Evolving complete robots with CPPN-NEAT

Abstract

This paper extends prior work using Compositional Pattern Producing Networks (CPPNs) as a generative encoding for the purpose of simultaneously evolving robot morphology and control. A method is presented for translating CPPNs into complete robots including their physical topologies, sensor placements, and embedded, closed-loop, neural network control policies. It is shown that this method can evolve robots for a given task. Additionally it is demonstrated how the performance of evolved robots can be significantly improved by allowing recurrent connections within the underlying CPPNs. The resulting robots are analyzed in the hopes of answering why these recurrent connections prove to be so beneficial in this domain. Several hypotheses are discussed, some of which are refuted from the available data while others will require further examination.