Extracting multi-modal dynamics of objects using RNNPB

Tetsuya Ogata, Hayato Ohba, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno

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

15 Citations (Scopus)

Abstract

Dynamic features play an important role in recognizing objects that have similar static features in colors and or shapes. This paper focuses on active sensing that exploits dynamic feature of an object. An extended version of the robot, Robovie-IIs, moves an object by its arm to obtain its dynamic features. Its issue is how to extract symbols from various kinds of temporal states of the object. We use the recurrent neural network with parametric bias (RNNPB) that generates selforganized nodes in the parametric bias space. The RNNPB with 42 neurons was trained with the data of sounds, trajectories, and tactile sensors generated while the robot was moving/hitting an object with its own arm. The clusters of 20 kinds of objects were successfully self-organized. The experiments with unknown (not trained) objects demonstrated that our method configured them in the PB space appropriately, which proves its generalization capability.

Original languageEnglish
Title of host publication2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Pages160-165
Number of pages6
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventIEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005 - Edmonton, AB
Duration: 2005 Aug 22005 Aug 6

Other

OtherIEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005
CityEdmonton, AB
Period05/8/205/8/6

Fingerprint

Recurrent neural networks
Robots
Neurons
Trajectories
Acoustic waves
Color
Sensors
Experiments

Keywords

  • Active sensing
  • Humanoid robot
  • Recurrent neural network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Control and Systems Engineering

Cite this

Ogata, T., Ohba, H., Tani, J., Komatani, K., & Okuno, H. G. (2005). Extracting multi-modal dynamics of objects using RNNPB. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 160-165). [1544975] https://doi.org/10.1109/IROS.2005.1544975

Extracting multi-modal dynamics of objects using RNNPB. / Ogata, Tetsuya; Ohba, Hayato; Tani, Jun; Komatani, Kazunori; Okuno, Hiroshi G.

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2005. p. 160-165 1544975.

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

Ogata, T, Ohba, H, Tani, J, Komatani, K & Okuno, HG 2005, Extracting multi-modal dynamics of objects using RNNPB. in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS., 1544975, pp. 160-165, IEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005, Edmonton, AB, 05/8/2. https://doi.org/10.1109/IROS.2005.1544975
Ogata T, Ohba H, Tani J, Komatani K, Okuno HG. Extracting multi-modal dynamics of objects using RNNPB. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2005. p. 160-165. 1544975 https://doi.org/10.1109/IROS.2005.1544975
Ogata, Tetsuya ; Ohba, Hayato ; Tani, Jun ; Komatani, Kazunori ; Okuno, Hiroshi G. / Extracting multi-modal dynamics of objects using RNNPB. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 2005. pp. 160-165
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