Raffaele Soloperto

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About

Raffaele Soloperto’s research centers on human-robot interaction, motion representation, and gesture recognition, with a focus on enabling seamless cooperation between humans and machines. His major contributions lie in developing novel mathematical frameworks for representing and reproducing complex human motions. In his most-cited work, “A bidirectional invariant representation of motion for gesture recognition and reproduction” (2015, 14 citations), Soloperto introduced a coordinate-free, scale-invariant representation of 6D motion trajectories—covering both position and orientation—that allows robots to recognize and replicate human gestures with high fidelity, independent of viewpoint or scale. This work, extended in a 2017 paper (12 citations), addresses a critical challenge in robotics: making motion data robust to variations in execution while preserving essential kinematic features. Soloperto’s approach has implications for assistive robotics, teleoperation, and skill transfer, where accurate gesture interpretation is vital. His research bridges theoretical geometry and practical robotics, offering tools that enhance the fluidity and safety of human-robot collaboration. With a growing citation record, Soloperto is recognized for advancing invariant motion representations that underpin more intuitive and adaptive robotic systems.