Skip to main content
SpringerLink
Log in
Book cover

The Human Hand as an Inspiration for Robot Hand Development pp 477–500Cite as

Physical Human Interactive Guidance: Identifying Grasping Principles from Human-Planned Grasps

  • Chapter
  • First Online:

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

Abstract

We present a novel and simple experimental method called Physical Human Interactive Guidance to study human-planned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose while the robot’s configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the Physical Human Interactive Guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object’s principal axes in the human-planned grasps (termed low skewness in this work), while the automated grasps used arbitrary wrist orientation. Preliminary tests shows that grasps with low skewness were significantly more robust than grasps with high skewness (77–93 %). We conclude with a detailed discussion of how the Physical Human Interactive Guidance method relates to existing methods for extracting the human principles for physical interaction.

Work was done when the authors were all at the University of Washington and Intel Labs Seattle.

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.

    http://www.barrett.com/robot/index.htm

  2. 2.

    http://www.barrett.com/robot/index.htm

  3. 3.

    http://www.ros.org/

  4. 4.

    In this particular experiment (see Fig. 2), a white rectangular box on which the objects were placed was used to align the object. This was only incidental to this experimental set-up, and any means of repeated accurate positioning of the object will suffice.

  5. 5.

    We also placed the objects randomly in three different locations on the table (left, right, and center with respect to the robot base) to ensure that the human-planned grasps were not unduly influenced by the specificity of the arm posture required for a particular location. Since we did not find any significant differences between the grasps from different locations in terms of the robot wrist and finger posture relative to the object, we combined all the human-planned grasps from the different locations into one set to be tested by the stationary robot.

  6. 6.

    http://grasping.cs.columbia.edu/

  7. 7.

    http://www.cyberglovesystems.com/

References

  1. R. Balasubramanian, L. Xu, P. Brook, J. R. Smith, Y. Matsuoka, Human-guided grasp measures improve grasp robustness on physical robot, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 2294–2301 (2010)

    Google Scholar 

  2. Y. Bekiroglu, J. Laaksonen, J. Jorgensen, V. Kyrki, D. Kragic, Assessing grasp stability based on learning and haptic data. IEEE Trans. Robot. 27(3), 619–629 (2011)

    Google Scholar 

  3. G.M. Bone, Y. Du, Multi-metric comparison of optimal 2d grasp planning algorithms, in Proceedings of IEEE International Conference on Robotics and Automation (2001)

    Google Scholar 

  4. L.Y. Chang, R.L. Klatzky, N.S. Pollard, Selection criteria for preparatory object rotation in manual lifting actions. J. Mot. Behav. 42(1), 11–27 (2010)

    Article  Google Scholar 

  5. L.Y. Chang, Y. Matsuoka, A kinematic thumb model for the act hand, in Proceedings of the 2006 IEEE International Conference on Robotics and Automation (2006)

    Google Scholar 

  6. L.Y. Chang, N.S. Pollard, Constrained least-squares optimization for robust estimation of center of rotation. J. Biomech. 40(6), 1392–1400 (2007)

    Google Scholar 

  7. E. Chinellato, A. Morales, R.B. Fisher, A.P. del Pobil, Visual quality measures for characterizing planar robot grasps. IEEE Trans. Syst. Man Cybernet. 35(1), 30–41 (2005)

    Google Scholar 

  8. A. Churchill, B. Hopkins, L. Ronnqvist, S. Vogt, Vision of the hand and environmental context in human prehension. Exp. Brain Res. 134, 81–89 (2000)

    Google Scholar 

  9. M.T. Ciocarlie, P.K. Allen, On-line interactive dexterous grasping, in Proceedings of Eurohaptics (2008)

    Google Scholar 

  10. S.T. Clanton, D.C. Wang, V.S. Chib, Y. Matsuoka, G.D. Stetten, Optical merger of direct vision with virtual images for scaled teleoperation. IEEE Trans. Vis. Comput. Graph. 12(2), 277–285 (2006)

