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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","title":"HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models","url":"https://arxiv.org/abs/2607.13072","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.13072v1 Announce Type: cross \nAbstract: 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. 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