Identification of Inputs for Cartesian Coordinate System Manipulation Interface using Machine Learning

Abstract

A novel interface we have developed allows the user to operate the robot intuitively because the user operates it according to a Cartesian coordinate system. However, interferences between axes in this interface cause unintended input by the user, reducing operability. In this study, we examine whether machine learning models can identify intentional or unintentional input in this interface. Input values during interface operation are acquired, and a model is built for each axis to estimate the input state. Models for all axes achieved an F1 score of 0.97 or higher.