Journal of Robotics, Networking and Artificial Life

Volume 7, Issue 4, March 2021, Pages 236 - 239

Control of a Ball Catch Robot Using Machine Learning

Authors
Shinichi Imai*
Graduate School, Tokyo Gakugei University, 4-1-1, Nukuikita-machi, Koganei, Tokyo 184-8501, Japan
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 Atlantis Press B.V.
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/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
7 - 4
Pages
236 - 239
Publication Date
2020/12
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.k.201215.005How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
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/).

Cite this article

TY  - JOUR
AU  - Shinichi Imai
PY  - 2020
DA  - 2020/12
TI  - Control of a Ball Catch Robot Using Machine Learning
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 236
EP  - 239
VL  - 7
IS  - 4
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.201215.005
DO  - https://doi.org/10.2991/jrnal.k.201215.005
ID  - Imai2020
ER  -