  This work contributes a novel deep navigation policy that enables
collision-free flight of aerial robots based on a modular approach exploiting
deep collision encoding and reinforcement learning. The proposed solution
builds upon a deep collision encoder that is trained on both simulated and real
depth images using supervised learning such that it compresses the
high-dimensional depth data to a low-dimensional latent space encoding
collision information while accounting for the robot size. This compressed
encoding is combined with an estimate of the robot's odometry and the desired
target location to train a deep reinforcement learning navigation policy that
offers low-latency computation and robust sim2real performance. A set of
simulation and experimental studies in diverse environments are conducted and
demonstrate the efficiency of the emerged behavior and its resilience in
real-life deployments.
