Control under uncertainty is a fundamental problem relevant to biology as well as engineering. Optimality models have explained numerous details of biological movements.  Indeed optimal control and optimal (i.e. Bayesian) estimation are becoming the framework of choice for studying sensorimotor function. However most demonstrations of optimality are limited to relatively simple behaviors. In more complex and interesting behaviors we still lack the algorithms to compute what is optimal. Continued progress requires more efficient algorithms for stochastic optimal control. In this University of Washington program, Emanuel Todorov, of MIT and UCSD, presents a new problem formulation that greatly simplifies the construction of optimal control laws, and yields original algorithms. From the Series:CSE Colloquia - 2008