, Volume 3, Issue 4, pp 172–180 | Cite as

A predictive network architecture for a robust and smooth robot docking behavior

  • Junpei ZhongEmail author
  • Cornelius Weber
  • Stefan Wermter
Research Article


Robots and living beings exhibit latencies in their sensorimotor processing due to mechanical and electronic or neural processing delays. A reaction typically occurs to input stimuli of the past. This is critical not only when the environment changes (e.g. moving objects) but also when the agent itself moves. An agent that does not predict while moving may need to remain static between sensory input acquisition and output response to guarantee that the response is appropriate to the percept. We propose a biologically-inspired learning model of predictive sensorimotor integration to compensate for this latency. In this model, an Elman network is developed for sensory prediction and sensory filtering; a Continuous Actor-Critic Learning Automaton (CACLA) is trained for continuous action generation. For a robot docking experiment, this architecture improves the smoothness of the robot’s sensory input and therefore results in a faster and more accurate continuous approach behavior.


Sensorimotor integration Continuous Actor-Critic Learning Automaton Elman network Sensory prediction 


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  1. [1]
    G. Foresti, IEEE Transactions on Circuits and Systems for Video Technology 9, 1045 (1999).CrossRefGoogle Scholar
  2. [2]
    E. Goldstein, Sensation and Perception (Wadsworth Publishing Company, 2010).Google Scholar
  3. [3]
    M. Bar, Journal of Cognitive Neuroscience 15, 600 (2003).CrossRefGoogle Scholar
  4. [4]
    K. Kveraga, A. Ghuman, and M. Bar, Brain and Cognition 65, 145 (2007).CrossRefGoogle Scholar
  5. [5]
    D. MacKay, Nature (1958).Google Scholar
  6. [6]
    R. Nijhawan, Nature (1994).Google Scholar
  7. [7]
    R. Nijhawan, Nature (1997).Google Scholar
  8. [8]
    W. Li, V. Piëch, and C. Gilbert, Nature Neuroscience 7, 651 (2004).CrossRefGoogle Scholar
  9. [9]
    L. Trainor, International Journal of Psychophysiology 83, 256 (2012).CrossRefGoogle Scholar
  10. [10]
    J. Hirsch and C. Gilbert, The Journal of Neuroscience 11, 1800 (1991).Google Scholar
  11. [11]
    Z. Kisvarday, E. Toth, M. Rausch, and U. Eysel, Cerebral Cortex 7, 605 (1997).CrossRefGoogle Scholar
  12. [12]
    V. Lamme and P. Roelfsema, Trends in Neurosciences 23, 571 (2000).CrossRefGoogle Scholar
  13. [13]
    G. Le Masson, S. Renaud-Le Masson, D. Debay, T. Bal, et al., Nature 417, 854 (2002).CrossRefGoogle Scholar
  14. [14]
    C. Darrin, G. Christopher, U. Ale2, and G. Cheng, International Journal of Humanoid Robotics 1, 585 (2004).CrossRefGoogle Scholar
  15. [15]
    L. Natale, F. Nori, G. Sandini, and G. Metta, in IEEE 6th International Conference on Development and Learning, ICDL (2007), pp. 324–329.Google Scholar
  16. [16]
    S. Nishide, T. Ogata, J. Tani, K. Komatani, and H. Okuno, Advanced Robotics 22, 527 (2008).Google Scholar
  17. [17]
    N. Pradhan, T. Burg, and S. Birchfield, in IEEE American Control Conference, ACC (2011), pp. 4628–4633.Google Scholar
  18. [18]
    T. H. Hong, C. Rasmussen, T. Chang, and M. Shneier, in Proc. of SPIE Aeroscience Conference (2002), pp. 311–319.Google Scholar
