A learning-based control architecture for an assistive robot providing social engagement during cognitively stimulating activities

Recent studies have shown that sustained engagement in cognitively stimulating activities has had positive effects on the cognitive functioning of humans. The objective of our work is to develop an intelligent socially assistive robot that can engage individuals in person-centered cognitively stimulating activities. In this paper, we present the design of a novel learning-based control architecture that enables the robot to act as a social motivator by providing assistance, encouragement and celebration during the course of an activity. A hierarchical reinforcement learning (HRL) approach is used to provide the robot with the ability to: (i) learn appropriate assistive behaviors based on the structure of the activity and (ii) personalize the interaction based on the person´s affective state during the activity. Preliminary experiments show that the proposed learning-based control architecture is effective in determining the optimal assistive behaviors of the robot during a memory game interaction.