ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
Neural Networks
Volume 19, Issue 3, April 2006, Pages 323-337
The Brain Mechanisms of Imitation Learning
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Article
Purchase PDF (844 K)

 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
View Record in Scopus
 
doi:10.1016/j.neunet.2006.02.007    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier Ltd All rights reserved.

2006 Special issue

Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model

Masato Itoa, Corresponding Author Contact Information, E-mail The Corresponding Author, Kuniaki Nodaa, E-mail The Corresponding Author, Yukiko Hoshinoa, E-mail The Corresponding Author and Jun Tanib, 1, E-mail The Corresponding Author

aSony Intelligence Dynamics Laboratories, Inc., Takanawa Muse Building 4F, 3-14-13 Higashigotanda, Shinagawa-ku, Tokyo 141-0022, Japan bBrain Science Institute, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan

Available online 17 April 2006.

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Abstract

This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural network with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative position between the robot's hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned different block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mechanisms for those observed situated behaviors of the robot

Keywords: Learning of object handling behavior; Dynamical systems approach; Recurrent neural network

Article Outline

1. Introduction
2. Mechanism, model and algorithm
2.1. The basic mechanism
2.2. Model and algorithm
2.2.1. The architecture
2.2.2. Learning process
2.2.3. Generation of behaviors
3. The robot system configuration
4. Robot experiments
4.1. Dynamic generation of ball handling behaviors
4.1.1. Learning of behaviors from human direct teaching
4.1.2. Dynamic generation and switching of learned behaviors
4.1.3. Analysis of the memory structure of RNNPB
4.2. Interactive generation of block handling behaviors
4.2.1. Learning of behaviors from human direct teaching
4.2.2. Interactive generation of learned behaviors
4.2.3. Analysis of the interaction structure and the memory structure of RNNPB
5. Discussion
5.1. Dynamical systems explanation
5.2. Comparison with related works
5.3. Imitation learning with more natural setting
6. Summary
Acknowledgements
References












Neural Networks
Volume 19, Issue 3, April 2006, Pages 323-337
The Brain Mechanisms of Imitation Learning
 
Home
Browse
My Settings
Alerts
Help
Elsevier.com (Opens new window)
About ScienceDirect  |  Contact Us  |  Information for Advertisers  |  Terms & Conditions  |  Privacy Policy
Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.