Skip to main content
SpringerLink
Log in

Multi-modal Deep Extreme Learning Machine for Robotic Grasping Recognition

  • Conference paper
  • First Online:
Proceedings of ELM-2015 Volume 2

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 7))

Abstract

Learning rich representations efficiently plays an important role in multi-modal recognition task, which is crucial to achieve high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Deep Extreme Learning Machine (MM-DELM) structure, while maintaining ELM’s advantages of training efficiency. In this structure, unsupervised hierarchical ELM is conducted for feature extraction for all modalities separately. Then, the shared layer is developed by combining these features from all of modalities. Finally, the Extreme Learning Machine (ELM) is used as supervised feature classifier for final decision. Experimental validation on Cornell grasping dataset illustrates that the proposed multiple modality fusion method achieves better grasp recognition performance.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251C257 (1991)

    Google Scholar 

  2. Huang, G., Babri, H.A.:.Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans. Neural Netw. 9(1), 224C229 (1998)

    Google Scholar 

  3. Leshno, M., Lin, V. Y., Pinkus, A., Schocken, S.: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw. 6, 861C867 (1993)

    Google Scholar 

  4. Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)

    Article  Google Scholar 

  5. Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of International Joint Conference on Neural Network(IJCNN), vol. 2, pp. 985–990 (2004)

    Google Scholar 

  6. Huang, G., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 42(2), 513–529 (2012)

    Article  Google Scholar 

  7. Li, M.B., Huang, G.B., Saratchandran, P., Sundararajan, N.: Fully complex extreme learning machine. Neurocomputing 68, 306C314 (2005)

    Google Scholar 

  8. Cambria, E., Huang, G.: Extreme learning machines-representational learning with ELMs for big data. IEEE Intell. Syst. 28(6), 30–59 (2013)

    Article  Google Scholar 

  9. Yu, W., Zhuang, F., He, Q., Shi, Z.: Learning deep representations via extreme learning machines. Neurocomputing 149, 308–315 (2015)

    Article  Google Scholar 

  10. Zhu, W., Miao, J., Qing, L., Huang, G.: Hierarchical extreme learning machine for unsupervised representation learning. Neurocomputing (in press)

    Google Scholar 

  11. Uzair, M., Shafait, F., Ghanem, B., Mian, A.: Representation learning with deep extreme learning machines for efficient image set classification, pp. 1–10 (2015). arXiv: arXiv:1503.02445

  12. Tang, J., Deng, C., Huang, G.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst., 1–13 (2015)

    Google Scholar 

  13. Feng, G., Huang, G., Lin, Q., Gay, R.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans. Neural Netw. 20(8), 1352–1357 (2009)

    Article  Google Scholar 

  14. Ding, S., Zhang, N., Xu, X., Guo, L., Zhang, J.: Deep extreme learning machine and its application in EEG classification. Math. Probl. Eng., 1–12 (2014)

    Google Scholar 

  15. Sahbani, A., El-Khoury, S., Bidaud, P.: An overview of 3D object grasp synthesis algorithms. Robot. Auton. Syst. 60, 326–336 (2012)

    Article  Google Scholar 

  16. Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven Grasp SynthesisłA survey. IEEE Trans. Robot. 30(2), 289–309 (2014)

    Article  Google Scholar 

  17. Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: International Conference on Robotics and Automation(ICRA), pp. 1817–1824 (2011)

    Google Scholar 

  18. Bai, J., Wu, Y.: SAE-RNN deep learning for RGB-D based object recognition. Intell. Comput. Theory, 235–240 (2014)

    Google Scholar 

  19. Beksi, W.J., Papanikolopoulos, N.: Object classification using dictionary learning and RGB-D covariance descriptors. In: International Conference on Robotics and Automation (ICRA), pp. 1–6 (2015)

    Google Scholar 

  20. Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Key Project for Basic Research of China under Grant 2013CB329403; in part by the National Natural Science Foundation of China under Grant 61210013; and in part by the Tsinghua University Initiative Scientific Research Program under Grant 20131089295.

Author information

Authors and Affiliations

  1. Department of Electronic Information, Taiyuan University of Technology, Taiyuan, Shanxi, People’s Republic of China

    Jie Wei & Gaowei Yan

  2. Department of Computer Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Tnlist, Beijing, People’s Republic of China

    Huaping Liu & Fuchun Sun

Authors
  1. Jie Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huaping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gaowei Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fuchun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huaping Liu .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Wei, J., Liu, H., Yan, G., Sun, F. (2016). Multi-modal Deep Extreme Learning Machine for Robotic Grasping Recognition. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28373-9_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28372-2

  • Online ISBN: 978-3-319-28373-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation