Yun Meng
Papers
2
Total Citations
9
H-Index
2
About
Yun Meng is a robotics researcher whose work focuses on solving one of the most critical challenges in humanoid robotics: energy-efficient locomotion. Their primary research areas include bipedal robot gait planning, energy optimization, and dynamic motion control. Meng’s major contributions center on developing novel algorithms that dramatically reduce the power consumption of biped robots—a key barrier to their real-world deployment. In their influential 2021 paper, which has garnered 7 citations, Meng introduced a grid gradient approximation method that optimizes gait by integrating 3D body motion with an allowable zero moment point region (AZR), using a five-mass inverted pendulum model to minimize joint torque and angular velocity. Building on this foundation, their 2024 work presents a descriptive parameter optimization approach that systematically identifies the most energy-efficient gait patterns. While these citation counts reflect a growing body of work, the practical significance of Meng’s research is notable: their algorithms directly address the energy bottleneck that limits biped robots’ operational time and autonomy. For students and researchers in robotics, Meng’s work offers a rigorous framework for understanding how mathematical optimization can transform robot walking from a power-hungry process into an efficient, sustainable motion.
Research Focus
Key Achievements
Top Papers
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