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H. Ishigaki is a robotics researcher whose work bridges control theory, machine learning, and medical robotics. His primary research areas include underactuated robot control, lazy learning control methods, and the development of master-slave manipulation systems for surgical applications. In his most cited work (16 citations), Ishigaki proposed a novel parameter identification method for the Acrobot—a two-link underactuated robot—using the unscented Kalman filter (UKF), enabling effective swing-up and balancing control. He further advanced control methodologies by introducing a lazy learning approach based on support vector regression (13 citations), which offers an innovative memory-based modeling technique for position control. Ishigaki has also made notable contributions to medical robotics, developing master-slave manipulation systems with force-feedback functionality for endoscopic surgery (9 and 7 citations). These systems, featuring motor-driven forceps, enhance precision and tactile feedback during minimally invasive procedures. His work demonstrates a consistent focus on practical, real-world applications, from dynamic robot control to improving surgical outcomes. With a citation record that reflects the niche but impactful nature of his research, Ishigaki’s contributions continue to influence both robotic control theory and surgical robotics.