fix: address PR review feedback — broken link, NaN guard, zizmor tags, fork skip

- Remove broken Triton issue link from Dockerfile.benchmark.libero - Add module-level _safe_int helper to guard n_episodes against NaN - Move _safe_float to module level alongside _safe_int - Add # zizmor: ignore[unpinned-uses] to all upload-artifact@v4 steps - Add if: env.HF_USER_TOKEN != '' to Libero smoke eval for fork PRs Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
chore: revert configs.py, factory.py, test_dispatch.py to main
2026-06-17 16:27:04 +00:00 · 2026-04-13 13:38:28 +02:00 · 2026-04-10 16:33:36 +02:00 · 2026-04-10 14:42:26 +02:00 · 2026-04-10 14:29:00 +02:00 · 2026-04-10 12:48:07 +02:00
348 changed files with 2806 additions and 3396 deletions
@@ -0,0 +1,311 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Integration tests: build an isolated Docker image per benchmark and run a
+# 1-episode smoke eval. Each benchmark gets its own image so incompatible
+# dependency trees (e.g. hf-libero vs metaworld==3.0.0) can never collide.
+#
+# To add a new benchmark:
+#   1. Add docker/Dockerfile.benchmark.<name>  (install only lerobot[<name>])
+#   2. Copy one of the jobs below and adjust the image name and eval command.
+name: Benchmark Integration Tests
+
+on:
+  # Run manually from the Actions tab
+  workflow_dispatch:
+
+  # Run every Monday at 02:00 UTC.
+  schedule:
+    - cron: "0 2 * * 1"
+
+  push:
+    branches:
+      - main
+    paths:
+      - "src/lerobot/envs/**"
+      - "src/lerobot/scripts/lerobot_eval.py"
+      - "docker/Dockerfile.benchmark.*"
+      - ".github/workflows/benchmark_tests.yml"
+      - "pyproject.toml"
+
+  pull_request:
+    branches:
+      - main
+      - feat/benchmark-ci
+    paths:
+      - "src/lerobot/envs/**"
+      - "src/lerobot/scripts/lerobot_eval.py"
+      - "docker/Dockerfile.benchmark.*"
+      - ".github/workflows/benchmark_tests.yml"
+      - "pyproject.toml"
+
+permissions:
+  contents: read
+
+env:
+  UV_VERSION: "0.8.0"
+  PYTHON_VERSION: "3.12"
+
+# Cancel in-flight runs for the same branch/PR.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  # ── LIBERO ────────────────────────────────────────────────────────────────
+  # Isolated image: lerobot[libero] only (hf-libero, dm-control, mujoco chain)
+  libero-integration-test:
+    name: Libero — build image + 1-episode eval
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
+
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        with:
+          persist-credentials: false
+          lfs: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          cache-binary: false
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+
+      # Build the benchmark-specific image. The Dockerfile separates dep-install
+      # from source-copy, so code-only changes skip the slow uv-sync layer
+      # when the runner has a warm Docker daemon cache.
+      - name: Build Libero benchmark image
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: docker/Dockerfile.benchmark.libero
+          push: false
+          load: true
+          tags: lerobot-benchmark-libero:ci
+
+      - name: Run Libero smoke eval (1 episode)
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          # Named container (no --rm) so we can docker cp artifacts out.
+          # Output to /tmp inside the container — /artifacts doesn't exist
+          # and user_lerobot cannot create root-level dirs.
+          docker run --name libero-eval --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
+            lerobot-benchmark-libero:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              lerobot-eval \
+                --policy.path=pepijn223/smolvla_libero \
+                --env.type=libero \
+                --env.task=libero_spatial \
+                --eval.batch_size=1 \
+                --eval.n_episodes=1 \
+                --eval.use_async_envs=false \
+                --policy.device=cuda \
+                '--env.camera_name_mapping={\"agentview_image\": \"camera1\", \"robot0_eye_in_hand_image\": \"camera2\"}' \
+                --policy.empty_cameras=1 \
+                --output_dir=/tmp/eval-artifacts
+              python scripts/ci/extract_task_descriptions.py \
+                --env libero --task libero_spatial \
+                --output /tmp/eval-artifacts/task_descriptions.json
+            "
+
+      - name: Copy Libero artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/libero-artifacts
+          docker cp libero-eval:/tmp/eval-artifacts/. /tmp/libero-artifacts/ 2>/dev/null || true
+          docker rm -f libero-eval || true
+
+      - name: Parse Libero eval metrics
+        if: always()
+        run: |
+          python3 scripts/ci/parse_eval_metrics.py \
+            --artifacts-dir /tmp/libero-artifacts \
+            --env libero \
+            --task libero_spatial \
+            --policy pepijn223/smolvla_libero
+
+      - name: Upload Libero rollout video
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: libero-rollout-video
+          path: /tmp/libero-artifacts/videos/
+          if-no-files-found: warn
+
+      - name: Upload Libero eval metrics
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: libero-metrics
+          path: /tmp/libero-artifacts/metrics.json
+          if-no-files-found: warn
+
+      # ── LIBERO TRAIN+EVAL SMOKE ──────────────────────────────────────────────
+      # Train SmolVLA for 1 step (batch_size=1, dataset episode 0 only) then
+      # immediately runs eval inside the training loop (eval_freq=1, 1 episode).
+      # Tests the full train→eval-within-training pipeline end-to-end.
+      - name: Run Libero train+eval smoke (1 step, eval_freq=1)
+        run: |
+          docker run --name libero-train-smoke --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
+            lerobot-benchmark-libero:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              accelerate launch --num_processes=1 \$(which lerobot-train) \
+                --policy.path=lerobot/smolvla_base \
+                --policy.load_vlm_weights=true \
+                --policy.scheduler_decay_steps=25000 \
+                --policy.freeze_vision_encoder=false \
+                --policy.train_expert_only=false \
+                --dataset.repo_id=lerobot/libero \
+                --dataset.episodes=[0] \
+                --dataset.use_imagenet_stats=false \
+                --env.type=libero \
+                --env.task=libero_spatial \
+                '--env.camera_name_mapping={\"agentview_image\": \"camera1\", \"robot0_eye_in_hand_image\": \"camera2\"}' \
+                --policy.empty_cameras=1 \
+                --output_dir=/tmp/train-smoke \
+                --steps=1 \
+                --batch_size=1 \
+                --eval_freq=1 \
+                --eval.n_episodes=1 \
+                --eval.batch_size=1 \
+                --eval.use_async_envs=false \
+                --save_freq=1 \
+                --policy.push_to_hub=false \
+                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.image2\": \"observation.images.camera2\"}'
+            "
+
+      - name: Copy Libero train-smoke artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/libero-train-smoke-artifacts
+          docker cp libero-train-smoke:/tmp/train-smoke/. /tmp/libero-train-smoke-artifacts/ 2>/dev/null || true
+          docker rm -f libero-train-smoke || true
+
+      - name: Upload Libero train-smoke eval video
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: libero-train-smoke-video
+          path: /tmp/libero-train-smoke-artifacts/eval/
+          if-no-files-found: warn
+
+  # ── METAWORLD ─────────────────────────────────────────────────────────────
+  # Isolated image: lerobot[metaworld] only (metaworld==3.0.0, mujoco>=3 chain)
+  metaworld-integration-test:
+    name: MetaWorld — build image + 1-episode eval
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
+
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        with:
+          persist-credentials: false
+          lfs: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          cache-binary: false
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+
+      - name: Build MetaWorld benchmark image
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: docker/Dockerfile.benchmark.metaworld
+          push: false
+          load: true
+          tags: lerobot-benchmark-metaworld:ci
+
+      - name: Run MetaWorld smoke eval (1 episode)
+        run: |
+          docker run --name metaworld-eval --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
+            lerobot-benchmark-metaworld:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              lerobot-eval \
+                --policy.path=pepijn223/smolvla_metaworld \
+                --env.type=metaworld \
+                --env.task=metaworld-push-v3 \
+                --eval.batch_size=1 \
+                --eval.n_episodes=1 \
+                --eval.use_async_envs=false \
+                --policy.device=cuda \
+                '--rename_map={\"observation.image\": \"observation.images.camera1\"}' \
+                --policy.empty_cameras=2 \
+                --output_dir=/tmp/eval-artifacts
+              python scripts/ci/extract_task_descriptions.py \
+                --env metaworld --task metaworld-push-v3 \
+                --output /tmp/eval-artifacts/task_descriptions.json
+            "
+
+      - name: Copy MetaWorld artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/metaworld-artifacts
+          docker cp metaworld-eval:/tmp/eval-artifacts/. /tmp/metaworld-artifacts/ 2>/dev/null || true
+          docker rm -f metaworld-eval || true
+
+      - name: Parse MetaWorld eval metrics
+        if: always()
+        run: |
+          python3 scripts/ci/parse_eval_metrics.py \
+            --artifacts-dir /tmp/metaworld-artifacts \
+            --env metaworld \
+            --task metaworld-push-v3 \
+            --policy pepijn223/smolvla_metaworld
+
+      - name: Upload MetaWorld rollout video
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: metaworld-rollout-video
+          path: /tmp/metaworld-artifacts/videos/
+          if-no-files-found: warn
+
+      - name: Upload MetaWorld eval metrics
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: metaworld-metrics
+          path: /tmp/metaworld-artifacts/metrics.json
+          if-no-files-found: warn
@@ -12,10 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-# This workflow validates each optional-dependency tier in isolation.
-# Each tier installs a different extra and runs the full test suite.
-# Tests that require an extra not installed in the current tier are
-# skipped automatically via pytest.importorskip guards.
+# This workflow handles fast testing.
 name: Fast Tests

 on:
@@ -57,9 +54,8 @@ concurrency:
  cancel-in-progress: true

 jobs:
-  # This job runs pytests in isolated dependency tiers.
-  # Each tier installs a different extra and runs the full suite;
-  # tests gated behind other extras skip automatically.
+  # This job runs pytests with the default dependencies.
+  # It runs everytime we commit to a PR or push to main
  fast-pytest-tests:
    name: Fast Pytest Tests
    runs-on: ubuntu-latest
@@ -93,9 +89,8 @@ jobs:
          version: ${{ env.UV_VERSION }}
          python-version: ${{ env.PYTHON_VERSION }}

-      # ── Tier 1: Base ──────────────────────────────────────
-      - name: "Tier 1 — Install: base"
-        run: uv sync --locked --extra test
+      - name: Install lerobot with test extras
+        run: uv sync --locked --extra "test"

      - name: Login to Hugging Face
        if: env.HF_USER_TOKEN != ''
@@ -103,26 +98,5 @@ jobs:
          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
          uv run hf auth whoami

-      - name: "Tier 1 — Test: base"
-        run: uv run pytest tests -vv --maxfail=10
-
-      # ── Tier 2: Dataset ──────────────────────────────────
-      - name: "Tier 2 — Install: dataset"
-        run: uv sync --locked --extra test --extra dataset
-
-      - name: "Tier 2 — Test: dataset"
-        run: uv run pytest tests -vv --maxfail=10
-
-      # ── Tier 3: Hardware ─────────────────────────────────
-      - name: "Tier 3 — Install: hardware"
-        run: uv sync --locked --extra test --extra hardware
-
-      - name: "Tier 3 — Test: hardware"
-        run: uv run pytest tests -vv --maxfail=10
-
-      # ── Tier 4: Viz ──────────────────────────────────────
-      - name: "Tier 4 — Install: viz"
-        run: uv sync --locked --extra test --extra viz
-
-      - name: "Tier 4 — Test: viz"
+      - name: Run pytest
        run: uv run pytest tests -vv --maxfail=10
@@ -0,0 +1,99 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Isolated benchmark image for LIBERO integration tests.
+# Installs only lerobot[libero] so its dep tree (hf-libero, dm-control, mujoco)
+# cannot conflict with other benchmarks.
+#
+# Build:  docker build -f docker/Dockerfile.benchmark.libero -t lerobot-benchmark-libero .
+# Run:    docker run --gpus all --rm lerobot-benchmark-libero lerobot-eval ...
+
+ARG CUDA_VERSION=12.4.1
+ARG OS_VERSION=22.04
+FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
+
+ARG PYTHON_VERSION=3.12
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    MUJOCO_GL=egl \
+    PATH=/lerobot/.venv/bin:$PATH \
+    CUDA_VISIBLE_DEVICES=0 \
+    DEVICE=cuda
+
+# System deps — same set as Dockerfile.internal
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common build-essential git curl \
+    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+    libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \
+    cmake pkg-config ninja-build \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends \
+       python${PYTHON_VERSION} \
+       python${PYTHON_VERSION}-venv \
+       python${PYTHON_VERSION}-dev \
+    && curl -LsSf https://astral.sh/uv/0.8.0/install.sh | sh \
+    && mv /root/.local/bin/uv /usr/local/bin/uv \
+    && useradd --create-home --shell /bin/bash user_lerobot \
+    && usermod -aG sudo user_lerobot \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /lerobot
+RUN chown -R user_lerobot:user_lerobot /lerobot
+USER user_lerobot
+
+ENV HOME=/home/user_lerobot \
+    HF_HOME=/home/user_lerobot/.cache/huggingface \
+    HF_LEROBOT_HOME=/home/user_lerobot/.cache/huggingface/lerobot \
+    TORCH_HOME=/home/user_lerobot/.cache/torch \
+    TRITON_CACHE_DIR=/home/user_lerobot/.cache/triton
+
+RUN uv venv --python python${PYTHON_VERSION}
+
+# ── Dependency layer (cached unless pyproject.toml / uv.lock change) ────────
+# Copy only the files uv needs to resolve deps, plus a minimal package stub
+# so the editable install can succeed without the full source tree.
+# Uses `uv pip install` instead of `uv sync` because uv sync validates the
+# entire lockfile across all extras — robomme's numpy<2.0 conflicts with the
+# base numpy>=2.0, making the full lockfile unsatisfiable. pip-style install
+# only resolves the requested extras for the current platform.
+COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
+RUN mkdir -p src/lerobot && touch src/lerobot/__init__.py src/lerobot/py.typed
+
+RUN uv pip install --no-cache -e ".[libero,smolvla]"
+
+# Pre-download lerobot/libero-assets from HF Hub so nothing is fetched at
+# runtime (which times out on CI). Point the libero config at the cached path.
+# libero/libero/__init__.py calls input() when ~/.libero/config.yaml is missing,
+# so we write the config before any libero import can happen.
+RUN LIBERO_DIR=$(python${PYTHON_VERSION} -c \
+      "import importlib.util, os; s=importlib.util.find_spec('libero'); \
+       print(os.path.join(os.path.dirname(s.origin), 'libero'))") && \
+    mkdir -p /home/user_lerobot/.libero && \
+    python${PYTHON_VERSION} -c "\
+from huggingface_hub import snapshot_download; \
+snapshot_download(repo_id='lerobot/libero-assets', repo_type='dataset', \
+                  local_dir='/home/user_lerobot/.libero/assets')" && \
+    printf "assets: /home/user_lerobot/.libero/assets\nbddl_files: ${LIBERO_DIR}/bddl_files\ndatasets: ${LIBERO_DIR}/../datasets\ninit_states: ${LIBERO_DIR}/init_files\n" \
+    > /home/user_lerobot/.libero/config.yaml
+
+# Workaround: Triton ships ptxas without the execute bit set.
+# Without this chmod, any JIT compilation (e.g. torch.compile) fails
+# with "Permission denied".
+RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
+
+# ── Source layer (rebuilds in seconds on code-only changes) ─────────────────
+COPY --chown=user_lerobot:user_lerobot . .
+
+CMD ["/bin/bash"]
@@ -0,0 +1,82 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Isolated benchmark image for MetaWorld integration tests.
+# Installs only lerobot[metaworld] so its dep tree (metaworld==3.0.0, mujoco>=3)
+# cannot conflict with other benchmarks.
+#
+# Build:  docker build -f docker/Dockerfile.benchmark.metaworld -t lerobot-benchmark-metaworld .
+# Run:    docker run --gpus all --rm lerobot-benchmark-metaworld lerobot-eval ...
+
+ARG CUDA_VERSION=12.4.1
+ARG OS_VERSION=22.04
+FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
+
+ARG PYTHON_VERSION=3.12
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    MUJOCO_GL=egl \
+    PATH=/lerobot/.venv/bin:$PATH \
+    CUDA_VISIBLE_DEVICES=0 \
+    DEVICE=cuda
+
+# System deps — same set as Dockerfile.internal
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common build-essential git curl \
+    libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
+    libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \
+    cmake pkg-config ninja-build \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends \
+       python${PYTHON_VERSION} \
+       python${PYTHON_VERSION}-venv \
+       python${PYTHON_VERSION}-dev \
+    && curl -LsSf https://astral.sh/uv/0.8.0/install.sh | sh \
+    && mv /root/.local/bin/uv /usr/local/bin/uv \
+    && useradd --create-home --shell /bin/bash user_lerobot \
+    && usermod -aG sudo user_lerobot \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /lerobot
+RUN chown -R user_lerobot:user_lerobot /lerobot
+USER user_lerobot
+
+ENV HOME=/home/user_lerobot \
+    HF_HOME=/home/user_lerobot/.cache/huggingface \
+    HF_LEROBOT_HOME=/home/user_lerobot/.cache/huggingface/lerobot \
+    TORCH_HOME=/home/user_lerobot/.cache/torch \
+    TRITON_CACHE_DIR=/home/user_lerobot/.cache/triton
+
+RUN uv venv --python python${PYTHON_VERSION}
+
+# ── Dependency layer (cached unless pyproject.toml / uv.lock change) ────────
+# Copy only the files uv needs to resolve deps, plus a minimal package stub
+# so the editable install can succeed without the full source tree.
+# Uses `uv pip install` instead of `uv sync` — see Dockerfile.benchmark.libero
+# for rationale (cross-extra numpy conflict with robomme).
+COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
+RUN mkdir -p src/lerobot && touch src/lerobot/__init__.py src/lerobot/py.typed
+
+RUN uv pip install --no-cache -e ".[metaworld,smolvla]"
+
+# Workaround: Triton ships ptxas without the execute bit set.
+# Without this chmod, any JIT compilation (e.g. torch.compile) fails
+# with "Permission denied". See: https://github.com/triton-lang/triton/issues/2due
+RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
+
+# ── Source layer (rebuilds in seconds on code-only changes) ─────────────────
+COPY --chown=user_lerobot:user_lerobot . .
+
+CMD ["/bin/bash"]
@@ -216,7 +216,7 @@ class MyBenchmarkEnvConfig(EnvConfig):

