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Socially assistive robots can autonomously provide activity assistance to vulnerable populations, including those living with cognitive impairments. To provide effective assistance, these robots should be capable of displaying appropriate behaviors and personalizing them to a user's cognitive abilities. Our research focuses on the development of a novel robot learning architecture that uniquely combines learning from demonstration (LfD) and reinforcement learning (RL) algorithms to effectively teach socially assistive robots personalized behaviors. Caregivers can demonstrate a series of assistive behaviors for an activity to the robot, which it uses to learn general behaviors via LfD. This information is used to obtain initial assistive state-behavior pairings using a decision tree. Then, the robot uses an RL algorithm to obtain a policy for selecting the appropriate behavior personalized to the user's cognition level. Experiments were conducted with the socially assistive robot Casper to investigate the effectiveness of our proposed learning architecture. Results showed that Casper was able to learn personalized behaviors for the new assistive activity of tea-making, and that combining LfD and RL algorithms significantly reduces the time required for a robot to learn a new activity.

Learning and Personalizing Socially Assistive Robot Behaviors to Aid with Activities of Daily Living