Nonverbal Cues Expressing Robot Personality - A Movement Analysts Perspective

Abstract

In social robotics, where people and robots interact in a social context, robot personality design is critical. Through voice, words, gestures, and nonverbal clues, social robots with expressive behaviors can display human-like actions, and the robot’s personality will ensure consistency. This research aims to create robot personalities expressed only by nonverbal cues. Differently from existing studies that test expressive behaviors with non-specialized participants, we look at how and why human movement analysts perceive distinct personalities in robots (introvert vs. extrovert) based on the robot’s movement and other dynamic features, such as joint position, head, and torso position, voice pitch, speed, and so on. We report the findings of a thematic analysis of the data obtained during a focus group with movement analysis experts who watched Pepper robot behaviors designed to be extrovert and introvert. Our findings lead to new guidelines for designing different robot movement features, including body symmetry, personality trait consistency, and social cue congruence during an interaction, all emphasized by the movement analyzers. Finally, we summarize the design principles for extrovert and introvert robot behaviors based on the combined findings of the focus group data analysis and literature review.