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

Gao, Xingen, Zhou, Changle, Chao, Fei, Yang, Longzhi, Lin, Chih-Min and Shang, Changjing (2019) A Robotic Writing Framework–Learning Human Aesthetic Preferences via Human–Machine Interactions. IEEE Access, 7. pp. 144043-144053. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2019.2944912

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’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.

Item Type: Article
Uncontrolled Keywords: Human-machine interaction, human preference, neural networks, robotic calligraphy, robotic writing trajectory
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 18 Oct 2019 08:34
Last Modified: 18 Oct 2019 08:45
URI: http://nrl.northumbria.ac.uk/id/eprint/41145

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