feat(depth maps writer): adding support for raw depth maps recording with image writer

log_rerun_data now accepts an optional `features` dict and uses the
`video.is_depth_map=True` info marker to detect depth observations.
Matching 2D arrays are logged as `rr.DepthImage(arr, meter=1.0,
colormap=rr.components.Colormap.Viridis)` and are never JPEG-compressed
(compression is lossy on float32 metric depth).

Detection covers both the namespaced dataset key
(e.g. `observation.depth.front`) and the raw observation keys the robot
emits (`front`, `front_depth`), so it works for both the typed
LeRobotDataset.features dict and the plain robot observation flow.

When `features` is None the previous behaviour is preserved (depth
arrays fall back to the generic `rr.Image` path), so non-depth
recordings and existing call sites are unaffected.

lerobot-record now forwards `dataset.features` so depth keys are picked
up automatically when `--display_data=true`.

Made-with: Cursor

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,11 +2,6 @@
 
 Short, imperative summary (e.g., "fix(robots): handle None in sensor parser"). See [CONTRIBUTING.md](../CONTRIBUTING.md) for PR conventions.
 
-## Type / Scope
-
-- **Type**: (Bug | Feature | Docs | Performance | Test | CI | Chore)
-- **Scope**: (optional — name of module or package affected)
-
 ## Summary / Motivation
 
 - One-paragraph description of what changes and why.
@@ -19,28 +14,14 @@ Short, imperative summary (e.g., "fix(robots): handle None in sensor parser"). S
 
 ## What changed
 
-- Short, concrete bullets of the modifications (files/behaviour).
+- Short, concrete bullets explaining the functional changes (how the behavior or output differs now).
 - Short note if this introduces breaking changes and migration steps.
 
 ## How was this tested (or how to run locally)
 
-- Tests added: list new tests or test files.
+- Tests added: list new tests or test files. `pytest -q tests/ -k <keyword>`
 - Manual checks / dataset runs performed.
-- Instructions for the reviewer
-
-Example:
-
-- Ran the relevant tests:
-
-  ```bash
-  pytest -q tests/ -k <keyword>
-  ```
-
-- Reproduce with a quick example or CLI (if applicable):
-
-  ```bash
-  lerobot-train --some.option=true
-  ```
+- Instructions for the reviewer for reproducing with a quick example or CLI (if applicable)
 
 ## Checklist (required before merge)
 
@@ -48,6 +29,7 @@ Example:
 - [ ] All tests pass locally (`pytest`)
 - [ ] Documentation updated
 - [ ] CI is green
+- [ ] Community Review: I have reviewed another contributor's open PR and linked it here: # (insert PR number/link)
 
 ## Reviewer notes
 

.github/workflows/benchmark_tests.yml

@@ -83,10 +83,13 @@ 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
@@ -115,7 +118,7 @@ jobs:
             bash -c "
               hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
               lerobot-eval \
-                --policy.path=pepijn223/smolvla_libero \
+                --policy.path=lerobot/smolvla_libero \
                 --env.type=libero \
                 --env.task=libero_spatial \
                 --eval.batch_size=1 \
@@ -144,7 +147,7 @@ jobs:
             --artifacts-dir /tmp/libero-artifacts \
             --env libero \
             --task libero_spatial \
-            --policy pepijn223/smolvla_libero
+            --policy lerobot/smolvla_libero
 
       - name: Upload Libero rollout video
         if: always()
@@ -238,10 +241,13 @@ 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]
@@ -264,7 +270,7 @@ jobs:
             bash -c "
               hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
               lerobot-eval \
-                --policy.path=pepijn223/smolvla_metaworld \
+                --policy.path=lerobot/smolvla_metaworld \
                 --env.type=metaworld \
                 --env.task=metaworld-push-v3 \
                 --eval.batch_size=1 \
@@ -293,7 +299,7 @@ jobs:
             --artifacts-dir /tmp/metaworld-artifacts \
             --env metaworld \
             --task metaworld-push-v3 \
-            --policy pepijn223/smolvla_metaworld
+            --policy lerobot/smolvla_metaworld
 
       - name: Upload MetaWorld rollout video
         if: always()
@@ -311,9 +317,121 @@ jobs:
           path: /tmp/metaworld-artifacts/metrics.json
           if-no-files-found: warn
 
-  # ── LIBERO-plus ───────────────────────────────────────────────────────────
-  libero-plus-integration-test:
-    name: LIBERO-plus — build image + 1-episode eval
+  # ── 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:
@@ -330,6 +448,328 @@ jobs:
         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: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+
       - name: Build LIBERO-plus benchmark image
         uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
         with:
@@ -349,14 +789,17 @@ jobs:
             -e HF_HOME=/tmp/hf \
             -e HF_USER_TOKEN="${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=lerobot/smolvla_libero_plus \
+                --policy.path=\"\$LIBERO_PLUS_POLICY\" \
                 --env.type=libero_plus \
-                --env.task=libero_spatial \
-                '--env.task_ids=[0,100,260,500,1000,1500,2000,2400]' \
+                --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 \
@@ -365,7 +808,7 @@ jobs:
                 --policy.empty_cameras=1 \
                 --output_dir=/tmp/eval-artifacts
               python scripts/ci/extract_task_descriptions.py \
-                --env libero_plus --task libero_spatial \
+                --env libero_plus --task \"\$LIBERO_PLUS_SUITE\" \
                 --output /tmp/eval-artifacts/task_descriptions.json
             "
 
@@ -382,8 +825,8 @@ jobs:
           python3 scripts/ci/parse_eval_metrics.py \
             --artifacts-dir /tmp/libero-plus-artifacts \
             --env libero_plus \
-            --task libero_spatial \
-            --policy lerobot/smolvla_libero_plus
+            --task "${LIBERO_PLUS_SUITE}" \
+            --policy "${LIBERO_PLUS_POLICY}"
 
       - name: Upload LIBERO-plus rollout video
         if: always()
@@ -401,16 +844,17 @@ jobs:
           path: /tmp/libero-plus-artifacts/metrics.json
           if-no-files-found: warn
 
-  # ── ROBOMME ───────────────────────────────────────────────────────────────
-  robomme-integration-test:
-    name: RoboMME — build image + 1-episode eval
+  # ── 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
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
         with:
           persist-credentials: false
           lfs: true
@@ -420,71 +864,82 @@ jobs:
         with:
           cache-binary: false
 
-      - name: Build RoboMME benchmark image
+      - 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.robomme
+          file: docker/Dockerfile.benchmark.vlabench
           push: false
           load: true
-          tags: lerobot-benchmark-robomme:ci
+          tags: lerobot-benchmark-vlabench:ci
+          build-args: |
+            VLABENCH_ASSETS_REPO=lerobot/vlabench-assets
 
-      - name: Run RoboMME smoke eval (1 episode)
+      - name: Run VLABench smoke eval (10 tasks, 1 episode each)
         if: env.HF_USER_TOKEN != ''
         run: |
-          docker run --name robomme-eval --gpus all \
+          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 \
-            lerobot-benchmark-robomme:ci \
+            -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_robomme \
-                --env.type=robomme \
-                --env.task=PickXtimes,BinFill,StopCube,MoveCube,InsertPeg \
-                --env.dataset_split=test \
+                --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.wrist_image\": \"observation.images.camera2\"}' \
-                --policy.empty_cameras=3 \
+                '--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 robomme --task PickXtimes,BinFill,StopCube,MoveCube,InsertPeg \
+                --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 RoboMME artifacts from container
+      - name: Copy VLABench 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
+          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 RoboMME eval metrics
+      - name: Parse VLABench eval metrics
         if: always()
         run: |
           python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/robomme-artifacts \
-            --env robomme \
-            --task PickXtimes \
-            --policy lerobot/smolvla_robomme
+            --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 RoboMME rollout video
+      - name: Upload VLABench rollout video
         if: always()
         uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
         with:
-          name: robomme-rollout-video
-          path: /tmp/robomme-artifacts/videos/
+          name: vlabench-rollout-video
+          path: /tmp/vlabench-artifacts/videos/
           if-no-files-found: warn
 
-      - name: Upload RoboMME eval metrics
+      - name: Upload VLABench eval metrics
         if: always()
         uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
         with:
-          name: robomme-metrics
-          path: /tmp/robomme-artifacts/metrics.json
+          name: vlabench-metrics
+          path: /tmp/vlabench-artifacts/metrics.json
           if-no-files-found: warn

.github/workflows/documentation-upload-pr.yml

@@ -33,7 +33,7 @@ jobs:
       github.event.workflow_run.event == 'pull_request' &&
       github.event.workflow_run.conclusion == 'success' &&
       github.repository == 'huggingface/lerobot'
-    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3  # main
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
     with:
       package_name: lerobot
     secrets:

.github/workflows/documentation.yml

@@ -55,7 +55,7 @@ jobs:
       github.repository == 'huggingface/lerobot'
     permissions:
       contents: read
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3  # main
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
     with:
       commit_sha: ${{ github.sha }}
       package: lerobot
@@ -78,7 +78,7 @@ jobs:
     permissions:
       contents: read
       pull-requests: write
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3  # main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
     with:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}

.github/workflows/latest_deps_tests.yml

@@ -217,6 +217,24 @@ jobs:
       - name: Run end-to-end tests
         run: make test-end-to-end
 
+  slack-notification:
+    name: Slack Notification
+    needs: [cpu-tests, gpu-tests, upgrade-lock]
+    if: always() && needs.upgrade-lock.outputs.changed == 'true'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    env:
+      CI_SLACK_CHANNEL: ${{ secrets.CI_SLACK_CHANNEL }}
+    steps:
+      - name: Post to a Slack channel
+        uses: huggingface/hf-workflows/.github/actions/post-slack@a88e7fa2eaee28de5a4d6142381b1fb792349b67  # main
+        with:
+          slack_channel: ${{ env.CI_SLACK_CHANNEL }}
+          title: "Results of the latest dependency tests (CPU + GPU)"
+          status: ${{ (needs.cpu-tests.result == 'success' && needs.gpu-tests.result == 'success') && 'success' || 'failure' }}
+          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+
   # This job creates or updates a PR with the upgraded lockfile
   open-pr:
     name: Open PR

AGENTS.md

@@ -1,5 +1,7 @@
 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

@@ -0,0 +1,410 @@
+# 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).

CONTRIBUTING.md

@@ -78,6 +78,9 @@ Use the templates for required fields and examples.
 - **Issues:** Follow the [ticket template](https://github.com/huggingface/lerobot/blob/main/.github/ISSUE_TEMPLATE/bug-report.yml).
 - **Pull requests:** Rebase on `upstream/main`, use a descriptive branch (don't work on `main`), run `pre-commit` and tests locally, and follow the [PR template](https://github.com/huggingface/lerobot/blob/main/.github/PULL_REQUEST_TEMPLATE.md).
 
-One member of the LeRobot team will then review your contribution.
+> [!IMPORTANT]
+> Community Review Policy: To help scale our efforts and foster a collaborative environment, we ask contributors to review at least one other person's open PR before their own receives attention. This shared responsibility multiplies our review capacity and helps everyone's code get merged faster!
+
+Once you have submitted your PR and completed a peer review, a member of the LeRobot team will review your contribution.
 
 Thank you for contributing to LeRobot!

MANIFEST.in

@@ -1,3 +1,4 @@
 include src/lerobot/templates/lerobot_modelcard_template.md
+include src/lerobot/templates/lerobot_rewardmodel_modelcard_template.md
 include src/lerobot/datasets/card_template.md
 include src/lerobot/envs/metaworld_config.json

benchmarks/__init__.py

@@ -1 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.

benchmarks/libero/README.md

@@ -1,60 +0,0 @@
-# LeRobot LIBERO Training Benchmark
-
-Train and evaluate all LeRobot policies on [LIBERO](https://libero-project.github.io/) and publish results as a HuggingFace leaderboard dataset.
-
-## Policies
-
-| Policy         | Base Model           | GPUs | LR     | Chunk | Notes                                 |
-| -------------- | -------------------- | ---- | ------ | ----- | ------------------------------------- |
-| pi0            | lerobot/pi0_base     | 8    | 2.5e-5 | 30    | PaliGemma + Gemma flow matching       |
-| pi0_fast       | lerobot/pi0fast-base | 8    | 2.5e-5 | 30    | Requires tokenizer pre-training       |
-| pi05           | lerobot/pi05_base    | 8    | 2.5e-5 | 30    | Quantiles normalization               |
-| groot          | nvidia/GR00T-N1.5-3B | 8    | 1e-4   | 30    | bf16, diffusion head + projector only |
-| act            | From scratch         | 1    | 1e-5   | 30    | ResNet-18, lightweight                |
-| diffusion      | From scratch         | 1    | 1e-4   | 32\*  | U-Net, horizon must be divisible by 8 |
-| smolvla        | lerobot/smolvla_base | 8    | 1e-4   | 30    | SmolVLM2-500M                         |
-| xvla           | lerobot/xvla-widowx  | 4    | 1e-4   | 32\*  | Florence2 + CLIP                      |
-| multi_task_dit | From scratch         | 1    | 2e-5   | 32\*  | CLIP + DiT                            |
-
-\* These policies use `horizon` rather than `chunk_size`. Set to 32 (nearest valid value to 30).
-
-## Training spec
-
-- **Steps**: 5,000 per policy
-- **Batch size**: 32 per GPU (effective BS = 256 for multi-GPU)
-- **Dataset**: `lerobot/libero` (libero_spatial)
-- **Evaluation**: 20 episodes after training
-- **LR**: each policy's default optimizer/scheduler preset
-- **Results**: each SLURM job publishes its own row to the HF leaderboard dataset automatically
-
-## Quick start
-
-### 1. Generate SLURM scripts
-
-```bash
-python benchmarks/libero/run_benchmark.py \
-    --output_dir /scratch/lerobot-benchmark \
-    --hub_org lerobot
-```
-
-### 2. Submit jobs
-
-```bash
-# If using pi0_fast, submit tokenizer first:
-sbatch /scratch/lerobot-benchmark/slurm_scripts/00_tokenizer.sh
-# Wait, then submit pi0_fast
-
-# All other policies can run in parallel:
-for script in /scratch/lerobot-benchmark/slurm_scripts/[0-9][0-9]_*.sh; do
-    [[ "$script" == *pi0_fast* ]] && continue
-    sbatch "$script"
-done
-```
-
-Each job publishes its result to `lerobot/benchmark-libero` on the Hub when it finishes.
-
-## Prerequisites
-
-- SLURM cluster with CUDA GPUs (A100 80GB recommended for VLM policies)
-- `pip install lerobot[pi,smolvla,groot,xvla,multi_task_dit,libero] datasets`
-- `huggingface-cli login`

benchmarks/libero/run_benchmark.py

@@ -1,606 +0,0 @@
-#!/usr/bin/env python
-"""Generate SLURM sbatch scripts for training all LeRobot policies on LIBERO.
-
-Each generated script trains one policy, evaluates it, and publishes its
-results row to a HuggingFace leaderboard dataset — no separate collection
-step needed.
-
-Usage:
-    # Generate scripts for all policies:
-    python benchmarks/libero/run_benchmark.py \\
-        --output_dir /scratch/lerobot-benchmark --hub_org lerobot
-
-    # Generate for a subset:
-    python benchmarks/libero/run_benchmark.py \\
-        --policies pi0 smolvla act \\
-        --output_dir /scratch/lerobot-benchmark --hub_org lerobot
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import subprocess
-import textwrap
-import uuid
-from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from pathlib import Path
-
-# ──────────────────────────────────────────────────────────────────────
-# Policy benchmark configs
-# ──────────────────────────────────────────────────────────────────────
-
-
-@dataclass
-class PolicyBenchmarkConfig:
-    """Training configuration for a single policy on a benchmark."""
-
-    policy_type: str
-    policy_path: str | None = None
-    num_gpus: int = 1
-    chunk_size: int | None = None  # Set on policies that use chunk_size (not horizon)
-    extra_policy_args: dict[str, str] = field(default_factory=dict)
-    needs_tokenizer: bool = False
-    tokenizer_args: dict[str, str] = field(default_factory=dict)
-
-
-COMMON_TRAINING_ARGS: dict[str, str] = {
-    "dataset.repo_id": "lerobot/libero",
-    "dataset.use_imagenet_stats": "false",
-    "env.type": "libero",
-    "env.task": "libero_spatial",
-    "steps": "5000",
-    "batch_size": "32",
-    "eval_freq": "0",
-    "save_freq": "5000",
-    "save_checkpoint": "true",
-    "log_freq": "100",
-    "wandb.enable": "true",
-    "policy.push_to_hub": "true",
-    "rename_map": (
-        '{"observation.images.image":"observation.images.camera1",'
-        '"observation.images.image2":"observation.images.camera2"}'
-    ),
-}
-
-EVAL_ARGS: dict[str, str] = {
-    "env.type": "libero",
-    "env.task": "libero_spatial",
-    "eval.n_episodes": "20",
-    "eval.batch_size": "10",
-}
-
-POLICY_CONFIGS: dict[str, PolicyBenchmarkConfig] = {
-    "pi0": PolicyBenchmarkConfig(
-        policy_type="pi0",
-        policy_path="lerobot/pi0_base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "pi0_fast": PolicyBenchmarkConfig(
-        policy_type="pi0_fast",
-        policy_path="lerobot/pi0fast-base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.scheduler_decay_steps": "5000",
-        },
-        needs_tokenizer=True,
-        tokenizer_args={
-            "repo_id": "lerobot/libero",
-            "action_horizon": "30",
-            "encoded_dims": "0:7",
-            "normalization_mode": "QUANTILES",
-            "vocab_size": "1024",
-            "scale": "10.0",
-            "push_to_hub": "true",
-        },
-    ),
-    "pi05": PolicyBenchmarkConfig(
-        policy_type="pi05",
-        policy_path="lerobot/pi05_base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "groot": PolicyBenchmarkConfig(
-        policy_type="groot",
-        policy_path=None,
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.base_model_path": "nvidia/GR00T-N1.5-3B",
-            "policy.tune_diffusion_model": "true",
-            "policy.tune_projector": "true",
-            "policy.tune_llm": "false",
-            "policy.tune_visual": "false",
-            "policy.use_bf16": "true",
-        },
-    ),
-    "act": PolicyBenchmarkConfig(
-        policy_type="act",
-        policy_path=None,
-        num_gpus=1,
-        chunk_size=30,
-        extra_policy_args={"policy.n_action_steps": "30"},
-    ),
-    "diffusion": PolicyBenchmarkConfig(
-        policy_type="diffusion",
-        policy_path=None,
-        num_gpus=1,
-        chunk_size=None,
-        extra_policy_args={
-            "policy.horizon": "32",
-            "policy.n_action_steps": "30",
-            "policy.n_obs_steps": "2",
-        },
-    ),
-    "smolvla": PolicyBenchmarkConfig(
-        policy_type="smolvla",
-        policy_path="lerobot/smolvla_base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.load_vlm_weights": "true",
-            "policy.freeze_vision_encoder": "false",
-            "policy.train_expert_only": "false",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "xvla": PolicyBenchmarkConfig(
-        policy_type="xvla",
-        policy_path="lerobot/xvla-widowx",
-        num_gpus=4,
-        chunk_size=32,
-        extra_policy_args={
-            "policy.n_action_steps": "32",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "multi_task_dit": PolicyBenchmarkConfig(
-        policy_type="multi_task_dit",
-        policy_path=None,
-        num_gpus=1,
-        chunk_size=None,
-        extra_policy_args={
-            "policy.horizon": "32",
-            "policy.n_action_steps": "30",
-        },
-    ),
-}
-
-ALL_POLICY_NAMES = list(POLICY_CONFIGS.keys())
-
-# GPU memory estimates (GB) for SLURM --mem allocation
-GPU_MEM_ESTIMATES: dict[str, int] = {
-    "pi0": 320,
-    "pi0_fast": 320,
-    "pi05": 280,
-    "groot": 320,
-    "act": 64,
-    "diffusion": 64,
-    "smolvla": 160,
-    "xvla": 160,
-    "multi_task_dit": 64,
-}
-
-
-# ──────────────────────────────────────────────────────────────────────
-# SLURM script generation
-# ──────────────────────────────────────────────────────────────────────
-
-
-def _cli_args(args: dict[str, str]) -> str:
-    """Build a backslash-continued CLI arg string with proper shell quoting."""
-    lines = []
-    for key, value in args.items():
-        if any(c in str(value) for c in ["{", "}", " ", '"', "'"]):
-            lines.append(f"    --{key}='{value}'")
-        else:
-            lines.append(f"    --{key}={value}")
-    return " \\\n".join(lines)
-
-
-def _training_cli_args(
-    policy_name: str,
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-) -> str:
-    cfg = POLICY_CONFIGS[policy_name]
-    args: dict[str, str] = {}
-    args.update(COMMON_TRAINING_ARGS)
-    args["policy.type"] = cfg.policy_type
-    if cfg.policy_path:
-        args["policy.path"] = cfg.policy_path
-    if cfg.chunk_size is not None:
-        args["policy.chunk_size"] = str(cfg.chunk_size)
-    args.update(cfg.extra_policy_args)
-    args["output_dir"] = str(output_dir / "train" / policy_name)
-    args["policy.repo_id"] = f"{hub_org}/{policy_name}_libero"
-    args["wandb.project"] = "lerobot-libero-benchmark"
-    args["wandb.run_name"] = f"{policy_name}_{benchmark_uuid[:8]}"
-    return _cli_args(args)
-
-
-def _publish_snippet(
-    policy_name: str,
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-    hub_dataset: str,
-) -> str:
-    """Inline Python that each SLURM job runs to publish its own result row."""
-    cfg = POLICY_CONFIGS[policy_name]
-    steps = int(COMMON_TRAINING_ARGS["steps"])
-    bs = int(COMMON_TRAINING_ARGS["batch_size"])
-    eff_bs = bs * cfg.num_gpus
-    train_dir = output_dir / "train" / policy_name
-
-    return textwrap.dedent(f"""\
-        python3 -c "
-        import json, os, re, sys
-        from pathlib import Path
-        from datetime import datetime, timezone
-
-        timing = {{}}
-        tp = Path('{output_dir}/logs/{policy_name}_timing.txt')
-        if tp.exists():
-            for ln in tp.read_text().splitlines():
-                if '=' in ln:
-                    k, _, v = ln.partition('=')
-                    timing[k.strip()] = v.strip()
-
-        # Parse eval results
-        eval_sr, eval_per_task, eval_n = None, '{{}}', 0
-        eval_dir = Path('{train_dir}/eval_results')
-        if eval_dir.exists():
-            for jf in eval_dir.glob('**/*.json'):
-                try:
-                    d = json.loads(jf.read_text())
-                except Exception:
-                    continue
-                if 'avg_success_rate' in d:
-                    eval_sr = d['avg_success_rate']
-                elif 'eval_info' in d and 'avg_success_rate' in d.get('eval_info', {{}}):
-                    eval_sr = d['eval_info']['avg_success_rate']
-                pt = {{k: v for k, v in d.items() if 'success_rate' in k and k != 'avg_success_rate'}}
-                if pt:
-                    eval_per_task = json.dumps(pt)
-                if 'n_episodes' in d:
-                    eval_n = d['n_episodes']
-
-        # Parse final loss from SLURM stdout
-        final_loss = None
-        for lf in sorted(Path('{output_dir}/logs').glob('{policy_name}_*.out'), reverse=True):
-            losses = re.findall(r'\\\"loss\\\"\\s*:\\s*([\\d.e+-]+)', lf.read_text())
-            if losses:
-                final_loss = float(losses[-1])
-                break
-
-        # Parse peak GPU mem
-        peak_mem = 0.0
-        csv_p = Path('{output_dir}/logs/{policy_name}_gpu_mem.csv')
-        if csv_p.exists():
-            for ln in csv_p.read_text().splitlines():
-                parts = ln.strip().split(',')
-                if len(parts) >= 2:
-                    try:
-                        peak_mem = max(peak_mem, float(parts[1].strip()))
-                    except ValueError:
-                        pass
-
-        # Parse train config for optimizer details
-        lr, opt_wd, sched_type, sched_warmup, sched_decay = 0.0, 0.0, '', 0, 0
-        freeze_ve, train_eo, grad_ckpt = False, False, False
-        cfg_path = Path('{train_dir}/checkpoints/{steps:06d}/pretrained_model/train_config.json')
-        if cfg_path.exists():
-            tc = json.loads(cfg_path.read_text())
-            o = tc.get('optimizer', {{}})
-            lr = o.get('lr', 0.0)
-            opt_wd = o.get('weight_decay', 0.0)
-            s = tc.get('scheduler', {{}})
-            sched_type = s.get('type', '')
-            sched_warmup = s.get('num_warmup_steps', 0)
-            sched_decay = s.get('num_decay_steps', 0)
-            p = tc.get('policy', {{}})
-            freeze_ve = p.get('freeze_vision_encoder', False)
-            train_eo = p.get('train_expert_only', False)
-            grad_ckpt = p.get('gradient_checkpointing', False)
-
-        row = {{
-            'benchmark_uuid': '{benchmark_uuid}',
-            'policy_type': '{policy_name}',
-            'policy_repo_id': '{hub_org}/{policy_name}_libero',
-            'base_model_repo_id': '{cfg.policy_path or ""}',
-            'dataset_repo_id': '{COMMON_TRAINING_ARGS["dataset.repo_id"]}',
-            'env_type': '{COMMON_TRAINING_ARGS["env.type"]}',
-            'env_task': '{COMMON_TRAINING_ARGS["env.task"]}',
-            'steps': {steps},
-            'batch_size_per_gpu': {bs},
-            'num_gpus': {cfg.num_gpus},
-            'effective_batch_size': {eff_bs},
-            'total_samples_seen': {steps * eff_bs},
-            'chunk_size': {cfg.chunk_size or 0},
-            'learning_rate': lr,
-            'optimizer_type': 'AdamW',
-            'optimizer_weight_decay': opt_wd,
-            'scheduler_type': sched_type,
-            'scheduler_warmup_steps': sched_warmup,
-            'scheduler_decay_steps': sched_decay,
-            'freeze_vision_encoder': freeze_ve,
-            'train_expert_only': train_eo,
-            'gradient_checkpointing': grad_ckpt,
-            'eval_success_rate': eval_sr,
-            'eval_success_rate_per_task': eval_per_task,
-            'eval_n_episodes': eval_n,
-            'final_train_loss': final_loss,
-            'training_time_s': float(timing.get('TRAINING_TIME_S', 0)),
-            'peak_gpu_memory_mb': peak_mem or float(timing.get('MAX_GPU_MEM_MB', 0)),
-            'gpu_type': timing.get('GPU_TYPE', 'unknown'),
-            'lerobot_commit': timing.get('LEROBOT_COMMIT', 'unknown'),
-            'timestamp': datetime.now(timezone.utc).isoformat(),
-        }}
-
-        # Save locally
-        Path('{train_dir}/benchmark_result.json').write_text(json.dumps(row, indent=2, default=str))
-
-        # Push to HF dataset
-        try:
-            from datasets import Dataset, load_dataset
-            try:
-                existing = load_dataset('{hub_dataset}', split='train')
-                rows = existing.to_list() + [row]
-            except Exception:
-                rows = [row]
-            Dataset.from_list(rows).push_to_hub('{hub_dataset}', split='train')
-            print('Published result to {hub_dataset}')
-        except ImportError:
-            print('datasets library not installed — result saved locally only')
-        except Exception as e:
-            print(f'Failed to push to hub: {{e}} — result saved locally')
-        "
-    """)
-
-
-def _generate_sbatch_script(
-    policy_name: str,
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-    hub_dataset: str,
-    lerobot_commit: str,
-) -> str:
-    cfg = POLICY_CONFIGS[policy_name]
-    steps = int(COMMON_TRAINING_ARGS["steps"])
-    log_dir = output_dir / "logs"
-    train_dir = output_dir / "train" / policy_name
-    checkpoint_path = train_dir / f"checkpoints/{steps:06d}/pretrained_model"
-
-    training_args = _training_cli_args(policy_name, output_dir, hub_org, benchmark_uuid)
-    eval_args = _cli_args(EVAL_ARGS)
-    publish = _publish_snippet(policy_name, output_dir, hub_org, benchmark_uuid, hub_dataset)
-
-    return textwrap.dedent(f"""\
-        #!/bin/bash
-        #SBATCH --job-name=bench_{policy_name}
-        #SBATCH --nodes=1
-        #SBATCH --ntasks-per-node=1
-        #SBATCH --gres=gpu:{cfg.num_gpus}
-        #SBATCH --cpus-per-task={cfg.num_gpus * 8}
-        #SBATCH --mem={GPU_MEM_ESTIMATES.get(policy_name, 128)}G
-        #SBATCH --time=06:00:00
-        #SBATCH --output={log_dir}/{policy_name}_%j.out
-        #SBATCH --error={log_dir}/{policy_name}_%j.err
-
-        set -euo pipefail
-
-        echo "=========================================="
-        echo "LeRobot LIBERO Benchmark — {policy_name}"
-        echo "UUID: {benchmark_uuid}"
-        echo "Start: $(date -Iseconds)"
-        echo "Host: $(hostname) | GPUs: {cfg.num_gpus}"
-        echo "=========================================="
-
-        START_TIME=$(date +%s)
-
-        # GPU memory monitoring (every 30s)
-        nvidia-smi --query-gpu=index,memory.used,memory.total,gpu_name \\
-            --format=csv,noheader,nounits -l 30 \\
-            > "{log_dir}/{policy_name}_gpu_mem.csv" &
-        GPU_MONITOR_PID=$!
-
-        # ── Training ──────────────────────────────────────────────────
-        echo "[$(date -Iseconds)] Starting training..."
-        accelerate launch --num_processes={cfg.num_gpus} \\
-            $(which lerobot-train) \\
-        {training_args}
-        TRAIN_EXIT=$?
-        TRAIN_END=$(date +%s)
-        echo "[$(date -Iseconds)] Training exit code: $TRAIN_EXIT"
-
-        # ── Evaluation ────────────────────────────────────────────────
-        EVAL_EXIT=1
-        if [ $TRAIN_EXIT -eq 0 ]; then
-            echo "[$(date -Iseconds)] Starting evaluation..."
-            lerobot-eval \\
-                --policy.path="{checkpoint_path}" \\
-            {eval_args} \\
-                --output_dir="{train_dir}/eval_results"
-            EVAL_EXIT=$?
-            echo "[$(date -Iseconds)] Eval exit code: $EVAL_EXIT"
-        else
-            echo "[$(date -Iseconds)] Skipping eval — training failed."
-        fi
-
-        # ── Timing ────────────────────────────────────────────────────
-        END_TIME=$(date +%s)
-        kill $GPU_MONITOR_PID 2>/dev/null || true
-
-        cat > "{log_dir}/{policy_name}_timing.txt" <<TIMING_EOF
-        BENCHMARK_UUID={benchmark_uuid}
-        POLICY_TYPE={policy_name}
-        TRAINING_TIME_S=$((TRAIN_END - START_TIME))
-        TOTAL_TIME_S=$((END_TIME - START_TIME))
-        TRAIN_EXIT=$TRAIN_EXIT
-        EVAL_EXIT=$EVAL_EXIT
-        MAX_GPU_MEM_MB=$(awk -F',' '{{print $2}}' "{log_dir}/{policy_name}_gpu_mem.csv" 2>/dev/null | sort -n | tail -1)
-        GPU_TYPE=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader | head -1 | xargs)
-        LEROBOT_COMMIT={lerobot_commit}
-        TIMING_EOF
-
-        # ── Publish result to HF dataset ──────────────────────────────
-        echo "[$(date -Iseconds)] Publishing result..."
-        {publish}
-
-        echo "=========================================="
-        echo "Done: $(date -Iseconds)"
-        echo "Training: $((TRAIN_END - START_TIME))s | Total: $((END_TIME - START_TIME))s"
-        echo "=========================================="
-    """)
-
-
-def _generate_tokenizer_script(
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-) -> str:
-    cfg = POLICY_CONFIGS["pi0_fast"]
-    log_dir = output_dir / "logs"
-    tokenizer_hub_repo = f"{hub_org}/fast-tokenizer-libero"
-
-    tok_args = dict(cfg.tokenizer_args)
-    tok_args["hub_repo_id"] = tokenizer_hub_repo
-
-    return textwrap.dedent(f"""\
-        #!/bin/bash
-        #SBATCH --job-name=bench_tokenizer
-        #SBATCH --nodes=1
-        #SBATCH --ntasks-per-node=1
-        #SBATCH --gres=gpu:1
-        #SBATCH --cpus-per-task=8
-        #SBATCH --mem=64G
-        #SBATCH --time=01:00:00
-        #SBATCH --output={log_dir}/tokenizer_%j.out
-        #SBATCH --error={log_dir}/tokenizer_%j.err
-
-        set -euo pipefail
-        echo "LeRobot — FAST Tokenizer | UUID: {benchmark_uuid}"
-
-        lerobot-train-tokenizer \\
-        {_cli_args(tok_args)}
-
-        echo "Tokenizer pushed to: {tokenizer_hub_repo}"
-    """)
-
-
-# ──────────────────────────────────────────────────────────────────────
-# Main
-# ──────────────────────────────────────────────────────────────────────
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Generate SLURM scripts for LeRobot LIBERO benchmark.")
-    parser.add_argument(
-        "--policies",
-        nargs="+",
-        default=ALL_POLICY_NAMES,
-        choices=ALL_POLICY_NAMES,
-        help="Policies to benchmark (default: all).",
-    )
-    parser.add_argument("--output_dir", type=Path, required=True, help="Root output directory.")
-    parser.add_argument("--hub_org", type=str, default="lerobot", help="HuggingFace org.")
-    parser.add_argument("--hub_dataset", type=str, default=None, help="HF dataset repo for results.")
-    parser.add_argument("--uuid", type=str, default=None, help="Override benchmark UUID.")
-    args = parser.parse_args()
-
-    benchmark_uuid = args.uuid or str(uuid.uuid4())
-    output_dir: Path = args.output_dir.resolve()
-    policies: list[str] = args.policies
-    hub_org: str = args.hub_org
-    hub_dataset: str = args.hub_dataset or f"{hub_org}/benchmark-libero"
-
-    try:
-        commit = subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip()
-    except (subprocess.CalledProcessError, FileNotFoundError):
-        commit = "unknown"
-
-    scripts_dir = output_dir / "slurm_scripts"
-    log_dir = output_dir / "logs"
-    scripts_dir.mkdir(parents=True, exist_ok=True)
-    log_dir.mkdir(parents=True, exist_ok=True)
-    for p in policies:
-        (output_dir / "train" / p).mkdir(parents=True, exist_ok=True)
-
-    generated: dict[str, Path] = {}
-
-    # Tokenizer job for pi0_fast
-    tokenizer_path = None
-    if "pi0_fast" in policies:
-        script = _generate_tokenizer_script(output_dir, hub_org, benchmark_uuid)
-        tokenizer_path = scripts_dir / "00_tokenizer.sh"
-        tokenizer_path.write_text(script)
-        tokenizer_path.chmod(0o755)
-        generated["tokenizer"] = tokenizer_path
-        tokenizer_hub_repo = f"{hub_org}/fast-tokenizer-libero"
-        POLICY_CONFIGS["pi0_fast"].extra_policy_args["policy.action_tokenizer_name"] = tokenizer_hub_repo
-
-    # Per-policy scripts
-    for i, name in enumerate(sorted(policies), start=1):
-        script = _generate_sbatch_script(name, output_dir, hub_org, benchmark_uuid, hub_dataset, commit)
-        path = scripts_dir / f"{i:02d}_{name}.sh"
-        path.write_text(script)
-        path.chmod(0o755)
-        generated[name] = path
-
-    # Manifest
-    manifest = {
-        "benchmark_uuid": benchmark_uuid,
-        "timestamp": datetime.now(UTC).isoformat(),
-        "lerobot_commit": commit,
-        "hub_org": hub_org,
-        "hub_dataset": hub_dataset,
-        "policies": policies,
-        "output_dir": str(output_dir),
-        "scripts": {k: str(v) for k, v in generated.items()},
-    }
-    manifest_path = output_dir / "benchmark_manifest.json"
-    manifest_path.write_text(json.dumps(manifest, indent=2))
-
-    # Instructions
-    print("=" * 60)
-    print("LeRobot LIBERO Benchmark — Scripts Generated")
-    print(f"UUID: {benchmark_uuid}")
-    print(f"Output: {output_dir}")
-    print(f"Results dataset: {hub_dataset}")
-    print("=" * 60)
-    print()
-    for _name, path in sorted(generated.items()):
-        print(f"  {path}")
-    print()
-
-    if tokenizer_path:
-        print("IMPORTANT: pi0_fast requires tokenizer training FIRST.")
-        print(f"  1. sbatch {tokenizer_path}")
-        print("  2. Wait for completion")
-        print(f"  3. sbatch {generated.get('pi0_fast', 'N/A')}")
-        print("  4. All other policies can run in parallel")
-    else:
-        print("All scripts can be submitted in parallel.")
-    print()
-    print("Each job publishes its result to the HF dataset automatically.")
-
-
-if __name__ == "__main__":
-    main()

