Papers
9
Total Citations
183
H-Index
6
About
Shoujun Wang is a robotics researcher whose work spans two dynamic frontiers: deep learning-based robot dynamics modeling and magnetically actuated soft micro-robotics. His most influential contributions address the longstanding challenge of parameter uncertainty in robotic systems, applying advanced machine learning techniques to improve precision and control. His 2020 paper on deep learning-aided dynamic parameter identification of 6-DOF manipulators (84 citations) and his 2019 work on LSTM-based inverse dynamics modeling (48 citations) have established him as a notable voice in intelligent robot control, particularly within the context of smart cities and automated factories. Alongside this, Wang has pursued pioneering research in untethered soft micro-robots driven by magnetoelastic composite materials, exploring undulatory swimming locomotion and closed-loop visual servoing for biomedical and microfluidic applications. His more recent work on bioinspired quadrupedal millirobots demonstrates a continued push toward multimodal locomotion on complex terrains. Collectively accumulating over 180 citations, Wang's research bridges theoretical modeling and practical innovation, making his profile highly relevant to students and researchers working at the intersection of machine learning, control systems, and micro-scale robotics.
Research Focus
Key Achievements
Top Papers
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- 3Motion characteristics of untethered swimmer with magnetoelastic material15 citations · 2021
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- 6Swimming Characteristics of Soft robot with Magnetoelastic Material7 citations · 2019
- 7Research and Development of Ball-Picking Robot Technology6 citations · 2017
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