5 citations · 7 across the 27 of their papers we have counts for
12 papers · 1 filter
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…
Reachability and Controllability Analysis of the State Covariance for Linear Stochastic Systems
Fengjiao Liu, Panagiotis Tsiotras
This paper studies the set of terminal state covariances that are reachable over a finite time horizon from a given initial state covariance for a linear stochastic system with add…
Distribution Steering for Discrete-Time Uncertain Ensemble Systems
Guangyu Wu, Panagiotis Tsiotras, Anders Lindquist
Ensemble systems appear frequently in many engineering applications and, as a result, they have become an important research topic in control theory. These systems are best charact…
Adaptive Dual Covariance Steering with Active Parameter Estimation
Jacob W. Knaup, Panagiotis Tsiotras
This work examines the optimal covariance steering problem for systems subject to unknown parameters that enter multiplicatively with the state and control, in addition to additive…
Distributionally Robust Density Control with Wasserstein Ambiguity Sets
Joshua Pilipovsky, Panagiotis Tsiotras
Precise control under uncertainty requires a good understanding and characterization of the noise affecting the system. This paper studies the problem of steering state distributio…