Enhancing Receptionist Roles with AI: A Standalone Edge Device based Humanoid Robot Receptionist
Chathushka Ranasinghe, Vidura Munasinghe, Sasinindu Vikasitha, Chamod Abeywickrama, Bhanuka Silva, Peshala G. Jayasekara · 2025
With the recent advancement of Large Language Models (LLMs), environmental perception methods, and frameworks for simplifying robot functionality implementation, robotic receptionists present an exciting opportunity to minimize humanrelated errors. However, most existing humanoid robots are costly and overqualified for such tasks, as they are primarily designed for research applications. To address this, we introduce a standalone, offline, cost-effective smart mobile robot receptionist built on an edge device that efficiently performs a predefined set of tasks while utilizing minimal hardware and computational resources. Our robot receptionist can engage in smooth conversations, recognize and interact with both new and returning users, perform hand gestures, provide navigation assistance through verbal and physical guidance, and autonomously navigate while avoiding static and dynamic obstacles. Additionally, its real-time location tracking feature is accessible via a custom-developed mobile application. Experimental results demonstrate that it delivers a seamless receptionist experience with low response time, enhancing human-robot interaction and making it a practical and effective alternative to traditional receptionist roles.