| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| --- |
| |
| # XR-1-Dataset-Sample |
|
|
| [[Project Page](https://xr-1-vla.github.io/)] [[Paper](https://huggingface.co/papers/2511.02776)] [[GitHub](https://github.com/Open-X-Humanoid/XR-1)] |
|
|
| This repository contains a representative sample of the **XR-1** project's multi-modal dataset. The data is organized to support cross-embodiment training for Humanoids, Manipulators, and Ego-centric vision. |
|
|
| ## 📂 Directory Structure |
|
|
| The dataset follows a hierarchy based on **Embodiment -> Task -> Format**: |
|
|
| ### 1. Robot Embodiment Data (LeRobot Format) |
| Standard robot data (like TienKung or UR5) is organized following the [LeRobot](https://github.com/huggingface/lerobot) convention: |
|
|
| ```text |
| XR-1-Dataset-Sample/ |
| └── DUAL_ARM_TIEN_KUNG2/ # Robot Embodiment |
| └── Press_Green_Button/ # Task Name |
| └── lerobot/ # Data in LeRobot format |
| ├── metadata.json |
| ├── episodes.jsonl |
| ├── videos/ |
| └── data/ |
| |
| ``` |
|
|
| ### 2. Human/Ego-centric Data (Ego4D Format) |
|
|
| For ego-centric data (e.g., Ego4D subsets used for Stage 1 UVMC pre-training), the structure is adapted to its native recording format: |
|
|
| ```text |
| XR-1-Dataset-Sample/ |
| └── Ego4D/ # Human ego-centric source |
| ├── files.json # Unified annotation/mapping file |
| └── files/ # Raw data storage |
| └── [video_id].mp4 # Egocentric video clips |
| ``` |
|
|
| ## 🤖 Data Modalities |
|
|
| * **Vision**: High-frequency RGB streams from multiple camera perspectives. |
| * **Motion**: Continuous state-action pairs, which are tokenized into **UVMC** (Unified Vision-Motion Codes) for XR-1 training. |
| * **Language**: Natural language instructions paired with each episode for VLA alignment. |
|
|
| ## 🛠 Usage |
|
|
| This sample is intended for use with the [XR-1 GitHub Repository](https://github.com/Open-X-Humanoid/XR-1). |
|
|
| ## 📝 Citation |
|
|
| ```bibtex |
| @inproceedings{fan2025xr, |
| title={XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations}, |
| author={Fan, Shichao and Wu, Kun and Che, Zhengping and Wang, Xinhua and Wu, Di and Liao, Fei and Liu, Ning and Zhang, Yixue and Zhao, Zhen and Xu, Zhiyuan and others}, |
| booktitle={Proceedings of the International Conference on Machine Learning (ICML)}, |
| year={2026} |
| } |
| ``` |
|
|
| ## 📜 License |
|
|
| This dataset is released under the [MIT License](https://github.com/Open-X-Humanoid/XR-1/blob/main/LICENSE). |
|
|
| --- |
|
|
| **Contact**: For questions, please open an issue on our [GitHub](https://github.com/Open-X-Humanoid/XR-1). |
|
|
| ## Discussions |
| If you're interested in XR-1, welcome to join our WeChat group for discussions. |
|
|
| <img src="https://cdn-uploads.huggingface.co/production/uploads/6776382cba4c337914f956d5/njWmHwX71fhItDBh_4Qo4.png" border=0 width=30%> |