benchmarks/publish_benchmark_result.py

@@ -1,156 +0,0 @@
-#!/usr/bin/env python
-
-# 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.
-
-"""Publish benchmark rows and lightweight artifacts to a Hub dataset."""
-
-from __future__ import annotations
-
-import argparse
-import json
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-from lerobot.utils.history_repo import UploadTarget, make_hub_file_url, upload_targets, utc_timestamp_slug
-
-
-def load_json_if_exists(path: Path) -> dict[str, Any] | None:
-    if not path.exists():
-        return None
-    return json.loads(path.read_text())
-
-
-def find_latest_train_config_path(run_root: Path) -> Path | None:
-    checkpoints_dir = run_root / "train" / "checkpoints"
-    if not checkpoints_dir.exists():
-        return None
-    candidates = sorted(
-        checkpoints_dir.glob("*/pretrained_model/train_config.json"),
-        key=lambda path: path.parts[-3],
-    )
-    return candidates[-1] if candidates else None
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--benchmark", required=True)
-    parser.add_argument("--policy", required=True)
-    parser.add_argument("--run_root", required=True, type=Path)
-    parser.add_argument("--results_repo", required=True)
-    parser.add_argument("--git_commit", required=True)
-    parser.add_argument("--num_gpus", required=True, type=int)
-    parser.add_argument("--microbatch_per_gpu", required=True, type=int)
-    parser.add_argument("--gradient_accumulation_steps", required=True, type=int)
-    parser.add_argument("--effective_batch_size", required=True, type=int)
-    parser.add_argument("--train_wall_time_s", required=True, type=float)
-    parser.add_argument("--eval_wall_time_s", required=True, type=float)
-    parser.add_argument("--slurm_job_id", default="")
-    parser.add_argument("--docker_image", required=True)
-    return parser.parse_args()
-
-
-def build_row(args: argparse.Namespace) -> tuple[dict[str, Any], list[UploadTarget]]:
-    now = datetime.now(UTC)
-    created_at = now.isoformat()
-    timestamp = utc_timestamp_slug(now)
-    run_id = f"{timestamp}__{args.benchmark}__{args.policy}__{args.slurm_job_id or 'manual'}"
-    eval_info = load_json_if_exists(args.run_root / "eval" / "eval_info.json") or {}
-    train_config_path = find_latest_train_config_path(args.run_root)
-    train_config = load_json_if_exists(train_config_path) or {}
-
-    artifact_prefix = f"artifacts/{args.benchmark}/{args.policy}/{run_id}"
-    row_path_in_repo = f"rows/{args.benchmark}/{args.policy}/{run_id}.json"
-
-    row = {
-        "schema_version": 1,
-        "created_at": created_at,
-        "run_id": run_id,
-        "benchmark": args.benchmark,
-        "policy": args.policy,
-        "git_commit": args.git_commit,
-        "slurm_job_id": args.slurm_job_id or None,
-        "docker_image": args.docker_image,
-        "resources": {
-            "num_gpus": args.num_gpus,
-            "microbatch_per_gpu": args.microbatch_per_gpu,
-            "gradient_accumulation_steps": args.gradient_accumulation_steps,
-            "effective_batch_size": args.effective_batch_size,
-        },
-        "timings": {
-            "train_wall_time_s": args.train_wall_time_s,
-            "eval_wall_time_s": args.eval_wall_time_s,
-            "total_wall_time_s": args.train_wall_time_s + args.eval_wall_time_s,
-        },
-        "eval": {
-            "overall": eval_info.get("overall", {}),
-            "per_group": eval_info.get("per_group", {}),
-            "per_task_count": len(eval_info.get("per_task", [])),
-        },
-        "paths": {
-            "run_root": str(args.run_root),
-            "train_dir": str(args.run_root / "train"),
-            "eval_dir": str(args.run_root / "eval"),
-        },
-        "train_config": train_config,
-        "artifact_urls": {
-            "row": make_hub_file_url(args.results_repo, row_path_in_repo),
-        },
-    }
-
-    row_path = args.run_root / "benchmark_row.json"
-    row_path.parent.mkdir(parents=True, exist_ok=True)
-    upload_list = [UploadTarget(local_path=row_path, path_in_repo=row_path_in_repo)]
-
-    eval_info_path = args.run_root / "eval" / "eval_info.json"
-    if eval_info_path.exists():
-        row["artifact_urls"]["eval_info"] = make_hub_file_url(
-            args.results_repo, f"{artifact_prefix}/eval_info.json"
-        )
-        upload_list.append(
-            UploadTarget(local_path=eval_info_path, path_in_repo=f"{artifact_prefix}/eval_info.json")
-        )
-
-    if train_config_path is not None and train_config_path.exists():
-        row["artifact_urls"]["train_config"] = make_hub_file_url(
-            args.results_repo, f"{artifact_prefix}/train_config.json"
-        )
-        upload_list.append(
-            UploadTarget(local_path=train_config_path, path_in_repo=f"{artifact_prefix}/train_config.json")
-        )
-
-    row_path.write_text(json.dumps(row, indent=2, sort_keys=True))
-    return row, upload_list
-
-
-def main() -> int:
-    args = parse_args()
-    row, upload_list = build_row(args)
-    uploaded = upload_targets(
-        repo_id=args.results_repo,
-        targets=upload_list,
-        repo_type="dataset",
-        private=False,
-        commit_message=f"Add benchmark row {row['run_id']}",
-    )
-    row["uploaded_paths"] = uploaded
-    row_path = args.run_root / "benchmark_row.json"
-    row_path.write_text(json.dumps(row, indent=2, sort_keys=True))
-    print(json.dumps(row, indent=2, sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

benchmarks/run_benchmark_matrix.py

@@ -1,647 +0,0 @@
-#!/usr/bin/env python
-
-# 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.
-
-"""Generate lightweight SLURM jobs for policy x benchmark benchmarking."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import subprocess
-from dataclasses import asdict, dataclass, field
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-from lerobot.utils.history_repo import utc_timestamp_slug
-
-MAX_GPUS = 8
-MIN_GPUS = 1
-DEFAULT_STEPS = 20_000
-DEFAULT_EFFECTIVE_BATCH_SIZE = 256
-DEFAULT_MICROBATCH_PER_GPU = 32
-DEFAULT_EVAL_BATCH_SIZE = 1
-DEFAULT_CPUS_PER_GPU = 8
-DEFAULT_MEMORY_PER_GPU_GB = 40
-
-
-@dataclass(frozen=True)
-class BenchmarkSpec:
-    name: str
-    dataset_repo_id: str
-    docker_image: str
-    eval_env_type: str
-    eval_task: str
-    eval_n_episodes: int
-    train_steps: int = DEFAULT_STEPS
-    effective_batch_size: int = DEFAULT_EFFECTIVE_BATCH_SIZE
-    train_extra_args: dict[str, Any] = field(default_factory=dict)
-    eval_extra_args: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass(frozen=True)
-class PolicySpec:
-    name: str
-    policy_type: str
-    num_gpus: int
-    policy_path: str | None = None
-    microbatch_per_gpu: int = DEFAULT_MICROBATCH_PER_GPU
-    extra_train_args: dict[str, Any] = field(default_factory=dict)
-    extra_eval_args: dict[str, Any] = field(default_factory=dict)
-    needs_tokenizer: bool = False
-    tokenizer_args: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass(frozen=True)
-class PlannedJob:
-    benchmark: str
-    policy: str
-    run_rel: str
-    num_gpus: int
-    microbatch_per_gpu: int
-    gradient_accumulation_steps: int
-    effective_batch_size: int
-    docker_image: str
-    train_args: dict[str, Any]
-    eval_args: dict[str, Any]
-    tokenizer_args: dict[str, Any] | None
-    script_path: str
-
-
-BENCHMARKS: dict[str, BenchmarkSpec] = {
-    "libero_plus": BenchmarkSpec(
-        name="libero_plus",
-        dataset_repo_id="lerobot/libero_plus",
-        docker_image="lerobot-benchmark-libero-plus:latest",
-        eval_env_type="libero_plus",
-        eval_task="libero_spatial,libero_object,libero_goal,libero_10",
-        eval_n_episodes=10,
-        train_extra_args={
-            "rename_map": {
-                "observation.images.image": "observation.images.camera1",
-                "observation.images.image2": "observation.images.camera2",
-            },
-        },
-        eval_extra_args={
-            "env.camera_name_mapping": {
-                "agentview_image": "camera1",
-                "robot0_eye_in_hand_image": "camera2",
-            },
-            "env.max_parallel_tasks": 1,
-            "eval.batch_size": DEFAULT_EVAL_BATCH_SIZE,
-            "eval.use_async_envs": False,
-            "eval.max_episodes_rendered": 0,
-            "policy.device": "cuda",
-        },
-    ),
-    "robomme": BenchmarkSpec(
-        name="robomme",
-        dataset_repo_id="lerobot/robomme",
-        docker_image="lerobot-benchmark-robomme:latest",
-        eval_env_type="robomme",
-        eval_task=(
-            "BinFill,PickXtimes,SwingXtimes,StopCube,VideoUnmask,VideoUnmaskSwap,"
-            "ButtonUnmask,ButtonUnmaskSwap,PickHighlight,VideoRepick,VideoPlaceButton,"
-            "VideoPlaceOrder,MoveCube,InsertPeg,PatternLock,RouteStick"
-        ),
-        eval_n_episodes=50,
-        train_extra_args={
-            "rename_map": {
-                "observation.images.image": "observation.images.camera1",
-                "observation.images.wrist_image": "observation.images.camera2",
-            },
-        },
-        eval_extra_args={
-            "env.dataset_split": "test",
-            "env.max_parallel_tasks": 1,
-            "rename_map": {
-                "observation.images.image": "observation.images.camera1",
-                "observation.images.wrist_image": "observation.images.camera2",
-            },
-            "eval.batch_size": DEFAULT_EVAL_BATCH_SIZE,
-            "eval.use_async_envs": False,
-            "eval.max_episodes_rendered": 0,
-            "policy.device": "cuda",
-        },
-    ),
-}
-
-
-POLICIES: dict[str, PolicySpec] = {
-    "pi0": PolicySpec(
-        name="pi0",
-        policy_type="pi0",
-        policy_path="lerobot/pi0_base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 0,
-        },
-    ),
-    "pi0_fast": PolicySpec(
-        name="pi0_fast",
-        policy_type="pi0_fast",
-        policy_path="lerobot/pi0fast-base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 0,
-        },
-        needs_tokenizer=True,
-        tokenizer_args={
-            "action_horizon": 30,
-            "encoded_dims": "0:7",
-            "normalization_mode": "QUANTILES",
-            "vocab_size": 1024,
-            "scale": 10.0,
-            "push_to_hub": True,
-        },
-    ),
-    "pi05": PolicySpec(
-        name="pi05",
-        policy_type="pi05",
-        policy_path="lerobot/pi05_base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 0,
-        },
-    ),
-    "groot": PolicySpec(
-        name="groot",
-        policy_type="groot",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.base_model_path": "nvidia/GR00T-N1.5-3B",
-            "policy.tune_diffusion_model": True,
-            "policy.tune_projector": True,
-            "policy.tune_llm": False,
-            "policy.tune_visual": False,
-            "policy.use_bf16": True,
-        },
-    ),
-    "act": PolicySpec(
-        name="act",
-        policy_type="act",
-        num_gpus=1,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-        },
-    ),
-    "diffusion": PolicySpec(
-        name="diffusion",
-        policy_type="diffusion",
-        num_gpus=1,
-        extra_train_args={
-            "policy.horizon": 32,
-            "policy.n_action_steps": 30,
-            "policy.n_obs_steps": 2,
-        },
-    ),
-    "smolvla": PolicySpec(
-        name="smolvla",
-        policy_type="smolvla",
-        policy_path="lerobot/smolvla_base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.load_vlm_weights": True,
-            "policy.freeze_vision_encoder": False,
-            "policy.train_expert_only": False,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 1,
-        },
-    ),
-    "xvla": PolicySpec(
-        name="xvla",
-        policy_type="xvla",
-        policy_path="lerobot/xvla-widowx",
-        num_gpus=4,
-        extra_train_args={
-            "policy.n_action_steps": 32,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 1,
-        },
-    ),
-    "multi_task_dit": PolicySpec(
-        name="multi_task_dit",
-        policy_type="multi_task_dit",
-        num_gpus=1,
-        extra_train_args={
-            "policy.horizon": 32,
-            "policy.n_action_steps": 30,
-        },
-    ),
-}
-
-
-def normalize_repo_id(hub_org: str, repo_or_id: str) -> str:
-    return repo_or_id if "/" in repo_or_id else f"{hub_org}/{repo_or_id}"
-
-
-def get_requested_names(
-    requested: list[str] | None,
-    available: dict[str, Any],
-    *,
-    kind: str,
-) -> list[str]:
-    if not requested:
-        return list(available)
-    unknown = sorted(set(requested) - set(available))
-    if unknown:
-        raise ValueError(f"Unknown {kind}: {', '.join(unknown)}. Available: {', '.join(available)}")
-    return requested
-
-
-def compute_gradient_accumulation_steps(
-    *,
-    effective_batch_size: int,
-    num_gpus: int,
-    microbatch_per_gpu: int,
-) -> int:
-    per_step_batch = num_gpus * microbatch_per_gpu
-    if effective_batch_size % per_step_batch != 0:
-        raise ValueError(
-            f"Cannot reach effective batch {effective_batch_size} with {num_gpus=} and "
-            f"{microbatch_per_gpu=}."
-        )
-    return effective_batch_size // per_step_batch
-
-
-def make_run_slug() -> str:
-    return utc_timestamp_slug()
-
-
-def shell_value(value: Any) -> str:
-    if isinstance(value, bool):
-        value = "true" if value else "false"
-    elif isinstance(value, (dict, list)):
-        value = json.dumps(value, sort_keys=True)
-    else:
-        value = str(value)
-    escaped = (
-        value.replace("\\", "\\\\")
-        .replace('"', '\\"')
-        .replace("$", "\\$")
-        .replace("`", "\\`")
-    )
-    return f'"{escaped}"'
-
-
-def format_cli_args(args: dict[str, Any]) -> str:
-    lines = []
-    for key, value in args.items():
-        lines.append(f"  --{key}={shell_value(value)}")
-    return " \\\n".join(lines)
-
-
-def build_train_args(
-    *,
-    benchmark: BenchmarkSpec,
-    policy: PolicySpec,
-    train_dir: str,
-    gradient_accumulation_steps: int,
-) -> dict[str, Any]:
-    args: dict[str, Any] = {
-        "dataset.repo_id": benchmark.dataset_repo_id,
-        "output_dir": train_dir,
-        "steps": benchmark.train_steps,
-        "batch_size": policy.microbatch_per_gpu,
-        "gradient_accumulation_steps": gradient_accumulation_steps,
-        "eval_freq": 0,
-        "save_freq": benchmark.train_steps,
-        "save_checkpoint": True,
-        "log_freq": 100,
-        "wandb.enable": False,
-        "policy.push_to_hub": False,
-        "policy.device": "cuda",
-    }
-    if policy.policy_path:
-        args["policy.path"] = policy.policy_path
-    else:
-        args["policy.type"] = policy.policy_type
-    args.update(benchmark.train_extra_args)
-    args.update(policy.extra_train_args)
-    return args
-
-
-def build_eval_args(
-    *,
-    benchmark: BenchmarkSpec,
-    policy: PolicySpec,
-    checkpoint_path: str,
-    eval_dir: str,
-) -> dict[str, Any]:
-    args: dict[str, Any] = {
-        "policy.path": checkpoint_path,
-        "env.type": benchmark.eval_env_type,
-        "env.task": benchmark.eval_task,
-        "eval.n_episodes": benchmark.eval_n_episodes,
-        "output_dir": eval_dir,
-    }
-    args.update(benchmark.eval_extra_args)
-    args.update(policy.extra_eval_args)
-    return args
-
-
-def plan_jobs(
-    *,
-    output_dir: Path,
-    hub_org: str,
-    results_repo: str,
-    policies: list[str],
-    benchmarks: list[str],
-) -> list[PlannedJob]:
-    _ = hub_org
-    _ = results_repo
-    scripts_dir = output_dir / "slurm"
-    jobs: list[PlannedJob] = []
-    for benchmark_name in benchmarks:
-        benchmark = BENCHMARKS[benchmark_name]
-        for policy_name in policies:
-            policy = POLICIES[policy_name]
-            num_gpus = max(MIN_GPUS, min(policy.num_gpus, MAX_GPUS))
-            run_rel = f"runs/{benchmark_name}/{policy_name}/{make_run_slug()}"
-            run_root = f"/benchmark-output/{run_rel}"
-            gradient_accumulation_steps = compute_gradient_accumulation_steps(
-                effective_batch_size=benchmark.effective_batch_size,
-                num_gpus=num_gpus,
-                microbatch_per_gpu=policy.microbatch_per_gpu,
-            )
-            train_dir = f"{run_root}/train"
-            checkpoint_path = f"{train_dir}/checkpoints/{benchmark.train_steps:06d}/pretrained_model"
-            eval_dir = f"{run_root}/eval"
-            train_args = build_train_args(
-                benchmark=benchmark,
-                policy=policy,
-                train_dir=train_dir,
-                gradient_accumulation_steps=gradient_accumulation_steps,
-            )
-            eval_args = build_eval_args(
-                benchmark=benchmark,
-                policy=policy,
-                checkpoint_path=checkpoint_path,
-                eval_dir=eval_dir,
-            )
-            tokenizer_args = None
-            if policy.needs_tokenizer:
-                tokenizer_repo_id = f"{hub_org}/{policy_name}-{benchmark_name}-tokenizer"
-                tokenizer_args = {
-                    "repo_id": benchmark.dataset_repo_id,
-                    "output_dir": f"{run_root}/tokenizer",
-                    "hub_repo_id": tokenizer_repo_id,
-                    **policy.tokenizer_args,
-                }
-                train_args["policy.action_tokenizer_name"] = tokenizer_repo_id
-            script_path = str(scripts_dir / f"{benchmark_name}__{policy_name}.sbatch")
-            jobs.append(
-                PlannedJob(
-                    benchmark=benchmark_name,
-                    policy=policy_name,
-                    run_rel=run_rel,
-                    num_gpus=num_gpus,
-                    microbatch_per_gpu=policy.microbatch_per_gpu,
-                    gradient_accumulation_steps=gradient_accumulation_steps,
-                    effective_batch_size=benchmark.effective_batch_size,
-                    docker_image=benchmark.docker_image,
-                    train_args=train_args,
-                    eval_args=eval_args,
-                    tokenizer_args=tokenizer_args,
-                    script_path=script_path,
-                )
-            )
-    return jobs
-
-
-def render_sbatch_script(
-    *,
-    job: PlannedJob,
-    output_dir: Path,
-    results_repo_id: str,
-    git_commit: str,
-) -> str:
-    host_output_dir = output_dir.resolve()
-    run_root = f"/benchmark-output/{job.run_rel}"
-    host_run_root = host_output_dir / job.run_rel
-    cpus_per_task = max(DEFAULT_CPUS_PER_GPU, DEFAULT_CPUS_PER_GPU * job.num_gpus)
-    mem_gb = max(DEFAULT_MEMORY_PER_GPU_GB, DEFAULT_MEMORY_PER_GPU_GB * job.num_gpus)
-    gpu_ids_expr = "${GPU_IDS}"
-    train_cli = format_cli_args(job.train_args)
-    eval_cli = format_cli_args(job.eval_args)
-    tokenizer_command = ""
-    if job.tokenizer_args:
-        tokenizer_cli = format_cli_args(job.tokenizer_args)
-        tokenizer_command = f"""
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES={gpu_ids_expr} \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    lerobot-train-tokenizer \\
-{tokenizer_cli}
-  '
-"""
-    return f"""#!/bin/bash
-#SBATCH --job-name=bench-{job.benchmark}-{job.policy}
-#SBATCH --gres=gpu:{job.num_gpus}
-#SBATCH --cpus-per-task={cpus_per_task}
-#SBATCH --mem={mem_gb}G
-#SBATCH --output={output_dir.resolve()}/logs/{job.benchmark}__{job.policy}__%j.out
-#SBATCH --error={output_dir.resolve()}/logs/{job.benchmark}__{job.policy}__%j.err
-
-set -euo pipefail
-
-HF_TOKEN="${{HF_TOKEN:-${{HF_USER_TOKEN:-}}}}"
-GPU_IDS="$(seq -s, 0 $(({job.num_gpus} - 1)))"
-RUN_ROOT="{run_root}"
-
-mkdir -p "{host_output_dir}/logs"
-mkdir -p "{host_run_root.parent}"
-
-{tokenizer_command}
-
-TRAIN_START="$(date +%s)"
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES="${{GPU_IDS}}" \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    accelerate launch --num_processes={job.num_gpus} $(which lerobot-train) \\
-{train_cli}
-  '
-TRAIN_END="$(date +%s)"
-
-EVAL_START="$(date +%s)"
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES="${{GPU_IDS}}" \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    lerobot-eval \\
-{eval_cli}
-  '
-EVAL_END="$(date +%s)"
-TRAIN_WALL_TIME_S="$((TRAIN_END - TRAIN_START))"
-EVAL_WALL_TIME_S="$((EVAL_END - EVAL_START))"
-
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES="${{GPU_IDS}}" \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -e RUN_ROOT="${{RUN_ROOT}}" \\
-  -e TRAIN_WALL_TIME_S="${{TRAIN_WALL_TIME_S}}" \\
-  -e EVAL_WALL_TIME_S="${{EVAL_WALL_TIME_S}}" \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    uv run python benchmarks/publish_benchmark_result.py \\
-      --benchmark={job.benchmark} \\
-      --policy={job.policy} \\
-      --run_root="${{RUN_ROOT}}" \\
-      --results_repo={results_repo_id} \\
-      --git_commit={git_commit} \\
-      --num_gpus={job.num_gpus} \\
-      --microbatch_per_gpu={job.microbatch_per_gpu} \\
-      --gradient_accumulation_steps={job.gradient_accumulation_steps} \\
-      --effective_batch_size={job.effective_batch_size} \\
-      --train_wall_time_s="${{TRAIN_WALL_TIME_S}}" \\
-      --eval_wall_time_s="${{EVAL_WALL_TIME_S}}" \\
-      --slurm_job_id="${{SLURM_JOB_ID:-}}" \\
-      --docker_image={job.docker_image}
-  '
-"""
-
-
-def write_manifest(
-    *,
-    output_dir: Path,
-    jobs: list[PlannedJob],
-    git_commit: str,
-    hub_org: str,
-    results_repo: str,
-) -> Path:
-    manifest = {
-        "generated_at": datetime.now(UTC).isoformat(),
-        "git_commit": git_commit,
-        "hub_org": hub_org,
-        "results_repo": results_repo,
-        "jobs": [asdict(job) for job in jobs],
-    }
-    manifest_path = output_dir / "manifest.json"
-    manifest_path.write_text(json.dumps(manifest, indent=2, sort_keys=True))
-    return manifest_path
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--policies", nargs="*", default=None)
-    parser.add_argument("--benchmarks", nargs="*", default=None)
-    parser.add_argument("--output_dir", required=True, type=Path)
-    parser.add_argument("--hub_org", required=True)
-    parser.add_argument("--results_repo", required=True)
-    parser.add_argument("--submit", action="store_true")
-    return parser.parse_args()
-
-
-def get_git_commit() -> str:
-    return subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip()
-
-
-def main() -> int:
-    args = parse_args()
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-    (args.output_dir / "slurm").mkdir(parents=True, exist_ok=True)
-    (args.output_dir / "logs").mkdir(parents=True, exist_ok=True)
-
-    selected_policies = get_requested_names(args.policies, POLICIES, kind="policies")
-    selected_benchmarks = get_requested_names(args.benchmarks, BENCHMARKS, kind="benchmarks")
-    git_commit = get_git_commit()
-    results_repo_id = normalize_repo_id(args.hub_org, args.results_repo)
-
-    jobs = plan_jobs(
-        output_dir=args.output_dir,
-        hub_org=args.hub_org,
-        results_repo=results_repo_id,
-        policies=selected_policies,
-        benchmarks=selected_benchmarks,
-    )
-
-    for job in jobs:
-        script = render_sbatch_script(
-            job=job,
-            output_dir=args.output_dir,
-            results_repo_id=results_repo_id,
-            git_commit=git_commit,
-        )
-        script_path = Path(job.script_path)
-        script_path.write_text(script)
-        script_path.chmod(0o755)
-        if args.submit:
-            subprocess.run(["sbatch", str(script_path)], check=True)
-
-    manifest_path = write_manifest(
-        output_dir=args.output_dir,
-        jobs=jobs,
-        git_commit=git_commit,
-        hub_org=args.hub_org,
-        results_repo=results_repo_id,
-    )
-    print(f"Wrote {len(jobs)} benchmark jobs to {args.output_dir}")
-    print(f"Manifest: {manifest_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

benchmarks/video/run_video_benchmark.py

@@ -39,6 +39,7 @@ from tqdm import tqdm
 
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.video_utils import (
+    VideoEncoderConfig,
     decode_video_frames,
     encode_video_frames,
 )
@@ -251,10 +252,13 @@ def benchmark_encoding_decoding(
             imgs_dir=imgs_dir,
             video_path=video_path,
             fps=fps,
-            vcodec=encoding_cfg["vcodec"],
-            pix_fmt=encoding_cfg["pix_fmt"],
-            g=encoding_cfg.get("g"),
-            crf=encoding_cfg.get("crf"),
+            camera_encoder_config=VideoEncoderConfig(
+                vcodec=encoding_cfg["vcodec"],
+                pix_fmt=encoding_cfg["pix_fmt"],
+                g=encoding_cfg.get("g"),
+                crf=encoding_cfg.get("crf"),
+                preset=encoding_cfg.get("preset"),
+            ),
             # fast_decode=encoding_cfg.get("fastdecode"),
             overwrite=True,
         )

docker/Dockerfile.benchmark.libero_plus

@@ -1,4 +1,4 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# 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.
@@ -12,8 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-FROM huggingface/lerobot-gpu:latest
+# 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 \
@@ -21,28 +31,54 @@ RUN apt-get update \
     && 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 easydict mujoco matplotlib wand scikit-image gym
+        "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 --depth=1 https://github.com/sylvestf/LIBERO-plus.git /home/user_lerobot/libero-plus \
+RUN git clone https://github.com/sylvestf/LIBERO-plus.git /home/user_lerobot/libero-plus \
+    && git -C /home/user_lerobot/libero-plus checkout ${LIBERO_PLUS_SHA} \
     && cd /home/user_lerobot/libero-plus && uv pip install --no-cache --no-deps -e "." \
-    && uv pip uninstall hf-libero 2>/dev/null || true
+    && (uv pip uninstall hf-libero 2>/dev/null || true)
 ENV PYTHONPATH="/home/user_lerobot/libero-plus:${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 \
-    && mv /tmp/libero-plus-dl/extract/inspire/hdd/project/embodied-multimodality/public/syfei/libero_new/release/dataset/LIBERO-plus-0/assets \
-          ${LIBERO_PLUS_ROOT}/assets \
+    && ASSETS_DIR=$(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

@@ -0,0 +1,71 @@
+# 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

@@ -0,0 +1,43 @@
+# 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

@@ -12,11 +12,24 @@
 # 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 \
@@ -27,6 +40,9 @@ RUN apt-get update \
     && 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 \
@@ -34,6 +50,7 @@ RUN printf 'gymnasium==0.29.1\nnumpy==1.26.4\n' > /tmp/robomme_override.txt \
          "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

@@ -0,0 +1,138 @@
+# 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

@@ -0,0 +1,99 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# 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
@@ -77,10 +79,22 @@
     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/act.mdx

@@ -90,6 +90,6 @@ lerobot-record \
   --dataset.single_task="Your task description" \
   --dataset.streaming_encoding=true \
   --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
+  # --dataset.camera_encoder_config.vcodec=auto \
   --policy.path=${HF_USER}/act_policy
 ```

docs/source/earthrover_mini_plus.mdx

@@ -194,7 +194,7 @@ lerobot-record \
     --dataset.single_task="Navigate around obstacles" \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
+    # --dataset.camera_encoder_config.vcodec=auto \
     --display_data=true
 ```
 

docs/source/groot.mdx

@@ -123,7 +123,7 @@ lerobot-record \
   --dataset.single_task="Grab and handover the red cube to the other arm" \
   --dataset.streaming_encoding=true \
   --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
+  # --dataset.camera_encoder_config.vcodec=auto \
   --policy.path=<user>/groot-bimanual \ # your trained model
   --dataset.episode_time_s=30 \
   --dataset.reset_time_s=10

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 \
@@ -108,11 +108,10 @@ python examples/hil/hil_data_collection.py \
     --teleop.port_left=/dev/ttyACM0 \
     --teleop.port_right=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
-    --dataset.repo_id=your-username/hil-dataset \
+    --dataset.repo_id=your-username/rollout_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 \
@@ -136,11 +135,10 @@ python examples/hil/hil_data_collection.py \
     --teleop.port_left=/dev/ttyACM0 \
     --teleop.port_right=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
-    --dataset.repo_id=your-username/hil-rtc-dataset \
+    --dataset.repo_id=your-username/rollout_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/hilserl.mdx

@@ -685,6 +685,10 @@ Example configuration for training the [reward classifier](https://huggingface.c
 
 ```json
 {
+  "dataset": {
+    "repo_id": "hf_username/dataset_name",
+    "root": null
+  },
   "policy": {
     "type": "reward_classifier",
     "model_name": "helper2424/resnet10",
@@ -705,8 +709,28 @@ Example configuration for training the [reward classifier](https://huggingface.c
         "type": "VISUAL",
         "shape": [3, 128, 128]
       }
-    }
-  }
+    },
+    "push_to_hub": true,
+    "repo_id": "hf_username/model_repo"
+  },
+  "batch_size": 16,
+  "num_workers": 4,
+  "steps": 5000,
+  "log_freq": 10,
+  "eval_freq": 1000,
+  "save_freq": 1000,
+  "save_checkpoint": true,
+  "seed": 2,
+  "resume": false,
+  "optimizer": {
+    "grad_clip_norm": 10.0
+  },
+  "wandb": {
+    "enable": true,
+    "project": "reward-classifier",
+    "disable_artifact": false
+  },
+  "job_name": "reward-classifier"
 }
 ```
 

docs/source/hope_jr.mdx

@@ -232,7 +232,7 @@ lerobot-record \
     --dataset.private=true \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
+    # --dataset.camera_encoder_config.vcodec=auto \
     --display_data=true
 ```
 
@@ -278,6 +278,6 @@ lerobot-record \
   --dataset.num_episodes=10 \
   --dataset.streaming_encoding=true \
   --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
+  # --dataset.camera_encoder_config.vcodec=auto \
   --policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
 ```

docs/source/il_robots.mdx

@@ -32,6 +32,12 @@ Once you’ve gathered enough trajectories, you’ll train a neural network to i
 
 If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support.
 
