Automatic modular design of robot swarms based on repertoires of behaviors generated via novelty search

Abstract

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.