Adaptive basis selection for compressed sensing in robotic tactile skins

Abstract

Tactile robotic skins consist of thousands to millions of tiny sensing elements that cover the surface of a robot. The data loads, timing requirements, and hardware constraints in tactile skins make real-time tactile data acquisition challenging. In previous work, we developed a compressed sensing tactile data acquisition system to address these challenges. In this work, we propose a method to adaptively select a basis for each compressed tactile signal to improve the overall signal reconstruction accuracy. A classifier is trained offline using a set of candidate bases and training signals. When each new compressed signal is acquired, the classifier identifies which basis to use in reconstructing the full tactile signal from the compressed one. We evaluate our method using data generated by our tactile skin simulation system. Our evaluations show that our adaptive basis selection method consistently outperforms approaches that use a single basis for signal reconstruction.