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Grasp Learning by Active Experimentation Using Continuous B-Spline Model

  • Jianwei Zhang
  • Bernd Rössler
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 116)

Abstract

In this chapter we present a self-valuing learning system based on continuous B-spline model which is capable of learning how to grasp unfamiliar objects and generalize the learned abilities. The learning system consists of two learners which distinguish between local and global grasping criteria. The local criteria are not object specific while the global criteria cover physical properties of each object. The system is self-valuing, i.e. rates its actions by evaluating sensory information and the use of image processing techniques. An experimental setup consisting of a PUMA-260 manipulator, equipped with hand-camera and force/torque sensor, was used to test this scheme. The system has shown the ability to grasp a wide range of objects and to apply previously learned knowledge to new objects.

Keywords

Learning System Local Criterion Action Execution Orientation Learner Unfamiliar Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    A. Bicchi and V. Kumar. Robotic grasping and contact: A review. In Proceedings of the IEEE International Conference on Robotics and Automation, 2000.Google Scholar
  2. 2.
    G. Smith, E. Lee, K. Goldberg, K. Boehringer, and J. Craig. Computing parallel-jaw grips. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 1897–1903, 1999.Google Scholar
  3. 3.
    A. Morales, P. J. Sanz G. Recatalâ, and Angel Pdel Pobil. Heuristic visionbased computation of planar antipodal grasps on unknown objects. In Proceedings of the IEEE International Conference on Robotics and Automation, 2001.Google Scholar
  4. 4.
    I. Kamon, T. Flash, and S. Edelman. Learning to grasp using visual information. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 2470–2476, 1996.CrossRefGoogle Scholar
  5. 5.
    J. Zhang, G. Brinkschröder, and A. Knoll. Visuelles Reinforcement-Lernen zur Feinpositionierung eines Roboterarms über kompakte Zustandskodierung. In Tagungsband Autonome Mobile Robotersysteme, München, 1999.Google Scholar
  6. 6.
    M.-C. Nguyen and V. Graefe. Self-learning vision-guided robots for searching and grasping objects. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 1633–1638, 2000.Google Scholar
  7. 7.
    K. C. Tai. The tree-to-tree correction problem. J. Assoc. Comput. Mach., 26(3):422–433, 1979.MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    T. Jiang, L. Wang, and K. Zhang. Alignment of Trees - An Alternative to Tree Edit. Theoretical Computer Science, 143(1):137–148, 1995.MathSciNetMATHGoogle Scholar
  9. 9.
    Ch. J. C. H. Watkins. Learning from Delayed Rewards. PhD thesis, King’s College, Cambridge, England, 1989.Google Scholar
  10. 10.
    R. S. Sutton. Learning to predict by the method of temporal differences. Machine Learning, 3: 9–44, 1988.Google Scholar
  11. 11.
    I. J. Schoenberg. Contributions to the problem of approximation of equidistant data by analytic functions. Quarterly of Applied Mathematics, 4:45–99, 112141, 1946.MathSciNetGoogle Scholar
  12. 12.
    R. F. Riesenfeld. Applications of B-Spline approximation to geometric problems of computer-aided design. PhD thesis, Syracuse University, 1973.Google Scholar
  13. 13.
    W. J. Gordon and R. F. Riesenfeld. Bspline curves and surfaces. In R. E. Barnhill and R. F. Riesenfeld, editors, Computer Aided Geometric Design. Academic Press, 1974.Google Scholar
  14. 14.
    M. Brown and C. J. Harris. Neural networks for modelling and control, chapter I, pages 17–55. In “Advances in Intelligent Control” , Ed., C. J. Harris, Taylor & Francis, London, 1994.Google Scholar
  15. 15.
    H. W. Werntges. Partition of unity improve neural function approximators. In Proceedings of IEEE International Conference on Neural Networks, San Francisco, volume 2, pages 914–918, 1993.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jianwei Zhang
    • 1
  • Bernd Rössler
    • 1
  1. 1.Faculty of TechnologyUniversity of BielefeldBielefeldGermany

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