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About

Kosei Demura is a pioneering researcher in robotics education and autonomous systems, with a career spanning from foundational RoboCup competitions to cutting-edge industrial automation. His work is defined by two core themes: advancing robotics curricula and developing robust perception and navigation methods for mobile robots. Demura’s most influential contribution is his 2020 comparative study of robotics curricula (17 citations), which provides a critical framework for engineering universities worldwide to design effective robotics programs. In the technical domain, he developed a novel self-localization method combining Monte Carlo localization with omni-directional camera template matching (6 citations), enabling robots to navigate using field lines rather than colored landmarks—a significant leap for RoboCup Middle-Size League. His collision estimation technique (3 citations), inspired by maritime radar, demonstrates his cross-disciplinary approach. More recently, Demura has applied his expertise to practical challenges, co-developing an automatic shelf-opening system for convenience stores in collaboration with Kanazawa Institute of Technology (3 citations), and designing a robot hand with four primitive motions for efficient bento box assembly (2025). His work on waypoint navigation with mutual feedback (3 citations) further showcases his commitment to solving real-world localization errors. Through these contributions, Demura has shaped both how robots learn to perceive their environment and how future engineers learn to build them.