A Hybrid Control Framework Teaching Robot to Write Chinese Characters: from Image to Handwriting

Teaching a robot to learn calligraphy writing has been an interesting and challenging topic for robot learning. Ideally, to achieve human-like behavior, a robot can imitate any character fonts out of image inputs only. In the past decades, many works have been done in different kinds of special calligraphy robots design and learning by demonstration. Recently, advanced learning algorithms such as reinforcement learning have also been used with a significant sacrifice in data collection and computational complexity. In this paper, we present a simple and compact hybrid learning approach, by combining offline learning in simulator and online motion planing. In our approach, we simplify the writing trajectory generation to an optimization problem considering the width of the stroke as a function of height (z-axis) only. Based on the skeleton from each extracted stroke, Dynamic programming and Gaussian process models are used to solve the widths at each sampling point and to convert into a smooth trajectory. In such manner, a robot can learn to write any Chinese character directly from an image input within half a minute.