Papers

4

Total Citations

54

H-Index

3

About

Xiaowen Kong’s research lies at the intersection of surgical robotics, continuum mechanisms, and data-driven control, with a focus on enhancing autonomy and precision in minimally invasive procedures. A key contribution is the development of accurate instance segmentation for surgical instruments in robotic surgery, achieving robust performance across diverse datasets—a foundational step for intelligent scene understanding in the operating room. Kong’s work on continuum robots is particularly notable: by proposing a data-efficient stochastic control strategy with online model updates, they enable precise manipulation in constrained environments without requiring analytical models, addressing a critical challenge in flexible robotics. This approach has been extended to automatic instrument tracking using a continuum laparoscope, where data-driven control allows the scope to autonomously adjust its field of view, reducing surgeon distraction and improving workflow. With over 54 citations across top venues, Kong’s research is shaping the future of autonomous surgical assistance. Earlier work on a novel two-degree-of-freedom caudal-fin propulsion mechanism for bionic robotic fish demonstrates a broader interest in bio-inspired design and efficient actuation. Kong’s contributions are paving the way for safer, more intuitive robotic systems in both surgery and beyond.

Research Focus

Key Achievements

3
H-Index
4
Papers
54
Total Citations
14
Avg Citations/Paper
🏆 Most Cited Paper
Accurate instance segmentation of surgical instruments in robotic surgery: model refinement and cross-dataset evaluation
27 citations · 2021
📈 Most Prolific Year: 2021 (1 Papers)
🤝 Key Collaborators: 13
🏛 Institutions: University of Science and Technology of China, City University of Hong Kong

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago