Constructing Task-Level Assembly Strategies in Robot Programming by Demonstration

Abstract

Programming by demonstration (PbD) is a technique for programming robots that holds much promise in making robots more accessible to ordinary, non-technical users. However, a well-known difficulty with the method is that a human will often demonstrate the task to be programmed inconsistently or even erroneously, leading to the inclusion of what is essentially noise in the demonstration. A number of techniques exist in the literature for filtering out this type of noise; however, most focus on very low level control command details. In this paper, we propose a new, complementary direction of research. We take a "task-level" view of the demonstration, and note that noise can exist at this level also. We propose a framework, based on a hybrid dynamic system modeling approach, to select the most optimal, task-level execution strategies that were demonstrated. We apply our framework to a real household task of inserting the compressible spindle of a paper towel holder into its supports. We conduct experiments to show that significant improvements in robot performance of the task can be achieved by a PbD regime that includes our method.