Improvement of Developmental Drawing Imitation Using Recurrent Neural Network Through Incorporation of AVITEWRITE Model

Cognitive developmental robotics is one of the keys to creating intelligent robots based on human development. In our research, we focus on drawing development of a human infant for creating a developing drawing robot. As the basis of our development model, we adopt five stages development proposed by Luquet. Multiple Timescales Recurrent Neural Network (MTRNN) model, is utilized as the learning model. In the first stage of drawing development, "Scribbling", the robot generates random arm motions to draw meaningless objects to learn the relation between the arm motion and drawing result. The robot arm/pen dynamics is learned using MTRNN in this stage. In the second/third stages, the robot imitates drawing motions from a human to improve its drawing skills to draw shapes through incremental learning. In this paper, we introduce model retraining and motion trajectory modification into the learning model to improve the drawing performance. The method is inspired by the Adaptive VITEWRITE (AVITEWRITE) model proposed by Grossberg, which describes drawing skill improvement of children through perception/action cycle involving vision, attention, learning, and movement. Experiments were conducted using a humanoid robot Nao drawing on a pen tablet. The results of the experiment show the effectiveness of the method improving the drawing performance using the proposed method.