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To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible automation. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by the mobile base’s parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base’s location, environment information, end-effector’s path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could …