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Learning Complex Tasks Using a Stepwise Approach

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Abstract

This paper explores a stepwise learning approach based on a system's decomposition into functional subsystems. Two case studies are examined: a visually guided robot that learns to track a maneuvering object, and a robot that learns to use the information from a force sensor in order to put a peg into a hole. These two applications show the features and advantages of the proposed approach: i) the subsystems naturally arise as functional components of the hardware and software; ii) these subsystems are building blocks of the robot behavior and can be combined in several ways for performing various tasks; iii) this decomposition makes it easier to check the performances and detect the cause of a malfunction; iv) only those subsystems for which a satisfactory solution is not available need to be learned; v) the strategy proposed for coordinating the optimization of all subsystems ensures an improvement at the task-level; vi) the overall system's behavior is significantly improved by the stepwise learning approach.

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References

  1. Powell, B.: The ‘spider’ and the yen, Newsweek 7 (August 1995), 46.

  2. Kreuziger, J.: Application of machine learning to robotics - An analysis, in: 2nd Int. Conf. on Automation, Robotics and Computer Vision, 1992, pp. RO:15.4.1–RO:15.4.5.

  3. Connell, J. H. and Mahadevan, S. (eds): Robot Learning, Kluwer Academic Publishers, Dordrecht, 1993.

    Google Scholar 

  4. Special issue on learning robots, Stud. Inform. Control 5(3) (1996).

  5. Monostori, L., Markus, A., Van Brussel, H., and Westkämper, E.: Machine learning approaches to manufacturing, Ann. of CIRP 45(2) (1996).

  6. Brooks, R. A. and Mataric, M. J.: Real robots, real learning problems, in: J. H. Connell and S. Mahadevan (eds), Robot Learning, Kluwer Academic Publishers, Dordrecht, 1993.

    Google Scholar 

  7. Nuttin, M. and Van Brussel, H.: Learning assembly operations: A case study with real-world objects, Stud. Inform. Control 5(3) (1996), 205–221.

    Google Scholar 

  8. Bernstein, N.: The Coordination and Regulation of Movements, Pergamon, Oxford, 1967.

    Google Scholar 

  9. Connell, J. H. and Mahadevan, S.: Rapid task learning for real robots, in: J. H. Connell and S. Mahadevan (eds), Robot Learning, Kluwer Academic Publishers, Dordrecht, 1993.

    Google Scholar 

  10. Brooks, R. A.: A robust layered control system for a mobile robot, IEEE J. Robotics Automat. 2(1) (1986).

  11. Mataric, M. J.: Learning in multi-robot systems, in: G. Weiss and S. Sen (eds), Adaptation and Learning in Multi-Agent Systems, Vol. 1042, Springer, Berlin, 1996, pp. 152–163.

    Google Scholar 

  12. Floreano, D. and Mondada, F.: Evolution of homing navigation in a real mobile robot, IEEE Trans. Systems Man Cybernet. 26(3) (1996), 396–407.

    Google Scholar 

  13. Chatila, R.: Deliberation and reactivity in autonomous mobile robots, Robotics and Autonom. Systems 16 (1995), 197–211.

    Google Scholar 

  14. Van Brussel, H.: Holonic manufacturing systems - the vision matching the problem, in: Proc. of the 1st European Conf. on Holonic Manufacturing Systems, Hannover, Germany, 1994.

  15. Abraham, R. and Marsden, J. E.: Foundation of Mechanics, Benjamin/Cummings, 1978.

  16. Lan, C.: Neural Networks, Theoretical Foundations and Analysis, 1990.

  17. Driankov, D., Hellendoorn, H., and Reinfrank, H.: An Introduction to Fuzzy Control, Springer, Berlin, 1993, 186–194.

    Google Scholar 

  18. Zadeh, L. A.: Fuzzy logic, neural networks and soft computing, Commun. ACM 37(3) (1994), 77–84.

    Google Scholar 

  19. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification and Regression Trees, Wadsworth Statistics/Probability Series, Wadsworth, Belmont, CA, 1984.

    Google Scholar 

  20. Bryson, A. E. and Ho, Y. C.: Applied Optimal Control, Halsted Press, New York, 1975.

    Google Scholar 

  21. Bischop, C. M.: Neural Networks for Pattern Recognition, Oxford Univ. Press, Oxford, 1995.

    Google Scholar 

  22. An, C. H., Atkenson, C. G., and Hollerbach, J. M.: Model-based Control of a Robot Manipulator, MIT Press, Cambridge, MA, 1988.

    Google Scholar 

  23. Slotine, J.-J. E. and Li, W.: Applied Nonlinear Control, Prentice-Hall, Englewood Cliffs, NJ, 1991.

    Google Scholar 

  24. Rumelhart, D. E., McClelland, J. L., and the PDP Research Group, Parallel Distributed Processing, MIT Press, Cambridge, MA, 1986.

    Google Scholar 

  25. Goldberg, D. E.: Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  26. Aboaf, E. W., Drucker, S. M., and Atkeson, C. M.: Task-level robot learning: Juggling a tennis ball more accurately, in: IEEE Int. Conf. on Robotics and Automation, 1989, pp. 1290–1295.

  27. Wen, J.: Hybrid approach of neural networks with knowledge-based explicit models, PhD Thesis, ETH, Zuerich, 1995.

    Google Scholar 

  28. Ferrigno, G. and Pedotti, A.: ELITE: A digital dedicated hardware system for movement analysis via real-time TV signal processing, IEEE Trans. Biomedical Engineering BME-32(11) (1985), 943–950.

    Google Scholar 

  29. Papanikolopoulos, N. P. and Khosla, P. K.: Adaptive robotic visual tracking: Theory and experiments, IEEE Trans. Automat. Control 38(3) (1993), 429–445.

