Papers
9
Total Citations
46
H-Index
4
About
Haiming Mou is a robotics researcher whose work centers on the design, modeling, and control of bipedal and mobile robots, with a particular focus on achieving dynamic, stable, and efficient locomotion. His major contributions lie in developing innovative mechanical designs and advanced control algorithms for underactuated bipedal systems, including robots with telescopic straight legs and symmetrical hips that require fewer actuators. Mou has pioneered the application of reinforcement learning methods—such as the Mean-Asynchronous Advantage Actor-Critic (M-A3C) and multi-agent approaches—for real-time gait planning and omnidirectional walking, as well as zeroing neural networks (ZNN) for gait optimization under inequality constraints. His work on the LZ-1 robot introduced flexible spoked mecanum wheels for multi-terrain mobility, and his cable-driven biped design achieves low inertia and high stiffness. With over 40 citations across his most-cited papers, Mou’s research has been published in leading robotics venues and demonstrates a clear trajectory from foundational platform design to sophisticated, learning-based control strategies. His achievements include the development of several original robotic platforms (L03, L04) and hierarchical optimization frameworks that combine model predictive control with whole-body planning.
Research Focus
Key Achievements
Top Papers
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- 2High Dynamic Bipedal Robot with Underactuated Telescopic Straight Legs7 citations · 2024
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