Self-organizing incremental neural network (SOINN) as a mechanism for motor babbling and sensory-motor learning in developmental robotics

Authors:
Tarek Najjar;Osamu Hasegawa
Affiliations:
Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

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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.