    Google Scholar 

  30. Burdet, E. and Luthiger, J.: Learning the coordination of robots motion with vision processes, subm. to IEEE Trans. Robotics Automat., (1998).

  31. Burdet, E. and Koeppe, R.: A method for expecting the features of objects and enabling realtime vision processing, in: Int. Conf. on Intelligent Robots and Systems, 1996.

  32. Kohonen, T.: Self-Organization and Associative Memory, Springer, Berlin, 1984.

    Google Scholar 

  33. Ritter, H., Martinez, T., and Schulten, K.: Neuronale Netze, Addison-Wesley, Reading, MA, 1991.

    Google Scholar 

  34. Bennett, D. J., Geiger, D., and Hollerbach, J. M.: Autonomous robot calibration for hand-eye coordination, Internat. J. of Robotics Res. 10(5) (1991), 550–559.

    Google Scholar 

  35. Burdet, E.: Algorithms of human motor control and their implementation in robotics, PhD Thesis, ETH, Zuerich, 1996.

    Google Scholar 

  36. Singer, R. A.: Estimating optimal tracking filter performance for manned maneuvering targets, IEEE Trans. Aerospace Electronic Systems AES-6(4) (1970), 473–483.

    Google Scholar 

  37. Burdet, E. and Mueller, J.: A robot learning reaching motions (video), in: IEEE Int. Conf. on Robotics and Automation, 1996.

  38. Hoff, B. R.: A computational description of the organization of human reaching and prehension, PhD Thesis, University of Southern California, 1992.

    Google Scholar 

  39. Bobrow, J. E.: Optimal robot path planning using the minimum-time criterion, IEEE J. of Robotics Automat. 4(4) (1988), 443–451.

    Google Scholar 

  40. Timcenko, A. and Allen, P. K.: Modeling dynamic uncertainty in robot motions, in: IEEE Int. Conf. on Robotics and Automation, Vol. 3, 1993, pp. 531–536.

    Google Scholar 

  41. Burdet, E. and Codourey, A.: Evaluating nonlinear adaptive control experimentally, in: IEEE Control Systems Magazine (April 1998), 39–47.

  42. Allen, P. K., Timcenko, A., Yoshimi, B., and Michelman, P.: Automated tracking and grasping of a moving object with a robotic hand-eye system, IEEE Trans. Robotics Automat. 9(2) (1993),152–165.

    Google Scholar 

  43. Buttazo, G. C., Allotta, B., and Fanizza, P.: Mousebuster: A robot for real-time catching, IEEE Robotics and Automation Magazine, (February 1994), pp. 49–55.

  44. De Schutter, J. and Van Brussel, H.: Compliant robot motion I: A formalism for specifying compliant motion tasks, Internat. J. of Robotics Res. 7(4) (1988), 3–17.

    Google Scholar 

  45. De Schutter, J. and Van Brussel, H.: Compliant robot motion II: A control approach based on external control loops, Internat. J. of Robotics Res. 7(4) (1988), 18–33.

    Google Scholar 

  46. Asada, H.: Teaching and learning of compliance using neural nets: Representation and generation of nonlinear compliance, in: Proc. of the 1990 IEEE Int. Conf. on Robotics and Automation, 1990, pp. 1237–1244.

  47. Nuttin, M. and Van Brussel, H.: Learning the peg-into-hole assembly operation with a connectionist reinforcement technique, Computers in Industry 33(1) (1997), 101–109.

    Google Scholar 

  48. Witvrouw, W., Van De Poel, P., Bruyninckx, H., and De Schutter, J.: ROSI: A task specification and simulation tool for force sensor based robot control, in: 24th Int. Symp. on Industrial Robots, November 1993.

  49. Witvrouw, W.: Development of experiments for sensor controlled robot tasks, PhD Thesis, Katholieke Universiteit Leuven, Department of Mechanical Engineering, Division PMA, Belgium, 1996.

    Google Scholar 

  50. Williams, R. J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine Learning 8 (1992), 229–256.

    Google Scholar 

  51. Fahlman, S. E.: An empirical study of learning speed in back propagation networks, Technical Report CMU-CS-88-162, Carnegie-Mellon University, 1988.

    Google Scholar 

  52. Wirth, N.: Algorithms and Data Structures, Prentice-Hall, Englewood Cliffs, NJ, 1986.

    Google Scholar 

  53. Suárez, R., Basañez, L., Nuttin, M., Van Brussel, H., and Rosell, J.: Learning versus analytical approach to contact estimation in assembly tasks with robots, in: Proc. of 7th ICAR, 1995, pp. 769–775.

  54. Tschichhold-Guerman, N. N.: The neural network model RuleNet and its application to mobile robot navigation, Fuzzy Sets and Systems 85 (1997), 287–303.

    Google Scholar 

  55. Baroglio, C., Giordana, A., Kaiser, M., Nuttin, M., and Piola, R.: Learning controllers for industrial robots, in: J. A. Franklin, T. M. Mitchell, and S. Thrun (eds), Recent Advances in Robot Learning, Kluwer Academic Publishers, Dordrecht, 1996, pp. 105–133.

    Google Scholar 

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Authors and Affiliations

  1. School of Kinesiology, Simon Fraser University, BC, Canada

    E. Burdet

  2. Department of Mechanical Engineering, Division PMA, K. U. Leuven, Belgium

    M. Nuttin

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  1. E. Burdet
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Burdet, E., Nuttin, M. Learning Complex Tasks Using a Stepwise Approach. Journal of Intelligent and Robotic Systems 24, 43–68 (1999). https://doi.org/10.1023/A:1008058131402

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