Hybrid LLM-based Intelligent Framework for Robot Task Scheduling
For construction robotics, this work proposes an LLM-based scheduling approach, but the evaluation is preliminary and lacks quantitative benchmarks.
The paper introduces a hybrid LLM-based framework for task scheduling of construction robots, using a generator (GPT-4) and supervisor (Gemma 3/Llama 4/Mistral 7b) to optimize time and resource allocation. Evaluation on a simple scenario shows improved scheduling, but no concrete numerical results are provided.
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.