Coordination and Synchronization of Locomotion in a Virtual Robot

This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a pareto optimal set of artificial neural networks that optimizes the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. Here we provide an insight into how the controller generates the emergent walking behavior in the creature by analyzing the evolved artificial neural networks in operation. A comparison between pareto optimal controllers showed that ANNs with varying numbers of hidden units resulted in noticeably different locomotion behaviors. We also found that a much higher level of sensory-motor coordination was present in the best evolved controller.