---
license: cc-by-4.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
  data_files: data/*/*.parquet
---

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

<font color="gray" style="text-decoration: underline;">Stay tuned for the full release!</font>


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}
}
```