Cognitive Modeling for Automating Learning in Visually-Guided Manipulative Tasks
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.
KeywordsCognitive robotics Computational cognition Artificial intelligence Visual servoing.
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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