Skill learning and inference framework

Abstract

We propose a skill learning and inference framework, which includes five processing modules as follows: 1) human demonstration process, 2) autonomous segmentation process, 3) process of learning dynamic movement primitives, 4) process of learning Bayesian networks, 5) process of constructing motivation graph and inferring skills. Based on the framework, the robot learns and infers situation-adequate and goal-oriented skills to cope with uncertainties and human perturbations. To validate the framework, we present the experimental results when using a robot arm that performs a daily-life task. © 2013 Springer-Verlag.