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

Published June 19, 2026 · Category: Robotics

Overview

arXiv:2601.15614v2 Announce Type: replace 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 \url{https://youtu.be/TgsUm6bb7zg}, code and model checkpoints are available at \url{https://github.com/Zichen-Yan/AION}.

Source

Originally published at arxiv.org.

Related Articles

• pdSTL: Probabilistic Differentiable Signal Temporal Logic for Stochastic Systems
• Fail-RAG : A Retrieval Augmented Generation Informed Framework for Robot Failure Identification
• CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion