  A common approach in robotics is to learn tasks by generalizing from special
cases given by a so-called demonstrator. In this paper, we apply this paradigm
and present an algorithm that uses a demonstrator (typically given by a
trajectory optimizer) to automatically synthesize feedback controllers for
steering a system described by ordinary differential equations into a goal set.
The resulting feedback control law switches between the demonstrations that it
uses as reference trajectories. In comparison to the direct use of trajectory
optimization as a control law, for example, in the form of model predictive
control, this allows for a much simpler and more efficient implementation of
the controller. The synthesis algorithm comes with rigorous convergence and
optimality results, and computational experiments confirm its efficiency.
