Yongsheng Gao
Papers
14
Total Citations
151
H-Index
7
About
Yongsheng Gao is a leading researcher in robotics, specializing in robot skill learning, human-robot interaction, and adaptive control systems. His work focuses on enabling robots to learn complex manipulation tasks from human demonstrations and generalize these skills to novel situations. Gao’s most impactful contribution is the development of advanced frameworks for movement generalization, particularly through Dynamic Movement Primitives (DMPs). His 2014 paper on adaptive control of a gyroscopically stabilized pendulum (42 citations) introduced a decoupling strategy that has been foundational for single-wheel pendulum robots and other unstable systems. He has also made significant strides in human intention understanding and behavior generalization, with his 2019 paper on DMPs and multiple demonstrations (14 citations) advancing how robots interpret and replicate human motion. Gao’s work on geometric optimal control for imitation learning (2023) and his earlier research on reconfigurable robot kinematics (2008) demonstrate a sustained commitment to practical, real-world robotic applications. With over 140 total citations, his research continues to shape the fields of robot learning and adaptive control, offering students and researchers a rich foundation for exploring autonomous, human-like robotic behavior.
Research Focus
Key Achievements
Top Papers
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- 3Generation of closed-form inverse kinematics for reconfigurable robots14 citations · 2008
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- 9Multi-robot tele-operation system based on multi-agent structure4 citations · 2006
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