Indirect online evolution - A conceptual framework for adaptation in industrial robotic systems

Abstract

A conceptual framework for online evolution in robotic systems called Indirect Online Evolution (IDOE) is presented. A model specie automatically infers models of a physical system and a parameter specie simultaneously optimizes the parameters of the inferred models according to a specified target behavior. Training vectors required for modelling are automatically provided online by the interplay between the two coevolving species and the physical system. At every generation, only the estimated fittest individual of the parameter specie is executed on the physical system, hence limiting both the evaluation time, the wear out and the potential hazards normally associated with direct online evolution (DOE), where every candidate solution has to be evaluated on the physical system. Features of IDOE are demonstrated by inferring models of a simple hidden system containing geometric shapes that are further optimized according to a target value. Simulated experiments indicate that the fitness of the IDOE approach is generally higher than the average fitness of DOE. © 2008 Springer-Verlag Berlin Heidelberg.