Papers
5
Total Citations
56
H-Index
5
About
Shouqian Sun is a pioneering researcher at the intersection of robotics, human-robot interaction, and affective computing. His work spans three transformative domains: adaptive control for assistive exoskeletons, intelligent trajectory planning for robotic arms, and emotion-aware retail service robots. Sun’s most influential contribution is his adaptive CPG-based impedance control framework for lower limb exoskeletons (2018, 22 citations), which broke from rigid predefined trajectories by enabling robots to dynamically adapt to a user’s unique impedance properties—a paradigm shift in assistive robotics. He also developed a novel hybrid algorithm for visualized trajectory planning of flexible redundant robotic arms (2016, 16 citations), advancing precision in complex manipulation tasks. More recently, Sun has pioneered empathy-driven retail robotics, creating the Consumer Shopping Emotion and Interest Database and a deep learning method that infers consumer shopping intentions from facial expressions—claiming performance “better than humans” (2022, 8 citations; 2021, 5 citations). His earlier work on music’s affective computing model using fuzzy logic (2006, 5 citations) laid foundational insights into emotional AI. Across his career, Sun has consistently pushed boundaries in making machines not only more capable but more emotionally intelligent, with applications from rehabilitation to retail.
Research Focus
Key Achievements
Top Papers
- 1Adaptive CPG-Based Impedance Control for Assistive Lower Limb Exoskeleton22 citations · 2018
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- 5Music's Affective Computing Model Based on Fuzzy Logic5 citations · 2006