Expanded linear dynamic affect-expression model for lingering emotional expression in social robot

Abstract

The service robot market is growing, and robots are replacing humans in many service industry jobs. Recently, as humans treat robots more emotionally and socially, designing robot emotions to improve human satisfaction in human–robot interaction (HRI) is crucial. Despite the importance of expressing the robot emotions, many robots only respond to the current stimulus when expressing emotions. Just as humans can feel the lingering effects of a strong stimulus after it has passed, social robots might do the same. For example, if a user hits a robot and the robot is very angry, even if the user praises the robot to make it feel better, the anger will not dissipate for some time. In this study, we propose the expanded linear dynamic affect-expression model (e-LDAEM) for expressing lingering emotion. Different intensity of stimuli in the e-LDAEM leads to different results, even if the robot is stimulated to the same emotion. The viscosity matrix and intensity vector show a positive correlation in the process of determining the emotion dynamics. This model enables the implementation of robots with diverse personalities by adjusting the lingering emotions to suit the robot’s form or situation. Through user evaluation results, the e-LDAEM has the proven effect of remaining in emotion for longer periods when the robot is given a strong stimulus. Thus, e-LDAEM is expected to improve the emotional bond between humans and robots.