  We consider the task of controlling a quadrotor to hover in front of a freely
moving user, using input data from an onboard camera. On this specific task we
compare two widespread learning paradigms: a mediated approach, which learns an
high-level state from the input and then uses it for deriving control signals;
and an end-to-end approach, which skips high-level state estimation altogether.
We show that despite their fundamental difference, both approaches yield
equivalent performance on this task. We finally qualitatively analyze the
behavior of a quadrotor implementing such approaches.
