Motor learning lies at the heart of how humans and animals acquire their skills. Understanding of this process enables many benefits in Robotics, physics-based Computer Animation, and other areas of science and engineering. In this thesis, we develop a computational framework for learning of agile, integrated motor skills. Our algorithm draws inspiration from the process by which humans and animals acquire their skills in nature. Specifically, all skills are learned through a process of staged, incremental learning, during which progressively more complex skills are acquired and subsequently integrated with prior abilities. Accordingly, our learning algorithm is comprised of three phases. In the first phase, a few seed motions that accomplish goals of a skill are acquired. In the second phase, additional motions are collected through active exploration. Finally, the third phase generalizes from observations made in the second phase to yield a dynamics model that is relevant to the goals of a skill. We apply our learning algorithm to a simple, planar character in a physical simulation and learn a variety of integrated skills such as hopping, ipping, rolling, stopping, getting up and continuous acrobatic maneuvers. Aspects of each skill, such as length, height and speed of the motion can be interactively controlled through a user interface. Furthermore, we show that the algorithm can be used without modification to learn all skills for a whole family of parameterized characters of similar structure. Finally, we demonstrate that our approach also scales to a more complex quadruped character.

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