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About

Stefano Michieletto is an Italian robotics researcher whose work spans human-robot interaction, computer vision, prosthetics, and robot control systems. His most influential contribution, "A quantitative taxonomy of human hand grasps" (2019, 78 citations), introduced a rigorous data-driven framework for classifying hand movements — a breakthrough for robotics, prosthetics, and rehabilitation communities that had previously relied on qualitative approaches. This work connects directly to his broader interest in prosthetic control, demonstrated through his research on EMG and IMU-based multi-joint regression models and Gaussian mixture model techniques for joint angle estimation, advancing natural and intuitive control of robotic prostheses. Michieletto has made significant contributions to human-robot collaboration, including fast motion prediction systems using wearable interfaces and machine learning-driven visual servoing for industrial settings — both critical for safe, responsive Industry 4.0 environments. His expertise in 3D perception is evident through work on RGB-D people tracking and point cloud-based action recognition. Notably, he has also shaped robotics education, advocating for ROS as a teaching framework in computer engineering curricula. With over 300 cumulative citations across his top publications, Michieletto's research bridges foundational human movement science with practical, real-world robotic applications.