| datasets: | |
| - ZichenYan/AION-dataset-files | |
| license: mit | |
| pipeline_tag: robotics | |
| tags: | |
| - reinforcement-learning | |
| - drones | |
| - aerial-navigation | |
| - object-goal-navigation | |
| # AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning | |
| AION is an end-to-end dual-policy reinforcement learning (RL) framework that decouples exploration and goal-reaching behaviors into two specialized policies for vision-based aerial ObjectNav without relying on external localization or global maps. | |
| ## Files | |
| | Checkpoint | Description | | |
| |------------|-------------| | |
| | `AION-g.dat` | Goal-reaching model | | |
| | `AION-e.dat` | Exploration model | | |
| ## Links | |
| - 📄 Paper: [arXiv](https://arxiv.org/abs/2601.15614) | |
| - 💻 Code: [GitHub](https://github.com/Zichen-Yan/AION) | |
| ## Citation | |
| ```bibtex | |
| @article{yan2026aion, | |
| title={AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning}, | |
| author={Yan, Zichen and Hou, Yuchen and Wang, Shenao and Gao, Yichao and Huang, Rui and Zhao, Lin}, | |
| journal={arXiv preprint arXiv:2601.15614}, | |
| year={2026} | |
| } | |
| ``` |