Human Robot Pouring Skill Transfer in Material Synthesis Using Vision-Based DMPs

Abstract

Pouring from one beaker to another is crucial in the preparation of coatings within material synthesis. In this study, a collaborative robot is utilized to imitate the behavior of experimenters in order to accomplish pouring tasks across various scenarios. Given that these tasks involve complex position-attitude relationships and the presence of obstacles, traditional rigid programming methods are hardly employed. Instead, learning from demonstration is incorporated to transfer experimenters’ pouring skills, which encompasses three phases: teaching, learning, and reproduction. We propose a vision-based dynamic movement primitives approach to generalize the skill based on visual feedback. Utilizing real-time visual information feedback regarding liquid level and beaker size, the teaching trajectory, which involves coupled position and attitude relationships, is generalized to adapt dynamically to differing experimental requirements. In experiments, we utilized the Kinect V2 camera and the Kinova Jaco2 manipulator to assess the efficacy of the proposed method.