Postural Synergies and Neural Network for Autonomous Grasping: A Tool for Dextrous Prosthetic and Robotic Hands

  • Fanny Ficuciello
  • Gianluca Palli
  • Claudio Melchiorri
  • Bruno Siciliano
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 1)


In this paper, a neural network model has been designed for planning grasps of a cybernetic hand prototype by means of postural synergies.The synergies subspace is derived by means of a joint-to-joint mapping from a human hand set of grasps. A library of motor primitives of the hand in a synergy based rendering has been built for a number of selected objects and tasks. The requirement of the task in a simplified approach is specified by the type of grasp, such as precision or power. A feed forward neural network has been trained using the grasping data from the library and running the Levenberg-Marquadt algorithm. By combining postural synergies and neural network the nonlinear relationship between the object geometric features and the hand configuration during grasping can be easily found with a good approximation. The experiments have been performed on the DEXMART hand prototype and the results demonstrate that integration of postural synergies and neural network is a promising tool toward simplified and autonomous grasping for artificial hands, such as anthropomorphic robotic hands and prostheses.


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  1. 1.
    Bicchi, A.: Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity. IEEE Transactions on Robotics and Automation 16(6), 652–662 (2000)CrossRefGoogle Scholar
  2. 2.
    Santello, M., Flanders, M., Soechting, J.: Postural hand synergies for tool use. Journal of Neuroscience 18(23), 10105–10115 (1998)Google Scholar
  3. 3.
    D. OttoBock HealthCare, Duderstdat,
  4. 4.
    U. Touch EMAS Ltd., Edinburgh,
  5. 5.
    Zecca, M., Micera, S., Carrozza, M.C., Dario, P.: Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering 30(4-6), 459–485 (2002)CrossRefGoogle Scholar
  6. 6.
    Perker, P., Englehart, K., Hudgins, B.: Myoelectric signal processing for control of powered limb prostheses. Journal of Electromyography and Kinesiology 16(6), 541–548 (2006)CrossRefGoogle Scholar
  7. 7.
    Shulz, S., Pylatiuk, C., Reischl, M., Martin, L., Mikut, R., Bretthauer, G.: A hydraulically driven multifunctional prosthetic hand. Robotica 23(3), 293–299 (2005)CrossRefGoogle Scholar
  8. 8.
    Cipriani, C., Controzzi, M., Carrozza, M.: Objectives, criteria and methods for the design of the smarthand transradial prosthesis. Robotica 28(6), 293–299 (2009)Google Scholar
  9. 9.
    Tenore, V., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., Thakor, N.V.: Decoding of individuated finger movements using surface electromyography. IEEE Transactions on Biomedical Engineering 56(5), 1427–1434 (2009)CrossRefGoogle Scholar
  10. 10.
    Cipriani, C., Zaccone, F., Micera, S., Carrozza, M.: On the shared control of an EMG-controlled prosthetic hand: analysis of user-prosthesis interaction. IEEE Transactions on Robotics 24(1), 170–184 (2008)CrossRefGoogle Scholar
  11. 11.
    Ciocarlie, M., Goldfeder, C., Allen, P.: Dimensionality reduction for hand-independent dexterous robotic grasping. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, pp. 3270–3275 (2007)Google Scholar
  12. 12.
    Ciocarlie, M., Allen, P.: Hand posture subspaces for dexterous robotic grasping. International Journal of Robotics Research 28(7), 851–867 (2009)CrossRefGoogle Scholar
  13. 13.
    Ciocarlie, M.T., Allen, P.K.: On-Line Interactive Dexterous Grasping. In: Ferre, M. (ed.) EuroHaptics 2008. LNCS, vol. 5024, pp. 104–113. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Matrone, G., Cipriani, C., Secco, E., Magenes, G., Carrozza, M.: Principal components analysis based control of a multi-dof underactuated prosthetic hand. Journal of Neuro Engineering and Rehabilitation 7(16), 1–16 (2010)Google Scholar
