Evolved center-crossing recurrent synaptic delay based neural networks for biped locomotion control

摘要

This paper combines the center-crossing condition in artificial neural networks that incorporate synaptic delays in their connections and which act as Central Pattern Generators (CPGs) for biped controllers. Recurrent synaptic delay based neural networks allow greater time reasoning capabilities in the neural controllers, outperforming the results of continuous time recurrent neural networks, the neural model most used as CPG for biped robot locomotion related behaviors. Simulated evolution is used to automatically obtain neural controllers for walking behaviors, showing the capabilities of the synaptic delay based neural networks for the temporal coordination of the biped joints in difficult surfaces.