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Computer Science > Robotics

arXiv:2309.06440 (cs)
[Submitted on 12 Sep 2023]

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

Authors:Kenneth Shaw, Ananye Agarwal, Deepak Pathak
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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 this https URL
Comments: Website at this https URL
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as: arXiv:2309.06440 [cs.RO]
  (or arXiv:2309.06440v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2309.06440

Submission history

From: Kenneth Shaw [view email]
[v1] Tue, 12 Sep 2023 17:59:20 UTC (7,229 KB)
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