| --- |
| license: cc-by-4.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| configs: |
| - config_name: default |
| data_files: data/*/*.parquet |
| --- |
| |
| We're thrilled to announce that the **LIBERO-X dataset** has been officially accepted by **[RSS 2026](https://roboticsconference.org/)** (Robotics: Science and Systems). The complete dataset is now available. |
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|
|
| This dataset was created using [LeRobot](https://github.com/huggingface/lerobot) (commit [`12f5263`](https://github.com/huggingface/lerobot/commit/12f5263)). |
|
|
| - **Homepage:** https://meituan.github.io/LIBERO-X/ |
| - **Paper:** https://arxiv.org/pdf/2602.06556 |
| - **License:** CC-BY-4.0 |
|
|
| ## Dataset Description |
|
|
| LIBERO-X introduces finer-grained task-level extensions to expose models to diverse task formulations and workspace configurations, includeing 2,520 demonstrations, 600 tasks, and 100 scenes, ensuring broad generalization across diverse scenarios, featuring: |
|
|
| - **Multi-Task Scene Design:** |
| Each scene averages 6 distinct tasks, a significant increase compared to the original LIBERO dataset’s average of 2.6 tasks per scene, enabling more complex and realistic multi-objective learning. |
|
|
| - **Attribute-Conditioned Manipulation:** |
| Actions are explicitly conditioned on fine-grained object properties (e.g., size, color, texture) beyond broad categories. |
|
|
| - **Spatial Relationship Reasoning:** |
| Tasks extend beyond target localization to require understanding and reasoning about spatial relationships among objects, including left/right, front/back, and near/far. |
|
|
| - **Human Demonstration Collection:** |
| All trajectories were human-collected via VR teleoperation using a Meta Quest 3. |
|
|
|
|
| ## Dataset Structure |
| [meta/info.json](meta/info.json): |
| ```json |
| { |
| "codebase_version": "v2.1", |
| "robot_type": "panda", |
| "total_episodes": 2520, |
| "total_frames": 889277, |
| "total_tasks": 428, |
| "total_videos": 0, |
| "total_chunks": 3, |
| "chunks_size": 1000, |
| "fps": 10, |
| "splits": { |
| "train": "0:2520" |
| }, |
| "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", |
| "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", |
| "features": { |
| "image": { |
| "dtype": "image", |
| "shape": [ |
| 256, |
| 256, |
| 3 |
| ], |
| "names": [ |
| "height", |
| "width", |
| "channel" |
| ] |
| }, |
| "wrist_image": { |
| "dtype": "image", |
| "shape": [ |
| 256, |
| 256, |
| 3 |
| ], |
| "names": [ |
| "height", |
| "width", |
| "channel" |
| ] |
| }, |
| "state": { |
| "dtype": "float32", |
| "shape": [ |
| 8 |
| ], |
| "names": [ |
| "state" |
| ] |
| }, |
| "actions": { |
| "dtype": "float32", |
| "shape": [ |
| 7 |
| ], |
| "names": [ |
| "actions" |
| ] |
| }, |
| "timestamp": { |
| "dtype": "float32", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "frame_index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "episode_index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| }, |
| "task_index": { |
| "dtype": "int64", |
| "shape": [ |
| 1 |
| ], |
| "names": null |
| } |
| } |
| } |
| ``` |
|
|
|
|
|
|
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2026libero, |
| title={LIBERO-X: Robustness Litmus for Vision-Language-Action Models}, |
| author={Wang, Guodong and Zhang, Chenkai and Liu, Qingjie and Zhang, Jinjin and Cai, Jiancheng and Liu, Junjie and Liu, Xinmin}, |
| journal={arXiv preprint arXiv:2602.06556}, |
| year={2026} |
| } |
| ``` |
