Yichen Wang
Papers
1
Total Citations
1
H-Index
1
About
Yichen Wang is a robotics researcher whose work focuses on the intersection of dynamic locomotion, model predictive control, and energy-efficient actuator design for legged systems. His most notable contribution is a kinodynamic model predictive control (MPC) framework that leverages unidirectional parallel springs (UPS) to significantly improve the energy efficiency of dynamic legged robots. By employing a hierarchical control structure that simplifies the dynamics while preserving key physical constraints, Wang’s approach enables robots to achieve agile, stable gaits with substantially lower power consumption—a critical step toward practical, long-duration autonomy in field robotics. His work bridges control theory and mechanical design, offering a principled method for exploiting passive elasticity in real-time planning. Though early in its citation trajectory, this research has already attracted attention from groups working on quadrupedal and bipedal platforms. Wang’s contributions are particularly relevant for students and engineers seeking to understand how model-based optimization can be harmonized with compliant hardware to unlock new levels of performance in legged locomotion.
Research Focus
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Top Papers
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