Guangzeng Chen
Papers
11
Total Citations
102
H-Index
6
About
Guangzeng Chen is a versatile robotics researcher whose work spans compliant actuation, nonlinear dynamics modeling, and generative AI-driven robot manipulation. He is perhaps best known for his pioneering contributions to magnetorheological clutch (MRC) technology, where he has systematically tackled one of the field's most persistent challenges: accurately modeling rate-dependent hysteresis and creep phenomena. His landmark 2021 paper demonstrating that the Preisach model is a special case of a diagonal recurrent neural network — enabling feedforward torque control of MRCs — has garnered 25 citations and reshaped how researchers approach hysteresis compensation in compliant robotic actuators. Alongside related MRC modeling and optimal design work accumulating a further 45+ citations, Chen has established himself as a leading voice in safe human-robot interaction. Beyond actuation, his research portfolio reveals impressive breadth: from monocular-vision-based robotic EV charging to quadruped leg mechanism design. Most recently, Chen has embraced large-scale generative pre-training for robot manipulation, contributing to GR-2, a video-language-action model trained on 38 million video clips. This trajectory — from precision mechatronics to foundation models for robotics — positions Chen as a dynamic researcher bridging classical control theory and modern AI-driven autonomy.
Research Focus
Key Achievements
Top Papers
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- 7Optimal Design of a Five-Bar Leg Mechanism for a Quadruped Robot4 citations · 2020
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- 10Music Melody Extraction Algorithm for Flute Robot2 citations · 2020