Cognitive Modeling for Automating Learning in Visually-guided Manipulative Tasks

Hendry Ferreira Chame (1) , Philippe Martinet (1)

1 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes

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.

Mots clés: Cognitive Robotics; Artificial In-telligence; Visual Servoing; Computational Cognition

Domaines: Automatique / Robotique; Intelligence artificielle [cs.AI]

Hendry Ferreira Chame, Philippe Martinet. Cognitive Modeling for Automating Learning in Visually-guided Manipulative Tasks. Informatics in Control, Automation and Robotics, Lecture Notes in Electrical Engineering, Springer International Publishing, pp.37-53, 2015, 978-3-319-10890-2. ⟨10.1007/978-3-319-10891-9_2⟩. ⟨hal-01230684⟩