Online-learning and planning in high dimensions with finite element goal babbling

Abstract

Goal babbling (GB) has proved to be a powerful tool in online learning of inverse kinematic models of high-dimensional redundant robots that are acting in low dimensional sensor-spaces. To only look for inverse models is however not sufficient. An inverse model will only tell the robot what posture it should have in order to reach a goal, but not how to reach that posture. As many environments restrict what motions are possible this becomes a limitation. This paper introduces a new method, Finite Element Goal Babbling (FEGB), that presents a natural extension to GB. By partitioning the sensor-space into a disjoint set of finite elements where every element is seen as an independent GB problem, a planning module can be added by observing transitions between the different elements. The method is evaluated on a high dimensional planar arm, acting in an environment that restricts its movements. The goal is to learn to control the position of the end-effector so that it can reach any position in the environment. The results show that FEGB is able to learn such control rapidly while naturally dealing with stationary obstacles and workspace limits that would prohibit the applicability of GB.