  19. [19]
    Y. Matsushita and J. Miura, Robotics and Autonomous Systems 59, 274 (2011).CrossRefGoogle Scholar
  20. [20]
    J. Zhong, C. Weber, and S. Wermter, Artificial Neural Networks and Machine Learning, ICANN pp. 539–546 (2012).Google Scholar
  21. [21]
    S. Chen, IEEE Transactions on Industrial Electronics 59, 4409 (2012), ISSN 0278-0046.CrossRefGoogle Scholar
  22. [22]
    V. Bonato, E. Marques, and G. Constantinides, Journal of Signal Processing Systems 56, 41 (2009), ISSN 1939-8018.CrossRefGoogle Scholar
  23. [23]
    T. Klein, J. Jeka, T. Kiemel, and M. Lewis, Biological Cybernetics pp. 1–14 (2012).Google Scholar
  24. [24]
    A. Schaefer, S. Udluft, and H. Zimmermann, Neurocomputing 71, 2481 (2008).CrossRefGoogle Scholar
  25. [25]
    R. Möller, Journal of Theoretical Biology (2012).Google Scholar
  26. [26]
    J. Hirel, P. Gaussier, and M. Quoy, in IEEE International Conference on Robotics and Biomimetics, ROBIO (2011), pp. 1627–1632.Google Scholar
  27. [27]
    R. Saegusa, F. Nori, G. Sandini, G. Metta, and S. Sakka, in 7th IEEE-RAS International Conference on Humanoid Robots (2007), pp. 102–108.Google Scholar
  28. [28]
    S. Thrun, Machine Learning 33, 41 (1998).zbMATHCrossRefGoogle Scholar
  29. [29]
    K. Cullen, Current Opinion in Neurobiology 14, 698 (2004).CrossRefGoogle Scholar
  30. [30]
    N. Navarro-Guerrero, C. Weber, P. Schroeter, and S. Wermter, Robotics and Autonomous Systems (2012).Google Scholar
  31. [31]
  32. [32]
    H. van Hasselt and M. Wiering, in IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL (2007), pp. 272–279.CrossRefGoogle Scholar
  33. [33]
    R. Shadmehr, M. Smith, and J. Krakauer, Annual Review of Neuroscience 33, 89 (2010).CrossRefGoogle Scholar
  34. [34]
    J. Izawa and R. Shadmehr, PLoS Computational Biology 7, e1002012 (2011).CrossRefGoogle Scholar
  35. [35]
    D. Ballard, Pattern Recognition 13, 111 (1981).zbMATHCrossRefGoogle Scholar
  36. [36]
    W. Yan, C. Weber, and S. Wermter, in International Joint Conference on Neural Networks, IJCNN (2012), pp. 1–8.Google Scholar
  37. [37]
    M. Spratling, Vision Research 48, 1391 (2008).CrossRefGoogle Scholar
  38. [38]
    K. Rauss, S. Schwartz, and G. Pourtois, Neuroscience & Biobehavioral Reviews 35, 1237 (2011).CrossRefGoogle Scholar
  39. [39]
    J. Anderson and L. Schooler, The Oxford Handbook of Memory. (2000).Google Scholar
  40. [40]
    A. Alink, C. Schwiedrzik, A. Kohler, W. Singer, and L. Muckli, The Journal of Neuroscience 30, 2960 (2010).CrossRefGoogle Scholar
  41. [41]
    M. Corbetta, G. Shulman, et al., Nature Reviews Neuroscience 3, 215 (2002).CrossRefGoogle Scholar
  42. [42]
    O. Khatib, in Proc. IEEE International Conference on Robotics and Automation. (1985), vol. 2, pp. 500–505.Google Scholar
  43. [43]
    L. Huang, Robotics and Autonomous Systems 57, 55 (2009).CrossRefGoogle Scholar
  44. [44]
    R. Olberg, Current Opinion in Neurobiology 22, 267 (2011).CrossRefGoogle Scholar
  45. [45]
    P. Hartono and S. Kakita, Memetic Computing 1, 305 (2009).CrossRefGoogle Scholar

Copyright information

© Versita Warsaw and Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Knowledge Technology, Department of Computer ScienceUniversity of HamburgHamburgGermany

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