外观
论文arXiv 2609.209821 个月前
ASGARD：通过强化学习实现UAV韧性的动作空间防护器
原文链接：ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning（arXiv 2609.20982 · 发布日期：2026-09-17）
ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning
ASGARD用教师-学生管线防御RL式UAV控制中的动作空间攻击，并在多种攻击场景中验证韧性。
核心要点
- RL控制器用于UAV导航控制，但动作命令在执行前可能被攻击覆写。
- 教师阶段用物理状态和攻击特权信息训练策略与动作校正监控器。
- 学生阶段通过监督学习蒸馏教师，仅用物理状态历史在机载运行。
- 实验显示其在多类攻击下完成任务，并可泛化到未见和隐蔽攻击。
验证范围与局限：摘要仅说明在针对不同动作命令的UAV攻击场景中评估，未说明仿真或真机。
英文摘要 (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.
作者：Mohsen Salehi, Karthik Pattabiraman
来源
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