chore(deps): bump the actions group with 15 updates

Bumps the actions group with 15 updates: | Package | From | To | | --- | --- | --- | | [actions/checkout](https://github.com/actions/checkout) | `6` | `7` | | [docker/setup-buildx-action](https://github.com/docker/setup-buildx-action) | `3` | `4` | | [docker/login-action](https://github.com/docker/login-action) | `3` | `4.4.0` | | [docker/build-push-action](https://github.com/docker/build-push-action) | `6` | `7` | | [actions/upload-artifact](https://github.com/actions/upload-artifact) | `4` | `7` | | [huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml](https://github.com/huggingface/doc-builder) | `2430c1ec91d04667414e2fa31ecfc36c153ea391` | `6108e850ae1cf2f71bb0815a600bcd50c39abfa7` | | [huggingface/doc-builder/.github/workflows/build_main_documentation.yml](https://github.com/huggingface/doc-builder) | `e60a538eea9817ab312196d0d233604b01697265` | `6108e850ae1cf2f71bb0815a600bcd50c39abfa7` | | [huggingface/doc-builder/.github/workflows/build_pr_documentation.yml](https://github.com/huggingface/doc-builder) | `e60a538eea9817ab312196d0d233604b01697265` | `6108e850ae1cf2f71bb0815a600bcd50c39abfa7` | | [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) | `6` | `8.3.2` | | [actions/github-script](https://github.com/actions/github-script) | `8` | `9` | | [actions/download-artifact](https://github.com/actions/download-artifact) | `4` | `8` | | [actions/labeler](https://github.com/actions/labeler) | `6` | `7` | | [actions/setup-python](https://github.com/actions/setup-python) | `6.2.0` | `7.0.0` | | [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) | `1.13.0` | `1.14.1` | | [trufflesecurity/trufflehog](https://github.com/trufflesecurity/trufflehog) | `3.90.0` | `3.95.9` | Updates `actions/checkout` from 6 to 7 - [Release notes](https://github.com/actions/checkout/releases) - [Commits](https://github.com/actions/checkout/compare/v6...v7) Updates `docker/setup-buildx-action` from 3 to 4 - [Release notes](https://github.com/docker/setup-buildx-action/releases) - [Commits](https://github.com/docker/setup-buildx-action/compare/v3...v4) Updates `docker/login-action` from 3 to 4.4.0 - [Release notes](https://github.com/docker/login-action/releases) - [Commits](https://github.com/docker/login-action/compare/v3...v4.4.0) Updates `docker/build-push-action` from 6 to 7 - [Release notes](https://github.com/docker/build-push-action/releases) - [Commits](https://github.com/docker/build-push-action/compare/v6...v7) Updates `actions/upload-artifact` from 4 to 7 - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v4...v7) Updates `huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml` from 2430c1ec91d04667414e2fa31ecfc36c153ea391 to 6108e850ae1cf2f71bb0815a600bcd50c39abfa7 - [Release notes](https://github.com/huggingface/doc-builder/releases) - [Commits](https://github.com/huggingface/doc-builder/compare/2430c1ec91d04667414e2fa31ecfc36c153ea391...6108e850ae1cf2f71bb0815a600bcd50c39abfa7) Updates `huggingface/doc-builder/.github/workflows/build_main_documentation.yml` from e60a538eea9817ab312196d0d233604b01697265 to 6108e850ae1cf2f71bb0815a600bcd50c39abfa7 - [Release notes](https://github.com/huggingface/doc-builder/releases) - [Commits](https://github.com/huggingface/doc-builder/compare/e60a538eea9817ab312196d0d233604b01697265...6108e850ae1cf2f71bb0815a600bcd50c39abfa7) Updates `huggingface/doc-builder/.github/workflows/build_pr_documentation.yml` from e60a538eea9817ab312196d0d233604b01697265 to 6108e850ae1cf2f71bb0815a600bcd50c39abfa7 - [Release notes](https://github.com/huggingface/doc-builder/releases) - [Commits](https://github.com/huggingface/doc-builder/compare/e60a538eea9817ab312196d0d233604b01697265...6108e850ae1cf2f71bb0815a600bcd50c39abfa7) Updates `astral-sh/setup-uv` from 6 to 8.3.2 - [Release notes](https://github.com/astral-sh/setup-uv/releases) - [Commits](https://github.com/astral-sh/setup-uv/compare/v6...v8.3.2) Updates `actions/github-script` from 8 to 9 - [Release notes](https://github.com/actions/github-script/releases) - [Commits](https://github.com/actions/github-script/compare/v8...v9) Updates `actions/download-artifact` from 4 to 8 - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v4...v8) Updates `actions/labeler` from 6 to 7 - [Release notes](https://github.com/actions/labeler/releases) - [Commits](https://github.com/actions/labeler/compare/v6...v7) Updates `actions/setup-python` from 6.2.0 to 7.0.0 - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/a309ff8b426b58ec0e2a45f0f869d46889d02405...5fda3b95a4ea91299a34e894583c3862153e4b97) Updates `pypa/gh-action-pypi-publish` from 1.13.0 to 1.14.1 - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e...ba38be9e461d3875417946c167d0b5f3d385a247) Updates `trufflesecurity/trufflehog` from 3.90.0 to 3.95.9 - [Release notes](https://github.com/trufflesecurity/trufflehog/releases) - [Commits](https://github.com/trufflesecurity/trufflehog/compare/eafb8c5f6a06175141c27f17bcc17941853d0047...27b0417c16317ca9a472a9a8092acce143b49c55) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: docker/setup-buildx-action dependency-version: '4' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: docker/login-action dependency-version: 4.4.0 dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: docker/build-push-action dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: actions/upload-artifact dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml dependency-version: 6108e850ae1cf2f71bb0815a600bcd50c39abfa7 dependency-type: direct:production dependency-group: actions - dependency-name: huggingface/doc-builder/.github/workflows/build_main_documentation.yml dependency-version: 6108e850ae1cf2f71bb0815a600bcd50c39abfa7 dependency-type: direct:production dependency-group: actions - dependency-name: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml dependency-version: 6108e850ae1cf2f71bb0815a600bcd50c39abfa7 dependency-type: direct:production dependency-group: actions - dependency-name: astral-sh/setup-uv dependency-version: 8.3.2 dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: actions/github-script dependency-version: '9' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: actions/download-artifact dependency-version: '8' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: actions/labeler dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: actions/setup-python dependency-version: 7.0.0 dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: pypa/gh-action-pypi-publish dependency-version: 1.14.1 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: actions - dependency-name: trufflesecurity/trufflehog dependency-version: 3.95.9 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: actions ... Signed-off-by: dependabot[bot] <support@github.com>
2026-07-29 20:49:42 +00:00 · 2026-07-27 11:25:27 +00:00
73 changed files with 414 additions and 2420 deletions
@@ -72,19 +72,19 @@ jobs:
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -95,7 +95,7 @@ jobs:
      # 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]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.libero
@@ -151,7 +151,7 @@ jobs:
      - name: Upload Libero rollout video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: libero-rollout-video
          path: /tmp/libero-artifacts/videos/
@@ -159,7 +159,7 @@ jobs:
      - name: Upload Libero eval metrics
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: libero-metrics
          path: /tmp/libero-artifacts/metrics.json
@@ -214,7 +214,7 @@ jobs:
      - name: Upload Libero train-smoke eval video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: libero-train-smoke-video
          path: /tmp/libero-train-smoke-artifacts/eval/
@@ -230,19 +230,19 @@ jobs:
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -250,7 +250,7 @@ jobs:
          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
      - name: Build MetaWorld benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.metaworld
@@ -303,7 +303,7 @@ jobs:
      - name: Upload MetaWorld rollout video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: metaworld-rollout-video
          path: /tmp/metaworld-artifacts/videos/
@@ -311,7 +311,7 @@ jobs:
      - name: Upload MetaWorld eval metrics
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: metaworld-metrics
          path: /tmp/metaworld-artifacts/metrics.json
@@ -332,19 +332,19 @@ jobs:
      ROBOTWIN_TASKS: beat_block_hammer,click_bell,handover_block,stack_blocks_two,click_alarmclock,open_microwave,adjust_bottle,lift_pot,stamp_seal,turn_switch
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -355,7 +355,7 @@ jobs:
      # simulation assets (~4 GB). Layer cache lives in the runner's local
      # Docker daemon — reused across re-runs on the same machine.
      - name: Build RoboTwin 2.0 benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.robotwin
@@ -413,7 +413,7 @@ jobs:
      - name: Upload RoboTwin rollout video
        if: always()
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
        with:
          name: robotwin-rollout-video
          path: /tmp/robotwin-artifacts/videos/
@@ -421,7 +421,7 @@ jobs:
      - name: Upload RoboTwin eval metrics
        if: always()
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
        with:
          name: robotwin-metrics
          path: /tmp/robotwin-artifacts/metrics.json
@@ -439,19 +439,19 @@ jobs:
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -459,7 +459,7 @@ jobs:
          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
      - name: Build RoboCasa365 benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.robocasa
@@ -514,7 +514,7 @@ jobs:
      - name: Upload RoboCasa365 rollout video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: robocasa-rollout-video
          path: /tmp/robocasa-artifacts/videos/
@@ -522,7 +522,7 @@ jobs:
      - name: Upload RoboCasa365 eval metrics
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: robocasa-metrics
          path: /tmp/robocasa-artifacts/metrics.json
@@ -540,19 +540,19 @@ jobs:
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -560,7 +560,7 @@ jobs:
          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
      - name: Build RoboCerebra benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.robocerebra
@@ -621,7 +621,7 @@ jobs:
      - name: Upload RoboCerebra rollout video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: robocerebra-rollout-video
          path: /tmp/robocerebra-artifacts/videos/
@@ -629,7 +629,7 @@ jobs:
      - name: Upload RoboCerebra eval metrics
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: robocerebra-metrics
          path: /tmp/robocerebra-artifacts/metrics.json
@@ -648,19 +648,19 @@ jobs:
      ROBOMME_TASKS: PickXtimes,BinFill,StopCube,MoveCube,InsertPeg,SwingXtimes,VideoUnmask,ButtonUnmask,PickHighlight,PatternLock
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -668,7 +668,7 @@ jobs:
          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
      - name: Build RoboMME benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.robomme
@@ -726,7 +726,7 @@ jobs:
      - name: Upload RoboMME rollout video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: robomme-rollout-video
          path: /tmp/robomme-artifacts/videos/
@@ -734,7 +734,7 @@ jobs:
      - name: Upload RoboMME eval metrics
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: robomme-metrics
          path: /tmp/robomme-artifacts/metrics.json
@@ -754,19 +754,19 @@ jobs:
      LIBERO_PLUS_TASK_IDS: "[0,100,260,500,1000,1500,2000,2400]"
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -774,7 +774,7 @@ jobs:
          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
      - name: Build LIBERO-plus benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.libero_plus
@@ -834,7 +834,7 @@ jobs:
      - name: Upload LIBERO-plus rollout video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: libero-plus-rollout-video
          path: /tmp/libero-plus-artifacts/videos/
@@ -842,7 +842,7 @@ jobs:
      - name: Upload LIBERO-plus eval metrics
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: libero-plus-metrics
          path: /tmp/libero-plus-artifacts/metrics.json
@@ -858,19 +858,19 @@ jobs:
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -878,7 +878,7 @@ jobs:
          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
      - name: Build VLABench benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: docker/Dockerfile.benchmark.vlabench
@@ -936,7 +936,7 @@ jobs:
      - name: Upload VLABench rollout video
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: vlabench-rollout-video
          path: /tmp/vlabench-artifacts/videos/
@@ -944,7 +944,7 @@ jobs:
      - name: Upload VLABench eval metrics
        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: vlabench-metrics
          path: /tmp/vlabench-artifacts/metrics.json
@@ -47,7 +47,7 @@ jobs:
    timeout-minutes: 30
    steps:
      - name: Checkout code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
@@ -52,21 +52,21 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
        with:
          lfs: true
          persist-credentials: false
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image CPU
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: ./docker/Dockerfile.user
@@ -87,21 +87,21 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
        with:
          lfs: true
          persist-credentials: false
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image GPU
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: ./docker/Dockerfile.internal
@@ -33,7 +33,7 @@ jobs:
      github.event.workflow_run.event == 'pull_request' &&
      github.event.workflow_run.conclusion == 'success' &&
      github.repository == 'huggingface/lerobot'
-    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
    with:
      package_name: lerobot
    secrets:
@@ -55,7 +55,7 @@ jobs:
      github.repository == 'huggingface/lerobot'
    permissions:
      contents: read
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # main
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
    with:
      commit_sha: ${{ github.sha }}
      package: lerobot
@@ -78,7 +78,7 @@ jobs:
    permissions:
      contents: read
      pull-requests: write
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
    with:
      commit_sha: ${{ github.event.pull_request.head.sha }}
      pr_number: ${{ github.event.number }}
@@ -69,7 +69,7 @@ jobs:
      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
          lfs: true
@@ -87,7 +87,7 @@ jobs:
          libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev
      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
+        uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -63,7 +63,7 @@ jobs:
      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          lfs: true
          persist-credentials: false
@@ -80,7 +80,7 @@ jobs:
          speech-dispatcher libgeos-dev portaudio19-dev
      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
+        uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -137,21 +137,21 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          lfs: true
          persist-credentials: false
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f  # v3
+        uses: docker/setup-buildx-action@bb05f3f5519dd87d3ba754cc423b652a5edd6d2c  # v4.2.0
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9  # v3
+        uses: docker/login-action@af1e73f918a031802d376d3c8bbc3fe56130a9b0  # v4.4.0
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image
-        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8  # v6
+        uses: docker/build-push-action@53b7df96c91f9c12dcc8a07bcb9ccacbed38856a  # v7.3.0
        with:
          context: .
          file: ./docker/Dockerfile.internal
@@ -29,7 +29,7 @@ jobs:
    runs-on: ubuntu-latest
    if: github.repository == 'huggingface/lerobot'
    steps:
-      - uses: actions/github-script@v8
+      - uses: actions/github-script@v9
        with:
          script: |
            // Setup Input Text
@@ -48,12 +48,12 @@ jobs:
    outputs:
      changed: ${{ steps.diff.outputs.changed }}
    steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
        with:
          persist-credentials: false
      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
+        uses: astral-sh/setup-uv@v8.3.2 # zizmor: ignore[unpinned-uses]
        with:
          version: ${{ env.UV_VERSION }}
          python-version: ${{ env.PYTHON_VERSION }}
@@ -74,7 +74,7 @@ jobs:
      - name: Upload updated lockfile
        if: steps.diff.outputs.changed == 'true'
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v7 # zizmor: ignore[unpinned-uses]
        with:
          name: uv-lock
          path: uv.lock
@@ -93,13 +93,13 @@ jobs:
      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
        with:
          lfs: true
          persist-credentials: false
      - name: Download updated lockfile
-        uses: actions/download-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/download-artifact@v8 # zizmor: ignore[unpinned-uses]
        with:
          name: uv-lock
@@ -115,7 +115,7 @@ jobs:
          speech-dispatcher libgeos-dev portaudio19-dev
      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
+        uses: astral-sh/setup-uv@v8.3.2 # zizmor: ignore[unpinned-uses]
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -153,27 +153,27 @@ jobs:
          sudo apt-get update
          sudo apt-get install git-lfs
          git lfs install
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
        with:
          lfs: true
          persist-credentials: false
      - name: Download updated lockfile
-        uses: actions/download-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/download-artifact@v8 # zizmor: ignore[unpinned-uses]
        with:
          name: uv-lock
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
        with:
          context: .
          file: ./docker/Dockerfile.internal
@@ -247,12 +247,12 @@ jobs:
    env:
      GH_TOKEN: ${{ secrets.UPDATE_LOCK_TOKEN }}
    steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
        with:
          persist-credentials: false
      - name: Download updated lockfile
-        uses: actions/download-artifact@v4 # zizmor: ignore[unpinned-uses]
+        uses: actions/download-artifact@v8 # zizmor: ignore[unpinned-uses]
        with:
          name: uv-lock
@@ -33,7 +33,7 @@ jobs:
    runs-on: ubuntu-latest
    if: github.repository == 'huggingface/lerobot' && !github.event.pull_request.draft
    steps:
-      - uses: actions/labeler@v6
+      - uses: actions/labeler@v7
        with:
          repo-token: ${{ secrets.GITHUB_TOKEN }}
          sync-labels: true # Removes labels if files are removed from the PR
@@ -43,12 +43,12 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
      - name: Set up Python
-        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405  # v6
+        uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97  # v6
        with:
          python-version: '3.12'
@@ -38,12 +38,12 @@ jobs:
    steps:
      - name: Checkout code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          persist-credentials: false
      - name: Set up Python
-        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405  # v6
+        uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97  # v6
        with:
          python-version: '3.12'
@@ -104,7 +104,7 @@ jobs:
      - name: Publish to TestPyPI for pre-releases
        # True for tags like 'v0.2.0-rc1'
        if: startsWith(github.ref, 'refs/tags/v') && contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e  # v1.13.0
+        uses: pypa/gh-action-pypi-publish@ba38be9e461d3875417946c167d0b5f3d385a247  # v1.14.1
        with:
          repository-url: https://test.pypi.org/legacy/
          verbose: true
@@ -112,7 +112,7 @@ jobs:
      - name: Publish to PyPI
        if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e  # v1.13.0
+        uses: pypa/gh-action-pypi-publish@ba38be9e461d3875417946c167d0b5f3d385a247  # v1.14.1
        with:
          verbose: true
          print-hash: true
@@ -127,7 +127,7 @@ jobs:
    env:
      MUJOCO_GL: egl
    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          lfs: true
          persist-credentials: false
@@ -137,7 +137,7 @@ jobs:
          git curl libglib2.0-0 libegl1-mesa-dev ffmpeg libusb-1.0-0-dev \
          speech-dispatcher libgeos-dev portaudio19-dev
      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
+        uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
        with:
          enable-cache: true # zizmor: ignore[cache-poisoning]
          version: ${{ env.UV_VERSION }}
@@ -43,12 +43,12 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
        with:
          fetch-depth: 0
          persist-credentials: false
      - name: Secret Scanning
-        uses: trufflesecurity/trufflehog@eafb8c5f6a06175141c27f17bcc17941853d0047  # v3.90.0
+        uses: trufflesecurity/trufflehog@27b0417c16317ca9a472a9a8092acce143b49c55  # v3.95.9
        with:
          extra_args: --only-verified
@@ -68,16 +68,17 @@ ENV HOME=/home/user_lerobot \
 # issues with MuJoCo and OpenGL drivers.
 RUN uv venv --python python${PYTHON_VERSION}
-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/
 RUN uv sync --locked --extra all --no-cache
 RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
-# Copy the application source code and install the local project
+# Copy the rest of the application source code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
 RUN uv sync --locked --extra all --no-cache
 # Set the default command
 CMD ["/bin/bash"]
@@ -60,14 +60,15 @@ ENV HOME=/home/user_lerobot \
 # run other Python projects in the same container without dependency conflicts.
 RUN uv venv
-# Install third-party dependencies separately for layer caching
+# Install Python dependencies for caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN uv sync --locked --extra all --no-install-project --no-cache
+COPY --chown=user_lerobot:user_lerobot src/ src/
-# Copy the application code and install the local project
+RUN uv sync --locked --extra all --no-cache
 # Copy the rest of the application code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
 RUN uv sync --locked --extra all --no-cache
 # Set the default command
 CMD ["/bin/bash"]
@@ -136,10 +136,6 @@ config = RealSenseCameraConfig(
    height=480,
    color_mode=ColorMode.RGB,
    use_depth=True,
    # Optional fixed color controls. Omit them to leave the current sensor settings unchanged.
    exposure=120,
    gain=64,
    white_balance=4600,
    rotation=Cv2Rotation.NO_ROTATION
 )
@@ -158,15 +154,6 @@ finally:
 ```
 <!-- prettier-ignore-end -->
 Manual color controls disable the corresponding automatic exposure or white-balance mode. Their
 supported ranges vary by camera model; an invalid value raises an error at connection time that
 includes the range reported by the sensor. Requesting an unsupported control also raises an error.
