  This article proposes a hierarchical learning architecture for safe
data-driven 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. In addition to
task-invariant system state and input constraints, a parameterized environment
model generates task-specific state constraints, which are satisfied by the
stored trajectories. Our goal is to use these trajectories to find a safe and
high-performing policy for a new task in a new, unknown environment. We propose
using the stored data to learn generalizable control strategies. At each time
step, based on a local forecast of the new task environment, the learned
strategy consists of a target region in the state space and input constraints
to guide the system evolution to the target region. These target regions are
used as terminal sets by a low-level model predictive 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 apply the proposed method to robotic path
planning, racing, and computer game applications.
