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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- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Bishop, C. 1996. Neural Network for Pattern Recognition. Cambridge University Press. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Kry, P. G., and Pai, D. K. 2006. Interaction capture and synthesis. ACM Trans. Graph. 25, 3, 872--880. Google ScholarDigital Library
- Kyota, F., and Saito, S. 2012. Fast grasp synthesis for various shaped objects. Comp. Graph. Forum 31, 2pt3 (May), 765--774. Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Liu, C. K. 2009. Dextrous manipulation from a grasping pose. ACM Trans. Graph. 28, 3, 59:1--59:6. Google ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Oikonomidis, I., Kyriazis, N., and Argyros, A. 2011. Efficient model-based 3d tracking of hand articulations using kinect. In British Machine Vision Conference.Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Rijpkema, H., and Girard, M. 1991. Computer animation of knowledge-based human grasping. SIGGRAPH Comput. Graph. 25, 4 (July), 339--348. Google ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- Suárez, R., Cornellà, J., and Garzón, M. R. 2006. Grasp quality measures. Institut d'Organització i Control de Sistemes Industrials.Google Scholar
- Vicon Systems, 2012. http://www.vicon.com.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
Index Terms
- Robust realtime physics-based motion control for human grasping
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