Hybrid LLM-based Intelligent Framework for Robot Task Scheduling

TL;DR

This paper presents a novel hybrid framework utilizing multiple Large Language Models to enhance robot task scheduling in construction, optimizing efficiency and adaptability in real-time scenarios.

Contribution

It introduces a dual-agent LLM system with a NLP interface for improved construction robot task scheduling and real-time site condition adaptation.

Findings

Demonstrated improved scheduling efficiency in a test scenario.

Validated the framework's ability to adapt to unexpected site conditions.

Showed the importance of LLMs in construction robot operations.

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.