  Autonomous UAV racing has recently emerged as an interesting research
problem. The dream is to beat humans in this new fast-paced sport. A common
approach is to learn an end-to-end policy that directly predicts controls from
raw images by imitating an expert. However, such a policy is limited by the
expert it imitates and scaling to other environments and vehicle dynamics is
difficult. One approach to overcome the drawbacks of an end-to-end policy is to
train a network only on the perception task and handle control with a PID or
MPC controller. However, a single controller must be extensively tuned and
cannot usually cover the whole state space. In this paper, we propose learning
an optimized controller using a DNN that fuses multiple controllers. The
network learns a robust controller with online trajectory filtering, which
suppresses noisy trajectories and imperfections of individual controllers. The
result is a network that is able to learn a good fusion of filtered
trajectories from different controllers leading to significant improvements in
overall performance. We compare our trained network to controllers it has
learned from, end-to-end baselines and human pilots in a realistic simulation;
our network beats all baselines in extensive experiments and approaches the
performance of a professional human pilot. A video summarizing this work is
available at https://youtu.be/hGKlE5X9Z5U
