Adaptive Emotional Expression in Social Robots: A Multimodal Approach to Dynamic Emotion Modeling

Abstract

Social robots have been extensively studied in recent decades, with many researchers exploring the use of modalities such as facial expressions to achieve more natural emotions in robots. Various methods have been attempted to generate and express robot emotions, including computational models that define an affect space and show dynamic emotion changes. However, the implementation of multimodal expression in previous models is ambiguous, and the generation of emotions in response to stimuli relies on heuristic methods. In this paper, we present a framework that enables robots to naturally express their emotions in a multimodal way, where the emotion can change over time based on the given stimulus values. By representing the robot's emotion as a position in an affect space of a computational emotion model, we consider the given stimuli values as driving forces that can shift the emotion position dynamically. In order to examine the feasibility of our proposed method, a mobile robot prototype was implemented that can recognize touch and express different emotions with facial expressions and movements. The experiment demonstrated that the emotion elicited by a given stimulus is contingent upon the robot's previous state, thereby imparting the impression that the robot possesses a distinctive emotion model. Furthermore, the Godspeed survey results indicated that our model was rated significantly higher than the baseline, which did not include a computational emotion model, in terms of anthropomorphism, animacy, and perceived intelligence. Notably, the unpredictability of emotion switching contributed to a perception of greater lifelikeness, which in turn enhanced the overall interaction experience.