Simulation control of a biped robot with Support Vector Regression

Abstract

This paper describes the control of an autonomous biped robot that uses the Support Vector Regression (SVR) method for its longitudinal balance. This SVR uses the Zero Moment Point (ZMP) position and its variation as input and the longitudinal correction of the robot's body is obtained as the output. The SVR was trained based on simulation data that was confirmed with the real robot. This method showed to be faster (with similar accuracy) than a recurrent network or a neuro-fuzzy control of the biped balance.