Skip to main content
SpringerLink
Log in
Book cover

The Human Hand as an Inspiration for Robot Hand Development pp 531–563Cite as

Grasp Planning Using Low Dimensional Subspaces

  • Chapter
  • First Online:

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 95))

Abstract

Recent advances in neuroscience research have shown that posture variation of the human hand during grasping is dominated by movement in a configuration space of highly reduced dimensionality. In this chapter we explore how robot and artificial hands may take advantage of similar subspaces to reduce the complexity of dexterous grasping. We first describe our method for grasp synthesis using a low-dimensional posture subspace, and apply it to a set of hand models with different kinematics and numbers of degrees of freedom. We then discuss two applications of the method: online interactive grasp planning and data-driven grasp planning using a pre-computed database of stable grasps.

This work has been supported by NSF grant IIS-0904514, NIH BRP grant 1RO1 NS 050256-01A2 and a Google Research Grant.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Even with perfect precision and recall, this theoretical algorithm may not truly be ‘ideal’, as the categories in the PSB are semantic rather than purely geometric. Nevertheless, since shape matching algorithms are regularly evaluated using these categories as a ground truth, we adopt the same convention.

References

  1. M.H. Schieber, M. Santello, Hand function: Neural control and peripheral limits to performance, (invited review). J. Appl. Physiol. 96, 2293–2300 (2004)

    Article  Google Scholar 

  2. M.R. Cutkosky, On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans. Robot. Autom. 5, 269–279 (1989)

    Article  Google Scholar 

  3. T. Iberall, Human prehension and dexterous robot hands. Int. J. Robot. Res. 16, 285–299 (1997)

    Article  Google Scholar 

  4. M. Santello, M. Flanders, J.F. Soechting, Postural hand synergies for tool use. J. Neurosci. 18(23), 10105–10115 (1998)

    Google Scholar 

  5. M. Santello, M. Flanders, J.F. Soechting, Patterns of hand motion during grasping and the influence of sensory guidance. J. Neurosci. 22, 1426–1435 (2002)

    Google Scholar 

  6. M. Santello, J.F. Soechting, Matching object size by controlling finger span and hand shape. Somatosens. Mot. Res. 14, 203–212 (1997)

    Article  Google Scholar 

  7. M. Santello, J.F. Soechting, Gradual molding of the hand to object contours. J. Neurophysiol. 79, 1307–1320 (1998)

    Google Scholar 

  8. C. Brown, H. Asada, Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis. In: IEEE-RAS International Conference on Intelligent Robots and Systems, pp. 2877–2882, 2007

    Google Scholar 

  9. C. Gosselin, F. Pelletier, T. Laliberte, An anthropomorphic underactuated robotic hand with 15 DoFs and a single actuator. In: IEEE International Conference on Robotics and Automation, 2008

    Google Scholar 

  10. V.C.K. Cheung, A. d’Avella, M.C. Tresch, E. Bizzi, Central and sensory contributions to the activation and organization of muscle synergies during natural motor behaviors. J. Neurosci. 25(27), 6419–6434 (2005)

    Article  Google Scholar 

  11. C.R. Mason, J.E. Gomez, T.J. Ebner, Hand synergies during reach-to-grasp. J. Neurophysiol. 86, 2896–2910 (2001)

    Google Scholar 

  12. E. Todorov, Z. Ghahramani, Analysis of the synergies underlying complex hand manipulation. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4637–4640, 2004

    Google Scholar 

  13. P.H. Thakur, A.J. Bastian, S. Hsiao, Multidigit movement synergies of the human hand in an unconstrained haptic exploration task. J. Neurosci. 28(6), 1271–1281 (2008)

    Article  Google Scholar 

  14. M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  15. A. Fod, M.J. Mataric, O.C. Jenkins, Automated derivation of primitives for movement classification. Autono. Robot. 12(16), 39–54 (2002)

    Google Scholar 

  16. A. Tsoli, O.C. Jenkins, 2d subspaces for user-driven robot grasping. In: Robotics, Science and Systems Conference: Workshop on Robot Manipulation, 2007

