Object-Goal Navigation (ObjectNav) requires an agent to autonomously explore an unknown environment and navigate toward target objects specified by a semantic label. While prior work has primarily studied zero-shot ObjectNav under 2D locomotion, extending it to aerial platforms with 3D locomotion capability remains underexplored. Aerial robots offer superior maneuverability and search efficiency, but they also introduce new challenges in spatial perception, dynamic control, and safety assurance. In this paper, we propose AION for vision-based aerial ObjectNav without relying on external localization or global maps. AION is an end-to-end dual-policy reinforcement learning (RL) framework that decouples exploration and goal-reaching behaviors into two specialized policies. We evaluate AION on the AI2-THOR benchmark and further assess its real-time performance in IsaacSim using high-fidelity drone models. Experimental results show that AION achieves superior performance across comprehensive evaluation metrics in exploration, navigation efficiency, and safety. The video can be found at https://youtu.be/TgsUm6bb7zg.

翻译：目标导向导航（ObjectNav）要求智能体自主探索未知环境，并导航至由语义标签指定的目标物体。现有研究主要关注二维运动下的零样本目标导向导航，而将其扩展至具备三维运动能力的空中平台仍待深入探索。空中机器人虽具备卓越的机动性与搜索效率，但也带来了空间感知、动态控制与安全保障等方面的新挑战。本文提出AION，一种不依赖外部定位或全局地图、基于视觉的空中目标导向导航方法。AION是一种端到端的双策略强化学习框架，将探索行为与目标抵达行为解耦为两个专用策略。我们在AI2-THOR基准测试中评估AION，并进一步使用高保真无人机模型在IsaacSim中评估其实时性能。实验结果表明，AION在探索能力、导航效率与安全性等综合评估指标上均取得优越性能。演示视频可见：https://youtu.be/TgsUm6bb7zg。