A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

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A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing. / Wu, Ruiqi; Chao, Fei; Zhou, Changle ; Huang, Yuxuan; Yang, Longzhi ; Lin, Chih-Min; Chang, Xiang; Shen, Qiang; Shang, Changjing.

In: IEEE Transactions on Cognitive and Developmental Systems, 21.07.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

Wu, R, Chao, F, Zhou, C, Huang, Y, Yang, L, Lin, C-M, Chang, X, Shen, Q & Shang, C 2021, 'A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing', IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2021.3098229

APA

Wu, R., Chao, F., Zhou, C., Huang, Y., Yang, L., Lin, C-M., Chang, X., Shen, Q., & Shang, C. (2021). A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2021.3098229

Vancouver

Wu R, Chao F, Zhou C, Huang Y, Yang L, Lin C-M et al. A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing. IEEE Transactions on Cognitive and Developmental Systems. 2021 Jul 21. https://doi.org/10.1109/TCDS.2021.3098229

Author

Wu, Ruiqi ; Chao, Fei ; Zhou, Changle ; Huang, Yuxuan ; Yang, Longzhi ; Lin, Chih-Min ; Chang, Xiang ; Shen, Qiang ; Shang, Changjing. / A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing. In: IEEE Transactions on Cognitive and Developmental Systems. 2021.

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@article{ecb2fd118a334ae893e8c5e7145b719e,
title = "A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing",
abstract = "The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This paper instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveal a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly.",
author = "Ruiqi Wu and Fei Chao and Changle Zhou and Yuxuan Huang and Longzhi Yang and Chih-Min Lin and Xiang Chang and Qiang Shen and Changjing Shang",
year = "2021",
month = jul,
day = "21",
doi = "10.1109/TCDS.2021.3098229",
language = "English",
journal = "IEEE Transactions on Cognitive and Developmental Systems",
issn = "2379-8920",
publisher = "IEEE Press",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

AU - Wu, Ruiqi

AU - Chao, Fei

AU - Zhou, Changle

AU - Huang, Yuxuan

AU - Yang, Longzhi

AU - Lin, Chih-Min

AU - Chang, Xiang

AU - Shen, Qiang

AU - Shang, Changjing

PY - 2021/7/21

Y1 - 2021/7/21

N2 - The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This paper instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveal a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly.

AB - The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This paper instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveal a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly.

U2 - 10.1109/TCDS.2021.3098229

DO - 10.1109/TCDS.2021.3098229

M3 - Article

JO - IEEE Transactions on Cognitive and Developmental Systems

JF - IEEE Transactions on Cognitive and Developmental Systems

SN - 2379-8920

ER -

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