Posts by Collection

Safe risk-averse bayesian optimization for controller tuning

Published in IEEE Robotics and Automation Letters, presented on IEEE ICRA 2023, 2023

RAGoOSe is a novel data-driven approach that combines safe learning with risk-averse Bayesian optimization to safely tune controllers in high-precision systems with variable noise, demonstrating improved performance over traditional methods in both synthetic and real semiconductor manufacturing applications.

Recommended citation: Christopher König, Miks Ozols, Anastasia Makarova, Efe C Balta, Andreas Krause, Alisa Rupenyan (2023). "Safe risk-averse bayesian optimization for controller tuning." IEEE Robotics and Automation Letters. 8(8208 - 8215).
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Robotics and Manufacturing Application

Published in The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024, 2024

A book chapter, focused on advanced control methods, suitable for industrial applications in robotics and precision motion systems.

Recommended citation: Alisa Rupenyan, Efe C. Balta (2024). "Robotics and Manufacturing Application." The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024.
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Time-varying safe data-driven optimization

Published in The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), 2024

We propose a method for optimization with unknown and time-varying optimization objective and constraints, based on Bayesian optimization.

Recommended citation: YJialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros, NeurIPS, 2024
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Efficient safe learning for controller- and process parameter tuning

Published in Engineering Applications of Artificial Intelligence, volume 143, 1 March 2025, 109894, 2025

We propose a method for safe and fast data-driven optimization by formulating a series of optimization problems instead of a grid search.

Recommended citation: YJialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros, NeurIPS, 2024
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Autonomy - adaptation

Published: January 30, 2025

Methods for adaptive tuning of control or process parameters

Autonomy - performance

Published: January 30, 2025

Methods for increasing the performance of manufacturing systems under unknown constraints (read more…)

Industrial robotics

Published: January 30, 2025

Our research in industrial robotics enables robot-based systems to autonomously and precisely perform manufacturing-related tasks

Keynote, Data-driven optimization for manufacturing applications

Published: March 28, 2024

I one of the keynotes, on the topic of data-driven optimization and control in manufacturing applications.

Plenary, Data-driven optimization and control in advanced manufacturing

Published: March 28, 2024

I gave the opening keynote about data-driven optimization and control in manufacturing applications. The slides are available here.

Keynote, Bayesian optimization algorithms for optimization in manufacturing

Published: June 28, 2024

During the this pre-ECC workshop I presented several Bayesian optimization algorithms, suitable for optimization in manufacturing, bacause they can handle unknown safety constraints, and they can ensure continuous optimization (thus taking into account process drifts).

Panel, Automatic Control Horizon: Roadmap and Industrial Innovation

Published: June 30, 2024

Also during the 2024 edition of ECC I participated in a panel discussion, focused on bridging control research to industry applications, during a tutorial session – Automatic Control Horizon: Roadmap and Industrial Innovation.

Workshop Control meets Practice in Advanced Manufacturing

Published: September 01, 2024

IEEE Conference on Automation, Science, and Engineering, September 2024, Bari, Italy – Workshop Translating Manufacturing Control and Automation Research to Practice: Examples, Challenges, and Opportunities. More information here.

Keynote, Flexible automation for manufacturing tasks

Published: January 30, 2025

I gave a keynote about flexible robotics ooperation on this event of the Innovation Booster Robotics. The slides are available upon request.

Artificial Intelligence 1

Undergraduate course, ZHAW, Computer Science, 2014

This is an introductory course on the basics of Artificial Intelligence, with a strong focus on applications. It covers topics from deep learning and from planning and search.

MLDM

Undergraduate course, ZHAW, Computer Science, 2024

This is an introductory course in Machine Learning. The lecture notes can be found here.