5 citations · 7 across the 27 of their papers we have counts for
31 papers
Continuous-Time Covariance Steering with Common Free-Final Time: Finite-Horizon Solutions and Infinite-Horizon Limits
Akan Selim, Fengjiao Liu, Siddhartha Ganguly +1
This article studies the optimal common free-final time problem for steering the state covariance of a continuous-time stochastic linear system between prescribed initial and termi…
Conflict-Based Lazy Search for Fast Multi-Manipulator Planning
Dongliang Zheng, Zhipeng Wang, Siqi Wang +4
Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do. However, real-time planning for multiple manipulators in a cluttered…
Lifted Schrödinger Bridges for Gaussian Mixture Endpoints: Projection Gaps and Path-Space Obstructions
Siddhartha Ganguly, George Rapakoulias, Panagiotis Tsiotras
We study stochastic density control between Gaussian-mixture endpoint distributions under Brownian prior dynamics. Since the direct Schrödinger bridge between Gaussian mixtures is…
Stochastic Transition-Map Distillation for Fast Probabilistic Inference
George Rapakoulias, Peter Garud, Lingjiong Zhu +1
Diffusion models achieve strong generation quality, diversity, and distribution coverage, but their performance often comes with expensive inference. In this work, we propose Stoch…
Asymmetric-Information Resource Allocation Games: An LP Approach to Purposeful Deception
Longxu Pan, Yue Guan, Daigo Shishika +1
In this work, we introduce the Deceptive Resource Allocation Game (DRAG), which studies purposeful deception within a Bayesian game framework. In DRAG, a Defender allocates resourc…
Reachability Analysis of the State Transition and State Covariance Matrices for an LTV System
Fengjiao Liu, Yixiao Zhang, Panagiotis Tsiotras
In this paper, we study the reachability of two closely related matrices appearing in the analysis of linear time-varying (LTV) systems over a finite time interval, namely, its clo…
Nonlinear Stochastic Density Steering via Gaussian Mixture Schrodinger Bridges and Multiple Linearizations
Mattia Mosso, George Rapakoulias, Yue Guan +1
The paper studies the optimal density steering problem for nonlinear continuous-time stochastic systems. To accurately capture nonlinear dynamics in high-uncertainty regions that d…
Covariance Steering of Discrete-Time Markov Jump Linear Systems with Multiplicative Noise
Fangji Wang, Siddhartha Ganguly, Panagiotis Tsiotras
We study a finite-horizon covariance steering problem for discrete-time Markov jump linear systems (MJLS) with both state- and control-dependent multiplicative noise. The objective…
Factor Graph-Based Shape Estimation for Continuum Robots via Magnus Expansion
Lorenzo Ticozzi, Patricio A. Vela, Panagiotis Tsiotras
Reconstructing the shape of continuum manipulators from sparse, noisy sensor data is a challenging task, owing to the infinite-dimensional nature of such systems. Existing approach…
Nonlinear Stochastic Optimal Control and Optimal Stopping using the Fokker-Planck Transformation
Akan Selim, Siddhartha Ganguly, Ali Pakniyat +1
In this paper, we develop a theoretical framework for nonlinear stochastic optimal control problems with optimal stopping by establishing a density-based deterministic representati…
Data-Driven Covariance Steering with Output Feedback
Dimitrios Moustroufis, Panagiotis Tsiotras
This paper addresses output-feedback covariance steering for stochastic discrete-time linear time-invariant systems with unknown dynamics. We construct a controllable non-minimal s…
Schrodinger Bridges and Density Steering Problems for Gaussian Mixtures Models in Discrete-Time
George Rapakoulias, Fengjiao Liu, Panagiotis Tsiotras
In this work, we revisit the discrete-time Schrödinger Bridge (SB) and Density Steering (DS) problems for Gaussian mixture model (GMM) boundary distributions. Building on the exist…
BaB-prob: Branch and Bound with Preactivation Splitting for Probabilistic Verification of Neural Networks
Fangji Wang, Panagiotis Tsiotras
Branch-and-bound with preactivation splitting has been shown highly effective for deterministic verification of neural networks. In this paper, we extend this framework to the prob…
Steering Large Agent Populations using Mean-Field Schrodinger Bridges with Gaussian Mixture Models
George Rapakoulias, Ali Reza Pedram, Panagiotis Tsiotras
The Mean-Field Schrodinger Bridge (MFSB) problem is an optimization problem aiming to find the minimum effort control policy to drive a McKean-Vlassov stochastic differential equat…
Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion
Jacob Knaup, Jovin D'sa, Behdad Chalaki +3
Interactive decision-making is essential in applications such as autonomous driving, where the agent must infer the behavior of nearby human drivers while planning in real-time. Tr…
Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions
Ji Yin, Oswin So, Eric Yang Yu +2
A common problem when using model predictive control (MPC) in practice is the satisfaction of safety specifications beyond the prediction horizon. While theoretical works have show…
SAVER: A Toolbox for Sampling-Based, Probabilistic Verification of Neural Networks
Vignesh Sivaramakrishnan, Krishna C. Kalagarla, Rosalyn Devonport +3
We present a neural network verification toolbox to 1) assess the probability of satisfaction of a constraint, and 2) synthesize a set expansion factor to achieve the probability o…
Go With the Flow: Fast Diffusion for Gaussian Mixture Models
George Rapakoulias, Ali Reza Pedram, Fengjiao Liu +2
Schrodinger Bridges (SBs) are diffusion processes that steer, in finite time, a given initial distribution to another final one while minimizing a suitable cost functional. Althoug…
Recursively Feasible Stochastic Model Predictive Control for Time-Varying Linear Systems Subject to Unbounded Disturbances
Jacob W. Knaup, Panagiotis Tsiotras
Model predictive control solves a constrained optimization problem online in order to compute an implicit closed-loop control policy. Recursive feasibility -- guaranteeing that the…
Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime
Luzia Knoedler, Oswin So, Ji Yin +5
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of di…
Residual Descent Differential Dynamic Game (RD3G) -- A Fast Newton Solver for Constrained General Sum Games
Zhiyuan Zhang, Panagiotis Tsiotras
We present Residual Descent Differential Dynamic Game (RD3G), a Newton-based solver for constrained multi-agent game-control problems. The proposed solver seeks a local Nash equili…
Stochastic Control of UAVs: An Optimal Tradeoff between Performance, Flight Smoothness and Control Effort
George Rapakoulias, Panagiotis Tsiotras
Safe and accurate control of unmanned aerial vehicles in the presence of winds is a challenging control problem due to the hard-to-model and highly stochastic nature of the disturb…
Discrete-Time Maximum Likelihood Neural Distribution Steering
George Rapakoulias, Panagiotis Tsiotras
This paper studies the problem of steering the distribution of a discrete-time dynamical system from an initial distribution to a target distribution in finite time. The formulatio…
DUST: A Framework for Data-Driven Density Steering
Joshua Pilipovsky, Panagiotis Tsiotras
We consider the problem of data-driven stochastic optimal control of an unknown LTI dynamical system. Assuming the process noise is normally distributed, we pose the problem of ste…