Skip to main content
SpringerLink
Log in
Book cover

The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications

SOCO’18-CISIS’18-ICEUTE’18 2018: International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 pp 367–376Cite as

Current Research Trends in Robot Grasping and Bin Picking

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 771))

Abstract

We provide a view of current research issues in Robotic Grasping and Bin Picking focused on the perception aspects of the problem, mainly related to computer vision algorithms. After recalling the evolution of the topics in the last decades, we focus on the modern use of Deep Learning Algorithms. Two main trends are followed in the approaches to innovative grasping techniques. First, Convolutional Neural Networks are used for grasping perceptual aspects. We discuss the different degrees of success of several published approaches. Second, Deep Reinforcement Learning is being extensively tested in order to develop integrated eye-hand coordination systems not requiring delicate calibration. We provide also a discussion of possible future lines of research.

This is a preview of subscription content, 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., Elsen, E., Engel, J., Fan, L., Fougner, C., Han, T., Hannun, A.Y., Jun, B., LeGresley, P., Lin, L., Narang, S., Ng, A.Y., Ozair, S., Prenger, R., Raiman, J., Satheesh, S., Seetapun, D., Sengupta, S., Wang, Y., Wang, Z., Wang, C., Xiao, B., Yogatama, D., Zhan, J., Zhu, Z.: Deep speech 2: end-to-end speech recognition in English and Mandarin. CoRR abs/1512.02595 (2015)

    Google Scholar 

  2. Asada, H.: Studies on prehension and handling by robot hands with elastic fingers. Ph.D. thesis, Kyoto University (1979)

    Google Scholar 

  3. Barnard, S.T., Thompson, W.B.: Disparity analysis of images. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 2(4), 333–340 (1980)

    Article  Google Scholar 

  4. Bicchi, A.: Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity. IEEE Trans. Robot. Autom. 16(6), 652–662 (2000)

    Article  Google Scholar 

  5. Bicchi, A., Kumar, V.: Robotic grasping and contact: a review. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 348–353 (2000)

    Google Scholar 

  6. Deriche, R., Zhang, Z., Luong, Q.T., Faugeras, O.: Robust recovery of the epipolar geometry from an uncalibrated stereo rig. In: Proceedings of the European Conference on Computer Vision, vol. 800, pp. 567–576 (1994)

    Chapter  Google Scholar 

  7. Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 998–1005 (2010)

    Google Scholar 

  8. Faugeras, O.D., Hebert, M.: The representation, recognition, and locating of 3-D objects. Int. J. Robot. Res. 5(3), 27–52 (1986)

    Article  Google Scholar 

  9. Ghalamzan, A.M., Mavrakis, N., Kopicki, M., Stolkin, R., Leonardis, A., E., A.M.G., Mavrakis, N., Kopicki, M., Stolkin, R., Leonardis, A.: Task-relevant grasp selection: a joint solution to planning grasps and manipulative motion trajectories. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 907–914, October 2016

    Google Scholar 

  10. Ghita, O., Whelan, P.: A systems engineering approach to robotic bin picking, November 2008

    Google Scholar 

  11. Graña, M., Torrealdea, F.: Hierarchically structured systems. Eur. J. Oper. Res. 25(1), 20–26 (1986). http://www.sciencedirect.com/science/article/pii/0377221786901104

    Article  Google Scholar 

  12. Gualtieri, M., ten Pas, A., Saenko, K., Platt, R.: High precision grasp pose detection in dense clutter. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 598–605, October 2016

    Google Scholar 

  13. Horn, B., Ikeuchi, K.: The mechanical manipulation of randomly oriented parts. Sci. Am. 251(2), 100–111 (1984)

    Article  Google Scholar 

  14. Khatib, O., Yokoi, K., Chang, K., Ruspini, D., Holmberg, R., Casal, A., Baader, A.: Force strategies for cooperative tasks in multiple mobile manipulation systems. In: International Symposium on Robotics Research (1995). https://doi.org/10.1007/978-1-4471-0765-1_37

