Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous and anthropomorphic hand for machine learning research. In contrast to previous hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4 hours at a cost of 2000 USD from readily available parts. It is capable of consistently exerting large torques over long durations of time. We show that LEAP Hand can be used to perform several manipulation tasks in the real world -- from visual teleoperation to learning from passive video data and sim2real. LEAP Hand significantly outperforms its closest competitor Allegro Hand in all our experiments while being 1/8th of the cost. We release detailed assembly instructions, the Sim2Real pipeline and a development platform with useful APIs on our website at https://leap-hand.github.io/

翻译：灵巧操作一直是机器人学中的长期挑战。虽然机器学习技术已展现出一定潜力，但相关成果目前仍主要局限在仿真环境中，这很大程度上归因于缺乏合适的硬件。本文提出LEAP Hand，一种面向机器学习研究的低成本灵巧人形手。与先前的手型不同，LEAP Hand采用新颖的运动学结构，可在任意手指姿态下实现最大灵巧度。该手成本低廉，使用现成部件可在4小时内组装完成，总成本约2000美元，并具备长时间持续输出大扭矩的能力。我们展示了LEAP Hand在真实世界中执行多种操作任务的能力——从视觉遥操作到被动视频数据学习再到仿真到现实迁移。在所有实验中，LEAP Hand的性能显著优于其最接近的竞品Allegro Hand，而成本仅为后者的八分之一。我们在网站https://leap-hand.github.io/ 上发布了详细的装配指南、仿真到现实迁移流程以及包含实用API的开发平台。