Neural network based model for visual-motor integration learning of robot's drawing behavior: Association of a drawing motion from a drawn image

• Published in: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 2736 - 2741
• Main Authors: Sasaki, Kazuma, Tjandra, Hadi, Noda, Kuniaki, Takahashi, Kuniyuki, Ogata, Tetsuya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2015
• Subjects: Context; Neurons; Recurrent neural networks; Robots; Shape; Training
• DOI: 10.1109/IROS.2015.7353752

In this study, we propose a neural network based model for learning a robot's drawing sequences in an unsupervised manner. We focus on the ability to learn visual-motor relationships, which can work as a reusable memory in association of drawing motion from a picture image. Assuming that a humanoid robot can draw a shape on a pen tablet, the proposed model learns drawing sequences, which comprises drawing motion and drawn picture image frames. To learn raw pixel data without any given specific features, we utilized a deep neural network for compressing large dimensional picture images and a continuous time recurrent neural network for integration of motion and picture images. To confirm the ability of the proposed model, we performed an experiment for learning 15 sequences comprising three types of shapes. The model successfully learns all the sequences and can associate a drawing motion from a not trained picture image and a trained picture with similar success. We also show that the proposed model self-organizes its behavior according to types shapes.