Abstract
This paper is concerned with motivation in autonomous robots. In particular we focus on the basic structure that is necessary for bootstrapping the initial stages of multiple skill learning within the motivational engine of the MDB cognitive architecture. To this end, taking inspiration from a series of computational models of the use of motivations in infants, we propose an approach that leverages two types of cognitive motivations: exploratory and proficiency based. The latter modulated by the concept of interestingness. We postulate that these make up the minimum set of motivational components required to initiate the unrewarded learning of a skill toolbox that may later be used in order to achieve operational goals. The approach is illustrated through an experiment with a real robot that is learning skills in a real environment.
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Acknowledgments
This work has been funded by the EU’s H2020 research programme (grant No 640891 DREAM), MINECO/FEDER (grant TIN2015-63646-C5-1-R), Xunta de Galicia/FEDER (grant ED431C 2017/12), and Spanish Ministry of Education, Culture and Sports for the FPU grant of A. Romero.
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Romero, A., Bellas, F., Becerra, J.A., Duro, R.J. (2019). Bootstrapping Autonomous Skill Learning in the MDB Cognitive Architecture. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_13
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DOI: https://doi.org/10.1007/978-3-030-19591-5_13
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