An IEC-driven Optimization of Gestures to Enhance Personality Traits in Communication Robots

Abstract

In this study, we propose a system that optimizes the gestures used to express the personality traits of a communication robot according to user preferences. In order for a communication robot to make people feel comfortable, it is necessary to convey a friendly impression. However, conventional robot personalities are designed based on general human impressions and do not take individual users’ preferences into account. In order to tackle this challenge, we present a system that utilizes Interactive Evolutionary Computation (IEC) to optimize the gestures employed to convey the personality traits of the robot, aligning them with the user’s specific preferences. The effectiveness of the system was then verified through an experimental evaluation. As a result, it was suggested that the proposed method could help the robot convey better impression to users, thereby improving its familiarity.