Authors
Shinichi Imai*
Graduate School, Tokyo Gakugei University, 4-1-1, Nukuikita-machi, Koganei, Tokyo 184-8501, Japan
*Email: [email protected]; http://www.u-gakugei.ac.jp/
Corresponding Author
Shinichi Imai
Received 10 October 2019, Accepted 14 June 2020, Available Online 31 December 2020.
DOI
https://doi.org/10.2991/jrnal.k.201215.005How to use a DOI?
Keywords
Machine learning; control; experiment; evaluation
Abstract
Robots, such as industrial robots, have been used in the world of industry since the 1970s. There has been particularly rapid development in the field of robots in recent years, and there has been progress in robot research in industries such as communications and automobiles. For this reason, in the near future, robots with a diverse range of applications will be required around us. In this paper, as part of foundational research on robots and artificial intelligence, we propose a method for learning ball trajectories, using machine learning, to estimate target values for the distance in which robots move. In the proposed method, we use a linear regression model for supervised learning, and validate its effectiveness through experimentation.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).