Reverse-engineering of artificially evolved controllers for swarms of robots

Abstract

It is generally challenging to design decentralized controllers for swarms of robots because there is often no obvious relation between the individual robot behaviors and the final behavior of the swarm. As a solution, we use artificial evolution to automatically discover neural controllers for swarming robots. Artificial evolution has the potential to find simple and efficient strategies which might otherwise have been overlooked by a human designer. However, evolved controllers are often unadapted when used in scenarios that differ even slightly from those encountered during the evolutionary process. By reverse-engineering evolved controllers we aim towards hand-designed controllers which capture the simplicity and efficiency of evolved neural controllers while being easy to optimize for a variety of scenarios.