Abstract
A physics-based control system that tracks a single motion trajectory produces high-quality animations, but does not recover from large disturbances that require deviating from this tracked trajectory. In order to enhance the responsiveness of physically simulated characters, we introduce algorithms that construct composite controllers that track multiple trajectories in parallel instead of sequentially switching from one control to the other. The composite controllers can blend or transition between different path controllers at arbitrary times according to the current system state. As a result, a composite control system generates both high-quality animations and natural responses to certain disturbances. We demonstrate its potential for improving robustness in performing several locomotion tasks. Then we consolidate these controllers into graphs that allow us to direct the character in real time.
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- Burridge, R. R., Rizzi, A. A., and Koditschek, D. E. 1999. Sequential composition of dynamically desterous robot behaviours. Int. J. of Robot. Res. 18, 6, 534--555.Google ScholarCross Ref
- Coros, S., Beaudoin, P., and van de Panne, M. 2009. Robust task-based control policies for physics-based Characters. ACM Trans. Graph. 28, 5. Google ScholarDigital Library
- Coros, S., Beaudoin, P., Yin, K., and van de Panne, M. 2008. Synthesis of constrained walking skills. ACM Trans. Graph. 27, 5, 113:1--113:9. Google ScholarDigital Library
- da Silva, M., Abe, Y., and Popović, J. 2008. Interactive simulation of stylized human locomotion. ACM Trans. Graph. 27, 3, 82:1--82:10. Google ScholarDigital Library
- da Silva, M., Durand, F., and Popović, J. 2009. Linear bellman combination for control of character animation. ACM Trans. Graph. 28, 3, 82:1--82:10. Google ScholarDigital Library
- Erez, T. and Smart, W. 2007. Bipedal walking on rough terrain using manifold control. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS). 1539--1544.Google Scholar
- Faloutsos, P., van de Panne, M., and Terzopoulos, D. 2001. Composable controllers for physics-based character animation. In Proceedings of ACM SIGGRAPH. Annual Conference Series. 251--260. Google ScholarDigital Library
- Fleming, W. H. 1978. Exit probabilities and optimal stochastic control. Appl. Math. Optimiz. 4, 329--346.Google ScholarDigital Library
- Hodgins, J. K. and Pollard, N. S. 1997. Adapting simulated behaviors for new characters. In Proceedings of SIGGRAPH. Annual Conference Series. 153--162. Google ScholarDigital Library
- Hodgins, J. K., Wooten, W. L., Brogan, D. C., and O'Brien, J. F. 1995. Animating human athletics. In Proceedings of ACM SIGGRAPH. Annual Conference Series. 71--78. Google ScholarDigital Library
- Holland, C. 1977. A new energy characterization of the smallest eigenvalue to the schrödinger equation. Comm. Pure Appl. Math. 30, 755--765.Google ScholarCross Ref
- Laszlo, J. F., van de Panne, M., and Fiume, E. L. 1996. Limit cycle control and its application to the animation of balancing and walking. In Proceedings of SIGGRAPH. Annual Conference Series. 155--162. Google ScholarDigital Library
- Lee, Y., Kim, S., and Lee, J. 2010. Data-Driven biped control. ACM Trans. Graph. 29, 4. Google ScholarDigital Library
- Muico, U., Lee, Y., Popović, J., and Popović, Z. 2009. Contact-aware nonlinear control of dynamic characters. ACM Trans. Graph. 28, 3, 81:1--81:9. Google ScholarDigital Library
- Pollard, N. S. and Behmaram-Mosavat, F. 2000. Force-Based motion editing for locomotion tasks. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). 663--669.Google Scholar
- Raibert, M., Blankespoor, K., Nelson, G., and Playter, R. 2008. Bigdog, the rough-terrain quadruped robot. In Proceedings of the International Federation of Automatic Control.Google Scholar
- Raibert, M. H. and Hodgins, J. K. 1991. Animation of dynamic legged locomotion. In Proceedings of SIGGRAPH. Annual Conference Series. 349--358. Google ScholarDigital Library
- Schittkowski, K. 2005. QL: A Fortran Code for Convex Quadratic Programming -- User's Guide Version 2.11. Department of Mathematics, University of Bayreuth.Google Scholar
- Sok, K. W., Kim, M., and Lee, J. 2007. Simulating biped behaviors from human motion data. ACM Trans. Graph. 26, 3, 107:1--107:9. Google ScholarDigital Library
- Todorov, E. 2009a. Compositionality of optimal control laws. In Advances in Neural Information Processing Systems (NIPS). Vol. 22. 1856--1864.Google Scholar
- Todorov, E. 2009b. Efficient computation of optimal actions. In Proceedings of the National Acad. Sci. 106, 11478--11483.Google ScholarCross Ref
- Wooten, W. L. and Hodgins, J. K. 2000. Simulating leaping, tumbling, landing and balancing humans. In Proceedings of the International Conference on Robotics and Automation (ICRA), 656--662.Google Scholar
- Ye, Y. and Liu, C. K. 2010. Optimal feedback control for character animation using an abstract model. ACM Trans. Graph. 29, 4. Google ScholarDigital Library
- Yin, K., Coros, S., Beaudoin, P., and van de Panne, M. 2008. Continuation methods for adapting simulated skills. ACM Trans. Graph. 27, 3, 81:1--81:7. Google ScholarDigital Library
- Yin, K., Loken, K., and van de Panne, M. 2007. SIMBICON: Simple biped locomotion control. In Proceedings of ACM SIGGRAPH: ACM SIGGRAPH 2007 Papers. 105. Google ScholarDigital Library
- Zordan, V. B. and Hodgins, J. K. 2002. Motion capture-driven simulations that hit and react. In Proceedings of the Symposium on Computer Animation (SCA). 89--96. Google ScholarDigital Library
Index Terms
- Composite control of physically simulated characters
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