Real-Time Physics-Based Human Legs Balancing Simulation
Date Issued
2015
Date
2015
Author(s)
Lin, Po-Han
Abstract
In this thesis we propose a new motion synthesis problem. For an upper body movement as input, system generates a corresponding lower body movement. When they animate at the same time in a physical simulation software, the human model should maintain body balance. To solve this problem, we try to use reinforce learning to let computer find the best control policy during iteratively testing and improving to adjust different upper body movement. When set an upper body movement as system input, first we execute a physical simulation for the upper-body-only movement to extract features of the movement. Then we pass these to a learned neural network model. The responses of the model is the features of corresponding lower body movement. We use a decoding algorithm to transfer output features to the lower body movement trajectory. In the last, we animate upper and lower body movement at the same time, it will be the final animation. This method makes us not to cost too much time in searching or revising objective and constraint function in traditional optimization methods. Also, the method is much like a human start to learn a new movement or skill.
Subjects
motion synthesis
reinforce learning
lower body balance
Type
thesis
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