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让球转动起来：为具有滚动接触关节的仿生肌腱驱动手学习灵巧策略

Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic Tendon-Driven Hand with Rolling Contact Joints

ETH Zurich（苏黎世联邦理工学院）；Max Planck ETH Center for Learning Systems（马克斯·普朗克-苏黎世联邦理工学院学习系统中心）

Yasunori Toshimitsu, Benedek Forrai, Barnabas Gavin Cangan, Ulrich Steger, Manuel Knecht, Stefan Weirich, Robert K. Katzschmann

备注：
for project website, see https://srl-ethz.github.io/get-ball-rolling/ . for video, see https://youtu.be/YahsMhqNU8o . for code, see https://github.com/srl-ethz/faive_gym_oss . Published to the 2023 IEEE-RAS International Conference on Humanoid Robots
发表信息：
2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), Austin, TX, USA, 2023, pp. 1-7
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AI总结：

本文提出仿生肌腱驱动Faive Hand及其GPU并行仿真RL训练架构，实现高自由度灵巧手的掌内转球策略零样本迁移到实物。

摘要：

仿生、灵巧的机器人手有潜力复刻人类能够完成的许多任务，并获得通用操作平台的地位。近年来，强化学习（RL）框架的进展已在四足运动和灵巧操作任务中取得显著性能。结合能够并行模拟数千个机器人的基于GPU的高度并行化仿真，基于RL的控制器已变得更具可扩展性且更易于使用。然而，为了将RL训练得到的策略带到真实世界，我们需要这样的训练框架：其输出的策略能够与物理执行器和传感器协同工作；同时还需要一个硬件平台，该平台能够用易获取的材料制造，并且足够鲁棒，能够运行交互式策略。本工作介绍了仿生肌腱驱动的Faive Hand及其系统架构，该手使用肌腱驱动的滚动接触关节，以实现可3D打印、鲁棒的高自由度手设计。我们对手的每个元件进行建模，并将其集成到GPU仿真环境中，以使用RL训练策略，同时实现了将灵巧的掌内球体旋转技能零样本迁移到物理机器人手。

英文摘要：

Biomimetic, dexterous robotic hands have the potential to replicate much of the tasks that a human can do, and to achieve status as a general manipulation platform. Recent advances in reinforcement learning (RL) frameworks have achieved remarkable performance in quadrupedal locomotion and dexterous manipulation tasks. Combined with GPU-based highly parallelized simulations capable of simulating thousands of robots in parallel, RL-based controllers have become more scalable and approachable. However, in order to bring RL-trained policies to the real world, we require training frameworks that output policies that can work with physical actuators and sensors as well as a hardware platform that can be manufactured with accessible materials yet is robust enough to run interactive policies. This work introduces the biomimetic tendon-driven Faive Hand and its system architecture, which uses tendon-driven rolling contact joints to achieve a 3D printable, robust high-DoF hand design. We model each element of the hand and integrate it into a GPU simulation environment to train a policy with RL, and achieve zero-shot transfer of a dexterous in-hand sphere rotation skill to the physical robot hand.

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。

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