Learning high-speed flight in the wild was published in Science Robotics. The paper presents an end-to-end approach to autonomous high-speed flight through complex natural and human-made environments, with purely onboard sensing and computation. The flight is controlled by a convolutional network that maps noisy sensory observations to collision-free trajectories in a receding-horizon fashion. It is trained exclusively in simulation via privileged learning.
