On-Line Interactive Dexterous Grasping

  • Matei T. Ciocarlie
  • Peter K. Allen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5024)

Abstract

In this paper we describe a system that combines human input and automatic grasp planning for controlling an artificial hand, with applications in the area of hand neuroprosthetics. We consider the case where a user attempts to grasp an object using a robotic hand, but has no direct control over the hand posture. An automated grasp planner searches for stable grasps of the target object and shapes the hand accordingly, allowing the user to successfully complete the task. We rely on two methods for achieving the computational rates required for effective user interaction: first, grasp planning is performed in a hand posture subspace of highly reduced dimensionality; second, our system uses real-time input provided by the human user, further simplifying the search for stable grasps to the point where solutions can be found at interactive rates. We demonstrate our approach on a number of different hand models and target objects, in both real and virtual environments.

Keywords

dexterous grasping human-machine interaction hand prosthetics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Taylor, D., Tillery, S., Schwartz, A.: Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002)CrossRefGoogle Scholar
  2. 2.
    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, 459–485 (2002)CrossRefGoogle Scholar
  3. 3.
    Taylor, D., Tillery, S., Schwartz, A.: Information conveyed through brain control: Cursor versus robot. IEEE Trans. Neural Syst. Rehab Eng. 1, 195–199 (2003)CrossRefGoogle Scholar
  4. 4.
    Cipriani, C., Zaccone, F., Stellin, G., Beccai, L., Cappiello, G., Carrozza, M., Dario, P.: Closed-loop controller for a bio-inspired multi-fingered underactuated prosthesis. In: IEEE Intl. Conf. on Robotics and Automation, pp. 2111–2116 (2006)Google Scholar
  5. 5.
    Shimoga, K.B.: Robot grasp synthesis algorithms: a survey. Intl. J. of Robotics Research 15, 230–266 (1996)CrossRefGoogle Scholar
  6. 6.
    Bicchi, A., Kumar, V.: Robotic grasping and contact: A review. In: IEEE Intl. Conf. on Robotics and Automation, pp. 348–353 (2000)Google Scholar
  7. 7.
    Roa, M., Suarez, R.: Geometrical approach for grasp synthesis on discretized 3d objects. In: IEEE-RSJ Intl. Conf. on Intelligent Robots and Systems (2007)Google Scholar
  8. 8.
    Kragic, D., Miller, A., Allen, P.: Real-time tracking meets online planning. In: IEEE Intl. Conf. on Robotics and Automation (2001)Google Scholar
  9. 9.
    Miller, A., Allen, P.K.: GraspIt!: A versatile simulator for robotic grasping. IEEE Robotics and Automation Magazine 11, 110–122 (2004)CrossRefGoogle Scholar
  10. 10.
    Santello, M., Flanders, M., Soechting, J.F.: Postural hand synergies for tool use. Journal of Neuroscience 18(23), 10 105–10 110 (1998)Google Scholar
  11. 11.
    Ciocarlie, M., Goldfeder, C., Allen, P.: Dexterous grasping via eigengrasps: A low-dimensional approach to a high-complexity problem. In: Robotics: Science and Systems Manipulation Workshop - Sensing and Adapting to the Real World (2007)Google Scholar
  12. 12.
    Brown, C., Asada, H.: Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis. In: IEEE-RAS Intl. Conf. on Intelligent Robots and Systems, pp. 2877–2882 (2007)Google Scholar
  13. 13.
    Peters, R.A., Jenkins, O.C.: Uncovering manifold structures in Robonaut’s sensory-data state space. In: IEEE-RAS Intl. Conf. on Humanoid Robots (2005)Google Scholar
  14. 14.
    Ciocarlie, M., Coldfeder, C., Allen, P.: Dimensionality reduction for hand-independent dexterous robotic grasping. In: IEEE-RSJ Intl. Conf. on Intelligent Robots and Systems (2007)Google Scholar
  15. 15.
    Ferrari, C., Canny, J.: Planning optimal grasps. In: IEEE Intl. Conf. on Robotics and Automation, pp. 2290–2295 (1992)Google Scholar
  16. 16.
    Ingber, L.: Very fast simulated re-annealing. J. Mathl. Comput. Modelling 12(8), 967–973 (1989)CrossRefMathSciNetMATHGoogle Scholar
  17. 17.
    Dollar, A., Howe, R.: Simple, robust autonomous grasping in unstructured environments. In: IEEE Intl. Conf. on Robotics and Automation (2007)Google Scholar
  18. 18.
    Carrozza, M.C., Cappiello, G., Micera, S., Edin, B.B., Beccai, L., Cipriani, C.: Design of a cybernetic hand for perception and action. Biol. Cybern. 95(6), 629–644 (2006)CrossRefMATHGoogle Scholar
  19. 19.
    Ciocarlie, M., Lackner, C., Allen, P.: Soft finger model with adaptive contact geometry for grasping and manipulation tasks. In: Joint Eurohaptics Conf. and IEEE Symp. on Haptic Interfaces (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Matei T. Ciocarlie
    • 1
  • Peter K. Allen
    • 1
  1. 1.Columbia UniversityNew YorkUSA

Personalised recommendations