  15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, NIPS 2012, vol. 25, pp. 1–9 (2012)

    Google Scholar 

  16. Laskey, M., Lee, J., Chuck, C., Gealy, D., Hsieh, W., Pokorny, F.T., Dragan, A.D., Goldberg, K.: Robot grasping in clutter: using a hierarchy of supervisors for learning from demonstrations. In: 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 827–834 (2016)

    Google Scholar 

  17. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2323 (1998)

    Article  Google Scholar 

  18. LeCun, Y.A., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  19. 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 

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

    Google Scholar 

  21. Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Ojea, J.A., Goldberg, K.: Dex-Net 2.0: deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics (2017)

    Google Scholar 

  22. Mason, M., Salisbury, J.J.: Robot Hands and the Mechanics of Manipulation. The MIT Press, Cambridge (1985)

    Google Scholar 

  23. Miller, A.T., Allen, P.K.: Examples of 3d grasp quality computations. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), vol. 2, pp. 1240–1246 (1999)

    Google Scholar 

  24. Miller, A.T., Allen, P.K.: Graspit: a versatile simulator for robotic grasping. IEEE Robot. Autom. Mag. 11(4), 110–122 (2004)

    Article  Google Scholar 

  25. Pinto, L., Gupta, A.: Supersizing self-supervision: learning to grasp from 50K tries and 700 robot hours. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3406–3413, June 2016

    Google Scholar 

  26. Rioux, M., Bechthold, G., Taylor, D., Duggan, M.: Design of a large depth of view 3-dimensional camera for robot vision. Opt. Eng. 26(12), 1245–1250 (1987)

    Article  Google Scholar 

  27. Roberts, L.G.: Machine perception of three-dimensional solids. Ph.D. thesis (1965)

    Google Scholar 

  28. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., Van Den Driessche, G., Graepel, T., Hassabis, D.: Mastering the game of Go without human knowledge. Nature 550(7676), 354–359 (2017)

    Article  Google Scholar 

  29. Trinkle, J.C., Abel, J.M., Paul, R.P.: An investigation of frictionless enveloping grasping in the plane. Int. J. Robot. Res. 7(3), 33–51 (1988)

    Article  Google Scholar 

  30. Varley, J., Weisz, J., Weiss, J., Allen, P.: Generating multi-fingered robotic grasps via deep learning. In: IEEE International Conference on Intelligent Robots and Systems, pp. 4415–4420, December 2015

    Google Scholar 

  31. Wohlkinger, W., Aldoma, A., Rusu, R.B., Vincze, M.: 3DNet: large-scale object class recognition from CAD models. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 5384–5391 (2012)

    Google Scholar 

  32. Yang, C., Medioni, G.: Object modelling by registration of multiple range images. Image Vis. Comput. 10(3), 145–155 (1992)

    Article  Google Scholar 

  33. Yoshikawa, T.: Multifingered robot hands: control for grasping and manipulation. Ann. Rev. Control 34(2), 199–208 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Group, CCIA Department, UPV/EHU, Paseo Manuel de Lardizabal 1, 20018, San Sebastian, Spain

    Marcos Alonso & Manuel Graña

  2. CIS and Electronics Department, University of Mondragon, Loramendi Kalea 5, 20500, Mondragon, Spain

    Alberto Izaguirre

Authors
  1. Marcos Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Izaguirre
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manuel Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Graña .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    Manuel Graña

  2. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Salamanca, Spain

    José Antonio Sáez

  6. Department of Industrial Engineering, University of A Coruña, La Coruña, La Coruña, Spain

    Héctor Quintián

  7. University of Salamanca, Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alonso, M., Izaguirre, A., Graña, M. (2019). Current Research Trends in Robot Grasping and Bin Picking. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_35

Download citation

Publish with us

Policies and ethics

Search

Navigation