Recognizing human touching behaviors using a haptic interface for a pet-robot

Abstract

This paper presents the preliminary results of classifying human touching behaviors using a haptic interface for a pet-like robot. The haptic interface uses gridded pressure-sensitive conductive ink sheets. Features of the measured pressure data are determined for classification in terms of 1) absolute values, 2) spatial distributions and 3) the temporal differences in measured pressure patterns. Touching behaviors include "slap," "pat," "stroke" and so forth. The experimental results show that a reliable classification of these touching patterns can be accomplished by using the sensor sheet and pressure features. The results of classification can be used as reward signals for reinforcement learning in controlling the behaviors of a pet-like robot that interacts with humans.