Optimizing Walking Controllers for Uncertain Inputs and Environments


We introduce methods for optimizing physics-based walking controllers for robustness to uncertainty. Many unknown factors, such as external forces, control torques, and user control inputs, cannot be known in advance and must be treated as uncertain. These variables are represented with probability distributions, and a return function scores the desirability of a single motion. Controller optimization entails maximizing the expected value of the return, which is computed by Monte Carlo methods. We demonstrate examples with different sources of uncertainty and task constraints. Optimizing control strategies under uncertainty increases robustness and produces natural variations in style.


Jack M. Wang
David J. Fleet
Aaron Hertzmann


Wang, J. M., Fleet, D. J., Hertzmann, A. Optimizing Walking Controllers for Uncertain Inputs and Environments. ACM Transactions on Graphics 29, 4 (Proceedings of SIGGRAPH 2010), Article 73, 8 pages.


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Thanks to Zoran Popović for early discussions, and the reviewers for their suggestions. This research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), the Ontario Ministry of Research and Innovation, and the Canadian Institute for Advanced Research (CIFAR). Part of this work was done while Aaron Hertzmann was on a sabbatical visit to Pixar Animation Studios.