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arXiv · 2509.08177

Quadrotor Navigation using Reinforcement Learning with Privileged Information

Jonathan Lee·Abhishek Rathod·Kshitij Goel·John Stecklein·Wennie Tabib
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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.

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Jonathan Lee, Abhishek Rathod, Kshitij Goel, John Stecklein, Wennie Tabib. 2025-09-09. Quadrotor Navigation using Reinforcement Learning with Privileged Information. https://arxiv.org/abs/2509.08177

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