Biped Controller for Character Animation

  • KangKang Yin
  • Stelian Coros
  • Michiel van de Panne
Reference work entry


In this chapter, we first overview the common methods for building biped controllers in physics-based character animation. Then we explain in detail two closely related biped controllers: SIMBICON and GENBICON. The simple biped locomotion control (SIMBICON) strategy adopts a simple linear feedback strategy for foot placement to maintain balance during locomotion. The generalized biped walking control (GENBICON) strategy improves the balance control using an inverted pendulum model for foot placement, in conjunction with Jacobian-transpose control for velocity fine-tuning and gravity compensation for all limb movement. Both SIMBICON and GENBICON use proportional-derivative joint servos to track a desired motion style, which can be interactively edited by users. The major advantages of such biped controllers include simplicity, robustness, and directable styles. Finally, we discuss our ongoing efforts toward building more versatile and robust controllers with minimal prior knowledge.


Biped control Physics-based character animation Motion control Balance feedback Motion capture Inverted pendulum Jacobian transpose control Foot placement 



We sincerely thank all our collaborators for their contributions to the work described in this chapter, especially Kevin Loken and Philippe Beaudoin. This work was funded in part by NSERC Discovery Grant RGPIN-2015-04843.


  1. Al Borno M, de Lasa M, Hertzmann A (2013) Trajectory optimization for full-body movements with complex contacts. IEEE Trans Vis Comput Graph 19(8):1405–1414CrossRefGoogle Scholar
  2. Coros S, Beaudoin P, van de Panne M (2010) Generalized biped walking control. ACM Trans Graph 29(4):Article 130. Scholar
  3. de Lasa M, Mordatch I, Hertzmann A (2010) Feature-based locomotion controllers. ACM Trans Graph 29(4):131:1–131:10. ISSN 0730-0301Google Scholar
  4. Geijtenbeek T, Pronost N (2012) Interactive character animation using simulated physics: a state-of-the-art review. Comput Graphics Forum 31:2492–2515. Wiley Online LibraryCrossRefGoogle Scholar
  5. Giovanni S, Yin KK (2011) Locotest: deploying and evaluating physics-based locomotion on multiple simulation platforms. Lect Notes Comput Sci 7060:227–241CrossRefGoogle Scholar
  6. Hämäläinen P, Rajamäki J, Karen Liu C (2015) Online control of simulated humanoids using particle belief propagation. ACM Trans Graph 34(4):81CrossRefzbMATHGoogle Scholar
  7. Hodgins JK, Wooten WL, Brogan DC, O’Brien JF (1995) Animating human athletics. In: Proceedings of the 22nd annual conference on computer graphics and interactive techniques, SIGGRAPH’95. ACM Press, New York, pp 71–78.
  8. Lee Y, Kim S, Lee J (2010) Data-driven biped control. ACM Trans Graph 29(4):129:1–129:8Google Scholar
  9. Liu L, Yin KK, van de Panne M, Shao T, Weiwei X (2010) Sampling-based contact-rich motion control. ACM Trans Graph 29(4):Article 128. Scholar
  10. Liu L, Yin KK, van de Panne M, Guo B (2012) Terrain runner: control, parameterization, composition, and planning for highly dynamic motions. ACM Trans Graph 31(6):Article 154. Scholar
  11. Liu L, Yin KK, Wang B, Guo B (2013) Simulation and control of skeleton-driven soft body characters. ACM Trans Graph 32(6):Article 215. Scholar
  12. Liu L, Yin KK, Guo B (2015) Improving sampling-based motion control. Comput Graphics Forum 34(2):415–423. Scholar
  13. Liu L, van de Panne M, Yin KK (2016) Guided learning of control graphs for physics-based characters. ACM Trans Graph 35(3):Article 29. Scholar
  14. Macchietto A, Zordan V, Shelton CR (2009) Momentum control for balance. ACM Trans Graph 28(3):Article 80CrossRefGoogle Scholar
  15. Muico U, Lee Y, Popović J, Popović Z (2009) Contactaware nonlinear control of dynamic characters. ACM Trans Graph 28(3):Article 81CrossRefGoogle Scholar
  16. Peng XB, Berseth G, van de Panne M (2016) Terrain-adaptive locomotion skills using deep reinforcement learning. ACM Trans Graph 35(4):Article 81Google Scholar
  17. Peng XB, Berseth G, Yin KK, van de Panne M (2017) Deeploco: dynamic locomotion skills using hierarchical deep reinforcement learning. ACM Trans Graph 36(4):Article 41CrossRefGoogle Scholar
  18. Pratt JE, Tedrake R (2006) Velocity based stability margins for fast bipedal walking. In: Fast motions in biomechanics and robots. Springer, BerlinGoogle Scholar
  19. Pratt J, Chew CM, Torres A, Dilworth P, Pratt G (2001) Virtual model control: an intuitive approach for bipedal locomotion. Int J Robot Res 20(2):129CrossRefGoogle Scholar
  20. Sunada C, Argaez D, Dubowsky S, Mavroidis C (1994) A coordinated Jacobian transpose control for mobile multi-limbed robotic systems. Proc IEEE Int Conf Robot Autom 1910–1915Google Scholar
  21. Witkin A, Kass M (1988) Spacetime constraints. SIGGRAPH’88 22:159–168CrossRefGoogle Scholar
  22. Yin KK, Loken K, van de Panne M (2007) SIMBICON: simple biped locomotion control. ACM Trans Graph 26(3):Article 105. Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • KangKang Yin
    • 1
    • 2
  • Stelian Coros
    • 3
  • Michiel van de Panne
    • 4
  1. 1.Simon Fraser UniversityBurnabyCanada
  2. 2.Department of Computer ScienceSingaporeSingapore
  3. 3.Carnegie Mellon UniversityPittsburghUSA
  4. 4.University of British ColumbiaVancouverCanada

Section editors and affiliations

  • Zhigang Deng
    • 1
  1. 1.Department of Computer Science,University of HoustonHoustonUSA

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