Identification of Prototypical Task Executions Based on Smoothness as Basis of Human-to-Robot Kinematic Skill Transfer
This work provides a method for analyzing and designing robotic applications by transferring human skills, potentially benefiting robot learning and automation researchers.
This paper explores human-to-robot skill transfer by identifying prototypical task executions through clustering human demonstrations. The method uses smoothness and kinematic features to represent skill and task performance, respectively, and successfully transfers these identified prototypes to a generic robot arm in simulation.
In this paper we investigate human-to-robot skill transfer based on the identification of prototypical task executions by clustering a set of examples performed by human demonstrators, where smoothness and kinematic features represent skill and task performance, respectively. We exemplify our skill transfer approach with data from an experimental task in which a tool touches a support surface with a target velocity. Prototypical task executions are identified and transferred to a generic robot arm in simulation. The results illustrate how task models based on skill and performance features can provide analysis and design criteria for robotic applications.