EyeSight Hand：集成视觉式触觉传感器与柔顺驱动的全驱动灵巧机器人手设计
EyeSight Hand: Design of a Fully-Actuated Dexterous Robot Hand with Integrated Vision-Based Tactile Sensors and Compliant Actuation
AI总结：
本研究提出集成视觉式触觉传感器与准直驱驱动的7自由度仿人灵巧手EyeSight Hand，通过三项操作任务实验验证，结合视觉丢弃策略的模仿学习证明触觉感知可显著提升灵巧操作性能。
摘要：
在本研究中，我们推出了EyeSight Hand——一款新型7自由度（DoF）仿人手，其集成了专为提升全手操作能力而定制的视觉式触觉传感器。此外，我们提出了一种以准直驱驱动为核心的驱动方案，可实现类人的力量与速度，同时为大规模数据采集提供鲁棒性保障。我们在三项具有挑战性的任务上对EyeSight Hand进行了评估：开瓶、切橡皮泥以及取放盘子，这些任务需要结合复杂操作、工具使用与精确施力。采用新型视觉丢弃策略在这些任务上训练的模仿学习模型，展现了触觉反馈在提升任务成功率方面的优势。我们的结果表明，触觉感知的集成显著提升了任务性能，凸显了触觉信息在灵巧操作中的关键作用。
英文摘要：
In this work, we introduce the EyeSight Hand, a novel 7 degrees of freedom (DoF) humanoid hand featuring integrated vision-based tactile sensors tailored for enhanced whole-hand manipulation. Additionally, we introduce an actuation scheme centered around quasi-direct drive actuation to achieve human-like strength and speed while ensuring robustness for large-scale data collection. We evaluate the EyeSight Hand on three challenging tasks: bottle opening, plasticine cutting, and plate pick and place, which require a blend of complex manipulation, tool use, and precise force application. Imitation learning models trained on these tasks, with a novel vision dropout strategy, showcase the benefits of tactile feedback in enhancing task success rates. Our results reveal that the integration of tactile sensing dramatically improves task performance, underscoring the critical role of tactile information in dexterous 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。
