홈 › Papers

GR-Dexter Technical Report

2025-12-30 · Ruoshi Wen, Guangzeng Chen, Zhongren Cui, Min Du, Yang Gou, Zhigang Han, Liqun Huang, Mingyu Lei, Yunfei Li, Zhuohang Li, Wenlei Liu, Yuxiao Liu, Xiao Ma, Hao Niu, Yutao Ouyang, Zeyu Ren, Haixin Shi, Wei Xu, Haoxiang Zhang, Jiajun Zhang, Xiao Zhang, Liwei Zheng, Weiheng Zhong, Yifei Zhou, Zhengming Zhu, Hang Li arxiv

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.

📄 PDF Abstract BibTeX arXiv:2512.24210

Code (0)

등록된 구현이 없습니다.

Tasks

Robot Manipulation

DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models

2025-10-16 · Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe, Giovanni Bellitto 외 arxiv

Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework that employs diffusion models and large la…

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

2018-02-26 · Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew 외

The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. The tasks include …

continuous-control Continuous Control Multi-Goal Reinforcement Learning OpenAI Gym+3

DexDrummer: In-Hand, Contact-Rich, and Long-Horizon Dexterous Robot Drumming

2026-03-23 · Hung-Chieh Fang, Amber Xie, Jennifer Grannen, Kenneth Llontop 외 arxiv

Performing in-hand, contact-rich, and long-horizon dexterous manipulation remains an unsolved challenge in robotics. Prior hand dexterity works have considered each of these three challenges in isolation, yet do not comb…

Reinforcement Learning Trajectory Planning

RLDX-1 Technical Report

2026-05-05 · Dongyoung Kim, Huiwon Jang, Myungkyu Koo, Suhyeok Jang 외 arxiv

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generali…

Scene Understanding

GR-3 Technical Report

2025-07-21 · Chilam Cheang, Sijin Chen, Zhongren Cui, Yingdong Hu 외 arxiv

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 …