  Evolving virtual creatures is a field with a rich history and recently it has
been getting more attention, especially in the soft robotics domain. The
compliance of soft materials endows soft robots with complex behavior, but it
also makes their design process unintuitive and in need of automated design.
Despite the great interest, evolved virtual soft robots lack the complexity,
and co-optimization of morphology and control remains a challenging problem.
Prior work identifies and investigates a major issue with the co-optimization
process -- fragile co-adaptation of brain and body resulting in premature
convergence of morphology. In this work, we expand the investigation of this
phenomenon by comparing learnable controllers with proprioceptive observations
and fixed controllers without any observations, whereas in the latter case, we
only have the optimization of the morphology. Our experiments in two morphology
spaces and two environments that vary in complexity show, concrete examples of
the existence of high-performing regions in the morphology space that are not
able to be discovered during the co-optimization of the morphology and control,
yet exist and are easily findable when optimizing morphologies alone. Thus this
work clearly demonstrates and characterizes the challenges of optimizing
morphology during co-optimization. Based on these results, we propose a new
body-centric framework to think about the co-optimization problem which helps
us understand the issue from a search perspective. We hope the insights we
share with this work attract more attention to the problem and help us to
enable efficient brain-body co-optimization.
