Papers
3
Total Citations
10
H-Index
2
About
Mingfeng Yao is a robotics researcher whose work focuses on enhancing the safety, precision, and autonomy of robotic manipulation systems, particularly in dynamic and delicate environments. His key research areas include haptic feedback for surgical robotics, real-time motion planning, and dynamic grasping for manipulators. Yao’s major contributions center on developing algorithms that enable robots to interact with soft tissues and moving objects more effectively. Notably, his 2025 paper on "Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light" addresses a critical gap in minimally invasive surgery by proposing a method to estimate haptic interaction forces without direct hardware sensors, a breakthrough for safer surgical procedures. His work on "Dynamic grasping of manipulator based on realtime smooth trajectory generation" (2023) introduces the SeqSPA framework, which allows robots to adapt to moving targets and obstacles in real time. With papers accumulating citations in the range of 2 to 4, Yao’s research is gaining recognition for its practical applications in surgical robotics and industrial automation, where smooth, efficient, and safe robot-object interaction is paramount.
Research Focus
Key Achievements
Top Papers
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