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
Organization: GigaAI

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.