+<Tip>
+
+Want to quickly get the right commands for your setup? The [quickstart notebook](https://github.com/huggingface/lerobot/blob/main/examples/notebooks/quickstart.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/lerobot/blob/main/examples/notebooks/quickstart.ipynb) lets you configure your robot once and generates all the commands below ready to paste.
+
+</Tip>
+
 ## Set up and Calibrate
 
 If you haven't yet set up and calibrated your robot and teleop device, please do so by following the robot-specific tutorial.
@@ -187,7 +193,7 @@ lerobot-record \
     --dataset.num_episodes=5 \
     --dataset.single_task="Grab the black cube" \
     --dataset.streaming_encoding=true \
-    # --dataset.vcodec=auto \
+    # --dataset.camera_encoder_config.vcodec=auto \
     --dataset.encoder_threads=2
 ```
 </hfoption>
@@ -503,121 +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=${HF_USER}/rollout_eval_data \
+    --dataset.single_task="Put lego brick into the box" \
+    --duration=3600
+```
+
+| Flag                                   | Description                                                 |
+| -------------------------------------- | ----------------------------------------------------------- |
+| `--strategy.upload_every_n_episodes`   | Push to Hub every N episodes (default: 5)                   |
+| `--strategy.target_video_file_size_mb` | Target video file size for episode rotation (default: auto) |
+| `--dataset.repo_id`                    | **Required.** Hub repository for the recorded dataset       |
+| `--dataset.push_to_hub`                | Whether to push to Hub on teardown (default: true)          |
+
+### Highlight (`--strategy.type=highlight`)
+
+Autonomous rollout with on-demand recording via a memory-bounded ring buffer. The robot runs continuously while the buffer captures the last N seconds of telemetry. Press the save key to flush the buffer and start live recording; press it again to save the episode.
+
+```bash
+lerobot-rollout \
+    --strategy.type=highlight \
+    --strategy.ring_buffer_seconds=30 \
+    --strategy.save_key=s \
+    --strategy.push_key=h \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=koch_follower \
+    --robot.port=/dev/ttyACM0 \
+    --dataset.repo_id=${HF_USER}/rollout_highlight_data \
+    --dataset.single_task="Pick up the red cube"
+```
+
+**Keyboard controls:**
+
+| Key                | Action                                                   |
+| ------------------ | -------------------------------------------------------- |
+| `s` (configurable) | Start recording (flushes buffer) / stop and save episode |
+| `h` (configurable) | Push dataset to Hub                                      |
+| `ESC`              | Stop the session                                         |
+
+| Flag                                   | Description                                    |
+| -------------------------------------- | ---------------------------------------------- |
+| `--strategy.ring_buffer_seconds`       | Duration of buffered telemetry (default: 30)   |
+| `--strategy.ring_buffer_max_memory_mb` | Memory cap for the ring buffer (default: 2048) |
+| `--strategy.save_key`                  | Key to toggle recording (default: `s`)         |
+| `--strategy.push_key`                  | Key to push to Hub (default: `h`)              |
+
+### DAgger (`--strategy.type=dagger`)
+
+Human-in-the-loop data collection. Alternates between autonomous policy execution and human intervention via a teleoperator. Intervention frames are tagged with `intervention=True`. Requires a teleoperator (`--teleop.type`).
+
+See the [Human-In-the-Loop Data Collection](./hil_data_collection) guide for a detailed walkthrough.
+
+**Corrections-only mode** (default): Only human correction windows are recorded. Each correction becomes one episode.
+
+```bash
+lerobot-rollout \
+    --strategy.type=dagger \
+    --strategy.num_episodes=20 \
+    --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --robot.type=bi_openarm_follower \
+    --teleop.type=openarm_mini \
+    --dataset.repo_id=${HF_USER}/rollout_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}/rollout_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/lerobot-dataset-v3.mdx

@@ -43,7 +43,7 @@ lerobot-record \
   --dataset.num_episodes=5 \
   --dataset.single_task="Grab the black cube" \
   --dataset.streaming_encoding=true \
-  # --dataset.vcodec=auto \
+  # --dataset.camera_encoder_config.vcodec=auto \
   --dataset.encoder_threads=2
 ```
 

docs/source/libero_plus.mdx

@@ -0,0 +1,188 @@
+# 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/reachy2.mdx

@@ -161,7 +161,7 @@ lerobot-record \
     --dataset.private=true \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
+    # --dataset.camera_encoder_config.vcodec=auto \
     --display_data=true
 ```
 
@@ -203,7 +203,7 @@ lerobot-record \
     --dataset.private=true \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.vcodec=auto \
+    # --dataset.camera_encoder_config.vcodec=auto \
     --display_data=true
 ```
 

docs/source/rename_map.mdx

@@ -61,17 +61,6 @@ lerobot-eval \
   --rename_map='{"observation.images.image": "observation.images.base_0_rgb", "observation.images.image2": "observation.images.left_wrist_0_rgb"}'
 ```
 
-### Recording
-
-`lerobot-record` also supports rename maps, nested under the dataset config:
-
-```bash
-lerobot-record \ # When running inference
-  --policy.path="<user>/smolVLA_finetuned" \
-  ... \
-  --dataset.rename_map='{"observation.images.glove2": "observation.images.image"}'
-```
-
 ## Alternative: edit the policy config directly
 
 If you always use the same dataset or environment, you can **edit the policy's `config.json`** so its observation keys match your data source. Then no rename map is needed.
@@ -105,10 +94,10 @@ XVLA-base has three visual inputs and `empty_cameras=0` by default. Your dataset
 
 ## Quick reference
 
-| Goal                                      | What to do                                                                  |
-| ----------------------------------------- | --------------------------------------------------------------------------- |
-| Dataset keys ≠ policy keys                | `--rename_map='{"dataset_key": "policy_key", ...}'`                         |
-| Env keys ≠ policy keys (eval)             | `--rename_map='{"env_key": "policy_key", ...}'`                             |
-| Recording with different keys (inference) | `--dataset.rename_map='{"source_key": "policy_key", ...}'`.                 |
-| Fewer cameras than policy expects         | `--policy.empty_cameras=N` (supported by PI0, PI05, PI0Fast, SmolVLA, XVLA) |
-| Avoid passing a rename map                | Edit the policy's `config.json` so its keys match your data source          |
+| Goal                                    | What to do                                                                  |
+| --------------------------------------- | --------------------------------------------------------------------------- |
+| Dataset keys ≠ policy keys              | `--rename_map='{"dataset_key": "policy_key", ...}'`                         |
+| Env keys ≠ policy keys (eval)           | `--rename_map='{"env_key": "policy_key", ...}'`                             |
+| Rollout with different keys (inference) | `--rename_map='{"source_key": "policy_key", ...}'`.                         |
+| Fewer cameras than policy expects       | `--policy.empty_cameras=N` (supported by PI0, PI05, PI0Fast, SmolVLA, XVLA) |
+| Avoid passing a rename map              | Edit the policy's `config.json` so its keys match your data source          |

docs/source/robocasa.mdx

@@ -0,0 +1,188 @@
+# 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

@@ -0,0 +1,99 @@
+# 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

@@ -0,0 +1,130 @@
+# 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

@@ -0,0 +1,223 @@
+# 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/sarm.mdx

@@ -46,7 +46,7 @@ This ensures identical task states map to consistent progress values, even acros
 
 ## Inputs and Targets (What the new code expects)
 
-SARM is trained through its processor (`src/lerobot/policies/sarm/processor_sarm.py`), which:
+SARM is trained through its processor (`src/lerobot/rewards/sarm/processor_sarm.py`), which:
 
 - **Encodes** images and task text with CLIP (ViT-B/32) into `video_features` and `text_features`
 - **Pads/truncates** robot state into `state_features` (up to `max_state_dim`)
@@ -347,7 +347,7 @@ Use `compute_rabc_weights.py` with `--visualize-only` to visualize model predict
 <hfoption id="single_stage">
 
 ```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
+python -m lerobot.rewards.sarm.compute_rabc_weights \
   --dataset-repo-id your-username/your-dataset \
   --reward-model-path your-username/sarm-model \
   --visualize-only \
@@ -360,7 +360,7 @@ python src/lerobot/policies/sarm/compute_rabc_weights.py \
 <hfoption id="dense_only">
 
 ```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
+python -m lerobot.rewards.sarm.compute_rabc_weights \
   --dataset-repo-id your-username/your-dataset \
   --reward-model-path your-username/sarm-model \
   --visualize-only \
@@ -373,7 +373,7 @@ python src/lerobot/policies/sarm/compute_rabc_weights.py \
 <hfoption id="dual">
 
 ```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
+python -m lerobot.rewards.sarm.compute_rabc_weights \
   --dataset-repo-id your-username/your-dataset \
   --reward-model-path your-username/sarm-model \
   --visualize-only \
@@ -429,7 +429,7 @@ The weighting follows **Equations 8-9** from the paper:
 First, run the SARM model on all frames in your dataset to compute progress values:
 
 ```bash
-python src/lerobot/policies/sarm/compute_rabc_weights.py \
+python -m lerobot.rewards.sarm.compute_rabc_weights \
   --dataset-repo-id your-username/your-dataset \
   --reward-model-path your-username/sarm-model \
   --head-mode sparse \
@@ -465,15 +465,15 @@ This script:
 
 ### Step 5b: Train Policy with RA-BC
 
-Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`). Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:
+Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`) if not explicitly provided. Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:
 
 ```bash
 lerobot-train \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=pi0 \
-  --use_rabc=true \
-  --rabc_head_mode=sparse \
-  --rabc_kappa=0.01 \
+  --sample_weighting.type=rabc \
+  --sample_weighting.head_mode=sparse \
+  --sample_weighting.kappa=0.01 \
   --output_dir=outputs/train/policy_rabc \
   --batch_size=32 \
   --steps=40000
@@ -488,12 +488,13 @@ The training script automatically:
 
 **RA-BC Arguments:**
 
-| Argument               | Description                                                | Default                            |
-| ---------------------- | ---------------------------------------------------------- | ---------------------------------- |
-| `--use_rabc`           | Enable RA-BC sample weighting                              | `false`                            |
-| `--rabc_progress_path` | Path to progress parquet file (auto-detected from dataset) | `sarm_progress.parquet` in dataset |
-| `--rabc_head_mode`     | Which SARM head's progress to use: `sparse` or `dense`     | `sparse`                           |
-| `--rabc_kappa`         | Threshold κ for high-quality samples                       | `0.01`                             |
+| Argument                           | Description                                            | Default                 |
+| ---------------------------------- | ------------------------------------------------------ | ----------------------- |
+| `--sample_weighting.type`          | Weighting strategy type (`rabc` or `uniform`)          | `rabc`                  |
+| `--sample_weighting.progress_path` | Path to progress parquet file                          | `sarm_progress.parquet` |
+| `--sample_weighting.head_mode`     | Which SARM head's progress to use: `sparse` or `dense` | `sparse`                |
+| `--sample_weighting.kappa`         | Threshold κ for high-quality samples                   | `0.01`                  |
+| `--sample_weighting.epsilon`       | Small constant for numerical stability                 | `1e-6`                  |
 
 ### Tuning RA-BC Kappa
 
@@ -511,30 +512,30 @@ The `kappa` parameter is the threshold that determines which samples get full we
 
 Monitor these WandB metrics during training:
 
-| Metric             | Healthy Range | Problem Indicator         |
-| ------------------ | ------------- | ------------------------- |
-| `rabc_mean_weight` | 0.3 - 0.8     | ≈ 1.0 means kappa too low |
-| `rabc_delta_mean`  | > 0           | Should be positive        |
-| `rabc_delta_std`   | > 0           | Variance in data quality  |
+| Metric                        | Healthy Range | Problem Indicator         |
+| ----------------------------- | ------------- | ------------------------- |
+| `sample_weight_mean_weight`   | 0.3 - 0.8     | ≈ 1.0 means kappa too low |
+| `sample_weighting/delta_mean` | > 0           | Should be positive        |
+| `sample_weighting/delta_std`  | > 0           | Variance in data quality  |
 
-**If `rabc_mean_weight ≈ 1.0`:** Your kappa is too low. Most samples have `delta > kappa` and bypass the soft-weighting entirely. RA-BC becomes equivalent to vanilla BC.
+**If `sample_weight_mean_weight ≈ 1.0`:** Your kappa is too low. Most samples have `delta > kappa` and bypass the soft-weighting entirely. RA-BC becomes equivalent to vanilla BC.
 
 **Setting kappa based on your data:**
 
-The default `kappa=0.01` was tuned for the paper's T-shirt folding task (~90s episodes at 30fps). For your dataset, check the logged `rabc_delta_mean` and `rabc_delta_std`:
+The default `kappa=0.01` was tuned for the paper's T-shirt folding task (~90s episodes at 30fps). For your dataset, check the logged `sample_weighting/delta_mean` and `sample_weighting/delta_std`:
 
 ```
 # If delta_mean ≈ 0.03 and delta_std ≈ 0.02:
 # Most deltas fall in range [0.01, 0.05]
 
 # Option 1: Set kappa = delta_mean (medium selectivity)
---rabc_kappa=0.03
+--sample_weighting.kappa=0.03
 
 # Option 2: Set kappa = delta_mean + delta_std (high selectivity)
---rabc_kappa=0.05
+--sample_weighting.kappa=0.05
 
 # Option 3: Set kappa = delta_mean + 2*delta_std (very selective)
---rabc_kappa=0.07
+--sample_weighting.kappa=0.07
 ```
 
 **When RA-BC may not help:**
@@ -550,8 +551,8 @@ accelerate launch \
   src/lerobot/scripts/lerobot_train.py \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=pi0 \
-  --use_rabc=true \
-  --rabc_kappa=0.01 \
+  --sample_weighting.type=rabc \
+  --sample_weighting.kappa=0.01 \
   --output_dir=outputs/train/policy_rabc \
   --batch_size=32 \
   --steps=40000
@@ -576,7 +577,7 @@ accelerate launch \
 ### RA-BC
 
 1. **Train SARM first**: RA-BC quality depends entirely on SARM quality
-2. **Monitor `rabc_mean_weight`**: If it's ≈ 1.0, increase kappa (see [Tuning RA-BC Kappa](#tuning-ra-bc-kappa))
+2. **Monitor `sample_weight_mean_weight`**: If it's ≈ 1.0, increase kappa (see [Tuning RA-BC Kappa](#tuning-ra-bc-kappa))
 
 ---
 

docs/source/smolvla.mdx

@@ -108,7 +108,7 @@ lerobot-record \
   --dataset.num_episodes=10 \
   --dataset.streaming_encoding=true \
   --dataset.encoder_threads=2 \
-  # --dataset.vcodec=auto \
+  # --dataset.camera_encoder_config.vcodec=auto \
   # <- Teleop optional if you want to teleoperate in between episodes \
   # --teleop.type=so100_leader \
   # --teleop.port=/dev/ttyACM0 \

docs/source/streaming_video_encoding.mdx

@@ -14,12 +14,22 @@ This makes `save_episode()` near-instant (the video is already encoded by the ti
 
 ## 2. Tuning Parameters
 
-| Parameter               | CLI Flag                          | Type          | Default       | Description                                                       |
-| ----------------------- | --------------------------------- | ------------- | ------------- | ----------------------------------------------------------------- |
-| `streaming_encoding`    | `--dataset.streaming_encoding`    | `bool`        | `True`        | Enable real-time encoding during capture                          |
-| `vcodec`                | `--dataset.vcodec`                | `str`         | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder                     |
-| `encoder_threads`       | `--dataset.encoder_threads`       | `int \| None` | `None` (auto) | Threads per encoder instance. `None` will leave the vcoded decide |
-| `encoder_queue_maxsize` | `--dataset.encoder_queue_maxsize` | `int`         | `60`          | Max buffered frames per camera (~2s at 30fps). Consumes RAM       |
+All encoding parameters are grouped under `camera_encoder_config` (a `VideoEncoderConfig` dataclass), accessible from the CLI via `--dataset.camera_encoder_config.<field>`.
+
+| Parameter               | CLI Flag                                      | Type          | Default       | Description                                                         |
+| ----------------------- | --------------------------------------------- | ------------- | ------------- | ------------------------------------------------------------------- |
+| `streaming_encoding`    | `--dataset.streaming_encoding`                | `bool`        | `True`        | Enable real-time encoding during capture                            |
+| `vcodec`                | `--dataset.camera_encoder_config.vcodec`      | `str`         | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder                       |
+| `pix_fmt`               | `--dataset.camera_encoder_config.pix_fmt`     | `str`         | `"yuv420p"`   | Pixel format                                                        |
+| `g`                     | `--dataset.camera_encoder_config.g`           | `int \| None` | `2`           | GOP size (keyframe interval)                                        |
+| `crf`                   | `--dataset.camera_encoder_config.crf`         | `int \| None` | `30`          | Quality level (mapped to codec-specific parameter)                  |
+| `preset`                | `--dataset.camera_encoder_config.preset`      | `int \| None` | `12`          | Speed preset (libsvtav1 only, 0 = slowest … 13 = fastest)           |
+| `fast_decode`           | `--dataset.camera_encoder_config.fast_decode` | `int`         | `0`           | Fast-decode tuning level                                            |
+| `encoder_threads`       | `--dataset.encoder_threads`                   | `int \| None` | `None` (auto) | Threads per encoder instance (global). `None` lets the codec decide |
+| `encoder_queue_maxsize` | `--dataset.encoder_queue_maxsize`             | `int`         | `60`          | Max buffered frames per camera (~2s at 30fps). Consumes RAM         |
+
+> [!TIP]
+> Not all parameters apply to every codec. `VideoEncoderConfig` will warn at startup if you set a parameter that your chosen codec ignores (e.g. `preset` with `h264_nvenc`).
 
 ## 3. Performance Considerations
 
@@ -40,7 +50,7 @@ Streaming encoding means the CPU is encoding video **during** the capture loop,
 
 ### `encoder_threads` Tuning
 
-This parameter controls how many threads each encoder instance uses internally:
+This parameter (`--dataset.encoder_threads`) controls how many threads each encoder instance uses internally:
 
 - **Higher values** (e.g., 4-5): Faster encoding, but uses more CPU cores per camera. Good for high-end systems with many cores.
 - **Lower values** (e.g., 1-2): Less CPU per camera, freeing cores for capture and visualization. Good for low-res images and capable CPUs.
@@ -82,15 +92,15 @@ Use HW encoding when:
 
 ### Available HW Encoders
 
-| Encoder             | Platform      | Hardware                                                                                         | CLI Value                            |
-| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------ |
-| `h264_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.vcodec=h264_videotoolbox` |
-| `hevc_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.vcodec=hevc_videotoolbox` |
-| `h264_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.vcodec=h264_nvenc`        |
-| `hevc_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.vcodec=hevc_nvenc`        |
-| `h264_vaapi`        | Linux         | Intel/AMD GPU                                                                                    | `--dataset.vcodec=h264_vaapi`        |
-| `h264_qsv`          | Linux/Windows | Intel Quick Sync                                                                                 | `--dataset.vcodec=h264_qsv`          |
-| `auto`              | Any           | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.vcodec=auto`              |
+| Encoder             | Platform      | Hardware                                                                                         | CLI Value                                                  |
+| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | ---------------------------------------------------------- |
+| `h264_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.camera_encoder_config.vcodec=h264_videotoolbox` |
+| `hevc_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.camera_encoder_config.vcodec=hevc_videotoolbox` |
+| `h264_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.camera_encoder_config.vcodec=h264_nvenc`        |
+| `hevc_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.camera_encoder_config.vcodec=hevc_nvenc`        |
+| `h264_vaapi`        | Linux         | Intel/AMD GPU                                                                                    | `--dataset.camera_encoder_config.vcodec=h264_vaapi`        |
+| `h264_qsv`          | Linux/Windows | Intel Quick Sync                                                                                 | `--dataset.camera_encoder_config.vcodec=h264_qsv`          |
+| `auto`              | Any           | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.camera_encoder_config.vcodec=auto`              |
 
 > [!NOTE]
 > In order to use the HW accelerated encoders you might need to upgrade your GPU drivers.
@@ -100,15 +110,15 @@ Use HW encoding when:
 
 ## 5. Troubleshooting
 
-| Symptom                                                            | Likely Cause                                 | Fix                                                                                                                                                                                                                                                                                  |
-| ------------------------------------------------------------------ | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage)                | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.vcodec=auto`) |
-| "Encoder queue full" warnings or dropped frames in dataset         | Encoder can't keep up (Queue overflow)       | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.vcodec=auto`).                                                                                                                                                    |
-| High RAM usage                                                     | Queue filling faster than encoding           | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding                                                                                                                                                                                     |
-| Large video files                                                  | Using HW encoder or H.264                    | Expected trade-off. Switch to `libsvtav1` if CPU allows                                                                                                                                                                                                                              |
-| `save_episode()` still slow                                        | `streaming_encoding` is `False`              | Set `--dataset.streaming_encoding=true`                                                                                                                                                                                                                                              |
-| Encoder thread crash                                               | Codec not available or invalid settings      | Check `vcodec` is installed, try `--dataset.vcodec=auto`                                                                                                                                                                                                                             |
-| Recorded dataset is missing frames                                 | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected.                                   |
+| Symptom                                                            | Likely Cause                                 | Fix                                                                                                                                                                                                                                                                                                        |
+| ------------------------------------------------------------------ | -------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage)                | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.camera_encoder_config.vcodec=auto`) |
+| "Encoder queue full" warnings or dropped frames in dataset         | Encoder can't keep up (Queue overflow)       | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.camera_encoder_config.vcodec=auto`).                                                                                                                                                    |
+| High RAM usage                                                     | Queue filling faster than encoding           | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding                                                                                                                                                                                                           |
+| Large video files                                                  | Using HW encoder or H.264                    | Expected trade-off. Switch to `libsvtav1` if CPU allows                                                                                                                                                                                                                                                    |
+| `save_episode()` still slow                                        | `streaming_encoding` is `False`              | Set `--dataset.streaming_encoding=true`                                                                                                                                                                                                                                                                    |
+| Encoder thread crash                                               | Codec not available or invalid settings      | Check `vcodec` is installed, try `--dataset.camera_encoder_config.vcodec=auto`                                                                                                                                                                                                                             |
+| Recorded dataset is missing frames                                 | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected.                                                         |
 
 ## 6. Recommended Configurations
 
@@ -146,10 +156,10 @@ On very constrained systems, streaming encoding may compete too heavily with the
 # 2camsx 640x480x3 @30fps: Requires some tuning.
 
