NDLI: Active Exploration in Building Hierarchical Neural Networks for Robotics

Content Provider	IEEE Xplore Digital Library
Author	Meng, Q. Lee, M.H.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Wales Univ., Aberystwyth (Meng, Q.; Lee, M.H.)
Abstract	During early robot learning, several mappings need to be set up for sensorimotor coordinations and transformation of sensory information from one modality to another. Usually these mappings are nonlinear and traditional passive learning approaches can not deal with these problems well. In this paper, a hierarchical clustering technique is introduced to group large mapping error locations and these error clusters drive the system to actively explore details of these clusters. Higher level local growing radial basis function subnetworks are used to approximate the mapping residual errors from previous mapping levels. Plastic radial basis function networks construct the substrate of the learning system and a simplified node-decoupled extended Kalman filter algorithm is presented to train these radial basis function networks. Experimental results are given to compare the performance between active learning and passive learning
Starting Page	2095
Ending Page	2100
File Size	275175
Page Count	6
File Format	PDF
ISBN	0780393597
ISSN	10915281
DOI	10.1109/IMTC.2006.328464
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-04-24
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Robot sensing systems Robot kinematics Radial basis function networks Learning systems Humanoid robots Plastics Clustering algorithms Robotics and automation Neurons growing radial basis function networks Robot learning active exploration hierarchical neural networks
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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