    def get_env_processors(self):
        """Override if your benchmark needs observation/action transforms."""
-        from lerobot.processor import PolicyProcessorPipeline
+        from lerobot.processor.pipeline import PolicyProcessorPipeline
        from lerobot.processor.env_processor import MyBenchmarkProcessorStep
        return (
            PolicyProcessorPipeline(steps=[MyBenchmarkProcessorStep()]),
@@ -170,7 +170,7 @@ python -m lerobot.async_inference.robot_client \
 ```python
 import threading
 from lerobot.robots.so_follower import SO100FollowerConfig
-from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.async_inference.configs import RobotClientConfig
 from lerobot.async_inference.robot_client import RobotClient
 from lerobot.async_inference.helpers import visualize_action_queue_size
@@ -41,7 +41,7 @@ The script:

 ```python
 # New usage pattern (after migration)
-from lerobot.policies import make_policy, make_pre_post_processors
+from lerobot.policies.factory import make_policy, make_pre_post_processors

 # Load model and processors separately
 policy = make_policy(config, ds_meta=dataset.meta)
@@ -47,9 +47,9 @@ Here is a template to get you started, customize the parameters and methods as n
 ```python
 # configuration_my_custom_policy.py
 from dataclasses import dataclass, field
-from lerobot.configs import PreTrainedConfig
-from lerobot.optim import AdamWConfig
-from lerobot.optim import CosineDecayWithWarmupSchedulerConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig

@PreTrainedConfig.register_subclass("my_custom_policy")
@dataclass
@@ -120,7 +120,7 @@ import torch
 import torch.nn as nn
 from typing import Any

-from lerobot.policies import PreTrainedPolicy
+from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.utils.constants import ACTION
 from .configuration_my_custom_policy import MyCustomPolicyConfig

@@ -79,8 +79,9 @@ The following examples show how to use the camera API to configure and capture f

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig
-from lerobot.cameras import ColorMode, Cv2Rotation
+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(
@@ -125,8 +126,9 @@ with OpenCVCamera(config) as camera:

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
-from lerobot.cameras import ColorMode, Cv2Rotation
+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(
@@ -95,7 +95,7 @@ After completing your annotation:
 When you load a dataset with subtask annotations, the subtask information is automatically available:

 ```python
-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset

 # Load a dataset with subtask annotations
 dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
@@ -133,10 +133,11 @@ if has_subtasks:
 The `TokenizerProcessor` automatically handles subtask tokenization for Vision-Language Action (VLA) models:

 ```python
-from lerobot.processor import TokenizerProcessorStep
+from lerobot.processor.tokenizer_processor import TokenizerProcessor
+from lerobot.processor.pipeline import ProcessorPipeline

-# Create a tokenizer processor step
-tokenizer_processor = TokenizerProcessorStep(
+# Create a tokenizer processor
+tokenizer_processor = TokenizerProcessor(
    tokenizer_name_or_path="google/paligemma-3b-pt-224",
    padding="max_length",
    max_length=64,
@@ -157,7 +158,7 @@ When subtasks are available in the batch, the tokenizer processor adds:

 ```python
 import torch
-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset

 dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")

@@ -181,7 +182,7 @@ for batch in dataloader:
 Try loading a dataset with subtask annotations:

 ```python
-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset

 # Example dataset with subtask annotations
 dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
@@ -66,10 +66,10 @@ The SDK gives you:

 Follow our [Installation Guide](./installation) to install LeRobot.

-In addition to the base installation, install the EarthRover Mini with hardware dependencies:
+In addition to the base installation, install the EarthRover Mini dependencies:

 ```bash
-pip install -e ".[hardware]"
+pip install -e .
 ```

 ## How It Works
@@ -173,8 +173,8 @@ observation = {
 The `make_env_pre_post_processors` function follows the same pattern as `make_pre_post_processors` for policies:

 ```python
-from lerobot.envs import make_env_pre_post_processors, PushtEnv
-from lerobot.envs.configs import LiberoEnv
+from lerobot.envs.factory import make_env_pre_post_processors
+from lerobot.envs.configs import LiberoEnv, PushtEnv

 # For LIBERO: Returns LiberoProcessorStep in preprocessor
 libero_cfg = LiberoEnv(task="libero_spatial", camera_name=["agentview"])
@@ -257,7 +257,7 @@ def eval_main(cfg: EvalPipelineConfig):
 The `LiberoProcessorStep` demonstrates a real-world environment processor:

 ```python
-from lerobot.processor import ObservationProcessorStep
+from lerobot.processor.pipeline import ObservationProcessorStep

@dataclass
@ProcessorStepRegistry.register(name="libero_processor")
@@ -34,7 +34,7 @@ Finally, your environment must implement the standard `gym.vector.VectorEnv` int
 Loading an environment from the Hub is as simple as:

 ```python
-from lerobot.envs import make_env
+from lerobot.envs.factory import make_env

 # Load a hub environment (requires explicit consent to run remote code)
 env = make_env("lerobot/cartpole-env", trust_remote_code=True)
@@ -191,7 +191,7 @@ api.upload_folder(
 ### Basic Usage

 ```python
-from lerobot.envs import make_env
+from lerobot.envs.factory import make_env

 # Load from the hub
 envs_dict = make_env(
@@ -314,7 +314,7 @@ env = make_env("trusted-org/verified-env@a1b2c3d4", trust_remote_code=True)
 Here's a complete example using the reference CartPole environment:

 ```python
-from lerobot.envs import make_env
+from lerobot.envs.factory import make_env
 import numpy as np

 # Load the environment
@@ -58,10 +58,10 @@ pip install -e .
 cd ..


-# 5. Install LeRobot (evaluation extra for env/policy evaluation)
+# 5. Install LeRobot
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
-pip install -e ".[evaluation]"
+pip install -e .
 cd ..


@@ -262,7 +262,7 @@ def main(cfg: EvalPipelineConfig):
    """Run random action rollout for IsaacLab Arena environment."""
    logging.info(pformat(asdict(cfg)))

-    from lerobot.envs import make_env
+    from lerobot.envs.factory import make_env

    env_dict = make_env(
        cfg.env,
@@ -74,7 +74,7 @@ EnvHub exposes every LeIsaac-supported task in a uniform interface. The examples
 # envhub_random_action.py

 import torch
-from lerobot.envs import make_env
+from lerobot.envs.factory import make_env

 # Load from the hub
 envs_dict = make_env("LightwheelAI/leisaac_env:envs/so101_pick_orange.py", n_envs=1, trust_remote_code=True)
@@ -142,7 +142,7 @@ from lerobot.teleoperators import (  # noqa: F401
 )
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import init_logging
-from lerobot.envs import make_env
+from lerobot.envs.factory import make_env


@dataclass
@@ -282,7 +282,7 @@ Note: when working with `bi_so101_fold_cloth`, call `initialize()` immediately a

 ```python
 import torch
-from lerobot.envs import make_env
+from lerobot.envs.factory import make_env

 # Load from the hub
 envs_dict = make_env("LightwheelAI/leisaac_env:envs/bi_so101_fold_cloth.py", n_envs=1, trust_remote_code=True)
@@ -58,8 +58,8 @@ lerobot-teleoperate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.teleoperators.so_leader import SO101Leader, SO101LeaderConfig
-from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
+from lerobot.teleoperators.so_leader import SO101LeaderConfig, SO101Leader
+from lerobot.robots.so_follower import SO101FollowerConfig, SO101Follower

 robot_config = SO101FollowerConfig(
    port="/dev/tty.usbmodem58760431541",
@@ -116,9 +116,9 @@ lerobot-teleoperate \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.teleoperators.koch_leader import KochLeader, KochLeaderConfig
-from lerobot.robots.koch_follower import KochFollower, KochFollowerConfig
+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)
@@ -195,12 +195,13 @@ lerobot-record \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.datasets import LeRobotDataset
-from lerobot.utils.feature_utils import hw_to_dataset_features
+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.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
-from lerobot.common.control_utils import init_keyboard_listener
+from lerobot.teleoperators.so_leader.config_so100_leader import SO100LeaderConfig
+from lerobot.teleoperators.so_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.scripts.lerobot_record import record_loop
@@ -409,8 +410,9 @@ lerobot-replay \
 ```python
 import time

-from lerobot.datasets import LeRobotDataset
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.robots.so_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so_follower.so100_follower import SO100Follower
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say

@@ -530,14 +532,15 @@ lerobot-record  \

 <!-- prettier-ignore-start -->
 ```python
-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.datasets import LeRobotDataset
-from lerobot.utils.feature_utils import hw_to_dataset_features
-from lerobot.policies.act import ACTPolicy
-from lerobot.policies import make_pre_post_processors
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.robots.so_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so_follower.so100_follower import SO100Follower
 from lerobot.scripts.lerobot_record import record_loop
-from lerobot.common.control_utils import init_keyboard_listener
+from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun

@@ -116,8 +116,6 @@ brew install ffmpeg

 ## Step 3: Install LeRobot 🤗

-The base `lerobot` install is intentionally **lightweight** — it includes only core ML dependencies (PyTorch, torchvision, numpy, opencv, einops, draccus, huggingface-hub, gymnasium, safetensors). Heavier dependencies are gated behind optional extras so you only install what you need.
-
 ### From Source

 First, clone the repository and navigate into the directory:
@@ -133,16 +131,12 @@ Then, install the library in editable mode. This is useful if you plan to contri
 <hfoptions id="install_lerobot_src">
 <hfoption id="conda">
 ```bash
-pip install -e ".[core_scripts]"  # For robot workflows (recording, replaying, calibrate)
-pip install -e ".[training]"      # For training policies
-pip install -e ".[all]"           # Everything (all policies, envs, hardware, dev tools)
+pip install -e .
 ```
 </hfoption>
 <hfoption id="uv">
 ```bash
-uv pip install -e ".[core_scripts]"  # For robot workflows (recording, replaying, calibrate)
-uv pip install -e ".[training]"      # For training policies
-uv pip install -e ".[all]"           # Everything (all policies, envs, hardware, dev tools)
+uv pip install -e .
 ```
 </hfoption>
 </hfoptions>
@@ -168,48 +162,26 @@ uv pip install lerobot
 </hfoptions>
 <!-- prettier-ignore-end -->

-_This installs only the core ML dependencies. You will need to add extras for most workflows._
+_This installs only the default dependencies._

-**Feature Extras:**
-LeRobot provides **feature-scoped extras** that map to common workflows. If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.
-
-| Extra      | What it adds                                | Typical use case                    |
-| ---------- | ------------------------------------------- | ----------------------------------- |
-| `dataset`  | `datasets`, `av`, `torchcodec`, `jsonlines` | Loading & creating datasets         |
-| `training` | `dataset` + `accelerate`, `wandb`           | Training policies                   |
-| `hardware` | `pynput`, `pyserial`, `deepdiff`            | Connecting to real robots           |
-| `viz`      | `rerun-sdk`                                 | Visualization during recording/eval |
-
-**Composite Extras** combine feature extras for common CLI scripts:
-
-| Extra          | Includes                       | Typical use case                                        |
-| -------------- | ------------------------------ | ------------------------------------------------------- |
-| `core_scripts` | `dataset` + `hardware` + `viz` | `lerobot-record`, `lerobot-replay`, `lerobot-calibrate` |
-| `evaluation`   | `av`                           | `lerobot-eval` (add policy + env extras as needed)      |
-| `dataset_viz`  | `dataset` + `viz`              | `lerobot-dataset-viz`, `lerobot-imgtransform-viz`       |
+**Extra Features:**
+To install additional functionality, use one of the following (If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.):

