GR-3 Technical Report
Authors: name: Chilam Cheang; name: Sijin Chen; name: Zhongren Cui; name: Yingdong Hu; name: Liqun Huang; name: Tao Kong; name: Hang Li; name: Yifeng Li; name: Yuxiao Liu; name: Xiao Ma; name: Hao Niu; name: Wenxuan Ou; name: Wanli Peng; name: Zeyu Ren; name: Haixin Shi; name: Jiawen Tian; name: Hongtao Wu; name: Xin Xiao; name: Yuyang Xiao; name: Jiafeng Xu; name: Yichu Yang
Published: 2025-07-21 10:54:13

Abstract: 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, pi_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.