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：仿生灵巧机器人手在复制人类多种操作任务方面具有巨大潜力，有望成为通用操作平台。近年来，强化学习框架在四足运动控制和灵巧操作任务中取得了显著进展。结合基于GPU的高并行仿真环境（可同时模拟数千台机器人），基于强化学习的控制器变得更具可扩展性和实用性。然而，为将强化学习训练的策略迁移到现实世界，需要训练框架既能输出兼容物理执行器和传感器的策略，又需采用易于制造且足够鲁棒的硬件平台来运行交互式策略。本文介绍了仿生肌腱驱动的Faive手及其系统架构：通过肌腱驱动的滚动接触关节，实现了可3D打印、鲁棒的高自由度手部设计。我们对手的每个组件进行建模，并将其集成到GPU仿真环境中训练强化学习策略，最终实现了灵巧的掌中球旋转技能从仿真到物理机器人手的零样本迁移。