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

Victor Parque*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021
EditorsW. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1538-1543
Number of pages6
ISBN (Electronic)9781665424639
DOIs
Publication statusPublished - 2021 Jul
Event45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021 - Virtual, Online, Spain
Duration: 2021 Jul 122021 Jul 16

Publication series

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

Conference

Conference45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021
Country/TerritorySpain
CityVirtual, Online
Period21/7/1221/7/16

Keywords

  • Kernels
  • Motion planning
  • Neural networks
  • Robot manipulator

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

Fingerprint

Dive into the research topics of 'Learning motion planning functions using a linear transition in the C-space: Networks and kernels'. Together they form a unique fingerprint.

Cite this