  We propose a hierarchical learning architecture for predictive control in
unknown environments. We consider a constrained nonlinear dynamical system and
assume the availability of state-input trajectories solving control tasks in
different environments. A parameterized environment model generates state
constraints specific to each task, which are satisfied by the stored
trajectories. Our goal is to find a feasible trajectory for a new task in an
unknown environment. From stored data, we learn strategies in the form of
target sets in a reduced-order state space. These strategies are applied to the
new task in real-time using a local forecast of the new environment, and the
resulting output is used as a terminal region by a low-level receding horizon
controller. We show how to i) design the target sets from past data and then
ii) incorporate them into a model predictive control scheme with shifting
horizon that ensures safety of the closed-loop system when performing the new
task. We prove the feasibility of the resulting control policy, and verify the
proposed method in a robotic path planning application.
