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Agile flights of autonomous quadrotors in cluttered environments requireconstrained motion planning and control subject to translational and rotationaldynamics. Traditional model-based methods typically demand complicated designand heavy computation. In this paper, we develop a novel deep reinforcementlearning-based method that tackles the challenging task of flying through adynamic narrow gate. We design a model predictive controller with its adaptivetracking references parameterized by a deep neural network (DNN). Thesereferences include the traversal time and the quadrotor SE(3) traversal posethat encourage the robot to fly through the gate with maximum safety marginsfrom various initial conditions. To cope with the difficulty of training inhighly dynamic environments, we develop a reinforce-imitate learning frameworkto train the DNN efficiently that generalizes well to diverse settings.Furthermore, we propose a binary search algorithm that allows online adaptionof the SE(3) references to dynamic gates in real-time. Finally, throughextensive high-fidelity simulations, we show that our approach is robust to thegate's velocity uncertainties and adaptive to different gate trajectories andorientations.

Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors