Walking Pattern Analysis of Humanoid Robot Using Support Vector Regression

Abstract

This work presents walking pattern analysis of a humanoid robot using support vector regression. The humanoid robot is highly suitable to work in human environments but the dynamics involved are highly nonlinear and unstable. So we are establishing empirical relationships based on the walking pattern analysis as dynamic stability of motion. Zero moment point is usually used as a basic component for dynamically stable motion. Kernel method and support vector machines (SVM) have become very popular as methods for learning from examples. We apply SVM to analyze humanoid robot walking. The experimental results show that the SVM based on the kernel substitution provides a promising alternative to model robot movements but also to control actual humanoid robots.