Adapting Preshaped Grasping Movements Using Vision Descriptors

  • Oliver Krömer
  • Renaud Detry
  • Justus Piater
  • Jan Peters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6226)

Abstract

Grasping is one of the most important abilities needed for future service robots. In the task of picking up an object from between clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which are often not available. Therefore, methods are needed that execute grasps robustly even with imprecise information gathered only from standard stereo vision. We propose techniques that reactively modify the robot’s learned motor primitives based on non-parametric potential fields centered on the Early Cognitive Vision descriptors. These allow both obstacle avoidance, and the adapting of finger motions to the object’s local geometry. The methods were tested on a real robot, where they led to improved adaptability and quality of grasping actions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bard, C., Troccaz, J., Vercelli, G.: Shape analysis and hand preshaping for grasping. In: IROS Proceedings (1991)Google Scholar
  2. 2.
    Iberall, T.: Grasp planning for human prehension. In: ICAI Proceedings (1987)Google Scholar
  3. 3.
    Morales, A., Asfour, T., Azad, P., Knoop, S., Dillmann, R.: Integrated grasp planning and visual object localization for a humanoid robot with five-fingered hands. In: IROS, pp. 5663–5668 (2006)Google Scholar
  4. 4.
    Xue, Z., Kasper, A., Zoellner, J.M., Dillmann, R.: An automatic grasp planning system for service robots. In: Proceedings of International Conference on Advanced Robotics, ICAR (2009)Google Scholar
  5. 5.
    Bertram, D., Kuffner, J., Dillmann, R., Asfour, T.: An integrated approach to inverse kinematics and path planning for redundant manipulators. In: ICRA, pp. 1874–1879 (2006)Google Scholar
  6. 6.
    Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot Modeling and Control. In: WSE (2005)Google Scholar
  7. 7.
    Khatib, M.: Sensor-based motion control for mobile robots (1996)Google Scholar
  8. 8.
    Sabe, K., Fukuchi, M., Gutmann, J.-S., Ohashi, T., Kawamoto, K., Yoshigahara, T.: Obstacle avoidance and path planning for humanoid robots using stereo vision. In: ICRA, pp. 592–597 (2004)Google Scholar
  9. 9.
    And, S.L.: Visual sonar: Fast obstacle avoidance using monocular vision (2003)Google Scholar
  10. 10.
    Tegin, J., Ekvall, S., Kragic, D., Wikander, J., Iliev, B.: Demonstration based learning and control for automatic grasping. In: Demonstration based Learning and Control for Automatic Grasping (2008)Google Scholar
  11. 11.
    Miller, A.T., Knoop, S., Christensen, H.I., Allen, P.K.: Automatic grasp planning using shape primitives. In: Proceedings of the International Conference on Robotics and Automation, ICRA (2003)Google Scholar
  12. 12.
    Park, D.-H., Hoffmann, H., Pastor, P., Schaal, S.: Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields. In: IEEE International Conference on Humanoid Robots(HUMANOIDS) (2008)Google Scholar
  13. 13.
    Bley, F., Schmirgel, V., Kraiss, K.-F.: Mobile manipulation based on generic object knowledge. In: Proceedings of Robot and Human Interactive Communication, ROMAN (2006)Google Scholar
  14. 14.
    Hsiao, K., Nangeroni, P., Huber, M., Saxena, A., Ng, A.: Reactive grasping using optical proximity sensors. In: ICRA Proceedings (2009)Google Scholar
  15. 15.
    Steffen, J., Haschke, R., Ritter, H.: Experience-based and tactile-driven dynamic grasp control. In: IRS Proceedings (2007)Google Scholar
  16. 16.
    Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: NIPS (2003)Google Scholar
  17. 17.
    Schaal, S., Peters, J., Nakanishi, J., Ijspeert, A.: Learning movement primitives. In: ISRR Proceedings (2003)Google Scholar
  18. 18.
    Ijspeert, A.J., Nakanishi, J., Schaal, S.: Movement imitation with nonlinear dynamical systems in humanoid robots. In: ICRA (2002)Google Scholar
  19. 19.
    Pugeault, N.: Early Cognitive Vision: Feedback Mechanisms for the Disambiguation of Early Visual Representation. Vdm Verlag Dr. Mueller (2008)Google Scholar
  20. 20.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)MATHGoogle Scholar
  21. 21.
    Krueger, N., Lappe, M., Woergoetter, F.: Biologically motivated multimodal processing of visual primitives. The Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour (2004)Google Scholar
  22. 22.
    Chieffi, S., Gentilucci, M.: Coordination between the transport and the grasp components during prehension movements (1993)Google Scholar
  23. 23.
    Oztop, E., Kawato, M.: Models for the control of grasping. In: Sensorimotor Control of Grasping: Physiology and Pathophysiology. Cambridge University Press, Cambridge (2009)Google Scholar
  24. 24.
    Jeannerod, M.: Grasping Objects: The Hand as a Pattern Recognition Device. In: Perspectives of Motor Behaviour and Its Neural Basis (1997)Google Scholar
  25. 25.
    Graziano, M.S.: Progress in understanding spatial coordinate systems in the primate brain. In: Neuron (2006)Google Scholar
  26. 26.
    Oztop, E., Bradley, N.S., Arbib, M.A.: Infant grasp learning: a computational model (2004)Google Scholar
  27. 27.
    Detry, R., Kroemer, O., Popovic, M., Touati, Y., Baseski, E., Krueger, N., Peters, J., Piater, J.: Object-specific grasp affordance densities. In: ICDL (2009)Google Scholar
  28. 28.
    Jeannerod, M.: The study of hand movements during grasping. A historical perspective. In: Sensorimotor Control of Grasping: Physiology and Pathophysiology. Cambridge University Press, Cambridge (2009)Google Scholar
  29. 29.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)MATHGoogle Scholar
  30. 30.
    Detry, R., Pugeault, N., Piater, J.: Probabilistic pose recovery using learned hierarchical object models. In: International Cognitive Vision Workshop (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Oliver Krömer
    • 1
  • Renaud Detry
    • 1
  • Justus Piater
    • 1
  • Jan Peters
    • 1
  1. 1.Max Planck Inistitute for Biological CyberneticsTübignenGermany

Personalised recommendations