 # Use H.264, disable streaming, consider batching encoding
-lerobot-record --dataset.vcodec=h264 --dataset.streaming_encoding=false ...
+lerobot-record --dataset.camera_encoder_config.vcodec=h264 --dataset.streaming_encoding=false ...
 ```
 
 ## 7. Closing note
 
 Performance ultimately depends on your exact setup — frames-per-second, resolution, CPU cores and load, available memory, episode length, and the encoder you choose. Always test with your target workload, be mindful about your CPU & system capabilities and tune `encoder_threads`, `encoder_queue_maxsize`, and
-`vcodec` reasonably. That said, a common practical configuration (for many applications) is three cameras at 640×480x3 @30fps; this usually runs fine with the default streaming video encoding settings in modern systems. Always verify your recorded dataset is healthy by comparing the video duration to the CLI episode duration and confirming the row count equals FPS × CLI duration.
+`camera_encoder_config.vcodec` reasonably. That said, a common practical configuration (for many applications) is three cameras at 640×480x3 @30fps; this usually runs fine with the default streaming video encoding settings in modern systems. Always verify your recorded dataset is healthy by comparing the video duration to the CLI episode duration and confirming the row count equals FPS × CLI duration.

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/using_dataset_tools.mdx

@@ -117,10 +117,10 @@ lerobot-edit-dataset \
     --repo_id lerobot/pusht_image \
     --operation.type convert_image_to_video \
     --operation.output_dir outputs/pusht_video \
-    --operation.vcodec libsvtav1 \
-    --operation.pix_fmt yuv420p \
-    --operation.g 2 \
-    --operation.crf 30
+    --operation.camera_encoder_config.vcodec libsvtav1 \
+    --operation.camera_encoder_config.pix_fmt yuv420p \
+    --operation.camera_encoder_config.g 2 \
+    --operation.camera_encoder_config.crf 30
 
 # Convert only specific episodes
 lerobot-edit-dataset \
@@ -147,11 +147,14 @@ lerobot-edit-dataset \
 **Parameters:**
 
 - `output_dir`: Custom output directory (optional - by default uses `new_repo_id` or `{repo_id}_video`)
-- `vcodec`: Video codec to use - options: `h264`, `hevc`, `libsvtav1` (default: `libsvtav1`)
-- `pix_fmt`: Pixel format - options: `yuv420p`, `yuv444p` (default: `yuv420p`)
-- `g`: Group of pictures (GOP) size - lower values give better quality but larger files (default: 2)
-- `crf`: Constant rate factor - lower values give better quality but larger files, 0 is lossless (default: 30)
-- `fast_decode`: Fast decode tuning option (default: 0)
+- `camera_encoder_config`: Video encoder settings — all sub-fields accessible via `--operation.camera_encoder_config.<field>`:
+  - `vcodec`: Video codec — `h264`, `hevc`, `libsvtav1`, `auto`, or hardware codecs (default: `libsvtav1`)
+  - `pix_fmt`: Pixel format — `yuv420p`, `yuv444p` (default: `yuv420p`)
+  - `g`: GOP size — lower values give better quality but larger files (default: 2)
+  - `crf`: Quality level — lower is better, 0 is lossless (default: 30)
+  - `preset`: Speed preset, libsvtav1 only (default: 12)
+  - `fast_decode`: Fast-decode tuning (default: 0)
+  - `encoder_threads`: Threads per encoder instance — global setting, separate from `camera_encoder_config` (default: None)
 - `episode_indices`: List of specific episodes to convert (default: all episodes)
 - `num_workers`: Number of parallel workers for processing (default: 4)
 

docs/source/vlabench.mdx

@@ -0,0 +1,176 @@
+# 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](/home/jade_choghari/robot/lerobot/src/lerobot/policies/xvla/action_hub.py) implementation for details.
+See the [action_hub.py](https://github.com/huggingface/lerobot/blob/main/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/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)
+- [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)
 
 ## Contributing
 

examples/dataset/slurm_compute_rabc.py

@@ -69,7 +69,7 @@ class ComputeProgressShards(PipelineStep):
         import torch
         from tqdm import tqdm
 
-        from lerobot.policies.sarm.compute_rabc_weights import (
+        from lerobot.rewards.sarm.compute_rabc_weights import (
             generate_all_frame_indices,
             interpolate_progress,
             load_sarm_resources,

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/notebooks/quickstart.ipynb

@@ -0,0 +1,342 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🤗 LeRobot Quickstart\n",
+    "\n",
+    "Calibration → teleoperation → data collection → training → evaluation.\n",
+    "\n",
+    "Install the required dependencies: `pip install -e .[notebook,dataset,training,viz,hardware]`.\n",
+    "\n",
+    "**How to use:**\n",
+    "1. Edit the **Configuration** cell with your settings.\n",
+    "2. Run all cells (`Run All`).\n",
+    "3. Each section prints a ready-to-paste terminal command - copy it and run it.\n",
+    "\n",
+    "Each setup is different, please refer to the [LeRobot documentation](https://huggingface.co/docs/lerobot/il_robots) for more details on each step and available options. <br>\n",
+    "Feel free to make this notebook your own and adapt it to your needs!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _cameras_arg(cameras: dict) -> str:\n",
+    "    if not cameras:\n",
+    "        return \"\"\n",
+    "    entries = [f\"{n}: {{{', '.join(f'{k}: {v}' for k, v in cfg.items())}}}\" for n, cfg in cameras.items()]\n",
+    "    return \"{ \" + \", \".join(entries) + \" }\"\n",
+    "\n",
+    "\n",
+    "def print_cmd(*parts: str) -> None:\n",
+    "    \"\"\"Print a shell command with line continuations, skipping empty parts.\"\"\"\n",
+    "    non_empty = [p for p in parts if p]\n",
+    "    print(\" \\\\\\n    \".join(non_empty))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Configuration\n",
+    "\n",
+    "Edit this cell, then **Run All** to generate all commands below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Robot (follower) - run `lerobot-find-port` to discover the port\n",
+    "ROBOT_TYPE = \"so101_follower\"\n",
+    "ROBOT_PORT = \"/dev/ttyACM0\"\n",
+    "ROBOT_ID = \"my_follower_arm\"\n",
+    "\n",
+    "# Teleop (leader) - run `lerobot-find-port` to discover the port\n",
+    "TELEOP_TYPE = \"so101_leader\"\n",
+    "TELEOP_PORT = \"/dev/ttyACM1\"\n",
+    "TELEOP_ID = \"my_leader_arm\"\n",
+    "\n",
+    "# Cameras - set to {} to disable\n",
+    "# Run `lerobot-find-cameras opencv` to list available cameras and their indices\n",
+    "CAMERAS = {\n",
+    "    \"top\": {\"type\": \"opencv\", \"index_or_path\": 2, \"width\": 640, \"height\": 480, \"fps\": 30},\n",
+    "    \"wrist\": {\"type\": \"opencv\", \"index_or_path\": 4, \"width\": 640, \"height\": 480, \"fps\": 30},\n",
+    "}\n",
+    "\n",
+    "# Dataset\n",
+    "HF_USER = \"your_hf_username\"  # `huggingface-cli whoami` to find your username\n",
+    "DATASET_NAME = \"my_so101_dataset\"\n",
+    "TASK_DESCRIPTION = \"pick and place the block\"\n",
+    "NUM_EPISODES = 10\n",
+    "\n",
+    "# Training\n",
+    "POLICY_TYPE = \"act\"  # act, diffusion, smolvla, ...\n",
+    "POLICY_DEVICE = \"cuda\"  # cuda / cpu / mps\n",
+    "TRAIN_STEPS = 10_000\n",
+    "SAVE_FREQ = 2_000\n",
+    "OUTPUT_DIR = f\"outputs/train/{DATASET_NAME}\"\n",
+    "\n",
+    "# Inference - Hub repo ID or local checkpoint path\n",
+    "# e.g. set to f\"{OUTPUT_DIR}/checkpoints/last\" to use a local checkpoint\n",
+    "POLICY_PATH = f\"{HF_USER}/{DATASET_NAME}_{POLICY_TYPE}\"\n",
+    "LAST_CHECKPOINT_PATH = f\"{OUTPUT_DIR}/checkpoints/last\"\n",
+    "\n",
+    "# Derived\n",
+    "DATASET_REPO_ID = f\"{HF_USER}/{DATASET_NAME}\"\n",
+    "DATASET_ROOT = f\"data/{DATASET_NAME}\"\n",
+    "POLICY_REPO_ID = f\"{HF_USER}/{DATASET_NAME}_{POLICY_TYPE}\"\n",
+    "EVAL_REPO_ID = f\"{HF_USER}/eval_{DATASET_NAME}\"\n",
+    "CAMERAS_ARG = _cameras_arg(CAMERAS)\n",
+    "CAMERAS_FLAG = f'--robot.cameras=\"{CAMERAS_ARG}\"' if CAMERAS_ARG else \"\"\n",
+    "\n",
+    "print(f\"Robot  : {ROBOT_TYPE} @ {ROBOT_PORT}\")\n",
+    "print(f\"Teleop : {TELEOP_TYPE} @ {TELEOP_PORT}\")\n",
+    "print(f\"Cameras: {list(CAMERAS) or 'none'}\")\n",
+    "print(f\"Dataset: {DATASET_REPO_ID} ({NUM_EPISODES} episodes) saved to {DATASET_ROOT}\")\n",
+    "print(f\"Policy : {POLICY_TYPE} -> {POLICY_REPO_ID}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Calibration\n",
+    "\n",
+    "Run once per arm before first use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Follower\n",
+    "print_cmd(\n",
+    "    \"lerobot-calibrate\",\n",
+    "    f\"--robot.type={ROBOT_TYPE}\",\n",
+    "    f\"--robot.port={ROBOT_PORT}\",\n",
+    "    f\"--robot.id={ROBOT_ID}\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leader\n",
+    "print_cmd(\n",
+    "    \"lerobot-calibrate\",\n",
+    "    f\"--teleop.type={TELEOP_TYPE}\",\n",
+    "    f\"--teleop.port={TELEOP_PORT}\",\n",
+    "    f\"--teleop.id={TELEOP_ID}\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 2. Teleoperation\n",
+    "\n",
+    "See the [teleoperation docs](https://huggingface.co/docs/lerobot/il_robots#teleoperate) and the [cameras guide](https://huggingface.co/docs/lerobot/cameras) for more options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print_cmd(\n",
+    "    \"lerobot-teleoperate\",\n",
+    "    f\"--robot.type={ROBOT_TYPE}\",\n",
+    "    f\"--robot.port={ROBOT_PORT}\",\n",
+    "    f\"--robot.id={ROBOT_ID}\",\n",
+    "    CAMERAS_FLAG,\n",
+    "    f\"--teleop.type={TELEOP_TYPE}\",\n",
+    "    f\"--teleop.port={TELEOP_PORT}\",\n",
+    "    f\"--teleop.id={TELEOP_ID}\",\n",
+    "    \"--display_data=true\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 3. Record Dataset\n",
+    "\n",
+    "See the [recording docs](https://huggingface.co/docs/lerobot/il_robots#record-a-dataset) for tips on gathering good data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print_cmd(\n",
+    "    \"lerobot-record\",\n",
+    "    f\"--robot.type={ROBOT_TYPE}\",\n",
+    "    f\"--robot.port={ROBOT_PORT}\",\n",
+    "    f\"--robot.id={ROBOT_ID}\",\n",
+    "    CAMERAS_FLAG,\n",
+    "    f\"--teleop.type={TELEOP_TYPE}\",\n",
+    "    f\"--teleop.port={TELEOP_PORT}\",\n",
+    "    f\"--teleop.id={TELEOP_ID}\",\n",
+    "    f\"--dataset.repo_id={DATASET_REPO_ID}\",\n",
+    "    f\"--dataset.num_episodes={NUM_EPISODES}\",\n",
+    "    f'--dataset.single_task=\"{TASK_DESCRIPTION}\"',\n",
+    "    \"--dataset.streaming_encoding=true\",\n",
+    "    \"--display_data=true\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Resume a previously interrupted recording session\n",
+    "print_cmd(\n",
+    "    \"lerobot-record\",\n",
+    "    f\"--robot.type={ROBOT_TYPE}\",\n",
+    "    f\"--robot.port={ROBOT_PORT}\",\n",
+    "    f\"--robot.id={ROBOT_ID}\",\n",
+    "    CAMERAS_FLAG,\n",
+    "    f\"--teleop.type={TELEOP_TYPE}\",\n",
+    "    f\"--teleop.port={TELEOP_PORT}\",\n",
+    "    f\"--teleop.id={TELEOP_ID}\",\n",
+    "    f\"--dataset.repo_id={DATASET_REPO_ID}\",\n",
+    "    f\"--dataset.root={DATASET_ROOT}\",\n",
+    "    f\"--dataset.num_episodes={NUM_EPISODES}\",\n",
+    "    f'--dataset.single_task=\"{TASK_DESCRIPTION}\"',\n",
+    "    \"--dataset.streaming_encoding=true\",\n",
+    "    \"--display_data=true\",\n",
+    "    \"--resume=true\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 4. Train Policy\n",
+    "\n",
+    "See the [training docs](https://huggingface.co/docs/lerobot/il_robots#train-a-policy) for configuration options and tips."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print_cmd(\n",
+    "    \"lerobot-train\",\n",
+    "    f\"--dataset.repo_id={DATASET_REPO_ID}\",\n",
+    "    f\"--policy.type={POLICY_TYPE}\",\n",
+    "    f\"--policy.device={POLICY_DEVICE}\",\n",
+    "    f\"--policy.repo_id={POLICY_REPO_ID}\",\n",
+    "    f\"--output_dir={OUTPUT_DIR}\",\n",
+    "    f\"--steps={TRAIN_STEPS}\",\n",
+    "    f\"--save_freq={SAVE_FREQ}\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Resume a previously interrupted training session\n",
+    "print_cmd(\n",
+    "    \"lerobot-train\",\n",
+    "    f\"--config_path={LAST_CHECKPOINT_PATH}/pretrained_model/train_config.json\",\n",
+    "    \"--resume=true\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 5. Inference\n",
+    "\n",
+    "Uses `POLICY_PATH` from the Configuration cell (defaults to the Hub repo ID). You can also put there the `LAST_CHECKPOINT_PATH`.\n",
+    "\n",
+    "See the [inference docs](https://huggingface.co/docs/lerobot/il_robots#run-inference-and-evaluate-your-policy) for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print_cmd(\n",
+    "    \"lerobot-record\",\n",
+    "    f\"--policy.path={POLICY_PATH}\",\n",
+    "    f\"--robot.type={ROBOT_TYPE}\",\n",
+    "    f\"--robot.port={ROBOT_PORT}\",\n",
+    "    f\"--robot.id={ROBOT_ID}\",\n",
+    "    CAMERAS_FLAG,\n",
+    "    f\"--teleop.type={TELEOP_TYPE}\",\n",
+    "    f\"--teleop.port={TELEOP_PORT}\",\n",
+    "    f\"--teleop.id={TELEOP_ID}\",\n",
+    "    f\"--dataset.repo_id={EVAL_REPO_ID}\",\n",
+    "    f\"--dataset.num_episodes={NUM_EPISODES}\",\n",
+    "    f'--dataset.single_task=\"{TASK_DESCRIPTION}\"',\n",
+    "    \"--dataset.streaming_encoding=true\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lerobot (3.12.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

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()

examples/tutorial/rl/hilserl_example.py

@@ -10,7 +10,7 @@ from lerobot.datasets import LeRobotDataset
 from lerobot.envs.configs import HILSerlProcessorConfig, HILSerlRobotEnvConfig
 from lerobot.policies import SACConfig
 from lerobot.policies.sac.modeling_sac import SACPolicy
-from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
+from lerobot.rewards.classifier.modeling_classifier import Classifier
 from lerobot.rl.buffer import ReplayBuffer
 from lerobot.rl.gym_manipulator import make_robot_env
 from lerobot.robots.so_follower import SO100FollowerConfig

examples/tutorial/rl/reward_classifier_example.py

@@ -1,7 +1,7 @@
 import torch
 
 from lerobot.datasets import LeRobotDataset
-from lerobot.policies import RewardClassifierConfig, make_policy, make_pre_post_processors
+from lerobot.rewards import RewardClassifierConfig, make_reward_model, make_reward_pre_post_processors
 
 
 def main():
@@ -22,10 +22,10 @@ def main():
         model_name="microsoft/resnet-18",
     )
 
-    # Make policy, preprocessor, and optimizer
-    policy = make_policy(config, ds_meta=dataset.meta)
-    optimizer = config.get_optimizer_preset().build(policy.parameters())
-    preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
+    # Make reward model, preprocessor, and optimizer
+    reward_model = make_reward_model(config, dataset_stats=dataset.meta.stats)
+    optimizer = config.get_optimizer_preset().build(reward_model.parameters())
+    preprocessor, _ = make_reward_pre_post_processors(config, dataset_stats=dataset.meta.stats)
 
     classifier_id = "<user>/reward_classifier_hil_serl_example"
 
@@ -42,7 +42,7 @@ def main():
             batch = preprocessor(batch)
 
             # Forward pass
-            loss, output_dict = policy.forward(batch)
+            loss, output_dict = reward_model.forward(batch)
 
             # Backward pass and optimization
             optimizer.zero_grad()
@@ -58,8 +58,8 @@ def main():
 
     print("Training finished!")
 
-    # You can now save the trained policy.
-    policy.push_to_hub(classifier_id)
+    # You can now save the trained reward model.
+    reward_model.push_to_hub(classifier_id)
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -108,9 +108,9 @@ training = [
     "wandb>=0.24.0,<0.25.0",
 ]
 hardware = [
-    "pynput>=1.7.8,<1.9.0",
-    "pyserial>=3.5,<4.0",
-    "deepdiff>=7.0.1,<9.0.0",
+    "lerobot[pynput-dep]",
+    "lerobot[pyserial-dep]",
+    "lerobot[deepdiff-dep]",
 ]
 viz = [
     "rerun-sdk>=0.24.0,<0.27.0",
@@ -136,10 +136,14 @@ scipy-dep = ["scipy>=1.14.0,<2.0.0"]
 diffusers-dep = ["diffusers>=0.27.2,<0.36.0"]
 qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
 matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
+pyserial-dep = ["pyserial>=3.5,<4.0"]
+deepdiff-dep = ["deepdiff>=7.0.1,<9.0.0"]
+pynput-dep = ["pynput>=1.7.8,<1.9.0"]
+pyzmq-dep = ["pyzmq>=26.2.1,<28.0.0"]
 
 # Motors
-feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
-dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"]
+feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0", "lerobot[pyserial-dep]", "lerobot[deepdiff-dep]"]
+dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0", "lerobot[pyserial-dep]", "lerobot[deepdiff-dep]"]
 damiao = ["lerobot[can-dep]"]
 robstride = ["lerobot[can-dep]"]
 
@@ -147,10 +151,11 @@ robstride = ["lerobot[can-dep]"]
 openarms = ["lerobot[damiao]"]
 gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
 hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
-lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
+lekiwi = ["lerobot[feetech]", "lerobot[pyzmq-dep]"]
 unitree_g1 = [
     # "unitree-sdk2==1.0.1",
-    "pyzmq>=26.2.1,<28.0.0",
+    "lerobot[pyzmq-dep]",
+    "lerobot[pyserial-dep]",
     "onnxruntime>=1.16.0,<2.0.0",
     "onnx>=1.16.0,<2.0.0",
     "meshcat>=0.3.0,<0.4.0",
@@ -196,7 +201,8 @@ async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
 peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
 
 # Development
-dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1", "ruff>=0.14.1"]
+dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1", "ruff>=0.14.1", "lerobot[notebook]"]
+notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
 test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
@@ -206,6 +212,20 @@ 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 = [
@@ -269,6 +289,7 @@ 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

@@ -35,9 +35,11 @@ import re
 import sys
 from pathlib import Path
 
-
 # LIBERO-plus derives task.language by space-joining the perturbation-variant
-# filename, so strip the perturbation metadata blob to recover the base prompt.
+# 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+)+)+$"
 )
@@ -72,29 +74,120 @@ def _metaworld_descriptions(task_name: str) -> dict[str, str]:
     return {f"{task_name}_0": label}
 
 
-def _robomme_descriptions(task_names: str) -> dict[str, str]:
-    return {
-        f"{task_name}_0": task_name.replace("_", " ").strip()
-        for task_name in (task.strip() for task in task_names.split(","))
-        if task_name
-    }
+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 in {"libero", "libero_plus"}:
+        if args.env == ("libero", "libero_plus"):
             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)
+            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}'.",

spaces/benchmark-leaderboard/README.md

@@ -1,27 +0,0 @@
----
-title: LeRobot Benchmark Leaderboard
-emoji: 🤖
-colorFrom: yellow
-colorTo: orange
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Benchmark history for LeRobot policy x benchmark runs
----
-
-# LeRobot Benchmark Leaderboard
-
-This Space reads immutable benchmark rows from a Hugging Face dataset and shows:
-
-- Latest result per policy and benchmark
-- Historical trends over time
-- Direct links to uploaded eval and config artifacts
-
-## Configuration
-
-Set `BENCHMARK_RESULTS_REPO` in the Space settings if you want to point the UI
-at a different public dataset. The default is:
-
-- `lerobot/benchmark-history`

spaces/benchmark-leaderboard/app.py

@@ -1,226 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-import json
-import os
-import time
-from pathlib import Path
-from typing import Any
-
-import gradio as gr
-import pandas as pd
-import plotly.express as px
-from huggingface_hub import HfApi, hf_hub_download
-
-RESULTS_REPO = os.environ.get("BENCHMARK_RESULTS_REPO", "lerobot/benchmark-history")
-CACHE_DIR = Path("/tmp/benchmark-leaderboard-cache")
-CACHE_DIR.mkdir(parents=True, exist_ok=True)
-CACHE_TTL_S = 300
-
-_CACHE: dict[str, tuple[float, pd.DataFrame]] = {}
-
-
-def _row_to_record(row: dict[str, Any]) -> dict[str, Any]:
-    overall = row.get("eval", {}).get("overall", {})
-    resources = row.get("resources", {})
-    timings = row.get("timings", {})
-    artifact_urls = row.get("artifact_urls", {})
-    return {
-        "created_at": row.get("created_at"),
-        "benchmark": row.get("benchmark"),
-        "policy": row.get("policy"),
-        "success_rate": overall.get("pc_success"),
-        "n_episodes": overall.get("n_episodes"),
-        "avg_sum_reward": overall.get("avg_sum_reward"),
-        "train_wall_time_s": timings.get("train_wall_time_s"),
-        "eval_wall_time_s": timings.get("eval_wall_time_s"),
-        "total_wall_time_s": timings.get("total_wall_time_s"),
-        "num_gpus": resources.get("num_gpus"),
-        "microbatch_per_gpu": resources.get("microbatch_per_gpu"),
-        "gradient_accumulation_steps": resources.get("gradient_accumulation_steps"),
-        "effective_batch_size": resources.get("effective_batch_size"),
-        "git_commit": row.get("git_commit"),
-        "row_url": artifact_urls.get("row"),
-        "eval_info_url": artifact_urls.get("eval_info"),
-        "train_config_url": artifact_urls.get("train_config"),
-    }
-
-
-def load_rows(repo_id: str = RESULTS_REPO) -> pd.DataFrame:
-    cache_key = f"rows::{repo_id}"
-    cached = _CACHE.get(cache_key)
-    if cached is not None and (time.monotonic() - cached[0]) < CACHE_TTL_S:
-        return cached[1]
-
-    api = HfApi()
-    files = [path for path in api.list_repo_files(repo_id=repo_id, repo_type="dataset") if path.startswith("rows/")]
-    records: list[dict[str, Any]] = []
-    for path_in_repo in sorted(files, reverse=True):
-        local_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=path_in_repo, cache_dir=CACHE_DIR)
-        with open(local_path) as f:
-            row = json.load(f)
-        records.append(_row_to_record(row))
-
-    df = pd.DataFrame.from_records(records)
-    if not df.empty:
-        df["created_at"] = pd.to_datetime(df["created_at"], utc=True)
-        df = df.sort_values("created_at", ascending=False).reset_index(drop=True)
-    _CACHE[cache_key] = (time.monotonic(), df)
-    return df
-
-
-def make_latest_table(df: pd.DataFrame) -> pd.DataFrame:
-    if df.empty:
-        return df
-    latest = (
-        df.sort_values("created_at", ascending=False)
-        .groupby(["benchmark", "policy"], as_index=False)
-        .first()
-        .sort_values(["benchmark", "success_rate"], ascending=[True, False], na_position="last")
-    )
-    return latest[
-        [
-            "benchmark",
-            "policy",
-            "success_rate",
-            "n_episodes",
-            "train_wall_time_s",
-            "eval_wall_time_s",
-            "num_gpus",
-            "effective_batch_size",
-            "git_commit",
-            "row_url",
-            "eval_info_url",
-            "train_config_url",
-        ]
-    ]
-
-
-def make_history_figure(df: pd.DataFrame, benchmark: str, policy: str | None) -> Any:
-    filtered = df[df["benchmark"] == benchmark]
-    if policy and policy != "All":
-        filtered = filtered[filtered["policy"] == policy]
-    if filtered.empty:
-        return px.line(title="No benchmark rows found")
-    fig = px.line(
-        filtered.sort_values("created_at"),
-        x="created_at",
-        y="success_rate",
-        color="policy",
-        markers=True,
-        hover_data=["git_commit", "num_gpus", "train_wall_time_s", "eval_wall_time_s"],
-        title=f"{benchmark} success rate history",
-    )
-    fig.update_layout(yaxis_title="Success rate (%)", xaxis_title="Run time")
-    return fig
-
-
-def make_run_markdown(df: pd.DataFrame, benchmark: str, policy: str | None) -> str:
-    filtered = df[df["benchmark"] == benchmark]
-    if policy and policy != "All":
-        filtered = filtered[filtered["policy"] == policy]
-    if filtered.empty:
-        return "No matching runs yet."
-    latest = filtered.sort_values("created_at", ascending=False).iloc[0]
-    row_link = latest["row_url"] if pd.notna(latest["row_url"]) else None
-    eval_link = latest["eval_info_url"] if pd.notna(latest["eval_info_url"]) else None
-    train_link = latest["train_config_url"] if pd.notna(latest["train_config_url"]) else None
-    lines = [
-        f"Latest run: `{latest['policy']}` on `{latest['benchmark']}`",
-        f"Success rate: `{latest['success_rate']}`",
-        f"GPUs: `{latest['num_gpus']}`",
-        f"Effective batch size: `{latest['effective_batch_size']}`",
-        f"Commit: `{latest['git_commit']}`",
-    ]
-    if row_link:
-        lines.append(f"Row JSON: [open]({row_link})")
-    if eval_link:
-        lines.append(f"Eval Info: [open]({eval_link})")
-    if train_link:
-        lines.append(f"Train Config: [open]({train_link})")
-    return "\n\n".join(lines)
-
-
-def refresh_view(benchmark: str, policy: str) -> tuple[pd.DataFrame, dict[str, Any], Any, str]:
-    df = load_rows()
-    latest_table = make_latest_table(df)
-    benchmark_names = sorted(df["benchmark"].dropna().unique().tolist()) if not df.empty else []
-    if benchmark not in benchmark_names and benchmark_names:
-        benchmark = benchmark_names[0]
-    policy_choices = ["All"]
-    if benchmark and not df.empty:
-        policy_choices.extend(sorted(df[df["benchmark"] == benchmark]["policy"].dropna().unique().tolist()))
-    if policy not in policy_choices:
-        policy = "All"
-    history = make_history_figure(df, benchmark, policy)
-    summary = make_run_markdown(df, benchmark, policy)
-    return latest_table, gr.update(choices=policy_choices, value=policy), history, summary
-
-
-with gr.Blocks(title="LeRobot Benchmark Leaderboard") as demo:
-    gr.Markdown(
-        f"""
-# LeRobot Benchmark Leaderboard
-
-Results dataset: [`{RESULTS_REPO}`](https://huggingface.co/datasets/{RESULTS_REPO})
-"""
-    )
-
-    with gr.Row():
-        benchmark_dropdown = gr.Dropdown(label="Benchmark", choices=[])
-        policy_dropdown = gr.Dropdown(label="Policy", choices=["All"], value="All")
-        refresh_button = gr.Button("Refresh")
-
-    latest_table = gr.Dataframe(label="Latest Results", interactive=False)
-    history_plot = gr.Plot(label="History")
-    latest_summary = gr.Markdown()
-
-    def _initial_state():
-        df = load_rows()
-        benchmarks = sorted(df["benchmark"].dropna().unique().tolist()) if not df.empty else []
-        benchmark = benchmarks[0] if benchmarks else ""
-        latest, policy_choices, history, summary = refresh_view(benchmark, "All")
-        return (
-            gr.update(choices=benchmarks, value=benchmark),
-            policy_choices,
-            latest,
-            history,
-            summary,
-        )
-
-    demo.load(
-        _initial_state,
-        outputs=[benchmark_dropdown, policy_dropdown, latest_table, history_plot, latest_summary],
-    )
-    refresh_button.click(
-        refresh_view,
-        inputs=[benchmark_dropdown, policy_dropdown],
-        outputs=[latest_table, policy_dropdown, history_plot, latest_summary],
-    )
-    benchmark_dropdown.change(
-        refresh_view,
-        inputs=[benchmark_dropdown, policy_dropdown],
-        outputs=[latest_table, policy_dropdown, history_plot, latest_summary],
-    )
-    policy_dropdown.change(
-        refresh_view,
-        inputs=[benchmark_dropdown, policy_dropdown],
-        outputs=[latest_table, policy_dropdown, history_plot, latest_summary],
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()

spaces/benchmark-leaderboard/requirements.txt

@@ -1,4 +0,0 @@
-gradio>=5.0.0,<6.0.0
-plotly>=5.18.0
-pandas>=2.0.0
-huggingface-hub>=1.0.0,<2.0.0

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -33,7 +33,7 @@ import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 import numpy as np  # type: ignore  # TODO: add type stubs for numpy
 
 from lerobot.utils.decorators import check_if_not_connected
-from lerobot.utils.import_utils import _reachy2_sdk_available
+from lerobot.utils.import_utils import _reachy2_sdk_available, require_package
 
 if TYPE_CHECKING or _reachy2_sdk_available:
     from reachy2_sdk.media.camera import CameraView
@@ -76,6 +76,7 @@ class Reachy2Camera(Camera):
         Args:
             config: The configuration settings for the camera.
         """
+        require_package("reachy2_sdk", extra="reachy2")
         super().__init__(config)
 
         self.config = config

src/lerobot/cameras/realsense/camera_realsense.py

@@ -17,18 +17,21 @@ 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 Any
+from typing import TYPE_CHECKING, Any
 
 import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 import numpy as np  # type: ignore  # TODO: add type stubs for numpy
 from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
 
-try:
-    import pyrealsense2 as rs  # type: ignore  # TODO: add type stubs for pyrealsense2
-except Exception as e:
-    logging.info(f"Could not import realsense: {e}")
+from lerobot.utils.import_utils import _pyrealsense2_available, require_package
+
+if TYPE_CHECKING or _pyrealsense2_available:
+    import pyrealsense2 as rs
+else:
+    rs = None
 
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.errors import DeviceNotConnectedError
@@ -39,6 +42,7 @@ 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):
@@ -112,7 +116,7 @@ class RealSenseCamera(Camera):
         Args:
             config: The configuration settings for the camera.
         """
-
+        require_package(pkg_name, extra="intelrealsense", import_name="pyrealsense2")
         super().__init__(config)
 
         self.config = config
@@ -129,6 +133,9 @@ class RealSenseCamera(Camera):
 
         self.rs_pipeline: rs.pipeline | None = None
         self.rs_profile: rs.pipeline_profile | None = None
+        # Meters per uint16 unit on the depth stream. Queried from the device
+        # at connect() time. Typical D-series value is 0.001 (= 1 mm/unit).
+        self.depth_scale: float | None = None
 
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
@@ -186,6 +193,17 @@ class RealSenseCamera(Camera):
             ) from e
 
         self._configure_capture_settings()
+
+        # Query depth scale (meters per uint16 unit) when depth is enabled so
+        # consumers can convert the raw z16 stream to metric distances.
+        if self.use_depth and self.rs_profile is not None:
+            try:
+                depth_sensor = self.rs_profile.get_device().first_depth_sensor()
+                self.depth_scale = float(depth_sensor.get_depth_scale())
+            except RuntimeError as e:
+                logger.warning(f"{self}: failed to query depth scale ({e}); falling back to 0.001 m/unit.")
+                self.depth_scale = 0.001
+
         self._start_read_thread()
 
         # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
@@ -528,7 +546,6 @@ class RealSenseCamera(Camera):
             self.latest_timestamp = None
             self.new_frame_event.clear()
 
-    # NOTE(Steven): Missing implementation for depth for now
     @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
@@ -571,7 +588,6 @@ class RealSenseCamera(Camera):
 
         return frame
 
-    # NOTE(Steven): Missing implementation for depth for now
     @check_if_not_connected
     def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
         """Return the most recent (color) frame captured immediately (Peeking).
@@ -607,6 +623,78 @@ class RealSenseCamera(Camera):
 
         return frame
 
+
+    @check_if_not_connected
+    def async_read_depth(self, timeout_ms: float = 200) -> NDArray[Any]:
+        """Read the latest depth frame asynchronously, in metric meters.
+
+        Mirrors :meth:`async_read` but returns the depth stream rather than the
+        color stream. Output is ``np.uint16`` of shape ``(H, W)``.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If ``use_depth`` is ``False`` for this camera, or if
+                the background read thread is not running.
+            TimeoutError: If no frame becomes available within ``timeout_ms``.
+        """
+        if not self.use_depth:
+            raise RuntimeError(
+                f"{self}: cannot read depth — camera was configured with use_depth=False."
+            )
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+            raise TimeoutError(
+                f"Timed out waiting for depth frame from camera {self} after {timeout_ms} ms."
+            )
+
+        with self.frame_lock:
+            depth_frame = self.latest_depth_frame
+            self.new_frame_event.clear()
+
+        if depth_frame is None:
+            raise RuntimeError(f"Internal error: Event set but no depth frame available for {self}.")
+
+        return depth_frame
+
+    @check_if_not_connected
+    def read_latest_depth(self, max_age_ms: int = 500) -> NDArray[Any]:
+        """Return the most recent depth frame in metric meters (peeking).
+
+        Non-blocking counterpart of :meth:`read_latest` for the depth stream.
+        Output is ``np.float32`` of shape ``(H, W)`` in meters.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If ``use_depth`` is ``False`` for this camera, or if
+                no depth frame has been captured yet.
+            TimeoutError: If the latest depth frame is older than ``max_age_ms``.
+        """
+        if not self.use_depth:
+            raise RuntimeError(
+                f"{self}: cannot read depth — camera was configured with use_depth=False."
+            )
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        with self.frame_lock:
+            depth_frame = self.latest_depth_frame
+            timestamp = self.latest_timestamp
+
+        if depth_frame is None or timestamp is None:
+            raise RuntimeError(f"{self} has not captured any depth frames yet.")
+
+        age_ms = (time.perf_counter() - timestamp) * 1e3
+        if age_ms > max_age_ms:
+            raise TimeoutError(
+                f"{self} latest depth frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+            )
+
+        return depth_frame
+
     def disconnect(self) -> None:
         """
         Disconnects from the camera, stops the pipeline, and cleans up resources.
@@ -630,6 +718,8 @@ class RealSenseCamera(Camera):
             self.rs_pipeline = None
             self.rs_profile = None
 
+        self.depth_scale = None
+
         with self.frame_lock:
             self.latest_color_frame = None
             self.latest_depth_frame = None

src/lerobot/cameras/zmq/camera_zmq.py

@@ -28,12 +28,19 @@ import json
 import logging
 import time
 from threading import Event, Lock, Thread
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import cv2
 import numpy as np
 from numpy.typing import NDArray
 
+from lerobot.utils.import_utils import _zmq_available, require_package
+
+if TYPE_CHECKING or _zmq_available:
+    import zmq
+else:
+    zmq = None
+
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.errors import DeviceNotConnectedError
 
@@ -74,8 +81,8 @@ class ZMQCamera(Camera):
     """
 
     def __init__(self, config: ZMQCameraConfig):
+        require_package("pyzmq", extra="pyzmq-dep", import_name="zmq")
         super().__init__(config)
-        import zmq
 
         self.config = config
         self.server_address = config.server_address
@@ -117,8 +124,6 @@ class ZMQCamera(Camera):
         logger.info(f"Connecting to {self}...")
 
         try:
-            import zmq
-
             self.context = zmq.Context()
             self.socket = self.context.socket(zmq.SUB)
             self.socket.setsockopt_string(zmq.SUBSCRIBE, "")
@@ -180,11 +185,8 @@ class ZMQCamera(Camera):
 
         try:
             message = self.socket.recv_string()
-        except Exception as e:
-            # zmq is lazy-imported in connect(), so check by name to avoid a top-level import
-            if type(e).__name__ == "Again":
-                raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e
-            raise
+        except zmq.Again as e:
+            raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e
 
         # Decode JSON message
         data = json.loads(message)

src/lerobot/common/control_utils.py

@@ -28,6 +28,12 @@ import numpy as np
 import torch
 
 from lerobot.policies import PreTrainedPolicy, prepare_observation_for_inference
+from lerobot.utils.import_utils import _deepdiff_available, require_package
+
+if TYPE_CHECKING or _deepdiff_available:
+    from deepdiff import DeepDiff
+else:
+    DeepDiff = None
 
 if TYPE_CHECKING:
     from lerobot.datasets import LeRobotDataset
@@ -217,10 +223,7 @@ def sanity_check_dataset_robot_compatibility(
     Raises:
         ValueError: If any of the checked metadata fields do not match.
     """
-    from lerobot.utils.import_utils import require_package
-
-    require_package("deepdiff", extra="hardware")
-    from deepdiff import DeepDiff
+    require_package("deepdiff", extra="deepdiff-dep")
 
     from lerobot.utils.constants import DEFAULT_FEATURES
 

src/lerobot/common/wandb_utils.py

@@ -41,8 +41,12 @@ def cfg_to_group(
             return tag
         return tag[:max_tag_length]
 
+    if cfg.is_reward_model_training:
+        trainable_tag = f"reward_model:{cfg.reward_model.type}"
+    else:
+        trainable_tag = f"policy:{cfg.policy.type}"
     lst = [
-        f"policy:{cfg.policy.type}",
+        trainable_tag,
         f"seed:{cfg.seed}",
     ]
     if cfg.dataset is not None:

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 +1,80 @@
+# 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
+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
+
+    def stamp_repo_id(self) -> None:
+        """Append a date-time tag to ``repo_id`` so each recording session gets a unique name.
+
+        Must be called explicitly at dataset *creation* time — not on resume,
+        where the existing ``repo_id`` (already stamped) must be preserved.
+        """
+        if self.repo_id:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            self.repo_id = f"{self.repo_id}_{timestamp}"

src/lerobot/configs/default.py

@@ -17,7 +17,7 @@
 from dataclasses import dataclass, field
 
 from lerobot.transforms import ImageTransformsConfig
-from lerobot.utils.import_utils import get_safe_default_codec
+from lerobot.utils.import_utils import get_safe_default_video_backend
 
 
 @dataclass
@@ -34,7 +34,10 @@ class DatasetConfig:
     image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
     revision: str | None = None
     use_imagenet_stats: bool = True
-    video_backend: str = field(default_factory=get_safe_default_codec)
+    video_backend: str = field(default_factory=get_safe_default_video_backend)
+    # When True, video frames are returned as uint8 tensors (0-255) instead of float32 (0.0-1.0).
+    # This reduces memory and speeds up DataLoader IPC. The training pipeline handles the conversion.
+    return_uint8: bool = False
     streaming: bool = False
 
     def __post_init__(self) -> None:
@@ -67,17 +70,11 @@ class EvalConfig:
     # `batch_size` specifies the number of environments to use in a gym.vector.VectorEnv.
     # Set to 0 for auto-tuning based on available CPU cores and n_episodes.
     batch_size: int = 0
-    # Number of rollout videos to save per evaluated task. Set to 0 to disable videos.
-    max_episodes_rendered: int = 10
     # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
     # Defaults to True; automatically downgraded to SyncVectorEnv when batch_size=1.
     use_async_envs: bool = True
 
