Mattia Penzotti
Papers
3
Total Citations
8
H-Index
2
About
Mattia Penzotti is a researcher whose work lies at the intersection of human-robot interaction, manipulation, and teleoperation. His key research areas include deformable object manipulation, robot-to-human handover fluency, and bimanual teleoperation safety. Penzotti’s major contributions are threefold: first, he systematically analyzed human manipulation strategies when handling objects of varying deformability and task properties, revealing insights that bridge robotic and human dexterity; second, he developed methods to enhance object release fluency in robot-to-human handovers by integrating proprioceptive and exteroceptive information, improving the perceived quality of collaborative actions; and third, he revised the CollisionIK algorithm for self-collision avoidance in bimanual teleoperation, reducing delays while maintaining safety. His work has garnered citations across these studies, with his 2024 and 2025 papers each receiving 3 citations, and his 2022 paper earning 2 citations. Notably, his research addresses practical challenges in physical human-robot collaboration, such as safe handovers and collision-free teleoperation, making his findings directly applicable to assistive robotics and industrial automation. Penzotti’s contributions are particularly valuable for students and researchers interested in designing more intuitive and safe robotic systems for real-world interaction.
Research Focus
Key Achievements
Top Papers
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