 Omitted controls leave the sensor's existing automatic or manual setting unchanged. These options
 require `use_rgb=True`.
 On the RealSense D405, the color stream is provided by the Stereo Module, so changing manual
 exposure or gain also affects the depth stream.
 </hfoption>
 </hfoptions>
@@ -494,19 +494,6 @@ ignore_errors = true
 module = "lerobot.envs.*"
 ignore_errors = false
 [[tool.mypy.overrides]]
 module = "lerobot.annotations.*"
 ignore_errors = false
 disallow_untyped_defs = true
 disallow_incomplete_defs = true
 check_untyped_defs = true
 [[tool.mypy.overrides]]
 module = "lerobot.transforms.*"
 ignore_errors = false
 disallow_untyped_defs = true
 disallow_incomplete_defs = true
 check_untyped_defs = true
 # [[tool.mypy.overrides]]
 # module = "lerobot.utils.*"
@@ -120,22 +120,14 @@ class OpenCVCamera(Camera):
        self.rotation: int | None = get_cv2_rotation(config.rotation)
        self.backend: int = config.backend
-        self.capture_width: int | None = None
+        if self.height and self.width:
        self.capture_height: int | None = None
        self._reset_connection_settings()
    def __str__(self) -> str:
        return f"{self.__class__.__name__}({self.index_or_path})"
    def _reset_connection_settings(self) -> None:
        """Restore settings that may have been auto-detected during a failed connection."""
        self.fps = self.config.fps
        self.width = self.config.width
        self.height = self.config.height
            self.capture_width, self.capture_height = self.width, self.height
            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
                self.capture_width, self.capture_height = self.height, self.width
    def __str__(self) -> str:
        return f"{self.__class__.__name__}({self.index_or_path})"
    @property
    def is_connected(self) -> bool:
        """Checks if the camera is currently connected and opened."""
@@ -172,7 +164,6 @@ class OpenCVCamera(Camera):
                f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
            )
        try:
        self._configure_capture_settings()
        self._start_read_thread()
@@ -184,13 +175,6 @@ class OpenCVCamera(Camera):
            with self.frame_lock:
                if self.latest_frame is None:
                    raise ConnectionError(f"{self} failed to capture frames during warmup.")
        except BaseException:
            try:
                self._cleanup_resources()
            except Exception:
                logger.exception(f"Failed to fully clean up {self} after connect() failed.")
            self._reset_connection_settings()
            raise
        logger.info(f"{self} connected.")
@@ -328,7 +312,6 @@ class OpenCVCamera(Camera):
        for target in targets_to_scan:
            camera = cv2.VideoCapture(target)
            try:
            if camera.isOpened():
                default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
                default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
@@ -338,9 +321,7 @@ class OpenCVCamera(Camera):
                # Get FOURCC code and convert to string
                default_fourcc_code = camera.get(cv2.CAP_PROP_FOURCC)
                default_fourcc_code_int = int(default_fourcc_code)
-                    default_fourcc = "".join(
+                default_fourcc = "".join([chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])
                        [chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)]
                    )
                camera_info = {
                    "name": f"OpenCV Camera @ {target}",
@@ -357,7 +338,6 @@ class OpenCVCamera(Camera):
                }
                found_cameras_info.append(camera_info)
            finally:
                camera.release()
        return found_cameras_info
@@ -516,26 +496,6 @@ class OpenCVCamera(Camera):
            self.latest_timestamp = None
            self.new_frame_event.clear()
    def _cleanup_resources(self) -> None:
        """Stop background reads and release the capture, including after partial setup."""
        read_thread = self.thread
        videocapture = self.videocapture
        try:
            self._stop_read_thread()
        finally:
            self.videocapture = None
            try:
                if videocapture is not None:
                    videocapture.release()
            finally:
                # Releasing the device may unblock a hardware read that outlived
                # the first bounded join in _stop_read_thread().
                if read_thread is not None and read_thread.is_alive():
                    read_thread.join(timeout=2.0)
                    if read_thread.is_alive():  # pragma: no cover
                        logger.warning(f"{self} read thread remained alive after releasing the capture.")
    @check_if_not_connected
    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
        """
@@ -626,6 +586,16 @@ class OpenCVCamera(Camera):
        if not self.is_connected and self.thread is None:
            raise DeviceNotConnectedError(f"{self} not connected.")
-        self._cleanup_resources()
+        if self.thread is not None:
            self._stop_read_thread()
        if self.videocapture is not None:
            self.videocapture.release()
            self.videocapture = None
        with self.frame_lock:
            self.latest_frame = None
            self.latest_timestamp = None
            self.new_frame_event.clear()
        logger.info(f"{self} disconnected.")
@@ -121,9 +121,6 @@ class RealSenseCamera(Camera):
        self.config = config
        self.width: int | None = config.width
        self.height: int | None = config.height
        if config.serial_number_or_name.isdigit():
            self.serial_number = config.serial_number_or_name
        else:
@@ -134,9 +131,6 @@ class RealSenseCamera(Camera):
        self.use_rgb = config.use_rgb
        self.use_depth = config.use_depth
        self.warmup_s = config.warmup_s
        self.exposure: int | None = config.exposure
        self.gain: int | None = config.gain
        self.white_balance: int | None = config.white_balance
        self.rs_pipeline: rs.pipeline | None = None
        self.rs_profile: rs.pipeline_profile | None = None
@@ -151,23 +145,14 @@ class RealSenseCamera(Camera):
        self.rotation: int | None = get_cv2_rotation(config.rotation)
-        self.capture_width: int | None = None
+        if self.height and self.width:
        self.capture_height: int | None = None
        self._reset_connection_settings()
    def __str__(self) -> str:
        return f"{self.__class__.__name__}({self.serial_number})"
    def _reset_connection_settings(self) -> None:
        """Restore settings that may have been auto-detected during a failed connection."""
        self.fps = self.config.fps
        self.width = self.config.width
        self.height = self.config.height
        self.warmup_s = self.config.warmup_s
            self.capture_width, self.capture_height = self.width, self.height
            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
                self.capture_width, self.capture_height = self.height, self.width
    def __str__(self) -> str:
        return f"{self.__class__.__name__}({self.serial_number})"
    @property
    def is_connected(self) -> bool:
        """Checks if the camera pipeline is started and streams are active."""
@@ -187,8 +172,7 @@ class RealSenseCamera(Camera):
        Raises:
            DeviceAlreadyConnectedError: If the camera is already connected.
-            ValueError: If the configuration is invalid, a requested sensor option is unsupported,
+            ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique).
                or a requested sensor value is invalid.
            ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all.
            RuntimeError: If the pipeline starts but fails to apply requested settings.
        """
@@ -206,9 +190,7 @@ class RealSenseCamera(Camera):
                f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
            ) from e
        try:
        self._configure_capture_settings()
            self._configure_sensor_options()
        self._start_read_thread()
        # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
@@ -224,13 +206,6 @@ class RealSenseCamera(Camera):
                self.use_depth and self.latest_depth_frame is None
            ):
                raise ConnectionError(f"{self} failed to capture frames during warmup.")
        except BaseException:
            try:
                self._cleanup_resources()
            except Exception:
                logger.exception(f"Failed to fully clean up {self} after connect() failed.")
            self._reset_connection_settings()
            raise
        logger.info(f"{self} connected.")
@@ -364,111 +339,6 @@ class RealSenseCamera(Camera):
        self.new_frame_event.clear()
        return self._async_read(timeout_ms=10000, read_depth=read_depth)
    def _get_color_sensor(self) -> "rs.sensor":
        """Returns the sensor that controls the color stream.
        Most RealSense cameras expose "RGB Camera" for color. The D405 has no
        separate RGB module — its color stream comes from "Stereo Module".
        We try RGB Camera first, then fall back to Stereo Module.
        """
        if self.rs_profile is None:
            raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
        device = self.rs_profile.get_device()
        sensors = {s.get_info(rs.camera_info.name): s for s in device.query_sensors()}
        for name in ("RGB Camera", "Stereo Module"):
            if name in sensors:
                return sensors[name]
        available = list(sensors.keys())
        raise RuntimeError(f"{self}: no color sensor found. Available sensors: {available}")
    def _set_sensor_option(self, sensor: "rs.sensor", option: "rs.option", value: float, label: str) -> None:
        """Sets a sensor option, re-raising range errors with actionable diagnostics."""
        try:
            sensor.set_option(option, value)
        except Exception as e:
            range_info = ""
            try:
                option_range = sensor.get_option_range(option)
                range_info = (
                    f" (supported range: min={option_range.min}, max={option_range.max}, "
                    f"step={option_range.step}, default={option_range.default})"
                )
            except Exception:
                range_info = " (option range unavailable)"
            raise ValueError(
                f"{self}: failed to set {label} to {value}{range_info}. Original error: {e}"
            ) from e
    def _configure_sensor_options(self) -> None:
        """Applies manual sensor options (exposure, gain, white balance) to the color sensor.
        When exposure or gain is set, auto-exposure is disabled first. When white_balance
        is set, auto white balance is disabled first. An omitted option is left unchanged,
        and configuration is skipped entirely if all options are omitted.
        Raises:
            ValueError: If the sensor does not support a requested option or a requested
                value is invalid. Invalid-value errors include the option name, requested
                value, and supported range when available.
        """
        if self.exposure is None and self.gain is None and self.white_balance is None:
            return
        color_sensor = self._get_color_sensor()
        requested_options = (
            (rs.option.exposure, self.exposure, "exposure"),
            (rs.option.gain, self.gain, "gain"),
            (rs.option.white_balance, self.white_balance, "white balance"),
        )
        unsupported_options = [
            label
            for option, value, label in requested_options
            if value is not None and not color_sensor.supports(option)
        ]
        if unsupported_options:
            raise ValueError(
                f"{self}: color sensor does not support requested manual options: {unsupported_options}."
            )
        manual_exposure_requested = self.exposure is not None or self.gain is not None
        if manual_exposure_requested:
            if color_sensor.supports(rs.option.enable_auto_exposure):
                self._set_sensor_option(color_sensor, rs.option.enable_auto_exposure, 0, "auto-exposure")
                logger.info(f"{self} auto-exposure disabled.")
            else:
                logger.warning(
                    f"{self} sensor does not support disabling auto-exposure; "
                    "applying manual exposure/gain directly."
                )
        if self.exposure is not None:
            self._set_sensor_option(color_sensor, rs.option.exposure, self.exposure, "exposure")
            logger.info(f"{self} exposure set to {self.exposure}.")
        if self.gain is not None:
            self._set_sensor_option(color_sensor, rs.option.gain, self.gain, "gain")
            logger.info(f"{self} gain set to {self.gain}.")
        if self.white_balance is not None:
            if color_sensor.supports(rs.option.enable_auto_white_balance):
                self._set_sensor_option(
                    color_sensor, rs.option.enable_auto_white_balance, 0, "auto white balance"
                )
                logger.info(f"{self} auto white balance disabled.")
            else:
                logger.warning(
                    f"{self} sensor does not support disabling auto white balance; "
                    "applying manual white balance directly."
                )
            self._set_sensor_option(
                color_sensor, rs.option.white_balance, self.white_balance, "white balance"
            )
            logger.info(f"{self} white balance set to {self.white_balance}.")
    @check_if_not_connected
    def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
        """
@@ -671,27 +541,6 @@ class RealSenseCamera(Camera):
            self.latest_timestamp = None
            self.new_frame_event.clear()
    def _cleanup_resources(self) -> None:
        """Stop background reads and stop the pipeline, including after partial setup."""
        read_thread = self.thread
        rs_pipeline = self.rs_pipeline
        try:
            self._stop_read_thread()
        finally:
            self.rs_pipeline = None
            self.rs_profile = None
            try:
                if rs_pipeline is not None:
                    rs_pipeline.stop()
            finally:
                # Stopping the pipeline may unblock a hardware read that outlived
                # the first bounded join in _stop_read_thread().
                if read_thread is not None and read_thread.is_alive():
                    read_thread.join(timeout=2.0)
                    if read_thread.is_alive():  # pragma: no cover
                        logger.warning(f"{self} read thread remained alive after stopping the pipeline.")
    def _async_read(self, timeout_ms: float, read_depth: bool = False) -> NDArray[Any]:
        """Shared helper for :meth:`async_read`/:meth:`async_read_depth`: return the latest buffered frame."""
        if self.thread is None or not self.thread.is_alive():
@@ -835,5 +684,18 @@ class RealSenseCamera(Camera):
                f"Attempted to disconnect {self}, but it appears already disconnected."
            )
-        self._cleanup_resources()
+        if self.thread is not None:
            self._stop_read_thread()
        if self.rs_pipeline is not None:
            self.rs_pipeline.stop()
            self.rs_pipeline = None
            self.rs_profile = None
        with self.frame_lock:
            self.latest_color_frame = None
            self.latest_depth_frame = None
            self.latest_timestamp = None
            self.new_frame_event.clear()
        logger.info(f"{self} disconnected.")
@@ -46,17 +46,6 @@ class RealSenseCameraConfig(CameraConfig):
        use_depth: Whether to enable depth stream. Defaults to False.
        rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
        warmup_s: Time reading frames before returning from connect (in seconds)
        exposure: Manual exposure value for the color sensor. When set, auto-exposure is
            disabled and this fixed value is used. Valid ranges are camera-model specific
            and reported if the value is rejected. Defaults to None (leave unchanged).
        gain: Manual gain value for the color sensor. When set, auto-exposure is disabled
            and this fixed gain is used, which also freezes exposure at its current value
            when no exposure is configured. Valid ranges are camera-model specific and
            reported if the value is rejected. Defaults to None (leave unchanged).
        white_balance: Manual white balance value for the color sensor. When set, auto
            white balance is disabled and this fixed value is used. Valid ranges are
            camera-model specific and reported if the value is rejected. Defaults to None
            (leave unchanged).
    Note:
        - Either name or serial_number must be specified.
@@ -72,9 +61,6 @@ class RealSenseCameraConfig(CameraConfig):
    use_depth: bool = False
    rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
    warmup_s: int = 1
    exposure: int | None = None
    gain: int | None = None
    white_balance: int | None = None
    def __post_init__(self) -> None:
        self.color_mode = ColorMode(self.color_mode)
@@ -83,18 +69,6 @@ class RealSenseCameraConfig(CameraConfig):
        if not self.use_rgb and not self.use_depth:
            raise ValueError("At least one of `use_rgb` or `use_depth` must be enabled.")
        manual_color_options = {
            "exposure": self.exposure,
            "gain": self.gain,
            "white_balance": self.white_balance,
        }
        configured_color_options = [name for name, value in manual_color_options.items() if value is not None]
        if configured_color_options and not self.use_rgb:
            raise ValueError(
                "Manual color sensor options require `use_rgb=True`. "
                f"Configured options: {configured_color_options}."
            )
        values = (self.fps, self.width, self.height)
        if any(v is not None for v in values) and any(v is None for v in values):
            raise ValueError(
@@ -71,19 +71,13 @@ class DatasetRecordConfig:
    # Number of threads per encoder instance. None = auto (codec default).
    # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
    encoder_threads: int | None = None
    # Skip appending the date-time tag to repo_id, keeping the user-provided name as-is
    # (e.g. self-managed versioned names intended for a later `lerobot-edit-dataset merge`).
    no_stamp: bool = False
    def stamp_repo_id(self) -> None:
        """Append a date-time tag to ``repo_id`` so each recording session gets a unique name.
        Must be called explicitly at dataset *creation* time — not on resume,
        where the existing ``repo_id`` (already stamped) must be preserved.
        No-op when ``no_stamp`` is set, preserving a user-managed ``repo_id``.
        """
        if self.no_stamp:
            return
        if self.repo_id:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            self.repo_id = f"{self.repo_id}_{timestamp}"
@@ -14,7 +14,6 @@
 import builtins
 import datetime as dt
 import json
 import multiprocessing
 import os
 import tempfile
 from dataclasses import dataclass, field
@@ -102,12 +101,6 @@ class TrainPipelineConfig(HubMixin):
    batch_size: int = 8
    prefetch_factor: int = 4
    persistent_workers: bool = True
    # DataLoader worker start method. "spawn" is safer than "fork" with
    # non-fork-safe libs (PyAV / torchcodec / ffmpeg), but adds some
    # worker-startup time per run since workers re-import modules instead
    # of inheriting parent state. Override with `--dataloader_multiprocessing_context=fork`
    # when appropriate, or set it to `null` to use Python's platform default.
    dataloader_multiprocessing_context: str | None = "spawn"
    steps: int = 100_000
    # Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
    env_eval_freq: int = 20_000
@@ -219,17 +212,6 @@ class TrainPipelineConfig(HubMixin):
            self.reward_model.pretrained_path = str(policy_dir)
    def validate(self) -> None:
        available_contexts = multiprocessing.get_all_start_methods()
        if (
            self.dataloader_multiprocessing_context is not None
            and self.dataloader_multiprocessing_context not in available_contexts
        ):
            raise ValueError(
                "`dataloader_multiprocessing_context` must be None or one of "
                f"{available_contexts} on this platform, got "
                f"{self.dataloader_multiprocessing_context!r}."
            )
        self._resolve_pretrained_from_cli()
        if self.policy is None and self.reward_model is None:
@@ -188,8 +188,8 @@ class LeRobotDatasetMetadata:
    def _load_metadata(self):
        self.info = load_info(self.root)
        check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
-        self.tasks = load_tasks(self.root) if self.total_tasks > 0 else None
+        self.tasks = load_tasks(self.root)
-        self.episodes = load_episodes(self.root) if self.total_episodes > 0 else None
+        self.episodes = load_episodes(self.root)
        self.stats = load_stats(self.root)
    def ensure_readable(self) -> None:
@@ -172,23 +172,6 @@ class DatasetWriter:
    def _get_image_file_dir(self, episode_index: int, image_key: str) -> Path:
        return self._get_image_file_path(episode_index, image_key, frame_index=0).parent
    def _get_episode_buffer_index(self) -> int:
        episode_index = self.episode_buffer["episode_index"]
        # episode_index is `int` when freshly created, but becomes `np.ndarray` after
        # save_episode() mutates the buffer. Handle both types here.
        if isinstance(episode_index, np.ndarray):
            episode_index = episode_index.item() if episode_index.size == 1 else episode_index[0]
        return int(episode_index)
    def _delete_camera_frame_dirs(self, camera_keys: list[str]) -> None:
        if self.image_writer is not None:
            self._wait_image_writer()
        episode_index = self._get_episode_buffer_index()
        for camera_key in camera_keys:
            img_dir = self._get_image_file_dir(episode_index, camera_key)
            if img_dir.is_dir():
                shutil.rmtree(img_dir)
    def _save_image(
        self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
    ) -> None:
@@ -386,9 +369,7 @@ class DatasetWriter:
                self._episodes_since_last_encoding = 0
        if episode_data is None:
-            if len(self._meta.image_keys) > 0:
+            self.clear_episode_buffer(delete_images=len(self._meta.image_keys) > 0)
                self._delete_camera_frame_dirs(self._meta.image_keys)
            self.episode_buffer = self._create_episode_buffer()
    def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None) -> None:
        """Batch save videos for multiple episodes."""
@@ -580,10 +561,10 @@ class DatasetWriter:
        return metadata
    def clear_episode_buffer(self, delete_images: bool = True) -> None:
-        """Discard the current episode buffer and optionally delete temp camera frames.
+        """Discard the current episode buffer and optionally delete temp images.
        Args:
-            delete_images: If ``True``, remove temporary camera frame directories
+            delete_images: If ``True``, remove temporary image directories
                written for the current episode.
