Adaptive Drawing Behavior by Visuornotor Learning Using Recurrent Neural Networks

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 paper, 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.