Quadrotor Navigation using Reinforcement Learning with Privileged Information
This addresses navigation challenges for quadrotors in cluttered environments, representing an incremental improvement over prior methods.
The paper tackles quadrotor navigation around large obstacles using reinforcement learning with privileged information, achieving an 86% success rate in simulation and deploying successfully in outdoor flights covering 589 meters without collisions.
This paper presents a reinforcement learning-based quadrotor navigation method that leverages efficient differentiable simulation, novel loss functions, and privileged information to navigate around large obstacles. Prior learning-based methods perform well in scenes that exhibit narrow obstacles, but struggle when the goal location is blocked by large walls or terrain. In contrast, the proposed method utilizes time-of-arrival (ToA) maps as privileged information and a yaw alignment loss to guide the robot around large obstacles. The policy is evaluated in photo-realistic simulation environments containing large obstacles, sharp corners, and dead-ends. Our approach achieves an 86% success rate and outperforms baseline strategies by 34%. We deploy the policy onboard a custom quadrotor in outdoor cluttered environments both during the day and night. The policy is validated across 20 flights, covering 589 meters without collisions at speeds up to 4 m/s.