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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/2410.00388 |
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| _version_ | 1866916934077906944 |
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| author | Choi, Daniel Fung, Angus Wang, Haitong Tan, Aaron Hao |
| author_facet | Choi, Daniel Fung, Angus Wang, Haitong Tan, Aaron Hao |
| contents | The Multi-Object Search (MOS) problem involves navigating to a sequence of locations to maximize the likelihood of finding target objects while minimizing travel costs. In this paper, we introduce a novel approach to the MOS problem, called Finder, which leverages vision language models (VLMs) to locate multiple objects across diverse environments. Specifically, our approach introduces multi-channel score maps to track and reason about multiple objects simultaneously during navigation, along with a score map technique that combines scene-level and object-level semantic correlations. Experiments in both simulated and real-world settings showed that Finder outperforms existing methods using deep reinforcement learning and VLMs. Ablation and scalability studies further validated our design choices and robustness with increasing numbers of target objects, respectively. Website: https://find-all-my-things.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00388 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Find Everything: A General Vision Language Model Approach to Multi-Object Search Choi, Daniel Fung, Angus Wang, Haitong Tan, Aaron Hao Robotics The Multi-Object Search (MOS) problem involves navigating to a sequence of locations to maximize the likelihood of finding target objects while minimizing travel costs. In this paper, we introduce a novel approach to the MOS problem, called Finder, which leverages vision language models (VLMs) to locate multiple objects across diverse environments. Specifically, our approach introduces multi-channel score maps to track and reason about multiple objects simultaneously during navigation, along with a score map technique that combines scene-level and object-level semantic correlations. Experiments in both simulated and real-world settings showed that Finder outperforms existing methods using deep reinforcement learning and VLMs. Ablation and scalability studies further validated our design choices and robustness with increasing numbers of target objects, respectively. Website: https://find-all-my-things.github.io/ |
| title | Find Everything: A General Vision Language Model Approach to Multi-Object Search |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.00388 |