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In this paper we present a novel method for robust, optimal control of nonlinear systems under probabilistic uncertainty. The method extends a previous approach for linear systems that approximates the distribution of the predicted system state using a finite number of particles. We couple this particle-based approach with a nonlinear solver that does not take into account uncertainty to give a new method for nonlinear, robust control. Any solution returned by the algorithm is guaranteed to be e-close to a local optimum of the nonlinear stochastic control problem.