device + task + warn fix

.github/workflows/benchmark_tests.yml

@@ -83,13 +83,10 @@ jobs:
           cache-binary: false
 
       - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
         uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
         with:
           username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
           password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
 
       # Build the benchmark-specific image. The Dockerfile separates dep-install
       # from source-copy, so code-only changes skip the slow uv-sync layer
@@ -118,7 +115,7 @@ jobs:
             bash -c "
               hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
               lerobot-eval \
-                --policy.path=lerobot/smolvla_libero \
+                --policy.path=pepijn223/smolvla_libero \
                 --env.type=libero \
                 --env.task=libero_spatial \
                 --eval.batch_size=1 \
@@ -147,7 +144,7 @@ jobs:
             --artifacts-dir /tmp/libero-artifacts \
             --env libero \
             --task libero_spatial \
-            --policy lerobot/smolvla_libero
+            --policy pepijn223/smolvla_libero
 
       - name: Upload Libero rollout video
         if: always()
@@ -241,13 +238,10 @@ jobs:
           cache-binary: false
 
       - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
         uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
         with:
           username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
           password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
 
       - name: Build MetaWorld benchmark image
         uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
@@ -270,7 +264,7 @@ jobs:
             bash -c "
               hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
               lerobot-eval \
-                --policy.path=lerobot/smolvla_metaworld \
+                --policy.path=pepijn223/smolvla_metaworld \
                 --env.type=metaworld \
                 --env.task=metaworld-push-v3 \
                 --eval.batch_size=1 \
@@ -299,7 +293,7 @@ jobs:
             --artifacts-dir /tmp/metaworld-artifacts \
             --env metaworld \
             --task metaworld-push-v3 \
-            --policy lerobot/smolvla_metaworld
+            --policy pepijn223/smolvla_metaworld
 
       - name: Upload MetaWorld rollout video
         if: always()
@@ -316,630 +310,3 @@ jobs:
           name: metaworld-metrics
           path: /tmp/metaworld-artifacts/metrics.json
           if-no-files-found: warn
-
-  # ── ROBOTWIN 2.0 ──────────────────────────────────────────────────────────
-  # Isolated image: full RoboTwin 2.0 stack — SAPIEN, mplib, CuRobo,
-  # pytorch3d, + simulation assets (~4 GB).
-  # Build takes ~20 min on first run; subsequent runs hit the layer cache.
-  # Requires an NVIDIA GPU runner with CUDA 12.1 drivers.
-  robotwin-integration-test:
-    name: RoboTwin 2.0 — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-      ROBOTWIN_POLICY: lerobot/smolvla_robotwin
-      ROBOTWIN_TASKS: beat_block_hammer,click_bell,handover_block,stack_blocks_two,click_alarmclock,open_microwave,adjust_bottle,lift_pot,stamp_seal,turn_switch
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-
-      # Build the full-install image: SAPIEN, mplib, CuRobo, pytorch3d +
-      # simulation assets (~4 GB). Layer cache lives in the runner's local
-      # Docker daemon — reused across re-runs on the same machine.
-      - name: Build RoboTwin 2.0 benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.robotwin
-          push: false
-          load: true
-          tags: lerobot-benchmark-robotwin:ci
-          cache-from: type=local,src=/tmp/.buildx-cache-robotwin
-          cache-to: type=local,dest=/tmp/.buildx-cache-robotwin,mode=max
-
-      - name: Run RoboTwin 2.0 smoke eval (10 tasks, 1 episode each)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          # Named container (no --rm) so we can docker cp artifacts out.
-          docker run --name robotwin-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e ROBOTWIN_POLICY="${ROBOTWIN_POLICY}" \
-            -e ROBOTWIN_TASKS="${ROBOTWIN_TASKS}" \
-            lerobot-benchmark-robotwin:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              cd /opt/robotwin && lerobot-eval \
-                --policy.path=\"\$ROBOTWIN_POLICY\" \
-                --env.type=robotwin \
-                --env.task=\"\$ROBOTWIN_TASKS\" \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--rename_map={\"observation.images.head_camera\": \"observation.images.camera1\", \"observation.images.left_camera\": \"observation.images.camera2\", \"observation.images.right_camera\": \"observation.images.camera3\"}' \
-                --output_dir=/tmp/eval-artifacts
-              python /lerobot/scripts/ci/extract_task_descriptions.py \
-                --env robotwin \
-                --task \"\$ROBOTWIN_TASKS\" \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy RoboTwin artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/robotwin-artifacts
-          docker cp robotwin-eval:/tmp/eval-artifacts/. /tmp/robotwin-artifacts/ 2>/dev/null || true
-          docker rm -f robotwin-eval || true
-
-      - name: Parse RoboTwin eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/robotwin-artifacts \
-            --env robotwin \
-            --task "${ROBOTWIN_TASKS}" \
-            --policy "${ROBOTWIN_POLICY}"
-
-      - name: Upload RoboTwin rollout video
-        if: always()
-        uses: actions/upload-artifact@v4
-        with:
-          name: robotwin-rollout-video
-          path: /tmp/robotwin-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload RoboTwin eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4
-        with:
-          name: robotwin-metrics
-          path: /tmp/robotwin-artifacts/metrics.json
-          if-no-files-found: warn
-
-  # ── ROBOCASA365 ──────────────────────────────────────────────────────────
-  # Isolated image: robocasa + robosuite installed manually as editable
-  # clones (no `lerobot[robocasa]` extra — robocasa's setup.py pins
-  # `lerobot==0.3.3`, which would shadow this repo's lerobot).
-  robocasa-integration-test:
-    name: RoboCasa365 — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-
-      - name: Build RoboCasa365 benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.robocasa
-          push: false
-          load: true
-          tags: lerobot-benchmark-robocasa:ci
-
-      - name: Run RoboCasa365 smoke eval (10 atomic tasks, 1 episode each)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name robocasa-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            -e MUJOCO_GL=egl \
-            lerobot-benchmark-robocasa:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=lerobot/smolvla_robocasa \
-                --env.type=robocasa \
-                --env.task=CloseFridge,OpenCabinet,OpenDrawer,TurnOnMicrowave,TurnOffStove,CloseToasterOvenDoor,SlideDishwasherRack,TurnOnSinkFaucet,NavigateKitchen,TurnOnElectricKettle \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--rename_map={\"observation.images.robot0_agentview_left\": \"observation.images.camera1\", \"observation.images.robot0_eye_in_hand\": \"observation.images.camera2\", \"observation.images.robot0_agentview_right\": \"observation.images.camera3\"}' \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env robocasa \
-                --task CloseFridge,OpenCabinet,OpenDrawer,TurnOnMicrowave,TurnOffStove,CloseToasterOvenDoor,SlideDishwasherRack,TurnOnSinkFaucet,NavigateKitchen,TurnOnElectricKettle \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy RoboCasa365 artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/robocasa-artifacts
-          docker cp robocasa-eval:/tmp/eval-artifacts/. /tmp/robocasa-artifacts/ 2>/dev/null || true
-          docker rm -f robocasa-eval || true
-
-      - name: Parse RoboCasa365 eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/robocasa-artifacts \
-            --env robocasa \
-            --task atomic_smoke_10 \
-            --policy lerobot/smolvla_robocasa
-
-      - name: Upload RoboCasa365 rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robocasa-rollout-video
-          path: /tmp/robocasa-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload RoboCasa365 eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robocasa-metrics
-          path: /tmp/robocasa-artifacts/metrics.json
-          if-no-files-found: warn
-
-  # ── ROBOCEREBRA ───────────────────────────────────────────────────────────
-  # Reuses the LIBERO simulator (libero_10 suite) with RoboCerebra camera
-  # defaults (image/wrist_image). The image is layered on
-  # huggingface/lerobot-gpu, which already ships [libero] as part of [all].
-  robocerebra-integration-test:
-    name: RoboCerebra — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-
-      - name: Build RoboCerebra benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.robocerebra
-          push: false
-          load: true
-          tags: lerobot-benchmark-robocerebra:ci
-          cache-from: type=local,src=/tmp/.buildx-cache-robocerebra
-          cache-to: type=local,dest=/tmp/.buildx-cache-robocerebra,mode=max
-
-      - name: Run RoboCerebra smoke eval (1 episode)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name robocerebra-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            -e LIBERO_DATA_FOLDER=/tmp/libero_data \
-            lerobot-benchmark-robocerebra:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=lerobot/smolvla_robocerebra \
-                --env.type=libero \
-                --env.task=libero_10 \
-                --env.fps=20 \
-                --env.obs_type=pixels_agent_pos \
-                --env.observation_height=256 \
-                --env.observation_width=256 \
-                '--env.camera_name_mapping={\"agentview_image\": \"image\", \"robot0_eye_in_hand_image\": \"wrist_image\"}' \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.wrist_image\": \"observation.images.camera2\"}' \
-                --policy.empty_cameras=1 \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env libero --task libero_10 \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy RoboCerebra artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/robocerebra-artifacts
-          docker cp robocerebra-eval:/tmp/eval-artifacts/. /tmp/robocerebra-artifacts/ 2>/dev/null || true
-          docker rm -f robocerebra-eval || true
-
-      - name: Parse RoboCerebra eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/robocerebra-artifacts \
-            --env robocerebra \
-            --task libero_10 \
-            --policy lerobot/smolvla_robocerebra
-
-      - name: Upload RoboCerebra rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robocerebra-rollout-video
-          path: /tmp/robocerebra-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload RoboCerebra eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robocerebra-metrics
-          path: /tmp/robocerebra-artifacts/metrics.json
-          if-no-files-found: warn
-
-  # ── ROBOMME ───────────────────────────────────────────────────────────────
-  # Isolated image: mani-skill/SAPIEN/Vulkan chain with gymnasium and numpy
-  # overrides (robomme can't be a pyproject extra due to numpy<2 pin).
-  robomme-integration-test:
-    name: RoboMME — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-      ROBOMME_POLICY: lerobot/smolvla_robomme
-      ROBOMME_TASKS: PickXtimes,BinFill,StopCube,MoveCube,InsertPeg,SwingXtimes,VideoUnmask,ButtonUnmask,PickHighlight,PatternLock
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-
-      - name: Build RoboMME benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.robomme
-          push: false
-          load: true
-          tags: lerobot-benchmark-robomme:ci
-
-      - name: Run RoboMME smoke eval (10 tasks, 1 episode each)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name robomme-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            -e ROBOMME_POLICY="${ROBOMME_POLICY}" \
-            -e ROBOMME_TASKS="${ROBOMME_TASKS}" \
-            lerobot-benchmark-robomme:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=\"\$ROBOMME_POLICY\" \
-                --env.type=robomme \
-                --env.task=\"\$ROBOMME_TASKS\" \
-                --env.dataset_split=test \
-                --env.task_ids=[0] \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.wrist_image\": \"observation.images.camera2\"}' \
-                --policy.empty_cameras=3 \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env robomme --task \"\$ROBOMME_TASKS\" \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy RoboMME artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/robomme-artifacts
-          docker cp robomme-eval:/tmp/eval-artifacts/. /tmp/robomme-artifacts/ 2>/dev/null || true
-          docker rm -f robomme-eval || true
-
-      - name: Parse RoboMME eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/robomme-artifacts \
-            --env robomme \
-            --task "${ROBOMME_TASKS}" \
-            --policy "${ROBOMME_POLICY}"
-
-      - name: Upload RoboMME rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robomme-rollout-video
-          path: /tmp/robomme-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload RoboMME eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robomme-metrics
-          path: /tmp/robomme-artifacts/metrics.json
-          if-no-files-found: warn
-
-  # ── LIBERO-plus ───────────────────────────────────────────────────────────
-  # Isolated image: LIBERO-plus fork cloned into /home/user_lerobot on top of
-  # huggingface/lerobot-gpu (see docker/Dockerfile.benchmark.libero_plus).
-  libero-plus-integration-test:
-    name: LIBERO-plus — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-      LIBERO_PLUS_SUITE: libero_spatial
-      LIBERO_PLUS_POLICY: lerobot/smolvla_libero_plus
-      LIBERO_PLUS_TASK_IDS: "[0,100,260,500,1000,1500,2000,2400]"
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-
-      - name: Build LIBERO-plus benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.libero_plus
-          push: false
-          load: true
-          tags: lerobot-benchmark-libero-plus:ci
-          cache-from: type=local,src=/tmp/.buildx-cache-libero-plus
-          cache-to: type=local,dest=/tmp/.buildx-cache-libero-plus,mode=max
-
-      - name: Run LIBERO-plus smoke eval (1 episode)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name libero-plus-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            -e LIBERO_PLUS_SUITE="${LIBERO_PLUS_SUITE}" \
-            -e LIBERO_PLUS_POLICY="${LIBERO_PLUS_POLICY}" \
-            -e LIBERO_PLUS_TASK_IDS="${LIBERO_PLUS_TASK_IDS}" \
-            lerobot-benchmark-libero-plus:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=\"\$LIBERO_PLUS_POLICY\" \
-                --env.type=libero_plus \
-                --env.task=\"\$LIBERO_PLUS_SUITE\" \
-                --env.task_ids=\"\$LIBERO_PLUS_TASK_IDS\" \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--env.camera_name_mapping={\"agentview_image\": \"camera1\", \"robot0_eye_in_hand_image\": \"camera2\"}' \
-                --policy.empty_cameras=1 \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env libero_plus --task \"\$LIBERO_PLUS_SUITE\" \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy LIBERO-plus artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/libero-plus-artifacts
-          docker cp libero-plus-eval:/tmp/eval-artifacts/. /tmp/libero-plus-artifacts/ 2>/dev/null || true
-          docker rm -f libero-plus-eval || true
-
-      - name: Parse LIBERO-plus eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/libero-plus-artifacts \
-            --env libero_plus \
-            --task "${LIBERO_PLUS_SUITE}" \
-            --policy "${LIBERO_PLUS_POLICY}"
-
-      - name: Upload LIBERO-plus rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: libero-plus-rollout-video
-          path: /tmp/libero-plus-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload LIBERO-plus eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: libero-plus-metrics
-          path: /tmp/libero-plus-artifacts/metrics.json
-          if-no-files-found: warn
-
-  # ── VLABENCH ─────────────────────────────────────────────────────────────
-  # Isolated image: lerobot[vlabench] only (VLABench, mujoco==3.2.2, dm-control chain)
-  vlabench-integration-test:
-    name: VLABench — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-        env:
-          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-
-      - name: Build VLABench benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.vlabench
-          push: false
-          load: true
-          tags: lerobot-benchmark-vlabench:ci
-          build-args: |
-            VLABENCH_ASSETS_REPO=lerobot/vlabench-assets
-
-      - name: Run VLABench smoke eval (10 tasks, 1 episode each)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name vlabench-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            -e MUJOCO_GL=egl \
-            lerobot-benchmark-vlabench:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=lerobot/smolvla_vlabench \
-                --env.type=vlabench \
-                --env.task=select_fruit,select_toy,select_book,select_painting,select_drink,select_ingredient,select_billiards,select_poker,add_condiment,insert_flower \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.second_image\": \"observation.images.camera2\", \"observation.images.wrist_image\": \"observation.images.camera3\"}' \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env vlabench \
-                --task select_fruit,select_toy,select_book,select_painting,select_drink,select_ingredient,select_billiards,select_poker,add_condiment,insert_flower \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy VLABench artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/vlabench-artifacts
-          docker cp vlabench-eval:/tmp/eval-artifacts/. /tmp/vlabench-artifacts/ 2>/dev/null || true
-          docker rm -f vlabench-eval || true
-
-      - name: Parse VLABench eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/vlabench-artifacts \
-            --env vlabench \
-            --task select_fruit,select_toy,select_book,select_painting,select_drink,select_ingredient,select_billiards,select_poker,add_condiment,insert_flower \
-            --policy lerobot/smolvla_vlabench
-
-      - name: Upload VLABench rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: vlabench-rollout-video
-          path: /tmp/vlabench-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload VLABench eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: vlabench-metrics
-          path: /tmp/vlabench-artifacts/metrics.json
-          if-no-files-found: warn

AGENTS.md

@@ -1,7 +1,5 @@
 This file provides guidance to AI agents when working with code in this repository.
 
-> **User-facing help → [`AGENT_GUIDE.md`](./AGENT_GUIDE.md)** (SO-101 setup, recording, picking a policy, training duration, eval — with copy-pasteable commands).
-
 ## Project Overview
 
 LeRobot is a PyTorch-based library for real-world robotics, providing datasets, pretrained policies, and tools for training, evaluation, data collection, and robot control. It integrates with Hugging Face Hub for model/dataset sharing.

AGENT_GUIDE.md

@@ -1,410 +0,0 @@
-# AGENT_GUIDE.md — LeRobot Helper for AI Agents & Users
-
-This file is a practical, copy-paste-friendly companion for any AI agent (Cursor, Claude, ChatGPT, Codex, etc.) helping a user work with LeRobot. It complements [`AGENTS.md`](./AGENTS.md) (dev/contributor context) with **user-facing guidance**: how to start, what to train, how long, how to record, and how to calibrate an SO-101.
-
----
-
-## 1. Start here — ask the user first (MANDATORY)
-
-Before suggesting any command, an agent MUST ask the user at least these questions and wait for answers:
-
-1. **What's your goal?** (e.g. "teach my SO-101 to fold a cloth", "train a policy on an existing HF dataset", "contribute a PR", "understand the codebase")
-2. **What hardware do you have?**
-   - Robot: none / SO-100 / SO-101 / Koch / LeKiwi / Reachy / other
-   - Teleop: leader arm / phone / keyboard / gamepad / none
-   - Cameras: how many, resolution, fixed or moving?
-3. **What machine will you train on?**
-   - GPU model + VRAM (e.g. "laptop 3060 6 GB", "RTX 4090 24 GB", "A100 80 GB", "CPU only")
-   - OS: macOS / Linux / Windows
-4. **Skill level & time budget?** First time, some ML, experienced? Hours, days, a weekend?
-5. **Do you already have a dataset?** Yes (HF repo id?) / no / want to record one
-6. **How can I help right now?** (pick one concrete next step)
-
-Only after you have answers, propose a concrete path. If something is ambiguous, ask again rather than guessing. Bias toward **the simplest thing that works** for the user's hardware and goal.
-
----
-
-## 2. LeRobot in 60 seconds
-
-LeRobot = **datasets + policies + envs + robot control**, unified by a small set of strong abstractions.
-
-- **`LeRobotDataset`** — episode-aware dataset (video or images + actions + state), loadable from the Hub or disk.
-- **Policies** (`ACT`, `Diffusion`, `SmolVLA`, `π0`, `π0.5`, `Wall-X`, `X-VLA`, `VQ-BeT`, `TD-MPC`, …) — all inherit `PreTrainedPolicy` and can be pushed/pulled from the Hub.
-- **Processors** — small composable transforms between dataset → policy → robot.
-- **Envs** (sim) and **Robots** (real) — same action/observation contract so code swaps cleanly.
-- **CLI** — `lerobot-record`, `lerobot-train`, `lerobot-eval`, `lerobot-teleoperate`, `lerobot-calibrate`, `lerobot-find-port`, `lerobot-setup-motors`, `lerobot-replay`.
-
-See [`AGENTS.md`](./AGENTS.md) for repo architecture.
-
----
-
-## 3. Quickstart paths (pick one)
-
-### Path A — "I have an SO-101 and want my first trained policy"
-
-Go to §4 (SO-101 end-to-end), then §5 (data tips), then §6 (pick a policy — likely **ACT**), then §7 (how long), then §8 (eval).
-
-### Path B — "No hardware, I want to train on an existing dataset"
-
-Skip §4. Pick a policy in §6, pick a duration in §7, then run `lerobot-train` per §4.9 with a Hub `--dataset.repo_id` and an `--env.type` for eval. Finish with §8.
-
-### Path C — "I just want to understand the codebase"
-
-Read §2 above, then `AGENTS.md` "Architecture", then open `src/lerobot/policies/act/` and `src/lerobot/datasets/lerobot_dataset.py` as canonical examples.
-
----
-
-## 4. SO-101 end-to-end cheat-sheet
-
-Full details in [`docs/source/so101.mdx`](./docs/source/so101.mdx) and [`docs/source/il_robots.mdx`](./docs/source/il_robots.mdx). Minimum commands in order. Confirm arms are assembled + powered before issuing.
-
-**4.1 Install**
-
-```bash
-pip install 'lerobot[feetech]'              # SO-100/SO-101 motor stack
-# pip install 'lerobot[all]'                # everything
-# pip install 'lerobot[aloha,pusht]'        # specific features
-# pip install 'lerobot[smolvla]'            # add SmolVLA deps
-git lfs install && git lfs pull
-hf auth login                               # required to push datasets/policies
-```
-
-Contributors can alternatively use `uv sync --locked --extra feetech` (see `AGENTS.md`).
-
-**4.2 Find USB ports** — run once per arm, unplug when prompted.
-
-```bash
-lerobot-find-port
-```
-
-macOS: `/dev/tty.usbmodem...`; Linux: `/dev/ttyACM0` (may need `sudo chmod 666 /dev/ttyACM0`).
-
-**4.3 Setup motor IDs & baudrate** (one-time, per arm)
-
-```bash
-lerobot-setup-motors --robot.type=so101_follower --robot.port=<FOLLOWER_PORT>
-lerobot-setup-motors --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>
-```
-
-**4.4 Calibrate** — center all joints, press Enter, sweep each joint through its full range. The `id` is the calibration key — reuse it everywhere.
-
-```bash
-lerobot-calibrate --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower
-lerobot-calibrate --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>   --teleop.id=my_leader
-```
-
-**4.5 Teleoperate** (sanity check, no recording)
-
-```bash
-lerobot-teleoperate \
-  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
-  --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>  --teleop.id=my_leader \
-  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-  --display_data=true
-```
-
-> **Feetech timeout / comms error on SO-100 / SO-101?** Before touching software, check the **red motor LEDs** on the daisy chain.
->
-> - **All steady red, gripper → base chain** → wiring OK.
-> - **One or more motors dark / chain stops mid-way** → wiring issue: reseat the 3-pin cables, check the controller-board power supply, and make sure each motor is fully clicked in.
-> - **LEDs blinking** → the motor is in an **error state**: usually overload (forcing a joint past its limit) **or wrong power supply voltage**. SO-100 / SO-101 ship in two variants — a **5 V / 7.4 V** build and a **12 V** build — they are NOT interchangeable. Using a 12 V PSU on a 5 V / 7.4 V arm (or vice-versa) will trip this error; confirm your motor variant before powering up.
->
-> Most "timeout" errors are physical, not code.
-
-**4.6 Record a dataset** — keys: **→** next, **←** redo, **ESC** finish & upload.
-
-```bash
-HF_USER=$(NO_COLOR=1 hf auth whoami | awk -F': *' 'NR==1 {print $2}')
-
-lerobot-record \
-  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
-  --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>  --teleop.id=my_leader \
-  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-  --dataset.repo_id=${HF_USER}/my_task \
-  --dataset.single_task="<describe the task in one sentence>" \
-  --dataset.num_episodes=50 \
-  --dataset.episode_time_s=30 \
-  --dataset.reset_time_s=10 \
-  --display_data=true
-```
-
-**4.7 Visualize** — **always** do this before training. Look for missing frames, camera blur, unreachable targets, inconsistent object positions.
-After upload: https://huggingface.co/spaces/lerobot/visualize_dataset → paste `${HF_USER}/my_task`. Works for **any LeRobot-formatted Hub dataset** — use it to scout other datasets, inspect episode quality, or debug your own data before retraining.
-
-**4.8 Replay an episode** (sanity check)
-
-```bash
-lerobot-replay --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
-  --dataset.repo_id=${HF_USER}/my_task --dataset.episode=0
-```
-
-**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. See §6/§7 for policy and duration.
-
-```bash
-lerobot-train \
-  --dataset.repo_id=${HF_USER}/my_task \
-  --policy.type=act \
-  --policy.device=cuda \
-  --output_dir=outputs/train/act_my_task \
-  --job_name=act_my_task \
-  --batch_size=8 \
-  --wandb.enable=true \
-  --policy.repo_id=${HF_USER}/act_my_task
-```
-
-**4.10 Evaluate on the real robot** — compare success rate to a teleoperated baseline.
-
-```bash
-lerobot-record \
-  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
-  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-  --dataset.repo_id=${HF_USER}/eval_my_task \
-  --dataset.single_task="<same task description as training>" \
-  --dataset.num_episodes=10 \
-  --policy.path=${HF_USER}/act_my_task
-```
-
----
-
-## 5. Data collection tips (beginner → reliable policy)
-
-Good data beats clever models. Adopt these defaults and deviate only with evidence.
-
-### 5.1 Setup & ergonomics
-
-- **Fix the rig and cameras** before touching the software. If the rig vibrates or the operator gets frustrated, fix that first — more bad data won't help.
-- **Lighting matters more than resolution.** Diffuse, consistent light. Avoid moving shadows.
-- **"Can you do the task from the camera view alone?"** If no, your cameras are wrong. Fix before recording.
-- Enable **action interpolation** for rollouts when available for smoother trajectories.
-
-### 5.2 Practice before you record
-
-- Do 5–10 demos without recording. Build a deliberate, repeatable strategy.
-- Hesitant or inconsistent demos teach the model hesitation.
-
-### 5.3 Quality over speed
-
-Deliberate, high-quality execution beats fast sloppy runs. Optimize for speed only **after** strategy is dialed in — never trade quality for it.
-
-### 5.4 Consistency within and across episodes
-
-Same grasp, approach vector, and timing. Coherent strategies are much easier to learn than wildly varying movements.
-
-### 5.5 Start small, then extend (the golden rule)
-
-- **First 50 episodes = constrained version** of the task: one object, fixed position, fixed camera setup, one operator.
-- Train a quick ACT model. See what fails.
-- **Then add diversity** along one axis at a time: more positions → more lighting → more objects → more operators.
-- Don't try to collect the "perfect dataset" on day one. Iterate.
-
-### 5.6 Policy choice for beginners
-
-- **Laptop / first time / want results fast → ACT.** Works surprisingly well, trains fast even on a laptop GPU.
-- **Bigger GPU / language-conditioned / multi-task → SmolVLA.** Unfreezing the vision encoder (see §7) is a big win here.
-- Defer π0 / π0.5 / Wall-X / X-VLA until you have a proven ACT baseline and a 20+ GB GPU.
-
-### 5.7 Recommended defaults for your first task
-
-| Setting          | Value                                                                                                                                                 |
-| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Episodes         | **50** to start, scale to 100–300 after first training                                                                                                |
-| Episode length   | 20–45 s (shorter is fine for grasp/place)                                                                                                             |
-| Reset time       | 10 s                                                                                                                                                  |
-| FPS              | 30                                                                                                                                                    |
-| Cameras          | **2 cameras recommended**: 1 fixed front + 1 wrist. Multi-view often outperforms single-view. A single fixed camera also works to keep things simple. |
-| Task description | Short, specific, action-phrased sentence                                                                                                              |
-
-### 5.8 Troubleshooting signal
-
-- Policy fails at one specific stage → record 10–20 more episodes **targeting that stage**.
-- Policy flaps / oscillates → likely inconsistent demos, or need more training; re-record worst episodes (use **←** to redo).
-- Policy ignores the object → camera framing or lighting issue, not a model issue.
-
-See also: [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset).
-
----
-
-## 6. Which policy should I train?
-
-Match the policy to the user's **GPU memory** and **time budget**. Numbers below come from an internal profiling run (one training update per policy). They are **indicative only** — see caveats.
-
-### 6.1 Profiling snapshot (indicative)
-
-All policies typically train for **5–10 epochs** (see §7).
-
-| Policy      | Batch | Update (ms) | Peak GPU mem (GB) | Best for                                                                                         |
-| ----------- | ----: | ----------: | ----------------: | ------------------------------------------------------------------------------------------------ |
-| `act`       |     4 |    **83.9** |          **0.94** | First-time users, laptops, single-task. Fast and reliable.                                       |
-| `diffusion` |     4 |       168.6 |              4.94 | Multi-modal action distributions; needs mid-range GPU.                                           |
-| `smolvla`   |     1 |       357.8 |              3.93 | Language-conditioned, multi-task, small VLA. **Unfreeze vision encoder for big gains** (see §7). |
-| `xvla`      |     1 |       731.6 |             15.52 | Large VLA, multi-task.                                                                           |
-| `wall_x`    |     1 |       716.5 |             15.95 | Large VLA with world-model objective.                                                            |
-| `pi0`       |     1 |       940.3 |             15.50 | Strong large VLA baseline (Physical Intelligence).                                               |
-| `pi05`      |     1 |      1055.8 |             16.35 | Newer π policy; similar footprint to `pi0`.                                                      |
-
-**Critical caveats:**
-
-- **Optimizer:** measured with **SGD**. LeRobot's default is **AdamW**, which keeps extra optimizer state → **peak memory will be noticeably higher** with the default, especially for `pi0`, `pi05`, `wall_x`, `xvla`.
-- **Batch size:** the large policies were profiled at batch 1. In practice use a **larger batch** for stable training (see §7.4). Memory scales roughly linearly with batch.
-
-### 6.2 Decision rules
-
-- **< 8 GB VRAM (laptop, 3060, M-series Mac):** → `act`. Maybe `diffusion` if you have ~6–8 GB free.
-- **12–16 GB VRAM (4070/4080, A4000):** → `smolvla` with defaults, or `act`/`diffusion` with larger batch. `pi0`/`pi05`/`wall_x`/`xvla` feasible only with small batch + gradient accumulation.
-- **24+ GB VRAM (3090/4090/A5000):** → any policy. Prefer `smolvla` (unfrozen) for multi-task; `act` for single-task grasp-and-place (still often the best ROI). Could experiment with `pi0` or `pi05` or `xvla`
-- **80 GB (A100/H100):** → any, with healthy batch. `pi05`, `xvla`, `wall_x` become comfortable.
-- **CPU only:** → don't train here. Use Google Colab (see [`docs/source/notebooks.mdx`](./docs/source/notebooks.mdx)) or a rented GPU.
-
----
-
-## 7. How long should I train?
-
-Robotics imitation learning usually converges in a **few epochs over the dataset**, not hundreds of thousands of raw steps. Think **epochs first**, then translate to steps.
-
-### 7.1 Rule of thumb
-
-- **Typical total: 5–10 epochs.** Start at 5, eval, then decide if more helps.
-- Very small datasets (< 30 episodes) may want slightly more epochs — but first, **collect more data**.
-- VLAs with a pretrained vision backbone typically need **fewer** epochs than training from scratch.
-
-### 7.2 Steps ↔ epochs conversion
-
-```
-total_frames     = sum of frames over all episodes      # e.g. 50 eps × 30 fps × 30 s ≈ 45,000
-steps_per_epoch  = ceil(total_frames / batch_size)
-total_steps      = epochs × steps_per_epoch
-```
-
-Examples for `--batch_size=8`:
-
-| Dataset size            |  Frames | Steps / epoch | 5 epochs | 10 epochs |
-| ----------------------- | ------: | ------------: | -------: | --------: |
-| 50 eps × 30 s @ 30 fps  |  45,000 |        ~5,625 |      28k |       56k |
-| 100 eps × 30 s @ 30 fps |  90,000 |       ~11,250 |      56k |      113k |
-| 300 eps × 30 s @ 30 fps | 270,000 |       ~33,750 |     169k |      338k |
-
-Pass the resulting total with `--steps=<N>`; eval at intermediate checkpoints (`outputs/train/.../checkpoints/`).
-
-### 7.3 Per-policy starting points (single-task, ~50 episodes)
-
-| Policy         | Batch | Steps (first run) | Notes                                                             |
-| -------------- | ----: | ----------------: | ----------------------------------------------------------------- |
-| `act`          |  8–16 |           30k–80k | Usually converges under 50k for single-task.                      |
-| `diffusion`    |  8–16 |          80k–150k | Benefits from longer training than ACT.                           |
-| `smolvla`      |   4–8 |           30k–80k | Pretrained VLM → converges fast.                                  |
-| `pi0` / `pi05` |   1–4 |           30k–80k | Memory-bound; use gradient accumulation for effective batch ≥ 16! |
-
-### 7.4 Batch size guidance
-
-- **Bigger batch is preferable** for stable gradients on teleop data.
-- If GPU memory is the bottleneck, use **gradient accumulation** to raise _effective_ batch without raising peak memory.
-- Scale **learning rate** gently with batch; most LeRobot defaults work fine for a 2–4× batch change.
-
-### 7.5 Scale LR schedule & checkpoints with `--steps`
-
-LeRobot's default schedulers (e.g. SmolVLA's cosine decay) use `scheduler_decay_steps=30_000`, which is sized for long training runs. When you shorten training (e.g. 5k–10k steps on a small dataset), **scale the scheduler down to match** — otherwise the LR stays near the peak and never decays. Same for checkpoint frequency.
-
-```bash
-lerobot-train ... \
-  --steps=5000 \
-  --policy.scheduler_decay_steps=5000 \
-  --save_freq=5000
-```
-
-Rule of thumb: set `scheduler_decay_steps ≈ steps`, and `save_freq` to whatever granularity you want for eval (e.g. every 1k–5k steps). Match `scheduler_warmup_steps` proportionally if your run is very short.
-
-### 7.6 SmolVLA: unfreeze the vision encoder for real gains
-
-SmolVLA ships with `freeze_vision_encoder=True`. Unfreezing usually **improves performance substantially** on specialized tasks, at the cost of more VRAM and slower steps. Enable with:
-
-```bash
-lerobot-train ... --policy.type=smolvla \
-  --policy.freeze_vision_encoder=false \
-  --policy.train_expert_only=false
-```
-
-### 7.7 Signals to stop / keep going
-
-- Train loss plateaus → stop, save a Hub checkpoint.
-- Train loss still dropping and you're under 10 epochs → keep going.
-
----
-
-## 8. Evaluation & benchmarks
-
-Two flavors of evaluation:
-
-### 8.1 Real-robot eval (SO-101, etc.)
-
-Reuse `lerobot-record` with `--policy.path` to run the trained policy on-robot and save the run as an eval dataset. Convention: prefix the dataset with `eval_`.
-
-```bash
-lerobot-record \
-  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
-  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-  --dataset.repo_id=${HF_USER}/eval_my_task \
-  --dataset.single_task="<same task description used during training>" \
-  --dataset.num_episodes=10 \
-  --policy.path=${HF_USER}/act_my_task
-```
-
-Report success rate across episodes. Compare to a teleoperated baseline and to an earlier checkpoint to catch regressions.
-
-### 8.2 Sim-benchmark eval
-
-For policies trained on sim datasets (PushT, Aloha, LIBERO, MetaWorld, RoboCasa, …) use `lerobot-eval` against the matching `env.type`:
-
-```bash
-lerobot-eval \
-  --policy.path=${HF_USER}/diffusion_pusht \
-  --env.type=pusht \
-  --eval.n_episodes=50 \
-  --eval.batch_size=10 \
-  --policy.device=cuda
-```
-
-- Use `--policy.path=outputs/train/.../checkpoints/<step>/pretrained_model` for local checkpoints.
-- `--eval.n_episodes` should be ≥ 50 for a stable success-rate estimate.
-- Available envs live in `src/lerobot/envs/`. See [`docs/source/libero.mdx`](./docs/source/libero.mdx), [`metaworld.mdx`](./docs/source/metaworld.mdx), [`robocasa.mdx`](./docs/source/robocasa.mdx), [`vlabench.mdx`](./docs/source/vlabench.mdx) for specific benchmarks.
-- To add a new benchmark, see [`docs/source/adding_benchmarks.mdx`](./docs/source/adding_benchmarks.mdx) and [`envhub.mdx`](./docs/source/envhub.mdx).
-
-### 8.2b Dockerfiles for benchmark eval
-
-Benchmark envs have native dependencies that are painful to install locally. The repo ships **pre-baked Dockerfiles** for each supported benchmark — use these to run `lerobot-eval` in a reproducible environment:
-
-| Benchmark   | Dockerfile                                                                             |
-| ----------- | -------------------------------------------------------------------------------------- |
-| LIBERO      | [`docker/Dockerfile.benchmark.libero`](./docker/Dockerfile.benchmark.libero)           |
-| LIBERO+     | [`docker/Dockerfile.benchmark.libero_plus`](./docker/Dockerfile.benchmark.libero_plus) |
-| MetaWorld   | [`docker/Dockerfile.benchmark.metaworld`](./docker/Dockerfile.benchmark.metaworld)     |
-| RoboCasa    | [`docker/Dockerfile.benchmark.robocasa`](./docker/Dockerfile.benchmark.robocasa)       |
-| RoboCerebra | [`docker/Dockerfile.benchmark.robocerebra`](./docker/Dockerfile.benchmark.robocerebra) |
-| RoboMME     | [`docker/Dockerfile.benchmark.robomme`](./docker/Dockerfile.benchmark.robomme)         |
-| RoboTwin    | [`docker/Dockerfile.benchmark.robotwin`](./docker/Dockerfile.benchmark.robotwin)       |
-| VLABench    | [`docker/Dockerfile.benchmark.vlabench`](./docker/Dockerfile.benchmark.vlabench)       |
-
-Build and run (adapt to your benchmark):
-
-```bash
-docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-bench-robomme .
-docker run --gpus all --rm -it \
-  -v $HOME/.cache/huggingface:/root/.cache/huggingface \
-  lerobot-bench-robomme \
-  lerobot-eval --policy.path=<your_policy> --env.type=<env> --eval.n_episodes=50
-```
-
-See [`docker/README.md`](./docker/README.md) for base-image details.
-
-### 8.3 Target success rates
-
-Single-task grasp-and-place with 50 clean episodes: ACT should reach **> 70% success** on the training configuration. Less → data problem (see §5), not model problem. Expect a drop when generalizing to new positions — scale episodes or diversity to recover.
-
----
-
-## 9. Further reading & resources
-
-- **Getting started:** [`installation.mdx`](./docs/source/installation.mdx) · [`il_robots.mdx`](./docs/source/il_robots.mdx) · [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets)
-- **Per-policy docs:** browse [`docs/source/*.mdx`](./docs/source/) (policies, hardware, benchmarks, advanced training).
-- **Community:** [Discord](https://discord.com/invite/s3KuuzsPFb) · [Hub `LeRobot` tag](https://huggingface.co/datasets?other=LeRobot) · [Dataset visualizer](https://huggingface.co/spaces/lerobot/visualize_dataset)
-
-> Keep this file current. If you learn a rule that would prevent a class of user mistakes, add it here and in [`AGENTS.md`](./AGENTS.md).

docker/Dockerfile.benchmark.libero_plus

@@ -1,84 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for LIBERO-plus integration tests.
-# Extends the nightly GPU image (which has lerobot[all]) with the LIBERO-plus
-# fork source + its 6.4 GB perturbation assets.
-#
-# Build:  docker build -f docker/Dockerfile.benchmark.libero_plus -t lerobot-benchmark-libero-plus .
-# Run:    docker run --gpus all --rm lerobot-benchmark-libero-plus lerobot-eval ...
-
-FROM huggingface/lerobot-gpu:latest
-ENV MUJOCO_GL=egl
-
-# unzip for the 6.4 GB assets.zip; the rest are LIBERO-plus build-time extras
-# (wand / ImageMagick / fontconfig) not in the nightly base.
-USER root
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends \
-         unzip libexpat1 libfontconfig1-dev libmagickwand-dev \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-USER user_lerobot
-
-# robosuite==1.4.1 is mandatory (the fork uses `single_arm_env` removed in
-# v1.5+). The rest are LIBERO-plus runtime deps pulled from its setup.py.
-# We install these explicitly instead of via the [libero_plus] extra because
-# the extra's `libero @ git+...` dep installs as a namespace package and then
-# clone and PYTHONPATH-override it below.
-RUN uv pip install --no-cache \
-        "robosuite==1.4.1" \
-        "bddl==1.0.1" \
-        "easydict==1.13" \
-        "mujoco==3.7.0" \
-        "matplotlib==3.10.8" \
-        "Wand==0.6.13" \
-        "scikit-image==0.25.2" \
-        "gym==0.26.2"
-
-# Clone LIBERO-plus and make it importable as `libero`. The nightly base has
-# hf-libero (10 tasks) preinstalled via lerobot[libero]; uninstall it so
-# Python resolves `import libero` to the 2402-task LIBERO-plus module instead.
-# Pinned to the current upstream main SHA so benchmark builds stay reproducible.
-ARG LIBERO_PLUS_SHA=4976dc3
-ENV LIBERO_PLUS_ROOT=/home/user_lerobot/libero-plus/libero/libero
-RUN git clone https://github.com/sylvestf/LIBERO-plus.git /home/user_lerobot/libero-plus \
-    && git -C /home/user_lerobot/libero-plus checkout ${LIBERO_PLUS_SHA} \
-    && cd /home/user_lerobot/libero-plus && uv pip install --no-cache --no-deps -e "." \
-    && (uv pip uninstall hf-libero 2>/dev/null || true)
-ENV PYTHONPATH="/home/user_lerobot/libero-plus:${PYTHONPATH}"
-
-# Perturbation textures/scenes: bddl_base_domain.py resolves XMLs via
-# DIR_PATH/../assets (package-relative, ignoring ~/.libero/config.yaml). All
-# 2402 tasks reference files that ship only in Sylvest/LIBERO-plus's
-# assets.zip (6.4 GB) under a deep author-internal prefix — extract and
-# flatten it under ${LIBERO_PLUS_ROOT}/assets.
-RUN python -c "\
-from huggingface_hub import hf_hub_download; \
-hf_hub_download(repo_id='Sylvest/LIBERO-plus', repo_type='dataset', \
-                filename='assets.zip', local_dir='/tmp/libero-plus-dl')" \
-    && unzip -q /tmp/libero-plus-dl/assets.zip -d /tmp/libero-plus-dl/extract \
-    && ASSETS_DIR=$(find /tmp/libero-plus-dl/extract -type d -name assets | head -1) \
-    && mv "${ASSETS_DIR}" ${LIBERO_PLUS_ROOT}/assets \
-    && rm -rf /tmp/libero-plus-dl
-
-# Point ~/.libero/config.yaml at the clone so LIBERO-plus's imports are
-# non-interactive (it calls input() when the config is missing).
-RUN mkdir -p /home/user_lerobot/.libero \
-    && printf "assets: ${LIBERO_PLUS_ROOT}/assets\nbddl_files: ${LIBERO_PLUS_ROOT}/bddl_files\ndatasets: ${LIBERO_PLUS_ROOT}/../datasets\ninit_states: ${LIBERO_PLUS_ROOT}/init_files\n" \
-       > /home/user_lerobot/.libero/config.yaml
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]

docker/Dockerfile.benchmark.robocasa

@@ -1,71 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for RoboCasa365 integration tests.
-# Extends the nightly GPU image (which already has all extras installed)
-# with the PR's source code and RoboCasa-specific asset setup.
-#
-# Build:  docker build -f docker/Dockerfile.benchmark.robocasa -t lerobot-benchmark-robocasa .
-# Run:    docker run --gpus all --rm lerobot-benchmark-robocasa lerobot-eval ...
-
-FROM huggingface/lerobot-gpu:latest
-
-# Install robocasa + robosuite as editable clones. pip-installing from git
-# omits data files like robocasa/models/assets/box_links/box_links_assets.json
-# (not declared in package_data), which download_kitchen_assets needs at import.
-#
-# `--no-deps` on robocasa is deliberate: its setup.py pins `lerobot==0.3.3`
-# in install_requires, which would shadow the editable lerobot baked into
-# this image. We install robocasa's actual runtime deps explicitly instead.
-# Pinned SHAs for reproducible benchmark runs. Bump when you need an
-# upstream fix; don't rely on `main`/`master` drift.
-ARG ROBOCASA_SHA=56e355ccc64389dfc1b8a61a33b9127b975ba681
-ARG ROBOSUITE_SHA=aaa8b9b214ce8e77e82926d677b4d61d55e577ab
-RUN git clone https://github.com/robocasa/robocasa.git ~/robocasa && \
-    git -C ~/robocasa checkout ${ROBOCASA_SHA} && \
-    git clone https://github.com/ARISE-Initiative/robosuite.git ~/robosuite && \
-    git -C ~/robosuite checkout ${ROBOSUITE_SHA} && \
-    uv pip install --no-cache -e ~/robocasa --no-deps && \
-    uv pip install --no-cache -e ~/robosuite && \
-    uv pip install --no-cache \
-      "numpy==2.2.5" "numba==0.61.2" "scipy==1.15.3" "mujoco==3.3.1" \
-      "pygame==2.6.1" "Pillow==12.2.0" "opencv-python==4.13.0.92" \
-      "pyyaml==6.0.3" "pynput==1.8.1" "tqdm==4.67.3" "termcolor==3.3.0" \
-      "imageio==2.37.3" "h5py==3.16.0" "lxml==6.0.4" "hidapi==0.14.0.post4" \
-      "tianshou==0.4.10" "gymnasium==1.2.3"
-
-# Set up robocasa macros and download kitchen assets. We need:
-#   - tex              : base environment textures
-#   - tex_generative   : AI-generated textures; kitchen fixture XMLs embed
-#                        refs to generative_textures/wall/tex*.png
-#                        unconditionally, so MjModel.from_xml_string fails
-#                        at reset time without them (even if the env is
-#                        constructed with generative_textures=None).
-#   - fixtures_lw      : lightwheel kitchen fixtures (fridge, counters...)
-#   - objs_lw          : lightwheel object meshes (stools, misc props)
-# We skip the objaverse/aigen object packs (~30GB combined) by pairing
-# this with --env.obj_registries=["lightwheel"] on the lerobot side.
-# The download script prompts interactively, so pipe 'y' to auto-accept.
-RUN python -m robocasa.scripts.setup_macros && \
-    yes y | python -m robocasa.scripts.download_kitchen_assets \
-      --type tex tex_generative fixtures_lw objs_lw
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-# Re-install lerobot editably so the new source (with RoboCasaEnv registration)
-# replaces the stale package baked into the nightly image.
-RUN uv pip install --no-cache --no-deps -e .
-
-CMD ["/bin/bash"]

docker/Dockerfile.benchmark.robocerebra

@@ -1,43 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for RoboCerebra integration tests.
-# RoboCerebra reuses LIBERO's simulator (libero_10 suite) with a different
-# rename_map, so this image is identical to the LIBERO benchmark image —
-# extends the nightly GPU base with LIBERO assets + the PR's source code.
-#
-# Build:  docker build -f docker/Dockerfile.benchmark.robocerebra -t lerobot-benchmark-robocerebra .
-# Run:    docker run --gpus all --rm lerobot-benchmark-robocerebra lerobot-eval ...
-
-FROM huggingface/lerobot-gpu:latest
-
-# Pre-download lerobot/libero-assets from HF Hub so nothing is fetched at
-# runtime (which times out on CI). Point the libero config at the cached path.
-# libero/libero/__init__.py calls input() when ~/.libero/config.yaml is missing,
-# so we write the config before any libero import can happen.
-RUN LIBERO_DIR=$(python -c \
-      "import importlib.util, os; s=importlib.util.find_spec('libero'); \
-       print(os.path.join(os.path.dirname(s.origin), 'libero'))") && \
-    mkdir -p /home/user_lerobot/.libero && \
-    python -c "\
-from huggingface_hub import snapshot_download; \
-snapshot_download(repo_id='lerobot/libero-assets', repo_type='dataset', \
-                  local_dir='/home/user_lerobot/.libero/assets')" && \
-    printf "assets: /home/user_lerobot/.libero/assets\nbddl_files: ${LIBERO_DIR}/bddl_files\ndatasets: ${LIBERO_DIR}/../datasets\ninit_states: ${LIBERO_DIR}/init_files\n" \
-    > /home/user_lerobot/.libero/config.yaml
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]

docker/Dockerfile.benchmark.robomme

@@ -1,56 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for RoboMME integration tests.
-# Extends the nightly GPU image (which has lerobot[all]) with Vulkan system
-# libs for ManiSkill/SAPIEN and the robomme extra. robomme isn't in [all]
-# because mani-skill hard-pins gymnasium==0.29.1 and numpy<2.0.0 which
-# conflict with lerobot's defaults; both are safe at runtime:
-#   - gymnasium 0.29.x has the same 5-tuple step() API as 1.x (since 0.26)
-#   - numpy 1.26.4 is API-compatible with lerobot's actual usage.
-#
-# Build:  docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-benchmark-robomme .
-# Run:    docker run --gpus all --rm lerobot-benchmark-robomme lerobot-eval ...
-
-FROM huggingface/lerobot-gpu:latest
-
-# NVIDIA Container Toolkit: expose Vulkan driver capability for headless rendering.
-ENV NVIDIA_DRIVER_CAPABILITIES=all \
-    VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/nvidia_icd.json
-
-# ManiSkill/SAPIEN's renderer needs Vulkan, which isn't in the base image.
-USER root
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends \
-         libvulkan1 libvulkan-dev mesa-vulkan-drivers \
-    && mkdir -p /usr/share/vulkan/icd.d \
-    && echo '{"file_format_version":"1.0.0","ICD":{"library_path":"libGLX_nvidia.so.0","api_version":"1.3.0"}}' \
-       > /usr/share/vulkan/icd.d/nvidia_icd.json \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-USER user_lerobot
-
-# Install smolvla + av-dep via the PR's pyproject, then layer robomme on top
-# with gymnasium/numpy overrides. robomme isn't a pyproject extra because its
-# mani-skill pin conflicts with lerobot's base numpy>=2 (see pyproject.toml).
-COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN printf 'gymnasium==0.29.1\nnumpy==1.26.4\n' > /tmp/robomme_override.txt \
-    && uv pip install --no-cache --override /tmp/robomme_override.txt \
-         -e ".[smolvla,av-dep]" \
-         "robomme @ git+https://github.com/RoboMME/robomme_benchmark.git@main" \
-    && python -c "import robomme; print('robomme import OK')"
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]

docker/Dockerfile.benchmark.robotwin

@@ -1,138 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for RoboTwin 2.0 integration tests.
-# Extends the nightly GPU image with the RoboTwin simulator stack:
-#   sapien/mplib/pytorch3d + NVlabs CuRobo + embodiments.zip + objects.zip
-# (~3.96 GB of assets; background_texture.zip ~11 GB skipped for smoke eval).
-#
-# Build: docker build -f docker/Dockerfile.benchmark.robotwin -t lerobot-benchmark-robotwin .
-# Run:   docker run --gpus all --rm lerobot-benchmark-robotwin \
-#            lerobot-eval --env.type=robotwin --env.task=beat_block_hammer ...
-
-FROM huggingface/lerobot-gpu:latest
-
-ENV NVIDIA_DRIVER_CAPABILITIES=all \
-    VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/nvidia_icd.json \
-    ROBOTWIN_ROOT=/opt/robotwin
-
-# The nightly base is CUDA -base (no compiler, no Vulkan loader). CuRobo's
-# `pip install -e .` runs nvcc, and SAPIEN renders via Vulkan — add both.
-USER root
-# Pinned upstream SHA for reproducible benchmark runs. Bump when we need
-# an upstream fix; don't rely on `main` drift.
-ARG ROBOTWIN_SHA=0aeea2d669c0f8516f4d5785f0aa33ba812c14b4
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends \
-         cuda-nvcc-12-4 cuda-cudart-dev-12-4 \
-         libvulkan1 vulkan-tools \
-    && mkdir -p /usr/share/vulkan/icd.d \
-    && echo '{"file_format_version":"1.0.0","ICD":{"library_path":"libGLX_nvidia.so.0","api_version":"1.3.0"}}' \
-       > /usr/share/vulkan/icd.d/nvidia_icd.json \
-    && git clone https://github.com/RoboTwin-Platform/RoboTwin.git ${ROBOTWIN_ROOT} \
-    && git -C ${ROBOTWIN_ROOT} checkout ${ROBOTWIN_SHA} \
-    && chown -R user_lerobot:user_lerobot ${ROBOTWIN_ROOT} \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-USER user_lerobot
-
-# RoboTwin runtime deps (av is already in the base via [av-dep]).
-RUN uv pip install --no-cache \
-        "sapien==3.0.0b1" "mplib==0.2.1" "transforms3d==0.4.2" "trimesh==4.4.3" \
-        "open3d==0.19.0" "imageio==2.34.2" termcolor zarr pydantic h5py
-
-# pytorch3d has no universal wheel; must be built from source (~10 min, cached).
-RUN uv pip install --no-cache --no-build-isolation \
-        "git+https://github.com/facebookresearch/pytorch3d.git@stable"
-
-# CuRobo — NVlabs motion generator; TORCH_CUDA_ARCH_LIST must be set or the
-# build aborts on an empty arch list. RoboTwin's own installer pins v0.7.8,
-# which still exposes the v1 API (`curobo.types.math`) that RoboTwin imports.
-ARG CUROBO_REF=v0.7.8
-RUN cd ${ROBOTWIN_ROOT}/envs \
-    && git clone --branch ${CUROBO_REF} --depth 1 https://github.com/NVlabs/curobo.git \
-    && cd curobo \
-    && TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;8.9;9.0" \
-       uv pip install -e . --no-build-isolation --no-cache
-
-# Upstream patches (mirror RoboTwin's script/_install.sh).
-# These patches target the exact versions pinned above; re-check when upgrading.
-# mplib==0.2.1: drop a broken `or collide` clause in planner.py.
-#   Safe to remove once mplib > 0.2.1 ships with the fix upstream.
-# sapien==3.0.0b1: fix URDF loader encoding + .srdf extension check.
-#   Safe to remove once sapien > 3.0.0b1 ships with the fix upstream.
-RUN python - <<'EOF'
-import pathlib, re, site
-for d in site.getsitepackages():
-    p = pathlib.Path(d) / "mplib" / "planner.py"
-    if p.exists():
-        p.write_text(re.sub(r"\bor collide\b", "", p.read_text(), count=1))
-        print(f"mplib patch applied: {p}")
-    p = pathlib.Path(d) / "sapien" / "wrapper" / "urdf_loader.py"
-    if p.exists():
-        src = p.read_text().replace(
-            "with open(srdf_path) as f:", 'with open(srdf_path, encoding="utf-8") as f:'
-        ).replace('"srdf"', '".srdf"')
-        p.write_text(src)
-        print(f"sapien patch applied: {p}")
-EOF
-
-# Simulation assets from TianxingChen/RoboTwin2.0: embodiments (~220 MB) +
-# objects (~3.74 GB). background_texture (~11 GB) is intentionally skipped.
-# The dataset is public — no auth token needed.
-RUN python - <<'EOF'
-import os, pathlib, zipfile
-from huggingface_hub import hf_hub_download
-
-assets_dir = pathlib.Path(os.environ["ROBOTWIN_ROOT"]) / "assets"
-assets_dir.mkdir(parents=True, exist_ok=True)
-for fname in ("embodiments.zip", "objects.zip"):
-    local = hf_hub_download(
-        repo_id="TianxingChen/RoboTwin2.0",
-        repo_type="dataset",
-        filename=fname,
-        local_dir=str(assets_dir),
-    )
-    with zipfile.ZipFile(local, "r") as z:
-        z.extractall(str(assets_dir))
-    pathlib.Path(local).unlink()
-EOF
-
-WORKDIR ${ROBOTWIN_ROOT}
-RUN python script/update_embodiment_config_path.py
-
-ENV PYTHONPATH="${ROBOTWIN_ROOT}"
-
-# Fail the image build early if the CuRobo package layout regresses. Importing
-# RoboTwin's planner here is too eager because CuRobo constructs CUDA-backed
-# defaults at import time, while Docker builds don't have access to an NVIDIA
-# driver.
-RUN python - <<'EOF'
-from pathlib import Path
-
-from curobo.types.math import Pose
-
-planner_src = (Path("/opt/robotwin/envs/robot/planner.py")).read_text()
-assert "from curobo.types.math import Pose as CuroboPose" in planner_src
-
-print("CuRobo import OK:", Pose.__name__)
-print("RoboTwin planner import references curobo.types.math")
-EOF
-
-# Return to the lerobot source directory (set by base image) before overlaying.
-WORKDIR /lerobot
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]

docker/Dockerfile.benchmark.vlabench

@@ -1,99 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for VLABench integration tests.
-# Extends the nightly GPU image with the PR's source code and VLABench setup.
-#
-# Build:  docker build -f docker/Dockerfile.benchmark.vlabench -t lerobot-benchmark-vlabench .
-# Run:    docker run --gpus all --rm lerobot-benchmark-vlabench lerobot-eval ...
-
-FROM huggingface/lerobot-gpu:latest
-
-# Install VLABench from GitHub (not on PyPI) and pin MuJoCo/dm-control.
-# Shallow-clone without submodule recursion (nested SSH-only submodules fail in CI).
-# Editable install (-e) because VLABench/utils/ has no __init__.py, so
-# find_packages() omits it from wheels; editable mode uses the source tree directly.
-# rrt-algorithms has the same packaging issue (rrt/ dir missing __init__.py).
-# Patch: constant.py calls os.listdir on ~100 asset/obj/meshes/* dirs at import
-# time. Guard the call so missing dirs return [] instead of crashing (in case
-# the asset download is partial).
-#
-# Pinned upstream SHAs for reproducible benchmark runs. Bump when you need
-# an upstream fix; don't rely on `main`/`develop` drift.
-ARG VLABENCH_SHA=cf588fe60c0c7282174fe979f5913170cfe69017
-ARG RRT_ALGORITHMS_SHA=e51d95ee489a225220d6ae2a764c4111f6ba7d85
-RUN git clone https://github.com/OpenMOSS/VLABench.git ~/VLABench && \
-    git -C ~/VLABench checkout ${VLABENCH_SHA} && \
-    git clone https://github.com/motion-planning/rrt-algorithms.git ~/rrt-algorithms && \
-    git -C ~/rrt-algorithms checkout ${RRT_ALGORITHMS_SHA} && \
-    python3 -c "\
-import pathlib; \
-p = pathlib.Path.home() / 'VLABench/VLABench/configs/constant.py'; \
-t = p.read_text(); \
-p.write_text(t.replace( \
-    'subdirs = os.listdir(xml_dir)', \
-    'if not os.path.isdir(xml_dir): return []\n    subdirs = os.listdir(xml_dir)'))" && \
-    uv pip install --no-cache -e ~/VLABench -e ~/rrt-algorithms \
-      mujoco==3.2.2 dm-control==1.0.22 \
-      open3d colorlog scikit-learn openai gdown
-
-# Download VLABench mesh assets. Task configs reference object meshes
-# (obj/meshes/fruit/, containers/basket/, tablewares/plates/, etc.); without
-# them the task builder picks from an empty mesh list and crashes with
-# IndexError at task-build time (random.choice([]) in config_manager.py).
-#
-# Preferred source: an HF Hub mirror. Set VLABENCH_ASSETS_REPO at build time
-# (e.g. --build-arg VLABENCH_ASSETS_REPO=lerobot/vlabench-assets) and we'll
-# snapshot_download the repo into VLABench's assets dir. This is the reliable
-# path for CI — Google Drive frequently returns HTTP 429 ("Too many users have
-# viewed or downloaded this file recently") on shared academic files.
-#
-# After download we *validate* that at least one XML exists under each
-# task-critical subtree and fail the build loudly if not. Silent-empty asset
-# dirs are the #1 cause of VLABench runtime crashes in CI, so we surface them
-# here rather than after a 10-minute eval build.
-#
-# Fallback: VLABench's own gdown-based script. Best-effort only.
-ARG VLABENCH_ASSETS_REPO=""
-RUN ASSETS_DIR="$HOME/VLABench/VLABench/assets" && \
-    if [ -n "${VLABENCH_ASSETS_REPO}" ]; then \
-        echo "Downloading VLABench assets from HF Hub: ${VLABENCH_ASSETS_REPO}" && \
-        uv pip install --no-cache "huggingface_hub[hf_xet]>=0.26" && \
-        python -c "from huggingface_hub import snapshot_download; \
-p = snapshot_download(repo_id='${VLABENCH_ASSETS_REPO}', repo_type='dataset', \
-    local_dir='${ASSETS_DIR}', allow_patterns=['obj/**', 'scenes/**']); \
-print('snapshot_download returned:', p)"; \
-    else \
-        echo "No VLABENCH_ASSETS_REPO set — falling back to gdown" && \
-        python ~/VLABench/scripts/download_assets.py --choice all; \
-    fi && \
-    python -c "\
-from pathlib import Path; \
-import sys; \
-root = Path('${ASSETS_DIR}'); \
-checks = ['obj/meshes/tablewares/plates', 'obj/meshes/containers/basket', 'obj/meshes/fruit', 'obj/meshes/containers/tray']; \
-failed = []; \
-print(f'Validating VLABench assets under {root}'); \
-[print(f'  {c}: {len(list((root/c).rglob(\"*.xml\")))} XMLs') for c in checks]; \
-[failed.append(c) for c in checks if not any((root/c).rglob('*.xml'))]; \
-sys.exit(f'Empty asset dirs (no *.xml): {failed}') if failed else print('All asset dirs populated.')"
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-# Re-install lerobot editably so the new source (with VLABenchEnv registration
-# and updated obs handling) replaces the stale package baked into the nightly image.
-RUN uv pip install --no-cache --no-deps -e .
-
-CMD ["/bin/bash"]

docs/source/_toctree.yml

@@ -61,6 +61,8 @@
     title: SARM
   title: "Reward Models"
 - sections:
+  - local: inference
+    title: Policy Deployment (lerobot-rollout)
   - local: async
     title: Use Async Inference
   - local: rtc
@@ -77,22 +79,10 @@
     title: Adding a New Benchmark
   - local: libero
     title: LIBERO
-  - local: libero_plus
-    title: LIBERO-plus
   - local: metaworld
     title: Meta-World
-  - local: robotwin
-    title: RoboTwin 2.0
-  - local: robocasa
-    title: RoboCasa365
-  - local: robocerebra
-    title: RoboCerebra
-  - local: robomme
-    title: RoboMME
   - local: envhub_isaaclab_arena
     title: NVIDIA IsaacLab Arena Environments
-  - local: vlabench
-    title: VLABench
   title: "Benchmarks"
 - sections:
   - local: introduction_processors

docs/source/hil_data_collection.mdx

@@ -50,30 +50,30 @@ This process can be repeated iteratively: deploy, collect, fine-tune, repeat. Ea
 
 ### Teleoperator Requirements
 
-The `examples/hil` HIL scripts require **teleoperators with active motors** that can:
+The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with active motors** that can:
 
 - Enable/disable torque programmatically
 - Move to target positions (to mirror the robot state when pausing)
 
-**Compatible teleoperators in the current `examples/hil` scripts:**
+**Compatible teleoperators:**
 
 - `openarm_mini` - OpenArm Mini
 - `so_leader` - SO100 / SO101 leader arm
 
 > [!IMPORTANT]
-> The provided `examples/hil` commands default to `bi_openarm_follower` + `openarm_mini`.
+> The provided commands default to `bi_openarm_follower` + `openarm_mini`.
 > `so_follower` + `so_leader` configs are also registered and can be used via CLI flags.
 
