Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model

The 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 tasks are mastered and learning is consolidated within neural networks. Experimental results and the analyses showed that 1) codevelopmental shaping of task behaviors stems from interactions between the robot and tutor, 2) dynamic structures for articulating and sequencing of behavior primitives are selforganized in the hierarchically organized network, and 3) such structures can afford both generalization and context-dependency in generating skilled behaviors.

• Type: Journal
• Year: 2008
• Where: TSMC
• Authors: Jun Tani, Ryunosuke Nishimoto, Jun Namikawa, Masato Ito
• Keywords: Dynamic Neural Network, Neural Networks, Robot, TSMC 2008