https://youtu.be/2N_wKXQ6MXA

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 work, we propose to teach 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 teach
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.