GigaBrain-0: A World Model-Powered Vision-Language-Action Model
Authors: name: GigaBrain Team; name: Angen Ye; name: Boyuan Wang; name: Chaojun Ni; name: Guan Huang; name: Guosheng Zhao; name: Haoyun Li; name: Jie Li; name: Jiagang Zhu; name: Lv Feng; name: Peng Li; name: Qiuping Deng; name: Runqi Ouyang; name: Wenkang Qin; name: Xinze Chen; name: Xiaofeng Wang; name: Yang Wang; name: Yifan Li; name: Yilong Li; name: Yiran Ding; name: Yuan Xu; name: Yun Ye; name: Yukun Zhou; name: Zhehao Dong; name: Zhenan Wang; name: Zhichao Liu; name: Zheng Zhu
Published: 2025-10-22 09:57:13

Abstract: 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.