Skip to main content
SpringerLink
Log in

Lighting- and Occlusion-Robust View-Based Teaching/Playback for Model-Free Robot Programming

  • Conference paper
  • First Online:
Intelligent Autonomous Systems 14 (IAS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 531))

Included in the following conference series:

Abstract

In this paper, we investigate a model-free method for robot programming referred to as view-based teaching/playback. It uses neural networks to map factor scores of input images onto robot motions. The method can achieve greater robustness to changes in the task conditions, including the initial pose of the object, as compared to conventional teaching/playback. We devised an online algorithm for adaptively switching between range and grayscale images used in view-based teaching/playback. In its application to pushing tasks using an industrial manipulator, view-based teaching/playback using the proposed algorithm succeeded even under changing lighting conditions. We also devised an algorithm to cope with occlusions using subimages, which worked successfully in experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscipl. Rev.: Comput. Stat. 2(4), 433–459 (2010)

    Article  Google Scholar 

  2. Argall, B.D., Chernovab, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)

    Article  Google Scholar 

  3. Billard, A., Calinon, S., Dillman, R., Schaal, S.: Robot programming by demonstration. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, chap. 59.2, pp. 1371–1394. Springer, Berlin (2008)

    Google Scholar 

  4. Dillmann, R., Asfour, T., Do, M., Jäkel, R., Kasper, A., Azad, P., Ude, A., Schmidt-Rohr, S.R., Lösch, M.: Advances in robot programming by demonstration. Künstliche Intelligenz 24(4), 295–303 (2010)

    Article  Google Scholar 

  5. Kato, M., Kobayashi, Y., Hosoe, S.: Optimizing resolution for feature extraction in robotic motion learning. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 1086–1091 (2005)

    Google Scholar 

  6. Levine, S., Pastor, P., Krizhevsky, A., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. arXiv preprint arXiv:1603.02199 (2016)

  7. Maeda, Y., Moriyama, Y.: View-based teaching/playback for industrial manipulators. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 4306–4311 (2011)

    Google Scholar 

  8. Maeda, Y., Nakamura, T.: View-based teaching/playback for robotic manipulation. ROBOMECH J. 2(1) (2015), doi:10.1186/s40648-014-0025-4

  9. Matsumoto, Y., Inaba, M., Inoue, H.: View-based navigation using an omniview sequence in a corridor environment. Mach. Vision Appl. 14(2), 121–128 (2003)

    Article  Google Scholar 

  10. Meireles, M.R.G., Almeida, P.E.M., Simões, M.G.: A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans. Ind. Electron. 50(3), 585–601 (2003)

    Article  Google Scholar 

  11. Moriyama, Y., Maeda, Y.: View-based teaching/playback for manipulation by industrial robots. Trans. Jpn. Soc. Mech. Eng. Series C 79(806), 3597–3698 (2013) (in Japanese)

    Google Scholar 

  12. Noda, K., Arie, H., Suga, Y., Ogata, T.: Multimodal integration learning of robot behavior using deep neural networks. Robot. Auton. Syst. 62(6), 721–736 (2014)

    Article  Google Scholar 

  13. Shibata, K., Iida, M.: Acquisition of box pushing by direct-vision-based reinforcement learning. In: Proceedings of SICE Annual Conference, pp. 1378–1383 (2003)

    Google Scholar 

  14. Zhang, J., Knoll, A., Schmidt, R.: A neuro-fuzzy control model for fine-positioning of manipulators. Robot. Auton. Syst. 32(2–3), 101–113 (2000)

    Article  Google Scholar 

  15. Zhao, Q., Sun, Z., Sun, F., Zhu, J.: Appearance-based robot visual servo via a wavelet neural network. Int. J. Control Autom. Syst. 6(4), 607–612 (2008)

    Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP24560286 and JP15K05890.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan

    Yusuke Maeda

  2. Ricoh Corp., Tokyo, Japan

    Yoshito Saito

Authors
  1. Yusuke Maeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshito Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yusuke Maeda .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University , Shanghai, China

    Weidong Chen

  2. Osaka University , Osaka, Japan

    Koh Hosoda

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. Osaka University , Osaka, Japan

    Masahiro Shimizu

  5. Shanghai Jiao Tong University , Shanghai, China

    Hesheng Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Maeda, Y., Saito, Y. (2017). Lighting- and Occlusion-Robust View-Based Teaching/Playback for Model-Free Robot Programming. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds) Intelligent Autonomous Systems 14. IAS 2016. Advances in Intelligent Systems and Computing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-319-48036-7_68

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48036-7_68

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48035-0

  • Online ISBN: 978-3-319-48036-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation