スパースガウス過程による情報獲得型計画とオンライン学習
Informative Planning and Online Learning with Sparse Gaussian Processes
海洋モニタリング用に、情報量の多いデータを集める計画とスパースガウス過程によるオンライン学習を交互に実行し、環境モデルを効率的に更新する手法を提案した。
著者: Kai-Chieh Ma, Lantao Liu, Gaurav S. Sukhatme
分類: cs.RO, cs.AI, cs.LG, stat.ML
原文アブストラクト
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining an environmental model. To alleviate the computational bottleneck caused by large-scale data accumulated, we propose a framework that iterates between a planning component aimed at collecting the most information-rich data, and a sparse Gaussian Process learning component where the environmental model and hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. Our simulations with ground-truth ocean data shows that the proposed method is both accurate and efficient.
