Online

Cappelle, Collin

Computer Science

2019

Ph. D.

Traditional techniques for the design of robots require human engineers to plan every aspect of the system, from body to controller. In contrast, the field of evolutionary robotics uses evolutionary algorithms to create optimized morphologies and neural controllers with minimal human intervention. In order to expand the capability of an evolved agent, it must be exposed to a variety of conditions and environments. This thesis investigates the design and benefits of virtual robots which can reflect the structure and modularity in the world around them. I show that when a robot's morphology and controller enable it to perceive each environment as a collection of independent components, rather than a monolithic entity, evolution only needs to optimize on a subset of environments in order to maintain performance in the overall larger environmental space. I explore previously unused methods in evolutionary robotics to aid in the evolution of modularity, including using morphological and neurological cost. I utilize a tree morphology which makes my results generalizable to other morphologies while also allowing in depth theoretical analysis about the properties relevant to modularity in embodied agents. In order to better frame the question of modularity in an embodied context, I provide novel definitions of morphological and neurological modularity as well as create the sub-goal interference metric which measures how much independence a robot exhibits with regards to environmental stimulus. My work extends beyond evolutionary robotics and can be applied to the optimization of embodied systems in general as well as provides insight into the evolution of form in biological organisms.