Authors:
Wataru Torii
;
Shinpei Fujimoto
;
Masahiro Furukawa
;
Hideyuki Ando
and
Taro Maeda
Affiliation:
Graduate School of Osaka University, Japan
Keyword(s):
Robot manipulation, RNN, FFT butterfly, associative memory, “Tsumori”
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Methodologies and Technologies
;
Model Design and Evaluation
;
Operational Research
;
Pattern Recognition, Clustering and Classification
;
Simulation
Abstract:
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.