Towards an HRI Tutoring Framework for Long-term Personalization and Real-time Adaptation

Abstract

Personalization and adaptation are key aspects of designing and developing effective and acceptable social robot tutors. They allow to tailor interactions towards individual needs and preferences, improve engagement and sense of familiarity over time, and facilitate trust between the user and the robot. To foster the development of autonomous adaptive social robots, we present a tutoring framework that recognizes new or previously met pupils and adapts the training experience through feedback about real-time performance and the tailoring of exercises and interaction based on users’ past encounters. The framework is suitable for multiparty scenarios, allowing for deployment in real-world tutoring contexts unfolding in groups.