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Integration of Machine Learning and Optimization for Robot Learning

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Recent Global Research and Education: Technological Challenges

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 519))

Abstract

Learning ability in Robotics is acknowledged as one of the major challenges facing artificial intelligence. Although in the numerous areas within Robotics machine learning (ML) has long identified as a core technology, recently Robot learning, in particular, has been witnessing major challenges due to the theoretical advancement at the boundary between optimization and ML. In fact the integration of ML and optimization reported to be able to dramatically increase the decision-making quality and learning ability in decision systems. Here the novel integration of ML and optimization which can be applied to the complex and dynamic contexts of Robot learning is described. Furthermore with the aid of an educational Robotics kit the proposed methodology is evaluated.

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References

  1. Connell, J.H., Mahadevan, S.: Robot Learning. Springer Science & Business Media (2012)

    Google Scholar 

  2. Knox, W.B., Glass, B.D., Love, B.C., Maddox, W.T., Stone, P.: How humans teach agents. Int. J. Social Robot. 4(4), 409–421 (2012)

    Article  Google Scholar 

  3. Bishop, CM., Nasrabadi, NM.: Pattern recognition and machine learning. J. Electron. Imaging 16(4) (2007)

    Google Scholar 

  4. Michalski, R.S., Carbonell, JG., Mitchell, TM. (eds.): Machine Learning: an Artificial Intelligence Approach. Springer Science & Business Media (2013)

    Google Scholar 

  5. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier (2011)

    Google Scholar 

  6. Mosavi, A., Vaezipour, A.: Reactive search optimization; application to multiobjective optimization problems. Appl. Math. 1572–1582 (2012)

    Google Scholar 

  7. Battiti. R., Brunato. M.: Reactive search optimization: learning while optimizing. In: Handbook of Metaheuristics, pp. 543–571. Springer, US (2010)

    Google Scholar 

  8. Murphy, R.R.: Human-robot interaction in rescue robotics. Syst. Man Cybernetics Appl. Rev. IEEE Trans. 34(2), 138–153 (2004)

    Article  Google Scholar 

  9. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Computer Vision–ECCV, pp. 430–443. Springer, Berlin (2006)

    Google Scholar 

  10. Sofman, B., et al.: Improving robot navigation through self-supervised online learning. J. Field Robotics. 23, 59–75 (2006)

    Article  Google Scholar 

  11. Yang, S.Y., Jin, S.M., Kwon, S.K.: Remote control system of industrial field robot. In: 6th IEEE International Conference on Industrial Informatics, pp. 442–447 (2008)

    Google Scholar 

  12. Peters, J., Vijayakumar, S., Schaal, S.: Reinforcement learning for humanoid robotics. In: Proceedings of the Third IEEE-RAS International Conference on Humanoid Robots (2003)

    Google Scholar 

  13. Kohl, N., Stone, P.: Machine learning for fast quadrupedal locomotion. In: AAAI, pp. 611–616 (2004)

    Google Scholar 

  14. Popp, K., Schiehlen, W.: Ground Vehicle Dynamics. Springer, Berlin (2010)

    Google Scholar 

  15. Taylor, R.H., Menciassi, A., Fichtinger, G., Dario, P.: Medical robotics and computer-integrated surgery. In: Handbook of Robotics, pp. 1199–1222. Springer, Berlin (2008)

    Google Scholar 

  16. Stavens, D., Thrun, S. A.: self-supervised terrain roughness estimator for off-road autonomous driving. arXiv preprint arXiv:1206.6872 (2012)

  17. Nehaniv, C.L., Dautenhahn, K.: Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communication. Cambridge University Press (2007)

    Google Scholar 

  18. Mombaur, K., Truong, A., Laumond, J.P.: From human to humanoid locomotion—an inverse optimal control approach. Auton. robots. 28(3), 369–383 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Mosavi, A., Varkonyi-Koczy, A., Fullsack, M.: Combination of machine learning and optimization for automated decision-making. In: Conference on Multiple Criteria Decision Making MCDM, Hamburg, Germany (2015)

    Google Scholar 

  21. Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32(11), 1238–1274 (2005)

