HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models
This paper introduces HRO, a hierarchical room-to-object framework for zero-shot object-goal navigation powered by large language models (LLMs). Unlike existing flat reasoning methods, HRO mimics human-like hierarchical spatial cognition, enabling the agent to explore from room-level to object-level in a coarse-to-fine manner. Experiments on Gibson and HM3D datasets demonstrate superior success rate and generalization over prior LLM-based approaches.
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[Submitted on 12 Jul 2026]
Title:HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models
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Abstract:Zero-shot object-goal navigation aims to enable an intelligent agent to explore and navigate to objects of unknown categories in an unfamiliar environment without specific target training. In zero-shot navigation tasks, pre-trained large models are usually employed to leverage their prior knowledge for guiding the agent's navigation. However, existing zero-shot object-goal navigation methods based on large language models (LLMs) merely utilize LLMs as flat reasoning tools to directly associate objects or regions. They lack the hierarchical spatial cognition modeling of human-like room semantics to object localization, which leads to strong blindness in exploration, insufficient accuracy in semantic association, and failure to fully unleash the common-sense reasoning potential of LLMs. This paper proposes an LLM-driven hierarchical room-to-object (HRO) framework for zero-shot object-goal navigation, which guides the agent to explore and navigate to the target object in a coarse-to-fine manner. Experiments on Gibson and HM3D datasets verify that our HRO framework achieves superior success rate and generalization over existing LLM-based methods, underscoring LLMs' strong potential for zero-shot object-goal navigation.
Comments: Main paper (6 pages). Accepted for publication by IEEE International Conference on Systems, Man, and Cybernetics 2026 (IEEE SMC 2026)
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
Cite as: arXiv:2607.13072 [cs.RO]
(or arXiv:2607.13072v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.13072
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yinfeng Yu [view email] [v1] Sun, 12 Jul 2026 06:28:30 UTC (848 KB)
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