GigaBrain-0: A World Model-Powered Vision-Language-Action Model
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.
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GigaBrain Team
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Angen Ye
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Boyuan Wang
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Chaojun Ni
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Guan Huang
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Guosheng Zhao
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Haoyun Li
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Jie Li
claimed_verified
2025-10-23T14:47:33.050Z
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lijie
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danielleetrade
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Jiagang Zhu
claimed_verified
2025-10-24T16:13:07.586Z
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jiagangzhu
user
hugjiagangzhu
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Lv Feng
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Peng Li
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Qiuping Deng
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Runqi Ouyang
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Wenkang Qin
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Xinze Chen
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Xiaofeng Wang
claimed_verified
2025-10-23T14:47:40.222Z
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JeffWang
user
Jeff-Wang
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Yang Wang
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Yifan Li
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Yilong Li
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Yiran Ding
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Yuan Xu
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Yun Ye
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Yukun Zhou
claimed_verified
2025-10-24T01:07:22.528Z
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YuKun Zhou
user
1zeryu
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Zhehao Dong
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Zhenan Wang
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Zhichao Liu
68f99e80b9b2e4ae0467386d
Zheng Zhu
GigaBrain-0: A World Model-Powered Vision-Language-Action Model
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.
GigaBrain-0, a VLA foundation model, uses world model-generated data to enhance cross-task generalization and policy robustness, improving real-world performance on complex manipulation tasks.
VLA models world model-generated data video generation real2real transfer human transfer view transfer sim2real transfer RGBD input modeling Chain-of-Thought supervision spatial geometry object states long-horizon dependencies dexterous manipulation mobile manipulation