Generative Adversarial Nets in Robotic Chinese Calligraphy

Chao, Fei, Lv, Jitu, Zhou, Dajun, Yang, Longzhi, Lin, Chih-Min, Shang, Changjing and Zhou, Changle (2018) Generative Adversarial Nets in Robotic Chinese Calligraphy. In: ICRA 2018 - IEEE International Conference on Robotics and Automation (ICRA). ICRA . IEEE, pp. 1104-1110. ISBN 9781538630822

[img]
Preview
Text (Full text)
Chao et al - Generative Adversarial Nets in Robotic Chinese Calligraphy AAM.pdf - Accepted Version

Download (1MB) | Preview
Official URL: https://ieeexplore.ieee.org/document/8460787/

Abstract

Conventional approaches of robotic writing of Chinese character strokes often suffer from limited font generation methods, and thus the writing results often lack of diversity. This has seriously restricted the high quality writing ability of robots. This paper proposes a generative adversarial nets-based calligraphic robotic framework, which enables a robot to learn writing fundamental Chinese strokes with rich diversity and good originality. In particular, the framework considers the learning process of robotic writing as an adversarial procedure which is implemented by three interactive modules including a stroke generation module, a stroke discriminative module and a training module. Noting that the stroke generative module included in the conventional generative adversarial nets cannot solve the non-differentiable problem, the policy gradient commonly used in reinforcement learning is thus adapted in this work to train the generative module by regarding the outputs from the discriminative module as rewards. Experimental results demonstrate that the proposed framework allows a calligraphic robot to successfully write fundamental Chinese strokes with good quality in various styles. The experiment also suggests the proposed approach can achieve human-level stroke writing quality without the requirement of a performance evaluation system. This approach therefore significantly boosts the robotic autonomous creation ability.

Item Type: Book Section
Uncontrolled Keywords: Writing, Trajectory, Training, Gallium nitride, Manipulators, Probability distribution
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 20 Sep 2018 09:01
Last Modified: 11 Dec 2018 12:31
URI: http://nrl.northumbria.ac.uk/id/eprint/35824

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence