Towards Adaptive Human-Robot Interaction: A Comfort-Driven Framework for Social Robots

Abstract

The ability to understand others' affective states and adjust behavior accordingly is crucial in social relationships. Enabling robots with these capabilities can advance their development as social agents and improve Human-Robot Interaction (HRI). This work introduces a cognitive framework to enhance social robots' adaptability and perception as intentional agents, aiming to foster personalized HRI. Inspired by attachment theory, the proposed framework endows robots with basic cognitive functionalities - such as perception and decision-making - and an internal motivation defined as comfort, which is dynamically influenced by the robot's needs and goals and by the stimuli it receives during the interaction. This innovative approach aims to allow robots to align their behavior with their partner's preferences and adapt accordingly. By monitoring their comfort-based motivation, robots can personalize the interaction with users, promoting more predictable and immediate exchanges.