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Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization. However, scaling BO to problems with large input dimensions (>10) remains an open challenge. In this paper, we propose to leverage results from optimal control to scale BO to higher dimensional control tasks and to reduce the need for manually selecting the optimization domain. Thedoi:10.1109/iros40897.2019.8967736 dblp:conf/iros/FrohlichKDZ19 fatcat:a6yyuapvdrgmlmrglomzjo6aue