Hybrid LLM-based Intelligent Framework for Robot Task Scheduling
This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The system utilizes a Natural Language Processing interface for communication and adapts in real-time to unexpected site conditions. It concurrently uses two LLM agents: a generator (GPT-4) and a supervisor (Gemma 3/Llama 4/Mistral 7b) to provide precise task schedules. Evaluation results highlight the crucial role of LLMs in construction robotic tasks.
Article intelligence
Key points
- Proposes a hybrid LLM-based framework for construction robot task scheduling
- Uses dual-agent architecture: generator (GPT-4) and supervisor (Gemma 3/Llama 4/Mistral 7b)
- Enables human-robot interaction via NLP interface and real-time adaptation
- Experimental results demonstrate improved time and resource optimization
Why it matters
This matters because proposes a hybrid LLM-based framework for construction robot task scheduling.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.15486] Hybrid LLM-based Intelligent Framework for Robot Task Scheduling
[Submitted on 15 May 2026]
Title:Hybrid LLM-based Intelligent Framework for Robot Task Scheduling
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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. 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.
Comments: 9 pages, 5 figures
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.15486 [cs.RO]
(or arXiv:2605.15486v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.15486
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Swayamjit Saha [view email] [v1] Fri, 15 May 2026 00:04:38 UTC (1,913 KB)
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