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 http://roboticsbenchmarks.org/platforms/dmanus

翻译：高昂的成本和缺乏可靠性阻碍了灵巧手在机器人领域的广泛应用。此外，缺乏能够在手部整个区域进行感知的可行触觉传感器，也限制了能够改善灵巧操作技能学习的丰富低级反馈。本文介绍了一种廉价、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需的大规模数据采集能力。对人类操作的研究表明，低级触觉反馈在执行日常灵巧任务中至关重要。DManus在整个手掌表面及指尖均配备了ReSkin感知技术。我们通过一项触觉感知任务——料箱拾取与分拣——展示了该完全集成系统的有效性。代码、文档、设计文件、详细组装说明、训练模型、任务视频以及重建该设置所需的所有补充材料均可在http://roboticsbenchmarks.org/platforms/dmanus 获取。