Robot learns Chinese calligraphy from Demonstrations

Abstract

Chinese calligraphy is a unique form of art in the world, whose aesthetic is mainly created by the proper manipulation of the brush. However, it is impossible for a person to figure out the 6-D motion of the brush from calligraphy images, if he has no experience of writing calligraphy. In this paper, we propose a Learning from Demonstration approach for our calligraphy robot, Callibot, to acquire calligraphy skills. We first propose a new stroke parametrization approach. Then we apply Locally Weighted Linear Regression to map from the stroke parameters to the trajectory of the brush. The training data are obtained from several demonstrations. Thereafter, Callibot is capable of writing a new stroke, if the stroke's parameters are given. The resulting motion is as natural as human writing. Experimental results prove the feasibility of our proposed approach. This approach is independent of the robot and is compatible with any robot with six or more degrees of freedom. This approach can be further integrated with our previous research, i.e. stroke extraction, so that Callibot will be able to replicate calligraphy from images.