CogExplore: Contextual Exploration with Language-Encoded Environment Representations
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910500220043264 |
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| author | Biggie, Harel Cooper, Patrick Albin, Doncey Such, Kristen Heckman, Christoffer |
| author_facet | Biggie, Harel Cooper, Patrick Albin, Doncey Such, Kristen Heckman, Christoffer |
| contents | Integrating language models into robotic exploration frameworks improves performance in unmapped environments by providing the ability to reason over semantic groundings, contextual cues, and temporal states. The proposed method employs large language models (GPT-3.5 and Claude Haiku) to reason over these cues and express that reasoning in terms of natural language, which can be used to inform future states. We are motivated by the context of search-and-rescue applications where efficient exploration is critical. We find that by leveraging natural language, semantics, and tracking temporal states, the proposed method greatly reduces exploration path distance and further exposes the need for environment-dependent heuristics. Moreover, the method is highly robust to a variety of environments and noisy vision detections, as shown with a 100% success rate in a series of comprehensive experiments across three different environments conducted in a custom simulation pipeline operating in Unreal Engine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CogExplore: Contextual Exploration with Language-Encoded Environment Representations Biggie, Harel Cooper, Patrick Albin, Doncey Such, Kristen Heckman, Christoffer Robotics I.2.9 Integrating language models into robotic exploration frameworks improves performance in unmapped environments by providing the ability to reason over semantic groundings, contextual cues, and temporal states. The proposed method employs large language models (GPT-3.5 and Claude Haiku) to reason over these cues and express that reasoning in terms of natural language, which can be used to inform future states. We are motivated by the context of search-and-rescue applications where efficient exploration is critical. We find that by leveraging natural language, semantics, and tracking temporal states, the proposed method greatly reduces exploration path distance and further exposes the need for environment-dependent heuristics. Moreover, the method is highly robust to a variety of environments and noisy vision detections, as shown with a 100% success rate in a series of comprehensive experiments across three different environments conducted in a custom simulation pipeline operating in Unreal Engine. |
| title | CogExplore: Contextual Exploration with Language-Encoded Environment Representations |
| topic | Robotics I.2.9 |
| url | https://arxiv.org/abs/2406.17180 |