GR-Dexter技术报告

摘要

视觉-语言-动作模型已实现语言条件化的长周期机器人操控，但现有系统大多局限于夹爪式机械手。由于动作空间扩展、频繁的手物遮挡以及真实机器人数据采集成本等问题，将VLA策略扩展到具有高自由度的双手灵巧手机器人仍面临挑战。我们提出GR-Dexter——一个面向双手灵巧手机器人的VLA通用操控整体框架，集成了紧凑型21自由度机械手设计、直观的双手遥操作系统用于真实机器人数据采集，以及融合遥操作轨迹与大规模视觉语言数据及精选跨本体数据集的训练方案。在涵盖长周期日常操作和泛化性抓放任务的真实环境测试中，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。我们期待GR-Dexter能成为通向通用灵巧手机器人操控的实践性一步。

English

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技术报告

GR-Dexter Technical Report

摘要

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