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Dataset Card for LIBERO-PRO Perturbation Dataset

This dataset contains the bddl and init files of LIBERO-PRO configurations under object, relation, semantic, task, and environment perturbations. The dataset supports direct integration with the LIBERO-PRO framework.


Dataset Details

This dataset extends the original LIBERO benchmark by introducing systematic perturbations in five dimensions:

  1. Object Perturbation: Modifies object appearance, color, and scale to test adaptability to visual shifts.
  2. Position Perturbation: Relocates objects within feasible spatial bounds to evaluate the model’s adaptability to spatial position changes.
  3. Semantic Perturbation: Paraphrases natural language commands to probe linguistic robustness.
  4. Task Perturbation: Redefines task logic and target states to test procedural generalization.
  5. Environment Perturbation: Replaces working environments to evaluate cross-environment robustness.

Each perturbation includes corresponding init files (initial environment configurations) and bddl files (behavioral descriptions in BDDL format).


Uses

How to use:

  1. Copy all .bddl files to:
    LIBERO-PRO/libero/libero/bddl_files/
    
  2. Copy all init files to:
    LIBERO-PRO/libero/libero/init_files/
    
  3. Follow the quick start instructions provided in the LIBERO-PRO README.

Dataset Structure

Each perturbation category contains:

  • init/: Environment initialization files defining object placement and world state.
  • bddl/: Task goal definitions in Behavior Domain Definition Language.

Citation

If you use this dataset, please cite the LIBERO-PRO project:

BibTeX:

@misc{zhou2025liberopro,
  title={LIBERO-PRO: Benchmarking Perturbation Robustness of Visual Language Action Models},
  author={Zhou, Xueyang and Tie, Guiyao and Zhang, Guowen and Wang, Hechang and Zhou, Pan},
  year={2025},
  howpublished={\url{https://github.com/Zxy-MLlab/LIBERO-PRO}},
}

Dataset Card Authors

  • Xueyang Zhou
  • Yangming Xu

Dataset Card Contact

For questions or issues, please contact:
📧 [email protected]

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