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

TL;DR

LEAP Hand is a low-cost, anthropomorphic robotic hand designed for machine learning research, offering high dexterity, affordability, and versatility for real-world manipulation tasks, surpassing existing solutions in performance and cost.

Contribution

The paper introduces LEAP Hand, a novel low-cost, highly dexterous robotic hand with a unique kinematic structure, enabling advanced manipulation and learning applications.

Findings

LEAP Hand outperforms Allegro Hand in all experiments.

LEAP Hand can be assembled in 4 hours for $2000.

LEAP Hand effectively performs real-world manipulation tasks.

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 detailed assembly instructions, the Sim2Real pipeline and a development platform with useful APIs on our website at https://leap-hand.github.io/