 ---
 
 ## Script
 
-A single script handles both synchronous and RTC-based inference. Toggle RTC with `--rtc.enabled=true`:
+Use `lerobot-rollout` with `--strategy.type=dagger` for HIL data collection. Select the inference backend with `--inference.type=sync|rtc`:
 
-| Mode                     | Flag                 | Models                |
-| ------------------------ | -------------------- | --------------------- |
-| Standard (default)       | _(no flag needed)_   | ACT, Diffusion Policy |
-| Real-Time Chunking (RTC) | `--rtc.enabled=true` | Pi0, Pi0.5, SmolVLA   |
+| Mode                     | Flag                   | Models                |
+| ------------------------ | ---------------------- | --------------------- |
+| Standard (default)       | _(no flag needed)_     | ACT, Diffusion Policy |
+| Real-Time Chunking (RTC) | `--inference.type=rtc` | Pi0, Pi0.5, SmolVLA   |
 
 ---
 
@@ -97,7 +97,7 @@ python src/lerobot/scripts/lerobot_train.py \
 **Standard inference (ACT, Diffusion Policy):**
 
 ```bash
-python examples/hil/hil_data_collection.py \
+lerobot-rollout --strategy.type=dagger \
     --robot.type=bi_openarm_follower \
     --robot.left_arm_config.port=can1 \
     --robot.left_arm_config.side=left \
@@ -111,8 +111,7 @@ python examples/hil/hil_data_collection.py \
     --dataset.repo_id=your-username/hil-dataset \
     --dataset.single_task="Fold the T-shirt properly" \
     --dataset.fps=30 \
-    --dataset.episode_time_s=1000 \
-    --dataset.num_episodes=50 \
+    --strategy.num_episodes=50 \
     --interpolation_multiplier=2
 ```
 
@@ -121,11 +120,11 @@ python examples/hil/hil_data_collection.py \
 For models with high inference latency, enable RTC for smooth execution:
 
 ```bash
-python examples/hil/hil_data_collection.py \
-    --rtc.enabled=true \
-    --rtc.execution_horizon=20 \
-    --rtc.max_guidance_weight=5.0 \
-    --rtc.prefix_attention_schedule=LINEAR \
+lerobot-rollout --strategy.type=dagger \
+    --inference.type=rtc \
+    --inference.rtc.execution_horizon=20 \
+    --inference.rtc.max_guidance_weight=5.0 \
+    --inference.rtc.prefix_attention_schedule=LINEAR \
     --robot.type=bi_openarm_follower \
     --robot.left_arm_config.port=can1 \
     --robot.left_arm_config.side=left \
@@ -139,8 +138,7 @@ python examples/hil/hil_data_collection.py \
     --dataset.repo_id=your-username/hil-rtc-dataset \
     --dataset.single_task="Fold the T-shirt properly" \
     --dataset.fps=30 \
-    --dataset.episode_time_s=1000 \
-    --dataset.num_episodes=50 \
+    --strategy.num_episodes=50 \
     --interpolation_multiplier=3
 ```
 
@@ -235,7 +233,7 @@ This HIL data collection approach builds on ideas from interactive imitation lea
 
 - **HG-DAgger** (Kelly et al., 2019) made this practical for robotics: a human expert monitors the robot and only intervenes when needed, rather than labeling every state. The gating between autonomous and human control is exactly the pause → takeover → return-to-policy loop used in the scripts here.
 
-- **RaC** (Hu et al., 2025) scales this loop to long-horizon tasks by explicitly decomposing interventions into **recovery** (teleoperating back to a good state) and **correction** (demonstrating the right behavior from there). This decomposition is the protocol followed by the HIL scripts in `examples/hil`.
+- **RaC** (Hu et al., 2025) scales this loop to long-horizon tasks by explicitly decomposing interventions into **recovery** (teleoperating back to a good state) and **correction** (demonstrating the right behavior from there). This decomposition is the protocol followed by the DAgger strategy in `lerobot-rollout`.
 
 - **π0.6/RECAP** (Physical Intelligence, 2025) applies the same iterative collect-and-finetune loop at scale with VLA models, showing that even large pretrained policies benefit substantially from targeted human corrections on their own failure modes. π0.6 is trained using RECAP.
 

docs/source/il_robots.mdx

@@ -509,121 +509,42 @@ hf upload ${HF_USER}/act_so101_test${CKPT} \
 
 ## Run inference and evaluate your policy
 
-You can use the `record` script from [`lerobot-record`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
+Use `lerobot-rollout` to deploy a trained policy on your robot. You can choose different strategies depending on your needs:
 
 <hfoptions id="eval">
-<hfoption id="Command">
+<hfoption id="Base mode (no recording)">
 ```bash
-lerobot-record  \
+lerobot-rollout \
+  --strategy.type=base \
+  --policy.path=${HF_USER}/my_policy \
   --robot.type=so100_follower \
   --robot.port=/dev/ttyACM1 \
   --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
-  --robot.id=my_awesome_follower_arm \
-  --display_data=false \
-  --dataset.repo_id=${HF_USER}/eval_so100 \
-  --dataset.single_task="Put lego brick into the transparent box" \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
-  # <- Teleop optional if you want to teleoperate in between episodes \
-  # --teleop.type=so100_leader \
-  # --teleop.port=/dev/ttyACM0 \
-  # --teleop.id=my_awesome_leader_arm \
-  --policy.path=${HF_USER}/my_policy
+  --task="Put lego brick into the transparent box" \
+  --duration=60
 ```
 </hfoption>
-<hfoption id="API example">
-
-<!-- prettier-ignore-start -->
-```python
-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.datasets import LeRobotDataset
-from lerobot.utils.feature_utils import hw_to_dataset_features
-from lerobot.policies.act import ACTPolicy
-from lerobot.policies import make_pre_post_processors
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.scripts.lerobot_record import record_loop
-from lerobot.common.control_utils import init_keyboard_listener
-from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
-
-
-NUM_EPISODES = 5
-FPS = 30
-EPISODE_TIME_SEC = 60
-TASK_DESCRIPTION = "My task description"
-HF_MODEL_ID = "<hf_username>/<model_repo_id>"
-HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
-
-# Create the robot configuration
-camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-robot_config = SO100FollowerConfig(
-    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", cameras=camera_config
-)
-
-# Initialize the robot
-robot = SO100Follower(robot_config)
-
-# Initialize the policy
-policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
-
-# Configure the dataset features
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-# Create the dataset
-dataset = LeRobotDataset.create(
-    repo_id=HF_DATASET_ID,
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# Initialize the keyboard listener and rerun visualization
-_, events = init_keyboard_listener()
-init_rerun(session_name="recording")
-
-# Connect the robot
-robot.connect()
-
-preprocessor, postprocessor = make_pre_post_processors(
-    policy_cfg=policy,
-    pretrained_path=HF_MODEL_ID,
-    dataset_stats=dataset.meta.stats,
-)
-
-for episode_idx in range(NUM_EPISODES):
-    log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
-
-    # Run the policy inference loop
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        policy=policy,
-        preprocessor=preprocessor,
-        postprocessor=postprocessor,
-        dataset=dataset,
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
-    )
-
-    dataset.save_episode()
-
-# Clean up
-robot.disconnect()
-dataset.push_to_hub()
+<hfoption id="Sentry mode (with recording)">
+```bash
+lerobot-rollout \
+  --strategy.type=sentry \
+  --strategy.upload_every_n_episodes=5 \
+  --policy.path=${HF_USER}/my_policy \
+  --robot.type=so100_follower \
+  --robot.port=/dev/ttyACM1 \
+  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/eval_so100 \
+  --dataset.single_task="Put lego brick into the transparent box" \
+  --duration=600
 ```
-<!-- prettier-ignore-end -->
-
 </hfoption>
 </hfoptions>
 
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
+The `--strategy.type` flag selects the execution mode:
 
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_so101_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_so101_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_so101_test`).
+- `base`: Autonomous rollout with no data recording (useful for quick evaluation)
+- `sentry`: Continuous recording with auto-upload (useful for large-scale evaluation)
+- `highlight`: Ring buffer recording with keystroke save (useful for capturing interesting events)
+- `dagger`: Human-in-the-loop data collection (see [HIL Data Collection](./hil_data_collection))
+
+All strategies support `--inference.type=rtc` for smooth execution with slow VLA models (Pi0, Pi0.5, SmolVLA).

docs/source/inference.mdx

@@ -0,0 +1,261 @@
+# Policy Deployment (lerobot-rollout)
+
+`lerobot-rollout` is the single CLI for deploying trained policies on real robots. It supports multiple execution strategies and inference backends, from quick evaluation to continuous recording and human-in-the-loop data collection.
+
+## Quick Start
+
+No extra dependencies are needed beyond your robot and policy extras.
+
+```bash
+lerobot-rollout \
+    --strategy.type=base \
+    --policy.path=lerobot/act_koch_real \
+    --robot.type=koch_follower \
+    --robot.port=/dev/ttyACM0 \
+    --task="pick up cube" \
+    --duration=30
+```
+
+This runs the policy for 30 seconds with no recording.
+
+---
+
+## Strategies
+
+Select a strategy with `--strategy.type=<name>`. Each strategy defines a different control loop with its own recording and interaction semantics.
+
+### Base (`--strategy.type=base`)
+
+Autonomous policy execution with no data recording. Use this for quick evaluation, demos, or when you only need to observe the robot.
+
+```bash
+lerobot-rollout \
+    --strategy.type=base \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=so100_follower \
+    --robot.port=/dev/ttyACM0 \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --task="Put lego brick into the box" \
+    --duration=60
+```
+
+| Flag             | Description                                            |
+| ---------------- | ------------------------------------------------------ |
+| `--duration`     | Run time in seconds (0 = infinite)                     |
+| `--task`         | Task description passed to the policy                  |
+| `--display_data` | Stream observations/actions to Rerun for visualization |
+
+### Sentry (`--strategy.type=sentry`)
+
+Continuous autonomous recording with periodic upload to the Hugging Face Hub. Episode boundaries are auto-computed from camera resolution and FPS so each saved episode produces a complete video file, keeping uploads efficient.
+
+Policy state (hidden state, RTC queue) persists across episode boundaries: the robot does not reset between episodes.
+
+```bash
+lerobot-rollout \
+    --strategy.type=sentry \
+    --strategy.upload_every_n_episodes=5 \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=so100_follower \
+    --robot.port=/dev/ttyACM0 \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --dataset.repo_id=${HF_USER}/eval_data \
+    --dataset.single_task="Put lego brick into the box" \
+    --duration=3600
+```
+
+| Flag                                   | Description                                                 |
+| -------------------------------------- | ----------------------------------------------------------- |
+| `--strategy.upload_every_n_episodes`   | Push to Hub every N episodes (default: 5)                   |
+| `--strategy.target_video_file_size_mb` | Target video file size for episode rotation (default: auto) |
+| `--dataset.repo_id`                    | **Required.** Hub repository for the recorded dataset       |
+| `--dataset.push_to_hub`                | Whether to push to Hub on teardown (default: true)          |
+
+### Highlight (`--strategy.type=highlight`)
+
+Autonomous rollout with on-demand recording via a memory-bounded ring buffer. The robot runs continuously while the buffer captures the last N seconds of telemetry. Press the save key to flush the buffer and start live recording; press it again to save the episode.
+
+```bash
+lerobot-rollout \
+    --strategy.type=highlight \
+    --strategy.ring_buffer_seconds=30 \
+    --strategy.save_key=s \
+    --strategy.push_key=h \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=koch_follower \
+    --robot.port=/dev/ttyACM0 \
+    --dataset.repo_id=${HF_USER}/highlight_data \
+    --dataset.single_task="Pick up the red cube"
+```
+
+**Keyboard controls:**
+
+| Key                | Action                                                   |
+| ------------------ | -------------------------------------------------------- |
+| `s` (configurable) | Start recording (flushes buffer) / stop and save episode |
+| `h` (configurable) | Push dataset to Hub                                      |
+| `ESC`              | Stop the session                                         |
+
+| Flag                                   | Description                                    |
+| -------------------------------------- | ---------------------------------------------- |
+| `--strategy.ring_buffer_seconds`       | Duration of buffered telemetry (default: 30)   |
+| `--strategy.ring_buffer_max_memory_mb` | Memory cap for the ring buffer (default: 2048) |
+| `--strategy.save_key`                  | Key to toggle recording (default: `s`)         |
+| `--strategy.push_key`                  | Key to push to Hub (default: `h`)              |
+
+### DAgger (`--strategy.type=dagger`)
+
+Human-in-the-loop data collection. Alternates between autonomous policy execution and human intervention via a teleoperator. Intervention frames are tagged with `intervention=True`. Requires a teleoperator (`--teleop.type`).
+
+See the [Human-In-the-Loop Data Collection](./hil_data_collection) guide for a detailed walkthrough.
+
+**Corrections-only mode** (default): Only human correction windows are recorded. Each correction becomes one episode.
+
+```bash
+lerobot-rollout \
+    --strategy.type=dagger \
+    --strategy.num_episodes=20 \
+    --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --robot.type=bi_openarm_follower \
+    --teleop.type=openarm_mini \
+    --dataset.repo_id=${HF_USER}/hil_data \
+    --dataset.single_task="Fold the T-shirt"
+```
+
+**Continuous recording mode** (`--strategy.record_autonomous=true`): Both autonomous and correction frames are recorded with time-based episode rotation (same as Sentry).
+
+```bash
+lerobot-rollout \
+    --strategy.type=dagger \
+    --strategy.record_autonomous=true \
+    --strategy.num_episodes=50 \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=so100_follower \
+    --robot.port=/dev/ttyACM0 \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/ttyACM1 \
+    --dataset.repo_id=${HF_USER}/dagger_data \
+    --dataset.single_task="Grasp the block"
+```
+
+**Keyboard controls** (default input device):
+
+| Key     | Action                                      |
+| ------- | ------------------------------------------- |
+| `Space` | Pause / resume policy execution             |
+| `Tab`   | Start / stop human correction               |
+| `Enter` | Push dataset to Hub (corrections-only mode) |
+| `ESC`   | Stop the session                            |
+
+Foot pedal input is also supported via `--strategy.input_device=pedal`. Configure pedal codes with `--strategy.pedal.*` flags.
+
+| Flag                                 | Description                                             |
+| ------------------------------------ | ------------------------------------------------------- |
+| `--strategy.num_episodes`            | Number of correction episodes to record (default: 10)   |
+| `--strategy.record_autonomous`       | Record autonomous frames too (default: false)           |
+| `--strategy.upload_every_n_episodes` | Push to Hub every N episodes (default: 5)               |
+| `--strategy.input_device`            | Input device: `keyboard` or `pedal` (default: keyboard) |
+| `--teleop.type`                      | **Required.** Teleoperator type                         |
+
+---
+
+## Inference Backends
+
+Select a backend with `--inference.type=<name>`. All strategies work with both backends.
+
+### Sync (default)
+
+One policy call per control tick. The main loop blocks until the action is computed.
+
+Works with all policies. No extra flags needed.
+
+### Real-Time Chunking (`--inference.type=rtc`)
+
+A background thread produces action chunks asynchronously. The main control loop polls for the next ready action while the policy computes the next chunk in parallel.
+
+Use RTC with large, slow VLA models (Pi0, Pi0.5, SmolVLA) for smooth, continuous motion despite high inference latency.
+
+```bash
+lerobot-rollout \
+    --strategy.type=base \
+    --inference.type=rtc \
+    --inference.rtc.execution_horizon=10 \
+    --inference.rtc.max_guidance_weight=10.0 \
+    --policy.path=${HF_USER}/pi0_policy \
+    --robot.type=so100_follower \
+    --robot.port=/dev/ttyACM0 \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --task="Pick up the cube" \
+    --duration=60 \
+    --device=cuda
+```
+
+| Flag                                        | Description                                                    |
+| ------------------------------------------- | -------------------------------------------------------------- |
+| `--inference.rtc.execution_horizon`         | Steps to blend with previous chunk (default: varies by policy) |
+| `--inference.rtc.max_guidance_weight`       | Consistency enforcement strength (default: varies by policy)   |
+| `--inference.rtc.prefix_attention_schedule` | Blend schedule: `LINEAR`, `EXP`, `ONES`, `ZEROS`               |
+| `--inference.queue_threshold`               | Max queue size before backpressure (default: 30)               |
+
+See the [Real-Time Chunking](./rtc) guide for details on tuning RTC parameters.
+
+---
+
+## Common Flags
+
+| Flag                              | Description                                                       | Default |
+| --------------------------------- | ----------------------------------------------------------------- | ------- |
+| `--policy.path`                   | **Required.** HF Hub model ID or local checkpoint path            | --      |
+| `--robot.type`                    | **Required.** Robot type (e.g. `so100_follower`, `koch_follower`) | --      |
+| `--robot.port`                    | Serial port for the robot                                         | --      |
+| `--robot.cameras`                 | Camera configuration (JSON dict)                                  | --      |
+| `--fps`                           | Control loop frequency                                            | 30      |
+| `--duration`                      | Run time in seconds (0 = infinite)                                | 0       |
+| `--device`                        | Torch device (`cpu`, `cuda`, `mps`)                               | auto    |
+| `--task`                          | Task description (used when no dataset is provided)               | --      |
+| `--display_data`                  | Stream telemetry to Rerun visualization                           | false   |
+| `--display_ip` / `--display_port` | Remote Rerun server address                                       | --      |
+| `--interpolation_multiplier`      | Action interpolation factor                                       | 1       |
+| `--use_torch_compile`             | Enable `torch.compile` for inference                              | false   |
+| `--resume`                        | Resume a previous recording session                               | false   |
+| `--play_sounds`                   | Vocal synthesis for events                                        | true    |
+
+---
+
+## Programmatic Usage
+
+For custom deployments (e.g. with kinematics processors), use the rollout module API directly:
+
+```python
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.utils.process import ProcessSignalHandler
+
+cfg = RolloutConfig(
+    robot=my_robot_config,
+    policy=my_policy_config,
+    strategy=BaseStrategyConfig(),
+    inference=SyncInferenceConfig(),
+    fps=30,
+    duration=60,
+    task="my task",
+)
+
+signal_handler = ProcessSignalHandler(use_threads=True)
+ctx = build_rollout_context(
+    cfg,
+    signal_handler.shutdown_event,
+    robot_action_processor=my_custom_action_processor,       # optional
+    robot_observation_processor=my_custom_obs_processor,     # optional
+)
+
+strategy = BaseStrategy(cfg.strategy)
+try:
+    strategy.setup(ctx)
+    strategy.run(ctx)
+finally:
+    strategy.teardown(ctx)
+```
+
+See `examples/so100_to_so100_EE/rollout.py` and `examples/phone_to_so100/rollout.py` for full examples with kinematics processors.

docs/source/libero_plus.mdx

@@ -1,188 +0,0 @@
-# LIBERO-plus
-
-LIBERO-plus is a **robustness benchmark** for Vision-Language-Action (VLA) models built on top of [LIBERO](./libero). It systematically stress-tests policies by applying **seven independent perturbation dimensions** to the original LIBERO task set, exposing failure modes that standard benchmarks miss.
-
-- Paper: [In-depth Robustness Analysis of Vision-Language-Action Models](https://arxiv.org/abs/2510.13626)
-- GitHub: [sylvestf/LIBERO-plus](https://github.com/sylvestf/LIBERO-plus)
-- Dataset: [lerobot/libero_plus](https://huggingface.co/datasets/lerobot/libero_plus)
-
-![An overview of the LIBERO-plus benchmark perturbation dimensions](https://github.com/sylvestf/LIBERO-plus/raw/main/static/images/libero-plus.jpg)
-
-## Perturbation dimensions
-
-LIBERO-plus creates ~10 000 task variants by perturbing each original LIBERO task along these axes:
-
-| Dimension             | What changes                                          |
-| --------------------- | ----------------------------------------------------- |
-| Objects layout        | Target position, presence of confounding objects      |
-| Camera viewpoints     | Camera position, orientation, field-of-view           |
-| Robot initial states  | Manipulator start pose                                |
-| Language instructions | LLM-rewritten task description (paraphrase / synonym) |
-| Light conditions      | Intensity, direction, color, shadow                   |
-| Background textures   | Scene surface and object appearance                   |
-| Sensor noise          | Photometric distortions and image degradation         |
-
-## Available task suites
-
-LIBERO-plus covers the same five suites as LIBERO:
-
-| Suite          | CLI name         | Tasks | Max steps | Description                                        |
-| -------------- | ---------------- | ----- | --------- | -------------------------------------------------- |
-| LIBERO-Spatial | `libero_spatial` | 10    | 280       | Tasks requiring reasoning about spatial relations  |
-| LIBERO-Object  | `libero_object`  | 10    | 280       | Tasks centered on manipulating different objects   |
-| LIBERO-Goal    | `libero_goal`    | 10    | 300       | Goal-conditioned tasks with changing targets       |
-| LIBERO-90      | `libero_90`      | 90    | 400       | Short-horizon tasks from the LIBERO-100 collection |
-| LIBERO-Long    | `libero_10`      | 10    | 520       | Long-horizon tasks from the LIBERO-100 collection  |
-
-<Tip warning={true}>
-  Installing LIBERO-plus **replaces** vanilla LIBERO — it uninstalls `hf-libero`
-  so that `import libero` resolves to the LIBERO-plus fork. You cannot have both
-  installed at the same time. To switch back to vanilla LIBERO, uninstall the
-  fork and reinstall with `pip install -e ".[libero]"`.
-</Tip>
-
-## Installation
-
-### System dependencies (Linux only)
-
-```bash
-sudo apt install libexpat1 libfontconfig1-dev libmagickwand-dev
-```
-
-### Python package
-
-```bash
-pip install -e ".[libero]" "robosuite==1.4.1" bddl easydict mujoco wand scikit-image gym
-git clone https://github.com/sylvestf/LIBERO-plus.git
-cd LIBERO-plus && pip install --no-deps -e .
-pip uninstall -y hf-libero  # so `import libero` resolves to the fork
-```
-
-LIBERO-plus is installed from its GitHub fork rather than a pyproject extra — the fork ships as a namespace package that pip can't handle, so it must be cloned and added to `PYTHONPATH`. See `docker/Dockerfile.benchmark.libero_plus` for the canonical install. MuJoCo is required, so only Linux is supported.
-
-<Tip>
-Set the MuJoCo rendering backend before running evaluation:
-
-```bash
-export MUJOCO_GL=egl   # headless / HPC / cloud
-```
-
-</Tip>
-
-### Download LIBERO-plus assets
-
-LIBERO-plus ships its extended asset pack separately. Download `assets.zip` from the [Hugging Face dataset](https://huggingface.co/datasets/Sylvest/LIBERO-plus/tree/main) and extract it into the LIBERO-plus package directory:
-
-```bash
-# After installing the package, find where it was installed:
-python -c "import libero; print(libero.__file__)"
-# Then extract assets.zip into <package_root>/libero/assets/
-```
-
-## Evaluation
-
-### Default evaluation (recommended)
-
-Evaluate across the four standard suites (10 episodes per task):
-
-```bash
-lerobot-eval \
-  --policy.path="your-policy-id" \
-  --env.type=libero_plus \
-  --env.task=libero_spatial,libero_object,libero_goal,libero_10 \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10 \
-  --env.max_parallel_tasks=1
-```
-
-### Single-suite evaluation
-
-Evaluate on one LIBERO-plus suite:
-
-```bash
-lerobot-eval \
-  --policy.path="your-policy-id" \
-  --env.type=libero_plus \
-  --env.task=libero_spatial \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10
-```
-
-- `--env.task` picks the suite (`libero_spatial`, `libero_object`, etc.).
-- `--env.task_ids` restricts to specific task indices (`[0]`, `[1,2,3]`, etc.). Omit to run all tasks in the suite.
-- `--eval.batch_size` controls how many environments run in parallel.
-- `--eval.n_episodes` sets how many episodes to run per task.
-
-### Multi-suite evaluation
-
-Benchmark a policy across multiple suites at once by passing a comma-separated list:
-
-```bash
-lerobot-eval \
-  --policy.path="your-policy-id" \
-  --env.type=libero_plus \
-  --env.task=libero_spatial,libero_object \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10
-```
-
-### Control mode
-
-LIBERO-plus supports two control modes — `relative` (default) and `absolute`. Different VLA checkpoints are trained with different action parameterizations, so make sure the mode matches your policy:
-
-```bash
---env.control_mode=relative   # or "absolute"
-```
-
-### Policy inputs and outputs
-
-**Observations:**
-
-- `observation.state` — 8-dim proprioceptive features (eef position, axis-angle orientation, gripper qpos)
-- `observation.images.image` — main camera view (`agentview_image`), HWC uint8
-- `observation.images.image2` — wrist camera view (`robot0_eye_in_hand_image`), HWC uint8
-
-**Actions:**
-
-- Continuous control in `Box(-1, 1, shape=(7,))` — 6D end-effector delta + 1D gripper
-
-### Recommended evaluation episodes
-
-For reproducible benchmarking, use **10 episodes per task** across all four standard suites (Spatial, Object, Goal, Long). This gives 400 total episodes and matches the protocol used for published results.
-
-## Training
-
-### Dataset
-
-A LeRobot-format training dataset for LIBERO-plus is available at:
-
-- [lerobot/libero_plus](https://huggingface.co/datasets/lerobot/libero_plus)
-
-### Example training command
-
-```bash
-lerobot-train \
-    --policy.type=smolvla \
-    --policy.repo_id=${HF_USER}/smolvla_libero_plus \
-    --policy.load_vlm_weights=true \
-    --dataset.repo_id=lerobot/libero_plus \
-    --env.type=libero_plus \
-    --env.task=libero_spatial \
-    --output_dir=./outputs/ \
-    --steps=100000 \
-    --batch_size=4 \
-    --eval.batch_size=1 \
-    --eval.n_episodes=1 \
-    --eval_freq=1000
-```
-
-## Relationship to LIBERO
-
-LIBERO-plus is a drop-in extension of LIBERO:
-
-- Same Python gym interface (`LiberoEnv`, `LiberoProcessorStep`)
-- Same camera names and observation/action format
-- Same task suite names
-- Installs under the same `libero` Python package name (different GitHub repo)
-
-To use the original LIBERO benchmark, see [LIBERO](./libero) and use `--env.type=libero`.

docs/source/robocasa.mdx

@@ -1,188 +0,0 @@
-# RoboCasa365
-
-[RoboCasa365](https://robocasa.ai) is a large-scale simulation framework for training and benchmarking **generalist robots** in everyday kitchen tasks. It ships 365 diverse manipulation tasks across 2,500 kitchen environments, 3,200+ object assets and 600+ hours of human demonstration data, on a PandaOmron 12-DOF mobile manipulator (Franka arm on a holonomic base).
-
-- Paper: [RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots](https://arxiv.org/abs/2406.02523)
-- GitHub: [robocasa/robocasa](https://github.com/robocasa/robocasa)
-- Project website: [robocasa.ai](https://robocasa.ai)
-- Pretrained policy: [`lerobot/smolvla_robocasa`](https://huggingface.co/lerobot/smolvla_robocasa)
-- Single-task dataset (CloseFridge): [`pepijn223/robocasa_CloseFridge`](https://huggingface.co/datasets/pepijn223/robocasa_CloseFridge)
-
-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/robocasa-banner.webp"
-  alt="RoboCasa365 benchmark overview"
-  width="85%"
-/>
-
-## Available tasks
-
-RoboCasa365 organizes its 365 tasks into two families and three upstream benchmark groups that LeRobot exposes as first-class `--env.task` shortcuts:
-
-| Family    | Tasks | Description                                                                     |
-| --------- | ----- | ------------------------------------------------------------------------------- |
-| Atomic    | ~65   | Single-skill tasks: pick-and-place, door/drawer manipulation, appliance control |
-| Composite | ~300  | Multi-step tasks across 60+ categories: cooking, cleaning, organizing, etc.     |
-
-**Atomic task examples:** `CloseFridge`, `OpenDrawer`, `OpenCabinet`, `TurnOnMicrowave`, `TurnOffStove`, `NavigateKitchen`, `PickPlaceCounterToStove`.
-
-**Composite task categories:** baking, boiling, brewing, chopping, clearing table, defrosting food, loading dishwasher, making tea, microwaving food, washing dishes, and more.
-
-`--env.task` accepts three forms:
-
-- a single task name (`CloseFridge`)
-- a comma-separated list (`CloseFridge,OpenBlenderLid,PickPlaceCoffee`)
-- a benchmark-group shortcut — `atomic_seen`, `composite_seen`, `composite_unseen`, `pretrain50`, `pretrain100`, `pretrain200`, `pretrain300` — which auto-expands to the upstream task list and auto-sets the dataset `split` (`target` or `pretrain`).
-
-## Installation
-
-RoboCasa and its dependency `robosuite` are not published on PyPI, and RoboCasa's own `setup.py` hardcodes `lerobot==0.3.3`, which conflicts with this repo's `lerobot`. LeRobot therefore does **not** expose a `robocasa` extra — install the two packages manually as editable clones (using `--no-deps` on `robocasa` to skip its shadowed `lerobot` pin):
-
-```bash
-# After following the standard LeRobot installation instructions.
-
-git clone https://github.com/robocasa/robocasa.git ~/robocasa
-git clone https://github.com/ARISE-Initiative/robosuite.git ~/robosuite
-pip install -e ~/robocasa --no-deps
-pip install -e ~/robosuite
-
-# Robocasa's runtime deps (the ones its setup.py would have pulled, minus
-# the bad lerobot pin).
-pip install numpy numba scipy mujoco pygame Pillow opencv-python \
-            pyyaml pynput tqdm termcolor imageio h5py lxml hidapi \
-            tianshou gymnasium
-
-python -m robocasa.scripts.setup_macros
-# Lightweight assets (lightwheel object meshes + textures). Enough for
-# the default env out of the box.
-python -m robocasa.scripts.download_kitchen_assets \
-  --type tex tex_generative fixtures_lw objs_lw
-# Optional: full objaverse/aigen registries (~30GB) for richer object
-# variety. Enable at eval time via --env.obj_registries (see below).
-# python -m robocasa.scripts.download_kitchen_assets --type objs_objaverse
-```
-
-<Tip>
-RoboCasa requires MuJoCo. Set the rendering backend before training or evaluation:
-
-```bash
-export MUJOCO_GL=egl  # for headless servers (HPC, cloud)
-```
-
-</Tip>
-
-### Object registries
-
-By default the env samples objects only from the `lightwheel` registry (what `--type objs_lw` ships), which avoids a `Probabilities contain NaN` crash when the objaverse / aigen packs aren't on disk. If you've downloaded the full asset set, enable the full registry at runtime:
-
-```bash
---env.obj_registries='[objaverse,lightwheel]'
-```
-
-## Evaluation
-
-All eval snippets below mirror the CI command (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps RoboCasa's native camera keys (`robot0_agentview_left` / `robot0_eye_in_hand` / `robot0_agentview_right`) onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_robocasa` policy was trained on.
-
-### Single-task evaluation (recommended for quick iteration)
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_robocasa \
-  --env.type=robocasa \
-  --env.task=CloseFridge \
-  --eval.batch_size=1 \
-  --eval.n_episodes=20 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
-```
-
-### Multi-task evaluation
-
-Pass a comma-separated list of tasks:
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_robocasa \
-  --env.type=robocasa \
-  --env.task=CloseFridge,OpenCabinet,OpenDrawer,TurnOnMicrowave,TurnOffStove \
-  --eval.batch_size=1 \
-  --eval.n_episodes=20 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
-```
-
-### Benchmark-group evaluation
-
-Run an entire upstream group (e.g. all 18 `atomic_seen` tasks with `split=target`):
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_robocasa \
-  --env.type=robocasa \
-  --env.task=atomic_seen \
-  --eval.batch_size=1 \
-  --eval.n_episodes=20 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
-```
-
-### Recommended evaluation episodes
-
-**20 episodes per task** for reproducible benchmarking. Matches the protocol used in published results.
-
-## Policy inputs and outputs
-
-**Observations** (raw RoboCasa camera names are preserved verbatim):
-
-- `observation.state` — 16-dim proprioceptive state (base position, base quaternion, relative end-effector position, relative end-effector quaternion, gripper qpos)
-- `observation.images.robot0_agentview_left` — left agent view, 256×256 HWC uint8
-- `observation.images.robot0_eye_in_hand` — wrist camera view, 256×256 HWC uint8
-- `observation.images.robot0_agentview_right` — right agent view, 256×256 HWC uint8
-
-**Actions:**
-
-- Continuous control in `Box(-1, 1, shape=(12,))` — base motion (4D) + control mode (1D) + end-effector position (3D) + end-effector rotation (3D) + gripper (1D).
-
-## Training
-
-### Single-task example
-
-A ready-to-use single-task dataset is on the Hub:
-[`pepijn223/robocasa_CloseFridge`](https://huggingface.co/datasets/pepijn223/robocasa_CloseFridge).
-
-Fine-tune a SmolVLA base on `CloseFridge`:
-
-```bash
-lerobot-train \
-  --policy.type=smolvla \
-  --policy.repo_id=${HF_USER}/smolvla_robocasa_CloseFridge \
-  --policy.load_vlm_weights=true \
-  --policy.push_to_hub=true \
-  --dataset.repo_id=pepijn223/robocasa_CloseFridge \
-  --env.type=robocasa \
-  --env.task=CloseFridge \
-  --output_dir=./outputs/smolvla_robocasa_CloseFridge \
-  --steps=100000 \
-  --batch_size=4 \
-  --eval_freq=5000 \
-  --eval.batch_size=1 \
-  --eval.n_episodes=5 \
-  --save_freq=10000
-```
-
-Evaluate the resulting checkpoint:
-
-```bash
-lerobot-eval \
-  --policy.path=${HF_USER}/smolvla_robocasa_CloseFridge \
-  --env.type=robocasa \
-  --env.task=CloseFridge \
-  --eval.batch_size=1 \
-  --eval.n_episodes=20
-```
-
-## Reproducing published results
-
-The released checkpoint [`lerobot/smolvla_robocasa`](https://huggingface.co/lerobot/smolvla_robocasa) is evaluated with the commands in the [Evaluation](#evaluation) section. CI runs a 10-atomic-task smoke eval (one episode each) on every PR touching the benchmark, picking fixture-centric tasks that don't require the objaverse asset pack.

docs/source/robocerebra.mdx

@@ -1,99 +0,0 @@
-# RoboCerebra
-
-[RoboCerebra](https://robocerebra-project.github.io/) is a long-horizon manipulation benchmark that evaluates **high-level reasoning, planning, and memory** in VLAs. Episodes chain multiple sub-goals with language-grounded intermediate instructions, built on top of LIBERO's simulator stack (MuJoCo + robosuite, Franka Panda 7-DOF).
-
-- Paper: [RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation](https://arxiv.org/abs/2506.06677)
-- Project website: [robocerebra-project.github.io](https://robocerebra-project.github.io/)
-- Dataset: [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) — LeRobot v3.0, 6,660 episodes / 571,116 frames at 20 fps, 1,728 language-grounded sub-tasks.
-- Pretrained policy: [`lerobot/smolvla_robocerebra`](https://huggingface.co/lerobot/smolvla_robocerebra)
-
-## Available tasks
-
-RoboCerebra reuses LIBERO's simulator, so evaluation runs against the LIBERO `libero_10` long-horizon suite:
-
-| Suite     | CLI name    | Tasks | Description                                                   |
-| --------- | ----------- | ----- | ------------------------------------------------------------- |
-| LIBERO-10 | `libero_10` | 10    | Long-horizon kitchen/living room tasks chaining 3–6 sub-goals |
-
-Each RoboCerebra episode in the dataset is segmented into multiple sub-tasks with natural-language instructions, which the unified dataset exposes as independent supervision signals.
-
-## Installation
-
-RoboCerebra piggybacks on LIBERO, so the `libero` extra is all you need:
-
-```bash
-pip install -e ".[libero]"
-```
-
-<Tip>
-RoboCerebra requires Linux (MuJoCo / robosuite). Set the rendering backend before training or evaluation:
-
-```bash
-export MUJOCO_GL=egl  # for headless servers (HPC, cloud)
-```
-
-</Tip>
-
-## Evaluation
-
-RoboCerebra eval runs against LIBERO's `libero_10` suite with RoboCerebra's camera naming (`image` + `wrist_image`) and an extra empty-camera slot so a three-view-trained policy receives the expected input layout:
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_robocerebra \
-  --env.type=libero \
-  --env.task=libero_10 \
-  --env.fps=20 \
-  --env.obs_type=pixels_agent_pos \
-  --env.observation_height=256 \
-  --env.observation_width=256 \
-  '--env.camera_name_mapping={"agentview_image": "image", "robot0_eye_in_hand_image": "wrist_image"}' \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.wrist_image": "observation.images.camera2"}' \
-  --policy.empty_cameras=1
-```
-
-### Recommended evaluation episodes
-
-**10 episodes per task** across the `libero_10` suite (100 total) for reproducible benchmarking. Matches the protocol used in the RoboCerebra paper.
-
-## Policy inputs and outputs
-
-**Observations:**
-
-- `observation.state` — 8-dim proprioceptive state (7 joint positions + gripper)
-- `observation.images.image` — third-person view, 256×256 HWC uint8
-- `observation.images.wrist_image` — wrist-mounted camera view, 256×256 HWC uint8
-
-**Actions:**
-
-- Continuous control in `Box(-1, 1, shape=(7,))` — end-effector delta (6D) + gripper (1D)
-
-## Training
-
-The unified dataset at [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) exposes two RGB streams and language-grounded sub-task annotations:
-
-| Feature                          | Shape         | Description          |
-| -------------------------------- | ------------- | -------------------- |
-| `observation.images.image`       | (256, 256, 3) | Third-person view    |
-| `observation.images.wrist_image` | (256, 256, 3) | Wrist-mounted camera |
-| `observation.state`              | (8,)          | Joint pos + gripper  |
-| `action`                         | (7,)          | EEF delta + gripper  |
-
-Fine-tune a SmolVLA base on it:
-
-```bash
-lerobot-train \
-  --policy.path=lerobot/smolvla_base \
-  --dataset.repo_id=lerobot/robocerebra_unified \
-  --env.type=libero \
-  --env.task=libero_10 \
-  --output_dir=outputs/smolvla_robocerebra
-```
-
-## Reproducing published results
-
-The released checkpoint [`lerobot/smolvla_robocerebra`](https://huggingface.co/lerobot/smolvla_robocerebra) was trained on `lerobot/robocerebra_unified` and evaluated with the command in the [Evaluation](#evaluation) section. CI runs the same command with `--eval.n_episodes=1` as a smoke test on every PR touching the benchmark.

docs/source/robomme.mdx

@@ -1,130 +0,0 @@
-# RoboMME
-
-[RoboMME](https://robomme.github.io) is a memory-augmented manipulation benchmark built on ManiSkill (SAPIEN). It evaluates a robot's ability to retain and use information across an episode — counting, object permanence, reference, and imitation.
-
-- **16 tasks** across 4 memory-skill suites
-- **1,600 training demos** (100 per task, 50 val, 50 test)
-- **Dataset**: [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) — LeRobot v3.0, 768K frames at 10 fps
-- **Simulator**: ManiSkill / SAPIEN, Panda arm, Linux only
-
-![RoboMME benchmark tasks overview](https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2603.04639/gradient.png)
-
-## Tasks
-
-| Suite                             | Tasks                                                         |
-| --------------------------------- | ------------------------------------------------------------- |
-| **Counting** (temporal memory)    | BinFill, PickXtimes, SwingXtimes, StopCube                    |
-| **Permanence** (spatial memory)   | VideoUnmask, VideoUnmaskSwap, ButtonUnmask, ButtonUnmaskSwap  |
-| **Reference** (object memory)     | PickHighlight, VideoRepick, VideoPlaceButton, VideoPlaceOrder |
-| **Imitation** (procedural memory) | MoveCube, InsertPeg, PatternLock, RouteStick                  |
-
-## Installation
-
-> RoboMME requires **Linux** (ManiSkill/SAPIEN uses Vulkan rendering). Docker is recommended to isolate dependency conflicts.
-
-### Native (Linux)
-
-```bash
-pip install --override <(printf 'gymnasium==0.29.1\nnumpy==1.26.4\n') \
-  -e '.[smolvla,av-dep]' \
-  'robomme @ git+https://github.com/RoboMME/robomme_benchmark.git@main'
-```
-
-> **Dependency note**: `mani-skill` (pulled by `robomme`) pins `gymnasium==0.29.1` and `numpy<2.0.0`, which conflict with lerobot's base `numpy>=2.0.0`. That's why `robomme` is not a pyproject extra — use the override install above, or the Docker approach below to avoid conflicts entirely.
-
-### Docker (recommended)
-
-```bash
-# Build base image first (from repo root)
-docker build -f docker/Dockerfile.eval-base -t lerobot-eval-base .
-
-# Build RoboMME eval image (applies gymnasium + numpy pin overrides)
-docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-robomme .
-```
-
-The `docker/Dockerfile.benchmark.robomme` image overrides `gymnasium==0.29.1` and `numpy==1.26.4` after lerobot's install. Both versions are runtime-safe for lerobot's actual API usage.
-
-## Running Evaluation
-
-### Default (single task, single episode)
-
-```bash
-lerobot-eval \
-    --policy.path=<your_policy_repo> \
-    --env.type=robomme \
-    --env.task=PickXtimes \
-    --env.dataset_split=test \
-    --env.task_ids=[0] \
-    --eval.batch_size=1 \
-    --eval.n_episodes=1
-```
-
-### Multi-task evaluation
-
-Evaluate multiple tasks in one run by comma-separating task names. Use `task_ids` to control which episodes are evaluated per task. Recommended: 50 episodes per task for the test split.
-
-```bash
-lerobot-eval \
-    --policy.path=<your_policy_repo> \
-    --env.type=robomme \
-    --env.task=PickXtimes,BinFill,StopCube,MoveCube,InsertPeg \
-    --env.dataset_split=test \
-    --env.task_ids=[0,1,2,3,4,5,6,7,8,9] \
-    --eval.batch_size=1 \
-    --eval.n_episodes=50
-```
-
-### Key CLI options for `env.type=robomme`
-
-| Option               | Default       | Description                                        |
-| -------------------- | ------------- | -------------------------------------------------- |
-| `env.task`           | `PickXtimes`  | Any of the 16 task names above (comma-separated)   |
-| `env.dataset_split`  | `test`        | `train`, `val`, or `test`                          |
-| `env.action_space`   | `joint_angle` | `joint_angle` (8-D) or `ee_pose` (7-D)             |
-| `env.episode_length` | `300`         | Max steps per episode                              |
-| `env.task_ids`       | `null`        | List of episode indices to evaluate (null = `[0]`) |
-
-## Dataset
-
-The dataset [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) is in **LeRobot v3.0 format** and can be loaded directly:
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-dataset = LeRobotDataset("lerobot/robomme")
-```
-
-### Dataset features
-
-| Feature            | Shape         | Description                     |
-| ------------------ | ------------- | ------------------------------- |
-| `image`            | (256, 256, 3) | Front camera RGB                |
-| `wrist_image`      | (256, 256, 3) | Wrist camera RGB                |
-| `actions`          | (8,)          | Joint angles + gripper          |
-| `state`            | (8,)          | Joint positions + gripper state |
-| `simple_subgoal`   | str           | High-level language annotation  |
-| `grounded_subgoal` | str           | Grounded language annotation    |
-| `episode_index`    | int           | Episode ID                      |
-| `frame_index`      | int           | Frame within episode            |
-
-### Feature key alignment (training)
-
-The env wrapper exposes `pixels/image` and `pixels/wrist_image` as observation keys. The `features_map` in `RoboMMEEnv` maps these to `observation.images.image` and `observation.images.wrist_image` for the policy. State is exposed as `agent_pos` and maps to `observation.state`.
-
-The dataset's `image` and `wrist_image` columns already align with the policy input keys, so no renaming is needed when fine-tuning.
-
-## Action Spaces
-
-| Type          | Dim | Description                                               |
-| ------------- | --- | --------------------------------------------------------- |
-| `joint_angle` | 8   | 7 joint angles + 1 gripper (−1 closed, +1 open, absolute) |
-| `ee_pose`     | 7   | xyz + roll/pitch/yaw + gripper                            |
-
-Set via `--env.action_space=joint_angle` (default) or `--env.action_space=ee_pose`.
-
-## Platform Notes
-
-- **Linux only**: ManiSkill requires SAPIEN/Vulkan. macOS and Windows are not supported.
-- **GPU recommended**: Rendering is CPU-capable but slow; CUDA + Vulkan gives full speed.
-- **gymnasium / numpy conflict**: See installation note above. Docker image handles this automatically.
-- **ManiSkill fork**: `robomme` depends on a specific ManiSkill fork (`YinpeiDai/ManiSkill`), pulled in automatically via the `robomme` package.

docs/source/robotwin.mdx

@@ -1,223 +0,0 @@
-# RoboTwin 2.0
-
-RoboTwin 2.0 is a **large-scale dual-arm manipulation benchmark** built on the SAPIEN physics engine. It provides a standardized evaluation protocol for bimanual robotic policies across 50 tasks (as of upstream `main`) with strong domain randomization (clutter, lighting, background, tabletop height, and language instructions).
-
-- Paper: [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://arxiv.org/abs/2506.18088)
-- GitHub: [RoboTwin-Platform/RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin)
-- Leaderboard: [robotwin-platform.github.io/leaderboard](https://robotwin-platform.github.io/leaderboard)
-- Dataset: [lerobot/robotwin_unified](https://huggingface.co/datasets/lerobot/robotwin_unified)
-
-![RoboTwin 2.0 benchmark overview](https://www.aitntnews.com/pictures/2025/7/8/9a7f79cb-5ba9-11f0-8581-fa163e47d677.png)
-
-## Overview
-
-| Property      | Value                                                    |
-| ------------- | -------------------------------------------------------- |
-| Tasks         | 50 dual-arm manipulation tasks                           |
-| Robot         | Aloha-AgileX bimanual (14 DOF, 7 per arm)                |
-| Action space  | 14-dim joint-space, continuous in `[-1, 1]`              |
-| Cameras       | `head_camera`, `left_camera`, `right_camera`             |
-| Simulator     | SAPIEN (not MuJoCo)                                      |
-| Eval protocol | 100 episodes/task, 50 demo_clean demonstrations          |
-| Eval settings | **Easy** (`demo_clean`) and **Hard** (`demo_randomized`) |
-
-## Available tasks
-
-RoboTwin 2.0 ships 50 dual-arm manipulation tasks in its upstream `envs/` directory. The canonical list is the `ROBOTWIN_TASKS` tuple in `src/lerobot/envs/robotwin.py`, mirrored verbatim from the upstream repo. Example tasks:
-
-| Task                     | CLI name                 | Category          |
-| ------------------------ | ------------------------ | ----------------- |
-| Beat block with hammer   | `beat_block_hammer`      | Tool use          |
-| Click bell / alarm clock | `click_bell`             | Precision press   |
-| Stack blocks (2 / 3)     | `stack_blocks_two/three` | Stacking          |
-| Stack bowls (2 / 3)      | `stack_bowls_two/three`  | Stacking          |
-| Handover block / mic     | `handover_block`         | Bimanual coord.   |
-| Lift pot                 | `lift_pot`               | Bimanual lift     |
-| Shake bottle             | `shake_bottle`           | Continuous motion |
-| Turn switch              | `turn_switch`            | Articulated obj   |
-| Stamp seal               | `stamp_seal`             | Precision place   |
-| Scan object              | `scan_object`            | Mobile manip.     |
-
-Pass a comma-separated list to `--env.task` to run multiple tasks in a single eval sweep.
-
-<Tip warning={true}>
-  `open_laptop` is currently broken upstream (its `check_success()` uses
-  `self.arm_tag`, which is only set inside the scripted-expert `play_once()`
-  path and therefore unavailable during normal policy eval). Avoid it until the
-  upstream bug is fixed, or patch the task to default `self.arm_tag = "left"` in
-  `load_actors()`.
-</Tip>
-
-## Dataset
-
-The RoboTwin 2.0 dataset is available in **LeRobot v3.0 format** on the Hugging Face Hub:
-
-```
-lerobot/robotwin_unified
-```
-
-It contains over 100,000 pre-collected trajectories across all 50 tasks (79.6 GB, Apache 2.0 license). No format conversion is needed — it is already in the correct LeRobot v3.0 schema with video observations and action labels.
-
-You can load it directly with the HF Datasets library:
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("lerobot/robotwin_unified", split="train")
-```
-
-## Installation
-
-RoboTwin 2.0 requires **Linux** with an NVIDIA GPU (CUDA 12.1 recommended). Installation takes approximately 20 minutes.
-
-### 1. Create a conda environment
-
-```bash
-conda create -n robotwin python=3.10 -y
-conda activate robotwin
-```
-
-### 2. Install LeRobot
-
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e "."
-```
-
-### 3. Install RoboTwin 2.0
-
-```bash
-git clone https://github.com/RoboTwin-Platform/RoboTwin.git
-cd RoboTwin
-bash script/_install.sh
-bash script/_download_assets.sh
-```
-
-The install script handles all Python dependencies including SAPIEN, CuRobo, mplib, and pytorch3d.
-
-<Tip warning={true}>
-If the automated install fails, install manually:
-
-```bash
-pip install -r requirements.txt
-pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
-cd envs && git clone https://github.com/NVlabs/curobo.git && cd curobo
-pip install -e . --no-build-isolation
-```
-
-Then apply the required mplib fix: in `mplib/planner.py` line 807, remove `or collide` from the conditional.
-
-</Tip>
-
-### 4. Add RoboTwin to PYTHONPATH
-
-The RoboTwin task modules must be importable by LeRobot. From within the `RoboTwin/` directory:
-
-```bash
-export PYTHONPATH="${PYTHONPATH}:$(pwd)"
-```
-
-Add this to your shell profile to make it permanent.
-
-## Evaluation
-
-### Standard evaluation (recommended)
-
-Evaluate a policy on a single task with the official protocol (100 episodes):
-
-```bash
-lerobot-eval \
-  --policy.path="your-hf-policy-id" \
-  --env.type=robotwin \
-  --env.task=beat_block_hammer \
-  --eval.batch_size=1 \
-  --eval.n_episodes=100
-```
-
-### Single-task quick check
-
-```bash
-lerobot-eval \
-  --policy.path="your-hf-policy-id" \
-  --env.type=robotwin \
-  --env.task=beat_block_hammer \
-  --eval.batch_size=1 \
-  --eval.n_episodes=5
-```
-
-### Multi-task sweep
-
-Evaluate on several tasks in one run:
-
-```bash
-lerobot-eval \
-  --policy.path="your-hf-policy-id" \
-  --env.type=robotwin \
-  --env.task=beat_block_hammer,click_bell,handover_block,stack_blocks_two \
-  --eval.batch_size=1 \
-  --eval.n_episodes=100
-```
-
-### Full benchmark (all 50 tasks)
-
-```bash
-lerobot-eval \
-  --policy.path="your-hf-policy-id" \
-  --env.type=robotwin \
-  --env.task=adjust_bottle,beat_block_hammer,blocks_ranking_rgb,blocks_ranking_size,click_alarmclock,click_bell,dump_bin_bigbin,grab_roller,handover_block,handover_mic,hanging_mug,lift_pot,move_can_pot,move_pillbottle_pad,move_playingcard_away,move_stapler_pad,open_microwave,pick_diverse_bottles,pick_dual_bottles,place_a2b_left,place_a2b_right,place_bread_basket,place_bread_skillet,place_burger_fries,place_can_basket,place_cans_plasticbox,place_container_plate,place_dual_shoes,place_empty_cup,place_fan,place_mouse_pad,place_object_basket,place_object_scale,place_object_stand,place_phone_stand,place_shoe,press_stapler,put_bottles_dustbin,put_object_cabinet,rotate_qrcode,scan_object,shake_bottle,shake_bottle_horizontally,stack_blocks_three,stack_blocks_two,stack_bowls_three,stack_bowls_two,stamp_seal,turn_switch \
-  --eval.batch_size=1 \
-  --eval.n_episodes=100
-```
-
-<Tip>
-  `open_laptop` is intentionally omitted above because of the upstream
-  `self.arm_tag` bug (see the **Available tasks** section). Re-add it once the
-  upstream fix lands.
-</Tip>
-
-## Camera configuration
-
-By default, all three cameras are included:
-
-| Camera key     | Description                    |
-| -------------- | ------------------------------ |
-| `head_camera`  | Torso-mounted overhead view    |
-| `left_camera`  | Left arm wrist-mounted camera  |
-| `right_camera` | Right arm wrist-mounted camera |
-
-To use a subset of cameras, override `--env.camera_names`:
-
-```bash
-lerobot-eval \
-  --policy.path="your-hf-policy-id" \
-  --env.type=robotwin \
-  --env.task=beat_block_hammer \
-  --env.camera_names="head_camera,left_camera" \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10
-```
-
-## Environment config reference
-
-Key parameters for `RoboTwinEnvConfig`:
-
-| Parameter            | Default                                  | Description                        |
-| -------------------- | ---------------------------------------- | ---------------------------------- |
-| `task`               | `"beat_block_hammer"`                    | Comma-separated task name(s)       |
-| `fps`                | `25`                                     | Simulation FPS                     |
-| `episode_length`     | `300`                                    | Max steps per episode              |
-| `obs_type`           | `"pixels_agent_pos"`                     | `"pixels"` or `"pixels_agent_pos"` |
-| `camera_names`       | `"head_camera,left_camera,right_camera"` | Comma-separated active cameras     |
-| `observation_height` | `240`                                    | Camera pixel height                |
-| `observation_width`  | `320`                                    | Camera pixel width                 |
-
-## Leaderboard submission
-
-Results can be submitted to the [RoboTwin 2.0 leaderboard](https://robotwin-platform.github.io/leaderboard). The official protocol requires:
-
-- Training on 50 `demo_clean` demonstrations per task
-- Evaluating 100 episodes per task
-- Reporting success rate separately for **Easy** (`demo_clean`) and **Hard** (`demo_randomized`) settings
-
-For submission instructions, refer to the [RoboTwin 2.0 documentation](https://robotwin-platform.github.io/doc/).

docs/source/rtc.mdx

@@ -34,7 +34,7 @@ pip install -e ".[smolvla]"
 
 ### Using RTC with Pi0
 
-You can find a complete reference implementation in [eval_with_real_robot.py](examples/rtc/eval_with_real_robot.py).
+You can use `lerobot-rollout --strategy.type=base --inference.type=rtc` for RTC deployment on real robots.
 The snippet below provides a simplified pseudo-example of how RTC operates with Pi0 in your pipeline:
 
 ```python
@@ -137,8 +137,12 @@ The script generates a visualization of the denoising process, comparing standar
 ## Testing RTC with a Real Robot
 
 ```bash
-python examples/rtc/eval_with_real_robot.py \
+lerobot-rollout \
+    --strategy.type=base \
     --policy.path=${HF_USERNAME}/policy_repo_id \
+    --inference.type=rtc \
+    --inference.rtc.execution_horizon=10 \
+    --inference.rtc.max_guidance_weight=10.0 \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58FA0834591 \
     --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
@@ -178,7 +182,7 @@ visualizer = RTCDebugVisualizer()
 # ... create plots
 ```
 
-See `examples/rtc/eval_dataset.py` for a complete example of visualization.
+See `examples/rtc/eval_dataset.py` for a complete example of offline RTC visualization.
 
