chore(deps): bump the uv group across 1 directory with 2 updates

Bumps the uv group with 2 updates in the / directory: [setuptools](https://github.com/pypa/setuptools) and [pytest](https://github.com/pytest-dev/pytest). Updates `setuptools` from 80.10.2 to 83.0.0 - [Release notes](https://github.com/pypa/setuptools/releases) - [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst) - [Commits](https://github.com/pypa/setuptools/compare/v80.10.2...v83.0.0) Updates `pytest` from 8.4.2 to 9.0.3 - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.4.2...9.0.3) --- updated-dependencies: - dependency-name: setuptools dependency-version: 83.0.0 dependency-type: direct:development dependency-group: uv - dependency-name: pytest dependency-version: 9.0.3 dependency-type: direct:production dependency-group: uv ... Signed-off-by: dependabot[bot] <support@github.com>
chore(mypy): cover annotations and transforms (#3860 )
2026-07-28 20:26:05 +00:00 · 2026-07-28 14:29:26 +00:00 · 2026-07-28 16:25:45 +02:00 · 2026-07-28 16:25:42 +02:00 · 2026-07-28 15:41:47 +02:00 · 2026-07-28 14:57:50 +02:00
59 changed files with 1671 additions and 504 deletions
@@ -72,19 +72,19 @@ jobs:
      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}

    steps:
-      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7
+        uses: actions/upload-artifact@v4
        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@v7
+        uses: actions/upload-artifact@v4
        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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false
          lfs: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        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@v7
+      - uses: actions/checkout@v6
        with:
          lfs: true
          persist-credentials: false
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image CPU
-        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7
+      - uses: actions/checkout@v6
        with:
          lfs: true
          persist-credentials: false
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image GPU
-        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # 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@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # 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@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # 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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        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@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
+        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
        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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        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@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
+        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
        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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          lfs: true
          persist-credentials: false
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@bb05f3f5519dd87d3ba754cc423b652a5edd6d2c  # v4.2.0
+        uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f  # v3
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@af1e73f918a031802d376d3c8bbc3fe56130a9b0  # v4.4.0
+        uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9  # v3
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image
-        uses: docker/build-push-action@53b7df96c91f9c12dcc8a07bcb9ccacbed38856a  # v7.3.0
+        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8  # v6
        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@v9
+      - uses: actions/github-script@v8
        with:
          script: |
            // Setup Input Text
@@ -48,12 +48,12 @@ jobs:
    outputs:
      changed: ${{ steps.diff.outputs.changed }}
    steps:
-      - uses: actions/checkout@v7
+      - uses: actions/checkout@v6
        with:
          persist-credentials: false

      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@v8.3.2 # zizmor: ignore[unpinned-uses]
+        uses: astral-sh/setup-uv@v6 # 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@v7 # zizmor: ignore[unpinned-uses]
+        uses: actions/upload-artifact@v4 # 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@v7
+      - uses: actions/checkout@v6
        with:
          lfs: true
          persist-credentials: false

      - name: Download updated lockfile
-        uses: actions/download-artifact@v8 # zizmor: ignore[unpinned-uses]
+        uses: actions/download-artifact@v4 # 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@v8.3.2 # zizmor: ignore[unpinned-uses]
+        uses: astral-sh/setup-uv@v6 # 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@v7
+      - uses: actions/checkout@v6
        with:
          lfs: true
          persist-credentials: false

      - name: Download updated lockfile
-        uses: actions/download-artifact@v8 # zizmor: ignore[unpinned-uses]
+        uses: actions/download-artifact@v4 # zizmor: ignore[unpinned-uses]
        with:
          name: uv-lock

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4 # zizmor: ignore[unpinned-uses]
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
      - name: Build and push Docker image
-        uses: docker/build-push-action@v7 # zizmor: ignore[unpinned-uses]
+        uses: docker/build-push-action@v6 # 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@v7
+      - uses: actions/checkout@v6
        with:
          persist-credentials: false

      - name: Download updated lockfile
-        uses: actions/download-artifact@v8 # zizmor: ignore[unpinned-uses]
+        uses: actions/download-artifact@v4 # 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@v7
+      - uses: actions/labeler@v6
        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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false

      - name: Set up Python
-        uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97  # v6
+        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405  # v6
        with:
          python-version: '3.12'

@@ -38,12 +38,12 @@ jobs:

    steps:
      - name: Checkout code
-        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          persist-credentials: false

      - name: Set up Python
-        uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97  # v6
+        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405  # 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@ba38be9e461d3875417946c167d0b5f3d385a247  # v1.14.1
+        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e  # v1.13.0
        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@ba38be9e461d3875417946c167d0b5f3d385a247  # v1.14.1
+        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e  # v1.13.0
        with:
          verbose: true
          print-hash: true
@@ -127,7 +127,7 @@ jobs:
    env:
      MUJOCO_GL: egl
    steps:
-      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        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@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
+        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e  # v6
        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@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
        with:
          fetch-depth: 0
          persist-credentials: false

      - name: Secret Scanning
-        uses: trufflesecurity/trufflehog@27b0417c16317ca9a472a9a8092acce143b49c55  # v3.95.9
+        uses: trufflesecurity/trufflehog@eafb8c5f6a06175141c27f17bcc17941853d0047  # v3.90.0
        with:
          extra_args: --only-verified
@@ -136,6 +136,10 @@ 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
 )

@@ -154,6 +158,15 @@ 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>

@@ -14,7 +14,7 @@

 [build-system]
 requires = ["setuptools"]
-build-backend = "setuptools.build_meta"
+build-backend = "setuptools*"

 [project.urls]
 homepage = "https://huggingface.co/lerobot"
@@ -87,7 +87,7 @@ dependencies = [

    # Build tools (required by opencv-python-headless on some platforms)
    "cmake>=3.29.0.1,<4.2.0",
-    "setuptools>=71.0.0,<81.0.0",
+    "setuptools>=71.0.0,<84.0.0",
 ]

 # Optional dependencies
@@ -261,7 +261,7 @@ annotations = [
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools>=1.73.1,<2.0.0", "mypy>=1.19.1", "ruff>=0.14.1", "lerobot[notebook]"]
 notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
-test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
+test = ["pytest>=8.1.0,<10.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]

 # Simulation
@@ -494,6 +494,19 @@ 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.*"
@@ -120,14 +120,22 @@ class OpenCVCamera(Camera):
        self.rotation: int | None = get_cv2_rotation(config.rotation)
        self.backend: int = config.backend

-        if self.height and self.width:
-            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
+        self.capture_width: int | None = None
+        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
+
    @property
    def is_connected(self) -> bool:
        """Checks if the camera is currently connected and opened."""
@@ -164,17 +172,25 @@ class OpenCVCamera(Camera):
                f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
            )

-        self._configure_capture_settings()
-        self._start_read_thread()
+        try:
+            self._configure_capture_settings()
+            self._start_read_thread()

-        if warmup and self.warmup_s > 0:
-            start_time = time.time()
-            while time.time() - start_time < self.warmup_s:
-                self.async_read(timeout_ms=self.warmup_s * 1000)
-                time.sleep(0.1)
-            with self.frame_lock:
-                if self.latest_frame is None:
-                    raise ConnectionError(f"{self} failed to capture frames during warmup.")
+            if warmup and self.warmup_s > 0:
+                start_time = time.time()
+                while time.time() - start_time < self.warmup_s:
+                    self.async_read(timeout_ms=self.warmup_s * 1000)
+                    time.sleep(0.1)
+                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.")

