Efficient neuroevolution for a quadruped robot

Shengbo Xu*, Hirotaka Moriguchi, Shinichi Honiden

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this research, we investigate whether CoSyNE and CMA-NeuroES algorithms can efficiently optimize neural policy of a quadruped robot. Both of these algorithms are proven to optimize connection weights efficiently on Pole Balancing benchmark. Due to their good results on that benchmark, they are expected to be efficient on other control problems like gait generation. In this research we experimentally show that CMA-NeuroES have higher scalability to optimize Artificial Neural Networks for generating gaits of quadruped robots in comparison with CoSyNE. The results can be helpful for researchers and practitioners to choose the optimal Neuroevolution algorithm for generating gaits.

本文言語English
ホスト出版物のタイトルSimulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings
ページ361-370
ページ数10
DOI
出版ステータスPublished - 2012 12月 26
外部発表はい
イベント9th International Conference on Simulated Evolution and Learning, SEAL 2012 - Hanoi, Viet Nam
継続期間: 2012 12月 162012 12月 19

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7673 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other9th International Conference on Simulated Evolution and Learning, SEAL 2012
国/地域Viet Nam
CityHanoi
Period12/12/1612/12/19

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

フィンガープリント

「Efficient neuroevolution for a quadruped robot」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル