Pouya Samandi

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About

Pouya Samandi is a robotics researcher whose work bridges the gap between surgical precision and intelligent automation. His primary research areas include medical robotics, kinematic calibration, and the application of machine learning to robotic control systems. Samandi’s most notable contribution is his pioneering work on the “Sina” surgical robot, where he developed a hybrid kinematic calibration method that combines analytical and data-driven approaches. This innovation addresses a critical challenge in robotics: the difficulty of calibrating complex inverse kinematics when traditional analytical solutions fail and numerical methods prove unreliable. By integrating model-free machine learning techniques, his approach offers faster, more robust calibration—a breakthrough for surgical robots requiring high accuracy. His seminal paper on this method has garnered 13 citations, reflecting its growing influence in the field. Samandi’s work is particularly valuable for students and researchers interested in the intersection of robotics, AI, and medical devices, as it demonstrates how intelligent algorithms can overcome fundamental engineering limitations. His contributions are paving the way for more reliable and adaptable surgical robots, ultimately advancing the safety and efficacy of robot-assisted surgery.