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.

翻译：视觉-语言-动作模型已能实现语言条件化的长时程机器人操控，但现有系统大多局限于夹爪。将VLA策略扩展到配备高自由度灵巧手的双臂机器人仍具挑战性，这源于动作空间的扩大、频繁的手-物体遮挡以及真实机器人数据收集的成本。我们提出GR-Dexter，一个面向双臂灵巧手机器人、基于VLA的通用操控整体硬件-模型-数据框架。我们的方法结合了紧凑型21自由度机械手设计、用于真实机器人数据采集的直观双臂遥操作系统，以及一种利用遥操作机器人轨迹、大规模视觉-语言数据和精心策划的跨具身数据集的训练方案。在涵盖长时程日常操作与泛化性拾放任务的真实世界评估中，GR-Dexter在领域内表现出色，并对未见过的物体和指令展现出更强的鲁棒性。我们希望GR-Dexter能成为迈向通用灵巧手机器人操控的实用一步。