Jinmin Wang

South China University of Technology, University of California System

Papers

Total Citations

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About

Jinmin Wang is a researcher whose work spans soft robotics, intelligent automation, and warehouse logistics. His key research areas include the characterization of smart materials for actuators and the application of machine learning to robotic systems. Wang’s most notable contribution is his in-depth study of PVC gel actuators, where he developed methods for in-situ characterization of their dynamic electromechanical properties—a critical step toward creating more responsive and durable soft robotic components. This work has garnered 13 citations since 2024, highlighting its relevance to the growing field of flexible electronics and bio-inspired robotics. In parallel, Wang has applied deep learning and reinforcement learning to optimize automated picking systems in warehouse robots, addressing the pressing need for efficiency in e-commerce logistics. His research in this area has accumulated 11 citations across two related papers. Earlier in his career, Wang contributed to robotic path planning by using cubic spline curves to ensure smooth, stable motion in dynamic environments, a foundational technique that remains cited today. Through his blend of materials science and computational intelligence, Wang is advancing both the hardware and software that power next-generation autonomous systems.