Distributed learning of cooperative robotic behaviors using particle swarm optimization

Abstract

In this paper we study the automatic synthesis of robotic controllers for the coordinated movement of multiple mobile robots. The algorithm used to learn the controllers is a noise-resistant version of Particle Swarm Optimization, which is applied in two different settings: centralized and distributed learning. In centralized learning, every robot runs the same controller and the performance is evaluated with a global metric. In the distributed learning, robots run different controllers and the performance is evaluated independently on each robot with a local metric. Our results from learning in simulation show that it is possible to learn a cooperative task in a fully distributed way employing a local metric, and we validate the simulations with real robot experiments where the best solutions from distributed and centralized learning achieve similar performances.