Layered learning for a soccer legged robot helped with a 3D simulator

Mobile robots can benefit from machine learning approaches for improving their behaviors in performing complex activities. In recent years, these techniques have been used to find optimal parameter sets for many behaviors. In particular, layered learning has been proposed to improve learning rate in robot learning tasks. In this paper, we consider a layered learning approach for learning optimal parameters of basic control routines, behaviours and strategy selection. We compare three different methods in the different layers: genetic algorithm, Nelder-Mead, and policy gradient. Moreover, we study how to use a 3D simulator for speeding up robot learning. The results of our experimental work on AIBO robots are useful not only to state differences and similarities between different robot learning approaches used within the layered learning framework, but also to evaluate a more effective learning methodology that makes use of a simulator. © 2008 Springer-Verlag Berlin Heidelberg.