推动事情发展：学习具有滚动接触关节的仿生腱驱动手的灵巧策略

摘要

仿生、灵巧的机器人手有潜力复制人类能够完成的许多任务，并实现成为通用操作平台的地位。最近在强化学习（RL）框架方面取得了显著进展，已在四足动物的运动和灵巧操作任务中取得了卓越表现。结合基于GPU的高度并行化仿真技术，能够同时模拟成千上万个机器人，基于RL的控制器变得更具可伸缩性和易接近性。然而，为了将经RL训练的策略应用于现实世界，我们需要训练框架，输出可以与物理执行器和传感器配合工作的策略，以及一个可以用易得材料制造但足够强大以运行交互策略的硬件平台。本文介绍了仿生腱驱动的Faive Hand及其系统架构，该系统利用腱驱动的滚动接触关节实现了一个可三维打印、稳健的高自由度手部设计。我们对手部的每个元素进行建模，并将其整合到GPU仿真环境中，通过RL训练一个策略，并实现了将手部内灵巧的球体旋转技能零次迁移到物理机器人手。

English

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.

推动事情发展：学习具有滚动接触关节的仿生腱驱动手的灵巧策略

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

摘要

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