     def __post_init__(self) -> None:
-        if self.max_episodes_rendered < 0:
-            raise ValueError(
-                f"`max_episodes_rendered` must be non-negative, got {self.max_episodes_rendered}."
-            )
         if self.batch_size == 0:
             self.batch_size = self._auto_batch_size()
         if self.batch_size > self.n_episodes:

src/lerobot/configs/rewards.py

@@ -0,0 +1,163 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import abc
+import builtins
+import json
+import logging
+import os
+import tempfile
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, TypeVar
+
+import draccus
+from huggingface_hub import hf_hub_download
+from huggingface_hub.constants import CONFIG_NAME
+from huggingface_hub.errors import HfHubHTTPError
+
+from lerobot.configs.types import PolicyFeature
+from lerobot.optim.optimizers import OptimizerConfig
+from lerobot.optim.schedulers import LRSchedulerConfig
+from lerobot.utils.device_utils import auto_select_torch_device, is_torch_device_available
+from lerobot.utils.hub import HubMixin
+
+T = TypeVar("T", bound="RewardModelConfig")
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RewardModelConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
+    """Base configuration for reward models.
+
+    Args:
+    input_features: A dictionary defining the PolicyFeature of the input data for the reward. The key represents
+        the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+    output_features: A dictionary defining the PolicyFeature of the output data for the reward. The key represents
+        the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+    """
+
+    # Reuses PolicyFeature
+    input_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    output_features: dict[str, PolicyFeature] = field(default_factory=dict)
+
+    device: str | None = None
+
+    pretrained_path: str | None = None
+
+    push_to_hub: bool = False
+    repo_id: str | None = None
+
+    # Hub metadata
+    license: str | None = None
+    tags: list[str] | None = None
+    private: bool | None = None
+
+    def __post_init__(self) -> None:
+        if not self.device or not is_torch_device_available(self.device):
+            auto_device = auto_select_torch_device()
+            logger.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.")
+            self.device = auto_device.type
+
+    @property
+    def type(self) -> str:
+        choice_name = self.get_choice_name(self.__class__)
+        if not isinstance(choice_name, str):
+            raise TypeError(f"Expected string from get_choice_name, got {type(choice_name)}")
+        return choice_name
+
+    @property
+    def observation_delta_indices(self) -> list | None:  # type: ignore[type-arg]
+        return None
+
+    @property
+    def action_delta_indices(self) -> list | None:  # type: ignore[type-arg]
+        return None
+
+    @property
+    def reward_delta_indices(self) -> list | None:  # type: ignore[type-arg]
+        return None
+
+    @abc.abstractmethod
+    def get_optimizer_preset(self) -> OptimizerConfig:
+        raise NotImplementedError
+
+    def get_scheduler_preset(self) -> LRSchedulerConfig | None:
+        return None
+
+    def validate_features(self) -> None:
+        pass
+
+    def _save_pretrained(self, save_directory: Path) -> None:
+        with open(save_directory / CONFIG_NAME, "w") as f, draccus.config_type("json"):
+            draccus.dump(self, f, indent=4)
+
+    @classmethod
+    def from_pretrained(
+        cls: builtins.type[T],
+        pretrained_name_or_path: str | Path,
+        *,
+        force_download: bool = False,
+        resume_download: bool | None = None,
+        proxies: dict[Any, Any] | None = None,
+        token: str | bool | None = None,
+        cache_dir: str | Path | None = None,
+        local_files_only: bool = False,
+        revision: str | None = None,
+        **reward_kwargs: Any,
+    ) -> T:
+        model_id = str(pretrained_name_or_path)
+        config_file: str | None = None
+        if Path(model_id).is_dir():
+            if CONFIG_NAME in os.listdir(model_id):
+                config_file = os.path.join(model_id, CONFIG_NAME)
+            else:
+                logger.error(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}")
+        else:
+            try:
+                config_file = hf_hub_download(
+                    repo_id=model_id,
+                    filename=CONFIG_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+            except HfHubHTTPError as e:
+                raise FileNotFoundError(
+                    f"{CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
+                ) from e
+
+        if config_file is None:
+            raise FileNotFoundError(f"{CONFIG_NAME} not found in {model_id}")
+
+        # HACK: Parse the original config to get the config subclass, so that we can
+        # apply cli overrides.
+        with draccus.config_type("json"):
+            orig_config = draccus.parse(cls, config_file, args=[])
+
+        with open(config_file) as f:
+            config = json.load(f)
+
+        config.pop("type", None)
+        with tempfile.NamedTemporaryFile("w+", delete=False, suffix=".json") as f:
+            json.dump(config, f)
+            config_file = f.name
+
+        cli_overrides = reward_kwargs.pop("cli_overrides", [])
+        with draccus.config_type("json"):
+            return draccus.parse(orig_config.__class__, config_file, args=cli_overrides)

src/lerobot/configs/train.py

@@ -13,7 +13,9 @@
 # limitations under the License.
 import builtins
 import datetime as dt
+import json
 import os
+import tempfile
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any
@@ -26,18 +28,57 @@ from lerobot import envs
 from lerobot.configs import parser
 from lerobot.optim import LRSchedulerConfig, OptimizerConfig
 from lerobot.utils.hub import HubMixin
+from lerobot.utils.sample_weighting import SampleWeightingConfig
 
 from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
+from .rewards import RewardModelConfig
 
 TRAIN_CONFIG_NAME = "train_config.json"
 
 
+def _migrate_legacy_rabc_fields(config: dict[str, Any]) -> dict[str, Any] | None:
+    """Return migrated payload for legacy RA-BC fields, or None when no migration is needed."""
+    legacy_fields = (
+        "use_rabc",
+        "rabc_progress_path",
+        "rabc_kappa",
+        "rabc_epsilon",
+        "rabc_head_mode",
+    )
+    if not any(key in config for key in legacy_fields):
+        return None
+
+    migrated_config = dict(config)
+    use_rabc = bool(migrated_config.pop("use_rabc", False))
+    rabc_progress_path = migrated_config.pop("rabc_progress_path", None)
+    rabc_kappa = migrated_config.pop("rabc_kappa", None)
+    rabc_epsilon = migrated_config.pop("rabc_epsilon", None)
+    rabc_head_mode = migrated_config.pop("rabc_head_mode", None)
+
+    # New configs may already define sample_weighting explicitly. In that case,
+    # legacy fields are ignored after being stripped from the payload.
+    if migrated_config.get("sample_weighting") is None and use_rabc:
+        sample_weighting: dict[str, Any] = {"type": "rabc"}
+        if rabc_progress_path is not None:
+            sample_weighting["progress_path"] = rabc_progress_path
+        if rabc_kappa is not None:
+            sample_weighting["kappa"] = rabc_kappa
+        if rabc_epsilon is not None:
+            sample_weighting["epsilon"] = rabc_epsilon
+        if rabc_head_mode is not None:
+            sample_weighting["head_mode"] = rabc_head_mode
+        migrated_config["sample_weighting"] = sample_weighting
+
+    return migrated_config
+
+
 @dataclass
 class TrainPipelineConfig(HubMixin):
     dataset: DatasetConfig
     env: envs.EnvConfig | None = None
     policy: PreTrainedConfig | None = None
+    reward_model: RewardModelConfig | None = None
     # Set `dir` to where you would like to save all of the run outputs. If you run another training session
     # with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
     output_dir: Path | None = None
@@ -56,7 +97,8 @@ class TrainPipelineConfig(HubMixin):
     # Number of workers for the dataloader.
     num_workers: int = 4
     batch_size: int = 8
-    gradient_accumulation_steps: int = 1
+    prefetch_factor: int = 4
+    persistent_workers: bool = True
     steps: int = 100_000
     eval_freq: int = 20_000
     log_freq: int = 200
@@ -71,27 +113,41 @@ class TrainPipelineConfig(HubMixin):
     wandb: WandBConfig = field(default_factory=WandBConfig)
     peft: PeftConfig | None = None
 
-    # RA-BC (Reward-Aligned Behavior Cloning) parameters
-    use_rabc: bool = False  # Enable reward-weighted training
-    rabc_progress_path: str | None = None  # Path to precomputed SARM progress parquet file
-    rabc_kappa: float = 0.01  # Hard threshold for high-quality samples
-    rabc_epsilon: float = 1e-6  # Small constant for numerical stability
-    rabc_head_mode: str | None = "sparse"  # For dual-head models: "sparse" or "dense"
+    # Sample weighting configuration (e.g., for RA-BC training)
+    sample_weighting: SampleWeightingConfig | None = None
 
     # Rename map for the observation to override the image and state keys
     rename_map: dict[str, str] = field(default_factory=dict)
     checkpoint_path: Path | None = field(init=False, default=None)
 
+    @property
+    def is_reward_model_training(self) -> bool:
+        """True when the config targets a reward model rather than a policy."""
+        return self.reward_model is not None
+
+    @property
+    def trainable_config(self) -> PreTrainedConfig | RewardModelConfig:
+        """Return whichever config (policy or reward_model) is active."""
+        if self.is_reward_model_training:
+            return self.reward_model  # type: ignore[return-value]
+        return self.policy  # type: ignore[return-value]
+
     def validate(self) -> None:
         # HACK: We parse again the cli args here to get the pretrained paths if there was some.
         policy_path = parser.get_path_arg("policy")
-        if policy_path:
-            # Only load the policy config
+        reward_model_path = parser.get_path_arg("reward_model")
+
+        if reward_model_path:
+            cli_overrides = parser.get_cli_overrides("reward_model")
+            self.reward_model = RewardModelConfig.from_pretrained(
+                reward_model_path, cli_overrides=cli_overrides
+            )
+            self.reward_model.pretrained_path = str(Path(reward_model_path))
+        elif policy_path:
             cli_overrides = parser.get_cli_overrides("policy")
             self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
             self.policy.pretrained_path = Path(policy_path)
         elif self.resume:
-            # The entire train config is already loaded, we just need to get the checkpoint dir
             config_path = parser.parse_arg("config_path")
             if not config_path:
                 raise ValueError(
@@ -107,18 +163,22 @@ class TrainPipelineConfig(HubMixin):
             policy_dir = Path(config_path).parent
             if self.policy is not None:
                 self.policy.pretrained_path = policy_dir
+            if self.reward_model is not None:
+                self.reward_model.pretrained_path = str(policy_dir)
             self.checkpoint_path = policy_dir.parent
 
-        if self.policy is None:
+        if self.policy is None and self.reward_model is None:
             raise ValueError(
-                "Policy is not configured. Please specify a pretrained policy with `--policy.path`."
+                "Neither policy nor reward_model is configured. "
+                "Please specify one with `--policy.path` or `--reward_model.path`."
             )
 
+        active_cfg = self.trainable_config
         if not self.job_name:
             if self.env is None:
-                self.job_name = f"{self.policy.type}"
+                self.job_name = f"{active_cfg.type}"
             else:
-                self.job_name = f"{self.env.type}_{self.policy.type}"
+                self.job_name = f"{self.env.type}_{active_cfg.type}"
 
         if not self.resume and isinstance(self.output_dir, Path) and self.output_dir.is_dir():
             raise FileExistsError(
@@ -133,34 +193,19 @@ class TrainPipelineConfig(HubMixin):
         if isinstance(self.dataset.repo_id, list):
             raise NotImplementedError("LeRobotMultiDataset is not currently implemented.")
 
-        if self.gradient_accumulation_steps <= 0:
-            raise ValueError(
-                f"`gradient_accumulation_steps` must be strictly positive, got {self.gradient_accumulation_steps}."
-            )
-
         if not self.use_policy_training_preset and (self.optimizer is None or self.scheduler is None):
             raise ValueError("Optimizer and Scheduler must be set when the policy presets are not used.")
         elif self.use_policy_training_preset and not self.resume:
-            self.optimizer = self.policy.get_optimizer_preset()
-            self.scheduler = self.policy.get_scheduler_preset()
+            self.optimizer = active_cfg.get_optimizer_preset()
+            self.scheduler = active_cfg.get_scheduler_preset()
 
-        if self.policy.push_to_hub and not self.policy.repo_id:
-            raise ValueError(
-                "'policy.repo_id' argument missing. Please specify it to push the model to the hub."
-            )
-
-        if self.use_rabc and not self.rabc_progress_path:
-            # Auto-detect from dataset path
-            repo_id = self.dataset.repo_id
-            if self.dataset.root:
-                self.rabc_progress_path = str(Path(self.dataset.root) / "sarm_progress.parquet")
-            else:
-                self.rabc_progress_path = f"hf://datasets/{repo_id}/sarm_progress.parquet"
+        if hasattr(active_cfg, "push_to_hub") and active_cfg.push_to_hub and not active_cfg.repo_id:
+            raise ValueError("'repo_id' argument missing. Please specify it to push the model to the hub.")
 
     @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"]
+        """Keys for draccus pretrained-path loading."""
+        return ["policy", "reward_model"]
 
     def to_dict(self) -> dict[str, Any]:
         return draccus.encode(self)  # type: ignore[no-any-return]  # because of the third-party library draccus uses Any as the return type
@@ -211,6 +256,15 @@ class TrainPipelineConfig(HubMixin):
                 ) from e
 
         cli_args = kwargs.pop("cli_args", [])
+        if config_file is not None:
+            with open(config_file) as f:
+                config = json.load(f)
+            migrated_config = _migrate_legacy_rabc_fields(config)
+            if migrated_config is not None:
+                with tempfile.NamedTemporaryFile("w+", delete=False, suffix=".json") as f:
+                    json.dump(migrated_config, f)
+                    config_file = f.name
+
         with draccus.config_type("json"):
             return draccus.parse(cls, config_file, args=cli_args)
 

src/lerobot/datasets/__init__.py

@@ -40,10 +40,21 @@ from .io_utils import load_episodes, write_stats
 from .lerobot_dataset import LeRobotDataset
 from .multi_dataset import MultiLeRobotDataset
 from .pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from .pyav_utils import (
+    check_video_encoder_config_pyav,
+    detect_available_encoders_pyav,
+    get_codec,
+)
 from .sampler import EpisodeAwareSampler
 from .streaming_dataset import StreamingLeRobotDataset
 from .utils import DEFAULT_EPISODES_PATH, create_lerobot_dataset_card
-from .video_utils import VideoEncodingManager
+from .video_utils import (
+    DepthEncoderConfig,
+    VideoEncoderConfig,
+    VideoEncodingManager,
+    camera_encoder_defaults,
+    depth_encoder_defaults,
+)
 
 # NOTE: Low-level I/O functions (cast_stats_to_numpy, get_parquet_file_size_in_mb, etc.)
 # and legacy migration constants are intentionally NOT re-exported here.
@@ -58,15 +69,22 @@ __all__ = [
     "LeRobotDatasetMetadata",
     "MultiLeRobotDataset",
     "StreamingLeRobotDataset",
+    "DepthEncoderConfig",
+    "VideoEncoderConfig",
     "VideoEncodingManager",
+    "camera_encoder_defaults",
+    "depth_encoder_defaults",
     "add_features",
     "aggregate_datasets",
     "aggregate_pipeline_dataset_features",
     "aggregate_stats",
+    "check_video_encoder_config_pyav",
     "convert_image_to_video_dataset",
     "create_initial_features",
     "create_lerobot_dataset_card",
     "delete_episodes",
+    "detect_available_encoders_pyav",
+    "get_codec",
     "get_feature_stats",
     "load_episodes",
     "make_dataset",

src/lerobot/datasets/aggregate.py

@@ -97,8 +97,8 @@ def update_data_df(df, src_meta, dst_meta):
         pd.DataFrame: Updated DataFrame with adjusted indices.
     """
 
-    df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"]
-    df["index"] = df["index"] + dst_meta.info["total_frames"]
+    df["episode_index"] = df["episode_index"] + dst_meta.info.total_episodes
+    df["index"] = df["index"] + dst_meta.info.total_frames
 
     src_task_names = src_meta.tasks.index.take(df["task_index"].to_numpy())
     df["task_index"] = dst_meta.tasks.loc[src_task_names, "task_index"].to_numpy()
@@ -225,9 +225,9 @@ def update_meta_data(
         # Clean up temporary columns
         df = df.drop(columns=["_orig_chunk", "_orig_file"])
 
-    df["dataset_from_index"] = df["dataset_from_index"] + dst_meta.info["total_frames"]
-    df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info["total_frames"]
-    df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"]
+    df["dataset_from_index"] = df["dataset_from_index"] + dst_meta.info.total_frames
+    df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info.total_frames
+    df["episode_index"] = df["episode_index"] + dst_meta.info.total_episodes
 
     return df
 
@@ -237,8 +237,8 @@ def aggregate_datasets(
     aggr_repo_id: str,
     roots: list[Path] | None = None,
     aggr_root: Path | None = None,
-    data_files_size_in_mb: float | None = None,
-    video_files_size_in_mb: float | None = None,
+    data_files_size_in_mb: int | None = None,
+    video_files_size_in_mb: int | None = None,
     chunk_size: int | None = None,
 ):
     """Aggregates multiple LeRobot datasets into a single unified dataset.
@@ -313,8 +313,8 @@ def aggregate_datasets(
         # to avoid interference between different source datasets
         data_idx.pop("src_to_dst", None)
 
-        dst_meta.info["total_episodes"] += src_meta.total_episodes
-        dst_meta.info["total_frames"] += src_meta.total_frames
+        dst_meta.info.total_episodes += src_meta.total_episodes
+        dst_meta.info.total_frames += src_meta.total_frames
 
     finalize_aggregation(dst_meta, all_metadata)
     logging.info("Aggregation complete.")
@@ -332,7 +332,6 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
         videos_idx: Dictionary tracking video chunk and file indices.
         video_files_size_in_mb: Maximum size for video files in MB (defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB)
         chunk_size: Maximum number of files per chunk (defaults to DEFAULT_CHUNK_SIZE)
-
     Returns:
         dict: Updated videos_idx with current chunk and file indices.
     """
@@ -417,6 +416,7 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
                 concatenate_video_files(
                     [dst_path, src_path],
                     dst_path,
+                    compatibility_check=True,
                 )
                 # Update duration of this destination file
                 dst_file_durations[dst_key] = current_dst_duration + src_duration
@@ -640,14 +640,10 @@ def finalize_aggregation(aggr_meta, all_metadata):
     write_tasks(aggr_meta.tasks, aggr_meta.root)
 
     logging.info("write info")
-    aggr_meta.info.update(
-        {
-            "total_tasks": len(aggr_meta.tasks),
-            "total_episodes": sum(m.total_episodes for m in all_metadata),
-            "total_frames": sum(m.total_frames for m in all_metadata),
-            "splits": {"train": f"0:{sum(m.total_episodes for m in all_metadata)}"},
-        }
-    )
+    aggr_meta.info.total_tasks = len(aggr_meta.tasks)
+    aggr_meta.info.total_episodes = sum(m.total_episodes for m in all_metadata)
+    aggr_meta.info.total_frames = sum(m.total_frames for m in all_metadata)
+    aggr_meta.info.splits = {"train": f"0:{sum(m.total_episodes for m in all_metadata)}"}
     write_info(aggr_meta.info, aggr_meta.root)
 
     logging.info("write stats")

src/lerobot/datasets/dataset_metadata.py

@@ -37,20 +37,18 @@ from .io_utils import (
     load_subtasks,
     load_tasks,
     write_info,
-    write_json,
     write_stats,
     write_tasks,
 )
 from .utils import (
     DEFAULT_EPISODES_PATH,
-    INFO_PATH,
     check_version_compatibility,
     get_safe_version,
     has_legacy_hub_download_metadata,
     is_valid_version,
     update_chunk_file_indices,
 )
-from .video_utils import get_video_info
+from .video_utils import VideoEncoderConfig, get_video_info
 
 CODEBASE_VERSION = "v3.0"
 
@@ -228,7 +226,7 @@ class LeRobotDatasetMetadata:
     @property
     def _version(self) -> packaging.version.Version:
         """Codebase version used to create this dataset."""
-        return packaging.version.parse(self.info["codebase_version"])
+        return packaging.version.parse(self.info.codebase_version)
 
     def get_data_file_path(self, ep_index: int) -> Path:
         """Return the relative parquet file path for the given episode index.
@@ -283,27 +281,27 @@ class LeRobotDatasetMetadata:
     @property
     def data_path(self) -> str:
         """Formattable string for the parquet files."""
-        return self.info["data_path"]
+        return self.info.data_path
 
     @property
     def video_path(self) -> str | None:
         """Formattable string for the video files."""
-        return self.info["video_path"]
+        return self.info.video_path
 
     @property
     def robot_type(self) -> str | None:
         """Robot type used in recording this dataset."""
-        return self.info["robot_type"]
+        return self.info.robot_type
 
     @property
     def fps(self) -> int:
         """Frames per second used during data collection."""
-        return self.info["fps"]
+        return self.info.fps
 
     @property
     def features(self) -> dict[str, dict]:
         """All features contained in the dataset."""
-        return self.info["features"]
+        return self.info.features
 
     @property
     def image_keys(self) -> list[str]:
@@ -315,6 +313,20 @@ class LeRobotDatasetMetadata:
         """Keys to access visual modalities stored as videos."""
         return [key for key, ft in self.features.items() if ft["dtype"] == "video"]
 
+    @property
+    def depth_keys(self) -> list[str]:
+        """Keys to access depth-map modalities stored as videos.
+
+        A depth video key is a feature whose ``info`` dict carries
+        ``"video.is_depth_map": True`` (set either at creation time by the user
+        or after the first encoded episode by :meth:`update_video_info`).
+        """
+        return [
+            key
+            for key, ft in self.features.items()
+            if ft["dtype"] == "video" and ft.get("info", {}).get("video.is_depth_map", False)
+        ]
+
     @property
     def camera_keys(self) -> list[str]:
         """Keys to access visual modalities (regardless of their storage method)."""
@@ -333,32 +345,32 @@ class LeRobotDatasetMetadata:
     @property
     def total_episodes(self) -> int:
         """Total number of episodes available."""
-        return self.info["total_episodes"]
+        return self.info.total_episodes
 
     @property
     def total_frames(self) -> int:
         """Total number of frames saved in this dataset."""
-        return self.info["total_frames"]
+        return self.info.total_frames
 
     @property
     def total_tasks(self) -> int:
         """Total number of different tasks performed in this dataset."""
-        return self.info["total_tasks"]
+        return self.info.total_tasks
 
     @property
     def chunks_size(self) -> int:
         """Max number of files per chunk."""
-        return self.info["chunks_size"]
+        return self.info.chunks_size
 
     @property
     def data_files_size_in_mb(self) -> int:
         """Max size of data file in mega bytes."""
-        return self.info["data_files_size_in_mb"]
+        return self.info.data_files_size_in_mb
 
     @property
     def video_files_size_in_mb(self) -> int:
         """Max size of video file in mega bytes."""
-        return self.info["video_files_size_in_mb"]
+        return self.info.video_files_size_in_mb
 
     def get_task_index(self, task: str) -> int | None:
         """
@@ -502,29 +514,48 @@ class LeRobotDatasetMetadata:
         self._save_episode_metadata(episode_dict)
 
         # Update info
-        self.info["total_episodes"] += 1
-        self.info["total_frames"] += episode_length
-        self.info["total_tasks"] = len(self.tasks)
-        self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"}
+        self.info.total_episodes += 1
+        self.info.total_frames += episode_length
+        self.info.total_tasks = len(self.tasks)
+        self.info.splits = {"train": f"0:{self.info.total_episodes}"}
 
         write_info(self.info, self.root)
 
         self.stats = aggregate_stats([self.stats, episode_stats]) if self.stats is not None else episode_stats
         write_stats(self.stats, self.root)
 
-    def update_video_info(self, video_key: str | None = None) -> None:
-        """
+    def update_video_info(
+        self,
+        video_key: str | None = None,
+        camera_encoder_config: VideoEncoderConfig | None = None,
+    ) -> None:
+        """Populate per-feature video info in ``info.json``.
+
         Warning: this function writes info from first episode videos, implicitly assuming that all videos have
         been encoded the same way. Also, this means it assumes the first episode exists.
+
+        Args:
+            video_key: If provided, only update this video key. Otherwise update
+                all video keys in the dataset.
+            camera_encoder_config: Encoder configuration used to produce the
+                videos. When provided, its fields are recorded as
+                ``video.<field>`` entries alongside the stream-derived
+                ``video.*`` entries (see :func:`get_video_info`).
         """
         if video_key is not None and video_key not in self.video_keys:
             raise ValueError(f"Video key {video_key} not found in dataset")
 
         video_keys = [video_key] if video_key is not None else self.video_keys
         for key in video_keys:
-            if not self.features[key].get("info", None):
+            existing = self.features[key].get("info") or {}
+            # Repopulate when codec metadata is missing — preserves user-provided
+            # markers like ``video.is_depth_map`` while still recording stream
+            # info on the first episode.
+            if not existing or "video.codec" not in existing:
                 video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
-                self.info["features"][key]["info"] = get_video_info(video_path)
+                stream_info = get_video_info(video_path, camera_encoder_config=camera_encoder_config)
+                merged = {**existing, **stream_info}
+                self.info.features[key]["info"] = merged
 
     def update_chunk_settings(
         self,
@@ -546,17 +577,17 @@ class LeRobotDatasetMetadata:
         if chunks_size is not None:
             if chunks_size <= 0:
                 raise ValueError(f"chunks_size must be positive, got {chunks_size}")
-            self.info["chunks_size"] = chunks_size
+            self.info.chunks_size = chunks_size
 
         if data_files_size_in_mb is not None:
             if data_files_size_in_mb <= 0:
                 raise ValueError(f"data_files_size_in_mb must be positive, got {data_files_size_in_mb}")
-            self.info["data_files_size_in_mb"] = data_files_size_in_mb
+            self.info.data_files_size_in_mb = data_files_size_in_mb
 
         if video_files_size_in_mb is not None:
             if video_files_size_in_mb <= 0:
                 raise ValueError(f"video_files_size_in_mb must be positive, got {video_files_size_in_mb}")
-            self.info["video_files_size_in_mb"] = video_files_size_in_mb
+            self.info.video_files_size_in_mb = video_files_size_in_mb
 
         # Update the info file on disk
         write_info(self.info, self.root)
@@ -653,7 +684,7 @@ class LeRobotDatasetMetadata:
                 f"Features contain video keys {obj.video_keys}, but 'use_videos' is set to False. "
                 "Either remove video features from the features dict, or set 'use_videos=True'."
             )
-        write_json(obj.info, obj.root / INFO_PATH)
+        write_info(obj.info, obj.root)
         obj.revision = None
         obj._pq_writer = None
         obj.latest_episode = None

src/lerobot/datasets/dataset_reader.py

@@ -16,6 +16,7 @@
 """Private reader component for LeRobotDataset. Handles random-access reading (HF dataset, delta indices, video decoding)."""
 
 from collections.abc import Callable
+from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
 
 import datasets
@@ -31,7 +32,13 @@ from .io_utils import (
     hf_transform_to_torch,
     load_nested_dataset,
 )
-from .video_utils import decode_video_frames
+from .video_utils import decode_depth_frames, decode_video_frames
+from .depth_utils import (
+    DEFAULT_DEPTH_MIN, 
+    DEFAULT_DEPTH_MAX, 
+    DEFAULT_DEPTH_SHIFT, 
+    DEFAULT_DEPTH_USE_LOG,
+)
 
 
 class DatasetReader:
@@ -49,6 +56,7 @@ class DatasetReader:
         video_backend: str,
         delta_timestamps: dict[str, list[float]] | None,
         image_transforms: Callable | None,
+        return_uint8: bool = False,
     ):
         """Initialize the reader with metadata, filtering, and transform config.
 
@@ -73,6 +81,7 @@ class DatasetReader:
         self._tolerance_s = tolerance_s
         self._video_backend = video_backend
         self._image_transforms = image_transforms
+        self._return_uint8 = return_uint8
 
         self.hf_dataset: datasets.Dataset | None = None
         self._absolute_to_relative_idx: dict[int, int] | None = None
@@ -105,10 +114,8 @@ class DatasetReader:
         """Build absolute-to-relative index mapping from loaded hf_dataset."""
         self._absolute_to_relative_idx = None
         if self.episodes is not None and self.hf_dataset is not None:
-            self._absolute_to_relative_idx = {
-                abs_idx.item() if isinstance(abs_idx, torch.Tensor) else abs_idx: rel_idx
-                for rel_idx, abs_idx in enumerate(self.hf_dataset["index"])
-            }
+            indices = self.hf_dataset.data.column("index").to_numpy()
+            self._absolute_to_relative_idx = dict(zip(indices.tolist(), range(len(indices)), strict=True))
 
     @property
     def num_frames(self) -> int:
@@ -235,16 +242,44 @@ class DatasetReader:
         Segmentation Fault.
         """
         ep = self._meta.episodes[ep_idx]
-        item = {}
-        for vid_key, query_ts in query_timestamps.items():
+
+        depth_keys = set(self._meta.depth_keys)
+
+        def _decode_single(vid_key: str, query_ts: list[float]) -> tuple[str, torch.Tensor]:
             from_timestamp = ep[f"videos/{vid_key}/from_timestamp"]
             shifted_query_ts = [from_timestamp + ts for ts in query_ts]
-
             video_path = self.root / self._meta.get_video_file_path(ep_idx, vid_key)
-            frames = decode_video_frames(video_path, shifted_query_ts, self._tolerance_s, self._video_backend)
-            item[vid_key] = frames.squeeze(0)
+            if vid_key in depth_keys:
+                feature_info = self._meta.features[vid_key].get("info") or {}
+                frames = decode_depth_frames(
+                    video_path,
+                    shifted_query_ts,
+                    self._tolerance_s,
+                    depth_min=feature_info.get("video.depth_min", DEFAULT_DEPTH_MIN),
+                    depth_max=feature_info.get("video.depth_max", DEFAULT_DEPTH_MAX),
+                    shift=feature_info.get("video.shift", DEFAULT_DEPTH_SHIFT),
+                    use_log=feature_info.get("video.use_log", DEFAULT_DEPTH_USE_LOG),
+                )
+            else:
+                frames = decode_video_frames(
+                    video_path,
+                    shifted_query_ts,
+                    self._tolerance_s,
+                    self._video_backend,
+                    return_uint8=self._return_uint8,
+                )
+            return vid_key, frames.squeeze(0)
 
-        return item
+        items = list(query_timestamps.items())
+
+        # Single camera: no threading overhead
+        if len(items) <= 1:
+            return {vid_key: _decode_single(vid_key, query_ts)[1] for vid_key, query_ts in items}
+
+        # Multi-camera: decode in parallel (video decoding releases the GIL)
+        with ThreadPoolExecutor(max_workers=len(items)) as pool:
+            futures = [pool.submit(_decode_single, k, ts) for k, ts in items]
+            return dict(f.result() for f in futures)
 
     def get_item(self, idx) -> dict:
         """Core __getitem__ logic. Assumes hf_dataset is loaded.

src/lerobot/datasets/dataset_tools.py

@@ -62,7 +62,7 @@ from .utils import (
     DEFAULT_EPISODES_PATH,
     update_chunk_file_indices,
 )
-from .video_utils import encode_video_frames, get_video_info
+from .video_utils import VideoEncoderConfig, encode_video_frames, get_video_info
 
 
 def _load_episode_with_stats(src_dataset: LeRobotDataset, episode_idx: int) -> dict:
@@ -92,6 +92,7 @@ def delete_episodes(
     episode_indices: list[int],
     output_dir: str | Path | None = None,
     repo_id: str | None = None,
+    camera_encoder_config: VideoEncoderConfig | None = None,
 ) -> LeRobotDataset:
     """Delete episodes from a LeRobotDataset and create a new dataset.
 
@@ -100,6 +101,7 @@ def delete_episodes(
         episode_indices: List of episode indices to delete.
         output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
         repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        camera_encoder_config: Video encoder settings used when re-encoding video segments (default: :class:`VideoEncoderConfig()`).
     """
     if not episode_indices:
         raise ValueError("No episodes to delete")
@@ -132,7 +134,7 @@ def delete_episodes(
 
     video_metadata = None
     if dataset.meta.video_keys:
-        video_metadata = _copy_and_reindex_videos(dataset, new_meta, episode_mapping)
+        video_metadata = _copy_and_reindex_videos(dataset, new_meta, episode_mapping, camera_encoder_config)
 
     data_metadata = _copy_and_reindex_data(dataset, new_meta, episode_mapping)
 
@@ -154,6 +156,7 @@ def split_dataset(
     dataset: LeRobotDataset,
     splits: dict[str, float | list[int]],
     output_dir: str | Path | None = None,
+    camera_encoder_config: VideoEncoderConfig | None = None,
 ) -> dict[str, LeRobotDataset]:
     """Split a LeRobotDataset into multiple smaller datasets.
 
@@ -162,6 +165,7 @@ def split_dataset(
         splits: Either a dict mapping split names to episode indices, or a dict mapping
                 split names to fractions (must sum to <= 1.0).
         output_dir: Root directory where the split datasets will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
+        camera_encoder_config: Video encoder settings used when re-encoding video segments (default: :class:`VideoEncoderConfig()`).
 
     Examples:
       Split by specific episodes
@@ -222,7 +226,9 @@ def split_dataset(
 
         video_metadata = None
         if dataset.meta.video_keys:
-            video_metadata = _copy_and_reindex_videos(dataset, new_meta, episode_mapping)
+            video_metadata = _copy_and_reindex_videos(
+                dataset, new_meta, episode_mapping, camera_encoder_config
+            )
 
         data_metadata = _copy_and_reindex_data(dataset, new_meta, episode_mapping)
 
@@ -578,8 +584,7 @@ def _keep_episodes_from_video_with_av(
     output_path: Path,
     episodes_to_keep: list[tuple[int, int]],
     fps: float,
-    vcodec: str = "libsvtav1",
-    pix_fmt: str = "yuv420p",
+    camera_encoder_config: VideoEncoderConfig | None = None,
 ) -> None:
     """Keep only specified episodes from a video file using PyAV.
 
@@ -593,9 +598,10 @@ def _keep_episodes_from_video_with_av(
             Ranges are half-open intervals: [start_frame, end_frame), where start_frame
             is inclusive and end_frame is exclusive.
         fps: Frame rate of the video.
-        vcodec: Video codec to use for encoding.
-        pix_fmt: Pixel format for output video.
+        camera_encoder_config: Video encoder settings (default: :class:`VideoEncoderConfig()`).
     """
+    if camera_encoder_config is None:
+        camera_encoder_config = VideoEncoderConfig()
     from fractions import Fraction
 
     import av
@@ -619,12 +625,12 @@ def _keep_episodes_from_video_with_av(
 
     # Convert fps to Fraction for PyAV compatibility.
     fps_fraction = Fraction(fps).limit_denominator(1000)
-    v_out = out.add_stream(vcodec, rate=fps_fraction)
+    v_out = out.add_stream(camera_encoder_config.vcodec, rate=fps_fraction)
 
     # PyAV type stubs don't distinguish video streams from audio/subtitle streams.
     v_out.width = v_in.codec_context.width
     v_out.height = v_in.codec_context.height
-    v_out.pix_fmt = pix_fmt
+    v_out.pix_fmt = camera_encoder_config.pix_fmt
 
     # Set time_base to match the frame rate for proper timestamp handling.
     v_out.time_base = Fraction(1, int(fps))
@@ -687,8 +693,7 @@ def _copy_and_reindex_videos(
     src_dataset: LeRobotDataset,
     dst_meta: LeRobotDatasetMetadata,
     episode_mapping: dict[int, int],
-    vcodec: str = "libsvtav1",
-    pix_fmt: str = "yuv420p",
+    camera_encoder_config: VideoEncoderConfig | None = None,
 ) -> dict[int, dict]:
     """Copy and filter video files, only re-encoding files with deleted episodes.
 
@@ -700,10 +705,13 @@ def _copy_and_reindex_videos(
         src_dataset: Source dataset to copy from
         dst_meta: Destination metadata object
         episode_mapping: Mapping from old episode indices to new indices
+        camera_encoder_config: Video encoder settings used when re-encoding segments (default: :class:`VideoEncoderConfig()`).
 
     Returns:
         dict mapping episode index to its video metadata (chunk_index, file_index, timestamps)
     """
+    if camera_encoder_config is None:
+        camera_encoder_config = VideoEncoderConfig()
     if src_dataset.meta.episodes is None:
         src_dataset.meta.episodes = load_episodes(src_dataset.meta.root)
 
@@ -792,8 +800,7 @@ def _copy_and_reindex_videos(
                     dst_video_path,
                     episodes_to_keep_ranges,
                     src_dataset.meta.fps,
-                    vcodec,
-                    pix_fmt,
+                    camera_encoder_config,
                 )
 
                 cumulative_ts = 0.0
@@ -897,14 +904,10 @@ def _copy_and_reindex_episodes_metadata(
 
     dst_meta.finalize()
 
-    dst_meta.info.update(
-        {
-            "total_episodes": len(episode_mapping),
-            "total_frames": total_frames,
-            "total_tasks": len(dst_meta.tasks) if dst_meta.tasks is not None else 0,
-            "splits": {"train": f"0:{len(episode_mapping)}"},
