Spatio-temporal neural data mining architecture in learning robots

摘要

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 behavior of others and create solutions to problems. We demonstrate a hybrid approach of differential ratio data mining to perform analysis on spatio-temporal robot behavioral data. The technique offers classification performance gains for recognition of robot actions by highlighting points of covariance and hence interest within the data.