  Quadrotors are highly nonlinear dynamical systems that require carefully
tuned controllers to be pushed to their physical limits. Recently,
learning-based control policies have been proposed for quadrotors, as they
would potentially allow learning direct mappings from high-dimensional raw
sensory observations to actions. Due to sample inefficiency, training such
learned controllers on the real platform is impractical or even impossible.
Training in simulation is attractive but requires to transfer policies between
domains, which demands trained policies to be robust to such domain gap. In
this work, we make two contributions: (i) we perform the first benchmark
comparison of existing learned control policies for agile quadrotor flight and
show that training a control policy that commands body-rates and thrust results
in more robust sim-to-real transfer compared to a policy that directly
specifies individual rotor thrusts, (ii) we demonstrate for the first time that
such a control policy trained via deep reinforcement learning can control a
quadrotor in real-world experiments at speeds over 45km/h.