-        }
-    )
+    dst_meta.info.total_episodes = len(episode_mapping)
+    dst_meta.info.total_frames = total_frames
+    dst_meta.info.total_tasks = len(dst_meta.tasks) if dst_meta.tasks is not None else 0
+    dst_meta.info.splits = {"train": f"0:{len(episode_mapping)}"}
     write_info(dst_meta.info, dst_meta.root)
 
     if not all_stats:
@@ -1069,21 +1072,20 @@ def _copy_episodes_metadata_and_stats(
     if episodes_dir.exists():
         shutil.copytree(episodes_dir, dst_episodes_dir, dirs_exist_ok=True)
 
-    dst_meta.info.update(
-        {
-            "total_episodes": src_dataset.meta.total_episodes,
-            "total_frames": src_dataset.meta.total_frames,
-            "total_tasks": src_dataset.meta.total_tasks,
-            "splits": src_dataset.meta.info.get("splits", {"train": f"0:{src_dataset.meta.total_episodes}"}),
-        }
+    dst_meta.info.total_episodes = src_dataset.meta.total_episodes
+    dst_meta.info.total_frames = src_dataset.meta.total_frames
+    dst_meta.info.total_tasks = src_dataset.meta.total_tasks
+    # Preserve original splits if available, otherwise create default
+    dst_meta.info.splits = (
+        src_dataset.meta.info.splits
+        if src_dataset.meta.info.splits
+        else {"train": f"0:{src_dataset.meta.total_episodes}"}
     )
 
     if dst_meta.video_keys and src_dataset.meta.video_keys:
         for key in dst_meta.video_keys:
             if key in src_dataset.meta.features:
-                dst_meta.info["features"][key]["info"] = src_dataset.meta.info["features"][key].get(
-                    "info", {}
-                )
+                dst_meta.info.features[key]["info"] = src_dataset.meta.info.features[key].get("info", {})
 
     write_info(dst_meta.info, dst_meta.root)
 
@@ -1269,11 +1271,7 @@ def _estimate_frame_size_via_calibration(
     episode_indices: list[int],
     temp_dir: Path,
     fps: int,
-    vcodec: str,
-    pix_fmt: str,
-    g: int,
-    crf: int,
-    fast_decode: int,
+    camera_encoder_config: VideoEncoderConfig,
     num_calibration_frames: int = 30,
 ) -> float:
     """Estimate MB per frame by encoding a small calibration sample.
@@ -1287,11 +1285,7 @@ def _estimate_frame_size_via_calibration(
         episode_indices: List of episode indices being processed.
         temp_dir: Temporary directory for calibration files.
         fps: Frames per second for video encoding.
-        vcodec: Video codec (libsvtav1, h264, hevc).
-        pix_fmt: Pixel format (yuv420p, etc.).
-        g: GOP size (group of pictures).
-        crf: Constant Rate Factor (quality).
-        fast_decode: Fast decode tuning parameter.
+        camera_encoder_config: Video encoder settings used for calibration encoding.
         num_calibration_frames: Number of frames to use for calibration (default: 30).
 
     Returns:
@@ -1327,11 +1321,7 @@ def _estimate_frame_size_via_calibration(
             imgs_dir=calibration_dir,
             video_path=calibration_video_path,
             fps=fps,
-            vcodec=vcodec,
-            pix_fmt=pix_fmt,
-            g=g,
-            crf=crf,
-            fast_decode=fast_decode,
+            camera_encoder_config=camera_encoder_config,
             overwrite=True,
         )
 
@@ -1525,7 +1515,7 @@ def modify_tasks(
     write_tasks(new_task_df, root)
 
     # Update info.json
-    dataset.meta.info["total_tasks"] = len(unique_tasks)
+    dataset.meta.info.total_tasks = len(unique_tasks)
     write_info(dataset.meta.info, root)
 
     # Reload metadata to reflect changes
@@ -1649,11 +1639,7 @@ def convert_image_to_video_dataset(
     dataset: LeRobotDataset,
     output_dir: Path | None = None,
     repo_id: str | None = None,
-    vcodec: str = "libsvtav1",
-    pix_fmt: str = "yuv420p",
-    g: int = 2,
-    crf: int = 30,
-    fast_decode: int = 0,
+    camera_encoder_config: VideoEncoderConfig | None = None,
     episode_indices: list[int] | None = None,
     num_workers: int = 4,
     max_episodes_per_batch: int | None = None,
@@ -1668,11 +1654,7 @@ def convert_image_to_video_dataset(
         dataset: The source LeRobot dataset with images
         output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
         repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
-        vcodec: Video codec (default: libsvtav1)
-        pix_fmt: Pixel format (default: yuv420p)
-        g: Group of pictures size (default: 2)
-        crf: Constant rate factor (default: 30)
-        fast_decode: Fast decode tuning (default: 0)
+        camera_encoder_config: Video encoder settings (default: :class:`VideoEncoderConfig()`).
         episode_indices: List of episode indices to convert (None = all episodes)
         num_workers: Number of threads for parallel processing (default: 4)
         max_episodes_per_batch: Maximum episodes per video batch to avoid memory issues (None = no limit)
@@ -1681,6 +1663,9 @@ def convert_image_to_video_dataset(
     Returns:
         New LeRobotDataset with images encoded as videos
     """
+    if camera_encoder_config is None:
+        camera_encoder_config = VideoEncoderConfig()
+
     # Check that it's an image dataset
     if len(dataset.meta.video_keys) > 0:
         raise ValueError(
@@ -1704,7 +1689,10 @@ def convert_image_to_video_dataset(
     logging.info(
         f"Converting {len(episode_indices)} episodes with {len(img_keys)} cameras from {dataset.repo_id}"
     )
-    logging.info(f"Video codec: {vcodec}, pixel format: {pix_fmt}, GOP: {g}, CRF: {crf}")
+    logging.info(
+        f"Video codec: {camera_encoder_config.vcodec}, pixel format: {camera_encoder_config.pix_fmt}, "
+        f"GOP: {camera_encoder_config.g}, CRF: {camera_encoder_config.crf}"
+    )
 
     # Create new features dict, converting image features to video features
     new_features = {}
@@ -1774,11 +1762,7 @@ def convert_image_to_video_dataset(
                 episode_indices=episode_indices,
                 temp_dir=temp_dir,
                 fps=fps,
-                vcodec=vcodec,
-                pix_fmt=pix_fmt,
-                g=g,
-                crf=crf,
-                fast_decode=fast_decode,
+                camera_encoder_config=camera_encoder_config,
             )
 
             logging.info(f"Processing camera: {img_key}")
@@ -1820,11 +1804,7 @@ def convert_image_to_video_dataset(
                     imgs_dir=imgs_dir,
                     video_path=video_path,
                     fps=fps,
-                    vcodec=vcodec,
-                    pix_fmt=pix_fmt,
-                    g=g,
-                    crf=crf,
-                    fast_decode=fast_decode,
+                    camera_encoder_config=camera_encoder_config,
                     overwrite=True,
                 )
 
@@ -1858,10 +1838,10 @@ def convert_image_to_video_dataset(
         episodes_df.to_parquet(episodes_path, index=False)
 
         # Update metadata info
-        new_meta.info["total_episodes"] = len(episode_indices)
-        new_meta.info["total_frames"] = sum(ep["length"] for ep in all_episode_metadata.values())
-        new_meta.info["total_tasks"] = dataset.meta.total_tasks
-        new_meta.info["splits"] = {"train": f"0:{len(episode_indices)}"}
+        new_meta.info.total_episodes = len(episode_indices)
+        new_meta.info.total_frames = sum(ep["length"] for ep in all_episode_metadata.values())
+        new_meta.info.total_tasks = dataset.meta.total_tasks
+        new_meta.info.splits = {"train": f"0:{len(episode_indices)}"}
 
         # Update video info for all image keys (now videos)
         # We need to manually set video info since update_video_info() checks video_keys first
@@ -1870,7 +1850,9 @@ def convert_image_to_video_dataset(
                 video_path = new_meta.root / new_meta.video_path.format(
                     video_key=img_key, chunk_index=0, file_index=0
                 )
-                new_meta.info["features"][img_key]["info"] = get_video_info(video_path)
+                new_meta.info.features[img_key]["info"] = get_video_info(
+                    video_path, camera_encoder_config=camera_encoder_config
+                )
 
         write_info(new_meta.info, new_meta.root)
 

src/lerobot/datasets/dataset_writer.py

@@ -46,15 +46,19 @@ from .io_utils import (
     write_info,
 )
 from .utils import (
+    DEFAULT_DEPTH_PATH,
     DEFAULT_EPISODES_PATH,
     DEFAULT_IMAGE_PATH,
     update_chunk_file_indices,
 )
 from .video_utils import (
+    DepthEncoderConfig,
     StreamingVideoEncoder,
+    VideoEncoderConfig,
     concatenate_video_files,
     encode_video_frames,
     get_video_duration_in_s,
+    is_depth_feature,
 )
 
 logger = logging.getLogger(__name__)
@@ -65,14 +69,19 @@ def _encode_video_worker(
     episode_index: int,
     root: Path,
     fps: int,
-    vcodec: str = "libsvtav1",
+    camera_encoder_config: VideoEncoderConfig | None = None,
     encoder_threads: int | None = None,
 ) -> Path:
     temp_path = Path(tempfile.mkdtemp(dir=root)) / f"{video_key}_{episode_index:03d}.mp4"
     fpath = DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=episode_index, frame_index=0)
     img_dir = (root / fpath).parent
     encode_video_frames(
-        img_dir, temp_path, fps, vcodec=vcodec, overwrite=True, encoder_threads=encoder_threads
+        img_dir,
+        temp_path,
+        fps,
+        camera_encoder_config=camera_encoder_config,
+        encoder_threads=encoder_threads,
+        overwrite=True,
     )
     shutil.rmtree(img_dir)
     return temp_path
@@ -89,33 +98,40 @@ class DatasetWriter:
         self,
         meta: LeRobotDatasetMetadata,
         root: Path,
-        vcodec: str,
+        camera_encoder_config: VideoEncoderConfig,
         encoder_threads: int | None,
         batch_encoding_size: int,
         streaming_encoder: StreamingVideoEncoder | None = None,
         initial_frames: int = 0,
+        depth_encoder_config: DepthEncoderConfig | None = None,
     ):
-        """Initialize the writer with metadata, codec, and encoding config.
+        """Initialize the writer with metadata, codec, and encoder config.
 
         Args:
             meta: Dataset metadata instance (used for feature schema, chunk
                 settings, and episode persistence).
             root: Local dataset root directory.
-            vcodec: Video codec for encoding (e.g. ``'libsvtav1'``, ``'h264'``).
-            encoder_threads: Threads per encoder instance. ``None`` for auto.
+            camera_encoder_config: Video encoder settings applied to all cameras.
+            encoder_threads: Number of encoder threads (global). ``None``
+                lets the codec decide.
             batch_encoding_size: Number of episodes to accumulate before
                 batch-encoding videos.
             streaming_encoder: Optional pre-built :class:`StreamingVideoEncoder`
                 for real-time encoding. ``None`` disables streaming mode.
             initial_frames: Starting frame count (non-zero when resuming).
+            depth_encoder_config: Optional depth-map encoder config used in
+                place of ``camera_encoder_config`` for keys present in
+                ``meta.depth_keys``.
         """
         self._meta = meta
         self._root = root
-        self._vcodec = vcodec
+        self._camera_encoder_config = camera_encoder_config
+        self._depth_encoder_config = depth_encoder_config
         self._encoder_threads = encoder_threads
         self._batch_encoding_size = batch_encoding_size
         self._streaming_encoder = streaming_encoder
 
+
         # Writer state
         self.image_writer: AsyncImageWriter | None = None
         self.episode_buffer: dict = self._create_episode_buffer()
@@ -135,8 +151,16 @@ class DatasetWriter:
             ep_buffer[key] = current_ep_idx if key == "episode_index" else []
         return ep_buffer
 
+    def _is_depth_image_key(self, image_key: str) -> bool:
+        """Whether *image_key* is a depth feature stored as per-frame images."""
+        ft = self._meta.features.get(image_key)
+        if ft is None or ft.get("dtype") != "image":
+            return False
+        return is_depth_feature(ft.get("info") or {})
+
     def _get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> Path:
-        fpath = DEFAULT_IMAGE_PATH.format(
+        path_template = DEFAULT_DEPTH_PATH if self._is_depth_image_key(image_key) else DEFAULT_IMAGE_PATH
+        fpath = path_template.format(
             image_key=image_key, episode_index=episode_index, frame_index=frame_index
         )
         return self._root / fpath
@@ -284,7 +308,7 @@ class DatasetWriter:
                             episode_index,
                             self._root,
                             self._meta.fps,
-                            self._vcodec,
+                            self._camera_encoder_config,
                             self._encoder_threads,
                         ): video_key
                         for video_key in self._meta.video_keys
@@ -495,7 +519,13 @@ class DatasetWriter:
 
         # Update video info (only needed when first episode is encoded)
         if episode_index == 0:
-            self._meta.update_video_info(video_key)
+            is_depth_key = video_key in set(self._meta.depth_keys)
+            cfg_for_info = (
+                self._depth_encoder_config
+                if is_depth_key and self._depth_encoder_config is not None
+                else self._camera_encoder_config
+            )
+            self._meta.update_video_info(video_key, camera_encoder_config=cfg_for_info)
             write_info(self._meta.info, self._meta.root)
 
         metadata = {
@@ -564,7 +594,12 @@ class DatasetWriter:
     def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> Path:
         """Use ffmpeg to convert frames stored as png into mp4 videos."""
         return _encode_video_worker(
-            video_key, episode_index, self._root, self._meta.fps, self._vcodec, self._encoder_threads
+            video_key,
+            episode_index,
+            self._root,
+            self._meta.fps,
+            self._camera_encoder_config,
+            self._encoder_threads,
         )
 
     def close_writer(self) -> None:
@@ -597,7 +632,7 @@ class DatasetWriter:
 
     def cleanup_interrupted_episode(self, episode_index: int) -> None:
         """Remove temporary image directories for an interrupted episode."""
-        for key in self._meta.video_keys:
+        for key in self._meta.camera_keys:
             img_dir = self._get_image_file_path(
                 episode_index=episode_index, image_key=key, frame_index=0
             ).parent

src/lerobot/datasets/depth_utils.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+
+# 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.
+"""
+Depth encoding/decoding helpers for :class:`VideoEncoderConfig`.
+"""
+
+import math
+from typing import Literal
+
+import numpy as np
+import torch
+from numpy.typing import NDArray
+
+DEPTH_QUANT_BITS: int = 12
+DEPTH_QMAX: int = (1 << DEPTH_QUANT_BITS) - 1  # 4095
+_MM_PER_METRE: float = 1000.0
+_UINT16_MAX: int = 65535
+
+DEFAULT_DEPTH_MIN: float = 0.01
+DEFAULT_DEPTH_MAX: float = 10.0
+DEFAULT_DEPTH_SHIFT: float = 3.5
+DEFAULT_DEPTH_USE_LOG: bool = True
+
+
+def _validate_log_quant_params(depth_min: float, shift: float) -> None:
+    """Ensure ``log(depth_min + shift)`` is finite."""
+    if depth_min + shift <= 0:
+        raise ValueError(
+            f"depth_min + shift must be positive for logarithmic quantization, "
+            f"got depth_min={depth_min} + shift={shift} = {depth_min + shift}"
+        )
+
+
+def _depth_input_to_float32_and_unit(
+    depth: NDArray[np.uint16] | NDArray[np.floating] | torch.Tensor,
+    input_unit: Literal["auto", "m", "mm"],
+) -> tuple[NDArray[np.float32], Literal["m", "mm"]]:
+    """Depth as float32 in the chosen unit, plus the resolved unit."""
+    if isinstance(depth, torch.Tensor):
+        t = depth.detach().cpu()
+        arr = t.numpy()
+        is_floating = t.is_floating_point()
+    else:
+        arr = np.asarray(depth)
+        is_floating = np.issubdtype(arr.dtype, np.floating)
+
+    resolved_unit: Literal["m", "mm"]
+    if input_unit == "auto":
+        resolved_unit = "m" if is_floating else "mm"
+    else:
+        resolved_unit = input_unit
+
+    # Convert to float32 to keep typing consistency
+    return np.asarray(arr, dtype=np.float32, order="K"), resolved_unit
+
+
+def quantize_depth(
+    depth: NDArray[np.uint16] | NDArray[np.floating] | torch.Tensor,
+    depth_min: float = DEFAULT_DEPTH_MIN,
+    depth_max: float = DEFAULT_DEPTH_MAX,
+    shift: float = DEFAULT_DEPTH_SHIFT,
+    use_log: bool = DEFAULT_DEPTH_USE_LOG,
+    *,
+    input_unit: Literal["auto", "m", "mm"] = "auto",
+) -> NDArray[np.uint16]:
+    """Quantize depth to 12-bit codes (``uint16``, values ``0…DEPTH_QMAX``).
+
+    Depth maps are packed into 12-bit integer frames so they fit in standard
+    high-bit-depth pixel formats (e.g. ``yuv420p12le`` / ``gray12le``)
+    and can be encoded by widely supported video codecs (HEVC Main 12, ffv1).
+    Logarithmic quantization is the default because it allocates more quanta
+    to near-range depth, which matches the (1/depth) error profile of typical
+    depth sensors. Math is ported from BEHAVIOR-1K's ``obs_utils.py``.
+
+    **Input units**:
+
+    - ``input_unit="auto"`` (default): infer from dtype (floating = m, non-floating = mm).
+    - ``input_unit="mm"``: interpret input values as millimetres.
+    - ``input_unit="m"``: interpret input values as metres.
+
+    Quantization math runs in the **resolved input unit**. 
+    
+    ``depth_min``, ``depth_max``, and ``shift`` are always in **metres**.
+
+    Args:
+        depth: Depth map; ``torch.Tensor`` is moved to CPU for conversion.
+        depth_min: Depth (metres) at quantum ``0``.
+        depth_max: Depth (metres) at quantum :data:`DEPTH_QMAX`.
+        shift: Depth shift (metres); used in log mode. Must satisfy ``depth_min + shift > 0``.
+        use_log: If ``True`` (default), quantize in log space.
+        input_unit: Input unit policy (``"auto"``, ``"mm"``, ``"m"``).
+
+    Returns:
+        ``numpy.ndarray``, ``dtype=uint16``, same shape as ``depth``, values in
+        ``[0, DEPTH_QMAX]``.
+
+    Raises:
+        ValueError: If ``input_unit`` is not ``"auto"``, ``"mm"``, or ``"m"``.
+        ValueError: If ``use_log=True`` and ``depth_min + shift <= 0``.
+    """
+    if input_unit not in ("auto", "m", "mm"):
+        raise ValueError(f"input_unit must be 'auto', 'm', or 'mm', got {input_unit!r}")
+
+    depth_f, resolved_unit = _depth_input_to_float32_and_unit(depth, input_unit=input_unit)
+    depth_min_u = np.float32(depth_min) if resolved_unit == "m" else np.float32(depth_min * _MM_PER_METRE)
+    depth_max_u = np.float32(depth_max) if resolved_unit == "m" else np.float32(depth_max * _MM_PER_METRE)
+    shift_u = np.float32(shift) if resolved_unit == "m" else np.float32(shift * _MM_PER_METRE)
+
+    if use_log:
+        _validate_log_quant_params(depth_min, shift)
+        log_min = math.log(float(depth_min_u + shift_u))
+        log_max = math.log(float(depth_max_u + shift_u))
+        norm = (np.log(depth_f + shift_u) - log_min) / (log_max - log_min)
+    else:
+        norm = (depth_f - depth_min_u) / (depth_max_u - depth_min_u)
+
+    out = np.rint(norm * DEPTH_QMAX).clip(0, DEPTH_QMAX)
+    return out.astype(np.uint16, copy=False)
+
+
+def dequantize_depth(
+    quantized: NDArray[np.uint16] | torch.Tensor,
+    depth_min: float = DEFAULT_DEPTH_MIN,
+    depth_max: float = DEFAULT_DEPTH_MAX,
+    shift: float = DEFAULT_DEPTH_SHIFT,
+    use_log: bool = DEFAULT_DEPTH_USE_LOG,
+    *,
+    output_unit: Literal["m", "mm"] = "mm",
+) -> NDArray[np.uint16] | NDArray[np.float32]:
+    """Inverse of :func:`quantize_depth`.
+
+    Tuning arguments **must match** :func:`quantize_depth`.
+
+    Decoding inverts the same normalized code mapping as :func:`quantize_depth`
+    using ``depth_min`` / ``depth_max`` / ``shift`` (in metres), then returns
+    the requested output unit.
+
+    Args:
+        quantized: 12-bit codes ``[0, DEPTH_QMAX]``, ``dtype=uint16``.
+        depth_min, depth_max, shift, use_log: Same as :func:`quantize_depth` (metres).
+        output_unit: ``\"mm\"`` returns ``uint16`` millimetres (``rint``, clip
+            ``[0, 65535]``). ``\"m\"`` returns ``float32`` metres in
+            ``[depth_min, depth_max]``.
+
+    Returns:
+        Depth map in the requested unit and dtype.
+
+    Raises:
+        ValueError: If ``use_log=True`` and ``depth_min + shift <= 0``.
+        ValueError: If ``output_unit`` is not ``\"m\"`` or ``\"mm\"``.
+    """
+    if output_unit not in ("m", "mm"):
+        raise ValueError(f"output_unit must be 'm' or 'mm', got {output_unit!r}")
+
+    if isinstance(quantized, torch.Tensor):
+        quantized = quantized.detach().cpu().numpy()
+    q = np.asarray(quantized, dtype=np.uint16, order="K")
+    norm = q.astype(np.float32, copy=False) / DEPTH_QMAX
+
+    depth_min_mm = np.float32(depth_min * _MM_PER_METRE)
+    depth_max_mm = np.float32(depth_max * _MM_PER_METRE)
+    shift_mm = np.float32(shift * _MM_PER_METRE)
+
+    if use_log:
+        _validate_log_quant_params(depth_min, shift)
+        log_min = math.log(float(depth_min_mm + shift_mm))
+        log_max = math.log(float(depth_max_mm + shift_mm))
+        depth_mm = np.exp(norm * (log_max - log_min) + log_min) - shift_mm
+    else:
+        depth_mm = norm * (depth_max_mm - depth_min_mm) + depth_min_mm
+
+    depth_mm = np.clip(depth_mm, depth_min_mm, depth_max_mm).astype(np.float32, copy=False)
+    if output_unit == "m":
+        return (depth_mm / np.float32(_MM_PER_METRE)).astype(np.float32, copy=False)
+    mm = np.rint(depth_mm).clip(0, _UINT16_MAX)
+    return mm.astype(np.uint16, copy=False)

src/lerobot/datasets/factory.py

@@ -19,6 +19,7 @@ from pprint import pformat
 import torch
 
 from lerobot.configs import PreTrainedConfig
+from lerobot.configs.rewards import RewardModelConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.transforms import ImageTransforms
 from lerobot.utils.constants import ACTION, IMAGENET_STATS, OBS_PREFIX, REWARD
@@ -30,12 +31,14 @@ from .streaming_dataset import StreamingLeRobotDataset
 
 
 def resolve_delta_timestamps(
-    cfg: PreTrainedConfig, ds_meta: LeRobotDatasetMetadata
+    cfg: PreTrainedConfig | RewardModelConfig, ds_meta: LeRobotDatasetMetadata
 ) -> dict[str, list] | None:
-    """Resolves delta_timestamps by reading from the 'delta_indices' properties of the PreTrainedConfig.
+    """Resolves delta_timestamps by reading from the 'delta_indices' properties of the config.
 
     Args:
-        cfg (PreTrainedConfig): The PreTrainedConfig to read delta_indices from.
+        cfg (PreTrainedConfig | RewardModelConfig): The config to read delta_indices from. Both
+            ``PreTrainedConfig`` and concrete ``RewardModelConfig`` subclasses expose the
+            ``{observation,action,reward}_delta_indices`` properties used below.
         ds_meta (LeRobotDatasetMetadata): The dataset from which features and fps are used to build
             delta_timestamps against.
 
@@ -82,7 +85,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
         ds_meta = LeRobotDatasetMetadata(
             cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision
         )
-        delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
+        delta_timestamps = resolve_delta_timestamps(cfg.trainable_config, ds_meta)
         if not cfg.dataset.streaming:
             dataset = LeRobotDataset(
                 cfg.dataset.repo_id,
@@ -92,6 +95,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 image_transforms=image_transforms,
                 revision=cfg.dataset.revision,
                 video_backend=cfg.dataset.video_backend,
+                return_uint8=True,
                 tolerance_s=cfg.tolerance_s,
             )
         else:
@@ -104,6 +108,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 revision=cfg.dataset.revision,
                 max_num_shards=cfg.num_workers,
                 tolerance_s=cfg.tolerance_s,
+                return_uint8=True,
             )
     else:
         raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")

src/lerobot/datasets/feature_utils.py

@@ -28,6 +28,7 @@ from .utils import (
     DEFAULT_DATA_PATH,
     DEFAULT_VIDEO_FILE_SIZE_IN_MB,
     DEFAULT_VIDEO_PATH,
+    DatasetInfo,
 )
 
 
@@ -78,8 +79,8 @@ def create_empty_dataset_info(
     chunks_size: int | None = None,
     data_files_size_in_mb: int | None = None,
     video_files_size_in_mb: int | None = None,
-) -> dict:
-    """Create a template dictionary for a new dataset's `info.json`.
+) -> DatasetInfo:
+    """Create a template ``DatasetInfo`` object for a new dataset's ``meta/info.json``.
 
     Args:
         codebase_version (str): The version of the LeRobot codebase.
@@ -87,25 +88,24 @@ def create_empty_dataset_info(
         features (dict): The LeRobot features dictionary for the dataset.
         use_videos (bool): Whether the dataset will store videos.
         robot_type (str | None): The type of robot used, if any.
+        chunks_size (int | None): Max files per chunk directory. Defaults to ``DEFAULT_CHUNK_SIZE``.
+        data_files_size_in_mb (int | None): Max parquet file size in MB. Defaults to ``DEFAULT_DATA_FILE_SIZE_IN_MB``.
+        video_files_size_in_mb (int | None): Max video file size in MB. Defaults to ``DEFAULT_VIDEO_FILE_SIZE_IN_MB``.
 
     Returns:
-        dict: A dictionary with the initial dataset metadata.
+        DatasetInfo: A typed dataset information object with initial metadata.
     """
-    return {
-        "codebase_version": codebase_version,
-        "robot_type": robot_type,
-        "total_episodes": 0,
-        "total_frames": 0,
-        "total_tasks": 0,
-        "chunks_size": chunks_size or DEFAULT_CHUNK_SIZE,
-        "data_files_size_in_mb": data_files_size_in_mb or DEFAULT_DATA_FILE_SIZE_IN_MB,
-        "video_files_size_in_mb": video_files_size_in_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB,
-        "fps": fps,
-        "splits": {},
-        "data_path": DEFAULT_DATA_PATH,
-        "video_path": DEFAULT_VIDEO_PATH if use_videos else None,
-        "features": features,
-    }
+    return DatasetInfo(
+        codebase_version=codebase_version,
+        fps=fps,
+        features=features,
+        robot_type=robot_type,
+        chunks_size=chunks_size or DEFAULT_CHUNK_SIZE,
+        data_files_size_in_mb=data_files_size_in_mb or DEFAULT_DATA_FILE_SIZE_IN_MB,
+        video_files_size_in_mb=video_files_size_in_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB,
+        data_path=DEFAULT_DATA_PATH,
+        video_path=DEFAULT_VIDEO_PATH if use_videos else None,
+    )
 
 
 def check_delta_timestamps(
@@ -294,10 +294,20 @@ def validate_feature_image_or_video(
     # Note: The check of pixels range ([0,1] for float and [0,255] for uint8) is done by the image writer threads.
     error_message = ""
     if isinstance(value, np.ndarray):
-        actual_shape = value.shape
-        c, h, w = expected_shape
-        if len(actual_shape) != 3 or (actual_shape != (c, h, w) and actual_shape != (h, w, c)):
-            error_message += f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{(c, h, w)}' or '{(h, w, c)}'.\n"
+        actual_shape = tuple(value.shape)
+        expected = tuple(expected_shape)
+        if len(expected) == 2:
+            # Single-channel features (e.g. depth maps) — accept (H,W), (1,H,W), (H,W,1)
+            h, w = expected
+            valid = actual_shape in {(h, w), (1, h, w), (h, w, 1)}
+            if not valid:
+                error_message += f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{(h, w)}', '{(1, h, w)}', or '{(h, w, 1)}'.\n"
+        elif len(expected) == 3:
+            c, h, w = expected
+            if len(actual_shape) != 3 or (actual_shape != (c, h, w) and actual_shape != (h, w, c)):
+                error_message += f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{(c, h, w)}' or '{(h, w, c)}'.\n"
+        else:
+            error_message += f"The feature '{name}' has an unsupported expected_shape '{expected}'.\n"
     elif isinstance(value, PILImage.Image):
         pass
     else:

src/lerobot/datasets/image_writer.py

@@ -30,26 +30,67 @@ def safe_stop_image_writer(func):
     def wrapper(*args, **kwargs):
         try:
             return func(*args, **kwargs)
-        except Exception as e:
+        except BaseException:
             dataset = kwargs.get("dataset")
             writer = getattr(dataset, "writer", None) if dataset else None
             if writer is not None and writer.image_writer is not None:
                 logger.warning("Waiting for image writer to terminate...")
                 writer.image_writer.stop()
-            raise e
+            raise
 
     return wrapper
 
 
-def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) -> PIL.Image.Image:
-    # TODO(aliberts): handle 1 channel and 4 for depth images
-    if image_array.ndim != 3:
-        raise ValueError(f"The array has {image_array.ndim} dimensions, but 3 is expected for an image.")
+# Single-channel dtypes that PIL natively maps to the matching mode
+# (``uint8`` → ``L``, ``uint16`` → ``I;16``, ``float32`` → ``F``).
+GRAYSCALE_DTYPES: tuple[np.dtype, ...] = (
+    np.dtype("uint8"),
+    np.dtype("uint16"),
+    np.dtype("float32"),
+)
 
+
+def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) -> PIL.Image.Image:
+    """Convert a NumPy array to a PIL Image, preserving precision for grayscale.
+
+    Behaviour by shape:
+
+    - ``(H, W)`` or ``(1, H, W)`` / ``(H, W, 1)``: single-channel grayscale.
+      The native dtype is preserved using the matching PIL mode
+      (``L`` / ``I;16`` / ``F``). This is the path used for raw depth maps (no rescaling, clamping, or downcasting)
+    - ``(3, H, W)`` / ``(H, W, 3)``: RGB. Channels-first inputs are transposed
+      to channels-last. Float inputs in ``[0, 1]`` are scaled to ``uint8``
+      (existing behaviour, gated by ``range_check``).
+
+    Other shapes / channel counts raise ``NotImplementedError`` or
+    ``ValueError``.
+    """
+    if image_array.ndim not in (2, 3):
+        raise ValueError(
+            f"The array has {image_array.ndim} dimensions, but 2 or 3 is expected for an image."
+        )
+
+    # Squeeze 3D single-channel inputs to 2D so depth maps work whether the
+    # caller emits (H, W), (1, H, W), or (H, W, 1).
+    if image_array.ndim == 3:
+        if image_array.shape[0] == 1:
+            image_array = image_array[0]
+        elif image_array.shape[-1] == 1:
+            image_array = image_array[..., 0]
+
+    if image_array.ndim == 2:
+        if image_array.dtype not in GRAYSCALE_DTYPES:
+            raise ValueError(
+                f"Unsupported single-channel image dtype: {image_array.dtype}. "
+                f"Supported dtypes: {sorted(str(d) for d in GRAYSCALE_DTYPES)}."
+            )
+
+        return PIL.Image.fromarray(np.ascontiguousarray(image_array))
+
+    # 3D path: must be RGB (3 channels), channels-first or channels-last.
     if image_array.shape[0] == 3:
         # Transpose from pytorch convention (C, H, W) to (H, W, C)
         image_array = image_array.transpose(1, 2, 0)
-
     elif image_array.shape[-1] != 3:
         raise NotImplementedError(
             f"The image has {image_array.shape[-1]} channels, but 3 is required for now."
@@ -71,13 +112,28 @@ def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True)
     return PIL.Image.fromarray(image_array)
 
 
+def save_kwargs_for_path(fpath: Path, compress_level: int) -> dict:
+    """Pick the right format-specific kwargs for :meth:`PIL.Image.Image.save`.
+
+    PNG uses ``compress_level`` (0–9, zlib). TIFF uses ``compression`` (raw) for lossless raw depth maps.
+    """
+    suffix = Path(fpath).suffix.lower()
+    if suffix == ".png":
+        return {"compress_level": compress_level}
+    if suffix in (".tif", ".tiff"):
+        return {"compression": "raw"}
+    return {}
+
+
 def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1):
     """
     Saves a NumPy array or PIL Image to a file.
 
     This function handles both NumPy arrays and PIL Image objects, converting
     the former to a PIL Image before saving. It includes error handling for
-    the save operation.
+    the save operation. The output format is inferred from the *fpath*
+    extension: ``.png`` → PNG with ``compress_level``, ``.tiff`` / ``.tif``
+    → lossless raw depth maps (TIFF).
 
     Args:
         image (np.ndarray | PIL.Image.Image): The image data to save.
@@ -101,7 +157,7 @@ def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level
             img = image
         else:
             raise TypeError(f"Unsupported image type: {type(image)}")
-        img.save(fpath, compress_level=compress_level)
+        img.save(fpath, **save_kwargs_for_path(Path(fpath), compress_level))
     except Exception as e:
         logger.error("Error writing image %s: %s", fpath, e)
 

src/lerobot/datasets/io_utils.py

@@ -39,6 +39,7 @@ from .utils import (
     EPISODES_DIR,
     INFO_PATH,
     STATS_PATH,
+    DatasetInfo,
     serialize_dict,
 )
 
@@ -115,25 +116,21 @@ def embed_images(dataset: datasets.Dataset) -> datasets.Dataset:
     return dataset
 
 
-def write_info(info: dict, local_dir: Path) -> None:
-    write_json(info, local_dir / INFO_PATH)
+def write_info(info: DatasetInfo, local_dir: Path) -> None:
+    write_json(info.to_dict(), local_dir / INFO_PATH)
 
 
-def load_info(local_dir: Path) -> dict:
+def load_info(local_dir: Path) -> DatasetInfo:
     """Load dataset info metadata from its standard file path.
 
-    Also converts shape lists to tuples for consistency.
-
     Args:
         local_dir (Path): The root directory of the dataset.
 
     Returns:
-        dict: The dataset information dictionary.
+        DatasetInfo: The typed dataset information object.
     """
-    info = load_json(local_dir / INFO_PATH)
-    for ft in info["features"].values():
-        ft["shape"] = tuple(ft["shape"])
-    return info
+    raw = load_json(local_dir / INFO_PATH)
+    return DatasetInfo.from_dict(raw)
 
 
 def write_stats(stats: dict, local_dir: Path) -> None:

src/lerobot/datasets/lerobot_dataset.py

@@ -35,9 +35,11 @@ from .utils import (
     is_valid_version,
 )
 from .video_utils import (
+    DepthEncoderConfig,
     StreamingVideoEncoder,
-    get_safe_default_codec,
-    resolve_vcodec,
+    VideoEncoderConfig,
+    get_safe_default_video_backend,
+    seed_depth_feature_info,
 )
 
 logger = logging.getLogger(__name__)
@@ -56,11 +58,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
         force_cache_sync: bool = False,
         download_videos: bool = True,
         video_backend: str | None = None,
+        return_uint8: bool = False,
         batch_encoding_size: int = 1,
-        vcodec: str = "libsvtav1",
+        camera_encoder_config: VideoEncoderConfig | None = None,
+        depth_encoder_config: DepthEncoderConfig | None = None,
+        encoder_threads: int | None = None,
         streaming_encoding: bool = False,
         encoder_queue_maxsize: int = 30,
-        encoder_threads: int | None = None,
     ):
         """
         2 modes are available for instantiating this class, depending on 2 different use cases:
@@ -176,16 +180,15 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
             batch_encoding_size (int, optional): Number of episodes to accumulate before batch encoding videos.
                 Set to 1 for immediate encoding (default), or higher for batched encoding. Defaults to 1.
-            vcodec (str, optional): Video codec for encoding videos during recording. Options: 'h264', 'hevc',
-                'libsvtav1', 'auto', or hardware-specific codecs like 'h264_videotoolbox', 'h264_nvenc'.
-                Defaults to 'libsvtav1'. Use 'auto' to auto-detect the best available hardware encoder.
+            camera_encoder_config (VideoEncoderConfig | None, optional): Video encoder settings for cameras
+                (codec, quality, etc.). Defaults to
+                :class:`~lerobot.datasets.video_utils.VideoEncoderConfig` defaults when ``None``.
+            encoder_threads (int | None, optional): Number of encoder threads (global). ``None`` lets the
+                codec decide.
             streaming_encoding (bool, optional): If True, encode video frames in real-time during capture
                 instead of writing PNG images first. This makes save_episode() near-instant. Defaults to False.
             encoder_queue_maxsize (int, optional): Maximum number of frames to buffer per camera when using
                 streaming encoding. Defaults to 30 (~1s at 30fps).
-            encoder_threads (int | None, optional): Number of threads per encoder instance. None lets the
-                codec auto-detect (default). Lower values reduce CPU usage per encoder. Maps to 'lp' (via svtav1-params) for
-                libsvtav1 and 'threads' for h264/hevc.
 
         Note:
             Write-mode parameters (``streaming_encoding``, ``batch_encoding_size``) passed to
@@ -201,9 +204,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.episodes = episodes
         self.tolerance_s = tolerance_s
         self.revision = revision if revision else CODEBASE_VERSION
-        self._video_backend = video_backend if video_backend else get_safe_default_codec()
+        self._video_backend = video_backend if video_backend else get_safe_default_video_backend()
+        self._return_uint8 = return_uint8
         self._batch_encoding_size = batch_encoding_size
-        self._vcodec = resolve_vcodec(vcodec)
+        if camera_encoder_config is None:
+            camera_encoder_config = VideoEncoderConfig()
+        self._camera_encoder_config = camera_encoder_config
+        self._depth_encoder_config = depth_encoder_config
         self._encoder_threads = encoder_threads
 
         if self._requested_root is not None:
@@ -225,6 +232,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             video_backend=self._video_backend,
             delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
+            return_uint8=self._return_uint8,
         )
 
         # Load actual data
@@ -245,16 +253,23 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 DeprecationWarning,
                 stacklevel=2,
             )
+            seed_depth_feature_info(self.meta.features, self._depth_encoder_config)
             streaming_enc = None
             if streaming_encoding and len(self.meta.video_keys) > 0:
                 streaming_enc = self._build_streaming_encoder(
-                    self.meta.fps, self._vcodec, encoder_queue_maxsize, encoder_threads
+                    self.meta.fps,
+                    self._camera_encoder_config,
+                    self._encoder_threads,
+                    encoder_queue_maxsize,
+                    depth_encoder_config=self._depth_encoder_config,
+                    depth_keys=self.meta.depth_keys,
                 )
             self.writer = DatasetWriter(
                 meta=self.meta,
                 root=self.root,
-                vcodec=self._vcodec,
-                encoder_threads=encoder_threads,
+                camera_encoder_config=self._camera_encoder_config,
+                depth_encoder_config=self._depth_encoder_config,
+                encoder_threads=self._encoder_threads,
                 batch_encoding_size=batch_encoding_size,
