EmoACT: a Framework to Embed Emotions into Artificial Agents Based on Affect Control Theory
Francesca Corrao, Alice Nardelli, Jennifer Renoux, Carmine Tommaso Recchiuto
TL;DR
EmoACT addresses embedding synthetic emotions in artificial agents to improve HRI. It introduces a platform-agnostic EmoACT framework based on Affect Control Theory, representing emotions in the $EPA$ space with coordinates $E$, $P$, and $A$, and translating impressions into perceivable cues. Implemented on the Pepper robot, the framework comprises Impression Detection, Emotion Generation, and Emotional Expression to produce frequency-controlled emotional displays during collaborative storytelling. Experiments show that higher-frequency emotional expressions enhance perceived emotional and cognitive agency, supporting ACT as a viable basis for more natural, social human-robot interactions.
Abstract
As robots and artificial agents become increasingly integrated into daily life, enhancing their ability to interact with humans is essential. Emotions, which play a crucial role in human interactions, can improve the naturalness and transparency of human-robot interactions (HRI) when embodied in artificial agents. This study aims to employ Affect Control Theory (ACT), a psychological model of emotions deeply rooted in interaction, for the generation of synthetic emotions. A platform-agnostic framework inspired by ACT was developed and implemented in a humanoid robot to assess its impact on human perception. Results show that the frequency of emotional displays impacts how users perceive the robot. Moreover, appropriate emotional expressions seem to enhance the robot's perceived emotional and cognitive agency. The findings suggest that ACT can be successfully employed to embed synthetic emotions into robots, resulting in effective human-robot interactions, where the robot is perceived more as a social agent than merely a machine.