Current research supports the use of cognitive training interventions to improve the brain functioning of both adults and children. Our work focuses on exploring the potential use of robot assistants to allow for these interventions to become more accessible. Namely, we aim to develop an intelligent, socially assistive robot that can engage individuals in person-centred cognitively stimulating activities. In this paper, we present the design of a novel control architecture for the robot Brian 2.0, which enables the robot to be a social motivator by providing assistance, encouragement and celebration during an activity. A hierarchical reinforcement learning approach is used in the architecture to allow the robot to: 1) learn appropriate assistive behaviours based on the structure of the activity, and 2) personalize an interaction based on user states. Experiments show that the control architecture is effective in determining the robot's optimal assistive behaviours during a memory game interaction.

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Recently, there has been a growing body of research that supports the effectiveness of using non-pharmacological cognitive and social training interventions to reduce the decline of or improve brain functioning in individuals suffering from cognitive impairments. However, implementing and sustaining such interventions on a long-term basis is difficult as they require considerable resources and people, and can be very time-consuming for healthcare staff. Our research focuses on making these interventions more accessible to healthcare professionals through the aid of robotic assistants. The objective of our work is to develop an intelligent socially assistive robot with abilities to recognize and identify human affective intent to determine its own appropriate emotion-based behavior while engaging in assistive interactions with people. In this paper, we present the design of a novel human-robot interaction (HRI) control architecture that allows the robot to provide social and cognitive stimulation in person-centered cognitive interventions. Namely, the novel control architecture is designed to allow a robot to act as a social motivator by encouraging, congratulating and assisting a person during the course of a cognitively stimulating activity. Preliminary experiments validate the effectiveness of the control architecture in providing assistive interactions during a HRI-based person-directed activity.

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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 personcentered 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.