Robot learning from demonstration and the problem of target defense by team of unmanned surface vehicles

Abstract

In this paper, we consider some approaches to the task-level robot learning from demonstration for the target defense by a team of unmanned water surface vehicles. We assume that the problem of target defense by a team of unmanned water surface vehicles represented as a problem of learning of rhythmic motor primitives. We consider for the problem neural networks as oscillators to learn rhythmic motor tasks. Also, we use the approximate period problem and introduce the approximate period problem for a set of strings with a set of restrictions. In this paper, we present experimental results for dierent synthetic test data set for stationary and moving targets.