Learning Pose Dynamical System for Contact Tasks under Human Interaction

Robots are expected to execute various operation tasks like a human by learning human working skills, especially for complex contact tasks. Increasing demands for human-robot interaction during task execution makes robot motion planning and control a considerable challenge, not only to reproduce demonstration motion and force in the contact space but also to resume working after interacting with a human without re-planning motion. In this article, we propose a novel framework based on a time-invariant dynamical system (DS), taking into account both human skills transfer and human-robot interaction. In the proposed framework, the human demonstration trajectory was modeled by the pose diffeomorphic DS to achieve online motion planning. Furthermore, the motion of the DS was modified by admittance control to satisfy different demands. We evaluated the method with a UR5e robot in the contact task of the composite woven layup. The experimental results show that our approach can effectively reproduce the trajectory and force learned from human demonstration, allow human-robot interaction safely during the task, and control the robot to return to work automatically after human interaction.