@@ -312,32 +328,36 @@ class OpenCVCamera(Camera):

        for target in targets_to_scan:
            camera = cv2.VideoCapture(target)
-            if camera.isOpened():
-                default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
-                default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                default_fps = camera.get(cv2.CAP_PROP_FPS)
-                default_format = camera.get(cv2.CAP_PROP_FORMAT)
+            try:
+                if camera.isOpened():
+                    default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
+                    default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                    default_fps = camera.get(cv2.CAP_PROP_FPS)
+                    default_format = camera.get(cv2.CAP_PROP_FORMAT)

-                # 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([chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])
+                    # 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(
+                        [chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)]
+                    )

-                camera_info = {
-                    "name": f"OpenCV Camera @ {target}",
-                    "type": "OpenCV",
-                    "id": target,
-                    "backend_api": camera.getBackendName(),
-                    "default_stream_profile": {
-                        "format": default_format,
-                        "fourcc": default_fourcc,
-                        "width": default_width,
-                        "height": default_height,
-                        "fps": default_fps,
-                    },
-                }
+                    camera_info = {
+                        "name": f"OpenCV Camera @ {target}",
+                        "type": "OpenCV",
+                        "id": target,
+                        "backend_api": camera.getBackendName(),
+                        "default_stream_profile": {
+                            "format": default_format,
+                            "fourcc": default_fourcc,
+                            "width": default_width,
+                            "height": default_height,
+                            "fps": default_fps,
+                        },
+                    }

-                found_cameras_info.append(camera_info)
+                    found_cameras_info.append(camera_info)
+            finally:
                camera.release()

        return found_cameras_info
@@ -496,6 +516,26 @@ 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]:
        """
@@ -586,16 +626,6 @@ class OpenCVCamera(Camera):
        if not self.is_connected and self.thread is None:
            raise DeviceNotConnectedError(f"{self} not connected.")

-        if self.thread is not None:
-            self._stop_read_thread()
-
-        if self.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()
+        self._cleanup_resources()

        logger.info(f"{self} disconnected.")
@@ -121,6 +121,9 @@ 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:
@@ -131,6 +134,9 @@ 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
@@ -145,14 +151,23 @@ class RealSenseCamera(Camera):

        self.rotation: int | None = get_cv2_rotation(config.rotation)

-        if self.height and self.width:
-            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
+        self.capture_width: int | None = None
+        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
+
    @property
    def is_connected(self) -> bool:
        """Checks if the camera pipeline is started and streams are active."""
@@ -172,7 +187,8 @@ class RealSenseCamera(Camera):

        Raises:
            DeviceAlreadyConnectedError: If the camera is already connected.
-            ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique).
+            ValueError: If the configuration is invalid, a requested sensor option is unsupported,
+                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.
        """
@@ -190,22 +206,31 @@ class RealSenseCamera(Camera):
                f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
            ) from e

-        self._configure_capture_settings()
-        self._start_read_thread()
+        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.
-        self.warmup_s = max(self.warmup_s, 1)
+            # 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.
+            self.warmup_s = max(self.warmup_s, 1)

-        warmup_read = self.async_read if self.use_rgb else self.async_read_depth
-        start_time = time.time()
-        while time.time() - start_time < self.warmup_s:
-            warmup_read(timeout_ms=self.warmup_s * 1000)
-            time.sleep(0.1)
-        with self.frame_lock:
-            if (self.use_rgb and self.latest_color_frame is None) or (
-                self.use_depth and self.latest_depth_frame is None
-            ):
-                raise ConnectionError(f"{self} failed to capture frames during warmup.")
+            warmup_read = self.async_read if self.use_rgb else self.async_read_depth
+            start_time = time.time()
+            while time.time() - start_time < self.warmup_s:
+                warmup_read(timeout_ms=self.warmup_s * 1000)
+                time.sleep(0.1)
+            with self.frame_lock:
+                if (self.use_rgb and self.latest_color_frame is None) or (
+                    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.")

@@ -339,6 +364,111 @@ 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]:
        """
@@ -541,6 +671,27 @@ 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():
@@ -684,18 +835,5 @@ class RealSenseCamera(Camera):
                f"Attempted to disconnect {self}, but it appears already disconnected."
            )

-        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()
-
+        self._cleanup_resources()
        logger.info(f"{self} disconnected.")
@@ -46,6 +46,17 @@ 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.
@@ -61,6 +72,9 @@ 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)
@@ -69,6 +83,18 @@ 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(
@@ -71,13 +71,19 @@ 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}"
@@ -14,6 +14,7 @@
 import builtins
 import datetime as dt
 import json
+import multiprocessing
 import os
 import tempfile
 from dataclasses import dataclass, field
@@ -101,6 +102,12 @@ 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
@@ -212,6 +219,17 @@ 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)
-        self.episodes = load_episodes(self.root)
+        self.tasks = load_tasks(self.root) if self.total_tasks > 0 else None
+        self.episodes = load_episodes(self.root) if self.total_episodes > 0 else None
        self.stats = load_stats(self.root)

    def ensure_readable(self) -> None:
@@ -172,6 +172,23 @@ 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:
@@ -369,7 +386,9 @@ class DatasetWriter:
                self._episodes_since_last_encoding = 0

        if episode_data is None:
-            self.clear_episode_buffer(delete_images=len(self._meta.image_keys) > 0)
+            if 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."""
@@ -561,10 +580,10 @@ class DatasetWriter:
        return metadata

    def clear_episode_buffer(self, delete_images: bool = True) -> None:
-        """Discard the current episode buffer and optionally delete temp images.
+        """Discard the current episode buffer and optionally delete temp camera frames.

        Args:
-            delete_images: If ``True``, remove temporary image directories
+            delete_images: If ``True``, remove temporary camera frame directories
                written for the current episode.
        """
        # Cancel streaming encoder if active
@@ -572,17 +591,7 @@ class DatasetWriter:
            self._streaming_encoder.cancel_episode()

        if delete_images:
-            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._delete_camera_frame_dirs(self._meta.camera_keys)

        self.episode_buffer = self._create_episode_buffer()

@@ -155,6 +155,7 @@ 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:
@@ -220,6 +221,8 @@ 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)
@@ -384,7 +384,9 @@ 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.zeros((self.observation_height, self.observation_width, 3), dtype=np.uint8)
+        self._black_frame: np.ndarray = np.zeros(
+            (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.zeros(ctrl_dim, dtype=np.float64)
+            padded: np.ndarray = np.zeros(ctrl_dim, dtype=np.float64)
            padded[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
            return padded

@@ -122,6 +122,9 @@ 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}
@@ -134,6 +137,9 @@ MODEL_RESOLUTION = {
    "xm430-w350": 4096,
    "xm540-w270": 4096,
    "xc430-w150": 4096,
+    "xh540-w150": 4096,
+    "xc330-t288": 4096,
+    "xc330-t181": 4096,
 }

 # {model: model_number}
@@ -145,6 +151,9 @@ 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}
@@ -156,6 +165,9 @@ 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 = {
@@ -166,6 +178,9 @@ 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 = {
@@ -176,6 +191,9 @@ 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 = [
@@ -44,12 +44,19 @@ 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
@@ -177,6 +184,7 @@ 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"),
@@ -333,12 +341,15 @@ 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.
-        from peft import PeftConfig, PeftModel
+        require_package("peft", extra="peft")

        logging.info("Loading policy's PEFT adapter.")

        peft_pretrained_path = str(cfg.pretrained_path)
-        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
+        peft_config = PeftConfig.from_pretrained(
+            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"]:
@@ -349,9 +360,14 @@ def make_policy(
                "the adapter was trained."
            )

+        kwargs["revision"] = peft_config.revision
        policy = policy_cls.from_pretrained(**kwargs)
        policy = PeftModel.from_pretrained(
-            policy, peft_pretrained_path, config=peft_config, is_trainable=True
+            policy,
+            peft_pretrained_path,
+            config=peft_config,
+            revision=cfg.pretrained_revision,
+            is_trainable=True,
        )

    else:
@@ -37,13 +37,19 @@ 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
@@ -475,6 +475,7 @@ 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,
@@ -511,6 +512,7 @@ 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,
@@ -526,6 +528,7 @@ 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,
@@ -540,6 +543,7 @@ 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,
@@ -547,6 +551,7 @@ 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,
@@ -43,11 +43,22 @@ from torch.distributions import Beta

 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.utils.constants import ACTION
-from lerobot.utils.import_utils import _scipy_available, _transformers_available, require_package
+from lerobot.utils.import_utils import (
+    _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__)


