人工智能
计算机科学
机器人学
概化理论
人机交互
模块化设计
任务(项目管理)
触觉传感器
计算机视觉
机器人
工程类
心理学
发展心理学
系统工程
操作系统
作者
Raunaq Bhirangi,Abigail DeFranco,Jacob Adkins,Carmel Majidi,Abhinav Gupta,Tess Hellebrekers,Vikash Kumar
出处
期刊:IEEE robotics and automation letters
日期:2023-12-01
卷期号:8 (12): 8311-8318
被引量:2
标识
DOI:10.1109/lra.2023.3327619
摘要
High cost and lack of reliability have precluded the widespread adoption of dexterous hands in robotics. Furthermore, the lack of a viable tactile sensor capable of sensing over the entire area of the hand impedes the rich, low-level feedback that would improve the learning of dexterous manipulation skills. This letter introduces an inexpensive, modular, and robust platform - the D'Manus - aimed at resolving these challenges while satisfying the large-scale data collection demands of deep robot learning paradigms. Studies on human manipulation point to the criticality of low-level tactile feedback in performing everyday dexterous tasks. The D'Manus comes with ReSkin sensing on the entire surface of the palm as well as the fingertips. We also demonstrate the generalizability of tactile models trained with the fully integrated system in a tactile-aware task - bin-picking and sorting.
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