Mitsuhiro Hayashibe
Institut national de recherche en sciences et technologies du numérique, Tohoku University, Jikei University School of Medicine, The University of Tokyo, Centre National de la Recherche Scientifique, Keio University, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Université de Montpellier, Tohoku Institute of Technology
Papers
69
Total Citations
1,119
H-Index
19
About
Mitsuhiro Hayashibe is a pioneering researcher whose work spans rehabilitation robotics, surgical systems, brain-machine interfaces, and deep reinforcement learning for bioinspired and soft robotics. Beginning his career with groundbreaking contributions to computer-assisted surgery — including laser-scan endoscope systems for intraoperative geometry acquisition (74 citations) and preoperative planning frameworks for surgical robotics — Hayashibe has consistently pushed the boundaries of human-robot interaction across medical and autonomous domains. His influential work on combining inertial sensors with Kinect for joint angle estimation in rehabilitation (116 citations) demonstrated his ability to bridge sensing technologies with real-world clinical applications. He has since expanded into neural interfaces, proposing synergetic brain-machine interfacing paradigms for multi-DOF robot control and advancing EEG-based motor imagery decoding using deep learning. More recently, Hayashibe has made significant strides in deep reinforcement learning, contributing foundational surveys on sim-to-real transfer for bioinspired robots (74 citations), hierarchical navigation frameworks (68 citations), and investigations into motor synergy emergence in learning agents. His body of work, accumulating hundreds of citations across multiple disciplines, reflects a rare interdisciplinary vision connecting neuroscience, surgical robotics, and intelligent autonomous systems — making him an essential reference point for researchers across all these fields.
Research Focus
Key Achievements
Top Papers
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- 5A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control49 citations · 2016
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