@@ -1731,13 +1742,11 @@ 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)
@@ -34,14 +34,22 @@ 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

-T = TypeVar("T", bound="PreTrainedPolicy")
+if TYPE_CHECKING or _peft_available:
+    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,
@@ -384,7 +392,7 @@ 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.
        """
-        from peft import get_peft_model
+        require_package("peft", extra="peft")

        # If user provided a complete config, use it directly (with overrides)
        if peft_config is not None:
@@ -455,7 +463,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
        Returns:
            Preprocessed dict with renamed keys and init_type mapped to method-specific key.
        """
-        from peft import PeftType
+        require_package("peft", extra="peft")

        cli_overrides = cli_overrides.copy()

@@ -480,7 +488,7 @@ 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."""
-        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType
+        require_package("peft", extra="peft")

        # Determine PEFT method type (default to LORA)
        method_type_str = cli_overrides.get("method_type") or "lora"
@@ -507,7 +515,7 @@ 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."""
-        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType
+        require_package("peft", extra="peft")

        # Get method type from existing config or CLI override
        method_type_str = cli_overrides.get("method_type")
@@ -132,10 +132,20 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
    def transform_features(
        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        for axis in ["x", "y", "z", "gripper"]:
+        for axis in ["x", "y", "z"]:
            features[PipelineFeatureType.ACTION].pop(f"delta_{axis}", None)
+        features[PipelineFeatureType.ACTION].pop("gripper", None)

-        for feat in ["enabled", "target_x", "target_y", "target_z", "target_wx", "target_wy", "target_wz"]:
+        for feat in [
+            "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,)
            )
@@ -713,6 +713,8 @@ 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,
@@ -731,7 +733,7 @@ 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
+            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs, is_local_source
        )

        # 4. Validate that all overrides were used
@@ -921,6 +923,7 @@ 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.

@@ -944,7 +947,7 @@ 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 not found locally
+           - **Hub fallback**: Download state file if the pipeline was loaded from the Hub
           - **Optional**: Only load if step has load_state_dict method

        4. **Override Tracking**:
@@ -962,6 +965,7 @@ 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)
@@ -975,7 +979,9 @@ 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(step_instance, step_entry, model_id, base_path, hub_download_kwargs)
+            cls._load_step_state(
+                step_instance, step_entry, model_id, base_path, hub_download_kwargs, is_local_source
+            )

        return steps, remaining_override_keys

@@ -1139,6 +1145,7 @@ 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.

@@ -1157,7 +1164,7 @@ 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 or base_path is None
+           - **When triggered**: Local file not found and the pipeline source is a Hub repo
           - **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
@@ -1178,6 +1185,7 @@ 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.
@@ -1191,6 +1199,12 @@ 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
+from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.transition import (
@@ -124,9 +124,7 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
    cfg.validate()
    display_pid = False
    if not use_threads(cfg):
-        import torch.multiprocessing as mp
-
-        mp.set_start_method("spawn")
+        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
        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
+from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
    format_big_number,
@@ -123,9 +123,7 @@ 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):
-        import torch.multiprocessing as mp
-
-        mp.set_start_method("spawn")
+        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)

    # Use the job_name from the config
    train(
@@ -323,6 +323,10 @@ 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
@@ -326,8 +326,17 @@ class RolloutConfig:

        policy_path = parser.get_path_arg("policy")
        if policy_path:
-            cli_overrides = parser.get_cli_overrides("policy")
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            yaml_overrides = parser.get_yaml_overrides("policy")
+            cli_overrides = parser.get_cli_overrides("policy") or []
+            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")
@@ -24,10 +24,11 @@ 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
+from lerobot.configs import FeatureType, PreTrainedConfig
 from lerobot.datasets import (
    LeRobotDataset,
    aggregate_pipeline_dataset_features,
@@ -47,6 +48,7 @@ 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 (
@@ -57,6 +59,12 @@ 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__)


@@ -159,6 +167,35 @@ 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,
@@ -176,7 +213,6 @@ 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
@@ -187,17 +223,7 @@ def build_rollout_context(
            "Please use `cpu` or `cuda` backend."
        )

-    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)
+    policy = _load_pretrained_policy(policy_config)

    if is_rtc:
        policy.config.rtc_config = cfg.inference.rtc
@@ -276,12 +302,22 @@ 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"))
+        if isinstance(v, tuple) or (v is float and k.endswith((".pos", ".vel")))
    }
-    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith(".pos")}
+    # Keep both joint-position (.pos) and base-velocity (.vel) action features so
+    # 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.
@@ -392,6 +428,7 @@ 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},
@@ -94,6 +94,8 @@ 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"

@@ -476,11 +478,11 @@ def convert_dataset(
    # First check if the dataset already has a v3.0 version
    if root is None and not force_conversion:
        try:
-            print("Trying to download v3.0 version of the dataset from the hub...")
+            logger.info("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:
-            print("Dataset does not have an uploaded v3.0 version. Continuing with conversion.")
+            logger.info("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
@@ -488,7 +490,7 @@ def convert_dataset(
    if root.exists():
        validate_local_dataset_version(root)
        use_local_dataset = True
-        print(f"Using local dataset at {root}")
+        logger.info(f"Using local dataset at {root}")

    old_root = root.parent / f"{root.name}_old"
    new_root = root.parent / f"{root.name}_v30"
@@ -523,7 +525,7 @@ def convert_dataset(
        try:
            hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
        except (HTTPError, RevisionNotFoundError) as e:
-            print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
+            logger.warning(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()
-    print(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...", flush=True)
+    logger.info(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...")
    api.create_repo(
        repo_id=repo_id,
        repo_type="dataset",
        private=cfg.push_private,
        exist_ok=True,
    )
-    print(f"[lerobot-annotate] uploading {root} -> {repo_id}...", flush=True)
+    logger.info(f"[lerobot-annotate] uploading {root} -> {repo_id}...")
    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"],
    )
-    print(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}", flush=True)
+    logger.info(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}")

    dataset_info = load_info(root)
    card = create_lerobot_dataset_card(dataset_info=dataset_info, license="apache-2.0", repo_id=repo_id)
@@ -200,14 +200,13 @@ 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)
-        print(f"[lerobot-annotate] tagged {repo_id} as {version_tag}", flush=True)
+        logger.info(f"[lerobot-annotate] tagged {repo_id} as {version_tag}")
    except Exception as exc:  # noqa: BLE001
-        print(
+        logger.warning(
            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)",
-            flush=True,
+            f"HfApi().create_tag({repo_id!r}, tag={version_tag!r}, repo_type='dataset', exist_ok=True)"
        )


@@ -89,6 +89,8 @@ 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

@@ -299,7 +301,7 @@ def visualize_dataset(
            while True:
                time.sleep(1)
        except KeyboardInterrupt:
-            print("Ctrl-C received. Exiting.")
+            logger.info("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 Any, TypedDict
+from typing import TYPE_CHECKING, 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 register_third_party_plugins
+from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
 from lerobot.utils.io_utils import write_video
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -95,6 +95,14 @@ 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()."""
@@ -444,15 +452,16 @@ def eval_policy(
        exc = ValueError(
            f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
        )
-        try:
-            from peft import PeftModel
-
-            if not isinstance(policy, PeftModel):
-                raise exc
-        except ImportError:
-            raise exc from None
+        if not _peft_available:
+            raise exc
+        require_package("peft", extra="peft")
+        if not isinstance(policy, PeftModel):
+            raise exc

    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
@@ -674,6 +683,8 @@ def eval_policy(
    if save_predicted_video:
        info["predicted_video_paths"] = predicted_video_paths

+    policy.train(was_training)
+
    return info


@@ -791,13 +802,13 @@ def eval_main(cfg: EvalPipelineConfig):
            recording_repo_id=cfg.eval.recording_repo_id,
            recording_private=cfg.eval.recording_private,
        )
-        print("Overall Aggregated Metrics:")
-        print(info["overall"])
+        logger.info("Overall Aggregated Metrics:")
+        logger.info(info["overall"])

