In the field of robot control, there have been several studies on humanoid robots operating in remote areas. We propose a methodology to control a robot using input from an operator with fewer degrees of freedom than the robot. This method is based on the concept that time-continuous actions can be segmented because human intentions are discrete in the time domain. Additionally, machine learning is used to determine components with a high correlation to input data that are often complex or large in quantity. In this study, we implemented a new structure on a conventional neural network to manipulate a robot using a fast Fourier transform. The neural network was expected to acquire robustness for amplitude and phase variations. Thus, our model can reflect a fluctuating operator input to control a robot. We applied the proposed neural network to manipulate a robot and verified the validity and performance compared with traditional models.
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