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Dexterous Robotic-Hand Grasp Learning Using Piecewise Linear Dynamic Systems Model

  • Wei Xiao
  • Fuchun Sun
  • Huaping Liu
  • Chao He
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

Learning from sensor data plays an important role in the field of robotic research, especially in dexterous robotic hand grasping. The manuscript puts efforts on learning from tactile dynamic process during robotic hand grasping. A piecewise linear dynamic systems and a group of models are presented, under the guidance of which, proper gesture according to different types of targets could then be selected to facilitate stable and accurate grasping. This is evaluated on the experimental testbed and shows promising results.

Notes

Acknowledgments

This work was jointly supported by Tsinghua Self-innovation Project (Grant No. 20111081111) and the National Natural Science Foundation of China (Grants No. 61210013, 61075027).

References

  1. 1.
    Salimi S, Bone GM (2008) Kinematic enveloping grasp planning method for robotic dexterous hands and three-dimensional objects. Robotica 26:331–344CrossRefGoogle Scholar
  2. 2.
    Lippiello V, Ruggiero F, Villani L (2009) Floating visual grasp of unknow objects. In: IEEE international conference on intelligent robots and systems, IEEE Press, New York, pp 1290–1295Google Scholar
  3. 3.
    Bekiroglu Y, Detry R, Kragic D (2011) Learning tactile characterizations of object- and pose-specific grasps. In: IEEE international conference on intelligent robots and systems, IEEE Press, New York, pp 1554–1560Google Scholar
  4. 4.
    Bicchi A (2000) Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity. IEEE Trans Rob Autom 16:652–662CrossRefGoogle Scholar
  5. 5.
    Bekiroglu Y, Huebner K, Kragic D (2011) Integrating grasp planning with online stability assessment using tactile sensing. In: IEEE international conference on robotics and automation, IEEE Press, New York, pp 4750–4755Google Scholar
  6. 6.
    Huebner K, Kragic D (2008) Selection of robot pre-grasp using box-based shape approximation. In: IEEE international conference on intelligent robots and systems, IEEE Press, New York, pp 1765–1770Google Scholar
  7. 7.
    Goldfeder C, Allen PK, Lackner C, Pelossof R (2007) Grasp planning via decomposition trees. In: IEEE international conference on robotics and automation, IEEE Press, New York, pp 4679–4684Google Scholar
  8. 8.
    Siddiqi SM (2009) Learning latent variable and predictive models of dynamical systems. Ph.D. dissertation, School of Computer Science Robotics Institute, Carnegie Mellon Univ., Pittsburgh, PAGoogle Scholar
  9. 9.
    Zhang Y, Ji Q (2006) Active and dynamic information fusion for multisensor systems with dynamic Bayesian Networks. IEEE Trans Syst Man Cybern Part B Cybern 36:467–472Google Scholar
  10. 10.
    Doretto G, Chiuso A, Wu Y, Soatto S (2003) Dynamic textures. Int J Comput Vision 51:91–109CrossRefMATHGoogle Scholar
  11. 11.
    Sun Z, Ge SS (2005) Analysis and synthesis of switched linear control systems. Automatica 41:181–195CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Xiao W, Sun FC, Liu HP, Liu HY, He C (2012) Dexterous robotic hand grasp modeling using piecewise linear dynamic model. In: 2012 international conference on multisensor fusion and information integration, IEEE Press, New York, pp 52–57Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Tsinghua National Laboratory for Information Science and Technology, The State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and TechnologyTsinghua UniversityBeijingPeople’s Republic of China

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