 ```bash
-pip install 'lerobot[core_scripts]'          # Record, replay, calibrate
-pip install 'lerobot[training]'              # Train policies
-pip install 'lerobot[core_scripts,training]' # Record + train
-pip install 'lerobot[all]'                   # Everything
+pip install 'lerobot[all]'          # All available features
+pip install 'lerobot[aloha,pusht]'  # Specific features (Aloha & Pusht)
+pip install 'lerobot[feetech]'      # Feetech motor support
 ```

-**Policy, environment, and hardware extras** are still available for specific dependencies:
+_Replace `[...]` with your desired features._

-```bash
-pip install 'lerobot[pi]'             # Pi0/Pi0.5/Pi0-FAST policy deps
-pip install 'lerobot[smolvla]'        # SmolVLA policy deps
-pip install 'lerobot[diffusion]'      # Diffusion policy deps (diffusers)
-pip install 'lerobot[aloha,pusht]'    # Simulation environments
-pip install 'lerobot[feetech]'        # Feetech motor support
-```
-
-_Multiple extras can be combined (e.g., `.[core_scripts,pi,pusht]`). For a full list of available extras, refer to `pyproject.toml`._
+**Available Tags:**
+For a full list of optional dependencies, see:
+https://pypi.org/project/lerobot/

 ### Troubleshooting

-If you encounter build errors, you may need to install additional system dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
+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
@@ -224,8 +196,8 @@ LeRobot provides optional extras for specific functionalities. Multiple extras c

 ### Simulations

-Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht)).
-These automatically include the `dataset` extra.
+Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
+Example:

 ```bash
 pip install -e ".[aloha]" # or "[pusht]" for example
@@ -241,7 +213,7 @@ pip install -e ".[feetech]" # or "[dynamixel]" for example

 ### Experiment Tracking

-Weights and Biases is included in the `training` extra. To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with:
+To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with

 ```bash
 wandb login
@@ -19,10 +19,10 @@ This means that your favorite policy can be used like this:
 ```python
 import torch

-from lerobot.datasets import LeRobotDataset
-from lerobot.policies import make_pre_post_processors
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_pre_post_processors
 from lerobot.policies.your_policy import YourPolicy
-from lerobot.processor import RobotProcessorPipeline, PolicyProcessorPipeline
+from lerobot.processor.pipeline import RobotProcessorPipeline, PolicyProcessorPipeline
 dataset = LeRobotDataset("hf_user/dataset", episodes=[0])
 sample = dataset[10]

@@ -260,7 +260,7 @@ Since processor pipelines can add new features (like velocity fields), change te
 These functions work together by starting with robot hardware specifications (`create_initial_features()`) then simulating the entire pipeline transformation (`aggregate_pipeline_dataset_features()`) to compute the final feature dictionary that gets passed to `LeRobotDataset.create()`, ensuring perfect alignment between what processors output and what datasets expect to store.

 ```python
-from lerobot.datasets import aggregate_pipeline_dataset_features
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features

 # Start with robot's raw features
 initial_features = create_initial_features(
@@ -89,7 +89,7 @@ A core v3 principle is **decoupling storage from the user API**: data is stored

 ```python
 import torch
-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset

 repo_id = "yaak-ai/L2D-v3"

@@ -135,7 +135,7 @@ for batch in data_loader:
 Use `StreamingLeRobotDataset` to iterate directly from the Hub without local copies. This allows to stream large datasets without the need to downloading them onto disk or loading them onto memory, and is a key feature of the new dataset format.

 ```python
-from lerobot.datasets import StreamingLeRobotDataset
+from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset

 repo_id = "yaak-ai/L2D-v3"
 dataset = StreamingLeRobotDataset(repo_id)  # streams directly from the Hub
@@ -167,8 +167,8 @@ Currently, transforms are applied during **training time only**, not during reco
 Use the `image_transforms` parameter when loading a dataset for training:

 ```python
-from lerobot.datasets import LeRobotDataset
-from lerobot.transforms import ImageTransforms, ImageTransformsConfig, ImageTransformConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig, ImageTransformConfig

 # Option 1: Use default transform configuration (disabled by default)
 transforms_config = ImageTransformsConfig(
@@ -290,7 +290,7 @@ python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DAT
 When creating or recording datasets, you **must** call `dataset.finalize()` to properly close parquet writers. See the [PR #1903](https://github.com/huggingface/lerobot/pull/1903) for more details.

 ```python
-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset

 # Create dataset and record episodes
 dataset = LeRobotDataset.create(...)
@@ -4,10 +4,10 @@ This guide shows you how to train policies on multiple GPUs using [Hugging Face

 ## Installation

-`accelerate` is included in the `training` extra. Install it with:
+First, ensure you have accelerate installed:

 ```bash
-pip install 'lerobot[training]'
+pip install accelerate
 ```

 ## Training with Multiple GPUs
@@ -45,8 +45,7 @@ Modify the examples to use `PhoneOS.IOS` or `PhoneOS.ANDROID` in `PhoneConfig`.
 Teleoperation example:

 ```python
-from lerobot.teleoperators.phone import Phone, PhoneConfig
-from lerobot.teleoperators.phone.config_phone import PhoneOS
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS

 teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
 teleop_device = Phone(teleop_config)
@@ -110,7 +110,8 @@ lerobot-edit-dataset \
 Or equivalently in Python:

 ```python
-from lerobot.datasets import LeRobotDataset, recompute_stats
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.dataset_tools import recompute_stats

 dataset = LeRobotDataset("your_dataset")
 recompute_stats(dataset, relative_action=True, chunk_size=50, relative_exclude_joints=["gripper"])
@@ -116,7 +116,8 @@ lerobot-edit-dataset \
 Or equivalently in Python:

 ```python
-from lerobot.datasets import LeRobotDataset, recompute_stats
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.dataset_tools import recompute_stats

 dataset = LeRobotDataset("your_dataset")
 recompute_stats(dataset, relative_action=True, chunk_size=50, relative_exclude_joints=["gripper"])
@@ -60,10 +60,11 @@ When `use_relative_actions=true`, the training script automatically:
 ### Recomputing stats for an existing dataset

 If you want to precompute relative action stats offline, use `recompute_stats` from
-`lerobot.datasets`:
+`lerobot.datasets.dataset_tools`:

 ```python
-from lerobot.datasets import LeRobotDataset, recompute_stats
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.dataset_tools import recompute_stats

 dataset = LeRobotDataset("your_org/your_dataset")
 dataset = recompute_stats(
@@ -39,8 +39,9 @@ The snippet below provides a simplified pseudo-example of how RTC operates with

 ```python
 from lerobot.policies.pi0 import PI0Policy, PI0Config
-from lerobot.configs import RTCAttentionSchedule
-from lerobot.policies.rtc import RTCConfig, ActionQueue
+from lerobot.configs.types import RTCAttentionSchedule
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.policies.rtc.action_queue import ActionQueue