    Article  Google Scholar 

  11. R.G. Cohen, D.A. Rosenbaum, Where grasps are made reveals how grasps are planned: generation and recall of motor plans. Exp. Brain Res. 157, 486–495 (2004). doi:10.1007/s00221-004-1862-9

    Article  Google Scholar 

  12. M.R. Cutkosky, On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans. Robot. Autom. 5(3), 269–279 (1989)

    Article  MathSciNet  Google Scholar 

  13. R. Diankov, J. Kuffner, OpenRAVE: A planning architecture for autonomous robotics. Technical Report CMU-RI-TR-08-34, The Robotics Institute, Pittsburgh, PA, July 2008

    Google Scholar 

  14. C. Fernández, M.A. Vicente, C. Pérez, O. Reinoso, R. Aracil, Learning to grasp from examples in telerobotics, in Proceeding Conference on Artificial Intelligence and Applications (2003)

    Google Scholar 

  15. C. Ferrari, J. Canny, Planning optimal grasps, in Proceeding of the IEEE International Conference on Robotics and Automation, pp. 2290–2295 (1992)

    Google Scholar 

  16. J. Friedman, T. Flash, Task-dependent selection of grasp kinematics and stiffness in human object manipulation. Cortex 43, 444–460 (2007)

    Article  Google Scholar 

  17. S.S.H.U. Gamage, J. Lasenby, New least squares solutions for estimating the average centre of rotation and the axis of rotation. J. Biomech. 35(1), 87–93, 01 (2002)

    Google Scholar 

  18. C. Goldfeder, M. Ciocarlie, H. Dang, P. Allen, The columbia grasp database, in Proceedings of International Conference on Robotics and Automation, pp. 1710–1716 (2009). doi:10.1109/ROBOT.2009.5152709

  19. W.B. Griffin, R.P. Findley, M.L. Turner, M.R. Cutkosky, Calibration and mapping of a human hand for dexterous telemanipulation, in Proceedings of ASME IMECE Conference on Haptic Interfaces for Virtual Environments and Teleoperator System Symposium (2000)

    Google Scholar 

  20. R.S. Johansson, G. Westling, Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Exp. Brain Res. 56(3), 550–564 (1984)

    Article  Google Scholar 

  21. S.H. Johnson-Frey, Whats so special about human tool use? Neuron (2003)

    Google Scholar 

  22. L.A. Jones, S.J. Lederman, Human Hand Function (Oxford University Press, Oxford, 2006)

    Google Scholar 

  23. S. Kakei, D.S. Hoffman, P.L. Strick, Muscle and movement representations in the primary motor cortex. Science 285(5436), 2136–2139 (1999)

    Article  Google Scholar 

  24. U. Kartoun, H. Stern, Y. Edan, Advances in e-engineering and digital enterprise technology-1: Proc. Int. Conf. on e-Engineering and Digital Enterprise Technology, chapter Virtual Reality Telerobotic System (John Wiley and Sons, New York, 2004)

    Google Scholar 

  25. D. Kirkpatrick, B. Mishra, C.K. Yap, Quantitative Steinitz’s theorms with applications to multifingered grasping, in ACM Symposium on Theory of Computing, pp. 341–351 (1990)

    Google Scholar 

  26. C. Lee, Learning Reduced-Dimension Models of Human Actions. PhD thesis, The Robotics Institute, Carnegie Mellon University, 2000

    Google Scholar 

  27. C. Lee, Y. Xu, Reduced-dimension representations of human performance data for human-to-robot skill transfer, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 84–90 (1998)

    Google Scholar 

  28. Z. Li, S.S. Sastry, Task-oriented optimal grasping by multifingered robot hands. IEEE J. Robot. Autom. 4(1), 32–44 (1988)

    Google Scholar 

  29. J. Lloyd, J. Beis, D. Pai, D. Lowe, Model-based telerobotics with vision, in Proceedings of IEEE International Conference on Robotics and Automation, vol. 2, pp. 1297–1304 (1997)