        """
        # Cancel streaming encoder if active
@@ -591,7 +572,17 @@ class DatasetWriter:
            self._streaming_encoder.cancel_episode()
        if delete_images:
-            self._delete_camera_frame_dirs(self._meta.camera_keys)
+            if self.image_writer is not None:
                self._wait_image_writer()
            episode_index = self.episode_buffer["episode_index"]
            # episode_index is `int` when freshly created, but becomes `np.ndarray` after
            # save_episode() mutates the buffer. Handle both types here.
            if isinstance(episode_index, np.ndarray):
                episode_index = episode_index.item() if episode_index.size == 1 else episode_index[0]
            for cam_key in self._meta.image_keys:
                img_dir = self._get_image_file_dir(episode_index, cam_key)
                if img_dir.is_dir():
                    shutil.rmtree(img_dir)
        self.episode_buffer = self._create_episode_buffer()
@@ -384,12 +384,7 @@ class LiberoEnv(gym.Env):
    def close(self):
        if self._env is not None:
            try:
            self._env.close()
            finally:
                # LIBERO deletes its inner env on close, so this wrapper must
                # be recreated before the next reset.
                self._env = None
 def _make_env_fns(
@@ -155,7 +155,6 @@ class MetaworldEnv(gym.Env):
            env.model.cam_pos[2] = [0.75, 0.075, 0.7]
        env.reset()
        env._freeze_rand_vec = False  # otherwise no randomization
        env.seeded_rand_vec = True  # use seeded RNG so reset(seed=X) controls object positions
        self._env = env
    def render(self) -> np.ndarray:
@@ -221,8 +220,6 @@ class MetaworldEnv(gym.Env):
        self._ensure_env()
        super().reset(seed=seed)
        if seed is not None:
            self._env.seed(seed)
        raw_obs, info = self._env.reset(seed=seed)
        observation = self._format_raw_obs(raw_obs)
@@ -384,9 +384,7 @@ class RoboTwinEnv(gym.Env):
        self._env: Any | None = None  # deferred — created on first reset() inside worker
        self._step_count: int = 0
-        self._black_frame: np.ndarray = np.zeros(
+        self._black_frame = np.zeros((self.observation_height, self.observation_width, 3), dtype=np.uint8)
            (self.observation_height, self.observation_width, 3), dtype=np.uint8
        )
        image_spaces = {
            cam: spaces.Box(
@@ -373,7 +373,7 @@ class VLABenchEnv(gym.Env):
        if action.shape[0] != 7:
            # Unknown layout — fall back to zero-pad so the sim doesn't crash.
-            padded: np.ndarray = np.zeros(ctrl_dim, dtype=np.float64)
+            padded = np.zeros(ctrl_dim, dtype=np.float64)
            padded[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
            return padded
@@ -122,9 +122,6 @@ MODEL_ENCODING_TABLE = {
    "xm430-w350": X_SERIES_ENCODINGS_TABLE,
    "xm540-w270": X_SERIES_ENCODINGS_TABLE,
    "xc430-w150": X_SERIES_ENCODINGS_TABLE,
    "xh540-w150": X_SERIES_ENCODINGS_TABLE,
    "xc330-t288": X_SERIES_ENCODINGS_TABLE,
    "xc330-t181": X_SERIES_ENCODINGS_TABLE,
 }
 # {model: model_resolution}
@@ -137,9 +134,6 @@ MODEL_RESOLUTION = {
    "xm430-w350": 4096,
    "xm540-w270": 4096,
    "xc430-w150": 4096,
    "xh540-w150": 4096,
    "xc330-t288": 4096,
    "xc330-t181": 4096,
 }
 # {model: model_number}
@@ -151,9 +145,6 @@ MODEL_NUMBER_TABLE = {
    "xm430-w350": 1020,
    "xm540-w270": 1120,
    "xc430-w150": 1070,
    "xh540-w150": 1110,
    "xc330-t288": 1220,
    "xc330-t181": 1210,
 }
 # {model: available_operating_modes}
@@ -165,9 +156,6 @@ MODEL_OPERATING_MODES = {
    "xm430-w350": [0, 1, 3, 4, 5, 16],
    "xm540-w270": [0, 1, 3, 4, 5, 16],
    "xc430-w150": [1, 3, 4, 16],
    "xh540-w150": [0, 1, 3, 4, 5, 16],
    "xc330-t288": [0, 1, 3, 4, 5, 16],
    "xc330-t181": [0, 1, 3, 4, 5, 16],
 }
 MODEL_CONTROL_TABLE = {
@@ -178,9 +166,6 @@ MODEL_CONTROL_TABLE = {
    "xm430-w350": X_SERIES_CONTROL_TABLE,
    "xm540-w270": X_SERIES_CONTROL_TABLE,
    "xc430-w150": X_SERIES_CONTROL_TABLE,
    "xh540-w150": X_SERIES_CONTROL_TABLE,
    "xc330-t288": X_SERIES_CONTROL_TABLE,
    "xc330-t181": X_SERIES_CONTROL_TABLE,
 }
 MODEL_BAUDRATE_TABLE = {
@@ -191,9 +176,6 @@ MODEL_BAUDRATE_TABLE = {
    "xm430-w350": X_SERIES_BAUDRATE_TABLE,
    "xm540-w270": X_SERIES_BAUDRATE_TABLE,
    "xc430-w150": X_SERIES_BAUDRATE_TABLE,
    "xh540-w150": X_SERIES_BAUDRATE_TABLE,
    "xc330-t288": X_SERIES_BAUDRATE_TABLE,
    "xc330-t181": X_SERIES_BAUDRATE_TABLE,
 }
 AVAILABLE_BAUDRATES = [
@@ -44,19 +44,12 @@ from lerobot.utils.constants import (
    POLICY_PREPROCESSOR_DEFAULT_NAME,
 )
 from lerobot.utils.feature_utils import dataset_to_policy_features
 from lerobot.utils.import_utils import _peft_available, require_package
 from .evo1.configuration_evo1 import Evo1Config
 from .groot.configuration_groot import GrootConfig
 from .pretrained import PreTrainedPolicy
 from .utils import validate_visual_features_consistency
 if TYPE_CHECKING or _peft_available:
    from peft import PeftConfig, PeftModel
 else:
    PeftConfig = None
    PeftModel = None
 def _reconnect_relative_absolute_steps(
    preprocessor: PolicyProcessorPipeline, postprocessor: PolicyProcessorPipeline
@@ -184,7 +177,6 @@ def make_pre_post_processors(
            return make_groot_pre_post_processors_from_pretrained(
                config=policy_cfg,
                pretrained_path=pretrained_path,
                revision=pretrained_revision,
                dataset_stats=kwargs.get("dataset_stats"),
                dataset_meta=kwargs.get("dataset_meta"),
                preprocessor_overrides=kwargs.get("preprocessor_overrides"),
@@ -341,15 +333,12 @@ def make_policy(
        # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo
        # of the adapter and the adapter's config contains the path to the base policy. So we need the
        # adapter config first, then load the correct policy and then apply PEFT.
-        require_package("peft", extra="peft")
+        from peft import PeftConfig, PeftModel
        logging.info("Loading policy's PEFT adapter.")
        peft_pretrained_path = str(cfg.pretrained_path)
-        peft_config = PeftConfig.from_pretrained(
+        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
            peft_pretrained_path,
            revision=cfg.pretrained_revision,
        )
        kwargs["pretrained_name_or_path"] = peft_config.base_model_name_or_path
        if not kwargs["pretrained_name_or_path"]:
@@ -360,14 +349,9 @@ def make_policy(
                "the adapter was trained."
            )
        kwargs["revision"] = peft_config.revision
        policy = policy_cls.from_pretrained(**kwargs)
        policy = PeftModel.from_pretrained(
-            policy,
+            policy, peft_pretrained_path, config=peft_config, is_trainable=True
            peft_pretrained_path,
            config=peft_config,
            revision=cfg.pretrained_revision,
            is_trainable=True,
        )
    else:
@@ -37,19 +37,13 @@ def is_image_feature(key: str) -> bool:
@dataclass
 class ConcurrencyConfig:
    """Configuration for the concurrency of the actor and learner.
    Possible values are:
    - "threads": Use threads for the actor and learner.
    - "processes": Use processes for the actor and learner.
    ``multiprocessing_context`` selects the process-wide start method when
    processes are used. Set it to ``None`` to preserve Python's default or a
    method already selected by the embedding application.
    """
    actor: str = "threads"
    learner: str = "threads"
    multiprocessing_context: str | None = "spawn"
@dataclass
@@ -475,7 +475,6 @@ def make_groot_pre_post_processors_from_pretrained(
    config: GrootConfig,
    pretrained_path: str,
    *,
    revision: str | None = None,
    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
    dataset_meta: Any | None = None,
    preprocessor_overrides: dict[str, Any] | None = None,
@@ -512,7 +511,6 @@ def make_groot_pre_post_processors_from_pretrained(
    preprocessor, postprocessor = _load_groot_processor_pipelines(
        pretrained_path,
        revision=revision,
        preprocessor_overrides=preprocessor_overrides,
        postprocessor_overrides=postprocessor_overrides,
        preprocessor_config_filename=preprocessor_config_filename,
@@ -528,7 +526,6 @@ def make_groot_pre_post_processors_from_pretrained(
 def _load_groot_processor_pipelines(
    pretrained_path: str,
    *,
    revision: str | None,
    preprocessor_overrides: dict[str, Any],
    postprocessor_overrides: dict[str, Any],
    preprocessor_config_filename: str,
@@ -543,7 +540,6 @@ def _load_groot_processor_pipelines(
    preprocessor = PolicyProcessorPipeline.from_pretrained(
        pretrained_model_name_or_path=pretrained_path,
        config_filename=preprocessor_config_filename,
        revision=revision,
        overrides=preprocessor_overrides,
        to_transition=batch_to_transition,
        to_output=transition_to_batch,
@@ -551,7 +547,6 @@ def _load_groot_processor_pipelines(
    postprocessor = PolicyProcessorPipeline.from_pretrained(
        pretrained_model_name_or_path=pretrained_path,
        config_filename=postprocessor_config_filename,
        revision=revision,
        overrides=postprocessor_overrides,
        to_transition=policy_action_to_transition,
        to_output=transition_to_policy_action,
@@ -43,22 +43,11 @@ from torch.distributions import Beta
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.utils.constants import ACTION
-from lerobot.utils.import_utils import (
+from lerobot.utils.import_utils import _scipy_available, _transformers_available, require_package
    _peft_available,
    _scipy_available,
    _transformers_available,
    require_package,
 )
 from ..rtc.modeling_rtc import RTCProcessor
 from .configuration_molmoact2 import MolmoAct2Config
 if TYPE_CHECKING or _peft_available:
    from peft import LoraConfig, get_peft_model
 else:
    LoraConfig = None
    get_peft_model = None
 logger = logging.getLogger(__name__)
@@ -1742,11 +1731,13 @@ class MolmoAct2Policy(PreTrainedPolicy):
    def _build_inner_lora_config(self):
        require_package("peft", extra="molmoact2")
        from peft import LoraConfig
        return LoraConfig(**self._get_inner_peft_targets())
    def _apply_lora_adapters(self) -> None:
        require_package("peft", extra="molmoact2")
        from peft import get_peft_model
        peft_config = self._build_inner_lora_config()
        self._validate_peft_config(peft_config)
@@ -34,22 +34,14 @@ from lerobot.configs import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.utils.device_utils import resolve_safetensors_device
 from lerobot.utils.hub import HubMixin
 from lerobot.utils.import_utils import _peft_available, require_package
 from .utils import log_model_loading_keys
-if TYPE_CHECKING or _peft_available:
+T = TypeVar("T", bound="PreTrainedPolicy")
    from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType, get_peft_model
 else:
    PEFT_TYPE_TO_CONFIG_MAPPING = None
    PeftType = None
    get_peft_model = None
 if TYPE_CHECKING:
    from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
 T = TypeVar("T", bound="PreTrainedPolicy")
 def _build_card_context(
    cfg: TrainPipelineConfig | None,
@@ -392,7 +384,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
            peft_cli_overrides: Optional dict of CLI overrides (method_type, target_modules, r, etc.)
                These are merged with policy defaults to build the final config.
        """
-        require_package("peft", extra="peft")
+        from peft import get_peft_model
        # If user provided a complete config, use it directly (with overrides)
        if peft_config is not None:
@@ -463,7 +455,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
        Returns:
            Preprocessed dict with renamed keys and init_type mapped to method-specific key.
        """
-        require_package("peft", extra="peft")
+        from peft import PeftType
        cli_overrides = cli_overrides.copy()
@@ -488,7 +480,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
    def _build_peft_config(self, cli_overrides: dict):
        """Build a PEFT config from policy defaults and CLI overrides."""
-        require_package("peft", extra="peft")
+        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType
        # Determine PEFT method type (default to LORA)
        method_type_str = cli_overrides.get("method_type") or "lora"
@@ -515,7 +507,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
    def _apply_peft_cli_overrides(self, peft_config, cli_overrides: dict):
        """Apply CLI overrides to an existing PEFT config."""
-        require_package("peft", extra="peft")
+        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType
        # Get method type from existing config or CLI override
        method_type_str = cli_overrides.get("method_type")
@@ -132,20 +132,10 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
    def transform_features(
        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        for axis in ["x", "y", "z"]:
+        for axis in ["x", "y", "z", "gripper"]:
            features[PipelineFeatureType.ACTION].pop(f"delta_{axis}", None)
        features[PipelineFeatureType.ACTION].pop("gripper", None)
-        for feat in [
+        for feat in ["enabled", "target_x", "target_y", "target_z", "target_wx", "target_wy", "target_wz"]:
            "enabled",
            "target_x",
            "target_y",
            "target_z",
            "target_wx",
            "target_wy",
            "target_wz",
            "gripper_vel",
        ]:
            features[PipelineFeatureType.ACTION][f"{feat}"] = PolicyFeature(
                type=FeatureType.ACTION, shape=(1,)
            )
@@ -713,8 +713,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            ProcessorMigrationError: If the model requires migration to processor format.
        """
        model_id = str(pretrained_model_name_or_path)
        model_path = Path(model_id)
        is_local_source = model_path.is_dir() or model_path.is_file()
        hub_download_kwargs = {
            "force_download": force_download,
            "resume_download": resume_download,
@@ -733,7 +731,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        # 3. Build steps with overrides
        steps, validated_overrides = cls._build_steps_with_overrides(
-            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs, is_local_source
+            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs
        )
        # 4. Validate that all overrides were used
@@ -923,7 +921,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        model_id: str,
        base_path: Path | None,
        hub_download_kwargs: dict[str, Any],
        is_local_source: bool = False,
    ) -> tuple[list[ProcessorStep], set[str]]:
        """Build all processor steps with overrides and state loading.
@@ -947,7 +944,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        3. **State Loading** (via _load_step_state):
           - **If step has "state_file"**: Load tensor state from .safetensors
           - **Local first**: Check base_path/state_file.safetensors
-           - **Hub fallback**: Download state file if the pipeline was loaded from the Hub
+           - **Hub fallback**: Download state file if not found locally
           - **Optional**: Only load if step has load_state_dict method
        4. **Override Tracking**:
@@ -965,7 +962,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            model_id: The model identifier (needed for Hub state file downloads)
            base_path: Local directory path for finding state files
            hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
            is_local_source: Whether model_id resolved to a local directory or config file.
        Returns:
            Tuple of (instantiated_steps_list, unused_override_keys)
@@ -979,9 +975,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        steps, remaining_override_keys = cls._build_steps_from_config(loaded_config, overrides)
        for step_instance, step_entry in zip(steps, loaded_config["steps"], strict=True):
-            cls._load_step_state(
+            cls._load_step_state(step_instance, step_entry, model_id, base_path, hub_download_kwargs)
                step_instance, step_entry, model_id, base_path, hub_download_kwargs, is_local_source
            )
        return steps, remaining_override_keys
@@ -1145,7 +1139,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        model_id: str,
        base_path: Path | None,
        hub_download_kwargs: dict[str, Any],
        is_local_source: bool = False,
    ) -> None:
        """Load state dictionary for a processor step if available.
@@ -1164,7 +1157,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
           - **Use case**: Loading from local saved model directory
        2. **Hub download fallback**: Download state file from repository
-           - **When triggered**: Local file not found and the pipeline source is a Hub repo
+           - **When triggered**: Local file not found or base_path is None
           - **Process**: Use hf_hub_download with same parameters as config
           - **Example**: Download "normalize_step_0.safetensors" from "user/repo"
           - **Result**: Downloaded to local cache, path returned
@@ -1185,7 +1178,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            model_id: The model identifier (used for Hub downloads if needed)
            base_path: Local directory path for finding state files (None for Hub-only)
            hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
            is_local_source: Whether model_id resolved to a local directory or config file.
        Note:
            This method modifies step_instance in-place and returns None.
@@ -1199,12 +1191,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
        # Try local file first
        if base_path and (base_path / state_filename).exists():
            state_path = str(base_path / state_filename)
        elif is_local_source:
            state_path = base_path / state_filename if base_path else Path(state_filename)
            raise FileNotFoundError(
                f"State file '{state_filename}' was not found for local processor pipeline "
                f"'{model_id}' at '{state_path}'."
            )
        else:
            # Download from Hub
            state_path = hf_hub_download(
@@ -91,7 +91,7 @@ from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
 from lerobot.teleoperators.utils import TeleopEvents
 from lerobot.utils.device_utils import get_safe_torch_device
-from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.transition import (
@@ -124,7 +124,9 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
    cfg.validate()
    display_pid = False
    if not use_threads(cfg):
-        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
+        import torch.multiprocessing as mp
        mp.set_start_method("spawn")
        display_pid = True
    # Create logs directory to ensure it exists
@@ -102,7 +102,7 @@ from lerobot.utils.constants import (
 )
 from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.io_utils import load_json, write_json
-from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
    format_big_number,
@@ -123,7 +123,9 @@ def train_cli(cfg: TrainRLServerPipelineConfig):
    # Fail fast with a friendly error if the optional ``hilserl`` extra is missing.
    require_package("grpcio", extra="hilserl", import_name="grpc")
    if not use_threads(cfg):
-        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
+        import torch.multiprocessing as mp
        mp.set_start_method("spawn")
    # Use the job_name from the config
    train(
@@ -323,10 +323,6 @@ class LeKiwiClient(Robot):
            np.ndarray: the action sent to the motors, potentially clipped.
