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The “Mechatronic Board”: A Tool to Study Intrinsic Motivations in Humans, Monkeys, and Humanoid Robots

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Abstract

In this chapter the design and fabrication of a new mechatronic platform (called “mechatronic board”) for behavioural analysis of children, non-human primates, and robots are presented and discussed. The platform is the result of a multidisciplinary design approach which merges indications coming from neuroscientists, psychologists, primatologists, roboticists, and bioengineers, with the main goal of studying learning mechanisms driven by intrinsic motivations and curiosity. This chapter firstly introduces the main requirements of the platform, coming from the different needs of the experiments involving the different types of participants. Then, it provides a detailed analysis of the main features of the mechatronic board, focusing on its key aspects which allow the study of intrinsically motivated learning in children and non-human primates. Finally, it shows some preliminary results on curiosity-driven learning coming from pilot experiments involving children, capuchin monkeys, and a computational model of the behaviour of these organisms tested with a humanoid robot (the iCub robot). These experiments investigate the capacity of children, capuchin monkeys, and a computational model implemented on the iCub robot to learn action-outcome contingencies on the basis of intrinsic motivations.

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Acknowledgements

This research was supported by the European Community 7th Framework Programme (FP7/2007–2013), “Challenge 2: Cognitive Systems, Interaction, Robotics”, Grant Agreement No. ICT-IP-231722, and Project “IM-CLeVeR: Intrinsically Motivated Cumulative Learning Versatile Robots”. It was also supported by the Italian Ministry of University and Research, FIRB Research Program 2006, no. RBAP06SPK5.

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Taffoni, F. et al. (2013). The “Mechatronic Board”: A Tool to Study Intrinsic Motivations in Humans, Monkeys, and Humanoid Robots. In: Baldassarre, G., Mirolli, M. (eds) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32375-1_16

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  • DOI: https://doi.org/10.1007/978-3-642-32375-1_16

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