All the Feels: A dexterous hand with large-area tactile sensing
This addresses the problem of enabling rich tactile feedback for dexterous manipulation in robotics, though it appears incremental as it builds on existing sensing and platform designs.
The paper tackles the high cost and lack of reliable tactile sensing in dexterous robotic hands by introducing the DManus, an inexpensive, modular platform with large-area tactile sensing, demonstrating its effectiveness in bin picking and sorting tasks.
High cost and lack of reliability has 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 learning of dexterous manipulation skills. This paper introduces an inexpensive, modular, robust, and scalable platform -- the DManus -- aimed at resolving these challenges while satisfying the large-scale data collection capabilities demanded by deep robot learning paradigms. Studies on human manipulation point to the criticality of low-level tactile feedback in performing everyday dexterous tasks. The DManus comes with ReSkin sensing on the entire surface of the palm as well as the fingertips. We demonstrate effectiveness of the fully integrated system in a tactile aware task -- bin picking and sorting. Code, documentation, design files, detailed assembly instructions, trained models, task videos, and all supplementary materials required to recreate the setup can be found on https://sites.google.com/view/roboticsbenchmarks/platforms/dmanus.