Quadrupedal Locomotion: GasNets, CTRNNs and Hybrid CTRNN/PNNs Compared

Abstract

Evolutionary Robotics seeks to use evolutionary techniques to create both physical and physically simulated robots capable of exhibiting characteristics commonly associated with living organisms. Typically, biologically inspired artificial neural networks are evolved to act as sensorimotor control systems. These networks include; GasNets, Continuous Time Recurrent Neural Networks (CTRNNs) and Plastic Neural Networks (PNNs). This paper seeks to compare the performance of such networks in solving the problem of locomotion in a physically simulated quadruped. The results in this paper, taken together with those of other studies (summarized in this paper) help us to assess the relative strengths and weaknesses of the these three different approaches.