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