  The discovery of complex multicellular organism development took millions of
years of evolution. The genome of such a multicellular organism guides the
development of its body from a single cell, including its control system. Our
goal is to imitate this natural process using a single neural cellular
automaton (NCA) as a genome for modular robotic agents. In the introduced
approach, called Neural Cellular Robot Substrate (NCRS), a single NCA guides
the growth of a robot and the cellular activity which controls the robot during
deployment. We also introduce three benchmark environments, which test the
ability of the approach to grow different robot morphologies. In this paper,
NCRSs are trained with covariance matrix adaptation evolution strategy
(CMA-ES), and covariance matrix adaptation MAP-Elites (CMA-ME) for quality
diversity, which we show leads to more diverse robot morphologies with higher
fitness scores. While the NCRS can solve the easier tasks from our benchmark
environments, the success rate reduces when the difficulty of the task
increases. We discuss directions for future work that may facilitate the use of
the NCRS approach for more complex domains.
