LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot
  Learning
Kenneth Shaw Ananye Agarwal Deepak Pathak
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/