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feat(envs): add RoboCerebra long-horizon manipulation benchmark (#3314)
* feat(ci): add RoboCerebra benchmark eval job - Docker image with robosuite/libero deps for RoboCerebra eval - CI workflow: 1-episode eval with pepijn223/smolvla_robocerebra - Reuses libero env with rename_map + empty_cameras=3 * docs(robocerebra): add benchmark page and toctree entry Add a dedicated docs page for RoboCerebra that points at the canonical dataset lerobot/robocerebra_unified and shows how to run eval + fine-tune against it. Wire it into the Benchmarks section of the toctree so doc-builder picks it up. * ci: point benchmark eval checkpoints at the lerobot/ org mirrors pepijn223/smolvla_* → lerobot/smolvla_* across every benchmark job in this branch (libero, metaworld, and the per-branch benchmark). The checkpoints were mirrored into the lerobot/ org and that's the canonical location going forward. * fix(robocerebra): drop alias extra + simplify docker image Two problems rolled up: 1. `uv sync --locked --extra test` was failing because pyproject.toml added a `robocerebra = ["lerobot[libero]"]` alias extra but uv.lock wasn't regenerated. Drop the alias — nothing in CI actually needs the extra name (the Dockerfile just installs what it needs directly), so this restores pyproject.toml and uv.lock to byte-exact origin/main. 2. Rebase docker/Dockerfile.benchmark.robocerebra on huggingface/lerobot-gpu:latest (same pattern as libero/metaworld/…). The nightly image already ships lerobot[all] which includes [libero], so the RoboCerebra image is essentially identical to the LIBERO one: fetch libero-assets, write ~/.libero/config.yaml, overlay source. 92 → 43 lines. Also repoint the CI workflow comment that referenced the removed extra. * ci: use dedicated lerobot/smolvla_robocerebra checkpoint for smoke eval Replace the generic pepijn223/smolvla_libero placeholder with the purpose-trained lerobot/smolvla_robocerebra model in the RoboCerebra CI smoke test. * fix(ci): align RoboCerebra eval with training pipeline Fixes 5 mismatches that caused 0% success rate: - env.type: robocerebra (unregistered) → libero - resolution: 360x360 (default) → 256x256 (matches dataset) - camera_name_mapping: map eye_in_hand → wrist_image (not image2) - empty_cameras: 3 → 1 (matches training) - add HF_USER_TOKEN guard on eval step * fix(ci): set env.fps=20 and explicit obs_type for RoboCerebra eval Match the dataset's 20 FPS (LiberoEnv defaults to 30) and make obs_type=pixels_agent_pos explicit for safety against future changes. * docs(robocerebra): align page with adding_benchmarks template Rework docs/source/robocerebra.mdx to follow the standard benchmark doc structure: intro + links + available tasks + installation + eval + recommended episodes + policy I/O + training + reproducing results. - Point everything at lerobot/smolvla_robocerebra (the released checkpoint), not the personal pepijn223 mirror. - Add the --env.fps=20 and --env.obs_type=pixels_agent_pos flags that CI actually uses, so copy-paste eval reproduces CI. - Split the "Training" block out of the recipe section into its own section with the feature table. - Add an explicit "Reproducing published results" section pointing at the CI smoke eval. * fix: integrate PR #3314 review feedback - ci(robocerebra): drop redundant hf auth login step (auth is already performed inside the eval step's container). - ci(robocerebra): add Docker Hub login before the image build to pick up the authenticated rate limit. - docs(robocerebra): align eval snippet with the CI command (observation size, camera_name_mapping, use_async_envs, device, empty_cameras=1). * fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs Port of #3416 onto this branch. * ci: gate Docker Hub login on secret availability * Update .github/workflows/benchmark_tests.yml Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update .github/workflows/benchmark_tests.yml Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
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@@ -525,3 +525,110 @@ jobs:
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name: robocasa-metrics
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path: /tmp/robocasa-artifacts/metrics.json
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if-no-files-found: warn
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# ── ROBOCEREBRA ───────────────────────────────────────────────────────────
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# Reuses the LIBERO simulator (libero_10 suite) with RoboCerebra camera
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# defaults (image/wrist_image). The image is layered on
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# huggingface/lerobot-gpu, which already ships [libero] as part of [all].
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robocerebra-integration-test:
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name: RoboCerebra — build image + 1-episode eval
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runs-on:
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group: aws-g6-4xlarge-plus
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env:
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HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
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steps:
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- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
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with:
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persist-credentials: false
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lfs: true
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
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with:
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cache-binary: false
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- name: Login to Docker Hub
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if: ${{ env.DOCKERHUB_USERNAME != '' }}
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uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
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with:
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username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
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password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
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env:
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DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
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- name: Build RoboCerebra benchmark image
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uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
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with:
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context: .
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file: docker/Dockerfile.benchmark.robocerebra
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push: false
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load: true
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tags: lerobot-benchmark-robocerebra:ci
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cache-from: type=local,src=/tmp/.buildx-cache-robocerebra
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cache-to: type=local,dest=/tmp/.buildx-cache-robocerebra,mode=max
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- name: Run RoboCerebra smoke eval (1 episode)
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if: env.HF_USER_TOKEN != ''
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run: |
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docker run --name robocerebra-eval --gpus all \
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--shm-size=4g \
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-e HF_HOME=/tmp/hf \
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-e HF_USER_TOKEN="${HF_USER_TOKEN}" \
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-e HF_HUB_DOWNLOAD_TIMEOUT=300 \
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-e LIBERO_DATA_FOLDER=/tmp/libero_data \
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lerobot-benchmark-robocerebra:ci \
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bash -c "
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hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
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lerobot-eval \
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--policy.path=lerobot/smolvla_robocerebra \
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--env.type=libero \
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--env.task=libero_10 \
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--env.fps=20 \
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--env.obs_type=pixels_agent_pos \
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--env.observation_height=256 \
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--env.observation_width=256 \
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'--env.camera_name_mapping={\"agentview_image\": \"image\", \"robot0_eye_in_hand_image\": \"wrist_image\"}' \
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--eval.batch_size=1 \
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--eval.n_episodes=1 \
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--eval.use_async_envs=false \
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--policy.device=cuda \
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'--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.wrist_image\": \"observation.images.camera2\"}' \
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--policy.empty_cameras=1 \
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--output_dir=/tmp/eval-artifacts
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python scripts/ci/extract_task_descriptions.py \
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--env libero --task libero_10 \
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--output /tmp/eval-artifacts/task_descriptions.json
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"
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- name: Copy RoboCerebra artifacts from container
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if: always()
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run: |
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mkdir -p /tmp/robocerebra-artifacts
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docker cp robocerebra-eval:/tmp/eval-artifacts/. /tmp/robocerebra-artifacts/ 2>/dev/null || true
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docker rm -f robocerebra-eval || true
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- name: Parse RoboCerebra eval metrics
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if: always()
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run: |
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python3 scripts/ci/parse_eval_metrics.py \
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--artifacts-dir /tmp/robocerebra-artifacts \
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--env robocerebra \
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--task libero_10 \
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--policy lerobot/smolvla_robocerebra
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- name: Upload RoboCerebra rollout video
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if: always()
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uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
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with:
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name: robocerebra-rollout-video
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path: /tmp/robocerebra-artifacts/videos/
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if-no-files-found: warn
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- name: Upload RoboCerebra eval metrics
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if: always()
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uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
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with:
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name: robocerebra-metrics
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path: /tmp/robocerebra-artifacts/metrics.json
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if-no-files-found: warn
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