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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Lungarella, M., Metta, G., Pfeifer, R., Sandini, G.: Developmental robotics: a survey. Connection Science 15(4), 151–190 (2003)
Weng, J., Hwang, W.: From neural networks to the brain: Autonomous mental development. IEEE Computational Intelligence Magazine 1(3), 15–31 (2006)
Stoytchev, A.: Five basic principles of developmental robotics. In: NIPS 2006 Workshop on Grounding Perception, Knowledge and Cognition in Sensori-Motor Experience (2006)
Piaget, J.: The origins of intelligence in
Van der Meer, A., Van der Weel, F., Lee, D., et al.: The functional significance of arm movements in neonates. Science-New York Then Washington, 693–693 (1995)
von Hofsten, C.: Eye–hand coordination in the newborn. Developmental Psychology 18(3), 450 (1982)
Hersch, M., Sauser, E., Billard, A.: Online learning of the body schema. International Journal of Humanoid Robotics 5(02), 161–181 (2008)
Sturm, J., Plagemann, C., Burgard, W.: Body schema learning for robotic manipulators from visual self-perception. Journal of Physiology-Paris 103(3), 220–231 (2009)
Metta, G., Sandini, G., Konczak, J.: A developmental approach to visually-guided reaching in artificial systems. Neural Networks 12(10), 1413–1427 (1999)
Caligiore, D., Parisi, D., Baldassarre, G.: Toward an integrated biomimetic model of reaching. In: IEEE 6th International Conference on Development and Learning, ICDL 2007, pp. 241–246. IEEE (2007)
Demiris, Y., Dearden, A.: From motor babbling to hierarchical learning by imitation: a robot developmental pathway (2005)
Kober, J., Peters, J.: Learning motor primitives for robotics. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 2112–2118. IEEE (2009)
Furao, S., Ogura, T., Hasegawa, O.: An enhanced self-organizing incremental neural network for online unsupervised learning. Neural Networks 20(8), 893–903 (2007)
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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
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