GigaBrain-0: A World Model-Powered Vision-Language-Action Model
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arXiv
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| Natura: | Preprint |
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2025
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| _version_ | 1866918230712385536 |
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| author | GigaBrain Team Ye, Angen Wang, Boyuan Ni, Chaojun Huang, Guan Zhao, Guosheng Li, Haoyun Li, Jie Zhu, Jiagang Feng, Lv Li, Peng Deng, Qiuping Ouyang, Runqi Qin, Wenkang Chen, Xinze Wang, Xiaofeng Wang, Yang Li, Yifan Li, Yilong Ding, Yiran Xu, Yuan Ye, Yun Zhou, Yukun Dong, Zhehao Wang, Zhenan Liu, Zhichao Zhu, Zheng |
| author_facet | GigaBrain Team Ye, Angen Wang, Boyuan Ni, Chaojun Huang, Guan Zhao, Guosheng Li, Haoyun Li, Jie Zhu, Jiagang Feng, Lv Li, Peng Deng, Qiuping Ouyang, Runqi Qin, Wenkang Chen, Xinze Wang, Xiaofeng Wang, Yang Li, Yifan Li, Yilong Ding, Yiran Xu, Yuan Ye, Yun Zhou, Yukun Dong, Zhehao Wang, Zhenan Liu, Zhichao Zhu, Zheng |
| contents | Training Vision-Language-Action (VLA) models for generalist robots typically requires large-scale real-world robot data, which is expensive and time-consuming to collect. The inefficiency of physical data collection severely limits the scalability, and generalization capacity of current VLA systems. To address this challenge, we introduce GigaBrain-0, a novel VLA foundation model empowered by world model-generated data (e.g., video generation, real2real transfer, human transfer, view transfer, sim2real transfer data). By leveraging world models to generate diverse data at scale, GigaBrain-0 significantly reduces reliance on real robot data while improving cross-task generalization. Our approach further improves policy robustness through RGBD input modeling and embodied Chain-of-Thought (CoT) supervision, enabling the model to reason about spatial geometry, object states, and long-horizon dependencies during task execution. This leads to substantial gains in real-world performance on dexterous, long-horizon, and mobile manipulation tasks. Extensive experiments demonstrate that GigaBrain-0 achieves superior generalization across variations in appearances (e.g., textures, colors), object placements, and camera viewpoints. Additionally, we present GigaBrain-0-Small, an optimized lightweight variant designed to run efficiently on devices such as the NVIDIA Jetson AGX Orin. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GigaBrain-0: A World Model-Powered Vision-Language-Action Model GigaBrain Team Ye, Angen Wang, Boyuan Ni, Chaojun Huang, Guan Zhao, Guosheng Li, Haoyun Li, Jie Zhu, Jiagang Feng, Lv Li, Peng Deng, Qiuping Ouyang, Runqi Qin, Wenkang Chen, Xinze Wang, Xiaofeng Wang, Yang Li, Yifan Li, Yilong Ding, Yiran Xu, Yuan Ye, Yun Zhou, Yukun Dong, Zhehao Wang, Zhenan Liu, Zhichao Zhu, Zheng Robotics Computer Vision and Pattern Recognition Training Vision-Language-Action (VLA) models for generalist robots typically requires large-scale real-world robot data, which is expensive and time-consuming to collect. The inefficiency of physical data collection severely limits the scalability, and generalization capacity of current VLA systems. To address this challenge, we introduce GigaBrain-0, a novel VLA foundation model empowered by world model-generated data (e.g., video generation, real2real transfer, human transfer, view transfer, sim2real transfer data). By leveraging world models to generate diverse data at scale, GigaBrain-0 significantly reduces reliance on real robot data while improving cross-task generalization. Our approach further improves policy robustness through RGBD input modeling and embodied Chain-of-Thought (CoT) supervision, enabling the model to reason about spatial geometry, object states, and long-horizon dependencies during task execution. This leads to substantial gains in real-world performance on dexterous, long-horizon, and mobile manipulation tasks. Extensive experiments demonstrate that GigaBrain-0 achieves superior generalization across variations in appearances (e.g., textures, colors), object placements, and camera viewpoints. Additionally, we present GigaBrain-0-Small, an optimized lightweight variant designed to run efficiently on devices such as the NVIDIA Jetson AGX Orin. |
| title | GigaBrain-0: A World Model-Powered Vision-Language-Action Model |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.19430 |