A Novel Data mining method for next-generation robots

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In this study, we challenge development of the novel modeling technique that is essential to a next-generation robot. We assume that our modeling technique has two functions. Function 1 detects the data which are short for modeling, and generates new data necessary for modeling by operating a robot. Function 2 is a function to choose only data necessary for modeling from a large amount of sampled data. When we controlled a 3-DOF manipulator using visual information, we examined the method based on the Kernel PCA method which can compress high-dimensional learning data using a non-linear mapping function to the low-dimensional data.