Learning of labeling room space for mobile robots based on visual motor experience

Tatsuro Yamada, Saki Ito, Hiroaki Arie, Tetsuya Ogata*

*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

A model was developed to allow a mobile robot to label the areas of a typical domestic room, using raw sequential visual and motor data, no explicit information on location was provided, and no maps were constructed. The model comprised a deep autoencoder and a recurrent neural network. The model was demonstrated to (1) learn to correctly label areas of different shapes and sizes, (2) be capable of adapting to changes in room shape and rearrangement of items in the room, and (3) attribute different labels to the same area, when approached from different angles. Analysis of the internal representations of the model showed that a topological structure corresponding to the room structure was self-organized as the trajectory of the internal activations of the network.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
編集者Paul F. Verschure, Alessandra Lintas, Alessandro E. Villa, Stefano Rovetta
出版社Springer Verlag
ページ35-42
ページ数8
ISBN(印刷版)9783319685991
DOI
出版ステータスPublished - 2017
イベント26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy
継続期間: 2017 9 112017 9 14

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)
10613 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other26th International Conference on Artificial Neural Networks, ICANN 2017
国/地域Italy
CityAlghero
Period17/9/1117/9/14

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

「Learning of labeling room space for mobile robots based on visual motor experience」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