Emotional Beeps and Blinks: Exploring Emotional Expressiveness in Non-Anthropomorphic Robots Through Sound Design and Color

Abstract

Effective affective communication is essential for collaborative human-robot interaction, as emotional expressions can enhance trust and collaboration with robots. This paper addresses this need by investigating whether a musically-informed sound design or a color-based display is more effective for emotional expression in a non-anthropomorphic kitchen assistant robot. Using a within-subjects VR experiment (N=14), participants completed three collaborative cooking tasks while the robot expressed happiness, sadness, and anger via synthesized sounds or colored lights. Results show that effectiveness is emotion-dependent: sound significantly outperformed color for happiness and sadness, while color (red) was more effective for anger. Sound also yielded higher believability, likeability, animacy, and perceived intelligence. Qualitative findings suggest sound’s advantage stems from both its functional benefit in visually demanding tasks and its capacity to create social presence. A key emergent finding is that contextual appropriateness shapes emotion interpretation, indicating that expression design must account for social expectations in collaborative settings.