所有触感：一种具有大面积触觉传感的灵巧手
All the Feels: A dexterous hand with large-area tactile sensing
AI总结：
本文提出低成本、模块化且可扩展的灵巧手平台 DManus，通过在整个手掌和指尖集成 ReSkin 大面积触觉传感，以支持深度机器人学习的大规模数据收集，并在料箱拣选与分拣任务中验证了系统的触觉感知有效性。
摘要：
高成本和缺乏可靠性阻碍了灵巧手在机器人领域的广泛采用。此外，缺乏一种能够在手的整个区域上进行感知的可行触觉传感器，也阻碍了丰富的低级反馈，而这种反馈将改善灵巧操作技能的学习。本文介绍了一个廉价、模块化、鲁棒且可扩展的平台——DManus——旨在解决这些挑战，同时满足深度机器人学习范式所要求的大规模数据收集能力。关于人类操作的研究指出，低级触觉反馈在执行日常灵巧任务中至关重要。DManus 在整个手掌表面以及指尖上都配备了 ReSkin 传感。我们展示了该完全集成系统在触觉感知任务——料箱拣选与分拣——中的有效性。代码、文档、设计文件、详细装配说明、训练模型、任务视频以及重现该设置所需的所有补充材料都可以在 https://sites.google.com/view/roboticsbenchmarks/platforms/dmanus 上找到。
英文摘要：
High cost and lack of reliability has precluded the widespread adoption of dexterous hands in robotics. Furthermore, the lack of a viable tactile sensor capable of sensing over the entire area of the hand impedes the rich, low-level feedback that would improve learning of dexterous manipulation skills. This paper introduces an inexpensive, modular, robust, and scalable platform -- the DManus -- aimed at resolving these challenges while satisfying the large-scale data collection capabilities demanded by deep robot learning paradigms. Studies on human manipulation point to the criticality of low-level tactile feedback in performing everyday dexterous tasks. The DManus comes with ReSkin sensing on the entire surface of the palm as well as the fingertips. We demonstrate effectiveness of the fully integrated system in a tactile aware task -- bin picking and sorting. Code, documentation, design files, detailed assembly instructions, trained models, task videos, and all supplementary materials required to recreate the setup can be found on https://sites.google.com/view/roboticsbenchmarks/platforms/dmanus.
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。
