ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Human-Robot Interaction
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1627937
This article is part of the Research TopicSynergizing Large Language Models and Computational Intelligence for Advanced Robotic SystemsView all 4 articles
Integrating Large Language Models for Intuitive Robot Navigation
Provisionally accepted
- 1University of Bristol, Bristol, United Kingdom
- 2Sheffield Hallam University, Sheffield, United Kingdom
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Home assistance robots face challenges in natural language interaction, object detection, and navigation, mainly when operating in resource-constrained home environments, which limits their practical deployment. In this study, we propose an AI agent framework based on Large Language Models (LLMs), which includes EnvNet, RoutePlanner, and AIBrain, to explore solutions for these issues. Utilizing quantized LLMs allows the system to operate on resource-limited devices while maintaining robust interaction capabilities. Our proposed method shows promising results in improving natural language understanding and navigation accuracy in home environments, also providing a valuable exploration for deploying home assistance robots.
Keywords: Home assistance robots, Large language models, AI Agent, LoRA fine-tuning, quantization
Received: 13 May 2025; Accepted: 18 Jul 2025.
Copyright: © 2025 Xue, Elksnis and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ning Wang, Sheffield Hallam University, Sheffield, United Kingdom
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