Biodiversity in evolved voxel-based soft robots

In many natural environments, there are different forms of living creatures that 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 non-biological 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 body and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, body and controller of VSRs for the task of locomotion. We investigate experimentally whether two key factors - -evolutionary algorithm (EA) and representation - -impact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise a way for measuring biodiversity, systematically characterizing the robots shape and behavior, and apply it to the VSRs evolved with three EAs and two representations. The experimental results suggest that the representation matters more than the EA and that there is not a clear trade-off between diversity and effectiveness.