Shouyi Wang

Rutgers, The State University of New Jersey, Delft University of Technology, The University of Texas at Arlington

Papers

Total Citations

156

H-Index

About

Shouyi Wang is a researcher whose work sits at the intersection of machine learning, robotics, and human-robot collaboration, with a particular focus on applying intelligent algorithms to complex control engineering challenges. His most influential contribution, "Machine Learning Algorithms in Bipedal Robot Control" (2012), has garnered 112 citations and stands as a comprehensive survey of how supervised, reinforcement, and unsupervised learning techniques are reshaping autonomous robotic systems. This work has become a key reference for researchers navigating the rapidly evolving landscape of intelligent robot control. Wang's foundational research on reinforcement learning for biped robots walking on uneven surfaces (2006) demonstrates his early commitment to developing more natural, energy-efficient locomotion strategies inspired by passive dynamic walking principles. More recently, his 2017 work on interactive multisensing frameworks for human-robot collaboration reflects a meaningful evolution in his research agenda — bridging Cyber-Physical Systems with personalized assistive training to promote safer, smarter manufacturing environments. Collectively, Wang's publications reveal a researcher who has consistently pushed the boundaries of autonomous control and adaptive learning, making significant contributions that span foundational robotics theory and practical human-centered applications.