Robot learning by Gaussian process regression

Abstract

Intelligent robots cannot be programmed in advance for all possible situations, but they should be able to generalize based on the acquired knowledge. In robot learning based on imitation of human activity we often use statistical methods that generalize observed (learned) movements. The acquired data is used to generate useful robots responses in situations for which the robot has not been specifically instructed how to respond. The paper describes the robot learning with Gaussian process regression that creates the model and estimates the parameters for generalization of the acquired motor knowledge, which is accumulated as a database of example movements. New actions are synthesized by applying Gaussian process regression, where the goal and other characteristics of an action are utilized as queries to create an optimal control policy with respect to the previously acquired knowledge. The paper demonstrates that the proposed methodology can be integrated with an active vision system of a humanoid robot. 3D vision data is used to provide query points for statistical generalization.