“Sampling-based contact-rich motion control” by Liu, Yin, Panne, Shao and Xu

Human motions are the product of internal and external forces, but these forces are very difficult to measure in a general setting. Given a motion capture trajectory, we propose a method to reconstruct its open-loop control and the implicit contact forces. The method employs a strategy based on randomized sampling of the control within user-specified bounds, coupled with forward dynamics simulation. Sampling-based techniques are well suited to this task because of their lack of dependence on derivatives, which are difficult to estimate in contact-rich scenarios. They are also easy to parallelize, which we exploit in our implementation on a compute cluster. We demonstrate reconstruction of a diverse set of captured motions, including walking, running, and contact rich tasks such as rolls and kip-up jumps. We further show how the method can be applied to physically based motion transformation and retargeting, physically plausible motion variations, and reference-trajectory-free idling motions. Alongside the successes, we point out a number of limitations and directions for future work.

1. Barbič, J., and Popović, J. 2008. Real-time control of physically based simulations using gentle forces. ACM Trans. Graph. 27, 5. Google ScholarDigital Library
2. Brubaker, M. A., Sigal, L., and Fleet, D. J. 2009. Estimating contact dynamics. In IEEE International Conference on Computer Vision (ICCV).Google Scholar
3. Chenney, S., and Forsyth, D. A. 2000. Sampling plausible solutions to multi-body constraint problems. In SIGGRAPH ’00: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, 219–228. Google ScholarDigital Library
4. Choi, M. G., Lee, J., and Shin, S. Y. 2003. Planning biped locomotion using motion capture data and probabilistic roadmaps. ACM Trans. Graph. 22, 2, 182–203. Google ScholarDigital Library
5. Faloutsos, P., van de Panne, M., and Terzopoulos, D. 2001. Composable controllers for physics-based character animation. In Proceedings of SIGGRAPH 2001, 251–260. Google ScholarDigital Library
6. Hodgins, J. K., and Pollard, N. S. 1997. Adapting simulated behaviors for new characters. In SIGGRAPH ’97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, 153–162. Google ScholarDigital Library
7. James, D. L., and Fatahalian, K. 2003. Precomputing interactive dynamic deformable scenes. In SIGGRAPH ’03: ACM SIGGRAPH 2003 Papers, 879–887. Google ScholarDigital Library
8. Jenkins, O., and Mataric, M. 2003. Automated derivation of behavior vocabularies for autonomous humanoid motion. In 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS). Google ScholarDigital Library
9. Jordan, M. I., and Wolpert, D. M. 1999. Computational motor control. In The Cognitive Neurosciences, M. Gazzaniga, Ed. MIT Press.Google Scholar
10. Kavraki, L. E., Svestka, P., Latombe, J.-C., and Overmars, M. H. 1996. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics & Automation 12, 4, 566–580.Google ScholarCross Ref
11. Kawato, M. 1999. Internal models for motor control and trajectory planning. Current Opinion in Neurobiology 9, 718–727.Google ScholarCross Ref
12. Kim, M., Hyun, K., Kim, J., and Lee, J. 2009. Synchronized multi-character motion editing. ACM Trans. Graph. 28, 3. Google ScholarDigital Library
13. Kovar, L., Gleicher, M., and Pighin, F. 2002. Motion graphs. In SIGGRAPH ’02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques, ACM, New York, NY, USA, 473–482. Google ScholarDigital Library
14. Lau, M., Bar-Joseph, Z., and Kuffner, J. 2009. Modeling spatial and temporal variation in motion data. ACM Trans. Graph. 28, 5. Google ScholarDigital Library
15. Laurent, F., Lionel, R., Christine, D., and Marie-Paule, C. 2004. Animal gaits from video. In SCA ’04: Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation, 277–286. Google ScholarDigital Library
