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20132023
most citedTractable Reinforcement Learning of Signal Temporal Logic Objectives

13 citations · 46 across the 11 of their papers we have counts for

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13 papers · 1 filter

eess.SY2023

Frequency-domain Gaussian Process Models for Uncertainties

Alex Devonport, Peter Seiler, Murat Arcak

Complex-valued Gaussian processes are commonly used in Bayesian frequency-domain system identification as prior models for regression. If each realization of such a process were an…

eess.SY2023

Trajectory-based Robustness Analysis for Nonlinear Systems

Peter Seiler, Raghu Venkataraman

This paper considers the robustness of an uncertain nonlinear system along a finite-horizon trajectory. The uncertain system is modeled as a connection of a nonlinear system and a…

eess.SY2022

Frequency Domain Gaussian Process Models for Uncertainties

Alex Devonport, Peter Seiler, Murat Arcak

Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression. If each realization of such a process were an $H_\inft…

eess.SY20207 cited

Imitation Learning with Stability and Safety Guarantees

He Yin, Peter Seiler, Ming Jin +1

A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL). Convex stability and safety conditions are deri…

eess.SY20202 cited

Iterative Best Response for Multi-Body Asset-Guarding Games

Emmanuel Sin, Murat Arcak, Douglas Philbrick +1

We present a numerical approach to finding optimal trajectories for players in a multi-body, asset-guarding game with nonlinear dynamics and non-convex constraints. Using the Itera…

eess.SY2020

An Efficient Algorithm to Compute Norms for Finite Horizon, Linear Time-Varying Systems

Jyot Buch, Murat Arcak, Peter Seiler

We present an efficient algorithm to compute the induced norms of finite-horizon Linear Time-Varying (LTV) systems. The formulation includes both induced and termin…