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.

[1]  P J Webros BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[2]  Tetsuya Ogata,et al.  Acquisition of viewpoint representation in imitative learning from own sensory-motor experiences , 2015, 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob).

[3]  Stephen Grossberg,et al.  A neural model of cortico-cerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements , 2000, Neural Networks.

[4]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[5]  Tetsuya Ogata,et al.  Developmental Human-Robot Imitation Learning of Drawing with a Neuro Dynamical System , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[6]  A. Meltzoff,et al.  Imitation of Facial and Manual Gestures by Human Neonates , 1977, Science.

[7]  Minoru Asada,et al.  Cognitive developmental robotics as a new paradigm for the design of humanoid robots , 2001, Robotics Auton. Syst..

[8]  Katsushi Ikeuchi,et al.  Painting Robot with Multi-Fingered Hands and Stereo Vision , 2006, MFI.

[9]  Jun Tani,et al.  Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment , 2008, PLoS Comput. Biol..

[10]  Aude Billard,et al.  Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies , 2018, Auton. Robots.

[11]  Georges Henri Luquet,et al.  Le dessin enfantin , 1927 .