GR-Dexter Technical Report
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.
6955e1ac832867f25352559b
Ruoshi Wen
6955e1ac832867f25352559c
Guangzeng Chen
6955e1ac832867f25352559d
Zhongren Cui
6955e1ac832867f25352559e
Min Du
6955e1ac832867f25352559f
Yang Gou
6955e1ac832867f2535255a0
Zhigang Han
6955e1ac832867f2535255a1
Liqun Huang
6955e1ac832867f2535255a2
Mingyu Lei
6955e1ac832867f2535255a3
Yunfei Li
6955e1ac832867f2535255a4
Zhuohang Li
6955e1ac832867f2535255a5
Wenlei Liu
6955e1ac832867f2535255a6
Yuxiao Liu
claimed_verified
2026-03-03T10:45:03.126Z
6772383bb19143bfea926c92
/avatars/4dbaaa791a731543381455821924a265.svg
Yuxiao Liu
TopoInside
user
TopoInside
6955e1ac832867f2535255a7
Xiao Ma
6955e1ac832867f2535255a8
Hao Niu
6955e1ac832867f2535255a9
Yutao Ouyang
6955e1ac832867f2535255aa
Zeyu Ren
6955e1ac832867f2535255ab
Haixin Shi
6955e1ac832867f2535255ac
Wei Xu
claimed_verified
2026-01-02T15:39:56.523Z
68907b2743f0900caad0fa53
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/OGhOkehAj6QDVeaFdU8-B.png
Wei Xu
xu-wei
user
xu-wei
6955e1ac832867f2535255ad
Haoxiang Zhang
6955e1ac832867f2535255ae
Jiajun Zhang
6955e1ac832867f2535255af
Xiao Zhang
6955e1ac832867f2535255b0
Liwei Zheng
6955e1ac832867f2535255b1
Weiheng Zhong
6955e1ac832867f2535255b2
Yifei Zhou
6955e1ac832867f2535255b3
Zhengming Zhu
6955e1ac832867f2535255b4
Hang Li
GR-Dexter Technical Report
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.
GR-Dexter introduces a hardware-model-data framework for bimanual dexterous-hand robot manipulation using vision-language-action models, combining teleoperation data and multimodal datasets to achieve robust generalization.
Vision-language-action (VLA) models training recipe large-scale vision-language datasets cross-embodiment datasets