Enhancing intention prediction and interpretability in service robots with LLM and KG

Abstract 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.