                 streaming_encoder=streaming_enc,
                 initial_frames=self.meta.total_frames,
@@ -288,25 +303,27 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 video_backend=self._video_backend,
                 delta_timestamps=self.delta_timestamps,
                 image_transforms=self.image_transforms,
+                return_uint8=self._return_uint8,
             )
         return self.reader
 
     @staticmethod
     def _build_streaming_encoder(
         fps: int,
-        vcodec: str,
-        encoder_queue_maxsize: int,
+        camera_encoder_config: VideoEncoderConfig,
         encoder_threads: int | None,
+        encoder_queue_maxsize: int,
+        *,
+        depth_encoder_config: DepthEncoderConfig | None = None,
+        depth_keys: list[str] | None = None,
     ) -> StreamingVideoEncoder:
         return StreamingVideoEncoder(
             fps=fps,
-            vcodec=vcodec,
-            pix_fmt="yuv420p",
-            g=2,
-            crf=30,
-            preset=None,
-            queue_maxsize=encoder_queue_maxsize,
+            camera_encoder_config=camera_encoder_config,
             encoder_threads=encoder_threads,
+            queue_maxsize=encoder_queue_maxsize,
+            depth_encoder_config=depth_encoder_config,
+            depth_keys=depth_keys,
         )
 
     # ── Metadata properties ───────────────────────────────────────────
@@ -621,11 +638,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
         image_writer_threads: int = 0,
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
-        vcodec: str = "libsvtav1",
+        camera_encoder_config: VideoEncoderConfig | None = None,
+        depth_encoder_config: DepthEncoderConfig | None = None,
         metadata_buffer_size: int = 10,
         streaming_encoding: bool = False,
         encoder_queue_maxsize: int = 30,
         encoder_threads: int | None = None,
+        video_files_size_in_mb: int | None = None,
+        data_files_size_in_mb: int | None = None,
     ) -> "LeRobotDataset":
         """Create a new LeRobotDataset from scratch for recording data.
 
@@ -650,20 +670,23 @@ class LeRobotDataset(torch.utils.data.Dataset):
             video_backend: Video decoding backend (used when reading back).
             batch_encoding_size: Number of episodes to accumulate before
                 batch-encoding videos. ``1`` means encode immediately.
-            vcodec: Video codec for encoding. Options include ``'libsvtav1'``,
-                ``'h264'``, ``'hevc'``, ``'auto'``.
+            camera_encoder_config: Video encoder settings for cameras; defaults
+                match :class:`~lerobot.datasets.video_utils.VideoEncoderConfig`
+                when ``None``.
+            encoder_threads: Number of encoder threads (global). ``None``
+                lets the codec decide.
             metadata_buffer_size: Number of episode metadata records to buffer
                 before flushing to parquet.
             streaming_encoding: If ``True``, encode video frames in real-time
                 during capture instead of writing images first.
             encoder_queue_maxsize: Max buffered frames per camera when using
                 streaming encoding.
-            encoder_threads: Threads per encoder instance. ``None`` for auto.
 
         Returns:
             A new :class:`LeRobotDataset` in write mode.
         """
-        vcodec = resolve_vcodec(vcodec)
+        if camera_encoder_config is None:
+            camera_encoder_config = VideoEncoderConfig()
         obj = cls.__new__(cls)
         obj.meta = LeRobotDatasetMetadata.create(
             repo_id=repo_id,
@@ -673,6 +696,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
             root=root,
             use_videos=use_videos,
             metadata_buffer_size=metadata_buffer_size,
+            video_files_size_in_mb=video_files_size_in_mb,
+            data_files_size_in_mb=data_files_size_in_mb,
         )
         obj.repo_id = obj.meta.repo_id
         obj._requested_root = obj.meta.root
@@ -682,22 +707,32 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.image_transforms = None
         obj.delta_timestamps = None
         obj.episodes = None
-        obj._video_backend = video_backend if video_backend is not None else get_safe_default_codec()
+        obj._video_backend = video_backend if video_backend is not None else get_safe_default_video_backend()
+        obj._return_uint8 = False
         obj._batch_encoding_size = batch_encoding_size
-        obj._vcodec = vcodec
+        obj._camera_encoder_config = camera_encoder_config
+        obj._depth_encoder_config = depth_encoder_config
         obj._encoder_threads = encoder_threads
+        seed_depth_feature_info(obj.meta.features, depth_encoder_config)
 
         # Reader is lazily created on first access (write-only mode)
         obj.reader = None
 
-        # Create writer
         streaming_enc = None
         if streaming_encoding and len(obj.meta.video_keys) > 0:
-            streaming_enc = cls._build_streaming_encoder(fps, vcodec, encoder_queue_maxsize, encoder_threads)
+            streaming_enc = cls._build_streaming_encoder(
+                fps,
+                camera_encoder_config,
+                encoder_threads,
+                encoder_queue_maxsize,
+                depth_encoder_config=depth_encoder_config,
+                depth_keys=obj.meta.depth_keys,
+            )
         obj.writer = DatasetWriter(
             meta=obj.meta,
             root=obj.root,
-            vcodec=vcodec,
+            camera_encoder_config=camera_encoder_config,
+            depth_encoder_config=depth_encoder_config,
             encoder_threads=encoder_threads,
             batch_encoding_size=batch_encoding_size,
             streaming_encoder=streaming_enc,
@@ -720,12 +755,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
         force_cache_sync: bool = False,
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
-        vcodec: str = "libsvtav1",
+        camera_encoder_config: VideoEncoderConfig | None = None,
+        depth_encoder_config: DepthEncoderConfig | None = None,
+        encoder_threads: int | None = None,
         image_writer_processes: int = 0,
         image_writer_threads: int = 0,
         streaming_encoding: bool = False,
         encoder_queue_maxsize: int = 30,
-        encoder_threads: int | None = None,
     ) -> "LeRobotDataset":
         """Resume recording on an existing dataset.
 
@@ -748,13 +784,16 @@ class LeRobotDataset(torch.utils.data.Dataset):
             video_backend: Video decoding backend for reading back data.
             batch_encoding_size: Number of episodes to accumulate before
                 batch-encoding videos.
-            vcodec: Video codec for encoding.
+            camera_encoder_config: Video encoder settings for cameras; defaults
+                match :class:`~lerobot.datasets.video_utils.VideoEncoderConfig`
+                when ``None``.
+            encoder_threads: Number of encoder threads (global). ``None``
+                lets the codec decide.
             image_writer_processes: Subprocesses for async image writing.
             image_writer_threads: Threads for async image writing.
             streaming_encoding: If ``True``, encode video in real-time during
                 capture.
             encoder_queue_maxsize: Max buffered frames per camera for streaming.
-            encoder_threads: Threads per encoder instance. ``None`` for auto.
 
         Returns:
             A :class:`LeRobotDataset` in write mode, ready to append episodes.
@@ -765,7 +804,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 "Writing into the revision-safe Hub snapshot cache (used when root=None) would corrupt "
                 "the shared cache. Please provide a local directory path."
             )
-        vcodec = resolve_vcodec(vcodec)
         obj = cls.__new__(cls)
         obj.repo_id = repo_id
         obj._requested_root = Path(root)
@@ -774,10 +812,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.image_transforms = None
         obj.delta_timestamps = None
         obj.episodes = None
-        obj._video_backend = video_backend if video_backend else get_safe_default_codec()
+        obj._video_backend = video_backend if video_backend else get_safe_default_video_backend()
+        obj._return_uint8 = False
         obj._batch_encoding_size = batch_encoding_size
-        obj._vcodec = vcodec
-        obj._encoder_threads = encoder_threads
 
         if obj._requested_root is not None:
             obj._requested_root.mkdir(exist_ok=True, parents=True)
@@ -786,21 +823,33 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.meta = LeRobotDatasetMetadata(
             obj.repo_id, obj._requested_root, obj.revision, force_cache_sync=force_cache_sync
         )
+
+        if camera_encoder_config is None:
+            camera_encoder_config = VideoEncoderConfig()
+        obj._camera_encoder_config = camera_encoder_config
+        obj._depth_encoder_config = depth_encoder_config
+        obj._encoder_threads = encoder_threads
         obj.root = obj.meta.root
+        seed_depth_feature_info(obj.meta.features, depth_encoder_config)
 
         # Reader is lazily created on first access (write-only mode)
         obj.reader = None
 
-        # Create writer for appending
         streaming_enc = None
         if streaming_encoding and len(obj.meta.video_keys) > 0:
             streaming_enc = cls._build_streaming_encoder(
-                obj.meta.fps, vcodec, encoder_queue_maxsize, encoder_threads
+                obj.meta.fps,
+                camera_encoder_config,
+                encoder_threads,
+                encoder_queue_maxsize,
+                depth_encoder_config=depth_encoder_config,
+                depth_keys=obj.meta.depth_keys,
             )
         obj.writer = DatasetWriter(
             meta=obj.meta,
             root=obj.root,
-            vcodec=vcodec,
+            camera_encoder_config=camera_encoder_config,
+            depth_encoder_config=depth_encoder_config,
             encoder_threads=encoder_threads,
             batch_encoding_size=batch_encoding_size,
             streaming_encoder=streaming_enc,

src/lerobot/datasets/multi_dataset.py

@@ -123,7 +123,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
 
         NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
         """
-        return self._datasets[0].meta.info["fps"]
+        return self._datasets[0].meta.info.fps
 
     @property
     def video(self) -> bool:
@@ -133,7 +133,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
 
         NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
         """
-        return self._datasets[0].meta.info.get("video", False)
+        return len(self._datasets[0].meta.video_keys) > 0
 
     @property
     def features(self) -> datasets.Features:

src/lerobot/datasets/pyav_utils.py

@@ -0,0 +1,311 @@
+#!/usr/bin/env python
+
+# 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.
+"""PyAV-based compatibility checks for :class:`VideoEncoderConfig`.
+
+Centralises all :mod:`av` introspection of the bundled FFmpeg build.
+Checks degrade to a no-op when the target codec isn't available locally.
+"""
+
+from __future__ import annotations
+
+import functools
+import logging
+from typing import TYPE_CHECKING, Any, Literal
+
+import av
+import numpy as np
+import torch
+
+from lerobot.datasets.depth_utils import (
+    DEFAULT_DEPTH_MAX,
+    DEFAULT_DEPTH_MIN,
+    DEFAULT_DEPTH_SHIFT,
+    DEFAULT_DEPTH_USE_LOG,
+    quantize_depth,
+    dequantize_depth,
+)
+
+if TYPE_CHECKING:
+    from lerobot.datasets.video_utils import VideoEncoderConfig
+
+logger = logging.getLogger(__name__)
+
+# Pixel formats supported by the depth encode/decode helpers below. Both are
+# 16-bit-word formats that carry 12 significant bits per sample, matching the
+# ``DEPTH_QMAX = 4095`` quantization range.
+DEPTH_PIX_FMTS: tuple[str, ...] = ("yuv420p12le", "gray12le")
+
+# Neutral chroma for 12-bit YUV (the midpoint of [0, 4095]). Filling the U/V
+# planes with this value keeps the encoder from spending bits on chroma noise
+# when only the Y plane carries information.
+_NEUTRAL_CHROMA_12BIT: int = 2048
+
+FFMPEG_NUMERIC_OPTION_TYPES = ("INT", "INT64", "UINT64", "FLOAT", "DOUBLE")
+FFMPEG_INTEGER_OPTION_TYPES = ("INT", "INT64", "UINT64")
+
+
+def _write_u16_plane(plane: av.video.plane.VideoPlane, src: np.ndarray, fill_value: int | None = None) -> None:
+    """Copy ``src`` into a uint16 plane respecting FFmpeg line padding."""
+    height, width = src.shape
+    stride_u16 = plane.line_size // np.dtype(np.uint16).itemsize
+    dst = np.frombuffer(plane, dtype=np.uint16).reshape(height, stride_u16)
+    if fill_value is not None:
+        dst.fill(fill_value)
+    dst[:, :width] = src
+
+
+def encode_depth_frame_pyav(
+    depth: np.ndarray | torch.Tensor,
+    *,
+    pix_fmt: str = "yuv420p12le",
+    depth_min: float = DEFAULT_DEPTH_MIN,
+    depth_max: float = DEFAULT_DEPTH_MAX,
+    shift: float = DEFAULT_DEPTH_SHIFT,
+    use_log: bool = DEFAULT_DEPTH_USE_LOG,
+    input_unit: Literal["auto", "m", "mm"] = "auto",
+) -> av.VideoFrame:
+    """Quantize depth and pack it into a 12-bit PyAV video frame.
+
+    Args:
+        depth: Depth frame to encode (H, W). Unit handling follows
+            :func:`lerobot.datasets.depth_utils.quantize_depth`.
+        pix_fmt: Target pixel format. Must be one of :data:`DEPTH_PIX_FMTS`.
+        depth_min, depth_max, shift, use_log, input_unit: Forwarded to
+            :func:`quantize_depth`.
+
+    Returns:
+        An :class:`av.VideoFrame` in ``pix_fmt`` with quantized depth in the
+        luminance plane.
+    """
+    if pix_fmt not in DEPTH_PIX_FMTS:
+        raise ValueError(f"Unsupported depth pix_fmt={pix_fmt!r}; expected one of {DEPTH_PIX_FMTS}")
+
+    quantized_depth = quantize_depth(
+        depth,
+        depth_min=depth_min,
+        depth_max=depth_max,
+        shift=shift,
+        use_log=use_log,
+        input_unit=input_unit,
+    )
+    if quantized_depth.ndim != 2:
+        raise ValueError(f"depth must be a 2D frame; got shape {quantized_depth.shape}")
+
+    quantized_depth = np.ascontiguousarray(quantized_depth, dtype=np.uint16)
+    height, width = quantized_depth.shape
+
+    if pix_fmt == "gray12le":
+        frame = av.VideoFrame(width=width, height=height, format="gray12le")
+        _write_u16_plane(frame.planes[0], quantized_depth)
+        return frame
+
+    if height % 2 != 0 or width % 2 != 0:
+        raise ValueError("yuv420p12le requires even H and W")
+
+    frame = av.VideoFrame(width=width, height=height, format="yuv420p12le")
+    _write_u16_plane(frame.planes[0], quantized_depth)
+    neutral_chroma = np.full((height // 2, width // 2), _NEUTRAL_CHROMA_12BIT, dtype=np.uint16)
+    _write_u16_plane(frame.planes[1], neutral_chroma, fill_value=_NEUTRAL_CHROMA_12BIT)
+    _write_u16_plane(frame.planes[2], neutral_chroma, fill_value=_NEUTRAL_CHROMA_12BIT)
+    return frame
+
+
+def decode_depth_frame_pyav(
+    frame: av.VideoFrame | list[av.VideoFrame],
+    *,
+    depth_min: float = DEFAULT_DEPTH_MIN,
+    depth_max: float = DEFAULT_DEPTH_MAX,
+    shift: float = DEFAULT_DEPTH_SHIFT,
+    use_log: bool = DEFAULT_DEPTH_USE_LOG,
+    return_quantized: bool = False,
+    output_unit: Literal["m", "mm"] = "m",
+) -> np.ndarray:
+    """Decode one or many depth video frames to quantized or metric depth.
+
+    Args:
+        frame: A single depth frame or a list of depth frames.
+        depth_min, depth_max, shift, use_log: Forwarded to
+            :func:`dequantize_depth`.
+        return_quantized: If ``True``, return raw 12-bit quanta as ``uint16``.
+        output_unit: Unit for dequantized output (``"m"`` or ``"mm"``).
+
+    Returns:
+        ``(H, W)`` array for a single frame, or ``(N, H, W)`` for a list.
+    """
+    frames = frame if isinstance(frame, list) else [frame]
+    quantized = np.stack([f.reformat(format="gray12le").to_ndarray() for f in frames]).astype(np.uint16, copy=False)
+    if return_quantized:
+        return quantized[0] if len(frames) == 1 else quantized
+
+    decoded = dequantize_depth(
+        quantized,
+        depth_min=depth_min,
+        depth_max=depth_max,
+        shift=shift,
+        use_log=use_log,
+        output_unit=output_unit,
+    )
+    return decoded[0] if len(frames) == 1 else decoded
+
+
+@functools.cache
+def get_codec(vcodec: str) -> av.codec.Codec | None:
+    """PyAV write-mode ``Codec`` for *vcodec*, or ``None`` if unavailable."""
+    try:
+        return av.codec.Codec(vcodec, "w")
+    except Exception:
+        return None
+
+
+@functools.cache
+def _get_codec_video_formats(vcodec: str) -> dict[str, av.option.Option]:
+    """Private-option name → PyAV ``Option`` for *vcodec* (empty if unavailable)."""
+    codec = get_codec(vcodec)
+    if codec is None:
+        return {}
+    return {opt.name: opt for opt in codec.descriptor.options}
+
+
+@functools.cache
+def _get_codec_video_formats(vcodec: str) -> tuple[str, ...]:
+    """Pixel formats accepted by *vcodec* in PyAV's preferred order (empty if unknown)."""
+    codec = get_codec(vcodec)
+    if codec is None:
+        return ()
+    return tuple(fmt.name for fmt in (codec.video_formats or []))
+
+
+def detect_available_encoders_pyav(encoders: list[str] | str) -> list[str]:
+    """Return the subset of *encoders* available as video encoders in the local FFmpeg build.
+
+    Each name is probed directly via :func:`get_codec`; input order is preserved.
+    """
+    if isinstance(encoders, str):
+        encoders = [encoders]
+
+    available: list[str] = []
+    for name in encoders:
+        codec = get_codec(name)
+        if codec is not None and codec.type == "video":
+            available.append(name)
+        else:
+            logger.debug("encoder '%s' not available as video encoder", name)
+    return available
+
+
+def _check_option_value(vcodec: str, label: str, value: Any, opt: av.option.Option) -> None:
+    """Range-check numeric *value* and choice-check string *value* against *opt*."""
+    type_name = opt.type.name
+    if type_name in FFMPEG_NUMERIC_OPTION_TYPES:
+        if isinstance(value, bool):
+            raise ValueError(
+                f"{label}={value!r} is not numeric; codec {vcodec!r} expects a number for this option."
+            )
+        elif isinstance(value, str):
+            try:
+                num_val = float(value)
+            except ValueError as e:
+                raise ValueError(
+                    f"{label}={value!r} is not numeric; codec {vcodec!r} expects a number for this option."
+                ) from e
+        elif isinstance(value, (float, int)):
+            num_val = value
+        else:
+            raise ValueError(
+                f"{label}={value!r} is not numeric; codec {vcodec!r} expects a number for this option."
+            )
+
+        # Check integer type compatibility
+        if type_name in FFMPEG_INTEGER_OPTION_TYPES and not num_val.is_integer():
+            raise ValueError(
+                f"{label}={num_val!r} must be an integer for codec {vcodec!r} "
+                f"(FFmpeg option {opt.name!r} is {type_name}); float values are not allowed."
+            )
+
+        # Check numeric range compatibility
+        lo, hi = float(opt.min), float(opt.max)
+        if lo < hi and not (lo <= num_val <= hi):
+            raise ValueError(
+                f"{label}={num_val} is out of range for codec {vcodec!r}; must be in [{lo}, {hi}]"
+            )
+
+    elif type_name == "STRING":
+        if isinstance(value, bool):
+            raise ValueError(f"{label}={value!r} is not a valid string value for codec {vcodec!r}.")
+        if isinstance(value, str):
+            str_val = value
+        elif isinstance(value, (int, float)):
+            str_val = str(value)
+        else:
+            raise ValueError(f"{label}={value!r} has unsupported type for STRING option on codec {vcodec!r}")
+
+        # Check string choice compatibility
+        choices = [c.name for c in (opt.choices or [])]
+        if choices and str_val not in choices:
+            raise ValueError(
+                f"{label}={str_val!r} is not a supported choice for codec "
+                f"{vcodec!r}; valid choices: {choices}"
+            )
+    else:
+        return
+
+
+def _check_pixel_format(vcodec: str, pix_fmt: str) -> None:
+    formats = _get_codec_video_formats(vcodec)
+    if formats and pix_fmt not in formats:
+        raise ValueError(
+            f"pix_fmt={pix_fmt!r} is not supported by codec {vcodec!r}; "
+            f"supported pixel formats: {list(formats)}"
+        )
+
+
+def _check_codec_options(vcodec: str, codec_options: dict[str, Any], config: VideoEncoderConfig) -> None:
+    """Validate merged encoder options (typed) against the codec's published AVOptions."""
+    supported_options = _get_codec_options_by_name(vcodec)
+    for key, value in codec_options.items():
+        # GOP size is not a codec-specific option, it has to be validated separately.
+        if key == "g":
+            if isinstance(value, bool) or not isinstance(value, int) or value < 1:
+                raise ValueError(f"g={value!r} must be a positive integer for codec {vcodec!r}")
+            continue
+        if key not in supported_options:
+            continue
+        opt = supported_options[key]
+        label = f"extra_options[{key!r}]" if key in config.extra_options else key
+        _check_option_value(vcodec, label, value, opt)
+
+
+def check_video_encoder_config_pyav(config: VideoEncoderConfig) -> None:
+    """Verify *config* is compatible with the bundled FFmpeg build.
+
+    Checks pixel format, abstract tuning-field compatibility, and each merged
+    encoder option from :meth:`~lerobot.datasets.video_utils.VideoEncoderConfig.get_codec_options`
+    against PyAV (including numeric ``extra_options`` present in that dict).
+    No-op when ``config.vcodec`` isn't in the local FFmpeg build.
+
+    Raises:
+        ValueError: on the first incompatibility encountered.
+    """
+    vcodec = config.vcodec
+    options = _get_codec_options_by_name(vcodec)
+    if not options:
+        logger.warning(
+            "Codec %r is not available in the bundled FFmpeg build; ",
+            vcodec,
+        )
+        return
+    _check_pixel_format(config.vcodec, config.pix_fmt)
+    _check_codec_options(config.vcodec, config.get_codec_options(), config)

src/lerobot/datasets/streaming_dataset.py

@@ -251,6 +251,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         seed: int = 42,
         rng: np.random.Generator | None = None,
         shuffle: bool = True,
+        return_uint8: bool = False,
     ):
         """Initialize a StreamingLeRobotDataset.
 
@@ -288,6 +289,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
 
         self.streaming = streaming
         self.buffer_size = buffer_size
+        self._return_uint8 = return_uint8
 
         # We cache the video decoders to avoid re-initializing them at each frame (avoiding a ~10x slowdown)
         self.video_decoder_cache = None
@@ -432,7 +434,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
 
     def _make_padding_camera_frame(self, camera_key: str):
         """Variable-shape padding frame for given camera keys, given in (H, W, C)"""
-        return torch.zeros(self.meta.info["features"][camera_key]["shape"]).permute(-1, 0, 1)
+        return torch.zeros(self.meta.info.features[camera_key]["shape"]).permute(-1, 0, 1)
 
     def _get_video_frame_padding_mask(
         self,
@@ -553,7 +555,11 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             root = self.meta.url_root if self.streaming and not self.streaming_from_local else self.root
             video_path = f"{root}/{self.meta.get_video_file_path(ep_idx, video_key)}"
             frames = decode_video_frames_torchcodec(
-                video_path, query_ts, self.tolerance_s, decoder_cache=self.video_decoder_cache
+                video_path,
+                query_ts,
+                self.tolerance_s,
+                decoder_cache=self.video_decoder_cache,
+                return_uint8=self._return_uint8,
             )
 
             item[video_key] = frames.squeeze(0) if len(query_ts) == 1 else frames

src/lerobot/datasets/utils.py

@@ -14,9 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import contextlib
+import dataclasses
 import importlib.resources
 import json
 import logging
+from dataclasses import dataclass, field
 from pathlib import Path
 
 import datasets
@@ -70,6 +72,9 @@ class ForwardCompatibilityError(CompatibilityError):
         super().__init__(message)
 
 
+logger = logging.getLogger(__name__)
+
+
 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
@@ -88,12 +93,133 @@ DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
 DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{frame_index:06d}.png"
+# Depth maps live alongside images on disk but use TIFF instead of PNG: PNG
+# cannot natively round-trip float32, and several common loaders silently
+# downcast 16-bit grayscale.
+DEFAULT_DEPTH_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{frame_index:06d}.tiff"
 
 LEGACY_EPISODES_PATH = "meta/episodes.jsonl"
 LEGACY_EPISODES_STATS_PATH = "meta/episodes_stats.jsonl"
 LEGACY_TASKS_PATH = "meta/tasks.jsonl"
 
 
+@dataclass
+class DatasetInfo:
+    """Typed representation of the ``meta/info.json`` file for a LeRobot dataset.
+
+    Replaces the previously untyped ``dict`` returned by ``load_info()`` and
+    created by ``create_empty_dataset_info()``.  Using a dataclass provides
+    explicit field definitions, IDE auto-completion, and validation at
+    construction time.
+    """
+
+    codebase_version: str
+    fps: int
+    features: dict[str, dict]
+
+    # Episode / frame counters — start at zero for new datasets
+    total_episodes: int = 0
+    total_frames: int = 0
+    total_tasks: int = 0
+
+    # Storage settings
+    chunks_size: int = field(default=DEFAULT_CHUNK_SIZE)
+    data_files_size_in_mb: int = field(default=DEFAULT_DATA_FILE_SIZE_IN_MB)
+    video_files_size_in_mb: int = field(default=DEFAULT_VIDEO_FILE_SIZE_IN_MB)
+
+    # File path templates
+    data_path: str = field(default=DEFAULT_DATA_PATH)
+    video_path: str | None = field(default=DEFAULT_VIDEO_PATH)
+
+    # Optional metadata
+    robot_type: str | None = None
+    splits: dict[str, str] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        # Coerce feature shapes from list to tuple — JSON deserialisation
+        # returns lists, but the rest of the codebase expects tuples.
+        for ft in self.features.values():
+            if isinstance(ft.get("shape"), list):
+                ft["shape"] = tuple(ft["shape"])
+
+        if self.fps <= 0:
+            raise ValueError(f"fps must be positive, got {self.fps}")
+        if self.chunks_size <= 0:
+            raise ValueError(f"chunks_size must be positive, got {self.chunks_size}")
+        if self.data_files_size_in_mb <= 0:
+            raise ValueError(f"data_files_size_in_mb must be positive, got {self.data_files_size_in_mb}")
+        if self.video_files_size_in_mb <= 0:
+            raise ValueError(f"video_files_size_in_mb must be positive, got {self.video_files_size_in_mb}")
+
+    def to_dict(self) -> dict:
+        """Return a JSON-serialisable dict.
+
+        Converts tuple shapes back to lists so ``json.dump`` can handle them.
+        """
+        d = dataclasses.asdict(self)
+        for ft in d["features"].values():
+            if isinstance(ft.get("shape"), tuple):
+                ft["shape"] = list(ft["shape"])
+        return d
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "DatasetInfo":
+        """Construct from a raw dict (e.g. loaded directly from JSON).
+
+        Unknown keys are ignored for forward compatibility with datasets that
+        carry additional fields (e.g. ``total_videos`` from v2.x). A warning is
+        logged when such fields are present.
+        """
+        known = {f.name for f in dataclasses.fields(cls)}
+        unknown = sorted(k for k in data if k not in known)
+        if unknown:
+            logger.warning(f"Unknown fields in DatasetInfo: {unknown}. These will be ignored.")
+        return cls(**{k: v for k, v in data.items() if k in known})
+
+    # ---------------------------------------------------------------------------
+    # Temporary dict-style compatibility layer
+    # Allows existing ``info["key"]`` call-sites to keep working without changes.
+    # Once all callers have been migrated to attribute access, remove these.
+    # ---------------------------------------------------------------------------
+    def __getitem__(self, key: str):
+        import warnings
+
+        warnings.warn(
+            f"Accessing DatasetInfo with dict-style syntax info['{key}'] is deprecated. "
+            f"Use attribute access info.{key} instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        try:
+            return getattr(self, key)
+        except AttributeError as err:
+            raise KeyError(key) from err
+
+    def __setitem__(self, key: str, value) -> None:
+        import warnings
+
+        warnings.warn(
+            f"Setting DatasetInfo with dict-style syntax info['{key}'] = ... is deprecated. "
+            f"Use attribute assignment info.{key} = ... instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        if not hasattr(self, key):
+            raise KeyError(f"DatasetInfo has no field '{key}'")
+        setattr(self, key, value)
+
+    def __contains__(self, key: str) -> bool:
+        """Check if a field exists (dict-like interface)."""
+        return hasattr(self, key)
+
+    def get(self, key: str, default=None):
+        """Get attribute value with default fallback (dict-like interface)."""
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            return default
+
+
 def has_legacy_hub_download_metadata(root: Path) -> bool:
     """Return ``True`` when *root* looks like a legacy Hub ``local_dir`` mirror.
 
@@ -294,7 +420,7 @@ def create_branch(repo_id: str, *, branch: str, repo_type: str | None = None) ->
 
 def create_lerobot_dataset_card(
     tags: list | None = None,
-    dataset_info: dict | None = None,
+    dataset_info: DatasetInfo | None = None,
     **kwargs,
 ) -> DatasetCard:
     """Create a `DatasetCard` for a LeRobot dataset.
@@ -305,7 +431,7 @@ def create_lerobot_dataset_card(
 
     Args:
         tags (list | None): A list of tags to add to the dataset card.
-        dataset_info (dict | None): The dataset's info dictionary, which will
+        dataset_info (DatasetInfo | None): The dataset's info object, which will
             be displayed on the card.
         **kwargs: Additional keyword arguments to populate the card template.
 
@@ -318,7 +444,7 @@ def create_lerobot_dataset_card(
         card_tags += tags
     if dataset_info:
         dataset_structure = "[meta/info.json](meta/info.json):\n"
-        dataset_structure += f"```json\n{json.dumps(dataset_info, indent=4)}\n```\n"
+        dataset_structure += f"```json\n{json.dumps(dataset_info.to_dict(), indent=4)}\n```\n"
         kwargs = {**kwargs, "dataset_structure": dataset_structure}
     card_data = DatasetCardData(
         license=kwargs.get("license"),

src/lerobot/datasets/video_utils.py

@@ -17,12 +17,13 @@ import contextlib
 import glob
 import importlib
 import logging
+import math
 import queue
 import shutil
 import tempfile
 import threading
 import warnings
-from dataclasses import dataclass, field
+from dataclasses import asdict, dataclass, field
 from fractions import Fraction
 from pathlib import Path
 from threading import Lock
@@ -37,7 +38,23 @@ import torchvision
 from datasets.features.features import register_feature
 from PIL import Image
 
-from lerobot.utils.import_utils import get_safe_default_codec
+from lerobot.datasets.pyav_utils import (
+    check_video_encoder_config_pyav,
+    depth_to_video_frame,
+    detect_available_encoders_pyav,
+    decode_depth_frame,
+    encode_depth_frame_pyav,
+    decode_depth_frame_pyav,
+)
+from lerobot.datasets.depth_utils import (
+    quantize_depth,
+    dequantize_depth,
+    DEFAULT_DEPTH_MIN,
+    DEFAULT_DEPTH_MAX,
+    DEFAULT_DEPTH_SHIFT,
+    DEFAULT_DEPTH_USE_LOG,
+)
+from lerobot.utils.import_utils import get_safe_default_video_backend
 
 logger = logging.getLogger(__name__)
 
@@ -52,70 +69,226 @@ HW_ENCODERS = [
     "h264_qsv",  # Intel Quick Sync
 ]
 
-VALID_VIDEO_CODECS = {"h264", "hevc", "libsvtav1", "auto"} | set(HW_ENCODERS)
+VALID_VIDEO_CODECS = {"h264", "hevc", "libsvtav1", "ffv1", "auto"} | set(HW_ENCODERS)
+
+LIBSVTAV1_DEFAULT_PRESET: int = 12
 
 
-def _get_codec_options(
-    vcodec: str,
-    g: int | None = 2,
-    crf: int | None = 30,
-    preset: int | None = None,
-) -> dict:
-    """Build codec-specific options dict for video encoding."""
-    options = {}
+@dataclass
+class VideoEncoderConfig:
+    """Video encoder configuration.
 
-    # GOP size (keyframe interval) - supported by VideoToolbox and software encoders
-    if g is not None and (vcodec in ("h264_videotoolbox", "hevc_videotoolbox") or vcodec not in HW_ENCODERS):
-        options["g"] = str(g)
+    Attributes:
+        vcodec: FFmpeg encoder name. ``"auto"`` is resolved during
+            construction (HW encoder if available, else ``libsvtav1``).
+        pix_fmt: Pixel format (e.g. ``"yuv420p"``).
+        g: GOP size (keyframe interval).
+        crf: Quality level — mapped to the native quality parameter of the
+            codec (``crf`` for software, ``qp`` for NVENC/VAAPI,
+            ``q:v`` for VideoToolbox, ``global_quality`` for QSV).
+        preset: Speed/quality preset. Accepted type is per-codec.
+        fast_decode: Fast-decode tuning. For ``libsvtav1`` this is a level (0-2)
+            embedded in ``svtav1-params``. For ``h264`` and ``hevc`` non-zero values
+            set ``tune=fastdecode``. Ignored for other codecs.
+        video_backend: Python library driving FFmpeg for encoding. Only ``"pyav"``
+            is currently supported.
+        extra_options: Free-form dictionary of additional FFmpeg options
+            (e.g. ``{"tune": "film", "profile:v": "high", "bf": 2}``).
+    """
 
-    # Quality control (codec-specific parameter names)
-    if crf is not None:
-        if vcodec in ("h264", "hevc", "libsvtav1"):
-            options["crf"] = str(crf)
-        elif vcodec in ("h264_videotoolbox", "hevc_videotoolbox"):
-            quality = max(1, min(100, int(100 - crf * 2)))
-            options["q:v"] = str(quality)
-        elif vcodec in ("h264_nvenc", "hevc_nvenc"):
-            options["rc"] = "constqp"
-            options["qp"] = str(crf)
-        elif vcodec in ("h264_vaapi",):
-            options["qp"] = str(crf)
-        elif vcodec in ("h264_qsv",):
-            options["global_quality"] = str(crf)
+    vcodec: str = "libsvtav1"
+    pix_fmt: str = "yuv420p"
+    g: int | None = 2
+    crf: int | None = 30
+    preset: int | str | None = None
+    fast_decode: int = 0
+    # TODO(CarolinePascal): add torchcodec support + find a way to unify the
+    # two backends (encoding and decoding).
+    video_backend: str = "pyav"
+    extra_options: dict[str, Any] = field(default_factory=dict)
 
-    # Preset (only for libsvtav1)
-    if vcodec == "libsvtav1":
-        options["preset"] = str(preset) if preset is not None else "12"
+    # Class-level marker persisted to ``info.json`` (via ``asdict``) so the
+    # reader can tell depth datasets from RGB ones without a separate dispatch
+    # path. ``init=False`` keeps it out of CLI/constructor surface; subclasses
+    # flip the default (see :class:`DepthEncoderConfig`).
+    is_depth_map: bool = field(default=False, init=False)
 
-    return options
+    def __post_init__(self) -> None:
+        self.resolve_vcodec()
+
+        # Empty-constructor ergonomics: ``VideoEncoderConfig()`` must "just work".
+        if self.preset is None and self.vcodec == "libsvtav1":
+            self.preset = LIBSVTAV1_DEFAULT_PRESET
+
+        self.validate()
+
+    def detect_available_encoders(self, encoders: list[str] | str) -> list[str]:
+        """Detect available encoders based on the video backend."""
+        if self.video_backend == "pyav":
+            return detect_available_encoders_pyav(encoders)
+        else:
+            return []
+
+    def validate(self) -> None:
+        """Validate the video encoder config."""
+        if self.video_backend == "pyav":
+            check_video_encoder_config_pyav(self)
+
+    def resolve_vcodec(self) -> None:
+        """Validate vcodec and resolve 'auto' to best available HW encoder, fallback to libsvtav1.
+
+        Any explicitly-requested codec that isn't in the local FFmpeg build is
+        also silently rewritten to ``libsvtav1`` so encoding never hard-fails on
+        a host missing the requested encoder.
+        """
+        # Backward compatibility: older datasets persist ``vcodec="av1"`` in
+        # ``info.json``. Rewrite to the canonical encoder name *before* the
+        # validation check below so loading those datasets keeps working.
+        if self.vcodec == "av1":
+            self.vcodec = "libsvtav1"
+
+        if self.vcodec not in VALID_VIDEO_CODECS:
+            raise ValueError(f"Invalid vcodec '{self.vcodec}'. Must be one of: {sorted(VALID_VIDEO_CODECS)}")
+        if self.vcodec == "auto":
+            available = self.detect_available_encoders(HW_ENCODERS)
+            for encoder in HW_ENCODERS:
+                if encoder in available:
+                    logger.info(f"Auto-selected video codec: {encoder}")
+                    self.vcodec = encoder
+                    return
+            logger.info("No hardware encoder available, falling back to software encoder 'libsvtav1'")
+            self.vcodec = "libsvtav1"
+
+        if self.detect_available_encoders(self.vcodec):
+            logger.info(f"Using video codec: {self.vcodec}")
+            self.vcodec = self.vcodec
+            return
+        raise ValueError(f"Unsupported video codec: {self.vcodec} with video backend {self.video_backend}")
+
+    def get_codec_options(
+        self, encoder_threads: int | None = None, as_strings: bool = False
+    ) -> dict[str, str]:
+        """Translate the tuning fields to codec-specific FFmpeg options.
+
+        ``VideoEncoderConfig.extra_options`` are merged last but never override a structured field.
+
+        Args:
+            encoder_threads: Number of encoder threads set globally for all VideoEncoderConfigs.
+                For libsvtav1, this is mapped to ``lp`` via ``svtav1-params``.
+                For h264/hevc, this is mapped to ``threads``.
+                Hardware encoders ignore this parameter.
+            as_strings: If ``True``, casts values to strings.
+        """
+        opts: dict[str, Any] = {}
+
+        def set_if(key: str, value: Any) -> None:
+            if value is not None:
+                opts[key] = value if not as_strings else str(value)
+
+        # GOP size is not a codec-specific option, so it is always set.
+        set_if("g", self.g)
+
+        if self.vcodec == "libsvtav1":
+            set_if("crf", self.crf)
+            set_if("preset", self.preset)
+            svtav1_parts: list[str] = []
+            if self.fast_decode is not None:
+                svtav1_parts.append(f"fast-decode={max(0, min(2, self.fast_decode))}")
+            if encoder_threads is not None:
+                svtav1_parts.append(f"lp={encoder_threads}")
