GR-Dexter 技术报告
GR-Dexter Technical Report
AI总结：
GR-Dexter 提出了一种综合框架，通过紧凑机械手、双臂遥控系统和训练配方，实现基于VLA的通用灵巧手机器人操作。
摘要：
视觉-语言-动作（VLA）模型已经使得语言引导的、长视界机器人操作成为可能，但大多数现有系统仅限于抓取手。将VLA策略扩展到具有高自由度（DoF）的双臂灵巧手机器人仍然具有挑战性，这主要是由于扩展的动作空间、频繁的手-物体遮挡以及收集真实机器人数据的成本。我们提出了GR-Dexter，一个针对基于VLA的通用操作的综合硬件-模型-数据框架，适用于双臂灵巧手机器人。我们的方法结合了紧凑型21自由度机械手的设计、直观的双臂遥控系统以收集真实机器人数据，以及一种训练配方，该配方利用遥控机器人轨迹以及大规模视觉-语言和精心挑选的跨身体验数据集。在覆盖长视界日常操作和可推广的拾取和放置任务的现实评估中，GR-Dexter在域内表现强劲，并且在未见过的对象和指令上表现出改进的鲁棒性。我们希望GR-Dexter能成为通用灵巧手机器人操作的实用一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
