Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
arXiv:2506.21129v2 Announce Type: replace Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation. However, global navigation satellite system (GNSS) spoofing attacks can induce out-of-distribution observation shifts that corrupt value estimation and degrade mission performance. Existing robust RL approaches typically improve resilience against specific attack models but often fail to generalize to attacks not encountered during training. To address this limitation, we propose a curriculum-guided adaptation framework that progressive
The increasing reliance on autonomous UAVs for critical missions makes their robustness against sophisticated attacks a pressing challenge, driving research in resilient AI.
This breakthrough in curriculum-adapted robust reinforcement learning offers a more generalizable solution for protecting autonomous systems against emergent adversarial threats, essential for their widespread adoption and reliability.
Existing robust RL approaches often fail against novel attacks, but this new framework proposes a method to progressively adapt and generalize resilience, significantly enhancing mission security for UAVs.
- · Defence contractors
- · Autonomous system developers
- · AI/ML research institutions
- · UAV operators
- · Adversarial actors targeting GNSS systems
- · Legacy robust RL approaches
- · Unresilient autonomous systems
More secure and reliable autonomous UAV operations in contested environments.
Accelerated deployment of autonomous systems in critical infrastructure and defence applications due to improved trustworthiness.
A competitive advantage for nations and companies that master generalizable robust AI for autonomous platforms.
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Read at arXiv cs.LG