Complex networks of simple neurons for bipedal locomotion

Abstract

Fluid bipedal locomotion remains a significant challenge for humanoid robotics. Recent bio-inspired approaches have made significant progress by using small numbers of tightly coupled neurons, called central pattern generators (CPGs). Our approach exchanges complexity of the neuron model for complexity of the network, gradually building a network of simple neurons capable of complex behaviors. We show this approach generates controllers <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de novo</i> that are able to control 3D bipedal locomotion up to 10 meters. This results holds for robots with human-proportionate morphologies across 95% of normal human variation. The resulting networks are then examined to discover neural structures that arise unusually often, lending some insight into the workings of otherwise opaque controllers.