arXiv:2606.06836v1 Announce Type: cross Abstract: Language-guided UAV agents must execute long-horizon semantic instructions while producing smooth, physically feasible continuous flight commands, yet existing Vision-Language Navigation (VLN) benchmarks typically use discrete or coarse actions and existing UAV Vision-Language-Action (VLA) tasks focus on short, atomic maneuvers. To address this gap in UAV task settings, we introduce \textbf{FLIGHT}, a \textbf{F}ine-grained \textbf{L}ong-horizon \textbf{I}nstruction-\textbf{G}uided benchmark for \textbf{H}ybrid UAV navigation and reasoning \text
The proliferation of advanced AI models and the increasing demand for autonomous systems capable of complex physical world interactions drives research into more sophisticated UAV navigation.
This development addresses a critical gap in combining long-horizon, fine-grained control with semantic instructions for UAVs, moving towards more intelligent and versatile autonomous aerial operations.
Existing benchmarks for Vision-Language Navigation (VLN) and UAV Vision-Language-Action (VLA) tasks are now extended by FLIGHT, enabling more sophisticated evaluation and development of truly autonomous UAV agents.
- · AI agents developers
- · 无人机制造商
- · Defense contractors
- · Logistics and delivery companies
- · Companies relying on manual UAV operation
- · Legacy VLN/VLA research without fine-grained control
More capable and reliable autonomous UAV systems will be developed for complex tasks.
This will accelerate the adoption of UAVs in various sectors, potentially displacing human operators in challenging environments.
The enhanced autonomy could lead to new doctrines in defense and logistics, prioritizing swarms of intelligent UAVs over traditional assets.
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Read at arXiv cs.AI