Hybrid LLM-based Intelligent Framework for Robot Task Scheduling
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910223422193664 |
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| author | Saha, Swayamjit Das, Subhabrata Duan, Haonan Liu, Xiao-Yang |
| author_facet | Saha, Swayamjit Das, Subhabrata Duan, Haonan Liu, Xiao-Yang |
| contents | This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed with key data about the desired task, such as agent action abilities, and the desired end goal to be achieved. A well-balanced allocation strategy is developed, optimizing both time efficiency and resource utilization. Our system utilizes a Natural Language Processing interface to streamline communication with construction professionals and adapt in real-time to unexpected site conditions. We concurrently use two LLM agents, specifically generator (GPT-4) and supervisor (Gemma 3/Llama 4/Mistral 7b) LLM agents to provide a more precise task schedule. We evaluate the proposed methodology using a straightforward scenario and provide metric scores to prove the efficacy of the frameworks. Our results highlight that the implementation of LLMs is crucial in construction operational tasks including robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15486 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hybrid LLM-based Intelligent Framework for Robot Task Scheduling Saha, Swayamjit Das, Subhabrata Duan, Haonan Liu, Xiao-Yang Robotics Artificial Intelligence This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed with key data about the desired task, such as agent action abilities, and the desired end goal to be achieved. A well-balanced allocation strategy is developed, optimizing both time efficiency and resource utilization. Our system utilizes a Natural Language Processing interface to streamline communication with construction professionals and adapt in real-time to unexpected site conditions. We concurrently use two LLM agents, specifically generator (GPT-4) and supervisor (Gemma 3/Llama 4/Mistral 7b) LLM agents to provide a more precise task schedule. We evaluate the proposed methodology using a straightforward scenario and provide metric scores to prove the efficacy of the frameworks. Our results highlight that the implementation of LLMs is crucial in construction operational tasks including robots. |
| title | Hybrid LLM-based Intelligent Framework for Robot Task Scheduling |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2605.15486 |