Towards Cognitive Robots: Building Hierarchical Task Representations of Manipulations from Human Demonstration

This paper deals with building up a knowledge base of manipulation tasks by extracting relevant knowledge from demonstrations of manipulation problems. Hereby the focus of the paper is on modeling and representing manipulation tasks enabling the system to reason and reorganize the gathered knowledge in terms of reusability, scalability and explainability of learned skills and tasks. The goal is to compare the newly acquired skill or task with already existing task knowledge and decide whether to add a new task representation or to expand the existing representation with an alternative. Furthermore, a constraint for the representation is that at execution time the built knowledge base can be integrated and used in a symbolic planner.