  Performing acrobatic maneuvers with quadrotors is extremely challenging.
Acrobatic flight requires high thrust and extreme angular accelerations that
push the platform to its physical limits. Professional drone pilots often
measure their level of mastery by flying such maneuvers in competitions. In
this paper, we propose to learn a sensorimotor policy that enables an
autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard
sensing and computation. We train the policy entirely in simulation by
leveraging demonstrations from an optimal controller that has access to
privileged information. We use appropriate abstractions of the visual input to
enable transfer to a real quadrotor. We show that the resulting policy can be
directly deployed in the physical world without any fine-tuning on real data.
Our methodology has several favorable properties: it does not require a human
expert to provide demonstrations, it cannot harm the physical system during
training, and it can be used to learn maneuvers that are challenging even for
the best human pilots. Our approach enables a physical quadrotor to fly
maneuvers such as the Power Loop, the Barrel Roll, and the Matty Flip, during
which it incurs accelerations of up to 3g.
