Papers
17
Total Citations
271
H-Index
10
About
Yuquan Wang is a robotics researcher whose work spans human-robot collaboration, impact-aware control, and mobile manipulation systems. His research addresses some of the most pressing challenges in modern robotics: enabling robots to work safely and efficiently alongside humans while dramatically improving their physical interaction capabilities. Wang's most influential contribution, "Deep Learning-based Multimodal Control Interface for Human-Robot Collaboration" (2018, 60 citations), pioneered intuitive interfaces that allow industrial robots to dynamically adapt to human operators beyond rigid pre-programmed routines. Complementing this, his context-aware safety framework further strengthened the foundation for shared human-robot workspaces. On the motion planning side, his energy-efficient trajectory optimization work demonstrates a practical commitment to sustainable robotics. Perhaps Wang's most technically distinctive contributions lie in impact-aware robotics — a field where robots, rather than timidly approaching surfaces at near-zero velocity, can execute high-speed contacts intelligently. His series of papers on impact dynamics modeling, robust control design, and impact-aware model predictive control (collectively accumulating over 80 citations) have meaningfully advanced this underexplored domain. His dual-arm mobile manipulation research further showcases his breadth, developing elegant virtual kinematic chain frameworks for complex coordinated robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Impact-Friendly Robust Control Design with Task-Space Quadratic Optimization27 citations · 2019
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- 5A Context-Aware Safety System for Human-Robot Collaboration20 citations · 2018
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- 9Impact-Aware Task-Space Quadratic-Programming Control13 citations · 2020
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