        # Print per-suite stats
        for task_group, task_group_info in info.items():
-            print(f"\nAggregated Metrics for {task_group}:")
-            print(task_group_info)
+            logger.info(f"\nAggregated Metrics for {task_group}:")
+            logger.info(task_group_info)
    # Close all vec envs
    close_envs(envs)

@@ -1010,40 +1021,48 @@ def eval_policy_all(
        recording_private=recording_private,
    )

-    if max_parallel_tasks <= 1:
-        prefetch_thread: threading.Thread | None = None
-        for i, (task_group, task_id, env) in enumerate(tasks):
-            if prefetch_thread is not None:
-                prefetch_thread.join()
-                prefetch_thread = None
+    # 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):
+                if prefetch_thread is not None:
+                    prefetch_thread.join()
+                    prefetch_thread = None

-            try:
-                tg, tid, metrics = task_runner(task_group, task_id, env)
-                _accumulate_to(tg, metrics)
-                per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics})
-            finally:
-                env.close()
-                # Prefetch next task's workers *after* closing current env to prevent
-                # GPU memory overlap between consecutive tasks.
-                if i + 1 < len(tasks):
-                    next_env = tasks[i + 1][2]
-                    if hasattr(next_env, "_ensure"):
-                        prefetch_thread = threading.Thread(target=next_env._ensure, daemon=True)
-                        prefetch_thread.start()
-    else:
-        with cf.ThreadPoolExecutor(max_workers=max_parallel_tasks) as executor:
-            fut2meta = {}
-            for task_group, task_id, env in tasks:
-                fut = executor.submit(task_runner, task_group, task_id, env)
-                fut2meta[fut] = (task_group, task_id, env)
-            for fut in cf.as_completed(fut2meta):
-                tg, tid, env = fut2meta[fut]
                try:
-                    tg, tid, metrics = fut.result()
+                    tg, tid, metrics = task_runner(task_group, task_id, env)
                    _accumulate_to(tg, metrics)
                    per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics})
                finally:
                    env.close()
+                    # Prefetch next task's workers *after* closing current env to prevent
+                    # GPU memory overlap between consecutive tasks.
+                    if i + 1 < len(tasks):
+                        next_env = tasks[i + 1][2]
+                        if hasattr(next_env, "_ensure"):
+                            prefetch_thread = threading.Thread(target=next_env._ensure, daemon=True)
+                            prefetch_thread.start()
+        else:
+            with cf.ThreadPoolExecutor(max_workers=max_parallel_tasks) as executor:
+                fut2meta = {}
+                for task_group, task_id, env in tasks:
+                    fut = executor.submit(task_runner, task_group, task_id, env)
+                    fut2meta[fut] = (task_group, task_id, env)
+                for fut in cf.as_completed(fut2meta):
+                    tg, tid, env = fut2meta[fut]
+                    try:
+                        tg, tid, metrics = fut.result()
+                        _accumulate_to(tg, metrics)
+                        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):
@@ -40,6 +40,7 @@ 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__)

@@ -285,6 +286,8 @@ def save_images_from_all_cameras(


 def main():
+    init_logging()
+
    parser = argparse.ArgumentParser(
        description="Unified camera utility script for listing cameras and capturing images."
    )
@@ -453,9 +453,11 @@ def record(
                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
            )

-        robot.connect()
+        # Connect the teleoperator before the robot so the robot isn't left idle (and possibly
+        # 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()

@@ -61,6 +61,7 @@ from lerobot.robots import (  # noqa: F401
    earthrover_mini_plus,
    hope_jr,
    koch_follower,
+    lekiwi,
    make_robot_from_config,
    omx_follower,
    openarm_follower,
@@ -165,6 +165,7 @@ from lerobot.robots import (  # noqa: F401
    earthrover_mini_plus,
    hope_jr,
    koch_follower,
+    lekiwi,
    omx_follower,
    openarm_follower,
    reachy2,
@@ -57,7 +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 register_third_party_plugins
+from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
 from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -68,9 +68,24 @@ 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


+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,
@@ -197,8 +212,6 @@ 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
@@ -302,7 +315,7 @@ 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. ")
-        from peft import PeftModel
+        require_package("peft", extra="peft")

        if isinstance(policy, PeftModel):
            logging.info("PEFT adapter already loaded from checkpoint, skipping wrap_with_peft.")
@@ -473,8 +486,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
        pin_memory=device.type == "cuda",
        drop_last=False,
        collate_fn=collate_fn,
-        prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
-        persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
+        **_dataloader_worker_kwargs(cfg),
    )

    # Build eval dataloader if a held-out split exists
@@ -500,8 +512,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
            pin_memory=device.type == "cuda",
            drop_last=False,
            collate_fn=eval_collate_fn,
-            prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
-            persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
+            **_dataloader_worker_kwargs(cfg),
        )

    # Prepare everything with accelerator
@@ -45,6 +45,7 @@ lerobot-train-tokenizer \
 """

 import json
+import logging
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -63,6 +64,9 @@ 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
@@ -274,11 +278,8 @@ def process_episode(args):

        return action_chunks

-    except Exception as e:
-        print(f"Error processing episode {ep_idx}: {e}")
-        import traceback
-
-        traceback.print_exc()
+    except Exception:
+        logger.exception("Error processing episode %s", ep_idx)
        return None


@@ -300,10 +301,10 @@ def train_fast_tokenizer(
    Returns:
        Trained FAST tokenizer
    """
-    print(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
-    print(f"Action chunk shape: {action_chunks.shape}")
-    print(f"Vocab size: {vocab_size}")
-    print(f"DCT scale: {scale}")
+    logger.info(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
+    logger.info(f"Action chunk shape: {action_chunks.shape}")
+    logger.info(f"Vocab size: {vocab_size}")
+    logger.info(f"DCT scale: {scale}")

    # download the tokenizer source code (not pretrained weights)
    # we'll train a new tokenizer on our own data
@@ -314,7 +315,7 @@ def train_fast_tokenizer(

    # train the new tokenizer on our action data using .fit()
    # this trains the BPE tokenizer on DCT coefficients
-    print("Training new tokenizer (this may take a few minutes)...")
+    logger.info("Training new tokenizer (this may take a few minutes)...")
    tokenizer = base_tokenizer.fit(
        action_data_list,
        scale=scale,
@@ -322,21 +323,21 @@ def train_fast_tokenizer(
        time_horizon=action_chunks.shape[1],  # action_horizon
        action_dim=action_chunks.shape[2],  # encoded dimensions
    )
-    print("✓ Tokenizer training complete!")
+    logger.info("✓ 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)
-    print(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")
+    logger.info(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."""
-    print("\nComputing compression statistics...")
+    logger.info("\nComputing compression statistics...")

    # sample for stats (use max 1000 chunks for speed)
    sample_size = min(1000, len(action_chunks))
@@ -366,12 +367,12 @@ def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
        "max_token_length": float(np.max(token_lengths)),
    }

-    print("Compression Statistics:")
-    print(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
-    print(f"  Mean token length: {stats['mean_token_length']:.1f}")
-    print(f"  P99 token length: {stats['p99_token_length']:.0f}")
-    print(f"  Min token length: {stats['min_token_length']:.0f}")
-    print(f"  Max token length: {stats['max_token_length']:.0f}")
+    logger.info("Compression Statistics:")
+    logger.info(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
+    logger.info(f"  Mean token length: {stats['mean_token_length']:.1f}")
+    logger.info(f"  P99 token length: {stats['p99_token_length']:.0f}")
+    logger.info(f"  Min token length: {stats['min_token_length']:.0f}")
+    logger.info(f"  Max token length: {stats['max_token_length']:.0f}")

    return stats

@@ -385,9 +386,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        cfg: TokenizerTrainingConfig dataclass with all configuration parameters
    """
    # load dataset
-    print(f"Loading dataset: {cfg.repo_id}")
+    logger.info(f"Loading dataset: {cfg.repo_id}")
    dataset = LeRobotDataset(repo_id=cfg.repo_id, root=cfg.root)
-    print(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+    logger.info(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")

