GR-Dexter Technical Report
Authors: name: Ruoshi Wen, name: Guangzeng Chen, name: Zhongren Cui, name: Min Du, name: Yang Gou, name: Zhigang Han, name: Liqun Huang, name: Mingyu Lei, name: Yunfei Li, name: Zhuohang Li, name: Wenlei Liu, name: Yuxiao Liu, name: Xiao Ma, name: Hao Niu, name: Yutao Ouyang, name: Zeyu Ren, name: Haixin Shi, name: Wei Xu, name: Haoxiang Zhang, name: Jiajun Zhang, name: Xiao Zhang, name: Liwei Zheng, name: Weiheng Zhong, name: Yifei Zhou, name: Zhengming Zhu, name: Hang Li
Organization: ByteDance Seed

Vision-language-action (VLA) models have enabled language-conditioned, long-horizon robot manipulation, but most existing systems are limited to grippers. Scaling VLA policies to bimanual robots with high degree-of-freedom (DoF) dexterous hands remains challenging due to the expanded action space, frequent hand-object occlusions, and the cost of collecting real-robot data. We present GR-Dexter, a holistic hardware-model-data framework for VLA-based generalist manipulation on a bimanual dexterous-hand robot. Our approach combines the design of a compact 21-DoF robotic hand, an intuitive bimanual teleoperation system for real-robot data collection, and a training recipe that leverages teleoperated robot trajectories together with large-scale vision-language and carefully curated cross-embodiment datasets. Across real-world evaluations spanning long-horizon everyday manipulation and generalizable pick-and-place, GR-Dexter achieves strong in-domain performance and improved robustness to unseen objects and unseen instructions. We hope GR-Dexter serves as a practical step toward generalist dexterous-hand robotic manipulation.