Abstract

This paper proposes a group of network elements, SONE, that self-organizes network topology, aiming at online and real-time learning and adaptation in robots. SONE, consisting of node elements and link elements, develops network topology by repeating generation and elimination of themselves based on reinforcement signals that are propagated and stored between the elements. This technique proved successful in simulations in which a mobile robot avoided obstacles, and it convinced us of its feasibility for online learning.

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