On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system
On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system
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DOI：
10.1177/105971230401200202
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发表时间：
2004-01-01
影响因子：
1.6
通讯作者：
Tani, J
中科院分区：
文献类型：
--
作者：
Ito, M;Tani, J
这项研究提出了小型人形机器人和用户之间模仿交互的实验。基于参数偏差循环神经网络模型（RNNPB），在仿人机器人中实现了镜像系统的动态神经网络模型。实验表明，在机器人学习了 RNPB 中嵌入的多个循环运动模式后，它可以与在机器人面前演示相应运动模式的人的运动同步地重新生成每个模式。此外，当用户展示新颖的循环运动模式时，机器人会表现出不同的交互响应。对这些答复进行了分析和分类。我们提出，机器人和用户运动之间的连贯性和不连贯性的动态可以增强它们之间的密切互动，并且它们还可以解释共同注意力的基本心理机制。
This study presents experiments on the imitative interactions between a small humanoid robot and a user. A dynamic neural network model of a mirror system was implemented in a humanoid robot, based on the recurrent neural network model with parametric bias (RNNPB). The experiments showed that after the robot learns multiple cyclic movement patterns as embedded in the RNNPB, it can regenerate each pattern synchronously with the movements of a human who is demonstrating the corresponding movement pattern in front of the robot. Further, the robot exhibits diverse interactive responses when the user demonstrates novel cyclic movement patterns. Those responses were analyzed and categorized. We propose that the dynamics of coherence and incoherence between the robot's and the user's movements could enhance close interactions between them, and that they could also explain the essential psychological mechanism of joint attention.