AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning

TL;DR

This paper introduces AION, a dual-policy reinforcement learning framework enabling aerial robots to perform object-goal navigation in unknown environments without external localization, demonstrating superior exploration and safety.

Contribution

AION is the first end-to-end dual-policy RL approach for aerial ObjectNav that does not rely on external localization or global maps.

Findings

Achieves high exploration efficiency and navigation success in AI2-THOR and IsaacSim.

Outperforms existing methods in exploration and safety metrics.

Demonstrates real-time performance on high-fidelity drone models.

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 https://youtu.be/TgsUm6bb7zg.