arXiv AI By Alam Noor, Luis Almeida, Kai Li, Jiyan Wu, Miguel Guti\'errez Gait\'an, Eduardo Tovar

Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision System

Read the original on arXiv AI →

arXiv:2608. 16142v1 Announce Type: cross Abstract: UAV on-board vision systems are widely used for different activities, including monitoring in no-fly zones.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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
arXiv AI
Aug 11

RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation

arXiv:2608. 09467v1 Announce Type: cross Abstract: Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments.

By Boxiong Wang, Hui Kang, Geng Sun, Jiahui Li, Chao Yu, Daxin Tian
arXiv AI
Jun 9

Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

arXiv:2510. 10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.

By Yang Li, Ruichen Zhang, Yinqiu Liu, Guangyuan Liu, Abbas Jamalipour, Xianbin Wang, Dong In Kim
Hugging Face Trending Papers
Aug 10

RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation

Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments. Although recent end-to-end UAV vision-language-action (UAV-VLA) policies reduce reliance on separately designed perception, planning, and control modules, their behavior-cloning objectives provide limited corrective supervision for interactive closed-loop execution.