Yongliang Wang

Shenyang Institute of Automation, University of Groningen

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About

Yongliang Wang is a robotics and human-robot interaction researcher whose work bridges intelligent control systems and human factors engineering. His research centers on two interconnected domains: assessing human cognitive states during robot-assisted operations and developing advanced motion planning algorithms for robotic manipulators. In his highly cited 2020 study (22 citations), Wang demonstrated the utility of heart rate variability (HRV) as a reliable physiological metric for measuring mental workload in human-dual-arm robot interaction scenarios — a critical contribution as robots increasingly operate in unstructured environments where operators must manage complex tasks through remote terminals rather than direct physical engagement. This work addresses a fundamental challenge in safe and effective human-robot collaboration: understanding and managing operator cognitive load. More recently, Wang's 2025 research introduced a reinforcement learning-based trajectory planning framework that enables robotic manipulators to navigate cluttered environments efficiently, bypassing the computational overhead traditionally associated with kinematic and dynamic equation solving. Together, these contributions reflect Wang's commitment to making robotic systems both smarter and more human-centered, advancing the field toward safer, more intuitive, and computationally efficient human-robot interaction paradigms.