 ## References
 

docs/source/unitree_g1.mdx

@@ -274,7 +274,8 @@ python src/lerobot/scripts/lerobot_train.py \
 Once trained, we recommend deploying policies using inference-time RTC:
 
 ```bash
-python examples/rtc/eval_with_real_robot.py \
+lerobot-rollout \
+  --strategy.type=base \
   --policy.path=your-username/your-repo-id \
   --policy.device=cuda \
   --robot.type=unitree_g1 \
@@ -284,7 +285,7 @@ python examples/rtc/eval_with_real_robot.py \
   --task="task_description" \
   --duration=1000 \
   --fps=30 \
-  --rtc.enabled=true
+  --inference.type=rtc
 ```
 
 ---

docs/source/vlabench.mdx

@@ -1,176 +0,0 @@
-# VLABench
-
-[VLABench](https://github.com/OpenMOSS/VLABench) is a large-scale benchmark for **language-conditioned robotic manipulation with long-horizon reasoning**. The upstream suite covers 100 task categories across 2,000+ objects and evaluates six dimensions of robot intelligence: mesh & texture understanding, spatial reasoning, world-knowledge transfer, semantic instruction comprehension, physical-law understanding, and long-horizon planning. Built on MuJoCo / dm_control with a Franka Panda 7-DOF arm. LeRobot exposes **43 of these tasks** through `--env.task` (21 primitives + 22 composites, see [Available tasks](#available-tasks) below).
-
-- Paper: [VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning](https://arxiv.org/abs/2412.18194)
-- GitHub: [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench)
-- Project website: [vlabench.github.io](https://vlabench.github.io)
-- Pretrained policy: [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench)
-
-<img
-  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/vlabench.png"
-  alt="VLABench benchmark overview"
-  width="85%"
-/>
-
-## Available tasks
-
-VLABench ships two task suites covering **43 task categories** in LeRobot's `--env.task` surface:
-
-| Suite     | CLI name    | Tasks | Description                                                      |
-| --------- | ----------- | ----- | ---------------------------------------------------------------- |
-| Primitive | `primitive` | 21    | Single / few-skill combinations (select, insert, physics QA)     |
-| Composite | `composite` | 22    | Multi-step reasoning and long-horizon planning (cook, rearrange) |
-
-**Primitive tasks:** `select_fruit`, `select_toy`, `select_chemistry_tube`, `add_condiment`, `select_book`, `select_painting`, `select_drink`, `insert_flower`, `select_billiards`, `select_ingredient`, `select_mahjong`, `select_poker`, and physical-reasoning tasks (`density_qa`, `friction_qa`, `magnetism_qa`, `reflection_qa`, `simple_cuestick_usage`, `simple_seesaw_usage`, `sound_speed_qa`, `thermal_expansion_qa`, `weight_qa`).
-
-**Composite tasks:** `cluster_billiards`, `cluster_book`, `cluster_drink`, `cluster_toy`, `cook_dishes`, `cool_drink`, `find_unseen_object`, `get_coffee`, `hammer_nail`, `heat_food`, `make_juice`, `play_mahjong`, `play_math_game`, `play_poker`, `play_snooker`, `rearrange_book`, `rearrange_chemistry_tube`, `set_dining_table`, `set_study_table`, `store_food`, `take_chemistry_experiment`, `use_seesaw_complex`.
-
-`--env.task` accepts three forms:
-
-- a single task name (`select_fruit`)
-- a comma-separated list (`select_fruit,heat_food`)
-- a suite shortcut (`primitive`, `composite`, or `primitive,composite`)
-
-## Installation
-
-VLABench is **not on PyPI** — its only distribution is the [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench) GitHub repo — so LeRobot does not expose a `vlabench` extra. Install it manually as an editable clone, alongside the MuJoCo / dm_control pins VLABench needs, then fetch the mesh assets:
-
-```bash
-# After following the standard LeRobot installation instructions.
-
-git clone https://github.com/OpenMOSS/VLABench.git ~/VLABench
-git clone https://github.com/motion-planning/rrt-algorithms.git ~/rrt-algorithms
-pip install -e ~/VLABench -e ~/rrt-algorithms
-pip install "mujoco==3.2.2" "dm-control==1.0.22" \
-            open3d colorlog scikit-learn openai gdown
-
-python ~/VLABench/scripts/download_assets.py
-```
-
-<Tip>
-VLABench requires Linux (`sys_platform == 'linux'`) and Python 3.10+. Set the MuJoCo rendering backend before running:
-
-```bash
-export MUJOCO_GL=egl  # for headless servers (HPC, cloud)
-```
-
-</Tip>
-
-## Evaluation
-
-All eval snippets below mirror the command CI runs (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps VLABench's `image` / `second_image` / `wrist_image` camera keys onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_vlabench` policy was trained on.
-
-### Single-task evaluation (recommended for quick iteration)
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_vlabench \
-  --env.type=vlabench \
-  --env.task=select_fruit \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
-```
-
-### Multi-task evaluation
-
-Pass a comma-separated list of tasks:
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_vlabench \
-  --env.type=vlabench \
-  --env.task=select_fruit,select_toy,add_condiment,heat_food \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
-```
-
-### Suite-wide evaluation
-
-Run an entire suite (all 21 primitives or all 22 composites):
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_vlabench \
-  --env.type=vlabench \
-  --env.task=primitive \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  --env.max_parallel_tasks=1 \
-  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
-```
-
-Or both suites:
-
-```bash
-lerobot-eval \
-  --policy.path=lerobot/smolvla_vlabench \
-  --env.type=vlabench \
-  --env.task=primitive,composite \
-  --eval.batch_size=1 \
-  --eval.n_episodes=10 \
-  --eval.use_async_envs=false \
-  --policy.device=cuda \
-  --env.max_parallel_tasks=1 \
-  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
-```
-
-### Recommended evaluation episodes
-
-**10 episodes per task** for reproducible benchmarking (210 total for the full primitive suite, 220 for composite). Matches the protocol in the VLABench paper.
-
-## Policy inputs and outputs
-
-**Observations:**
-
-- `observation.state` — 7-dim end-effector state (position xyz + Euler xyz + gripper)
-- `observation.images.image` — front camera, 480×480 HWC uint8
-- `observation.images.second_image` — second camera, 480×480 HWC uint8
-- `observation.images.wrist_image` — wrist camera, 480×480 HWC uint8
-
-**Actions:**
-
-- Continuous control in `Box(-1, 1, shape=(7,))` — 3D position + 3D Euler orientation + 1D gripper.
-
-## Training
-
-### Datasets
-
-Pre-collected VLABench datasets in LeRobot format on the Hub:
-
-- [`VLABench/vlabench_primitive_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_primitive_ft_lerobot_video) — 5,000 episodes, 128 tasks, 480×480 images.
-- [`VLABench/vlabench_composite_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_composite_ft_lerobot_video) — 5,977 episodes, 167 tasks, 224×224 images.
-
-### Example training command
-
-Fine-tune a SmolVLA base on the primitive suite:
-
-```bash
-lerobot-train \
-  --policy.type=smolvla \
-  --policy.repo_id=${HF_USER}/smolvla_vlabench_primitive \
-  --policy.load_vlm_weights=true \
-  --policy.push_to_hub=true \
-  --dataset.repo_id=VLABench/vlabench_primitive_ft_lerobot_video \
-  --env.type=vlabench \
-  --env.task=select_fruit \
-  --output_dir=./outputs/smolvla_vlabench_primitive \
-  --steps=100000 \
-  --batch_size=4 \
-  --eval_freq=5000 \
-  --eval.batch_size=1 \
-  --eval.n_episodes=1 \
-  --save_freq=10000
-```
-
-## Reproducing published results
-
-The released checkpoint [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench) was trained on the primitive-suite dataset above and is evaluated with the [Single-task](#single-task-evaluation-recommended-for-quick-iteration) / [Suite-wide](#suite-wide-evaluation) commands. CI runs a 10-primitive-task smoke eval (one episode each) on every PR touching the benchmark.

