Abstract
Learning how to control arm joints for goal-directed reaching tasks is one of the earliest skills that need to be acquired by Developmental Robotics in order to scaffold into tasks of higher Intelligence. Motor Babbling seems as a promising approach toward the generation of internal models and control policies for robotic arms. In this paper we propose a mechanism for learning sensory-motor associations using layered arrangement of Self-Organizing Neural Network (SOINN) and joint-egocentric representations. The robot starts off by random exploratory motion, then it gradually shift into more coordinated, goal-directed actions based on the measure of error-change. The main contribution of this research is in the proposition of a novel architecture for online sensory-motor learning using SOINN networks without the need to provide the system with a kinematic model or a preprogrammed joint control scheme. The viability of the proposed mechanism is demonstrated using a simulated planar robotic arm.
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Najjar, T., Hasegawa, O. (2013). Self-Organizing Incremental Neural Network (SOINN) as a Mechanism for Motor Babbling and Sensory-Motor Learning in Developmental Robotics. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_31
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DOI: https://doi.org/10.1007/978-3-642-38679-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38678-7
Online ISBN: 978-3-642-38679-4
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