        """
        # Action values may be torch tensors (e.g. replayed from a dataset) or numpy
        # scalars; json.dumps only serializes Python primitives, so coerce each value to a
        # plain float before sending.
        action = {key: float(value) for key, value in action.items()}
        self.zmq_cmd_socket.send_string(json.dumps(action))  # action is in motor space
        # TODO(Steven): Remove the np conversion when it is possible to record a non-numpy array value
@@ -46,12 +46,6 @@ class SOFollowerConfig:
    position_i_coefficient: int = 0
    position_d_coefficient: int = 32
    # Number of extra attempts when a `sync_read` of the motors fails. Feetech buses can occasionally
    # return a corrupted status packet ("Incorrect status packet!"), especially when several joints move
    # at once, which otherwise aborts the control loop. Retries are immediate (no sleep) and only happen on
    # failure, so the steady-state read cost is unchanged.
    num_read_retries: int = 2
@RobotConfig.register_subclass("so101_follower")
@RobotConfig.register_subclass("so100_follower")
@@ -180,7 +180,7 @@ class SOFollower(Robot):
    def get_observation(self) -> RobotObservation:
        # Read arm position
        start = time.perf_counter()
-        obs_dict = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
+        obs_dict = self.bus.sync_read("Present_Position")
        obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
        dt_ms = (time.perf_counter() - start) * 1e3
        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
@@ -221,7 +221,7 @@ class SOFollower(Robot):
        # Cap goal position when too far away from present position.
        # /!\ Slower fps expected due to reading from the follower.
        if self.config.max_relative_target is not None:
-            present_pos = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
+            present_pos = self.bus.sync_read("Present_Position")
            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
            goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
@@ -326,17 +326,8 @@ class RolloutConfig:
        policy_path = parser.get_path_arg("policy")
        if policy_path:
-            yaml_overrides = parser.get_yaml_overrides("policy")
+            cli_overrides = parser.get_cli_overrides("policy")
-            cli_overrides = parser.get_cli_overrides("policy") or []
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
            policy_overrides = yaml_overrides + cli_overrides
            pretrained_revision = parser.parse_arg("pretrained_revision", cli_overrides)
            if pretrained_revision is None:
                pretrained_revision = parser.parse_arg("pretrained_revision", yaml_overrides)
            self.policy = PreTrainedConfig.from_pretrained(
                policy_path,
                revision=pretrained_revision,
                cli_overrides=policy_overrides,
            )
            self.policy.pretrained_path = policy_path
        if self.policy is None:
            raise ValueError("--policy.path is required for rollout")
@@ -24,11 +24,10 @@ from __future__ import annotations
 import logging
 from dataclasses import dataclass, field
 from threading import Event
 from typing import TYPE_CHECKING
 import torch
-from lerobot.configs import FeatureType, PreTrainedConfig
+from lerobot.configs import FeatureType
 from lerobot.datasets import (
    LeRobotDataset,
    aggregate_pipeline_dataset_features,
@@ -48,7 +47,6 @@ from lerobot.processor.relative_action_processor import RelativeActionsProcessor
 from lerobot.robots import make_robot_from_config
 from lerobot.teleoperators import Teleoperator, make_teleoperator_from_config
 from lerobot.utils.feature_utils import combine_feature_dicts, hw_to_dataset_features
 from lerobot.utils.import_utils import _peft_available, require_package
 from .configs import BaseStrategyConfig, DAggerStrategyConfig, RolloutConfig
 from .inference import (
@@ -59,12 +57,6 @@ from .inference import (
 )
 from .robot_wrapper import ThreadSafeRobot
 if TYPE_CHECKING or _peft_available:
    from peft import PeftConfig, PeftModel
 else:
    PeftConfig = None
    PeftModel = None
 logger = logging.getLogger(__name__)
@@ -167,35 +159,6 @@ class RolloutContext:
 # ---------------------------------------------------------------------------
 def _load_pretrained_policy(policy_config: PreTrainedConfig) -> PreTrainedPolicy:
    """Load policy weights, keeping adapter and base-model revisions independent."""
    pretrained_revision = policy_config.pretrained_revision
    policy_class = get_policy_class(policy_config.type)
    if not policy_config.use_peft:
        return policy_class.from_pretrained(
            policy_config.pretrained_path,
            config=policy_config,
            revision=pretrained_revision,
        )
    require_package("peft", extra="peft")
    peft_path = policy_config.pretrained_path
    peft_config = PeftConfig.from_pretrained(peft_path, revision=pretrained_revision)
    policy = policy_class.from_pretrained(
        pretrained_name_or_path=peft_config.base_model_name_or_path,
        config=policy_config,
        revision=peft_config.revision,
    )
    return PeftModel.from_pretrained(
        policy,
        peft_path,
        config=peft_config,
        revision=pretrained_revision,
    )
 def build_rollout_context(
    cfg: RolloutConfig,
    shutdown_event: Event,
@@ -213,6 +176,7 @@ def build_rollout_context(
    # --- 1. Policy (heavy I/O, but no hardware yet) -------------------
    logger.info("Loading policy from '%s'...", cfg.policy.pretrained_path)
    policy_config = cfg.policy
    policy_class = get_policy_class(policy_config.type)
    if hasattr(policy_config, "compile_model"):
        policy_config.compile_model = cfg.use_torch_compile
@@ -223,7 +187,17 @@ def build_rollout_context(
            "Please use `cpu` or `cuda` backend."
        )
-    policy = _load_pretrained_policy(policy_config)
+    if policy_config.use_peft:
        from peft import PeftConfig, PeftModel
        peft_path = policy_config.pretrained_path
        peft_config = PeftConfig.from_pretrained(peft_path)
        policy = policy_class.from_pretrained(
            pretrained_name_or_path=peft_config.base_model_name_or_path, config=policy_config
        )
        policy = PeftModel.from_pretrained(policy, peft_path, config=peft_config)
    else:
        policy = policy_class.from_pretrained(policy_config.pretrained_path, config=policy_config)
    if is_rtc:
        policy.config.rtc_config = cfg.inference.rtc
@@ -302,22 +276,12 @@ def build_rollout_context(
    # ``observation_features`` values are either a tuple (camera shape) or the
    # ``float`` type itself used as a sentinel for scalar motor features —
    # see ``dict[str, type | tuple]`` annotation on ``Robot.observation_features``.
    # Keep cameras (tuple) plus both joint-position (.pos) and base-velocity (.vel)
    # scalar state features. LeKiwi's observation.state is 9-dim (6 arm .pos +
    # x/y/theta.vel) and the policy was trained/normalized on all 9; the old .pos-only
    # filter fed a 6-dim state into a 9-dim normalizer → RuntimeError (size 6 vs 9).
    # Pure-arm robots have no .vel state keys, so this is a no-op for them.
    observation_features_hw = {
        k: v
        for k, v in all_obs_features.items()
-        if isinstance(v, tuple) or (v is float and k.endswith((".pos", ".vel")))
+        if isinstance(v, tuple) or (v is float and k.endswith(".pos"))
    }
-    # Keep both joint-position (.pos) and base-velocity (.vel) action features so
+    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith(".pos")}
    # mobile manipulators command the base too (e.g. LeKiwi: 6 arm .pos +
    # x/y/theta.vel = 9-dim action). Pure-arm robots have no .vel keys, so this is
    # a no-op for them. Without the .vel keys the base velocities are silently
    # dropped from dataset_features[ACTION]/ordered_action_keys and the base never moves.
    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith((".pos", ".vel"))}
    # The action side is always needed: sync inference reads action names from
    # ``dataset_features[ACTION]`` to map policy tensors back to robot actions.
@@ -428,7 +392,6 @@ def build_rollout_context(
    preprocessor, postprocessor = make_pre_post_processors(
        policy_cfg=policy_config,
        pretrained_path=cfg.policy.pretrained_path,
        pretrained_revision=policy_config.pretrained_revision,
        dataset_stats=dataset_stats,
        preprocessor_overrides={
            "device_processor": {"device": cfg.device},
@@ -36,7 +36,6 @@ python src/lerobot/scripts/augment_dataset_quantile_stats.py \
 import argparse
 import concurrent.futures
 import logging
 import os
 from pathlib import Path
 import numpy as np
@@ -53,7 +52,6 @@ from lerobot.datasets import (
    get_feature_stats,
    write_stats,
 )
 from lerobot.datasets.compute_stats import sample_indices
 from lerobot.utils.utils import init_logging
@@ -79,14 +77,12 @@ def has_quantile_stats(stats: dict[str, dict] | None, quantile_list_keys: list[s
    return False
-def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampling: bool = True) -> dict:
+def process_single_episode(dataset: LeRobotDataset, episode_idx: int) -> dict:
    """Process a single episode and return its statistics.
    Args:
        dataset: The LeRobot dataset
        episode_idx: Index of the episode to process
        use_sampling: If True, sub-sample image/video frames per episode to bound
            memory. If False, use every frame (exact, higher memory).
    Returns:
        Dictionary containing episode statistics
@@ -96,31 +92,16 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampli
    start_idx = dataset.meta.episodes[episode_idx]["dataset_from_index"]
    end_idx = dataset.meta.episodes[episode_idx]["dataset_to_index"]
    episode_len = end_idx - start_idx
    # Images/video are the memory hog, so sub-sample those frames per episode;
    # numeric columns are cheap, so read them in full (exact).
    image_keys = [k for k in dataset.features if dataset.features[k]["dtype"] in ("image", "video")]
    numeric_keys = [
        k for k in dataset.features if dataset.features[k]["dtype"] not in ("image", "video", "string")
    ]
    collected_data: dict[str, list] = {}
    for idx in range(start_idx, end_idx):
        item = dataset[idx]
        for key, value in item.items():
            if key not in dataset.features:
                continue
-    # Numeric features: every frame, read directly from the underlying table.
+            if key not in collected_data:
-    if numeric_keys:
+                collected_data[key] = []
-        numeric_cols = dataset.hf_dataset.select_columns(numeric_keys)[start_idx:end_idx]
+            collected_data[key].append(value)
        for key in numeric_keys:
            collected_data[key] = [torch.as_tensor(v) for v in numeric_cols[key]]
    # Image/video features: decode only a sampled subset of frames.
    if image_keys:
        sampled_offsets = sample_indices(episode_len) if use_sampling else list(range(episode_len))
        for offset in sampled_offsets:
            item = dataset[start_idx + offset]
            for key in image_keys:
                if key in item:
                    collected_data.setdefault(key, []).append(item[key])
    ep_stats = {}
    for key, data_list in collected_data.items():
@@ -150,13 +131,11 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampli
    return ep_stats
-def compute_quantile_stats_for_dataset(dataset: LeRobotDataset, use_sampling: bool = True) -> dict[str, dict]:
+def compute_quantile_stats_for_dataset(dataset: LeRobotDataset) -> dict[str, dict]:
    """Compute quantile statistics for all episodes in the dataset.
    Args:
        dataset: The LeRobot dataset to compute statistics for
        use_sampling: If True, sub-sample image/video frames per episode to bound
            memory. If False, use every frame (exact, higher memory).
    Returns:
        Dictionary containing aggregated statistics with quantiles
@@ -174,15 +153,15 @@ def compute_quantile_stats_for_dataset(dataset: LeRobotDataset, use_sampling: bo
    if has_videos:
        logging.info("Dataset contains video keys - using sequential processing for thread safety")
        for episode_idx in tqdm(range(dataset.num_episodes), desc="Processing episodes"):
-            ep_stats = process_single_episode(dataset, episode_idx, use_sampling)
+            ep_stats = process_single_episode(dataset, episode_idx)
            episode_stats_list.append(ep_stats)
    else:
        logging.info("Dataset has no video keys - using parallel processing for better performance")
-        max_workers = min(dataset.num_episodes, int(os.environ.get("LEROBOT_STATS_MAX_WORKERS", 16)))
+        max_workers = min(dataset.num_episodes, 16)
        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
            future_to_episode = {
-                executor.submit(process_single_episode, dataset, episode_idx, use_sampling): episode_idx
+                executor.submit(process_single_episode, dataset, episode_idx): episode_idx
                for episode_idx in range(dataset.num_episodes)
            }
@@ -209,7 +188,6 @@ def augment_dataset_with_quantile_stats(
    repo_id: str,
    root: str | Path | None = None,
    overwrite: bool = False,
    use_sampling: bool = True,
 ) -> None:
    """Augment a dataset with quantile statistics if they are missing.
@@ -217,8 +195,6 @@ def augment_dataset_with_quantile_stats(
        repo_id: Repository ID of the dataset
        root: Local root directory for the dataset
        overwrite: Overwrite existing quantile statistics if they already exist
        use_sampling: If True, sub-sample image/video frames per episode to bound
            memory. If False, use every frame (exact, higher memory).
    """
    logging.info(f"Loading dataset: {repo_id}")
    dataset = LeRobotDataset(
@@ -232,7 +208,7 @@ def augment_dataset_with_quantile_stats(
    logging.info("Dataset does not contain quantile statistics. Computing them now...")
-    new_stats = compute_quantile_stats_for_dataset(dataset, use_sampling=use_sampling)
+    new_stats = compute_quantile_stats_for_dataset(dataset)
    logging.info("Updating dataset metadata with new quantile statistics")
    dataset.meta.stats = new_stats
@@ -272,14 +248,6 @@ def main():
        action="store_true",
        help="Overwrite existing quantile statistics if they already exist",
    )
    parser.add_argument(
        "--no-sampling",
        action="store_true",
        help=(
            "Compute stats over every frame (exact, higher memory). By default, "
            "image/video frames are sub-sampled per episode to bound memory."
        ),
    )
    args = parser.parse_args()
    root = Path(args.root) if args.root else None
@@ -290,7 +258,6 @@ def main():
        repo_id=args.repo_id,
        root=root,
        overwrite=args.overwrite,
        use_sampling=not args.no_sampling,
    )
@@ -94,8 +94,6 @@ from lerobot.datasets.video_utils import concatenate_video_files, get_video_dura
 from lerobot.utils.constants import HF_LEROBOT_HOME
 from lerobot.utils.utils import flatten_dict, init_logging
 logger = logging.getLogger(__name__)
 V21 = "v2.1"
 V30 = "v3.0"
@@ -478,11 +476,11 @@ def convert_dataset(
    # First check if the dataset already has a v3.0 version
    if root is None and not force_conversion:
        try:
-            logger.info("Trying to download v3.0 version of the dataset from the hub...")
+            print("Trying to download v3.0 version of the dataset from the hub...")
            snapshot_download(repo_id, repo_type="dataset", revision=V30, local_dir=HF_LEROBOT_HOME / repo_id)
            return
        except Exception:
-            logger.info("Dataset does not have an uploaded v3.0 version. Continuing with conversion.")
+            print("Dataset does not have an uploaded v3.0 version. Continuing with conversion.")
    # Set root based on whether local dataset path is provided
    use_local_dataset = False
@@ -490,7 +488,7 @@ def convert_dataset(
    if root.exists():
        validate_local_dataset_version(root)
        use_local_dataset = True
-        logger.info(f"Using local dataset at {root}")
+        print(f"Using local dataset at {root}")
    old_root = root.parent / f"{root.name}_old"
    new_root = root.parent / f"{root.name}_v30"
@@ -525,7 +523,7 @@ def convert_dataset(
        try:
            hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
        except (HTTPError, RevisionNotFoundError) as e:
-            logger.warning(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
+            print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
            pass
        hub_api.delete_files(
            delete_patterns=["data/chunk*/episode_*", "meta/*.jsonl", "videos/chunk*"],
@@ -154,14 +154,14 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
    repo_id = cfg.new_repo_id or cfg.repo_id
    commit_message = cfg.push_commit_message or "Add steerable annotations (lerobot-annotate)"
    api = HfApi()
-    logger.info(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...")
+    print(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...", flush=True)
    api.create_repo(
        repo_id=repo_id,
        repo_type="dataset",
        private=cfg.push_private,
        exist_ok=True,
    )
-    logger.info(f"[lerobot-annotate] uploading {root} -> {repo_id}...")
+    print(f"[lerobot-annotate] uploading {root} -> {repo_id}...", flush=True)
    commit_info = api.upload_folder(
        folder_path=str(root),
        repo_id=repo_id,
@@ -172,7 +172,7 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
        # at the source dataset; a fresh card is generated below instead.
        ignore_patterns=[".annotate_staging/**", "**/.DS_Store", "README.md"],
    )
-    logger.info(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}")
+    print(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}", flush=True)
    dataset_info = load_info(root)
    card = create_lerobot_dataset_card(dataset_info=dataset_info, license="apache-2.0", repo_id=repo_id)
@@ -200,13 +200,14 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
        with suppress(RevisionNotFoundError):
            api.delete_tag(repo_id, tag=version_tag, repo_type="dataset")
        api.create_tag(**tag_kwargs)
-        logger.info(f"[lerobot-annotate] tagged {repo_id} as {version_tag}")
+        print(f"[lerobot-annotate] tagged {repo_id} as {version_tag}", flush=True)
    except Exception as exc:  # noqa: BLE001
-        logger.warning(
+        print(
            f"[lerobot-annotate] WARNING: could not create tag {version_tag!r} on {repo_id}: {exc}. "
            "Dataset is uploaded but ``LeRobotDataset`` won't be able to load it until it's tagged. "
            "Run: from huggingface_hub import HfApi; "
-            f"HfApi().create_tag({repo_id!r}, tag={version_tag!r}, repo_type='dataset', exist_ok=True)"
+            f"HfApi().create_tag({repo_id!r}, tag={version_tag!r}, repo_type='dataset', exist_ok=True)",
            flush=True,
        )
@@ -89,8 +89,6 @@ from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD, SUCCESS
 from lerobot.utils.utils import init_logging
 logger = logging.getLogger(__name__)
 DEFAULT_FOXGLOVE_PORT = 8765
 DEFAULT_RERUN_PORT = 9090
@@ -301,7 +299,7 @@ def visualize_dataset(
            while True:
                time.sleep(1)
        except KeyboardInterrupt:
-            logger.info("Ctrl-C received. Exiting.")
+            print("Ctrl-C received. Exiting.")
 def main():
@@ -62,7 +62,7 @@ from dataclasses import asdict
 from functools import partial
 from pathlib import Path
 from pprint import pformat
-from typing import TYPE_CHECKING, Any, TypedDict
+from typing import Any, TypedDict
 import einops
 import gymnasium as gym
@@ -87,7 +87,7 @@ from lerobot.processor import PolicyProcessorPipeline
 from lerobot.types import PolicyAction
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STR, REWARD
 from lerobot.utils.device_utils import get_safe_torch_device
-from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
+from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.io_utils import write_video
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -95,14 +95,6 @@ from lerobot.utils.utils import (
    inside_slurm,
 )
 if TYPE_CHECKING or _peft_available:
    from peft import PeftModel
 else:
    PeftModel = None
 logger = logging.getLogger(__name__)
 def _env_features_to_dataset_features(env_features: dict) -> dict:
    """Convert EnvConfig.features to the dict format expected by LeRobotDataset.create()."""
@@ -452,16 +444,15 @@ def eval_policy(
        exc = ValueError(
            f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
        )
-        if not _peft_available:
+        try:
-            raise exc
+            from peft import PeftModel
-        require_package("peft", extra="peft")
+
            if not isinstance(policy, PeftModel):
                raise exc
        except ImportError:
            raise exc from None
    start = time.time()
    # Preserve the mode for direct callers. eval_policy_all scopes the mode
    # around all tasks so parallel evaluations cannot race with each other.
    was_training = policy.training
    policy.eval()
    # Determine how many batched rollouts we need to get n_episodes. Note that if n_episodes is not evenly
@@ -564,7 +555,7 @@ def eval_policy(
        if seeds:
            all_seeds.extend(seeds)
        else:
-            all_seeds.extend([None] * env.num_envs)
+            all_seeds.append(None)
        # FIXME: episode_data is either None or it doesn't exist
        if return_episode_data:
@@ -683,8 +674,6 @@ def eval_policy(
    if save_predicted_video:
        info["predicted_video_paths"] = predicted_video_paths
    policy.train(was_training)
    return info
@@ -802,13 +791,13 @@ def eval_main(cfg: EvalPipelineConfig):
            recording_repo_id=cfg.eval.recording_repo_id,
            recording_private=cfg.eval.recording_private,
        )
-        logger.info("Overall Aggregated Metrics:")
+        print("Overall Aggregated Metrics:")
-        logger.info(info["overall"])
+        print(info["overall"])
        # Print per-suite stats
        for task_group, task_group_info in info.items():
-            logger.info(f"\nAggregated Metrics for {task_group}:")
+            print(f"\nAggregated Metrics for {task_group}:")
-            logger.info(task_group_info)
+            print(task_group_info)
    # Close all vec envs
    close_envs(envs)
@@ -1021,12 +1010,6 @@ def eval_policy_all(
        recording_private=recording_private,
    )
    # Set the shared policy's mode before launching any workers. Restoring it
    # inside individual tasks would let one task enable training mode while
    # another task is still evaluating.
    was_training = policy.training
    policy.eval()
    try:
    if max_parallel_tasks <= 1:
        prefetch_thread: threading.Thread | None = None
        for i, (task_group, task_id, env) in enumerate(tasks):
@@ -1061,8 +1044,6 @@ def eval_policy_all(
                    per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics})
                finally:
                    env.close()
    finally:
        policy.train(was_training)
    # compute aggregated metrics helper (robust to lists/scalars)
    def _agg_from_list(xs):
@@ -40,7 +40,6 @@ from PIL import Image
 from lerobot.cameras import ColorMode
 from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig
 from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
 from lerobot.utils.utils import init_logging
 logger = logging.getLogger(__name__)
@@ -286,8 +285,6 @@ def save_images_from_all_cameras(
 def main():
    init_logging()
    parser = argparse.ArgumentParser(
        description="Unified camera utility script for listing cameras and capturing images."
