Papers
12
Total Citations
505
H-Index
10
About
Xiaomei Wang is a robotics researcher whose work sits at the intersection of soft robotics, continuum robot control, and medical robotics. Her research focuses on developing intelligent control systems for compliant robotic platforms, with particular emphasis on machine learning-based approaches to overcome the inherent modeling challenges posed by soft and continuum robots. Wang's most cited contribution, "Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression" (2019, 115 citations), pioneered adaptive learning frameworks that allow soft robots to achieve precise motion control despite their elastic, nonlinear properties. Her comprehensive survey on machine learning-based control of continuum robots (2021, 110 citations) has become a key reference for researchers navigating this rapidly evolving field. Beyond soft robotics, Wang has made significant contributions to medical robotics, developing MRI-guided platforms for cardiac catheter navigation and stereotactic neurosurgery, as well as focused ultrasound systems. Her multi-sensor fusion work, combining visual servoing with strain sensing for continuum robot control, reflects her commitment to robust, real-world deployable solutions. With over 490 citations across her portfolio, Wang's research is shaping the future of safe, intelligent robotic systems for both surgical and industrial applications.
Research Focus
Key Achievements
Top Papers
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- 2A Survey for Machine Learning-Based Control of Continuum Robots110 citations · 2021
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- 6A Robotic Platform to Navigate MRI-guided Focused Ultrasound System27 citations · 2021
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