 # Load Pi0 with RTC enabled
 policy_cfg = PI0Config()
@@ -418,7 +418,7 @@ Create a custom preprocessing pipeline for your environment:

 ```python
 from lerobot.processor import PolicyProcessorPipeline
-from lerobot.policies.xvla import (
+from lerobot.policies.xvla.processor_xvla import (
    XVLAImageToFloatProcessorStep,
    XVLAImageNetNormalizeProcessorStep,
    XVLAAddDomainIdProcessorStep,
@@ -35,7 +35,7 @@ from pprint import pformat

 import draccus

-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.robots import (  # noqa: F401
    Robot,
    RobotConfig,
@@ -31,11 +31,17 @@ from pprint import pprint
 import torch
 from huggingface_hub import HfApi

-from lerobot.datasets import LeRobotDataset, LeRobotDatasetMetadata
+import lerobot
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.lerobot_dataset import LeRobotDataset


 def main():
-    # Browse datasets created/ported by the community on the hub using the hub api:
+    # We ported a number of existing datasets ourselves, use this to see the list:
+    print("List of available datasets:")
+    pprint(lerobot.available_datasets)
+
+    # You can also browse through the datasets created/ported by the community on the hub using the hub api:
    hub_api = HfApi()
    repo_ids = [info.id for info in hub_api.list_datasets(task_categories="robotics", tags=["LeRobot"])]
    pprint(repo_ids)
@@ -231,7 +231,7 @@ class AggregateProgress(PipelineStep):
        import pyarrow as pa
        import pyarrow.parquet as pq

-        from lerobot.datasets import LeRobotDataset
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
        from lerobot.utils.utils import init_logging

        init_logging()
@@ -26,8 +26,8 @@ import torch
 from torchvision.transforms import v2
 from torchvision.transforms.functional import to_pil_image

-from lerobot.datasets import LeRobotDataset
-from lerobot.transforms import ImageTransformConfig, ImageTransforms, ImageTransformsConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.transforms import ImageTransformConfig, ImageTransforms, ImageTransformsConfig


 def save_image(tensor, filename):
@@ -29,8 +29,7 @@ Usage:

 import numpy as np

-from lerobot.datasets import (
-    LeRobotDataset,
+from lerobot.datasets.dataset_tools import (
    add_features,
    delete_episodes,
    merge_datasets,
@@ -38,6 +37,7 @@ from lerobot.datasets import (
    remove_feature,
    split_dataset,
 )
+from lerobot.datasets.lerobot_dataset import LeRobotDataset


 def main():
@@ -112,18 +112,17 @@ from hil_utils import (
    teleop_smooth_move_to,
 )

-from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.cameras.realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.common.control_utils import is_headless, predict_action
-from lerobot.configs import PreTrainedConfig, parser
-from lerobot.datasets import (
-    LeRobotDataset,
-    VideoEncodingManager,
-    aggregate_pipeline_dataset_features,
-    create_initial_features,
-    safe_stop_image_writer,
-)
-from lerobot.policies import PreTrainedPolicy, get_policy_class, make_policy, make_pre_post_processors
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.configs import parser
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.feature_utils import build_dataset_frame, combine_feature_dicts, hw_to_dataset_features
+from lerobot.datasets.image_writer import safe_stop_image_writer
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.video_utils import VideoEncodingManager
+from lerobot.policies.factory import get_policy_class, make_policy, make_pre_post_processors
+from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.rtc import ActionInterpolator, ActionQueue, LatencyTracker, RTCConfig
 from lerobot.policies.utils import make_robot_action
 from lerobot.processor import (
@@ -132,18 +131,18 @@ from lerobot.processor import (
    RelativeActionsProcessorStep,
    TransitionKey,
    create_transition,
-    rename_stats,
-    to_relative_actions,
 )
+from lerobot.processor.relative_action_processor import to_relative_actions
+from lerobot.processor.rename_processor import rename_stats
 from lerobot.robots import Robot, RobotConfig, make_robot_from_config
-from lerobot.robots.bi_openarm_follower import BiOpenArmFollowerConfig
-from lerobot.robots.so_follower import SOFollowerRobotConfig  # noqa: F401
+from lerobot.robots.bi_openarm_follower.config_bi_openarm_follower import BiOpenArmFollowerConfig
+from lerobot.robots.so_follower.config_so_follower import SOFollowerRobotConfig  # noqa: F401
 from lerobot.teleoperators import Teleoperator, TeleoperatorConfig, make_teleoperator_from_config
-from lerobot.teleoperators.openarm_mini import OpenArmMiniConfig  # noqa: F401
-from lerobot.teleoperators.so_leader import SOLeaderTeleopConfig  # noqa: F401
-from lerobot.utils import get_safe_torch_device
+from lerobot.teleoperators.openarm_mini.config_openarm_mini import OpenArmMiniConfig  # noqa: F401
+from lerobot.teleoperators.so_leader.config_so_leader import SOLeaderTeleopConfig  # noqa: F401
 from lerobot.utils.constants import ACTION, OBS_STATE, OBS_STR
-from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts, hw_to_dataset_features
+from lerobot.utils.control_utils import is_headless, predict_action
+from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import init_logging, log_say
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
@@ -19,12 +19,13 @@ import time
 from dataclasses import dataclass, field
 from pathlib import Path

-from lerobot.common.control_utils import is_headless
 from lerobot.processor import (
    IdentityProcessorStep,
    RobotAction,
    RobotObservation,
    RobotProcessorPipeline,
+)
+from lerobot.processor.converters import (
    observation_to_transition,
    robot_action_observation_to_transition,
    transition_to_observation,
@@ -32,6 +33,7 @@ from lerobot.processor import (
 )
 from lerobot.robots import Robot
 from lerobot.teleoperators import Teleoperator
+from lerobot.utils.control_utils import is_headless
 from lerobot.utils.robot_utils import precise_sleep

 logger = logging.getLogger(__name__)
@@ -14,15 +14,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.datasets import LeRobotDataset
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.act import ACTPolicy
+from lerobot.datasets.feature_utils import hw_to_dataset_features
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
 from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.constants import ACTION, OBS_STR
-from lerobot.utils.feature_utils import hw_to_dataset_features
+from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun

@@ -14,15 +14,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.feature_utils import hw_to_dataset_features
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.processor import make_default_processors
-from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
+from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
 from lerobot.utils.constants import ACTION, OBS_STR
-from lerobot.utils.feature_utils import hw_to_dataset_features
+from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun

@@ -16,8 +16,9 @@

 import time

-from lerobot.datasets import LeRobotDataset
-from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
+from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
 from lerobot.utils.constants import ACTION
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
@@ -14,16 +14,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.configs import FeatureType, PolicyFeature
-from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.datasets.feature_utils import combine_feature_dicts
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.act import ACTPolicy
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
 from lerobot.processor import (
    RobotProcessorPipeline,
    make_default_teleop_action_processor,
+)
+from lerobot.processor.converters import (
    observation_to_transition,
    robot_action_observation_to_transition,
    transition_to_observation,
@@ -36,7 +39,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 )
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun

@@ -14,12 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.feature_utils import combine_feature_dicts
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import (
-    RobotProcessorPipeline,
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
    observation_to_transition,
    robot_action_observation_to_transition,
    transition_to_observation,
@@ -34,11 +35,11 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
    InverseKinematicsEEToJoints,
 )
 from lerobot.scripts.lerobot_record import record_loop
-from lerobot.teleoperators.phone import Phone, PhoneConfig
-from lerobot.teleoperators.phone.config_phone import PhoneOS
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.teleoperators.phone.teleop_phone import Phone
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun

@@ -16,10 +16,10 @@

 import time

-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import (
-    RobotProcessorPipeline,
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
    robot_action_observation_to_transition,
    transition_to_robot_action,
 )
@@ -16,8 +16,8 @@
 import time

 from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import (
-    RobotProcessorPipeline,
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
    robot_action_observation_to_transition,
    transition_to_robot_action,
 )
@@ -28,9 +28,9 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
    GripperVelocityToJoint,
    InverseKinematicsEEToJoints,
 )
-from lerobot.teleoperators.phone import Phone, PhoneConfig
-from lerobot.teleoperators.phone.config_phone import PhoneOS
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
+from lerobot.teleoperators.phone.teleop_phone import Phone
 from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
@@ -22,7 +22,8 @@ from pathlib import Path
 import numpy as np
 import tensorflow_datasets as tfds

-from lerobot.datasets import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.utils.utils import get_elapsed_time_in_days_hours_minutes_seconds

 DROID_SHARDS = 2048
@@ -36,7 +36,7 @@ class AggregateDatasets(PipelineStep):
    def run(self, data=None, rank: int = 0, world_size: int = 1):
        import logging

-        from lerobot.datasets import aggregate_datasets
+        from lerobot.datasets.aggregate import aggregate_datasets
        from lerobot.utils.utils import init_logging

        init_logging()
@@ -26,7 +26,8 @@ from huggingface_hub import HfApi
 from huggingface_hub.constants import REPOCARD_NAME
 from port_droid import DROID_SHARDS

-from lerobot.datasets import CODEBASE_VERSION, LeRobotDatasetMetadata, create_lerobot_dataset_card
+from lerobot.datasets.dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
+from lerobot.datasets.utils import create_lerobot_dataset_card
 from lerobot.utils.utils import init_logging


@@ -154,7 +155,7 @@ class UploadDataset(PipelineStep):
        from datasets.utils.tqdm import disable_progress_bars
        from huggingface_hub import CommitOperationAdd, preupload_lfs_files

-        from lerobot.datasets import LeRobotDatasetMetadata
+        from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
        from lerobot.utils.utils import init_logging

        init_logging()
@@ -109,10 +109,15 @@ except ImportError:
    MATPLOTLIB_AVAILABLE = False
    plt = None

-from lerobot.configs import DatasetConfig, PreTrainedConfig, RTCAttentionSchedule, parser
-from lerobot.datasets import LeRobotDataset, LeRobotDatasetMetadata, resolve_delta_timestamps
-from lerobot.policies import get_policy_class, make_pre_post_processors
-from lerobot.policies.rtc import RTCConfig
+from lerobot.configs import parser
+from lerobot.configs.default import DatasetConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import RTCAttentionSchedule
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.factory import resolve_delta_timestamps
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
 from lerobot.policies.rtc.debug_visualizer import RTCDebugVisualizer
 from lerobot.utils.hub import HubMixin
 from lerobot.utils.utils import init_logging
@@ -101,21 +101,26 @@ from threading import Event, Lock, Thread
 import torch
 from torch import Tensor

-from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.cameras.realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.cameras.zmq import ZMQCameraConfig  # noqa: F401
-from lerobot.configs import PreTrainedConfig, RTCAttentionSchedule, parser
-from lerobot.policies import get_policy_class, make_pre_post_processors
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
+from lerobot.configs import parser
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import RTCAttentionSchedule
+from lerobot.datasets.feature_utils import build_dataset_frame, hw_to_dataset_features
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
 from lerobot.policies.rtc import ActionInterpolator, ActionQueue, LatencyTracker, RTCConfig
 from lerobot.processor import (
    NormalizerProcessorStep,
    RelativeActionsProcessorStep,
    TransitionKey,
    create_transition,
+)
+from lerobot.processor.factory import (
    make_default_robot_action_processor,
    make_default_robot_observation_processor,
-    to_relative_actions,
 )
+from lerobot.processor.relative_action_processor import to_relative_actions
 from lerobot.rl.process import ProcessSignalHandler
 from lerobot.robots import (  # noqa: F401
    Robot,
@@ -128,7 +133,6 @@ from lerobot.robots import (  # noqa: F401
 )
 from lerobot.robots.utils import make_robot_from_config
 from lerobot.utils.constants import OBS_IMAGES, OBS_STATE
-from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
 from lerobot.utils.hub import HubMixin
 from lerobot.utils.utils import init_logging

@@ -14,16 +14,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.configs import FeatureType, PolicyFeature
-from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.datasets.feature_utils import combine_feature_dicts
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.act import ACTPolicy
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
 from lerobot.processor import (
    RobotProcessorPipeline,
    make_default_teleop_action_processor,
+)
+from lerobot.processor.converters import (
    observation_to_transition,
    robot_action_observation_to_transition,
    transition_to_observation,
@@ -36,7 +39,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 )
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun

@@ -15,12 +15,13 @@
 # limitations under the License.


-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.feature_utils import combine_feature_dicts
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import (
-    RobotProcessorPipeline,
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
    observation_to_transition,
    robot_action_observation_to_transition,
    transition_to_observation,
@@ -35,7 +36,7 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun

@@ -17,10 +17,10 @@

 import time

-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import (
-    RobotProcessorPipeline,
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
    robot_action_observation_to_transition,
    transition_to_robot_action,
 )
@@ -17,8 +17,8 @@
 import time

 from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import (
-    RobotProcessorPipeline,
+from lerobot.processor import RobotProcessorPipeline
+from lerobot.processor.converters import (
    robot_action_observation_to_transition,
    robot_action_to_transition,
    transition_to_robot_action,
@@ -18,11 +18,13 @@ from pathlib import Path

 import torch

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


 def main():
@@ -19,12 +19,14 @@ from pathlib import Path

 import torch

-from lerobot.configs import FeatureType
-from lerobot.datasets import LeRobotDatasetMetadata, StreamingLeRobotDataset
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.act import ACTConfig, ACTPolicy
+from lerobot.configs.types import FeatureType
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.feature_utils import dataset_to_policy_features
+from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
+from lerobot.policies.act.configuration_act import ACTConfig
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
 from lerobot.utils.constants import ACTION
-from lerobot.utils.feature_utils import dataset_to_policy_features


 def main():
@@ -4,11 +4,13 @@ from pathlib import Path

 import torch

-from lerobot.configs import FeatureType
-from lerobot.datasets import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.act import ACTConfig, ACTPolicy
-from lerobot.utils.feature_utils import dataset_to_policy_features
+from lerobot.configs.types import FeatureType
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.feature_utils import dataset_to_policy_features
