GR-3 Technical Report
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916857148080128 |
|---|---|
| author | Cheang, Chilam Chen, Sijin Cui, Zhongren Hu, Yingdong Huang, Liqun Kong, Tao Li, Hang Li, Yifeng Liu, Yuxiao Ma, Xiao Niu, Hao Ou, Wenxuan Peng, Wanli Ren, Zeyu Shi, Haixin Tian, Jiawen Wu, Hongtao Xiao, Xin Xiao, Yuyang Xu, Jiafeng Yang, Yichu |
| author_facet | Cheang, Chilam Chen, Sijin Cui, Zhongren Hu, Yingdong Huang, Liqun Kong, Tao Li, Hang Li, Yifeng Liu, Yuxiao Ma, Xiao Niu, Hao Ou, Wenxuan Peng, Wanli Ren, Zeyu Shi, Haixin Tian, Jiawen Wu, Hongtao Xiao, Xin Xiao, Yuyang Xu, Jiafeng Yang, Yichu |
| contents | We report our recent progress towards building generalist robot policies, the development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model. It showcases exceptional capabilities in generalizing to novel objects, environments, and instructions involving abstract concepts. Furthermore, it can be efficiently fine-tuned with minimal human trajectory data, enabling rapid and cost-effective adaptation to new settings. GR-3 also excels in handling long-horizon and dexterous tasks, including those requiring bi-manual manipulation and mobile movement, showcasing robust and reliable performance. These capabilities are achieved through a multi-faceted training recipe that includes co-training with web-scale vision-language data, efficient fine-tuning from human trajectory data collected via VR devices, and effective imitation learning with robot trajectory data. In addition, we introduce ByteMini, a versatile bi-manual mobile robot designed with exceptional flexibility and reliability, capable of accomplishing a wide range of tasks when integrated with GR-3. Through extensive real-world experiments, we show GR-3 surpasses the state-of-the-art baseline method, $π_0$, on a wide variety of challenging tasks. We hope GR-3 can serve as a step towards building generalist robots capable of assisting humans in daily life. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GR-3 Technical Report Cheang, Chilam Chen, Sijin Cui, Zhongren Hu, Yingdong Huang, Liqun Kong, Tao Li, Hang Li, Yifeng Liu, Yuxiao Ma, Xiao Niu, Hao Ou, Wenxuan Peng, Wanli Ren, Zeyu Shi, Haixin Tian, Jiawen Wu, Hongtao Xiao, Xin Xiao, Yuyang Xu, Jiafeng Yang, Yichu Robotics Artificial Intelligence Computer Vision and Pattern Recognition We report our recent progress towards building generalist robot policies, the development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model. It showcases exceptional capabilities in generalizing to novel objects, environments, and instructions involving abstract concepts. Furthermore, it can be efficiently fine-tuned with minimal human trajectory data, enabling rapid and cost-effective adaptation to new settings. GR-3 also excels in handling long-horizon and dexterous tasks, including those requiring bi-manual manipulation and mobile movement, showcasing robust and reliable performance. These capabilities are achieved through a multi-faceted training recipe that includes co-training with web-scale vision-language data, efficient fine-tuning from human trajectory data collected via VR devices, and effective imitation learning with robot trajectory data. In addition, we introduce ByteMini, a versatile bi-manual mobile robot designed with exceptional flexibility and reliability, capable of accomplishing a wide range of tasks when integrated with GR-3. Through extensive real-world experiments, we show GR-3 surpasses the state-of-the-art baseline method, $π_0$, on a wide variety of challenging tasks. We hope GR-3 can serve as a step towards building generalist robots capable of assisting humans in daily life. |
| title | GR-3 Technical Report |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.15493 |