Safe Control of Large-scale MAS: Recent Developments and Open Challenges

Overview

Multi-agent systems (MAS) have received tremendous attention from scholars in different disciplines, including computer science and robotics, as a means to solve complex problems by subdividing them into smaller tasks. Some examples of MAS include smart grids, search and rescue teams, edge computing, wireless communication networks, space systems, package delivery, and micro-grids. The design and analysis of MAS controllers present unique challenges, such as scalability, verification, and robustness to factors such as communication issues, adversarial or non-cooperative agents, and partial observability.

Most common tools used for design and analysis of safety of MAS include shielding-based methods, learning certificates for safety, such as control barrier functions, and reinforcement learning based methods. When it comes to the performance of any of these tools, there is generally a tradeoff between the scalability of the tool and the ability to formally verify it for correctness. While there has been a lot of progress in the field with the hope of mitigating this tradeoff, much work still needs to be done in the development of a formally verifiable method that is both scalable and practically useful. 

The main goal of the workshop is to highlight recent advances and developments in the role of control theory and machine learning in solving scalability problems of safe control design for MAS, and discuss some of the important open problems in the field. This workshop aims to bring together experts from machine learning, robotics, and control theory to discuss the recent developments and utility of the variety of available tools for safe MAS, as well as their drawbacks and the directions to be pursued given the current state of the art. 

The expected outcome of the workshop is to strengthen the knowledge of the researchers from various backgrounds on the topic of safety of large-scale MAS, on how control-theoretic tools can be used in tandem with model learning methods for the development of practically useful and theoretically provable algorithms, and to discuss some of the pressing issues in the field with the domain experts.

Timeliness: As the applications of large-scale MAS increase in both military and civilian space, it has become inevitable to develop tools that can provide guarantees on both performance and safety of both the agents and their environments. With modern methods, relying on AI/ML technology, demonstrating success in practice when it comes to robotics applications, it is essential that efficient tools be developed for analysis and verification of the correctness of these methods.  

The target audience comprises graduate-level control theorists, computer scientists, and engineers, as well as researchers with a strong interest in the safety, scalability, and robustness of MAS, either from a theoretical or an application perspective. In particular, the main topics being covered are:

• Characterization of MAS: centralized vs decentralized

• Reachability-based methods for MAS

• Control barrier functions for large-scale MAS

• Constrained and unconstrained Multi-agent RL (MARL) algorithms

• Formal verification and testing methods

(Confirmed) Invited speakers

We propose a full-day workshop that can tentatively host up to 8 invited speakers. We already have the following as the confirmed speakers for our workshop:

• Jonathan How (MIT) 

• Dimitra Panagou (University of Michigan)

• Samuel Coogan (Georgia Tech)

• Lars Lindemann (ETH Zurich)

• Karl Johansson (KTH)

• Kunal Garg (ASU)

Speaker bio

Jonathan How: Jonathan P. How is the Richard C. Maclaurin Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He received a B.A.Sc. (aerospace) from the University of Toronto in 1987, and his S.M. and Ph.D. in Aeronautics and Astronautics from MIT in 1990 and 1993, respectively, and then studied for 1.5 years at MIT as a postdoctoral associate. Prior to joining MIT in 2000, he was an assistant professor in the Department of Aeronautics and Astronautics at Stanford University. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the American Institute of Aeronautics and Astronautics (AIAA). He was elected to the National Academy of Engineering (NAE) in 2021. Dr. How’s research focuses on robust planning and learning under uncertainty with an emphasis on multi-agent systems, and he was the planning and control lead for the MIT DARPA Urban Challenge team in 2007. His work has been recognized with multiple awards, including the 2020 IEEE CSS Distinguished Member Award, the 2020 AIAA Intelligent Systems Award, the 2002 Institute of Navigation Burka Award, the 2011 IFAC Automatica award for best applications paper, the 2015 AeroLion Technologies Outstanding Paper Award for Unmanned Systems, the 2015 winner of the IEEE Control Systems Society Video Clip Contest, the IROS Best Paper Award on Cognitive Robotics (2017 and 2019), the 2020 ICRA Best Paper Award in Service Robotics, and three AIAA Best Paper in Conference Awards (2011-2013). He received the Amazon Machine Learning Research Award in 2018 and 2020, and he was awarded the Air Force Commander’s Public Service Award in 2017 for his contributions to the SAB. Dr. How serves on the AIAA Fellows Committee for Information Systems and Systems Integration and is the 2026-2028 president-elect for the IEEE Control System Society.

