A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions

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A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions. / Gao, Xingen; Zhou, Changle ; Chao, Fei; Yang, Longzhi; Lin, Chih-Min; Shang, Changjing.

In: IEEE Access, Vol. 7, 01.10.2019, p. 144043-144053.

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Gao, Xingen ; Zhou, Changle ; Chao, Fei ; Yang, Longzhi ; Lin, Chih-Min ; Shang, Changjing. / A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions. In: IEEE Access. 2019 ; Vol. 7. pp. 144043-144053.

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@article{d75d9961bc954711a05a21951cc47eb0,
title = "A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions",
abstract = "Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user{\textquoteright}s aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fr{\'e}chet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user",
keywords = "human-machine interaction, human preference, neural networks, robotic colligraphy, robotic writing trajectory",
author = "Xingen Gao and Changle Zhou and Fei Chao and Longzhi Yang and Chih-Min Lin and Changjing Shang",
year = "2019",
month = oct,
day = "1",
doi = "10.1109/ACCESS.2019.2944912",
language = "English",
volume = "7",
pages = "144043--144053",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE Press",

}

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TY - JOUR

T1 - A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions

AU - Gao, Xingen

AU - Zhou, Changle

AU - Chao, Fei

AU - Yang, Longzhi

AU - Lin, Chih-Min

AU - Shang, Changjing

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user’s aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fréchet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user

AB - Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user’s aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fréchet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user

KW - human-machine interaction

KW - human preference

KW - neural networks

KW - robotic colligraphy

KW - robotic writing trajectory

U2 - 10.1109/ACCESS.2019.2944912

DO - 10.1109/ACCESS.2019.2944912

M3 - Article

VL - 7

SP - 144043

EP - 144053

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -

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