  In this paper, we revisit the problem of learning a stabilizing controller
from a finite number of demonstrations by an expert. By first focusing on
feedback linearizable systems, we show how to combine expert demonstrations
into a stabilizing controller, provided that demonstrations are sufficiently
long and there are at least $n+1$ of them, where $n$ is the number of states of
the system being controlled. When we have more than $n+1$ demonstrations, we
discuss how to optimally choose the best $n+1$ demonstrations to construct the
stabilizing controller. We then extend these results to a class of systems that
can be embedded into a higher-dimensional system containing a chain of
integrators. The feasibility of the proposed algorithm is demonstrated by
applying it on a CrazyFlie 2.0 quadrotor.
