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

Kenneth Shaw
Carnegie Mellon University
Ananye Agarwal
Carnegie Mellon University
Deepak Pathak
Carnegie Mellon University
Paper Website

Paper ID 89

Session 12. Robot Mechanisms & Control

Poster Session Thursday, July 13

Poster 25

Abstract: 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 the URDF model, 3D CAD files, tuned simulation environment, and a development platform with useful APIs on our website at https://leap-hand.github.io/ .