Advertisement

Simply Grasping Simple Shapes: Commanding a Humanoid Hand with a Shape-Based Synergy

  • Logan C. Farrell
  • Troy A. Dennis
  • Julia Badger
  • Marcia K. O’MalleyEmail author
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

Despite rapid advancements in dexterity and mechanical design, the utility of humanoid robots outside of a controlled laboratory setting is limited in part due to the complexity involved in programming robots to grasp common objects. There exists a need for an efficient method to command high degree-of-freedom (DoF) position-controlled dexterous manipulators to grasp a range of objects such that explicit models are not needed for every interaction. The authors propose a method termed geometrical synergies that, similar to the neuroscience concept of postural synergies, aims to decrease the commanded DoF of the humanoid hand. In the geometrical synergy approach, the method relies on grasp design based on intuitive measurements of the object to be grasped, in contrast to postural synergy methods that focus on the principal components of human grasps to determine robot hand joint commands. For this paper, a synergy was designed to grasp cylinder-shaped objects. Using the SynGrasp toolbox, a model of a twelve-DoF hand was created to perform contact analysis around a small set of cylinders defined by a single variable, diameter. Experiments were performed with the robot to validate and update the synergy-based models. Successful manipulation of a large range of cylindrical objects not previously introduced to the robot was demonstrated. This geometric synergy-based grasp planning method can be applied to any position-controlled humanoid hand to decrease the number of commanded DoF based on simple, measurable inputs in order to grasp commonly shaped objects. This method has the potential to vastly expand the library of objects the robot can manipulate.

