Identification of self-body based on dynamic predictability using neuro-dynamical system

Harumitsu Nobuta, Shun Nishide, Hiroshi G. Okuno, Tetsuya Ogata

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

Abstract

The goal of our work is to acquire an internal model through a robot's experience. The internal model has the ability for mutual conversion between motor commands and movement of the body (e.g. hand) in view. Unlike other works, which assume the robot's body to be extracted in its view, we assume that external moving objects are also included in its view. We introduce predictability as a measure to segregate such objects from the robot's body: the robot's body is predictable while moving objects are not. Prediction is conducted using a neuro-dynamical system called the multiple timescales recurrent neural network (MTRNN). The prediction results of the robot's body are compared with the actual motion to distinguish the robot's body from other objects. For evaluation, we conducted an experiment with the robot moving its hand while moving objects were in view. The results of the experiment showed that the prediction of the robot's hand is 3.86 times as accurate as that of others on average. These results show the effectiveness of using predictability as a measure to acquire an internal model in an environment that includes both a robot's body and other moving objects in view.

Original languageEnglish
Title of host publication2011 IEEE/SICE International Symposium on System Integration, SII 2011
Pages256-261
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE/SICE International Symposium on System Integration, SII 2011 - Kyoto
Duration: 2011 Dec 202011 Dec 22

Other

Other2011 IEEE/SICE International Symposium on System Integration, SII 2011
CityKyoto
Period11/12/2011/12/22

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Dynamical systems
Robots
End effectors
Recurrent neural networks
Experiments

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering

Cite this

Nobuta, H., Nishide, S., Okuno, H. G., & Ogata, T. (2011). Identification of self-body based on dynamic predictability using neuro-dynamical system. In 2011 IEEE/SICE International Symposium on System Integration, SII 2011 (pp. 256-261). [6147456] https://doi.org/10.1109/SII.2011.6147456

Identification of self-body based on dynamic predictability using neuro-dynamical system. / Nobuta, Harumitsu; Nishide, Shun; Okuno, Hiroshi G.; Ogata, Tetsuya.

2011 IEEE/SICE International Symposium on System Integration, SII 2011. 2011. p. 256-261 6147456.

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

Nobuta, H, Nishide, S, Okuno, HG & Ogata, T 2011, Identification of self-body based on dynamic predictability using neuro-dynamical system. in 2011 IEEE/SICE International Symposium on System Integration, SII 2011., 6147456, pp. 256-261, 2011 IEEE/SICE International Symposium on System Integration, SII 2011, Kyoto, 11/12/20. https://doi.org/10.1109/SII.2011.6147456
Nobuta H, Nishide S, Okuno HG, Ogata T. Identification of self-body based on dynamic predictability using neuro-dynamical system. In 2011 IEEE/SICE International Symposium on System Integration, SII 2011. 2011. p. 256-261. 6147456 https://doi.org/10.1109/SII.2011.6147456
Nobuta, Harumitsu ; Nishide, Shun ; Okuno, Hiroshi G. ; Ogata, Tetsuya. / Identification of self-body based on dynamic predictability using neuro-dynamical system. 2011 IEEE/SICE International Symposium on System Integration, SII 2011. 2011. pp. 256-261
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