Papers
78
Total Citations
1,959
H-Index
25
About
Lorenzo Molinari Tosatti is a prominent robotics researcher whose work sits at the intersection of industrial automation, human-robot collaboration, and advanced control systems. With a career spanning over a decade of influential contributions, his research has fundamentally shaped how robots interact safely and intelligently with both humans and uncertain environments. His most celebrated work focuses on impedance and force-tracking control, developing sophisticated strategies that allow robotic manipulators to perform delicate interaction tasks — such as polishing leather or assembling fragile components — without dangerous force overshoots. His 2020 paper on model-based reinforcement learning for variable impedance control (192 citations) represents a landmark fusion of machine learning and classical control theory, enabling robots to adaptively collaborate with human partners. Complementing this, his iterative learning and reinforcement frameworks teach robots to master repetitive industrial tasks with remarkable precision. Molinari Tosatti has equally advanced the safety frameworks governing human-robot cooperation in industrial environments, addressing critical standards like EN ISO 10218. His work spans rehabilitation robotics — notably a spherical parallel ankle rehabilitation device — multi-robot welding cell design, and workspace-sharing algorithms. Collectively accumulating over 900 citations, his contributions have meaningfully accelerated the deployment of intelligent, safe collaborative robots across manufacturing and healthcare settings.
Research Focus
Key Achievements
Top Papers
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- 3Safe Human-Robot Cooperation in an Industrial Environment113 citations · 2013
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