“Push-recovery stability of biped locomotion”
Conference:
Type(s):
Title:
- Push-recovery stability of biped locomotion
Session/Category Title: Faces and Characters
Presenter(s)/Author(s):
Abstract:
Biped controller design pursues two fundamental goals; simulated walking should look human-like and robust against perturbation while maintaining its balance. Normal gait is a pattern of walking that humans normally adopt in undisturbed situations. It has previously been postulated that normal gait is more energy efficient than abnormal or impaired gaits. However, it is not clear whether normal gait is also superior to abnormal gait patterns with respect to other factors, such as stability. Understanding the correlation between gait and stability is an important aspect of biped controller design. We studied this issue in two sets of experiments with human participants and a simulated biped. The experiments evaluated the degree of resilience to external pushes for various gait patterns. We identified four gait factors that affect the balance-recovery capabilities of both human and simulated walking. We found that crouch gait is significantly more stable than normal gait against lateral push. Walking speed and the timing/magnitude of disturbance also affect gait stability. Our work would provide a potential way to compare the performance of biped controllers by normalizing their output gaits and improve their performance by adjusting these decisive factors.
References:
1. Al Borno, M., De Lasa, M., and Hertzmann, A. 2013. Trajectory optimization for full-body movements with complex contacts. IEEE Transactions on Visualization and Computer Graphics 19(8), 1405–1414.
2. Bauby, C. E., and Kuo, A. D. 2000. Active control of lateral balance in human walking. Journal of Biomechanics 33, 11 (Nov.), 1433–1440.
3. Brauer, S. G., Woollacott, M., and Shumway-Cook, A. 2001. The Interacting Effects of Cognitive Demand and Recovery of Postural Stability in Balance-Impaired Elderly Persons. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 56, 8 (Jan.), M489–M496.
4. Bruijn, S. M., van Dien, J. H., Meijer, O. G., and Beek, P. J. 2009. Statistical precision and sensitivity of measures of dynamic gait stability. Journal of Neuroscience Methods 178, 2 (Apr.), 327–333.
5. Cnaan, A., Laird, N. M., and Slasor, P. 1997. Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine 16(20), 2349–2380.
6. Collins, S. H., Ruina, A. L., Tedrake, R., and Wisse, M. 2005. Efficient bipedal robots based on passive-dynamic walkers. Science 307, 1082–1085.
7. Coros, S., Beaudoin, P., and Panne, M. v. c. 2010. Generalized biped walking control. ACM Transactions on Graphics (SIGGRAPH 2010) 29(4).
8. da Silva, M., Abe, Y., and Popović, J. 2008. Interactive simulation of stylized human locomotion. ACM Transactions on Graphics (SIGGRAPH 2008) 27(3).
9. de Lasa, M., Mordatch, I., and Hertzmann, A. 2010. Feature-based locomotion controllers. ACM Transactions on Graphics (SIGGRAPH 2010) 29(4).
10. England, S. A., and Granata, K. P. 2007. The influence of gait speed on local dynamic stability of walking. Gait & Posture 25, 2 (Feb.), 172–178.
11. Gage, J. R. 2004. In The treatment of gait problems in cerebral palsy, Mac Keith Press, J. R. Gage, Ed., 49–51.
12. Geijtenbeek, T., van de Panne, M., and van der Stappen, A. F. 2013. Flexible muscle-based locomotion for bipedal creatures. ACM Transactions on Graphics (SIGGRAPH Asia 2013) 32(6).
13. Goswami, A. 1999. Postural stability of biped robots and the foot rotation indicator point. International journal of robotics research 18(6), 523–533.
14. Hansen, N. 2006. The CMA evolution strategy: A comparing review. In Towards a New Evolutionary Computation, vol. 192 of Studies in Fuzziness and Soft Computing. 75–102.
15. Hirai, K., Hirose, M., Haikawa, Y., and Takenaka, T. 1998. The development of honda humanoid robot. In Proceedings of IEEE International conference on robotics and automation (ICRA), 1321–1326.
16. Jain, S., and Liu, C. K. 2011. Controlling physics-based characters using soft contacts. ACM Transactions on Graphics (SIGGRAPH Asia 2011) 30(6).
17. Kuo, A. D. 2002. Energetics of actively powered locomotion using the simplest walking model. Journal of Biomechanical Engineering 124, 113–120.
