  Soft robotics is a rapidly growing area of robotics research that would
benefit greatly from design automation, given the challenges of manually
engineering complex, compliant, and generally non-intuitive robot body plans
and behaviors. It has been suggested that a major hurdle currently limiting
soft robot brain-body co-optimization is the fragile specialization between a
robot's controller and the particular body plan it controls, resulting in
premature convergence. Here we posit that modular controllers are more robust
to changes to a robot's body plan. We demonstrate a decreased reduction in
locomotion performance after morphological mutations to soft robots with
modular controllers, relative to those with similar global controllers -
leading to fitter offspring. Moreover, we show that the increased
transferability of modular controllers to similar body plans enables more
effective brain-body co-optimization of soft robots, resulting in an increased
rate of positive morphological mutations and higher overall performance of
evolved robots. We hope that this work helps provide specific methods to
improve soft robot design automation in this particular setting, while also
providing evidence to support our understanding of the challenges of brain-body
co-optimization more generally.
