Kim Sangwook, Yu Zhibin, Lee Minho
School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.
College of Information Science and Engineering, Ocean University of China (OUC), 238 Songling Road Qingdao, China.
Neural Netw. 2017 Aug;92:29-38. doi: 10.1016/j.neunet.2017.01.009. Epub 2017 Feb 11.
To develop an advanced human-robot interaction system, it is important to first understand how human beings learn to perceive, think, and act in an ever-changing world. In this paper, we propose an intention understanding system that uses an Object Augmented-Supervised Multiple Timescale Recurrent Neural Network (OA-SMTRNN) and demonstrate the effects of perception-action connected learning in an artificial agent, which is inspired by psychological and neurological phenomena in humans. We believe that action and perception are not isolated processes in human mental development, and argue that these psychological and neurological interactions can be replicated in a human-machine scenario. The proposed OA-SMTRNN consists of perception and action modules and their connection, which are constructed of supervised multiple timescale recurrent neural networks and the deep auto-encoder, respectively, and connects their perception and action for understanding human intention. Our experimental results show the effects of perception-action connected learning, and demonstrate that robots can understand human intention with OA-SMTRNN through perception-action connected learning.
要开发一个先进的人机交互系统，首先了解人类如何在不断变化的世界中学习感知、思考和行动非常重要。在本文中，我们提出了一种意图理解系统，该系统使用对象增强监督多时间尺度递归神经网络（OA-SMTRNN），并在受人类心理和神经现象启发的人工智能体中展示了感知-行动关联学习的效果。我们认为，行动和感知在人类心理发展中不是孤立的过程，并认为这些心理和神经交互可以在人机场景中复制。所提出的OA-SMTRNN由感知和行动模块及其连接组成，它们分别由监督多时间尺度递归神经网络和深度自动编码器构建，并连接它们的感知和行动以理解人类意图。我们的实验结果显示了感知-行动关联学习的效果，并证明机器人可以通过感知-行动关联学习利用OA-SMTRNN理解人类意图。
