Human-Guided Learning of Social Action Selection for Robot-Assisted Therapy

This paper presents a method for progressively increasing autonomous action selection capabilities in sensitive environments, where random exploration-based learning is not desirable, using guidance provided by a human supervisor. We describe the global framework and a simulation case study based on a scenario in Robot Assisted Therapy for children with Autism Spectrum Disorder. This simulation illustrates the functional features of our proposed approach, and demonstrates how a system following these principles adapts to different interaction contexts while maintaining an appropriate behaviour for the system at all times.

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