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Several methods have already been proposed to automatically design control software for robot swarms by assembling predefined modules. Yet, so far, the modules on which these methods operate have always been defined manually in a process that is time consuming, requires domain knowledge, and must be performed by an expert. Motivated by the goal of automatizing the definition of these modules, we propose an approach in which repertoires of modules, in the form of neural networks, are automatically generated via a quality-diversity evolutionary algorithm. To illustrate the proposal, we introduce Nata, a novel approach belonging to the AutoMoDe family. Nata automatically generates probabilistic finite-state machines in which states are selected from a repertoire of neural networks, and transition conditions are selected from a set of rules based on the sensory capabilities of the robotic platform considered. Both the repertoire of neural networks and the set of transition rules are automatically generated a priori, once and for all, in a mission-agnostic way. We study Nata on three missions, both in simulation and with real robots. Nata is the first modular automatic design method that assembles modules that were themselves generated automatically. [Display omitted] • Automatic modular methods for designing robot swarms can be automatically generated. • Nata selects and combines neural networks generated a priori via novelty search. • Nata outperforms the classical neuroevolutionary approach. [ABSTRACT FROM AUTHOR]

• Automatic methods can design spatially-organizing behaviors for robot swarms. • AutoMoDe-Mate is a design method specialized in spatial organization problems. • AutoMoDe-Mate includes a module that enables robot pattern formation. • Experiments with physical robots show effectiveness in coverage and networking. • AutoMoDe-Mate outperforms baseline modular and neuro-evolutionary methods. [Display omitted] We present Mate, an automatic off-line design method specialized in the design of spatially-organizing behaviors for robot swarms. Mate belongs to the family of modular methods known as AutoMoDe. We introduce Mate to study the automatic design of collective behaviors for missions in which the swarm is subject to spatial distribution constrains. In this paper, we produce control software for three missions with specifications related to the distribution of the swarm in the environment. We conduct experiments in simulation and with a swarm of 20 e-puck robots. Alongside Mate, we also conduct experiments with two other automatic design methods: Chocolate—a state-of-the-art instance of AutoMoDe; and EvoSpace—a method based on neuro-evolution. Early studies conducted with existing modular design methods have shown their limitations in the design of spatially-organizing behaviors for robots that operate under spatial constrains. By introducing a specialized method like Mate, we expect to overcome these limitations. The aggregate results of our experiments show that Mate performs significantly better than Chocolate and EvoSpace in the missions we consider. [ABSTRACT FROM AUTHOR]