Terrain Runner: Control, Parameterization, Composition, and Planning for Highly Dynamic Motions

ACM Transactions on Graphics (SIGGRAPH ASIA 2012)

Libin Liu, KangKang Yin, Michiel van de Panne, Baining Guo


Libin Liu, KangKang Yin, Michiel van de Panne, Baining Guo



In this paper we learn the skills required by real-time physics-based avatars to perform parkour-style fast terrain crossing using a mix of running, jumping, speed-vaulting, and drop-rolling. We begin with a single motion capture example of each skill and then learn reduced-order linear feedback control laws that provide robust ex- ecution of the motions during forward dynamic simulation. We then parameterize each skill with respect to the environment, such as the height of obstacles, or with respect to the task parameters, such as running speed and direction. We employ a continuation process to achieve the required parameterization of the motions and their affine feedback laws. The continuation method uses a predictor-corrector method based on radial basis functions. Lastly, we build control laws specific to the sequential composition of dif- ferent skills, so that the simulated character can robustly transition to obstacle clearing maneuvers from running whenever obstacles are encountered. The learned transition skills work in tandem with a simple online step-based planning algorithm, and together they robustly guide the character to achieve a state that is well-suited for the chosen obstacle-clearing motion.

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  title={Terrain runner: control, parameterization, composition, and planning for highly dynamic motions},
  author={Liu, Libin and Yin, KangKang and van de Panne, Michiel and Guo, Baining},
  journal={ACM Transactions on Graphics (TOG)},

   NSERC (Natural Sciences and Engineering Research Council of Canada)
   GRAND NCE: Graphics, Animation, and New Media