Manipulative Tasks Identification by Learning and Generalizing Hand Motions

  • Diego R. Faria
  • Ricardo Martins
  • Jorge Lobo
  • Jorge Dias
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 349)


In this work is proposed an approach to learn patterns and recognize a manipulative task by the extracted features among multiples observations. The diversity of information such as hand motion, fingers flexure and object trajectory are important to represent a manipulative task. By using the relevant features is possible to generate a general form of the signals that represents a specific dataset of trials. The hand motion generalization process is achieved by polynomial regression. Later, given a new observation, it is performed a classification and identification of a task by using the learned features.


Motion Patterns Task Recognition Task Generalization 


  1. 1.
    Johansson, R.S., Flanagan, J.R.: Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009)CrossRefGoogle Scholar
  2. 2.
    Calinon, S., Guenter, F., Billard, A.: On Learning, Representing and Generalizing a Task in a Humanoid Robot. IEEE Transactions on Systems, Man and Cybernetics, Part B 37(2), 286–298 ; Special issue on robot learning by observationGoogle Scholar
  3. 3.
    Ogawara, K., Tanabe, Y., Kurazume, R., Hasegawa, T.: Detecting repeated motion patterns via dynamic programming using motion density. In: Proc. 2009 IEEE Int. Conf. on Robotics and Automation (ICRA 2009), pp. 1743–1749 (2009)Google Scholar
  4. 4.
    Pastor, P., Hoffmann, H., Asfour, T., Schaal, S.: Learning and generalization of motor skills by learning from demonstration. In: Int. Conference on Robotics and Automation, ICRA 2009 (2009)Google Scholar
  5. 5.
    Polhemus Liberty 240/8 Motion Tracking System,
  6. 6.
  7. 7.
  8. 8.
    Faria, D.R., Martins, R., Dias, J.: Learning Motion Patterns from Multiple Observations along the Actions Phases of Manipulative Tasks. In: Workshop on Grasping Planning and Task Learning by Imitation: IEEE/RSJ IROS 2010, Taipei, Taiwan (October 2010)Google Scholar
  9. 9.
    Faria, D.R., Dias, J.: 3D Hand Trajectory Segmentation by Curvatures and Hand Orientation for Classification through a Probabilistic Approach. In: Proceedings of The IEEE/RSJ Int.Conf. on Intelligent Robots and Systems, IROS 2009, St. Louis, MO, USA (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Diego R. Faria
    • 1
  • Ricardo Martins
    • 1
  • Jorge Lobo
    • 1
  • Jorge Dias
    • 1
  1. 1.Institute of Systems and Robotics, DEECUniversity of CoimbraPortugal

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