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Found 8 papers, 1 papers with code
Denoising Diffusion-Based Control of Nonlinear Systems
We learn to control a dynamical system in reverse such that the terminal state belongs to the target set.
Noise in the reverse process improves the approximation capabilities of diffusion models
We use a control theoretic perspective by posing the approximation of the reverse process as a trajectory tracking problem.
Density Stabilization Strategies for Nonholonomic Agents on Compact Manifolds
We relax this assumption on the ellipticity of the generator of the stochastic processes, and consider the more practical case of the stabilization problem for a swarm of agents whose dynamics are given by a controllable driftless control-affine system.
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs
To address this shortcoming, in this paper we study a class of neural ordinary differential equations that, by design, leave a given manifold invariant, and characterize their properties by leveraging the controllability properties of control affine systems.
Neural ODE Control for Trajectory Approximation of Continuity Equation
As a corollary to this result, we establish that the continuity equation of the neural ODE is approximately controllable on the set of compactly supported probability measures that are absolutely continuous with respect to the Lebesgue measure.
A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling
As a consequence of our convergence result, we identify conditions on the target function and data distribution for which convexity of the energy landscape emerges in the continuum limit.
Multi-Robot Target Search using Probabilistic Consensus on Discrete Markov Chains
The simulations demonstrate that all robots achieve consensus in finite time with the proposed search strategy over a range of robot densities in the environment.
Robotics Multiagent Systems
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution
In this paper, we present a reinforcement learning approach to designing a control policy for a "leader" agent that herds a swarm of "follower" agents, via repulsive interactions, as quickly as possible to a target probability distribution over a strongly connected graph.
