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.

Dementia is currently a growing epidemic, bringing forth severe health, social, and economic strains. As an alternative to pharmacological measures, current research supports the effectiveness of using cognitive training interventions to slow the decline of or even improve brain functioning in persons with dementia. However, implementing and sustaining these interventions on a long-term basis can be challenging as they demand considerable resources and people. Our research focuses on investigating the potential use of robotic assistants to allow for these interventions to become more accessible to users and caregivers. Namely, the aim of our work is to develop socially assistive robots that can provide cognitive and social stimulation for persons with dementia. In this paper, we study the social interaction attributes of the human-like robot, Brian 2.0, during a one-on-one person-centered cognitively stimulating activity to determine if the robot is capable of minimizing task-induced stress by providing assistance, encouragement, and celebration, while adapting its behavior to a user state during the course of the activity. Our preliminary study shows that the social intelligence of Brian 2.0 is effective in engaging individuals in a cognitively stimulating game while minimizing stress during gameplay.