All the Feels: A Dexterous Hand With Large-Area Tactile Sensing
Raunaq Bhirangi, Abigail DeFranco, Jacob Adkins, Carmel Majidi, Abhinav Gupta, Tess Hellebrekers, Vikash Kumar
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D'Manus</i> - 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D'Manus</i> 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.