障害物環境における知覚を考慮した俊敏飛行の学習
Learning Perception-Aware Agile Flight in Cluttered Environments
強化学習で全状態情報を用いた教師方策を訓練し、それを模倣学習でカメラ入力のみの生徒方策に蒸留することで、障害物環境を最短時間で飛行する知覚考慮型のドローン制御を実現した。
著者: Yunlong Song, Kexin Shi, Robert Penicka, Davide Scaramuzza
分類: cs.RO, cs.AI
原文アブストラクト
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.
