Papers
24
Total Citations
473
H-Index
11
About
Matteo Rubagotti is a robotics and control systems researcher whose work spans mobile robotics, human-robot interaction, and advanced control theory. His research career traces a clear trajectory from foundational work in sliding mode control and harmonic potential fields for autonomous mobile robots — including dynamic obstacle avoidance and time-optimal motion planning — toward increasingly sophisticated applications in collaborative and semi-autonomous robotics. Rubagotti has made particularly significant contributions to model predictive control (MPC) for robotic systems, pioneering its application to variable stiffness actuators, semi-autonomous teleoperation, and safety-critical physical human-robot interaction. His 2019 paper on semi-autonomous teleoperation with obstacle avoidance via MPC has accumulated 73 citations, reflecting its strong influence on shared-control robotics. His work on closed-loop control of variable stiffness actuated robots (51 citations) has similarly shaped understanding of compliant robotic systems. More recently, Rubagotti has embraced machine learning methods, proposing deep imitation learning and deep reinforcement learning frameworks that preserve the safety guarantees of traditional MPC while dramatically reducing computational costs. His development of an open-source wearable arm motion-tracking system further demonstrates a commitment to accessible, reproducible research. Collectively, his publications represent a cohesive and forward-looking research program at the intersection of control theory, human-robot collaboration, and intelligent autonomy.
Research Focus
Key Achievements
Top Papers
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- 4Time-optimal sliding-mode control of a mobile robot in a dynamic environment44 citations · 2011
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- 7Time-Optimal Control of Variable-Stiffness-Actuated Systems24 citations · 2017
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