Task Learning Using Graphical Programming and Human Demonstrations

This paper proposes to extend the classical PbD approach with a graphical language that makes robot coaching easier and is based on graphical programming where the user designs complex robot tasks by using a set of low-level action primitives.

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