Zhou Jincao, Su Xuezhong, Fu Weiping, Lv Yang, Liu Bo
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, China.
School of Engineering, Xi'an International University, Xi'an, China.
Sci Rep. 2024 Nov 6;14(1):26999. doi: 10.1038/s41598-024-77916-3.
The rapid advancement of artificial intelligence has significantly expanded the role of service robots in everyday life. This expansion necessitates the accurate recognition and prediction of human intentions to provide timely and appropriate services. However, existing methods often struggle to perform effectively in complex and unstructured environments. To address this challenge, we propose the Large language model and Knowledge graph based Intention Recognition Framework (LKIRF), which combines large language model (LLM) with knowledge graphs (KG) to enhance the intention recognition capabilities of service robots. Our approach constructs an offline KG from human motion and environmental data and builds an online reasoning graph through real-time interaction, utilizing LLM for interpretation. Experimental results indicate that compared to traditional methods, LKIRF not only improves prediction accuracy across various scenarios but also enhances the transparency and interpretability of the intention reasoning process.
人工智能的快速发展显著扩大了服务机器人在日常生活中的作用。这种扩展需要准确识别和预测人类意图，以便提供及时和适当的服务。然而，现有方法在复杂和非结构化环境中往往难以有效运行。为应对这一挑战，我们提出了基于大语言模型和知识图谱的意图识别框架（LKIRF），该框架将大语言模型（LLM）与知识图谱（KG）相结合，以增强服务机器人的意图识别能力。我们的方法从人类运动和环境数据构建离线知识图谱，并通过实时交互构建在线推理图，利用大语言模型进行解释。实验结果表明，与传统方法相比，LKIRF不仅提高了各种场景下的预测准确率，还增强了意图推理过程的透明度和可解释性。
