Papers
24
Total Citations
795
H-Index
14
About
Cheng Fang is a robotics researcher whose work spans human-robot interaction, impedance control, skill learning, and biomechanical modeling. His most influential contribution, a Dynamic Movement Primitives (DMPs)-based framework for learning and generalizing variable impedance skills from human demonstration, has garnered over 210 citations and represents a significant advance in enabling robots to replicate nuanced human manipulation behaviors. Complementing this, Fang has made substantial contributions to teleimpedance control, developing reduced-complexity models of human arm endpoint and joint stiffness that allow remote robotic systems to more faithfully replicate human motor strategies — work accumulated across multiple papers totaling over 160 citations. A recurring theme in Fang's research is human ergonomics in collaborative settings. His selective muscle fatigue management framework (123 citations) introduced anticipatory robotic responses to prevent overexertion during co-manipulation tasks, reflecting a deep integration of biomechanical insight and control engineering. He has also addressed humanoid robot challenges, including self-collision avoidance and bipedal locomotion planning. More recently, his survey on human modeling in physical human-robot interaction signals a broadening scholarly perspective. Taken together, Fang's body of work meaningfully advances the design of robots that are safer, more adaptive, and genuinely responsive to human physical capabilities.
Research Focus
Key Achievements
Top Papers
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- 4Magnetically Controllable Liquid Metal Marbles69 citations · 2019
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- 6Online Model Based Estimation of Complete Joint Stiffness of Human Arm44 citations · 2017
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- 9Human Modeling in Physical Human-Robot Interaction: A Brief Survey26 citations · 2023
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