Inter-modality mapping in robot with recurrent neural network
Inter-modality mapping in robot with recurrent neural network
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DOI：
10.1016/j.patrec.2010.05.002
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发表时间：
2010-09
期刊：
影响因子：
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通讯作者：
T. Ogata;S. Nishide;H. Kozima;Kazunori Komatani;HIroshi G. Okuno
中科院分区：
文献类型：
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作者：
T. Ogata;S. Nishide;H. Kozima;Kazunori Komatani;HIroshi G. Okuno
为机器人系统开发了一种不同感觉模态之间的映射系统，使其能够产生表达物体运动产生的听觉信号和声音的运动。采用具有良好泛化能力的参数偏置递归神经网络模型作为学习模型。由于听觉信号和视觉信号之间的对应关系太多而难以记忆，因此概括的能力是必不可少的。该系统在“Keepon”机器人中实现，通过操纵箱体物体，显示机器人水平往复或旋转的摩擦声运动和跌落或倾覆的碰撞声运动。Keepon不仅从已知事件中表现出适当的行为，而且从未知事件中也表现出适当的行为，并根据观察到的动作产生各种声音。
A system for mapping between different sensory modalities was developed for a robot system to enable it to generate motions expressing auditory signals and sounds generated by object movement. A recurrent neural network model with parametric bias, which has good generalization ability, is used as a learning model. Since the correspondences between auditory signals and visual signals are too numerous to memorize, the ability to generalize is indispensable. This system was implemented in the “Keepon” robot, and the robot was shown horizontal reciprocating or rotating motions with the sound of friction and falling or overturning motion with the sound of collision by manipulating a box object. Keepon behaved appropriately not only from learned events but also from unknown events and generated various sounds in accordance with observed motions.