  We propose a sampling-based trajectory optimization methodology for
constrained problems. We extend recent works on stochastic search to deal with
box control constraints,as well as nonlinear state constraints for discrete
dynamical systems. Regarding the former, our strategy is to optimize over
truncated parameterized distributions on control inputs. Furthermore, we show
how non-smooth penalty functions can be incorporated into our framework to
handle state constraints. Simulations on cartpole and quadcopter show that our
approach outperforms previous methods on constrained sampling-based
optimization, in terms of quality of solutions and convergence speed.