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.act.configuration_act import ACTConfig
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors


 def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[float]:
@@ -1,9 +1,9 @@
 import torch

-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.datasets import LeRobotDatasetMetadata
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.act import ACTPolicy
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
 from lerobot.policies.utils import build_inference_frame, make_robot_action
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig

@@ -3,7 +3,7 @@ import threading
 from lerobot.async_inference.configs import RobotClientConfig
 from lerobot.async_inference.helpers import visualize_action_queue_size
 from lerobot.async_inference.robot_client import RobotClient
-from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.robots.so_follower import SO100FollowerConfig


@@ -4,11 +4,13 @@ from pathlib import Path

 import torch

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


 def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[float]:
@@ -1,9 +1,9 @@
 import torch

-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.datasets import LeRobotDatasetMetadata
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.diffusion import DiffusionPolicy
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.policies.factory import make_pre_post_processors
 from lerobot.policies.utils import build_inference_frame, make_robot_action
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig

@@ -1,11 +1,11 @@
 import torch

-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.pi0 import PI0Policy
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.feature_utils import hw_to_dataset_features
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.pi0.modeling_pi0 import PI0Policy
 from lerobot.policies.utils import build_inference_frame, make_robot_action
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.utils.feature_utils import hw_to_dataset_features

 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
@@ -6,17 +6,17 @@ from queue import Empty, Full
 import torch
 import torch.optim as optim

-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.feature_utils import hw_to_dataset_features
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.envs.configs import HILSerlProcessorConfig, HILSerlRobotEnvConfig
-from lerobot.policies import SACConfig
+from lerobot.policies.sac.configuration_sac import SACConfig
 from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
 from lerobot.rl.buffer import ReplayBuffer
 from lerobot.rl.gym_manipulator import make_robot_env
 from lerobot.robots.so_follower import SO100FollowerConfig
-from lerobot.teleoperators import TeleopEvents
 from lerobot.teleoperators.so_leader import SO100LeaderConfig
-from lerobot.utils.feature_utils import hw_to_dataset_features
+from lerobot.teleoperators.utils import TeleopEvents

 LOG_EVERY = 10
 SEND_EVERY = 10
@@ -1,7 +1,8 @@
 import torch

-from lerobot.datasets import LeRobotDataset
-from lerobot.policies import RewardClassifierConfig, make_policy, make_pre_post_processors
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig


 def main():
@@ -1,11 +1,11 @@
 import torch

-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.policies import make_pre_post_processors
-from lerobot.policies.smolvla import SmolVLAPolicy
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.feature_utils import hw_to_dataset_features
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 from lerobot.policies.utils import build_inference_frame, make_robot_action
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.utils.feature_utils import hw_to_dataset_features

 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
@@ -58,74 +58,45 @@ classifiers = [
 keywords = ["lerobot", "huggingface", "robotics",  "machine learning", "artificial intelligence"]

 dependencies = [
-    # Core ML
-    "torch>=2.7,<2.11.0",
-    "torchvision>=0.22.0,<0.26.0",
-    "numpy>=2.0.0,<2.3.0", # NOTE: Explicitly listing numpy helps the resolver converge faster. Upper bound imposed by opencv-python-headless.
-    "opencv-python-headless>=4.9.0,<4.14.0",
-    "Pillow>=10.0.0,<13.0.0",
-    "einops>=0.8.0,<0.9.0",

-    # Config & Hub
-    "draccus==0.10.0", # TODO: Relax version constraint
+    # Hugging Face dependencies
+    "datasets>=4.0.0,<5.0.0",
+    "diffusers>=0.27.2,<0.36.0",
    "huggingface-hub>=1.0.0,<2.0.0",
-    "requests>=2.32.0,<3.0.0",
+    "accelerate>=1.10.0,<2.0.0",

-    # Environments
-    # NOTE: gymnasium is used in lerobot.envs (lerobot-train, lerobot-eval), policies/factory,
-    # and robots/unitree. Moving it to an optional extra would require import guards across many
-    # tightly-coupled modules. Candidate for a future refactor to decouple envs from the core.
-    "gymnasium>=1.1.1,<2.0.0",
-
-    # Serialization & checkpointing
-    "safetensors>=0.4.3,<1.0.0",
-
-    # Lightweight utilities
-    "packaging>=24.2,<26.0",
-    "termcolor>=2.4.0,<4.0.0",
-    "tqdm>=4.66.0,<5.0.0",
-
-    # Build tools (required by opencv-python-headless on some platforms)
-    "cmake>=3.29.0.1,<4.2.0",
+    # Core dependencies
+    "numpy>=2.0.0,<2.3.0", # NOTE: Explicitly listing numpy helps the resolver converge faster. Upper bound imposed by opencv-python-headless.
    "setuptools>=71.0.0,<81.0.0",
+    "cmake>=3.29.0.1,<4.2.0",
+    "packaging>=24.2,<26.0",
+
+    "torch>=2.7,<2.11.0",
+    "torchcodec>=0.3.0,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # NOTE: Windows support starts at version 0.7 (needs torch==2.8), ffmpeg>=8 support starts at version 0.8.1 (needs torch==2.9), system-wide ffmpeg support starts at version 0.10 (needs torch==2.10).
+    "torchvision>=0.22.0,<0.26.0",
+
+    "einops>=0.8.0,<0.9.0",
+    "opencv-python-headless>=4.9.0,<4.14.0",
+    "av>=15.0.0,<16.0.0",
+    "jsonlines>=4.0.0,<5.0.0",
+    "pynput>=1.7.8,<1.9.0",
+    "pyserial>=3.5,<4.0",
+
+    "wandb>=0.24.0,<0.25.0",
+    "draccus==0.10.0", # TODO: Relax version constraint
+    "gymnasium>=1.1.1,<2.0.0",
+    "rerun-sdk>=0.24.0,<0.27.0",
+
+    # Support dependencies
+    "deepdiff>=7.0.1,<9.0.0",
+    "imageio[ffmpeg]>=2.34.0,<3.0.0",
+    "termcolor>=2.4.0,<4.0.0",
 ]

 # Optional dependencies
 [project.optional-dependencies]

-# ── Feature-scoped extras ──────────────────────────────────
-dataset = [
-    "datasets>=4.0.0,<5.0.0",
-    "pandas>=2.0.0,<3.0.0", # NOTE: Transitive dependency of datasets
-    "pyarrow>=21.0.0,<30.0.0", # NOTE: Transitive dependency of datasets
-    "lerobot[av-dep]",
-    "torchcodec>=0.3.0,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # NOTE: Windows support starts at version 0.7 (needs torch==2.8), ffmpeg>=8 support starts at version 0.8.1 (needs torch==2.9), system-wide ffmpeg support starts at version 0.10 (needs torch==2.10).
-    "jsonlines>=4.0.0,<5.0.0",
-]
-training = [
-    "lerobot[dataset]",
-    "accelerate>=1.10.0,<2.0.0",
-    "wandb>=0.24.0,<0.25.0",
-]
-hardware = [
-    "pynput>=1.7.8,<1.9.0",
-    "pyserial>=3.5,<4.0",
-    "deepdiff>=7.0.1,<9.0.0",
-]
-viz = [
-    "rerun-sdk>=0.24.0,<0.27.0",
-]
-# ── User-facing composite extras (map to CLI scripts) ─────
-# lerobot-record, lerobot-replay, lerobot-calibrate, lerobot-teleoperate, etc.
-core_scripts = ["lerobot[dataset]", "lerobot[hardware]", "lerobot[viz]"]
-# lerobot-eval -- base evaluation framework. You also need the policy's extra (e.g., lerobot[pi])
-# and the environment's extra (e.g., lerobot[pusht]) if evaluating in simulation.
-evaluation = ["lerobot[av-dep]"]
-# lerobot-dataset-viz, lerobot-imgtransform-viz
-dataset_viz = ["lerobot[dataset]", "lerobot[viz]"]
-
 # Common
-av-dep = ["av>=15.0.0,<16.0.0"]
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 placo-dep = ["placo>=0.9.6,<0.9.17"]
 transformers-dep = ["transformers==5.3.0"] # TODO(Steven): https://github.com/huggingface/lerobot/pull/3249
@@ -133,7 +104,6 @@ grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
 peft-dep = ["peft>=0.18.0,<1.0.0"]
 scipy-dep = ["scipy>=1.14.0,<2.0.0"]
-diffusers-dep = ["diffusers>=0.27.2,<0.36.0"]
 qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
 matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.

@@ -166,28 +136,28 @@ intelrealsense = [
 phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0", "lerobot[scipy-dep]"]

 # Policies
-diffusion = ["lerobot[diffusers-dep]"]
 wallx = [
    "lerobot[transformers-dep]",
-    "lerobot[peft-dep]",
+    "lerobot[peft]",
    "lerobot[scipy-dep]",
    "torchdiffeq>=0.2.4,<0.3.0",
    "lerobot[qwen-vl-utils-dep]",
 ]
 pi = ["lerobot[transformers-dep]", "lerobot[scipy-dep]"]
-smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0"]
-multi_task_dit = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]"]
+smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]
+multi_task_dit = ["lerobot[transformers-dep]"]
 groot = [
    "lerobot[transformers-dep]",
-    "lerobot[peft-dep]",
-    "lerobot[diffusers-dep]",
+    "lerobot[peft]",
    "dm-tree>=0.1.8,<1.0.0",
    "timm>=1.0.0,<1.1.0",
+    "safetensors>=0.4.3,<1.0.0",
+    "Pillow>=10.0.0,<13.0.0",
    "decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
    "ninja>=1.11.1,<2.0.0",
    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-sarm = ["lerobot[transformers-dep]", "pydantic>=2.0.0,<3.0.0", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
+sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
 xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]

@@ -196,42 +166,31 @@ async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
 peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]

 # Development
-dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1", "ruff>=0.14.1"]
+dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
 test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]

 # Simulation
 # NOTE: Explicitly listing scipy helps flatten the dependecy tree.
-aloha = ["lerobot[dataset]", "gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
-pusht = ["lerobot[dataset]", "gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-libero = ["lerobot[dataset]", "lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
-metaworld = ["lerobot[dataset]", "metaworld==3.0.0", "lerobot[scipy-dep]"]
+aloha = ["gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
+pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
+libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
+metaworld = ["metaworld==3.0.0", "lerobot[scipy-dep]"]

 # All
 all = [
-    # Feature-scoped extras
-    "lerobot[dataset]",
-    "lerobot[training]",
-    "lerobot[hardware]",
-    "lerobot[viz]",
    # NOTE(resolver hint): scipy is pulled in transitively via lerobot[scipy-dep] through
    # multiple extras (aloha, metaworld, pi, wallx, phone). Listing it explicitly
    # helps pip's resolver converge by constraining scipy early, before it encounters
    # the loose scipy requirements from transitive deps like dm-control and metaworld.
    "scipy>=1.14.0,<2.0.0",
    "lerobot[dynamixel]",
-    "lerobot[feetech]",
-    "lerobot[damiao]",
-    "lerobot[robstride]",
    "lerobot[gamepad]",
    "lerobot[hopejr]",
    "lerobot[lekiwi]",
-    "lerobot[openarms]",
    "lerobot[reachy2]",
    "lerobot[kinematics]",
    "lerobot[intelrealsense]",
-    "lerobot[diffusion]",
-    "lerobot[multi_task_dit]",
    "lerobot[wallx]",
    "lerobot[pi]",
    "lerobot[smolvla]",
@@ -308,9 +267,7 @@ ignore = [
 ]

 [tool.ruff.lint.per-file-ignores]
-"__init__.py" = ["F401", "F403", "E402"]
-# E402: conditional-import guards (TYPE_CHECKING / is_package_available) must precede the imports they protect
-"src/lerobot/scripts/convert_dataset_v21_to_v30.py" = ["E402"]
+"__init__.py" = ["F401", "F403"]
 "src/lerobot/policies/wall_x/**" = ["N801", "N812", "SIM102", "SIM108", "SIM210", "SIM211", "B006", "B007", "SIM118"] # Supprese these as they are coming from original Qwen2_5_vl code TODO(pepijn): refactor original

 [tool.ruff.lint.isort]
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Extract natural-language task descriptions for a benchmark suite.
+
+Runs inside the benchmark Docker container (where the env library is installed)
+immediately after lerobot-eval, writing a JSON file that parse_eval_metrics.py
+picks up and embeds in metrics.json.
+
+Output format: {"<suite>_<task_idx>": "<nl instruction>", ...}
+
+Usage:
+    python scripts/ci/extract_task_descriptions.py \\
+        --env libero --task libero_spatial \\
+        --output /tmp/eval-artifacts/task_descriptions.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+
+def _libero_descriptions(task_suite: str) -> dict[str, str]:
+    from libero.libero import benchmark  # type: ignore[import-untyped]
+
+    suite_dict = benchmark.get_benchmark_dict()
+    if task_suite not in suite_dict:
+        print(
+            f"[extract_task_descriptions] Unknown LIBERO suite '{task_suite}'. "
+            f"Available: {list(suite_dict.keys())}",
+            file=sys.stderr,
+        )
+        return {}
+    suite = suite_dict[task_suite]()
+    return {f"{task_suite}_{i}": suite.get_task(i).language for i in range(suite.n_tasks)}
+
+
+def _metaworld_descriptions(task_name: str) -> dict[str, str]:
+    # MetaWorld tasks don't expose a separate NL description attribute;
+    # use a cleaned version of the task name as the description.
+    label = task_name.removeprefix("metaworld-").replace("-", " ").strip()
+    return {f"{task_name}_0": label}
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--env", required=True, help="Environment family (libero, metaworld, ...)")
+    parser.add_argument("--task", required=True, help="Task/suite name (e.g. libero_spatial)")
+    parser.add_argument("--output", required=True, help="Path to write task_descriptions.json")
+    args = parser.parse_args()
+
+    descriptions: dict[str, str] = {}
+    try:
+        if args.env == "libero":
+            descriptions = _libero_descriptions(args.task)
+        elif args.env == "metaworld":
+            descriptions = _metaworld_descriptions(args.task)
+        else:
+            print(
+                f"[extract_task_descriptions] No description extractor for env '{args.env}'.",
+                file=sys.stderr,
+            )
+    except Exception as exc:
+        print(f"[extract_task_descriptions] Warning: {exc}", file=sys.stderr)
+
+    out_path = Path(args.output)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(json.dumps(descriptions, indent=2))
+    print(f"[extract_task_descriptions] {len(descriptions)} descriptions → {out_path}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
@@ -0,0 +1,147 @@
+#!/usr/bin/env python3
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Parse lerobot-eval output into a small metrics.json artifact.
+
+Reads eval_info.json written by lerobot-eval --output_dir and extracts the
+key metrics needed by the health dashboard. Handles both single-task and
+multi-task eval output formats.
+
+NOTE: This script runs on the bare CI runner (not inside Docker), so it
+must use only Python stdlib modules. Do not add third-party imports.
+
+Usage:
+    python scripts/ci/parse_eval_metrics.py \\
+        --artifacts-dir /tmp/libero-artifacts \\
+        --env libero \\
+        --task libero_spatial \\
+        --policy pepijn223/smolvla_libero
+
+Writes <artifacts-dir>/metrics.json. The CI workflow then uploads this file
+as a GitHub Actions artifact named "<env>-metrics".
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+from pathlib import Path
+
+
+def _safe_float(v: float | int | None) -> float | None:
+    if v is None:
+        return None
+    f = float(v)
+    return None if math.isnan(f) else f
+
+
+def _safe_int(v: float | int | None) -> int | None:
+    if v is None:
+        return None
+    f = float(v)
+    return None if math.isnan(f) else int(f)
+
+
+def _extract_metrics(info: dict) -> tuple[float | None, int | None, float | None, float | None]:
+    """Extract (pc_success, n_episodes, avg_sum_reward, eval_s) from eval_info.json.
+
+    Handles two output shapes:
+      - Single-task: {"aggregated": {"pc_success": 80.0, ...}}
+      - Multi-task:  {"overall": {"pc_success": 80.0, "n_episodes": 5, ...}}
+    """
+    for key in ("aggregated", "overall"):
+        if key not in info:
+            continue
+        agg = info[key]
+        pc = agg.get("pc_success")
+        n = agg.get("n_episodes")
+        reward = agg.get("avg_sum_reward")
+        eval_s = agg.get("eval_s")
+
+        if pc is not None and not math.isnan(pc):
+            return (
+                float(pc),
+                _safe_int(n),
+                _safe_float(reward),
+                _safe_float(eval_s),
+            )
+
+    return None, None, None, None
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--artifacts-dir", required=True, help="Path to the mounted artifacts volume")
