Stairway to Success: Zero-Shot Floor-Aware Object-Goal Navigation via LLM-Driven Coarse-to-Fine Exploration
Object-Goal Navigation (OGN) remains challenging in real-world, multi-floor environments and under open-vocabulary object descriptions. We observe that most episodes in widely used benchmarks such as HM3D and MP3D involve multi-floor buildings, with many requiring explicit floor transitions. However, existing methods are often limited to single-floor settings or predefined object categories. To address these limitations, we tackle two key challenges: (1) efficient cross-level planning and (2) zero-shot object-goal navigation (ZS-OGN), where agents must interpret novel object descriptions without prior exposure. We propose ASCENT, a framework that combines a Multi-Floor Spatial Abstraction module for hierarchical semantic mapping and a Coarse-to-Fine Frontier Reasoning module leveraging Large Language Models (LLMs) for context-aware exploration, without requiring additional training on new object semantics or locomotion data. Our method outperforms state-of-the-art ZS-OGN approaches on HM3D and MP3D benchmarks while enabling efficient multi-floor navigation. We further validate its practicality through real-world deployment on a quadruped robot, achieving successful object exploration across unseen floors.
View on arXiv@article{gong2025_2505.23019,
title={ Stairway to Success: Zero-Shot Floor-Aware Object-Goal Navigation via LLM-Driven Coarse-to-Fine Exploration },
author={ Zeying Gong and Rong Li and Tianshuai Hu and Ronghe Qiu and Lingdong Kong and Lingfeng Zhang and Yiyi Ding and Leying Zhang and Junwei Liang },
journal={arXiv preprint arXiv:2505.23019},
year={ 2025 }
}