Exerting human control over decentralized robot swarms

Robot swarms are capable of performing tasks with robustness and flexibility using only local interactions between the agents. Such a system can lead to emergent behavior that is often desirable, but difficult to control and manipulate post-design. These properties make the real-time control of swarms by a human operator challenging-a problem that has not been adequately addressed in the literature. In this paper we present preliminary work on two possible forms of control: top-down control of global swarm characteristics and bottom-up control by influencing a subset of the swarm members. We present learning methods to address each of these. The first method uses instance-based learning to produce a generalized model from a sampling of the parameter space and global characteristics for specific situations. The second method uses evolutionary learning to learn placement and parameterization of virtual agents that can influence the robots in the swarm. Finally we show how these methods generalize and can be used by a human operator to dynamically control a swarm in real time.