    Google Scholar 

  30. J. Lukos, C. Ansuini, M. Santello, Choice of contact points during multidigit grasping: effect of predictability of object center of mass location. J. Neurosci. 27(4), 3894–3903 (2007). doi:10.1523/JNEUROSCI.4693-06.2007

    Article  Google Scholar 

  31. A. Miller, P.K. Allen, Graspit!: a versatile simulator for robotic grasping, in IEEE Robotics and Automation Magazine (2004)

    Google Scholar 

  32. B. Mirtich, J. Canny, Easily computable optimum grasps in 2-D and 3-D, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 739–747 (1994)

    Google Scholar 

  33. N. Miyata, M. Kouchi, T. Kurihara, M. Mochimaru, Modeling of human hand link structure from optical motion capture data, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2129–2135 (2004)

    Google Scholar 

  34. A. Morales, E. Chinellato, A. Fagg, A. del Pobil, An active learning approach for assessing robot grasp reliability, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems 2004 (IROS 2004), vol. 1, pp. 485–490 (2004)

    Google Scholar 

  35. J. Ponce, B. Faveqon, On computing three-finger force-closure grasps of polygonal objects. IEEE Trans. Robot. Autom. 11, 868–881 (1995)

    Google Scholar 

  36. M. Ralph, M. Moussa, An integrated system for user-adaptive robotic grasping. IEEE Trans. Robot. 26(4), 698–709 (2010)

    Google Scholar 

  37. J. Romano, K. Hsiao, G. Niemeyer, S. Chitta, K. Kuchenbecker, Human-inspired robotic grasp control with tactile sensing. IEEE Trans. Robot. 27(6), 1067–1079 (2011)

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  40. A. Saxena, L.L.S. Wong, A. Ng, Learning grasp strategies with partial shape information, in Proceedings of AAAI Conference on Artificial Intelligence (2008)

    Google Scholar 

  41. K.B. Shimoga, Robot grasp synthesis algorithms: a survey. Int. J. Robot. Res. (1996). doi:10.1177/027836499601500302

    Google Scholar 

  42. W.T. Townsend, The BarrettHand grasper—programmably flexible part handling and assembly. Ind. Robot Int. J. 27(3), 181–188 (2000)

    Article  MathSciNet  Google Scholar 

  43. M. Veber, T. Bajd, Assessment of human hand kinematics, in Proceedings of 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 2966–2971 (2006)

    Google Scholar 

  44. G. Westling, R. Johansson, Factors influencing the force control during precision grip. Exp. Brain Res. 53, 277–284 (1984)

    Article  Google Scholar 

  45. R. Wistort, J.R. Smith, Electric field servoing for robotic manipulation, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (2008)

    Google Scholar 

  46. D.M. Wolpert, Z. Ghahramani, M.I. Jordan, Perceptual distortion contributes to the curvature of human reaching movements. Exp. Brain Res. 98, 153–156 (1994)

    Article  Google Scholar 

  47. V.M. Zatsiorsky, M.L. Latash, Multifinger prehension: an overview. J. Motor Behav. 40(5), 446–475 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank Brian Mayton for help with the robot experiment set-up and Louis LeGrand for interesting discussions on grasp metrics. Gratitude is also due to Matei Ciocarlie and Peter Allen of the GraspIt! team for helping the authors use the GraspIt! code.

Author information

Authors and Affiliations

  1. School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA

    Ravi Balasubramanian

  2. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Ling Xu

  3. Department of Computer Science, The University of Washington, Seattle, WA, USA

    Peter D. Brook, Joshua R. Smith & Yoky Matsuoka

Authors
  1. Ravi Balasubramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ling Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter D. Brook
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joshua R. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yoky Matsuoka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ravi Balasubramanian .

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

Balasubramanian, R., Xu, L., Brook, P.D., Smith, J.R., Matsuoka, Y. (2014). Physical Human Interactive Guidance: Identifying Grasping Principles from Human-Planned Grasps. 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_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03017-3_22

  • 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