Abstract
Robot manipulators, as general-purpose machines, can be used to perform various tasks. Though, adaptations to specific scenarios require of some technical efforts. In particular, the descriptions of the task result in a robot program which must be modified whenever changes are introduced. Another source of variations are undesired changes due to the entropic properties of systems; in effect, robots must be re-calibrated with certain frequency to produce the desired results. To ensure adaptability, cognitive robotists aim to design systems capable of learning and decision making. Moreover, control techniques such as visual-servoing allow robust control under inaccuracies in the estimates of the system’s parameters. This paper reports the design of a platform called CRR, which combines the computational cognition paradigm for decision making and learning, with the visual-servoing control technique for the automation of manipulative tasks.
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Acknowledgments
This research was accomplished thanks to the founding of the National Agency of Research through the EQUIPEX ROBOTEX project (ANR-10-EQX-44), of the European Union through the FEDER ROBOTEX project 2011-2015, and of the Ecole Centrale of Nantes.
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Chame, H.F., Martinet, P. (2015). Cognitive Modeling for Automating Learning in Visually-Guided Manipulative Tasks. In: Ferrier, JL., Gusikhin, O., Madani, K., Sasiadek, J. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-319-10891-9_2
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DOI: https://doi.org/10.1007/978-3-319-10891-9_2
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