docs/source/xvla.mdx

@@ -220,7 +220,7 @@ REAL_DIM = 12
 # Postprocessing: Trim 20D predictions to 12D for deployment
 ```
 
-See the [action_hub.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py) implementation for details.
+See the [action_hub.py](/home/jade_choghari/robot/lerobot/src/lerobot/policies/xvla/action_hub.py) implementation for details.
 
 #### Auto Action Mode (Recommended)
 
@@ -519,9 +519,9 @@ If you use X-VLA in your research, please cite:
 
 - [X-VLA Paper](https://arxiv.org/pdf/2510.10274)
 - [LeRobot Documentation](https://github.com/huggingface/lerobot)
-- [Action Registry Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py)
-- [Processor Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/processor_xvla.py)
-- [Model Configuration](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/configuration_xvla.py)
+- [Action Registry Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/action_hub.py)
+- [Processor Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/processor_xvla.py)
+- [Model Configuration](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/configuration_xvla.py)
 
 ## Contributing
 

examples/hil/hil_utils.py

@@ -1,226 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Shared utilities for Human-in-the-Loop data collection scripts."""
-
-import logging
-import time
-from dataclasses import dataclass, field
-from pathlib import Path
-
-from lerobot.common.control_utils import is_headless
-from lerobot.processor import (
-    IdentityProcessorStep,
-    RobotAction,
-    RobotObservation,
-    RobotProcessorPipeline,
-    observation_to_transition,
-    robot_action_observation_to_transition,
-    transition_to_observation,
-    transition_to_robot_action,
-)
-from lerobot.robots import Robot
-from lerobot.teleoperators import Teleoperator
-from lerobot.utils.robot_utils import precise_sleep
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class HILDatasetConfig:
-    repo_id: str
-    single_task: str
-    root: str | Path | None = None
-    fps: int = 30
-    episode_time_s: float = 120
-    num_episodes: int = 50
-    video: bool = True
-    push_to_hub: bool = True
-    private: bool = False
-    tags: list[str] | None = None
-    num_image_writer_processes: int = 0
-    num_image_writer_threads_per_camera: int = 4
-    video_encoding_batch_size: int = 1
-    vcodec: str = "auto"
-    streaming_encoding: bool = True
-    encoder_queue_maxsize: int = 30
-    encoder_threads: int | None = None
-    rename_map: dict[str, str] = field(default_factory=dict)
-
-
-def teleop_has_motor_control(teleop: Teleoperator) -> bool:
-    """Check if teleoperator has motor control capabilities."""
-    return all(hasattr(teleop, attr) for attr in ("enable_torque", "disable_torque", "write_goal_positions"))
-
-
-def teleop_disable_torque(teleop: Teleoperator) -> None:
-    """Disable teleop torque if supported."""
-    if hasattr(teleop, "disable_torque"):
-        teleop.disable_torque()
-
-
-def teleop_enable_torque(teleop: Teleoperator) -> None:
-    """Enable teleop torque if supported."""
-    if hasattr(teleop, "enable_torque"):
-        teleop.enable_torque()
-
-
-def teleop_smooth_move_to(teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 50):
-    """Smoothly move teleop to target position if motor control is available."""
-    if not teleop_has_motor_control(teleop):
-        logger.warning("Teleop does not support motor control - cannot mirror robot position")
-        return
-
-    teleop_enable_torque(teleop)
-    current = teleop.get_action()
-    steps = max(int(duration_s * fps), 1)
-
-    for step in range(steps + 1):
-        t = step / steps
-        interp = {}
-        for k in current:
-            if k in target_pos:
-                interp[k] = current[k] * (1 - t) + target_pos[k] * t
-            else:
-                interp[k] = current[k]
-        teleop.write_goal_positions(interp)
-        time.sleep(1 / fps)
-
-
-def init_keyboard_listener():
-    """Initialize keyboard listener with HIL controls."""
-    events = {
-        "exit_early": False,
-        "rerecord_episode": False,
-        "stop_recording": False,
-        "policy_paused": False,
-        "correction_active": False,
-        "resume_policy": False,
-        "in_reset": False,
-        "start_next_episode": False,
-    }
-
-    if is_headless():
-        logger.warning("Headless environment - keyboard controls unavailable")
-        return None, events
-
-    from pynput import keyboard
-
-    def on_press(key):
-        try:
-            if events["in_reset"]:
-                if key in [keyboard.Key.space, keyboard.Key.right]:
-                    logger.info("[HIL] Starting next episode...")
-                    events["start_next_episode"] = True
-                elif hasattr(key, "char") and key.char == "c":
-                    events["start_next_episode"] = True
-                elif key == keyboard.Key.esc:
-                    logger.info("[HIL] ESC - Stop recording, pushing to hub...")
-                    events["stop_recording"] = True
-                    events["start_next_episode"] = True
-            else:
-                if key == keyboard.Key.space:
-                    if not events["policy_paused"] and not events["correction_active"]:
-                        logger.info("[HIL] PAUSED - Press 'c' to take control or 'p' to resume policy")
-                        events["policy_paused"] = True
-                elif hasattr(key, "char") and key.char == "c":
-                    if events["policy_paused"] and not events["correction_active"]:
-                        logger.info("[HIL] Taking control...")
-                        events["start_next_episode"] = True
-                elif hasattr(key, "char") and key.char == "p":
-                    if events["policy_paused"] or events["correction_active"]:
-                        logger.info("[HIL] Resuming policy...")
-                        events["resume_policy"] = True
-                elif key == keyboard.Key.right:
-                    logger.info("[HIL] End episode")
-                    events["exit_early"] = True
-                elif key == keyboard.Key.left:
-                    logger.info("[HIL] Re-record episode")
-                    events["rerecord_episode"] = True
-                    events["exit_early"] = True
-                elif key == keyboard.Key.esc:
-                    logger.info("[HIL] ESC - Stop recording...")
-                    events["stop_recording"] = True
-                    events["exit_early"] = True
-        except Exception as e:
-            logger.info(f"Key error: {e}")
-
-    listener = keyboard.Listener(on_press=on_press)
-    listener.start()
-    return listener, events
-
-
-def make_identity_processors():
-    """Create identity processors for recording."""
-    teleop_proc = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[IdentityProcessorStep()],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-    obs_proc = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[IdentityProcessorStep()],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-    return teleop_proc, obs_proc
-
-
-def reset_loop(robot: Robot, teleop: Teleoperator, events: dict, fps: int):
-    """Reset period where human repositions environment."""
-    logger.info("[HIL] RESET")
-
-    events["in_reset"] = True
-    events["start_next_episode"] = False
-
-    obs = robot.get_observation()
-    robot_pos = {k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features}
-    teleop_smooth_move_to(teleop, robot_pos, duration_s=2.0, fps=50)
-
-    logger.info("Press any key to enable teleoperation")
-    while not events["start_next_episode"] and not events["stop_recording"]:
-        precise_sleep(0.05)
-
-    if events["stop_recording"]:
-        return
-
-    events["start_next_episode"] = False
-    teleop_disable_torque(teleop)
-    logger.info("Teleop enabled - press any key to start episode")
-
-    while not events["start_next_episode"] and not events["stop_recording"]:
-        loop_start = time.perf_counter()
-        action = teleop.get_action()
-        robot.send_action(action)
-        precise_sleep(1 / fps - (time.perf_counter() - loop_start))
-
-    events["in_reset"] = False
-    events["start_next_episode"] = False
-    events["exit_early"] = False
-    events["policy_paused"] = False
-    events["correction_active"] = False
-    events["resume_policy"] = False
-
-
-def print_controls(rtc: bool = False):
-    """Print control instructions."""
-    mode = "Human-in-the-Loop Data Collection" + (" (RTC)" if rtc else "")
-    logger.info(
-        "%s\n  Controls:\n"
-        "    SPACE  - Pause policy\n"
-        "    c      - Take control\n"
-        "    p      - Resume policy after pause/correction\n"
-        "    →      - End episode\n"
-        "    ESC    - Stop and push to hub",
-        mode,
-    )

examples/lekiwi/evaluate.py

@@ -14,17 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from lerobot.common.control_utils import init_keyboard_listener
+import logging
+import time
+
+from lerobot.common.control_utils import init_keyboard_listener, predict_action
 from lerobot.datasets import LeRobotDataset
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
+from lerobot.policies.utils import make_robot_action
 from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
-from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.constants import ACTION, OBS_STR
-from lerobot.utils.feature_utils import hw_to_dataset_features
+from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
+from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 NUM_EPISODES = 2
 FPS = 30
@@ -35,6 +39,9 @@ HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
 
 
 def main():
+    # NOTE: For production policy deployment, use `lerobot-rollout` CLI instead.
+    # This script provides a self-contained example for educational purposes.
+
     # Create the robot configuration & robot
     robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
@@ -83,43 +90,67 @@ def main():
             raise ValueError("Robot is not connected!")
 
         print("Starting evaluate loop...")
+        control_interval = 1 / FPS
         recorded_episodes = 0
         while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
             log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
 
-            # Main record loop
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
-            )
+            # Inline evaluation loop: predict actions and send to robot
+            timestamp = 0
+            start_episode_t = time.perf_counter()
+            while timestamp < EPISODE_TIME_SEC:
+                start_loop_t = time.perf_counter()
+
+                if events["exit_early"]:
+                    events["exit_early"] = False
+                    break
+
+                # Get robot observation
+                obs = robot.get_observation()
+                obs_processed = robot_observation_processor(obs)
+                observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
+
+                # Predict action using the policy
+                action_tensor = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=policy.config.device,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.device.type == "cuda",
+                    task=TASK_DESCRIPTION,
+                    robot_type=robot.name,
+                )
+
+                # Convert policy output to robot action dict
+                action_values = make_robot_action(action_tensor, dataset.features)
+
+                # Process and send action to robot
+                robot_action_to_send = robot_action_processor((action_values, obs))
+                robot.send_action(robot_action_to_send)
+
+                # Write to dataset
+                action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
+                frame = {**observation_frame, **action_frame, "task": TASK_DESCRIPTION}
+                dataset.add_frame(frame)
+
+                log_rerun_data(observation=obs_processed, action=action_values)
+
+                dt_s = time.perf_counter() - start_loop_t
+                sleep_time_s = control_interval - dt_s
+                if sleep_time_s < 0:
+                    logging.warning(
+                        f"Evaluate loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({FPS} Hz)."
+                    )
+                precise_sleep(max(sleep_time_s, 0.0))
+                timestamp = time.perf_counter() - start_episode_t
 
             # Reset the environment if not stopping or re-recording
             if not events["stop_recording"] and (
                 (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
             ):
                 log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
-                )
+                log_say("Waiting for environment reset, press right arrow key when ready...")
 
             if events["rerecord_episode"]:
                 log_say("Re-record episode")

examples/lekiwi/record.py

@@ -45,9 +45,6 @@ def main():
     leader_arm = SO100Leader(leader_arm_config)
     keyboard = KeyboardTeleop(keyboard_config)
 
-    # TODO(Steven): Update this example to use pipelines
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-
     # Configure the dataset features
     action_features = hw_to_dataset_features(robot.action_features, ACTION)
     obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
@@ -77,6 +74,10 @@ def main():
         if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
             raise ValueError("Robot or teleop is not connected!")
 
+        teleop_action_processor, robot_action_processor, robot_observation_processor = (
+            make_default_processors()
+        )
+
         print("Starting record loop...")
         recorded_episodes = 0
         while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
@@ -87,14 +88,14 @@ def main():
                 robot=robot,
                 events=events,
                 fps=FPS,
+                teleop_action_processor=teleop_action_processor,
+                robot_action_processor=robot_action_processor,
+                robot_observation_processor=robot_observation_processor,
                 dataset=dataset,
                 teleop=[leader_arm, keyboard],
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -106,13 +107,13 @@ def main():
                     robot=robot,
                     events=events,
                     fps=FPS,
+                    teleop_action_processor=teleop_action_processor,
+                    robot_action_processor=robot_action_processor,
+                    robot_observation_processor=robot_observation_processor,
                     teleop=[leader_arm, keyboard],
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
                 )
 
             if events["rerecord_episode"]:

examples/lekiwi/rollout.py

@@ -0,0 +1,77 @@
+# !/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Run a trained policy on LeKiwi without recording (base rollout).
+
+Uses the rollout engine's :class:`BaseStrategy` (autonomous execution,
+no dataset) with :class:`SyncInferenceConfig` (inline policy call per
+control tick).  For a CLI entry point with the same capabilities plus
+recording, upload, and human-in-the-loop variants, see ``lerobot-rollout``.
+"""
+
+from lerobot.configs import PreTrainedConfig
+from lerobot.robots.lekiwi import LeKiwiClientConfig
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+
+FPS = 30
+DURATION_SEC = 60
+TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+
+
+def main():
+    init_logging()
+
+    # Robot: LeKiwi client — make sure lekiwi_host is already running on the robot.
+    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+
+    # Policy: load the pretrained config.  ``pretrained_path`` is read downstream
+    # by ``build_rollout_context`` to reload the full model.
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+
+    # Assemble the rollout config: base strategy (no recording) + sync inference.
+    cfg = RolloutConfig(
+        robot=robot_config,
+        policy=policy_config,
+        strategy=BaseStrategyConfig(),
+        inference=SyncInferenceConfig(),
+        fps=FPS,
+        duration=DURATION_SEC,
+        task=TASK_DESCRIPTION,
+    )
+
+    # Graceful Ctrl-C: the strategy loop exits when shutdown_event is set.
+    signal_handler = ProcessSignalHandler(use_threads=True)
+
+    # Build the context (connects robot, loads policy, wires the inference strategy).
+    # No custom processors here — LeKiwi runs on raw joint features.
+    ctx = build_rollout_context(cfg, signal_handler.shutdown_event)
+
+    strategy = BaseStrategy(cfg.strategy)
+    try:
+        strategy.setup(ctx)
+        strategy.run(ctx)
+    finally:
+        strategy.teardown(ctx)
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/evaluate.py

@@ -14,13 +14,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
+import time
+
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
+from lerobot.common.control_utils import init_keyboard_listener, predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
+from lerobot.policies.utils import make_robot_action
 from lerobot.processor import (
     RobotProcessorPipeline,
     make_default_teleop_action_processor,
@@ -34,11 +38,12 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     ForwardKinematicsJointsToEE,
     InverseKinematicsEEToJoints,
 )
-from lerobot.scripts.lerobot_record import record_loop
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
+from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 NUM_EPISODES = 5
 FPS = 30
@@ -49,6 +54,9 @@ HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
 
 def main():
+    # NOTE: For production policy deployment, use `lerobot-rollout` CLI instead.
+    # This script provides a self-contained example for educational purposes.
+
     # Create the robot configuration & robot
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     robot_config = SO100FollowerConfig(
@@ -143,43 +151,67 @@ def main():
             raise ValueError("Robot is not connected!")
 
         print("Starting evaluate loop...")
+        control_interval = 1 / FPS
         episode_idx = 0
         for episode_idx in range(NUM_EPISODES):
             log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
-            )
+            # Inline evaluation loop: predict actions and send to robot
+            timestamp = 0
+            start_episode_t = time.perf_counter()
+            while timestamp < EPISODE_TIME_SEC:
+                start_loop_t = time.perf_counter()
+
+                if events["exit_early"]:
+                    events["exit_early"] = False
+                    break
+
+                # Get robot observation
+                obs = robot.get_observation()
+                obs_processed = robot_joints_to_ee_pose_processor(obs)
+                observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
+
+                # Predict action using the policy
+                action_tensor = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=policy.config.device,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.device.type == "cuda",
+                    task=TASK_DESCRIPTION,
+                    robot_type=robot.name,
+                )
+
+                # Convert policy output to robot action dict
+                action_values = make_robot_action(action_tensor, dataset.features)
+
+                # Process and send action to robot (EE -> joints via IK)
+                robot_action_to_send = robot_ee_to_joints_processor((action_values, obs))
+                robot.send_action(robot_action_to_send)
+
+                # Write to dataset
+                action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
+                frame = {**observation_frame, **action_frame, "task": TASK_DESCRIPTION}
+                dataset.add_frame(frame)
+
+                log_rerun_data(observation=obs_processed, action=action_values)
+
+                dt_s = time.perf_counter() - start_loop_t
+                sleep_time_s = control_interval - dt_s
+                if sleep_time_s < 0:
+                    logging.warning(
+                        f"Evaluate loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({FPS} Hz)."
+                    )
+                precise_sleep(max(sleep_time_s, 0.0))
+                timestamp = time.perf_counter() - start_episode_t
 
             # Reset the environment if not stopping or re-recording
             if not events["stop_recording"] and (
                 (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
             ):
                 log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=make_default_teleop_action_processor(),
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose_processor,
-                )
+                log_say("Waiting for environment reset, press right arrow key when ready...")
 
             if events["rerecord_episode"]:
                 log_say("Re-record episode")
@@ -190,7 +222,6 @@ def main():
 
             # Save episode
             dataset.save_episode()
-            episode_idx += 1
     finally:
         # Clean up
         log_say("Stop recording")

examples/phone_to_so100/record.py

@@ -65,14 +65,15 @@ def main():
     robot = SO100Follower(robot_config)
     phone = Phone(teleop_config)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+    #   https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
     kinematics_solver = RobotKinematics(
         urdf_path="./SO101/so101_new_calib.urdf",
         target_frame_name="gripper_frame_link",
         joint_names=list(robot.bus.motors.keys()),
     )
 
-    # Build pipeline to convert phone action to EE action
+    # Build pipeline to convert phone action to EE action (with gripper velocity mapped to joint).
     phone_to_robot_ee_pose_processor = RobotProcessorPipeline[
         tuple[RobotAction, RobotObservation], RobotAction
     ](
@@ -94,7 +95,7 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Build pipeline to convert EE action to joints action
+    # Build pipeline to convert EE action to joints action (IK).
     robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
         steps=[
             InverseKinematicsEEToJoints(
@@ -107,7 +108,7 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Build pipeline to convert joint observation to EE observation
+    # Build pipeline to convert joint observation to EE observation (FK).
     robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
             ForwardKinematicsJointsToEE(
@@ -118,13 +119,12 @@ def main():
         to_output=transition_to_observation,
     )
 
-    # Create the dataset
+    # Create the dataset, deriving features from the pipelines so the on-disk schema
+    # matches exactly what the pipelines produce at runtime.
     dataset = LeRobotDataset.create(
         repo_id=HF_REPO_ID,
         fps=FPS,
         features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
             aggregate_pipeline_dataset_features(
                 pipeline=phone_to_robot_ee_pose_processor,
                 initial_features=create_initial_features(action=phone.action_features),
@@ -163,14 +163,14 @@ def main():
                 robot=robot,
                 events=events,
                 fps=FPS,
+                teleop_action_processor=phone_to_robot_ee_pose_processor,
+                robot_action_processor=robot_ee_to_joints_processor,
+                robot_observation_processor=robot_joints_to_ee_pose,
                 teleop=phone,
                 dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=phone_to_robot_ee_pose_processor,
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -182,13 +182,13 @@ def main():
                     robot=robot,
                     events=events,
                     fps=FPS,
+                    teleop_action_processor=phone_to_robot_ee_pose_processor,
+                    robot_action_processor=robot_ee_to_joints_processor,
+                    robot_observation_processor=robot_joints_to_ee_pose,
                     teleop=phone,
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=phone_to_robot_ee_pose_processor,
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose,
                 )
 
             if events["rerecord_episode"]:

examples/phone_to_so100/rollout.py

@@ -0,0 +1,126 @@
+# !/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Run a trained EE-space policy on SO100 (phone-trained) without recording.
+
+Mirrors ``examples/so100_to_so100_EE/rollout.py`` — the model was trained
+with phone teleoperation in EE space, so at deployment we only need the
+joint↔EE conversion on the robot side; the phone is not used.
+
+Uses :class:`BaseStrategy` (no recording) + :class:`SyncInferenceConfig`
+(inline policy call).  For recording during rollout, switch to Sentry,
+Highlight, or DAgger via ``lerobot-rollout --strategy.type=...``.
+"""
+
+from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.configs import PreTrainedConfig
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import (
+    RobotProcessorPipeline,
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    ForwardKinematicsJointsToEE,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+
+FPS = 30
+DURATION_SEC = 60
+TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+
+
+def main():
+    init_logging()
+
+    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+    robot_config = SO100FollowerConfig(
+        port="/dev/tty.usbmodem58760434471",
+        id="my_awesome_follower_arm",
+        cameras=camera_config,
+        use_degrees=True,
+    )
+
+    # Peek at motor names once to build the kinematic solver.
+    temp_robot = SO100Follower(robot_config)
+    motor_names = list(temp_robot.bus.motors.keys())
+
+    kinematics_solver = RobotKinematics(
+        urdf_path="./SO101/so101_new_calib.urdf",
+        target_frame_name="gripper_frame_link",
+        joint_names=motor_names,
+    )
+
+    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=motor_names)],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+
+    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            InverseKinematicsEEToJoints(
+                kinematics=kinematics_solver,
+                motor_names=motor_names,
+                initial_guess_current_joints=True,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+
+    cfg = RolloutConfig(
+        robot=robot_config,
+        policy=policy_config,
+        strategy=BaseStrategyConfig(),
+        inference=SyncInferenceConfig(),
+        fps=FPS,
+        duration=DURATION_SEC,
+        task=TASK_DESCRIPTION,
+    )
+
+    signal_handler = ProcessSignalHandler(use_threads=True)
+
+    ctx = build_rollout_context(
+        cfg,
+        signal_handler.shutdown_event,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
+    )
+
+    strategy = BaseStrategy(cfg.strategy)
+    try:
+        strategy.setup(ctx)
+        strategy.run(ctx)
+    finally:
+        strategy.teardown(ctx)
+
+
+if __name__ == "__main__":
+    main()

examples/rtc/eval_with_real_robot.py

@@ -1,673 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Demo script showing how to use Real-Time Chunking (RTC) with action chunking policies on real robots.
-
-This script demonstrates:
-1. Creating a robot and policy (SmolVLA, Pi0, etc.) with RTC
-2. Consuming actions from the policy while the robot executes
-3. Periodically requesting new action chunks in the background using threads
-4. Managing action buffers and timing for real-time operation
-
-For simulation environments, see eval_with_simulation.py
-
-Usage:
-    # Run RTC with Real robot with RTC
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --policy.device=mps \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with Real robot without RTC
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/smolvla_check_rtc_last3 \
-        --policy.device=mps \
-        --rtc.enabled=false \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with Real robot with pi0.5 policy
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=<USER>/pi05_check_rtc \
-        --policy.device=mps \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with bi_openarm_follower (dual-arm OpenArms) and pi0.5 policy
-    python examples/rtc/eval_with_real_robot.py \
-        --policy.path=lerobot-data-collection/folding_final \
-        --robot.type=bi_openarm_follower \
-        --robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}}' \
-        --robot.left_arm_config.port=can0 \
-        --robot.left_arm_config.side=left \
-        --robot.left_arm_config.can_interface=socketcan \
-        --robot.left_arm_config.disable_torque_on_disconnect=true \
-        --robot.left_arm_config.max_relative_target=8.0 \
-        --robot.right_arm_config.port=can1 \
-        --robot.right_arm_config.side=right \
-        --robot.right_arm_config.can_interface=socketcan \
-        --robot.right_arm_config.disable_torque_on_disconnect=true \
-        --robot.right_arm_config.max_relative_target=8.0 \
-        --task="Fold the T-shirt properly" \
-        --fps=30 \
-        --duration=2000 \
-        --interpolation_multiplier=3 \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --rtc.max_guidance_weight=5.0 \
-        --rtc.prefix_attention_schedule=LINEAR \
-        --device=cuda
-"""
-
-import logging
-import math
-import sys
-import time
-import traceback
-from dataclasses import dataclass, field
-from threading import Event, Lock, Thread
-
-import torch
-from torch import Tensor
-
-from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.cameras.realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.cameras.zmq import ZMQCameraConfig  # noqa: F401
-from lerobot.configs import PreTrainedConfig, RTCAttentionSchedule, parser
-from lerobot.policies import get_policy_class, make_pre_post_processors
-from lerobot.policies.rtc import ActionInterpolator, ActionQueue, LatencyTracker, RTCConfig
-from lerobot.processor import (
-    NormalizerProcessorStep,
-    RelativeActionsProcessorStep,
-    TransitionKey,
-    create_transition,
-    make_default_robot_action_processor,
-    make_default_robot_observation_processor,
-    to_relative_actions,
-)
-from lerobot.rl.process import ProcessSignalHandler
-from lerobot.robots import (  # noqa: F401
-    Robot,
-    RobotConfig,
-    bi_openarm_follower,
-    bi_so_follower,
-    koch_follower,
-    so_follower,
-    unitree_g1,
-)
-from lerobot.robots.utils import make_robot_from_config
-from lerobot.utils.constants import OBS_IMAGES, OBS_STATE
-from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
-from lerobot.utils.hub import HubMixin
-from lerobot.utils.utils import init_logging
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class RobotWrapper:
-    def __init__(self, robot: Robot):
-        self.robot = robot
-        self.lock = Lock()
-
-    def get_observation(self) -> dict[str, Tensor]:
-        with self.lock:
-            return self.robot.get_observation()
-
-    def send_action(self, action: Tensor):
-        with self.lock:
-            self.robot.send_action(action)
-
-    def observation_features(self) -> list[str]:
-        with self.lock:
-            return self.robot.observation_features
-
-    def action_features(self) -> list[str]:
-        with self.lock:
-            return self.robot.action_features
-
-
-@dataclass
-class RTCDemoConfig(HubMixin):
-    """Configuration for RTC demo with action chunking policies and real robots."""
-
-    # Policy configuration
-    policy: PreTrainedConfig | None = None
-
-    # Robot configuration
-    robot: RobotConfig | None = None
-
-    # RTC configuration
-    rtc: RTCConfig = field(
-        default_factory=lambda: RTCConfig(
-            execution_horizon=10,
-            max_guidance_weight=1.0,
-            prefix_attention_schedule=RTCAttentionSchedule.EXP,
-        )
-    )
-
-    # Demo parameters
-    duration: float = 30.0  # Duration to run the demo (seconds)
-    fps: float = 10.0  # Action execution frequency (Hz)
-    interpolation_multiplier: int = 1  # Control rate multiplier (1=off, 2=2x, 3=3x)
-
-    # Compute device
-    device: str | None = None  # Device to run on (cuda, cpu, auto)
-
-    # Get new actions horizon. The amount of executed steps after which will be requested new actions.
-    # It should be higher than inference delay + execution horizon.
-    action_queue_size_to_get_new_actions: int = 30
-
-    # Task to execute
-    task: str = field(default="", metadata={"help": "Task to execute"})
-
-    # Torch compile configuration
-    use_torch_compile: bool = field(
-        default=False,
-        metadata={"help": "Use torch.compile for faster inference (PyTorch 2.0+)"},
-    )
-
-    torch_compile_backend: str = field(
-        default="inductor",
-        metadata={"help": "Backend for torch.compile (inductor, aot_eager, cudagraphs)"},
-    )
-
-    torch_compile_mode: str = field(
-        default="default",
-        metadata={"help": "Compilation mode (default, reduce-overhead, max-autotune)"},
-    )
-
-    torch_compile_disable_cudagraphs: bool = field(
-        default=True,
-        metadata={
-            "help": "Disable CUDA graphs in torch.compile. Required due to in-place tensor "
-            "operations in denoising loop (x_t += dt * v_t) which cause tensor aliasing issues."
-        },
-    )
-
-    def __post_init__(self):
-        # HACK: We parse again the cli args here to get the pretrained path if there was one.
-        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)
-            self.policy.pretrained_path = policy_path
-        else:
-            raise ValueError("Policy path is required")
-
-        # Validate that robot configuration is provided
-        if self.robot is None:
-            raise ValueError("Robot configuration must be provided")
-
-    @classmethod
-    def __get_path_fields__(cls) -> list[str]:
-        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
-        return ["policy"]
-
-
-def is_image_key(k: str) -> bool:
-    return k.startswith(OBS_IMAGES)
-
-
-def _reanchor_relative_rtc_prefix(
-    prev_actions_absolute: Tensor,
-    current_state: Tensor,
-    relative_step: RelativeActionsProcessorStep,
-    normalizer_step: NormalizerProcessorStep | None,
-    policy_device: torch.device | str,
-) -> Tensor:
-    """Convert absolute leftovers into model-space for relative-action RTC policies.
-
-    When a policy uses relative actions, the RTC prefix (leftover actions from
-    the previous chunk) is stored in absolute space. Before feeding it back to
-    the policy we need to re-express it relative to the *current* robot state
-    and then re-normalize.
-    """
-    state = current_state.detach().cpu()
-    if state.dim() == 1:
-        state = state.unsqueeze(0)
-
-    action_cpu = prev_actions_absolute.detach().cpu()
-    mask = relative_step._build_mask(action_cpu.shape[-1])
-    relative_actions = to_relative_actions(action_cpu, state, mask)
-
-    transition = create_transition(action=relative_actions)
-    if normalizer_step is not None:
-        transition = normalizer_step(transition)
-
-    return transition[TransitionKey.ACTION].to(policy_device)
-
-
-def get_actions(
-    policy,
-    robot: RobotWrapper,
-    robot_observation_processor,
-    action_queue: ActionQueue,
-    shutdown_event: Event,
-    cfg: RTCDemoConfig,
-):
-    """Thread function to request action chunks from the policy.
-
-    Args:
-        policy: The policy instance (SmolVLA, Pi0, etc.)
-        robot: The robot instance for getting observations
-        robot_observation_processor: Processor for raw robot observations
-        action_queue: Queue to put new action chunks
-        shutdown_event: Event to signal shutdown
-        cfg: Demo configuration
-    """
-    try:
-        logger.info("[GET_ACTIONS] Starting get actions thread")
-
-        latency_tracker = LatencyTracker()  # Track latency of action chunks
-        fps = cfg.fps
-        time_per_chunk = 1.0 / fps
-
-        # Only keep .pos joints + camera streams if the policy was trained on positions,
-        # not the full pos/vel/torque state the robot exposes.
-        observation_features_hw = {
-            key: value
-            for key, value in robot.observation_features().items()
-            if key.endswith(".pos") or isinstance(value, tuple)
-        }
-
-        dataset_features = hw_to_dataset_features(observation_features_hw, "observation")
-        policy_device = policy.config.device
-
-        # Load preprocessor and postprocessor from pretrained files
-        # The stats are embedded in the processor .safetensors files
-        logger.info(f"[GET_ACTIONS] Loading preprocessor/postprocessor from {cfg.policy.pretrained_path}")
-
-        preprocessor, postprocessor = make_pre_post_processors(
-            policy_cfg=cfg.policy,
-            pretrained_path=cfg.policy.pretrained_path,
-            dataset_stats=None,  # Will load from pretrained processor files
-            preprocessor_overrides={
-                "device_processor": {"device": cfg.policy.device},
-            },
-        )
-
-        logger.info("[GET_ACTIONS] Preprocessor/postprocessor loaded successfully with embedded stats")
-
-        relative_step = next(
-            (s for s in preprocessor.steps if isinstance(s, RelativeActionsProcessorStep) and s.enabled),
-            None,
-        )
-        normalizer_step = next(
-            (s for s in preprocessor.steps if isinstance(s, NormalizerProcessorStep)),
-            None,
-        )
-        if relative_step is not None:
-            if relative_step.action_names is None:
-                cfg_names = getattr(cfg.policy, "action_feature_names", None)
-                if cfg_names:
-                    relative_step.action_names = list(cfg_names)
-                else:
-                    relative_step.action_names = [
-                        k for k in robot.robot.action_features if k.endswith(".pos")
-                    ]
-            logger.info("[GET_ACTIONS] Relative actions enabled: will re-anchor RTC prefix")
-
-        get_actions_threshold = cfg.action_queue_size_to_get_new_actions
-
-        if not cfg.rtc.enabled:
-            get_actions_threshold = 0
-
-        while not shutdown_event.is_set():
-            if action_queue.qsize() <= get_actions_threshold:
-                current_time = time.perf_counter()
-                action_index_before_inference = action_queue.get_action_index()
-                prev_actions = action_queue.get_left_over()
-
-                inference_latency = latency_tracker.max()
-                inference_delay = math.ceil(inference_latency / time_per_chunk)
-
-                obs = robot.get_observation()
-
-                # Apply robot observation processor
-                obs_processed = robot_observation_processor(obs)
-
-                obs_with_policy_features = build_dataset_frame(
-                    dataset_features, obs_processed, prefix="observation"
-                )
-
-                for name in obs_with_policy_features:
-                    obs_with_policy_features[name] = torch.from_numpy(obs_with_policy_features[name])
-                    if "image" in name:
-                        obs_with_policy_features[name] = (
-                            obs_with_policy_features[name].type(torch.float32) / 255
-                        )
-                        obs_with_policy_features[name] = (
-                            obs_with_policy_features[name].permute(2, 0, 1).contiguous()
-                        )
-                    obs_with_policy_features[name] = obs_with_policy_features[name].unsqueeze(0)
-                    obs_with_policy_features[name] = obs_with_policy_features[name].to(policy_device)
-
-                obs_with_policy_features["task"] = [cfg.task]  # Task should be a list, not a string!
-                obs_with_policy_features["robot_type"] = (
-                    robot.robot.name if hasattr(robot.robot, "name") else ""
-                )
-
-                preproceseded_obs = preprocessor(obs_with_policy_features)
-
-                # Re-anchor leftover actions for relative-action policies.
-                # We need the *postprocessed* (absolute) leftover, not the original
-                # (normalized/relative) one that get_left_over() returns.
-                if (
-                    prev_actions is not None
-                    and relative_step is not None
-                    and OBS_STATE in obs_with_policy_features
-                ):
-                    with action_queue.lock:
-                        if action_queue.queue is not None:
-                            prev_actions_abs = action_queue.queue[action_queue.last_index :].clone()
-                        else:
-                            prev_actions_abs = None
-                    if prev_actions_abs is not None and prev_actions_abs.numel() > 0:
-                        prev_actions = _reanchor_relative_rtc_prefix(
-                            prev_actions_absolute=prev_actions_abs,
-                            current_state=obs_with_policy_features[OBS_STATE],
-                            relative_step=relative_step,
-                            normalizer_step=normalizer_step,
-                            policy_device=policy_device,
-                        )
-
-                # Generate actions WITH RTC
-                actions = policy.predict_action_chunk(
-                    preproceseded_obs,
-                    inference_delay=inference_delay,
-                    prev_chunk_left_over=prev_actions,
-                )
-
-                # Store original actions (before postprocessing) for RTC
-                original_actions = actions.squeeze(0).clone()
-
-                postprocessed_actions = postprocessor(actions)
-
-                postprocessed_actions = postprocessed_actions.squeeze(0)
-
-                new_latency = time.perf_counter() - current_time
-                new_delay = math.ceil(new_latency / time_per_chunk)
-                latency_tracker.add(new_latency)
-
-                if cfg.action_queue_size_to_get_new_actions < cfg.rtc.execution_horizon + new_delay:
-                    logger.warning(
-                        "[GET_ACTIONS] cfg.action_queue_size_to_get_new_actions Too small, It should be higher than inference delay + execution horizon."
-                    )
-
-                action_queue.merge(
-                    original_actions, postprocessed_actions, new_delay, action_index_before_inference
-                )
-            else:
-                # Small sleep to prevent busy waiting
-                time.sleep(0.1)
-
-        logger.info("[GET_ACTIONS] get actions thread shutting down")
-    except Exception as e:
-        logger.error(f"[GET_ACTIONS] Fatal exception in get_actions thread: {e}")
-        logger.error(traceback.format_exc())
-        sys.exit(1)
-
-
-def actor_control(
-    robot: RobotWrapper,
-    robot_action_processor,
-    action_queue: ActionQueue,
-    shutdown_event: Event,
-    cfg: RTCDemoConfig,
-):
-    """Thread function to execute actions on the robot.
-
-    Args:
-        robot: The robot instance
-        action_queue: Queue to get actions from
-        shutdown_event: Event to signal shutdown
-        cfg: Demo configuration
-    """
-    try:
-        logger.info("[ACTOR] Starting actor thread")
-
-        action_keys = [k for k in robot.action_features() if k.endswith(".pos")]
-
-        action_count = 0
-        interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
-        action_interval = interpolator.get_control_interval(cfg.fps)
-
-        while not shutdown_event.is_set():
-            start_time = time.perf_counter()
-
-            if interpolator.needs_new_action():
-                new_action = action_queue.get()
-                if new_action is not None:
-                    interpolator.add(new_action.cpu())
-
-            action = interpolator.get()
-            if action is not None:
-                action = action.cpu()
-                action_dict = {key: action[i].item() for i, key in enumerate(action_keys)}
-                action_processed = robot_action_processor((action_dict, None))
-                robot.send_action(action_processed)
-                action_count += 1
-
-            dt_s = time.perf_counter() - start_time
-            time.sleep(max(0, (action_interval - dt_s) - 0.001))
-
-        logger.info(f"[ACTOR] Actor thread shutting down. Total actions executed: {action_count}")
-    except Exception as e:
-        logger.error(f"[ACTOR] Fatal exception in actor_control thread: {e}")
-        logger.error(traceback.format_exc())
-        sys.exit(1)
-
-
-def _apply_torch_compile(policy, cfg: RTCDemoConfig):
-    """Apply torch.compile to the policy's predict_action_chunk method.
-
-    Args:
-        policy: Policy instance to compile
-        cfg: Configuration containing torch compile settings
-
-    Returns:
-        Policy with compiled predict_action_chunk method
-    """
-
-    # PI models handle their own compilation
-    if policy.type == "pi05" or policy.type == "pi0":
-        return policy
-
-    try:
-        # Check if torch.compile is available (PyTorch 2.0+)
-        if not hasattr(torch, "compile"):
-            logger.warning(
-                f"torch.compile is not available. Requires PyTorch 2.0+. "
-                f"Current version: {torch.__version__}. Skipping compilation."
-            )
-            return policy
-
-        logger.info("Applying torch.compile to predict_action_chunk...")
-        logger.info(f"  Backend: {cfg.torch_compile_backend}")
-        logger.info(f"  Mode: {cfg.torch_compile_mode}")
-        logger.info(f"  Disable CUDA graphs: {cfg.torch_compile_disable_cudagraphs}")
-
-        # Compile the predict_action_chunk method
-        # - CUDA graphs disabled to prevent tensor aliasing from in-place ops (x_t += dt * v_t)
-        compile_kwargs = {
-            "backend": cfg.torch_compile_backend,
-            "mode": cfg.torch_compile_mode,
-        }
-
-        # Disable CUDA graphs if requested (prevents tensor aliasing issues)
-        if cfg.torch_compile_disable_cudagraphs:
-            compile_kwargs["options"] = {"triton.cudagraphs": False}
-
-        original_method = policy.predict_action_chunk
-        compiled_method = torch.compile(original_method, **compile_kwargs)
-        policy.predict_action_chunk = compiled_method
-        logger.info("✓ Successfully compiled predict_action_chunk")
-
-    except Exception as e:
-        logger.error(f"Failed to apply torch.compile: {e}")
-        logger.warning("Continuing without torch.compile")
-
-    return policy
-
-
-@parser.wrap()
-def demo_cli(cfg: RTCDemoConfig):
-    """Main entry point for RTC demo with draccus configuration."""
-
-    # Initialize logging
-    init_logging()
-
-    logger.info(f"Using device: {cfg.device}")
-
-    # Setup signal handler for graceful shutdown
-    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
-    shutdown_event = signal_handler.shutdown_event
-
-    policy = None
-    robot = None
-    get_actions_thread = None
-    actor_thread = None
-
-    policy_class = get_policy_class(cfg.policy.type)
-
-    # Load config and set compile_model for pi0/pi05 models
-    config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
-
-    if cfg.policy.type == "pi05" or cfg.policy.type == "pi0":
-        config.compile_model = cfg.use_torch_compile
-
-    if config.use_peft:
-        from peft import PeftConfig, PeftModel
-
-        peft_pretrained_path = cfg.policy.pretrained_path
-        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
-
-        policy = policy_class.from_pretrained(
-            pretrained_name_or_path=peft_config.base_model_name_or_path, config=config
-        )
-        policy = PeftModel.from_pretrained(policy, peft_pretrained_path, config=peft_config)
-    else:
-        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
-
-    # Turn on RTC
-    policy.config.rtc_config = cfg.rtc
-
-    # Init RTC processort, as by default if RTC disabled in the config
-    # The processor won't be created
-    policy.init_rtc_processor()
-
-    assert policy.name in ["smolvla", "pi05", "pi0"], "Only smolvla, pi05, and pi0 are supported for RTC"
-
-    policy = policy.to(cfg.device)
-    policy.eval()
-
-    # Apply torch.compile to predict_action_chunk method if enabled
-    if cfg.use_torch_compile:
-        policy = _apply_torch_compile(policy, cfg)
-
-    # Create robot
-    logger.info(f"Initializing robot: {cfg.robot.type}")
-    robot = make_robot_from_config(cfg.robot)
-    robot.connect()
-    robot_wrapper = RobotWrapper(robot)
-
-    # Create robot observation processor
-    robot_observation_processor = make_default_robot_observation_processor()
-    robot_action_processor = make_default_robot_action_processor()
-
-    # Create action queue for communication between threads
-    action_queue = ActionQueue(cfg.rtc)
-
-    # Start chunk requester thread
-    get_actions_thread = Thread(
-        target=get_actions,
-        args=(policy, robot_wrapper, robot_observation_processor, action_queue, shutdown_event, cfg),
-        daemon=True,
-        name="GetActions",
-    )
-    get_actions_thread.start()
-    logger.info("Started get actions thread")
-
-    # Start action executor thread
-    actor_thread = Thread(
-        target=actor_control,
-        args=(robot_wrapper, robot_action_processor, action_queue, shutdown_event, cfg),
-        daemon=True,
-        name="Actor",
-    )
-    actor_thread.start()
-    logger.info("Started actor thread")
-
-    logger.info("Started stop by duration thread")
-
-    # Main thread monitors for duration or shutdown
-    logger.info(f"Running demo for {cfg.duration} seconds...")
-    start_time = time.time()
-
-    while not shutdown_event.is_set() and (time.time() - start_time) < cfg.duration:
-        time.sleep(10)
-
-        # Log queue status periodically
-        if int(time.time() - start_time) % 5 == 0:
-            logger.info(f"[MAIN] Action queue size: {action_queue.qsize()}")
-
-        if time.time() - start_time > cfg.duration:
-            break
-
-    logger.info("Demo duration reached or shutdown requested")
-
-    # Signal shutdown
-    shutdown_event.set()
-
-    # Wait for threads to finish
-    if get_actions_thread and get_actions_thread.is_alive():
-        logger.info("Waiting for chunk requester thread to finish...")
-        get_actions_thread.join()
-
-    if actor_thread and actor_thread.is_alive():
-        logger.info("Waiting for action executor thread to finish...")
-        actor_thread.join()
-
-    # Cleanup robot
-    if robot:
-        robot.disconnect()
-        logger.info("Robot disconnected")
-
-    logger.info("Cleanup completed")
-
-
-if __name__ == "__main__":
-    demo_cli()
-    logging.info("RTC demo finished")

examples/so100_to_so100_EE/evaluate.py

@@ -14,13 +14,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
+import time
+
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import init_keyboard_listener
+from lerobot.common.control_utils import init_keyboard_listener, predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
+from lerobot.policies.utils import make_robot_action
 from lerobot.processor import (
     RobotProcessorPipeline,
     make_default_teleop_action_processor,
@@ -34,11 +38,12 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
     ForwardKinematicsJointsToEE,
     InverseKinematicsEEToJoints,
 )
-from lerobot.scripts.lerobot_record import record_loop
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.feature_utils import combine_feature_dicts
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
+from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_rerun
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 NUM_EPISODES = 5
 FPS = 30
@@ -49,6 +54,9 @@ HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
 
 def main():
+    # NOTE: For production policy deployment, use `lerobot-rollout` CLI instead.
+    # This script provides a self-contained example for educational purposes.
+
     # Create the robot configuration & robot
     camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
     robot_config = SO100FollowerConfig(
@@ -143,43 +151,67 @@ def main():
             raise ValueError("Robot is not connected!")
 
         print("Starting evaluate loop...")
+        control_interval = 1 / FPS
         episode_idx = 0
         for episode_idx in range(NUM_EPISODES):
             log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
-            )
+            # Inline evaluation loop: predict actions and send to robot
+            timestamp = 0
+            start_episode_t = time.perf_counter()
+            while timestamp < EPISODE_TIME_SEC:
+                start_loop_t = time.perf_counter()
+
+                if events["exit_early"]:
+                    events["exit_early"] = False
+                    break
+
+                # Get robot observation
+                obs = robot.get_observation()
+                obs_processed = robot_joints_to_ee_pose_processor(obs)
+                observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
+
+                # Predict action using the policy
+                action_tensor = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=policy.config.device,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.device.type == "cuda",
+                    task=TASK_DESCRIPTION,
+                    robot_type=robot.name,
+                )
+
+                # Convert policy output to robot action dict
+                action_values = make_robot_action(action_tensor, dataset.features)
+
+                # Process and send action to robot (EE -> joints via IK)
+                robot_action_to_send = robot_ee_to_joints_processor((action_values, obs))
+                robot.send_action(robot_action_to_send)
+
+                # Write to dataset
+                action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
+                frame = {**observation_frame, **action_frame, "task": TASK_DESCRIPTION}
+                dataset.add_frame(frame)
+
+                log_rerun_data(observation=obs_processed, action=action_values)
+
+                dt_s = time.perf_counter() - start_loop_t
+                sleep_time_s = control_interval - dt_s
+                if sleep_time_s < 0:
+                    logging.warning(
+                        f"Evaluate loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({FPS} Hz)."
+                    )
+                precise_sleep(max(sleep_time_s, 0.0))
+                timestamp = time.perf_counter() - start_episode_t
 
             # Reset the environment if not stopping or re-recording
             if not events["stop_recording"] and (
                 (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
             ):
                 log_say("Reset the environment")
-                record_loop(
-                    robot=robot,
-                    events=events,
-                    fps=FPS,
-                    control_time_s=EPISODE_TIME_SEC,
-                    single_task=TASK_DESCRIPTION,
-                    display_data=True,
-                    teleop_action_processor=make_default_teleop_action_processor(),
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose_processor,
-                )
+                log_say("Waiting for environment reset, press right arrow key when ready...")
 
             if events["rerecord_episode"]:
                 log_say("Re-record episode")
@@ -190,7 +222,6 @@ def main():
 
             # Save episode
             dataset.save_episode()
-            episode_idx += 1
     finally:
         # Clean up
         log_say("Stop recording")

examples/so100_to_so100_EE/record.py

@@ -62,21 +62,20 @@ def main():
     follower = SO100Follower(follower_config)
     leader = SO100Leader(leader_config)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+    #   https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
     follower_kinematics_solver = RobotKinematics(
         urdf_path="./SO101/so101_new_calib.urdf",
         target_frame_name="gripper_frame_link",
         joint_names=list(follower.bus.motors.keys()),
     )
-
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
     leader_kinematics_solver = RobotKinematics(
         urdf_path="./SO101/so101_new_calib.urdf",
         target_frame_name="gripper_frame_link",
         joint_names=list(leader.bus.motors.keys()),
     )
 
-    # Build pipeline to convert follower joints to EE observation
+    # Build pipeline to convert follower joints to EE observation.
     follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
             ForwardKinematicsJointsToEE(
@@ -87,7 +86,7 @@ def main():
         to_output=transition_to_observation,
     )
 
-    # Build pipeline to convert leader joints to EE action
+    # Build pipeline to convert leader joints to EE action.
     leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
         steps=[
             ForwardKinematicsJointsToEE(
@@ -98,9 +97,9 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Build pipeline to convert EE action to follower joints
+    # Build pipeline to convert EE action to follower joints (with safety bounds).
     ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        [
+        steps=[
             EEBoundsAndSafety(
                 end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
                 max_ee_step_m=0.10,
@@ -115,13 +114,12 @@ def main():
         to_output=transition_to_robot_action,
     )
 
-    # Create the dataset
+    # Create the dataset, deriving features from the pipelines so the on-disk schema
+    # matches exactly what the pipelines produce at runtime.
     dataset = LeRobotDataset.create(
         repo_id=HF_REPO_ID,
         fps=FPS,
         features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
             aggregate_pipeline_dataset_features(
                 pipeline=leader_joints_to_ee,
                 initial_features=create_initial_features(action=leader.action_features),
@@ -144,7 +142,7 @@ def main():
 
     # Initialize the keyboard listener and rerun visualization
     listener, events = init_keyboard_listener()
-    init_rerun(session_name="recording_phone")
+    init_rerun(session_name="recording_so100_ee")
 
     try:
         if not leader.is_connected or not follower.is_connected:
@@ -160,14 +158,14 @@ def main():
                 robot=follower,
                 events=events,
                 fps=FPS,
+                teleop_action_processor=leader_joints_to_ee,
+                robot_action_processor=ee_to_follower_joints,
+                robot_observation_processor=follower_joints_to_ee,
                 teleop=leader,
                 dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=leader_joints_to_ee,
-                robot_action_processor=ee_to_follower_joints,
-                robot_observation_processor=follower_joints_to_ee,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -179,13 +177,13 @@ def main():
                     robot=follower,
                     events=events,
                     fps=FPS,
+                    teleop_action_processor=leader_joints_to_ee,
+                    robot_action_processor=ee_to_follower_joints,
+                    robot_observation_processor=follower_joints_to_ee,
                     teleop=leader,
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=leader_joints_to_ee,
-                    robot_action_processor=ee_to_follower_joints,
-                    robot_observation_processor=follower_joints_to_ee,
                 )
 
             if events["rerecord_episode"]:

examples/so100_to_so100_EE/rollout.py

@@ -0,0 +1,134 @@
+# !/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Run a trained EE-space policy on SO100 without recording (base rollout).
+
+Uses the rollout engine's :class:`BaseStrategy` (autonomous execution,
+no dataset) with :class:`SyncInferenceConfig` (inline policy call per
+control tick).  The custom observation/action processors convert between
+joint space (robot hardware) and end-effector space (policy I/O) via
+forward/inverse kinematics.
+"""
+
+from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.configs import PreTrainedConfig
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import (
+    RobotProcessorPipeline,
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    ForwardKinematicsJointsToEE,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
+from lerobot.rollout.inference import SyncInferenceConfig
+from lerobot.rollout.strategies import BaseStrategy
+from lerobot.types import RobotAction, RobotObservation
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+
+FPS = 30
+DURATION_SEC = 60
+TASK_DESCRIPTION = "My task description"
+HF_MODEL_ID = "<hf_username>/<model_repo_id>"
+
+
+def main():
+    init_logging()
+
+    # Robot configuration — the rollout engine will connect it inside build_rollout_context.
+    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+    robot_config = SO100FollowerConfig(
+        port="/dev/tty.usbmodem5A460814411",
+        id="my_awesome_follower_arm",
+        cameras=camera_config,
+        use_degrees=True,
+    )
+
+    # Kinematic solver: we need the motor-name list, so peek at the robot once.
+    # (The rollout engine owns the connected instance; we only use this for introspection.)
+    temp_robot = SO100Follower(robot_config)
+    motor_names = list(temp_robot.bus.motors.keys())
+
+    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+    #   https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+    kinematics_solver = RobotKinematics(
+        urdf_path="./SO101/so101_new_calib.urdf",
+        target_frame_name="gripper_frame_link",
+        joint_names=motor_names,
+    )
+
+    # Joint-space observation → EE-space observation (consumed by the policy).
+    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=motor_names)],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+
+    # EE-space action (produced by the policy) → joint-space action (sent to robot).
+    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            InverseKinematicsEEToJoints(
+                kinematics=kinematics_solver,
+                motor_names=motor_names,
+                initial_guess_current_joints=True,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    # Policy config (full model is loaded inside build_rollout_context).
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+
+    cfg = RolloutConfig(
+        robot=robot_config,
+        policy=policy_config,
+        strategy=BaseStrategyConfig(),
+        inference=SyncInferenceConfig(),
+        fps=FPS,
+        duration=DURATION_SEC,
+        task=TASK_DESCRIPTION,
+    )
+
+    signal_handler = ProcessSignalHandler(use_threads=True)
+
+    # Pass the EE kinematic processors via kwargs; the defaults (identity) would
+    # otherwise skip the joint↔EE conversion and the policy would receive the
+    # wrong observation/action space.
+    ctx = build_rollout_context(
+        cfg,
+        signal_handler.shutdown_event,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
+    )
+
+    strategy = BaseStrategy(cfg.strategy)
+    try:
+        strategy.setup(ctx)
+        strategy.run(ctx)
+    finally:
+        strategy.teardown(ctx)
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -212,20 +212,6 @@ aloha = ["lerobot[dataset]", "gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
 pusht = ["lerobot[dataset]", "gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
 libero = ["lerobot[dataset]", "lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
 metaworld = ["lerobot[dataset]", "metaworld==3.0.0", "lerobot[scipy-dep]"]
-# NOTE: vlabench is NOT exposed as a `lerobot` extra. Its only distribution
-# is the OpenMOSS/VLABench GitHub repo (package name `VLABench`, no PyPI
-# release), so any `vlabench>=X` pip spec is unresolvable. Install it
-# manually alongside MuJoCo / dm-control — see docs/source/vlabench.mdx
-# for the recipe.
-# NOTE: robomme is NOT a pyproject extra — mani-skill hard-pins numpy<2
-# which conflicts with lerobot's numpy>=2 base pin, so the two trees can't
-# resolve into a single env. Install it only in the RoboMME Docker image
-# via `uv pip install --override` (see docker/Dockerfile.benchmark.robomme).
-# NOTE: robocasa is NOT exposed as a `lerobot` extra. Its setup.py pins
-# `lerobot==0.3.3` in install_requires, which cyclically shadows our own
-# workspace `lerobot` and makes the graph unsolvable under any resolver
-# (uv, pip). Install it manually alongside robosuite — see
-# docs/source/robocasa.mdx for the recipe.
 
 # All
 all = [
@@ -289,6 +275,7 @@ lerobot-find-joint-limits="lerobot.scripts.lerobot_find_joint_limits:main"
 lerobot-imgtransform-viz="lerobot.scripts.lerobot_imgtransform_viz:main"
 lerobot-edit-dataset="lerobot.scripts.lerobot_edit_dataset:main"
 lerobot-setup-can="lerobot.scripts.lerobot_setup_can:main"
+lerobot-rollout="lerobot.scripts.lerobot_rollout:main"
 
 # ---------------- Tool Configurations ----------------
 [tool.setuptools.package-data]

scripts/ci/extract_task_descriptions.py

@@ -31,23 +31,9 @@ from __future__ import annotations
 
 import argparse
 import json
-import re
 import sys
 from pathlib import Path
 
-# LIBERO-plus derives task.language by space-joining the perturbation-variant
-# filename (grab_language_from_filename in libero/libero/benchmark/__init__.py),
-# so non-_language_ variants inherit a trailing metadata blob like
-# "view 0 0 100 0 0 initstate 0 noise 45" or "add 16". Strip those tokens so
-# the description matches the base instruction used in the training dataset.
-_LIBERO_PERTURBATION_TAIL_RE = re.compile(
-    r"(?:\s(?:view|initstate|noise|add|tb|table|light|level)(?:\s\d+)+)+$"
-)
-
-
-def _strip_libero_perturbation_tail(instruction: str) -> str:
-    return _LIBERO_PERTURBATION_TAIL_RE.sub("", instruction).strip()
-
 
 def _libero_descriptions(task_suite: str) -> dict[str, str]:
     from libero.libero import benchmark  # type: ignore[import-untyped]
@@ -61,10 +47,7 @@ def _libero_descriptions(task_suite: str) -> dict[str, str]:
         )
         return {}
     suite = suite_dict[task_suite]()
-    return {
-        f"{task_suite}_{i}": _strip_libero_perturbation_tail(suite.get_task(i).language)
-        for i in range(suite.n_tasks)
-    }
+    return {f"{task_suite}_{i}": suite.get_task(i).language for i in range(suite.n_tasks)}
 
 
 def _metaworld_descriptions(task_name: str) -> dict[str, str]:
@@ -74,120 +57,19 @@ def _metaworld_descriptions(task_name: str) -> dict[str, str]:
     return {f"{task_name}_0": label}
 
 
-def _robotwin_descriptions(task_names: str) -> dict[str, str]:
-    """Return descriptions for each requested RoboTwin task. Reads
-    `description/task_instruction/<task>.json` from the RoboTwin clone
-    (cwd is /opt/robotwin in CI). Falls back to the task name if missing."""
-    out: dict[str, str] = {}
-    root = Path("description/task_instruction")
-    for name in (t.strip() for t in task_names.split(",") if t.strip()):
-        desc_file = root / f"{name}.json"
-        desc = name.replace("_", " ")
-        if desc_file.is_file():
-            data = json.loads(desc_file.read_text())
-            full = data.get("full_description") or desc
-            # Strip the schema placeholders ({A}, {a}) — keep the sentence readable.
-            desc = full.replace("<", "").replace(">", "")
-        out[f"{name}_0"] = desc
-    return out
-
-
-def _robocasa_descriptions(task_spec: str) -> dict[str, str]:
-    """For each task in the comma-separated list, emit a cleaned-name label.
-
-    RoboCasa episodes carry their language instruction in the env's
-    `ep_meta['lang']`, populated per reset. Pulling it requires spinning
-    up the full kitchen env per task (~seconds each); we use the task
-    name as the key here and let the eval's episode info carry the
-    actual instruction.
-    """
-    out: dict[str, str] = {}
-    for task in (t.strip() for t in task_spec.split(",") if t.strip()):
-        # Split CamelCase into words: "CloseFridge" → "close fridge".
-        label = "".join(f" {c.lower()}" if c.isupper() else c for c in task).strip()
-        out[f"{task}_0"] = label or task
-    return out
-
-
-_ROBOMME_DESCRIPTIONS = {
-    "BinFill": "Fill the target bin with the correct number of cubes",
-    "PickXtimes": "Pick the indicated cube the specified number of times",
-    "SwingXtimes": "Swing the object the specified number of times",
-    "StopCube": "Grasp and stop the moving cube",
-    "VideoUnmask": "Pick the cube shown in the reference video",
-    "VideoUnmaskSwap": "Pick the cube matching the reference video after a swap",
-    "ButtonUnmask": "Press the button indicated by the reference",
-    "ButtonUnmaskSwap": "Press the correct button after objects are swapped",
-    "PickHighlight": "Pick the highlighted cube",
-    "VideoRepick": "Repick the cube shown in the reference video",
-    "VideoPlaceButton": "Place the cube on the button shown in the video",
-    "VideoPlaceOrder": "Place cubes in the order shown in the video",
-    "MoveCube": "Move the cube to the target location",
-    "InsertPeg": "Insert the peg into the target hole",
-    "PatternLock": "Unlock the pattern by pressing buttons in sequence",
-    "RouteStick": "Route the stick through the required waypoints",
-}
-
-
-def _robomme_descriptions(task_names: str, task_ids: list[int] | None = None) -> dict[str, str]:
-    """Return descriptions for each requested RoboMME task. Keys match the
-    video filename pattern `<task>_<task_id>` used by the eval script."""
-    if task_ids is None:
-        task_ids = [0]
-    out: dict[str, str] = {}
-    for name in (t.strip() for t in task_names.split(",") if t.strip()):
-        desc = _ROBOMME_DESCRIPTIONS.get(name, name)
-        for tid in task_ids:
-            out[f"{name}_{tid}"] = desc
-    return out
-
-
-def _vlabench_descriptions(task_spec: str) -> dict[str, str]:
-    """For each task in the comma-separated list, emit a cleaned-name label.
-
-    VLABench tasks carry language instructions on their dm_control task
-    object, but pulling them requires loading the full env per task
-    (~seconds each). The CI smoke-eval already captures the instruction
-    inside its episode info; this mapping is just enough to key
-    `metrics.json` by `<task>_0`.
-    """
-    out: dict[str, str] = {}
-    for task in (t.strip() for t in task_spec.split(",") if t.strip()):
-        out[f"{task}_0"] = task.replace("_", " ").strip()
-    return out
-
-
 def main() -> int:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--env", required=True, help="Environment family (libero, metaworld, ...)")
     parser.add_argument("--task", required=True, help="Task/suite name (e.g. libero_spatial)")
-    parser.add_argument(
-        "--task-ids",
-        type=str,
-        default=None,
-        help="Comma-separated task IDs (e.g. '0,1,2'). Default: [0]",
-    )
     parser.add_argument("--output", required=True, help="Path to write task_descriptions.json")
     args = parser.parse_args()
 
-    task_ids: list[int] | None = None
-    if args.task_ids:
-        task_ids = [int(x.strip()) for x in args.task_ids.split(",")]
-
     descriptions: dict[str, str] = {}
     try:
-        if args.env == ("libero", "libero_plus"):
+        if args.env == "libero":
             descriptions = _libero_descriptions(args.task)
         elif args.env == "metaworld":
             descriptions = _metaworld_descriptions(args.task)
-        elif args.env == "robotwin":
-            descriptions = _robotwin_descriptions(args.task)
-        elif args.env == "robocasa":
-            descriptions = _robocasa_descriptions(args.task)
-        elif args.env == "robomme":
-            descriptions = _robomme_descriptions(args.task, task_ids=task_ids)
-        elif args.env == "vlabench":
-            descriptions = _vlabench_descriptions(args.task)
         else:
             print(
                 f"[extract_task_descriptions] No description extractor for env '{args.env}'.",

src/lerobot/cameras/realsense/camera_realsense.py

@@ -17,7 +17,6 @@ Provides the RealSenseCamera class for capturing frames from Intel RealSense cam
 """
 
 import logging
-import sys
 import time
 from threading import Event, Lock, Thread
 from typing import TYPE_CHECKING, Any
@@ -42,7 +41,6 @@ from ..utils import get_cv2_rotation
 from .configuration_realsense import RealSenseCameraConfig
 
 logger = logging.getLogger(__name__)
-pkg_name = "pyrealsense2-macosx" if sys.platform == "darwin" else "pyrealsense2"
 
 
 class RealSenseCamera(Camera):
@@ -116,7 +114,7 @@ class RealSenseCamera(Camera):
         Args:
             config: The configuration settings for the camera.
         """
-        require_package(pkg_name, extra="intelrealsense", import_name="pyrealsense2")
+        require_package("pyrealsense2", extra="intelrealsense")
         super().__init__(config)
 
         self.config = config

src/lerobot/configs/__init__.py

@@ -21,6 +21,7 @@ are intentionally NOT re-exported here to avoid circular dependencies
 Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
 """
 
+from .dataset import DatasetRecordConfig
 from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .types import (
@@ -39,6 +40,7 @@ __all__ = [
     "PolicyFeature",
     "RTCAttentionSchedule",
     # Config classes
+    "DatasetRecordConfig",
     "DatasetConfig",
     "EvalConfig",
     "PeftConfig",

src/lerobot/configs/dataset.py

@@ -0,0 +1,77 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Shared dataset recording configuration used by both ``lerobot-record`` and ``lerobot-rollout``."""
+
+from dataclasses import dataclass, field
+from datetime import datetime
+from pathlib import Path
+
+
+@dataclass
+class DatasetRecordConfig:
+    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
+    repo_id: str = ""
+    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
+    single_task: str = ""
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    root: str | Path | None = None
+    # Limit the frames per second.
+    fps: int = 30
+    # Number of seconds for data recording for each episode.
+    episode_time_s: int | float = 60
+    # Number of seconds for resetting the environment after each episode.
+    reset_time_s: int | float = 60
+    # Number of episodes to record.
+    num_episodes: int = 50
+    # Encode frames in the dataset into video
+    video: bool = True
+    # Upload dataset to Hugging Face hub.
+    push_to_hub: bool = True
+    # Upload on private repository on the Hugging Face hub.
+    private: bool = False
+    # Add tags to your dataset on the hub.
+    tags: list[str] | None = None
+    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
+    # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
+    # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
+    # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.
+    num_image_writer_processes: int = 0
+    # Number of threads writing the frames as png images on disk, per camera.
+    # Too many threads might cause unstable teleoperation fps due to main thread being blocked.
+    # Not enough threads might cause low camera fps.
+    num_image_writer_threads_per_camera: int = 4
+    # Number of episodes to record before batch encoding videos
+    # Set to 1 for immediate encoding (default behavior), or higher for batched encoding
+    video_encoding_batch_size: int = 1
+    # Video codec for encoding videos. Options: 'h264', 'hevc', 'libsvtav1', 'auto',
+    # or hardware-specific: 'h264_videotoolbox', 'h264_nvenc', 'h264_vaapi', 'h264_qsv'.
+    # Use 'auto' to auto-detect the best available hardware encoder.
+    vcodec: str = "libsvtav1"
+    # Enable streaming video encoding: encode frames in real-time during capture instead
+    # of writing PNG images first. Makes save_episode() near-instant. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding
+    streaming_encoding: bool = False
+    # Maximum number of frames to buffer per camera when using streaming encoding.
+    # ~1s buffer at 30fps. Provides backpressure if the encoder can't keep up.
+    encoder_queue_maxsize: int = 30
+    # 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
+    # Rename map for the observation to override the image and state keys
+    rename_map: dict[str, str] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        if self.repo_id:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            self.repo_id = f"{self.repo_id}_{timestamp}"

src/lerobot/datasets/utils.py

@@ -71,8 +71,8 @@ class ForwardCompatibilityError(CompatibilityError):
 
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of files per chunk
-DEFAULT_DATA_FILE_SIZE_IN_MB = 100  # Max size per file
-DEFAULT_VIDEO_FILE_SIZE_IN_MB = 200  # Max size per file
+DEFAULT_DATA_FILE_SIZE_IN_MB = 50  # Max size per file
+DEFAULT_VIDEO_FILE_SIZE_IN_MB = 100  # Max size per file
 
 INFO_PATH = "meta/info.json"
 STATS_PATH = "meta/stats.json"

src/lerobot/envs/configs.py

@@ -331,7 +331,6 @@ class LiberoEnv(EnvConfig):
     camera_name_mapping: dict[str, str] | None = None
     observation_height: int = 360
     observation_width: int = 360
-    is_libero_plus: bool = False
     features: dict[str, PolicyFeature] = field(
         default_factory=lambda: {
             ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
@@ -433,7 +432,6 @@ class LiberoEnv(EnvConfig):
             control_mode=self.control_mode,
             episode_length=self.episode_length,
             camera_name_mapping=self.camera_name_mapping,
-            is_libero_plus=self.is_libero_plus,
         )
 
     def get_env_processors(self):
@@ -498,146 +496,6 @@ class MetaworldEnv(EnvConfig):
         )
 
 
-@EnvConfig.register_subclass("robocasa")
-@dataclass
-class RoboCasaEnv(EnvConfig):
-    task: str = "CloseFridge"
-    fps: int = 20
-    episode_length: int = 1000
-    obs_type: str = "pixels_agent_pos"
-    render_mode: str = "rgb_array"
-    camera_name: str = "robot0_agentview_left,robot0_eye_in_hand,robot0_agentview_right"
-    observation_height: int = 256
-    observation_width: int = 256
-    visualization_height: int = 512
-    visualization_width: int = 512
-    split: str | None = None
-    # Object-mesh registries to sample from. Upstream default is
-    # ("objaverse", "lightwheel"), but objaverse is ~30GB and the CI image
-    # only ships the lightwheel pack. Override to include objaverse once
-    # you've run `python -m robocasa.scripts.download_kitchen_assets
-    # --type objaverse` locally.
-    obj_registries: list[str] = field(default_factory=lambda: ["lightwheel"])
-    features: dict[str, PolicyFeature] = field(
-        default_factory=lambda: {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(12,))}
-    )
-    features_map: dict[str, str] = field(default_factory=lambda: {ACTION: ACTION, "agent_pos": OBS_STATE})
-
-    def __post_init__(self):
-        if self.obs_type not in ("pixels", "pixels_agent_pos"):
-            raise ValueError(f"Unsupported obs_type: {self.obs_type}")
-
-        # Preserve raw RoboCasa camera names end-to-end (e.g.
-        # `observation.images.robot0_agentview_left`). This matches the
-        # naming convention used by the RoboCasa datasets on the Hub, so
-        # trained policies don't need a `--rename_map` at eval time.
-        cams = [c.strip() for c in self.camera_name.split(",") if c.strip()]
-        for cam in cams:
-            self.features[f"pixels/{cam}"] = PolicyFeature(
-                type=FeatureType.VISUAL,
-                shape=(self.observation_height, self.observation_width, 3),
-            )
-            self.features_map[f"pixels/{cam}"] = f"{OBS_IMAGES}.{cam}"
-
-        if self.obs_type == "pixels_agent_pos":
-            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(16,))
-
-    @property
-    def gym_kwargs(self) -> dict:
-        kwargs: dict[str, Any] = {
-            "obs_type": self.obs_type,
-            "render_mode": self.render_mode,
-            "observation_height": self.observation_height,
-            "observation_width": self.observation_width,
-            "visualization_height": self.visualization_height,
-            "visualization_width": self.visualization_width,
-        }
-        if self.split is not None:
-            kwargs["split"] = self.split
-        return kwargs
-
-    def create_envs(self, n_envs: int, use_async_envs: bool = False):
-        from .robocasa import create_robocasa_envs
-
-        if self.task is None:
-            raise ValueError("RoboCasaEnv requires a task to be specified")
-        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
-        return create_robocasa_envs(
-            task=self.task,
-            n_envs=n_envs,
-            camera_name=self.camera_name,
-            gym_kwargs=self.gym_kwargs,
-            env_cls=env_cls,
-            episode_length=self.episode_length,
-            obj_registries=tuple(self.obj_registries),
-        )
-
-
-@EnvConfig.register_subclass("vlabench")
-@dataclass
-class VLABenchEnv(EnvConfig):
-    task: str = "select_fruit"
-    fps: int = 10
-    episode_length: int = 500
-    obs_type: str = "pixels_agent_pos"
-    render_mode: str = "rgb_array"
-    render_resolution: tuple[int, int] = (480, 480)
-    robot: str = "franka"
-    action_mode: str = "eef"
-    features: dict[str, PolicyFeature] = field(
-        default_factory=lambda: {
-            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
-        }
-    )
-    features_map: dict[str, str] = field(
-        default_factory=lambda: {
-            ACTION: ACTION,
-            "agent_pos": OBS_STATE,
-            "pixels/image": f"{OBS_IMAGES}.image",
-            "pixels/second_image": f"{OBS_IMAGES}.second_image",
-            "pixels/wrist_image": f"{OBS_IMAGES}.wrist_image",
-        }
-    )
-
-    def __post_init__(self):
-        h, w = self.render_resolution
-        if self.obs_type == "pixels":
-            self.features["pixels/image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
-            self.features["pixels/second_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
-            self.features["pixels/wrist_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
-        elif self.obs_type == "pixels_agent_pos":
-            self.features["pixels/image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
-            self.features["pixels/second_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
-            self.features["pixels/wrist_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
-            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(7,))
-        else:
-            raise ValueError(f"Unsupported obs_type: {self.obs_type}")
-
-    @property
-    def gym_kwargs(self) -> dict:
-        return {
-            "obs_type": self.obs_type,
-            "render_mode": self.render_mode,
-            "render_resolution": self.render_resolution,
-            "robot": self.robot,
-            "max_episode_steps": self.episode_length,
-            "action_mode": self.action_mode,
-        }
-
-    def create_envs(self, n_envs: int, use_async_envs: bool = False):
-        from .vlabench import create_vlabench_envs
-
-        if self.task is None:
-            raise ValueError("VLABenchEnv requires a task to be specified")
-        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
-        return create_vlabench_envs(
-            task=self.task,
-            n_envs=n_envs,
-            gym_kwargs=self.gym_kwargs,
-            env_cls=env_cls,
-        )
-
-
 @EnvConfig.register_subclass("isaaclab_arena")
 @dataclass
 class IsaaclabArenaEnv(HubEnvConfig):
@@ -716,171 +574,3 @@ class IsaaclabArenaEnv(HubEnvConfig):
