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Learning in Robotics

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Amir Mosavi, Annamaria Varkonyi
10.5120/ijca2017911661

Amir Mosavi and Annamaria Varkonyi. Learning in Robotics. International Journal of Computer Applications 157(1):8-11, January 2017. BibTeX

@article{10.5120/ijca2017911661,
	author = {Amir Mosavi and Annamaria Varkonyi},
	title = {Learning in Robotics},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {1},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {8-11},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume157/number1/26793-2016911661},
	doi = {10.5120/ijca2017911661},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Machine learning is currently identified as one of the major parts of the research in Robotics. However the advanced concept of machine learning plus optimization reported effective for developing learning systems. This article considers the novel integration of machine learning and optimization for the complex and dynamic context of Robot learning. Further the proposed case study presents an effective framework for learning and solving the global optimization problem within the context of Robotics and learning.

References

  1. Connell, J.H., Mahadevan, S.: editors. Robot learning. Springer Science & Business Media (2012)
  2. Knox, W.B., Glass, B.D., Love, B.C., Maddox, W.T., Stone, P.: How humans teach agents. International Journal of Social Robotics 4(4):409-21 (2012)
  3. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In Computer Vision–ECCV. Springer Berlin Heidelberg 430-443 (2006)
  4. Sofman, B., Lin, E., Bagnell, J.A., Cole, J., Vandapel., N., Stentz, A.: Improving robot navigation through self‐supervised online learning. Journal of Field Robotics. 23, 59-75. (2006)
  5. Yang, S.Y., Jin, S.M., Kwon, S.K.: Remote control system of industrial field robot. InIndustrial Informatics, 6th IEEE International Conference on, 442-447 (2008)
  6. Peters, J., Vijayakumar, S., Schaal, S.: Reinforcement learning for humanoid robotics. In Proceedings of the third IEEE-RAS international conference on humanoid robots 1-20 (2003)
  7. Bishop, CM., Nasrabadi, NM.: Pattern Recognition and Machine Learning. Journal of Electronic Imaging 16(4), (2007)
  8. Michalski, R.S., Carbonell, JG., Mitchell, TM.: editors. Machine learning: An artificial intelligence approach. Springer Science & Business Media (2013)
  9. Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques. Elsevier (2011)
  10. Waller, M.A., Fawcett, SE.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics 34(2):77-84 (2013)
  11. Battiti. R., Brunato. M.: Reactive search optimization: learning while optimizing. InHandbook of Metaheuristics. Springer US. 543-571 (2010)
  12. Murphy, R.R.: Human-robot interaction in rescue robotics. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 34(2) 138-53 (2004)
  13. Kohl N, Stone P.: Machine learning for fast quadrupedal locomotion. InAAAI 611-616 (2004)
  14. Popp, K., Schiehlen, W.: Ground vehicle dynamics. Springer Berlin Heidelberg; 2010.
  15. Taylor, R.H., Menciassi, A., Fichtinger, G., Dario, P., Medical robotics and computer-integrated surgery. InSpringer handbook of robotics, Springer Berlin Heidelberg 1199-1222 (2008)
  16. Nehaniv, C.L., Dautenhahn, K.: editors. Imitation and social learning in robots, humans and animals: behavioural, social and communicative dimensions. Cambridge University Press (2007)
  17. Mombaur, K., Truong, A., Laumond, J.P.: From human to humanoid locomotion—an inverse optimal control approach. Autonomous robots. 28(3) 369-83 (2010)
  18. Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robotics and autonomous systems. 57(5) 469-83 (2009)
  19. Mozos, O.M., Stachniss, C., Burgard, W.: Supervised learning of places from range data using adaboost. In Robotics and Automation, Proceedings of the 2005 IEEE International Conference on 1730-1735 (2005)
