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

Emmanuel Senft, Paul Baxter, Tony Belpaeme
Proceedings of The 4th Workshop on Machine Learning for Interactive Systems at ICML 2015, PMLR 43:15-20, 2015.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v43-senft15, title = {Human-Guided Learning of Social Action Selection for Robot-Assisted Therapy}, author = {Senft, Emmanuel and Baxter, Paul and Belpaeme, Tony}, booktitle = {Proceedings of The 4th Workshop on Machine Learning for Interactive Systems at ICML 2015}, pages = {15--20}, year = {2015}, editor = {Cuayáhuitl, Heriberto and Dethlefs, Nina and Frommberger, Lutz and Van Otterlo, Martijn and Pietquin, Olivier}, volume = {43}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {11 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v43/senft15.pdf}, url = {https://proceedings.mlr.press/v43/senft15.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Human-Guided Learning of Social Action Selection for Robot-Assisted Therapy %A Emmanuel Senft %A Paul Baxter %A Tony Belpaeme %B Proceedings of The 4th Workshop on Machine Learning for Interactive Systems at ICML 2015 %C Proceedings of Machine Learning Research %D 2015 %E Heriberto Cuayáhuitl %E Nina Dethlefs %E Lutz Frommberger %E Martijn Van Otterlo %E Olivier Pietquin %F pmlr-v43-senft15 %I PMLR %P 15--20 %U https://proceedings.mlr.press/v43/senft15.html %V 43 %X 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.
RIS
TY - CPAPER TI - Human-Guided Learning of Social Action Selection for Robot-Assisted Therapy AU - Emmanuel Senft AU - Paul Baxter AU - Tony Belpaeme BT - Proceedings of The 4th Workshop on Machine Learning for Interactive Systems at ICML 2015 DA - 2015/06/18 ED - Heriberto Cuayáhuitl ED - Nina Dethlefs ED - Lutz Frommberger ED - Martijn Van Otterlo ED - Olivier Pietquin ID - pmlr-v43-senft15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 43 SP - 15 EP - 20 L1 - http://proceedings.mlr.press/v43/senft15.pdf UR - https://proceedings.mlr.press/v43/senft15.html AB - 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. ER -
APA
Senft, E., Baxter, P. & Belpaeme, T.. (2015). 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, in Proceedings of Machine Learning Research 43:15-20 Available from https://proceedings.mlr.press/v43/senft15.html.

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