  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. The contributions of this paper are twofold: 1) We
show how we can make use of a learned dynamics model in combination with a
model-based controller to simplify the BO problem by focusing onto the most
relevant regions of the optimization domain. 2) Based on (1) we present a
method to find an embedding in parameter space that reduces the effective
dimensionality of the optimization problem. To evaluate the effectiveness of
the proposed approach, we present an experimental evaluation on real hardware,
as well as simulated tasks including a 48-dimensional policy for a quadcopter.