+    parser.add_argument("--env", required=True, help="Environment name (e.g. libero)")
+    parser.add_argument("--task", required=True, help="Task name (e.g. libero_spatial)")
+    parser.add_argument("--policy", required=True, help="Policy hub path (e.g. pepijn223/smolvla_libero)")
+    args = parser.parse_args()
+
+    artifacts_dir = Path(args.artifacts_dir)
+    eval_info_path = artifacts_dir / "eval_info.json"
+
+    pc_success: float | None = None
+    n_episodes: int | None = None
+    avg_sum_reward: float | None = None
+    eval_s: float | None = None
+
+    if eval_info_path.exists():
+        try:
+            info = json.loads(eval_info_path.read_text())
+            pc_success, n_episodes, avg_sum_reward, eval_s = _extract_metrics(info)
+        except (json.JSONDecodeError, KeyError, TypeError) as exc:
+            print(f"[parse_eval_metrics] Warning: could not parse eval_info.json: {exc}", file=sys.stderr)
+    else:
+        print(
+            f"[parse_eval_metrics] Warning: {eval_info_path} not found — eval may have failed.",
+            file=sys.stderr,
+        )
+
+    task_descriptions: dict[str, str] = {}
+    task_desc_path = artifacts_dir / "task_descriptions.json"
+    if task_desc_path.exists():
+        try:
+            task_descriptions = json.loads(task_desc_path.read_text())
+        except json.JSONDecodeError as exc:
+            print(
+                f"[parse_eval_metrics] Warning: could not parse task_descriptions.json: {exc}",
+                file=sys.stderr,
+            )
+
+    metrics = {
+        "env": args.env,
+        "task": args.task,
+        "policy": args.policy,
+        "pc_success": pc_success,
+        "n_episodes": n_episodes,
+        "avg_sum_reward": avg_sum_reward,
+        "eval_s": eval_s,
+        "task_descriptions": task_descriptions,
+    }
+
+    out_path = artifacts_dir / "metrics.json"
+    out_path.write_text(json.dumps(metrics, indent=2))
+    print(f"[parse_eval_metrics] Written: {out_path}")
+    print(json.dumps(metrics, indent=2))
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
@@ -13,39 +13,188 @@
 # 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.
-
 """
-LeRobot -- PyTorch library for real-world robotics.
+This file contains lists of available environments, dataset and policies to reflect the current state of LeRobot library.
+We do not want to import all the dependencies, but instead we keep it lightweight to ensure fast access to these variables.

-Provides datasets, pretrained policies, and tools for training, evaluation,
-data collection, and robot control. Integrates with Hugging Face Hub for
-model and dataset sharing.
+Example:
+    ```python
+        import lerobot
+        print(lerobot.available_envs)
+        print(lerobot.available_tasks_per_env)
+        print(lerobot.available_datasets)
+        print(lerobot.available_datasets_per_env)
+        print(lerobot.available_real_world_datasets)
+        print(lerobot.available_policies)
+        print(lerobot.available_policies_per_env)
+        print(lerobot.available_robots)
+        print(lerobot.available_cameras)
+        print(lerobot.available_motors)
+    ```

-The base install is intentionally lightweight. Feature-specific dependencies
-are gated behind optional extras::
+When implementing a new dataset loadable with LeRobotDataset follow these steps:
+- Update `available_datasets_per_env` in `lerobot/__init__.py`

-    pip install 'lerobot[dataset]'       # dataset loading & creation
-    pip install 'lerobot[training]'      # training loop + wandb
-    pip install 'lerobot[hardware]'      # real robot control
-    pip install 'lerobot[core_scripts]'  # dataset + hardware + viz (record, replay, calibrate, etc.)
-    pip install 'lerobot[all]'           # everything
+When implementing a new environment (e.g. `gym_aloha`), follow these steps:
+- Update `available_tasks_per_env` and `available_datasets_per_env` in `lerobot/__init__.py`
+
+When implementing a new policy class (e.g. `DiffusionPolicy`) follow these steps:
+- Update `available_policies` and `available_policies_per_env`, in `lerobot/__init__.py`
+- Set the required `name` class attribute.
+- Update variables in `tests/test_available.py` by importing your new Policy class
 """

-from lerobot.__version__ import __version__
+import itertools

-# Maps optional extras to the CLI entry-points they unlock.
-available_extras: dict[str, list[str]] = {
-    "dataset": ["lerobot-dataset-viz", "lerobot-imgtransform-viz", "lerobot-edit-dataset"],
-    "training": ["lerobot-train"],
-    "hardware": [
-        "lerobot-calibrate",
-        "lerobot-find-port",
-        "lerobot-find-cameras",
-        "lerobot-find-joint-limits",
-        "lerobot-setup-motors",
+from lerobot.__version__ import __version__  # noqa: F401
+
+# TODO(rcadene): Improve policies and envs. As of now, an item in `available_policies`
+# refers to a yaml file AND a modeling name. Same for `available_envs` which refers to
+# a yaml file AND a environment name. The difference should be more obvious.
+available_tasks_per_env = {
+    "aloha": [
+        "AlohaInsertion-v0",
+        "AlohaTransferCube-v0",
    ],
-    "core_scripts": ["lerobot-record", "lerobot-replay", "lerobot-teleoperate"],
-    "evaluation": ["lerobot-eval"],
+    "pusht": ["PushT-v0"],
+}
+available_envs = list(available_tasks_per_env.keys())
+
+available_datasets_per_env = {
+    "aloha": [
+        "lerobot/aloha_sim_insertion_human",
+        "lerobot/aloha_sim_insertion_scripted",
+        "lerobot/aloha_sim_transfer_cube_human",
+        "lerobot/aloha_sim_transfer_cube_scripted",
+        "lerobot/aloha_sim_insertion_human_image",
+        "lerobot/aloha_sim_insertion_scripted_image",
+        "lerobot/aloha_sim_transfer_cube_human_image",
+        "lerobot/aloha_sim_transfer_cube_scripted_image",
+    ],
+    # TODO(alexander-soare): Add "lerobot/pusht_keypoints". Right now we can't because this is too tightly
+    # coupled with tests.
+    "pusht": ["lerobot/pusht", "lerobot/pusht_image"],
 }

-__all__ = ["__version__", "available_extras"]
+available_real_world_datasets = [
+    "lerobot/aloha_mobile_cabinet",
+    "lerobot/aloha_mobile_chair",
+    "lerobot/aloha_mobile_elevator",
+    "lerobot/aloha_mobile_shrimp",
+    "lerobot/aloha_mobile_wash_pan",
+    "lerobot/aloha_mobile_wipe_wine",
+    "lerobot/aloha_static_battery",
+    "lerobot/aloha_static_candy",
+    "lerobot/aloha_static_coffee",
+    "lerobot/aloha_static_coffee_new",
+    "lerobot/aloha_static_cups_open",
+    "lerobot/aloha_static_fork_pick_up",
+    "lerobot/aloha_static_pingpong_test",
+    "lerobot/aloha_static_pro_pencil",
+    "lerobot/aloha_static_screw_driver",
+    "lerobot/aloha_static_tape",
+    "lerobot/aloha_static_thread_velcro",
+    "lerobot/aloha_static_towel",
+    "lerobot/aloha_static_vinh_cup",
+    "lerobot/aloha_static_vinh_cup_left",
+    "lerobot/aloha_static_ziploc_slide",
+    "lerobot/umi_cup_in_the_wild",
+    "lerobot/unitreeh1_fold_clothes",
+    "lerobot/unitreeh1_rearrange_objects",
+    "lerobot/unitreeh1_two_robot_greeting",
+    "lerobot/unitreeh1_warehouse",
+    "lerobot/nyu_rot_dataset",
+    "lerobot/utokyo_saytap",
+    "lerobot/imperialcollege_sawyer_wrist_cam",
+    "lerobot/utokyo_xarm_bimanual",
+    "lerobot/tokyo_u_lsmo",
+    "lerobot/utokyo_pr2_opening_fridge",
+    "lerobot/cmu_franka_exploration_dataset",
+    "lerobot/cmu_stretch",
+    "lerobot/asu_table_top",
+    "lerobot/utokyo_pr2_tabletop_manipulation",
+    "lerobot/utokyo_xarm_pick_and_place",
+    "lerobot/ucsd_kitchen_dataset",
+    "lerobot/austin_buds_dataset",
+    "lerobot/dlr_sara_grid_clamp",
+    "lerobot/conq_hose_manipulation",
+    "lerobot/columbia_cairlab_pusht_real",
+    "lerobot/dlr_sara_pour",
+    "lerobot/dlr_edan_shared_control",
+    "lerobot/ucsd_pick_and_place_dataset",
+    "lerobot/berkeley_cable_routing",
+    "lerobot/nyu_franka_play_dataset",
+    "lerobot/austin_sirius_dataset",
+    "lerobot/cmu_play_fusion",
+    "lerobot/berkeley_gnm_sac_son",
+    "lerobot/nyu_door_opening_surprising_effectiveness",
+    "lerobot/berkeley_fanuc_manipulation",
+    "lerobot/jaco_play",
+    "lerobot/viola",
+    "lerobot/kaist_nonprehensile",
+    "lerobot/berkeley_mvp",
+    "lerobot/uiuc_d3field",
+    "lerobot/berkeley_gnm_recon",
+    "lerobot/austin_sailor_dataset",
+    "lerobot/utaustin_mutex",
+    "lerobot/roboturk",
+    "lerobot/stanford_hydra_dataset",
+    "lerobot/berkeley_autolab_ur5",
+    "lerobot/stanford_robocook",
+    "lerobot/toto",
+    "lerobot/fmb",
+    "lerobot/droid_100",
+    "lerobot/berkeley_rpt",
+    "lerobot/stanford_kuka_multimodal_dataset",
+    "lerobot/iamlab_cmu_pickup_insert",
+    "lerobot/taco_play",
+    "lerobot/berkeley_gnm_cory_hall",
+    "lerobot/usc_cloth_sim",
+]
+
+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 robots from `lerobot/robots`
+available_robots = [
+    "koch",
+    "koch_bimanual",
+    "aloha",
+    "so100",
+    "so101",
+]
+
+# lists all available cameras from `lerobot/cameras`
+available_cameras = [
+    "opencv",
+    "intelrealsense",
+]
+
+# lists all available motors from `lerobot/motors`
+available_motors = [
+    "dynamixel",
+    "feetech",
+]
+
+# keys and values refer to yaml files
+available_policies_per_env = {
+    "aloha": ["act"],
+    "pusht": ["diffusion", "vqbet"],
+    "koch_real": ["act_koch_real"],
+    "aloha_real": ["act_aloha_real"],
+}
+
+env_task_pairs = [(env, task) for env, tasks in available_tasks_per_env.items() for task in tasks]
+env_dataset_pairs = [
+    (env, dataset) for env, datasets in available_datasets_per_env.items() for dataset in datasets
+]
+env_dataset_policy_triplets = [
+    (env, dataset, policy)
+    for env, datasets in available_datasets_per_env.items()
+    for dataset in datasets
+    for policy in available_policies_per_env[env]
+]
@@ -1,30 +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.
-
-"""
-Async inference server/client.
-
-Requires: ``pip install 'lerobot[async]'``
-
-Available modules (import directly)::
-
-    from lerobot.async_inference.policy_server import ...
-    from lerobot.async_inference.robot_client import ...
-"""
-
-from lerobot.utils.import_utils import require_package
-
-require_package("grpcio", extra="async", import_name="grpc")
-
-__all__: list[str] = []
@@ -22,7 +22,8 @@ from typing import Any

 import torch

-from lerobot.configs import PolicyFeature
+from lerobot.configs.types import PolicyFeature
+from lerobot.datasets.feature_utils import build_dataset_frame, hw_to_dataset_features

 # NOTE: Configs need to be loaded for the client to be able to instantiate the policy config
 from lerobot.policies import (  # noqa: F401
@@ -35,7 +36,6 @@ from lerobot.policies import (  # noqa: F401
 )
 from lerobot.robots.robot import Robot
 from lerobot.utils.constants import OBS_IMAGES, OBS_STATE, OBS_STR
-from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
 from lerobot.utils.utils import init_logging

 Action = torch.Tensor
@@ -38,7 +38,7 @@ import draccus
 import grpc
 import torch

-from lerobot.policies import get_policy_class, make_pre_post_processors
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.transport import (
    services_pb2,  # type: ignore
@@ -47,8 +47,8 @@ import draccus
 import grpc
 import torch

-from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.cameras.realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
 from lerobot.robots import (  # noqa: F401
    Robot,
    RobotConfig,
@@ -15,9 +15,3 @@
 from .camera import Camera
 from .configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
 from .utils import make_cameras_from_configs
-
-# NOTE: Camera submodule configs and implementations (OpenCVCameraConfig, RealSenseCamera, etc.)
-# are intentionally NOT re-exported here to avoid pulling backend-specific dependencies.
-# Import from submodules: ``from lerobot.cameras.opencv import OpenCVCameraConfig``
-
-__all__ = ["Camera", "CameraConfig", "ColorMode", "Cv2Backends", "Cv2Rotation", "make_cameras_from_configs"]
@@ -14,5 +14,3 @@

 from .configuration_reachy2_camera import Reachy2CameraConfig
 from .reachy2_camera import Reachy2Camera
-
-__all__ = ["Reachy2Camera", "Reachy2CameraConfig"]
@@ -14,5 +14,3 @@

 from .camera_realsense import RealSenseCamera
 from .configuration_realsense import RealSenseCameraConfig
-
-__all__ = ["RealSenseCamera", "RealSenseCameraConfig"]
@@ -31,8 +31,8 @@ import cv2
 import numpy as np
 import zmq

-from ..configs import ColorMode
-from ..opencv import OpenCVCamera, OpenCVCameraConfig
+from lerobot.cameras.configs import ColorMode
+from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig

 logger = logging.getLogger(__name__)

@@ -1,30 +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.
-
-"""
-Cross-cutting modules that bridge multiple lerobot packages.
-
-Unlike ``lerobot.utils`` (which must remain dependency-free), modules here
-are allowed to import from ``lerobot.policies``, ``lerobot.processor``,
-``lerobot.configs``, etc.  They are deliberately NOT re-exported from the
-top-level ``lerobot`` package.
-
-Available modules (import directly)::
-
-    from lerobot.common.control_utils import predict_action, ...
-    from lerobot.common.train_utils import save_checkpoint, ...
-    from lerobot.common.wandb_utils import WandBLogger, ...
-"""
-
-__all__: list[str] = []
@@ -1,47 +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.
-
-"""
-Public API for lerobot configuration types and base config classes.
-
-NOTE: TrainPipelineConfig, EvalPipelineConfig, and TrainRLServerPipelineConfig
-are intentionally NOT re-exported here to avoid circular dependencies
-(they import lerobot.envs and lerobot.policies at module level).
-Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
-"""
-
-from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
-from .policies import PreTrainedConfig
-from .types import (
-    FeatureType,
-    NormalizationMode,
-    PipelineFeatureType,
-    PolicyFeature,
-    RTCAttentionSchedule,
-)
-
-__all__ = [
-    # Types
-    "FeatureType",
-    "NormalizationMode",
-    "PipelineFeatureType",
-    "PolicyFeature",
-    "RTCAttentionSchedule",
-    # Config classes
-    "DatasetConfig",
-    "EvalConfig",
-    "PeftConfig",
-    "PreTrainedConfig",
-    "WandBConfig",
-]
@@ -16,8 +16,8 @@

 from dataclasses import dataclass, field

-from lerobot.transforms import ImageTransformsConfig
-from lerobot.utils.import_utils import get_safe_default_codec
+from lerobot.datasets.transforms import ImageTransformsConfig
+from lerobot.datasets.video_utils import get_safe_default_codec


@dataclass
@@ -19,9 +19,8 @@ from pathlib import Path

 from lerobot import envs, policies  # noqa: F401
 from lerobot.configs import parser
-
-from .default import EvalConfig
-from .policies import PreTrainedConfig
+from lerobot.configs.default import EvalConfig
+from lerobot.configs.policies import PreTrainedConfig

 logger = getLogger(__name__)

@@ -26,13 +26,13 @@ from huggingface_hub import hf_hub_download
 from huggingface_hub.constants import CONFIG_NAME
 from huggingface_hub.errors import HfHubHTTPError

-from lerobot.optim import LRSchedulerConfig, OptimizerConfig
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.optim.optimizers import OptimizerConfig
+from lerobot.optim.schedulers import LRSchedulerConfig
 from lerobot.utils.constants import ACTION, OBS_STATE
 from lerobot.utils.device_utils import auto_select_torch_device, is_amp_available, is_torch_device_available
 from lerobot.utils.hub import HubMixin

-from .types import FeatureType, PolicyFeature
-
 T = TypeVar("T", bound="PreTrainedConfig")
 logger = getLogger(__name__)

@@ -24,12 +24,12 @@ from huggingface_hub.errors import HfHubHTTPError

 from lerobot import envs
 from lerobot.configs import parser
-from lerobot.optim import LRSchedulerConfig, OptimizerConfig
+from lerobot.configs.default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.optim import OptimizerConfig
+from lerobot.optim.schedulers import LRSchedulerConfig
 from lerobot.utils.hub import HubMixin

-from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
-from .policies import PreTrainedConfig
-
 TRAIN_CONFIG_NAME = "train_config.json"


@@ -11,13 +11,3 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-"""
-Data processing utilities (annotation tools, dataset transformations).
-
-Available sub-modules (import directly)::
-
-    from lerobot.data_processing.sarm_annotations import ...
-"""
-
-__all__: list[str] = []
@@ -11,13 +11,3 @@
 # 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.
-
-"""
-SARM subtask annotation tools.
-
-Available modules (import directly)::
-
-    from lerobot.data_processing.sarm_annotations.subtask_annotation import ...
-"""
-
-__all__: list[str] = []
@@ -76,7 +76,7 @@ import torch
 from pydantic import BaseModel, Field
 from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration

