Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
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DOI：
10.1109/tsmcb.2007.907738
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发表时间：
2008-02
期刊：
影响因子：
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通讯作者：
J. Tani;Ryunosuke Nishimoto;Jun Namikawa;Masato Ito
中科院分区：
文献类型：
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作者：
J. Tani;Ryunosuke Nishimoto;Jun Namikawa;Masato Ito
本文研究了受人类顶叶皮层功能启发的具有动态神经网络模型的人类导师与机器人之间互动学习的特点。类人机器人具有具有层次结构的递归神经网络，可以学习操纵物体。在人类互动的帮助下，机器人在反复的自我试验中学习任务，人类互动提供物理指导，直到掌握任务并在神经网络中巩固学习。实验结果和分析表明：1)任务行为的共同发展塑造源于机器人与导师之间的互动；2)行为原语的表达和排序的动态结构在分层组织的网络中是自组织的；3)这种结构在产生熟练行为时既能提供泛化又能提供上下文依赖。
This paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural-network model, which is inspired by human parietal cortex functions. A humanoid robot, with a recurrent neural network that has a hierarchical structure, learns to manipulate objects. Robots learn tasks in repeated self-trials with the assistance of human interaction, which provides physical guidance until the tasks are mastered and learning is consolidated within the neural networks. Experimental results and the analyses showed the following: 1) codevelopmental shaping of task behaviors stems from interactions between the robot and a tutor; 2) dynamic structures for articulating and sequencing of behavior primitives are self-organized in the hierarchically organized network; and 3) such structures can afford both generalization and context dependency in generating skilled behaviors.