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

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

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Document Type:Preprint
Author:Michał Stolarz, Alex Mitrevski, Mohammad Wasil, Paul G. Plöger
ArXiv Id:http://arxiv.org/abs/2207.12144
Date of first publication:2022/07/25
Publication status:Accepted for presentation at the 6th Workshop on Behavior Adaptation and Learning for Assistive Robotics (BAILAR) at RO-MAN 2022
Funding Information:This work is conducted in the context of the MigrAVE project, which is funded by the German Ministry of Education and Research (BMBF). We hereby thank our partners, M¨unster University of Applied Sciences (FHM) and the RFH - University of Applied Sciences, Cologne. The research in the MigrAVE project has received an ethics approval by the German Psychological Society (DGPs).
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2022/08/04