[2509.08177] Quadrotor Navigation using Reinforcement Learning with Privileged Information

Computer Science > Robotics arXiv:2509.08177 (cs) [Submitted on 9 Sep 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:Quadrotor Navigation using Reinforcement Learning with Privileged Information Authors:Jonathan Lee, Abhishek Rathod, Kshitij Goel, John Stecklein, Wennie Tabib View a PDF of the paper titled Quadrotor Navigation using Reinforcement Learning with Privileged Information, by Jonathan Lee and 4 other authors View PDF HTML (experimental) Abstract: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. Subjects: Robotics (cs.RO); Artificial Intelligence (cs.A...