    )
@@ -453,11 +453,9 @@ def record(
                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
            )
-        # Connect the teleoperator before the robot so the robot isn't left idle (and possibly
+        robot.connect()
        # tripping a firmware watchdog) during teleop init. Matches lerobot_teleoperate.py.
        if teleop is not None:
            teleop.connect()
        robot.connect()
        listener, events = init_keyboard_listener()
@@ -61,7 +61,6 @@ from lerobot.robots import (  # noqa: F401
    earthrover_mini_plus,
    hope_jr,
    koch_follower,
    lekiwi,
    make_robot_from_config,
    omx_follower,
    openarm_follower,
@@ -165,7 +165,6 @@ from lerobot.robots import (  # noqa: F401
    earthrover_mini_plus,
    hope_jr,
    koch_follower,
    lekiwi,
    omx_follower,
    openarm_follower,
    reachy2,
@@ -22,8 +22,7 @@ import dataclasses
 import logging
 import sys
 import time
-from collections.abc import Iterator
+from contextlib import nullcontext
 from contextlib import contextmanager, nullcontext
 from pprint import pformat
 from typing import TYPE_CHECKING, Any
@@ -58,7 +57,7 @@ from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.rewards import make_reward_pre_post_processors
 from lerobot.utils.collate import lerobot_collate_fn
-from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
+from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -69,38 +68,9 @@ from lerobot.utils.utils import (
    inside_slurm,
 )
 if TYPE_CHECKING or _peft_available:
    from peft import PeftModel
 else:
    PeftModel = None
 from .lerobot_eval import eval_policy_all
@contextmanager
 def _make_eval_envs(cfg: TrainPipelineConfig) -> Iterator[dict[str, dict[int, Any]]]:
    """Create evaluation environments for one run and always dispose of them."""
    envs = make_env(
        cfg.env,
        n_envs=cfg.eval.batch_size,
        use_async_envs=cfg.eval.use_async_envs,
    )
    try:
        yield envs
    finally:
        close_envs(envs)
 def _dataloader_worker_kwargs(cfg: TrainPipelineConfig) -> dict[str, Any]:
    """Return worker-only DataLoader options, disabling them for single-process loading."""
    workers_enabled = cfg.num_workers > 0
    return {
        "prefetch_factor": cfg.prefetch_factor if workers_enabled else None,
        "persistent_workers": cfg.persistent_workers and workers_enabled,
        "multiprocessing_context": cfg.dataloader_multiprocessing_context if workers_enabled else None,
    }
 def update_policy(
    train_metrics: MetricsTracker,
    policy: PreTrainedPolicy,
@@ -227,6 +197,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if cfg.job.is_remote:
        return submit_to_hf(cfg)
    from lerobot.utils.import_utils import require_package
    require_package("accelerate", extra="training")
    from accelerate import Accelerator
    from accelerate.utils import DistributedDataParallelKwargs, DistributedType
@@ -295,6 +267,14 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if not is_main_process:
        dataset, eval_dataset = make_train_eval_datasets(cfg)
    # Create environment used for evaluating checkpoints during training on simulation data.
    # On real-world data, no need to create an environment as evaluations are done outside train.py,
    # using the eval.py instead, with gym_dora environment and dora-rs.
    eval_env = None
    if cfg.env_eval_freq > 0 and cfg.env is not None and is_main_process:
        logging.info("Creating env")
        eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
    if cfg.is_reward_model_training:
        if is_main_process:
            logging.info("Creating reward model")
@@ -322,7 +302,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if cfg.peft is not None:
        if cfg.is_reward_model_training:
            raise ValueError("PEFT is only supported for policy training. ")
-        require_package("peft", extra="peft")
+        from peft import PeftModel
        if isinstance(policy, PeftModel):
            logging.info("PEFT adapter already loaded from checkpoint, skipping wrap_with_peft.")
@@ -493,7 +473,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
        pin_memory=device.type == "cuda",
        drop_last=False,
        collate_fn=collate_fn,
-        **_dataloader_worker_kwargs(cfg),
+        prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
        persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
    )
    # Build eval dataloader if a held-out split exists
@@ -519,7 +500,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
            pin_memory=device.type == "cuda",
            drop_last=False,
            collate_fn=eval_collate_fn,
-            **_dataloader_worker_kwargs(cfg),
+            prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
            persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
        )
    # Prepare everything with accelerator
@@ -702,7 +684,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
            if is_main_process:
                step_id = get_step_identifier(step, cfg.steps)
                logging.info(f"Eval policy at step {step}")
-                with _make_eval_envs(cfg) as eval_env, torch.no_grad(), accelerator.autocast():
+                with torch.no_grad(), accelerator.autocast():
                    eval_info = eval_policy_all(
                        envs=eval_env,  # dict[suite][task_id] -> vec_env
                        policy=accelerator.unwrap_model(policy),
@@ -750,6 +732,9 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    if is_main_process:
        progbar.close()
    if eval_env:
        close_envs(eval_env)
    is_fsdp = accelerator.distributed_type == DistributedType.FSDP
    model_state_dict = accelerator.get_state_dict(policy) if is_fsdp else None
    if is_main_process:
@@ -45,7 +45,6 @@ lerobot-train-tokenizer \
 """
 import json
 import logging
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -64,9 +63,6 @@ else:
 from lerobot.configs import NormalizationMode, parser
 from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, OBS_STATE
 from lerobot.utils.utils import init_logging
 logger = logging.getLogger(__name__)
@dataclass
@@ -278,8 +274,11 @@ def process_episode(args):
        return action_chunks
-    except Exception:
+    except Exception as e:
-        logger.exception("Error processing episode %s", ep_idx)
+        print(f"Error processing episode {ep_idx}: {e}")
        import traceback
        traceback.print_exc()
        return None
@@ -301,10 +300,10 @@ def train_fast_tokenizer(
    Returns:
        Trained FAST tokenizer
    """
-    logger.info(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
+    print(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
-    logger.info(f"Action chunk shape: {action_chunks.shape}")
+    print(f"Action chunk shape: {action_chunks.shape}")
-    logger.info(f"Vocab size: {vocab_size}")
+    print(f"Vocab size: {vocab_size}")
-    logger.info(f"DCT scale: {scale}")
+    print(f"DCT scale: {scale}")
    # download the tokenizer source code (not pretrained weights)
    # we'll train a new tokenizer on our own data
@@ -315,7 +314,7 @@ def train_fast_tokenizer(
    # train the new tokenizer on our action data using .fit()
    # this trains the BPE tokenizer on DCT coefficients
-    logger.info("Training new tokenizer (this may take a few minutes)...")
+    print("Training new tokenizer (this may take a few minutes)...")
    tokenizer = base_tokenizer.fit(
        action_data_list,
        scale=scale,
@@ -323,21 +322,21 @@ def train_fast_tokenizer(
        time_horizon=action_chunks.shape[1],  # action_horizon
        action_dim=action_chunks.shape[2],  # encoded dimensions
    )
-    logger.info("✓ Tokenizer training complete!")
+    print("✓ Tokenizer training complete!")
    # validate it works
    sample_chunk = action_chunks[0]
    encoded = tokenizer(sample_chunk[None])[0]
    if isinstance(encoded, list):
        encoded = np.array(encoded)
-    logger.info(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")
+    print(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")
    return tokenizer
 def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
    """Compute compression statistics."""
-    logger.info("\nComputing compression statistics...")
+    print("\nComputing compression statistics...")
    # sample for stats (use max 1000 chunks for speed)
    sample_size = min(1000, len(action_chunks))
@@ -367,12 +366,12 @@ def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
        "max_token_length": float(np.max(token_lengths)),
    }
-    logger.info("Compression Statistics:")
+    print("Compression Statistics:")
-    logger.info(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
+    print(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
-    logger.info(f"  Mean token length: {stats['mean_token_length']:.1f}")
+    print(f"  Mean token length: {stats['mean_token_length']:.1f}")
-    logger.info(f"  P99 token length: {stats['p99_token_length']:.0f}")
+    print(f"  P99 token length: {stats['p99_token_length']:.0f}")
-    logger.info(f"  Min token length: {stats['min_token_length']:.0f}")
+    print(f"  Min token length: {stats['min_token_length']:.0f}")
-    logger.info(f"  Max token length: {stats['max_token_length']:.0f}")
+    print(f"  Max token length: {stats['max_token_length']:.0f}")
    return stats
@@ -386,9 +385,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        cfg: TokenizerTrainingConfig dataclass with all configuration parameters
    """
    # load dataset
-    logger.info(f"Loading dataset: {cfg.repo_id}")
+    print(f"Loading dataset: {cfg.repo_id}")
    dataset = LeRobotDataset(repo_id=cfg.repo_id, root=cfg.root)
-    logger.info(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+    print(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
    # parse normalization mode
    try:
@@ -398,7 +397,7 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
            f"Invalid normalization_mode: {cfg.normalization_mode}. "
            f"Must be one of: {', '.join([m.value for m in NormalizationMode])}"
        ) from err
-    logger.info(f"Normalization mode: {norm_mode.value}")
+    print(f"Normalization mode: {norm_mode.value}")
    # parse encoded dimensions
    encoded_dim_ranges = []
@@ -407,38 +406,38 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        encoded_dim_ranges.append((start, end))
    total_encoded_dims = sum(end - start for start, end in encoded_dim_ranges)
-    logger.info(f"Encoding {total_encoded_dims} dimensions: {cfg.encoded_dims}")
+    print(f"Encoding {total_encoded_dims} dimensions: {cfg.encoded_dims}")
    # parse relative dimensions
    relative_dim_list = None
    if cfg.relative_dims is not None and cfg.relative_dims.strip():
        relative_dim_list = [int(d.strip()) for d in cfg.relative_dims.split(",")]
-        logger.info(f"Relative dimensions: {relative_dim_list}")
+        print(f"Relative dimensions: {relative_dim_list}")
    else:
-        logger.info("No relative dimensions specified")
+        print("No relative dimensions specified")
-    logger.info(f"Use relative transform: {cfg.use_relative_transform}")
+    print(f"Use relative transform: {cfg.use_relative_transform}")
    if cfg.use_relative_transform and (relative_dim_list is None or len(relative_dim_list) == 0):
-        logger.warning(
+        print(
            "Warning: use_relative_transform=True but no relative_dims specified. "
            "No relative transform will be applied."
        )
-    logger.info(f"Action horizon: {cfg.action_horizon}")
+    print(f"Action horizon: {cfg.action_horizon}")
-    logger.info(f"State key: {cfg.state_key}")
+    print(f"State key: {cfg.state_key}")
    # determine episodes to process
    num_episodes = dataset.num_episodes
    if cfg.max_episodes is not None:
        num_episodes = min(cfg.max_episodes, num_episodes)
-    logger.info(f"Processing {num_episodes} episodes...")
+    print(f"Processing {num_episodes} episodes...")
    # process episodes sequentially (to avoid pickling issues with dataset)
    all_chunks = []
    for ep_idx in range(num_episodes):
        if ep_idx % 10 == 0:
-            logger.info(f"  Processing episode {ep_idx}/{num_episodes}...")
+            print(f"  Processing episode {ep_idx}/{num_episodes}...")
        chunks = process_episode(
            (
@@ -456,19 +455,19 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
    # concatenate all chunks
    all_chunks = np.concatenate(all_chunks, axis=0)
-    logger.info(f"Collected {len(all_chunks)} action chunks")
+    print(f"Collected {len(all_chunks)} action chunks")
    # extract only encoded dimensions FIRST (before normalization)
    encoded_chunks = []
    for start, end in encoded_dim_ranges:
        encoded_chunks.append(all_chunks[:, :, start:end])
    encoded_chunks = np.concatenate(encoded_chunks, axis=-1)  # [N, H, D_encoded]
-    logger.info(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")
+    print(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")
    # apply normalization to encoded dimensions
-    logger.info("\nBefore normalization - overall stats:")
+    print("\nBefore normalization - overall stats:")
-    logger.info(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+    print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-    logger.info(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+    print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
    # get normalization stats from dataset
    norm_stats = dataset.meta.stats
@@ -490,9 +489,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                    encoded_stats[stat_name] = stat_array[encoded_dim_indices]
        if encoded_stats:
-            logger.info(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
+            print(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
            for stat_name, stat_values in encoded_stats.items():
-                logger.info(
+                print(
                    f"  {stat_name}: shape={stat_values.shape}, "
                    f"range=[{np.min(stat_values):.4f}, {np.max(stat_values):.4f}]"
                )
@@ -500,27 +499,27 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
            # apply normalization based on mode
            try:
                encoded_chunks = apply_normalization(encoded_chunks, encoded_stats, norm_mode, eps=1e-8)
-                logger.info(f"\nApplied {norm_mode.value} normalization")
+                print(f"\nApplied {norm_mode.value} normalization")
            except ValueError as e:
-                logger.warning(f"Warning: {e}. Using raw actions without normalization.")
+                print(f"Warning: {e}. Using raw actions without normalization.")
-            logger.info("\nAfter normalization - overall stats:")
+            print("\nAfter normalization - overall stats:")
-            logger.info(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+            print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-            logger.info(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+            print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
-            logger.info("\nPer-dimension stats (after normalization):")
+            print("\nPer-dimension stats (after normalization):")
            for d in range(encoded_chunks.shape[-1]):
                dim_data = encoded_chunks[:, :, d]
-                logger.info(
+                print(
                    f"  Dim {d}: min={np.min(dim_data):7.4f}, max={np.max(dim_data):7.4f}, "
                    f"mean={np.mean(dim_data):7.4f}, std={np.std(dim_data):7.4f}"
                )
        else:
-            logger.warning("Warning: Could not extract stats for encoded dimensions, using raw actions")
+            print("Warning: Could not extract stats for encoded dimensions, using raw actions")
    else:
-        logger.warning("Warning: No normalization stats found in dataset, using raw actions")
+        print("Warning: No normalization stats found in dataset, using raw actions")
-    logger.info(f"Encoded chunks shape: {encoded_chunks.shape}")
+    print(f"Encoded chunks shape: {encoded_chunks.shape}")
    # train FAST tokenizer
    tokenizer = train_fast_tokenizer(
@@ -562,8 +561,8 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
    with open(output_path / "metadata.json", "w") as f:
        json.dump(metadata, f, indent=2)
-    logger.info(f"\nSaved FAST tokenizer to {output_path}")
+    print(f"\nSaved FAST tokenizer to {output_path}")
-    logger.info(f"Metadata: {json.dumps(metadata, indent=2)}")
+    print(f"Metadata: {json.dumps(metadata, indent=2)}")
    # push to Hugging Face Hub if requested
    if cfg.push_to_hub:
@@ -571,10 +570,10 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        hub_repo_id = cfg.hub_repo_id
        if hub_repo_id is None:
            hub_repo_id = output_path.name
-            logger.info(f"\nNo hub_repo_id provided, using: {hub_repo_id}")
+            print(f"\nNo hub_repo_id provided, using: {hub_repo_id}")
-        logger.info(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
+        print(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
-        logger.info(f"   Private: {cfg.hub_private}")
+        print(f"   Private: {cfg.hub_private}")
        try:
            # use the tokenizer's push_to_hub method
@@ -594,15 +593,14 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                commit_message="Upload tokenizer metadata",
            )
-            logger.info(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
+            print(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
        except Exception as e:
-            logger.error(f"Error pushing to hub: {e}")
+            print(f"Error pushing to hub: {e}")
-            logger.error("   Make sure you're logged in with `huggingface-cli login`")
+            print("   Make sure you're logged in with `huggingface-cli login`")
 def main():
    """CLI entry point that parses arguments and runs the tokenizer training."""
    init_logging()
    train_tokenizer()
@@ -29,12 +29,6 @@ class SOLeaderConfig:
    # Whether to use degrees for angles
    use_degrees: bool = True
    # Number of extra attempts when a `sync_read` of the motors fails. Feetech buses can occasionally
    # return a corrupted status packet ("Incorrect status packet!"), especially when several joints move
    # at once, which otherwise aborts the teleoperation loop. Retries are immediate (no sleep) and only
    # happen on failure, so the steady-state read cost is unchanged.
    num_read_retries: int = 2
@TeleoperatorConfig.register_subclass("so101_leader")
@TeleoperatorConfig.register_subclass("so100_leader")
@@ -145,7 +145,7 @@ class SOLeader(Teleoperator):
    @check_if_not_connected
    def get_action(self) -> dict[str, float]:
        start = time.perf_counter()
-        action = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
+        action = self.bus.sync_read("Present_Position")
        action = {f"{motor}.pos": val for motor, val in action.items()}
        dt_ms = (time.perf_counter() - start) * 1e3
        logger.debug(f"{self} read action: {dt_ms:.1f}ms")
@@ -13,34 +13,18 @@
 # limitations under the License.
 from .transforms import (
    CoarseDropout,
    GammaCorrection,
    GaussianNoise,
    GaussianPatchBrightness,
    ImageTransformConfig,
    ImageTransforms,
    ImageTransformsConfig,
    JPEGCompression,
    MotionBlur,
    PlanckianJitter,
    RandomShadow,
    RandomSubsetApply,
    SharpnessJitter,
    make_transform_from_config,
 )
 __all__ = [
    "CoarseDropout",
    "GammaCorrection",
    "GaussianNoise",
    "GaussianPatchBrightness",
    "ImageTransformConfig",
    "ImageTransforms",
    "ImageTransformsConfig",
    "JPEGCompression",
    "MotionBlur",
    "PlanckianJitter",
    "RandomShadow",
    "RandomSubsetApply",
    "SharpnessJitter",
    "make_transform_from_config",
@@ -14,13 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import collections
 import math
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass, field
 from typing import Any
 import torch
 from torchvision.io import decode_image, encode_jpeg
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import (
    Transform,
@@ -43,7 +41,7 @@ class RandomSubsetApply(Transform):
    def __init__(
        self,
-        transforms: Sequence[Callable[..., Any]],
+        transforms: Sequence[Callable],
        p: list[float] | None = None,
        n_subset: int | None = None,
        random_order: bool = False,
@@ -52,7 +50,7 @@ class RandomSubsetApply(Transform):
        if not isinstance(transforms, Sequence):
            raise TypeError("Argument transforms should be a sequence of callables")
        if p is None:
-            p = [1.0] * len(transforms)
+            p = [1] * len(transforms)
        elif len(p) != len(transforms):
            raise ValueError(
                f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
@@ -71,7 +69,7 @@ class RandomSubsetApply(Transform):
        self.n_subset = n_subset
        self.random_order = random_order
-        self.selected_transforms: list[Callable[..., Any]] = []
+        self.selected_transforms = None
    def forward(self, *inputs: Any) -> Any:
        needs_unpacking = len(inputs) > 1
@@ -121,7 +119,7 @@ class SharpnessJitter(Transform):
        super().__init__()
        self.sharpness = self._check_input(sharpness)
-    def _check_input(self, sharpness: float | Sequence[float]) -> tuple[float, float]:
+    def _check_input(self, sharpness):
        if isinstance(sharpness, (int | float)):
            if sharpness < 0:
                raise ValueError("If sharpness is a single number, it must be non negative.")
@@ -146,471 +144,6 @@ class SharpnessJitter(Transform):
        return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
 class GaussianNoise(Transform):
    """Add Gaussian noise to simulate camera sensor noise.
    Models readout noise from ADC quantization, which increases in low-light conditions.
    Common in real-robot setups where wrist cameras operate in suboptimal lighting.
    Args:
        std: Range (min, max) for noise standard deviation in pixel-value scale (0-255).
    """
    def __init__(self, std: float | Sequence[float] = (5.0, 25.0)) -> None:
        super().__init__()
        if isinstance(std, (int, float)):
            self.std = (0.0, float(std))
        elif isinstance(std, Sequence) and len(std) == 2:
            self.std = (float(std[0]), float(std[1]))
        else:
            raise TypeError("std must be a number or a sequence with length 2.")
        if not 0.0 <= self.std[0] <= self.std[1]:
            raise ValueError(f"std must satisfy 0 <= min <= max, but got {self.std}.")
