ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning

Researchers proposed ASGARD, a two-phase teacher-student pipeline that makes reinforcement-learning-based UAV control resilient to action-space attacks, according to a new arXiv paper (arXiv:2609.20982v1). ASGARD's teacher phase uses an encoder combining the UAV's physical state with action-attack-related privileged information to train the RL control policy and a monitor that outputs corrected action commands to the actuators, while the student phase trains both components via supervised learning to run on-board using only the UAV's physical state history. The authors report ASGARD completes missions despite action-space attacks, generalizes to unseen attacks, and remains resilient against stealthy attacks.

arXiv:2609.20982v1 Announce Type: new Abstract: Reinforcement learning RL controllers have been recently adopted for Unmanned Aerial Vehicles UAV navigation and control. However, they are susceptible to action-space attacks that overwrite the action commands after the policy generates them and before the actuators execute them. While most existing defenses target attacks on the policy's inputs, those addressing action-space attacks retrain the policy at training time and are not resilient to corrupted actions at runtime. We propose ASGARD, a two-phase teacher-student pipeline for making RL-based UAV control resilient to action-space attacks. In the teacher phase, an encoder combines the UAV's physical state with action-attack-related privileged information to produce an action-attack-aware latent that trains the RL control policy and a monitor that outputs corrected action commands to the actuators. In the student phase, both the encoder and the monitor are trained via supervised learning from their teacher counterparts to run on-board using only the UAV's physical state history. We evaluate ASGARD across attack scenarios targeting different action commands on UAV. We find that ASGARD is resilient to action-space attacks and completes the missions despite the attack. We further find that ASGARD generalizes to unseen attacks and remains resilient against stealthy attacks.