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About

Stefano Mauro is a robotics researcher whose work spans human-robot collaboration, robot safety, dynamic modeling, and soft robotics. He is perhaps best known for his contributions to collaborative robotics, where his research on collision avoidance systems — including vision-based approaches using depth sensor fusion and real-time algorithms implemented on UR3 robots — has helped lay practical groundwork for safe human-robot coexistence in shared workspaces. His 2021 paper on effective layouts for human-robot collaborative assembly tasks, alongside his vision-based hand-over control architecture, reflects a sustained commitment to making industrial collaboration both functional and safe, collectively earning nearly 90 citations between them. Mauro has also made significant contributions to robot reliability and modeling, including a widely cited overview of harmonic drive gear failures in industrial robots and investigations into how temperature and mounting configurations affect dynamic parameter identification. His more recent multibody modeling work supports prognostics and health management in collaborative manipulators. Complementing this, his exploration of soft robotic arm design and artificial potential field navigation methods demonstrates impressive breadth. With a research career spanning over two decades — from pneumatic robot control in 2002 to cutting-edge collaborative systems today — Mauro has established himself as a versatile and enduring voice in applied robotics research.