Interactive personalization for socially assistive robots

Abstract

In this work, we seek to define a new problem of interactive personalization in the context of socially assistive robotics. We analyze a robotic tutor's elicitation of learning-sensitive information to be leveraged by interactive machine learning methods for personalized education. Our results, evaluated using a variety of subjective measures, demonstrate that a humans-in-the-loop approach positively benefits the human-robotic tutor interaction, while minimizing the computational complexity of personalization.