Uncertainty propagation and mitigation is at the core of all robotic and control systems. The standard approach so far has followed the spirit of controlling a system “with uncertainties,” as opposed to the direct control “of uncertainties.” Borrowing ideas from the classical Optimal Mass Transport (OMT) and Schrödinger Bridge problems, distributional control has recently emerged as a principled approach to characterize and mitigate uncertainty in stochastic systems with strict performance guarantees. In this talk, I will review some recent results on covariance and distribution control for stochastic systems subject to chance constraints, including data-driven and distributionally robust implementations; I will demonstrate the application of the theory to a variety of problems ranging from model predictive control, robot motion planning under uncertainty, multi-agent mean-field control, and generative AI.