Local Discrimination Based on Piezoelectric Sensing in Robots Composed of Soft Matter with Different Physical Properties

Abstract

The coronavirus epidemic has attracted significant attention to the applications of pet robots which can be used to treat and entertain people in their homes. However, pet robots are fabricated using hard materials and it is difficult for them to communicate with people through contact. Soft robots are expected to realize communication through contact similar to that of actual pets. Soft robots provide people with a sense of healing and security owing to their softness and can extract rich information through external stimuli by applying a machine learning framework called physical-reservoir computing. It is crucial to determine the differences between the physical properties of soft materials that affect the information extracted from a soft body to develop an intelligent soft robot. In this study, two owl-shaped soft robots with different softnesses were developed to analyze the characteristics of the signal data obtained via piezoelectric film sensors embedded in models with different physical properties. An accuracy of 94.2% and 95.9% was obtained for touched part classification using 1D CNN and logistic regression models, respectively. Additionally, the relationship between the softness of material and classification performance was investigated by comparing the distribution of part classification accuracy for different hyper-parameters of two owl models.