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Pages
Bridge Discovery Grant Accepted!
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Posts
Robotics Lab growing
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Our robotics lab is growing. Here are some recent impressions.
Bridge Discovery Grant Accepted!
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Our collaborative grant focused on robotic 3D-printing has been accepted and jointly funded by the Swiss National Science Foundation (SNSF) and the Swiss Innovation Agency (Innosuisse). It is a 4-year project for 2 MCHF, and a collaboration with Efe C. Balta at inspire AG and with John Lygeros at the Automatic Control Lab at ETH Zurich.
End of the year 2024
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What an year that was. The group grew with two more people (although they will join in a few weeks), we published several cool publications in top venues (such as NeurIPS and Engineering Applications of AI for AI, and in IEEE TCST and IEEE TASE for control and learning). I actively joined the second phase of NCCR Automation, with one PhD project, one postdoc project, and one industry collaboration.
publications
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).
Download Paper | Download Slides
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: Jialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros, NeurIPS, 2024
Download Paper | Download Slides
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: M Zagorowska, C König, H Yu, EC Balta, A Rupenyan, J Lygeros Engineering Applications of Artificial Intelligence, 2025
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In-situ controller autotuning by Bayesian optimization for closed-loop feedback control of laser powder bed fusion process
Published in Additive Manufacturing, Elsevier, volume 99, 5 February 2025, 104641, 2025
We experimentally apply Bayesian Optimization in LPBF to tune an in-layer PI controller to modulate laser power, assessing its performance on wedge geometries prone to overheating.
Recommended citation: Barış Kavas, Efe C Balta, Michael R Tucker, Raamadaas Krishnadas, Alisa Rupenyan, John Lygeros, Markus Bambach, Additive Manufacturing, volume 99, 2025
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research
Autonomy - adaptation
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Methods for adaptive tuning of control or process parameters
Autonomy - performance
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Methods for increasing the performance of manufacturing systems under unknown constraints (read more…)
Industrial robotics
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Our research in industrial robotics enables robot-based systems to autonomously and precisely perform manufacturing-related tasks
research_group
Our Research Group
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Welcome to the Industrial AI Research Group. Our team focuses on developing solutions for smart manufacturing and industrial systems, in particular industrial autonomous robotics, data-driven optimization for manufacturing, and integration of AI-based methods in various aspects of manufacturing.
Alisa Rupenyan
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Prof. Dr.
Deepak Ingole
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Dr.
Gabriele Fadini
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Dr.
Diego Bolliger
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Yu Tang
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Melvin Pariyadan
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talks
Keynote, Data-driven optimization for manufacturing applications
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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
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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
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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
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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
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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
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I gave a keynote about flexible robotics ooperation on this event of the Innovation Booster Robotics. The slides are available upon request.
teaching
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.