When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918485489090560 |
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| author | Aghaei, Matin Zhang, Lingfeng Alomrani, Mohammad Ali Biparva, Mahdi Zhang, Yingxue |
| author_facet | Aghaei, Matin Zhang, Lingfeng Alomrani, Mohammad Ali Biparva, Mahdi Zhang, Yingxue |
| contents | Recent ObjectNav systems credit large language models (LLMs) for sizable zero-shot gains, yet it remains unclear how much comes from language versus geometry. We revisit this question by re-evaluating an instruction-guided pipeline, InstructNav, under a detector-controlled setting and introducing two training-free variants that only alter the action value map: a geometry-only Frontier Proximity Explorer (FPE) and a lightweight Semantic-Heuristic Frontier (SHF) that polls the LLM with simple frontier votes. Across HM3D and MP3D, FPE matches or exceeds the detector-controlled instruction follower while using no API calls and running faster; SHF attains comparable accuracy with a smaller, localized language prior. These results suggest that carefully engineered frontier geometry accounts for much of the reported progress, and that language is most reliable as a light heuristic rather than an end-to-end planner. Code available at: https://github.com/matinaghaei/instructnav-scrutinized |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20021 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation Aghaei, Matin Zhang, Lingfeng Alomrani, Mohammad Ali Biparva, Mahdi Zhang, Yingxue Robotics Artificial Intelligence Machine Learning Recent ObjectNav systems credit large language models (LLMs) for sizable zero-shot gains, yet it remains unclear how much comes from language versus geometry. We revisit this question by re-evaluating an instruction-guided pipeline, InstructNav, under a detector-controlled setting and introducing two training-free variants that only alter the action value map: a geometry-only Frontier Proximity Explorer (FPE) and a lightweight Semantic-Heuristic Frontier (SHF) that polls the LLM with simple frontier votes. Across HM3D and MP3D, FPE matches or exceeds the detector-controlled instruction follower while using no API calls and running faster; SHF attains comparable accuracy with a smaller, localized language prior. These results suggest that carefully engineered frontier geometry accounts for much of the reported progress, and that language is most reliable as a light heuristic rather than an end-to-end planner. Code available at: https://github.com/matinaghaei/instructnav-scrutinized |
| title | When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.20021 |