Spatio-Temporal Neural Data Mining Architecture in Learning Robots

Abstract

There has been little research into the use of hybrid neural data mining to improve robot performance or enhance their capability. This paper presents a novel neural data mining technique that analyses robot sensor data for imitation learning. Learning by imitation allows a robot to learn from observing either another robot or a human to gain skills, understand the behaviour of others and create solutions to problems. We demonstrate a hybrid approach of differential ratio data mining to perform analysis on spatiotemporal robot behavioural data. The technique offers classification performance gains for recognition of robot actions by highlighting points of covariance and hence interest

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Cite this paper

@inproceedings{Malone2000SpatioTemporalND, title={Spatio-Temporal Neural Data Mining Architecture in Learning Robots}, author={James Malone and Mark Elshaw and Ken McGarry and Chris Bowerman and Stefan Wermter}, year={2000} }