Tsutomu Fujinami

Japan Advanced Institute of Science and Technology

Papers

Total Citations

H-Index

About

Tsutomu Fujinami is a pioneering researcher at the intersection of human-robot interaction and machine learning, with a focus on how robots mediate human communication and learn autonomously. His most impactful work explores the use of Telenoid, a tele-operated humanoid robot, in real-world educational settings. In a landmark 2012 study (25 citations), Fujinami investigated how this minimalistic robot affects communication among children in a classroom, demonstrating that even a simplified robotic presence can significantly alter social dynamics and foster interaction—a key contribution to understanding embodied telepresence. This work bridges robotics, psychology, and education, offering insights into how robots can support learning environments. Earlier, Fujinami addressed a fundamental challenge in autonomous robotics: sensor selection. In his 2005 paper, he formulated the problem as a multi-armed bandit task, enabling mobile robots to dynamically choose the most relevant sensors while learning state-action functions—a computationally efficient approach that reduces complexity in reinforcement learning. Though less cited (2 citations), this work showcases his technical depth in adaptive systems. Fujinami’s research stands out for its human-centered focus, applying rigorous computational methods to real-world social contexts, and his studies remain influential for researchers designing robots that communicate, learn, and coexist with people.