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
Organization: ByteDance Seed

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.