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.

翻译：仿生灵巧机器人手具有复制人类大多数任务的潜力，有望成为通用操控平台。强化学习框架的最新进展已在四足移动和灵巧操控任务中取得显著成效。结合基于GPU的高并行模拟系统（可并行模拟数千个机器人），基于强化学习的控制器变得更加可扩展和易用。然而，为使强化学习训练的策略应用于现实世界，我们需要训练框架既能输出适配物理执行器和传感器的策略，又需硬件平台可用可及材料制造且足够鲁棒以运行交互式策略。本文介绍了仿生腱驱Faive Hand及其系统架构，该设计采用腱驱动滚动接触关节实现可3D打印、鲁棒的高自由度手部结构。我们对手的每个元件进行建模，并将其集成至GPU仿真环境以训练强化学习策略，最终实现了在真实机器人手上零样本迁移灵巧的球体旋转技能。