Papers
2
Total Citations
18
H-Index
2
About
Wanyue Li is a rising robotics researcher whose work focuses on advancing legged locomotion, particularly for quadruped robots navigating complex and hazardous environments. Her key research areas include model predictive control (MPC) for dynamic locomotion, deep reinforcement learning (DRL) for terrain adaptation, and the integration of active spinal mechanisms to enhance robotic agility. Li’s most cited paper, “Dynamic Locomotion of a Quadruped Robot with Active Spine via Model Predictive Control” (2023, 16 citations), tackles the challenge of controlling robots with increased degrees of freedom and time-varying inertia introduced by an active spine. By developing efficient optimization strategies, she enables real-time locomotion control on embedded platforms, a critical step toward practical deployment. Her more recent work, “MARG: MAstering Risky Gap Terrains for Legged Robots With Elevation Mapping” (2025, 2 citations), addresses safety in blind locomotion controllers, using elevation mapping to help robots traverse risky gaps. Li’s contributions bridge theoretical control methods and applied robotics, offering scalable solutions for agile, safe movement in unstructured settings. Her research holds promise for search-and-rescue, exploration, and industrial inspection, marking her as a notable emerging voice in the field.
Research Focus
Key Achievements
Top Papers
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