Optimal transport began as the problem of how to efficiently redistribute goods between
production and consumers and evolved into a far-reaching geometric variational framework …

We present a survey on the application of fluid approximations, in the form of mean-field
models, to the design of control strategies in swarm robotics. Mean-field models that consist …

Motion planning is still an open problem for many disciplines, eg, robotics, autonomous
driving, due to their need for high computational resources that hinder real-time, efficient …

Monge map refers to the optimal transport map between two probability distributions and
provides a principled approach to transform one distribution to another. In spite of the rapid …

As a counterpoint to classical stochastic particle methods for linear diffusion equations, such
as Langevin dynamics for the Fokker-Planck equation, we develop a deterministic particle …

In this paper, we study the finite-horizon optimal density steering problem for discrete-time
stochastic linear dynamical systems for the case when the state distribution can be …

We consider the optimal control problem of steering an agent population to a desired
distribution over an infinite horizon. This is an optimal transport problem over dynamical …

Monge map refers to the optimal transport map between two probability distributions and
provides a principled approach to transform one distribution to another. Neural network …

In this article, we consider the problem of controlling the group behavior of a large number of
dynamic systems that are constantly interacting with each other. These systems are …

We present hierarchical policy blending as optimal transport (HiPBOT). HiPBOT
hierarchically adjusts the weights of low-level reactive expert policies of different agents by …