📝 SummarXiv

Abstract

We develop a \emph{flow-matching framework} for transporting probability measures under control-affine dynamics and for stabilizing systems to points or target sets. Starting from the continuity equation associated with the control affine system dx/dt = f_0(x) + \sum_{i=1}^m u_i f_i(x), we construct measure interpolations through exact and approximate flow matching, and extend the approach to \emph{output flow matching} when only output distributions must align. These constructions allow to directly import standard control tools, such as feedback design, oscillatory inputs, and trajectory steering, and yield sample-efficient, regression-based controllers for measure-to-measure transport. We also introduce a complementary ``noising + time-reversal'' perspective for classical state or set stabilization, inspired by denoising diffusion models. Here stabilization is interpreted as a denoising problem. We propose two methods for constructing the noising process: (i) PMP-based noising, which leverages the Hamiltonian system from Pontryagin's Maximum Principle and recovers the optimal controller for linear systems with convex costs, while providing feasible feedback laws in the nonlinear case; and (ii) randomized-control noising, which employs regular (non-white noise) controls through the endpoint map and naturally accommodates control constraints. Both approaches avoid the score blow-up seen in stochastic differential equation-based denoising methods. We establish existence of solutions to the corresponding ODEs and regularity of the induced flows on measures, even when control laws are nonsmooth. Finally, we illustrate the framework on linear and nonlinear systems, demonstrating its effectiveness for both measure transport and stabilization problems.