Papers
8
Total Citations
75
H-Index
5
About
Mingshan Chi is a robotics researcher whose work spans assistive robotics, robot learning from demonstration, motion planning, and underwater perception systems. His most significant contributions center on developing intelligent robotic solutions for elderly and disabled individuals, particularly through wheelchair-mounted robotic arms (WMRA). Chi's pioneering research on Dynamic Movement Primitives combined with Dynamic Potential Fields—his most cited work with 35 citations—introduced a robust framework enabling service robots to generalize learned tasks and autonomously avoid obstacles in unstructured home environments. Complementing this, his series of studies on learning from demonstration, trajectory segmentation, and motion primitive extraction has progressively advanced how assistive robots acquire and reproduce complex daily-living skills with minimal user burden. Beyond assistive robotics, Chi has contributed to hydraulic quadruped robot joint control using double internal model controllers, demonstrating breadth across legged locomotion systems. More recently, he has extended his research to underwater robotics, developing a multi-scale fusion image enhancement method paired with an improved YOLOv5s architecture for ROV-based underwater target detection. His cumulative body of work, totaling over 75 citations, reflects a sustained commitment to bridging intelligent learning algorithms with real-world robotic applications that meaningfully improve human quality of life.
Research Focus
Key Achievements
Top Papers
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- 3Learning motion primitives from demonstration10 citations · 2017
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- 6WMRA skill learning through segmentation of demonstration2 citations · 2017
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