In many natural environments, different forms of living organisms successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce biodiversity in artificial agents, while still optimizing them for a particular task, might increase their applicability to scenarios where human response to unexpected changes is not possible. In this work, we focus on Voxel-based Soft Robots (VSRs), a form of robots that grants great freedom in the design of both morphology and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, morphology and controller of VSRs for the task of locomotion. We investigate experimentally whether three key factors—representation, Evolutionary Algorithm (EA), and environment—impact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise an automatic machine learning pipeline for systematically characterizing the morphology and behavior of robots resulting from the optimization process. We classify the robots into species and then measure biodiversity in populations of robots evolved in a multitude of conditions resulting from the combination of different morphology representations, controller representations, EAs, and environments. The experimental results suggest that, in general, EA and environment matter more than representation. We also propose a novel EA based on a speciation mechanism that operates on morphology and behavior descriptors and we show that it allows to jointly evolve morphology and controller of effective and diverse VSRs.
