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About

Gennaro Vessio is a researcher at the intersection of social robotics, human-computer interaction, and biometrics. His work explores how robots can engage people in cultural settings, such as museums, and how machine learning can analyze human movement for both security and health applications. Vessio’s most cited paper, “Pepper4Museum: Towards a Human-like Museum Guide” (8 citations), proposes using social robots to create more engaging visitor experiences. He has also made notable contributions to signature verification, developing neural network models that integrate kinematic and dynamic features—a challenging task that broadens the applicability of biometric systems. In a novel direction, Vessio applies robotic kinematics and dynamics to classify dysgraphia in children, a learning disorder affecting handwriting. His 2025 deep learning framework for this purpose (3 citations) demonstrates a creative fusion of robotics and educational technology. Additionally, his work on gesture recognition for social robots, including recognizing waving gestures, advances the goal of making human-robot interaction more natural and socially believable. With a growing citation record and a focus on translating robotic and AI techniques into real-world impact, Vessio is shaping how machines understand and assist humans.