    # parse normalization mode
    try:
@@ -397,7 +398,7 @@ 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
-    print(f"Normalization mode: {norm_mode.value}")
+    logger.info(f"Normalization mode: {norm_mode.value}")

    # parse encoded dimensions
    encoded_dim_ranges = []
@@ -406,38 +407,38 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        encoded_dim_ranges.append((start, end))

    total_encoded_dims = sum(end - start for start, end in encoded_dim_ranges)
-    print(f"Encoding {total_encoded_dims} dimensions: {cfg.encoded_dims}")
+    logger.info(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(",")]
-        print(f"Relative dimensions: {relative_dim_list}")
+        logger.info(f"Relative dimensions: {relative_dim_list}")
    else:
-        print("No relative dimensions specified")
+        logger.info("No relative dimensions specified")

-    print(f"Use relative transform: {cfg.use_relative_transform}")
+    logger.info(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):
-        print(
+        logger.warning(
            "Warning: use_relative_transform=True but no relative_dims specified. "
            "No relative transform will be applied."
        )

-    print(f"Action horizon: {cfg.action_horizon}")
-    print(f"State key: {cfg.state_key}")
+    logger.info(f"Action horizon: {cfg.action_horizon}")
+    logger.info(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)

-    print(f"Processing {num_episodes} episodes...")
+    logger.info(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:
-            print(f"  Processing episode {ep_idx}/{num_episodes}...")
+            logger.info(f"  Processing episode {ep_idx}/{num_episodes}...")

        chunks = process_episode(
            (
@@ -455,19 +456,19 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):

    # concatenate all chunks
    all_chunks = np.concatenate(all_chunks, axis=0)
-    print(f"Collected {len(all_chunks)} action chunks")
+    logger.info(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]
-    print(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")
+    logger.info(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")

    # apply normalization to encoded dimensions
-    print("\nBefore normalization - overall stats:")
-    print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-    print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+    logger.info("\nBefore normalization - overall stats:")
+    logger.info(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}")

    # get normalization stats from dataset
    norm_stats = dataset.meta.stats
@@ -489,9 +490,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                    encoded_stats[stat_name] = stat_array[encoded_dim_indices]

        if encoded_stats:
-            print(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
+            logger.info(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
            for stat_name, stat_values in encoded_stats.items():
-                print(
+                logger.info(
                    f"  {stat_name}: shape={stat_values.shape}, "
                    f"range=[{np.min(stat_values):.4f}, {np.max(stat_values):.4f}]"
                )
@@ -499,27 +500,27 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
            # apply normalization based on mode
            try:
                encoded_chunks = apply_normalization(encoded_chunks, encoded_stats, norm_mode, eps=1e-8)
-                print(f"\nApplied {norm_mode.value} normalization")
+                logger.info(f"\nApplied {norm_mode.value} normalization")
            except ValueError as e:
-                print(f"Warning: {e}. Using raw actions without normalization.")
+                logger.warning(f"Warning: {e}. Using raw actions without normalization.")

-            print("\nAfter normalization - overall stats:")
-            print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-            print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+            logger.info("\nAfter normalization - overall stats:")
+            logger.info(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("\nPer-dimension stats (after normalization):")
+            logger.info("\nPer-dimension stats (after normalization):")
            for d in range(encoded_chunks.shape[-1]):
                dim_data = encoded_chunks[:, :, d]
-                print(
+                logger.info(
                    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:
-            print("Warning: Could not extract stats for encoded dimensions, using raw actions")
+            logger.warning("Warning: Could not extract stats for encoded dimensions, using raw actions")
    else:
-        print("Warning: No normalization stats found in dataset, using raw actions")
+        logger.warning("Warning: No normalization stats found in dataset, using raw actions")

-    print(f"Encoded chunks shape: {encoded_chunks.shape}")
+    logger.info(f"Encoded chunks shape: {encoded_chunks.shape}")

    # train FAST tokenizer
    tokenizer = train_fast_tokenizer(
@@ -561,8 +562,8 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
    with open(output_path / "metadata.json", "w") as f:
        json.dump(metadata, f, indent=2)

-    print(f"\nSaved FAST tokenizer to {output_path}")
-    print(f"Metadata: {json.dumps(metadata, indent=2)}")
+    logger.info(f"\nSaved FAST tokenizer to {output_path}")
+    logger.info(f"Metadata: {json.dumps(metadata, indent=2)}")

    # push to Hugging Face Hub if requested
    if cfg.push_to_hub:
@@ -570,10 +571,10 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
        hub_repo_id = cfg.hub_repo_id
        if hub_repo_id is None:
            hub_repo_id = output_path.name
-            print(f"\nNo hub_repo_id provided, using: {hub_repo_id}")
+            logger.info(f"\nNo hub_repo_id provided, using: {hub_repo_id}")

-        print(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
-        print(f"   Private: {cfg.hub_private}")
+        logger.info(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
+        logger.info(f"   Private: {cfg.hub_private}")

        try:
            # use the tokenizer's push_to_hub method
@@ -593,14 +594,15 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                commit_message="Upload tokenizer metadata",
            )

-            print(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
+            logger.info(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
        except Exception as e:
-            print(f"Error pushing to hub: {e}")
-            print("   Make sure you're logged in with `huggingface-cli login`")
+            logger.error(f"Error pushing to hub: {e}")
+            logger.error("   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()


@@ -41,7 +41,7 @@ class RandomSubsetApply(Transform):

    def __init__(
        self,
-        transforms: Sequence[Callable],
+        transforms: Sequence[Callable[..., Any]],
        p: list[float] | None = None,
        n_subset: int | None = None,
        random_order: bool = False,
@@ -50,7 +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] * len(transforms)
+            p = [1.0] * len(transforms)
        elif len(p) != len(transforms):
            raise ValueError(
                f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
@@ -69,7 +69,7 @@ class RandomSubsetApply(Transform):
        self.n_subset = n_subset
        self.random_order = random_order

-        self.selected_transforms = None
+        self.selected_transforms: list[Callable[..., Any]] = []

    def forward(self, *inputs: Any) -> Any:
        needs_unpacking = len(inputs) > 1
@@ -119,7 +119,7 @@ class SharpnessJitter(Transform):
        super().__init__()
        self.sharpness = self._check_input(sharpness)

-    def _check_input(self, sharpness):
+    def _check_input(self, sharpness: float | Sequence[float]) -> tuple[float, float]:
        if isinstance(sharpness, (int | float)):
            if sharpness < 0:
                raise ValueError("If sharpness is a single number, it must be non negative.")
@@ -215,7 +215,7 @@ class ImageTransformsConfig:
    )


-def make_transform_from_config(cfg: ImageTransformConfig):
+def make_transform_from_config(cfg: ImageTransformConfig) -> Transform:
    if cfg.type == "SharpnessJitter":
        return SharpnessJitter(**cfg.kwargs)

@@ -236,8 +236,8 @@ class ImageTransforms(Transform):
        super().__init__()
        self._cfg = cfg

-        self.weights = []
-        self.transforms = {}
+        self.weights: list[float] = []
+        self.transforms: dict[str, Transform] = {}
        for tf_name, tf_cfg in cfg.tfs.items():
            if tf_cfg.weight <= 0.0:
                continue
@@ -16,11 +16,39 @@
 # 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.

