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About

Yingshi Wang is a robotics researcher whose work focuses on the intersection of trajectory planning, motion control, and real-time prediction for dynamic, high-speed environments. His key research areas include humanoid robotics, kinematic optimization, and nonlinear state estimation. Wang’s most significant contributions lie in enabling robots to perform complex, reactive tasks, such as table tennis, where precise prediction and rapid motion are critical. His 2014 paper on online minimum-acceleration trajectory planning under kinematic constraints, which has garnered 16 citations, provides a foundational method for generating smooth, feasible robot motions in real time. Earlier, his 2009 work on ball flight trajectory prediction for humanoid table-tennis robots, supported by China’s national 863 program, addressed the challenge of compensating for visual feedback delays and limited motion ability—a problem central to interactive robotics. His 2010 paper further advanced this by introducing a nonlinear output feedback observer for trajectory prediction, tackling the nonlinear dynamics of a flying ball. With a total of 35 citations across these works, Wang’s research has practical implications for sports robotics, autonomous manipulation, and human-robot interaction.