Michael E. Napoli
Papers
7
Total Citations
52
H-Index
4
About
Michael E. Napoli is a robotics researcher whose work bridges autonomous navigation and medical robotics, with a particular focus on human-robot collaboration and tissue elasticity imaging. His most impactful contribution lies in robot-assisted medical ultrasound, where he developed hybrid force/velocity control systems integrated with strain elastography to improve breast cancer screening. This work, his most cited paper (21 citations), enables quantitative compressional elastography through extended Kalman filtering—a significant advance over qualitative methods. In autonomous robotics, Napoli pioneered contextual awareness systems that allow robots to interpret monologic natural language instructions, alongside selective adaptation techniques for state lattices that optimize motion planning in cluttered environments. His research on probabilistic mapping of tissue elasticity and learning models for predictive adaptation demonstrates a consistent theme: enabling robots to operate more intelligently in uncertain, real-world conditions. With papers spanning 2017-2022, Napoli’s work is particularly notable for its dual focus on both the cognitive (language understanding, adaptive planning) and physical (force control, elastography) aspects of robotic interaction, making him a versatile contributor to the field.
Research Focus
Key Achievements
Top Papers
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- 6Learning Models for Predictive Adaptation in State Lattices3 citations · 2017
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