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LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning
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LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning
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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/
Forward citations
Cited by 17 Pith papers
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Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning
Real2Sim tactile calibration, layout-aware encoder pretraining, and diffusion policy aggregation from object-specific RL experts enable 27% real-world success in blind grasping on a LEAP Hand for 10 seen and 10 unseen...
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Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
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