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JRM Vol.31 No.4 p. 519
doi: 10.20965/jrm.2019.p0519
(2019)

Editorial:

Special Issue on Machine Learning for Robotics and Swarm Systems

Masahito Yamamoto, Takashi Kawakami, and Keitaro Naruse

Professor, Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan
Professor, Department of Information and Computer Science, Hokkaido University of Science
7-Jo 15-4-1 Maeda, Teine, Sapporo, Hokkaido 006-8585, Japan
Professor, Department of Computer Science and Engineering, University of Aizu
Aizu Wakamatsu, Fukushima 965-8580, Japan

Published:
August 20, 2019

In recent years, machine-learning applications have been rapidly expanding in the fields of robotics and swarm systems, including multi-agent systems. Swarm systems were developed in the field of robotics as a kind of distributed autonomous robotic systems, imbibing the concepts of the emergent methodology for extremely redundant systems. They typically consist of homogeneous autonomous robots, which resemble living animals that build swarms. Machine-learning techniques such as deep learning have played a remarkable role in controlling robotic behaviors in the real world or multi-agents in the simulation environment.

In this special issue, we highlight five interesting papers that cover topics ranging from the analysis of the relationship between the congestion among autonomous robots and the task performances, to the decision making process among multiple autonomous agents.

We thank the authors and reviewers of the papers and hope that this special issue encourages readers to explore recent topics and future studies in machine-learning applications for robotics and swarm systems.

Cite this article as:
Masahito Yamamoto, Takashi Kawakami, and Keitaro Naruse, “Special Issue on Machine Learning for Robotics and Swarm Systems,” J. Robot. Mechatron., Vol.31, No.4, p. 519, 2019.
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Last updated on Nov. 25, 2021