Regression and Classification Methods for Learning Sound Wave Amplitude Modulation in Soft Tactile Sensing

This paper presents a novel soft tactile skin (STS) technology operating with sound waves. In this innovative approach, the sound waves generated by a speaker travel in channels embedded in a soft membrane and get modulated due to a deformation of the channel when pressed by an external force and received by a microphone at the end of the channel. The sensor leverages regression and classification methods for estimating the normal force and its contact location. Our sensor can be affixed to any robot part, e.g., end effectors or arm. We tested several regression and classifier methods to learn the relation between sound wave modulation, the applied force, and its location, respectively and picked the best-performing models for force and location predictions. Our novel tactile sensor yields 93% of the force estimation within 1.5 N tolerances for a range of 0-30+1 N and estimates contact locations with over 96% accuracy. We also demonstrated the performance of STS technology for a real-time gripping force control application.