16. LaValle, S. M., and Kuffner, J. J. 2000. Rapidly-exploring random trees: Progress and prospects. In Proceedings Workshop on the Algorithmic Foundations of Robotics.Google Scholar
17. LaValle, S. M. 2006. Planning Algorithms. Cambridge University Press. Google ScholarDigital Library
18. Liu, C. K., Hertzmann, A., and Popović, Z. 2006. Composition of complex optimal multi-character motions. In SCA ’06: Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation, Eurographics Association, 215–222. Google ScholarDigital Library
19. Macchietto, A., Zordan, V., and Shelton, C. R. 2009. Momentum control for balance. ACM Trans. Graph. 28, 3. Google ScholarDigital Library
20. Muico, U., Lee, Y., Popović, J., and Popović, Z. 2009. Contact-aware nonlinear control of dynamic characters. ACM Trans. Graph. 28, 3. Google ScholarDigital Library
21. Ngo, J. T., and Marks, J. 1993. Spacetime constraints revisited. In Proceedings of SIGGRAPH 1993, 343–350. Google ScholarDigital Library
22. Payton, C., and Bartlett, R. 2007. Biomechanical Evaluation of Movement in Sport and Exercise. Routledge.Google Scholar
23. Perlin, K. 1995. Real time responsive animation with personality. IEEE Transactions on Visualization and Computer Graphics 1, 1, 5–15. Google ScholarDigital Library
24. Sharon, D., and van de Panne, M. 2005. Synthesis of controllers for stylized planar bipedal walking. In ICRA05, 2387–2392.Google Scholar
25. Sims, K. 1994. Evolving virtual creatures. In Proceedings of SIGGRAPH 1994, 15–22. Google ScholarDigital Library
26. Sok, K. W., Kim, M., and Lee, J. 2007. Simulating biped behaviors from human motion data. ACM Trans. Graph. 26, 3, Article 107. Google ScholarDigital Library
27. Tsianos, K. I., Sucan, I. A., and Kavraki, L. E. 2007. Sampling-based robot motion planning: Towards realistic applications. Computer Science Review 1 (August), 2–11. Google ScholarDigital Library
28. Twigg, C. D., and James, D. L. 2007. Many-worlds browsing for control of multibody dynamics. ACM Trans. Graph. 26, 3. Google ScholarDigital Library
29. Van de Panne, M., and Fiume, E. 1993. Sensor-actuator networks. In Proc. ACM SIGGRAPH, ACM, 335–342. Google ScholarDigital Library
30. Wampler, K., and Popović, Z. 2009. Optimal gait and form for animal locomotion. ACM Trans. Graph. 28, 3, Article 60. Google ScholarDigital Library
31. Wang, J. M., Fleet, D. J., and Hertzmann, A. 2009. Optimizing walking controllers. ACM Trans. Graph. 28, 5, Article 168. Google ScholarDigital Library
32. Witkin, A., and Kass, M. 1988. Spacetime constraints. In Proceedings of SIGGRAPH 1988, 159–168. Google ScholarDigital Library
33. Yamane, K., Kuffner, J. J., and Hodgins, J. K. 2004. Synthesizing animations of human manipulation tasks. ACM Trans. Graph. 23, 3, 532–539. Google ScholarDigital Library
34. Yin, K., Cline, M. B., and Pai, D. K. 2003. Motion perturbation based on simple neuromotor control models. In PG’03: Proceedings of the 11th Pacific Conference on Computer Graphics and Applications, 445–449. Google ScholarDigital Library
35. Yin, K., Loken, K., and van de Panne, M. 2007. Simbicon: Simple biped locomotion control. ACM Trans. Graph. 26, 3, Article 105. Google ScholarDigital Library
36. Yin, K., Coros, S., Beaudoin, P., and van de Panne, M. 2008. Continuation methods for adapting simulated skills. ACM Trans. Graph. 27, 3, Article 81. Google ScholarDigital Library
37. Zordan, V. B., and Hodgins, J. K. 2002. Motion capture-driven simulations that hit and react. In ACM SIGGRAPH Symposium on Computer Animation, 89–96. Google ScholarDigital Library
38. Zordan, V. B., Majkowska, A., Chiu, B., and Fast, M. 2005. Dynamic response for motion capture animation. ACM Trans. Graph., 697–701. Google ScholarDigital Library