arXiv AI
LiLib: Lifelong Air-to-Ground Path-Loss Prediction on UAVs via a Drift-Triggered Model Library
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arXiv AI
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Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
arXiv:2606. 24483v1 Announce Type: cross Abstract: The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage.
arXiv Machine Learning
Jun 2
SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search
arXiv:2606. 01325v1 Announce Type: cross Abstract: Unmanned Aerial Vehicles (UAVs) equipped with radar sensors are deployed for target search missions in diverse environments, where targets exhibit characteristic signatures (e.
arXiv Machine Learning
Sep 4
Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments
The paper addresses lifelong aerial autonomy by treating visual place recognition (VPR) as a mission‑based domain‑incremental learning problem. It introduces a heterogeneous memory framework that first trains on a static satellite exemplar memory and then uses a bounded replay buffer to retain selected airborne observations across missions. The proposed DBS‑Hybrid replay strategy, which blends prototype‑based diversity trimming with representative‑first feature‑space coverage, outperforms baseline methods in accuracy, generalization, and knowledge retention across multiple UAV missions.
arXiv AI
Jun 6
Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
arXiv:2606. 05661v1 Announce Type: new Abstract: Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it.
arXiv Machine Learning
Sep 17
Changepoint-Aware World Models: Detecting Dynamics Shifts and Recovering by Forgetting Stale Replay in Model-Based RL
Changepoint-Aware World Models (CAWM) is a DreamerV3 agent that detects abrupt dynamics shifts in a robot’s environment using an online CUSUM test on internal prediction error. Upon detection, CAWM selectively forgets stale replay data while preserving the learned representation, enabling rapid recovery from shifts such as doubled gravity or halved actuator gain. Experiments on simulated locomotion show CAWM recovers faster than passive retraining and outperforms a baseline that respawns a fresh dynamics model, achieving significant return gains in the first 30k post‑shift frames.
arXiv Computer Vision
Oct 7
Catastrophic Forgetting in Sequential Thermal Anti-UAV Detection: The Role of Scale-Conditioned Gradient Imbalance
arXiv:2610.08315v1 Announce Type: new Abstract: Counter-UAV systems based on thermal infrared detection must stay accurate as operational datasets evolve, yet sequential fine-tuning causes catastroph...