文献类型：期刊文献

英文题名：Research on a Dual-Mode Human-Robot Interaction Method Based on a Large Language Model

作者：Guo, Jingjing[1,2];Han, Xi[1,2]

第一作者：Guo, Jingjing

通讯作者：Han, X[1];Han, X[2]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2026

卷号：8

期号：1

外文期刊名：IET CYBER-SYSTEMS AND ROBOTICS

收录：EI(收录号：20260219894938);Scopus(收录号：2-s2.0-105026953104);WOS:【ESCI(收录号:WOS:001656899700001)】；

语种：英文

外文关键词：artificial intelligence; guide robot; human-robot interaction; large language model; query routing-adaptive response mechanism

摘要：Current guide robot systems have two main issues: (1) they only support a single mode of interaction (proactive or reactive) and lack a coordination mechanism and (2) they rely heavily on predefined content, which hinders the realisation of a natural and flexible human-like interaction experience. To address these issues, this paper proposes a dual-mode human-robot interaction (HRI) method based on a large language model (LLM). This method includes the following: (1) proactive interaction module. This module uses the robot's own sensors to perceive environmental information in real time, enabling it to provide various human-like services, such as safety alerts, situational announcements, and personalised recommendations. (2) Reactive interaction module. This integrates a query router with retrieval-augmented generation (RAG) method to build an adaptive response mechanism, which aims to provide more accurate responses while optimising response efficiency. Validation in guided tour scenarios confirms the efficiency of the proposed method. Results demonstrate that the proposed method achieves a 92% F1-score (improving 8 percentage points [PPs] over pure LLM and 6 PPs over traditional RAG), has a 48.4% improvement in response latency compared to the standard retrieval-cosine method (the fastest baseline among static RAG approaches) and achieves higher Likert-scale ratings in naturalness (4.35), intelligence (4.05), dependability (4.48) and stimulation (4.45) than other evaluated methods. This study proposes a scalable technical pathway for advancing human-robot interaction systems towards more natural and anthropomorphic interaction paradigms.