Starting with simple bodies and simple brains, co-evolution gradually increases the complexity of both together. Our work investigates both evolving controllers for robots and co-evolution. The first part of this project involved developing controllers for simple robots to perform a simple task - pole balancing and Luxo locomotion. In the second part we move to more complex robots performing the more complex task of pursuit and evasion. Ultimately, we aim for inexpensive robots that design themselves to address problems.
SANs are a type of neural network that uses sensors for inputs and outputs to actuators.
In this work an initial population of controllers was generated for a character and standard evolutionary techniques were used to improve the NN controllers. Karl Sims also used evolutionary algorithms to evolve controllers for animated characters. In Sims' work he also evolved character morphology. He radically improved on van de Panne's work by evolving the entire morphology. In this work the character morphology consisted of variable sized blocks connected by revolute joints.