GR-Dexter技术报告

摘要

視覺-語言-動作模型已實現了語言條件化的長時程機器人操作，但現有系統多侷限於夾爪機械手。將VLA策略擴展至配備高自由度靈巧雙手的雙臂機器人仍面臨挑戰，原因在於動作空間擴展、頻繁的手物遮擋以及真實機器人數據採集成本。我們提出GR-Dexter——一個面向雙臂靈巧手機器人的VLA通用操作整體框架，整合了緊湊型21自由度機械手設計、直觀的雙臂遙操作數據採集系統，以及融合遙操作軌跡、大規模視覺語言數據與精選跨具身數據的訓練方案。在涵蓋長時程日常操作與泛化性抓放的現實場景測試中，GR-Dexter不僅展現出優異的域內性能，更對未見過物體與指令表現出更強健的適應能力。我們期望GR-Dexter能為實現通用靈巧手機器人操作邁出實質性一步。

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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