Matteo Maggi

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About

Matteo Maggi is a robotics researcher whose work focuses on advancing adaptive grasping technologies for robotic end-effectors, particularly through the innovative integration of underactuation and vacuum grasping. His primary research areas include robotic gripper design, shape adaptation, and grip stability for handling objects with complex geometries—such as spherical and cylindrical surfaces—that challenge traditional flat-surface suction cup systems. Maggi’s major contributions are exemplified in his most-cited paper, "Influence of the Dynamic Effects and Grasping Location on the Performance of an Adaptive Vacuum Gripper" (2022, 10 citations), which systematically analyzes how dynamic factors and grasping positions affect the efficacy of adaptive vacuum grippers. He further advanced the field with his 2024 paper, "Combining underactuation with vacuum grasping for improved robotic grippers" (7 citations), where he introduced the concept of the underactuated vacuum gripper (UVG)—a novel hybrid approach that merges underactuation’s shape-adaptability with vacuum suction’s grip stability. Maggi’s work, including his study on partial contact loss in vacuum grasping (2022, 2 citations), addresses critical gaps in robotic manipulation, offering theoretical frameworks and practical insights that enhance robotic dexterity and reliability in industrial and service applications.