A passive robot controller aiding human coaching for kinematic behavior modifications. (February 2020)

• Record Type: Journal Article
• Authors: Papageorgiou, D.; Kastritsi, T.; Doulgeri, Z.
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 61(2020)
• Publication Date: 2020-02
• Subjects: Kinesthetic teaching -- Virtual fixtures -- Phri -- Learning by demonstration; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2019.101824
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: A controller for the kinesthetic modification of a kinematic behavior encoded by Dynamic Movement Primitives (DMP) is proposed. The controller enables the human teacher to haptically "inspect" the spatial properties of the learned behavior in SE(3). The user can significantly modify segments of the behavior. Experiments on teaching a variant of an emulated milling task with a KUKA LWR4+ manipulator are performed. Results are compared with the case of using a gravity compensated robot agnostic of the previously learned task. It is shown that the time duration of teaching and the user's cognitive load are reduced. Abstract: Precise programming of robots for industrial tasks is inflexible to variations and time-consuming. Teaching a kinematic behavior by demonstration and encoding it with dynamical systems that are robust with respect to perturbations, is proposed in order to address this issue. Given a kinematic behavior encoded by Dynamic Movement Primitives (DMP), this work proposes a passive control scheme for assisting kinesthetic modifications of the learned behavior in task variations. It employs the utilization of penetrable spherical Virtual Fixtures (VFs) around the DMP's virtual evolution that follows the teacher's motion. The controller enables the user to haptically 'inspect' the spatial properties of the learned behavior in SE (3) and significantly modify it at any required segment, while facilitating the following of already learned segments. A demonstration within the VFs could signify that the kinematic behavior is taught correctly and could lead to autonomous execution, with the DMP generating the newly learned reference commands. The proposed control scheme is theoretically proved to be passive and experimentally validated with a KUKA LWR4+ robot. Results are compared with the case of using a gravity compensated robot agnostic of the previously learned task. It is shown that the time duration of teaching and the user's cognitive load are reduced.