Yanting Ye
Papers
2
Total Citations
25
H-Index
2
About
Yanting Ye is a robotics researcher whose work bridges the gap between human-like cognition and bipedal locomotion. Her key research areas include bipedal walking control, humanoid robot motion planning, and cognitive architectures for autonomous decision-making. Ye’s most impactful contribution is her "Natural Walking Reference Generation Based on Double-Link LIPM Gait Planning Algorithm" (2017, 19 citations), which enhanced the classic linear inverted pendulum model (LIPM) to generate more natural and stable gait patterns for humanoid robots. This work provides a simplified yet effective framework for planning center-of-mass trajectories from given zero moment point trajectories, advancing practical bipedal locomotion. In her notable paper "Robots That Think Fast and Slow: An Example of Throwing the Ball Into the Basket" (2016, 6 citations), Ye explores integrating dual-process cognitive theory into robotics, enabling robots to exhibit human-like thinking behavior during dynamic tasks. This interdisciplinary approach—combining biomechanics, control theory, and cognitive science—demonstrates her commitment to creating robots that not only move naturally but also reason adaptively. Ye’s research continues to inspire students and researchers working at the intersection of locomotion and artificial intelligence.
Research Focus
Key Achievements
Top Papers
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