Human-Guided Learning of Social Action Selection for Robot-Assisted Therapy
Proceedings of The 4th Workshop on Machine Learning for Interactive Systems at ICML 2015, PMLR 43:15-20, 2015.
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.