  Recently, neural control policies have outperformed existing model-based
planning-and-control methods for autonomously navigating quadrotors through
cluttered environments in minimum time. However, they are not perception aware,
a crucial requirement in vision-based navigation due to the camera's limited
field of view and the underactuated nature of a quadrotor. We propose a
learning-based system that achieves perception-aware, agile flight in cluttered
environments. Our method combines imitation learning with reinforcement
learning (RL) by leveraging a privileged learning-by-cheating framework. Using
RL, we first train a perception-aware teacher policy with full-state
information to fly in minimum time through cluttered environments. Then, we use
imitation learning to distill its knowledge into a vision-based student policy
that only perceives the environment via a camera. Our approach tightly couples
perception and control, showing a significant advantage in computation speed
(10 times faster) and success rate. We demonstrate the closed-loop control
performance using hardware-in-the-loop simulation.
