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Robot learning from demonstration and the problem of target defense by team of unmanned surface vehicles
2014
Applied Mathematical Sciences
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
doi:10.12988/ams.2014.46496
fatcat:wlwe5zu4knc2zbkshqnn3xos5a