Learning motion planning functions using a linear transition in the C-space: Networks and kernels

Victor Parque*

*この研究の対応する著者

研究成果: Conference contribution

抄録

Motion planning approaches aided by learning schemes have achieved relevant results in the community, particularly in terms of rendering new paths efficiently and adapting to new environments/situations through encoder-decoder frameworks and latent space configurations. This paper evaluates the feasibility of learning motion planning functions for robot manipulators using a linear transition of the configuration space. Our computational experiments involving a relevant set of learning architectures have shown the feasibility and the efficiency in finding motion planning functions that meet user-defined criteria. Our approach contributes to realizing the practical efficiency to tackle the learning-based motion planning problem. Due to the amenability to parallelization schemes, our approach is potential to tackle larger degrees of freedom.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021
編集者W. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1538-1543
ページ数6
ISBN(電子版)9781665424639
DOI
出版ステータスPublished - 2021 7
イベント45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021 - Virtual, Online, Spain
継続期間: 2021 7 122021 7 16

出版物シリーズ

名前Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021

Conference

Conference45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021
国/地域Spain
CityVirtual, Online
Period21/7/1221/7/16

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • ソフトウェア

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