An Emotional Model Based on Fuzzy Logic and Social Psychology for a Personal Assistant Robot

Abstract

Personal assistants and social robotics have evolved significantly in recent years thanks to the development of artificial intelligence and affective computing. Today’s main challenge is achieving a more natural and human interaction with these systems. Integrating emotional models into social robotics is necessary to accomplish this goal. This paper presents an emotional model whose design has been supervised by psychologists, and its implementation on a social robot. Based on social psychology, this dimensional model has six dimensions with twelve emotions. Fuzzy logic has been selected for defining: (i) how the input stimuli affect the emotions and (ii) how the emotions affect the responses generated by the robot. The most significant contribution of this work is that the proposed methodology, which allows engineers to easily adapt the robot personality designed by a team of psychologists. It also allows expert psychologists to define the rules that relate the inputs and outputs to the emotions, even without technical knowledge. This methodology has been developed and validated on a personal assistant robot. It consists of three input stimuli, (i) the battery level, (ii) the brightness of the room, and (iii) the touch of caresses. In a simplified implementation of the general model, these inputs affect two emotions that generate an externalized emotional response through the robot’s heartbeat, facial expression, and tail movement. The three experiments performed verify the correct functioning of the emotional model developed, demonstrating that stimuli, independently or jointly, generate changes in emotions that, in turn, affect the robot’s responses.