  15. 15.
    Geng, T., Lee, M., Hulse, M.: Transferring human grasping synergies to a robot. Mechatronics 21(1), 272–284 (2011)CrossRefGoogle Scholar
  16. 16.
    Vilaplana, J., Coronado, J.: A neural network model for coordination of hand gesture during reach to grasp. Neural Networks 19(1), 12–30 (2006)zbMATHCrossRefGoogle Scholar
  17. 17.
    Ficuciello, F., Palli, G., Melchiorri, C., Siciliano, B.: Experimental evaluation of postural synergies during reach to grasp with the UB Hand IV. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, pp. 1775–1780 (2011)Google Scholar
  18. 18.
    Ficuciello, F., Palli, G., Melchiorri, C., Siciliano, B.: Planning and control during reach to grasp using the three predominant ub hand iv postural synergies. In: Proc. IEEE International Conference on Robotics and Automation, Saint Paul, MN, pp. 1775–1780 (2012)Google Scholar
  19. 19.
    Palli, G., Melchiorri, C., Vassura, G., Berselli, G., Pirozzi, S., Natale, C., De Maria, G., May, C.: Innovative Technologies for the Next Generation of Robotic Hands. In: Siciliano, B. (ed.) Advanced Bimanual Manipulation. STAR, vol. 80, pp. 173–217. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Borghesan, G., Palli, G., Melchiorri, C.: Design of tendon-driven robotic fingers: Modelling and control issues. In: Proc. IEEE International Conference on Robotics and Automation, Anchorage, pp. 793–798 (2010)Google Scholar
  21. 21.
    DEXMART Project website,
  22. 22.
    Berselli, G., Borghesan, G., Brandi, M., Melchiorri, C., Natale, C., Palli, G., Pirozzi, S., Vassura, G.: Integrated mechatronic design for a new generation of robotic hands. In: Proc. IFAC Symposium on Robot Control, Gifu, Japan (2009)Google Scholar
  23. 23.
    Biagiotti, L., Lotti, F., Melchiorri, C., Palli, G., Tiezzi, P., Vassura, G.: Development of UB Hand 3: Early results. In: Proc. IEEE International Conference on Robotics and Automation, Barcelona, pp. 4488–4493 (2005)Google Scholar
  24. 24.
    Lotti, F., Vassura, G.: A novel approach to mechanical design of articulated finger for robotic hands. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 1687–1692 (2002)Google Scholar
  25. 25.
    Palli, G., Borghesan, G., Melchiorri, C.: Modeling, identification and control of tendon-based actuation systems. IEEE Transactions on Robotics 28(2), 277–290 (2012)CrossRefGoogle Scholar
  26. 26.
    Berselli, G., Vassura, G.: Differentiated layer design to modify the compliance of soft pads for robotic limbs. In: Proc. IEEE International Conference on Robotics and Automation, Kobe, pp. 1285–1290 (2009)Google Scholar
  27. 27.
    Berselli, G., Piccinini, M., Palli, G., Vassura, G.: Engineering Design of Fluid-Filled Soft Covers for Robotic Contact Interfaces: Guidelines, Nonlinear Modeling, and Experimental Validation. IEEE Transactions on Robotics 27(3), 436–449 (2011)CrossRefGoogle Scholar
  28. 28.
    Romero, J., Feix, T., Kjellstrom, H., Kragic, D.: Spatio-temporal modelling of grasping actions. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, pp. 2103–2108 (2010)Google Scholar
  29. 29.
    Beale, M., Hagan, M., Demuth, H.: Neural network toolbox: User’s guide,
  30. 30.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  31. 31.
    Villani, L., Ficuciello, F., Lippiello, V., Palli, G., Ruggiero, F., Siciliano, B.: Grasping and Control of Multi-Fingered Hands. In: Siciliano, B. (ed.) Advanced Bimanual Manipulation. STAR, vol. 80, pp. 219–265. Springer, Heidelberg (2012)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fanny Ficuciello
    • 1
  • Gianluca Palli
    • 2
  • Claudio Melchiorri
    • 2
  • Bruno Siciliano
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità degli Studi di Napoli Federico IINapoliItaly
  2. 2.Dipartimento di Elettronica, Informatica e SistemisticaAlma Mater Studiorum Università di BolognaBolognaItaly

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