  20. Stavens, D., Thrun, S. A.: self-supervised terrain roughness estimator for off-road autonomous driving. arXiv preprint arXiv:1206.6872 (2012)
  21. Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: A survey. International Journal of Robotics Research. 32(11) 1238-74 (2005)
  22. Kalra, N., Zlot, R., Dias, M.B., Stentz, A.: Market-Based Multirobot Coordination: A Comprehensive Survey and Analysis. Report Carnegie Mellon University (2005)
  23. Hebert, MH., Thorpe, C.E., Stentz, A.: editors. Intelligent unmanned ground vehicles: autonomous navigation research at Carnegie Mellon. Springer Science & Business Media (2012)
  24. Wing, J.M.: Computational thinking. Communications of the ACM 49(3) (2006)
  25. Billard, A., Calinon, S., Dillmann, R., Schaal, S.: Robot programming by demonstration. InSpringer handbook of robotics Springer Berlin Heidelberg 1371-1394 (2008)
  26. Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11(3) 387-434 (2005)
  27. Sra, S., Nowozin, S., Wright, S.J.: Optimization for machine learning. Mit Press (2012)
  28. Battiti, R., Brunato, M., Mascia, F.: Reactive search and intelligent optimization. Springer Science & Business Media (2008)
  29. Amir Mosavi, Annamaria Varkonyi, Integration of Machine Learning and Optimization for Robot Learning, Advances in Intelligent Systems and Computing, Springer-Verlag Berlin Heidelberg (2016)
  30. Shim, J.P., Warkentin, M., Courtney, J.F., Power D.J, Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decision support systems 33(2) 111-126 (2002)
  31. Toussaint, M., Ritter, H., Brock, O.: The Optimization Route to Robotics—and Alternatives. KI-Künstliche Intelligenz 29(4) 379-88 (2015)
  32. Mosavi, Amir, and Annamaria Varkonyi, Machine learning for artificial intelligence and Robotics, International Journal of Applied Mathematics, Electronics and Computers, (2016)
  33. Stone, P., Veloso, M.: Multiagent systems: A survey from a machine learning perspective. Autonomous Robots. 8(3) 345-83 (2000)
  34. Battiti, R., Brunato, M.: The LION way. Machine Learning Plus Intelligent Optimization. Applied Simulation and Modelling (2013)
  35. Mosavi, A.: Decision-Making in Complicated Geometrical Problems. International Journal of Computer Applications 87(19) (2014)
  36. Brunato M, Battiti R. Learning and intelligent optimization (LION): one ring to rule them all. Proceedings of the VLDB Endowment 6(11) 1176-7 (2013)
  37. Mosavi, A., Vaezipour, A.: Reactive Search Optimization; Application to Multiobjective Optimization Problems. Applied Mathematics 1572-82 (2012)
  38. 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)
  39. Battiti, R., Brunato, M.: The LION Way: Machine Learning plus Intelligent Optimization. Trento University, LIONlab (2014)
  40. Battiti, R., Brunato, M.: Reactive Business Intelligence. From Data to Models to Insight, Reactive Search Srl, Italy. (2011)
  41. 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)
  42. Horst, R., Pardalos, P.M.: editors. Handbook of global optimization. Springer Science & Business Media (2013)
  43. Battiti, R., Passerini, A.: Brain–computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker. Evolutionary Computation, IEEE Transactions on. 14(5) 671-87 (2010)
  44. Kim, S.H., Jeon. J.W.: Introduction for Freshmen to Embedded Systems Using LEGO Mindstorms. IEEE Transactions on Education. 52(1) 99-108 (2009)
  45. Parsons, S., Sklar, E.: Teaching AI using LEGO mindstorms. In AAAI Spring Symposium (2004)
  46. Goupy, J., Creighton, L.: Introduction to design of experiments with JMP examples. SAS Publishing (2007)
  47. Mosavi, Amir, and Annamaria R. Varkonyi-Koczy. Integration of Machine Learning and Optimization for Robot Learning. In Recent Global Research and Education: Technological Challenges, pp. 349-355. Springer International Publishing, (2017)

Keywords

Predictive analytics, machine learning, optimization, robotics