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


 # Pydantic Models for SARM Subtask Annotation
@@ -746,7 +746,8 @@ def save_annotations_to_dataset(
    dataset_path: Path, annotations: dict[int, SubtaskAnnotation], fps: int, prefix: str = "sparse"
 ):
    """Save annotations to LeRobot dataset parquet format."""
-    from lerobot.datasets import DEFAULT_EPISODES_PATH, load_episodes
+    from lerobot.datasets.io_utils import load_episodes
+    from lerobot.datasets.utils import DEFAULT_EPISODES_PATH

    episodes_dataset = load_episodes(dataset_path)
    if not episodes_dataset or len(episodes_dataset) == 0:
@@ -840,7 +841,7 @@ def generate_auto_sparse_annotations(

 def load_annotations_from_dataset(dataset_path: Path, prefix: str = "sparse") -> dict[int, SubtaskAnnotation]:
    """Load annotations from LeRobot dataset parquet files."""
-    from lerobot.datasets import load_episodes
+    from lerobot.datasets.io_utils import load_episodes

    episodes_dataset = load_episodes(dataset_path)
    if not episodes_dataset or len(episodes_dataset) == 0:
@@ -15,68 +15,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from lerobot.utils.import_utils import require_package
-
-require_package("datasets", extra="dataset")
-require_package("av", extra="dataset")
-
-from .aggregate import aggregate_datasets
-from .compute_stats import DEFAULT_QUANTILES, aggregate_stats, get_feature_stats
-from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
-from .dataset_tools import (
-    add_features,
-    convert_image_to_video_dataset,
-    delete_episodes,
-    merge_datasets,
-    modify_features,
-    modify_tasks,
-    recompute_stats,
-    remove_feature,
-    split_dataset,
-)
-from .factory import make_dataset, resolve_delta_timestamps
-from .image_writer import safe_stop_image_writer
-from .io_utils import load_episodes, write_stats
-from .lerobot_dataset import LeRobotDataset
-from .multi_dataset import MultiLeRobotDataset
-from .pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-from .sampler import EpisodeAwareSampler
-from .streaming_dataset import StreamingLeRobotDataset
-from .utils import DEFAULT_EPISODES_PATH, create_lerobot_dataset_card
-from .video_utils import VideoEncodingManager
-
-# NOTE: Low-level I/O functions (cast_stats_to_numpy, get_parquet_file_size_in_mb, etc.)
-# and legacy migration constants are intentionally NOT re-exported here.
-# Import directly: ``from lerobot.datasets.io_utils import ...``
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.multi_dataset import MultiLeRobotDataset
+from lerobot.datasets.sampler import EpisodeAwareSampler
+from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
+from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig

 __all__ = [
-    "CODEBASE_VERSION",
-    "DEFAULT_EPISODES_PATH",
-    "DEFAULT_QUANTILES",
    "EpisodeAwareSampler",
+    "ImageTransforms",
+    "ImageTransformsConfig",
    "LeRobotDataset",
    "LeRobotDatasetMetadata",
    "MultiLeRobotDataset",
    "StreamingLeRobotDataset",
-    "VideoEncodingManager",
-    "add_features",
-    "aggregate_datasets",
-    "aggregate_pipeline_dataset_features",
-    "aggregate_stats",
-    "convert_image_to_video_dataset",
-    "create_initial_features",
-    "create_lerobot_dataset_card",
-    "delete_episodes",
-    "get_feature_stats",
-    "load_episodes",
-    "make_dataset",
-    "merge_datasets",
-    "modify_features",
-    "modify_tasks",
-    "recompute_stats",
-    "remove_feature",
-    "resolve_delta_timestamps",
-    "safe_stop_image_writer",
-    "split_dataset",
-    "write_stats",
 ]
@@ -23,10 +23,10 @@ import datasets
 import pandas as pd
 import tqdm

-from .compute_stats import aggregate_stats
-from .dataset_metadata import LeRobotDatasetMetadata
-from .feature_utils import get_hf_features_from_features
-from .io_utils import (
+from lerobot.datasets.compute_stats import aggregate_stats
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.feature_utils import get_hf_features_from_features
+from lerobot.datasets.io_utils import (
    get_file_size_in_mb,
    get_parquet_file_size_in_mb,
    to_parquet_with_hf_images,
@@ -34,7 +34,7 @@ from .io_utils import (
    write_stats,
    write_tasks,
 )
-from .utils import (
+from lerobot.datasets.utils import (
    DEFAULT_CHUNK_SIZE,
    DEFAULT_DATA_FILE_SIZE_IN_MB,
    DEFAULT_DATA_PATH,
@@ -43,7 +43,7 @@ from .utils import (
    DEFAULT_VIDEO_PATH,
    update_chunk_file_indices,
 )
-from .video_utils import concatenate_video_files, get_video_duration_in_s
+from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s


 def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
@@ -19,11 +19,9 @@ import logging

 import numpy as np

-from lerobot.processor import RelativeActionsProcessorStep
+from lerobot.datasets.io_utils import load_image_as_numpy
 from lerobot.utils.constants import ACTION, OBS_STATE

-from .io_utils import load_image_as_numpy
-
 DEFAULT_QUANTILES = [0.01, 0.10, 0.50, 0.90, 0.99]


@@ -698,6 +696,8 @@ def compute_relative_action_stats(
        ValueError: If the dataset has fewer frames than ``chunk_size``.
        RuntimeError: If no valid (single-episode) chunks are found.
    """
+    from lerobot.processor.relative_action_processor import RelativeActionsProcessorStep
+
    if exclude_joints is None:
        exclude_joints = []

@@ -23,13 +23,9 @@ import pyarrow as pa
 import pyarrow.parquet as pq
 from huggingface_hub import snapshot_download

-from lerobot.utils.constants import DEFAULT_FEATURES, HF_LEROBOT_HOME, HF_LEROBOT_HUB_CACHE
-from lerobot.utils.feature_utils import _validate_feature_names
-from lerobot.utils.utils import flatten_dict
-
-from .compute_stats import aggregate_stats
-from .feature_utils import create_empty_dataset_info
-from .io_utils import (
+from lerobot.datasets.compute_stats import aggregate_stats
+from lerobot.datasets.feature_utils import _validate_feature_names, create_empty_dataset_info
+from lerobot.datasets.io_utils import (
    get_file_size_in_mb,
    load_episodes,
    load_info,
@@ -41,16 +37,19 @@ from .io_utils import (
    write_stats,
    write_tasks,
 )
-from .utils import (
+from lerobot.datasets.utils import (
    DEFAULT_EPISODES_PATH,
+    DEFAULT_FEATURES,
    INFO_PATH,
    check_version_compatibility,
+    flatten_dict,
    get_safe_version,
    has_legacy_hub_download_metadata,
    is_valid_version,
    update_chunk_file_indices,
 )
-from .video_utils import get_video_info
+from lerobot.datasets.video_utils import get_video_info
+from lerobot.utils.constants import HF_LEROBOT_HOME, HF_LEROBOT_HUB_CACHE

 CODEBASE_VERSION = "v3.0"

@@ -21,17 +21,17 @@ from pathlib import Path
 import datasets
 import torch

-from .dataset_metadata import LeRobotDatasetMetadata
-from .feature_utils import (
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.feature_utils import (
    check_delta_timestamps,
    get_delta_indices,
    get_hf_features_from_features,
 )
-from .io_utils import (
+from lerobot.datasets.io_utils import (
    hf_transform_to_torch,
    load_nested_dataset,
 )
-from .video_utils import decode_video_frames
+from lerobot.datasets.video_utils import decode_video_frames


 class DatasetReader:
@@ -36,25 +36,22 @@ import pyarrow.parquet as pq
 import torch
 from tqdm import tqdm

-from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME, OBS_IMAGE, OBS_STATE
-from lerobot.utils.utils import flatten_dict
-
-from .aggregate import aggregate_datasets
-from .compute_stats import (
+from lerobot.datasets.aggregate import aggregate_datasets
+from lerobot.datasets.compute_stats import (
    aggregate_stats,
    compute_episode_stats,
    compute_relative_action_stats,
 )
-from .dataset_metadata import LeRobotDatasetMetadata
-from .io_utils import (
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.io_utils import (
    get_parquet_file_size_in_mb,
    load_episodes,
    write_info,
    write_stats,
    write_tasks,
 )
-from .lerobot_dataset import LeRobotDataset
-from .utils import (
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import (
    DATA_DIR,
    DEFAULT_CHUNK_SIZE,
    DEFAULT_DATA_FILE_SIZE_IN_MB,
@@ -62,7 +59,8 @@ from .utils import (
    DEFAULT_EPISODES_PATH,
    update_chunk_file_indices,
 )
-from .video_utils import encode_video_frames, get_video_info
+from lerobot.datasets.video_utils import encode_video_frames, get_video_info
+from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME, OBS_IMAGE, OBS_STATE


 def _load_episode_with_stats(src_dataset: LeRobotDataset, episode_idx: int) -> dict:
@@ -831,6 +829,8 @@ def _copy_and_reindex_episodes_metadata(
        data_metadata: Dict mapping new episode index to its data file metadata
        video_metadata: Optional dict mapping new episode index to its video metadata
    """
+    from lerobot.datasets.utils import flatten_dict
+
    if src_dataset.meta.episodes is None:
        src_dataset.meta.episodes = load_episodes(src_dataset.meta.root)

@@ -922,8 +922,8 @@ def _write_parquet(df: pd.DataFrame, path: Path, meta: LeRobotDatasetMetadata) -

    This ensures images are properly embedded and the file can be loaded correctly by HF datasets.
    """
-    from .feature_utils import get_hf_features_from_features
-    from .io_utils import embed_images
+    from lerobot.datasets.feature_utils import get_hf_features_from_features
+    from lerobot.datasets.io_utils import embed_images

    hf_features = get_hf_features_from_features(meta.features)
    ep_dataset = datasets.Dataset.from_dict(df.to_dict(orient="list"), features=hf_features, split="train")
@@ -1367,7 +1367,7 @@ def _copy_data_without_images(
        episode_indices: Episodes to include
        img_keys: Image keys to remove
    """
-    from .utils import DATA_DIR
+    from lerobot.datasets.utils import DATA_DIR

    data_dir = src_dataset.root / DATA_DIR
    parquet_files = sorted(data_dir.glob("*/*.parquet"))
@@ -31,26 +31,26 @@ import PIL.Image
 import pyarrow.parquet as pq
 import torch

-from .compute_stats import compute_episode_stats
-from .dataset_metadata import LeRobotDatasetMetadata
-from .feature_utils import (
+from lerobot.datasets.compute_stats import compute_episode_stats
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.feature_utils import (
    get_hf_features_from_features,
    validate_episode_buffer,
    validate_frame,
 )
-from .image_writer import AsyncImageWriter, write_image
-from .io_utils import (
+from lerobot.datasets.image_writer import AsyncImageWriter, write_image
+from lerobot.datasets.io_utils import (
    embed_images,
    get_file_size_in_mb,
    load_episodes,
    write_info,
 )
-from .utils import (
+from lerobot.datasets.utils import (
    DEFAULT_EPISODES_PATH,
    DEFAULT_IMAGE_PATH,
    update_chunk_file_indices,
 )
-from .video_utils import (
+from lerobot.datasets.video_utils import (
    StreamingVideoEncoder,
    concatenate_video_files,
    encode_video_frames,
@@ -18,15 +18,19 @@ from pprint import pformat

 import torch

-from lerobot.configs import PreTrainedConfig
+from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig
-from lerobot.transforms import ImageTransforms
-from lerobot.utils.constants import ACTION, IMAGENET_STATS, OBS_PREFIX, REWARD
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.multi_dataset import MultiLeRobotDataset
+from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
+from lerobot.datasets.transforms import ImageTransforms
+from lerobot.utils.constants import ACTION, OBS_PREFIX, REWARD

-from .dataset_metadata import LeRobotDatasetMetadata
-from .lerobot_dataset import LeRobotDataset
-from .multi_dataset import MultiLeRobotDataset
-from .streaming_dataset import StreamingLeRobotDataset
+IMAGENET_STATS = {
+    "mean": [[[0.485]], [[0.456]], [[0.406]]],  # (c,1,1)
+    "std": [[[0.229]], [[0.224]], [[0.225]]],  # (c,1,1)
+}


 def resolve_delta_timestamps(
@@ -14,21 +14,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from pprint import pformat
+from typing import Any

 import datasets
 import numpy as np
 from PIL import Image as PILImage

-from lerobot.utils.constants import DEFAULT_FEATURES
-from lerobot.utils.utils import is_valid_numpy_dtype_string
-
-from .utils import (
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.datasets.utils import (
    DEFAULT_CHUNK_SIZE,
    DEFAULT_DATA_FILE_SIZE_IN_MB,
    DEFAULT_DATA_PATH,
+    DEFAULT_FEATURES,
    DEFAULT_VIDEO_FILE_SIZE_IN_MB,
    DEFAULT_VIDEO_PATH,
 )
+from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_STR
+from lerobot.utils.utils import is_valid_numpy_dtype_string


 def get_hf_features_from_features(features: dict) -> datasets.Features:
@@ -69,6 +71,199 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
    return datasets.Features(hf_features)


+def _validate_feature_names(features: dict[str, dict]) -> None:
+    """Validate that feature names do not contain invalid characters.
+
+    Args:
+        features (dict): The LeRobot features dictionary.
+
+    Raises:
+        ValueError: If any feature name contains '/'.
+    """
+    invalid_features = {name: ft for name, ft in features.items() if "/" in name}
+    if invalid_features:
+        raise ValueError(f"Feature names should not contain '/'. Found '/' in '{invalid_features}'.")
+
+
+def hw_to_dataset_features(
+    hw_features: dict[str, type | tuple], prefix: str, use_video: bool = True
+) -> dict[str, dict]:
+    """Convert hardware-specific features to a LeRobot dataset feature dictionary.
+
+    This function takes a dictionary describing hardware outputs (like joint states
+    or camera image shapes) and formats it into the standard LeRobot feature
+    specification.
+
+    Args:
+        hw_features (dict): Dictionary mapping feature names to their type (float for
+            joints) or shape (tuple for images).
+        prefix (str): The prefix to add to the feature keys (e.g., "observation"
+            or "action").
+        use_video (bool): If True, image features are marked as "video", otherwise "image".
+
+    Returns:
+        dict: A LeRobot features dictionary.
+    """
+    features = {}
+    joint_fts = {
+        key: ftype
+        for key, ftype in hw_features.items()
+        if ftype is float or (isinstance(ftype, PolicyFeature) and ftype.type != FeatureType.VISUAL)
+    }
+    cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
+
+    if joint_fts and prefix == ACTION:
+        features[prefix] = {
+            "dtype": "float32",
+            "shape": (len(joint_fts),),
+            "names": list(joint_fts),
+        }
+
+    if joint_fts and prefix == OBS_STR:
+        features[f"{prefix}.state"] = {
+            "dtype": "float32",
+            "shape": (len(joint_fts),),
+            "names": list(joint_fts),
+        }
+
+    for key, shape in cam_fts.items():
+        features[f"{prefix}.images.{key}"] = {
+            "dtype": "video" if use_video else "image",
+            "shape": shape,
+            "names": ["height", "width", "channels"],
+        }
+
+    _validate_feature_names(features)
+    return features
+
+
+def build_dataset_frame(
+    ds_features: dict[str, dict], values: dict[str, Any], prefix: str
+) -> dict[str, np.ndarray]:
+    """Construct a single data frame from raw values based on dataset features.
+
+    A "frame" is a dictionary containing all the data for a single timestep,
+    formatted as numpy arrays according to the feature specification.
+
+    Args:
+        ds_features (dict): The LeRobot dataset features dictionary.
+        values (dict): A dictionary of raw values from the hardware/environment.
+        prefix (str): The prefix to filter features by (e.g., "observation"
+            or "action").
+
+    Returns:
+        dict: A dictionary representing a single frame of data.
+    """
+    frame = {}
+    for key, ft in ds_features.items():
+        if key in DEFAULT_FEATURES or not key.startswith(prefix):
+            continue
+        elif ft["dtype"] == "float32" and len(ft["shape"]) == 1:
+            frame[key] = np.array([values[name] for name in ft["names"]], dtype=np.float32)
+        elif ft["dtype"] in ["image", "video"]:
+            frame[key] = values[key.removeprefix(f"{prefix}.images.")]
+
+    return frame
+
+
+def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFeature]:
+    """Convert dataset features to policy features.
+
+    This function transforms the dataset's feature specification into a format
+    that a policy can use, classifying features by type (e.g., visual, state,
+    action) and ensuring correct shapes (e.g., channel-first for images).
+
+    Args:
+        features (dict): The LeRobot dataset features dictionary.
+
+    Returns:
+        dict: A dictionary mapping feature keys to `PolicyFeature` objects.
+
+    Raises:
+        ValueError: If an image feature does not have a 3D shape.
+    """
+    # TODO(aliberts): Implement "type" in dataset features and simplify this
+    policy_features = {}
