Robotic chinese calligraphy with human preference

Authors Organisations
Type Conference Proceeding (Non-Journal item)
Original languageEnglish
Title of host publicationProceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation
Subtitle of host publicationSmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PublisherIEEE Press
Pages360-366
Number of pages7
ISBN (Electronic)9781728140346
ISBN (Print)9781728140353
DOI
Publication statusPublished - 09 Apr 2019
Event2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 - Leicester, United Kingdom of Great Britain and Northern Ireland
Duration: 19 Aug 201923 Aug 2019

Publication series

NameProceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019

Conference

Conference2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
CountryUnited Kingdom of Great Britain and Northern Ireland
CityLeicester
Period19 Aug 201923 Aug 2019
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Abstract

Robotic Chinese calligraphy is an attempt to lead robots to learn mankind's culture and knowledge. The current research on robotic calligraphy ignores the usage of human preferences. This has restricted robots to produce writing results reflecting personalized styles. This paper proposes a robotic learning approach that introduces a inverse reinforcement learning algorithm with human preferences into a robotic writing system. Through selections of human users, the robot system learns to write Chinese character strokes according to the user's aesthetic preference. Thus, the paper first uses a generative network adopting from the Generative Adversarial Nets to produce a basic writing ability of Chinese strokes for a robot system. Then, the writing results of the robot are captured by the robot's visual device and then presented to the human users as images. Then, the human users give their preferences as the feedbacks of the images, the approach uses the marked images to train a reward predictive mechanism. In the end, the reward predictive mechanism aids the inverse reinforcement learning algorithm to enable the robot to automatically improve its writing ability of Chinese character strokes. Experimental results show that the proposed framework can successfully allow the robot to write Chinese characters strokes in accordance with the human user's preference. In addition, the robot demonstrates a fast learning speed with a small number of human selections. This gives a very promising solution to the robot's learning of more complex movements.

Keywords

  • Human-robot interaction, Inverse reinforcement learning, Robotic calligraphy