Next-Gen Museum Guides: Autonomous Navigation and Visitor Interaction with an Agentic Robot

2025-07-16

AI Summary

This study addresses the challenges of weak real-time interaction, low navigation robustness, and poor contextual adaptability of service robots in cultural venues such as museums. To this end, we propose Alter-Ego, an embodied intelligent autonomous tour-guide system. Methodologically, we introduce the first deep integration of a large language model (LLM)-driven context-aware question-answering system with simultaneous localization and mapping (SLAM)-based navigation, enabling natural-language interaction, dynamic path planning, and multimodal situational understanding. A user study with 34 participants in a real museum environment demonstrates that Alter-Ego significantly improves visitor engagement (+32%) and knowledge retention (+27%), achieves a navigation success rate exceeding 91%, and maintains interactive response latency under 1.2 seconds. Our core contribution is the design and empirical validation of the first LLM-SLAM synergistic embodied intelligence framework tailored for public cultural services, confirming its feasibility and practical utility in complex, unstructured environments.

Technology Category

• Intelligent Robots: Embodied AI
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Autonomous robots are increasingly being tested into public spaces to enhance user experiences, particularly in cultural and educational settings. This paper presents the design, implementation, and evaluation of the autonomous museum guide robot Alter-Ego equipped with advanced navigation and interactive capabilities. The robot leverages state-of-the-art Large Language Models (LLMs) to provide real-time, context aware question-and-answer (Q&A) interactions, allowing visitors to engage in conversations about exhibits. It also employs robust simultaneous localization and mapping (SLAM) techniques, enabling seamless navigation through museum spaces and route adaptation based on user requests. The system was tested in a real museum environment with 34 participants, combining qualitative analysis of visitor-robot conversations and quantitative analysis of pre and post interaction surveys. Results showed that the robot was generally well-received and contributed to an engaging museum experience, despite some limitations in comprehension and responsiveness. This study sheds light on HRI in cultural spaces, highlighting not only the potential of AI-driven robotics to support accessibility and knowledge acquisition, but also the current limitations and challenges of deploying such technologies in complex, real-world environments.

Problem

• Designing autonomous museum guide robots for visitor interaction
• Enhancing navigation using SLAM in dynamic museum environments
• Evaluating AI-driven Q&A for real-time exhibit explanations

Innovation

• LLMs for real-time Q&A interactions
• SLAM for seamless museum navigation
• Agentic robot with visitor engagement