Mapping Grasps from the Human Hand to the DEXMART Hand by Means of Postural Synergies and Vision

  • Fanny Ficuciello
  • Gianluca Palli
  • Claudio Melchiorri
  • Bruno Siciliano
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 88)


This work aims at defining a suitable postural synergies subspace for the DEXMART Hand from observation of human hand grasping postures. Previous works were carried out on a preliminary prototype (the UB Hand IV), without neither proprioceptive integrated sensors nor external sensors, by means of a joint-to- joint mapping technique. Using an RGB camera and depth sensor for 3D motion capture, the human hand palm pose and fingertip positions have been measured for a reference set of grasping postures. The proposed method for the determination of the synergies subspace is based on the kinematics mapping from the human hand to the robotic hand using data from experiments involving five subjects. The subjects’ hand configurations have been mapped to the robotic hand by matching the hand pose and fingertip positions and applying a closed-loop inverse kinematic algorithm. Suitable scaling factors have been used to adapt the DEXMART Hand kinematics to the subjects’ hand dimension. By means of Principal Component Analysis (PCA), the kinematic patterns of the first three predominant synergies have been computed and a brief comparison with the previous method and kinematics is reported. Finally, a synergy-based control strategy has been used for testing the efficiency of the grasp synthesis method.


Joint Angle Human Hand Kinect Sensor Robot Hand Robotic Hand 
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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  1. 1.
    DEXMART Project website,
  2. 2.
    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
  3. 3.
    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 Trans. on Robotics 27(3), 436–449 (2011)CrossRefGoogle Scholar
  4. 4.
    Berselli, G., Vassura, G.: Differentiated layer design to modify the compliance of soft pads for robotic limbs. In: Proc. IEEE Int. Conf. on Robotics and Automation, Kobe, Japan, pp. 1285–1290 (2009)Google Scholar
  5. 5.
    Biagiotti, L., Lotti, F., Melchiorri, C., Palli, G., Tiezzi, P., Vassura, G.: Development of UB Hand 3: Early results. In: Proc. IEEE Int. Conf. on Robotics and Automation, Barcelona, Spain, pp. 4488–4493 (2005)Google Scholar
  6. 6.
    De Maria, G., Natale, C., Pirozzi, S.: Force/tactile sensor for robotic applications. Sensors and Actuators A: Physical (2012), doi:10.1016/j.sna.2011.12.042Google Scholar
  7. 7.
    Drenckhahn, D., Benninghoff, A.: Anatomie: Makroskopische Anatomie, Embryologie und Histologie des Menschen. Zellen-und Gewebelehre, Entwiklungsbiologie, Bewegungsapparat, Herz-Kreisluf-System, Immunsystem, Atem-und Verdauungsapparat. Anatomie: Makroskopische Anatomie, Embryologie und Histologie des Menschen. Urban & Schwarzenberg (1994)Google Scholar
  8. 8.
    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 Int. Conf. on Intelligent Robots and Systems, San Francisco, pp. 1775–1780 (2011)Google Scholar
  9. 9.
    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 Int. Conf. Robotics and Automation, Saint Paul, MN, pp. 1775–1780 (2012)Google Scholar
  10. 10.
    Frati, V., Prattichizzo, D.: Using kinect for hand tracking and rendering in wearable haptics. In: IEEE World Haptics Conference, Istanbul (2011)Google Scholar
  11. 11.
    Geng, T., Lee, M., Hulse, M.: Transferring human grasping synergies to a robot. Mechatronics 21(1), 272–284 (2011)CrossRefGoogle Scholar
  12. 12.
    Gioioso, G., Salvietti, G., Malvezzi, M., Prattichizzo, D.: Mapping synergies from human to robotic hands with dissimilar kinematics: An object based approach. In: Proc. IEEE Int. Conf. on Robotics and Automation, Workshop on Manipulation Under Uncertainty, Shangai (2011)Google Scholar
  13. 13.
    Kapandji, I., Honoré, L.: The Physiology of the Joints: The upper limb. The Physiology of the Joints, Churchill Livingstone (2007)Google Scholar
  14. 14.
    Lotti, F., Vassura, G.: A novel approach to mechanical design of articulated finger for robotic hands. Proc. IEEE/RSJ Int. Conf. on Intelligent Robot and Systems 2, 1687–1692 (2002)CrossRefGoogle Scholar
  15. 15.
    Grebenstein, M., et al.: A method for hand kinematics designers 7 billion perfect hands. In: Proc. of 1st International Conference on Applied Bionics and Biomechanics, Venice, Italy (2010)Google Scholar
  16. 16.
    Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Full dof tracking of a hand interacting with an object by modeling occlusions and physical constraints. In: Proc. 13th International Conference on Computer Vision, Barcelona, pp. 1260–1264 (2011)Google Scholar
  17. 17.
    Palli, G., Borghesan, G., Melchiorri, C.: Modeling, identification and control of tendon-based actuation systems. IEEE Trans. on Robotics 28(2), 277–290 (2012)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Palli, G., Natale, C., May, C., Melchiorri, C., Wurtz, T.: Modeling and control of the twisted string actuation system. IEEE/ASME Transactions on Mechatronics 18(2), 664–673 (2013), doi:10.1109/TMECH.2011.2181855CrossRefGoogle Scholar
  20. 20.
    Palli, G., Pirozzi, S.: Miniaturized optical-based force sensors for tendon-driven robots. In: Proc. IEEE Int. Conf. on Robotics and Automation, Shanghai, China, pp. 5344–5349 (2011)Google Scholar
  21. 21.
    Romero, J., Feix, T., Kjellstrom, H., Kragic, D.: Spatio-temporal modelling of grasping actions. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Taipei, pp. 2103–2108 (2010)Google Scholar
  22. 22.
    Santello, M., Flanders, M., Soechting, J.: Postural hand synergies for tool use. Journal of Neuroscience 18(23), 10,105–10,115 (1998)Google Scholar
  23. 23.
    Siciliano, B., Khatib, O. (eds.): Springer Handbook of Robotics. Springer (2008)Google Scholar
  24. 24.
    Zhang, C., Zhang, Z.: Calibration between depth and color sensors for commodity depth cameras. In: Proc. Int. Conf. on Multimedia and Expo, Barcelona, Spain, pp. 1–6 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Fanny Ficuciello
    • 1
  • Gianluca Palli
    • 2
  • Claudio Melchiorri
    • 2
  • Bruno Siciliano
    • 1
  1. 1.DIETIUniversity of Naples Federico IINapoliItaly
  2. 2.DEIUniversity of BolognaBolognaItaly

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