Evolution of neural oscillator network for the biped walking control of a four-link robot

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a four-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to test the adaptability of the presented method, feedback information is entrained with the best evolved CPG network to realize slope terrain adaptive walking and anti-disturbance capability. Our results confirm that the biologically inspired CPG model is well suited for legged walking, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.