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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.21037 |
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| _version_ | 1866914993741496320 |
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| author | Yuan, Shuaihang Unlu, Halil Utku Huang, Hao Wen, Congcong Tzes, Anthony Fang, Yi |
| author_facet | Yuan, Shuaihang Unlu, Halil Utku Huang, Hao Wen, Congcong Tzes, Anthony Fang, Yi |
| contents | In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach introduces a multi-expert decision framework to address the nonsensical or irrelevant reasoning often seen in foundation model-based systems. The method comprises two key components: Diversified Expert Frontier Analysis (DEFA) and Consensus Decision Making (CDM). DEFA utilizes three expert models: furniture arrangement, room type analysis, and visual scene reasoning, while CDM aggregates their outputs, prioritizing unanimous or majority consensus for more reliable decisions. Demonstrating state-of-the-art performance on the RoboTHOR and HM3D datasets, our method excels at navigating towards untrained objects or goals and outperforms various baselines, showcasing its adaptability to dynamic real-world conditions and superior generalization capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21037 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the Reliability of Foundation Model-Based Frontier Selection in Zero-Shot Object Goal Navigation Yuan, Shuaihang Unlu, Halil Utku Huang, Hao Wen, Congcong Tzes, Anthony Fang, Yi Robotics In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach introduces a multi-expert decision framework to address the nonsensical or irrelevant reasoning often seen in foundation model-based systems. The method comprises two key components: Diversified Expert Frontier Analysis (DEFA) and Consensus Decision Making (CDM). DEFA utilizes three expert models: furniture arrangement, room type analysis, and visual scene reasoning, while CDM aggregates their outputs, prioritizing unanimous or majority consensus for more reliable decisions. Demonstrating state-of-the-art performance on the RoboTHOR and HM3D datasets, our method excels at navigating towards untrained objects or goals and outperforms various baselines, showcasing its adaptability to dynamic real-world conditions and superior generalization capabilities. |
| title | Exploring the Reliability of Foundation Model-Based Frontier Selection in Zero-Shot Object Goal Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.21037 |