18. Kwon, T., and Hodgins, J. K. 2010. Control systems for human running using an inverted pendulum model and a reference motion capture sequence. In Proceedings of SIGGRAPH/Eurographics Symposium on Computer Animation, 129–138.
19. Lee, J., and Shin, S. Y. 1999. A hierarchical approach to interactive motion editing for human-like figures. In Proceedings of SIGGRAPH ’99, 39–48.
20. Lee, Y., Kim, S., and Lee, J. 2010. Data-driven biped control. ACM Transactions on graphics (SIGGRAPH) 29(4).
21. Lee, Y., Kwon, T., Park, M. A., and Lee, J. 2014. Locomotion control for many-muscle humanoids. ACM Transactions on graphics (SIGGRAPH Asia) 33(6), Article 218.
22. Lee, J. 2008. Representing rotations and orientations in geometric computing. IEEE Computer Graphics and Applications 28(2), 75–83.
23. Liu, L., Yin, K., van de Panne, M., and Guo, B. 2012. Terrain runner: control, parameterization, composition, and planning for highly dynamic motions. ACM Transactions on Graphics (SIGGRAPH Asia 2012) 31(6).
24. McGeer, T. 1990. Passive dynamic walking. International Journal of Robotics Research 9, 62–82.
25. Mordatch, I., Wang, J. M., Todorov, E., and Koltun, V. 2013. Animating human lower limbs using contact-invariant optimization. ACM Transactions on Graphics (SIGGRAPH Asia 2013) 32(6).
26. Ralston, H. J. 1958. Energy-speed relation and optimal speed during level walking. Internationale Zeitschrift für Angewandte Physiologie Einschliesslich Arbeitsphysiologie 17(4), 277–283.
27. Rodda, J. M., Graham, H. K., Carson, L., Galea, M. P., and Wolfe, R. 2004. Sagittal gait patterns in spastic diplegia. The Journal of bone and joint surgery 86, 251–258.
28. Rogers, M. W., Hedman, L. D., Johnson, M. E., Cain, T. D., and Hanke, T. A. 2001. Lateral Stability During Forward-Induced Stepping for Dynamic Balance Recovery in Young and Older Adults. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 56, 9 (Jan.), M589–M594.
29. Shiratori, T., Coley, B., Cham, R., and Hodgins, J. K. 2009. Simulating balance recovery responses to trips based on biomechanical principles. In Proceedings of SIGGRAPH/Eurographics Symposium on Computer Animation.
30. Snaterse, M., Ton, R., Kuo, A. D., and Donelan, J. M. 2011. Distinct fast and slow processes contribute to the selection of preferred step frequency during human walking. Journal of Applied Physiology 110(6), 1682–1690.
31. Sok, K. W., Kim, M., and Lee, J. 2007. Simulating biped behaviors from human motion data. ACM Transactions on Graphics (SIGGRAPH 2007) 26(3).
32. Steele, K. M., Seth, A., Hicks, J. L., Schwartz, M. S., and Delp, S. L. 2010. Muscle contributions to support and progression during single-limb stance in crouch gait. Journal of biomechanics 43, 2099–2105.
33. Tsai, Y.-Y., Lin, W.-C., Cheng, K. B., Lee, J., and Lee, T.-Y. 2010. Real-time physics-based 3D biped character animation using an inverted pendulum model. IEEE Transactions on Visualization and Computer Graphics 16(2), 325–337.
34. Wang, J. M., J., F. D., and Hertzmann, A. 2010. Optimizing walking controllers for uncertain inputs and environments. ACM Transactions on graphics (SIGGRAPH) 29(4).
35. Wang, J. M., Hamner, S. R., Delp, S. L., and Koltun, V. 2012. Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Transactions on graphics (SIGGRAPH) 31(4).
36. Waters, R. L., and Mulroy, S. 1999. The energy expenditure of normal and pathologic gait. Gait & Posture 9, 207–231.
37. Ye, Y., and Liu, C. K. 2010. Optimal feedback control for character animation using an abstract model. ACM Transactions on Graphics (SIGGRAPH 2010) 29(4).
38. Yin, K., Loken, K., and Panne, M. v. d. 2007. SIMBICON: simple biped locomotion control. ACM Transactions on Graphics (SIGGRAPH 2007) 26(3).
