Tool-body assimilation model using a neuro-dynamical system for acquiring representation of tool function and motion

Kuniyuki Takahshi, Tetsuya Ogata, Hadi Tjandra, Yuki Yamaguchi, Yuki Suga, Shigeki Sugano

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

    3 Citations (Scopus)

    Abstract

    In this paper, we propose a tool-body assimilation model that implements a multiple time-scales recurrent neural network (MTRNN). Our model allows a robot to acquire the representation of a tool function and the required motion without having any prior knowledge of the tool. It is composed of five modules: image feature extraction, body model, tool dynamics feature, tool recognition, and motion recognition. Self-organizing maps (SOM) are used for image feature extraction from raw images. The MTRNN is used for body model learning. Parametric bias (PB) nodes are used to learn tool dynamic features. The PB nodes are attached to the neurons of the MTRNN to modulate the body model. A hierarchical neural network (HNN) is implemented for tool and motion recognition. Experiments were conducted using OpenHRP3, a robotics simulator, with multiple tools. The results show that the tool-body assimilation model is capable of recognizing tools, including those having an unlearned shape, and acquires the required motions accordingly.

    Original languageEnglish
    Title of host publicationIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1255-1260
    Number of pages6
    ISBN (Print)9781479957361
    DOIs
    Publication statusPublished - 2014
    Event2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2014 - Besancon
    Duration: 2014 Jul 82014 Jul 11

    Other

    Other2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2014
    CityBesancon
    Period14/7/814/7/11

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    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Control and Systems Engineering
    • Computer Science Applications
    • Software

    Cite this

    Takahshi, K., Ogata, T., Tjandra, H., Yamaguchi, Y., Suga, Y., & Sugano, S. (2014). Tool-body assimilation model using a neuro-dynamical system for acquiring representation of tool function and motion. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM (pp. 1255-1260). [6878254] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2014.6878254