Learning of a Rapid Prototyping Gait Library for a Quadruped Robot Using PD-ILC and Gaussian Processes

Abstract

This work presents a body velocity control strategy for quadruped robots. Such control typically requires accurate kinematic and dynamic model knowledge, which is very challenging because of the multidimensional input-output system and the ground contact. Based on the inverse kinematics, we propose a Proportional-Derivative controlled robot that uses Iterative Learning Control to learn discrete body velocities, which are then generalized using the Gaussian Process Regression model for each joint separately. This controller design enables onboard control and learning in real-time without any simulation. This study illustrates the effectiveness of the proposed methodology over a range of velocities while emphasizing the minimal computational effort associated with its application in a practical context.