  The combination of policy search and deep neural networks holds the promise
of automating a variety of decision-making tasks. Model Predictive Control
(MPC) provides robust solutions to robot control tasks by making use of a
dynamical model of the system and solving an optimization problem online over a
short planning horizon. In this work, we leverage probabilistic decision-making
approaches and the generalization capability of artificial neural networks to
the powerful online optimization by learning a deep high-level policy for the
MPC (High-MPC). Conditioning on robot's local observations, the trained neural
network policy is capable of adaptively selecting high-level decision variables
for the low-level MPC controller, which then generates optimal control commands
for the robot. First, we formulate the search of high-level decision variables
for MPC as a policy search problem, specifically, a probabilistic inference
problem. The problem can be solved in a closed-form solution. Second, we
propose a self-supervised learning algorithm for learning a neural network
high-level policy, which is useful for online hyperparameter adaptations in
highly dynamic environments. We demonstrate the importance of incorporating the
online adaption into autonomous robots by using the proposed method to solve a
challenging control problem, where the task is to control a simulated quadrotor
to fly through a swinging gate. We show that our approach can handle situations
that are difficult for standard MPC.
