Evaluation of deviation detection and isolation in task execution

Robot Programming by Demonstration (PbD) allows the use of a general purpose robot without specific programming skills. The most approaches in this field use Machine Learning methods to learn a robot behavior or skill. Besides, there are a few One-Shot PbD-approaches, which reduce the programming effort to a single demonstration and therefore allow a non-expert the quick and intuitive programming of a robot. However, a common drawback of One-Shot approaches is their decreased robustness during the program execution compared to Multi-Shot approaches. This leads to our main motivation: Making One-Shot PbD programs more robust. An important aspect of robustness is the ability to detect and handle unexpected events, e.g. a dropped object or a storage place, which should be free, but is already occupied. We call such events deviations. In our previous work we proposed an approach that provides the detection and isolation of deviations by a monitoring concept using entity-based resources. The advantages of this approach include a unified detection and complete classification principle. In this paper we evaluate our concept in a user study and give several insights to learned lessons to improve usability and comparability of our approach.