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
Connell, J.H., Mahadevan, S.: Robot Learning. Springer Science & Business Media (2012)
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)
Bishop, CM., Nasrabadi, NM.: Pattern recognition and machine learning. J. Electron. Imaging 16(4) (2007)
Michalski, R.S., Carbonell, JG., Mitchell, TM. (eds.): Machine Learning: an Artificial Intelligence Approach. Springer Science & Business Media (2013)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier (2011)
Mosavi, A., Vaezipour, A.: Reactive search optimization; application to multiobjective optimization problems. Appl. Math. 1572–1582 (2012)
Battiti. R., Brunato. M.: Reactive search optimization: learning while optimizing. In: Handbook of Metaheuristics, pp. 543–571. Springer, US (2010)
Murphy, R.R.: Human-robot interaction in rescue robotics. Syst. Man Cybernetics Appl. Rev. IEEE Trans. 34(2), 138–153 (2004)
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Computer Vision–ECCV, pp. 430–443. Springer, Berlin (2006)
Sofman, B., et al.: Improving robot navigation through self-supervised online learning. J. Field Robotics. 23, 59–75 (2006)
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)
Peters, J., Vijayakumar, S., Schaal, S.: Reinforcement learning for humanoid robotics. In: Proceedings of the Third IEEE-RAS International Conference on Humanoid Robots (2003)
Kohl, N., Stone, P.: Machine learning for fast quadrupedal locomotion. In: AAAI, pp. 611–616 (2004)
Popp, K., Schiehlen, W.: Ground Vehicle Dynamics. Springer, Berlin (2010)
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)
Stavens, D., Thrun, S. A.: self-supervised terrain roughness estimator for off-road autonomous driving. arXiv preprint arXiv:1206.6872 (2012)
Nehaniv, C.L., Dautenhahn, K.: Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communication. Cambridge University Press (2007)
Mombaur, K., Truong, A., Laumond, J.P.: From human to humanoid locomotion—an inverse optimal control approach. Auton. robots. 28(3), 369–383 (2010)
Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)
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)
Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32(11), 1238–1274 (2005)
Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Auton. Agent. Multi-Agent Syst. 11(3), 387–434 (2005)
Sra, S., Nowozin, S., Wright, S.J.: Optimization for Machine Learning. Mit Press (2012)
Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Springer Science & Business Media (2008)
Battiti, R.: Reactive search: Toward self-tuning heuristics. Mod. Heuristic Search Methods 61–83 (1996)
Battiti, R., Brunato, M.: Reactive Business Intelligence. From Data to Models to Insight. Reactive Search Srl, Italy (2011)
Toussaint, M., Ritter, H., Brock, O.: The optimization route to robotics—and alternatives. KI-Künstliche Intelligenz 29(4), 379–388 (2015)
Battiti, R., Campigotto, P.: Reactive search optimization: Learning while optimizing. an experiment in interactive multi-objective optimization. In: Proceedings of MIC (2009)
Stone, P., Veloso, M.: Multiagent systems: a survey from a machine learning perspective. Auton. Robot. 8(3), 345–383 (2000)
Battiti, R., Brunato, M.: The LION way. Machine learning plus intelligent optimization. Appl. Simul. Model. (2013)
Mosavi, A.: Decision-Making in Complicated Geometrical Problems. Int. J. Comput. Appl. 87(19) (2014)
Horst, R., Pardalos, P.M.: editors. Handbook of Global Optimization. Springer Science & Business Media (2013)
Brunato, M., Battiti, R.: Learning and intelligent optimization (LION): one ring to rule them all. Proc. VLDB Endowment 6(11), 1176–1177 (2013)
Battiti, R., Brunato, M.: The LION Way: Machine Learning Plus Intelligent Optimization. Trento University, LIONlab (2014)
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)
Parsons, S., Sklar, E.: Teaching AI using LEGO mindstorms. In: AAAI Spring Symposium (2004)
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.
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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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