Karl Johansson: Karl H. Johansson is Swedish Research Council Distinguished Professor in Electrical Engineering and Computer Science at KTH Royal Institute of Technology in Sweden and Founding Director of Digital Futures. He earned his MSc degree in Electrical Engineering and PhD in Automatic Control from Lund University. He has held visiting positions at UC Berkeley, Caltech, NTU and other prestigious institutions. His research interests focus on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. For his scientific contributions, he has received numerous best paper awards and various other distinctions from IEEE, IFAC, and other organizations. He has been awarded Distinguished Professor by the Swedish Research Council, Wallenberg Scholar by the Knut and Alice Wallenberg Foundation, Future Research Leader by the Swedish Foundation for Strategic Research. He has also received the triennial IFAC Young Author Prize, IEEE CSS Distinguished Lecturer, IFAC Outstanding Service Award, and IEEE CSS Hendrik W. Bode Lecture Prize. His extensive service to the academic community includes being President of the European Control Association, IEEE CSS Vice President Diversity, Outreach & Development, and Member of IEEE CSS Board of Governors and IFAC Council. He has served on the editorial boards of Automatica, IEEE TAC, IEEE TCNS and many other journals. He has also been a member of the Swedish Scientific Council for Natural Sciences and Engineering Sciences. He is Fellow of both the IEEE and the Royal Swedish Academy of Engineering Sciences. 

Dimitra Panagou: Dimitra Panagou received the Diploma and PhD degrees in Mechanical Engineering from the National Technical University of Athens, Greece, in 2006 and 2012, respectively. In September 2014 she joined the Department of Aerospace Engineering, University of Michigan as an Assistant Professor. Since July 2022 she is an Associate Professor with the newly established Department of Robotics, with a courtesy appointment with the Department of Aerospace Engineering, University of Michigan. Prior to joining the University of Michigan, she was a postdoctoral research associate with the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign (2012-2014), a visiting research scholar with the GRASP Lab, University of Pennsylvania (June 2013, Fall 2010) and a visiting research scholar with the University of Delaware, Mechanical Engineering Department (Spring 2009). Her research program spans the areas of nonlinear systems and control; multi-agent systems, autonomy and control; and aerospace robotics. She is particularly interested in the development of provably-correct methods for the safe and secure (resilient) operation of autonomous systems in complex missions, with applications in robot/sensor networks and multi-vehicle systems (ground, marine, aerial, space) under uncertainty. She is a recipient of the NASA Early Career Faculty Award, the AFOSR Young Investigator Award, the NSF CAREER Award, the George J. Huebner, Jr. Research Excellence Award, and a Senior Member of the IEEE and the AIAA.

Samuel Coogan: Sam Coogan received the B.S. degree in Electrical Engineering from Georgia Tech and the M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley. In 2015, he was a postdoctoral research engineer at Sensys Networks, Inc., and in 2012 he spent time at NASA's Jet Propulsion Lab. Before joining Georgia Tech in 2017, he was an assistant professor in the Electrical Engineering department at UCLA from 2015–2017. His awards and recognitions include the 2020 Donald P Eckman Award from the American Automatic Control Council recognizing "an outstanding young engineer in the field of automatic control", a Young Investigator Award from the Air Force Office of Scientific Research in 2019, a CAREER Award from the National Science Foundation in 2018, and the Outstanding paper award for the IEEE Transactions on Control of Network Systems in 2017.

Lars Lindemann: Lars is an Assistant Professor at the Automatic Control Laboratory at ETH Zürich where he is leading the Algorithmic Systems Theory (AST) Laboratory. His research interests include systems and control theory, formal methods, and machine learning with applications in autonomous systems and robotics. From 2023 to 2025 he was an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California. Between 2020 and 2022, he was a Postdoctoral Fellow in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received the Ph.D. degree in Electrical Engineering from KTH Royal Institute of Technology in 2020. Prior to that, he received the M.Sc. Degree in Systems, Control and Robotics from KTH in 2020 and two B.Sc. Degrees in Electrical and Information Engineering and in Engineering Management from the Christian-Albrecht University of Kiel in 2014. He received the Outstanding Student Paper Award at the 58th IEEE Conference on Decision and Control and the Student Best Paper Award (as an advisor) at the 60th IEEE Conference on Decision and Control. He was a finalist for the Best Paper Award (as an advisor) at the 2024 International Conference on Cyber-Physical Systems, the Best Paper Award at the 2022 Conference on Hybrid Systems: Computation and Control, and the Best Student Paper Award at the 2018 American Control Conference.

Kunal Garg: Kunal Garg is an assistant professor in the mechanical and aerospace engineering program at the School for Engineering of Matter, Transport and Energy at ASU. He received his master's degree in engineering and doctoral degree in aerospace engineering from the University of Michigan in 2019 and 2021. Before joining ASU, he was a postdoctoral associate in the Laboratory for Information & Decision Systems (LIDS) and the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology. Garg was a 2022 DAAD AInet Fellow for the Postdoctoral Networking Tour in Artificial Intelligence in the AI and Robotics domain. He also received the Professor Pierre T. Kabamba Award for Excellence in Control Systems, and the Richard and Eleanor Towner Prize for Distinguished Academic Achievement for his PhD research work in 2021. Garg's research interests include robust control synthesis for multi-agent coordination using machine learning methods, finite- and fixed-time control synthesis for spatiotemporal specifications, and continuous-time optimization.

Organizers

Kunal Garg, School for Engineering of Matter, Transport, and Energy, ASU (kgarg24@asu.edu)

Chuchu Fan, Department of Aeronautics and Astronautics, MIT (chuchu@mit.edu)