Modular Fuzzy Neural Networks for Imitative Learning of A Partner Robot
Modular Fuzzy Neural Networks for Imitative Learning of A Partner Robot
复制标题
DOI：
10.1109/ijcnn.2006.246931
复制
发表时间：
2006-10
期刊：
影响因子：
--
通讯作者：
N. Kubota;Toshiyuki Shimizu
中科院分区：
文献类型：
--
作者：
N. Kubota;Toshiyuki Shimizu
模仿是行为学习和人类交流的有力工具。模仿学习基本上由模型观察和模型再现组成。本文采用脉冲神经网络和自组织映射进行模型观测，采用模块化模糊神经网络和稳态遗传算法进行模型再生。所提出的方法适用于一个合作伙伴机器人与人类互动。实验结果表明，该方法使机器人能够通过模仿学习行为，并能有效地与人类进行交互。
Imitation is a powerful tool for behavior learning and human communication. Basically, imitative learning is composed of model observation and model reproduction. This paper applies a spiking neural network and self-organizing map for model observation, and modular fuzzy neural networks and a steady-state genetic algorithm for model reproduction. The proposed method is applied for a partner robot interacting with a human. Experimental results show that the proposed method enables a robot to learn behaviors through imitation and can interact with a human efficiently.