Papers
3
Total Citations
25
H-Index
3
About
Zeguo Yang’s research focuses on advancing robotic manipulation and learning, with a particular emphasis on whole-body control and imitation learning for mobile manipulators. His most cited work, “Design and analysis of a whole-body controller for a velocity controlled robot mobile manipulator” (2020, 19 citations), presents a novel control framework that enables coordinated motion between a robot’s base and arm, a critical step toward more versatile and agile robots in real-world environments. Yang has also made significant contributions to the field of robot learning from demonstration. His work on Dynamic Movement Primitives (DMPs) explores how robots can build a library of movements by observing human demonstrations, addressing the challenge of learning unknown trajectories. In his 2021 study, he introduces an imitation learning framework for a wheeled mobile manipulator, demonstrating how DMPs can be applied to transfer human skills to robots with complex kinematics. With a growing citation impact, Yang’s research is laying the groundwork for robots that can learn, adapt, and perform tasks autonomously—promising advances for manufacturing, service robotics, and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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