  Policy Search and Model Predictive Control~(MPC) are two different paradigms
for robot control: policy search has the strength of automatically learning
complex policies using experienced data, while MPC can offer optimal control
performance using models and trajectory optimization. An open research question
is how to leverage and combine the advantages of both approaches. In this work,
we provide an answer by using policy search for automatically choosing
high-level decision variables for MPC, which leads to a novel
policy-search-for-model-predictive-control framework. Specifically, we
formulate the MPC as a parameterized controller, where the hard-to-optimize
decision variables are represented as high-level policies. Such a formulation
allows optimizing policies in a self-supervised fashion. We validate this
framework by focusing on a challenging problem in agile drone flight: flying a
quadrotor through fast-moving gates. Experiments show that our controller
achieves robust and real-time control performance in both simulation and the
real world. The proposed framework offers a new perspective for merging
learning and control.
