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Robotics is the next frontier in the progress of Artificial Intelligence(AI), as the real world in which robots operate represents an enormous,complex, continuous state space with inherent real-time requirements. Oneextreme challenge in robotics is currently formed by autonomous drone racing.Human drone racers can fly through complex tracks at speeds of up to 190 km/h.Achieving similar speeds with autonomous drones signifies tackling fundamentalproblems in AI under extreme restrictions in terms of resources. In thisarticle, we present the winning solution of the first AI Robotic Racing (AIRR)Circuit, a competition consisting of four races in which all participatingteams used the same drone, to which they had limited access. The core of ourapproach is inspired by how human pilots combine noisy observations of the racegates with their mental model of the drone's dynamics to achieve fast control.Our approach has a large focus on gate detection with an efficient deep neuralsegmentation network and active vision. Further, we make contributions torobust state estimation and risk-based control. This allowed us to reach speedsof ~9.2m/s in the last race, unrivaled by previous autonomous drone racecompetitions. Although our solution was the fastest and most robust, it stilllost against one of the best human pilots, Gab707. The presented approachindicates a promising direction to close the gap with human drone pilots,forming an important step in bringing AI to the real world.

The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition