Handroid：连接灵巧手与人形机器人
Handroid: Bridging Dexterous Hand and Humanoid
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- University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
研究旨在开发兼具灵巧手与拟人机器人功能的平台，提出Handroid，它能在单一可重构平台集成两种能力，有统一控制学习框架，经多种任务验证，成为推进形态可重构机器人技术及跨实体学习的紧凑可复制平台。
中文摘要 AI 辅助
灵巧手和人形机器人通常是作为不同的实体开发的：前者能在物体尺度上进行丰富的接触式操作，后者能在以人类为中心的环境中实现移动和全身交互。我们介绍了Handroid，一个桌面级的双实体机器人，它在单个可重构平台上集成了这两种能力。Handroid重用一个27自由度的机电体，既可以作为灵巧手，也可以作为桌面人形机器人，高0.33米，重2.05千克。在灵巧手模式下，20个自由度形成一个与人手运动结构紧密匹配的拟人化手。在人形模式下，相同的关节模块重新配置成一个有头、手臂和腿的人形机器人，包括一个用于移动和全身运动的12自由度下肢结构。Handroid还提供了一个统一的控制和学习框架，支持手部遥操作、灵巧抓取、手中操作、人形机器人移动、步态生成和交互式运动创作。我们通过实际的灵巧操作、基于强化学习的移动、关键帧运动部署以及一个涉及实体重新配置、移动、对接和灵巧抓取放置的长期任务来验证该平台。这些结果使Handroid成为一个紧凑且可复制的平台，用于推进形态可重构机器人技术和跨实体机器人学习。
英文摘要
Dexterous hands and humanoid robots are typically developed as distinct embodiments: the former enable contact-rich manipulation at the object scale, whereas the latter provide mobility and whole-body interaction in human-centered environments. We introduce \textbf{Handroid}, a desktop-scale dual-embodiment robot that integrates both capabilities within a single reconfigurable platform. Handroid reuses one 27-DoF electromechanical body as either a dexterous hand or a desktop humanoid, measuring 0.33 m in height and 2.05 kg in weight. In the dexterous hand embodiment, 20 DoFs form an anthropomorphic hand closely matching the kinematic structure of the human hand. In the humanoid embodiment, the same articulated modules are reconfigured into a humanoid with a head, arms, and legs, including a 12-DoF lower-limb structure for locomotion and whole-body motion. Handroid further provides a unified control and learning framework supporting hand teleoperation, dexterous grasping, in-hand manipulation, humanoid locomotion, gait generation, and interactive motion authoring. We validate the platform through real-world dexterous manipulation, reinforcement-learning-based locomotion, keyframe motion deployment, and a long-horizon task involving embodiment reconfiguration, locomotion, docking, and dexterous pick-and-place. These results position Handroid as a compact and reproducible platform for advancing morphology-reconfigurable robotics and cross-embodiment robot learning.
