Robot Model Learning with Gaussian Process Mixture Model

Abstract

The mechanical design of a modern robot system is becoming more and more complex as robots are often required to do physical interactions with an unforeseen changing environment. To deal with challenging contexts in the field of robotics, capability to learn a model of kinematics and dynamics is becoming mandatory for a robot. In this paper, we proposed a novel on-line learning algorithm based on a mixture of local sparse Gaussian process. By segmenting a data domain into smaller regions and handling each region with an independent local Gaussian process model, the algorithm achieved linear computational cost with comparable accuracy. The algorithm was validated in experiments with real robot data. In the experiments, the algorithm achieved comparable performance to existing on-line regression approaches.