Deep Neural Network Classification of Tactile Materials Explored by Tactile Sensor Array with Various Active-Cell Formations

Reducing the input data of tactile sensory systems brings a large degree of freedom to real-world implementations from the perspectives of bandwidth and computational complexity. For this, we suggest efficient active-cell formations with a high classification accuracy of tactile materials. By revealing that averaged Kullback-Leibler (KL)-divergence and common frequency component power to variance ratio (CFVR) are proportional to the classification accuracy, we showed that those methods can be useful in estimating valid active-cell formations.