             ),
             PolicyProcessorPipeline(steps=[]),
         )
-
-
-@EnvConfig.register_subclass("libero_plus")
-@dataclass
-class LiberoPlusEnv(LiberoEnv):
-    """Config for LIBERO-plus robustness benchmark evaluation.
-
-    LIBERO-plus extends LIBERO with 7 perturbation dimensions (camera viewpoints,
-    object layouts, robot initial states, language instructions, lighting, background
-    textures, sensor noise) producing ~10k task variants.
-
-    The gym interface is identical to LIBERO so this class reuses ``LiberoEnv``
-    entirely — only the registered name and default task suite differ.
-
-    Install: see docker/Dockerfile.benchmark.libero_plus — LIBERO-plus ships
-    as a namespace package from a git fork and must be cloned + PYTHONPATH'd
-    rather than installed as a pyproject extra.
-
-    See Also:
-        https://github.com/sylvestf/LIBERO-plus
-    """
-
-    task: str = "libero_spatial"
-    is_libero_plus: bool = True
-
-
-@EnvConfig.register_subclass("robotwin")
-@dataclass
-class RoboTwinEnvConfig(EnvConfig):
-    """Configuration for RoboTwin 2.0 benchmark environments.
-
-    RoboTwin 2.0 is a dual-arm manipulation benchmark with 50 tasks built on the
-    SAPIEN simulator. The robot is an Aloha-AgileX bimanual platform with 14 DOF
-    (7 per arm). All three cameras are enabled by default.
-
-    See: https://robotwin-platform.github.io
-    Dataset: https://huggingface.co/datasets/lerobot/robotwin_unified
-    """
-
-    task: str = "beat_block_hammer"  # single task or comma-separated list
-    fps: int = 25
-    episode_length: int = 300
-    obs_type: str = "pixels_agent_pos"
-    render_mode: str = "rgb_array"
-    # Available cameras from RoboTwin's aloha-agilex embodiment: head_camera
-    # (torso-mounted) + left_camera / right_camera (wrists).
-    camera_names: str = "head_camera,left_camera,right_camera"
-    # Match the D435 dims in task_config/demo_clean.yml (_camera_config.yml).
-    # Gym's vector-env concatenate pre-allocates buffers of this shape, so it
-    # must equal what SAPIEN actually renders.
-    observation_height: int = 240
-    observation_width: int = 320
-    features: dict[str, PolicyFeature] = field(
-        default_factory=lambda: {
-            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(14,)),
-        }
-    )
-    features_map: dict[str, str] = field(
-        default_factory=lambda: {
-            ACTION: ACTION,
-            "pixels/head_camera": f"{OBS_IMAGES}.head_camera",
-            "pixels/left_camera": f"{OBS_IMAGES}.left_camera",
-            "pixels/right_camera": f"{OBS_IMAGES}.right_camera",
-            "agent_pos": OBS_STATE,
-        }
-    )
-
-    def __post_init__(self):
-        cam_list = [c.strip() for c in self.camera_names.split(",") if c.strip()]
-        for cam in cam_list:
-            self.features[f"pixels/{cam}"] = PolicyFeature(
-                type=FeatureType.VISUAL,
-                shape=(self.observation_height, self.observation_width, 3),
-            )
-            # Keep features_map entry if already set (default_factory); add if missing.
-            key = f"pixels/{cam}"
-            if key not in self.features_map:
-                self.features_map[key] = f"{OBS_IMAGES}.{cam}"
-
-        if self.obs_type == "pixels_agent_pos":
-            self.features["agent_pos"] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(14,),  # 14 DOF: 7 per arm
-            )
-        elif self.obs_type != "pixels":
-            raise ValueError(
-                f"Unsupported obs_type '{self.obs_type}'. "
-                "RoboTwinEnvConfig supports 'pixels' and 'pixels_agent_pos'."
-            )
-
-    @property
-    def gym_kwargs(self) -> dict:
-        return {}
-
-    def create_envs(self, n_envs: int, use_async_envs: bool = True):
-        from lerobot.envs.robotwin import create_robotwin_envs
-
-        if not self.task:
-            raise ValueError("RoboTwinEnvConfig requires `task` to be specified.")
-
-        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
-        cam_list = [c.strip() for c in self.camera_names.split(",") if c.strip()]
-        return create_robotwin_envs(
-            task=self.task,
-            n_envs=n_envs,
-            env_cls=env_cls,
-            camera_names=cam_list,
-            observation_height=self.observation_height,
-            observation_width=self.observation_width,
-            episode_length=self.episode_length,
-        )
-
-
-@EnvConfig.register_subclass("robomme")
-@dataclass
-class RoboMMEEnv(EnvConfig):
-    """RoboMME memory-augmented manipulation benchmark (ManiSkill/SAPIEN).
-
-    16 tasks across 4 suites: Counting, Permanence, Reference, Imitation.
-    Dataset: lerobot/robomme (LeRobot v3.0, 1,600 episodes).
-    Benchmark: https://github.com/RoboMME/robomme_benchmark
-
-    Requires the `robomme` git package installed separately (Linux only);
-    see docker/Dockerfile.benchmark.robomme for the canonical install.
-    """
-
-    task: str = "PickXtimes"
-    fps: int = 10
-    episode_length: int = 300
-    action_space: str = "joint_angle"  # or "ee_pose" (7-D)
-    dataset_split: str = "test"  # "train" | "val" | "test"
-    task_ids: list[int] | None = None
-    features: dict[str, PolicyFeature] = field(default_factory=dict)
-    features_map: dict[str, str] = field(
-        default_factory=lambda: {
-            ACTION: ACTION,
-            "pixels/image": f"{OBS_IMAGES}.image",
-            "pixels/wrist_image": f"{OBS_IMAGES}.wrist_image",
-            "agent_pos": OBS_STATE,
-        }
-    )
-
-    def __post_init__(self):
-        action_dim = 8 if self.action_space == "joint_angle" else 7
-        self.features = {
-            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,)),
-            "pixels/image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
-            "pixels/wrist_image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
-            "agent_pos": PolicyFeature(type=FeatureType.STATE, shape=(8,)),
-        }
-
-    @property
-    def gym_kwargs(self) -> dict:
-        return {}
-
-    def create_envs(self, n_envs: int, use_async_envs: bool = True):
-        from lerobot.envs.robomme import create_robomme_envs
-
-        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
-        return create_robomme_envs(
-            task=self.task,
-            n_envs=n_envs,
-            action_space_type=self.action_space,
-            dataset=self.dataset_split,
-            episode_length=self.episode_length,
-            task_ids=self.task_ids,
-            env_cls=env_cls,
-        )

src/lerobot/envs/libero.py

@@ -16,7 +16,6 @@
 from __future__ import annotations
 
 import os
-import re
 from collections import defaultdict
 from collections.abc import Callable, Iterable, Mapping, Sequence
 from functools import partial
@@ -32,7 +31,20 @@ from libero.libero.envs import OffScreenRenderEnv
 
 from lerobot.types import RobotObservation
 
-from .utils import _LazyAsyncVectorEnv, parse_camera_names
+from .utils import _LazyAsyncVectorEnv
+
+
+def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
+    """Normalize camera_name into a non-empty list of strings."""
+    if isinstance(camera_name, str):
+        cams = [c.strip() for c in camera_name.split(",") if c.strip()]
+    elif isinstance(camera_name, (list | tuple)):
+        cams = [str(c).strip() for c in camera_name if str(c).strip()]
+    else:
+        raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}")
+    if not cams:
+        raise ValueError("camera_name resolved to an empty list.")
+    return cams
 
 
 def _get_suite(name: str) -> benchmark.Benchmark:
@@ -57,34 +69,14 @@ def _select_task_ids(total_tasks: int, task_ids: Iterable[int] | None) -> list[i
     return ids
 
 
-# LIBERO-plus perturbation variants encode the perturbation in the filename
-# but on disk only the base `.pruned_init` exists — strip the suffix to match
-# LIBERO-plus's own suite.get_task_init_states() (we reimplement it here so we
-# can pass weights_only=False for PyTorch 2.6+ numpy pickles).
-_LIBERO_PERTURBATION_SUFFIX_RE = re.compile(r"_(?:language|view|light)_[^.]*|_(?:table|tb)_\d+")
-
-
-def get_task_init_states(task_suite: Any, i: int, is_libero_plus: bool = False) -> np.ndarray:
-    task = task_suite.tasks[i]
-    filename = Path(task.init_states_file)
-    root = Path(get_libero_path("init_states"))
-
-    if not is_libero_plus:
-        init_states_path = root / task.problem_folder / filename.name
-        return torch.load(init_states_path, weights_only=False)  # nosec B614
-
-    # LIBERO-plus: `_add_` / `_level` variants store extra-object layouts under
-    # libero_newobj/ as a flat array that must be reshaped to (1, -1).
-    if "_add_" in filename.name or "_level" in filename.name:
-        init_states_path = root / "libero_newobj" / task.problem_folder / filename.name
-        init_states = torch.load(init_states_path, weights_only=False)  # nosec B614
-        return init_states.reshape(1, -1)
-
-    # LIBERO-plus perturbation variants encode the perturbation in the filename
-    # but on disk only the base `.pruned_init` exists — strip the suffix to match.
-    stripped = _LIBERO_PERTURBATION_SUFFIX_RE.sub("", filename.stem) + filename.suffix
-    init_states_path = root / task.problem_folder / stripped
-    return torch.load(init_states_path, weights_only=False)  # nosec B614
+def get_task_init_states(task_suite: Any, i: int) -> np.ndarray:
+    init_states_path = (
+        Path(get_libero_path("init_states"))
+        / task_suite.tasks[i].problem_folder
+        / task_suite.tasks[i].init_states_file
+    )
+    init_states = torch.load(init_states_path, weights_only=False)  # nosec B614
+    return init_states
 
 
 def get_libero_dummy_action():
@@ -126,11 +118,9 @@ class LiberoEnv(gym.Env):
         camera_name_mapping: dict[str, str] | None = None,
         num_steps_wait: int = 10,
         control_mode: str = "relative",
-        is_libero_plus: bool = False,
     ):
         super().__init__()
         self.task_id = task_id
-        self.is_libero_plus = is_libero_plus
         self.obs_type = obs_type
         self.render_mode = render_mode
         self.observation_width = observation_width
@@ -138,7 +128,7 @@ class LiberoEnv(gym.Env):
         self.visualization_width = visualization_width
         self.visualization_height = visualization_height
         self.init_states = init_states
-        self.camera_name = parse_camera_names(
+        self.camera_name = _parse_camera_names(
             camera_name
         )  # agentview_image (main) or robot0_eye_in_hand_image (wrist)
 
@@ -157,11 +147,7 @@ class LiberoEnv(gym.Env):
         self.episode_index = episode_index
         self.episode_length = episode_length
         # Load once and keep
-        self._init_states = (
-            get_task_init_states(task_suite, self.task_id, is_libero_plus=self.is_libero_plus)
-            if self.init_states
-            else None
-        )
+        self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
         self._reset_stride = n_envs  # when performing a reset, append `_reset_stride` to `init_state_id`.
 
         self.init_state_id = self.episode_index  # tie each sub-env to a fixed init state
@@ -394,7 +380,6 @@ def _make_env_fns(
     gym_kwargs: Mapping[str, Any],
     control_mode: str,
     camera_name_mapping: dict[str, str] | None = None,
-    is_libero_plus: bool = False,
 ) -> list[Callable[[], LiberoEnv]]:
     """Build n_envs factory callables for a single (suite, task_id)."""
 
@@ -411,7 +396,6 @@ def _make_env_fns(
             n_envs=n_envs,
             control_mode=control_mode,
             camera_name_mapping=camera_name_mapping,
-            is_libero_plus=is_libero_plus,
             **local_kwargs,
         )
 
@@ -434,7 +418,6 @@ def create_libero_envs(
     control_mode: str = "relative",
     episode_length: int | None = None,
     camera_name_mapping: dict[str, str] | None = None,
-    is_libero_plus: bool = False,
 ) -> dict[str, dict[int, Any]]:
     """
     Create vectorized LIBERO environments with a consistent return shape.
@@ -454,7 +437,7 @@ def create_libero_envs(
     gym_kwargs = dict(gym_kwargs or {})
     task_ids_filter = gym_kwargs.pop("task_ids", None)  # optional: limit to specific tasks
 
-    camera_names = parse_camera_names(camera_name)
+    camera_names = _parse_camera_names(camera_name)
     suite_names = [s.strip() for s in str(task).split(",") if s.strip()]
     if not suite_names:
         raise ValueError("`task` must contain at least one LIBERO suite name.")
@@ -479,7 +462,6 @@ def create_libero_envs(
         # Probe once and reuse to avoid creating a temp env per task.
         cached_obs_space: spaces.Space | None = None
         cached_act_space: spaces.Space | None = None
-        cached_metadata: dict[str, Any] | None = None
 
         for tid in selected:
             fns = _make_env_fns(
@@ -493,14 +475,12 @@ def create_libero_envs(
                 gym_kwargs=gym_kwargs,
                 control_mode=control_mode,
                 camera_name_mapping=camera_name_mapping,
-                is_libero_plus=is_libero_plus,
             )
             if is_async:
-                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
+                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space)
                 if cached_obs_space is None:
                     cached_obs_space = lazy.observation_space
                     cached_act_space = lazy.action_space
-                    cached_metadata = lazy.metadata
                 out[suite_name][tid] = lazy
             else:
                 out[suite_name][tid] = env_cls(fns)

src/lerobot/envs/metaworld.py

@@ -311,7 +311,6 @@ def create_metaworld_envs(
     is_async = env_cls is gym.vector.AsyncVectorEnv
     cached_obs_space = None
     cached_act_space = None
-    cached_metadata = None
     out: dict[str, dict[int, Any]] = defaultdict(dict)
 
     for group in task_groups:
@@ -325,11 +324,10 @@ def create_metaworld_envs(
             fns = [(lambda tn=task_name: MetaworldEnv(task=tn, **gym_kwargs)) for _ in range(n_envs)]
 
             if is_async:
-                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
+                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space)
                 if cached_obs_space is None:
                     cached_obs_space = lazy.observation_space
                     cached_act_space = lazy.action_space
-                    cached_metadata = lazy.metadata
                 out[group][tid] = lazy
             else:
                 out[group][tid] = env_cls(fns)

src/lerobot/envs/robocasa.py

@@ -1,425 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import annotations
-
-import logging
-from collections import defaultdict
-from collections.abc import Callable, Sequence
-from functools import partial
-from typing import Any
-
-import gymnasium as gym
-import numpy as np
-from gymnasium import spaces
-
-from lerobot.types import RobotObservation
-
-from .utils import _LazyAsyncVectorEnv, parse_camera_names
-
-logger = logging.getLogger(__name__)
-
-# Dimensions for the flat action/state vectors used by the LeRobot wrapper.
-# These correspond to the PandaOmron robot in RoboCasa365.
-OBS_STATE_DIM = 16  # base_pos(3) + base_quat(4) + ee_pos_rel(3) + ee_quat_rel(4) + gripper_qpos(2)
-ACTION_DIM = 12  # base_motion(4) + control_mode(1) + ee_pos(3) + ee_rot(3) + gripper(1)
-ACTION_LOW = -1.0
-ACTION_HIGH = 1.0
-
-# Default PandaOmron cameras. We surface these raw names directly as
-# `observation.images.<name>` so the LeRobot dataset/policy keys match
-# RoboCasa's native convention (no implicit renaming).
-DEFAULT_CAMERAS = [
-    "robot0_agentview_left",
-    "robot0_eye_in_hand",
-    "robot0_agentview_right",
-]
-
-# Object-mesh registries to sample from. RoboCasa's upstream default is
-# ("objaverse", "lightwheel"), but the objaverse pack is huge (~30GB) and
-# most users — including our CI image — only download the lightwheel pack
-# (`--type objs_lw` in `download_kitchen_assets`). When a sampled object
-# category has zero candidates in every registry, robocasa crashes with
-# `ValueError: Probabilities contain NaN` (0/0 divide in the probability
-# normalization). Restricting to registries that are actually on disk
-# avoids the NaN and matches what the asset download provides.
-DEFAULT_OBJ_REGISTRIES: tuple[str, ...] = ("lightwheel",)
-
-# Task-group shortcuts accepted as `--env.task`. When the user passes one of
-# these names, we expand it to the upstream RoboCasa task list and auto-set
-# the dataset split. Individual task names (optionally comma-separated) still
-# take precedence; this only triggers on an exact group-name match.
-_TASK_GROUP_SPLITS = {
-    "atomic_seen": "target",
-    "composite_seen": "target",
-    "composite_unseen": "target",
-    "pretrain50": "pretrain",
-    "pretrain100": "pretrain",
-    "pretrain200": "pretrain",
-    "pretrain300": "pretrain",
-}
-
-
-def _resolve_tasks(task: str) -> tuple[list[str], str | None]:
-    """Resolve a `--env.task` value to (task_names, split_override).
-
-    If `task` is a known task-group name (e.g. `atomic_seen`, `pretrain100`),
-    expand it via `robocasa.utils.dataset_registry.{TARGET,PRETRAINING}_TASKS`
-    and return the matching split. Otherwise treat `task` as a single task or
-    comma-separated list and leave the split untouched (None).
-    """
-    key = task.strip()
-    if key in _TASK_GROUP_SPLITS:
-        from robocasa.utils.dataset_registry import PRETRAINING_TASKS, TARGET_TASKS
-
-        combined = {**TARGET_TASKS, **PRETRAINING_TASKS}
-        if key not in combined:
-            raise ValueError(
-                f"Task group '{key}' is not available in this version of robocasa. "
-                f"Known groups: {sorted(combined.keys())}."
-            )
-        return list(combined[key]), _TASK_GROUP_SPLITS[key]
-
-    names = [t.strip() for t in task.split(",") if t.strip()]
-    if not names:
-        raise ValueError("`task` must contain at least one RoboCasa task name.")
-    return names, None
-
-
-def convert_action(flat_action: np.ndarray) -> dict[str, Any]:
-    """Split a flat (12,) action vector into a RoboCasa action dict.
-
-    Layout: base_motion(4) + control_mode(1) + ee_pos(3) + ee_rot(3) + gripper(1)
-    """
-    return {
-        "action.base_motion": flat_action[0:4],
-        "action.control_mode": flat_action[4:5],
-        "action.end_effector_position": flat_action[5:8],
-        "action.end_effector_rotation": flat_action[8:11],
-        "action.gripper_close": flat_action[11:12],
-    }
-
-
-class RoboCasaEnv(gym.Env):
-    """LeRobot gym.Env wrapper for RoboCasa365 kitchen environments.
-
-    Wraps RoboCasaGymEnv from the robocasa package and converts its
-    dict-based observations and actions into the flat arrays LeRobot expects.
-    Raw RoboCasa camera names are preserved verbatim under `pixels/<cam>`.
-    """
-
-    metadata = {"render_modes": ["rgb_array"], "render_fps": 20}
-
-    def __init__(
-        self,
-        task: str,
-        camera_name: str | Sequence[str] = ",".join(DEFAULT_CAMERAS),
-        obs_type: str = "pixels_agent_pos",
-        render_mode: str = "rgb_array",
-        observation_width: int = 256,
-        observation_height: int = 256,
-        visualization_width: int = 512,
-        visualization_height: int = 512,
-        split: str | None = None,
-        episode_length: int | None = None,
-        obj_registries: Sequence[str] = DEFAULT_OBJ_REGISTRIES,
-        episode_index: int = 0,
-    ):
-        super().__init__()
-        self.task = task
-        self.obs_type = obs_type
-        self.render_mode = render_mode
-        self.observation_width = observation_width
-        self.observation_height = observation_height
-        self.visualization_width = visualization_width
-        self.visualization_height = visualization_height
-        self.split = split
-        self.obj_registries = tuple(obj_registries)
-        # Per-worker index (0..n_envs-1) used to spread the user-provided
-        # seed across factories so each sub-env explores a distinct layout
-        # even when the same seed is passed to `reset()`.
-        self.episode_index = int(episode_index)
-
-        self.camera_name = parse_camera_names(camera_name)
-
-        self._max_episode_steps = episode_length if episode_length is not None else 1000
-
-        # Deferred — created on first reset() inside the worker subprocess
-        # to avoid inheriting stale GPU/EGL contexts across fork().
-        self._env: Any = None
-        self.task_description = ""
-
-        images = {
-            cam: spaces.Box(
-                low=0,
-                high=255,
-                shape=(self.observation_height, self.observation_width, 3),
-                dtype=np.uint8,
-            )
-            for cam in self.camera_name
-        }
-
-        if self.obs_type == "pixels":
-            self.observation_space = spaces.Dict({"pixels": spaces.Dict(images)})
-        elif self.obs_type == "pixels_agent_pos":
-            self.observation_space = spaces.Dict(
-                {
-                    "pixels": spaces.Dict(images),
-                    "agent_pos": spaces.Box(
-                        low=-np.inf,
-                        high=np.inf,
-                        shape=(OBS_STATE_DIM,),
-                        dtype=np.float32,
-                    ),
-                }
-            )
-        else:
-            raise ValueError(f"Unsupported obs_type '{self.obs_type}'. Use 'pixels' or 'pixels_agent_pos'.")
-
-        self.action_space = spaces.Box(
-            low=ACTION_LOW,
-            high=ACTION_HIGH,
-            shape=(ACTION_DIM,),
-            dtype=np.float32,
-        )
-
-    def _ensure_env(self) -> None:
-        """Create the underlying RoboCasaGymEnv on first use.
-
-        Called inside the worker subprocess after fork(), so each worker gets
-        its own clean rendering context rather than inheriting a stale one from
-        the parent process (which causes crashes with AsyncVectorEnv).
-        """
-        if self._env is not None:
-            return
-        from robocasa.wrappers.gym_wrapper import RoboCasaGymEnv
-
-        # RoboCasaGymEnv defaults split="test", which create_env rejects
-        # (only None/"all"/"pretrain"/"target" are valid). Always pass a
-        # valid value so we don't hit that default. Extra kwargs are
-        # forwarded to the underlying kitchen env via create_env/robosuite.make.
-        self._env = RoboCasaGymEnv(
-            env_name=self.task,
-            camera_widths=self.observation_width,
-            camera_heights=self.observation_height,
-            split=self.split if self.split is not None else "all",
-            obj_registries=self.obj_registries,
-        )
-
-        ep_meta = self._env.env.get_ep_meta()
-        self.task_description = ep_meta.get("lang", self.task)
-
-    def _format_raw_obs(self, raw_obs: dict) -> RobotObservation:
-        """Convert RoboCasaGymEnv observation dict to LeRobot format."""
-        # RoboCasaGymEnv emits camera frames under "video.<cam>".
-        images = {cam: raw_obs[f"video.{cam}"] for cam in self.camera_name if f"video.{cam}" in raw_obs}
-
-        if self.obs_type == "pixels":
-            return {"pixels": images}
-
-        # `state.*` keys come from PandaOmronKeyConverter inside the wrapper.
-        agent_pos = np.concatenate(
-            [
-                raw_obs.get("state.base_position", np.zeros(3)),
-                raw_obs.get("state.base_rotation", np.zeros(4)),
-                raw_obs.get("state.end_effector_position_relative", np.zeros(3)),
-                raw_obs.get("state.end_effector_rotation_relative", np.zeros(4)),
-                raw_obs.get("state.gripper_qpos", np.zeros(2)),
-            ],
-            axis=-1,
-        ).astype(np.float32)
-
-        return {"pixels": images, "agent_pos": agent_pos}
-
-    def render(self) -> np.ndarray:
-        self._ensure_env()
-        assert self._env is not None
-        return self._env.render()
-
-    def reset(self, seed=None, **kwargs):
-        self._ensure_env()
-        assert self._env is not None
-        super().reset(seed=seed)
-        # Spread the seed across workers so n_envs factories don't all
-        # roll the same scene. With an explicit user seed we shift it by
-        # episode_index; with no seed we fall back to episode_index so
-        # each worker is still distinct rather than inheriting the same
-        # global RNG state.
-        worker_seed = seed + self.episode_index if seed is not None else self.episode_index
-        raw_obs, info = self._env.reset(seed=worker_seed)
-
-        ep_meta = self._env.env.get_ep_meta()
-        self.task_description = ep_meta.get("lang", self.task)
-
-        observation = self._format_raw_obs(raw_obs)
-        info = {"is_success": False}
-        return observation, info
-
-    def step(self, action: np.ndarray) -> tuple[RobotObservation, float, bool, bool, dict[str, Any]]:
-        self._ensure_env()
-        assert self._env is not None
-        if action.ndim != 1:
-            raise ValueError(
-                f"Expected action to be 1-D (shape (action_dim,)), "
-                f"but got shape {action.shape} with ndim={action.ndim}"
-            )
-
-        action_dict = convert_action(action)
-        raw_obs, reward, done, truncated, info = self._env.step(action_dict)
-
-        is_success = bool(info.get("success", False))
-        terminated = done or is_success
-        info.update({"task": self.task, "done": done, "is_success": is_success})
-
-        observation = self._format_raw_obs(raw_obs)
-        if terminated:
-            info["final_info"] = {
-                "task": self.task,
-                "done": bool(done),
-                "is_success": bool(is_success),
-            }
-            self.reset()
-
-        return observation, reward, terminated, truncated, info
-
-    def close(self):
-        if self._env is not None:
-            self._env.close()
-
-
-def _make_env_fns(
-    *,
-    task: str,
-    n_envs: int,
-    camera_names: list[str],
-    obs_type: str,
-    render_mode: str,
-    observation_width: int,
-    observation_height: int,
-    visualization_width: int,
-    visualization_height: int,
-    split: str | None,
-    episode_length: int | None,
-    obj_registries: Sequence[str],
-) -> list[Callable[[], RoboCasaEnv]]:
-    """Build n_envs factory callables for a single task.
-
-    Each factory carries a distinct ``episode_index`` (``0..n_envs-1``) so
-    ``RoboCasaEnv.reset()`` can derive a per-worker seed series from the
-    user-provided seed.
-    """
-
-    def _make_env(episode_index: int) -> RoboCasaEnv:
-        return RoboCasaEnv(
-            task=task,
-            camera_name=camera_names,
-            obs_type=obs_type,
-            render_mode=render_mode,
-            observation_width=observation_width,
-            observation_height=observation_height,
-            visualization_width=visualization_width,
-            visualization_height=visualization_height,
-            split=split,
-            episode_length=episode_length,
-            obj_registries=obj_registries,
-            episode_index=episode_index,
-        )
-
-    return [partial(_make_env, i) for i in range(n_envs)]
-
-
-def create_robocasa_envs(
-    task: str,
-    n_envs: int,
-    gym_kwargs: dict[str, Any] | None = None,
-    camera_name: str | Sequence[str] = ",".join(DEFAULT_CAMERAS),
-    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
-    episode_length: int | None = None,
-    obj_registries: Sequence[str] = DEFAULT_OBJ_REGISTRIES,
-) -> dict[str, dict[int, Any]]:
-    """Create vectorized RoboCasa365 environments with a consistent return shape.
-
-    Returns:
-        dict[task_name][task_id] -> vec_env (env_cls([...]) with exactly n_envs factories)
-
-    `task` can be:
-      - a single task name (e.g. `CloseFridge`)
-      - a comma-separated list of task names (e.g. `CloseFridge,PickPlaceCoffee`)
-      - a benchmark-group shortcut (`atomic_seen`, `composite_seen`,
-        `composite_unseen`, `pretrain50`, `pretrain100`, `pretrain200`,
-        `pretrain300`), which auto-expands to the upstream task list and
-        auto-sets the dataset `split` ("target" or "pretrain").
-    """
-    if env_cls is None or not callable(env_cls):
-        raise ValueError("env_cls must be a callable that wraps a list of environment factory callables.")
-    if not isinstance(n_envs, int) or n_envs <= 0:
-        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
-
-    gym_kwargs = dict(gym_kwargs or {})
-    obs_type = gym_kwargs.pop("obs_type", "pixels_agent_pos")
-    render_mode = gym_kwargs.pop("render_mode", "rgb_array")
-    observation_width = gym_kwargs.pop("observation_width", 256)
-    observation_height = gym_kwargs.pop("observation_height", 256)
-    visualization_width = gym_kwargs.pop("visualization_width", 512)
-    visualization_height = gym_kwargs.pop("visualization_height", 512)
-    split = gym_kwargs.pop("split", None)
-
-    camera_names = parse_camera_names(camera_name)
-    task_names, group_split = _resolve_tasks(str(task))
-    if group_split is not None and split is None:
-        split = group_split
-
-    logger.info(
-        "Creating RoboCasa envs | tasks=%s | split=%s | n_envs(per task)=%d",
-        task_names,
-        split,
-        n_envs,
-    )
-
-    is_async = env_cls is gym.vector.AsyncVectorEnv
-
-    cached_obs_space: spaces.Space | None = None
-    cached_act_space: spaces.Space | None = None
-    cached_metadata: dict[str, Any] | None = None
-    out: dict[str, dict[int, Any]] = defaultdict(dict)
-
-    for task_name in task_names:
-        fns = _make_env_fns(
-            task=task_name,
-            n_envs=n_envs,
-            camera_names=camera_names,
-            obs_type=obs_type,
-            render_mode=render_mode,
-            observation_width=observation_width,
-            observation_height=observation_height,
-            visualization_width=visualization_width,
-            visualization_height=visualization_height,
-            split=split,
-            episode_length=episode_length,
-            obj_registries=obj_registries,
-        )
-
-        if is_async:
-            lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
-            if cached_obs_space is None:
-                cached_obs_space = lazy.observation_space
-                cached_act_space = lazy.action_space
-                cached_metadata = lazy.metadata
-            out[task_name][0] = lazy
-        else:
-            out[task_name][0] = env_cls(fns)
-        logger.info("Built vec env | task=%s | n_envs=%d", task_name, n_envs)
-
-    return {name: dict(task_map) for name, task_map in out.items()}

src/lerobot/envs/robomme.py

@@ -1,245 +0,0 @@
-"""RoboMME environment wrapper for LeRobot evaluation.
-
-Wraps the RoboMME ``BenchmarkEnvBuilder`` into a Gymnasium-compatible
-``VectorEnv`` suitable for ``lerobot_eval``.
-
-RoboMME tasks:
-  Counting:    BinFill, PickXtimes, SwingXtimes, StopCube
-  Permanence:  VideoUnmask, VideoUnmaskSwap, ButtonUnmask, ButtonUnmaskSwap
-  Reference:   PickHighlight, VideoRepick, VideoPlaceButton, VideoPlaceOrder
-  Imitation:   MoveCube, InsertPeg, PatternLock, RouteStick
-
-Dataset: lerobot/robomme (LeRobot v3.0, 1,600 episodes)
-Install: see docker/Dockerfile.benchmark.robomme  (Linux only — mani-skill vs numpy pin conflict)
-Benchmark: https://github.com/RoboMME/robomme_benchmark
-"""
-
-from __future__ import annotations
-
-from collections.abc import Callable, Sequence
-from functools import partial
-from typing import Any
-
-import gymnasium as gym
-import numpy as np
-from gymnasium import spaces
-
-from .utils import _LazyAsyncVectorEnv
-
-ROBOMME_TASKS = [
-    "BinFill",
-    "PickXtimes",
-    "SwingXtimes",
-    "StopCube",
-    "VideoUnmask",
-    "VideoUnmaskSwap",
-    "ButtonUnmask",
-    "ButtonUnmaskSwap",
-    "PickHighlight",
-    "VideoRepick",
-    "VideoPlaceButton",
-    "VideoPlaceOrder",
-    "MoveCube",
-    "InsertPeg",
-    "PatternLock",
-    "RouteStick",
-]
-
-
-class RoboMMEGymEnv(gym.Env):
-    """Thin Gymnasium wrapper around a single RoboMME episode env."""
-
-    metadata = {"render_modes": ["rgb_array"], "render_fps": 10}
-
-    def __init__(
-        self,
-        task: str = "PickXtimes",
-        action_space_type: str = "joint_angle",
-        dataset: str = "test",
-        episode_idx: int = 0,
-        max_steps: int = 300,
-    ):
-        super().__init__()
-        from robomme.env_record_wrapper import BenchmarkEnvBuilder
-
-        self._task = task
-        self._action_space_type = action_space_type
-        self._dataset = dataset
-        self._episode_idx = episode_idx
-        self._max_steps = max_steps
-        self._max_episode_steps = max_steps
-
-        self._builder = BenchmarkEnvBuilder(
-            env_id=task,
-            dataset=dataset,
-            action_space=action_space_type,
-            gui_render=False,
-            max_steps=max_steps,
-        )
-        self._env = None
-        self._last_raw_obs: dict | None = None
-
-        action_dim = 8 if action_space_type == "joint_angle" else 7
-        self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(action_dim,), dtype=np.float32)
-        # `pixels` must be a nested Dict so `preprocess_observation()` in
-        # envs/utils.py picks it up and maps each camera to
-        # `observation.images.<cam>`. A flat layout (`pixels/image`,
-        # `pixels/wrist_image`) silently drops every image from the batch.
-        self.observation_space = spaces.Dict(
-            {
-                "pixels": spaces.Dict(
-                    {
-                        "image": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
-                        "wrist_image": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
-                    }
-                ),
-                "agent_pos": spaces.Box(-np.inf, np.inf, shape=(8,), dtype=np.float32),
-            }
-        )
-
-    def reset(self, *, seed=None, options=None):
-        super().reset(seed=seed)
-        self._env = self._builder.make_env_for_episode(
-            episode_idx=self._episode_idx,
-            max_steps=self._max_steps,
-        )
-        obs, info = self._env.reset()
-        self._last_raw_obs = obs
-        return self._convert_obs(obs), self._convert_info(info)
-
-    def step(self, action):
-        obs, reward, terminated, truncated, info = self._env.step(action)
-        self._last_raw_obs = obs
-
-        terminated_bool = bool(terminated.item()) if hasattr(terminated, "item") else bool(terminated)
-        truncated_bool = bool(truncated.item()) if hasattr(truncated, "item") else bool(truncated)
-
-        status = info.get("status", "ongoing")
-        is_success = status == "success"
-        conv_info = self._convert_info(info)
-        conv_info["is_success"] = is_success
-
-        return self._convert_obs(obs), float(reward), terminated_bool, truncated_bool, conv_info
-
-    def render(self) -> np.ndarray | None:
-        """Return the front camera image from the last observation for video recording."""
-        if self._last_raw_obs is None:
-            return np.zeros((256, 256, 3), dtype=np.uint8)
-        front = self._last_raw_obs.get("front_rgb_list")
-        if front is None:
-            return np.zeros((256, 256, 3), dtype=np.uint8)
-        frame = front[-1] if isinstance(front, list) else front
-        return np.asarray(frame, dtype=np.uint8)
-
-    def _convert_obs(self, obs: dict) -> dict:
-        front_rgb = (
-            obs["front_rgb_list"][-1] if isinstance(obs["front_rgb_list"], list) else obs["front_rgb_list"]
-        )
-        wrist_rgb = (
-            obs["wrist_rgb_list"][-1] if isinstance(obs["wrist_rgb_list"], list) else obs["wrist_rgb_list"]
-        )
-        joint_state = (
-            obs["joint_state_list"][-1]
-            if isinstance(obs["joint_state_list"], list)
-            else obs["joint_state_list"]
-        )
-        gripper_state = (
-            obs["gripper_state_list"][-1]
-            if isinstance(obs["gripper_state_list"], list)
-            else obs["gripper_state_list"]
-        )
-
-        front_rgb = np.asarray(front_rgb, dtype=np.uint8)
-        wrist_rgb = np.asarray(wrist_rgb, dtype=np.uint8)
-        joint = np.asarray(joint_state, dtype=np.float32).flatten()[:7]
-        gripper = np.asarray(gripper_state, dtype=np.float32).flatten()[:1]
-        state = np.concatenate([joint, gripper])
-
-        return {
-            "pixels": {"image": front_rgb, "wrist_image": wrist_rgb},
-            "agent_pos": state,
-        }
-
-    def _convert_info(self, info: dict) -> dict:
-        return {
-            "status": info.get("status", "ongoing"),
-            "task_goal": info.get("task_goal", ""),
-        }
-
-
-def _make_env_fns(
-    *,
-    task: str,
-    n_envs: int,
-    action_space_type: str,
-    dataset: str,
-    episode_length: int,
-    task_id: int,
-) -> list[Callable[[], RoboMMEGymEnv]]:
-    """Build n_envs factory callables for one RoboMME task id."""
-
-    def _make_one(episode_index: int) -> RoboMMEGymEnv:
-        return RoboMMEGymEnv(
-            task=task,
-            action_space_type=action_space_type,
-            dataset=dataset,
-            episode_idx=episode_index,
-            max_steps=episode_length,
-        )
-
-    return [partial(_make_one, task_id + i) for i in range(n_envs)]
-
-
-def create_robomme_envs(
-    task: str,
-    n_envs: int = 1,
-    action_space_type: str = "joint_angle",
-    dataset: str = "test",
-    episode_length: int = 300,
-    task_ids: list[int] | None = None,
-    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
-) -> dict[str, dict[int, gym.vector.VectorEnv]]:
-    """Create vectorized RoboMME environments for evaluation.
-
-    `task` may be a single RoboMME task name (e.g. "PickXtimes") or a
-    comma-separated list (e.g. "PickXtimes,BinFill,StopCube"). Each task
-    becomes its own suite in the returned mapping.
-
-    Returns {suite_name: {task_id: VectorEnv}} matching lerobot's expected format.
-    """
-    if env_cls is None or not callable(env_cls):
-        raise ValueError("env_cls must be a callable that wraps a list of env factory callables.")
-    if not isinstance(n_envs, int) or n_envs <= 0:
-        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
-
-    if task_ids is None:
-        task_ids = [0]
-
-    task_names = [t.strip() for t in task.split(",") if t.strip()]
-    is_async = env_cls is gym.vector.AsyncVectorEnv
-    cached_obs_space: spaces.Space | None = None
-    cached_act_space: spaces.Space | None = None
-    cached_metadata: dict[str, Any] | None = None
-    out: dict[str, dict[int, gym.vector.VectorEnv]] = {}
-    for task_name in task_names:
-        envs_by_task: dict[int, gym.vector.VectorEnv] = {}
-        for task_id in task_ids:
-            fns = _make_env_fns(
-                task=task_name,
-                n_envs=n_envs,
-                action_space_type=action_space_type,
-                dataset=dataset,
-                episode_length=episode_length,
-                task_id=task_id,
-            )
-            if is_async:
-                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
-                if cached_obs_space is None:
-                    cached_obs_space = lazy.observation_space
-                    cached_act_space = lazy.action_space
-                    cached_metadata = lazy.metadata
-                envs_by_task[task_id] = lazy
-            else:
-                envs_by_task[task_id] = env_cls(fns)
-        out[task_name] = envs_by_task
-    return out

src/lerobot/envs/robotwin.py

@@ -1,488 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import annotations
-
-import importlib
-import logging
-from collections import defaultdict
-from collections.abc import Callable, Sequence
-from functools import partial
-from typing import Any
-
-import gymnasium as gym
-import numpy as np
-import torch
-from gymnasium import spaces
-
-from lerobot.types import RobotObservation
-
-from .utils import _LazyAsyncVectorEnv
-
-logger = logging.getLogger(__name__)
-
-# Camera names as used by RoboTwin 2.0. The wrapper appends "_rgb" when looking
-# up keys in get_obs() output (e.g. "head_camera" → "head_camera_rgb").
-ROBOTWIN_CAMERA_NAMES: tuple[str, ...] = (
-    "head_camera",
-    "left_camera",
-    "right_camera",
-)
-
-ACTION_DIM = 14  # 7 DOF × 2 arms
-ACTION_LOW = -1.0
-ACTION_HIGH = 1.0
-DEFAULT_EPISODE_LENGTH = 300
-# D435 dims from task_config/_camera_config.yml (what demo_clean.yml selects).
-DEFAULT_CAMERA_H = 240
-DEFAULT_CAMERA_W = 320
-
-# Task list from RoboTwin 2.0's `envs/` directory — mirrors upstream exactly
-# (50 tasks as of main; earlier revisions had 60 with a different split).
-# Keep this in sync with:
-#   gh api /repos/RoboTwin-Platform/RoboTwin/contents/envs --paginate \
-#     | jq -r '.[].name' | grep -E '\.py$' | grep -v '^_' | sed 's/\.py$//'
-ROBOTWIN_TASKS: tuple[str, ...] = (
-    "adjust_bottle",
-    "beat_block_hammer",
-    "blocks_ranking_rgb",
-    "blocks_ranking_size",
-    "click_alarmclock",
-    "click_bell",
-    "dump_bin_bigbin",
-    "grab_roller",
-    "handover_block",
-    "handover_mic",
-    "hanging_mug",
-    "lift_pot",
-    "move_can_pot",
-    "move_pillbottle_pad",
-    "move_playingcard_away",
-    "move_stapler_pad",
-    "open_laptop",
-    "open_microwave",
-    "pick_diverse_bottles",
-    "pick_dual_bottles",
-    "place_a2b_left",
-    "place_a2b_right",
-    "place_bread_basket",
-    "place_bread_skillet",
-    "place_burger_fries",
-    "place_can_basket",
-    "place_cans_plasticbox",
-    "place_container_plate",
-    "place_dual_shoes",
-    "place_empty_cup",
-    "place_fan",
-    "place_mouse_pad",
-    "place_object_basket",
-    "place_object_scale",
-    "place_object_stand",
-    "place_phone_stand",
-    "place_shoe",
-    "press_stapler",
-    "put_bottles_dustbin",
-    "put_object_cabinet",
-    "rotate_qrcode",
-    "scan_object",
-    "shake_bottle",
-    "shake_bottle_horizontally",
-    "stack_blocks_three",
-    "stack_blocks_two",
-    "stack_bowls_three",
-    "stack_bowls_two",
-    "stamp_seal",
-    "turn_switch",
-)
-
-
-_ROBOTWIN_SETUP_CACHE: dict[str, dict[str, Any]] = {}
-
-
-def _load_robotwin_setup_kwargs(task_name: str) -> dict[str, Any]:
-    """Build the kwargs dict RoboTwin's setup_demo expects.
-
-    Mirrors the config loading done by RoboTwin's ``script/eval_policy.py``:
-    reads ``task_config/demo_clean.yml``, resolves the embodiment file from
-    ``_embodiment_config.yml``, loads the robot's own ``config.yml``, and
-    reads camera dimensions from ``_camera_config.yml``.
-
-    Uses ``aloha-agilex`` single-robot dual-arm by default (the only embodiment
-    used by beat_block_hammer and most smoke-test tasks).
-    """
-    if task_name in _ROBOTWIN_SETUP_CACHE:
-        return dict(_ROBOTWIN_SETUP_CACHE[task_name])
-
-    import os
-
-    import yaml  # type: ignore[import-untyped]
-    from envs import CONFIGS_PATH  # type: ignore[import-not-found]
-
-    task_config = "demo_clean"
-    with open(os.path.join(CONFIGS_PATH, f"{task_config}.yml"), encoding="utf-8") as f:
-        args = yaml.safe_load(f)
-
-    # Resolve embodiment — demo_clean.yml uses [aloha-agilex] (dual-arm single robot)
-    with open(os.path.join(CONFIGS_PATH, "_embodiment_config.yml"), encoding="utf-8") as f:
-        embodiment_types = yaml.safe_load(f)
-    embodiment = args.get("embodiment", ["aloha-agilex"])
-    if len(embodiment) == 1:
-        robot_file = embodiment_types[embodiment[0]]["file_path"]
-        args["left_robot_file"] = robot_file
-        args["right_robot_file"] = robot_file
-        args["dual_arm_embodied"] = True
-    elif len(embodiment) == 3:
-        args["left_robot_file"] = embodiment_types[embodiment[0]]["file_path"]
-        args["right_robot_file"] = embodiment_types[embodiment[1]]["file_path"]
-        args["embodiment_dis"] = embodiment[2]
-        args["dual_arm_embodied"] = False
-    else:
-        raise ValueError(f"embodiment must have 1 or 3 items, got {len(embodiment)}")
-
-    with open(os.path.join(args["left_robot_file"], "config.yml"), encoding="utf-8") as f:
-        args["left_embodiment_config"] = yaml.safe_load(f)
-    with open(os.path.join(args["right_robot_file"], "config.yml"), encoding="utf-8") as f:
-        args["right_embodiment_config"] = yaml.safe_load(f)
-
-    # Camera dimensions
-    with open(os.path.join(CONFIGS_PATH, "_camera_config.yml"), encoding="utf-8") as f:
-        camera_config = yaml.safe_load(f)
-    head_cam = args["camera"]["head_camera_type"]
-    args["head_camera_h"] = camera_config[head_cam]["h"]
-    args["head_camera_w"] = camera_config[head_cam]["w"]
-
-    # Headless overrides
-    args["render_freq"] = 0
-    args["task_name"] = task_name
-    args["task_config"] = task_config
-
-    _ROBOTWIN_SETUP_CACHE[task_name] = args
-    return dict(args)
-
-
-def _load_robotwin_task(task_name: str) -> type:
-    """Dynamically import and return a RoboTwin 2.0 task class.
-
-    RoboTwin tasks live in ``envs/<task_name>.py`` relative to the repository
-    root and are expected to be on ``sys.path`` after installation.
-    """
-    try:
-        module = importlib.import_module(f"envs.{task_name}")
-    except ModuleNotFoundError as e:
-        raise ModuleNotFoundError(
-            f"Could not import RoboTwin task '{task_name}'. "
-            "Ensure RoboTwin 2.0 is installed and its 'envs/' directory is on PYTHONPATH. "
-            "See the RoboTwin installation guide: https://robotwin-platform.github.io/doc/usage/robotwin-install.html"
-        ) from e
-    task_cls = getattr(module, task_name, None)
-    if task_cls is None:
-        raise AttributeError(f"Task class '{task_name}' not found in envs/{task_name}.py")
-    return task_cls
-
-
-class RoboTwinEnv(gym.Env):
-    """Gymnasium wrapper around a single RoboTwin 2.0 task.
-
-    RoboTwin uses a custom SAPIEN-based API (``setup_demo`` / ``get_obs`` /
-    ``take_action`` / ``check_success``) rather than the standard gym interface.
-    This class bridges that API to Gymnasium so that ``lerobot-eval`` can drive
-    RoboTwin exactly like LIBERO or Meta-World.
-
-    The underlying SAPIEN environment is created lazily on the first ``reset()``
-    call *inside the worker process*.  This is required for
-    ``gym.vector.AsyncVectorEnv`` compatibility: SAPIEN allocates EGL/GPU
-    contexts that must not be forked from the parent process.
-
-    Observations
-    ------------
-    The ``pixels`` dict uses the raw RoboTwin camera names as keys (e.g.
-    ``"head_camera"``, ``"left_camera"``). ``preprocess_observation`` in
-    ``envs/utils.py`` then converts these to ``observation.images.<cam>``.
-
-    Actions
-    -------
-    14-dim float32 array in ``[-1, 1]`` (joint-space, 7 DOF per arm).
-
-    Autograd
-    --------
-    ``setup_demo`` and ``take_action`` drive CuRobo's Newton trajectory
-    optimizer, which calls ``cost.backward()`` internally. lerobot_eval wraps
-    the rollout in ``torch.no_grad()``, so both call sites re-enable grad.
-    """
-
-    metadata = {"render_modes": ["rgb_array"], "render_fps": 25}
-
-    def __init__(
-        self,
-        task_name: str,
-        episode_index: int = 0,
-        n_envs: int = 1,
-        camera_names: Sequence[str] = ROBOTWIN_CAMERA_NAMES,
-        observation_height: int | None = None,
-        observation_width: int | None = None,
-        episode_length: int = DEFAULT_EPISODE_LENGTH,
-        render_mode: str = "rgb_array",
-    ):
-        super().__init__()
-        self.task_name = task_name
-        self.task = task_name  # used by add_envs_task() in utils.py
-        self.task_description = task_name.replace("_", " ")
-        self.episode_index = episode_index
-        self._reset_stride = n_envs
-        self.camera_names = list(camera_names)
-        # Default to D435 dims (the camera type baked into task_config/demo_clean.yml).
-        # The YAML-driven lookup is deferred to reset() so construction doesn't
-        # import RoboTwin's `envs` module — fast-tests run without RoboTwin installed.
-        self.observation_height = observation_height or DEFAULT_CAMERA_H
-        self.observation_width = observation_width or DEFAULT_CAMERA_W
-        self.episode_length = episode_length
-        self._max_episode_steps = episode_length  # lerobot_eval.rollout reads this
-        self.render_mode = render_mode
-
-        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)
-
-        image_spaces = {
-            cam: spaces.Box(
-                low=0,
-                high=255,
-                shape=(self.observation_height, self.observation_width, 3),
-                dtype=np.uint8,
-            )
-            for cam in self.camera_names
-        }
-        self.observation_space = spaces.Dict(
-            {
-                "pixels": spaces.Dict(image_spaces),
-                "agent_pos": spaces.Box(low=-np.inf, high=np.inf, shape=(ACTION_DIM,), dtype=np.float32),
-            }
-        )
-        self.action_space = spaces.Box(
-            low=ACTION_LOW, high=ACTION_HIGH, shape=(ACTION_DIM,), dtype=np.float32
-        )
-
-    def _ensure_env(self) -> None:
-        """Create the SAPIEN environment on first use.
-
-        Called inside the worker subprocess after fork(), so each worker gets
-        its own EGL/GPU context rather than inheriting a stale one from the
-        parent process (which causes crashes with AsyncVectorEnv).
-        """
-        if self._env is not None:
-            return
-        task_cls = _load_robotwin_task(self.task_name)
-        self._env = task_cls()
-
-    def _get_obs(self) -> RobotObservation:
-        assert self._env is not None, "_get_obs called before _ensure_env()"
-        raw = self._env.get_obs()
-        cameras_raw = raw.get("observation", {})
-
-        images: dict[str, np.ndarray] = {}
-        for cam in self.camera_names:
-            cam_data = cameras_raw.get(cam)
-            img = cam_data.get("rgb") if cam_data else None
-            if img is None:
-                images[cam] = self._black_frame
-                continue
-            img = np.asarray(img, dtype=np.uint8)
-            if img.ndim == 2:
-                img = np.stack([img, img, img], axis=-1)
-            elif img.shape[-1] != 3:
-                img = img[..., :3]
-            images[cam] = img
-
-        ja = raw.get("joint_action") or {}
-        vec = ja.get("vector")
-        if vec is not None:
-            arr = np.asarray(vec, dtype=np.float32).ravel()
-            joint_state = (
-                arr[:ACTION_DIM] if arr.size >= ACTION_DIM else np.zeros(ACTION_DIM, dtype=np.float32)
-            )
-        else:
-            joint_state = np.zeros(ACTION_DIM, dtype=np.float32)
-
-        return {"pixels": images, "agent_pos": joint_state}
-
-    def reset(self, seed: int | None = None, **kwargs) -> tuple[RobotObservation, dict]:
-        self._ensure_env()
-        super().reset(seed=seed)
-        assert self._env is not None  # set by _ensure_env() above
-
-        actual_seed = self.episode_index if seed is None else seed
-        setup_kwargs = _load_robotwin_setup_kwargs(self.task_name)
-        setup_kwargs.update(seed=actual_seed, is_test=True)
-        with torch.enable_grad():
-            self._env.setup_demo(**setup_kwargs)
-        self.episode_index += self._reset_stride
-        self._step_count = 0
-
-        obs = self._get_obs()
-        return obs, {"is_success": False, "task": self.task_name}
-
-    def step(self, action: np.ndarray) -> tuple[RobotObservation, float, bool, bool, dict[str, Any]]:
-        assert self._env is not None, "step() called before reset()"
-        if action.ndim != 1 or action.shape[0] != ACTION_DIM:
-            raise ValueError(f"Expected 1-D action of shape ({ACTION_DIM},), got {action.shape}")
-
-        with torch.enable_grad():
-            if hasattr(self._env, "take_action"):
-                self._env.take_action(action)
-            else:
-                self._env.step(action)
-
-        self._step_count += 1
-
-        is_success = bool(getattr(self._env, "eval_success", False))
-        if not is_success and hasattr(self._env, "check_success"):
-            is_success = bool(self._env.check_success())
-
-        obs = self._get_obs()
-        reward = float(is_success)
-        terminated = is_success
-        truncated = self._step_count >= self.episode_length
-
-        info: dict[str, Any] = {
-            "task": self.task_name,
-            "is_success": is_success,
-            "step": self._step_count,
-        }
-        if terminated or truncated:
-            info["final_info"] = {
-                "task": self.task_name,
-                "is_success": is_success,
-            }
-            self.reset()
-
-        return obs, reward, terminated, truncated, info
-
-    def render(self) -> np.ndarray:
-        self._ensure_env()
-        obs = self._get_obs()
-        # Prefer head camera for rendering; fall back to first available.
-        if "head_camera" in obs["pixels"]:
-            return obs["pixels"]["head_camera"]
-        return next(iter(obs["pixels"].values()))
-
-    def close(self) -> None:
-        if self._env is not None:
-            if hasattr(self._env, "close_env"):
-                import contextlib
-
-                with contextlib.suppress(TypeError):
-                    self._env.close_env()
-            self._env = None
-
-
-# ---- Multi-task factory --------------------------------------------------------
-
-
-def _make_env_fns(
-    *,
-    task_name: str,
-    n_envs: int,
-    camera_names: list[str],
-    observation_height: int,
-    observation_width: int,
-    episode_length: int,
-) -> list[Callable[[], RoboTwinEnv]]:
-    """Return n_envs factory callables for a single task."""
-
-    def _make_one(episode_index: int) -> RoboTwinEnv:
-        return RoboTwinEnv(
-            task_name=task_name,
-            episode_index=episode_index,
-            n_envs=n_envs,
-            camera_names=camera_names,
-            observation_height=observation_height,
-            observation_width=observation_width,
-            episode_length=episode_length,
-        )
-
-    return [partial(_make_one, i) for i in range(n_envs)]
-
-
-def create_robotwin_envs(
-    task: str,
-    n_envs: int,
-    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
-    camera_names: Sequence[str] = ROBOTWIN_CAMERA_NAMES,
-    observation_height: int = DEFAULT_CAMERA_H,
-    observation_width: int = DEFAULT_CAMERA_W,
-    episode_length: int = DEFAULT_EPISODE_LENGTH,
-) -> dict[str, dict[int, Any]]:
-    """Create vectorized RoboTwin 2.0 environments.
-
-    Returns:
-        ``dict[task_name][0] -> VectorEnv`` — one entry per task, each wrapping
-        ``n_envs`` parallel rollouts.
-
-    Args:
-        task: Comma-separated list of task names (e.g. ``"beat_block_hammer"``
-            or ``"beat_block_hammer,click_bell"``).
-        n_envs: Number of parallel rollouts per task.
-        env_cls: Vector env constructor (e.g. ``gym.vector.AsyncVectorEnv``).
-        camera_names: Cameras to include in observations.
-        observation_height: Pixel height for all cameras.
-        observation_width: Pixel width for all cameras.
-        episode_length: Max steps before truncation.
-    """
-    if env_cls is None or not callable(env_cls):
-        raise ValueError("env_cls must be callable (e.g. gym.vector.AsyncVectorEnv).")
-    if not isinstance(n_envs, int) or n_envs <= 0:
-        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
-
-    task_names = [t.strip() for t in str(task).split(",") if t.strip()]
-    if not task_names:
-        raise ValueError("`task` must contain at least one RoboTwin task name.")
-
-    unknown = [t for t in task_names if t not in ROBOTWIN_TASKS]
-    if unknown:
-        raise ValueError(f"Unknown RoboTwin tasks: {unknown}. Available tasks: {sorted(ROBOTWIN_TASKS)}")
-
-    logger.info(
-        "Creating RoboTwin envs | tasks=%s | n_envs(per task)=%d",
-        task_names,
-        n_envs,
-    )
-
-    is_async = env_cls is gym.vector.AsyncVectorEnv
-    cached_obs_space: spaces.Space | None = None
-    cached_act_space: spaces.Space | None = None
-    cached_metadata: dict[str, Any] | None = None
-
-    out: dict[str, dict[int, Any]] = defaultdict(dict)
-    for task_name in task_names:
-        fns = _make_env_fns(
-            task_name=task_name,
-            n_envs=n_envs,
-            camera_names=list(camera_names),
-            observation_height=observation_height,
-            observation_width=observation_width,
-            episode_length=episode_length,
-        )
-        if is_async:
-            lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
-            if cached_obs_space is None:
-                cached_obs_space = lazy.observation_space
-                cached_act_space = lazy.action_space
-                cached_metadata = lazy.metadata
-            out[task_name][0] = lazy
-        else:
-            out[task_name][0] = env_cls(fns)
-        logger.info("Built vec env | task=%s | n_envs=%d", task_name, n_envs)
-
-    return {k: dict(v) for k, v in out.items()}

src/lerobot/envs/utils.py

@@ -34,25 +34,6 @@ from lerobot.utils.utils import get_channel_first_image_shape
 from .configs import EnvConfig
 
 
-def parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
-    """Normalize ``camera_name`` into a non-empty list of strings.
-
-    Accepts a comma-separated string (``"cam_a,cam_b"``) or a sequence of
-    strings (tuples/lists). Whitespace is stripped; empty entries are
-    dropped. Raises ``TypeError`` for unsupported input types and
-    ``ValueError`` when the normalized list is empty.
-    """
-    if isinstance(camera_name, str):
-        cams = [c.strip() for c in camera_name.split(",") if c.strip()]
-    elif isinstance(camera_name, (list | tuple)):
-        cams = [str(c).strip() for c in camera_name if str(c).strip()]
-    else:
-        raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}")
-    if not cams:
-        raise ValueError("camera_name resolved to an empty list.")
-    return cams
-
-
 def _convert_nested_dict(d):
     result = {}
     for k, v in d.items():
@@ -172,20 +153,17 @@ class _LazyAsyncVectorEnv:
         env_fns: list[Callable],
         observation_space=None,
         action_space=None,
-        metadata=None,
     ):
         self._env_fns = env_fns
         self._env: gym.vector.AsyncVectorEnv | None = None
         self.num_envs = len(env_fns)
-        if observation_space is not None and action_space is not None and metadata is not None:
+        if observation_space is not None and action_space is not None:
             self.observation_space = observation_space
             self.action_space = action_space
-            self.metadata = metadata
         else:
             tmp = env_fns[0]()
             self.observation_space = tmp.observation_space
             self.action_space = tmp.action_space
-            self.metadata = tmp.metadata
             tmp.close()
         self.single_observation_space = self.observation_space
         self.single_action_space = self.action_space
@@ -194,10 +172,6 @@ class _LazyAsyncVectorEnv:
         if self._env is None:
             self._env = gym.vector.AsyncVectorEnv(self._env_fns, context="forkserver", shared_memory=True)
 
-    @property
-    def unwrapped(self):
-        return self
-
     def reset(self, **kwargs):
         self._ensure()
         return self._env.reset(**kwargs)

src/lerobot/envs/vlabench.py

@@ -1,589 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""VLABench environment wrapper for LeRobot.
-
-VLABench is a large-scale benchmark for language-conditioned robotic manipulation
-with long-horizon reasoning, built on MuJoCo/dm_control.
-
-- Paper: https://arxiv.org/abs/2412.18194
-- GitHub: https://github.com/OpenMOSS/VLABench
-- Website: https://vlabench.github.io
-"""
-
-from __future__ import annotations
-
-import contextlib
-import logging
-from collections import defaultdict
-from collections.abc import Callable, Sequence
-from typing import Any
-
-import cv2
-import gymnasium as gym
-import numpy as np
-from gymnasium import spaces
-from scipy.spatial.transform import Rotation
-
-from lerobot.types import RobotObservation
-
-from .utils import _LazyAsyncVectorEnv
-
-logger = logging.getLogger(__name__)
-
-ACTION_DIM = 7  # pos(3) + euler(3) + gripper(1)
-ACTION_LOW = np.array([-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0], dtype=np.float32)
-ACTION_HIGH = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=np.float32)
-
-# Default max episode steps per task type
-DEFAULT_MAX_EPISODE_STEPS = 500
-
-# VLABench task suites
-PRIMITIVE_TASKS = [
-    "select_fruit",
-    "select_toy",
-    "select_chemistry_tube",
-    "add_condiment",
-    "select_book",
-    "select_painting",
-    "select_drink",
-    "insert_flower",
-    "select_billiards",
-    "select_ingredient",
-    "select_mahjong",
-    "select_poker",
-    # Physical series
-    "density_qa",
-    "friction_qa",
-    "magnetism_qa",
-    "reflection_qa",
-    "simple_cuestick_usage",
-    "simple_seesaw_usage",
-    "sound_speed_qa",
-    "thermal_expansion_qa",
-    "weight_qa",
-]
-
-COMPOSITE_TASKS = [
-    "cluster_billiards",
-    "cluster_book",
-    "cluster_drink",
-    "cluster_toy",
-    "cook_dishes",
-    "cool_drink",
-    "find_unseen_object",
-    "get_coffee",
-    "hammer_nail",
-    "heat_food",
-    "make_juice",
-    "play_mahjong",
-    "play_math_game",
-    "play_poker",
-    "play_snooker",
-    "rearrange_book",
-    "rearrange_chemistry_tube",
-    "set_dining_table",
-    "set_study_table",
-    "store_food",
-    "take_chemistry_experiment",
-    "use_seesaw_complex",
-]
-
-SUITE_TASKS: dict[str, list[str]] = {
-    "primitive": PRIMITIVE_TASKS,
-    "composite": COMPOSITE_TASKS,
-}
-
-
-class VLABenchEnv(gym.Env):
-    """Gymnasium wrapper for VLABench environments.
-
-    Wraps the dm_control-based VLABench simulator behind a standard gym.Env interface.
-    Supports multiple cameras (front, second, wrist) and end-effector control.
-    """
-
-    metadata = {"render_modes": ["rgb_array"], "render_fps": 10}
-
-    def __init__(
-        self,
-        task: str = "select_fruit",
-        obs_type: str = "pixels_agent_pos",
-        render_mode: str = "rgb_array",
-        render_resolution: tuple[int, int] = (480, 480),
-        robot: str = "franka",
-        max_episode_steps: int = DEFAULT_MAX_EPISODE_STEPS,
-        action_mode: str = "eef",
-    ):
-        super().__init__()
-        self.task = task
-        self.obs_type = obs_type
-        self.render_mode = render_mode
-        self.render_resolution = render_resolution
-        self.robot = robot
-        self._max_episode_steps = max_episode_steps
-        self.action_mode = action_mode
-
-        # Deferred — created on first reset() inside worker subprocess to avoid
-        # inheriting stale GPU/EGL contexts when AsyncVectorEnv spawns workers.
-        # We never cache `env.physics`: dm_control exposes it as a weakref
-        # proxy that goes stale across resets (rebuilds the sim), so we always
-        # refetch it via `self._env.physics` at the call site.
-        self._env = None
-        self.task_description = ""  # populated on first reset
-        # Cached world-frame XYZ of the robot base link. The VLABench datasets
-        # log both `observation.state` positions and `actions` positions in
-        # robot-base frame (see VLABench/scripts/convert_to_lerobot.py which
-        # subtracts `robot_frame_pos` from ee_pos). The robot is attached at a
-        # fixed offset per task so this is safe to cache once per env build.
-        self._robot_base_xyz: np.ndarray | None = None
-
-        h, w = self.render_resolution
-
-        if self.obs_type == "state":
-            raise NotImplementedError(
-                "The 'state' observation type is not supported in VLABenchEnv. "
-                "Please use 'pixels' or 'pixels_agent_pos'."
-            )
-        elif self.obs_type == "pixels":
-            self.observation_space = spaces.Dict(
-                {
-                    "pixels": spaces.Dict(
-                        {
-                            "image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
-                            "second_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
-                            "wrist_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
-                        }
-                    ),
-                }
-            )
-        elif self.obs_type == "pixels_agent_pos":
-            self.observation_space = spaces.Dict(
-                {
-                    "pixels": spaces.Dict(
-                        {
-                            "image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
-                            "second_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
-                            "wrist_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
-                        }
-                    ),
-                    "agent_pos": spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float64),
-                }
-            )
-        else:
-            raise ValueError(f"Unsupported obs_type: {self.obs_type}")
-
-        self.action_space = spaces.Box(low=ACTION_LOW, high=ACTION_HIGH, dtype=np.float32)
-
-    # Max attempts to rebuild the underlying env when MuJoCo throws
-    # `PhysicsError` (e.g. mjWARN_BADQACC) during VLABench's 20-step
-    # reset warm-up. Some random task/layout samples land in unstable
-    # initial configurations; re-sampling the layout almost always
-    # gives a stable one. A handful of upstream tasks (notably
-    # `select_mahjong`) have layout samplers that diverge often enough
-    # to need >>5 retries, so we pick a generous ceiling.
-    _ENSURE_ENV_MAX_ATTEMPTS = 20
-
-    def _ensure_env(self) -> None:
-        """Create the underlying VLABench env on first use.
-
-        Called inside the worker subprocess after fork(), so each worker gets
-        its own clean rendering context rather than inheriting a stale one from
-        the parent process (which causes crashes with AsyncVectorEnv).
-
-        Retries on `PhysicsError`: VLABench's `LM4ManipDMEnv.reset()` runs 20
-        warm-up `step()` calls while toggling gravity/fluids to let the scene
-        settle; for some random layouts MuJoCo's integrator diverges and
-        raises `mjWARN_BADQACC`. Re-sampling the layout almost always yields
-        a stable one, so we retry a number of times before giving up. Between
-        attempts we reseed NumPy's global RNG from OS entropy so the upstream
-        task sampler explores fresh initial states — without this, retries
-        can replay the same diverging configuration when the sampler is
-        deterministic given the current RNG state.
-        """
-        if self._env is not None:
-            return
-
-        import VLABench.robots  # noqa: F401  # type: ignore[import-untyped]
-        import VLABench.tasks  # noqa: F401  # type: ignore[import-untyped]
-        from dm_control.rl.control import PhysicsError  # type: ignore[import-untyped]
-        from VLABench.envs import load_env  # type: ignore[import-untyped]
-
-        h, w = self.render_resolution
-        last_exc: PhysicsError | None = None
-        for attempt in range(1, self._ENSURE_ENV_MAX_ATTEMPTS + 1):
-            try:
-                env = load_env(task=self.task, robot=self.robot, render_resolution=(h, w))
-                self._env = env
-                break
-            except PhysicsError as exc:
-                last_exc = exc
-                logger.warning(
-                    "PhysicsError on attempt %d/%d while building task '%s': %s. Retrying with fresh layout…",
-                    attempt,
-                    self._ENSURE_ENV_MAX_ATTEMPTS,
-                    self.task,
-                    exc,
-                )
-                np.random.seed(None)
-        if self._env is None:
-            assert last_exc is not None
-            raise RuntimeError(
-                f"VLABench task '{self.task}' failed to produce a stable "
-                f"initial layout after {self._ENSURE_ENV_MAX_ATTEMPTS} "
-                f"attempts. This task's upstream sampler diverges too "
-                f"often for the configured robot; consider removing it "
-                f"from the eval set. Last physics error: {last_exc}"
-            ) from last_exc
-
-        # Extract task description from the dm_control task
-        task_obj = self._env.task
-        if hasattr(task_obj, "task_description"):
-            self.task_description = task_obj.task_description
-        elif hasattr(task_obj, "language_instruction"):
-            self.task_description = task_obj.language_instruction
-        else:
-            self.task_description = self.task
-
-        # Cache robot base world position so `_build_ctrl_from_action` and
-        # `_get_obs` can translate between robot-frame (dataset) and
-        # world-frame (dm_control) without hitting physics every call.
-        try:
-            self._robot_base_xyz = np.asarray(self._env.get_robot_frame_position(), dtype=np.float64).reshape(
-                3
-            )
-        except Exception:
-            # Fallback to VLABench's default Franka base position.
-            self._robot_base_xyz = np.array([0.0, -0.4, 0.78], dtype=np.float64)
-
-    def _get_obs(self) -> dict:
-        """Get current observation from the environment."""
-        assert self._env is not None
-
-        obs = self._env.get_observation()
-        h, w = self.render_resolution
-
-        def _to_hwc3(arr: np.ndarray) -> np.ndarray:
-            """Coerce any camera array to the declared (h, w, 3) uint8 shape."""
-            a = np.asarray(arr)
-            # Drop a leading singleton batch dim if present.
-            while a.ndim > 3 and a.shape[0] == 1:
-                a = a[0]
-            if a.ndim == 3 and a.shape[0] in (1, 3, 4) and a.shape[-1] not in (1, 3, 4):
-                # CHW → HWC
-                a = np.transpose(a, (1, 2, 0))
-            if a.ndim == 2:
-                a = np.stack([a] * 3, axis=-1)
-            if a.ndim != 3:
-                return np.zeros((h, w, 3), dtype=np.uint8)
-            # Force 3 channels.
-            if a.shape[-1] == 1:
-                a = np.repeat(a, 3, axis=-1)
-            elif a.shape[-1] == 4:
-                a = a[..., :3]
-            elif a.shape[-1] != 3:
-                return np.zeros((h, w, 3), dtype=np.uint8)
-            if a.shape[:2] != (h, w):
-                a = cv2.resize(a, (w, h), interpolation=cv2.INTER_AREA)
-            return a.astype(np.uint8)
-
-        # Extract camera images — VLABench returns (n_cameras, C, H, W) or individual arrays
-        raw_frames: list[np.ndarray] = []
-        if "rgb" in obs:
-            rgb = obs["rgb"]
-            if isinstance(rgb, np.ndarray):
-                if rgb.ndim == 4:
-                    raw_frames = [rgb[i] for i in range(rgb.shape[0])]
-                elif rgb.ndim == 3:
-                    raw_frames = [rgb]
-
-        image_keys = ["image", "second_image", "wrist_image"]
-        images: dict[str, np.ndarray] = {}
-        for i, key in enumerate(image_keys):
-            if i < len(raw_frames):
-                images[key] = _to_hwc3(raw_frames[i])
-            else:
-                images[key] = np.zeros((h, w, 3), dtype=np.uint8)
-
-        # Convert VLABench's raw ee_state `[pos_world(3), quat_wxyz(4), open(1)]`
-        # to the dataset's observation.state layout `[pos_robot(3), euler_xyz(3),
-        # gripper(1)]`. See VLABench/scripts/convert_to_lerobot.py — positions
-        # are stored in robot-base frame and orientations as scipy extrinsic
-        # 'xyz' euler angles.
-        raw = np.asarray(obs.get("ee_state", np.zeros(8)), dtype=np.float64).ravel()
-        pos_world = raw[:3] if raw.size >= 3 else np.zeros(3, dtype=np.float64)
-        quat_wxyz = raw[3:7] if raw.size >= 7 else np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float64)
-        gripper = float(raw[7]) if raw.size >= 8 else 0.0
-
-        base = self._robot_base_xyz if self._robot_base_xyz is not None else np.zeros(3, dtype=np.float64)
-        pos_robot = pos_world - base
-        euler_xyz = Rotation.from_quat([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]]).as_euler(
-            "xyz", degrees=False
-        )
-
-        ee_state = np.concatenate([pos_robot, euler_xyz, [gripper]]).astype(np.float64)
-
-        if self.obs_type == "pixels":
-            return {"pixels": images}
-        elif self.obs_type == "pixels_agent_pos":
-            return {
-                "pixels": images,
-                "agent_pos": ee_state.astype(np.float64),
-            }
-        else:
-            raise ValueError(f"Unknown obs_type: {self.obs_type}")
-
-    # ---- Action adaptation (EEF → joint ctrl) --------------------------------
-    #
-    # The HF vlabench datasets log 7D actions
-    # `[x, y, z (robot frame), rx, ry, rz (scipy extrinsic xyz), gripper]`,
-    # exactly matching VLABench's own eval pipeline (evaluator.base):
-    #   pos, euler, g = policy(...)
-    #   quat = euler_to_quaternion(*euler)      # extrinsic xyz -> wxyz
-    #   _, qpos = robot.get_qpos_from_ee_pos(physics, pos=pos + base, quat=quat)
-    #   env.step(np.concatenate([qpos, [g, g]]))
-    #
-    # VLABench's dm_control task writes `data.ctrl[:] = action` directly — for
-    # Franka that's 9 entries (7 arm joints + 2 gripper fingers). We mirror the
-    # above conversion so the policy's EEF commands actually drive the robot.
-
-    _FRANKA_FINGER_OPEN = 0.04  # qpos when gripper fully open
-
-    def _build_ctrl_from_action(self, action: np.ndarray, ctrl_dim: int) -> np.ndarray:
-        """Convert a 7D EEF action into the `ctrl_dim`-sized joint command vector.
-
-        For the Franka default (ctrl_dim=9): 7 arm joint qposes (via IK) +
-        2 gripper finger qposes (open/closed based on the gripper scalar).
-        If the action is already joint-space (shape matches ctrl_dim), pass
-        through.
-        """
-        if action.shape[0] == ctrl_dim:
-            return action.astype(np.float64, copy=False)
-
-        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[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
-            return padded
-
-        from dm_control.utils.inverse_kinematics import qpos_from_site_pose
-
-        # Action position is in robot-base frame (see convert_to_lerobot.py);
-        # dm_control's IK expects a world-frame target.
-        base = self._robot_base_xyz if self._robot_base_xyz is not None else np.zeros(3, dtype=np.float64)
-        pos_world = np.asarray(action[:3], dtype=np.float64) + base
-        rx, ry, rz = float(action[3]), float(action[4]), float(action[5])
-        gripper = float(np.clip(action[6], 0.0, 1.0))
-
-        # Dataset euler is scipy extrinsic 'xyz' (same as VLABench's
-        # `euler_to_quaternion`). scipy emits `[x, y, z, w]`; dm_control's IK
-        # and MuJoCo use `[w, x, y, z]`, so reorder.
-        qxyzw = Rotation.from_euler("xyz", [rx, ry, rz], degrees=False).as_quat()
-        quat = np.array([qxyzw[3], qxyzw[0], qxyzw[1], qxyzw[2]], dtype=np.float64)
-
-        assert self._env is not None
-        robot = self._env.task.robot
-        site_name = robot.end_effector_site.full_identifier
-
-        # inplace=False so IK doesn't mutate physics state mid-step — we only
-        # want the solved qpos. Fetch a fresh physics handle — caching it can
-        # yield a stale weakref after a reset.
-        ik_result = qpos_from_site_pose(
-            self._env.physics,
-            site_name=site_name,
-            target_pos=pos_world,
-            target_quat=quat,
-            inplace=False,
-            max_steps=100,
-        )
-        n_dof = robot.n_dof  # 7 for Franka
-        arm_qpos = ik_result.qpos[:n_dof]
-
-        # Dataset gripper convention: 1 = open (finger qpos = 0.04),
-        # 0 = closed (finger qpos = 0.0). See VLABench/scripts/convert_to_lerobot.py
-        # where `trajectory[i][-1] > 0.03` is encoded as `1`.
-        finger_qpos = gripper * self._FRANKA_FINGER_OPEN
-
-        ctrl = np.zeros(ctrl_dim, dtype=np.float64)
-        ctrl[:n_dof] = arm_qpos
-        # Remaining entries are gripper fingers (usually 2 for Franka).
-        ctrl[n_dof:] = finger_qpos
-        return ctrl
-
-    def reset(self, seed=None, **kwargs) -> tuple[RobotObservation, dict[str, Any]]:
-        self._ensure_env()
-        assert self._env is not None
-        super().reset(seed=seed)
-
-        if seed is not None:
-            self._seed_inner_env(int(self.np_random.integers(0, 2**31 - 1)))
-
-        self._env.reset()
-
-        observation = self._get_obs()
-        info = {"is_success": False}
-        return observation, info
-
-    def _seed_inner_env(self, seed: int) -> None:
-        """Propagate `seed` to the inner dm_control env. `Environment.reset()`
-        doesn't accept a seed, so we re-seed the task and environment
-        `RandomState`s directly. Best-effort: silently skipped when the
-        expected attributes are absent on a given VLABench version.
-        """
-        for owner_attr, rng_attr in (("task", "random"), (None, "_random_state")):
-            owner = getattr(self._env, owner_attr) if owner_attr else self._env
-            rng = getattr(owner, rng_attr, None)
-            rng_seed = getattr(rng, "seed", None)
-            if callable(rng_seed):
-                rng_seed(seed)
-
-    def step(self, action: np.ndarray) -> tuple[RobotObservation, float, bool, bool, dict[str, Any]]:
-        from dm_control.rl.control import PhysicsError  # type: ignore[import-untyped]
-
-        self._ensure_env()
-        assert self._env is not None
-
-        if action.ndim != 1:
-            raise ValueError(
-                f"Expected action to be 1-D (shape (action_dim,)), "
-                f"but got shape {action.shape} with ndim={action.ndim}"
-            )
-
-        if self.action_mode not in ("eef", "joint", "delta_eef"):
-            raise ValueError(f"Unknown action_mode: {self.action_mode}")
-
-        # Always refetch physics — dm_control returns a weakref proxy that can
-        # go stale across resets.
-        physics = self._env.physics
-        ctrl_dim = int(physics.data.ctrl.shape[0])
-        ctrl = self._build_ctrl_from_action(action, ctrl_dim)
-        try:
-            timestep = self._env.step(ctrl)
-        except PhysicsError as exc:
-            # Physics integrator diverged (e.g. mjWARN_BADQACC). Treat it as
-            # a graceful failed termination rather than a hard crash — the
-            # rest of the multi-task eval should still run.
-            logger.warning(
-                "PhysicsError during step on task '%s': %s. Terminating episode.",
-                self.task,
-                exc,
-            )
-            observation = self._get_obs()
-            info = {"task": self.task, "is_success": False, "physics_error": True}
-            # Drop the stale env so the next reset() rebuilds it cleanly.
-            with contextlib.suppress(Exception):
-                self._env.close()
-            self._env = None
-            return observation, 0.0, True, False, info
-
-        # Extract reward from dm_control timestep
-        reward = float(timestep.reward) if timestep.reward is not None else 0.0
-
-        # Check success via the task's termination condition
-        is_success = False
-        if hasattr(self._env, "task") and hasattr(self._env.task, "should_terminate_episode"):
-            is_success = bool(self._env.task.should_terminate_episode(self._env.physics))
-
-        terminated = is_success
-        truncated = False
-        info = {
-            "task": self.task,
-            "is_success": is_success,
-        }
-
-        observation = self._get_obs()
-
-        if terminated:
-            self.reset()
-
-        return observation, reward, terminated, truncated, info
-
-    def render(self) -> np.ndarray:
-        self._ensure_env()
-        obs = self._get_obs()
-        return obs["pixels"]["image"]
-
-    def close(self):
-        if self._env is not None:
-            self._env.close()
-            self._env = None
-
-
-# ---- Main API ----------------------------------------------------------------
-
-
-def create_vlabench_envs(
-    task: str,
-    n_envs: int,
-    gym_kwargs: dict[str, Any] | None = None,
-    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
-) -> dict[str, dict[int, Any]]:
-    """
-    Create vectorized VLABench environments with a consistent return shape.
-
-    Returns:
-        dict[suite_name][task_id] -> vec_env (env_cls([...]) with exactly n_envs factories)
-
-    Notes:
-        - n_envs is the number of rollouts *per task*.
-        - `task` can be a suite name ("primitive", "composite"), a comma-separated list of
-          suite names, or individual task names (e.g. "select_fruit,heat_food").
-    """
-    if env_cls is None or not callable(env_cls):
-        raise ValueError("env_cls must be a callable that wraps a list of environment factory callables.")
-    if not isinstance(n_envs, int) or n_envs <= 0:
-        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
-
-    gym_kwargs = dict(gym_kwargs or {})
-    task_groups = [t.strip() for t in task.split(",") if t.strip()]
-    if not task_groups:
-        raise ValueError("`task` must contain at least one VLABench task or suite name.")
-
-    logger.info(
-        "Creating VLABench envs | task_groups=%s | n_envs(per task)=%d",
-        task_groups,
-        n_envs,
-    )
-
-    is_async = env_cls is gym.vector.AsyncVectorEnv
-    cached_obs_space = None
-    cached_act_space = None
-    cached_metadata = None
-    out: dict[str, dict[int, Any]] = defaultdict(dict)
-
-    for group in task_groups:
-        # Check if it's a suite name, otherwise treat as individual task
-        tasks = SUITE_TASKS.get(group, [group])
-
-        for tid, task_name in enumerate(tasks):
-            logger.info(
-                "Building vec env | group=%s | task_id=%d | task=%s",
-                group,
-                tid,
-                task_name,
-            )
-
-            fns = [(lambda tn=task_name: VLABenchEnv(task=tn, **gym_kwargs)) for _ in range(n_envs)]
-
-            if is_async:
-                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
-                if cached_obs_space is None:
-                    cached_obs_space = lazy.observation_space
-                    cached_act_space = lazy.action_space
-                    cached_metadata = lazy.metadata
-                out[group][tid] = lazy
-            else:
-                out[group][tid] = env_cls(fns)
-
-    return {group: dict(task_map) for group, task_map in out.items()}

src/lerobot/policies/__init__.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from lerobot.utils.action_interpolator import ActionInterpolator as ActionInterpolator
+
 from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .factory import get_policy_class, make_policy, make_policy_config, make_pre_post_processors
@@ -21,7 +23,6 @@ from .pi0.configuration_pi0 import PI0Config as PI0Config
 from .pi0_fast.configuration_pi0_fast import PI0FastConfig as PI0FastConfig
 from .pi05.configuration_pi05 import PI05Config as PI05Config
 from .pretrained import PreTrainedPolicy as PreTrainedPolicy
