Visualization of Focal Cues for Visuomotor Coordination by Gradient-based Methods: A Recurrent Neural Network Shifts the Attention Depending on Task Requirements

Hiroshi Ito, Kenjiro Yamamoto, Hiroki Mori, Shuki Goto, Tetsuya Ogata

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

Abstract

For an autonomous robot to flexibly move in response to various tasks or environmental changes, an attention mechanism is required that is based on the robot's behavioral experience. In this paper, we visualize how attention is acquired inside a neural network learned using supervised learning and describe how to acquire a suitable representation for performing a task. Our experimental evaluation shows that the attention was automatically acquired for objects that are needed to perform tasks by learning the time-series of both vision and motor information rather than only vision information. By multimodal learning, the attention is robust against unlearned conditions which background changes or obstacles.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages188-194
Number of pages7
ISBN (Electronic)9781728166674
DOIs
Publication statusPublished - 2020 Jan
Event2020 IEEE/SICE International Symposium on System Integration, SII 2020 - Honolulu, United States
Duration: 2020 Jan 122020 Jan 15

Publication series

NameProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020

Conference

Conference2020 IEEE/SICE International Symposium on System Integration, SII 2020
CountryUnited States
CityHonolulu
Period20/1/1220/1/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation

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  • Cite this

    Ito, H., Yamamoto, K., Mori, H., Goto, S., & Ogata, T. (2020). Visualization of Focal Cues for Visuomotor Coordination by Gradient-based Methods: A Recurrent Neural Network Shifts the Attention Depending on Task Requirements. In Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020 (pp. 188-194). [9026205] (Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SII46433.2020.9026205