Abstract
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 this https URL.
Abstract (translated)
物体目标导航（ObjectNav）要求代理自主探索未知环境，并根据语义标签向特定的目标对象移动。尽管先前的工作主要研究了在二维运动情况下零样本的ObjectNav问题，但将其扩展到具有三维运动能力的空中平台仍然是一个未充分探讨的研究领域。空中机器人提供了更好的机动性和搜索效率，但也引入了空间感知、动态控制和安全保证等方面的新挑战。在这篇论文中，我们提出了AION，这是一种基于视觉的无人飞行器（UAV）物体导航框架，无需依赖外部定位或全局地图。AION是一个端到端的双策略强化学习（RL）框架，它将探索行为与目标达到行为分解为两个专门化的策略。我们在AI2-THOR基准上评估了AION，并进一步在IsaacSim中使用高保真无人机模型对其实时性能进行了测试。实验结果表明，AION在探索、导航效率和安全性等综合评价指标方面取得了卓越的性能。相关视频可以在此URL找到（原文中的链接未提供，您可以访问原始论文或提供的URL以获取视频）。
URL
https://arxiv.org/abs/2601.15614
