LIBERO-Pro2.0-Train / docs /simulation_assets.md
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LIBERO-Pro simulation assets

This archive supplies the environment assets for the 1,688-task release of LIBERO-Pro. It contains the existing LIBERO objects, scenes, textures, and the additional perturbation assets. BDDL files, initial states, and evaluation code remain in the GitHub main branch. The HDF5 training data in data/ and the Dataset Viewer are unchanged.

Download

The ZIP is approximately 483 MB and expands to approximately 821 MB. Download only the asset supplement:

pip install -U huggingface_hub
hf download zhouxueyang/LIBERO-Pro2.0-Train --repo-type dataset \
  --include "assets.zip" "assets.zip.sha256" "manifests/assets_manifest.json" \
            "scripts/verify_assets.py" "docs/simulation_assets.md" \
  --local-dir ./libero-pro-assets
python ./libero-pro-assets/scripts/verify_assets.py \
  --archive ./libero-pro-assets/assets.zip \
  --manifest ./libero-pro-assets/manifests/assets_manifest.json

Install into the current evaluation release

The compatible source commit is 3ac451e6e85ee6995bf684b56f6c04f8ef7c8829. Use a fresh checkout so existing environments and running jobs are unaffected. The GitHub repository currently requires access from its owner.

GIT_LFS_SKIP_SMUDGE=1 git clone --branch main \
  https://github.com/Zxy-MLlab/LIBERO-Pro2.0.git ./LIBERO-Pro-eval
git -C ./LIBERO-Pro-eval checkout 3ac451e6e85ee6995bf684b56f6c04f8ef7c8829
python -m zipfile -e ./libero-pro-assets/assets.zip \
  ./LIBERO-Pro-eval/simulator-source/libero/libero
python ./libero-pro-assets/scripts/verify_assets.py \
  --assets-dir ./LIBERO-Pro-eval/simulator-source/libero/libero/assets \
  --manifest ./libero-pro-assets/manifests/assets_manifest.json

The archive already contains the assets/ directory. Extract into its parent, not into another assets/ directory:

LIBERO-Pro-eval/
└── simulator-source/libero/libero/
    └── assets/
        ├── articulated_objects/
        ├── perturbation_assets/
        ├── scenes/
        ├── stable_hope_objects/
        ├── stable_scanned_objects/
        ├── textures/
        ├── turbosquid_objects/
        └── LICENSE_LIBERO

This is the source layout of the pinned release. If using an already validated native LIBERO package layout, the equivalent parent directory is libero/libero/. The archive does not install Python, MuJoCo, robosuite, model checkpoints, or evaluation dependencies. Follow the source release installation instructions for those components. Downloading the assets alone does not run the benchmark or change the number of evaluation trials.

Contents and verification

The archive contains 1,116 tracked asset files from the source release, plus the upstream LIBERO license. All 76 Git LFS assets were expanded and verified against their pinned SHA-256 values and sizes. All 181 vendor files match the original vendor manifest. For 103 vendor text files, Git had normalized the line endings; the original CRLF bytes were restored only after their size and SHA-256 matched that source manifest. Other files use canonical Git content.

manifests/assets_manifest.json records the source commit, every source path, file size, and SHA-256 checksum. The complete ZIP and every decompressed member were checked before publication. This verifies the release payload; it is not a new model evaluation or a claim that all policy rollouts succeed.

The historical asset catalog may mention internal or retired perturbation IDs. The authoritative 1,688-task inventory is the pinned source's assets/task_manifest.json. Public D19 corresponds to internal D20, and public D20 corresponds to internal D21. The asset supplement does not rename tasks.

Asset provenance and licenses

The HDF5 dataset's CC BY-NC 4.0 declaration does not replace upstream asset licenses. The archive retains the original LIBERO license, vendor licenses, and assets/perturbation_assets/THIRD_PARTY_NOTICES.md with source attribution. The selected asset collection includes MIT, Apache-2.0, CC BY 4.0, and LGPL-2.1-only components; consult each retained notice for its scope and any additional original dataset terms.