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.