A Robot for Test Bed Aimed at Improving Telepresence System and Evasion from Discomfort Stimuli by Online Learning

Abstract

This study contributes to improving the comfort of telepresence communication by adaptations to people. We developed a handheld telepresence robot comprised of a binaural microphone and a head mounted display as a test bed and conducted surveys on unpleasantness caused by special devices in use. We found that numerous people experienced an increase in unpleasant sound when the binaural microphone was being used. It was also found that types of unpleasant stimuli differed from person to person. Furthermore, we propose an automatic unpleasant stimuli avoidance system using online machine learning architecture constructed from echo state networks (ESNs) and Accumulator Based Arbitration Models (ABAMs) that can also flexibly adapt to remote users. Because the handheld telepresence robot can avoid unpleasant stimuli before remote users experience these stimuli, it provides them with a more comfortable communication environment while notifying people around the robot that uncomfortable stimulation is being avoided.