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Physics‐based trajectory optimization with automatic time warping
Author(s) -
Han Daseong,
Noh Junyong,
Shin Joseph S.
Publication year - 2018
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1813
Subject(s) - dynamic time warping , computer science , image warping , robustness (evolution) , trajectory , dynamic programming , optimal control , sampling (signal processing) , character (mathematics) , motion (physics) , trajectory optimization , control theory (sociology) , artificial intelligence , control (management) , algorithm , computer vision , mathematical optimization , physics , mathematics , astronomy , biochemistry , chemistry , geometry , filter (signal processing) , gene
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.