-from .rtc import ActionInterpolator as ActionInterpolator
 from .sac.configuration_sac import SACConfig as SACConfig
 from .sac.reward_model.configuration_classifier import RewardClassifierConfig as RewardClassifierConfig
 from .sarm.configuration_sarm import SARMConfig as SARMConfig

src/lerobot/policies/act/modeling_act.py

@@ -142,10 +142,9 @@ class ACTPolicy(PreTrainedPolicy):
 
         actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
 
-        abs_err = F.l1_loss(batch[ACTION], actions_hat, reduction="none")
-        valid_mask = ~batch["action_is_pad"].unsqueeze(-1)
-        num_valid = valid_mask.sum() * abs_err.shape[-1]
-        l1_loss = (abs_err * valid_mask).sum() / num_valid.clamp_min(1)
+        l1_loss = (
+            F.l1_loss(batch[ACTION], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
+        ).mean()
 
         loss_dict = {"l1_loss": l1_loss.item()}
         if self.config.use_vae:

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -380,9 +380,7 @@ class DiffusionModel(nn.Module):
                     f"{self.config.do_mask_loss_for_padding=}."
                 )
             in_episode_bound = ~batch["action_is_pad"]
-            mask = in_episode_bound.unsqueeze(-1)
-            num_valid = mask.sum() * loss.shape[-1]
-            return (loss * mask).sum() / num_valid.clamp_min(1)
+            loss = loss * in_episode_bound.unsqueeze(-1)
 
         return loss.mean()
 

src/lerobot/policies/groot/groot_n1.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from dataclasses import dataclass, field
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -173,14 +174,17 @@ N_COLOR_CHANNELS = 3
 
 
 # config
+@dataclass
 class GR00TN15Config(PretrainedConfig):
     model_type = "gr00t_n1_5"
+    backbone_cfg: dict = field(init=False, metadata={"help": "Backbone configuration."})
 
-    backbone_cfg: dict
-    action_head_cfg: dict
-    action_horizon: int
-    action_dim: int
-    compute_dtype: str = "float32"
+    action_head_cfg: dict = field(init=False, metadata={"help": "Action head configuration."})
+
+    action_horizon: int = field(init=False, metadata={"help": "Action horizon."})
+
+    action_dim: int = field(init=False, metadata={"help": "Action dimension."})
+    compute_dtype: str = field(default="float32", metadata={"help": "Compute dtype."})
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)

src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

@@ -688,9 +688,8 @@ class DiffusionObjective(nn.Module):
         loss = F.mse_loss(predicted, target, reduction="none")
 
         if self.do_mask_loss_for_padding and "action_is_pad" in batch:
-            mask = ~batch["action_is_pad"].unsqueeze(-1)
-            num_valid = mask.sum() * loss.shape[-1]
-            return (loss * mask).sum() / num_valid.clamp_min(1)
+            valid_actions = ~batch["action_is_pad"]
+            loss = loss * valid_actions.unsqueeze(-1)
 
         return loss.mean()
 
@@ -753,9 +752,8 @@ class FlowMatchingObjective(nn.Module):
         loss = F.mse_loss(predicted_velocity, target_velocity, reduction="none")
 
         if self.do_mask_loss_for_padding and "action_is_pad" in batch:
-            mask = ~batch["action_is_pad"].unsqueeze(-1)
-            num_valid = mask.sum() * loss.shape[-1]
-            return (loss * mask).sum() / num_valid.clamp_min(1)
+            valid_mask = ~batch["action_is_pad"]
+            loss = loss * valid_mask.unsqueeze(-1)
 
         return loss.mean()
 

src/lerobot/policies/rtc/action_interpolator.py

@@ -1,116 +1,4 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
+# Moved to lerobot.utils.action_interpolator — re-exported for backwards compatibility.
+from lerobot.utils.action_interpolator import ActionInterpolator
 
-"""Action interpolation for smoother robot control.
-
-Provides configurable Nx control rate by interpolating between consecutive actions.
-Useful with RTC and action-chunking policies to reduce jerkiness.
-"""
-
-from torch import Tensor
-
-
-class ActionInterpolator:
-    """Interpolates between consecutive actions for smoother control.
-
-    When enabled with multiplier N, produces N actions per policy action
-    by linearly interpolating between the previous and current action.
-
-    Example with multiplier=3:
-        prev_action -> [1/3 interpolated, 2/3 interpolated, current_action]
-
-    This effectively multiplies the control rate for smoother motion.
-
-    Usage:
-        interpolator = ActionInterpolator(multiplier=2)  # 2x control rate
-
-        # In control loop:
-        if interpolator.needs_new_action():
-            new_action = queue.get()
-            if new_action:
-                interpolator.add(new_action.cpu())
-
-        action = interpolator.get()
-        if action:
-            robot.send_action(action)
-    """
-
-    def __init__(self, multiplier: int = 1):
-        """Initialize the interpolator.
-
-        Args:
-            multiplier: Control rate multiplier (1 = no interpolation, 2 = 2x, 3 = 3x, etc.)
-        """
-        if multiplier < 1:
-            raise ValueError(f"multiplier must be >= 1, got {multiplier}")
-        self.multiplier = multiplier
-        self._prev: Tensor | None = None
-        self._buffer: list[Tensor] = []
-        self._idx = 0
-
-    @property
-    def enabled(self) -> bool:
-        """Whether interpolation is active (multiplier > 1)."""
-        return self.multiplier > 1
-
-    def reset(self):
-        """Reset interpolation state (call between episodes)."""
-        self._prev = None
-        self._buffer = []
-        self._idx = 0
-
-    def needs_new_action(self) -> bool:
-        """Check if a new action is needed from the queue."""
-        return self._idx >= len(self._buffer)
-
-    def add(self, action: Tensor) -> None:
-        """Add a new action and compute interpolated sequence.
-
-        Args:
-            action: New action tensor from policy/queue (already on CPU).
-        """
-        if self.multiplier > 1 and self._prev is not None:
-            self._buffer = []
-            for i in range(1, self.multiplier + 1):
-                t = i / self.multiplier
-                interp = self._prev + t * (action - self._prev)
-                self._buffer.append(interp)
-        else:
-            # First step: no previous action yet, so run at base FPS without interpolation.
-            self._buffer = [action.clone()]
-        self._prev = action.clone()
-        self._idx = 0
-
-    def get(self) -> Tensor | None:
-        """Get the next interpolated action.
-
-        Returns:
-            Next action tensor, or None if buffer is exhausted.
-        """
-        if self._idx >= len(self._buffer):
-            return None
-        action = self._buffer[self._idx]
-        self._idx += 1
-        return action
-
-    def get_control_interval(self, fps: float) -> float:
-        """Get the control interval based on interpolation multiplier.
-
-        Args:
-            fps: Base frames per second.
-
-        Returns:
-            Control interval in seconds (divided by multiplier).
-        """
-        return 1.0 / (fps * self.multiplier)
+__all__ = ["ActionInterpolator"]

src/lerobot/policies/rtc/action_queue.py

@@ -92,10 +92,10 @@ class ActionQueue:
         Returns:
             int: Number of unconsumed actions.
         """
-        if self.queue is None:
-            return 0
-        length = len(self.queue)
-        return length - self.last_index
+        with self.lock:
+            if self.queue is None:
+                return 0
+            return len(self.queue) - self.last_index
 
     def empty(self) -> bool:
         """Check if the queue is empty.
@@ -103,11 +103,10 @@ class ActionQueue:
         Returns:
             bool: True if no actions remain, False otherwise.
         """
-        if self.queue is None:
-            return True
-
-        length = len(self.queue)
-        return length - self.last_index <= 0
+        with self.lock:
+            if self.queue is None:
+                return True
+            return len(self.queue) - self.last_index <= 0
 
     def get_action_index(self) -> int:
         """Get the current action consumption index.
@@ -115,7 +114,8 @@ class ActionQueue:
         Returns:
             int: Index of the next action to be consumed.
         """
-        return self.last_index
+        with self.lock:
+            return self.last_index
 
     def get_left_over(self) -> Tensor | None:
         """Get leftover original actions for RTC prev_chunk_left_over.

src/lerobot/policies/sarm/processor_sarm.py

@@ -455,13 +455,7 @@ class SARMEncodingProcessorStep(ProcessorStep):
             inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
             # Get image embeddings
-            # transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
-            output = self.clip_model.get_image_features(**inputs)
-            if not isinstance(output, torch.Tensor):
-                output = output.pooler_output
-                if output is None:
-                    raise ValueError("pooler_output should not be None for CLIP models.")
-            embeddings = output.detach().cpu()
+            embeddings = self.clip_model.get_image_features(**inputs).detach().cpu()
 
             # Handle single frame case
             if embeddings.dim() == 1:
@@ -488,13 +482,7 @@ class SARMEncodingProcessorStep(ProcessorStep):
         inputs = self.clip_processor.tokenizer([text], return_tensors="pt", padding=True, truncation=True)
         inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
-        # transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
-        output = self.clip_model.get_text_features(**inputs)
-        if not isinstance(output, torch.Tensor):
-            output = output.pooler_output
-            if output is None:
-                raise ValueError("pooler_output should not be None for CLIP models.")
-        text_embedding = output.detach().cpu()
+        text_embedding = self.clip_model.get_text_features(**inputs).detach().cpu()
         text_embedding = text_embedding.expand(batch_size, -1)
 
         return text_embedding

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -394,21 +394,13 @@ class SmolVLAPolicy(PreTrainedPolicy):
         loss_dict["losses_after_rm_padding"] = losses.clone().mean().item()
 
         if reduction == "none":
-            # Return per-sample losses (B,) by averaging over valid (time, action) entries
-            if actions_is_pad is None:
-                per_sample_loss = losses.mean(dim=(1, 2))
-            else:
-                num_valid = ((~actions_is_pad).sum(dim=1) * losses.shape[-1]).clamp_min(1)
-                per_sample_loss = losses.sum(dim=(1, 2)) / num_valid
+            # Return per-sample losses (B,) by averaging over time and action dims
+            per_sample_loss = losses.mean(dim=(1, 2))
             loss_dict["loss"] = per_sample_loss.mean().item()
             return per_sample_loss, loss_dict
         else:
-            # Default: return scalar mean loss over valid (time, action) entries
-            if actions_is_pad is None:
-                loss = losses.mean()
-            else:
-                num_valid = ((~actions_is_pad).sum() * losses.shape[-1]).clamp_min(1)
-                loss = losses.sum() / num_valid
+            # Default: return scalar mean loss
+            loss = losses.mean()
             loss_dict["loss"] = loss.item()
             return loss, loss_dict
 

src/lerobot/processor/hil_processor.py

@@ -321,7 +321,6 @@ class GymHILAdapterProcessorStep(ProcessorStep):
     This step normalizes the `transition` object by:
     1. Copying `teleop_action` from `info` to `complementary_data`.
     2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
-    3. Copying `discrete_penalty` from `info` to `complementary_data`.
     """
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
@@ -331,9 +330,6 @@ class GymHILAdapterProcessorStep(ProcessorStep):
         if TELEOP_ACTION_KEY in info:
             complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
 
-        if DISCRETE_PENALTY_KEY in info:
-            complementary_data[DISCRETE_PENALTY_KEY] = info[DISCRETE_PENALTY_KEY]
-
         if "is_intervention" in info:
             info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
 
@@ -352,24 +348,18 @@ class GymHILAdapterProcessorStep(ProcessorStep):
 @ProcessorStepRegistry.register("gripper_penalty_processor")
 class GripperPenaltyProcessorStep(ProcessorStep):
     """
-    Applies a small per-transition cost on the discrete gripper action.
+    Applies a penalty for inefficient gripper usage.
 
-    Fires only when the commanded action would actually transition the gripper
-    from one extreme to the other (close-while-open or open-while-closed).
-    This discourages gripper oscillation while leaving "stay" and saturating-further
-    commands unpenalized.
+    This step penalizes actions that attempt to close an already closed gripper or
+    open an already open one, based on position thresholds.
 
     Attributes:
         penalty: The negative reward value to apply.
         max_gripper_pos: The maximum position value for the gripper, used for normalization.
-        open_threshold: Normalized state below which the gripper is considered "open".
-        closed_threshold: Normalized state above which the gripper is considered "closed".
     """
 
-    penalty: float = -0.02
+    penalty: float = -0.01
     max_gripper_pos: float = 30.0
-    open_threshold: float = 0.1
-    closed_threshold: float = 0.9
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         """
@@ -401,13 +391,9 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         gripper_state_normalized = current_gripper_pos / self.max_gripper_pos
 
         # Calculate penalty boolean as in original
-        #   - currently open  AND target is closed  -> close transition
-        #   - currently closed AND target is open   -> open transition
-        is_open = gripper_state_normalized < self.open_threshold
-        is_closed = gripper_state_normalized > self.closed_threshold
-        cmd_close = gripper_action_normalized > self.closed_threshold
-        cmd_open = gripper_action_normalized < self.open_threshold
-        gripper_penalty_bool = (is_open and cmd_close) or (is_closed and cmd_open)
+        gripper_penalty_bool = (gripper_state_normalized < 0.5 and gripper_action_normalized > 0.5) or (
+            gripper_state_normalized > 0.75 and gripper_action_normalized < 0.5
+        )
 
         gripper_penalty = self.penalty * int(gripper_penalty_bool)
 
@@ -423,14 +409,11 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         Returns the configuration of the step for serialization.
 
         Returns:
-            A dictionary containing the penalty value, max gripper position,
-            and the open/closed thresholds.
+            A dictionary containing the penalty value and max gripper position.
         """
         return {
             "penalty": self.penalty,
             "max_gripper_pos": self.max_gripper_pos,
-            "open_threshold": self.open_threshold,
-            "closed_threshold": self.closed_threshold,
         }
 
     def reset(self) -> None:

src/lerobot/processor/normalize_processor.py

@@ -134,15 +134,6 @@ class _NormalizationMixin:
         if self.dtype is None:
             self.dtype = torch.float32
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
-        self._reshape_visual_stats()
-
-    def _reshape_visual_stats(self) -> None:
-        """Reshape visual stats from ``[C]`` to ``[C, 1, 1]`` for image broadcasting."""
-        for key, feature in self.features.items():
-            if feature.type == FeatureType.VISUAL and key in self._tensor_stats:
-                for stat_name, stat_tensor in self._tensor_stats[key].items():
-                    if isinstance(stat_tensor, Tensor) and stat_tensor.ndim == 1:
-                        self._tensor_stats[key][stat_name] = stat_tensor.reshape(-1, 1, 1)
 
     def to(
         self, device: torch.device | str | None = None, dtype: torch.dtype | None = None
@@ -161,7 +152,6 @@ class _NormalizationMixin:
         if dtype is not None:
             self.dtype = dtype
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
-        self._reshape_visual_stats()
         return self
 
     def state_dict(self) -> dict[str, Tensor]:
@@ -211,7 +201,6 @@ class _NormalizationMixin:
             # Don't load from state_dict, keep the explicitly provided stats
             # But ensure _tensor_stats is properly initialized
             self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)  # type: ignore[assignment]
-            self._reshape_visual_stats()
             return
 
         # Normal behavior: load stats from state_dict
@@ -222,7 +211,6 @@ class _NormalizationMixin:
             self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to(
                 dtype=torch.float32, device=self.device
             )
-        self._reshape_visual_stats()
 
         # Reconstruct the original stats dict from tensor stats for compatibility with to() method
         # and other functions that rely on self.stats

src/lerobot/rl/actor.py

@@ -60,7 +60,7 @@ from torch.multiprocessing import Event, Queue
 from lerobot.cameras import opencv  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
-from lerobot.policies import make_policy, make_pre_post_processors
+from lerobot.policies import make_policy
 from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -76,6 +76,7 @@ from lerobot.transport.utils import (
 )
 from lerobot.types import TransitionKey
 from lerobot.utils.device_utils import get_safe_torch_device
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.transition import (
@@ -89,12 +90,11 @@ from lerobot.utils.utils import (
 )
 
 from .gym_manipulator import (
+    create_transition,
     make_processors,
     make_robot_env,
-    reset_and_build_transition,
     step_env_and_process_transition,
 )
-from .process import ProcessSignalHandler
 from .queue import get_last_item_from_queue
 
 # Main entry point
@@ -261,12 +261,13 @@ def act_with_policy(
     policy = policy.eval()
     assert isinstance(policy, nn.Module)
 
-    preprocessor, postprocessor = make_pre_post_processors(
-        policy_cfg=cfg.policy,
-        dataset_stats=cfg.policy.dataset_stats,
-    )
+    obs, info = online_env.reset()
+    env_processor.reset()
+    action_processor.reset()
 
-    transition = reset_and_build_transition(online_env, env_processor, action_processor)
+    # Process initial observation
+    transition = create_transition(observation=obs, info=info)
+    transition = env_processor(transition)
 
     # NOTE: For the moment we will solely handle the case of a single environment
     sum_reward_episode = 0
@@ -290,21 +291,8 @@ def act_with_policy(
 
         # Time policy inference and check if it meets FPS requirement
         with policy_timer:
-            normalized_observation = preprocessor.process_observation(observation)
-            action = policy.select_action(batch=normalized_observation)
-            # Unnormalize only the continuous part. When `num_discrete_actions` is set,
-            # `select_action` concatenates an argmax index in env space at the last dim;
-            # action stats cover the continuous dims only, so feeding the full vector to
-            # the unnormalizer would shape-mismatch and would also corrupt the discrete
-            # index by treating it as a normalized value.
-            if cfg.policy.num_discrete_actions is not None:
-                continuous_action = postprocessor.process_action(action[..., :-1])
-                discrete_action = action[..., -1:].to(
-                    device=continuous_action.device, dtype=continuous_action.dtype
-                )
-                action = torch.cat([continuous_action, discrete_action], dim=-1)
-            else:
-                action = postprocessor.process_action(action)
+            # Extract observation from transition for policy
+            action = policy.select_action(batch=observation)
         policy_fps = policy_timer.fps_last
 
         log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
@@ -338,8 +326,7 @@ def act_with_policy(
 
         # Check for intervention from transition info
         intervention_info = new_transition[TransitionKey.INFO]
-        is_intervention = bool(intervention_info.get(TeleopEvents.IS_INTERVENTION, False))
-        if is_intervention:
+        if intervention_info.get(TeleopEvents.IS_INTERVENTION, False):
             episode_intervention = True
             episode_intervention_steps += 1
 
@@ -347,10 +334,6 @@ def act_with_policy(
             "discrete_penalty": torch.tensor(
                 [new_transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)]
             ),
-            # Forward the intervention flag so the learner can route this transition
-            # into the offline replay buffer (see `process_transitions` in learner.py).
-            # Use the plain string key so the payload survives torch.load(weights_only=True).
-            TeleopEvents.IS_INTERVENTION.value: is_intervention,
         }
         # Create transition for learner (convert to old format)
         list_transition_to_send_to_learner.append(
@@ -407,7 +390,14 @@ def act_with_policy(
             episode_intervention_steps = 0
             episode_total_steps = 0
 
-            transition = reset_and_build_transition(online_env, env_processor, action_processor)
+            # Reset environment and processors
+            obs, info = online_env.reset()
+            env_processor.reset()
+            action_processor.reset()
+
+            # Process initial observation
+            transition = create_transition(observation=obs, info=info)
+            transition = env_processor(transition)
 
         if cfg.env.fps is not None:
             dt_time = time.perf_counter() - start_time

src/lerobot/rl/gym_manipulator.py

@@ -383,21 +383,10 @@ def make_processors(
             GymHILAdapterProcessorStep(),
             Numpy2TorchActionProcessorStep(),
             VanillaObservationProcessorStep(),
+            AddBatchDimensionProcessorStep(),
+            DeviceProcessorStep(device=device),
         ]
 
-        # Add time limit processor if reset config exists
-        if cfg.processor.reset is not None:
-            env_pipeline_steps.append(
-                TimeLimitProcessorStep(max_episode_steps=int(cfg.processor.reset.control_time_s * cfg.fps))
-            )
-
-        env_pipeline_steps.extend(
-            [
-                AddBatchDimensionProcessorStep(),
-                DeviceProcessorStep(device=device),
-            ]
-        )
-
         return DataProcessorPipeline(
             steps=env_pipeline_steps, to_transition=identity_transition, to_output=identity_transition
         ), DataProcessorPipeline(
@@ -562,19 +551,8 @@ def step_env_and_process_transition(
     terminated = terminated or processed_action_transition[TransitionKey.DONE]
     truncated = truncated or processed_action_transition[TransitionKey.TRUNCATED]
     complementary_data = processed_action_transition[TransitionKey.COMPLEMENTARY_DATA].copy()
-
-    if hasattr(env, "get_raw_joint_positions"):
-        raw_joint_positions = env.get_raw_joint_positions()
-        if raw_joint_positions is not None:
-            complementary_data["raw_joint_positions"] = raw_joint_positions
-
-    # Merge env and action-processor info: env wins for str keys, action-processor
-    # wins for `TeleopEvents` enum keys
-    action_info = processed_action_transition[TransitionKey.INFO]
     new_info = info.copy()
-    for key, value in action_info.items():
-        if isinstance(key, TeleopEvents):
-            new_info[key] = value
+    new_info.update(processed_action_transition[TransitionKey.INFO])
 
     new_transition = create_transition(
         observation=obs,
@@ -590,24 +568,6 @@ def step_env_and_process_transition(
     return new_transition
 
 
-def reset_and_build_transition(
-    env: gym.Env,
-    env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
-    action_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
-) -> EnvTransition:
-    """Reset env + processors and return the first env-processed transition."""
-    obs, info = env.reset()
-    env_processor.reset()
-    action_processor.reset()
-    complementary_data: dict[str, Any] = {}
-    if hasattr(env, "get_raw_joint_positions"):
-        raw_joint_positions = env.get_raw_joint_positions()
-        if raw_joint_positions is not None:
-            complementary_data["raw_joint_positions"] = raw_joint_positions
-    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
-    return env_processor(data=transition)
-
-
 def control_loop(
     env: gym.Env,
     env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
@@ -633,7 +593,17 @@ def control_loop(
     print("- When not intervening, robot will stay still")
     print("- Press Ctrl+C to exit")
 
-    transition = reset_and_build_transition(env, env_processor, action_processor)
+    # Reset environment and processors
+    obs, info = env.reset()
+    complementary_data = (
+        {"raw_joint_positions": info.pop("raw_joint_positions")} if "raw_joint_positions" in info else {}
+    )
+    env_processor.reset()
+    action_processor.reset()
+
+    # Process initial observation
+    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
+    transition = env_processor(data=transition)
 
     # Determine if gripper is used
     use_gripper = cfg.env.processor.gripper.use_gripper if cfg.env.processor.gripper is not None else True
@@ -695,7 +665,7 @@ def control_loop(
         # Create a neutral action (no movement)
         neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32)
         if use_gripper:
-            neutral_action = torch.cat([neutral_action, torch.tensor([1.0])])  # Gripper stay
+            neutral_action = torch.cat([neutral_action, torch.tensor([0.0])])  # Gripper stay
 
         # Use the new step function
         transition = step_env_and_process_transition(
@@ -753,7 +723,12 @@ def control_loop(
                     dataset.save_episode()
 
             # Reset for new episode
-            transition = reset_and_build_transition(env, env_processor, action_processor)
+            obs, info = env.reset()
+            env_processor.reset()
+            action_processor.reset()
+
+            transition = create_transition(observation=obs, info=info)
+            transition = env_processor(transition)
 
         # Maintain fps timing
         precise_sleep(max(dt - (time.perf_counter() - step_start_time), 0.0))

src/lerobot/rl/learner.py

@@ -70,7 +70,7 @@ from lerobot.common.wandb_utils import WandBLogger
 from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
 from lerobot.datasets import LeRobotDataset, make_dataset
-from lerobot.policies import make_policy, make_pre_post_processors
+from lerobot.policies import make_policy
 from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -90,6 +90,7 @@ from lerobot.utils.constants import (
     TRAINING_STATE_DIR,
 )
 from lerobot.utils.device_utils import get_safe_torch_device
+from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.transition import move_state_dict_to_device, move_transition_to_device
 from lerobot.utils.utils import (
@@ -99,7 +100,6 @@ from lerobot.utils.utils import (
 
 from .buffer import ReplayBuffer, concatenate_batch_transitions
 from .learner_service import MAX_WORKERS, SHUTDOWN_TIMEOUT, LearnerService
-from .process import ProcessSignalHandler
 
 
 @parser.wrap()
@@ -317,11 +317,6 @@ def add_actor_information_and_train(
 
     policy.train()
 
-    preprocessor, _postprocessor = make_pre_post_processors(
-        policy_cfg=cfg.policy,
-        dataset_stats=cfg.policy.dataset_stats,
-    )
-
     push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)
 
     last_time_policy_pushed = time.time()
@@ -410,8 +405,8 @@ def add_actor_information_and_train(
 
             actions = batch[ACTION]
             rewards = batch["reward"]
-            observations = preprocessor.process_observation(batch["state"])
-            next_observations = preprocessor.process_observation(batch["next_state"])
+            observations = batch["state"]
+            next_observations = batch["next_state"]
             done = batch["done"]
             check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
 
@@ -468,8 +463,8 @@ def add_actor_information_and_train(
 
         actions = batch[ACTION]
         rewards = batch["reward"]
-        observations = preprocessor.process_observation(batch["state"])
-        next_observations = preprocessor.process_observation(batch["next_state"])
+        observations = batch["state"]
+        next_observations = batch["next_state"]
         done = batch["done"]
 
         check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
@@ -1168,7 +1163,7 @@ def process_transitions(
 
             # Add to offline buffer if it's an intervention
             if dataset_repo_id is not None and transition.get("complementary_info", {}).get(
-                TeleopEvents.IS_INTERVENTION.value
+                TeleopEvents.IS_INTERVENTION
             ):
                 offline_replay_buffer.add(**transition)
 

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -353,8 +353,7 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
         speed_factor: A scaling factor to convert the normalized velocity command to a position change.
         clip_min: The minimum allowed gripper joint position.
         clip_max: The maximum allowed gripper joint position.
-        discrete_gripper: If True, interpret the input as a discrete class index
-            {0 = close, 1 = stay, 2 = open}, matching `GamepadTeleop.GripperAction`.
+        discrete_gripper: If True, treat the input action as discrete (0: open, 1: close, 2: stay).
     """
 
     speed_factor: float = 20.0
@@ -378,10 +377,10 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
             raise ValueError("Joints observation is require for computing robot kinematics")
 
         if self.discrete_gripper:
-            # Map discrete command {0=close, 1=stay, 2=open} -> signed velocity.
-            # Negation accounts for SO100 sign (joint position increases on close).
-            #   0 -> +clip_max (close), 1 -> 0 (stay), 2 -> -clip_max (open)
-            gripper_vel = -(gripper_vel - 1) * self.clip_max
+            # Discrete gripper actions are in [0, 1, 2]
+            # 0: open, 1: close, 2: stay
+            # We need to shift them to [-1, 0, 1] and then scale them to clip_max
+            gripper_vel = (gripper_vel - 1) * self.clip_max
 
         # Compute desired gripper position
         delta = gripper_vel * float(self.speed_factor)

src/lerobot/rollout/__init__.py

@@ -0,0 +1,80 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Policy deployment engine with pluggable rollout strategies."""
+
+from lerobot.utils.import_utils import require_package
+
+require_package("datasets", extra="dataset")
+
+from .configs import (
+    BaseStrategyConfig,
+    DAggerKeyboardConfig,
+    DAggerPedalConfig,
+    DAggerStrategyConfig,
+    HighlightStrategyConfig,
+    RolloutConfig,
+    RolloutStrategyConfig,
+    SentryStrategyConfig,
+)
+from .context import (
+    DatasetContext,
+    HardwareContext,
+    PolicyContext,
+    ProcessorContext,
+    RolloutContext,
+    RuntimeContext,
+    build_rollout_context,
+)
+from .inference import (
+    InferenceEngine,
+    InferenceEngineConfig,
+    RTCInferenceConfig,
+    RTCInferenceEngine,
+    SyncInferenceConfig,
+    SyncInferenceEngine,
+    create_inference_engine,
+)
+from .ring_buffer import RolloutRingBuffer
+from .robot_wrapper import ThreadSafeRobot
+from .strategies import RolloutStrategy, create_strategy
+
+__all__ = [
+    "BaseStrategyConfig",
+    "DAggerKeyboardConfig",
+    "DAggerPedalConfig",
+    "DAggerStrategyConfig",
+    "DatasetContext",
+    "HardwareContext",
+    "HighlightStrategyConfig",
+    "InferenceEngine",
+    "InferenceEngineConfig",
+    "PolicyContext",
+    "ProcessorContext",
+    "RTCInferenceConfig",
+    "RTCInferenceEngine",
+    "RolloutConfig",
+    "RolloutContext",
+    "RolloutRingBuffer",
+    "RolloutStrategy",
+    "RolloutStrategyConfig",
+    "RuntimeContext",
+    "SentryStrategyConfig",
+    "SyncInferenceConfig",
+    "SyncInferenceEngine",
+    "ThreadSafeRobot",
+    "build_rollout_context",
+    "create_inference_engine",
+    "create_strategy",
+]

src/lerobot/rollout/configs.py

@@ -0,0 +1,310 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Configuration dataclasses for the rollout deployment engine."""
+
+from __future__ import annotations
+
+import abc
+import logging
+from dataclasses import dataclass, field
+
+import draccus
+
+from lerobot.configs import PreTrainedConfig, parser
+from lerobot.configs.dataset import DatasetRecordConfig
+from lerobot.robots.config import RobotConfig
+from lerobot.teleoperators.config import TeleoperatorConfig
+
+from .inference import InferenceEngineConfig, SyncInferenceConfig
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Strategy configs (polymorphic dispatch via draccus ChoiceRegistry)
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RolloutStrategyConfig(draccus.ChoiceRegistry, abc.ABC):
+    """Abstract base for rollout strategy configurations.
+
+    Use ``--strategy.type=<name>`` on the CLI to select a strategy.
+    """
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+
+@RolloutStrategyConfig.register_subclass("base")
+@dataclass
+class BaseStrategyConfig(RolloutStrategyConfig):
+    """Autonomous rollout with no data recording."""
+
+    pass
+
+
+@RolloutStrategyConfig.register_subclass("sentry")
+@dataclass
+class SentryStrategyConfig(RolloutStrategyConfig):
+    """Continuous autonomous rollout with always-on recording.
+
+    Episode duration is derived from camera resolution, FPS, and
+    ``target_video_file_size_mb`` so that each saved episode produces a
+    video file that has crossed the target size.  This aligns episode
+    boundaries with the dataset's video file chunking, so each
+    ``push_to_hub`` call uploads complete video files rather than
+    re-uploading a growing file that hasn't crossed the chunk boundary.
+    """
+
+    upload_every_n_episodes: int = 5
+    # Target video file size in MB for episode rotation.  Episodes are
+    # saved once the estimated video duration would exceed this limit.
+    # Defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB when set to None.
+    target_video_file_size_mb: float | None = None
+
+
+@RolloutStrategyConfig.register_subclass("highlight")
+@dataclass
+class HighlightStrategyConfig(RolloutStrategyConfig):
+    """Autonomous rollout with on-demand recording via ring buffer.
+
+    A memory-bounded ring buffer continuously captures telemetry.  When
+    the user presses the save key, the buffer contents are flushed to
+    the dataset and live recording continues until the key is pressed
+    again.
+    """
+
+    ring_buffer_seconds: float = 30.0
+    ring_buffer_max_memory_mb: float = 2048.0
+    save_key: str = "s"
+    push_key: str = "h"
+
+
+@dataclass
+class DAggerKeyboardConfig:
+    """Keyboard key bindings for DAgger controls.
+
+    Keys are specified as single characters (e.g. ``"c"``, ``"h"``) or
+    special key names (``"space"``).
+    """
+
+    pause_resume: str = "space"
+    correction: str = "tab"
+    upload: str = "enter"
+
+
+@dataclass
+class DAggerPedalConfig:
+    """Foot pedal configuration for DAgger controls.
+
+    Pedal codes are evdev key code strings (e.g. ``"KEY_A"``).
+    """
+
+    device_path: str = "/dev/input/by-id/usb-PCsensor_FootSwitch-event-kbd"
+    pause_resume: str = "KEY_A"
+    correction: str = "KEY_B"
+    upload: str = "KEY_C"
+
+
+@RolloutStrategyConfig.register_subclass("dagger")
+@dataclass
+class DAggerStrategyConfig(RolloutStrategyConfig):
+    """Human-in-the-loop data collection (DAgger / RaC).
+
+    Alternates between autonomous policy execution and human intervention.
+    Intervention frames are tagged with ``intervention=True``.
+
+    Input is controlled via either a keyboard or foot pedal, selected by
+    ``input_device``.  Each device exposes three actions:
+
+    1. **pause_resume** — toggle policy execution on/off.
+    2. **correction** — toggle human correction recording.
+    3. **upload** — push dataset to hub on demand (corrections-only mode).
+
+    When ``record_autonomous=True`` (default) both autonomous and correction
+    frames are recorded with size-based episode rotation (same as Sentry)
+    and background uploading.  ``push_to_hub`` is blocked while a correction
+    is in progress.  Set to ``False`` to record only the human-correction
+    windows, where each correction becomes its own episode.
+    """
+
+    # Number of correction episodes to collect (corrections-only mode).
+    # When None, falls back to ``--dataset.num_episodes``.
+    num_episodes: int | None = None
+    record_autonomous: bool = False
+    upload_every_n_episodes: int = 5
+    # Target video file size in MB for episode rotation (record_autonomous
+    # mode only).  Defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB when None.
+    target_video_file_size_mb: float | None = None
+    input_device: str = "keyboard"
+    keyboard: DAggerKeyboardConfig = field(default_factory=DAggerKeyboardConfig)
+    pedal: DAggerPedalConfig = field(default_factory=DAggerPedalConfig)
+
+    def __post_init__(self):
+        if self.input_device not in ("keyboard", "pedal"):
+            raise ValueError(f"DAgger input_device must be 'keyboard' or 'pedal', got '{self.input_device}'")
+
+
+# ---------------------------------------------------------------------------
+# Top-level rollout config
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RolloutConfig:
+    """Top-level configuration for the ``lerobot-rollout`` CLI.
+
+    Combines hardware, policy, strategy, and runtime settings.  The
+    ``__post_init__`` method performs fail-fast validation to reject
+    invalid flag combinations early.
+    """
+
+    # Hardware
+    robot: RobotConfig | None = None
+    teleop: TeleoperatorConfig | None = None
+
+    # Policy (loaded from --policy.path via __post_init__)
+    policy: PreTrainedConfig | None = None
+
+    # Strategy (polymorphic: --strategy.type=base|sentry|highlight|dagger)
+    strategy: RolloutStrategyConfig = field(default_factory=BaseStrategyConfig)
+
+    # Inference backend (polymorphic: --inference.type=sync|rtc)
+    inference: InferenceEngineConfig = field(default_factory=SyncInferenceConfig)
+
+    # Dataset (required for sentry, highlight, dagger; None for base)
+    dataset: DatasetRecordConfig | None = None
+
+    # Runtime
+    fps: float = 30.0
+    duration: float = 0.0  # 0 = infinite (24/7 mode)
+    interpolation_multiplier: int = 1
+    device: str | None = None
+    task: str = ""
+    display_data: bool = False
+    # Display data on a remote Rerun server
+    display_ip: str | None = None
+    # Port of the remote Rerun server
+    display_port: int | None = None
+    # Whether to display compressed images in Rerun
+    display_compressed_images: bool = False
+    # Use vocal synthesis to read events
+    play_sounds: bool = True
+    resume: bool = False
+
+    # Torch compile
+    use_torch_compile: bool = False
+    torch_compile_backend: str = "inductor"
+    torch_compile_mode: str = "default"
+    compile_warmup_inferences: int = 2
+
+    def __post_init__(self):
+        """Validate config invariants and load the policy config from ``--policy.path``."""
+        # --- Strategy-specific validation ---
+        if isinstance(self.strategy, DAggerStrategyConfig) and self.teleop is None:
+            raise ValueError("DAgger strategy requires --teleop.type to be set")
+
+        # TODO(Steven): DAgger shouldn't require a dataset (user may want to just rollout+intervene without recording), but for now we require it to simplify the implementation.
+        needs_dataset = isinstance(
+            self.strategy, (SentryStrategyConfig, HighlightStrategyConfig, DAggerStrategyConfig)
+        )
+        if needs_dataset and (self.dataset is None or not self.dataset.repo_id):
+            raise ValueError(f"{self.strategy.type} strategy requires --dataset.repo_id to be set")
+
+        # if isinstance(self.strategy, BaseStrategyConfig) and self.dataset is not None:
+        #     raise ValueError(
+        #         "Base strategy does not record data. Use sentry, highlight, or dagger for recording."
+        #     )
+
+        # Sentry MUST use streaming encoding to avoid disk I/O blocking the control loop
+        if (
+            isinstance(self.strategy, SentryStrategyConfig)
+            and self.dataset is not None
+            and not self.dataset.streaming_encoding
+        ):
+            logger.warning("Sentry mode forces streaming_encoding=True")
+            self.dataset.streaming_encoding = True
+
+        # Highlight writes frames while the policy is still running, so streaming is mandatory.
+        if (
+            isinstance(self.strategy, HighlightStrategyConfig)
+            and self.dataset is not None
+            and not self.dataset.streaming_encoding
+        ):
+            logger.warning("Highlight mode forces streaming_encoding=True")
+            self.dataset.streaming_encoding = True
+
+        # DAgger: streaming is mandatory only when the autonomous phase is also recorded.
+        if isinstance(self.strategy, DAggerStrategyConfig) and self.dataset is not None:
+            if self.strategy.record_autonomous and not self.dataset.streaming_encoding:
+                logger.warning("DAgger with record_autonomous=True forces streaming_encoding=True")
+                self.dataset.streaming_encoding = True
+            elif not self.strategy.record_autonomous and not self.dataset.streaming_encoding:
+                logger.info(
+                    "Streaming encoding is disabled for DAgger corrections-only mode. "
+                    "Consider enabling it for faster episode saving: "
+                    "--dataset.streaming_encoding=true --dataset.encoder_threads=2"
+                )
+
+        # DAgger: resolve num_episodes from dataset config when not explicitly set.
+        if isinstance(self.strategy, DAggerStrategyConfig) and self.strategy.num_episodes is None:
+            if self.dataset is not None:
+                self.strategy.num_episodes = self.dataset.num_episodes
+                logger.info(
+                    "DAgger num_episodes not set — using --dataset.num_episodes=%d",
+                    self.strategy.num_episodes,
+                )
+            else:
+                raise ValueError(
+                    "DAgger num_episodes must be set either via --strategy.num_episodes or --dataset.num_episodes"
+                )
+
+        # --- Policy loading ---
+        if self.robot is None:
+            raise ValueError("--robot.type is required for rollout")
+
+        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)
+            self.policy.pretrained_path = policy_path
+        if self.policy is None:
+            raise ValueError("--policy.path is required for rollout")
+
+        # --- Task resolution ---
+        # When --dataset.rename_map (or any --dataset.* flag) is passed, draccus
+        # creates a DatasetRecordConfig with single_task="".  If the user set
+        # the task via the top-level --task flag, propagate it so that all
+        # downstream consumers (inference engine, dataset frame builders) see it.
+        if self.dataset is not None and not self.dataset.single_task and self.task:
+            self.dataset.single_task = self.task
+        elif self.dataset is not None and self.dataset.single_task and not self.task:
+            self.task = self.dataset.single_task
+
+        # --- Device resolution ---
+        # Resolve device from the policy config when not explicitly set so all
+        # components (policy.to, preprocessor, inference engine) use the same
+        # device string instead of inconsistent fallbacks.
+        if self.device is None and self.policy is not None:
+            resolved = getattr(self.policy, "device", None)
+            if resolved:
+                self.device = resolved
+                logger.info("Resolved device from policy config: %s", self.device)
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        return ["policy"]

src/lerobot/rollout/context.py

@@ -0,0 +1,496 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Rollout context: shared state created once before strategy dispatch.
+
+Grouped into five topical sub-contexts — :class:`RuntimeContext`,
+:class:`HardwareContext`, :class:`PolicyContext`, :class:`ProcessorContext`,
+and :class:`DatasetContext` — assembled into :class:`RolloutContext`.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from threading import Event
+
+import torch
+
+from lerobot.configs import FeatureType, PreTrainedConfig
+from lerobot.datasets import (
+    LeRobotDataset,
+    aggregate_pipeline_dataset_features,
+    create_initial_features,
+)
+from lerobot.policies import get_policy_class, make_pre_post_processors
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.processor import (
+    PolicyProcessorPipeline,
+    RobotAction,
+    RobotObservation,
+    RobotProcessorPipeline,
+    make_default_processors,
+    rename_stats,
+)
+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 .configs import BaseStrategyConfig, DAggerStrategyConfig, RolloutConfig
+from .inference import (
+    InferenceEngine,
+    RTCInferenceConfig,
+    create_inference_engine,
+)
+from .robot_wrapper import ThreadSafeRobot
+
+logger = logging.getLogger(__name__)
+
+
+def _resolve_action_key_order(
+    policy_action_names: list[str] | None, dataset_action_names: list[str]
+) -> list[str]:
+    """Choose action name ordering for mapping policy tensor outputs to robot action dicts."""
+    if not policy_action_names:
+        return dataset_action_names
+    policy_action_names = list(policy_action_names)
+    if len(policy_action_names) != len(dataset_action_names):
+        logger.warning(
+            "policy.action_feature_names length (%d) != dataset action dim (%d); using dataset order",
+            len(policy_action_names),
+            len(dataset_action_names),
+        )
+        return dataset_action_names
+    if set(dataset_action_names) != set(policy_action_names):
+        logger.warning("policy.action_feature_names keys don't match dataset; using dataset order")
+        return dataset_action_names
+    return policy_action_names
+
+
+# ---------------------------------------------------------------------------
+# Sub-contexts
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RuntimeContext:
+    """Runtime knobs shared with every strategy."""
+
+    cfg: RolloutConfig
+    shutdown_event: Event
+
+
+@dataclass
+class HardwareContext:
+    """Connected hardware.
+
+    The raw robot is available via ``robot_wrapper.inner`` when needed
+    (e.g. for disconnect); strategies should otherwise go through the
+    thread-safe wrapper.
+
+    ``initial_position`` stores the robot's joint positions at connect
+    time.  Strategies use it to return the robot to a safe pose before
+    shutting down.
+    """
+
+    robot_wrapper: ThreadSafeRobot
+    teleop: Teleoperator | None
+    initial_position: dict | None = None
+
+
+@dataclass
+class PolicyContext:
+    """Loaded policy and its inference engine."""
+
+    policy: PreTrainedPolicy
+    preprocessor: PolicyProcessorPipeline
+    postprocessor: PolicyProcessorPipeline
+    inference: InferenceEngine
+
+
+@dataclass
+class ProcessorContext:
+    """Robot-side pipelines (run outside the policy)."""
+
+    teleop_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]
+    robot_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]
+    robot_observation_processor: RobotProcessorPipeline[RobotObservation, RobotObservation]
+
+
+@dataclass
+class DatasetContext:
+    """Dataset and feature bookkeeping."""
+
+    dataset: LeRobotDataset | None
+    dataset_features: dict = field(default_factory=dict)
+    hw_features: dict = field(default_factory=dict)
+    ordered_action_keys: list[str] = field(default_factory=list)
+
+
+@dataclass
+class RolloutContext:
+    """Bundle of sub-contexts passed to every rollout strategy.
+
+    Built once by :func:`build_rollout_context` before strategy dispatch.
+    """
+
+    runtime: RuntimeContext
+    hardware: HardwareContext
+    policy: PolicyContext
+    processors: ProcessorContext
+    data: DatasetContext
+
+
+# ---------------------------------------------------------------------------
+# Build
+# ---------------------------------------------------------------------------
+
+
+def build_rollout_context(
+    cfg: RolloutConfig,
+    shutdown_event: Event,
+    teleop_action_processor: RobotProcessorPipeline | None = None,
+    robot_action_processor: RobotProcessorPipeline | None = None,
+    robot_observation_processor: RobotProcessorPipeline | None = None,
+) -> RolloutContext:
+    """Wire up policy, processors, hardware, dataset, and inference engine.
+
+    The order is policy-first / hardware-last so a bad ``--policy.path``
+    fails fast without touching the robot.
+    """
+    is_rtc = isinstance(cfg.inference, RTCInferenceConfig)
+
+    # --- 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)
+
+    full_config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
+    for attr in ("device", "use_amp"):
+        if hasattr(cfg.policy, attr) and hasattr(full_config, attr):
+            cli_val = getattr(cfg.policy, attr)
+            if cli_val is not None:
+                setattr(full_config, attr, cli_val)
+
+    if hasattr(full_config, "compile_model"):
+        full_config.compile_model = cfg.use_torch_compile
+
+    if full_config.type == "vqbet" and cfg.device == "mps":
+        raise NotImplementedError(
+            "Current implementation of VQBeT does not support `mps` backend. "
+            "Please use `cpu` or `cuda` backend."
+        )
+
+    if full_config.use_peft:
+        from peft import PeftConfig, PeftModel
+
+        peft_path = cfg.policy.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=full_config
+        )
+        policy = PeftModel.from_pretrained(policy, peft_path, config=peft_config)
+    else:
+        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=full_config)
+
+    if is_rtc:
+        policy.config.rtc_config = cfg.inference.rtc
+        if hasattr(policy, "init_rtc_processor"):
+            policy.init_rtc_processor()
+
+    policy = policy.to(cfg.device)
+    policy.eval()
+    logger.info("Policy loaded: type=%s, device=%s", policy_config.type, cfg.device)
+
+    if cfg.use_torch_compile and policy.type not in ("pi0", "pi05"):
+        try:
+            if hasattr(torch, "compile"):
+                compile_kwargs = {
+                    "backend": cfg.torch_compile_backend,
+                    "mode": cfg.torch_compile_mode,
+                    "options": {"triton.cudagraphs": False},
+                }
+                policy.predict_action_chunk = torch.compile(policy.predict_action_chunk, **compile_kwargs)
+                logger.info("torch.compile applied to predict_action_chunk")
+        except Exception as e:
+            logger.warning("Failed to apply torch.compile: %s", e)
+
+    # --- 2. Robot-side processors (user-supplied or defaults) --------
+    if (
+        teleop_action_processor is None
+        or robot_action_processor is None
+        or robot_observation_processor is None
+    ):
+        _t, _r, _o = make_default_processors()
+        teleop_action_processor = teleop_action_processor or _t
+        robot_action_processor = robot_action_processor or _r
+        robot_observation_processor = robot_observation_processor or _o
+
+    # --- 3. Hardware (heaviest side-effect, deferred) -----------------
+    logger.info("Connecting robot (%s)...", cfg.robot.type if cfg.robot else "?")
+    robot = make_robot_from_config(cfg.robot)
+    robot.connect()
+    logger.info("Robot connected: %s", robot.name)
+
+    # Store the initial joint positions so we can return to a safe pose on shutdown.
+    initial_obs = robot.get_observation()
+    initial_position = {k: v for k, v in initial_obs.items() if k.endswith(".pos")}
+    logger.info("Captured initial robot position (%d keys)", len(initial_position))
+
+    robot_wrapper = ThreadSafeRobot(robot)
+
+    teleop = None
+    if cfg.teleop is not None:
+        logger.info("Connecting teleoperator (%s)...", cfg.teleop.type if cfg.teleop else "?")
+        teleop = make_teleoperator_from_config(cfg.teleop)
+        teleop.connect()
+        logger.info("Teleoperator connected")
+
+    # DAgger requires teleop with motor control capabilities (enable_torque,
+    # disable_torque, write_goal_positions).
+    # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+    # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+    # if isinstance(cfg.strategy, DAggerStrategyConfig) and teleop is not None:
+    #     required_teleop_methods = ("enable_torque", "disable_torque", "write_goal_positions")
+    #     missing = [m for m in required_teleop_methods if not callable(getattr(teleop, m, None))]
+    #     if missing:
+    #         teleop.disconnect()
+    #         raise ValueError(
+    #             f"DAgger strategy requires a teleoperator with motor control methods "
+    #             f"{required_teleop_methods}. '{type(teleop).__name__}' is missing: {missing}"
+    #         )
+
+    # --- 4. Features + action-key reconciliation ---------------------
+    # Only `.pos` joint features are used for policy inference — velocity and
+    # torque channels are observation-only and must be excluded from the state
+    # and action tensors that the policy sees.  This matches the filtering
+    # applied by the old ``hil_data_collection`` script.
+    all_obs_features = robot.observation_features
+    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"))
+    }
+    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith(".pos")}
+
+    # The action side is always needed: sync inference reads action names from
+    # ``dataset_features[ACTION]`` to map policy tensors back to robot actions.
+    action_dataset_features = aggregate_pipeline_dataset_features(
+        pipeline=teleop_action_processor,
+        initial_features=create_initial_features(action=action_features_hw),
+        use_videos=cfg.dataset.video if cfg.dataset else True,
+    )
+    # Observation-side aggregation is needed because of build_dataset_frame
+    observation_dataset_features = aggregate_pipeline_dataset_features(
+        pipeline=robot_observation_processor,
+        initial_features=create_initial_features(observation=observation_features_hw),
+        use_videos=cfg.dataset.video if cfg.dataset else True,
+    )
+    dataset_features = combine_feature_dicts(action_dataset_features, observation_dataset_features)
+    hw_features = hw_to_dataset_features(observation_features_hw, "observation")
+    raw_action_keys = list(action_features_hw.keys())
+    policy_action_names = getattr(policy_config, "action_feature_names", None)
+    ordered_action_keys = _resolve_action_key_order(
+        list(policy_action_names) if policy_action_names else None,
+        raw_action_keys,
+    )
+
+    # --- Diagnostic logging ---
+    _act_ft = dataset_features.get("action", {})
+    _obs_ft = dataset_features.get("observation.state", {})
+    logger.info(
+        "Feature reconciliation: action_dim=%d, obs_state_dim=%d, ordered_action_keys=%d",
+        _act_ft.get("shape", (0,))[0],
+        _obs_ft.get("shape", (0,))[0],
+        len(ordered_action_keys),
+    )
+    logger.info("  action names  : %s", _act_ft.get("names", []))
+    logger.info("  obs state names: %s", _obs_ft.get("names", []))
+    logger.info("  ordered keys   : %s", ordered_action_keys)
+    logger.info(
+        "  policy.action_feature_names: %s",
+        list(policy_action_names) if policy_action_names else "None (using raw_action_keys)",
+    )
+    if full_config.input_features:
+        logger.info("  policy input_features: %s", list(full_config.input_features.keys()))
+    else:
+        logger.warning("  policy input_features is EMPTY — policy may not process images!")
+    if full_config.output_features:
+        for k, v in full_config.output_features.items():
+            logger.info("  policy output_features[%s]: shape=%s", k, v.shape)
+    # Validate action dimension consistency
+    if full_config.output_features:
+        for ft in full_config.output_features.values():
+            policy_action_dim = ft.shape[0]
+            if len(ordered_action_keys) != policy_action_dim:
+                logger.error(
+                    "ACTION DIM MISMATCH: policy expects %d dims, hardware produces %d keys. "
+                    "First 5 keys: %s",
+                    policy_action_dim,
+                    len(ordered_action_keys),
+                    ordered_action_keys[:5],
+                )
+            break
+
+    # Validate visual features if no rename_map is active
+    rename_map = cfg.dataset.rename_map if cfg.dataset else {}
+    if not rename_map:
+        expected_visuals = {k for k, v in full_config.input_features.items() if v.type == FeatureType.VISUAL}
+        provided_visuals = {
+            f"observation.images.{k}" for k, v in robot.observation_features.items() if isinstance(v, tuple)
+        }
+        policy_subset = expected_visuals.issubset(provided_visuals)
+        hw_subset = provided_visuals.issubset(expected_visuals)
+        if not (policy_subset or hw_subset):
+            raise ValueError(
+                f"Visual feature mismatch between policy and robot hardware.\n"
+                f"Policy expects: {expected_visuals}\n"
+                f"Robot provides: {provided_visuals}"
+            )
+
+    # --- 5. Dataset -------------
+    dataset = None
+    if cfg.dataset is not None and not isinstance(cfg.strategy, BaseStrategyConfig):
+        logger.info("Setting up dataset (repo_id=%s)...", cfg.dataset.repo_id)
+        if cfg.resume:
+            dataset = LeRobotDataset.resume(
+                cfg.dataset.repo_id,
+                root=cfg.dataset.root,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                vcodec=cfg.dataset.vcodec,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+                encoder_threads=cfg.dataset.encoder_threads,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
+                * len(robot.cameras if hasattr(robot, "cameras") else []),
+            )
+        else:
+            if isinstance(cfg.strategy, DAggerStrategyConfig):
+                dataset_features["intervention"] = {
+                    "dtype": "bool",
+                    "shape": (1,),
+                    "names": None,
+                }
+
+            dataset = LeRobotDataset.create(
+                cfg.dataset.repo_id,
+                cfg.dataset.fps,
+                root=cfg.dataset.root,
+                robot_type=robot.name,
+                features=dataset_features,
+                use_videos=cfg.dataset.video,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
+                * len(robot.cameras if hasattr(robot, "cameras") else []),
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                vcodec=cfg.dataset.vcodec,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+                encoder_threads=cfg.dataset.encoder_threads,
+            )
+
+    if dataset is not None:
+        logger.info("Dataset ready: %s (%d existing episodes)", dataset.repo_id, dataset.num_episodes)
+
+    # --- 6. Policy pre/post processors (needs dataset stats if any) ---
+    dataset_stats = None
+    if dataset is not None:
+        dataset_stats = rename_stats(
+            dataset.meta.stats,
+            cfg.dataset.rename_map if cfg.dataset else {},
+        )
+
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy_config,
+        pretrained_path=cfg.policy.pretrained_path,
+        dataset_stats=dataset_stats,
+        preprocessor_overrides={
+            "device_processor": {"device": cfg.device},
+            "rename_observations_processor": {"rename_map": cfg.dataset.rename_map if cfg.dataset else {}},
+        },
+    )
+
+    # --- Debug: verify normalizer stats loaded from pretrained ---
+    from lerobot.processor import NormalizerProcessorStep, UnnormalizerProcessorStep
+
+    for step in preprocessor.steps:
+        if isinstance(step, NormalizerProcessorStep):
+            n_stats = sum(len(v) for v in step._tensor_stats.values()) if step._tensor_stats else 0
+            logger.info(
+                "Preprocessor normalizer: %d stat tensors, keys=%s",
+                n_stats,
+                list(step._tensor_stats.keys())[:3],
+            )
+            if n_stats == 0:
+                logger.error("PREPROCESSOR NORMALIZER HAS NO STATS — observations will NOT be normalized!")
+    for step in postprocessor.steps:
+        if isinstance(step, UnnormalizerProcessorStep):
+            n_stats = sum(len(v) for v in step._tensor_stats.values()) if step._tensor_stats else 0
+            logger.info(
+                "Postprocessor unnormalizer: %d stat tensors, keys=%s",
+                n_stats,
+                list(step._tensor_stats.keys())[:3],
+            )
+            if n_stats == 0:
+                logger.error("POSTPROCESSOR UNNORMALIZER HAS NO STATS — actions will NOT be denormalized!")
+
+    # --- 7. Inference strategy (needs policy + pre/post + hardware) --
+    logger.info(
+        "Creating inference engine (type=%s)...",
+        cfg.inference.type if hasattr(cfg.inference, "type") else "sync",
+    )
+    task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+    inference_strategy = create_inference_engine(
+        cfg.inference,
+        policy=policy,
+        preprocessor=preprocessor,
+        postprocessor=postprocessor,
+        robot_wrapper=robot_wrapper,
+        hw_features=hw_features,
+        dataset_features=dataset_features,
+        ordered_action_keys=ordered_action_keys,
+        task=task_str,
+        fps=cfg.fps,
+        device=cfg.device,
+        use_torch_compile=cfg.use_torch_compile,
+        compile_warmup_inferences=cfg.compile_warmup_inferences,
+        shutdown_event=shutdown_event,
+    )
+
+    # --- 8. Assemble ---------------------------------------------------
+    logger.info("Rollout context assembled successfully")
+    return RolloutContext(
+        runtime=RuntimeContext(cfg=cfg, shutdown_event=shutdown_event),
+        hardware=HardwareContext(
+            robot_wrapper=robot_wrapper, teleop=teleop, initial_position=initial_position
+        ),
+        policy=PolicyContext(
+            policy=policy,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            inference=inference_strategy,
+        ),
+        processors=ProcessorContext(
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
+        ),
+        data=DatasetContext(
+            dataset=dataset,
+            dataset_features=dataset_features,
+            hw_features=hw_features,
+            ordered_action_keys=ordered_action_keys,
+        ),
+    )

src/lerobot/rollout/inference/__init__.py

@@ -0,0 +1,39 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Inference engine package — backend-agnostic action production.
+
+Concrete strategies (sync, RTC, …) expose the same small interface so
+rollout strategies never branch on the inference backend.
+"""
+
+from .base import InferenceEngine
+from .factory import (
+    InferenceEngineConfig,
+    RTCInferenceConfig,
+    SyncInferenceConfig,
+    create_inference_engine,
+)
+from .rtc import RTCInferenceEngine
+from .sync import SyncInferenceEngine
+
+__all__ = [
+    "InferenceEngine",
+    "InferenceEngineConfig",
+    "RTCInferenceConfig",
+    "RTCInferenceEngine",
+    "SyncInferenceConfig",
+    "SyncInferenceEngine",
+    "create_inference_engine",
+]

src/lerobot/rollout/inference/base.py

@@ -0,0 +1,88 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Inference engine ABC.
+
+Rollout strategies consume actions through this small interface so they
+do not need to know whether the inference engine is synchronous, runs in
+a background thread (RTC), or comes from an external source.
+"""
+
+from __future__ import annotations
+
+import abc
+
+import torch
+
+
+class InferenceEngine(abc.ABC):
+    """Abstract backend for producing actions during rollout.
+
+    Subclasses decide whether inference happens inline, in a background
+    thread, or externally. The contract is minimal so new backends can
+    be added without touching rollout strategies.
+
+    Lifecycle
+    ---------
+    ``start`` — prepare the backend (e.g. launch a background thread).
+    ``stop`` — shut the backend down cleanly.
+    ``reset`` — clear episode-scoped state (policy hidden state, queues…).
+
+    Action production
+    -----------------
+    ``get_action(obs_frame)`` — return the next action tensor, or
+    ``None`` if none is available (e.g. async queue empty).  Sync
+    backends always compute from ``obs_frame``; async backends may
+    ignore it (they get observations via ``notify_observation``).
+
+    Optional hooks
+    --------------
+    ``notify_observation`` / ``pause`` / ``resume`` have a no-op default
+    so rollout strategies can invoke them unconditionally.
+    """
+
+    @abc.abstractmethod
+    def start(self) -> None:
+        """Initialise the backend."""
+
+    @abc.abstractmethod
+    def stop(self) -> None:
+        """Tear the backend down."""
+
+    @abc.abstractmethod
+    def reset(self) -> None:
+        """Clear episode-scoped state."""
+
+    @abc.abstractmethod
+    def get_action(self, obs_frame: dict | None) -> torch.Tensor | None:
+        """Return the next action tensor, or ``None`` if unavailable."""
+
+    def notify_observation(self, obs: dict) -> None:  # noqa: B027
+        """Publish the latest processed observation.  Default: no-op."""
+
+    def pause(self) -> None:  # noqa: B027
+        """Pause background inference.  Default: no-op."""
+
+    def resume(self) -> None:  # noqa: B027
+        """Resume background inference.  Default: no-op."""
+
+    @property
+    def ready(self) -> bool:
+        """True once the backend can produce actions (e.g. warmup done)."""
+        return True
+
+    @property
+    def failed(self) -> bool:
+        """True if an unrecoverable error occurred in the backend."""
+        return False

src/lerobot/rollout/inference/factory.py

@@ -0,0 +1,129 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Inference engine configs and factory.
+
+Selection is explicit via ``--inference.type=sync|rtc``.  Adding a new
+backend requires registering its config subclass and dispatching it in
+:func:`create_inference_engine`.
+"""
+
+from __future__ import annotations
+
+import abc
+import logging
+from dataclasses import dataclass, field
+from threading import Event
+
+import draccus
+
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.processor import PolicyProcessorPipeline
+
+from ..robot_wrapper import ThreadSafeRobot
+from .base import InferenceEngine
+from .rtc import RTCInferenceEngine
+from .sync import SyncInferenceEngine
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Configs
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class InferenceEngineConfig(draccus.ChoiceRegistry, abc.ABC):
+    """Abstract base for inference backend configuration.
+
+    Use ``--inference.type=<name>`` on the CLI to select a backend.
+    """
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+
+@InferenceEngineConfig.register_subclass("sync")
+@dataclass
+class SyncInferenceConfig(InferenceEngineConfig):
+    """Inline synchronous inference (one policy call per control tick)."""
+
+
+@InferenceEngineConfig.register_subclass("rtc")
+@dataclass
+class RTCInferenceConfig(InferenceEngineConfig):
+    """Real-Time Chunking: async policy inference in a background thread."""
+
+    # ``RTCConfig`` is a small dataclass with default-only fields, so eagerly
+    # constructing one here costs nothing and keeps draccus' CLI surface flat
+    # (``--inference.rtc.execution_horizon=...`` etc.).  No need to lazy-init.
+    rtc: RTCConfig = field(default_factory=RTCConfig)
+    queue_threshold: int = 30
+
+
+# ---------------------------------------------------------------------------
+# Factory
+# ---------------------------------------------------------------------------
+
+
+def create_inference_engine(
+    config: InferenceEngineConfig,
+    *,
+    policy: PreTrainedPolicy,
+    preprocessor: PolicyProcessorPipeline,
+    postprocessor: PolicyProcessorPipeline,
+    robot_wrapper: ThreadSafeRobot,
+    hw_features: dict,
+    dataset_features: dict,
+    ordered_action_keys: list[str],
+    task: str,
+    fps: float,
+    device: str | None,
+    use_torch_compile: bool = False,
+    compile_warmup_inferences: int = 2,
+    shutdown_event: Event | None = None,
+) -> InferenceEngine:
+    """Instantiate the appropriate inference engine from a config object."""
+    logger.info("Creating inference engine: %s", config.type)
+    if isinstance(config, SyncInferenceConfig):
+        return SyncInferenceEngine(
+            policy=policy,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            dataset_features=dataset_features,
+            ordered_action_keys=ordered_action_keys,
+            task=task,
+            device=device,
+            robot_type=robot_wrapper.robot_type,
+        )
+    if isinstance(config, RTCInferenceConfig):
+        return RTCInferenceEngine(
+            policy=policy,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            robot_wrapper=robot_wrapper,
+            rtc_config=config.rtc,
+            hw_features=hw_features,
+            task=task,
+            fps=fps,
+            device=device,
+            use_torch_compile=use_torch_compile,
+            compile_warmup_inferences=compile_warmup_inferences,
+            rtc_queue_threshold=config.queue_threshold,
+            shutdown_event=shutdown_event,
+        )
+    raise ValueError(f"Unknown inference engine type: {type(config).__name__}")

src/lerobot/rollout/inference/rtc.py

@@ -0,0 +1,391 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Real-Time Chunking inference engine.
+
+A background thread produces action chunks asynchronously via
+:meth:`policy.predict_action_chunk`.  The main control loop polls
+``get_action`` for the next ready action; observations flow the other
+way via ``notify_observation``.
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+import time
+import traceback
+from threading import Event, Lock, Thread
+from typing import Any
+
+import torch
+
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc import ActionQueue, LatencyTracker
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.policies.utils import prepare_observation_for_inference
+from lerobot.processor import (
+    NormalizerProcessorStep,
+    PolicyProcessorPipeline,
+    RelativeActionsProcessorStep,
+    TransitionKey,
+    create_transition,
+    to_relative_actions,
+)
+from lerobot.utils.constants import OBS_STATE
+from lerobot.utils.feature_utils import build_dataset_frame
+
+from ..robot_wrapper import ThreadSafeRobot
+from .base import InferenceEngine
+
+logger = logging.getLogger(__name__)
+
+# How long the RTC loop sleeps when paused, idle, or backpressured by a full queue.
+_RTC_IDLE_SLEEP_S: float = 0.01
+# Backoff between transient inference errors (per consecutive failure).
+_RTC_ERROR_RETRY_DELAY_S: float = 0.5
+# Consecutive transient errors tolerated before giving up and propagating shutdown.
+_RTC_MAX_CONSECUTIVE_ERRORS: int = 10
+# Hard timeout for joining the RTC thread on stop().
+_RTC_JOIN_TIMEOUT_S: float = 3.0
+
+
+# ---------------------------------------------------------------------------
+# RTC helpers
+# ---------------------------------------------------------------------------
+
+
+def _reanchor_relative_rtc_prefix(
+    prev_actions_absolute: torch.Tensor,
+    current_state: torch.Tensor,
+    relative_step: RelativeActionsProcessorStep,
+    normalizer_step: NormalizerProcessorStep | None,
+    policy_device: torch.device | str,
+) -> torch.Tensor:
+    """Convert absolute leftover actions into model-space for relative-action RTC policies.
+
+    When using relative actions, the RTC prefix (previous chunk's unexecuted tail)
+    is stored in absolute coordinates. Before feeding it back to the policy, this
+    helper re-expresses those actions relative to the robot's current joint state
+    and optionally normalizes them so the policy receives correctly scaled inputs.
+    """
+    state = current_state.detach().cpu()
+    if state.dim() == 1:
+        state = state.unsqueeze(0)
+
+    action_cpu = prev_actions_absolute.detach().cpu()
+    mask = relative_step._build_mask(action_cpu.shape[-1])
+    relative_actions = to_relative_actions(action_cpu, state, mask)
+
+    transition = create_transition(action=relative_actions)
+    if normalizer_step is not None:
+        transition = normalizer_step(transition)
+
+    return transition[TransitionKey.ACTION].to(policy_device)
+
+
+def _normalize_prev_actions_length(prev_actions: torch.Tensor, target_steps: int) -> torch.Tensor:
+    """Pad or truncate RTC prefix actions to a fixed length for stable compiled inference."""
+    if prev_actions.ndim != 2:
+        raise ValueError(f"Expected 2D [T, A] tensor, got shape={tuple(prev_actions.shape)}")
+    steps, action_dim = prev_actions.shape
+    if steps == target_steps:
+        return prev_actions
+    if steps > target_steps:
+        return prev_actions[:target_steps]
+    padded = torch.zeros((target_steps, action_dim), dtype=prev_actions.dtype, device=prev_actions.device)
+    padded[:steps] = prev_actions
+    return padded
+
+
+# ---------------------------------------------------------------------------
+# RTCInferenceEngine
+# ---------------------------------------------------------------------------
+
+
+class RTCInferenceEngine(InferenceEngine):
+    """Async RTC inference: a background thread produces action chunks.
+
+    ``get_action`` pops the next action from the shared queue (or
+    returns ``None`` if the queue is empty).  The main loop should call
+    ``notify_observation`` every tick and ``pause``/``resume`` around
+    human-intervention phases.
+    """
+
+    def __init__(
+        self,
+        policy: PreTrainedPolicy,
+        preprocessor: PolicyProcessorPipeline,
+        postprocessor: PolicyProcessorPipeline,
+        robot_wrapper: ThreadSafeRobot,
+        rtc_config: RTCConfig,
+        hw_features: dict,
+        task: str,
+        fps: float,
+        device: str | None,
+        use_torch_compile: bool = False,
+        compile_warmup_inferences: int = 2,
+        rtc_queue_threshold: int = 30,
+        shutdown_event: Event | None = None,
+    ) -> None:
+        self._policy = policy
+        self._preprocessor = preprocessor
+        self._postprocessor = postprocessor
+        self._robot = robot_wrapper
+        self._rtc_config = rtc_config
+        self._hw_features = hw_features
+        self._task = task
+        self._fps = fps
+        self._device = device or "cpu"
+        self._use_torch_compile = use_torch_compile
+        self._compile_warmup_inferences = compile_warmup_inferences
+        self._rtc_queue_threshold = rtc_queue_threshold
+
+        self._action_queue: ActionQueue | None = None
+        self._obs_holder: dict[str, Any] = {}
+        self._obs_lock = Lock()
+        self._policy_active = Event()
+        self._compile_warmup_done = Event()
+        self._shutdown_event = Event()
+        self._rtc_error = Event()
+        self._global_shutdown_event = shutdown_event
+        self._rtc_thread: Thread | None = None
+
+        if not self._use_torch_compile:
+            self._compile_warmup_done.set()
+            logger.info("RTCInferenceEngine initialized (torch.compile disabled, no warmup needed)")
+        else:
+            logger.info(
+                "RTCInferenceEngine initialized (torch.compile enabled, %d warmup inferences)",
+                compile_warmup_inferences,
+            )
+
+        # Processor introspection for relative-action re-anchoring.
+        self._relative_step = next(
+            (s for s in preprocessor.steps if isinstance(s, RelativeActionsProcessorStep) and s.enabled),
+            None,
+        )
+        self._normalizer_step = next(
+            (s for s in preprocessor.steps if isinstance(s, NormalizerProcessorStep)),
+            None,
+        )
+        if self._relative_step is not None:
+            if self._relative_step.action_names is None:
+                cfg_names = getattr(policy.config, "action_feature_names", None)
+                if cfg_names:
+                    self._relative_step.action_names = list(cfg_names)
+                else:
+                    self._relative_step.action_names = [
+                        k for k in robot_wrapper.action_features if k.endswith(".pos")
+                    ]
+            logger.info("Relative actions enabled: RTC prefix will be re-anchored")
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    @property
+    def ready(self) -> bool:
+        """True once torch.compile warmup is complete (or immediately if compile is disabled)."""
+        return self._compile_warmup_done.is_set()
+
+    @property
+    def failed(self) -> bool:
+        """True if the RTC background thread exited due to an unrecoverable error."""
+        return self._rtc_error.is_set()
+
+    @property
+    def action_queue(self) -> ActionQueue | None:
+        """The shared action queue between the RTC thread and the main loop."""
+        return self._action_queue
+
+    def start(self) -> None:
+        """Launch the RTC background thread."""
+        self._action_queue = ActionQueue(self._rtc_config)
+        self._obs_holder = {
+            "obs": None,
+            "robot_type": self._robot.robot_type,
+        }
+        self._shutdown_event.clear()
+        self._rtc_thread = Thread(
+            target=self._rtc_loop,
+            daemon=True,
+            name="RTCInference",
+        )
+        self._rtc_thread.start()
+        logger.info("RTC inference thread started")
+
+    def stop(self) -> None:
+        """Signal the RTC thread to stop and wait for it."""
+        logger.info("Stopping RTC inference thread...")
+        self._shutdown_event.set()
+        self._policy_active.clear()
+        if self._rtc_thread is not None and self._rtc_thread.is_alive():
+            self._rtc_thread.join(timeout=_RTC_JOIN_TIMEOUT_S)
+            if self._rtc_thread.is_alive():
+                logger.warning("RTC thread did not join within %.1fs", _RTC_JOIN_TIMEOUT_S)
+            else:
+                logger.info("RTC inference thread stopped")
+            self._rtc_thread = None
+
+    def pause(self) -> None:
+        """Pause the RTC background thread."""
+        logger.info("Pausing RTC inference thread")
+        self._policy_active.clear()
+
+    def resume(self) -> None:
+        """Resume the RTC background thread."""
+        logger.info("Resuming RTC inference thread")
+        self._policy_active.set()
+
+    def reset(self) -> None:
+        """Reset the policy, processors, and action queue."""
+        logger.info("Resetting RTC inference state (policy + processors + queue)")
+        self._policy.reset()
+        self._preprocessor.reset()
+        self._postprocessor.reset()
+        if self._action_queue is not None:
+            self._action_queue.clear()
+
+    # ------------------------------------------------------------------
+    # Action production (called from main thread)
+    # ------------------------------------------------------------------
+
+    def get_action(self, obs_frame: dict | None) -> torch.Tensor | None:
+        """Pop the next action from the RTC queue (ignores ``obs_frame``)."""
+        if self._action_queue is None:
+            return None
+        return self._action_queue.get()
+
+    def notify_observation(self, obs: dict) -> None:
+        """Publish the latest observation for the RTC thread to consume."""
+        with self._obs_lock:
+            self._obs_holder["obs"] = obs
+
+    # ------------------------------------------------------------------
+    # RTC: background inference thread
+    # ------------------------------------------------------------------
+
+    def _rtc_loop(self) -> None:
+        """Background thread that generates action chunks via RTC."""
+        try:
+            latency_tracker = LatencyTracker()
+            time_per_chunk = 1.0 / self._fps
+            policy_device = torch.device(self._device)
+
+            warmup_required = max(1, self._compile_warmup_inferences) if self._use_torch_compile else 0
+            inference_count = 0
+            consecutive_errors = 0
+
+            while not self._shutdown_event.is_set():
+                if not self._policy_active.is_set():
+                    time.sleep(_RTC_IDLE_SLEEP_S)
+                    continue
+
+                queue = self._action_queue
+                with self._obs_lock:
+                    obs = self._obs_holder.get("obs")
+                if queue is None or obs is None:
+                    time.sleep(_RTC_IDLE_SLEEP_S)
+                    continue
+
+                if queue.qsize() <= self._rtc_queue_threshold:
+                    try:
+                        current_time = time.perf_counter()
+                        idx_before = queue.get_action_index()
+                        prev_actions = queue.get_left_over()
+
+                        latency = latency_tracker.max()
+                        delay = math.ceil(latency / time_per_chunk) if latency else 0
+
+                        obs_batch = build_dataset_frame(self._hw_features, obs, prefix="observation")
+                        obs_batch = prepare_observation_for_inference(
+                            obs_batch, policy_device, self._task, self._robot.robot_type
+                        )
+                        obs_batch["task"] = [self._task]
+
+                        preprocessed = self._preprocessor(obs_batch)
+
+                        if prev_actions is not None and self._relative_step is not None:
+                            state_tensor = preprocessed.get(OBS_STATE)
+                            if state_tensor is not None:
+                                prev_abs = queue.get_processed_left_over()
+                                if prev_abs is not None and prev_abs.numel() > 0:
+                                    prev_actions = _reanchor_relative_rtc_prefix(
+                                        prev_actions_absolute=prev_abs,
+                                        current_state=state_tensor,
+                                        relative_step=self._relative_step,
+                                        normalizer_step=self._normalizer_step,
+                                        policy_device=policy_device,
+                                    )
+
+                        if prev_actions is not None:
+                            prev_actions = _normalize_prev_actions_length(
+                                prev_actions, target_steps=self._rtc_config.execution_horizon
+                            )
+
+                        actions = self._policy.predict_action_chunk(
+                            preprocessed, inference_delay=delay, prev_chunk_left_over=prev_actions
+                        )
+
+                        original = actions.squeeze(0).clone()
+                        processed = self._postprocessor(actions).squeeze(0)