    Google Scholar 

  17. C.S. Lovchik, M.A. Diftler, The robonaut hand: A dextrous robot hand for space. In: IEEE International Conference on Robotics and Automation, pp. 907–912, 1998

    Google Scholar 

  18. J. Butterfass, G. Hirzinger, S. Knoch, H. Liu. DLR’s articulated hand, part I: Hard- and software architecture. In: International Conference on Robotics and Automation, pp. 2081–2086, 1998

    Google Scholar 

  19. L. Ingber, Very fast simulated re-annealing. J. Math. Comput. Model. 12(8), 967–973 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  20. A. Miller, P.K. Allen, Graspit!: a versatile simulator for robotic grasping. IEEE Robot. Autom. Mag. 11(4), 110–122 (2004)

    Article  Google Scholar 

  21. M. Mason, K. Salisbury, Robot hands and the mechanics of manipulation (MIT Press, Cambridge, 1985)

    Google Scholar 

  22. D.M. Taylor, S.H. Tillery, A.B. Schwartz, Direct cortical control of 3D neuroprosthetic devices. Science 296(5574), 1829–1832 (2002)

    Article  Google Scholar 

  23. M. Zecca, S. Micera, M.C. Carrozza, P. Dario, Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit. Rev. Biomed. Eng. 30, 459–485 (2002)

    Article  Google Scholar 

  24. D. Taylor, S.H. Tillery, A. Schwartz, Information conveyed through brain control: Cursor versus robot. IEEE Trans. Neural Syst. Rehab. Eng. 1(2), 195–199 (2003)

    Article  Google Scholar 

  25. D. Kragic, A. Miller, P. Allen, Real-time tracking meets online planning. In: IEEE International Conference on Robotics and Automation, Seoul, 2001

    Google Scholar 

  26. C. Ferrari, J. Canny, Planning optimal grasps. In: IEEE Internationl Conference on Robotics and Automation, pp. 2290–2295, 1992

    Google Scholar 

  27. A. Miller, P. Allen, Examples of 3-d grasp quality computations. In: IEEE International Conference on Robotics and Automation, Detroit, MI, pp. 1240–1246, 1999

    Google Scholar 

  28. R.D. Howe, M.R. Cutkosky, Practical force-motion models for sliding manipulation. Intl. J. of Robot. Res. 15(6), 557–572 (1996)

    Article  Google Scholar 

  29. M. Ciocarlie, C. Lackner, P. Allen, Soft finger model with adaptive contact geometry for grasping and manipulation tasks. In: Joint Eurohaptics Conference and IEEE Symposium on Haptic Interfaces, 2007

    Google Scholar 

  30. A. Dollar, R. Howe, Simple, robust autonomous grasping in unstructured environments. In: IEEE International Conference on Robotics and Automation, 2007

    Google Scholar 

  31. M.C. Carrozza, G. Cappiello, S. Micera, B.B. Edin, L. Beccai, C. Cipriani, Design of a cybernetic hand for perception and action. Biol. Cybern. 95(6), 629–644 (2006)

    Article  MATH  Google Scholar 

  32. M.T. Ciocarlie, S.T. Clanton, M. C. Spalding, P.K. Allen, Biomimetic grasp planning for cortical control of a robotic hand. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008

    Google Scholar 

  33. C. Goldfeder, M. Ciocarlie, H. Dang, P.K. Allen, The Columbia grasp database. In: IEEE International Conference on Robotics and Automation, pp. 1710–1716, May 2009

    Google Scholar 

  34. D.L. Bowers, R. Lumia, Manipulation of unmodeled objects using intelligent grasping schemes. Trans. Fuzzy Syst. 11(3), 320–329 (2003)

    Google Scholar 

  35. A. Morales, T. Asfour, P. Azad, S. Knoop, R. Dillmann, Integrated grasp planning and visual object localization for a humanoid robot with five-fingered hands. In: International Conference on Intelligent Robots and Systems, 2006

