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In robot-assisted therapy for individuals with Autism Spectrum Disorder, theworkload of therapists during a therapeutic session is increased if they haveto control the robot manually. To allow therapists to focus on the interactionwith the person instead, the robot should be more autonomous, namely it shouldbe able to interpret the person's state and continuously adapt its actionsaccording to their behaviour. In this paper, we develop a personalised robotbehaviour model that can be used in the robot decision-making process during anactivity; this behaviour model is trained with the help of a user model thathas been learned from real interaction data. We use Q-learning for this task,such that the results demonstrate that the policy requires about 10,000iterations to converge. We thus investigate policy transfer for improving theconvergence speed; we show that this is a feasible solution, but aninappropriate initial policy can lead to a suboptimal final return.

Personalised Robot Behaviour Modelling for Robot-Assisted Therapy in the Context of Autism Spectrum Disorder