Pourya Shahverdi

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About

Pourya Shahverdi is a robotics researcher whose work focuses on bridging the gap between human motion and humanoid robot control, with particular emphasis on real-time imitation and dynamic balance. His most influential contribution, "A simple and fast geometric kinematic solution for imitation of human arms by a NAO humanoid robot" (2016, 24 citations), introduced an efficient method for mapping human arm movements to a NAO robot using a Microsoft Kinect sensor. By calculating exact shoulder, elbow, and wrist positions, Shahverdi’s approach enabled rapid, geometrically sound imitation without complex inverse kinematics—a practical breakthrough for intuitive human-robot interaction. Building on this, his work "Balance Strategy for Human Imitation by a NAO Humanoid Robot" (2017) tackled the critical challenge of stability during motion transfer. He developed an ankle-based balance strategy using an inverted pendulum model and support polygon computation, ensuring the robot could maintain equilibrium during both double and single support phases. While early in his career, Shahverdi’s research has already demonstrated clear impact in humanoid robotics, offering computationally lightweight solutions that make human-robot collaboration more accessible. His work is particularly valuable for students and researchers interested in affordable, sensor-driven approaches to robotic imitation and balance control.