diff --git a/.github/workflows/benchmark_tests.yml b/.github/workflows/benchmark_tests.yml index 001037ad3..37d6e3fd2 100644 --- a/.github/workflows/benchmark_tests.yml +++ b/.github/workflows/benchmark_tests.yml @@ -334,6 +334,12 @@ jobs: with: cache-binary: false + - name: Login to Docker Hub + uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses] + with: + username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }} + password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }} + # 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. @@ -349,6 +355,7 @@ jobs: cache-to: type=local,dest=/tmp/.buildx-cache-robotwin,mode=max - name: Run RoboTwin 2.0 smoke eval (1 episode) + if: env.HF_USER_TOKEN != '' run: | # Named container (no --rm) so we can docker cp artifacts out. docker run --name robotwin-eval --gpus all \ diff --git a/docker/Dockerfile.benchmark.robotwin b/docker/Dockerfile.benchmark.robotwin index 936fa2379..7a24071eb 100644 --- a/docker/Dockerfile.benchmark.robotwin +++ b/docker/Dockerfile.benchmark.robotwin @@ -60,8 +60,11 @@ RUN cd ${ROBOTWIN_ROOT}/envs \ uv pip install -e . --no-build-isolation --no-cache # Upstream patches (mirror RoboTwin's script/_install.sh). -# mplib: drop a broken `or collide` clause in planner.py. -# sapien: fix URDF loader encoding + .srdf extension check. +# 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(): @@ -80,7 +83,7 @@ EOF # Simulation assets from TianxingChen/RoboTwin2.0: embodiments (~220 MB) + # objects (~3.74 GB). background_texture (~11 GB) is intentionally skipped. -ARG HF_TOKEN="" +# The dataset is public — no auth token needed. RUN python - <<'EOF' import os, pathlib, zipfile from huggingface_hub import hf_hub_download @@ -92,7 +95,6 @@ for fname in ("embodiments.zip", "objects.zip"): repo_id="TianxingChen/RoboTwin2.0", repo_type="dataset", filename=fname, - token=os.environ.get("HF_TOKEN") or None, local_dir=str(assets_dir), ) with zipfile.ZipFile(local, "r") as z: @@ -100,10 +102,14 @@ for fname in ("embodiments.zip", "objects.zip"): pathlib.Path(local).unlink() EOF -RUN cd ${ROBOTWIN_ROOT} && python script/update_embodiment_config_path.py +WORKDIR ${ROBOTWIN_ROOT} +RUN python script/update_embodiment_config_path.py ENV PYTHONPATH="${ROBOTWIN_ROOT}:${PYTHONPATH}" +# 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 . . diff --git a/docs/source/robotwin.mdx b/docs/source/robotwin.mdx index 815e157dd..555c0bf74 100644 --- a/docs/source/robotwin.mdx +++ b/docs/source/robotwin.mdx @@ -2,22 +2,30 @@ 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 60 tasks 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://robotwin-platform.github.io) +- Paper: [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://arxiv.org/abs/2504.13495) - 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) +
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