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

Tatsuro Yamada, Saki Ito, Hiroaki Arie, Tetsuya Ogata

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

    1 Citation (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
    PublisherSpringer-Verlag
    Pages35-42
    Number of pages8
    ISBN (Print)9783319685991
    DOIs
    Publication statusPublished - 2017 Jan 1
    Event26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy
    Duration: 2017 Sep 112017 Sep 14

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10613 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

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

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    Keywords

    • Deep autoencoder
    • Indoor scene labeling
    • Mobile robots
    • Recurrent neural network
    • Symbol grounding

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Yamada, T., Ito, S., Arie, H., & Ogata, T. (2017). Learning of labeling room space for mobile robots based on visual motor experience. In Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings (pp. 35-42). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10613 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-68600-4_5