+                        new_latency = time.perf_counter() - current_time
+                        new_delay = math.ceil(new_latency / time_per_chunk)
+
+                        inference_count += 1
+                        consecutive_errors = 0
+                        is_warmup = self._use_torch_compile and inference_count <= warmup_required
+                        if is_warmup:
+                            latency_tracker.reset()
+                        else:
+                            latency_tracker.add(new_latency)
+
+                        queue.merge(original, processed, new_delay, idx_before)
+
+                        if (
+                            is_warmup
+                            and inference_count >= warmup_required
+                            and not self._compile_warmup_done.is_set()
+                        ):
+                            self._compile_warmup_done.set()
+                            logger.info("Compile warmup complete (%d inferences)", inference_count)
+
+                        logger.debug("RTC inference latency=%.2fs, queue=%d", new_latency, queue.qsize())
+
+                    except Exception as e:
+                        consecutive_errors += 1
+                        logger.error(
+                            "RTC inference error (%d/%d): %s",
+                            consecutive_errors,
+                            _RTC_MAX_CONSECUTIVE_ERRORS,
+                            e,
+                        )
+                        logger.debug(traceback.format_exc())
+                        if consecutive_errors >= _RTC_MAX_CONSECUTIVE_ERRORS:
+                            # Persistent failure: stop retrying and propagate shutdown.
+                            raise
+                        time.sleep(_RTC_ERROR_RETRY_DELAY_S)
+                else:
+                    time.sleep(_RTC_IDLE_SLEEP_S)
+
+        except Exception as e:
+            logger.error("Fatal error in RTC thread: %s", e)
+            logger.error(traceback.format_exc())
+            self._rtc_error.set()
+            # Unblock any warmup waiters so the main loop doesn't spin forever
+            self._compile_warmup_done.set()
+            # Signal the top-level shutdown so strategies exit their control loops
+            if self._global_shutdown_event is not None:
+                self._global_shutdown_event.set()

src/lerobot/rollout/inference/sync.py

@@ -0,0 +1,127 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Synchronous inference engine: inline policy call per control tick."""
+
+from __future__ import annotations
+
+import logging
+from contextlib import nullcontext
+from copy import copy
+
+import torch
+
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.utils import make_robot_action, prepare_observation_for_inference
+from lerobot.processor import PolicyProcessorPipeline
+
+from .base import InferenceEngine
+
+logger = logging.getLogger(__name__)
+
+
+class SyncInferenceEngine(InferenceEngine):
+    """Inline synchronous inference: compute one action per call.
+
+    ``get_action`` runs the full policy pipeline (pre/post-processor +
+    ``select_action``) on the given observation frame and returns a
+    CPU action tensor reordered to match the dataset action keys.
+    """
+
+    def __init__(
+        self,
+        policy: PreTrainedPolicy,
+        preprocessor: PolicyProcessorPipeline,
+        postprocessor: PolicyProcessorPipeline,
+        dataset_features: dict,
+        ordered_action_keys: list[str],
+        task: str,
+        device: str | None,
+        robot_type: str,
+    ) -> None:
+        self._policy = policy
+        self._preprocessor = preprocessor
+        self._postprocessor = postprocessor
+        self._dataset_features = dataset_features
+        self._ordered_action_keys = ordered_action_keys
+        self._task = task
+        self._device = torch.device(device or "cpu")
+        self._robot_type = robot_type
+        logger.info(
+            "SyncInferenceEngine initialized (device=%s, action_keys=%d)",
+            self._device,
+            len(ordered_action_keys),
+        )
+
+    def start(self) -> None:
+        """No background resources to start."""
+        logger.info("SyncInferenceEngine started (inline mode — no background thread)")
+
+    def stop(self) -> None:
+        """No background resources to stop."""
+        logger.info("SyncInferenceEngine stopped")
+
+    def reset(self) -> None:
+        """Reset the policy and pre/post-processors."""
+        logger.info("Resetting sync inference state (policy + processors)")
+        self._policy.reset()
+        self._preprocessor.reset()
+        self._postprocessor.reset()
+
+    def get_action(self, obs_frame: dict | None) -> torch.Tensor | None:
+        """Run the full inference pipeline on ``obs_frame`` and return an action tensor."""
+        if obs_frame is None:
+            return None
+        # Shallow copy is intentional: the caller (`send_next_action`) builds
+        # ``obs_frame`` fresh per tick via ``build_dataset_frame``, so the
+        # tensor/array values are not shared with any other reader.
+        observation = copy(obs_frame)
+        autocast_ctx = (
+            torch.autocast(device_type=self._device.type)
+            if self._device.type == "cuda" and self._policy.config.use_amp
+            else nullcontext()
+        )
+        with torch.inference_mode(), autocast_ctx:
+            observation = prepare_observation_for_inference(
+                observation, self._device, self._task, self._robot_type
+            )
+            observation = self._preprocessor(observation)
+            action_raw = self._policy.select_action(observation)
+            action = self._postprocessor(action_raw)
+        action_tensor = action.squeeze(0).cpu()
+
+        if not hasattr(self, "_log_count"):
+            self._log_count = 0
+        if self._log_count < 3:
+            raw_flat = action_raw.squeeze(0).cpu()
+            logger.info(
+                "[Sync tick %d] raw action (first 5): %s | post-processed (first 5): %s",
+                self._log_count,
+                raw_flat[:5].tolist(),
+                action_tensor[:5].tolist(),
+            )
+            obs_state = obs_frame.get("observation.state")
+            if obs_state is not None:
+                logger.info(
+                    "[Sync tick %d] obs_frame['observation.state'] (first 5): %s | shape: %s",
+                    self._log_count,
+                    obs_state[:5].tolist() if hasattr(obs_state, "tolist") else str(obs_state)[:80],
+                    obs_state.shape if hasattr(obs_state, "shape") else "?",
+                )
+            self._log_count += 1
+
+        # Reorder to match dataset action ordering so the caller can treat
+        # the returned tensor uniformly across backends.
+        action_dict = make_robot_action(action_tensor, self._dataset_features)
+        return torch.tensor([action_dict[k] for k in self._ordered_action_keys])

src/lerobot/rollout/ring_buffer.py

@@ -0,0 +1,112 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Memory-bounded ring buffer for the Highlight Reel rollout strategy."""
+
+from __future__ import annotations
+
+from collections import deque
+
+import numpy as np
+import torch
+
+
+class RolloutRingBuffer:
+    """Fixed-capacity circular buffer for observation/action frames.
+
+    Stores the last *N* seconds of telemetry in memory, bounded by both
+    time (``max_frames``) and memory (``max_memory_bytes``).  When either
+    limit is reached the oldest frames are evicted.
+
+    .. note::
+       This class is **single-threaded**.  ``append``/``drain``/``clear``
+       must all be called from the same thread (the rollout main loop).
+       Concurrent access from a background thread will corrupt
+       ``_current_bytes`` accounting.
+
+    Parameters
+    ----------
+    max_seconds:
+        Maximum duration of buffered telemetry.
+    max_memory_mb:
+        Hard memory cap in MiB.  Frames are evicted when the estimated
+        total size exceeds this.
+    fps:
+        Frames per second — used to convert ``max_seconds`` to a frame
+        count.
+    """
+
+    def __init__(self, max_seconds: float = 30.0, max_memory_mb: float = 2048.0, fps: float = 30.0) -> None:
+        self._max_frames = int(max_seconds * fps)
+        self._max_bytes = int(max_memory_mb * 1024 * 1024)
+        self._buffer: deque[dict] = deque(maxlen=self._max_frames)
+        self._current_bytes: int = 0
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def append(self, frame: dict) -> None:
+        """Add *frame* to the buffer, evicting the oldest if at capacity."""
+        frame_bytes = _estimate_frame_bytes(frame)
+
+        # Evict oldest frames until we are under the memory cap
+        while self._current_bytes + frame_bytes > self._max_bytes and self._buffer:
+            evicted = self._buffer.popleft()
+            self._current_bytes -= _estimate_frame_bytes(evicted)
+
+        self._buffer.append(frame)
+        self._current_bytes += frame_bytes
+
+    def drain(self) -> list[dict]:
+        """Return all buffered frames and clear the buffer."""
+        frames = list(self._buffer)
+        self._buffer.clear()
+        self._current_bytes = 0
+        return frames
+
+    def clear(self) -> None:
+        """Discard all buffered frames."""
+        self._buffer.clear()
+        self._current_bytes = 0
+
+    def __len__(self) -> int:
+        return len(self._buffer)
+
+    @property
+    def estimated_bytes(self) -> int:
+        """Estimated total byte size of all buffered frames."""
+        return self._current_bytes
+
+
+# ------------------------------------------------------------------
+# Helpers
+# ------------------------------------------------------------------
+
+
+def _estimate_frame_bytes(frame: dict) -> int:
+    """Rough byte estimate for a single frame dictionary."""
+    total = 0
+    for v in frame.values():
+        if isinstance(v, torch.Tensor):
+            # ``torch.Tensor`` has no ``nbytes``; compute it explicitly so the
+            # memory cap is honoured even when frames hold unconverted tensors.
+            total += v.nelement() * v.element_size()
+        elif isinstance(v, np.ndarray) or hasattr(v, "nbytes"):
+            total += v.nbytes
+        elif isinstance(v, (int, float)):
+            total += 8
+        elif isinstance(v, (str, bytes)):
+            total += len(v)
+    return max(total, 1)  # avoid zero-size frames

src/lerobot/rollout/robot_wrapper.py

@@ -0,0 +1,79 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Thread-safe robot wrapper for concurrent observation/action access."""
+
+from __future__ import annotations
+
+from threading import Lock
+from typing import Any
+
+from lerobot.robots import Robot
+
+
+class ThreadSafeRobot:
+    """Lock-protected wrapper around a :class:`Robot` for use with background threads.
+
+    When RTC inference runs in a background thread while the main loop
+    executes actions, both threads may access the robot concurrently.
+    This wrapper serialises ``get_observation`` and ``send_action`` calls.
+
+    Read-only properties are proxied without the lock since they don't
+    mutate hardware state.
+    """
+
+    def __init__(self, robot: Robot) -> None:
+        self._robot = robot
+        self._lock = Lock()
+
+    # -- Lock-protected I/O --------------------------------------------------
+
+    def get_observation(self) -> dict[str, Any]:
+        with self._lock:
+            return self._robot.get_observation()
+
+    def send_action(self, action: dict[str, Any] | Any) -> Any:
+        with self._lock:
+            return self._robot.send_action(action)
+
+    # -- Read-only proxies (no lock needed) -----------------------------------
+
+    @property
+    def observation_features(self) -> dict:
+        return self._robot.observation_features
+
+    @property
+    def action_features(self) -> dict:
+        return self._robot.action_features
+
+    @property
+    def name(self) -> str:
+        return self._robot.name
+
+    @property
+    def robot_type(self) -> str:
+        return self._robot.robot_type
+
+    @property
+    def cameras(self):
+        return getattr(self._robot, "cameras", {})
+
+    @property
+    def is_connected(self) -> bool:
+        return self._robot.is_connected
+
+    @property
+    def inner(self) -> Robot:
+        """Access the underlying robot (e.g. for connect/disconnect)."""
+        return self._robot

src/lerobot/rollout/strategies/__init__.py

@@ -0,0 +1,36 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Rollout strategies — public API re-exports."""
+
+from .base import BaseStrategy
+from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
+from .dagger import DAggerEvents, DAggerPhase, DAggerStrategy
+from .factory import create_strategy
+from .highlight import HighlightStrategy
+from .sentry import SentryStrategy
+
+__all__ = [
+    "BaseStrategy",
+    "DAggerEvents",
+    "DAggerPhase",
+    "DAggerStrategy",
+    "HighlightStrategy",
+    "RolloutStrategy",
+    "SentryStrategy",
+    "create_strategy",
+    "estimate_max_episode_seconds",
+    "safe_push_to_hub",
+    "send_next_action",
+]

src/lerobot/rollout/strategies/base.py

@@ -0,0 +1,90 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Base rollout strategy: autonomous policy execution with no data recording."""
+
+from __future__ import annotations
+
+import logging
+import time
+
+from lerobot.utils.robot_utils import precise_sleep
+
+from ..context import RolloutContext
+from .core import RolloutStrategy, send_next_action
+
+logger = logging.getLogger(__name__)
+
+
+class BaseStrategy(RolloutStrategy):
+    """Autonomous policy rollout with no data recording.
+
+    All actions flow through the ``robot_action_processor`` pipeline
+    before reaching the robot.
+    """
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine."""
+        self._init_engine(ctx)
+        logger.info("Base strategy ready")
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run the autonomous control loop until shutdown or duration expires."""
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        interpolator = self._interpolator
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+
+        # Flush a few observation reads so CAN bus / sensor state is fresh
+        # before the first inference.  Without this, the first observation(s)
+        # can return stale or identical values for all joints, poisoning the
+        # entire first action chunk.
+        _OBS_WARMUP_READS = 5
+        for _ in range(_OBS_WARMUP_READS):
+            robot.get_observation()
+            precise_sleep(1 / cfg.fps)
+        logger.info("Flushed %d observation warmup reads", _OBS_WARMUP_READS)
+
+        start_time = time.perf_counter()
+        engine.resume()
+        logger.info("Base strategy control loop started")
+
+        while not ctx.runtime.shutdown_event.is_set():
+            loop_start = time.perf_counter()
+
+            if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                break
+
+            obs = robot.get_observation()
+            obs_processed = ctx.processors.robot_observation_processor(obs)
+            engine.notify_observation(obs_processed)
+
+            if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                continue
+
+            action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+            self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+
+            dt = time.perf_counter() - loop_start
+            self._warn_if_slow(dt, control_interval, cfg.fps)
+            if (sleep_t := control_interval - dt) > 0:
+                precise_sleep(sleep_t)
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Disconnect hardware and stop inference."""
+        self._teardown_hardware(ctx.hardware)
+        logger.info("Base strategy teardown complete")

src/lerobot/rollout/strategies/core.py

@@ -0,0 +1,302 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Rollout strategy ABC and shared action-dispatch helper."""
+
+from __future__ import annotations
+
+import abc
+import logging
+import time
+from typing import TYPE_CHECKING
+
+from lerobot.datasets.utils import DEFAULT_VIDEO_FILE_SIZE_IN_MB
+from lerobot.utils.action_interpolator import ActionInterpolator
+from lerobot.utils.constants import OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.visualization_utils import log_rerun_data
+
+from ..inference import InferenceEngine
+
+if TYPE_CHECKING:
+    from ..configs import RolloutStrategyConfig
+    from ..context import HardwareContext, RolloutContext, RuntimeContext
+
+logger = logging.getLogger(__name__)
+
+
+class RolloutStrategy(abc.ABC):
+    """Abstract base for rollout execution strategies.
+
+    Each concrete strategy implements a self-contained control loop with
+    its own recording/interaction semantics.  Strategies are mutually
+    exclusive — only one runs per session.
+    """
+
+    def __init__(self, config: RolloutStrategyConfig) -> None:
+        self.config = config
+        self._engine: InferenceEngine | None = None
+        self._interpolator: ActionInterpolator | None = None
+        self._warmup_flushed: bool = False
+
+    def _init_engine(self, ctx: RolloutContext) -> None:
+        """Attach the inference engine and action interpolator, then start the backend.
+
+        Creates an :class:`ActionInterpolator` from the config's
+        ``interpolation_multiplier`` and starts the inference engine.
+        Call this from ``setup()`` so strategies share identical
+        initialisation without duplicating code.
+        """
+        self._interpolator = ActionInterpolator(multiplier=ctx.runtime.cfg.interpolation_multiplier)
+        self._engine = ctx.policy.inference
+        logger.info("Starting inference engine...")
+        self._engine.start()
+        # Reset policy and processor state so the first inference starts clean
+        # (matches the old HIL script which called policy.reset() / preprocessor.reset()
+        # at the beginning of each episode).
+        self._engine.reset()
+        self._warmup_flushed = False
+        logger.info("Inference engine started")
+
+    def _handle_warmup(self, use_torch_compile: bool, loop_start: float, control_interval: float) -> bool:
+        """Handle torch.compile warmup phase.
+
+        Returns ``True`` if the caller should ``continue`` (still warming
+        up).  On the first post-warmup iteration the engine and
+        interpolator are reset so stale warmup state is discarded.
+        """
+        engine = self._engine
+        interpolator = self._interpolator
+        if not use_torch_compile:
+            return False
+        if not engine.ready:
+            dt = time.perf_counter() - loop_start
+            if (sleep_t := control_interval - dt) > 0:
+                precise_sleep(sleep_t)
+            return True
+        if not self._warmup_flushed:
+            logger.info("Warmup complete — flushing stale state and resuming engine")
+            engine.reset()
+            interpolator.reset()
+            self._warmup_flushed = True
+            engine.resume()
+        return False
+
+    def _teardown_hardware(self, hw: HardwareContext) -> None:
+        """Stop the inference engine, return robot to initial position, and disconnect hardware."""
+        if self._engine is not None:
+            logger.info("Stopping inference engine...")
+            self._engine.stop()
+        robot = hw.robot_wrapper.inner
+        if robot.is_connected:
+            if hw.initial_position:
+                logger.info("Returning robot to initial position before shutdown...")
+                self._return_to_initial_position(hw)
+            logger.info("Disconnecting robot...")
+            robot.disconnect()
+        teleop = hw.teleop
+        if teleop is not None and teleop.is_connected:
+            logger.info("Disconnecting teleoperator...")
+            teleop.disconnect()
+
+    @staticmethod
+    def _return_to_initial_position(hw: HardwareContext, duration_s: float = 3.0, fps: int = 50) -> None:
+        """Smoothly interpolate the robot back to its initial position."""
+        robot = hw.robot_wrapper
+        target = hw.initial_position
+        try:
+            current_obs = robot.get_observation()
+            current_pos = {k: v for k, v in current_obs.items() if k in target}
+            steps = max(int(duration_s * fps), 1)
+            for step in range(1, steps + 1):
+                t = step / steps
+                interp = {}
+                for k in current_pos:
+                    interp[k] = current_pos[k] * (1 - t) + target[k] * t
+                robot.send_action(interp)
+                precise_sleep(1 / fps)
+        except Exception as e:
+            logger.warning("Could not return to initial position: %s", e)
+
+    @staticmethod
+    def _log_telemetry(
+        obs_processed: dict | None,
+        action_dict: dict | None,
+        runtime_ctx: RuntimeContext,
+    ) -> None:
+        """Log observation/action telemetry to Rerun if display_data is enabled."""
+        cfg = runtime_ctx.cfg
+        if not cfg.display_data:
+            return
+        log_rerun_data(
+            observation=obs_processed,
+            action=action_dict,
+            compress_images=cfg.display_compressed_images,
+        )
+
+    @staticmethod
+    def _warn_if_slow(dt: float, control_interval: float, fps: float) -> None:
+        """Log a warning when the control loop runs slower than target FPS."""
+        if dt > control_interval:
+            actual_fps = 1.0 / dt if dt > 0 else 0
+            logger.warning(
+                "Control loop is running slower (%.1f Hz) than target FPS (%.0f Hz). "
+                "Dataset frames might be dropped and robot control might be unstable. "
+                "Common causes: 1) Camera FPS not keeping up "
+                "2) Policy inference taking too long 3) CPU starvation",
+                actual_fps,
+                fps,
+            )
+
+    @abc.abstractmethod
+    def setup(self, ctx: RolloutContext) -> None:
+        """Strategy-specific initialisation (keyboard listeners, buffers, etc.)."""
+
+    @abc.abstractmethod
+    def run(self, ctx: RolloutContext) -> None:
+        """Main rollout loop.  Returns when shutdown is requested or duration expires."""
+
+    @abc.abstractmethod
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Cleanup: save dataset, stop threads, disconnect hardware."""
+
+
+# ---------------------------------------------------------------------------
+# Shared helpers
+# ---------------------------------------------------------------------------
+
+
+def safe_push_to_hub(dataset, tags=None, private=False) -> bool:
+    """Push dataset to hub, skipping if no episodes have been saved.
+
+    Returns ``True`` if the push was attempted, ``False`` if skipped.
+    """
+    if dataset.num_episodes == 0:
+        logger.warning("No episodes saved — skipping push to hub")
+        return False
+    dataset.push_to_hub(tags=tags, private=private)
+    return True
+
+
+def estimate_max_episode_seconds(
+    dataset_features: dict,
+    fps: float,
+    target_size_mb: float = DEFAULT_VIDEO_FILE_SIZE_IN_MB,
+) -> float:
+    """Conservatively estimate how many seconds of video will exceed *target_size_mb*.
+
+    Each camera produces its own video file, so the episode duration is
+    driven by the **slowest** camera to fill ``target_size_mb`` — i.e.
+    the one with the fewest pixels per frame (lowest bitrate).
+
+    Uses a deliberately **low** bits-per-pixel estimate so the computed
+    duration is *longer* than reality.  By the time the timer fires the
+    actual video file is guaranteed to have crossed the target size,
+    which aligns episode boundaries with the dataset's video-file
+    chunking — each ``push_to_hub`` uploads complete files rather than
+    re-uploading a still-growing one.
+
+    The estimate ignores codec-specific settings (CRF, preset) on purpose:
+    we only need a rough lower bound on bitrate, not a precise prediction.
+
+    Falls back to 600 s (10 min) when no video features are present.
+    """
+    # 0.1 bits-per-pixel is a *low* estimate for CRF-30 streaming video of
+    # robot footage (real-world is typically 0.1 – 0.3 bpp).  Under-
+    # estimating the bitrate over-estimates the time → the episode will be
+    # *larger* than target_size_mb when we save, which is what we want.
+    conservative_bpp = 0.1
+
+    # Collect per-camera pixel counts — each camera has its own video file.
+    camera_pixels = []
+    for feat in dataset_features.values():
+        if feat.get("dtype") == "video":
+            shape = feat.get("shape", ())
+
+            # Assuming shape could be (C, H, W) or (T, C, H, W)
+            # We want to extract the spatial dimensions.
+            if len(shape) >= 3:
+                h, w = shape[-2], shape[-1]
+                pixels = h * w
+                if pixels > 0:
+                    camera_pixels.append(pixels)
+
+    if not camera_pixels:
+        return 600.0
+
+    # Use the smallest camera: it produces the lowest bitrate and therefore
+    # takes the longest to reach the target — the conservative choice.
+    min_pixels = min(camera_pixels)
+    bits_per_frame = min_pixels * conservative_bpp
+    bytes_per_second = (bits_per_frame * fps) / 8
+
+    # Guard against division by zero just in case
+    if bytes_per_second <= 0:
+        return 600.0
+
+    return (target_size_mb * 1024 * 1024) / bytes_per_second
+
+
+# ---------------------------------------------------------------------------
+# Shared action-dispatch helper
+# ---------------------------------------------------------------------------
+
+
+def send_next_action(
+    obs_processed: dict,
+    obs_raw: dict,
+    ctx: RolloutContext,
+    interpolator: ActionInterpolator,
+) -> dict | None:
+    """Dispatch the next action to the robot.
+
+    Pulls the next action tensor from the inference engine, feeds the
+    interpolator, and sends the interpolated action through the
+    ``robot_action_processor`` to the robot.  Works identically for
+    sync and async backends — the rollout strategy never needs to branch.
+
+    Returns the action dict that was sent, or ``None`` if no action was
+    ready (e.g. empty async queue, interpolator not yet primed).
+    """
+    engine = ctx.policy.inference
+    features = ctx.data.dataset_features
+    ordered_keys = ctx.data.ordered_action_keys
+
+    if interpolator.needs_new_action():
+        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+        action_tensor = engine.get_action(obs_frame)
+        if action_tensor is not None:
+            interpolator.add(action_tensor.cpu())
+
+    interp = interpolator.get()
+    if interp is None:
+        return None
+
+    action_dict = {k: interp[i].item() for i, k in enumerate(ordered_keys) if i < len(interp)}
+    processed = ctx.processors.robot_action_processor((action_dict, obs_raw))
+
+    if not hasattr(send_next_action, "_log_count"):
+        send_next_action._log_count = 0
+    if send_next_action._log_count < 3:
+        sample = {k: round(v, 4) for k, v in list(processed.items())[:5]}
+        logger.info(
+            "[send_next_action tick %d] action sent to robot (first 5): %s",
+            send_next_action._log_count,
+            sample,
+        )
+        send_next_action._log_count += 1
+
+    ctx.hardware.robot_wrapper.send_action(processed)
+    return action_dict

src/lerobot/rollout/strategies/dagger.py

@@ -0,0 +1,740 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""DAgger rollout strategy: Human-in-the-Loop data collection.
+
+Implements the RaC paradigm (Recovery and Correction) for interactive
+imitation learning.  Alternates between autonomous policy execution and
+human intervention via teleoperator.
+
+Input is controlled via either a keyboard or foot pedal, selected by
+the ``input_device`` config field.  Each device exposes three actions:
+
+    1. **pause_resume** — Toggle policy execution (AUTONOMOUS <-> PAUSED).
+    2. **correction**   — Toggle correction recording (PAUSED <-> CORRECTING).
+    3. **upload**        — Push dataset to hub on demand (corrections-only mode).
+    ESC (keyboard only)  — Stop session.
+
+Recording Modes:
+    ``record_autonomous=True``:  Sentry-like continuous recording with
+        time-based episode rotation.  Both autonomous and correction
+        frames are recorded; corrections tagged ``intervention=True``.
+    ``record_autonomous=False``: Only correction windows are recorded.
+        Each correction (start to stop) becomes one episode.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import enum
+import logging
+import os
+import sys
+import time
+from concurrent.futures import Future, ThreadPoolExecutor
+from threading import Event, Lock
+from typing import Any
+
+import numpy as np
+
+from lerobot.common.control_utils import is_headless
+from lerobot.datasets import VideoEncodingManager
+from lerobot.datasets.utils import DEFAULT_VIDEO_FILE_SIZE_IN_MB
+from lerobot.teleoperators import Teleoperator
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.import_utils import _pynput_available
+from lerobot.utils.pedal import start_pedal_listener
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+
+from ..configs import DAggerKeyboardConfig, DAggerPedalConfig, DAggerStrategyConfig
+from ..context import RolloutContext
+from ..robot_wrapper import ThreadSafeRobot
+from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
+
+PYNPUT_AVAILABLE = _pynput_available
+keyboard = None
+if PYNPUT_AVAILABLE:
+    try:
+        if ("DISPLAY" not in os.environ) and ("linux" in sys.platform):
+            logging.info("No DISPLAY set. Skipping pynput import.")
+            PYNPUT_AVAILABLE = False
+        else:
+            from pynput import keyboard
+    except Exception as e:
+        PYNPUT_AVAILABLE = False
+        logging.info(f"Could not import pynput: {e}")
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# DAgger state machine
+# ---------------------------------------------------------------------------
+
+
+class DAggerPhase(enum.Enum):
+    """Observable phases of a DAgger episode."""
+
+    AUTONOMOUS = "autonomous"  # Policy driving
+    PAUSED = "paused"  # Engine paused, teleop aligned, awaiting input
+    CORRECTING = "correcting"  # Human driving via teleop, recording interventions
+
+
+# Valid (current_phase, event) -> next_phase
+_DAGGER_TRANSITIONS: dict[tuple[DAggerPhase, str], DAggerPhase] = {
+    (DAggerPhase.AUTONOMOUS, "pause_resume"): DAggerPhase.PAUSED,
+    (DAggerPhase.PAUSED, "pause_resume"): DAggerPhase.AUTONOMOUS,
+    (DAggerPhase.PAUSED, "correction"): DAggerPhase.CORRECTING,
+    (DAggerPhase.CORRECTING, "correction"): DAggerPhase.PAUSED,
+}
+
+
+class DAggerEvents:
+    """Thread-safe container for DAgger input device events.
+
+    The keyboard/pedal threads write transition requests; the main loop
+    consumes them.
+    """
+
+    def __init__(self) -> None:
+        self._lock = Lock()
+        self._phase = DAggerPhase.AUTONOMOUS
+        self._pending_transition: str | None = None
+
+        # Session-level flags
+        self.stop_recording = Event()
+        self.upload_requested = Event()
+
+    # -- Thread-safe phase access ------------------------------------------
+
+    @property
+    def phase(self) -> DAggerPhase:
+        """Current phase of the DAgger state machine."""
+        with self._lock:
+            return self._phase
+
+    @phase.setter
+    def phase(self, value: DAggerPhase) -> None:
+        with self._lock:
+            self._phase = value
+
+    def request_transition(self, event: str) -> None:
+        """Request a phase transition (called from keyboard/pedal threads).
+
+        Only enqueues the request if it corresponds to a valid transition
+        from the current phase, preventing impossible state changes.
+        """
+        with self._lock:
+            if (self._phase, event) in _DAGGER_TRANSITIONS:
+                self._pending_transition = event
+
+    def consume_transition(self) -> tuple[DAggerPhase, DAggerPhase] | None:
+        """Consume a pending transition (called from main loop)."""
+        with self._lock:
+            if self._pending_transition is None:
+                return None
+            key = (self._phase, self._pending_transition)
+            self._pending_transition = None
+            new_phase = _DAGGER_TRANSITIONS.get(key)
+            if new_phase is None:
+                return None
+            old_phase = self._phase
+            self._phase = new_phase
+            return old_phase, new_phase
+
+    def reset(self) -> None:
+        """Reset all transient state for a fresh session."""
+        with self._lock:
+            self._phase = DAggerPhase.AUTONOMOUS
+            self._pending_transition = None
+        self.upload_requested.clear()
+
+
+# ---------------------------------------------------------------------------
+# Teleoperator helpers
+# ---------------------------------------------------------------------------
+
+
+# TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+# user is responsible for moving the teleop to the same position as the robot when starting the correction.
+def _teleop_smooth_move_to(
+    teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 50
+) -> None:
+    """Smoothly move teleop to target position via linear interpolation.
+
+    Requires the teleoperator to support motor control methods
+    (``enable_torque``, ``write_goal_positions``, ``get_action``).
+    """
+    teleop.enable_torque()
+    current = teleop.get_action()
+    steps = max(int(duration_s * fps), 1)
+
+    for step in range(steps + 1):
+        t = step / steps
+        interp = {}
+        for k in current:
+            if k in target_pos:
+                interp[k] = current[k] * (1 - t) + target_pos[k] * t
+            else:
+                interp[k] = current[k]
+        teleop.write_goal_positions(interp)
+        time.sleep(1 / fps)
+
+
+# ---------------------------------------------------------------------------
+# Input device handlers
+# ---------------------------------------------------------------------------
+
+
+def _init_dagger_keyboard(events: DAggerEvents, cfg: DAggerKeyboardConfig):
+    """Initialise keyboard listener with DAgger 3-key controls.
+
+    Returns the pynput Listener (or ``None`` in headless mode or when
+    pynput is unavailable).
+    """
+    if not PYNPUT_AVAILABLE or is_headless():
+        logger.warning("Headless environment or pynput unavailable — keyboard controls disabled")
+        return None
+
+    # Map config key names to pynput Key objects for special keys
+    special_keys = {
+        "space": keyboard.Key.space,
+        "tab": keyboard.Key.tab,
+        "enter": keyboard.Key.enter,
+    }
+
+    def _resolve_key(key) -> str | None:
+        """Resolve a pynput key event to a config-comparable string."""
+        if key == keyboard.Key.esc:
+            return "esc"
+        for name, pynput_key in special_keys.items():
+            if key == pynput_key:
+                return name
+        if hasattr(key, "char") and key.char:
+            return key.char
+        return None
+
+    # Build mapping: resolved key string -> DAgger event name
+    key_to_event = {
+        cfg.pause_resume: "pause_resume",
+        cfg.correction: "correction",
+    }
+
+    def on_press(key):
+        try:
+            resolved = _resolve_key(key)
+            if resolved is None:
+                return
+            if resolved == "esc":
+                logger.info("Stop recording...")
+                events.stop_recording.set()
+                return
+            if resolved in key_to_event:
+                events.request_transition(key_to_event[resolved])
+            if resolved == cfg.upload:
+                events.upload_requested.set()
+        except Exception as e:
+            logger.debug("Key error: %s", e)
+
+    listener = keyboard.Listener(on_press=on_press)
+    listener.start()
+    logger.info(
+        "DAgger keyboard listener started (pause_resume='%s', correction='%s', upload='%s', ESC=stop)",
+        cfg.pause_resume,
+        cfg.correction,
+        cfg.upload,
+    )
+    return listener
+
+
+def _init_dagger_pedal(events: DAggerEvents, cfg: DAggerPedalConfig):
+    """Initialise foot pedal listener with DAgger 3-pedal controls.
+
+    Returns the pedal listener thread (or ``None`` if evdev is unavailable).
+    """
+    code_to_event = {
+        cfg.pause_resume: "pause_resume",
+        cfg.correction: "correction",
+    }
+
+    def on_press(code: str) -> None:
+        if code in code_to_event:
+            events.request_transition(code_to_event[code])
+        if code == cfg.upload:
+            events.upload_requested.set()
+
+    logger.info("Initializing DAgger foot pedal listener (device=%s)", cfg.device_path)
+    return start_pedal_listener(on_press, device_path=cfg.device_path)
+
+
+# ---------------------------------------------------------------------------
+# DAgger Strategy
+# ---------------------------------------------------------------------------
+
+
+class DAggerStrategy(RolloutStrategy):
+    """Human-in-the-Loop data collection with intervention tagging.
+
+    State machine::
+
+        AUTONOMOUS --(key1)--> PAUSED --(key2)--> CORRECTING --(key2)--> PAUSED
+                               --(key1)--> AUTONOMOUS
+
+    Recording modes:
+        ``record_autonomous=True``: Sentry-like continuous recording with
+            time-based episode rotation.  Intervention frames tagged True.
+        ``record_autonomous=False``: Only correction windows recorded.
+            Each correction = one episode.  Upload on demand via key3.
+    """
+
+    config: DAggerStrategyConfig
+
+    def __init__(self, config: DAggerStrategyConfig):
+        super().__init__(config)
+        self._listener = None
+        self._pedal_thread = None
+        self._events = DAggerEvents()
+        self._push_executor: ThreadPoolExecutor | None = None
+        self._pending_push: Future | None = None
+        self._needs_push = Event()
+        self._episode_lock = Lock()
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine and input device listener."""
+        self._init_engine(ctx)
+        self._push_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="dagger-push")
+        target_mb = self.config.target_video_file_size_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB
+        self._episode_duration_s = estimate_max_episode_seconds(
+            ctx.data.dataset_features, ctx.runtime.cfg.fps, target_size_mb=target_mb
+        )
+
+        if self.config.input_device == "keyboard":
+            self._listener = _init_dagger_keyboard(self._events, self.config.keyboard)
+        else:
+            self._pedal_thread = _init_dagger_pedal(self._events, self.config.pedal)
+
+        record_mode = "all frames (sentry-like)" if self.config.record_autonomous else "corrections only"
+        logger.info(
+            "DAgger strategy ready (input=%s, episodes=%d, record=%s, episode_duration=%.0fs)",
+            self.config.input_device,
+            self.config.num_episodes,
+            record_mode,
+            self._episode_duration_s,
+        )
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run DAgger episodes with human-in-the-loop intervention."""
+        if self.config.record_autonomous:
+            self._run_continuous(ctx)
+        else:
+            self._run_corrections_only(ctx)
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Stop listeners, finalise the dataset, and disconnect hardware."""
+        play_sounds = ctx.runtime.cfg.play_sounds
+        logger.info("Stopping DAgger recording")
+        log_say("Stopping DAgger recording", play_sounds)
+
+        if self._listener is not None and not is_headless():
+            logger.info("Stopping keyboard listener")
+            self._listener.stop()
+
+        # Flush any queued/running push cleanly
+        if self._push_executor is not None:
+            logger.info("Shutting down push executor (waiting for pending pushes)...")
+            self._push_executor.shutdown(wait=True)
+            self._push_executor = None
+
+        if ctx.data.dataset is not None:
+            logger.info("Finalizing dataset...")
+            ctx.data.dataset.finalize()
+            if self._needs_push.is_set() and ctx.runtime.cfg.dataset and ctx.runtime.cfg.dataset.push_to_hub:
+                logger.info("Pushing final dataset to hub...")
+                if safe_push_to_hub(
+                    ctx.data.dataset,
+                    tags=ctx.runtime.cfg.dataset.tags,
+                    private=ctx.runtime.cfg.dataset.private,
+                ):
+                    logger.info("Dataset uploaded to hub")
+                    log_say("Dataset uploaded to hub", play_sounds)
+
+        self._teardown_hardware(ctx.hardware)
+        logger.info("DAgger strategy teardown complete")
+
+    # ------------------------------------------------------------------
+    # Continuous recording mode (record_autonomous=True)
+    # ------------------------------------------------------------------
+
+    def _run_continuous(self, ctx: RolloutContext) -> None:
+        """Sentry-like continuous recording with intervention tagging.
+
+        Episodes are auto-rotated every ``episode_time_s`` seconds and
+        uploaded in the background every ``upload_every_n_episodes`` episodes.
+        Both autonomous and correction frames are recorded; corrections are
+        tagged with ``intervention=True``.
+        """
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        teleop = ctx.hardware.teleop
+        dataset = ctx.data.dataset
+        events = self._events
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+        record_stride = max(1, cfg.interpolation_multiplier)
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        play_sounds = cfg.play_sounds
+
+        engine.reset()
+        interpolator.reset()
+        events.reset()
+        # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+        # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+        # teleop.disable_torque()
+        engine.resume()
+
+        last_action: dict[str, Any] | None = None
+        record_tick = 0
+        start_time = time.perf_counter()
+        episode_start = time.perf_counter()
+        episodes_since_push = 0
+        episode_duration_s = self._episode_duration_s
+        logger.info("DAgger continuous recording started (episode_duration=%.0fs)", episode_duration_s)
+
+        with VideoEncodingManager(dataset):
+            try:
+                while not events.stop_recording.is_set() and not ctx.runtime.shutdown_event.is_set():
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    # Process transitions
+                    transition = events.consume_transition()
+                    if transition is not None:
+                        old_phase, new_phase = transition
+                        self._apply_transition(old_phase, new_phase, engine, interpolator, robot, teleop)
+                        last_action = None
+
+                    phase = events.phase
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+                    obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+
+                    # --- CORRECTING: human teleop control ---
+                    if phase == DAggerPhase.CORRECTING:
+                        teleop_action = teleop.get_action()
+                        processed_teleop = ctx.processors.teleop_action_processor((teleop_action, obs))
+                        robot_action_to_send = ctx.processors.robot_action_processor((processed_teleop, obs))
+                        robot.send_action(robot_action_to_send)
+                        last_action = robot_action_to_send
+                        self._log_telemetry(obs_processed, processed_teleop, ctx.runtime)
+                        action_frame = build_dataset_frame(features, processed_teleop, prefix=ACTION)
+                        if record_tick % record_stride == 0:
+                            frame = {
+                                **obs_frame,
+                                **action_frame,
+                                "task": task_str,
+                                "intervention": np.array([True], dtype=bool),
+                            }
+                            dataset.add_frame(frame)
+                        record_tick += 1
+
+                    # --- PAUSED: hold position ---
+                    elif phase == DAggerPhase.PAUSED:
+                        if last_action:
+                            robot.send_action(last_action)
+
+                    # --- AUTONOMOUS: policy control ---
+                    else:
+                        engine.notify_observation(obs_processed)
+
+                        if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                            continue
+
+                        action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+                        if action_dict is not None:
+                            self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                            last_action = ctx.processors.robot_action_processor((action_dict, obs))
+                            action_frame = build_dataset_frame(features, action_dict, prefix=ACTION)
+                            if record_tick % record_stride == 0:
+                                frame = {
+                                    **obs_frame,
+                                    **action_frame,
+                                    "task": task_str,
+                                    "intervention": np.array([False], dtype=bool),
+                                }
+                                dataset.add_frame(frame)
+                            record_tick += 1
+
+                    # Episode rotation derived from video file-size target.
+                    # Do NOT save mid-correction — wait for the correction
+                    # to finish so the episode boundary is clean.
+                    elapsed = time.perf_counter() - episode_start
+                    if elapsed >= episode_duration_s and phase != DAggerPhase.CORRECTING:
+                        with self._episode_lock:
+                            dataset.save_episode()
+                        episodes_since_push += 1
+                        self._needs_push.set()
+                        logger.info(
+                            "Episode saved (total: %d, elapsed: %.1fs)",
+                            dataset.num_episodes,
+                            elapsed,
+                        )
+                        log_say(f"Episode {dataset.num_episodes} saved", play_sounds)
+
+                        if episodes_since_push >= self.config.upload_every_n_episodes:
+                            self._background_push(dataset, cfg)
+                            episodes_since_push = 0
+
+                        episode_start = time.perf_counter()
+
+                    dt = time.perf_counter() - loop_start
+                    self._warn_if_slow(dt, control_interval, cfg.fps)
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+
+            finally:
+                logger.info("DAgger continuous control loop ended — pausing engine")
+                engine.pause()
+                # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+                # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+                # teleop.disable_torque()
+                with contextlib.suppress(Exception):
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+                    logger.info("Final in-progress episode saved")
+
+    # ------------------------------------------------------------------
+    # Corrections-only mode (record_autonomous=False)
+    # ------------------------------------------------------------------
+
+    def _run_corrections_only(self, ctx: RolloutContext) -> None:
+        """Record only human correction windows.  Each correction = one episode.
+
+        The policy runs autonomously without recording.  When the user
+        pauses and starts a correction, frames are recorded with
+        ``intervention=True``.  Stopping the correction saves the episode.
+        The dataset can be uploaded on demand via the upload key/pedal.
+        """
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        teleop = ctx.hardware.teleop
+        dataset = ctx.data.dataset
+        events = self._events
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+        record_stride = max(1, cfg.interpolation_multiplier)
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        play_sounds = cfg.play_sounds
+
+        engine.reset()
+        interpolator.reset()
+        events.reset()
+        # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+        # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+        # teleop.disable_torque()
+        engine.resume()
+
+        last_action: dict[str, Any] | None = None
+        start_time = time.perf_counter()
+        record_tick = 0
+        recorded = 0
+        logger.info(
+            "DAgger corrections-only recording started (target: %d episodes)", self.config.num_episodes
+        )
+
+        with VideoEncodingManager(dataset):
+            try:
+                while (
+                    recorded < self.config.num_episodes
+                    and not events.stop_recording.is_set()
+                    and not ctx.runtime.shutdown_event.is_set()
+                ):
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    # Process transitions
+                    transition = events.consume_transition()
+                    if transition is not None:
+                        old_phase, new_phase = transition
+                        self._apply_transition(old_phase, new_phase, engine, interpolator, robot, teleop)
+                        last_action = None
+
+                        # Correction ended -> save episode (blocking if not streaming)
+                        if old_phase == DAggerPhase.CORRECTING and new_phase == DAggerPhase.PAUSED:
+                            with self._episode_lock:
+                                dataset.save_episode()
+                            recorded += 1
+                            self._needs_push.set()
+                            logger.info(
+                                "Correction %d/%d saved",
+                                recorded,
+                                self.config.num_episodes,
+                            )
+                            log_say(f"Correction {recorded} saved", play_sounds)
+
+                    # On-demand upload
+                    if events.upload_requested.is_set():
+                        events.upload_requested.clear()
+                        logger.info("Upload requested by user")
+                        self._background_push(dataset, cfg)
+
+                    phase = events.phase
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+
+                    # --- CORRECTING: human teleop control + recording ---
+                    if phase == DAggerPhase.CORRECTING:
+                        teleop_action = teleop.get_action()
+                        processed_teleop = ctx.processors.teleop_action_processor((teleop_action, obs))
+                        robot_action_to_send = ctx.processors.robot_action_processor((processed_teleop, obs))
+                        robot.send_action(robot_action_to_send)
+                        last_action = robot_action_to_send
+                        self._log_telemetry(obs_processed, processed_teleop, ctx.runtime)
+
+                        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+                        action_frame = build_dataset_frame(features, processed_teleop, prefix=ACTION)
+                        if record_tick % record_stride == 0:
+                            dataset.add_frame(
+                                {
+                                    **obs_frame,
+                                    **action_frame,
+                                    "task": task_str,
+                                    "intervention": np.array([True], dtype=bool),
+                                }
+                            )
+                        record_tick += 1
+
+                    # --- PAUSED: hold position ---
+                    elif phase == DAggerPhase.PAUSED:
+                        if last_action:
+                            robot.send_action(last_action)
+
+                    # --- AUTONOMOUS: policy control (no recording) ---
+                    else:
+                        engine.notify_observation(obs_processed)
+
+                        if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                            continue
+
+                        action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+                        if action_dict is not None:
+                            self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                            last_action = ctx.processors.robot_action_processor((action_dict, obs))
+
+                    dt = time.perf_counter() - loop_start
+                    self._warn_if_slow(dt, control_interval, cfg.fps)
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+
+            finally:
+                logger.info("DAgger corrections-only loop ended — pausing engine")
+                engine.pause()
+                # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+                # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+                # teleop.disable_torque()
+                with contextlib.suppress(Exception):
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+                    logger.info("Final in-progress episode saved")
+
+    # ------------------------------------------------------------------
+    # State-machine transition side-effects
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _apply_transition(
+        old_phase: DAggerPhase,
+        new_phase: DAggerPhase,
+        engine,
+        interpolator,
+        robot: ThreadSafeRobot,
+        teleop: Teleoperator,
+    ) -> None:
+        """Execute side-effects for a validated phase transition."""
+        logger.info("Phase transition: %s -> %s", old_phase.value, new_phase.value)
+        if old_phase == DAggerPhase.AUTONOMOUS and new_phase == DAggerPhase.PAUSED:
+            logger.info("Pausing engine — robot holds position")
+            engine.pause()
+            obs = robot.get_observation()
+            _robot_pos = {
+                k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features
+            }
+            # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+            # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+            # _teleop_smooth_move_to(teleop, robot_pos, duration_s=2.0, fps=50)
+
+        elif new_phase == DAggerPhase.CORRECTING:
+            logger.info("Entering correction mode — human teleop control")
+            # TODO(Steven): either enforce this (meaning all teleop must implement these methods) or
+            # user is responsible for moving the teleop to the same position as the robot when starting the correction.
+            # teleop.disable_torque()
+
+        elif new_phase == DAggerPhase.AUTONOMOUS:
+            logger.info("Resuming autonomous mode — resetting engine and interpolator")
+            interpolator.reset()
+            engine.reset()
+            engine.resume()
+
+    # ------------------------------------------------------------------
+    # Background push (shared by both modes)
+    # ------------------------------------------------------------------
+
+    def _background_push(self, dataset, cfg) -> None:
+        """Queue a Hub push on the single-worker executor.
+
+        The executor's max_workers=1 guarantees at most one push runs at
+        a time; submitted tasks are queued rather than dropped.  Pushes
+        are blocked while the operator is mid-correction to avoid
+        uploading a partially-recorded episode.
+        """
+        if self._push_executor is None:
+            return
+
+        if self._events.phase == DAggerPhase.CORRECTING:
+            logger.info("Skipping push — correction in progress")
+            return
+
+        if self._pending_push is not None and not self._pending_push.done():
+            logger.info("Previous push still in progress; queueing next")
+
+        def _push():
+            try:
+                with self._episode_lock:
+                    if safe_push_to_hub(
+                        dataset,
+                        tags=cfg.dataset.tags if cfg.dataset else None,
+                        private=cfg.dataset.private if cfg.dataset else False,
+                    ):
+                        self._needs_push.clear()
+                        logger.info("Background push to hub complete")
+            except Exception as e:
+                logger.error("Background push failed: %s", e)
+
+        self._pending_push = self._push_executor.submit(_push)
+        logger.info("Background push task submitted")

src/lerobot/rollout/strategies/factory.py

@@ -0,0 +1,45 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Strategy factory: config type-name → strategy class dispatch."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .base import BaseStrategy
+from .core import RolloutStrategy
+from .dagger import DAggerStrategy
+from .highlight import HighlightStrategy
+from .sentry import SentryStrategy
+
+if TYPE_CHECKING:
+    from ..configs import RolloutStrategyConfig
+
+
+def create_strategy(config: RolloutStrategyConfig) -> RolloutStrategy:
+    """Instantiate the appropriate strategy from a config object.
+
+    Dispatches on ``config.type`` (the name registered via
+    ``draccus.ChoiceRegistry``).
+    """
+    if config.type == "base":
+        return BaseStrategy(config)
+    if config.type == "sentry":
+        return SentryStrategy(config)
+    if config.type == "highlight":
+        return HighlightStrategy(config)
+    if config.type == "dagger":
+        return DAggerStrategy(config)
+    raise ValueError(f"Unknown strategy type '{config.type}'. Available: base, sentry, highlight, dagger")

src/lerobot/rollout/strategies/highlight.py

@@ -0,0 +1,278 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Highlight Reel strategy: on-demand recording via ring buffer."""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import os
+import sys
+import time
+from concurrent.futures import Future, ThreadPoolExecutor
+from threading import Event as ThreadingEvent
+
+from lerobot.common.control_utils import is_headless
+from lerobot.datasets import VideoEncodingManager
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.import_utils import _pynput_available, require_package
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+
+from ..configs import HighlightStrategyConfig
+from ..context import RolloutContext
+from ..ring_buffer import RolloutRingBuffer
+from .core import RolloutStrategy, safe_push_to_hub, send_next_action
+
+PYNPUT_AVAILABLE = _pynput_available
+keyboard = None
+if PYNPUT_AVAILABLE:
+    try:
+        if ("DISPLAY" not in os.environ) and ("linux" in sys.platform):
+            logging.info("No DISPLAY set. Skipping pynput import.")
+            PYNPUT_AVAILABLE = False
+        else:
+            from pynput import keyboard
+    except Exception as e:
+        PYNPUT_AVAILABLE = False
+        logging.info(f"Could not import pynput: {e}")
+
+logger = logging.getLogger(__name__)
+
+
+class HighlightStrategy(RolloutStrategy):
+    """Autonomous rollout with on-demand recording via ring buffer.
+
+    The robot runs autonomously while a memory-bounded ring buffer
+    captures continuous telemetry.  When the user presses the save key:
+
+    1. The ring buffer is flushed to the dataset (last *Z* seconds).
+    2. Live recording continues until the save key is pressed again.
+    3. The episode is saved and the ring buffer resumes capturing.
+
+    Requires ``streaming_encoding=True`` (enforced in config validation)
+    so that ``dataset.add_frame`` is a non-blocking queue put — draining
+    900 frames stays sub-ms per frame.
+    """
+
+    config: HighlightStrategyConfig
+
+    def __init__(self, config: HighlightStrategyConfig):
+        super().__init__(config)
+        require_package("pynput", extra="pynput-dep")
+        self._ring: RolloutRingBuffer | None = None
+        self._listener = None
+        self._save_requested = ThreadingEvent()
+        self._recording_live = ThreadingEvent()
+        self._push_requested = ThreadingEvent()
+        self._push_executor: ThreadPoolExecutor | None = None
+        self._pending_push: Future | None = None
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine, ring buffer, and keyboard listener."""
+        self._init_engine(ctx)
+
+        self._ring = RolloutRingBuffer(
+            max_seconds=self.config.ring_buffer_seconds,
+            max_memory_mb=self.config.ring_buffer_max_memory_mb,
+            fps=ctx.runtime.cfg.fps,
+        )
+
+        self._push_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="highlight-push")
+        logger.info(
+            "Ring buffer initialized (max_seconds=%.0f, max_memory=%.0fMB)",
+            self.config.ring_buffer_seconds,
+            self.config.ring_buffer_max_memory_mb,
+        )
+        self._setup_keyboard(ctx.runtime.shutdown_event)
+        logger.info(
+            "Highlight strategy ready (buffer=%.0fs, save='%s', push='%s')",
+            self.config.ring_buffer_seconds,
+            self.config.save_key,
+            self.config.push_key,
+        )
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run the autonomous loop, buffering frames and recording on demand."""
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        dataset = ctx.data.dataset
+        ring = self._ring
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+
+        engine.resume()
+        play_sounds = cfg.play_sounds
+
+        start_time = time.perf_counter()
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        logger.info("Highlight strategy recording started (press '%s' to save)", self.config.save_key)
+
+        with VideoEncodingManager(dataset):
+            try:
+                while not ctx.runtime.shutdown_event.is_set():
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+                    engine.notify_observation(obs_processed)
+
+                    if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                        continue
+
+                    action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+
+                    if action_dict is not None:
+                        self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+                        action_frame = build_dataset_frame(features, action_dict, prefix=ACTION)
+                        frame = {**obs_frame, **action_frame, "task": task_str}
+
+                        # NOTE: ``is_set()`` then ``clear()`` is not atomic
+                        # against the keyboard thread setting the flag again
+                        # in between — but that is benign: we lose at most one
+                        # toggle, processed on the next iteration.  The
+                        # ``_recording_live`` branch below is reached in the
+                        # SAME iteration after ``clear()`` runs, so a frame
+                        # finalised by ``save_episode()`` is never re-added to
+                        # the next episode.
+                        if self._save_requested.is_set():
+                            self._save_requested.clear()
+                            if not self._recording_live.is_set():
+                                logger.info(
+                                    "Flushing ring buffer (%d frames) + starting live recording",
+                                    len(ring),
+                                )
+                                for buffered_frame in ring.drain():
+                                    dataset.add_frame(buffered_frame)
+                                self._recording_live.set()
+                            else:
+                                dataset.add_frame(frame)
+                                dataset.save_episode()
+                                logger.info("Episode saved (total: %d)", dataset.num_episodes)
+                                log_say(
+                                    f"Episode {dataset.num_episodes} saved",
+                                    play_sounds,
+                                )
+                                self._recording_live.clear()
+
+                        if self._push_requested.is_set():
+                            self._push_requested.clear()
+                            logger.info("Push requested by user")
+                            self._background_push(dataset, cfg)
+
+                        if self._recording_live.is_set():
+                            dataset.add_frame(frame)
+                        else:
+                            ring.append(frame)
+
+                    dt = time.perf_counter() - loop_start
+                    self._warn_if_slow(dt, control_interval, cfg.fps)
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+
+            finally:
+                logger.info("Highlight control loop ended")
+                if self._recording_live.is_set():
+                    logger.info("Saving in-progress live episode")
+                    with contextlib.suppress(Exception):
+                        dataset.save_episode()
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Stop listeners, finalise the dataset, and disconnect hardware."""
+        play_sounds = ctx.runtime.cfg.play_sounds
+        logger.info("Stopping highlight recording")
+        log_say("Stopping highlight recording", play_sounds)
+
+        if self._listener is not None:
+            logger.info("Stopping keyboard listener")
+            self._listener.stop()
+
+        if self._push_executor is not None:
+            logger.info("Shutting down push executor (waiting for pending pushes)...")
+            self._push_executor.shutdown(wait=True)
+            self._push_executor = None
+
+        if ctx.data.dataset is not None:
+            logger.info("Finalizing dataset...")
+            ctx.data.dataset.finalize()
+            if ctx.runtime.cfg.dataset and ctx.runtime.cfg.dataset.push_to_hub:
+                logger.info("Pushing final dataset to hub...")
+                if safe_push_to_hub(
+                    ctx.data.dataset,
+                    tags=ctx.runtime.cfg.dataset.tags,
+                    private=ctx.runtime.cfg.dataset.private,
+                ):
+                    logger.info("Dataset uploaded to hub")
+                    log_say("Dataset uploaded to hub", play_sounds)
+
+        self._teardown_hardware(ctx.hardware)
+        logger.info("Highlight strategy teardown complete")
+
+    def _setup_keyboard(self, shutdown_event: ThreadingEvent) -> None:
+        """Set up keyboard listener for save and push keys."""
+        if is_headless():
+            logger.warning("Headless environment — highlight keys unavailable")
+            return
+
+        try:
+            save_key = self.config.save_key
+            push_key = self.config.push_key
+
+            def on_press(key):
+                with contextlib.suppress(Exception):
+                    if hasattr(key, "char") and key.char == save_key:
+                        self._save_requested.set()
+                    elif hasattr(key, "char") and key.char == push_key:
+                        self._push_requested.set()
+                    elif key == keyboard.Key.esc:
+                        self._save_requested.clear()
+                        shutdown_event.set()
+
+            self._listener = keyboard.Listener(on_press=on_press)
+            self._listener.start()
+            logger.info("Keyboard listener started (save='%s', push='%s', ESC=stop)", save_key, push_key)
+        except ImportError:
+            logger.warning("pynput not available — keyboard listener disabled")
+
+    def _background_push(self, dataset, cfg) -> None:
+        """Queue a Hub push on the single-worker executor."""
+        if self._push_executor is None:
+            return
+
+        if self._pending_push is not None and not self._pending_push.done():
+            logger.info("Previous push still in progress; queueing next")
+
+        def _push():
+            try:
+                if safe_push_to_hub(
+                    dataset,
+                    tags=cfg.dataset.tags if cfg.dataset else None,
+                    private=cfg.dataset.private if cfg.dataset else False,
+                ):
+                    logger.info("Background push to hub complete")
+            except Exception as e:
+                logger.error("Background push failed: %s", e)
+
+        self._pending_push = self._push_executor.submit(_push)
+        logger.info("Background push task submitted")

src/lerobot/rollout/strategies/sentry.py

@@ -0,0 +1,226 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Sentry rollout strategy: continuous autonomous recording with auto-upload."""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import time
+from concurrent.futures import Future, ThreadPoolExecutor
+from threading import Event, Lock
+
+from lerobot.datasets import VideoEncodingManager
+from lerobot.datasets.utils import DEFAULT_VIDEO_FILE_SIZE_IN_MB
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+
+from ..configs import SentryStrategyConfig
+from ..context import RolloutContext
+from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
+
+logger = logging.getLogger(__name__)
+
+
+class SentryStrategy(RolloutStrategy):
+    """Continuous autonomous rollout with always-on recording.
+
+    Episode duration is derived from camera resolution, FPS, and
+    ``DEFAULT_VIDEO_FILE_SIZE_IN_MB`` so that each saved episode
+    produces a video file that has crossed the chunk-size boundary.
+    This keeps ``push_to_hub`` efficient — it uploads complete video
+    files rather than re-uploading a still-growing one.
+
+    The dataset is pushed to the Hub via a bounded single-worker executor
+    so no push is ever silently dropped and exactly one push runs at a
+    time.
+
+    Policy state (hidden state, RTC queue) intentionally persists across
+    episode boundaries — Sentry slices one continuous rollout, the robot
+    does not reset between slices.
+
+    Requires ``streaming_encoding=True`` (enforced in config validation)
+    to prevent disk I/O from blocking the control loop.
+    """
+
+    config: SentryStrategyConfig
+
+    def __init__(self, config: SentryStrategyConfig):
+        super().__init__(config)
+        self._push_executor: ThreadPoolExecutor | None = None
+        self._pending_push: Future | None = None
+        self._needs_push = Event()
+        self._episode_lock = Lock()
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Initialise the inference engine and background push executor."""
+        self._init_engine(ctx)
+        self._push_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="sentry-push")
+        target_mb = self.config.target_video_file_size_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB
+        self._episode_duration_s = estimate_max_episode_seconds(
+            ctx.data.dataset_features, ctx.runtime.cfg.fps, target_size_mb=target_mb
+        )
+        logger.info(
+            "Sentry strategy ready (episode_duration=%.0fs, upload_every=%d eps)",
+            self._episode_duration_s,
+            self.config.upload_every_n_episodes,
+        )
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Run the continuous recording loop with automatic episode rotation."""
+        engine = self._engine
+        cfg = ctx.runtime.cfg
+        robot = ctx.hardware.robot_wrapper
+        dataset = ctx.data.dataset
+        interpolator = self._interpolator
+        features = ctx.data.dataset_features
+
+        control_interval = interpolator.get_control_interval(cfg.fps)
+
+        engine.resume()
+        play_sounds = cfg.play_sounds
+        episode_duration_s = self._episode_duration_s
+
+        start_time = time.perf_counter()
+        episode_start = time.perf_counter()
+        episodes_since_push = 0
+        task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
+        logger.info("Sentry recording started (episode_duration=%.0fs)", episode_duration_s)
+
+        with VideoEncodingManager(dataset):
+            try:
+                while not ctx.runtime.shutdown_event.is_set():
+                    loop_start = time.perf_counter()
+
+                    if cfg.duration > 0 and (time.perf_counter() - start_time) >= cfg.duration:
+                        logger.info("Duration limit reached (%.0fs)", cfg.duration)
+                        break
+
+                    obs = robot.get_observation()
+                    obs_processed = ctx.processors.robot_observation_processor(obs)
+                    engine.notify_observation(obs_processed)
+
+                    if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
+                        continue
+
+                    action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+
+                    if action_dict is not None:
+                        self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+                        obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+                        action_frame = build_dataset_frame(features, action_dict, prefix=ACTION)
+                        frame = {**obs_frame, **action_frame, "task": task_str}
+                        # ``add_frame`` writes to the in-progress episode buffer; the
+                        # background pusher only ever touches *finalised* episode
+                        # artifacts on disk.  The two operate on disjoint state, so
+                        # ``add_frame`` does not need ``_episode_lock``.
+                        dataset.add_frame(frame)
+
+                    # Episode rotation derived from video file-size target.
+                    # The duration is a conservative estimate so the actual
+                    # video has crossed DEFAULT_VIDEO_FILE_SIZE_IN_MB by now,
+                    # keeping push_to_hub efficient (uploads complete files).
+                    elapsed = time.perf_counter() - episode_start
+                    if elapsed >= episode_duration_s:
+                        # ``save_episode`` finalises the in-progress episode and
+                        # flushes it to disk; ``_episode_lock`` serialises this with
+                        # ``push_to_hub`` (run in the background executor) so the
+                        # pusher never reads a half-written episode.
+                        with self._episode_lock:
+                            dataset.save_episode()
+                        episodes_since_push += 1
+                        self._needs_push.set()
+                        logger.info(
+                            "Episode saved (total: %d, elapsed: %.1fs)",
+                            dataset.num_episodes,
+                            elapsed,
+                        )
+                        log_say(f"Episode {dataset.num_episodes} saved", play_sounds)
+
+                        if episodes_since_push >= self.config.upload_every_n_episodes:
+                            self._background_push(dataset, cfg)
+                            episodes_since_push = 0
+
+                        episode_start = time.perf_counter()
+
+                    dt = time.perf_counter() - loop_start
+                    self._warn_if_slow(dt, control_interval, cfg.fps)
+                    if (sleep_t := control_interval - dt) > 0:
+                        precise_sleep(sleep_t)
+
+            finally:
+                logger.info("Sentry control loop ended — saving final episode")
+                with contextlib.suppress(Exception):
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Flush pending pushes, finalise the dataset, and disconnect hardware."""
+        play_sounds = ctx.runtime.cfg.play_sounds
+        logger.info("Stopping sentry recording")
+        log_say("Stopping sentry recording", play_sounds)
+
+        # Flush any queued/running push cleanly.
+        if self._push_executor is not None:
+            logger.info("Shutting down push executor (waiting for pending pushes)...")
+            self._push_executor.shutdown(wait=True)
+            self._push_executor = None
+
+        if ctx.data.dataset is not None:
+            logger.info("Finalizing dataset...")
+            ctx.data.dataset.finalize()
+            if self._needs_push.is_set() and ctx.runtime.cfg.dataset and ctx.runtime.cfg.dataset.push_to_hub:
+                logger.info("Pushing final dataset to hub...")
+                if safe_push_to_hub(
+                    ctx.data.dataset,
+                    tags=ctx.runtime.cfg.dataset.tags,
+                    private=ctx.runtime.cfg.dataset.private,
+                ):
+                    logger.info("Dataset uploaded to hub")
+                    log_say("Dataset uploaded to hub", play_sounds)
+
+        self._teardown_hardware(ctx.hardware)
+        logger.info("Sentry strategy teardown complete")
+
+    def _background_push(self, dataset, cfg) -> None:
+        """Queue a Hub push on the single-worker executor.
+
+        The executor's max_workers=1 guarantees at most one push runs at
+        a time; submitted tasks are queued rather than dropped.
+        """
+        if self._push_executor is None:
+            return
+
+        if self._pending_push is not None and not self._pending_push.done():
+            logger.info("Previous push still in progress; queueing next")
+
+        def _push():
+            try:
+                with self._episode_lock:
+                    if safe_push_to_hub(
+                        dataset,
+                        tags=cfg.dataset.tags if cfg.dataset else None,
+                        private=cfg.dataset.private if cfg.dataset else False,
+                    ):
+                        self._needs_push.clear()
+                        logger.info("Background push to hub complete")
+            except Exception as e:
+                logger.error("Background push failed: %s", e)
+
+        self._pending_push = self._push_executor.submit(_push)
+        logger.info("Background push task submitted")

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -150,24 +150,11 @@ Show dataset information without feature details:
         --operation.type info \
         --operation.show_features false
 
-Recompute dataset statistics (saves to lerobot/pusht_recomputed_stats by default):
+Recompute dataset statistics:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type recompute_stats
 
-Recompute stats and save to a specific new repo_id:
-    lerobot-edit-dataset \
-        --repo_id lerobot/pusht \
-        --new_repo_id lerobot/pusht_new_stats \
-        --operation.type recompute_stats
-
-Recompute stats in-place (overwrites original dataset stats):
-    lerobot-edit-dataset \
-        --repo_id lerobot/pusht \
-        --new_repo_id lerobot/pusht \
-        --operation.type recompute_stats \
-        --operation.overwrite true
-
 Recompute stats for relative actions and push to hub:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
@@ -269,7 +256,6 @@ class RecomputeStatsConfig(OperationConfig):
     relative_exclude_joints: list[str] | None = None
     chunk_size: int = 50
     num_workers: int = 0
-    overwrite: bool = False
 
 
 @OperationConfig.register_subclass("info")
@@ -294,30 +280,16 @@ class EditDatasetConfig:
     push_to_hub: bool = False
 
 
-def _resolve_io_paths(
-    repo_id: str,
-    new_repo_id: str | None,
-    root: Path | str | None,
-    new_root: Path | str | None,
-    default_new_repo_id: str | None = None,
-) -> tuple[str, Path, Path]:
-    """Resolve input/output paths and repo_id for dataset operations.
-
-    Returns (output_repo_id, input_path, output_path) with resolved (symlink-safe) paths.
-    """
-    input_path = (Path(root) if root else HF_LEROBOT_HOME / repo_id).resolve()
-    output_repo_id = new_repo_id or default_new_repo_id or repo_id
-    output_path = (Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id).resolve()
-    return output_repo_id, input_path, output_path
-
-
 def get_output_path(
     repo_id: str,
     new_repo_id: str | None,
     root: Path | str | None,
     new_root: Path | str | None,
 ) -> tuple[str, Path]:
-    output_repo_id, input_path, output_path = _resolve_io_paths(repo_id, new_repo_id, root, new_root)
+    input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
+
+    output_repo_id = new_repo_id if new_repo_id else repo_id
+    output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
 
     # In case of in-place modification, create a backup of the original dataset (if it exists)
     if output_path == input_path:
@@ -585,39 +557,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
     if not isinstance(cfg.operation, RecomputeStatsConfig):
         raise ValueError("Operation config must be RecomputeStatsConfig")
 
-    # Determine whether this is an in-place operation
-    output_repo_id, input_root, output_root = _resolve_io_paths(
-        cfg.repo_id,
-        cfg.new_repo_id,
-        cfg.root,
-        cfg.new_root,
-        default_new_repo_id=f"{cfg.repo_id}_recomputed_stats",
-    )
-    in_place = output_root == input_root
-
-    if in_place and not cfg.operation.overwrite:
-        raise ValueError(
-            f"recompute_stats would overwrite the dataset in-place at {input_root}. "
-            "Pass --operation.overwrite true to allow in-place modification, "
-            "or use --new_repo_id / --new_root to write to a different location. "
-            f"Default output repo_id when neither is set: '{cfg.repo_id}_recomputed_stats'."
-        )
-
-    if in_place:
-        logging.warning(
-            f"Overwriting dataset stats in-place at {input_root}. The original stats will be lost."
-        )
-        dataset = LeRobotDataset(cfg.repo_id, root=input_root)
-    else:
-        logging.info(f"Copying dataset from {input_root} to {output_root}")
-        if output_root.exists():
-            backup_path = output_root.with_name(output_root.name + "_old")
-            logging.warning(f"Output directory {output_root} already exists. Moving to {backup_path}")
-            if backup_path.exists():
-                shutil.rmtree(backup_path)
-            shutil.move(output_root, backup_path)
-        shutil.copytree(input_root, output_root)
-        dataset = LeRobotDataset(output_repo_id, root=output_root)
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
 
     logging.info(f"Recomputing stats for {cfg.repo_id}")
     if cfg.operation.relative_action:
@@ -638,7 +578,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
     logging.info(f"Stats written to {dataset.root}")
 
     if cfg.push_to_hub:
-        logging.info(f"Pushing to hub as {dataset.repo_id}...")
+        logging.info(f"Pushing to hub as {dataset.meta.repo_id}...")
         dataset.push_to_hub()
 
 

src/lerobot/scripts/lerobot_record.py

@@ -13,70 +13,62 @@
 # limitations under the License.
 
 """
-Records a dataset. Actions for the robot can be either generated by teleoperation or by a policy.
+Records a dataset via teleoperation.  This is a pure data-collection
+tool — no policy inference.  For deploying trained policies, use
+``lerobot-rollout`` instead.
 
 Requires: pip install 'lerobot[core_scripts]'  (includes dataset + hardware + viz extras)
 
 Example:
 
