activity
20182022
most citedLearning Stability Certificates from Data

28 citations · 93 across the 15 of their papers we have counts for

collaborators

27 papers

cs.LG20223 cited

Learning to Control Linear Systems can be Hard

Anastasios Tsiamis, Ingvar Ziemann, Manfred Morari +2

In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regre…

eess.SY2022

Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control

Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani +1

While methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastica…

cs.RO20222 cited

Uncertainty-driven Planner for Exploration and Navigation

Georgios Georgakis, Bernadette Bucher, Anton Arapin +3

We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constit…

cs.LG20227 cited

Single Trajectory Nonparametric Learning of Nonlinear Dynamics

Ingvar Ziemann, Henrik Sandberg, Nikolai Matni

Given a single trajectory of a dynamical system, we analyze the performance of the nonparametric least squares estimator (LSE). More precisely, we give nonasymptotic expected

eess.SY2021

Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control

Fengjun Yang, Nikolai Matni

When designing large-scale distributed controllers, the information-sharing constraints between sub-controllers, as defined by a communication topology interconnecting them, are as…

cs.LG20211 cited

How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?

Jingxi Xu, Bruce Lee, Nikolai Matni +1

The difficulty of optimal control problems has classically been characterized in terms of system properties such as minimum eigenvalues of controllability/observability gramians. W…