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

Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation, yet 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 methods often lack generalization to unseen attacks. To address this, we propose a curriculum-guided adaptation framework that progressively exposes a robust policy to gradient-based adversarial observation perturbations of increasing intensity while aligning temporal-difference (TD) error distributions across curriculum stages. By preserving TD-error consistency rather than targeting specific attack models, the framework promotes resilience across attack conditions. We further derive a TD-space generalization certificate that bounds performance degradation when the TD-error distribution under a test-time attack remains sufficiently close to that of the final curriculum stage. We evaluate the framework in a UAV deconfliction environment with dynamic 3D obstacles under unseen fixed and dynamic GNSS spoofing attacks. Under fixed spoofing, the curriculum-adapted (CurAd) policy achieves approximately 99-100% mission success across evaluated traffic densities, compared with 20-24% for the best-performing robust baseline, while also outperforming the non-robust vanilla baseline. Under dynamic obstacle-luring spoofing, CurAd achieves the highest episodic rewards and reduces median mission completion steps by up to 22% relative to the best-performing robust RL baseline. These findings demonstrate that adversarial curriculum adaptation transfers resilience from synthetic perturbations to unseen GNSS spoofing attacks without attack-specific retraining, enhancing robust autonomous UAV navigation.

Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks. However, its practical use still depends heavily on human designed reward functions and repeated manual fine tuning, which is time consuming and does not guarantee high success in the desired task. This paper presents AgenticRL, agent guided reinforcement learning framework that increases autonomy in reward design, policy refinement, and real world deployment for unmanned aerial vehicles (UAV) navigation tasks. AgenticRL uses a multimodal generative pre-trained transformer (GPT) agent to interpret task information and visual scene observations, generate task specific reward functions, train policies using Proximal Policy Optimization (PPO) algorithm, and then act as a critic by evaluating the trained policy through diagnosis packets to generate feedback. Based on this feedback, the agent identifies failure modes and refines the reward function in a closed loop self improvement process. To further leverage the multimodal GPT agent during inference, AgenticRL uses real world images and natural language task information to automatically identify the active scenario and select the appropriate trained policy for execution. The framework is evaluated on multiple navigational tasks, including gate traversal, obstacle avoidance, wall barrier crossing with landing, trajectory following, and motion behavior learning. Experimental results show that the closed loop refinement process improves policy behavior compared with initial rewards by 71%. We also demonstrate sim-to-real transfer of the proposed framework, achieving a real world success rate of 91% and a sim-to-real accuracy of 94%.

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. However, autonomous UAV navigation in dense environments remains challenging under sparse perception and dynamic constraints. Most reinforcement learning (RL) methods lack explicit safety mechanisms, leading to unsafe exploration, unstable training, and risky behaviors, especially during high-speed flight. Even in safe RL approaches, safety is often enforced by projecting policy outputs onto a safe action set, which may introduce instability. Meanwhile, many learning-based methods rely on dense inputs or large networks, increasing computational burden and limiting lightweight onboard deployment. Facing the above challenges, we propose a safety-constrained perception-control integrated framework for UAV navigation. A lightweight network encodes sparse observations into collision-risk-aware features using asymmetric and depthwise separable convolutions. We formulate the task as a constrained Markov decision process within a hierarchical control architecture and solve it using a Lagrangian-based safe PPO algorithm. Curriculum learning further improves training stability. Experiments with varying obstacle densities and flight speeds demonstrate higher success rates, improved safety, and better efficiency than existing reinforcement learning baselines.