Gai Li

About

Gai Li is a researcher in robotics and intelligent control systems, with a primary focus on enhancing the stability and autonomy of humanoid robots. Their most cited work, "Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter" (2016, 4 citations), addresses a critical challenge in the field: maintaining balance under time-varying external disturbances that can cause robots to tip over. Li introduced a novel trapezoidal fuzzy least squares support vector regression (TF-LSSVR)-based control system, which learns and compensates for external disturbances to improve zero-moment-point (ZMP) stability—a key metric for dynamic balance. This data-driven approach integrates fuzzy logic and machine learning to create more robust control frameworks, advancing the practical deployment of humanoid robots in real-world environments. While their citation count is modest, Li's work contributes to the foundational intersection of soft computing and robotics, offering a methodology that reduces reliance on precise physical models. Their research is particularly relevant for students and engineers working on adaptive control, bipedal locomotion, and disturbance rejection in autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

   4 citations · 2016

Key Collaborators

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