A Reinforcement Learning-Based Social Robot for Personalized Learning in Children with Autism

This work hypothesizes that a social robot that uses reinforcement learning can effectively adapt to individual differences in teaching imitation skills (e.g., facial expressions) to children with autism spectrum disorder. We developed an active learning method based on reinforcement learning to personalize human-robot interaction sessions based on each child's imitation performance and preference. We evaluated this method with five children with autism spectrum disorder, and the results demonstrated varying responses to different methods of presenting facial expressions to teach imitation skills. We found that the robot consistently promoted increased shared attention, including visual contact and physical proximity during imitation tasks. This suggests that adaptive human-robot interactions can cater to the unique needs of children with autism, offering a promising avenue for personalized intervention. Additionally, we discuss observed qualitative insights from our study and considerations for robot behavior mitigation strategies to sustain engagement.