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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.