Hybrid LLM-based Intelligent Framework for Robot Task Scheduling
A new study presents a framework that utilizes Large Language Models (LLMs) to enhance task scheduling for construction robots. The system aims to optimize time efficiency and resource utilization while adapting to real-time site conditions. The research demonstrates the effectiveness of using LLMs in improving operational tasks in construction robotics.
- ▪The framework uses LLMs to improve task scheduling for construction robots.
- ▪It incorporates a Natural Language Processing interface for better communication with construction professionals.
- ▪Two LLM agents are employed to generate a precise task schedule.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15486 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Robotics arXiv:2605.15486 (cs) [Submitted on 15 May 2026] Title:Hybrid LLM-based Intelligent Framework for Robot Task Scheduling Authors:Swayamjit Saha, Subhabrata Das, Haonan Duan, Xiao-Yang Liu View a PDF of the paper titled Hybrid LLM-based Intelligent Framework for Robot Task Scheduling, by Swayamjit Saha and 3 other authors View PDF HTML (experimental) Abstract: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.
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