Task Learning Using Graphical Programming and Human Demonstrations

Abstract

The next generation of robots will have to learn new tasks or refine the existing ones through direct interaction with the environment or through a teaching/coaching process in programming by demonstration (PbD) and learning by instruction frameworks. In this paper, we propose to extend the classical PbD approach with a graphical language that makes robot coaching easier. The main idea is based on graphical programming where the user designs complex robot tasks by using a set of low-level action primitives. Different to other systems, our action primitives are made general and flexible so that the user can train them online and therefore easily design high level tasks