Experimental Verification of Learning Strategy Fusion for Varying Environments

Akihiko Yamaguchi, Masahiro Oshita, Jun Takamatsu, Tsukasa Ogasawara

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

Abstract

We investigate a real robot applicability of our method, general-purpose behavior-learning for high degree-of-freedom robots in varying environments. Our method is based on the learning strategy fusion proposed in [3], and extended theo-retically in [4]. This report discusses its applicability to real robot systems, and demonstrates some positive experimen-tal results.

Original languageEnglish
Title of host publicationHRI 2015 - Proceedings of the 2015 ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts
PublisherIEEE Computer Society
Pages171-172
Number of pages2
ISBN (Electronic)9781450333184
DOIs
Publication statusPublished - 2015 Mar 2
Externally publishedYes
Event10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015 - Portland, United States
Duration: 2015 Mar 22015 Mar 5

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
Volume02-05-March-2015
ISSN (Electronic)2167-2148

Other

Other10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015
CountryUnited States
CityPortland
Period15/3/215/3/5

Keywords

  • crawling
  • learning strategy
  • robot learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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