Dynamic resolution in the co-evolution of morphology and control

Evolutionary robotics is a promising approach to overcom-ing the limitations and biases of human designers in pro-ducing control strategies for autonomous robots. However, most work in evolutionary robotics remains solely concerned with optimizing control strategies for existing morphologies. By contrast, natural evolution, the only process that has pro-duced intelligent agents to date, may modify both the control (brain) and morphology (body) of organisms. Therefore, co-evolving morphology along with control may provide a better path towards realizing intelligent robots. This paper presents a novel method for co-evolving morphology and control us-ing CPPN-NEAT. This method is capable of dynamically ad-justing the resolution at which components of the robot are created: a large number of small sized components may be present in some body locations while a smaller number of larger sized components is present in other locations. Ad-vantages of this capability are demonstrated on a simple task, and implications for using this methodology to create more complex robots are discussed