Physics-based trajectory optimization with automatic time warping

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This paper presents a novel online model predictive control framework based on automatic time warping. In general, existing model predictive control frameworks employ reference motions with sampling time uniform and fixed. Unlike these, our framework allows to change the sampling time of a reference motion based on physics-based simulation so that the character effectively responds to external forces unexpectedly applied to it. In order to do so, we formulate an optimal control problem, taking into account both optimal time warping and full-body dynamics simultaneously. We adopt differential dynamic programming to produce an optimal control policy by solving the problem, which is used to compute the optimal feedback information for character motion and sampling time. We show the robustness of our framework to external perturbations through experiments. We also show the effectiveness of this framework for rhythmic motion synthesis.
Publisher
The International Conference on Computer Animation and Social Agent
Issue Date
2018-05-22
Language
English
Citation

The International Conference on Computer Animation and Social Agent(CASA) 2018

DOI
10.1002/cav.1813
URI
http://hdl.handle.net/10203/244147
Appears in Collection
GCT-Conference Papers(학술회의논문)
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