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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.20241 |
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| _version_ | 1866915213787267072 |
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| author | Uno, Mitsuaki Tanaka, Kanji Iwata, Daiki Noda, Yudai Miyazaki, Shoya Terashima, Kouki |
| author_facet | Uno, Mitsuaki Tanaka, Kanji Iwata, Daiki Noda, Yudai Miyazaki, Shoya Terashima, Kouki |
| contents | Object Goal Navigation (OGN) is a fundamental task for robots and AI, with key applications such as mobile robot image databases (MRID). In particular, mapless OGN is essential in scenarios involving unknown or dynamic environments. This study aims to enhance recent modular mapless OGN systems by leveraging the commonsense reasoning capabilities of large language models (LLMs). Specifically, we address the challenge of determining the visiting order in frontier-based exploration by framing it as a frontier ranking problem. Our approach is grounded in recent findings that, while LLMs cannot determine the absolute value of a frontier, they excel at evaluating the relative value between multiple frontiers viewed within a single image using the view image as context. We dynamically manage the frontier list by adding and removing elements, using an LLM as a ranking model. The ranking results are represented as reciprocal rank vectors, which are ideal for multi-view, multi-query information fusion. We validate the effectiveness of our method through evaluations in Habitat-Sim. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LGR: LLM-Guided Ranking of Frontiers for Object Goal Navigation Uno, Mitsuaki Tanaka, Kanji Iwata, Daiki Noda, Yudai Miyazaki, Shoya Terashima, Kouki Robotics Artificial Intelligence Object Goal Navigation (OGN) is a fundamental task for robots and AI, with key applications such as mobile robot image databases (MRID). In particular, mapless OGN is essential in scenarios involving unknown or dynamic environments. This study aims to enhance recent modular mapless OGN systems by leveraging the commonsense reasoning capabilities of large language models (LLMs). Specifically, we address the challenge of determining the visiting order in frontier-based exploration by framing it as a frontier ranking problem. Our approach is grounded in recent findings that, while LLMs cannot determine the absolute value of a frontier, they excel at evaluating the relative value between multiple frontiers viewed within a single image using the view image as context. We dynamically manage the frontier list by adding and removing elements, using an LLM as a ranking model. The ranking results are represented as reciprocal rank vectors, which are ideal for multi-view, multi-query information fusion. We validate the effectiveness of our method through evaluations in Habitat-Sim. |
| title | LGR: LLM-Guided Ranking of Frontiers for Object Goal Navigation |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2503.20241 |