Spatial Iterative Learning Control With Human Guidance and Visual Detection for Path Learning and Tracking

A popular path learning method is to use off-line programming by demonstration (PbD) to plan a rough path, but it is subjected to uncertainties in the environment so needs to be updated during the task execution. For this purpose, a spatial iterative learning control (sILC) is developed to learn an accurate path through intuitive online correction based on human-robot interaction (HRI). To improve the efficiency and accuracy of the path learning, a visual assistance system is added to HRI, which helps the robot to find the initial path point and complement the correction of the learning error. This method mitigates the requirement on classic ILC that the time period should be consistent in the repetitive interaction task and utilizes the complementary advantages of vision and force sensing, thus addressing the limitations of the vision-based or HRI methods. The rigorous proof of learning convergence and the results of the simulation and experiments on a 7-degree-of-freedom (DoF) Sawyer robot platform illustrate the significance and advantages of the proposed method.
Note to Practitioners
—The problem of accurate path learning of robotic manipulators is addressed in this paper, which is found in ample applications such as welding and laser cutting. When the required path is irregular, it is difficult to define it based on offline programming and calibration. This paper presents a new human-robot interactive learning framework, in which the interaction force and machine vision are combined with sILC to achieve online detection and correction for learning and tracking an unknown path. This framework leads to an intuitive human-robot collaboration system where the human operator can fine tune the robot’s motion through direct physical interaction, and at the same time the robot improves its tracking performance automatically based on visual servoing.