+    for key, ft in features.items():
+        shape = ft["shape"]
+        if ft["dtype"] in ["image", "video"]:
+            type = FeatureType.VISUAL
+            if len(shape) != 3:
+                raise ValueError(f"Number of dimensions of {key} != 3 (shape={shape})")
+
+            names = ft["names"]
+            # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
+            if names[2] in ["channel", "channels"]:  # (h, w, c) -> (c, h, w)
+                shape = (shape[2], shape[0], shape[1])
+        elif key == OBS_ENV_STATE:
+            type = FeatureType.ENV
+        elif key.startswith(OBS_STR):
+            type = FeatureType.STATE
+        elif key.startswith(ACTION):
+            type = FeatureType.ACTION
+        else:
+            continue
+
+        policy_features[key] = PolicyFeature(
+            type=type,
+            shape=shape,
+        )
+
+    return policy_features
+
+
+def combine_feature_dicts(*dicts: dict) -> dict:
+    """Merge LeRobot grouped feature dicts.
+
+    - For 1D numeric specs (dtype not image/video/string) with "names": we merge the names and recompute the shape.
+    - For others (e.g. `observation.images.*`), the last one wins (if they are identical).
+
+    Args:
+        *dicts: A variable number of LeRobot feature dictionaries to merge.
+
+    Returns:
+        dict: A single merged feature dictionary.
+
+    Raises:
+        ValueError: If there's a dtype mismatch for a feature being merged.
+    """
+    out: dict = {}
+    for d in dicts:
+        for key, value in d.items():
+            if not isinstance(value, dict):
+                out[key] = value
+                continue
+
+            dtype = value.get("dtype")
+            shape = value.get("shape")
+            is_vector = (
+                dtype not in ("image", "video", "string")
+                and isinstance(shape, tuple)
+                and len(shape) == 1
+                and "names" in value
+            )
+
+            if is_vector:
+                # Initialize or retrieve the accumulating dict for this feature key
+                target = out.setdefault(key, {"dtype": dtype, "names": [], "shape": (0,)})
+                # Ensure consistent data types across merged entries
+                if "dtype" in target and dtype != target["dtype"]:
+                    raise ValueError(f"dtype mismatch for '{key}': {target['dtype']} vs {dtype}")
+
+                # Merge feature names: append only new ones to preserve order without duplicates
+                seen = set(target["names"])
+                for n in value["names"]:
+                    if n not in seen:
+                        target["names"].append(n)
+                        seen.add(n)
+                # Recompute the shape to reflect the updated number of features
+                target["shape"] = (len(target["names"]),)
+            else:
+                # For images/videos and non-1D entries: override with the latest definition
+                out[key] = value
+    return out
+
+
 def create_empty_dataset_info(
    codebase_version: str,
    fps: int,
@@ -13,6 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import json
 from pathlib import Path
 from typing import Any

@@ -28,10 +29,7 @@ from datasets.table import embed_table_storage
 from PIL import Image as PILImage
 from torchvision import transforms

-from lerobot.utils.io_utils import load_json, write_json
-from lerobot.utils.utils import SuppressProgressBars, flatten_dict, unflatten_dict
-
-from .utils import (
+from lerobot.datasets.utils import (
    DEFAULT_DATA_FILE_SIZE_IN_MB,
    DEFAULT_EPISODES_PATH,
    DEFAULT_SUBTASKS_PATH,
@@ -39,8 +37,11 @@ from .utils import (
    EPISODES_DIR,
    INFO_PATH,
    STATS_PATH,
+    flatten_dict,
    serialize_dict,
+    unflatten_dict,
 )
+from lerobot.utils.utils import SuppressProgressBars


 def get_parquet_file_size_in_mb(parquet_path: str | Path) -> float:
@@ -115,6 +116,33 @@ def embed_images(dataset: datasets.Dataset) -> datasets.Dataset:
    return dataset


+def load_json(fpath: Path) -> Any:
+    """Load data from a JSON file.
+
+    Args:
+        fpath (Path): Path to the JSON file.
+
+    Returns:
+        Any: The data loaded from the JSON file.
+    """
+    with open(fpath) as f:
+        return json.load(f)
+
+
+def write_json(data: dict, fpath: Path) -> None:
+    """Write data to a JSON file.
+
+    Creates parent directories if they don't exist.
+
+    Args:
+        data (dict): The dictionary to write.
+        fpath (Path): The path to the output JSON file.
+    """
+    fpath.parent.mkdir(exist_ok=True, parents=True)
+    with open(fpath, "w") as f:
+        json.dump(data, f, indent=4, ensure_ascii=False)
+
+
 def write_info(info: dict, local_dir: Path) -> None:
    write_json(info, local_dir / INFO_PATH)

@@ -24,21 +24,20 @@ import torch.utils
 from huggingface_hub import HfApi, snapshot_download
 from huggingface_hub.errors import RevisionNotFoundError

-from lerobot.utils.constants import HF_LEROBOT_HUB_CACHE
-
-from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
-from .dataset_reader import DatasetReader
-from .dataset_writer import DatasetWriter
-from .utils import (
+from lerobot.datasets.dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
+from lerobot.datasets.dataset_reader import DatasetReader
+from lerobot.datasets.dataset_writer import DatasetWriter
+from lerobot.datasets.utils import (
    create_lerobot_dataset_card,
    get_safe_version,
    is_valid_version,
 )
-from .video_utils import (
+from lerobot.datasets.video_utils import (
    StreamingVideoEncoder,
    get_safe_default_codec,
    resolve_vcodec,
 )
+from lerobot.utils.constants import HF_LEROBOT_HUB_CACHE

 logger = logging.getLogger(__name__)

@@ -21,13 +21,12 @@ import datasets
 import torch
 import torch.utils

+from lerobot.datasets.compute_stats import aggregate_stats
+from lerobot.datasets.feature_utils import get_hf_features_from_features
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.video_utils import VideoFrame
 from lerobot.utils.constants import HF_LEROBOT_HOME

-from .compute_stats import aggregate_stats
-from .feature_utils import get_hf_features_from_features
-from .lerobot_dataset import LeRobotDataset
-from .video_utils import VideoFrame
-
 logger = logging.getLogger(__name__)


@@ -16,11 +16,11 @@ import re
 from collections.abc import Sequence
 from typing import Any

-from lerobot.configs import PipelineFeatureType
+from lerobot.configs.types import PipelineFeatureType
+from lerobot.datasets.feature_utils import hw_to_dataset_features
 from lerobot.processor import DataProcessorPipeline
 from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
-from lerobot.utils.feature_utils import hw_to_dataset_features


 def create_initial_features(
@@ -22,21 +22,20 @@ import numpy as np
 import torch
 from datasets import load_dataset

-from lerobot.utils.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE
-
-from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
-from .feature_utils import get_delta_indices
-from .io_utils import item_to_torch
-from .utils import (
+from lerobot.datasets.dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
+from lerobot.datasets.feature_utils import get_delta_indices
+from lerobot.datasets.io_utils import item_to_torch
+from lerobot.datasets.utils import (
    check_version_compatibility,
    find_float_index,
    is_float_in_list,
    safe_shard,
 )
-from .video_utils import (
+from lerobot.datasets.video_utils import (
    VideoDecoderCache,
    decode_video_frames_torchcodec,
 )
+from lerobot.utils.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE


 class LookBackError(Exception):
@@ -17,7 +17,9 @@ import contextlib
 import importlib.resources
 import json
 import logging
+from collections.abc import Iterator
 from pathlib import Path
+from typing import Any

 import datasets
 import numpy as np
@@ -26,8 +28,6 @@ import torch
 from huggingface_hub import DatasetCard, DatasetCardData, HfApi
 from huggingface_hub.errors import RevisionNotFoundError

-from lerobot.utils.utils import flatten_dict, unflatten_dict
-
 V30_MESSAGE = """
 The dataset you requested ({repo_id}) is in {version} format.

@@ -93,6 +93,14 @@ LEGACY_EPISODES_PATH = "meta/episodes.jsonl"
 LEGACY_EPISODES_STATS_PATH = "meta/episodes_stats.jsonl"
 LEGACY_TASKS_PATH = "meta/tasks.jsonl"

+DEFAULT_FEATURES = {
+    "timestamp": {"dtype": "float32", "shape": (1,), "names": None},
+    "frame_index": {"dtype": "int64", "shape": (1,), "names": None},
+    "episode_index": {"dtype": "int64", "shape": (1,), "names": None},
+    "index": {"dtype": "int64", "shape": (1,), "names": None},
+    "task_index": {"dtype": "int64", "shape": (1,), "names": None},
+}
+

 def has_legacy_hub_download_metadata(root: Path) -> bool:
    """Return ``True`` when *root* looks like a legacy Hub ``local_dir`` mirror.
@@ -115,6 +123,59 @@ def update_chunk_file_indices(chunk_idx: int, file_idx: int, chunks_size: int) -
    return chunk_idx, file_idx


+def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict:
+    """Flatten a nested dictionary by joining keys with a separator.
+
+    Example:
+        >>> dct = {"a": {"b": 1, "c": {"d": 2}}, "e": 3}
+        >>> print(flatten_dict(dct))
+        {'a/b': 1, 'a/c/d': 2, 'e': 3}
+
+    Args:
+        d (dict): The dictionary to flatten.
+        parent_key (str): The base key to prepend to the keys in this level.
+        sep (str): The separator to use between keys.
+
+    Returns:
+        dict: A flattened dictionary.
+    """
+    items = []
+    for k, v in d.items():
+        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+        if isinstance(v, dict):
+            items.extend(flatten_dict(v, new_key, sep=sep).items())
+        else:
+            items.append((new_key, v))
+    return dict(items)
+
+
+def unflatten_dict(d: dict, sep: str = "/") -> dict:
+    """Unflatten a dictionary with delimited keys into a nested dictionary.
+
+    Example:
+        >>> flat_dct = {"a/b": 1, "a/c/d": 2, "e": 3}
+        >>> print(unflatten_dict(flat_dct))
+        {'a': {'b': 1, 'c': {'d': 2}}, 'e': 3}
+
+    Args:
+        d (dict): A dictionary with flattened keys.
+        sep (str): The separator used in the keys.
+
+    Returns:
+        dict: A nested dictionary.
+    """
+    outdict = {}
+    for key, value in d.items():
+        parts = key.split(sep)
+        d = outdict
+        for part in parts[:-1]:
+            if part not in d:
+                d[part] = {}
+            d = d[part]
+        d[parts[-1]] = value
+    return outdict
+
+
 def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict:
    """Serialize a dictionary containing tensors or numpy arrays to be JSON-compatible.

@@ -271,6 +332,27 @@ def get_safe_version(repo_id: str, version: str | packaging.version.Version) ->
    raise ForwardCompatibilityError(repo_id, min(upper_versions))


+def cycle(iterable: Any) -> Iterator[Any]:
+    """Create a dataloader-safe cyclical iterator.
+
+    This is an equivalent of `itertools.cycle` but is safe for use with
+    PyTorch DataLoaders with multiple workers.
+    See https://github.com/pytorch/pytorch/issues/23900 for details.
+
+    Args:
+        iterable: The iterable to cycle over.
+
+    Yields:
+        Items from the iterable, restarting from the beginning when exhausted.
+    """
+    iterator = iter(iterable)
+    while True:
+        try:
+            yield next(iterator)
+        except StopIteration:
+            iterator = iter(iterable)
+
+
 def create_branch(repo_id: str, *, branch: str, repo_type: str | None = None) -> None:
    """Create a branch on an existing Hugging Face repo.

@@ -37,8 +37,6 @@ import torchvision
 from datasets.features.features import register_feature
 from PIL import Image

-from lerobot.utils.import_utils import get_safe_default_codec
-
 logger = logging.getLogger(__name__)

 # List of hardware encoders to probe for auto-selection. Availability depends on the platform and FFmpeg build.
@@ -118,6 +116,16 @@ def resolve_vcodec(vcodec: str) -> str:
    return "libsvtav1"


+def get_safe_default_codec():
+    if importlib.util.find_spec("torchcodec"):
+        return "torchcodec"
+    else:
+        logger.warning(
+            "'torchcodec' is not available in your platform, falling back to 'pyav' as a default decoder"
+        )
+        return "pyav"
+
+
 def decode_video_frames(
    video_path: Path | str,
    timestamps: list[float],
@@ -263,10 +271,7 @@ class VideoDecoderCache:
        if importlib.util.find_spec("torchcodec"):
            from torchcodec.decoders import VideoDecoder
        else:
-            raise ImportError(
-                "'torchcodec' is required but not installed. "
-                "Install it with: pip install 'lerobot[dataset]' (or uv pip install 'lerobot[dataset]')"
-            )
+            raise ImportError("torchcodec is required but not available.")

        video_path = str(video_path)

@@ -601,7 +606,7 @@ class _CameraEncoderThread(threading.Thread):
        self.encoder_threads = encoder_threads

    def run(self) -> None:
-        from .compute_stats import RunningQuantileStats, auto_downsample_height_width
+        from lerobot.datasets.compute_stats import RunningQuantileStats, auto_downsample_height_width

        container = None
        output_stream = None
@@ -12,27 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-# NOTE: gymnasium is currently a core dependency but is a candidate for moving to an
-# optional extra in the future. When that transition happens, uncomment the guard below
-# and update the extra name to the one that will contain gymnasium.
-# from lerobot.utils.import_utils import require_package
-# require_package("gymnasium", extra="<update_extra>", import_name="gymnasium")
-
-from .configs import AlohaEnv, EnvConfig, HILSerlRobotEnvConfig, HubEnvConfig, PushtEnv
-from .factory import make_env, make_env_config, make_env_pre_post_processors
-from .utils import check_env_attributes_and_types, close_envs, env_to_policy_features, preprocess_observation
-
-__all__ = [
-    "AlohaEnv",
-    "EnvConfig",
-    "HILSerlRobotEnvConfig",
-    "HubEnvConfig",
-    "PushtEnv",
-    "check_env_attributes_and_types",
-    "close_envs",
-    "env_to_policy_features",
-    "make_env",
-    "make_env_config",
-    "make_env_pre_post_processors",
-    "preprocess_observation",
-]
+from .configs import AlohaEnv, EnvConfig, HubEnvConfig, PushtEnv  # noqa: F401
@@ -23,8 +23,7 @@ import draccus
 import gymnasium as gym
 from gymnasium.envs.registration import registry as gym_registry

-from lerobot.configs import FeatureType, PolicyFeature
-from lerobot.processor import IsaaclabArenaProcessorStep, LiberoProcessorStep, PolicyProcessorPipeline
+from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.robots import RobotConfig
 from lerobot.teleoperators.config import TeleoperatorConfig
 from lerobot.utils.constants import (
@@ -125,6 +124,8 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):

    def get_env_processors(self):
        """Return (preprocessor, postprocessor) for this env. Default: identity."""
+        from lerobot.processor.pipeline import PolicyProcessorPipeline
+
        return PolicyProcessorPipeline(steps=[]), PolicyProcessorPipeline(steps=[])


@@ -417,7 +418,7 @@ class LiberoEnv(EnvConfig):
        return kwargs

    def create_envs(self, n_envs: int, use_async_envs: bool = False):
-        from .libero import create_libero_envs
+        from lerobot.envs.libero import create_libero_envs

        if self.task is None:
            raise ValueError("LiberoEnv requires a task to be specified")
@@ -435,6 +436,9 @@ class LiberoEnv(EnvConfig):
        )

    def get_env_processors(self):
+        from lerobot.processor.env_processor import LiberoProcessorStep
+        from lerobot.processor.pipeline import PolicyProcessorPipeline
+
        return (
            PolicyProcessorPipeline(steps=[LiberoProcessorStep()]),
            PolicyProcessorPipeline(steps=[]),
@@ -483,7 +487,7 @@ class MetaworldEnv(EnvConfig):
        }

    def create_envs(self, n_envs: int, use_async_envs: bool = False):
-        from .metaworld import create_metaworld_envs
+        from lerobot.envs.metaworld import create_metaworld_envs

        if self.task is None:
            raise ValueError("MetaWorld requires a task to be specified")
@@ -564,6 +568,9 @@ class IsaaclabArenaEnv(HubEnvConfig):
        return {}

    def get_env_processors(self):
+        from lerobot.processor.env_processor import IsaaclabArenaProcessorStep
+        from lerobot.processor.pipeline import PolicyProcessorPipeline
+
        state_keys = tuple(k.strip() for k in (self.state_keys or "").split(",") if k.strip())
        camera_keys = tuple(k.strip() for k in (self.camera_keys or "").split(",") if k.strip())
        if not state_keys and not camera_keys:
--- a/Show More
+++ b/Show More