 ```shell
-lerobot-record \
-    --robot.type=so100_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.cameras="{laptop: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-    --robot.id=black \
-    --dataset.repo_id=<my_username>/<my_dataset_name> \
-    --dataset.num_episodes=2 \
-    --dataset.single_task="Grab the cube" \
-    --dataset.streaming_encoding=true \
-    --dataset.encoder_threads=2 \
+lerobot-record \\
+    --robot.type=so100_follower \\
+    --robot.port=/dev/tty.usbmodem58760431541 \\
+    --robot.cameras="{laptop: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \\
+    --robot.id=black \\
+    --teleop.type=so100_leader \\
+    --teleop.port=/dev/tty.usbmodem58760431551 \\
+    --teleop.id=blue \\
+    --dataset.repo_id=<my_username>/<my_dataset_name> \\
+    --dataset.num_episodes=2 \\
+    --dataset.single_task="Grab the cube" \\
+    --dataset.streaming_encoding=true \\
+    --dataset.encoder_threads=2 \\
     --display_data=true
-    # <- Optional: specify video codec (auto, h264, hevc, libsvtav1). Default is libsvtav1. \
-    # --dataset.vcodec=h264 \
-    # <- Teleop optional if you want to teleoperate to record or in between episodes with a policy \
-    # --teleop.type=so100_leader \
-    # --teleop.port=/dev/tty.usbmodem58760431551 \
-    # --teleop.id=blue \
-    # <- Policy optional if you want to record with a policy \
-    # --policy.path=${HF_USER}/my_policy \
 ```
 
 Example recording with bimanual so100:
 ```shell
-lerobot-record \
-  --robot.type=bi_so_follower \
-  --robot.left_arm_config.port=/dev/tty.usbmodem5A460822851 \
-  --robot.right_arm_config.port=/dev/tty.usbmodem5A460814411 \
-  --robot.id=bimanual_follower \
+lerobot-record \\
+  --robot.type=bi_so_follower \\
+  --robot.left_arm_config.port=/dev/tty.usbmodem5A460822851 \\
+  --robot.right_arm_config.port=/dev/tty.usbmodem5A460814411 \\
+  --robot.id=bimanual_follower \\
   --robot.left_arm_config.cameras='{
     wrist: {"type": "opencv", "index_or_path": 1, "width": 640, "height": 480, "fps": 30},
     top: {"type": "opencv", "index_or_path": 3, "width": 640, "height": 480, "fps": 30},
   }' --robot.right_arm_config.cameras='{
     wrist: {"type": "opencv", "index_or_path": 2, "width": 640, "height": 480, "fps": 30},
     front: {"type": "opencv", "index_or_path": 4, "width": 640, "height": 480, "fps": 30},
-  }' \
-  --teleop.type=bi_so_leader \
-  --teleop.left_arm_config.port=/dev/tty.usbmodem5A460852721 \
-  --teleop.right_arm_config.port=/dev/tty.usbmodem5A460819811 \
-  --teleop.id=bimanual_leader \
-  --display_data=true \
-  --dataset.repo_id=${HF_USER}/bimanual-so-handover-cube \
-  --dataset.num_episodes=25 \
-  --dataset.single_task="Grab and handover the red cube to the other arm" \
-  --dataset.streaming_encoding=true \
-  # --dataset.vcodec=auto \
+  }' \\
+  --teleop.type=bi_so_leader \\
+  --teleop.left_arm_config.port=/dev/tty.usbmodem5A460852721 \\
+  --teleop.right_arm_config.port=/dev/tty.usbmodem5A460819811 \\
+  --teleop.id=bimanual_leader \\
+  --display_data=true \\
+  --dataset.repo_id=${HF_USER}/bimanual-so-handover-cube \\
+  --dataset.num_episodes=25 \\
+  --dataset.single_task="Grab and handover the red cube to the other arm" \\
+  --dataset.streaming_encoding=true \\
   --dataset.encoder_threads=2
 ```
 """
 
 import logging
 import time
-from dataclasses import asdict, dataclass, field
-from pathlib import Path
+from dataclasses import asdict, dataclass
 from pprint import pformat
-from typing import Any
-
-import torch
 
 from lerobot.cameras import CameraConfig  # noqa: F401
 from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
@@ -86,11 +78,10 @@ from lerobot.cameras.zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.common.control_utils import (
     init_keyboard_listener,
     is_headless,
-    predict_action,
-    sanity_check_dataset_name,
     sanity_check_dataset_robot_compatibility,
 )
-from lerobot.configs import PreTrainedConfig, parser
+from lerobot.configs import parser
+from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.datasets import (
     LeRobotDataset,
     VideoEncodingManager,
@@ -98,21 +89,11 @@ from lerobot.datasets import (
     create_initial_features,
     safe_stop_image_writer,
 )
-from lerobot.policies import (
-    ActionInterpolator,
-    PreTrainedPolicy,
-    make_policy,
-    make_pre_post_processors,
-    make_robot_action,
-)
 from lerobot.processor import (
-    PolicyAction,
-    PolicyProcessorPipeline,
     RobotAction,
     RobotObservation,
     RobotProcessorPipeline,
     make_default_processors,
-    rename_stats,
 )
 from lerobot.robots import (  # noqa: F401
     Robot,
@@ -146,7 +127,6 @@ from lerobot.teleoperators import (  # noqa: F401
 )
 from lerobot.teleoperators.keyboard import KeyboardTeleop
 from lerobot.utils.constants import ACTION, OBS_STR
-from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.robot_utils import precise_sleep
@@ -157,71 +137,12 @@ from lerobot.utils.utils import (
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 
-@dataclass
-class DatasetRecordConfig:
-    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
-    repo_id: str
-    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
-    single_task: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
-    root: str | Path | None = None
-    # Limit the frames per second.
-    fps: int = 30
-    # Number of seconds for data recording for each episode.
-    episode_time_s: int | float = 60
-    # Number of seconds for resetting the environment after each episode.
-    reset_time_s: int | float = 60
-    # Number of episodes to record.
-    num_episodes: int = 50
-    # Encode frames in the dataset into video
-    video: bool = True
-    # Upload dataset to Hugging Face hub.
-    push_to_hub: bool = True
-    # Upload on private repository on the Hugging Face hub.
-    private: bool = False
-    # Add tags to your dataset on the hub.
-    tags: list[str] | None = None
-    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
-    # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
-    # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
-    # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.
-    num_image_writer_processes: int = 0
-    # Number of threads writing the frames as png images on disk, per camera.
-    # Too many threads might cause unstable teleoperation fps due to main thread being blocked.
-    # Not enough threads might cause low camera fps.
-    num_image_writer_threads_per_camera: int = 4
-    # Number of episodes to record before batch encoding videos
-    # Set to 1 for immediate encoding (default behavior), or higher for batched encoding
-    video_encoding_batch_size: int = 1
-    # Video codec for encoding videos. Options: 'h264', 'hevc', 'libsvtav1', 'auto',
-    # or hardware-specific: 'h264_videotoolbox', 'h264_nvenc', 'h264_vaapi', 'h264_qsv'.
-    # Use 'auto' to auto-detect the best available hardware encoder.
-    vcodec: str = "libsvtav1"
-    # Enable streaming video encoding: encode frames in real-time during capture instead
-    # of writing PNG images first. Makes save_episode() near-instant. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding
-    streaming_encoding: bool = False
-    # Maximum number of frames to buffer per camera when using streaming encoding.
-    # ~1s buffer at 30fps. Provides backpressure if the encoder can't keep up.
-    encoder_queue_maxsize: int = 30
-    # 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
-    # Rename map for the observation to override the image and state keys
-    rename_map: dict[str, str] = field(default_factory=dict)
-
-    def __post_init__(self):
-        if self.single_task is None:
-            raise ValueError("You need to provide a task as argument in `single_task`.")
-
-
 @dataclass
 class RecordConfig:
     robot: RobotConfig
     dataset: DatasetRecordConfig
-    # Whether to control the robot with a teleoperator
+    # Teleoperator to control the robot (required)
     teleop: TeleoperatorConfig | None = None
-    # Whether to control the robot with a policy
-    policy: PreTrainedConfig | None = None
     # Display all cameras on screen
     display_data: bool = False
     # Display data on a remote Rerun server
@@ -234,27 +155,14 @@ class RecordConfig:
     play_sounds: bool = True
     # Resume recording on an existing dataset.
     resume: bool = False
-    # Action interpolation multiplier for smoother policy control (1=off, 2=2x, 3=3x)
-    # Only applies when using a policy (not teleop)
-    interpolation_multiplier: int = 1
 
     def __post_init__(self):
-        # HACK: We parse again the cli args here to get the pretrained path if there was one.
-        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)
-            self.policy.pretrained_path = policy_path
-
-        if self.teleop is None and self.policy is None:
-            raise ValueError("Choose a policy, a teleoperator or both to control the robot")
-
-    @classmethod
-    def __get_path_fields__(cls) -> list[str]:
-        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
-        return ["policy"]
+        if self.teleop is None:
+            raise ValueError(
+                "A teleoperator is required for recording. "
+                "Use --teleop.type=... to specify one. "
+                "For policy-based deployment, use lerobot-rollout instead."
+            )
 
 
 """ --------------- record_loop() data flow --------------------------
@@ -264,18 +172,14 @@ class RecordConfig:
            V
      [ robot_observation_processor ] ---> processed_obs
            V
-     .-----( ACTION LOGIC )------------------.
-     V                                       V
-     [ From Teleoperator ]                   [ From Policy ]
-     |                                       |
-     |  [teleop.get_action] -> raw_action    |   [predict_action]
-     |          |                            |          |
-     |          V                            |          V
-     | [teleop_action_processor]             |          |
-     |          |                            |          |
-     '---> processed_teleop_action           '---> processed_policy_action
-     |                                       |
-     '-------------------------.-------------'
+     [ Teleoperator ]
+     |
+     |  [teleop.get_action] -> raw_action
+     |          |
+     |          V
+     | [teleop_action_processor]
+     |          |
+     '---> processed_teleop_action
                                V
                   [ robot_action_processor ] --> robot_action_to_send
                                V
@@ -303,13 +207,9 @@ def record_loop(
     ],  # runs after robot
     dataset: LeRobotDataset | None = None,
     teleop: Teleoperator | list[Teleoperator] | None = None,
-    policy: PreTrainedPolicy | None = None,
-    preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]] | None = None,
-    postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction] | None = None,
     control_time_s: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
-    interpolator: ActionInterpolator | None = None,
     display_compressed_images: bool = False,
 ):
     if dataset is not None and dataset.fps != fps:
@@ -340,21 +240,7 @@ def record_loop(
                 "For multi-teleop, the list must contain exactly one KeyboardTeleop and one arm teleoperator. Currently only supported for LeKiwi robot."
             )
 
-    # Reset policy and processor if they are provided
-    if policy is not None and preprocessor is not None and postprocessor is not None:
-        policy.reset()
-        preprocessor.reset()
-        postprocessor.reset()
-
-    # Reset interpolator if provided
-    if interpolator is not None:
-        interpolator.reset()
-
-    # Calculate control interval based on interpolation
-    use_interpolation = interpolator is not None and interpolator.enabled and policy is not None
-    control_interval = interpolator.get_control_interval(fps) if interpolator else 1 / fps
-    # Pre-compute action key order outside the hot loop — it won't change mid-episode.
-    action_keys = sorted(robot.action_features) if use_interpolation else []
+    control_interval = 1 / fps
 
     no_action_count = 0
     timestamp = 0
@@ -372,63 +258,11 @@ def record_loop(
         # Applies a pipeline to the raw robot observation, default is IdentityProcessor
         obs_processed = robot_observation_processor(obs)
 
-        if policy is not None or dataset is not None:
+        if dataset is not None:
             observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
 
-        # Track whether this iteration should be recorded to the dataset.
-        # Interpolated-only iterations send actions to the robot but don't record frames,
-        # keeping the dataset at the original fps while the robot moves at the higher rate.
-        is_record_frame = True
-
-        # Get action from either policy or teleop
-        if policy is not None and preprocessor is not None and postprocessor is not None:
-            # With interpolation: only call policy when interpolator needs new action
-            if use_interpolation:
-                ran_inference = False
-
-                if interpolator.needs_new_action():
-                    action_values = predict_action(
-                        observation=observation_frame,
-                        policy=policy,
-                        device=get_safe_torch_device(policy.config.device),
-                        preprocessor=preprocessor,
-                        postprocessor=postprocessor,
-                        use_amp=policy.config.use_amp,
-                        task=single_task,
-                        robot_type=robot.robot_type,
-                    )
-                    act_processed_policy = make_robot_action(action_values, dataset.features)
-                    robot_action_to_send = robot_action_processor((act_processed_policy, obs))
-
-                    action_tensor = torch.tensor([robot_action_to_send[k] for k in action_keys])
-                    interpolator.add(action_tensor)
-                    ran_inference = True
-
-                interp_action = interpolator.get()
-                if interp_action is not None:
-                    robot_action_to_send = {k: interp_action[i].item() for i, k in enumerate(action_keys)}
-                    action_values = robot_action_to_send
-                else:
-                    continue
-
-                is_record_frame = ran_inference
-            else:
-                action_values = predict_action(
-                    observation=observation_frame,
-                    policy=policy,
-                    device=get_safe_torch_device(policy.config.device),
-                    preprocessor=preprocessor,
-                    postprocessor=postprocessor,
-                    use_amp=policy.config.use_amp,
-                    task=single_task,
-                    robot_type=robot.robot_type,
-                )
-                act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
-                # Applies a pipeline to the action, default is IdentityProcessor
-                robot_action_to_send = robot_action_processor((act_processed_policy, obs))
-                action_values = robot_action_to_send
-
-        elif policy is None and isinstance(teleop, Teleoperator):
+        # Get action from teleop
+        if isinstance(teleop, Teleoperator):
             act = teleop.get_action()
             if robot.name == "unitree_g1":
                 teleop.send_feedback(obs)
@@ -438,7 +272,7 @@ def record_loop(
             action_values = act_processed_teleop
             robot_action_to_send = robot_action_processor((act_processed_teleop, obs))
 
-        elif policy is None and isinstance(teleop, list):
+        elif isinstance(teleop, list):
             arm_action = teleop_arm.get_action()
             arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
             keyboard_action = teleop_keyboard.get_action()
@@ -451,7 +285,7 @@ def record_loop(
             no_action_count += 1
             if no_action_count == 1 or no_action_count % 10 == 0:
                 logging.warning(
-                    "No policy or teleoperator provided, skipping action generation. "
+                    "No teleoperator provided, skipping action generation. "
                     "This is likely to happen when resetting the environment without a teleop device. "
                     "The robot won't be at its rest position at the start of the next episode."
                 )
@@ -463,8 +297,8 @@ def record_loop(
         # TODO(steven, pepijn, adil): we should use a pipeline step to clip the action, so the sent action is the action that we input to the robot.
         _sent_action = robot.send_action(robot_action_to_send)
 
-        # Write to dataset (only on real policy frames, not interpolated-only iterations)
-        if dataset is not None and is_record_frame:
+        # Write to dataset
+        if dataset is not None:
             action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
             frame = {**observation_frame, **action_frame, "task": single_task}
             dataset.add_frame(frame)
@@ -488,7 +322,12 @@ def record_loop(
 
 
 @parser.wrap()
-def record(cfg: RecordConfig) -> LeRobotDataset:
+def record(
+    cfg: RecordConfig,
+    teleop_action_processor: RobotProcessorPipeline | None = None,
+    robot_action_processor: RobotProcessorPipeline | None = None,
+    robot_observation_processor: RobotProcessorPipeline | None = None,
+) -> LeRobotDataset:
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
@@ -502,7 +341,16 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
     robot = make_robot_from_config(cfg.robot)
     teleop = make_teleoperator_from_config(cfg.teleop) if cfg.teleop is not None else None
 
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    # Fall back to identity pipelines when the caller doesn't supply processors.
+    if (
+        teleop_action_processor is None
+        or robot_action_processor is None
+        or robot_observation_processor is None
+    ):
+        _t, _r, _o = make_default_processors()
+        teleop_action_processor = teleop_action_processor or _t
+        robot_action_processor = robot_action_processor or _r
+        robot_observation_processor = robot_observation_processor or _o
 
     dataset_features = combine_feature_dicts(
         aggregate_pipeline_dataset_features(
@@ -540,8 +388,12 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
             )
             sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features)
         else:
-            # Create empty dataset or load existing saved episodes
-            sanity_check_dataset_name(cfg.dataset.repo_id, cfg.policy)
+            # Reject eval_ prefix — for policy evaluation use lerobot-rollout
+            if cfg.dataset.repo_id.startswith("eval_"):
+                raise ValueError(
+                    "Dataset names starting with 'eval_' are reserved for policy evaluation. "
+                    "lerobot-record is for data collection only. Use lerobot-rollout for policy deployment."
+                )
             dataset = LeRobotDataset.create(
                 cfg.dataset.repo_id,
                 cfg.dataset.fps,
@@ -558,30 +410,6 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                 encoder_threads=cfg.dataset.encoder_threads,
             )
 
-        # Load pretrained policy
-        policy = (
-            None
-            if cfg.policy is None
-            else make_policy(cfg.policy, ds_meta=dataset.meta, rename_map=cfg.dataset.rename_map)
-        )
-        preprocessor = None
-        postprocessor = None
-        interpolator = None
-        if cfg.policy is not None:
-            preprocessor, postprocessor = make_pre_post_processors(
-                policy_cfg=cfg.policy,
-                pretrained_path=cfg.policy.pretrained_path,
-                dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map),
-                preprocessor_overrides={
-                    "device_processor": {"device": cfg.policy.device},
-                    "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
-                },
-            )
-            # Create interpolator for smoother policy control
-            if cfg.interpolation_multiplier > 1:
-                interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
-                logging.info(f"Action interpolation enabled: {cfg.interpolation_multiplier}x control rate")
-
         robot.connect()
         if teleop is not None:
             teleop.connect()
@@ -605,14 +433,10 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                     robot_action_processor=robot_action_processor,
                     robot_observation_processor=robot_observation_processor,
                     teleop=teleop,
-                    policy=policy,
-                    preprocessor=preprocessor,
-                    postprocessor=postprocessor,
                     dataset=dataset,
                     control_time_s=cfg.dataset.episode_time_s,
                     single_task=cfg.dataset.single_task,
                     display_data=cfg.display_data,
-                    interpolator=interpolator,
                     display_compressed_images=display_compressed_images,
                 )
 
@@ -660,7 +484,10 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
             listener.stop()
 
         if cfg.dataset.push_to_hub:
-            dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+            if dataset and dataset.num_episodes > 0:
+                dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+            else:
+                logging.warning("No episodes saved — skipping push to hub")
 
         log_say("Exiting", cfg.play_sounds)
     return dataset

src/lerobot/scripts/lerobot_rollout.py

@@ -0,0 +1,211 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Policy deployment engine with pluggable rollout strategies.
+
+``lerobot-rollout`` is the single CLI for running trained policies on
+real robots.
+
+Strategies
+----------
+    --strategy.type=base       Autonomous rollout, no recording
+    --strategy.type=sentry     Continuous recording with auto-upload
+    --strategy.type=highlight  Ring buffer + keystroke save
+    --strategy.type=dagger     Human-in-the-loop (DAgger / RaC)
+
+Inference backends
+------------------
+    --inference.type=sync      One policy call per control tick (default)
+    --inference.type=rtc       Real-Time Chunking for slow VLA models
+
+Usage examples
+--------------
+::
+
+    # Base mode — quick evaluation with sync inference
+    lerobot-rollout \\
+        --strategy.type=base \\
+        --policy.path=lerobot/act_koch_real \\
+        --robot.type=koch_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --task="pick up cube" --duration=30
+
+    # Base mode — RTC inference for slow VLAs (Pi0, Pi0.5, SmolVLA)
+    lerobot-rollout \\
+        --strategy.type=base \\
+        --policy.path=lerobot/pi0_base \\
+        --inference.type=rtc \\
+        --inference.rtc.execution_horizon=10 \\
+        --inference.rtc.max_guidance_weight=10.0 \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \\
+        --task="pick up cube" --duration=60
+
+    # Sentry mode — continuous recording with periodic upload
+    lerobot-rollout \\
+        --strategy.type=sentry \\
+        --strategy.upload_every_n_episodes=5 \\
+        --policy.path=lerobot/pi0_base \\
+        --inference.type=rtc \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --dataset.repo_id=user/sentry-data \\
+        --dataset.single_task="patrol" --duration=3600
+
+    # Highlight mode — ring buffer, press 's' to save, 'h' to push
+    lerobot-rollout \\
+        --strategy.type=highlight \\
+        --strategy.ring_buffer_seconds=30 \\
+        --policy.path=lerobot/act_koch_real \\
+        --robot.type=koch_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --dataset.repo_id=user/highlight-data \\
+        --dataset.single_task="pick up cube"
+
+    # DAgger mode — human-in-the-loop corrections only
+    lerobot-rollout \\
+        --strategy.type=dagger \\
+        --strategy.num_episodes=20 \\
+        --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \\
+        --robot.type=bi_openarm_follower \\
+        --teleop.type=openarm_mini \\
+        --dataset.repo_id=user/hil-data \\
+        --dataset.single_task="Fold the T-shirt"
+
+    # DAgger mode — continuous recording with RTC inference
+    lerobot-rollout \\
+        --strategy.type=dagger \\
+        --strategy.record_autonomous=true \\
+        --strategy.num_episodes=50 \\
+        --inference.type=rtc \\
+        --inference.rtc.execution_horizon=10 \\
+        --policy.path=user/my_pi0_policy \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --teleop.type=so101_leader \\
+        --teleop.port=/dev/ttyACM1 \\
+        --dataset.repo_id=user/dagger-rtc-data \\
+        --dataset.single_task="Grasp the block"
+
+    # With Rerun visualization and torch.compile
+    lerobot-rollout \\
+        --strategy.type=base \\
+        --policy.path=lerobot/act_koch_real \\
+        --robot.type=koch_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --task="pick up cube" --duration=60 \\
+        --display_data=true \\
+        --use_torch_compile=true
+
+    # Resume a previous sentry recording session
+    lerobot-rollout \\
+        --strategy.type=sentry \\
+        --policy.path=user/my_policy \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --dataset.repo_id=user/sentry-data \\
+        --dataset.single_task="patrol" \\
+        --resume=true
+"""
+
+import logging
+
+from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.zmq import ZMQCameraConfig  # noqa: F401
+from lerobot.configs import parser
+from lerobot.robots import (  # noqa: F401
+    Robot,
+    RobotConfig,
+    bi_openarm_follower,
+    bi_so_follower,
+    earthrover_mini_plus,
+    hope_jr,
+    koch_follower,
+    omx_follower,
+    openarm_follower,
+    reachy2,
+    so_follower,
+    unitree_g1 as unitree_g1_robot,
+)
+from lerobot.rollout import RolloutConfig, build_rollout_context, create_strategy
+from lerobot.teleoperators import (  # noqa: F401
+    Teleoperator,
+    TeleoperatorConfig,
+    bi_openarm_leader,
+    bi_so_leader,
+    homunculus,
+    koch_leader,
+    omx_leader,
+    openarm_leader,
+    openarm_mini,
+    reachy2_teleoperator,
+    so_leader,
+    unitree_g1,
+)
+from lerobot.utils.import_utils import register_third_party_plugins
+from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.utils import init_logging
+from lerobot.utils.visualization_utils import init_rerun
+
+logger = logging.getLogger(__name__)
+
+
+@parser.wrap()
+def rollout(cfg: RolloutConfig):
+    """Main entry point for policy deployment."""
+    init_logging()
+
+    if cfg.display_data:
+        logger.info("Initializing Rerun visualization (ip=%s, port=%s)", cfg.display_ip, cfg.display_port)
+        init_rerun(session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
+
+    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
+    shutdown_event = signal_handler.shutdown_event
+
+    logger.info("Building rollout context...")
+    ctx = build_rollout_context(cfg, shutdown_event)
+
+    strategy = create_strategy(cfg.strategy)
+    logger.info("Rollout strategy: %s", cfg.strategy.type)
+    logger.info(
+        "Robot: %s | FPS: %.0f | Duration: %s",
+        cfg.robot.type if cfg.robot else "?",
+        cfg.fps,
+        f"{cfg.duration}s" if cfg.duration > 0 else "infinite",
+    )
+
+    try:
+        strategy.setup(ctx)
+        logger.info("Rollout setup complete, starting rollout...")
+        strategy.run(ctx)
+    except KeyboardInterrupt:
+        logger.info("Interrupted by user")
+    finally:
+        strategy.teardown(ctx)
+
+    logger.info("Rollout finished")
+
+
+def main():
+    """CLI entry point for ``lerobot-rollout``."""
+    register_third_party_plugins()
+    rollout()
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/utils/action_interpolator.py

@@ -0,0 +1,116 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Action interpolation for smoother robot control.
+
+Provides configurable Nx control rate by interpolating between consecutive actions.
+Useful with RTC and action-chunking policies to reduce jerkiness.
+"""
+
+from torch import Tensor
+
+
+class ActionInterpolator:
+    """Interpolates between consecutive actions for smoother control.
+
+    When enabled with multiplier N, produces N actions per policy action
+    by linearly interpolating between the previous and current action.
+
+    Example with multiplier=3:
+        prev_action -> [1/3 interpolated, 2/3 interpolated, current_action]
+
+    This effectively multiplies the control rate for smoother motion.
+
+    Usage:
+        interpolator = ActionInterpolator(multiplier=2)  # 2x control rate
+
+        # In control loop:
+        if interpolator.needs_new_action():
+            new_action = queue.get()
+            if new_action:
+                interpolator.add(new_action.cpu())
+
+        action = interpolator.get()
+        if action:
+            robot.send_action(action)
+    """
+
+    def __init__(self, multiplier: int = 1):
+        """Initialize the interpolator.
+
+        Args:
+            multiplier: Control rate multiplier (1 = no interpolation, 2 = 2x, 3 = 3x, etc.)
+        """
+        if multiplier < 1:
+            raise ValueError(f"multiplier must be >= 1, got {multiplier}")
+        self.multiplier = multiplier
+        self._prev: Tensor | None = None
+        self._buffer: list[Tensor] = []
+        self._idx = 0
+
+    @property
+    def enabled(self) -> bool:
+        """Whether interpolation is active (multiplier > 1)."""
+        return self.multiplier > 1
+
+    def reset(self):
+        """Reset interpolation state (call between episodes)."""
+        self._prev = None
+        self._buffer = []
+        self._idx = 0
+
+    def needs_new_action(self) -> bool:
+        """Check if a new action is needed from the queue."""
+        return self._idx >= len(self._buffer)
+
+    def add(self, action: Tensor) -> None:
+        """Add a new action and compute interpolated sequence.
+
+        Args:
+            action: New action tensor from policy/queue (already on CPU).
+        """
+        if self.multiplier > 1 and self._prev is not None:
+            self._buffer = []
+            for i in range(1, self.multiplier + 1):
+                t = i / self.multiplier
+                interp = self._prev + t * (action - self._prev)
+                self._buffer.append(interp)
+        else:
+            # First step: no previous action yet, so run at base FPS without interpolation.
+            self._buffer = [action.clone()]
+        self._prev = action.clone()
+        self._idx = 0
+
+    def get(self) -> Tensor | None:
+        """Get the next interpolated action.
+
+        Returns:
+            Next action tensor, or None if buffer is exhausted.
+        """
+        if self._idx >= len(self._buffer):
+            return None
+        action = self._buffer[self._idx]
+        self._idx += 1
+        return action
+
+    def get_control_interval(self, fps: float) -> float:
+        """Get the control interval based on interpolation multiplier.
+
+        Args:
+            fps: Base frames per second.
+
+        Returns:
+            Control interval in seconds (divided by multiplier).
+        """
+        return 1.0 / (fps * self.multiplier)

src/lerobot/utils/import_utils.py

@@ -115,9 +115,7 @@ _feetech_sdk_available = is_package_available("feetech-servo-sdk", import_name="
 _reachy2_sdk_available = is_package_available("reachy2_sdk")
 _can_available = is_package_available("python-can", "can")
 _unitree_sdk_available = is_package_available("unitree-sdk2py", "unitree_sdk2py")
-_pyrealsense2_available = is_package_available("pyrealsense2") or is_package_available(
-    "pyrealsense2-macosx", import_name="pyrealsense2"
-)
+_pyrealsense2_available = is_package_available("pyrealsense2")
 _zmq_available = is_package_available("pyzmq", import_name="zmq")
 _hebi_available = is_package_available("hebi-py", import_name="hebi")
 _teleop_available = is_package_available("teleop")

src/lerobot/utils/pedal.py

@@ -0,0 +1,83 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Generic foot pedal listener using evdev.
+
+Callers supply a callback receiving the pressed key code (e.g. ``"KEY_A"``)
+and an optional device path.  The listener runs in a daemon thread and
+silently no-ops when :mod:`evdev` is not installed or the device is
+unavailable.  Strategy-specific key mapping logic lives in the caller.
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+from collections.abc import Callable
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_PEDAL_DEVICE = "/dev/input/by-id/usb-PCsensor_FootSwitch-event-kbd"
+
+
+def start_pedal_listener(
+    on_press: Callable[[str], None],
+    device_path: str = DEFAULT_PEDAL_DEVICE,
+) -> threading.Thread | None:
+    """Spawn a daemon thread that forwards pedal key-press codes to ``on_press``.
+
+    Parameters
+    ----------
+    on_press:
+        Callback invoked with the pressed key code string (e.g. ``"KEY_A"``)
+        on each pedal press event.  The callback runs in the listener thread
+        and must be thread-safe.
+    device_path:
+        Linux input device path (e.g. ``/dev/input/by-id/...``).
+
+    Returns
+    -------
+    The started daemon :class:`threading.Thread`, or ``None`` when
+    :mod:`evdev` is not installed (optional dependency; silent no-op).
+    """
+    try:
+        from evdev import InputDevice, categorize, ecodes
+    except ImportError:
+        return None
+
+    def pedal_reader() -> None:
+        try:
+            dev = InputDevice(device_path)
+            logger.info("Pedal connected: %s", dev.name)
+            for ev in dev.read_loop():
+                if ev.type != ecodes.EV_KEY:
+                    continue
+                key = categorize(ev)
+                code = key.keycode
+                if isinstance(code, (list, tuple)):
+                    code = code[0]
+                if key.keystate != 1:  # only key-down events
+                    continue
+                try:
+                    on_press(code)
+                except Exception as cb_err:  # pragma: no cover - defensive
+                    logger.warning("Pedal callback error: %s", cb_err)
+        except (FileNotFoundError, PermissionError):
+            pass
+        except Exception as e:
+            logger.warning("Pedal error: %s", e)
+
+    thread = threading.Thread(target=pedal_reader, daemon=True, name="PedalListener")
+    thread.start()
+    return thread

tests/envs/test_robotwin.py

@@ -1,282 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Unit tests for the RoboTwin 2.0 Gymnasium wrapper.
-
-These tests mock out the SAPIEN-based RoboTwin runtime (task modules +
-YAML config loader) so they run without the full RoboTwin installation
-(SAPIEN, CuRobo, mplib, asset downloads, etc.).
-"""
-
-from __future__ import annotations
-
-from contextlib import contextmanager
-from unittest.mock import MagicMock, patch
-
-import gymnasium as gym
-import numpy as np
-import pytest
-
-from lerobot.envs.robotwin import (
-    ACTION_DIM,
-    ROBOTWIN_CAMERA_NAMES,
-    ROBOTWIN_TASKS,
-    RoboTwinEnv,
-    create_robotwin_envs,
-)
-
-# ---------------------------------------------------------------------------
-# Fixtures / helpers
-# ---------------------------------------------------------------------------
-
-
-def _make_mock_task_env(
-    height: int = 240,
-    width: int = 320,
-    cameras: tuple[str, ...] = ROBOTWIN_CAMERA_NAMES,
-) -> MagicMock:
-    """Return a mock that mimics the RoboTwin task class API.
-
-    RoboTwin's real get_obs returns
-        {"observation": {cam: {"rgb": img}}, "joint_action": {"vector": np.ndarray}, ...}
-    so the mock follows the same nested shape.
-    """
-    obs_dict = {
-        "observation": {cam: {"rgb": np.zeros((height, width, 3), dtype=np.uint8)} for cam in cameras},
-        "joint_action": {"vector": np.zeros(ACTION_DIM, dtype=np.float32)},
-        "endpose": {},
-    }
-
-    mock = MagicMock()
-    mock.get_obs.return_value = obs_dict
-    mock.setup_demo.return_value = None
-    mock.take_action.return_value = None
-    mock.eval_success = False
-    mock.check_success.return_value = False
-    mock.close_env.return_value = None
-    return mock
-
-
-@contextmanager
-def _patch_runtime(mock_task_instance: MagicMock):
-    """Patch both the task-class loader and the YAML config loader so the
-    env can construct + reset without a real RoboTwin install."""
-    task_cls = MagicMock(return_value=mock_task_instance)
-    fake_setup = {
-        "head_camera_h": 240,
-        "head_camera_w": 320,
-        "left_embodiment_config": {},
-        "right_embodiment_config": {},
-        "left_robot_file": "",
-        "right_robot_file": "",
-        "dual_arm_embodied": True,
-        "render_freq": 0,
-        "task_name": "beat_block_hammer",
-        "task_config": "demo_clean",
-    }
-    with (
-        patch("lerobot.envs.robotwin._load_robotwin_task", return_value=task_cls),
-        patch("lerobot.envs.robotwin._load_robotwin_setup_kwargs", return_value=fake_setup),
-    ):
-        yield
-
-
-# ---------------------------------------------------------------------------
-# RoboTwinEnv unit tests
-# ---------------------------------------------------------------------------
-
-
-class TestRoboTwinEnv:
-    def test_observation_space_shape(self):
-        """observation_space should have the configured h×w×3 for every camera."""
-        h, w = 240, 320
-        env = RoboTwinEnv(
-            task_name="beat_block_hammer",
-            observation_height=h,
-            observation_width=w,
-            camera_names=["head_camera", "left_camera"],
-        )
-        pixels_space = env.observation_space["pixels"]
-        assert pixels_space["head_camera"].shape == (h, w, 3)
-        assert pixels_space["left_camera"].shape == (h, w, 3)
-        assert "right_camera" not in pixels_space
-
-    def test_action_space(self):
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        assert env.action_space.shape == (ACTION_DIM,)
-        assert env.action_space.dtype == np.float32
-
-    def test_reset_returns_correct_obs_keys(self):
-        mock_task = _make_mock_task_env()
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        with _patch_runtime(mock_task):
-            obs, info = env.reset()
-
-        assert "pixels" in obs
-        for cam in ROBOTWIN_CAMERA_NAMES:
-            assert cam in obs["pixels"], f"Missing camera '{cam}' in obs"
-        assert "agent_pos" in obs
-        assert obs["agent_pos"].shape == (ACTION_DIM,)
-        assert info["is_success"] is False
-
-    def test_reset_calls_setup_demo(self):
-        mock_task = _make_mock_task_env()
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        with _patch_runtime(mock_task):
-            env.reset(seed=42)
-        # setup_demo receives the full YAML-derived kwargs plus seed + is_test;
-        # we only assert the caller-provided bits.
-        assert mock_task.setup_demo.call_count == 1
-        call_kwargs = mock_task.setup_demo.call_args.kwargs
-        assert call_kwargs["seed"] == 42
-        assert call_kwargs["is_test"] is True
-
-    def test_step_returns_correct_types(self):
-        mock_task = _make_mock_task_env()
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        action = np.zeros(ACTION_DIM, dtype=np.float32)
-        with _patch_runtime(mock_task):
-            env.reset()
-            obs, reward, terminated, truncated, info = env.step(action)
-
-        assert isinstance(obs, dict)
-        assert isinstance(reward, float)
-        assert isinstance(terminated, bool)
-        assert isinstance(truncated, bool)
-        assert isinstance(info, dict)
-
-    def test_step_wrong_action_shape_raises(self):
-        mock_task = _make_mock_task_env()
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        bad_action = np.zeros(7, dtype=np.float32)  # wrong dim
-        with _patch_runtime(mock_task):
-            env.reset()
-            with pytest.raises(ValueError, match="Expected 1-D action"):
-                env.step(bad_action)
-
-    def test_success_terminates_episode(self):
-        mock_task = _make_mock_task_env()
-        mock_task.check_success.return_value = True
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        action = np.zeros(ACTION_DIM, dtype=np.float32)
-        with _patch_runtime(mock_task):
-            env.reset()
-            _, _, terminated, _, info = env.step(action)
-        assert terminated is True
-        assert info["is_success"] is True
-
-    def test_truncation_after_episode_length(self):
-        mock_task = _make_mock_task_env()
-        env = RoboTwinEnv(task_name="beat_block_hammer", episode_length=2)
-        action = np.zeros(ACTION_DIM, dtype=np.float32)
-        with _patch_runtime(mock_task):
-            env.reset()
-            env.step(action)  # step 1
-            _, _, _, truncated, _ = env.step(action)  # step 2 → truncated
-        assert truncated is True
-
-    def test_close_calls_close_env(self):
-        mock_task = _make_mock_task_env()
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        with _patch_runtime(mock_task):
-            env.reset()
-            env.close()
-        mock_task.close_env.assert_called_once()
-
-    def test_black_frame_for_missing_camera(self):
-        """If a camera key is absent from get_obs(), a black frame is returned."""
-        # Mock exposes only head_camera; we ask for both head_camera + left_camera.
-        mock_task = _make_mock_task_env(height=10, width=10, cameras=("head_camera",))
-        env = RoboTwinEnv(
-            task_name="beat_block_hammer",
-            camera_names=["head_camera", "left_camera"],
-            observation_height=10,
-            observation_width=10,
-        )
-        with _patch_runtime(mock_task):
-            obs, _ = env.reset()
-        assert obs["pixels"]["left_camera"].shape == (10, 10, 3)
-        assert obs["pixels"]["left_camera"].sum() == 0
-
-    def test_task_and_task_description_attributes(self):
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        assert env.task == "beat_block_hammer"
-        assert isinstance(env.task_description, str)
-
-    def test_deferred_init_env_is_none_before_reset(self):
-        env = RoboTwinEnv(task_name="beat_block_hammer")
-        assert env._env is None  # noqa: SLF001  (testing internal state)
-
-
-# ---------------------------------------------------------------------------
-# create_robotwin_envs tests
-# ---------------------------------------------------------------------------
-
-
-class TestCreateRoboTwinEnvs:
-    def test_returns_correct_structure(self):
-        mock_task = _make_mock_task_env()
-        with _patch_runtime(mock_task):
-            envs = create_robotwin_envs(
-                task="beat_block_hammer",
-                n_envs=1,
-                env_cls=gym.vector.SyncVectorEnv,
-            )
-        assert "beat_block_hammer" in envs
-        assert 0 in envs["beat_block_hammer"]
-        assert isinstance(envs["beat_block_hammer"][0], gym.vector.SyncVectorEnv)
-
-    def test_multi_task(self):
-        mock_task = _make_mock_task_env()
-        with _patch_runtime(mock_task):
-            envs = create_robotwin_envs(
-                task="beat_block_hammer,click_bell",
-                n_envs=1,
-                env_cls=gym.vector.SyncVectorEnv,
-            )
-        assert set(envs.keys()) == {"beat_block_hammer", "click_bell"}
-
-    def test_unknown_task_raises(self):
-        with pytest.raises(ValueError, match="Unknown RoboTwin tasks"):
-            create_robotwin_envs(
-                task="not_a_real_task",
-                n_envs=1,
-                env_cls=gym.vector.SyncVectorEnv,
-            )
-
-    def test_invalid_n_envs_raises(self):
-        with pytest.raises(ValueError, match="n_envs must be a positive int"):
-            create_robotwin_envs(
-                task="beat_block_hammer",
-                n_envs=0,
-                env_cls=gym.vector.SyncVectorEnv,
-            )
-
-
-# ---------------------------------------------------------------------------
-# ROBOTWIN_TASKS list
-# ---------------------------------------------------------------------------
-
-
-def test_task_list_not_empty():
-    assert len(ROBOTWIN_TASKS) >= 50
-
-
-def test_all_tasks_are_strings():
-    assert all(isinstance(t, str) and t for t in ROBOTWIN_TASKS)
-
-
-def test_no_duplicate_tasks():
-    assert len(ROBOTWIN_TASKS) == len(set(ROBOTWIN_TASKS))

tests/policies/rtc/test_action_interpolator.py

@@ -17,9 +17,9 @@
 import pytest
 import torch
 
-from lerobot.policies.rtc.action_interpolator import ActionInterpolator
 from lerobot.policies.rtc.action_queue import ActionQueue
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.utils.action_interpolator import ActionInterpolator
 
 # ====================== Fixtures ======================
 

tests/policies/rtc/test_rtc_relative_actions.py

@@ -187,7 +187,7 @@ class TestRTCDenoiseWithRelativeLeftovers:
 
 
 class TestFullPipelineRelativeRTC:
-    """End-to-end test of the RTC + relative actions pipeline matching eval_with_real_robot.py flow."""
+    """End-to-end test of the RTC + relative actions pipeline matching lerobot-rollout flow."""
 
     def test_preprocessor_caches_state_for_postprocessor(self):
         """Preprocessor's relative step should cache state so postprocessor can convert back."""
@@ -240,7 +240,7 @@ class TestFullPipelineRelativeRTC:
         torch.testing.assert_close(recovered, actions, atol=1e-5, rtol=1e-5)
 
     def test_eval_loop_simulation(self):
-        """Simulate the eval_with_real_robot.py loop with relative actions.
+        """Simulate the lerobot-rollout loop with relative actions.
 
         Iteration 1: No leftovers → model generates relative actions → store for RTC
         Iteration 2: Use leftovers as RTC guidance → model generates new relative actions
@@ -401,12 +401,12 @@ class TestStateRebasingApproximation:
 
 
 def _detect_relative_actions(preprocessor) -> bool:
-    """Mirror of the helper in eval_with_real_robot.py for testing without importing it."""
+    """Mirror of the helper in lerobot-rollout for testing without importing it."""
     return any(isinstance(step, RelativeActionsProcessorStep) and step.enabled for step in preprocessor.steps)
 
 
 class TestDetectRelativeActions:
-    """Test the _detect_relative_actions helper logic used by eval_with_real_robot.py."""
+    """Test the _detect_relative_actions helper logic used by lerobot-rollout."""
 
     def test_detects_enabled_relative_step(self):
         class FakePipeline:

tests/test_cli_peft.py

@@ -24,10 +24,6 @@ def lerobot_train(args):
     return run_command(cmd="lerobot-train", module="lerobot_train", args=args)
 
 
-def lerobot_record(args):
-    return run_command(cmd="lerobot-record", module="lerobot_record", args=args)
-
-
 def resolve_model_id_for_peft_training(policy_type):
     """PEFT training needs pretrained models, this finds the pretrained model of a policy type for PEFT training."""
     if policy_type == "smolvla":
@@ -155,81 +151,3 @@ def test_peft_training_params_are_fewer(policy_type, tmp_path):
                 f"--output_dir={output_dir}",
             ]
         )
-
-
-class DummyRobot:
-    name = "dummy"
-    cameras = []
-    action_features = {"foo": 1.0, "bar": 2.0}
-    observation_features = {"obs1": 1.0, "obs2": 2.0}
-    is_connected = True
-
-    def connect(self, *args):
-        pass
-
-    def disconnect(self):
-        pass
-
-
-def dummy_make_robot_from_config(*args, **kwargs):
-    return DummyRobot()
-
-
-@pytest.mark.parametrize("policy_type", ["smolvla"])
-@skip_if_package_missing("peft")
-def test_peft_record_loads_policy(policy_type, tmp_path):
-    """Train a policy with PEFT and attempt to load it with `lerobot-record`."""
-    from peft import PeftModel
-
-    output_dir = tmp_path / f"output_{policy_type}"
-    model_id = resolve_model_id_for_peft_training(policy_type)
-
-    lerobot_train(
-        [
-            f"--policy.path={model_id}",
-            "--policy.push_to_hub=false",
-            "--policy.input_features=null",
-            "--policy.output_features=null",
-            "--peft.method=LORA",
-            "--dataset.repo_id=lerobot/pusht",
-            "--dataset.episodes=[0, 1]",
-            "--steps=1",
-            f"--output_dir={output_dir}",
-        ]
-    )
-
-    policy_dir = output_dir / "checkpoints" / "last" / "pretrained_model"
-    dataset_dir = tmp_path / "eval_pusht"
-    single_task = "move the table"
-    loaded_policy = None
-
-    def dummy_record_loop(*args, **kwargs):
-        nonlocal loaded_policy
-
-        if "dataset" not in kwargs:
-            return
-
-        dataset = kwargs["dataset"]
-        dataset.add_frame({"task": single_task})
-        loaded_policy = kwargs["policy"]
-
-    with (
-        patch("lerobot.scripts.lerobot_record.make_robot_from_config", dummy_make_robot_from_config),
-        # disable record loop since we're only interested in successful loading of the policy.
-        patch("lerobot.scripts.lerobot_record.record_loop", dummy_record_loop),
-        # disable speech output
-        patch("lerobot.utils.utils.say"),
-    ):
-        lerobot_record(
-            [
-                f"--policy.path={policy_dir}",
-                "--robot.type=so101_follower",
-                "--robot.port=/dev/null",
-                "--dataset.repo_id=lerobot/eval_pusht",
-                f'--dataset.single_task="{single_task}"',
-                f"--dataset.root={dataset_dir}",
-                "--dataset.push_to_hub=false",
-            ]
-        )
-
-        assert isinstance(loaded_policy, PeftModel)

tests/test_control_robot.py

@@ -21,8 +21,9 @@ import pytest
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 pytest.importorskip("deepdiff", reason="deepdiff is required (install lerobot[hardware])")
 
+from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.scripts.lerobot_calibrate import CalibrateConfig, calibrate
-from lerobot.scripts.lerobot_record import DatasetRecordConfig, RecordConfig, record
+from lerobot.scripts.lerobot_record import RecordConfig, record
 from lerobot.scripts.lerobot_replay import DatasetReplayConfig, ReplayConfig, replay
 from lerobot.scripts.lerobot_teleoperate import TeleoperateConfig, teleoperate
 from tests.fixtures.constants import DUMMY_REPO_ID

tests/test_robomme_env.py

@@ -1,232 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Unit tests for the RoboMME env wrapper and config.
-
-RoboMME requires Linux + ManiSkill (Vulkan/SAPIEN), so tests that touch the
-env wrapper mock the ``robomme`` package. Tests that only exercise the
-dataclass config run without any mocking.
-"""
-
-from __future__ import annotations
-
-import sys
-from types import ModuleType
-from unittest.mock import MagicMock
-
-import numpy as np
-
-
-def _install_robomme_stub():
-    """Register a minimal stub for the ``robomme`` package on sys.modules."""
-    stub = ModuleType("robomme")
-    wrapper_stub = ModuleType("robomme.env_record_wrapper")
-
-    class FakeBuilder:
-        def __init__(self, **kwargs):
-            pass
-
-        def make_env_for_episode(self, episode_idx: int, max_steps: int):
-            env = MagicMock()
-            obs = {
-                "front_rgb_list": [np.zeros((256, 256, 3), dtype=np.uint8)],
-                "wrist_rgb_list": [np.zeros((256, 256, 3), dtype=np.uint8)],
-                "joint_state_list": [np.zeros(7, dtype=np.float32)],
-                "gripper_state_list": [np.zeros(2, dtype=np.float32)],
-            }
-            env.reset.return_value = (obs, {"status": "ongoing", "task_goal": "pick the cube"})
-            env.step.return_value = (obs, 0.0, False, False, {"status": "ongoing", "task_goal": ""})
-            return env
-
-    wrapper_stub.BenchmarkEnvBuilder = FakeBuilder
-    stub.env_record_wrapper = wrapper_stub
-    sys.modules["robomme"] = stub
-    sys.modules["robomme.env_record_wrapper"] = wrapper_stub
-
-
-def _uninstall_robomme_stub():
-    sys.modules.pop("robomme", None)
-    sys.modules.pop("robomme.env_record_wrapper", None)
-
-
-# ---------------------------------------------------------------------------
-# Config tests (no sim required)
-# ---------------------------------------------------------------------------
-
-
-def test_robomme_env_config_defaults():
-    from lerobot.envs.configs import RoboMMEEnv
-
-    cfg = RoboMMEEnv()
-    assert cfg.task == "PickXtimes"
-    assert cfg.fps == 10
-    assert cfg.episode_length == 300
-    assert cfg.action_space == "joint_angle"
-    assert cfg.dataset_split == "test"
-    assert cfg.task_ids is None
-
-
-def test_robomme_env_config_type():
-    from lerobot.envs.configs import RoboMMEEnv
-
-    cfg = RoboMMEEnv()
-    assert cfg.type == "robomme"
-
-
-def test_robomme_features_map():
-    from lerobot.envs.configs import RoboMMEEnv
-    from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
-
-    cfg = RoboMMEEnv()
-    assert cfg.features_map[ACTION] == ACTION
-    assert cfg.features_map["pixels/image"] == f"{OBS_IMAGES}.image"
-    assert cfg.features_map["pixels/wrist_image"] == f"{OBS_IMAGES}.wrist_image"
-    assert cfg.features_map["agent_pos"] == OBS_STATE
-
-
-def test_robomme_features_action_dim_joint_angle():
-    from lerobot.envs.configs import RoboMMEEnv
-    from lerobot.utils.constants import ACTION
-
-    cfg = RoboMMEEnv(action_space="joint_angle")
-    assert cfg.features[ACTION].shape == (8,)
-
-
-def test_robomme_features_action_dim_ee_pose():
-    """`ee_pose` uses a 7-D action; __post_init__ sets the correct shape."""
-    from lerobot.envs.configs import RoboMMEEnv
-    from lerobot.utils.constants import ACTION
-
-    cfg = RoboMMEEnv(action_space="ee_pose")
-    assert cfg.features[ACTION].shape == (7,)
-
-
-# ---------------------------------------------------------------------------
-# Obs conversion (pure Python, no sim)
-# ---------------------------------------------------------------------------
-
-
-def test_convert_obs_list_format():
-    """_convert_obs takes the last element from list-format obs fields and
-    emits a nested ``pixels`` dict (image, wrist_image) plus ``agent_pos``.
-
-    The nested layout is required so ``preprocess_observation()`` in
-    ``envs/utils.py`` maps each camera to ``observation.images.<cam>``.
-    """
-    _install_robomme_stub()
-    try:
-        from lerobot.envs.robomme import RoboMMEGymEnv
-
-        env = RoboMMEGymEnv.__new__(RoboMMEGymEnv)
-
-        front = np.full((256, 256, 3), 42, dtype=np.uint8)
-        wrist = np.full((256, 256, 3), 7, dtype=np.uint8)
-        joints = np.arange(7, dtype=np.float32)
-        gripper = np.array([0.5, 0.5], dtype=np.float32)
-
-        obs_raw = {
-            "front_rgb_list": [np.zeros_like(front), front],
-            "wrist_rgb_list": [np.zeros_like(wrist), wrist],
-            "joint_state_list": [np.zeros(7, dtype=np.float32), joints],
-            "gripper_state_list": [np.zeros(2, dtype=np.float32), gripper],
-        }
-
-        result = env._convert_obs(obs_raw)
-        np.testing.assert_array_equal(result["pixels"]["image"], front)
-        np.testing.assert_array_equal(result["pixels"]["wrist_image"], wrist)
-        assert result["agent_pos"].shape == (8,)
-        np.testing.assert_array_almost_equal(result["agent_pos"][:7], joints)
-        assert result["agent_pos"][7] == gripper[0]
-    finally:
-        _uninstall_robomme_stub()
-
-
-def test_convert_obs_array_format():
-    """_convert_obs also handles non-list (direct array) obs."""
-    _install_robomme_stub()
-    try:
-        from lerobot.envs.robomme import RoboMMEGymEnv
-
-        env = RoboMMEGymEnv.__new__(RoboMMEGymEnv)
-
-        front = np.zeros((256, 256, 3), dtype=np.uint8)
-        obs_raw = {
-            "front_rgb_list": front,
-            "wrist_rgb_list": front,
-            "joint_state_list": np.zeros(7, dtype=np.float32),
-            "gripper_state_list": np.zeros(2, dtype=np.float32),
-        }
-        result = env._convert_obs(obs_raw)
-        assert result["pixels"]["image"].shape == (256, 256, 3)
-        assert result["pixels"]["wrist_image"].shape == (256, 256, 3)
-        assert result["agent_pos"].shape == (8,)
-    finally:
-        _uninstall_robomme_stub()
-
-
-# ---------------------------------------------------------------------------
-# create_robomme_envs (mocked sim)
-# ---------------------------------------------------------------------------
-
-
-def test_create_robomme_envs_returns_correct_structure():
-    """Single task -> {task_name: {task_id: VectorEnv}} with one entry per task_id."""
-    _install_robomme_stub()
-    try:
-        from lerobot.envs.robomme import create_robomme_envs
-
-        env_cls = MagicMock(return_value=MagicMock())
-        result = create_robomme_envs(
-            task="PickXtimes",
-            n_envs=1,
-            task_ids=[0, 1],
-            env_cls=env_cls,
-        )
-
-        assert "PickXtimes" in result
-        assert 0 in result["PickXtimes"]
-        assert 1 in result["PickXtimes"]
-        assert env_cls.call_count == 2
-    finally:
-        _uninstall_robomme_stub()
-
-
-def test_create_robomme_envs_multi_task():
-    """Comma-separated task list produces one suite per task."""
-    _install_robomme_stub()
-    try:
-        from lerobot.envs.robomme import create_robomme_envs
-
-        env_cls = MagicMock(return_value=MagicMock())
-        result = create_robomme_envs(
-            task="PickXtimes,BinFill,StopCube",
-            n_envs=1,
-            env_cls=env_cls,
-        )
-
-        assert set(result.keys()) == {"PickXtimes", "BinFill", "StopCube"}
-    finally:
-        _uninstall_robomme_stub()
-
-
-def test_create_robomme_envs_raises_on_invalid_env_cls():
-    _install_robomme_stub()
-    try:
-        import pytest
-
-        from lerobot.envs.robomme import create_robomme_envs
-
-        with pytest.raises(ValueError, match="env_cls must be a callable"):
-            create_robomme_envs(task="PickXtimes", n_envs=1, env_cls=None)
-    finally:
-        _uninstall_robomme_stub()

tests/test_rollout.py

@@ -0,0 +1,338 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Minimal tests for the rollout module's public API."""
+
+from __future__ import annotations
+
+import dataclasses
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+# ---------------------------------------------------------------------------
+# Import smoke tests
+# ---------------------------------------------------------------------------
+
+
+def test_rollout_top_level_imports():
+    import lerobot.rollout
+
+    for name in lerobot.rollout.__all__:
+        assert hasattr(lerobot.rollout, name), f"Missing export: {name}"
+
+
+def test_inference_submodule_imports():
+    import lerobot.rollout.inference
+
+    for name in lerobot.rollout.inference.__all__:
+        assert hasattr(lerobot.rollout.inference, name), f"Missing export: {name}"
+
+
+def test_strategies_submodule_imports():
+    import lerobot.rollout.strategies
+
+    for name in lerobot.rollout.strategies.__all__:
+        assert hasattr(lerobot.rollout.strategies, name), f"Missing export: {name}"
+
+
+# ---------------------------------------------------------------------------
+# Config tests
+# ---------------------------------------------------------------------------
+
+
+def test_strategy_config_types():
+    from lerobot.rollout import (
+        BaseStrategyConfig,
+        DAggerStrategyConfig,
+        HighlightStrategyConfig,
+        SentryStrategyConfig,
+    )
+
+    assert BaseStrategyConfig().type == "base"
+    assert SentryStrategyConfig().type == "sentry"
+    assert HighlightStrategyConfig().type == "highlight"
+    assert DAggerStrategyConfig().type == "dagger"
+
+
+def test_dagger_config_invalid_input_device():
+    from lerobot.rollout import DAggerStrategyConfig
+
+    with pytest.raises(ValueError, match="input_device must be 'keyboard' or 'pedal'"):
+        DAggerStrategyConfig(input_device="joystick")
+
+
+def test_dagger_config_defaults():
+    from lerobot.rollout import DAggerStrategyConfig
+
+    cfg = DAggerStrategyConfig()
+    assert cfg.num_episodes is None
+    assert cfg.record_autonomous is False
+    assert cfg.input_device == "keyboard"
+
+
+def test_inference_config_types():
+    from lerobot.rollout.inference import RTCInferenceConfig, SyncInferenceConfig
+
+    assert SyncInferenceConfig().type == "sync"
+
+    rtc = RTCInferenceConfig()
+    assert rtc.type == "rtc"
+    assert rtc.queue_threshold == 30
+    assert rtc.rtc is not None
+
+
+def test_sentry_config_defaults():
+    from lerobot.rollout import SentryStrategyConfig
+
+    cfg = SentryStrategyConfig()
+    assert cfg.upload_every_n_episodes == 5
+    assert cfg.target_video_file_size_mb is None
+
+
+# ---------------------------------------------------------------------------
+# RolloutRingBuffer
+# ---------------------------------------------------------------------------
+
+
+def test_ring_buffer_append_and_eviction():
+    from lerobot.rollout import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=0.5, max_memory_mb=100.0, fps=10.0)
+    # max_frames = 5
+    for i in range(8):
+        buf.append({"val": i})
+    assert len(buf) == 5
+
+
+def test_ring_buffer_drain():
+    from lerobot.rollout import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=1.0, max_memory_mb=100.0, fps=10.0)
+    for i in range(3):
+        buf.append({"val": i})
+    frames = buf.drain()
+    assert len(frames) == 3
+    assert len(buf) == 0
+    assert buf.estimated_bytes == 0
+
+
+def test_ring_buffer_clear():
+    from lerobot.rollout import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=1.0, max_memory_mb=100.0, fps=10.0)
+    buf.append({"val": 1})
+    buf.clear()
+    assert len(buf) == 0
+    assert buf.estimated_bytes == 0
+
+
+def test_ring_buffer_tensor_bytes():
+    from lerobot.rollout import RolloutRingBuffer
+
+    buf = RolloutRingBuffer(max_seconds=1.0, max_memory_mb=100.0, fps=10.0)
+    t = torch.zeros(100, dtype=torch.float32)  # 400 bytes
+    buf.append({"tensor": t})
+    assert buf.estimated_bytes >= 400
+
+
+# ---------------------------------------------------------------------------
+# ThreadSafeRobot
+# ---------------------------------------------------------------------------
+
+
+def test_thread_safe_robot_delegates():
+    from lerobot.rollout import ThreadSafeRobot
+    from tests.mocks.mock_robot import MockRobot, MockRobotConfig
+
+    robot = MockRobot(MockRobotConfig(n_motors=3))
+    robot.connect()
+    wrapper = ThreadSafeRobot(robot)
+
+    obs = wrapper.get_observation()
+    assert "motor_1.pos" in obs
+    assert "motor_2.pos" in obs
+    assert "motor_3.pos" in obs
+
+    action = {"motor_1.pos": 0.0, "motor_2.pos": 1.0, "motor_3.pos": 2.0}
+    result = wrapper.send_action(action)
+    assert result == action
+
+    robot.disconnect()
+
+
+def test_thread_safe_robot_properties():
+    from lerobot.rollout import ThreadSafeRobot
+    from tests.mocks.mock_robot import MockRobot, MockRobotConfig
+
+    robot = MockRobot(MockRobotConfig(n_motors=3))
+    robot.connect()
+    wrapper = ThreadSafeRobot(robot)
+
+    assert wrapper.name == "mock_robot"
+    assert "motor_1.pos" in wrapper.observation_features
+    assert "motor_1.pos" in wrapper.action_features
+    assert wrapper.is_connected is True
+    assert wrapper.inner is robot
+
+    robot.disconnect()
+
+
+# ---------------------------------------------------------------------------
+# Strategy factory
+# ---------------------------------------------------------------------------
+
+
+def test_create_strategy_dispatches():
+    from lerobot.rollout import BaseStrategyConfig, DAggerStrategyConfig, SentryStrategyConfig
+    from lerobot.rollout.strategies import BaseStrategy, DAggerStrategy, SentryStrategy, create_strategy
+
+    assert isinstance(create_strategy(BaseStrategyConfig()), BaseStrategy)
+    assert isinstance(create_strategy(SentryStrategyConfig()), SentryStrategy)
+    assert isinstance(create_strategy(DAggerStrategyConfig()), DAggerStrategy)
+
+
+def test_create_strategy_unknown_raises():
+    from lerobot.rollout.strategies import create_strategy
+
+    cfg = MagicMock()
+    cfg.type = "bogus"
+    with pytest.raises(ValueError, match="Unknown strategy type"):
+        create_strategy(cfg)
+
+
+# ---------------------------------------------------------------------------
+# Inference factory
+# ---------------------------------------------------------------------------
+
+
+def test_create_inference_engine_sync():
+    from lerobot.rollout.inference import SyncInferenceConfig, SyncInferenceEngine, create_inference_engine
+
+    engine = create_inference_engine(
+        SyncInferenceConfig(),
+        policy=MagicMock(),
+        preprocessor=MagicMock(),
+        postprocessor=MagicMock(),
+        robot_wrapper=MagicMock(robot_type="mock"),
+        hw_features={},
+        dataset_features={},
+        ordered_action_keys=["k"],
+        task="test",
+        fps=30.0,
+        device="cpu",
+    )
+    assert isinstance(engine, SyncInferenceEngine)
+
+
+# ---------------------------------------------------------------------------
+# Pure functions
+# ---------------------------------------------------------------------------
+
+
+def test_estimate_max_episode_seconds_no_video():
+    from lerobot.rollout.strategies import estimate_max_episode_seconds
+
+    assert estimate_max_episode_seconds({}, fps=30.0) == 600.0
+
+
+def test_estimate_max_episode_seconds_with_video():
+    from lerobot.rollout.strategies import estimate_max_episode_seconds
+
+    features = {"cam": {"dtype": "video", "shape": (3, 480, 640)}}
+    result = estimate_max_episode_seconds(features, fps=30.0)
+    assert result > 0
+    # With a real camera, duration should differ from the fallback
+    assert result != 600.0
+
+
+def test_safe_push_to_hub():
+    from lerobot.rollout.strategies import safe_push_to_hub
+
+    ds = MagicMock()
+    ds.num_episodes = 0
+    assert safe_push_to_hub(ds) is False
+    ds.push_to_hub.assert_not_called()
+
+    ds.num_episodes = 5
+    assert safe_push_to_hub(ds, tags=["test"]) is True
+    ds.push_to_hub.assert_called_once_with(tags=["test"], private=False)
+
+
+# ---------------------------------------------------------------------------
+# DAgger state machine
+# ---------------------------------------------------------------------------
+
+
+def test_dagger_full_transition_cycle():
+    from lerobot.rollout.strategies import DAggerEvents, DAggerPhase
+
+    events = DAggerEvents()
+    assert events.phase == DAggerPhase.AUTONOMOUS
+
+    # AUTONOMOUS -> PAUSED
+    events.request_transition("pause_resume")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.AUTONOMOUS, DAggerPhase.PAUSED)
+
+    # PAUSED -> CORRECTING
+    events.request_transition("correction")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.PAUSED, DAggerPhase.CORRECTING)
+
+    # CORRECTING -> PAUSED
+    events.request_transition("correction")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.CORRECTING, DAggerPhase.PAUSED)
+
+    # PAUSED -> AUTONOMOUS
+    events.request_transition("pause_resume")
+    old, new = events.consume_transition()
+    assert (old, new) == (DAggerPhase.PAUSED, DAggerPhase.AUTONOMOUS)
+
+
+def test_dagger_invalid_transition_ignored():
+    from lerobot.rollout.strategies import DAggerEvents, DAggerPhase
+
+    events = DAggerEvents()
+    events.request_transition("correction")  # Not valid from AUTONOMOUS
+    assert events.consume_transition() is None
+    assert events.phase == DAggerPhase.AUTONOMOUS
+
+
+def test_dagger_events_reset():
+    from lerobot.rollout.strategies import DAggerEvents, DAggerPhase
+
+    events = DAggerEvents()
+    events.request_transition("pause_resume")
+    events.consume_transition()  # -> PAUSED
+    events.upload_requested.set()
+    events.reset()
+    assert events.phase == DAggerPhase.AUTONOMOUS
+    assert not events.upload_requested.is_set()
+
+
+# ---------------------------------------------------------------------------
+# Context dataclass
+# ---------------------------------------------------------------------------
+
+
+def test_rollout_context_fields():
+    from lerobot.rollout import RolloutContext
+
+    field_names = {f.name for f in dataclasses.fields(RolloutContext)}
+    assert field_names == {"runtime", "hardware", "policy", "processors", "data"}

tests/utils/test_process.py

@@ -24,7 +24,7 @@ import pytest
 
 pytest.importorskip("grpc")
 
-from lerobot.rl.process import ProcessSignalHandler  # noqa: E402
+from lerobot.utils.process import ProcessSignalHandler  # noqa: E402
 
 
 # Fixture to reset shutdown_event_counter and original signal handlers before and after each test

uv.lock

@@ -2,39 +2,30 @@ version = 1
 revision = 2
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.15' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
-    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "python_full_version == '3.14.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux'",
     "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
-    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'linux'",
     "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "(python_full_version >= '3.15' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'armv7l' and sys_platform == 'linux')",
-    "(python_full_version == '3.14.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.14.*' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version == '3.14.*' and platform_machine == 'armv7l' and sys_platform == 'linux')",
+    "(python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'armv7l' and sys_platform == 'linux')",
     "(python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'armv7l' and sys_platform == 'linux')",
     "(python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'armv7l' and sys_platform == 'linux')",
-    "(python_full_version >= '3.15' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'emscripten'",
-    "(python_full_version == '3.14.*' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version >= '3.14' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
     "(python_full_version == '3.13.*' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
     "(python_full_version < '3.13' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
     "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
-    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
-    "(python_full_version >= '3.15' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'win32')",
-    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'win32'",
-    "(python_full_version == '3.14.*' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'win32')",
+    "(python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32')",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32'",
     "(python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32')",
-    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'win32'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'",
     "(python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32')",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
@@ -1119,7 +1110,8 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "absl-py" },
     { name = "dm-env" },
-    { name = "dm-tree" },
+    { name = "dm-tree", version = "0.1.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "dm-tree", version = "0.1.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
     { name = "glfw" },
     { name = "labmaze" },
     { name = "lxml" },
@@ -1144,7 +1136,8 @@ version = "1.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "absl-py" },
-    { name = "dm-tree" },
+    { name = "dm-tree", version = "0.1.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+    { name = "dm-tree", version = "0.1.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
     { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/62/c9/93e8d6239d5806508a2ee4b370e67c6069943ca149f59f533923737a99b7/dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de", size = 20187, upload-time = "2022-12-21T00:25:29.306Z" }
@@ -1156,11 +1149,22 @@ wheels = [
 name = "dm-tree"
 version = "0.1.9"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux'",
+    "(python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'armv7l' and sys_platform == 'linux')",
+    "(python_full_version >= '3.14' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "(python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32')",
+    "python_full_version >= '3.14' and platform_machine == 's390x' 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 = [
@@ -1177,6 +1181,58 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c5/37/15603079854394f16e3833a7b50696c1f3cbf30a2243a119f64f18a16f36/dm_tree-0.1.9-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1f5d1e96b3a7de22b25b13a5eb30f41f8cf9c02dd4479a24920de99e780903c", size = 153052, upload-time = "2025-01-30T20:45:35.907Z" },
 ]
 
+[[package]]
+name = "dm-tree"
+version = "0.1.10"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'linux'",
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 [package.optional-dependencies]