Wei-Zh Lai

Papers

Total Citations

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About

Wei-Zh Lai is a robotics researcher whose work bridges the critical gap between theoretical modeling and real-time control in humanoid and manipulator systems. His primary research areas include robot inverse dynamics, machine learning for control, and humanoid robot stabilization. Lai’s most impactful contribution is his pioneering approach to learning inverse dynamics without prior kinematic information, using a structured Reproducing Kernel Hilbert Space. This work, which has garnered 27 citations, offers a powerful alternative to restrictive rigid-body models, enabling robots to compensate for complex dynamics and friction through data-driven learning. In parallel, Lai has advanced the practical control of humanoid robots, developing a real-time stabilizer that synergizes position-based and torque-based controllers. By leveraging the ZMP (Zero Moment Point) distributor and the COG (Center of Gravity) Jacobian, his 2017 work provides a robust framework for dynamic balancing and feedback control. Though his citation counts reflect an emerging career, the conceptual depth of his inverse dynamics learning and the applied significance of his humanoid control strategies mark him as a thoughtful contributor to the field, particularly for researchers interested in the intersection of machine learning and real-time robotic autonomy.