UAV-ON：用于空中智能体的开放世界目标导航基准测试
UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents
AI总结：
介绍用于空中智能体开放世界目标导航的UAV-ON基准测试，含14个环境及1270个目标对象，通过实例级指令编码。实现多种基线方法评估，结果凸显空中导航和语义目标基础的复合挑战，推动复杂环境下无人机可扩展自主性研究。
摘要：
空中导航是具身智能中一项基础但未充分探索的能力，能让智能体在传统导航范式失效的大规模非结构化环境中运行。现有多数研究遵循视觉与语言导航（VLN）范式，依赖序列语言指令，限制了扩展性和自主性。为此引入UAV-ON基准测试，用于空中智能体在开放世界环境中的大规模目标导航。它包含14个高保真虚幻引擎环境，定义了1270个带注释的目标对象，通过实例级指令编码类别、物理足迹和视觉描述符。实现了多种基线方法进行评估，结果显示所有基线在该环境中都面临挑战。UAV-ON旨在推动复杂真实世界环境中由语义目标描述驱动的无人机可扩展自主性研究。
英文摘要：
Aerial navigation is a fundamental yet underexplored capability in embodied intelligence, enabling agents to operate in large-scale, unstructured environments where traditional navigation paradigms fall short. However, most existing research follows the Vision-and-Language Navigation (VLN) paradigm, which heavily depends on sequential linguistic instructions, limiting its scalability and autonomy. To address this gap, we introduce UAV-ON, a benchmark for large-scale Object Goal Navigation (ObjectNav) by aerial agents in open-world environments, where agents operate based on high-level semantic goals without relying on detailed instructional guidance as in VLN. UAV-ON comprises 14 high-fidelity Unreal Engine environments with diverse semantic regions and complex spatial layouts, covering urban, natural, and mixed-use settings. It defines 1270 annotated target objects, each characterized by an instance-level instruction that encodes category, physical footprint, and visual descriptors, allowing grounded reasoning. These instructions serve as semantic goals, introducing realistic ambiguity and complex reasoning challenges for aerial agents. To evaluate the benchmark, we implement several baseline methods, including Aerial ObjectNav Agent (AOA), a modular policy that integrates instruction semantics with egocentric observations for long-horizon, goal-directed exploration. Empirical results show that all baselines struggle in this setting, highlighting the compounded challenges of aerial navigation and semantic goal grounding. UAV-ON aims to advance research on scalable UAV autonomy driven by semantic goal descriptions in complex real-world environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