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        return {
            "std": torch.empty(1).uniform_(self.std[0], self.std[1]).item(),
            "seed": torch.randint(0, torch.iinfo(torch.int64).max, ()).item(),
        }
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if isinstance(inpt, torch.Tensor) and inpt.is_floating_point():
            generator = torch.Generator(device=inpt.device).manual_seed(params["seed"])
            noise = torch.randn(inpt.shape, device=inpt.device, dtype=inpt.dtype, generator=generator)
            return (inpt + noise * (params["std"] / 255.0)).clamp(0.0, 1.0)
        return inpt
 class MotionBlur(Transform):
    """Apply directional motion blur to simulate fast robot or object movement.
    Generates a 1D averaging kernel along a random direction, applied via depthwise convolution.
    Args:
        kernel_size: An odd kernel size or a range containing at least one odd kernel size.
    """
    def __init__(self, kernel_size: int | Sequence[int] = (3, 11)) -> None:
        super().__init__()
        if isinstance(kernel_size, int):
            self.kernel_size = (kernel_size, kernel_size)
        elif isinstance(kernel_size, Sequence) and len(kernel_size) == 2:
            self.kernel_size = (int(kernel_size[0]), int(kernel_size[1]))
        else:
            raise TypeError("kernel_size must be an int or a sequence with length 2.")
        if not 1 <= self.kernel_size[0] <= self.kernel_size[1]:
            raise ValueError(f"kernel_size must satisfy 1 <= min <= max, but got {self.kernel_size}.")
        self._first_odd_kernel_size = self.kernel_size[0] + (self.kernel_size[0] + 1) % 2
        if self._first_odd_kernel_size > self.kernel_size[1]:
            raise ValueError(f"kernel_size range must contain an odd value, but got {self.kernel_size}.")
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        num_odd_sizes = (self.kernel_size[1] - self._first_odd_kernel_size) // 2 + 1
        size_index = int(torch.randint(0, num_odd_sizes, ()).item())
        ks = self._first_odd_kernel_size + 2 * size_index
        angle = torch.empty(1).uniform_(0, 360).item()
        return {"kernel_size": ks, "angle": angle}
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
            return inpt
        if inpt.ndim < 3:
            raise ValueError(f"MotionBlur expects [..., C, H, W] input, but got shape {inpt.shape}.")
        kernel_size = params["kernel_size"]
        radius = kernel_size // 2
        angle = math.radians(params["angle"])
        positions = torch.linspace(-radius, radius, kernel_size, device=inpt.device)
        x_coords = (positions * math.cos(angle)).round().to(torch.long) + radius
        y_coords = (positions * math.sin(angle)).round().to(torch.long) + radius
        kernel = torch.zeros((kernel_size, kernel_size), device=inpt.device, dtype=inpt.dtype)
        kernel[y_coords, x_coords] = 1
        kernel /= kernel.sum()
        channels, height, width = inpt.shape[-3:]
        flat_input = inpt.reshape(-1, channels, height, width)
        depthwise_kernel = kernel.expand(channels, 1, kernel_size, kernel_size)
        padded = torch.nn.functional.pad(flat_input, (radius,) * 4, mode="replicate")
        output = torch.nn.functional.conv2d(padded, depthwise_kernel, groups=channels)
        return output.reshape(inpt.shape).clamp(0.0, 1.0)
 class JPEGCompression(Transform):
    """Simulate JPEG compression artifacts (block artifacts, color banding).
    Models quality degradation from video compression in network-streamed camera feeds.
    Args:
        quality: Range (min, max) for JPEG quality factor (lower = more artifacts).
    """
    def __init__(self, quality: int | Sequence[int] = (15, 75)) -> None:
        super().__init__()
        if isinstance(quality, int):
            self.quality = (quality, quality)
        elif isinstance(quality, Sequence) and len(quality) == 2:
            self.quality = (int(quality[0]), int(quality[1]))
        else:
            raise TypeError("quality must be an int or a sequence with length 2.")
        if not 1 <= self.quality[0] <= self.quality[1] <= 100:
            raise ValueError(f"quality must satisfy 1 <= min <= max <= 100, but got {self.quality}.")
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        return {"quality": int(torch.randint(self.quality[0], self.quality[1] + 1, (1,)).item())}
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
            return inpt
        if inpt.ndim < 3:
            raise ValueError(f"JPEGCompression expects [..., C, H, W] input, but got shape {inpt.shape}.")
        channels, height, width = inpt.shape[-3:]
        if channels not in (1, 3):
            raise ValueError(f"JPEGCompression expects 1 or 3 channels, but got {channels}.")
        flat_input = inpt.reshape(-1, channels, height, width)
        flat_uint8 = (flat_input.clamp(0.0, 1.0) * 255).round().to(torch.uint8).cpu()
        decoded_frames = [
            decode_image(encode_jpeg(frame, quality=params["quality"])) for frame in flat_uint8.unbind()
        ]
        output = torch.stack(decoded_frames).to(device=inpt.device, dtype=inpt.dtype) / 255.0
        return output.reshape(inpt.shape)
 class GaussianPatchBrightness(Transform):
    """Apply spatially-varying brightness with Gaussian patches.
    Simulates uneven overhead lighting, spotlights, and shadow patches commonly
    encountered in real robot workspaces with multiple light sources.
    Args:
        num_patches: Range (min, max) for number of brightness patches.
        sigma_range: Range for Gaussian sigma as fraction of image size.
        factor_range: Range for brightness factor (< 1 darkens, > 1 brightens).
    """
    def __init__(
        self,
        num_patches: int | Sequence[int] = (1, 4),
        sigma_range: Sequence[float] = (0.05, 0.25),
        factor_range: Sequence[float] = (0.4, 1.6),
    ) -> None:
        super().__init__()
        if isinstance(num_patches, int):
            self.num_patches = (num_patches, num_patches)
        elif isinstance(num_patches, Sequence) and len(num_patches) == 2:
            self.num_patches = (int(num_patches[0]), int(num_patches[1]))
        else:
            raise TypeError("num_patches must be an int or a sequence with length 2.")
        if not 1 <= self.num_patches[0] <= self.num_patches[1]:
            raise ValueError(f"num_patches must satisfy 1 <= min <= max, but got {self.num_patches}.")
        if not isinstance(sigma_range, Sequence) or len(sigma_range) != 2:
            raise TypeError("sigma_range must be a sequence with length 2.")
        self.sigma_range = (float(sigma_range[0]), float(sigma_range[1]))
        if not 0.0 < self.sigma_range[0] <= self.sigma_range[1]:
            raise ValueError(f"sigma_range must satisfy 0 < min <= max, but got {self.sigma_range}.")
        if not isinstance(factor_range, Sequence) or len(factor_range) != 2:
            raise TypeError("factor_range must be a sequence with length 2.")
        self.factor_range = (float(factor_range[0]), float(factor_range[1]))
        if not 0.0 <= self.factor_range[0] <= self.factor_range[1]:
            raise ValueError(f"factor_range must satisfy 0 <= min <= max, but got {self.factor_range}.")
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        n = int(torch.randint(self.num_patches[0], self.num_patches[1] + 1, (1,)).item())
        return {
            "centers": torch.rand(n, 2).tolist(),
            "sigmas": torch.empty(n).uniform_(self.sigma_range[0], self.sigma_range[1]).tolist(),
            "factors": torch.empty(n).uniform_(self.factor_range[0], self.factor_range[1]).tolist(),
        }
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
            return inpt
        h, w = inpt.shape[-2:]
        mask = torch.ones(h, w, device=inpt.device, dtype=inpt.dtype)
        grid_y = torch.linspace(0, 1, h, device=inpt.device, dtype=inpt.dtype)
        grid_x = torch.linspace(0, 1, w, device=inpt.device, dtype=inpt.dtype)
        yy, xx = torch.meshgrid(grid_y, grid_x, indexing="ij")
        for (cy, cx), sigma, factor in zip(
            params["centers"], params["sigmas"], params["factors"], strict=True
        ):
            gauss = torch.exp(-((yy - cy) ** 2 + (xx - cx) ** 2) / (2 * sigma**2))
            mask = mask * (1.0 + (factor - 1.0) * gauss)
        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
 class RandomShadow(Transform):
    """Add random vertical band shadow with smooth edges.
    Simulates cast shadows from objects or people near the robot workspace.
    Symmetric: randomly brightens or darkens to prevent BatchNorm stats shift.
    Args:
        opacity: Range (min, max) for shadow/highlight opacity.
    """
    def __init__(self, opacity: float | Sequence[float] = (0.3, 0.6)) -> None:
        super().__init__()
        if isinstance(opacity, (int, float)):
            self.opacity = (float(opacity), float(opacity))
        elif isinstance(opacity, Sequence) and len(opacity) == 2:
            self.opacity = (float(opacity[0]), float(opacity[1]))
        else:
            raise TypeError("opacity must be a number or a sequence with length 2.")
        if not 0.0 <= self.opacity[0] <= self.opacity[1] <= 1.0:
            raise ValueError(f"opacity must satisfy 0 <= min <= max <= 1, but got {self.opacity}.")
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        return {
            "opacity": torch.empty(1).uniform_(self.opacity[0], self.opacity[1]).item(),
            "start": torch.rand(1).item(),
            "width": torch.empty(1).uniform_(1 / 3, 2 / 3).item(),
            "direction": -1.0 if torch.rand(1).item() < 0.5 else 1.0,
        }
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
            return inpt
        if inpt.ndim < 3:
            raise ValueError(f"RandomShadow expects [..., C, H, W] input, but got shape {inpt.shape}.")
        h, w = inpt.shape[-2:]
        band_width = max(1, min(w, round(params["width"] * w)))
        x_start = round(params["start"] * (w - band_width))
        x_end = x_start + band_width
        mask = torch.ones(h, w, device=inpt.device, dtype=inpt.dtype)
        mask[:, x_start:x_end] = 1.0 + params["direction"] * params["opacity"]
        smoothing_size = min(8, h, w)
        if smoothing_size > 1:
            batched_mask = mask[None, None]
            small = torch.nn.functional.avg_pool2d(batched_mask, smoothing_size, stride=smoothing_size)
            mask = torch.nn.functional.interpolate(small, size=(h, w), mode="bilinear", align_corners=False)[
                0, 0
            ]
        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
 class CoarseDropout(Transform):
    """Drop random rectangular patches to simulate partial occlusion.
    Models objects, hands, or cables passing through the camera field of view
    during robot manipulation.
    Args:
        max_holes: Maximum number of rectangular patches to drop.
        max_height_frac: Maximum patch height as fraction of image height.
        max_width_frac: Maximum patch width as fraction of image width.
        fill_value: Value to fill dropped regions with.
    """
    def __init__(
        self,
        max_holes: int = 8,
        max_height_frac: float = 0.07,
        max_width_frac: float = 0.07,
        fill_value: float = 0.0,
    ) -> None:
        super().__init__()
        if not isinstance(max_holes, int):
            raise TypeError("max_holes must be an int.")
        if max_holes < 1:
            raise ValueError(f"max_holes must be at least 1, but got {max_holes}.")
        if not 0.0 < max_height_frac <= 1.0:
            raise ValueError(f"max_height_frac must be in (0, 1], but got {max_height_frac}.")
        if not 0.0 < max_width_frac <= 1.0:
            raise ValueError(f"max_width_frac must be in (0, 1], but got {max_width_frac}.")
        if not 0.0 <= fill_value <= 1.0:
            raise ValueError(f"fill_value must be in [0, 1], but got {fill_value}.")
        self.max_holes = max_holes
        self.max_height_frac = max_height_frac
        self.max_width_frac = max_width_frac
        self.fill_value = fill_value
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        n = int(torch.randint(1, self.max_holes + 1, (1,)).item())
        sizes = torch.rand(n, 2)
        sizes[:, 0] *= self.max_height_frac
        sizes[:, 1] *= self.max_width_frac
        return {"sizes": sizes.tolist(), "positions": torch.rand(n, 2).tolist()}
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
            return inpt
        if inpt.ndim < 3:
            raise ValueError(f"CoarseDropout expects [..., C, H, W] input, but got shape {inpt.shape}.")
        h, w = inpt.shape[-2:]
        result = inpt.clone()
        for (height_frac, width_frac), (y_frac, x_frac) in zip(
            params["sizes"], params["positions"], strict=True
        ):
            hole_h = max(1, min(h, round(height_frac * h)))
            hole_w = max(1, min(w, round(width_frac * w)))
            y = round(y_frac * (h - hole_h))
            x = round(x_frac * (w - hole_w))
            result[..., y : y + hole_h, x : x + hole_w] = self.fill_value
        return result
 class GammaCorrection(Transform):
    """Apply random gamma correction to simulate exposure variation.
    Models different camera auto-exposure settings and sensor response curves.
    Uses log-symmetric sampling so brightening and darkening are equally likely,
    preventing BatchNorm statistics shift.
    Args:
        gamma: Range (min, max) for gamma value. Values < 1 brighten, > 1 darken.
    """
    def __init__(self, gamma: float | Sequence[float] = (0.5, 2.0)) -> None:
        super().__init__()
        if isinstance(gamma, (int, float)):
            gamma = float(gamma)
            if gamma <= 0:
                raise ValueError(f"gamma must be positive, but got {gamma}.")
            self.gamma = (min(gamma, 1.0 / gamma), max(gamma, 1.0 / gamma))
        elif isinstance(gamma, Sequence) and len(gamma) == 2:
            self.gamma = (float(gamma[0]), float(gamma[1]))
        else:
            raise TypeError("gamma must be a number or a sequence with length 2.")
        if not 0.0 < self.gamma[0] <= self.gamma[1]:
            raise ValueError(f"gamma must satisfy 0 < min <= max, but got {self.gamma}.")
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        log_lo = math.log(self.gamma[0])
        log_hi = math.log(self.gamma[1])
        gamma = math.exp(torch.empty(1).uniform_(log_lo, log_hi).item())
        return {"gamma": gamma}
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if isinstance(inpt, torch.Tensor) and inpt.is_floating_point():
            return inpt.pow(params["gamma"]).clamp(0.0, 1.0)
        return inpt
 # From the paper authors' MIT-licensed reference implementation:
 # https://github.com/TheZino/PlanckianJitter
 _PLANCKIAN_BLACKBODY_COEFFICIENTS = (
    (0.6743, 0.4029, 0.0013),
    (0.6281, 0.4241, 0.1665),
    (0.5919, 0.4372, 0.2513),
    (0.5623, 0.4457, 0.3154),
    (0.5376, 0.4515, 0.3672),
    (0.5163, 0.4555, 0.4103),
    (0.4979, 0.4584, 0.4468),
    (0.4816, 0.4604, 0.4782),
    (0.4672, 0.4619, 0.5053),
    (0.4542, 0.4630, 0.5289),
    (0.4426, 0.4638, 0.5497),
    (0.4320, 0.4644, 0.5681),
    (0.4223, 0.4648, 0.5844),
    (0.4135, 0.4651, 0.5990),
    (0.4054, 0.4653, 0.6121),
    (0.3980, 0.4654, 0.6239),
    (0.3911, 0.4655, 0.6346),
    (0.3847, 0.4656, 0.6444),
    (0.3787, 0.4656, 0.6532),
    (0.3732, 0.4656, 0.6613),
    (0.3680, 0.4655, 0.6688),
    (0.3632, 0.4655, 0.6756),
    (0.3586, 0.4655, 0.6820),
    (0.3544, 0.4654, 0.6878),
    (0.3503, 0.4653, 0.6933),
 )
 _PLANCKIAN_MIN_TEMPERATURE = 3_000
 _PLANCKIAN_MAX_TEMPERATURE = 15_000
 _PLANCKIAN_TEMPERATURE_STEP = 500
 class PlanckianJitter(Transform):
    """Simulate color temperature shift along the Planckian locus.
    Samples one black-body temperature and applies the corresponding correlated red
    and blue channel scaling while preserving the green channel. Coefficients between
    the tabulated 500 K intervals are linearly interpolated.
    Reference: Zini et al., "Planckian Jitter", CVPR 2022 Workshop.
    Args:
        temperature: A fixed color temperature or range in Kelvin. Supported values
            are between 3000 K and 15000 K.
    """
    def __init__(self, temperature: int | Sequence[int] = (3_000, 15_000)) -> None:
        super().__init__()
        if isinstance(temperature, int):
            self.temperature = (temperature, temperature)
        elif isinstance(temperature, Sequence) and len(temperature) == 2:
            self.temperature = (int(temperature[0]), int(temperature[1]))
        else:
            raise TypeError("temperature must be an int or a sequence with length 2.")
        if not (
            _PLANCKIAN_MIN_TEMPERATURE
            <= self.temperature[0]
            <= self.temperature[1]
            <= _PLANCKIAN_MAX_TEMPERATURE
        ):
            raise ValueError(
                "temperature must satisfy "
                f"{_PLANCKIAN_MIN_TEMPERATURE} <= min <= max <= {_PLANCKIAN_MAX_TEMPERATURE}, "
                f"but got {self.temperature}."
            )
    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
        temperature = int(torch.randint(self.temperature[0], self.temperature[1] + 1, ()).item())
        return {"temperature": temperature}
    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
            return inpt
        if inpt.ndim < 3 or inpt.shape[-3] != 3:
            raise ValueError(f"PlanckianJitter expects [..., 3, H, W] input, but got shape {inpt.shape}.")
        table_position = (params["temperature"] - _PLANCKIAN_MIN_TEMPERATURE) / _PLANCKIAN_TEMPERATURE_STEP
        left_index = math.floor(table_position)
        right_index = min(left_index + 1, len(_PLANCKIAN_BLACKBODY_COEFFICIENTS) - 1)
        interpolation_weight = table_position - left_index
        left = torch.tensor(
            _PLANCKIAN_BLACKBODY_COEFFICIENTS[left_index],
            device=inpt.device,
            dtype=inpt.dtype,
        )
        right = torch.tensor(
            _PLANCKIAN_BLACKBODY_COEFFICIENTS[right_index],
            device=inpt.device,
            dtype=inpt.dtype,
        )
        coefficients = torch.lerp(left, right, interpolation_weight)
        scale = torch.stack(
            (
                coefficients[0] / coefficients[1],
                coefficients.new_tensor(1.0),
                coefficients[2] / coefficients[1],
            )
        )
        broadcast_shape = (1,) * (inpt.ndim - 3) + (3, 1, 1)
        return (inpt * scale.reshape(broadcast_shape)).clamp(0.0, 1.0)
 _CUSTOM_TRANSFORMS: dict[str, type[Transform]] = {
    "SharpnessJitter": SharpnessJitter,
    "GaussianNoise": GaussianNoise,
    "MotionBlur": MotionBlur,
    "JPEGCompression": JPEGCompression,
    "GaussianPatchBrightness": GaussianPatchBrightness,
    "RandomShadow": RandomShadow,
    "CoarseDropout": CoarseDropout,
    "GammaCorrection": GammaCorrection,
    "PlanckianJitter": PlanckianJitter,
 }
@dataclass
 class ImageTransformConfig:
    """
@@ -682,18 +215,17 @@ class ImageTransformsConfig:
    )
-def make_transform_from_config(cfg: ImageTransformConfig) -> Transform:
+def make_transform_from_config(cfg: ImageTransformConfig):
-    if cfg.type in _CUSTOM_TRANSFORMS:
+    if cfg.type == "SharpnessJitter":
-        return _CUSTOM_TRANSFORMS[cfg.type](**cfg.kwargs)
+        return SharpnessJitter(**cfg.kwargs)
    transform_cls = getattr(v2, cfg.type, None)
    if isinstance(transform_cls, type) and issubclass(transform_cls, Transform):
        return transform_cls(**cfg.kwargs)
    valid_custom = ", ".join(sorted(_CUSTOM_TRANSFORMS.keys()))
    raise ValueError(
        f"Transform '{cfg.type}' is not valid. It must be a class in "
-        f"torchvision.transforms.v2 or one of: {valid_custom}."
+        f"torchvision.transforms.v2 or 'SharpnessJitter'."
