AI-Based Affective Interaction System in Emotion-Aware Social Robots

Social robots (SRs) are interactive systems designed to engage in human-centered communication through both verbal and nonverbal behaviors. To enable emotionally responsive interaction, SRs require affective feedback systems capable of recognizing user emotions and generating appropriate expressive responses. This study investigates the design of affective feedback systems in SRs. Guided by the FCBPSS framework, the system is analyzed through two core components. The first is the use of artificial intelligence techniques to process physiological and behavioral signals for emotion recognition. The second involves the generation of affective responses through multimodal output channels such as facial expressions, speech, haptics, and lighting. Based on this analysis, this study proposes an affective feedback framework that integrates AI-based emotion recognition with multimodal expressive interaction to support dynamic, human-centered robot behavior. The framework provides a structured foundation for analyzing and developing affective interaction systems in SRs, contributing to emotionally intelligent interaction models that are adaptive, socially aware, and grounded in systematic design principles.