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UBC Theses and Dissertations

Learning reduced order linear feedback policies for motion skills Ding, Kai


Skilled character motions need to adapt to their circumstances and this is typically accomplished with the use of feedback. However, good feedback strategies are difficult to author and this has been a major stumbling block in the development of physics-based animated characters. In this thesis we present a framework for the automated design of compact linear feedback strategies. We show that this can be an effective substitute for manually-designed abstract models such as the use of inverted pendulums for the control of simulated walking. Results are demonstrated for a variety of motion skills, including balancing, hopping, ball kicking, single-ball juggling, ball volleying, and bipedal walking. The framework uses policy search in the space of reduced-order linear feedback matrices as a means of developing an optimized linear feedback strategy. The generality of the method allows for the automated development of highly-effective unconventional feedback loops, such as the use of foot pressure feedback to achieve robust physics-based bipedal walking.

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