LEAP 手：用於機器人學習的低成本、高效率和人形化手部

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

September 12, 2023

作者: Kenneth Shaw, Ananye Agarwal, Deepak Pathak

cs.AI

摘要

靈巧操控一直是機器人領域的長期挑戰。儘管機器學習技術顯示出了一些潛力，但目前的成果主要僅限於模擬環境。這主要歸因於缺乏適合的硬體。本文介紹了 LEAP 手，一款用於機器學習研究的低成本靈巧且類人化手部。與以往的手部不同，LEAP 手具有一種新穎的運動結構，可以實現最大程度的靈活性，無論手指姿勢如何。LEAP 手成本低廉，可在4小時內以2000美元的成本從現有零件中組裝而成。它能夠持續施加大扭矩長時間運作。我們展示了LEAP 手可用於在現實世界中執行多項操控任務--從視覺遠端操作到從被動視頻數據和模擬到真實世界的學習。LEAP 手在所有實驗中明顯優於其最接近的競爭對手 Allegro 手，而成本僅為其1/8。我們在 https://leap-hand.github.io/ 網站上發布了詳細的組裝說明、Sim2Real 流程和一個帶有有用 API 的開發平台。

English

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/