Papers
8
Total Citations
73
H-Index
5
About
Davide Bazzi is a robotics researcher whose work sits at the intersection of human-robot interaction, collaborative manufacturing, and novel robotic mechanisms. His primary research areas include variable admittance control for manual guidance, safe motion planning for industrial cobots, and the development of specialized robotic systems for challenging environments. Bazzi’s most significant contributions center on his “goal-driven variable admittance control” framework, which intelligently adapts a robot’s compliance based on the operator’s intended target, making physical human-robot collaboration more intuitive and precise—especially for complex rotational movements. His work on proactive path planning for collaborative robots has also advanced the safety and efficiency of human-robot teams in Industry 4.0 settings. With his most-cited paper accumulating 23 citations and several others gaining traction, Bazzi’s research is steadily influencing the field. Notably, he has extended his expertise to marine robotics, exploring an underactuated cable-driven parallel robot for automated launch and recovery operations at sea—a testament to his ability to apply control and design principles across domains. For students and researchers, Bazzi’s work offers a compelling model of how theoretical control advances can directly improve real-world human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 4Identification of Robot Forward Dynamics via Neural Network6 citations · 2020
- 5Goal-driven variable admittance control for robot manual guidance5 citations · 2020
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