LEAP手：用于机器人学习的低成本、高效和拟人化手。

摘要

灵巧操控一直是机器人领域的长期挑战。虽然机器学习技术显示出一些潜力，但目前的结果主要局限于模拟环境。这在很大程度上归因于缺乏合适的硬件。本文介绍了LEAP Hand，这是一种用于机器学习研究的低成本灵巧且类人化的手。与先前的手相比，LEAP Hand具有一种新颖的运动学结构，可以实现最大程度的灵活性，无论手指姿势如何。LEAP Hand成本低廉，可以在4小时内使用现成零件组装，成本为2000美元。它能够持续施加大扭矩。我们展示了LEAP Hand可以用于在现实世界中执行多项操控任务，从视觉远程操作到从被动视频数据和Sim2Real中学习。LEAP Hand在所有实验中明显优于其最接近的竞争对手Allegro Hand，而成本仅为其1/8。我们在网站https://leap-hand.github.io/上发布了详细的组装说明、Sim2Real流程和一个带有有用API的开发平台。

English

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/

LEAP手：用于机器人学习的低成本、高效和拟人化手。

LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning

摘要

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