+            if svtav1_parts:
+                opts["svtav1-params"] = ":".join(svtav1_parts)
+        elif self.vcodec in ("h264", "hevc"):
+            set_if("crf", self.crf)
+            set_if("preset", self.preset)
+            if self.fast_decode:
+                opts["tune"] = "fastdecode"
+            set_if("threads", encoder_threads)
+        elif self.vcodec in ("h264_videotoolbox", "hevc_videotoolbox"):
+            if self.crf is not None:
+                opts["q:v"] = max(1, min(100, 100 - self.crf * 2))
+        elif self.vcodec in ("h264_nvenc", "hevc_nvenc"):
+            opts["rc"] = "constqp"
+            set_if("qp", self.crf)
+            set_if("preset", self.preset)
+        elif self.vcodec == "h264_vaapi":
+            set_if("qp", self.crf)
+        elif self.vcodec == "h264_qsv":
+            set_if("global_quality", self.crf)
+            set_if("preset", self.preset)
+        elif self.vcodec == "ffv1":
+            # Lossless intra-frame codec. ``crf``/``preset``/``fast_decode`` 
+            # are not meaningful.
+            set_if("threads", encoder_threads)
+        else:
+            set_if("crf", self.crf)
+            set_if("preset", self.preset)
+
+        # Extra options are merged last but never override structured fields (values are kept as given).
+        for k, v in self.extra_options.items():
+            if k not in opts:
+                set_if(k, v)
+
+        return opts
 
 
-def detect_available_hw_encoders() -> list[str]:
-    """Probe PyAV/FFmpeg for available hardware video encoders."""
-    available = []
-    for codec_name in HW_ENCODERS:
-        try:
-            av.codec.Codec(codec_name, "w")
-            available.append(codec_name)
-        except Exception:  # nosec B110
-            logger.debug("HW encoder '%s' not available", codec_name)  # nosec B110
-    return available
+@dataclass
+class DepthEncoderConfig(VideoEncoderConfig):
+    """Encoder configuration for depth-map streams.
+
+    Inherits the full :class:`VideoEncoderConfig` surface (codec, GOP, CRF,
+    preset, ``extra_options``…) and adds the four parameters of the depth
+    quantization pipeline (:func:`quantize_depth`). Inheritance — rather
+    than composition — keeps the CLI flat: ``--dataset.depth_encoder_config.<field>``
+    works identically to its RGB counterpart.
+
+    Defaults flip ``vcodec`` to ``"hevc"`` (Main 12 profile) and ``pix_fmt``
+    to ``"yuv420p12le"``, the most widely available 12-bit pixel format.
+    For archive-grade lossless storage use ``vcodec="ffv1"`` together with
+    ``pix_fmt="gray12le"`` (and clear ``crf``/``preset`` to ``None`` since
+    ``ffv1`` doesn't expose those tuning knobs).
+
+    The :attr:`is_depth_map` marker is class-fixed to ``True`` (``init=False``,
+    so it's hidden from CLI and constructor args) and is what the reader
+    side keys on to tell depth datasets from RGB ones.
+
+    Attributes:
+        depth_min: Minimum depth in physical units (e.g. metres) represented
+            by quantum ``0``.
+        depth_max: Maximum depth represented by quantum :data:`DEPTH_QMAX`.
+        shift: Pre-log offset for numerical stability near zero.
+        use_log: ``True`` for logarithmic quantization (default; matches
+            sensor error profile), ``False`` for linear.
+    """
+
+    vcodec: str = "hevc"
+    pix_fmt: str = "yuv420p12le"
+
+    depth_min: float = DEFAULT_DEPTH_MIN
+    depth_max: float = DEFAULT_DEPTH_MAX
+    shift: float = DEFAULT_DEPTH_SHIFT
+    use_log: bool = DEFAULT_DEPTH_USE_LOG
+
+    # Class invariant — kept out of ``__init__`` (and CLI) but persisted
+    # via ``asdict`` into ``info.json`` for the reader to detect depth.
+    is_depth_map: bool = field(default=True, init=False)
+
+    def quantize(self, depth: torch.Tensor | np.ndarray) -> torch.Tensor:
+        """Apply :func:`quantize_depth` bound to this config's parameters."""
+        return quantize_depth(depth, self.depth_min, self.depth_max, self.shift, self.use_log)
+
+    def dequantize(self, quantized: torch.Tensor | np.ndarray) -> torch.Tensor:
+        """Apply :func:`dequantize_depth` bound to this config's parameters."""
+        return dequantize_depth(quantized, self.depth_min, self.depth_max, self.shift, self.use_log)
 
 
-def resolve_vcodec(vcodec: str) -> str:
-    """Validate vcodec and resolve 'auto' to best available HW encoder, fallback to libsvtav1."""
-    if vcodec not in VALID_VIDEO_CODECS:
-        raise ValueError(f"Invalid vcodec '{vcodec}'. Must be one of: {sorted(VALID_VIDEO_CODECS)}")
-    if vcodec != "auto":
-        logger.info(f"Using video codec: {vcodec}")
-        return vcodec
-    available = detect_available_hw_encoders()
-    for encoder in HW_ENCODERS:
-        if encoder in available:
-            logger.info(f"Auto-selected video codec: {encoder}")
-            return encoder
-    logger.info("No hardware encoder available, falling back to software encoder 'libsvtav1'")
-    return "libsvtav1"
+def depth_encoder_defaults() -> DepthEncoderConfig:
+    """Return a :class:`DepthEncoderConfig` with depth-camera defaults."""
+    return DepthEncoderConfig()
+
+def camera_encoder_defaults() -> VideoEncoderConfig:
+    """Return a :class:`VideoEncoderConfig` with RGB-camera defaults."""
+    return VideoEncoderConfig()
 
 
 def decode_video_frames(
@@ -123,6 +296,7 @@ def decode_video_frames(
     timestamps: list[float],
     tolerance_s: float,
     backend: str | None = None,
+    return_uint8: bool = False,
 ) -> torch.Tensor:
     """
     Decodes video frames using the specified backend.
@@ -131,19 +305,23 @@ def decode_video_frames(
         video_path (Path): Path to the video file.
         timestamps (list[float]): List of timestamps to extract frames.
         tolerance_s (float): Allowed deviation in seconds for frame retrieval.
-        backend (str, optional): Backend to use for decoding. Defaults to "torchcodec" when available in the platform; otherwise, defaults to "pyav"..
+        backend (str, optional): Backend to use for decoding. Defaults to "torchcodec" when available in the platform; otherwise, defaults to "pyav".
+        return_uint8 (bool): If True, return raw uint8 frames without float32 normalization.
+            This reduces memory for DataLoader IPC; normalization can be done on GPU afterward.
 
     Returns:
-        torch.Tensor: Decoded frames.
+        torch.Tensor: Decoded frames (float32 in [0,1] by default, or uint8 if return_uint8=True).
 
     Currently supports torchcodec on cpu and pyav.
     """
     if backend is None:
-        backend = get_safe_default_codec()
+        backend = get_safe_default_video_backend()
     if backend == "torchcodec":
-        return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s)
+        return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s, return_uint8=return_uint8)
     elif backend in ["pyav", "video_reader"]:
-        return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
+        return decode_video_frames_torchvision(
+            video_path, timestamps, tolerance_s, backend, return_uint8=return_uint8
+        )
     else:
         raise ValueError(f"Unsupported video backend: {backend}")
 
@@ -154,6 +332,7 @@ def decode_video_frames_torchvision(
     tolerance_s: float,
     backend: str = "pyav",
     log_loaded_timestamps: bool = False,
+    return_uint8: bool = False,
 ) -> torch.Tensor:
     """Loads frames associated to the requested timestamps of a video
 
@@ -240,14 +419,17 @@ def decode_video_frames_torchvision(
     if log_loaded_timestamps:
         logger.info(f"{closest_ts=}")
 
-    # convert to the pytorch format which is float32 in [0,1] range (and channel first)
-    closest_frames = closest_frames.type(torch.float32) / 255
-
     if len(timestamps) != len(closest_frames):
         raise FrameTimestampError(
             f"Number of retrieved frames ({len(closest_frames)}) does not match "
             f"number of queried timestamps ({len(timestamps)})"
         )
+
+    if return_uint8:
+        return closest_frames
+
+    # convert to the pytorch format which is float32 in [0,1] range (and channel first)
+    closest_frames = closest_frames.type(torch.float32) / 255
     return closest_frames
 
 
@@ -306,6 +488,7 @@ def decode_video_frames_torchcodec(
     tolerance_s: float,
     log_loaded_timestamps: bool = False,
     decoder_cache: VideoDecoderCache | None = None,
+    return_uint8: bool = False,
 ) -> torch.Tensor:
     """Loads frames associated with the requested timestamps of a video using torchcodec.
 
@@ -373,33 +556,149 @@ def decode_video_frames_torchcodec(
     if log_loaded_timestamps:
         logger.info(f"{closest_ts=}")
 
-    # convert to float32 in [0,1] range
-    closest_frames = (closest_frames / 255.0).type(torch.float32)
-
     if not len(timestamps) == len(closest_frames):
         raise FrameTimestampError(
             f"Retrieved timestamps differ from queried {set(closest_frames) - set(timestamps)}"
         )
 
+    if return_uint8:
+        return closest_frames
+
+    # convert to float32 in [0,1] range
+    closest_frames = (closest_frames / 255.0).type(torch.float32)
     return closest_frames
 
 
+def decode_depth_frames(
+    video_path: Path | str,
+    timestamps: list[float],
+    tolerance_s: float,
+    *,
+    depth_min: float = DEFAULT_DEPTH_MIN,
+    depth_max: float = DEFAULT_DEPTH_MAX,
+    shift: float = DEFAULT_DEPTH_SHIFT,
+    use_log: bool = DEFAULT_DEPTH_USE_LOG,
+    return_quantized: bool = False,
+    log_loaded_timestamps: bool = False,
+) -> torch.Tensor:
+    """Decode depth-map frames at the requested timestamps using PyAV.
+
+    Mirrors the timestamp-tolerance / closest-frame contract of
+    :func:`decode_video_frames` but operates entirely through PyAV (the
+    ``torchvision`` and ``torchcodec`` backends don't currently round-trip
+    12-bit pixel formats reliably).
+
+    Each decoded frame is reformatted to ``gray12le`` so the same path
+    handles ``yuv420p12le`` (HEVC default) and ``gray12le`` (ffv1 archive)
+    sources transparently.
+
+    Args:
+        video_path: Path to a depth video produced with a
+            :class:`DepthEncoderConfig`.
+        timestamps: Frame timestamps to retrieve, in seconds.
+        tolerance_s: Maximum allowed deviation between the queried and the
+            actually-decoded timestamps.
+        depth_min, depth_max, shift, use_log: Parameters used at quantization
+            time. Should match :func:`info_to_depth_kwargs` extracted from
+            ``info.json`` for the source dataset.
+        return_quantized: If ``True``, skip the dequantization step and
+            return raw 12-bit ``uint16`` quanta.
+        log_loaded_timestamps: Debug logging.
+
+    Returns:
+        ``torch.Tensor`` of shape ``(N, H, W)``:
+
+        * ``dtype=torch.float32`` (metric depth, default)
+        * ``dtype=torch.uint16`` when ``return_quantized=True``.
+
+    Raises:
+        FrameTimestampError: If a query timestamp can't be matched within
+            *tolerance_s*, or if no frames are decoded.
+    """
+    video_path_str = str(video_path)
+    first_ts = min(timestamps)
+    last_ts = max(timestamps)
+
+    loaded_frames: list[np.ndarray] = []
+    loaded_ts: list[float] = []
+
+    av.logging.set_level(av.logging.WARNING)
+    with av.open(video_path_str, "r") as container:
+        try:
+            stream = container.streams.video[0]
+        except IndexError as e:
+            raise FrameTimestampError(f"No video stream in {video_path_str}") from e
+
+        # Seek to the keyframe at-or-before first_ts (PyAV doesn't do
+        # accurate seek, so we still iterate forward to the requested range).
+        seek_pts = int(first_ts / stream.time_base)
+        container.seek(seek_pts, stream=stream, any_frame=False, backward=True)
+
+        for frame in container.decode(stream):
+            if frame.pts is None:
+                continue
+            current_ts = float(frame.pts * stream.time_base)
+            if log_loaded_timestamps:
+                logger.info(f"depth frame loaded at timestamp={current_ts:.4f}")
+            loaded_frames.append(
+                decode_depth_frame(
+                    frame,
+                    depth_min=depth_min,
+                    depth_max=depth_max,
+                    shift=shift,
+                    use_log=use_log,
+                    return_quantized=True,
+                )
+            )
+            loaded_ts.append(current_ts)
+            if current_ts >= last_ts:
+                break
+
+    av.logging.restore_default_callback()
+
+    if not loaded_frames:
+        raise FrameTimestampError(
+            f"No depth frames decoded from {video_path_str} for timestamps {timestamps}"
+        )
+
+    query_ts = torch.tensor(timestamps)
+    loaded_ts_t = torch.tensor(loaded_ts)
+    dist = torch.cdist(query_ts[:, None], loaded_ts_t[:, None], p=1)
+    min_, argmin_ = dist.min(1)
+
+    is_within_tol = min_ < tolerance_s
+    if not is_within_tol.all():
+        raise FrameTimestampError(
+            f"One or several query timestamps violate the tolerance "
+            f"({min_[~is_within_tol]} > {tolerance_s=})."
+            f"\nqueried timestamps: {query_ts}"
+            f"\nloaded timestamps: {loaded_ts_t}"
+            f"\nvideo: {video_path_str}"
+        )
+
+    closest = np.stack([loaded_frames[i] for i in argmin_])  # (N, H, W) uint16
+    quantized = torch.from_numpy(closest)
+
+    if return_quantized:
+        return quantized
+    return dequantize_depth(quantized, depth_min, depth_max, shift, use_log)
+
+
 def encode_video_frames(
     imgs_dir: Path | str,
     video_path: Path | str,
     fps: int,
-    vcodec: str = "libsvtav1",
-    pix_fmt: str = "yuv420p",
-    g: int | None = 2,
-    crf: int | None = 30,
-    fast_decode: int = 0,
+    camera_encoder_config: VideoEncoderConfig | None = None,
+    encoder_threads: int | None = None,
+    *,
     log_level: int | None = av.logging.WARNING,
     overwrite: bool = False,
-    preset: int | None = None,
-    encoder_threads: int | None = None,
 ) -> None:
     """More info on ffmpeg arguments tuning on `benchmark/video/README.md`"""
-    vcodec = resolve_vcodec(vcodec)
+    if camera_encoder_config is None:
+        camera_encoder_config = VideoEncoderConfig()
+    vcodec = camera_encoder_config.vcodec
+    pix_fmt = camera_encoder_config.pix_fmt
 
     video_path = Path(video_path)
     imgs_dir = Path(imgs_dir)
@@ -410,42 +709,18 @@ def encode_video_frames(
 
     video_path.parent.mkdir(parents=True, exist_ok=True)
 
-    # Encoders/pixel formats incompatibility check
-    if (vcodec == "libsvtav1" or vcodec == "hevc") and pix_fmt == "yuv444p":
-        logger.warning(
-            f"Incompatible pixel format 'yuv444p' for codec {vcodec}, auto-selecting format 'yuv420p'"
-        )
-        pix_fmt = "yuv420p"
-
     # Get input frames
     template = "frame-" + ("[0-9]" * 6) + ".png"
     input_list = sorted(
         glob.glob(str(imgs_dir / template)), key=lambda x: int(x.split("-")[-1].split(".")[0])
     )
 
-    # Define video output frame size (assuming all input frames are the same size)
     if len(input_list) == 0:
         raise FileNotFoundError(f"No images found in {imgs_dir}.")
     with Image.open(input_list[0]) as dummy_image:
         width, height = dummy_image.size
 
-    # Define video codec options
-    video_options = _get_codec_options(vcodec, g, crf, preset)
-
-    if fast_decode:
-        key = "svtav1-params" if vcodec == "libsvtav1" else "tune"
-        value = f"fast-decode={fast_decode}" if vcodec == "libsvtav1" else "fastdecode"
-        video_options[key] = value
-
-    if encoder_threads is not None:
-        if vcodec == "libsvtav1":
-            lp_param = f"lp={encoder_threads}"
-            if "svtav1-params" in video_options:
-                video_options["svtav1-params"] += f":{lp_param}"
-            else:
-                video_options["svtav1-params"] = lp_param
-        else:
-            video_options["threads"] = str(encoder_threads)
+    video_options = camera_encoder_config.get_codec_options(encoder_threads, as_strings=True)
 
     # Set logging level
     if log_level is not None:
@@ -482,7 +757,10 @@ def encode_video_frames(
 
 
 def concatenate_video_files(
-    input_video_paths: list[Path | str], output_video_path: Path, overwrite: bool = True
+    input_video_paths: list[Path | str],
+    output_video_path: Path,
+    overwrite: bool = True,
+    compatibility_check: bool = False,
 ):
     """
     Concatenate multiple video files into a single video file using pyav.
@@ -495,6 +773,7 @@ def concatenate_video_files(
         input_video_paths: Ordered list of input video file paths to concatenate.
         output_video_path: Path to the output video file.
         overwrite: Whether to overwrite the output video file if it already exists. Default is True.
+        compatibility_check: Whether to check if the input videos are compatible. Default is False.
 
     Note:
         - Creates a temporary directory for intermediate files that is cleaned up after use.
@@ -513,6 +792,22 @@ def concatenate_video_files(
     if len(input_video_paths) == 0:
         raise FileNotFoundError("No input video paths provided.")
 
+    # This check may be skipped at recording time as videos are encoded with the same encoder config.
+    if compatibility_check:
+        reference_video_info = get_video_info(input_video_paths[0])
+        for input_path in input_video_paths[1:]:
+            video_info = get_video_info(input_path)
+            if (
+                video_info["video.height"] != reference_video_info["video.height"]
+                or video_info["video.width"] != reference_video_info["video.width"]
+                or video_info["video.fps"] != reference_video_info["video.fps"]
+                or video_info["video.codec"] != reference_video_info["video.codec"]
+                or video_info["video.pix_fmt"] != reference_video_info["video.pix_fmt"]
+            ):
+                raise ValueError(
+                    f"Input video {input_path} is not compatible with the reference video {input_video_paths[0]}."
+                )
+
     # Create a temporary .ffconcat file to list the input video paths
     with tempfile.NamedTemporaryFile(mode="w", suffix=".ffconcat", delete=False) as tmp_concatenate_file:
         tmp_concatenate_file.write("ffconcat version 1.0\n")
@@ -579,33 +874,31 @@ class _CameraEncoderThread(threading.Thread):
         fps: int,
         vcodec: str,
         pix_fmt: str,
-        g: int | None,
-        crf: int | None,
-        preset: int | None,
+        codec_options: dict[str, str],
         frame_queue: queue.Queue,
         result_queue: queue.Queue,
         stop_event: threading.Event,
-        encoder_threads: int | None = None,
+        depth_encoder_config: "DepthEncoderConfig | None" = None,
     ):
         super().__init__(daemon=True)
         self.video_path = video_path
         self.fps = fps
         self.vcodec = vcodec
         self.pix_fmt = pix_fmt
-        self.g = g
-        self.crf = crf
-        self.preset = preset
+        self.codec_options = codec_options
         self.frame_queue = frame_queue
         self.result_queue = result_queue
         self.stop_event = stop_event
-        self.encoder_threads = encoder_threads
+        self.depth_encoder_config = depth_encoder_config
+
 
     def run(self) -> None:
         from .compute_stats import RunningQuantileStats, auto_downsample_height_width
 
         container = None
         output_stream = None
-        stats_tracker = RunningQuantileStats()
+        is_depth = self.depth_encoder_config is not None
+        stats_tracker = RunningQuantileStats() if not is_depth else None
         frame_count = 0
 
         try:
@@ -623,51 +916,45 @@ class _CameraEncoderThread(threading.Thread):
                     # Sentinel: flush and close
                     break
 
-                # Ensure HWC uint8 numpy array
+                # Ensure HWC (RGB or depth) uint8 (RGB only) numpy array
                 if isinstance(frame_data, np.ndarray):
                     if frame_data.ndim == 3 and frame_data.shape[0] == 3:
                         # CHW -> HWC
                         frame_data = frame_data.transpose(1, 2, 0)
-                    if frame_data.dtype != np.uint8:
+                    if frame_data.dtype != np.uint8 and not is_depth:
                         frame_data = (frame_data * 255).astype(np.uint8)
 
                 # Open container on first frame (to get width/height)
                 if container is None:
                     height, width = frame_data.shape[:2]
-                    video_options = _get_codec_options(self.vcodec, self.g, self.crf, self.preset)
-                    if self.encoder_threads is not None:
-                        if self.vcodec == "libsvtav1":
-                            lp_param = f"lp={self.encoder_threads}"
-                            if "svtav1-params" in video_options:
-                                video_options["svtav1-params"] += f":{lp_param}"
-                            else:
-                                video_options["svtav1-params"] = lp_param
-                        else:
-                            video_options["threads"] = str(self.encoder_threads)
                     Path(self.video_path).parent.mkdir(parents=True, exist_ok=True)
                     container = av.open(str(self.video_path), "w")
-                    output_stream = container.add_stream(self.vcodec, self.fps, options=video_options)
+                    output_stream = container.add_stream(self.vcodec, self.fps, options=self.codec_options)
                     output_stream.pix_fmt = self.pix_fmt
                     output_stream.width = width
                     output_stream.height = height
                     output_stream.time_base = Fraction(1, self.fps)
 
                 # Encode frame with explicit timestamps
-                pil_img = Image.fromarray(frame_data)
-                video_frame = av.VideoFrame.from_image(pil_img)
+                if is_depth:
+                    video_frame = encode_depth_frame_pyav(frame_data, pix_fmt=self.pix_fmt, depth_min=self.depth_encoder_config.depth_min, depth_max=self.depth_encoder_config.depth_max, shift=self.depth_encoder_config.shift, use_log=self.depth_encoder_config.use_log)
+                else:
+                    pil_img = Image.fromarray(frame_data)
+                    video_frame = av.VideoFrame.from_image(pil_img)
                 video_frame.pts = frame_count
                 video_frame.time_base = Fraction(1, self.fps)
                 packet = output_stream.encode(video_frame)
                 if packet:
                     container.mux(packet)
 
-                # Update stats with downsampled frame (per-channel stats like compute_episode_stats)
-                img_chw = frame_data.transpose(2, 0, 1)  # HWC -> CHW
-                img_downsampled = auto_downsample_height_width(img_chw)
-                # Reshape CHW to (H*W, C) for per-channel stats
-                channels = img_downsampled.shape[0]
-                img_for_stats = img_downsampled.transpose(1, 2, 0).reshape(-1, channels)
-                stats_tracker.update(img_for_stats)
+                if not is_depth:
+                    # Update stats with downsampled frame (per-channel stats like compute_episode_stats)
+                    img_chw = frame_data.transpose(2, 0, 1)  # HWC -> CHW
+                    img_downsampled = auto_downsample_height_width(img_chw)
+                    # Reshape CHW to (H*W, C) for per-channel stats
+                    channels = img_downsampled.shape[0]
+                    img_for_stats = img_downsampled.transpose(1, 2, 0).reshape(-1, channels)
+                    stats_tracker.update(img_for_stats)
 
                 frame_count += 1
 
@@ -682,8 +969,10 @@ class _CameraEncoderThread(threading.Thread):
 
             av.logging.restore_default_callback()
 
-            # Get stats and put on result queue
-            if frame_count >= 2:
+            # Get stats and put on result queue (depth streams skip stats)
+            if is_depth:
+                self.result_queue.put(("ok", None))
+            elif frame_count >= 2:
                 stats = stats_tracker.get_statistics()
                 self.result_queue.put(("ok", stats))
             else:
@@ -712,22 +1001,40 @@ class StreamingVideoEncoder:
     def __init__(
         self,
         fps: int,
-        vcodec: str = "libsvtav1",
-        pix_fmt: str = "yuv420p",
-        g: int | None = 2,
-        crf: int | None = 30,
-        preset: int | None = None,
-        queue_maxsize: int = 30,
+        camera_encoder_config: VideoEncoderConfig | None = None,
         encoder_threads: int | None = None,
+        *,
+        queue_maxsize: int = 30,
+        depth_encoder_config: "DepthEncoderConfig | None" = None,
+        depth_keys: list[str] | None = None,
     ):
+        """
+        Args:
+            fps: Frames per second for the output videos.
+            camera_encoder_config: Video encoder settings applied to all cameras.
+                When ``None``, :class:`VideoEncoderConfig` defaults are used.
+            encoder_threads: Number of encoder threads (global setting).
+                ``None`` lets the codec decide.
+            queue_maxsize: Max frames to buffer per camera before
+                back-pressure drops frames.
+            depth_encoder_config: Optional depth encoder configuration applied
+                to all depth video keys listed in ``depth_keys``.
+            depth_keys: Video keys (matching the dataset feature names) that
+                must be encoded as quantized depth maps using
+                ``depth_encoder_config``. Required when ``depth_encoder_config``
+                is provided.
+        """
         self.fps = fps
-        self.vcodec = resolve_vcodec(vcodec)
-        self.pix_fmt = pix_fmt
-        self.g = g
-        self.crf = crf
-        self.preset = preset
+        self._camera_encoder_config = camera_encoder_config or VideoEncoderConfig()
+        self._encoder_threads = encoder_threads
         self.queue_maxsize = queue_maxsize
-        self.encoder_threads = encoder_threads
+        self._depth_encoder_config = depth_encoder_config
+        self._depth_keys: set[str] = set(depth_keys or [])
+        if self._depth_keys and self._depth_encoder_config is None:
+            raise ValueError(
+                "StreamingVideoEncoder received depth_keys without a depth_encoder_config; "
+                "either pass a DepthEncoderConfig or remove depth_keys."
+            )
 
         self._frame_queues: dict[str, queue.Queue] = {}
         self._result_queues: dict[str, queue.Queue] = {}
@@ -758,18 +1065,28 @@ class StreamingVideoEncoder:
             temp_video_dir = Path(tempfile.mkdtemp(dir=temp_dir))
             video_path = temp_video_dir / f"{video_key.replace('/', '_')}_streaming.mp4"
 
+            is_depth_key = video_key in self._depth_keys
+            encoder_cfg: VideoEncoderConfig
+            depth_cfg = None
+            if is_depth_key:
+                assert self._depth_encoder_config is not None  # guaranteed by __init__
+                encoder_cfg = self._depth_encoder_config
+                depth_cfg = self._depth_encoder_config
+            else:
+                encoder_cfg = self._camera_encoder_config
+
+            vcodec = encoder_cfg.vcodec
+            codec_options = encoder_cfg.get_codec_options(self._encoder_threads)
             encoder_thread = _CameraEncoderThread(
                 video_path=video_path,
                 fps=self.fps,
-                vcodec=self.vcodec,
-                pix_fmt=self.pix_fmt,
-                g=self.g,
-                crf=self.crf,
-                preset=self.preset,
+                vcodec=vcodec,
+                pix_fmt=encoder_cfg.pix_fmt,
+                codec_options=codec_options,
                 frame_queue=frame_queue,
                 result_queue=result_queue,
                 stop_event=stop_event,
-                encoder_threads=self.encoder_threads,
+                depth_encoder_config=depth_cfg,
             )
             encoder_thread.start()
 
@@ -974,8 +1291,18 @@ def get_audio_info(video_path: Path | str) -> dict:
     return audio_info
 
 
-def get_video_info(video_path: Path | str) -> dict:
-    # Set logging level
+def get_video_info(
+    video_path: Path | str,
+    video_encoder_config: "VideoEncoderConfig | None" = None,
+) -> dict:
+    """Build the ``video.*`` / ``audio.*`` info dict persisted in ``info.json``.
+
+    Args:
+        video_path: Path to the encoded video file to probe.
+        video_encoder_config: If provided, record the exact encoder settings used to encode this
+            video. Stream-derived values take precedence — encoder fields are only written for keys
+            not already populated from the video file itself.
+    """
     logging.getLogger("libav").setLevel(av.logging.WARNING)
 
     # Getting video stream information
@@ -992,7 +1319,6 @@ def get_video_info(video_path: Path | str) -> dict:
         video_info["video.width"] = video_stream.width
         video_info["video.codec"] = video_stream.codec.canonical_name
         video_info["video.pix_fmt"] = video_stream.pix_fmt
-        video_info["video.is_depth_map"] = False
 
         # Calculate fps from r_frame_rate
         video_info["video.fps"] = int(video_stream.base_rate)
@@ -1006,9 +1332,67 @@ def get_video_info(video_path: Path | str) -> dict:
     # Adding audio stream information
     video_info.update(**get_audio_info(video_path))
 
+    # Add additional encoder configuration if provided (no override of stream-derived values)
+    # Depth related fields flow naturally through this path.
+    if video_encoder_config is not None:
+        for field_name, field_value in asdict(video_encoder_config).items():
+            video_info.setdefault(f"video.{field_name}", field_value)
+
+    # Fallback case where no encoder config is provided or the video is not a depth map.
+    video_info.setdefault("video.is_depth_map", False)
+
     return video_info
 
 
+# ─── Depth metadata helpers (reader side) ────────────────────────────
+
+
+_DEPTH_INFO_KEYS: tuple[str, ...] = (
+    "video.depth_min",
+    "video.depth_max",
+    "video.shift",
+    "video.use_log",
+)
+
+
+def seed_depth_feature_info(
+    features: dict[str, dict],
+    depth_encoder_config: "DepthEncoderConfig | None",
+) -> None:
+    """Pre-populate per-feature ``video.<field>`` entries from *depth_encoder_config*.
+
+    ``update_video_info`` only runs after the first episode video is encoded,
+    so without this seeding step ``features[key]["info"]`` carries no
+    quantization range until then. Consumers that read the dataset feature
+    spec mid-recording (e.g. the rerun visualizer pinning the depth colormap
+    to ``video.depth_min`` / ``video.depth_max``) would otherwise see no
+    range during episode 1 and re-normalize per frame.
+
+    Stream-derived values written later by :func:`get_video_info` /
+    ``update_video_info`` win over these seeds (the merge is
+    ``{**existing, **stream_info}``), so callers can safely re-run this on
+    a partially-populated info dict.
+
+    No-op when ``depth_encoder_config`` is ``None`` or no feature is flagged
+    as a depth map.
+    """
+    if depth_encoder_config is None:
+        return
+    encoder_fields = {
+        f"video.{name}": value for name, value in asdict(depth_encoder_config).items()
+    }
+    for ft in features.values():
+        if ft.get("dtype") != "video":
+            continue
+        info = ft.get("info") or {}
+        if not info.get("video.is_depth_map", False):
+            continue
+        # Only fill fields not already set, so explicit user-provided info is preserved.
+        for k, v in encoder_fields.items():
+            info.setdefault(k, v)
+        ft["info"] = info
+
+
 def get_video_pixel_channels(pix_fmt: str) -> int:
     if "gray" in pix_fmt or "depth" in pix_fmt or "monochrome" in pix_fmt:
         return 1

src/lerobot/envs/__init__.py

@@ -18,15 +18,7 @@
 # from lerobot.utils.import_utils import require_package
 # require_package("gymnasium", extra="<update_extra>", import_name="gymnasium")
 
-from .configs import (
-    AlohaEnv,
-    EnvConfig,
-    HILSerlRobotEnvConfig,
-    HubEnvConfig,
-    LiberoPlusEnv,
-    PushtEnv,
-    RoboMMEEnv,
-)
+from .configs import AlohaEnv, EnvConfig, HILSerlRobotEnvConfig, HubEnvConfig, PushtEnv
 from .factory import make_env, make_env_config, make_env_pre_post_processors
 from .utils import check_env_attributes_and_types, close_envs, env_to_policy_features, preprocess_observation
 
@@ -35,9 +27,7 @@ __all__ = [
     "EnvConfig",
     "HILSerlRobotEnvConfig",
     "HubEnvConfig",
-    "LiberoPlusEnv",
     "PushtEnv",
-    "RoboMMEEnv",
     "check_env_attributes_and_types",
     "close_envs",
     "env_to_policy_features",

src/lerobot/envs/configs.py

@@ -331,6 +331,7 @@ 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,)),
@@ -432,6 +433,7 @@ 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):
@@ -496,6 +498,146 @@ 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):
@@ -579,45 +721,158 @@ class IsaaclabArenaEnv(HubEnvConfig):
 @EnvConfig.register_subclass("libero_plus")
 @dataclass
 class LiberoPlusEnv(LiberoEnv):
-    """Config for LIBERO-plus robustness benchmark evaluation."""
+    """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("robomme")
+@EnvConfig.register_subclass("robotwin")
 @dataclass
-class RoboMMEEnv(EnvConfig):
-    """RoboMME memory-augmented manipulation benchmark."""
+class RoboTwinEnvConfig(EnvConfig):
+    """Configuration for RoboTwin 2.0 benchmark environments.
 
-    task: str = "PickXtimes"
-    fps: int = 10
+    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
-    action_space: str = "joint_angle"
-    dataset_split: str = "test"
-    task_ids: list[int] | None = None
+    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=(8,)),
-            "image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
-            "wrist_image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
-            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(8,)),
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(14,)),
         }
     )
     features_map: dict[str, str] = field(
         default_factory=lambda: {
             ACTION: ACTION,
-            "image": f"{OBS_IMAGES}.image",
-            "wrist_image": f"{OBS_IMAGES}.wrist_image",
-            OBS_STATE: OBS_STATE,
+            "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 .robomme import create_robomme_envs
+        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(

src/lerobot/envs/libero.py

@@ -32,20 +32,7 @@ from libero.libero.envs import OffScreenRenderEnv
 
 from lerobot.types import RobotObservation
 
-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
+from .utils import _LazyAsyncVectorEnv, parse_camera_names
 
 
 def _get_suite(name: str) -> benchmark.Benchmark:
@@ -77,18 +64,24 @@ def _select_task_ids(total_tasks: int, task_ids: Iterable[int] | None) -> list[i
 _LIBERO_PERTURBATION_SUFFIX_RE = re.compile(r"_(?:language|view|light)_[^.]*|_(?:table|tb)_\d+")
 
 
-def get_task_init_states(task_suite: Any, i: int) -> np.ndarray:
+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"))
 
-    # `_add_` / `_level` variants store extra-object layouts under libero_newobj/
-    # as a flat array that must be reshaped to (1, -1).
+    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
@@ -133,9 +126,11 @@ 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
@@ -143,7 +138,7 @@ 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)
 
@@ -162,7 +157,11 @@ 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) if self.init_states else None
+        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._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
@@ -395,6 +394,7 @@ 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)."""
 
@@ -411,6 +411,7 @@ 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,
         )
 
@@ -433,6 +434,7 @@ 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.
@@ -452,7 +454,7 @@ 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.")
@@ -477,6 +479,7 @@ 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(
@@ -490,12 +493,14 @@ 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)
+                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[suite_name][tid] = lazy
             else:
                 out[suite_name][tid] = env_cls(fns)

src/lerobot/envs/metaworld.py

@@ -311,6 +311,7 @@ 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:
@@ -324,10 +325,11 @@ 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)
+                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)

src/lerobot/envs/robocasa.py

@@ -0,0 +1,425 @@
+#!/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

@@ -1,20 +1,18 @@
-#!/usr/bin/env python
+"""RoboMME environment wrapper for LeRobot evaluation.
 
-# 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.
+Wraps the RoboMME ``BenchmarkEnvBuilder`` into a Gymnasium-compatible
+``VectorEnv`` suitable for ``lerobot_eval``.
 
-"""RoboMME environment wrapper for LeRobot evaluation."""
+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
 
@@ -26,6 +24,8 @@ import gymnasium as gym
 import numpy as np
 from gymnasium import spaces
 
+from .utils import _LazyAsyncVectorEnv
+
 ROBOMME_TASKS = [
     "BinFill",
     "PickXtimes",
@@ -62,6 +62,13 @@ class RoboMMEGymEnv(gym.Env):
         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,
@@ -69,19 +76,24 @@ class RoboMMEGymEnv(gym.Env):
             gui_render=False,
             max_steps=max_steps,
         )
-        self._max_episode_steps = max_steps
-        self._episode_idx = episode_idx
-        self._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(
             {
-                "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),
-                "state": spaces.Box(-np.inf, np.inf, shape=(8,), dtype=np.float32),
+                "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),
             }
         )
 
@@ -103,12 +115,14 @@ class RoboMMEGymEnv(gym.Env):
         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"] = status == "success"
+        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")
@@ -135,14 +149,15 @@ class RoboMMEGymEnv(gym.Env):
             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 {
-            "image": np.asarray(front_rgb, dtype=np.uint8),
-            "wrist_image": np.asarray(wrist_rgb, dtype=np.uint8),
-            "state": state,
+            "pixels": {"image": front_rgb, "wrist_image": wrist_rgb},
+            "agent_pos": state,
         }
 
     def _convert_info(self, info: dict) -> dict:
@@ -161,6 +176,8 @@ def _make_env_fns(
     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,
@@ -182,7 +199,14 @@ def create_robomme_envs(
     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."""
+    """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:
@@ -192,6 +216,10 @@ def create_robomme_envs(
         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] = {}
@@ -204,6 +232,14 @@ def create_robomme_envs(
                 episode_length=episode_length,
                 task_id=task_id,
             )
-            envs_by_task[task_id] = env_cls(fns)
+            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