Papers
6
Total Citations
288
H-Index
4
About
Yunkuan Wang is a leading researcher in robotics and intelligent manufacturing, whose work spans industrial robot path planning, trajectory optimization, and human-robot interaction. His most influential contribution is the development of an improved RRT algorithm for path planning in complex environments, which has garnered 188 citations and addresses the critical challenge of autonomous robot programming in modern manufacturing. Wang further advanced industrial robotics with a time-optimal trajectory planning method for Delta robots using quintic Pythagorean-Hodograph curves, achieving smooth high-speed operation with 63 citations. His recent work on the MoFCNet framework for IMU-based human motion forecasting (19 citations) demonstrates a shift toward assistive exoskeletons, enabling accurate prediction of human intention for effective robotic support. Wang also introduced the novel task of Class Incremental Robotic Pick-and-Place, tackling few-shot object detection to allow robots to learn new categories without forgetting previous skills. His contributions to 3D instance segmentation through the Instance-Augmented Net and Instance-Guided Net further enhance robotic perception in cluttered scenes. With over 288 total citations and a growing portfolio of high-impact work, Wang is shaping the future of autonomous and assistive robotics.
Research Focus
Key Achievements
Top Papers
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- 5IAN: Instance-Augmented Net for 3D Instance Segmentation3 citations · 2023
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