    )
@@ -704,8 +236,8 @@ class ImageTransforms(Transform):
        super().__init__()
        self._cfg = cfg
-        self.weights: list[float] = []
+        self.weights = []
-        self.transforms: dict[str, Transform] = {}
+        self.transforms = {}
        for tf_name, tf_cfg in cfg.tfs.items():
            if tf_cfg.weight <= 0.0:
                continue
@@ -16,39 +16,11 @@
 # limitations under the License.
 import logging
 import multiprocessing
 import os
 import signal
 import sys
 def ensure_multiprocessing_start_method(start_method: str | None) -> None:
    """Set a multiprocessing start method once, or verify the existing method matches.
    Passing ``None`` leaves Python's process-wide default untouched. This is useful
    when LeRobot is embedded in an application that owns multiprocessing setup.
    """
    if start_method is None:
        return
    available_methods = multiprocessing.get_all_start_methods()
    if start_method not in available_methods:
        raise ValueError(
            f"Multiprocessing start method must be one of {available_methods} on this platform, "
            f"got {start_method!r}."
        )
    current_method = multiprocessing.get_start_method(allow_none=True)
    if current_method is None:
        multiprocessing.set_start_method(start_method)
    elif current_method != start_method:
        raise RuntimeError(
            f"Multiprocessing start method is already {current_method!r}; cannot change it to "
            f"{start_method!r}. Set the configured multiprocessing context to null to keep the "
            "application's existing method, or launch LeRobot in a fresh process."
        )
 class ProcessSignalHandler:
    """Utility class to attach graceful shutdown signal handlers.
@@ -133,13 +133,10 @@ def say(text: str, blocking: bool = False):
    else:
        raise RuntimeError("Unsupported operating system for text-to-speech.")
    try:
    if blocking:
-            subprocess.run(cmd, check=True, timeout=5)
+        subprocess.run(cmd, check=True)
    else:
        subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
    except (FileNotFoundError, subprocess.TimeoutExpired) as e:
        logging.warning("Text-to-speech command failed: %s | Error: %s", cmd, e)
 def log_say(text: str, play_sounds: bool = True, blocking: bool = False):
@@ -20,7 +20,7 @@
 # ```
 from pathlib import Path
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
 import cv2
 import numpy as np
@@ -123,73 +123,6 @@ def test_invalid_width_connect():
        camera.connect(warmup=False)
 def test_connect_cleans_up_after_settings_failure_and_allows_retry():
    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
    camera = OpenCVCamera(config)
    opened_captures = []
    def fail_settings():
        opened_captures.append(camera.videocapture)
        raise RuntimeError("settings failed")
    with (
        patch.object(camera, "_configure_capture_settings", side_effect=fail_settings),
        pytest.raises(RuntimeError, match="settings failed"),
    ):
        camera.connect(warmup=False)
    assert camera.videocapture is None
    assert camera.thread is None
    assert not camera.is_connected
    assert opened_captures[0] is not None
    assert not opened_captures[0].isOpened()
    camera.connect(warmup=False)
    assert camera.is_connected
    camera.disconnect()
 def test_connect_cleans_up_after_warmup_failure_and_allows_retry():
    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=1)
    camera = OpenCVCamera(config)
    read_threads = []
    def fail_warmup(*_args, **_kwargs):
        read_threads.append(camera.thread)
        raise TimeoutError("no frame")
    with (
        patch.object(camera, "async_read", side_effect=fail_warmup),
        pytest.raises(TimeoutError, match="no frame"),
    ):
        camera.connect()
    assert camera.videocapture is None
    assert camera.thread is None
    assert not camera.is_connected
    assert read_threads[0] is not None
    assert not read_threads[0].is_alive()
    camera.connect(warmup=False)
    assert camera.is_connected
    camera.disconnect()
 def test_find_cameras_releases_unopened_handles():
    module_path = OpenCVCamera.__module__
    unopened_capture = MagicMock()
    unopened_capture.isOpened.return_value = False
    with (
        patch(f"{module_path}.platform.system", return_value="Darwin"),
        patch(f"{module_path}.MAX_OPENCV_INDEX", 1),
        patch(f"{module_path}.cv2.VideoCapture", return_value=unopened_capture),
    ):
        assert OpenCVCamera.find_cameras() == []
    unopened_capture.release.assert_called_once_with()
@pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES)
 def test_read(index_or_path):
    config = OpenCVCameraConfig(index_or_path=index_or_path, warmup_s=0)
@@ -20,7 +20,7 @@
 # ```
 from pathlib import Path
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
 import numpy as np
 import pytest
@@ -30,8 +30,6 @@ from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnected
 pytest.importorskip("pyrealsense2")
 import pyrealsense2 as rs
 from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
 TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "cameras"
@@ -63,17 +61,6 @@ def test_abc_implementation():
    _ = RealSenseCamera(config)
@pytest.mark.parametrize("option", ["exposure", "gain", "white_balance"])
 def test_manual_color_option_requires_rgb(option):
    with pytest.raises(ValueError, match="use_rgb=True"):
        RealSenseCameraConfig(
            serial_number_or_name="042",
            use_rgb=False,
            use_depth=True,
            **{option: 100},
        )
 def test_connect():
    config = RealSenseCameraConfig(serial_number_or_name="042", warmup_s=0)
@@ -96,27 +83,6 @@ def test_connect_invalid_camera_path(patch_realsense):
        camera.connect(warmup=False)
 def test_connect_cleans_up_when_sensor_configuration_fails():
    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120)
    camera = RealSenseCamera(config)
    pipeline = MagicMock()
    pipeline.start.return_value = MagicMock()
    with (
        patch("lerobot.cameras.realsense.camera_realsense.rs.pipeline", return_value=pipeline),
        patch.object(camera, "_configure_rs_pipeline_config"),
        patch.object(camera, "_configure_capture_settings"),
        patch.object(camera, "_configure_sensor_options", side_effect=ValueError("invalid exposure")),
        pytest.raises(ValueError, match="invalid exposure"),
    ):
        camera.connect(warmup=False)
    pipeline.stop.assert_called_once_with()
    assert camera.rs_pipeline is None
    assert camera.rs_profile is None
    assert not camera.is_connected
 def test_invalid_width_connect():
    config = RealSenseCameraConfig(serial_number_or_name="042", width=99999, height=480, fps=30)
    camera = RealSenseCamera(config)
@@ -125,33 +91,6 @@ def test_invalid_width_connect():
        camera.connect(warmup=False)
 def test_connect_cleans_up_after_warmup_failure_and_allows_retry():
    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30)
    camera = RealSenseCamera(config)
    read_threads = []
    def fail_warmup(*_args, **_kwargs):
        read_threads.append(camera.thread)
        raise TimeoutError("no frame")
    with (
        patch.object(camera, "async_read", side_effect=fail_warmup),
        pytest.raises(TimeoutError, match="no frame"),
    ):
        camera.connect()
    assert camera.rs_pipeline is None
    assert camera.rs_profile is None
    assert camera.thread is None
    assert not camera.is_connected
    assert read_threads[0] is not None
    assert not read_threads[0].is_alive()
    camera.connect(warmup=False)
    assert camera.is_connected
    camera.disconnect()
 def test_read():
    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
    with RealSenseCamera(config) as camera:
@@ -289,203 +228,6 @@ def test_read_latest_too_old():
            _ = camera.read_latest(max_age_ms=0)  # immediately too old
 def _make_mock_sensor(name: str, supported_options: set | None = None) -> MagicMock:
    """Build a fake rs.sensor that reports a name and a configurable supported-options set."""
    supported = supported_options if supported_options is not None else set()
    sensor = MagicMock()
    sensor.get_info.return_value = name
    sensor.supports.side_effect = lambda opt: opt in supported
    return sensor
 def _attach_mock_color_sensor(camera: RealSenseCamera, sensor: MagicMock) -> None:
    """Wire camera.rs_profile so _get_color_sensor finds the given sensor."""
    profile = MagicMock()
    device = MagicMock()
    device.query_sensors.return_value = [sensor]
    profile.get_device.return_value = device
    camera.rs_profile = profile
 def test_get_color_sensor_prefers_rgb_camera():
    config = RealSenseCameraConfig(serial_number_or_name="042")
    camera = RealSenseCamera(config)
    rgb = _make_mock_sensor("RGB Camera")
    stereo = _make_mock_sensor("Stereo Module")
    profile = MagicMock()
    device = MagicMock()
    device.query_sensors.return_value = [stereo, rgb]
    profile.get_device.return_value = device
    camera.rs_profile = profile
    assert camera._get_color_sensor() is rgb
 def test_get_color_sensor_falls_back_to_stereo_module():
    """D405 has no separate RGB module; color comes from Stereo Module."""
    config = RealSenseCameraConfig(serial_number_or_name="042")
    camera = RealSenseCamera(config)
    stereo = _make_mock_sensor("Stereo Module")
    _attach_mock_color_sensor(camera, stereo)
    assert camera._get_color_sensor() is stereo
 def test_get_color_sensor_raises_with_available_sensors():
    config = RealSenseCameraConfig(serial_number_or_name="042")
    camera = RealSenseCamera(config)
    other = _make_mock_sensor("Motion Module")
    _attach_mock_color_sensor(camera, other)
    with pytest.raises(RuntimeError, match="Motion Module"):
        camera._get_color_sensor()
 def test_configure_sensor_options_skipped_when_none():
    config = RealSenseCameraConfig(serial_number_or_name="042")
    camera = RealSenseCamera(config)
    with patch.object(RealSenseCamera, "_get_color_sensor") as mock_get:
        camera._configure_sensor_options()
        mock_get.assert_not_called()
 def test_configure_sensor_options_applies_all_values():
    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120, gain=64, white_balance=4600)
    camera = RealSenseCamera(config)
    sensor = _make_mock_sensor(
        "RGB Camera",
        supported_options={
            rs.option.enable_auto_exposure,
            rs.option.exposure,
            rs.option.gain,
            rs.option.enable_auto_white_balance,
            rs.option.white_balance,
        },
    )
    _attach_mock_color_sensor(camera, sensor)
    camera._configure_sensor_options()
    sensor.set_option.assert_any_call(rs.option.enable_auto_exposure, 0)
    sensor.set_option.assert_any_call(rs.option.exposure, 120)
    sensor.set_option.assert_any_call(rs.option.gain, 64)
    sensor.set_option.assert_any_call(rs.option.enable_auto_white_balance, 0)
    sensor.set_option.assert_any_call(rs.option.white_balance, 4600)
@pytest.mark.parametrize(
    ("config_field", "option", "label"),
    [
        ("exposure", rs.option.exposure, "exposure"),
        ("gain", rs.option.gain, "gain"),
        ("white_balance", rs.option.white_balance, "white balance"),
    ],
 )
 def test_configure_sensor_options_raises_when_requested_option_is_unsupported(config_field, option, label):
    config = RealSenseCameraConfig(serial_number_or_name="042", **{config_field: 100})
    camera = RealSenseCamera(config)
    sensor = _make_mock_sensor("RGB Camera", supported_options=set())
    _attach_mock_color_sensor(camera, sensor)
    with pytest.raises(ValueError, match=label):
        camera._configure_sensor_options()
    sensor.supports.assert_any_call(option)
    sensor.set_option.assert_not_called()
@pytest.mark.parametrize(
    ("config_field", "option", "value"),
    [
        ("exposure", rs.option.exposure, 120),
        ("gain", rs.option.gain, 64),
    ],
 )
 def test_configure_sensor_options_exposure_or_gain_disables_auto_exposure(config_field, option, value):
    """white_balance=None should not touch auto white balance."""
    config = RealSenseCameraConfig(serial_number_or_name="042", **{config_field: value})
    camera = RealSenseCamera(config)
    sensor = _make_mock_sensor(
        "RGB Camera",
        supported_options={rs.option.enable_auto_exposure, option},
    )
    _attach_mock_color_sensor(camera, sensor)
    camera._configure_sensor_options()
    calls = [call.args for call in sensor.set_option.call_args_list]
    assert (rs.option.enable_auto_exposure, 0) in calls
    assert (option, value) in calls
    for opt, _ in calls:
        assert opt != rs.option.enable_auto_white_balance
        assert opt != rs.option.white_balance
 def test_configure_sensor_options_warns_when_auto_exposure_control_is_unsupported(caplog):
    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120)
    camera = RealSenseCamera(config)
    sensor = _make_mock_sensor("RGB Camera", supported_options={rs.option.exposure})
    _attach_mock_color_sensor(camera, sensor)
    with caplog.at_level("WARNING"):
        camera._configure_sensor_options()
    sensor.set_option.assert_called_once_with(rs.option.exposure, 120)
    assert "does not support disabling auto-exposure" in caplog.text
 def test_configure_sensor_options_warns_when_auto_white_balance_control_is_unsupported(caplog):
    config = RealSenseCameraConfig(serial_number_or_name="042", white_balance=4600)
    camera = RealSenseCamera(config)
    sensor = _make_mock_sensor("RGB Camera", supported_options={rs.option.white_balance})
    _attach_mock_color_sensor(camera, sensor)
    with caplog.at_level("WARNING"):
        camera._configure_sensor_options()
    sensor.set_option.assert_called_once_with(rs.option.white_balance, 4600)
    assert "does not support disabling auto white balance" in caplog.text
 def test_configure_sensor_options_out_of_range_raises_value_error():
    """set_option errors should be re-raised as ValueError with range diagnostics."""
    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=999999)
    camera = RealSenseCamera(config)
    sensor = _make_mock_sensor(
        "RGB Camera",
        supported_options={rs.option.enable_auto_exposure, rs.option.exposure},
    )
    def fake_set_option(option, value):
        if option == rs.option.exposure:
            raise RuntimeError("value out of range")
    sensor.set_option.side_effect = fake_set_option
    option_range = MagicMock(min=1, max=10000, step=1, default=156)
    sensor.get_option_range.return_value = option_range
    _attach_mock_color_sensor(camera, sensor)
    with pytest.raises(ValueError, match="exposure") as exc_info:
        camera._configure_sensor_options()
    msg = str(exc_info.value)
    assert "999999" in msg
    assert "min=1" in msg
    assert "max=10000" in msg
@pytest.mark.parametrize(
    "rotation",
    [
@@ -1,104 +0,0 @@
 # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import numpy as np
 import pytest
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 from lerobot.scripts.augment_dataset_quantile_stats import (
    compute_quantile_stats_for_dataset,
    has_quantile_stats,
 )
 def _numeric_keys(dataset):
    return [k for k, v in dataset.features.items() if v["dtype"] not in ("image", "video", "string")]
 def _image_keys(dataset):
    return [k for k, v in dataset.features.items() if v["dtype"] in ("image", "video")]
 def test_numeric_stats_are_unaffected_by_sampling(tmp_path, lerobot_dataset_factory):
    """Sampling only touches image/video frames; numeric features are read in
    full either way, so their stats must be identical with and without sampling."""
    dataset = lerobot_dataset_factory(
        root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
    )
    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
    numeric_keys = _numeric_keys(dataset)
    assert numeric_keys, "fixture should expose numeric features"
    for key in numeric_keys:
        if key not in exact:
            continue
        for stat in ("mean", "std", "q01", "q50", "q99"):
            if stat in exact[key]:
                np.testing.assert_allclose(
                    sampled[key][stat],
                    exact[key][stat],
                    rtol=1e-6,
                    atol=1e-6,
                    err_msg=f"numeric feature '{key}' stat '{stat}' changed under sampling",
                )
 def test_image_sampling_reduces_data_but_keeps_stats_close(tmp_path, lerobot_dataset_factory):
    """For images, sampling should reduce the number of samples considered while
    keeping the resulting statistics close to the exact ones."""
    dataset = lerobot_dataset_factory(
        root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
    )
    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
    image_keys = _image_keys(dataset)
    assert image_keys, "fixture should expose at least one image feature"
    for key in image_keys:
        # sampling actually looked at fewer pixels
        assert sampled[key]["count"][0] < exact[key]["count"][0]
        # but per-channel mean stays close
        np.testing.assert_allclose(
            sampled[key]["mean"],
            exact[key]["mean"],
            rtol=0.15,
            err_msg=f"image feature '{key}' mean drifted too far under sampling",
        )
 def test_short_episodes_use_all_frames(tmp_path, lerobot_dataset_factory):
    """With episodes shorter than the sampling floor, sampling is a no-op and
    must produce exactly the same stats as the exact path."""
    dataset = lerobot_dataset_factory(
        root=tmp_path / "ds", total_episodes=2, total_frames=40, use_videos=False
    )
    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
    for key in _image_keys(dataset):
        assert sampled[key]["count"][0] == exact[key]["count"][0]
 def test_quantile_stats_present_after_compute(tmp_path, lerobot_dataset_factory):
    """The computed stats should contain quantile keys for the dataset."""
    dataset = lerobot_dataset_factory(
        root=tmp_path / "ds", total_episodes=2, total_frames=200, use_videos=False
    )
    stats = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
    assert has_quantile_stats(stats)
@@ -204,38 +204,6 @@ def test_clear_resets_buffer(tmp_path):
    assert dataset.writer.episode_buffer["size"] == 0
 def test_clear_removes_video_frame_staging_dir(tmp_path):
    """clear_episode_buffer() removes PNG staging dirs for video features."""
    video_key = "observation.images.cam"
    features = {
        video_key: {
            "dtype": "video",
            "shape": (64, 96, 3),
            "names": ["height", "width", "channels"],
        },
        "action": {"dtype": "float32", "shape": (2,), "names": None},
    }
    dataset = LeRobotDataset.create(
        repo_id=DUMMY_REPO_ID,
        fps=DEFAULT_FPS,
        features=features,
        root=tmp_path / "ds",
        use_videos=True,
    )
    dataset.add_frame(_make_frame(features))
    video_staging_dir = (
        dataset.root
        / Path(DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=0, frame_index=0)).parent
    )
    assert video_staging_dir.is_dir()
    dataset.clear_episode_buffer()
    assert dataset.writer.episode_buffer["size"] == 0
    assert not video_staging_dir.exists()
 def test_finalize_is_idempotent(tmp_path):
    """Calling finalize() twice does not raise."""
    dataset = LeRobotDataset.create(
@@ -28,17 +28,9 @@ from lerobot.scripts.lerobot_imgtransform_viz import (
    save_each_transform,
 )
 from lerobot.transforms import (
    CoarseDropout,
    GammaCorrection,
    GaussianNoise,
    GaussianPatchBrightness,
    ImageTransformConfig,
    ImageTransforms,
    ImageTransformsConfig,
    JPEGCompression,
    MotionBlur,
    PlanckianJitter,
    RandomShadow,
    RandomSubsetApply,
    SharpnessJitter,
    make_transform_from_config,
@@ -463,153 +455,3 @@ def test_save_each_transform(img_tensor_factory, tmp_path):
            assert (transform_dir / file_name).exists(), (
                f"{file_name} was not found in {transform} directory."