    Article  Google Scholar 

  22. Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Auton. Agent. Multi-Agent Syst. 11(3), 387–434 (2005)

    Article  Google Scholar 

  23. Sra, S., Nowozin, S., Wright, S.J.: Optimization for Machine Learning. Mit Press (2012)

    Google Scholar 

  24. Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Springer Science & Business Media (2008)

    Google Scholar 

  25. Battiti, R.: Reactive search: Toward self-tuning heuristics. Mod. Heuristic Search Methods 61–83 (1996)

    Google Scholar 

  26. Battiti, R., Brunato, M.: Reactive Business Intelligence. From Data to Models to Insight. Reactive Search Srl, Italy (2011)

    Google Scholar 

  27. Toussaint, M., Ritter, H., Brock, O.: The optimization route to robotics—and alternatives. KI-Künstliche Intelligenz 29(4), 379–388 (2015)

    Article  Google Scholar 

  28. Battiti, R., Campigotto, P.: Reactive search optimization: Learning while optimizing. an experiment in interactive multi-objective optimization. In: Proceedings of MIC (2009)

    Google Scholar 

  29. Stone, P., Veloso, M.: Multiagent systems: a survey from a machine learning perspective. Auton. Robot. 8(3), 345–383 (2000)

    Article  Google Scholar 

  30. Battiti, R., Brunato, M.: The LION way. Machine learning plus intelligent optimization. Appl. Simul. Model. (2013)

    Google Scholar 

  31. Mosavi, A.: Decision-Making in Complicated Geometrical Problems. Int. J. Comput. Appl. 87(19) (2014)

    Google Scholar 

  32. Horst, R., Pardalos, P.M.: editors. Handbook of Global Optimization. Springer Science & Business Media (2013)

    Google Scholar 

  33. Brunato, M., Battiti, R.: Learning and intelligent optimization (LION): one ring to rule them all. Proc. VLDB Endowment 6(11), 1176–1177 (2013)

    Article  Google Scholar 

  34. Battiti, R., Brunato, M.: The LION Way: Machine Learning Plus Intelligent Optimization. Trento University, LIONlab (2014)

    Google Scholar 

  35. Battiti, R., Brunato, M., Delai, A.: Optimal Wireless Access Point Placement for Location-Dependent Services. Technical Report # DIT-03-052, University of Trento, Italy (2010)

    Google Scholar 

  36. Parsons, S., Sklar, E.: Teaching AI using LEGO mindstorms. In: AAAI Spring Symposium (2004)

    Google Scholar 

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Acknowledgment

This work is sponsored by Hungarian National Scientific Fund under contract OTKA 105846 and Research and Development Operational Program for the project “Modernization and Improvement of Technical Infrastructure for Research and Development of J. Selye University in the Fields of Nanotechnology and Intelligent Space”, ITMS 26210120042, co-funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Institute of Mechatronics and Vehicle Engineering, Obuda University, Budapest, Hungary

    Amir Mosavi & Annamaria R. Varkonyi-Koczy

  2. Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    Annamaria R. Varkonyi-Koczy

Authors
  1. Amir Mosavi
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  2. Annamaria R. Varkonyi-Koczy
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Corresponding author

Correspondence to Annamaria R. Varkonyi-Koczy .

Editor information

Editors and Affiliations

  1. Institute of Metrology and Biomedical En, Warsaw University of Technology Institute of Metrology and Biomedical En, Warsaw, Poland

    Ryszard Jabłoński

  2. Institute of Metrology and Biomedical En, Warsaw University of Technology Institute of Metrology and Biomedical En, Warsaw, Poland

    Roman Szewczyk

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Mosavi, A., Varkonyi-Koczy, A.R. (2017). Integration of Machine Learning and Optimization for Robot Learning. In: Jabłoński, R., Szewczyk, R. (eds) Recent Global Research and Education: Technological Challenges. Advances in Intelligent Systems and Computing, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-319-46490-9_47

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  • DOI: https://doi.org/10.1007/978-3-319-46490-9_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46489-3

  • Online ISBN: 978-3-319-46490-9

  • eBook Packages: EngineeringEngineering (R0)

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