@@ -20,7 +20,7 @@
 # ```

 from pathlib import Path
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch

 import cv2
 import numpy as np
@@ -123,6 +123,73 @@ 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 patch
+from unittest.mock import MagicMock, patch

 import numpy as np
 import pytest
@@ -30,6 +30,8 @@ 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"
@@ -61,6 +63,17 @@ 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)

@@ -83,6 +96,27 @@ 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)
@@ -91,6 +125,33 @@ 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:
@@ -228,6 +289,203 @@ 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",
    [
@@ -204,6 +204,38 @@ 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(
@@ -482,6 +482,20 @@ 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"
@@ -0,0 +1,79 @@
+# 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.
+
+import sys
+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)
+    monkeypatch.setitem(
+        sys.modules,
+        "peft",
+        SimpleNamespace(
+            PeftConfig=SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+            PeftModel=SimpleNamespace(from_pretrained=peft_model_from_pretrained),
+        ),
+    )
+
+    policy = policy_factory.make_policy(cfg, ds_meta=dataset_meta)
+
+    assert policy is adapted_policy
+    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,
+    )
@@ -113,6 +113,7 @@ 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)
@@ -152,6 +153,7 @@ 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():
@@ -26,8 +26,17 @@ import tempfile
 from pathlib import Path

 import pytest
+import torch
+from safetensors.torch import save_file

-from lerobot.processor.pipeline import DataProcessorPipeline, ProcessorMigrationError
+from lerobot.configs import PipelineFeatureType, PolicyFeature
+from lerobot.processor.pipeline import (
+    DataProcessorPipeline,
+    ProcessorMigrationError,
+    ProcessorStep,
+    ProcessorStepRegistry,
+)
+from lerobot.types import EnvTransition

 # Simplified Config Loading Tests

@@ -98,6 +107,140 @@ 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


@@ -17,6 +17,8 @@
 from __future__ import annotations

 import dataclasses
+import sys
+from types import SimpleNamespace
 from unittest.mock import MagicMock

 import pytest
@@ -106,6 +108,116 @@ 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
 # ---------------------------------------------------------------------------
@@ -402,10 +402,10 @@ name = "bddl"
 version = "1.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupytext" },
-    { name = "networkx" },
-    { name = "numpy" },
-    { name = "pytest" },
+    { name = "jupytext", marker = "sys_platform == 'linux'" },
+    { name = "networkx", marker = "sys_platform == 'linux'" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "pytest", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5c/37/0211f82891a9f14efcfd2b2096f8d9e4351398ad637fdd1ee59cfc580b0e/bddl-1.0.1.tar.gz", hash = "sha256:1fa4e6e5050b93888ff6fd8455c39bfb29d3864ce06b4c37c0f781f513a2ae26", size = 164809, upload-time = "2022-03-08T01:48:23.564Z" }

@@ -1010,7 +1010,7 @@ name = "cuda-bindings"
 version = "12.9.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder" },
+    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/32/45/557d4ed1fa54f0c7db8aee083229f624990d69f7d00f55477eed5c7e169a/cuda_bindings-12.9.7-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0666d3c082ef8f4b2d670950589373550e9f3bf564d635dd883f24a0b40402ff", size = 7071026, upload-time = "2026-05-27T18:44:13.356Z" },
@@ -1043,37 +1043,37 @@ wheels = [

 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime-cu12" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "sys_platform == 'linux'" },
 ]
 cufft = [
-    { name = "nvidia-cufft-cu12" },
+    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux'" },
 ]
 cufile = [
-    { name = "nvidia-cufile-cu12" },
+    { name = "nvidia-cufile-cu12", marker = "sys_platform == 'linux'" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti-cu12" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "sys_platform == 'linux'" },
 ]
 curand = [
-    { name = "nvidia-curand-cu12" },
+    { name = "nvidia-curand-cu12", marker = "sys_platform == 'linux'" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver-cu12" },
+    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux'" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse-cu12" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc-cu12" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx-cu12" },
+    { name = "nvidia-nvtx-cu12", marker = "sys_platform == 'linux'" },
 ]

