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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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2081-4836
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A Predictive Network Architecture for a Robust and Smooth Robot Docking Behavior

Junpei Zhong
  • Knowledge Technology, Department of Computer Science, University of Hamburg Vogt-Kölln-Str. 30, 22527 Hamburg, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
 / Cornelius Weber
  • Knowledge Technology, Department of Computer Science, University of Hamburg Vogt-Kölln-Str. 30, 22527 Hamburg, Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
 / Stefan Wermter
  • Knowledge Technology, Department of Computer Science, University of Hamburg Vogt-Kölln-Str. 30, 22527 Hamburg, Germany
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Published Online: 2013-04-15 | DOI: https://doi.org/10.2478/s13230-013-0106-8

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Abstract

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.

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

References

  • [1] G. Foresti, IEEE Transactions on Circuits and Systems for Video Technology 9, 1045 (1999).CrossrefGoogle Scholar

  • [2] E. Goldstein, Sensation and Perception (Wadsworth Publishing Company, 2010).Google Scholar

  • [3] M. Bar, Journal of Cognitive Neuroscience 15, 600 (2003).PubMedGoogle Scholar

  • [4] K. Kveraga, A. Ghuman, and M. Bar, Brain and Cognition 65, 145 (2007).CrossrefGoogle Scholar

  • [5] D. MacKay, Nature (1958).Google Scholar

  • [6] R. Nijhawan, Nature (1994).PubMedGoogle Scholar

  • [7] R. Nijhawan, Nature (1997).PubMedGoogle Scholar

  • [8] W. Li, V. Piëch, and C. Gilbert, Nature Neuroscience 7, 651 (2004).CrossrefGoogle Scholar

  • [9] L. Trainor, International Journal of Psychophysiology 83, 256 (2012).Google Scholar

  • [10] J. Hirsch and C. Gilbert, The Journal of Neuroscience 11, 1800 (1991).Google Scholar

  • [11] Z. Kisvarday, E. Toth, M. Rausch, and U. Eysel, Cerebral Cortex 7, 605 (1997).CrossrefGoogle Scholar

  • [12] V. Lamme and P. Roelfsema, Trends in Neurosciences 23, 571 (2000).CrossrefGoogle Scholar

  • [13] G. Le Masson, S. Renaud-Le Masson, D. Debay, T. Bal, et al., Nature 417, 854 (2002).Google Scholar

  • [14] C. Darrin, G. Christopher, U. Ale2, and G. Cheng, International Journal of Humanoid Robotics 1, 585 (2004).Google Scholar

  • [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] S. Nishide, T. Ogata, J. Tani, K. Komatani, and H. Okuno, Advanced Robotics 22, 527 (2008).Google Scholar

  • [17] N. Pradhan, T. Burg, and S. Birchfield, in IEEE American Control Conference, ACC (2011), pp. 4628–4633.Google Scholar

  • [18] T. H. Hong, C. Rasmussen, T. Chang, and M. Shneier, in Proc. of SPIE Aeroscience Conference (2002), pp. 311–319.Google Scholar

  • [19] Y. Matsushita and J. Miura, Robotics and Autonomous Systems 59, 274 (2011).CrossrefGoogle Scholar

  • [20] J. Zhong, C. Weber, and S. Wermter, Artificial Neural Networks and Machine Learning, ICANN pp. 539–546 (2012).Google Scholar

  • [21] S. Chen, IEEE Transactions on Industrial Electronics 59, 4409 (2012), ISSN 0278-0046.CrossrefGoogle Scholar

  • [22] V. Bonato, E. Marques, and G. Constantinides, Journal of Signal Processing Systems 56, 41 (2009), ISSN 1939-8018.CrossrefGoogle Scholar

  • [23] T. Klein, J. Jeka, T. Kiemel, and M. Lewis, Biological Cybernetics pp. 1–14 (2012).Google Scholar

  • [24] A. Schaefer, S. Udluft, and H. Zimmermann, Neurocomputing 71, 2481 (2008).CrossrefGoogle Scholar

  • [25] R. Möller, Journal of Theoretical Biology (2012).Google Scholar

  • [26] J. Hirel, P. Gaussier, and M. Quoy, in IEEE International Conference on Robotics and Biomimetics, ROBIO (2011), pp. 1627–1632.Google Scholar

  • [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] S. Thrun, Machine Learning 33, 41 (1998).Google Scholar

  • [29] K. Cullen, Current Opinion in Neurobiology 14, 698 (2004).CrossrefGoogle Scholar

  • [30] N. Navarro-Guerrero, C. Weber, P. Schroeter, and S. Wermter, Robotics and Autonomous Systems (2012).Google Scholar

  • [31] Aldebaran, http://www.aldebaran-robotics.com (2009).Google Scholar

  • [32] H. van Hasselt and M. Wiering, in IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL (2007), pp. 272–279.Google Scholar

  • [33] R. Shadmehr, M. Smith, and J. Krakauer, Annual Review of Neu-roscience 33, 89 (2010).CrossrefGoogle Scholar

  • [34] J. Izawa and R. Shadmehr, PLoS Computational Biology 7, e1002012 (2011).CrossrefGoogle Scholar

  • [35] D. Ballard, Pattern Recognition 13, 111 (1981).Google Scholar

  • [36] W. Yan, C. Weber, and S. Wermter, in International Joint Conference on Neural Networks, IJCNN (2012), pp. 1–8.Google Scholar

  • [37] M. Spratling, Vision Research 48, 1391 (2008).PubMedGoogle Scholar

  • [38] K. Rauss, S. Schwartz, and G. Pourtois, Neuroscience & Biobehavioral Reviews 35, 1237 (2011).CrossrefGoogle Scholar

  • [39] J. Anderson and L. Schooler, The Oxford Handbook of Memory. (2000).Google Scholar

  • [40] A. Alink, C. Schwiedrzik, A. Kohler, W. Singer, and L. Muckli, The Journal of Neuroscience 30, 2960 (2010).CrossrefGoogle Scholar

  • [41] M. Corbetta, G. Shulman, et al., Nature Reviews Neuroscience 3, 215 (2002).CrossrefGoogle Scholar

  • [42] O. Khatib, in Proc. IEEE International Conference on Robotics and Automation. (1985), vol. 2, pp. 500–505.Google Scholar

  • [43] L. Huang, Robotics and Autonomous Systems 57, 55 (2009).Google Scholar

  • [44] R. Olberg, Current Opinion in Neurobiology 22, 267 (2011).Google Scholar

  • [45] P. Hartono and S. Kakita, Memetic Computing 1, 305 (2009).Web of ScienceCrossrefGoogle Scholar

About the article

Received: 2012-12-14

Accepted: 2013-03-27

Published Online: 2013-04-15

Published in Print: 2012-12-01

Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 3, Issue 4, Pages 172–180, ISSN (Online) 2081-4836, DOI: https://doi.org/10.2478/s13230-013-0106-8.

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© Junpei Zhong et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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