Identifying Reusable Primitives in Narrated Demonstrations

The assumption that we can preprogram robots with all the information necessary for their function becomes impractical as the range of robotics applications grows. One widely proposed solution is the specification of reusable task plans, or recipes, consisting of a sequence of primitive actions. However, the primitive actions, such as unscrewing a nut in a car maintenance task, cannot always be predefined and thus must be learned for a particular robot platform and workspace. In this work, we present a novel algorithm that enables a robot to identify a reusable motion trajectory associated with each primitive action of a plan. Our approach segments the motion data captured during the demonstration of a human performing the given task, while also leveraging the human's verbal cues. We evaluated our algorithms in a pilot study with 6 users executing 90 primitive actions.