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
Published: 2025-12-30 13:22:16

Abstract: 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.