arXiv cs.AI· Christopher Conrad, Deepak Panda, Weisi Guo, Antonios Tsourdos·· 12 小时前AI 评分24
面向临时接管下 UAS 导航的中断感知循环强化学习
Interruption-Aware Recurrent Reinforcement Learning for UAS Navigation Under Temporary Overrides
AI 导读
针对 UAS 学习策略被远程飞手、机载感知避让或运行时保障机制临时接管后性能退化的问题，研究提出三种中断感知架构，分别通过独立特征流、专用中断记忆和门控环境特征将中断上下文注入循环处理。最强架构相对循环基线将接管后碰撞减少 74%、中断导致的任务失败减少 69%，且不损害无中断场景性能。基准证据显示中断会降低循环隐藏状态中任务变量的可解码性并提高接管后碰撞风险。
正文 · 原文
Abstract:Safe autonomous navigation of UASs requires tactical deconfliction to address conflicts that strategic coordination cannot prevent. This task is challenging under partial observability, when sensor or traffic data are missing, delayed, or degraded. Geometry-based methods can offer separation assurances but face limitations in dense encounters. Recurrent reinforcement learning has therefore been proposed to learn navigation policies that integrate observation-action histories to optimise long-term safety-efficiency trade-offs. However, operational safeguards may require a remote pilot, onboard detect-and-avoid system, or runtime assurance mechanism to temporarily override the learned policy. A research gap remains in understanding how these interruptions affect recurrent policies when the agent regains authority. Benchmark evidence shows that interruptions make task variables less decodable from the recurrent hidden state and increase post-interruption collision risk. The findings suggest that altered recurrent context prolongs post-handback degradation through an action-observation feedback loop, in which changed actions alter the flight path and subsequent recurrent updates. To address this vulnerability, we propose three interruption-aware architectures that condition recurrent processing on interruption context through separate feature streams, dedicated interruption memory, or gated environmental features. Across simulations, all three architectures preserve more decodable task information than a standard recurrent policy. Moreover, the strongest architecture reduces post-interruption collisions by 74% and interruption-induced mission failures by 69% relative to the recurrent baseline, without degrading interruption-free performance. These results show that safe interruption does not guarantee safe handback, and support interruption-aware recurrent design for resilient UAS autonomy.
| Comments: | This work has been submitted to IEEE for possible publication |
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2610.28121 [cs.AI] |
| (or arXiv:2610.28121v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28121 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Christopher Conrad [view email]
[v1]
Tue, 20 Oct 2026 12:30:41 UTC (29,418 KB)
来源：arXiv cs.AI · arxiv.org