  Applications of large-scale mobile multi-robot systems can be beneficial over
monolithic robots because of higher potential for robustness and scalability.
Developing controllers for multi-robot systems is challenging because the
multitude of interactions is hard to anticipate and difficult to model.
Automatic design using machine learning or evolutionary robotics seem to be
options to avoid that challenge, but bring the challenge of designing reward or
fitness functions. Generic reward and fitness functions seem unlikely to exist
and task-specific rewards often have undesired side effects. Approaches of
so-called innate motivation try to avoid the specific formulation of rewards
and work instead with different drivers, such as curiosity. Our approach to
innate motivation is to minimize surprise, which we implement by maximizing the
accuracy of the swarm robot's sensor predictions using neuroevolution. A unique
advantage of the swarm robot case is that swarm members populate the robot's
environment and can trigger more active behaviors in a self-referential loop.
We summarize our previous simulation-based results concerning behavioral
diversity, robustness, scalability, and engineered self-organization, and put
them into context. In several new studies, we analyze the influence of the
optimizer's hyperparameters, the scalability of evolved behaviors, and the
impact of realistic robot simulations. Finally, we present results using real
robots that show how the reality gap can be bridged.
