  In nature, biological organisms jointly evolve both their morphology and
their neurological capabilities to improve their chances for survival.
Consequently, task information is encoded in both their brains and their
bodies. In robotics, the development of complex control and planning algorithms
often bears sole responsibility for improving task performance. This dependence
on centralized control can be problematic for systems with computational
limitations, such as mechanical systems and robots on the microscale. In these
cases we need to be able to offload complex computation onto the physical
morphology of the system. To this end, we introduce a methodology for
algorithmically arranging sensing and actuation components into a robot design
while maintaining a low level of design complexity (quantified using a measure
of graph entropy), and a high level of task embodiment (evaluated by analyzing
the Kullback-Leibler divergence between physical executions of the robot and
those of an idealized system). This approach computes an idealized,
unconstrained control policy which is projected onto a limited selection of
sensors and actuators in a given library, resulting in intelligence that is
distributed away from a central processor and instead embodied in the physical
body of a robot. The method is demonstrated by computationally optimizing a
simulated synthetic cell.
