Search Results for author: Karthik Elamvazhuthi

Found 8 papers, 1 papers with code

Denoising Diffusion-Based Control of Nonlinear Systems

1 code implementation3 Feb 2024 Karthik Elamvazhuthi, Darshan Gadginmath, Fabio Pasqualetti

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

no code implementations13 Dec 2023 Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti

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

no code implementations30 Aug 2023 Karthik Elamvazhuthi, Spring Berman

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

no code implementations15 May 2023 Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti

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

no code implementations18 May 2022 Karthik Elamvazhuthi, Bahman Gharesifard, Andrea Bertozzi, Stanley Osher

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

no code implementations25 Feb 2022 Katy Craig, Karthik Elamvazhuthi, Matt Haberland, Olga Turanova

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

no code implementations20 Sep 2020 Aniket Shirsat, Karthik Elamvazhuthi, Spring Berman

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

no code implementations29 Jun 2020 Zahi M. Kakish, Karthik Elamvazhuthi, Spring Berman

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.

Q-Learning reinforcement-learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.