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arXiv Machine Learning

Bayesian Ambiguity Contraction-based Adaptive Robust Markov Decision Processes for Adversarial Surveillance Missions

The paper introduces an adaptive Robust Markov Decision Process framework for autonomous Intelligence, Surveillance, and Reconnaissance missions using Collaborative Combat Aircraft. It models adversarial uncertainty with finite threat prototypes and refines policies by eliminating inconsistent threat models, enabling a shift from worst‑case protection to more efficient mission strategies while preserving robustness. Experiments demonstrate higher mission rewards and fewer exposure events compared to nominal and static robust planners across various network topologies.

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arXiv AI
Aug 25

Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing

The paper presents a robust multi‑agent reinforcement learning framework for small unmanned aircraft systems (sUAS) to maintain separation assurance when GPS data is degraded or spoofed. By modeling state observation corruption as a zero‑sum game, the authors derive a closed‑form adversarial perturbation that eliminates iterative inner optimization and can be evaluated in linear time. Integrating this perturbation into a policy‑gradient MARL algorithm yields a counter‑policy that achieves near‑zero collision rates in high‑density simulations even with up to 35% observation corruption, outperforming non‑adversarial baselines.

By Alex Zongo, Filippos Fotiadis, Ufuk Topcu, Peng Wei
agentsreinforcement-learningsafety
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arXiv AI
Aug 17

Sensor-Driven Mission Synthesis for UAV/UGV Swarms: A TB-CSPN Coordination Architecture with Hardware-Enforced Safety

arXiv:2608. 14306v1 Announce Type: new Abstract: This paper presents a coordination architecture for heterogeneous UAV/UGV swarms that synthesises mission actions from uncertain, multi-modal sensor evidence while preserving hardware-enforced safety at the actuation boundary.

By Uwe M. Borghoff, Paolo Bottoni, Remo Pareschi
agentsmultimodalsafety
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arXiv Machine Learning
Oct 15

Adversarial Reinforcement Learning for Robust Planetary Landing

The paper presents a method for improving the robustness of deep reinforcement learning (DRL) policies used in planetary landing by employing adversarial reinforcement learning. A secondary agent generates physically constrained perturbations that challenge the landing controller, creating an asymmetric zero‑sum game. Training is conducted in three stages—baseline learning, stochastic robustness enhancement, and iterative adversarial optimization—showing that adversarial disturbances uncover vulnerabilities missed by conventional randomization and hint at better generalization to unseen perturbations.

By Miguel Leiva-V\'elez, Nicolas Gaston-Rozado, Andrea Scorsoglio, Hodei Urrutxua, V\'ictor Rodr\'iguez-Fern\'andez
agentsreinforcement-learningsafety
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arXiv AI
Jul 3

Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation

arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.

By Shenghui Zhang, YuXuan Gao, Songwei Zhao, Jifeng Hu, Zijing Zhang, Hechang Chen
agentsreinforcement-learningsafety
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