    Google Scholar 

  36. Y. Li, N.S. Pollard, A shape matching algorithm for synthesizing humanlike enveloping grasps. In: IEEE-RAS International Conference on Humanoid Robots, 2005

    Google Scholar 

  37. J. Aleotti, S. Caselli, Robot grasp synthesis from virtual demonstration and topology-preserving environment reconstruction. In: International Conference on Intelligent Robots and Systems, 2007

    Google Scholar 

  38. A. Saxena, J. Driemeyer, A.Y. Ng, Robotic grasping of novel objects using vision. Int. J. Robot. Res. 27(2), 157–173 (2008)

    Google Scholar 

  39. C. Goldfeder, P.K. Allen, C. Lackner, R. Pelossof, Grasp planning via decomposition trees. In: International Conference on Robotics and Automation, 2007

    Google Scholar 

  40. A.T. Miller, S. Knoop, H.I. Christensen, P.K. Allen, Automatic grasp planning using shape primitives. In: International Conference on Robotics and Automation, 2003

    Google Scholar 

  41. P. Shilane, P. Min, M. Kazhdan, T. Funkhouser, The princeton shape benchmark. In: Shape Modeling and Applications, 2004

    Google Scholar 

  42. R.K. Sivamani, J. Goodman, N.V. Gitis, H.I. Maibach, Coefficient of friction: tribological studies in man an overview. Skin Res. Technol. 9(3), 227–234 (2003)

    Google Scholar 

  43. M. Ciocarlie, C. Goldfeder, P.K. Allen, Dimensionality reduction for hand-independent dexterous robotic grasping. In: International Conference on Intelligent Robots and Systems, 2007

    Google Scholar 

  44. R. Balasubramanian, L. Xu, P. Brook, J.R. Smith, Y. Matsuoka, Human-guided grasp measures improve grasp robustness on physical robot. In: International Conference on Robotics and Automation, pp. 2294–2301, 2010

    Google Scholar 

  45. R. Balasubramanian, L. Xu, P. Brook, J.R. Smith, Y. Matsuoka, Physical human interactive guidance: Identifying grasping principles from human planned grasps. IEEE Trans. Robot. (2012) (in press)

    Google Scholar 

  46. R. Pelossof, A. Miller, P. Allen, T. Jebara, An SVM learning approach to robotic grasping. In: International Conference on Robotics and Automation, 2004

    Google Scholar 

  47. M. Novotni, R. Klein, 3D zernike descriptors for content based shape retrieval. In: Solid Modeling and Applications, June 2003

    Google Scholar 

  48. C. Goldfeder, P.K. Allen, Autotagging to improve text search for 3d models. In: Joint Conference on Digital Libraries, 2008

    Google Scholar 

  49. C. Goldfeder, P.K. Allen, Data-driven grasping. Auton. Robot. 31(1), 1–20 (2011)

    Google Scholar 

  50. C. Goldfeder, M. Ciocarlie, J. Peretzman, H. Dang, P.K. Allen, Data-driven grasping with partial sensor data. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1278–1284, October 2009

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Hao Dang for his help in building the Columbia Grasp Database.

Author information

Authors and Affiliations

  1. Department of Computer Science, Columbia University, New York, NY, USA

    Peter K. Allen

  2. Willow Garage, Inc., Menlo Park, CA, USA

    Matei Ciocarlie

  3. Google Inc., New York, NY, USA

    Corey Goldfeder

Authors
  1. Peter K. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matei Ciocarlie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Corey Goldfeder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter K. Allen .

Editor information

Editors and Affiliations

  1. Oregon State University, Corvallis, Oregon, USA

    Ravi Balasubramanian

  2. Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona, USA

    Veronica J. Santos

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Allen, P.K., Ciocarlie, M., Goldfeder, C. (2014). Grasp Planning Using Low Dimensional Subspaces. In: Balasubramanian, R., Santos, V. (eds) The Human Hand as an Inspiration for Robot Hand Development. Springer Tracts in Advanced Robotics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-03017-3_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03017-3_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03016-6

  • Online ISBN: 978-3-319-03017-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation