Learning Perception-Aware Agile Flight in Cluttered Environments

TL;DR: We propose a perception-aware, minimum-time, vision-based navigation method to fly a quadrotor through a cluttered environment, combining reinforcement learning and imitation learning through a learning-by-cheating framework.

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 method to learn neural network policies that achieve perception-aware, minimum-time flight in cluttered environments. Our method combines imitation learning and 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× faster) and success rate. We demonstrate closed-loop control using a physical quadrotor and hardware-in-the-loop simulation at speeds up to 50 km/h.