Continuous Adaptation in Robotic Systems by Indirect Online Evolution

A conceptual framework for online evolution in robotic systems called indirect online evolution (IDOE) is presented. A model specie automatically infers models of a hidden physical system by the use of gene expression programming (GEP). A parameter specie simultaneously optimizes the parameters of the inferred models according to a specified target vector. 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. This approach thus limits both the evaluation time, the wear out and the potential hazards normally associated with direct online evolution (DOE) where every individual has to be evaluated on the physical system. Additionally, the approach enables continuous system identification and adaptation during normal operation. Features of IDOE are illustrated by inferring models of a simplified, robotic arm, and further optimizing the parameters of the system according to a target position of the end effector. Simulated experiments indicate that the fitness of the IDOE approach is generally higher than the average fitness of DOE.