            )
 # --- Tests for robotics-relevant augmentations ---
 ROBOTICS_TRANSFORMS = [
    ("GaussianNoise", GaussianNoise, {"std": (5.0, 25.0)}),
    ("MotionBlur", MotionBlur, {"kernel_size": (3, 11)}),
    ("JPEGCompression", JPEGCompression, {"quality": (15, 75)}),
    ("GaussianPatchBrightness", GaussianPatchBrightness, {}),
    ("RandomShadow", RandomShadow, {"opacity": (0.3, 0.6)}),
    ("CoarseDropout", CoarseDropout, {"max_holes": 8}),
    ("GammaCorrection", GammaCorrection, {"gamma": (0.5, 2.0)}),
    ("PlanckianJitter", PlanckianJitter, {"temperature": (3_000, 15_000)}),
 ]
@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
 def test_robotics_transform_shape_preserved(name, cls, kwargs, img_tensor_factory):
    img = img_tensor_factory()
    tf = cls(**kwargs)
    out = tf(img)
    assert out.shape == img.shape, f"{name} changed shape: {img.shape} -> {out.shape}"
@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
 def test_robotics_transform_output_range(name, cls, kwargs, img_tensor_factory):
    img = img_tensor_factory()
    tf = cls(**kwargs)
    out = tf(img)
    assert out.min() >= -0.01, f"{name} min below range: {out.min():.4f}"
    assert out.max() <= 1.01, f"{name} max above range: {out.max():.4f}"
@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
 def test_robotics_transform_float_output(name, cls, kwargs, img_tensor_factory):
    img = img_tensor_factory()
    tf = cls(**kwargs)
    out = tf(img)
    assert out.is_floating_point(), f"{name} output dtype={out.dtype}"
@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
 def test_robotics_transform_non_float_passthrough(name, cls, kwargs):
    int_img = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
    tf = cls(**kwargs)
    out = tf(int_img)
    assert torch.equal(out, int_img), f"{name} modified non-float input"
@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
 def test_robotics_transform_via_config(name, cls, kwargs):
    cfg = ImageTransformConfig(type=name, kwargs=kwargs)
    tf = make_transform_from_config(cfg)
    assert isinstance(tf, cls), f"Config produced {type(tf)}, expected {cls}"
 def test_make_transform_error_message_includes_custom():
    """Error message should list all registered custom transforms."""
    with pytest.raises(ValueError, match="GaussianNoise"):
        make_transform_from_config(ImageTransformConfig(type="NonExistent"))
@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
@pytest.mark.parametrize("shape", [(4, 3, 32, 32), (2, 4, 3, 16, 16)])
 def test_robotics_transform_supports_temporal_batches(name, cls, kwargs, shape):
    img = torch.rand(shape)
    out = cls(**kwargs)(img)
    assert out.shape == img.shape, f"{name} changed shape: {img.shape} -> {out.shape}"
    assert out.min() >= 0
    assert out.max() <= 1
@pytest.mark.parametrize(
    "cls,kwargs",
    [
        (GaussianNoise, {"std": (25.0, 25.0)}),
        (MotionBlur, {"kernel_size": 5}),
        (JPEGCompression, {"quality": 10}),
        (
            GaussianPatchBrightness,
            {"num_patches": 1, "sigma_range": (0.2, 0.2), "factor_range": (0.5, 0.5)},
        ),
        (RandomShadow, {"opacity": 0.5}),
        (CoarseDropout, {"max_holes": 1, "fill_value": 0.0}),
        (GammaCorrection, {"gamma": (2.0, 2.0)}),
        (PlanckianJitter, {"temperature": 3_000}),
    ],
 )
 def test_robotics_transform_is_not_silent_noop(cls, kwargs):
    img = torch.rand(3, 32, 32)
    out = cls(**kwargs)(img)
    assert not torch.equal(out, img)
@pytest.mark.parametrize(
    "transform",
    [
        GaussianNoise(std=25),
        RandomShadow(opacity=0.5),
        CoarseDropout(max_holes=4),
    ],
 )
 def test_robotics_transform_random_params_are_reused(transform):
    img = torch.rand(3, 32, 32)
    params = transform.make_params([img])
    torch.testing.assert_close(transform.transform(img, params), transform.transform(img, params))
 def test_motion_blur_kernel_size_stays_in_configured_range():
    transform = MotionBlur(kernel_size=(4, 10))
    sampled_sizes = {transform.make_params([])["kernel_size"] for _ in range(100)}
    assert sampled_sizes <= {5, 7, 9}
    assert sampled_sizes
 def test_gamma_correction_scalar_below_one_defines_symmetric_range():
    transform = GammaCorrection(gamma=0.5)
    assert transform.gamma == (0.5, 2.0)
    assert transform(torch.rand(3, 8, 8)).shape == (3, 8, 8)
 def test_planckian_jitter_uses_correlated_temperature_coefficients():
    img = torch.full((2, 3, 8, 8), 0.25)
    out = PlanckianJitter(temperature=3_000)(img)
    torch.testing.assert_close(out[:, 1], img[:, 1])
    assert torch.all(out[:, 0] > out[:, 1])
    assert torch.all(out[:, 2] < out[:, 1])
 def test_random_shadow_supports_small_images():
    img = torch.rand(3, 7, 7)
    assert RandomShadow()(img).shape == img.shape
@pytest.mark.parametrize(
    "cls,kwargs",
    [
        (GaussianNoise, {"std": (-1.0, 1.0)}),
        (MotionBlur, {"kernel_size": 4}),
        (JPEGCompression, {"quality": (0, 75)}),
        (GaussianPatchBrightness, {"sigma_range": (0.0, 0.25)}),
        (RandomShadow, {"opacity": (0.3, 1.1)}),
        (CoarseDropout, {"max_holes": 0}),
        (GammaCorrection, {"gamma": 0.0}),
        (PlanckianJitter, {"temperature": (2_000, 6_500)}),
    ],
 )
 def test_robotics_transform_rejects_invalid_config(cls, kwargs):
    with pytest.raises(ValueError):
        cls(**kwargs)
@@ -482,20 +482,6 @@ def test_add_frame_works_in_write_mode(tmp_path):
 # ── Resume mode ──────────────────────────────────────────────────────
 def test_resume_freshly_created_empty_dataset(tmp_path):
    """resume() accepts a local dataset created before any episode was recorded."""
    root = tmp_path / "resume_empty_ds"
    LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=DEFAULT_FPS, features=SIMPLE_FEATURES, root=root)
    resumed = LeRobotDataset.resume(repo_id=DUMMY_REPO_ID, root=root)
    assert isinstance(resumed.writer, DatasetWriter)
    assert resumed.meta.total_episodes == 0
    assert resumed.meta.total_frames == 0
    assert resumed.meta.tasks is None
    assert resumed.meta.episodes is None
 def test_resume_creates_writer(tmp_path):
    """After resume(), writer is a DatasetWriter."""
    root = tmp_path / "resume_ds"
@@ -294,19 +294,6 @@ def test__sync_read(addr, length, ids_values, mock_motors, dummy_motors):
    assert read_values == ids_values
 def test__sync_read_retries_after_transient_failure(mock_motors, dummy_motors):
    addr, length, ids_values = (10, 4, {1: 1337})
    stub = mock_motors.build_sync_read_stub(addr, length, ids_values, num_invalid_try=1)
    bus = FeetechMotorsBus(port=mock_motors.port, motors=dummy_motors)
    bus.connect(handshake=False)
    read_values, read_comm = bus._sync_read(addr, length, list(ids_values), num_retry=1)
    assert read_comm == scs.COMM_SUCCESS
    assert read_values == ids_values
    assert mock_motors.stubs[stub].calls == 2
@pytest.mark.parametrize("raise_on_error", (True, False))
 def test__sync_read_comm(raise_on_error, mock_motors, dummy_motors):
    addr, length, ids_values = (10, 4, {1: 1337})
@@ -1,83 +0,0 @@
 # Copyright 2026 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from types import SimpleNamespace
 from unittest.mock import MagicMock
 import torch
 import lerobot.policies.factory as policy_factory
 def test_make_policy_keeps_peft_adapter_and_base_revisions_separate(monkeypatch):
    cfg = SimpleNamespace(
        type="mock",
        device="cpu",
        pretrained_path="user/adapter",
        pretrained_revision="adapter-sha",
        use_peft=True,
        input_features={},
        output_features={},
    )
    dataset_meta = SimpleNamespace(features={}, stats={})
    base_policy = torch.nn.Linear(1, 1)
    policy_from_pretrained = MagicMock(return_value=base_policy)
    policy_class = SimpleNamespace(from_pretrained=policy_from_pretrained)
    monkeypatch.setattr(policy_factory, "get_policy_class", lambda _: policy_class)
    monkeypatch.setattr(policy_factory, "dataset_to_policy_features", lambda _: {})
    monkeypatch.setattr(policy_factory, "validate_visual_features_consistency", lambda *args: None)
    peft_config = SimpleNamespace(
        base_model_name_or_path="user/base-policy",
        revision="base-sha",
    )
    peft_config_from_pretrained = MagicMock(return_value=peft_config)
    adapted_policy = torch.nn.Linear(1, 1)
    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
    require_package = MagicMock()
    monkeypatch.setattr(policy_factory, "require_package", require_package)
    monkeypatch.setattr(
        policy_factory,
        "PeftConfig",
        SimpleNamespace(from_pretrained=peft_config_from_pretrained),
    )
    monkeypatch.setattr(
        policy_factory,
        "PeftModel",
        SimpleNamespace(from_pretrained=peft_model_from_pretrained),
    )
    policy = policy_factory.make_policy(cfg, ds_meta=dataset_meta)
    assert policy is adapted_policy
    require_package.assert_called_once_with("peft", extra="peft")
    peft_config_from_pretrained.assert_called_once_with(
        "user/adapter",
        revision="adapter-sha",
    )
    policy_from_pretrained.assert_called_once_with(
        config=cfg,
        dataset_stats=dataset_meta.stats,
        dataset_meta=dataset_meta,
        pretrained_name_or_path="user/base-policy",
        revision="base-sha",
    )
    peft_model_from_pretrained.assert_called_once_with(
        base_policy,
        "user/adapter",
        config=peft_config,
        revision="adapter-sha",
        is_trainable=True,
    )
@@ -113,7 +113,6 @@ def test_gaussian_actor_config_default_initialization():
    # Concurrency configuration
    assert config.concurrency.actor == "threads"
    assert config.concurrency.learner == "threads"
    assert config.concurrency.multiprocessing_context == "spawn"
    assert isinstance(config.actor_network_kwargs, ActorNetworkConfig)
    assert isinstance(config.policy_kwargs, PolicyConfig)
@@ -153,7 +152,6 @@ def test_concurrency_config():
    config = ConcurrencyConfig()
    assert config.actor == "threads"
    assert config.learner == "threads"
    assert config.multiprocessing_context == "spawn"
 def test_gaussian_actor_config_custom_initialization():
@@ -26,17 +26,8 @@ import tempfile
 from pathlib import Path
 import pytest
 import torch
 from safetensors.torch import save_file
-from lerobot.configs import PipelineFeatureType, PolicyFeature
+from lerobot.processor.pipeline import DataProcessorPipeline, ProcessorMigrationError
 from lerobot.processor.pipeline import (
    DataProcessorPipeline,
    ProcessorMigrationError,
    ProcessorStep,
    ProcessorStepRegistry,
 )
 from lerobot.types import EnvTransition
 # Simplified Config Loading Tests
@@ -107,140 +98,6 @@ def test_load_config_nonexistent_path_tries_hub():
        DataProcessorPipeline._load_config("nonexistent/path", "processor.json", {})
 def test_from_pretrained_local_directory_missing_state_does_not_call_hub(monkeypatch):
    """Local processor dirs must fail locally when a state file is missing."""
    @ProcessorStepRegistry.register("local_missing_state_step")
    class LocalMissingStateStep(ProcessorStep):
        def __call__(self, transition: EnvTransition) -> EnvTransition:
            return transition
        def transform_features(
            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
            return features
        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
            pass
    try:
        with tempfile.TemporaryDirectory() as tmp_dir:
            tmp_path = Path(tmp_dir)
            config = {
                "name": "LocalMissingStatePipeline",
                "steps": [{"registry_name": "local_missing_state_step", "state_file": "missing.safetensors"}],
            }
            (tmp_path / "processor.json").write_text(json.dumps(config))
            def fail_hub_download(*args, **kwargs):
                pytest.fail("local missing processor state should not call hf_hub_download")
            monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fail_hub_download)
            with pytest.raises(FileNotFoundError, match="missing.safetensors.*local processor pipeline"):
                DataProcessorPipeline.from_pretrained(tmp_path, config_filename="processor.json")
    finally:
        ProcessorStepRegistry.unregister("local_missing_state_step")
 def test_from_pretrained_local_config_file_missing_state_does_not_call_hub(monkeypatch):
    """Local single-file processor configs must also keep missing state resolution local."""
    @ProcessorStepRegistry.register("local_file_missing_state_step")
    class LocalFileMissingStateStep(ProcessorStep):
        def __call__(self, transition: EnvTransition) -> EnvTransition:
            return transition
        def transform_features(
            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
            return features
        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
            pass
    try:
        with tempfile.TemporaryDirectory() as tmp_dir:
            tmp_path = Path(tmp_dir)
            config_path = tmp_path / "processor.json"
            config = {
                "name": "LocalFileMissingStatePipeline",
                "steps": [
                    {"registry_name": "local_file_missing_state_step", "state_file": "missing.safetensors"}
                ],
            }
            config_path.write_text(json.dumps(config))
            def fail_hub_download(*args, **kwargs):
                pytest.fail("local missing processor state should not call hf_hub_download")
            monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fail_hub_download)
            with pytest.raises(FileNotFoundError, match="missing.safetensors.*local processor pipeline"):
                DataProcessorPipeline.from_pretrained(config_path, config_filename="ignored.json")
    finally:
        ProcessorStepRegistry.unregister("local_file_missing_state_step")
 def test_from_pretrained_hub_source_missing_local_state_still_calls_hub(monkeypatch, tmp_path):
    """Hub sources still fall back to hf_hub_download for state files."""
    @ProcessorStepRegistry.register("hub_state_step")
    class HubStateStep(ProcessorStep):
        def __init__(self):
            self.value = torch.tensor(0)
        def __call__(self, transition: EnvTransition) -> EnvTransition:
            return transition
        def transform_features(
            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
            return features
        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
            self.value = state["value"]
    try:
        state_path = tmp_path / "downloaded.safetensors"
        save_file({"value": torch.tensor(7)}, state_path)
        loaded_config = {
            "name": "HubStatePipeline",
            "steps": [{"registry_name": "hub_state_step", "state_file": "hub_state.safetensors"}],
        }
        calls = []
        def fake_load_config(cls, model_id, config_filename, hub_download_kwargs):
            return loaded_config, tmp_path / "hub_cache"
        def fake_hub_download(**kwargs):
            calls.append(kwargs)
            return str(state_path)
        monkeypatch.setattr(DataProcessorPipeline, "_load_config", classmethod(fake_load_config))
        monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fake_hub_download)
        pipeline = DataProcessorPipeline.from_pretrained("user/repo", config_filename="processor.json")
        assert calls == [
            {
                "repo_id": "user/repo",
                "filename": "hub_state.safetensors",
                "repo_type": "model",
                "force_download": False,
                "resume_download": None,
                "proxies": None,
                "token": None,
                "cache_dir": None,
                "local_files_only": False,
                "revision": None,
            }
        ]
        assert pipeline.steps[0].value.item() == 7
    finally:
        ProcessorStepRegistry.unregister("hub_state_step")
 # Config Validation Tests
@@ -49,7 +49,7 @@ def _make_bus_mock() -> MagicMock:
@pytest.fixture
-def follower(tmp_path):
+def follower():
    bus_mock = _make_bus_mock()
    def _bus_side_effect(*_args, **kwargs):
@@ -71,7 +71,7 @@ def follower(tmp_path):
        ),
        patch.object(SO100Follower, "configure", lambda self: None),
    ):
-        cfg = SO100FollowerConfig(port="/dev/null", calibration_dir=tmp_path)
+        cfg = SO100FollowerConfig(port="/dev/null")
        robot = SO100Follower(cfg)
        yield robot
        if robot.is_connected:
@@ -99,27 +99,6 @@ def test_get_observation(follower):
        assert obs[f"{motor}.pos"] == idx
 def test_get_observation_uses_read_retries(follower):
    # Feetech buses can intermittently fail a sync_read; the follower should forward the configured
    # retry count so transient failures don't abort the control loop (see #3131).
    follower.config.num_read_retries = 7
    follower.connect()
    follower.get_observation()
    follower.bus.sync_read.assert_called_once_with("Present_Position", num_retry=7)
 def test_send_action_uses_read_retries(follower):
    follower.config.max_relative_target = 10.0
    follower.config.num_read_retries = 7
    follower.connect()
    action = {f"{motor}.pos": value * 10 for value, motor in enumerate(follower.bus.motors, 1)}
    follower.send_action(action)
    follower.bus.sync_read.assert_called_once_with("Present_Position", num_retry=7)
 def test_send_action(follower):
    follower.connect()
@@ -17,8 +17,6 @@
 from __future__ import annotations
 import dataclasses
 import sys
 from types import SimpleNamespace
 from unittest.mock import MagicMock
 import pytest
@@ -108,116 +106,6 @@ def test_sentry_config_defaults():
    assert cfg.target_video_file_size_mb is None
 def test_rollout_config_passes_policy_pretrained_revision(monkeypatch):
    from lerobot.configs import PreTrainedConfig, parser
    from lerobot.rollout import RolloutConfig
    from tests.mocks.mock_robot import MockRobotConfig
    captured = {}
    def fake_from_pretrained(cls, pretrained_name_or_path, **kwargs):
        captured["pretrained_name_or_path"] = pretrained_name_or_path
        captured.update(kwargs)
        return SimpleNamespace(device="cpu", pretrained_revision=kwargs["revision"])
    monkeypatch.setattr(parser, "get_yaml_overrides", lambda _: ["--pretrained_revision=yaml-sha"])
    monkeypatch.setattr(
        sys,
        "argv",
        ["lerobot-rollout", "--policy.path=user/policy", "--policy.pretrained_revision=cli-sha"],
    )
    monkeypatch.setattr(PreTrainedConfig, "from_pretrained", classmethod(fake_from_pretrained))
    cfg = RolloutConfig(robot=MockRobotConfig())
    assert captured["pretrained_name_or_path"] == "user/policy"
    assert captured["revision"] == "cli-sha"
    assert captured["cli_overrides"] == [
        "--pretrained_revision=yaml-sha",
        "--pretrained_revision=cli-sha",
    ]
    assert cfg.policy.pretrained_path == "user/policy"
    assert cfg.policy.pretrained_revision == "cli-sha"
 def test_load_pretrained_policy_passes_revision(monkeypatch):
    import lerobot.rollout.context as rollout_context
    policy_config = SimpleNamespace(
        type="mock",
        use_peft=False,
        pretrained_path="user/policy",
        pretrained_revision="policy-sha",
    )
    policy_class = MagicMock()
    loaded_policy = MagicMock()
    policy_class.from_pretrained.return_value = loaded_policy
    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
    policy = rollout_context._load_pretrained_policy(policy_config)
    assert policy is loaded_policy
    policy_class.from_pretrained.assert_called_once_with(
        "user/policy",
        config=policy_config,
        revision="policy-sha",
    )
 def test_load_pretrained_peft_policy_keeps_adapter_and_base_revisions_separate(monkeypatch):
    import lerobot.rollout.context as rollout_context
    policy_config = SimpleNamespace(
        type="mock",
        use_peft=True,
        pretrained_path="user/adapter",
        pretrained_revision="adapter-sha",
    )
    policy_class = MagicMock()
    base_policy = MagicMock()
    policy_class.from_pretrained.return_value = base_policy
    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
    peft_config = SimpleNamespace(
        base_model_name_or_path="user/base-policy",
        revision="base-sha",
    )
    peft_config_from_pretrained = MagicMock(return_value=peft_config)
    adapted_policy = MagicMock()
    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
    require_package = MagicMock()
    monkeypatch.setattr(rollout_context, "require_package", require_package)
    monkeypatch.setattr(
        rollout_context,
        "PeftConfig",
        SimpleNamespace(from_pretrained=peft_config_from_pretrained),
        raising=False,
    )
    monkeypatch.setattr(
        rollout_context,
        "PeftModel",
        SimpleNamespace(from_pretrained=peft_model_from_pretrained),
        raising=False,
    )
    policy = rollout_context._load_pretrained_policy(policy_config)
    assert policy is adapted_policy
    require_package.assert_called_once_with("peft", extra="peft")
    peft_config_from_pretrained.assert_called_once_with("user/adapter", revision="adapter-sha")
    policy_class.from_pretrained.assert_called_once_with(
        pretrained_name_or_path="user/base-policy",
        config=policy_config,
        revision="base-sha",
    )
    peft_model_from_pretrained.assert_called_once_with(
        base_policy,
        "user/adapter",
        config=peft_config,
        revision="adapter-sha",
    )
 # ---------------------------------------------------------------------------
 # RolloutRingBuffer
 # ---------------------------------------------------------------------------