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About

Matteo Terreran is a robotics and computer vision researcher whose work centers on human-robot collaboration (HRC), skeleton-based action recognition, and robotic perception in industrial environments. His research addresses one of Industry 4.0's central challenges: enabling robots and human workers to collaborate safely, intuitively, and efficiently on the factory floor. Terreran's most impactful contribution is his development of skeleton-based frameworks for recognizing human actions and gestures in collaborative settings, with his general framework paper accumulating 41 citations and a related conference companion garnering 15 more. These systems allow robots to interpret worker gestures and anticipate task sequences in real time — a critical capability for adaptive automation. Complementing this, his work on scalable, low-cost people tracking (14 citations) and dynamic task planning (9 citations) demonstrates a systems-level approach to HRC deployment. On the perception side, Terreran has made notable contributions to hand-eye calibration, proposing both unified iterative methods and multi-camera extensions that improve robot localization accuracy in occluded workcell environments. His portfolio also extends to aerospace inspection and autonomous manufacturing, reflecting a broad engineering versatility. Collectively, his publications paint the picture of a researcher steadily building the foundational perception and planning tools needed to make collaborative robotics a practical industrial reality.