# Quadrotor Navigation using Reinforcement Learning with Privileged Information

arXiv: https://arxiv.org/abs/2509.08177 · HTML: https://mlsanity.com/paper/2509.08177/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics](https://mlsanity.com/paper/2407.10648/) (2024)
- [A Review of Differentiable Simulators](https://mlsanity.com/paper/2407.05560/) (2024)
- [Learning Quadrotor Control From Visual Features Using Differentiable Simulation](https://mlsanity.com/paper/2410.15979/) (2024)
- [VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight](https://mlsanity.com/paper/2407.14783/) (2024)
- [RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation](https://mlsanity.com/paper/2502.02054/) (2025)
- [Rapid Quadrotor Navigation in Diverse Environments using an Onboard Depth Camera](https://mlsanity.com/paper/2411.04326/) (2024)

## Built on by (most cited first, 6 of 6)

- [Reinforcement Learning for Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2602.01266/) (2026)
- [AirDreamer: Generalist Drone Navigation with World Models](https://mlsanity.com/paper/2606.03252/) (2026)
- [High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning](https://mlsanity.com/paper/2602.08653/) (2026)
- [CMRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments](https://mlsanity.com/paper/2605.14810/) (2026)
- [Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics](https://mlsanity.com/paper/2607.02472/) (2026)
- [ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics](https://mlsanity.com/paper/2610.17419/) (2026)

## Abstract (arXiv)

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.