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http://dx.doi.org/10.15701/kcgs.2019.25.3.133

On-line Motion Synthesis Using Analytically Differentiable System Dynamics  

Han, Daseong (Handong Global University)
Noh, Junyong (KAIST)
Shin, Joseph S. (Handong Global University)
Publication Information
Journal of the Korea Computer Graphics Society / v.25, no.3, 2019 , pp. 133-142 More about this Journal
Abstract
In physics-based character animation, trajectory optimization has been widely adopted for automatic motion synthesis, through the prediction of an optimal sequence of future states of the character based on its system dynamics model. In general, the system dynamics model is neither in a closed form nor differentiable when it handles the contact dynamics between a character and the environment with rigid body collisions. Employing smoothed contact dynamics, researchers have suggested efficient trajectory optimization techniques based on numerical differentiation of the resulting system dynamics. However, the numerical derivative of the system dynamics model could be inaccurate unlike its analytical counterpart, which may affect the stability of trajectory optimization. In this paper, we propose a novel method to derive the closed-form derivative for the system dynamics by properly approximating the contact model. Based on the resulting derivatives of the system dynamics model, we also present a model predictive control (MPC)-based motion synthesis framework to robustly control the motion of a biped character according to on-line user input without any example motion data.
Keywords
Trajectory Optimization; Physics-based; Character Animation; Model Predictive Control;
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