Learning novel objects using out-of-vocabulary word segmentation and object extraction for home assistant robots

Muhammad Attamimi*, Akira Mizutani, Tomoaki Nakamura, Komei Sugiura, Takayuki Nagai, Naoto Iwahashi, Hiroyuki Okada, Takashi Omori

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

This paper presents a method for learning novel objects from audio-visual input. Objects are learned using out-of-vocabulary word segmentation and object extraction. The latter half of this paper is devoted to evaluations. We propose the use of a task adopted from the RoboCup@Home league as a standard evaluation for real world applications. We have implemented proposed method on a real humanoid robot and evaluated it through a task called "Supermarket". The results reveal that our integrated system works well in the real application. In fact, our robot outperformed the maximum score obtained in RoboCup@Home 2009 competitions.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Robotics and Automation, ICRA 2010
Pages745-750
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Robotics and Automation, ICRA 2010 - Anchorage, AK, United States
Duration: 3 May 20107 May 2010

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2010 IEEE International Conference on Robotics and Automation, ICRA 2010
Country/TerritoryUnited States
CityAnchorage, AK
Period3/05/107/05/10

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