no code implementations • 23 Feb 2022 • Jack Umenberger, Max Simchowitz, Juan C. Perdomo, Kaiqing Zhang, Russ Tedrake
In this paper, we provide a new perspective on this challenging problem based on the notion of $\textit{informativity}$, which intuitively requires that all components of a filter's internal state are representative of the true state of the underlying dynamical system.
no code implementations • NeurIPS 2021 • Juan C. Perdomo, Jack Umenberger, Max Simchowitz
Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering.
no code implementations • 29 Jul 2021 • Max Revay, Jack Umenberger, Ian R. Manchester
This paper proposes methods for identification of large-scale networked systems with guarantees that the resulting model will be contracting -- a strong form of nonlinear stability -- and/or monotone, i. e. order relations between states are preserved.
1 code implementation • 27 Jan 2021 • Tobia Marcucci, Jack Umenberger, Pablo A. Parrilo, Russ Tedrake
Given a graph, the shortest-path problem requires finding a sequence of edges with minimum cumulative length that connects a source vertex to a target vertex.
Robot Navigation Discrete Mathematics Optimization and Control
no code implementations • L4DC 2020 • Jack Umenberger, Thomas B. Schon
Recent work by Mania et al. has proved that certainty equivalent control achieves nearly optimal regret for linear systems with quadratic costs.
1 code implementation • NeurIPS 2019 • Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson
This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function.
1 code implementation • 2 May 2019 • Antônio H. Ribeiro, Koen Tiels, Jack Umenberger, Thomas B. Schön, Luis A. Aguirre
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems.
no code implementations • 6 Mar 2019 • Jack Umenberger, Thomas B. Schön
We propose an input design method for a general class of parametric probabilistic models, including nonlinear dynamical systems with process noise.
no code implementations • NeurIPS 2018 • Jack Umenberger, Thomas B. Schön
Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics, to medicine and finance.
no code implementations • 2 Mar 2018 • Jack Umenberger, Ian R. Manchester
Estimation of nonlinear dynamic models from data poses many challenges, including model instability and non-convexity of long-term simulation fidelity.