Development and Evaluation of a Human-Robot Collaborative Training System for Retail Stores Using Virtual Reality and Digital Twin Technologies
Year: 2025
Type: article
Abstract: This paper presents a virtual reality and digital twin-based training system designed to improve human-robot collaboration in retail store environments, particularly under disaster scenarios. This system enables dynamic role adaptation between humans and AI-controlled avatars or robots, facilitating diverse collaborative configurations. In a virtual retail environment replicating post-disaster conditions, human subjects—paired either with AI or another human participant—engage in collaborative object-retrieval tasks, distinguishing between safe and hazardous items. Experimental results indicate that human-human collaborations outperform human-AI collaborations in both task efficiency and safety. Participants exhibited improved movement efficiency and higher accuracy in retrieving safe items when paired with another human. These findings suggest that human-robot interaction training can benefit from human-human collaboration configurations for skill enhancement. This system also demonstrates potential for broader applications in simulating complex hazardous environments where real-world training is challenging.
Authors: Tetsunari Inamura, Hiroki Yamada, Kazumi Morinaga, Natsuki Yamanobe, Ryo Hanai, Yukiyasu Domae
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409593398