Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot

Wu, Ruiqi, Zhou, Changle, Chao, Fei, Yang, Longzhi, Lin, Chih-Min and Shang, Changjing (2020) Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot. Neurocomputing, 388. pp. 12-23. ISSN 0925-2312

Preview

Text BH7FFB1P_Ruiqi_Neurocomp_edited_final.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2MB) | Preview

Abstract

As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly.

Actions (login required)

View Item

Downloads