William S. Owen

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—High performance control of robotic systems, including the new generation of humanoid, assistive and entertainment robots, requires adequate knowledge of the dynamics of the system. This can be problematic in the presence of modeling uncertainties as the performance of classical, model-based controllers is highly dependant upon accurate knowledge of the(More)
Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship of a ma-nipulator, eliminating the need for any knowledge of the system model. Ideally, such algorithms should be able to process large amounts of data in an online and incremental manner, thus allowing the system to adapt to changes in its(More)
— Model-based control strategies for robot manip-ulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. This paper proposes an(More)
This paper proposes an approach for online learning of the dynamic model of a robot manipulator. The dynamic model is formulated as a weighted sum of locally linear models, and Locally Weighted Projection Regression (LWPR) is used to learn the models based on training data obtained during operation. The LWPR model can be initialized with partial knowledge(More)
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