README.md

metadata

license: cc-by-4.0
task_categories:
  - robotics
tags:
  - vision-language-action
  - vla
  - benchmark
  - robot-learning

Dataset Card for LIBERO-PRO Perturbation Dataset

Project Page | Paper | Code

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 to evaluate Vision-Language-Action (VLA) models beyond rote memorization.

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Dataset Details

Dataset Description

• Curated by: LIBERO-PRO Research Team
• Affiliation: MLLab, Huazhong University of Science and Technology
• Language(s) (NLP): English (instructional text)
• License: CC-BY-4.0 (for dataset artifacts), MIT (for codebase)
• Primary Purpose: Evaluation of VLA models under structured perturbations

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).

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Seven-Case Robustness Extension

The repository also includes a 40-task evaluation set covering seven BDDL-configured robustness cases. Each category contains 10 tasks from each of libero_spatial, libero_object, libero_goal, and libero_10.

Folder	Evaluation case
01_visual_noise_glare	Lighting and observation noise
02_camera_view_angle	Camera position and orientation
03_runtime_object_move	Runtime target-object movement
04_object_texture	Object appearance and texture
05_view_occlusion	View occlusion by scene objects
06_object_shape	Target-object shape scaling
07_initial_pose_position_angle	Initial position and yaw changes

The 280 BDDL files use this layout:

bddl_files/<category>/bddl/<suite>/<task>.bddl

Shared original initialization states are stored under:

init_files/<suite>/<task>.pruned_init

Where available, the corresponding .init files are included as well. The runtime object movement case uses a near-grasp trigger with a maximum end-effector-to-target distance of 0.09 m and a step-160 fallback.

The metadata/ directory contains a portable dataset index, task-specific perturbation manifest, and the latest static validation report. File checksums are listed in SHA256SUMS.txt.

The custom :perturbation_config fields require the LIBERO-Pro-aware parser and evaluation integration from the project codebase.

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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.

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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.

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Citation

If you use this dataset, please cite both the original LIBERO benchmark and the LIBERO-PRO project:

BibTeX:

@article{zhou2025liberopro,
  title={LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization},
  author={Xueyang Zhou and Yangming Xu and Guiyao Tie and Yongchao Chen and Guowen Zhang and Duanfeng Chu and Pan Zhou and Lichao Sun},
  journal={arXiv preprint arXiv:2510.03827},
  year={2025}
}

@article{liu2023libero,
  title={LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
  author={Liu, Bo and Zhu, Yifeng and Gao, Chongkai and Feng, Yihao and Liu, Qiang and Zhu, Yuke and Stone, Peter},
  journal={arXiv preprint arXiv:2306.03310},
  year={2023}
}

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Dataset Card Authors

• Xueyang Zhou
• Yangming Xu

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Dataset Card Contact

For questions or issues, please contact:
📧 d202480819@hust.edu.cn