype=value.dtype, compressor=cpr
+                    )
+                else:
+                    # copy data to prevent modify
+                    arr = np.zeros(shape=new_shape, dtype=value.dtype)
+                    self.data[key] = arr
+            else:
+                arr = self.data[key]
+                assert value.shape[1:] == arr.shape[1:]
+                # same method for both zarr and numpy
+                if is_zarr:
+                    arr.resize(new_shape)
+                else:
+                    arr.resize(new_shape, refcheck=False)
+            # copy data
+            arr[-value.shape[0] :] = value
+
+        # append to episode ends
+        episode_ends = self.episode_ends
+        if is_zarr:
+            episode_ends.resize(episode_ends.shape[0] + 1)
+        else:
+            episode_ends.resize(episode_ends.shape[0] + 1, refcheck=False)
+        episode_ends[-1] = new_len
+
+        # rechunk
+        if is_zarr and episode_ends.chunks[0] < episode_ends.shape[0]:
+            rechunk_recompress_array(self.meta, "episode_ends", chunk_length=int(episode_ends.shape[0] * 1.5))
+
+    def drop_episode(self):
+        is_zarr = self.backend == "zarr"
+        episode_ends = self.episode_ends[:].copy()
+        assert len(episode_ends) > 0
+        start_idx = 0
+        if len(episode_ends) > 1:
+            start_idx = episode_ends[-2]
+        for value in self.data.values():
+            new_shape = (start_idx,) + value.shape[1:]
+            if is_zarr:
+                value.resize(new_shape)
+            else:
+                value.resize(new_shape, refcheck=False)
+        if is_zarr:
+            self.episode_ends.resize(len(episode_ends) - 1)
+        else:
+            self.episode_ends.resize(len(episode_ends) - 1, refcheck=False)
+
+    def pop_episode(self):
+        assert self.n_episodes > 0
+        episode = self.get_episode(self.n_episodes - 1, copy=True)
+        self.drop_episode()
+        return episode
+
+    def extend(self, data):
+        self.add_episode(data)
+
+    def get_episode(self, idx, copy=False):
+        idx = list(range(len(self.episode_ends)))[idx]
+        start_idx = 0
+        if idx > 0:
+            start_idx = self.episode_ends[idx - 1]
+        end_idx = self.episode_ends[idx]
+        result = self.get_steps_slice(start_idx, end_idx, copy=copy)
+        return result
+
+    def get_episode_slice(self, idx):
+        start_idx = 0
+        if idx > 0:
+            start_idx = self.episode_ends[idx - 1]
+        end_idx = self.episode_ends[idx]
+        return slice(start_idx, end_idx)
+
+    def get_steps_slice(self, start, stop, step=None, copy=False):
+        _slice = slice(start, stop, step)
+
+        result = {}
+        for key, value in self.data.items():
+            x = value[_slice]
+            if copy and isinstance(value, np.ndarray):
+                x = x.copy()
+            result[key] = x
+        return result
+
+    # =========== chunking =============
+    def get_chunks(self) -> dict:
+        assert self.backend == "zarr"
+        chunks = {}
+        for key, value in self.data.items():
+            chunks[key] = value.chunks
+        return chunks
+
+    def set_chunks(self, chunks: dict):
+        assert self.backend == "zarr"
+        for key, value in chunks.items():
+            if key in self.data:
+                arr = self.data[key]
+                if value != arr.chunks:
+                    check_chunks_compatible(chunks=value, shape=arr.shape)
+                    rechunk_recompress_array(self.data, key, chunks=value)
+
+    def get_compressors(self) -> dict:
+        assert self.backend == "zarr"
+        compressors = {}
+        for key, value in self.data.items():
+            compressors[key] = value.compressor
+        return compressors
+
+    def set_compressors(self, compressors: dict):
+        assert self.backend == "zarr"
+        for key, value in compressors.items():
+            if key in self.data:
+                arr = self.data[key]
+                compressor = self.resolve_compressor(value)
+                if compressor != arr.compressor:
+                    rechunk_recompress_array(self.data, key, compressor=compressor)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Simon Alibert	c7a36bb661	Install ffmpeg for integration tests	2025-03-13 14:51:34 +01:00
Remi	9466da0ddf	Merge branch 'main' into torchcodec-cpu	2025-03-13 14:00:22 +01:00
Jade Choghari	bb7542d799	Merge branch 'main' into torchcodec-cpu	2025-03-12 21:10:00 +03:00
Jade Choghari	1a1740d90d	update torchcodec version	2025-03-12 21:02:16 +03:00
Jade Choghari	0b379e9c4e	update benchmark to new dataset format	2025-03-08 11:12:58 +03:00
Jade Choghari	2295e6c45b	fix arg	2025-03-04 13:31:25 +03:00
pre-commit-ci[bot]	920079230e	[pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci	2025-03-04 10:27:47 +00:00
Jade Choghari	744906098a	Update lerobot/common/datasets/lerobot_dataset.py Co-authored-by: Remi <re.cadene@gmail.com>	2025-03-04 13:27:40 +03:00
Jade Choghari	e1732b4954	Update lerobot/common/datasets/video_utils.py Co-authored-by: Remi <re.cadene@gmail.com>	2025-03-04 13:27:34 +03:00
Jade Choghari	c03b0db8aa	Update lerobot/common/datasets/video_utils.py Co-authored-by: Remi <re.cadene@gmail.com>	2025-03-04 13:27:24 +03:00
pre-commit-ci[bot]	a8fcd3512d	[pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci	2025-03-03 07:26:28 +00:00
root	a963dba256	add dependency	2025-03-03 07:25:56 +00:00
root	2f9cbfbc4f	add dependency	2025-03-03 06:44:26 +00:00
pre-commit-ci[bot]	e8126dc3d6	[pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci	2025-03-03 06:33:39 +00:00
root	4e2dc91e59	add torchcodec cpu	2025-03-02 20:47:33 +00:00
				`@@ -1 +0,0 @@`
				`../../src/lerobot/robots/koch_follower/koch.mdx`
				`@@ -1 +0,0 @@`
				`../../src/lerobot/robots/so100_follower/so100.mdx`
				`@@ -1 +0,0 @@`
				`../../src/lerobot/robots/so101_follower/so101.mdx`
				`@@ -0,0 +1 @@`
				`https://drive.google.com/drive/folders/1S8eFg98IaGAIKVZ8QFWG1bx4mHa-O204`