 [[package]]
@@ -1145,7 +1145,7 @@ name = "decord"
 version = "0.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", marker = "(platform_machine != 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/11/79/936af42edf90a7bd4e41a6cac89c913d4b47fa48a26b042d5129a9242ee3/decord-0.6.0-py3-none-manylinux2010_x86_64.whl", hash = "sha256:51997f20be8958e23b7c4061ba45d0efcd86bffd5fe81c695d0befee0d442976", size = 13602299, upload-time = "2021-06-14T21:30:55.486Z" },
@@ -1283,10 +1283,10 @@ resolution-markers = [
    "python_full_version == '3.14.*' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py" },
-    { name = "attrs" },
-    { name = "numpy" },
-    { name = "wrapt" },
+    { name = "absl-py", marker = "python_full_version >= '3.14'" },
+    { name = "attrs", marker = "python_full_version >= '3.14'" },
+    { name = "numpy", marker = "python_full_version >= '3.14'" },
+    { name = "wrapt", marker = "python_full_version >= '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a6/83/ce29720ccf934c6cfa9b9c95ebbe96558386e66886626066632b5e44afed/dm_tree-0.1.9.tar.gz", hash = "sha256:a4c7db3d3935a5a2d5e4b383fc26c6b0cd6f78c6d4605d3e7b518800ecd5342b", size = 35623, upload-time = "2025-01-30T20:45:37.13Z" }
 wheels = [
@@ -1324,10 +1324,10 @@ resolution-markers = [
    "python_full_version < '3.13' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py" },
-    { name = "attrs" },
-    { name = "numpy" },
-    { name = "wrapt" },
+    { name = "absl-py", marker = "python_full_version < '3.14'" },
+    { name = "attrs", marker = "python_full_version < '3.14'" },
+    { name = "numpy", marker = "python_full_version < '3.14'" },
+    { name = "wrapt", marker = "python_full_version < '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/66/a3ec619d22b6baffa5ab853e8dc6ec9d0c837127948af59bb15b988d7312/dm_tree-0.1.10.tar.gz", hash = "sha256:22f37b599e01cc3402a17f79c257a802aebd8d326de05b54657650845956208a", size = 35748, upload-time = "2026-03-31T17:35:39.03Z" }
 wheels = [
@@ -1912,7 +1912,7 @@ name = "h5py"
 version = "3.16.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" }
 wheels = [
@@ -1956,23 +1956,23 @@ name = "hf-libero"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "bddl" },
-    { name = "cloudpickle" },
-    { name = "easydict" },
-    { name = "einops" },
-    { name = "future" },
-    { name = "gymnasium" },
-    { name = "hf-egl-probe" },
-    { name = "hydra-core" },
-    { name = "matplotlib" },
-    { name = "mujoco" },
-    { name = "numpy" },
-    { name = "opencv-python" },
-    { name = "robomimic" },
-    { name = "robosuite" },
-    { name = "thop" },
-    { name = "transformers" },
-    { name = "wandb" },
+    { name = "bddl", marker = "sys_platform == 'linux'" },
+    { name = "cloudpickle", marker = "sys_platform == 'linux'" },
+    { name = "easydict", marker = "sys_platform == 'linux'" },
+    { name = "einops", marker = "sys_platform == 'linux'" },
+    { name = "future", marker = "sys_platform == 'linux'" },
+    { name = "gymnasium", marker = "sys_platform == 'linux'" },
+    { name = "hf-egl-probe", marker = "sys_platform == 'linux'" },
+    { name = "hydra-core", marker = "sys_platform == 'linux'" },
+    { name = "matplotlib", marker = "sys_platform == 'linux'" },
+    { name = "mujoco", marker = "sys_platform == 'linux'" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "opencv-python", marker = "sys_platform == 'linux'" },
+    { name = "robomimic", marker = "sys_platform == 'linux'" },
+    { name = "robosuite", marker = "sys_platform == 'linux'" },
+    { name = "thop", marker = "sys_platform == 'linux'" },
+    { name = "transformers", marker = "sys_platform == 'linux'" },
+    { name = "wandb", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/af/aa/4e9eb8715e0bff9cb6553db563a35d253393097d446f82bd53575e8b253d/hf_libero-0.1.4.tar.gz", hash = "sha256:c058d67ad5a2b589529c14d614282ef4cca3a7763dafa134f58a6c9039657e34", size = 2961319, upload-time = "2026-06-10T09:56:13.994Z" }
 wheels = [
@@ -2123,9 +2123,9 @@ name = "hydra-core"
 version = "1.3.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "antlr4-python3-runtime" },
-    { name = "omegaconf" },
-    { name = "packaging" },
+    { name = "antlr4-python3-runtime", marker = "sys_platform == 'linux'" },
+    { name = "omegaconf", marker = "sys_platform == 'linux'" },
+    { name = "packaging", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/10/dd/220f0e91743136725352497e98540772a01fc7c3ab96ff16c3c74424e984/hydra_core-1.3.4.tar.gz", hash = "sha256:ad0f7b05a0242255a8984d5a4ed2f6847f7b783ed727368a2c0155ec52d6c34c", size = 3263348, upload-time = "2026-07-04T16:25:38.891Z" }
 wheels = [
@@ -2678,11 +2678,11 @@ name = "jupytext"
 version = "1.19.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py" },
-    { name = "mdit-py-plugins" },
-    { name = "nbformat" },
-    { name = "packaging" },
-    { name = "pyyaml" },
+    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
+    { name = "mdit-py-plugins", marker = "sys_platform == 'linux'" },
+    { name = "nbformat", marker = "sys_platform == 'linux'" },
+    { name = "packaging", marker = "sys_platform == 'linux'" },
+    { name = "pyyaml", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/473f8ebb101553fb2ea6ab1d34324d6677844c968947ac050c759d539f2c/jupytext-1.19.5.tar.gz", hash = "sha256:605026446d605aa54fd7f7fc69df6ae51c7a46053d4cebf05afdc64d66de3df0", size = 4600916, upload-time = "2026-07-21T22:00:29.198Z" }
 wheels = [
@@ -3472,7 +3472,7 @@ requires-dist = [
    { name = "pyrealsense2", marker = "sys_platform != 'darwin' and extra == 'intelrealsense'", specifier = ">=2.55.1.6486,<2.57.0" },
    { name = "pyrealsense2-macosx", marker = "sys_platform == 'darwin' and extra == 'intelrealsense'", specifier = ">=2.54,<2.57.0" },
    { name = "pyserial", marker = "extra == 'pyserial-dep'", specifier = ">=3.5,<4.0" },
-    { name = "pytest", marker = "extra == 'test'", specifier = ">=8.1.0,<9.0.0" },
+    { name = "pytest", marker = "extra == 'test'", specifier = ">=8.1.0,<10.0.0" },
    { name = "pytest-cov", marker = "extra == 'test'", specifier = ">=5.0.0,<8.0.0" },
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+    { name = "nvidia-cudnn-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparselt-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvshmem-cu12", marker = "sys_platform == 'linux'" },
+    { name = "setuptools", marker = "sys_platform == 'linux'" },
+    { name = "sympy", marker = "sys_platform == 'linux'" },
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Author	SHA1	Message	Date
dependabot[bot]	5292f96b8b	chore(deps): bump the uv group across 1 directory with 2 updates Bumps the uv group with 2 updates in the / directory: [setuptools](https://github.com/pypa/setuptools) and [pytest](https://github.com/pytest-dev/pytest). Updates `setuptools` from 80.10.2 to 83.0.0 - [Release notes](https://github.com/pypa/setuptools/releases) - [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst) - [Commits](https://github.com/pypa/setuptools/compare/v80.10.2...v83.0.0) Updates `pytest` from 8.4.2 to 9.0.3 - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.4.2...9.0.3) --- updated-dependencies: - dependency-name: setuptools dependency-version: 83.0.0 dependency-type: direct:development dependency-group: uv - dependency-name: pytest dependency-version: 9.0.3 dependency-type: direct:production dependency-group: uv ... Signed-off-by: dependabot[bot] <support@github.com>	2026-07-28 14:29:26 +00:00
Alexandre Edmond	a05c0833e1	chore(mypy): cover annotations and transforms (#3860 ) Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-28 16:25:45 +02:00
Alexandre Edmond	7b76d94c5b	Handle resuming empty local datasets (#3859 ) Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-28 16:25:42 +02:00
Khalil Meftah	ec2dbc1c98	fix(policy): honor revisions when loading PEFT checkpoints (#4189 )	2026-07-28 15:41:47 +02:00
Steven Palma	d526785e47	fix(dependencies): protect peft import (#4188 ) Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-28 14:57:50 +02:00
Steven Palma	4af7c70664	refactor(logging): standardize logging with getLogger(__name__) in scripts (#4192 ) * refactor(logging): replace print with logger in lerobot_info * refactor(logging): replace print with logger in convert_dataset_v21_to_v30 * refactor(logging): replace print with logger in lerobot_annotate * refactor(logging): replace print with logger in lerobot_dataset_viz * refactor(logging): replace print with logger in lerobot_eval * refactor(logging): replace print with logger in lerobot_find_cameras * refactor(logging): replace print with logger in lerobot_find_joint_limits * refactor(logging): replace print with logger in lerobot_find_port * refactor(logging): replace print with logger in lerobot_imgtransform_viz * refactor(logging): replace print with logger in lerobot_setup_can * refactor(logging): replace print with logger in lerobot_teleoperate * refactor(logging): replace print with logger in lerobot_train_tokenizer * fix(logging): preserve CLI output semantics --------- Co-authored-by: ailisilob <2248345706@qq.com>	2026-07-28 14:42:38 +02:00
charlie8612	a855570097	feat(motors): add XH540-W150, XC330-T288, XC330-T181 to Dynamixel tables (#3815 ) Register three X-series Dynamixel models so they can be driven by DynamixelMotorsBus: XH540-W150 (model 1110), XC330-T288 (1220) and XC330-T181 (1210). All are standard Protocol 2.0 X-series motors that share the existing X_SERIES control/baudrate/encoding tables and 4096 resolution; only the model number and operating-mode list are model-specific. Values verified against the ROBOTIS e-manual. These motors are used by the ROBOTIS OMY-L100 arm, among others. Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-28 14:27:48 +02:00
Steven Palma	167e22ba51	feat(record): add `--dataset.no_stamp` to opt out of repo_id timestamping (#4190 ) * feat(record): add `--dataset.no_stamp` to opt out of repo_id timestamping stamp_repo_id() unconditionally appended a date-time tag to repo_id for every new (non-resume) dataset, so users managing their own versioned repo names (e.g. for a later lerobot-edit-dataset merge) could not opt out. Add a no_stamp field to DatasetRecordConfig and make stamp_repo_id() a no-op when it is set. The flag covers both lerobot-record and lerobot-rollout since they share this config, and no call-site changes are needed. Fixes #3722. * chore(tests): delete dataset config test --------- Co-authored-by: Philipp Sinitsin <ph.sinitsin@gmail.com>	2026-07-28 14:06:28 +02:00
