Adaptive Drawing Behavior by Visuomotor Learning Using Recurrent Neural Networks

Kazuma Sasaki, Tetsuya Ogata

    Research output: Contribution to journalArticlepeer-review


    Drawing is a medium that represents an idea as drawn lines, and drawing behavior requires complex cognitive abilities to process visual and motor information. One way to understand aspects of these abilities is constructing computational models that can replicate these abilities rather than explaining the phenomena by building plausible models by a top-down manner. In this study, we proposed a supervised learning model that can be trained using examples of visuomotor sequences from drawings made by human. Additionally, we demonstrated that the proposed model has functions of 1) associating motions to depict the given picture image and 2) adapting to drawing behavior to complete a given part of the drawing process. This dynamical model is implemented by recurrent neural networks that have images and motion as their input and output. Through experiments that involved learning human drawing sequences, the model was able to associate appropriate motions to achieve depiction targets while adapting to a given part of the drawing process. Furthermore, we demonstrate that including visual information in the model improved performance robustness against noisy lines in the input data.

    Original languageEnglish
    JournalIEEE Transactions on Cognitive and Developmental Systems
    Publication statusAccepted/In press - 2018 Aug 31


    • Adaptation
    • Adaptation models
    • Computational modeling
    • Drawing ability
    • Predictive models
    • Recurrent neural networks
    • Recurrent Neural Networks
    • Supervised learning
    • Training
    • Visualization
    • Visuomotor Learning.

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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