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Natural Teaching of Robot-Assisted Rearranging Exercises for Cognitive Training

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11876))

Abstract

Social Assistive Robots are a powerful tool to be used in patients’ cognitive training. The purpose of this study is to evaluate a new methodology to enable caregivers to teach cognitive exercises to the robot in an easy and natural way. We build upon our existing framework, in which a robot is employed to provide encouragement and hints while a patient is physically playing a cognitive exercise. In this paper, we focus on empowering the caregiver to easily teach new board exercises to the robot by providing positive examples.

The proposed learning method has two main advantages (i) the teaching procedure is human-friendly (ii) the produced exercise rules are human-understandable. The learning algorithm is validated in 6 exercises with different characteristics, correctly identifying and representing the rules from a few examples.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement SOCRATES MSCA-ITN-721619, by the Spanish Ministry of Science and Innovation HuMoUR TIN2017-90086-R, and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).

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Acknowledgements

Authors would like to thank Patrick Grosch, Sergi Hernandez and Alejandro López for assembling and programming the electronic board. Thanks to Nofar Sinai (http://www.vikkiacademy.com/) for allowing us to use some frames of the SOCRATES video.

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Correspondence to Antonio Andriella .

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Andriella, A., Suárez-Hernández, A., Segovia-Aguas, J., Torras, C., Alenyà, G. (2019). Natural Teaching of Robot-Assisted Rearranging Exercises for Cognitive Training. In: Salichs, M., et al. Social Robotics. ICSR 2019. Lecture Notes in Computer Science(), vol 11876. Springer, Cham. https://doi.org/10.1007/978-3-030-35888-4_57

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  • DOI: https://doi.org/10.1007/978-3-030-35888-4_57

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  • Online ISBN: 978-3-030-35888-4

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