Dynamic Modulation of Emotional Expressions in Social Robots: Effects on Liveliness and Naturalness
Haeun Park, Sun Jun Hwang, Hyojin Kim, Jiyeon Lee, Hui Sung Lee
Abstract
Humans naturally express emotions with subtle variations, and exaggerated expressions often appear as heightened intensity in facial, bodily, or vocal cues. This paper introduces a method for exaggerating robotic emotional expressions by dynamically adjusting intensity within an emotion dynamics model. By systematically manipulating the damping ratio, we generated five distinct intensity levels for each emotion, thereby producing emotional expressions that exhibited different degrees of overshoot. A user study revealed that liveliness ratings for surprise increased linearly with intensity, suggesting that exaggerated, high-energy dynamics are particularly effective for conveying surprise. In contrast, other emotions exhibited optimal points at intermediate levels, indicating that excessive exaggeration can reduce perceived naturalness. These findings highlight the need for emotion-specific and user-specific calibration of expression intensity, supporting more nuanced and engaging human-robot interactions.