activity
20192022
most citedSelf-Optimizing Grinding Machines using Gaussian Process Models and Constrained Bayesian Optimization

34 citations · 34 across the 2 of their papers we have counts for

collaborators

13 papers

eess.SY2022

Drone-based Volume Estimation in Indoor Environments

Samuel Balula, Dominic Liao-McPherson, Stefan Stevšić +2

Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous syste…

eess.SY2022

Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements

Xavier Guidetti, Marino Kühne, Yannick Nagel +3

The tuning of fused filament fabrication parameters is notoriously challenging. We propose an autonomous data-driven method to select parameters based on in situ measurements. We u…

eess.SY2022

Controller-Aware Dynamic Network Management for Industry 4.0

Efe C. Balta, Mohammad H. Mamduhi, John Lygeros +1

In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected…

eess.SY2022

On Robustness in Optimization-Based Constrained Iterative Learning Control

Dominic Liao-McPherson, Efe C. Balta, Alisa Rupenyan +1

Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance. Optimization-based IL…

eess.SY2021

Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

Christopher König, Matteo Turchetta, John Lygeros +2

Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approa…

cs.RO2020

Learning from Simulation, Racing in Reality

Eugenio Chisari, Alexander Liniger, Alisa Rupenyan +2

We present a reinforcement learning-based solution to autonomously race on a miniature race car platform. We show that a policy that is trained purely in simulation using a relativ…