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http://dx.doi.org/10.5302/J.ICROS.2014.14.9033

Computational Model of a Mirror Neuron System for Intent Recognition through Imitative Learning of Objective-directed Action  

Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University)
Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.20, no.6, 2014 , pp. 606-611 More about this Journal
Abstract
The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model.
Keywords
mirror neuron system; imitative learning; human-robot interaction; intent recognition;
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