Keywords

Manipulation Grasp Synergy Humanoid Dexterous Hand 

References

  1. 1.
    Badger, J., Hulse, A., Tayler, R., Curtis, A., Gooding, D., Thackston, A.: Model-based robotic dynamic motion control for the Robonaut 2 humanoid robot. In: Proceedings of the IEEE-RAS international conference on humanoid robots (Humanoids), pp. 62–67, Atlanta, GA (2013). https://doi.org/10.1109/HUMANOIDS.2013.7029956, http://ieeexplore.ieee.org.ezproxy.rice.edu/stamp/stamp.jsp?tp=&arnumber=7029956&isnumber=7029946
  2. 2.
    Bicchi, A.: On the closure properties of robotic grasping. 14(4), 319–334Google Scholar
  3. 3.
    Bridgwater, L.B., Ihrke, C.A., Diftler, M.A., Abdallah, M.E., Radford, N.A., Rogers, J.M., Yayathi, S., Askew, R.S., Linn, D.M.: The Robonaut 2 hand - designed to do work with tools. In: 2012 IEEE International Conference on Robotics and Automation, pp. 3425–3430. IEEE, Minnesota (2012)Google Scholar
  4. 4.
    Catalano, M.G., Grioli, G., Serio, A., Farnioli, E., Piazza, C., Bicchi, A.: Adaptive synergies for a humanoid robot hand. In: 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 7–14. IEEE, Osaka (2012)Google Scholar
  5. 5.
    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, Citeseer (2007)Google Scholar
  6. 6.
    Ciocarlie, M., Goldfeder, C., Allen, P.: Dimensionality reduction for hand-independent dexterous robotic grasping. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3270–3275. IEEE, San Diego (2007)Google Scholar
  7. 7.
    Ciocarlie, M.T., Allen, P.K.: Hand posture subspaces for dexterous robotic grasping. Int. J. Robot. Res. 28(7), 851–867 (2009)CrossRefGoogle Scholar
  8. 8.
    Cutkosky, M.R.: On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans. Robot. Autom. 5(3), 269–279 (1989)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Diftler, M.A., Mehling, J.S., Abdallah, M.E., Radford, N.A., Bridgwater, L.B., Sanders, A.M., Askew, R.S., Linn, D.M., Yamokoski, J.D., Permenter, F.A., Hargrave, B.K., Platt, R., Savely, R.T., Ambrose, R.O.: Robonaut 2 - the first humanoid robot in space. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2178–2183 (2011)Google Scholar
  10. 10.
    García, N., Suárez, R., Rosell, J.: Task-dependent synergies for motion planning of an anthropomorphic dual-arm system. IEEE Trans. Robot. 33(3), 756–764 (2017). JuneCrossRefGoogle Scholar
  11. 11.
    Grebenstein, M., Chalon, M., Hirzinger, G., Siegwart, R.: Antagonistically driven finger design for the anthropomorphic DLR hand arm system. In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, pp. 609–616. IEEE, Nashville (2010)Google Scholar
  12. 12.
    Hart, S., Dinh, P., Hambuchen, K.: The affordance template ROS package for robot task programming. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 6227–6234 (2015)Google Scholar
  13. 13.
    Johnson, M., Shrewsbury, B., Bertrand, S., Wu, T., Duran, D., Floyd, M., Abeles, P., Stephen, D., Mertins, N., Lesman, A., Carff, J., Rifenburgh, W., Kaveti, P., Straatman, W., Smith, J., Griffioen, M., Layton, B., de Boer, T., Koolen, T., Neuhaus, P., Pratt, J.: Team IHMC’s lessons learned from the DARPA robotics challenge trials. J. Field Robot. 32(2), 192–208 (2015)CrossRefGoogle Scholar
  14. 14.
    Jörntell, H.: Synergy control in subcortical circuitry: insights from neurophysiology. In: Human and Robot Hands, pp. 61–68. Springer, Cham (2016)Google Scholar
  15. 15.
    Kaneko, K., Harada, K., Kanehiro, F., Miyamori, G., Akachi, K.: Humanoid robot HRP-3. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2471–2478. IEEE, Nice (2008)Google Scholar
  16. 16.
    Malvezzi, M., Gioioso, G., Salvietti, G., Prattichizzo, D., Bicchi, A.: Syngrasp: a MATLAB toolbox for grasp analysis of human and robotic hands. In: 2013 IEEE International Conference on Robotics and Automation, pp. 1088–1093 (2013)Google Scholar
  17. 17.
    Nagatani, K., Kiribayashi, S., Okada, Y., Otake, K., Yoshida, K., Tadokoro, S., Nishimura, T., Yoshida, T., Koyanagi, E., Fukushima, M., Kawatsuma, S.: Emergency response to the nuclear accident at the Fukushima Daiichi nuclear power plants using mobile rescue robots. J. Field Robot. 30(1), 44–63 (2013)CrossRefGoogle Scholar
  18. 18.
    Roa, M.A., Argus, M.J., Leidner, D., Borst, C., Hirzinger, G.: Power grasp planning for anthropomorphic robot hands. In: 2012 IEEE International Conference on Robotics and Automation, pp. 563–569Google Scholar
  19. 19.
    Salvietti, G., Gioioso, G., Malvezzi, M., Prattichizzo, D.: How to map human hand synergies onto robotic hands using the syngrasp matlab toolbox. In: Human and Robot Hands, pp. 195–209. Springer, Cham (2016)Google Scholar
  20. 20.
    Santello, M., Flanders, M., Soechting, J.F.: Postural hand synergies for tool use. J. Neurosci. 18(23), 10105–10115 (1998)CrossRefGoogle Scholar
  21. 21.
    Santina, C.D., Grioli, G., Catalano, M., Brando, A., Bicchi, A.: Dexterity augmentation on a synergistic hand: the Pisa/IIT SoftHand+. In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 497–503 (2015)Google Scholar
  22. 22.
    Yanco, H.A., Norton, A., Ober, W., Shane, D., Skinner, A., Vice, J.: Analysis of human-robot interaction at the DARPA robotics challenge trials. J. Field Robot. 32(3), 420–444 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Logan C. Farrell
    • 1
    • 2
  • Troy A. Dennis
    • 1
  • Julia Badger
    • 2
  • Marcia K. O’Malley
    • 1
    Email author
  1. 1.Mechanical EngineeringRice UniversityHoustonUSA
  2. 2.Johnson Space Center, NASAHoustonUSA

Personalised recommendations