Intuitive Task-Level Programming by Demonstration Through Semantic Skill Recognition

Abstract

Intuitive robot programming for non-experts will be essential to increasing automation in small and medium-sized enterprises. Programming by demonstration (PbD) is a fast and intuitive approach, whereas programs created with task-level programming (TLP) are easy to understand and flexible in their execution. In this letter, we propose an approach which combines these complementary advantages of PbD and TLP. Users define complete task-level programs including all parameters through PbD alone. Therefore, we call this approach task-level programming by demonstration (TLPbD). TLPbD extends skill-based approaches by enabling experts to semantically annotate robot skills with their conditions and effects, which facilitates online skill recognition from pure demonstrations by a non-expert. In a user study with 21 participants, the approach is compared with an existing intuitive TLP approach. The results show that the new approach drastically reduces the programming time while at the same time being more intuitive, reducing mental load, and achieving the same or even better skill sequences.