Punctuated anytime learning for hexapod gait generation

Abstract

Punctuated anytime learning is presented as the solution for two problems: the use of anytime learning with an off-line learning module and the linking of the actual robot to its simulation during evolutionary robotics. Two methods of punctuated anytime learning, fitness biasing and the co-evolution of model parameters, are described and compared using the common task of gait generation for a hexapod robot with changing capabilities.