Steven Palma	00c25c65c2	feat(camera): add manual exposure, gain, and white balance options for RealSense cameras (#4130 ) * feat(camera): add manual exposure, gain, and white balance options for RealSense cameras The RealSense camera integration lacked sensor-level controls, causing issues like unstable lighting from auto-exposure hunting. This adds optional `exposure`, `gain`, and `white_balance` fields to RealSenseCameraConfig that disable the corresponding auto modes and apply fixed values when set. * fix: support D405 stereo module for sensor options D405 exposes color stream via "Stereo Module", not "RGB Camera". Fall back to Stereo Module when RGB Camera is not found. * test(camera): add unit tests + range-aware errors for RealSense sensor options Address PR #3220 review: - Wrap set_option calls; re-raise ValueError with option name, value, and sensor.get_option_range() diagnostics on out-of-range values. - Add unit tests for _get_color_sensor (RGB Camera, D405 Stereo Module fallback, diagnostic error) and _configure_sensor_options (no-op, all values, unsupported warns, partial config, out-of-range raise). * fix(realsense): validate manual color controls * refactor(camera): apply feedback --------- Co-authored-by: Lev Kozlov <kozlov.l.a10@gmail.com>	2026-07-28 13:41:09 +02:00
Steven Palma	23f6d5dabd	fix(cameras): release device handle when connect() setup fails (#4187 ) Co-authored-by: Ryan Rana <39924576+RyanRana@users.noreply.github.com>	2026-07-28 13:21:06 +02:00
Xingdong Zuo	9b25b7fe0a	feat(lekiwi): support LeKiwi in the rollout/eval CLI (#3742 ) * feat(lekiwi): support LeKiwi in the rollout/eval CLI Register the lekiwi robot in lerobot_rollout.py so policies can be rolled out on a LeKiwi, and keep base-velocity (.vel) features in build_rollout_context. LeKiwi's observation.state and action are 9-dim (6 arm .pos + x/y/theta.vel) and the policy is normalized on all 9. The old filter kept only .pos features, so it fed a 6-dim vector into a 9-dim normalizer (RuntimeError, size 6 vs 9) and silently dropped the base velocities from the action, leaving the base unable to move. Keeping both .pos and .vel fixes both. Pure-arm robots have no .vel keys, so this is a no-op for them. * style: format LeKiwi rollout action features --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Steven Palma <steven.palma@huggingface.co>	2026-07-28 12:52:02 +02:00
Steven Palma	c1b6ea85d6	feat(rl): add multiprocessing option to training pipeline and sets spawn as default + guard (#4140 )	2026-07-28 12:19:53 +02:00
Steven Palma	ffe25afb8f	fix(processors): wrong feature key dropped in delta-action transform_features (#4165 )	2026-07-28 11:18:23 +02:00
Steven Palma	95211b98f1	feat(config): add `multiprocessing` option to `DataLoader` context and sets `spawn` as default (#4139 ) * Add dataloader_multiprocessing_context, default to spawn Make the DataLoader multiprocessing start method configurable on TrainPipelineConfig and default it to 'spawn'. The previous default (fork on Linux) is unsafe with libraries that hold non-fork-safe state in the parent process — common ones in this codebase are PyAV, torchcodec, and the ffmpeg shared libs they wrap. Symptoms reported in #2488, #2209, and observed locally include: - multiprocessing.context.AuthenticationError: digest received was wrong - RuntimeError: Pin memory thread exited unexpectedly - RuntimeError: DataLoader worker exited unexpectedly - Random SIGSEGV inside worker processes during video decode Switching to spawn re-imports modules cleanly in each worker and eliminates these failure modes. Added the setting as a config field rather than hard-coding so users on platforms where fork is preferred can opt back in via --dataloader-multiprocessing-context=fork. * Address review: shorten config comment, note spawn startup tradeoff Per @jashshah999, mention that spawn workers re-import modules and so add some startup time vs fork. Also trim the failure-mode dump from the inline comment — the linked issue covers the symptoms in detail. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(scripts): add multiprocessing_context safeguards * chore(config): add libs note --------- Co-authored-by: 0o8o0-blip <0o8o0-blip@users.noreply.github.com>	2026-07-28 00:42:55 +02:00
Xingdong Zuo	95256d766d	feat(lekiwi): support LeKiwi in lerobot-replay CLI (#3739 ) Register the `lekiwi` robot module in `lerobot_replay.py` so episodes can be replayed on a LeKiwi via `--robot.type=lekiwi_client`. The module is already registered in `lerobot_calibrate.py` and `lerobot_setup_motors.py`; this fills the gap so the replay CLI recognizes the same robot. Replayed actions are loaded from the dataset as torch tensors, which `json.dumps` cannot serialize when `LeKiwiClient.send_action` ships them over ZMQ. Coerce each action value to a plain float before sending. This is scoped to the LeKiwi network client and does not affect any other robot. Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 19:29:44 +02:00
Thomas Landeg	fd53716688	fix(envs): make metaworld seeding reproducible (#3727 ) Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>	2026-07-27 18:42:17 +02:00
Steven Palma	a96540a2c4	fix rollout policy revision loading (#4161 ) Co-authored-by: RaviTeja-Kondeti <rkondet3@asu.edu>	2026-07-27 18:20:39 +02:00
WOLIKIMCHENG	acd42b4d85	fix(processor): keep missing local state resolution local (#3715 ) Co-authored-by: root <kinsonnee@gmail.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 15:53:31 +02:00
Steven Palma	bbeacfe57d	fix(record): connect teleoperator before robot to avoid watchdog jump (#4166 ) * fix(record): connect teleoperator before robot to avoid watchdog jump lerobot-record connected the robot before the teleoperator. A robot's connect()/reset() can leave it holding a default pose under a firmware watchdog (e.g. Unitree G1); if teleop.connect() (model loading, IK init, network setup) then takes longer than that watchdog, the joints drop to damping and the first send_action() makes the robot jump. Swap the order so the teleoperator connects first, matching the ordering already used in lerobot_teleoperate.py. Pure ordering fix, no API change. Fixes #3684 * fix(record): trim comment and add connect-order regression test Address review feedback on #3684: - Trim the verbose ordering comment down to two lines. - Add test_record_connects_teleop_before_robot to tests/test_control_robot.py, asserting teleop.connect() runs before robot.connect() in record(). * chore(test): remove test --------- Co-authored-by: Jaimin Patel <jpatel@tuvalabs.com> Co-authored-by: Martino Russi <77496684+nepyope@users.noreply.github.com>	2026-07-27 14:09:58 +02:00
Steven Palma	801346e18c	fix(scripts): restore policy training mode after eval_policy() in lerobot-eval (#4162 ) * fix(scripts/eval): restore policy training mode after eval_policy() `eval_policy` calls `policy.eval()` before the rollout but never restores the prior mode on return. When called from the training loop (`lerobot_train.py`'s `eval_policy_all -> run_one -> eval_one -> eval_policy` chain), the policy is left in eval mode for every subsequent training step, which silently: * disables Dropout (no regularisation), * freezes BatchNorm running stats (no further EMA updates). Under DDP only `is_main_process` runs eval (lerobot_train.py:527), so the main rank ends up in eval mode while workers stay in train mode — the all-reduced gradients then combine forward passes computed with different dropout masks and different BN behaviour, a real DDP-correctness issue. Scope of impact: * Affects every policy with Dropout in its forward path. In-tree, that includes the default ACT (6 Dropout layers at p=0.1), Diffusion (vision backbone), VQ-BeT, Multi-Task DiT, X-VLA, plus all VLA policies that inherit Dropout from their pretrained HF backbone (PI0/PI0.5/PI0-FAST, SmolVLA, GR00T-N1.5, EO1, Wall-X). * Triggers from the first eval onward. On the default config (steps=100k, eval_freq=20k) that's 80% of training; on the LIBERO / RoboCasa / VLABench example commands in docs/ (eval_freq=1k–5k) it's 95–99% of training. * Policies using only LayerNorm/GroupNorm and no Dropout (TDMPC, RTC) are unaffected. Policies using `FrozenBatchNorm2d` (ACT's ResNet backbone) are immune to the BN-stat half; the Dropout half still bites. Fix: * Snapshot `policy.training` on entry to `eval_policy`. * Restore it on normal return. * Save-and-restore is a strict no-op for callers that pass an already-eval-mode policy (e.g. the standalone `lerobot-eval` script loading a frozen checkpoint). * Restoration is placed before the normal return only, not in a try/finally — exception paths leave the policy in eval mode, same as today. A try/finally upgrade would require re-indenting ~165 lines and can land as a separate cleanup if desired. Tests (tests/scripts/test_eval.py, 7 tests total, ~1.6s): * Regression gates on the lerobot_eval fix itself: training-mode preservation, eval-mode preservation, dropout-active behavioural check, non-crash for both entry modes. * Quantitative mechanism demonstration (`test_missing_mode_restoration_hurts_generalisation`): trains a tiny Dropout+BatchNorm MLP under both the bug pattern and the fix pattern on identical data and seed, then asserts the buggy variant generalises at least 5% worse on a held-out val set. In repeated runs we see 10-25% deltas on this toy problem; real policies (more layers, more Dropout, longer training) generally see larger gaps. Lives alongside the regression tests so the empirical proof is reproducible from the repo without adding a separate benchmarks/ directory. * fix(scripts): keep policy train/eval --------- Co-authored-by: ModeEric <ericjm4@illinois.edu>	2026-07-27 14:08:11 +02:00
MihaiAnca13	ab87fd9764	fix(datasets): clear video frame staging on episode reset (#3683 ) * fix video frame staging cleanup on episode reset * linting --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-27 13:44:32 +02:00