# ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks

arXiv: https://arxiv.org/abs/2506.22423 · HTML: https://mlsanity.com/paper/2506.22423/

## Key facts

- Cited by 1 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 24 of its 41 references that are in the map.
- First posted on arXiv 27 Jun 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, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [Robust Deep Reinforcement Learning with Adversarial Attacks](https://mlsanity.com/paper/1712.03632/) (2017)
- [Learning to be Safe: Deep RL with a Safety Critic](https://mlsanity.com/paper/2010.14603/) (2020)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control](https://mlsanity.com/paper/2103.02142/) (2021)
- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning](https://mlsanity.com/paper/2001.09684/) (2020)
- [Risk Averse Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1904.00511/) (2019)
- [safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics](https://mlsanity.com/paper/2109.06325/) (2021)
- [PuVAE: A Variational Autoencoder to Purify Adversarial Examples](https://mlsanity.com/paper/1903.00585/) (2019)
- [Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards](https://mlsanity.com/paper/2410.15185/) (2024)
- [Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization](https://mlsanity.com/paper/1910.13399/) (2019)
- [Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates](https://mlsanity.com/paper/1912.09957/) (2019)
- [Learning Resilient Behaviors for Navigation Under Uncertainty](https://mlsanity.com/paper/1910.09998/) (2019)
- [Injected and Delivered: Fabricating Implicit Control over Actuation Systems by Spoofing Inertial Sensors](https://mlsanity.com/paper/1806.07558/) (2018)
- [Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation](https://mlsanity.com/paper/2312.12861/) (2023)
- [SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attacks](https://mlsanity.com/paper/2408.15200/) (2024)

## Built on by (most cited first, 1 of 1)

- [ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning](https://mlsanity.com/paper/2609.20982/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) depend on onboard sensors for perception, navigation, and control. However, these sensors are susceptible to physical attacks, such as GPS spoofing, that can corrupt state estimates and lead to unsafe behavior. While reinforcement learning (RL) offers adaptive control capabilities, existing safe RL methods are ineffective against such attacks. We present ARMOR (Adaptive Robust Manipulation-Optimized State Representations), an attack-resilient, model-free RL controller that enables robust UAV operation under adversarial sensor manipulation. Instead of relying on raw sensor observations, ARMOR learns a robust latent representation of the UAV's physical state via a two-stage training framework. In the first stage, a teacher encoder, trained with privileged attack information, generates attack-aware latent states for RL policy training. In the second stage, a student encoder is trained via supervised learning to approximate the teacher's latent states using only historical sensor data, enabling real-world deployment without privileged information. Our experiments show that ARMOR outperforms conventional methods, ensuring UAV safety. Additionally, ARMOR improves generalization to unseen attacks and reduces training cost by eliminating the need for iterative adversarial training.