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Learning Utility Surfaces for Movement Selection

Matthew Howard, Michael Gienger, Christian Goerick, "Learning Utility Surfaces for Movement Selection", Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO), 2006.


Here we present a novel approach for designing cost functions for optimal control in the null-space by exploiting recent advances in statistical machine learning. The behaviour of a (kinematically or dynamically controlled) mechanical system performing some task is observed and separated into task- and null-space components. The null-space component is then modelled as a first order differential equation with the cost as the independent variable. Numerical solution of this equation provides training data for a statistical learning algorithm that is used to build an open-form model of the cost function.

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