用双策略强化学习实现无人机室内寻物，无需地图与定位。
AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning
- 分拆探索与寻物为两个专用策略，提升导航效率。
- 在AI2-THOR和IsaacSim中表现优于现有方法，安全高效。
- 适合研究无人机自主导航或强化学习应用的开发者。
目标导向导航（ObjectNav）要求智能体在未知环境中自主探索并导航至由语义标签指定的目标物体。尽管先前研究主要聚焦于二维移动平台上的零样本导航，但将该任务扩展到具备三维运动能力的空中平台仍鲜有探索。无人机具有更优的机动性和搜索效率，但也带来了空间感知、动态控制和安全性保障的新挑战。本文提出AION，一种基于视觉的端到端双策略强化学习框架，用于无需外部定位或全局地图的空中目标导航。AION将探索与目标抵达行为解耦为两个专用策略。我们在AI2-THOR基准上评估AION，并在IsaacSim中使用高保真无人机模型测试其实时性能。实验结果表明，AION在探索能力、导航效率和安全性等综合指标上均表现优异。项目代码已公开于https://github.com/Zichen-Yan/AION。
原文摘要 · Abstract (English)
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 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 project is available at https://github.com/Zichen-Yan/AION.
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