title: LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot
    Learning
summary: 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/
paper_authors._id: 6501349b1e14749e840f9250
paper_authors.name: Kenneth Shaw
paper_authors._id: 6501349b1e14749e840f9251
paper_authors.name: Ananye Agarwal
paper_authors._id: 6501349b1e14749e840f9252
paper_authors.name: Deepak Pathak
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-14T08:27:18.937Z
paper_authors._id: 648098bac0d3c70316d21346
paper_authors.avatarUrl: /avatars/e4bf018c85730eda57f43e66c3deaeef.svg
paper_authors.fullname: Deepak pathak
paper_authors.type: user
paper_authors.user: dpkpathak
paper_title: LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot
    Learning
paper_summary: 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/
paper_ai_keywords: dexterous manipulation machine learning techniques simulation hardware LEAP Hand kinematic structure dexterity anthropomorphic hand visual teleoperation learning from passive video data sim2real Allegro Hand assembly instructions Sim2Real pipeline development platform APIs