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Robust realtime physics-based motion control for human grasping

Published:01 November 2013Publication History
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Abstract

This paper presents a robust physics-based motion control system for realtime synthesis of human grasping. Given an object to be grasped, our system automatically computes physics-based motion control that advances the simulation to achieve realistic manipulation with the object. Our solution leverages prerecorded motion data and physics-based simulation for human grasping. We first introduce a data-driven synthesis algorithm that utilizes large sets of prerecorded motion data to generate realistic motions for human grasping. Next, we present an online physics-based motion control algorithm to transform the synthesized kinematic motion into a physically realistic one. In addition, we develop a performance interface for human grasping that allows the user to act out the desired grasping motion in front of a single Kinect camera. We demonstrate the power of our approach by generating physics-based motion control for grasping objects with different properties such as shapes, weights, spatial orientations, and frictions. We show our physics-based motion control for human grasping is robust to external perturbations and changes in physical quantities.

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  1. Amor, H. B., Heumer, G., Jung, B., and Vitzthum, A. 2008. Grasp synthesis from low-dimensional probabilistic grasp models. Comput. Animat. Virtual Worlds 19, 3--4 (Sept.), 445--454. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Baerentzen, J., and Aanaes, H. 2005. Signed distance computation using the angle weighted pseudonormal. IEEE Transactions on Visualization and Computer Graphics 11, 3, 243--253. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bishop, C. 1996. Neural Network for Pattern Recognition. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Clerc, M., and Kennedy, J. 2002. The particle swarm-- explosion, stability, and convergence in a multidimensional complex space. In IEEE Transactions on Evolutionary Computation. 6(1):58--73. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Elkoura, G., and Singh, K. 2003. Handrix: animating the human hand. In Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation, Eurographics Association, 110--119. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Jörg, S., Hodgins, J., and Safonova, A. 2012. Data-driven finger motion synthesis for gesturing characters. ACM Trans. Graph. 31, 6 (Nov.), 189:1--189:7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Kry, P. G., and Pai, D. K. 2006. Interaction capture and synthesis. ACM Trans. Graph. 25, 3, 872--880. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Kyota, F., and Saito, S. 2012. Fast grasp synthesis for various shaped objects. Comp. Graph. Forum 31, 2pt3 (May), 765--774. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Lee, S.-H., and Goswami, A. 2010. Ground reaction force control at each foot: A momentum-based humanoid balance controller for non-level and non-stationary ground. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 3157--3162.Google ScholarGoogle Scholar
  10. Li, Y., Fu, J. L., and Pollard, N. S. 2007. Data-driven grasp synthesis using shape matching and task-based pruning. IEEE Transactions on Visualization and Computer Graphics 13, 4 (July), 732--747. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Liu, C. K. 2008. Synthesis of interactive hand manipulation. In Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA '08, 163--171. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Liu, C. K. 2009. Dextrous manipulation from a grasping pose. ACM Trans. Graph. 28, 3, 59:1--59:6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Miller, A. T., and Allen, P. K. 1999. Examples of 3d grasp quality computations. In Proceedings of IEEE International Conference on Robotics and Automation, 1240--1246.Google ScholarGoogle Scholar
  14. Miller, A. T., Knoop, S., Christensen, H. I., and Allen, P. K. 2003. Automatic grasp planning using shape primitives. Proceedings of IEEE International Conference on Robotics and Automation, 1824--1829.Google ScholarGoogle Scholar
  15. Min, J., Chen, Y.-L., and Chai, J. 2009. Interactive Generation of Human Animation with Deformable Motion Models. ACM Transactions on Graphics. 29(1): article No. 9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Mordatch, I., Popović, Z., and Todorov, E. 2012. Contact-invariant optimization for hand manipulation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA '12, 137--144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Oikonomidis, I., Kyriazis, N., and Argyros, A. 2011. Efficient model-based 3d tracking of hand articulations using kinect. In British Machine Vision Conference.Google ScholarGoogle Scholar
  18. Pollard, N., and Reitsma, P. 2001. Animation of Humanlike Characters: Dynamic Motion Filtering with A Physically Plausible Contact Model. In In Yale Workshop on Adaptive and Learning Systems.Google ScholarGoogle Scholar
  19. Pollard, N. S., and Zordan, V. B. 2005. Physically based grasping control from example. In Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation, SCA '05, 311--318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Pollard, N. S. 2004. Closure and quality equivalence for efficient synthesis of grasps from examples. International Journal of Robotics Research 23, 6, 595--614.Google ScholarGoogle ScholarCross RefCross Ref
  21. Rijpkema, H., and Girard, M. 1991. Computer animation of knowledge-based human grasping. SIGGRAPH Comput. Graph. 25, 4 (July), 339--348. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Sanso, R. M., and Thalmann, D. 1994. A hand control and automatic grasping system for synthetic actors. Computer Graphics Forum 13, 3, 167--177.Google ScholarGoogle ScholarCross RefCross Ref
  23. Sorkine, O., Cohen-Or, D., Lipman, Y., Alexa, M., Rössl, C., and Seidel, H. 2004. Laplacian surface editing. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, ACM, 175--184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Suárez, R., Cornellà, J., and Garzón, M. R. 2006. Grasp quality measures. Institut d'Organització i Control de Sistemes Industrials.Google ScholarGoogle Scholar
  25. Vicon Systems, 2012. http://www.vicon.com.Google ScholarGoogle Scholar
  26. Wang, Y., Min, J., Zhang, J., Liu, Y., Xu, F., Dai, Q., and Chai, J. 2013. Video-based hand manipulation capture through composite motion control. ACM Trans. Graph. 32, 4 (July), 43:1--43:14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Ye, Y., and Liu, C. K. 2012. Synthesis of detailed hand manipulations using contact sampling. ACM Trans. Graph. 31, 4 (July), 41:1--41:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Zhao, W., Chai, J., and Xu, Y.-Q. 2012. Combining marker-based mocap and rgb-d camera for acquiring high-fidelity hand motion data. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA '12, 33--42. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Transactions on Graphics
          ACM Transactions on Graphics  Volume 32, Issue 6
          November 2013
          671 pages
          ISSN:0730-0301
          EISSN:1557-7368
          DOI:10.1145/2508363
          Issue’s Table of Contents

          Copyright © 2013 ACM

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          Publication History

          • Published: 1 November 2013
          Published in tog Volume 32, Issue 6

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