Dynamical Systems
Exploiting nonlinearity and shaping dynamics in creative and deeply mathematical ways.
We apply dynamical systems theory in applications ranging from space probe trajectory optimization to elevator suspensions. We also develop fundamental theory and computational methods in fluid dynamics.
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Researchers
Stefano
Di Cairano
Yebin
Wang
Avishai
Weiss
Scott A.
Bortoff
Abraham P.
Vinod
Christopher R.
Laughman
Ankush
Chakrabarty
Saviz
Mowlavi
Hassan
Mansour
Hongtao
Qiao
Daniel N.
Nikovski
Petros T.
Boufounos
Purnanand
Elango
Chungwei
Lin
Abraham
Goldsmith
Devesh K.
Jha
James
Queeney
Yanting
Ma
Pedro
Miraldo
Philip V.
Orlik
Diego
Romeres
Alexander
Schperberg
William S.
Yerazunis
Jianlin
Guo
Kieran
Parsons
Arvind
Raghunathan
Joshua
Rapp
Hongbo
Sun
Bingnan
Wang
Pu
(Perry)
Wang
Jinyun
Zhang
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Awards
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AWARD University of Padua and MERL team wins the AI Olympics with RealAIGym competition at IROS24 Date: October 17, 2024
Awarded to: Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, RoboticsBrief- The team composed of the control group at the University of Padua and MERL's Optimization and Robotic team ranked 1st out of the 4 finalist teams that arrived to the 2nd AI Olympics with RealAIGym competition at IROS 24, which focused on control of under-actuated robots. The team was composed by Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli and Diego Romeres. The competition was organized by the German Research Center for Artificial Intelligence (DFKI), Technical University of Darmstadt and Chalmers University of Technology.
The competition and award ceremony was hosted by IEEE International Conference on Intelligent Robots and Systems (IROS) on October 17, 2024 in Abu Dhabi, UAE. Diego Romeres presented the team's method, based on a model-based reinforcement learning algorithm called MC-PILCO.
- The team composed of the control group at the University of Padua and MERL's Optimization and Robotic team ranked 1st out of the 4 finalist teams that arrived to the 2nd AI Olympics with RealAIGym competition at IROS 24, which focused on control of under-actuated robots. The team was composed by Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli and Diego Romeres. The competition was organized by the German Research Center for Artificial Intelligence (DFKI), Technical University of Darmstadt and Chalmers University of Technology.
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AWARD MERL’s Paper on Wi-Fi Sensing Earns Top 3% Paper Recognition at ICASSP 2023, Selected as a Best Student Paper Award Finalist Date: June 9, 2023
Awarded to: Cristian J. Vaca-Rubio, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Petros Boufounos and Petar Popovski
MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Pu (Perry) Wang; Ye Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Signal ProcessingBrief- A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.
Performed during Cristian’s stay at MERL first as a visiting Marie Skłodowska-Curie Fellow and then as a full-time intern in 2022, this work capitalizes on standards-compliant Wi-Fi signals to perform indoor localization and sensing. The paper uses a neural dynamic learning framework to address technical issues such as low sampling rate and irregular sampling intervals.
ICASSP, a flagship conference of the IEEE Signal Processing Society (SPS), was hosted on the Greek island of Rhodes from June 04 to June 10, 2023. ICASSP 2023 marked the largest ICASSP in history, boasting over 4000 participants and 6128 submitted papers, out of which 2709 were accepted.
- A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.
See All Awards for MERL -
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News & Events
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NEWS MERL researchers present 9 papers at ACC 2024 Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
In addition, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
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NEWS Diego Romeres gave an invited talk at the Padua University's Seminar series on "AI in Action" Date: April 9, 2024
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- Diego Romeres, Principal Research Scientist and Team Leader in the Optimization and Robotics Team, was invited to speak as a guest lecturer in the seminar series on "AI in Action" in the Department of Management and Engineering, at the University of Padua.
The talk, entitled "Machine Learning for Robotics and Automation" described MERL's recent research on machine learning and model-based reinforcement learning applied to robotics and automation.
- Diego Romeres, Principal Research Scientist and Team Leader in the Optimization and Robotics Team, was invited to speak as a guest lecturer in the seminar series on "AI in Action" in the Department of Management and Engineering, at the University of Padua.
See All News & Events for Dynamical Systems -
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Internships
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ST0103: Internship - Data-Driven Control for High-Dimensional Dynamics
MERL is seeking a motivated and qualified individual to work on data-driven estimation and control of high-dimensional dynamical systems, with applications in indoor airflow optimization. The ideal candidate will be a PhD student in engineering or related fields with a solid background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, data-driven control, nonlinear control, reduced-order modeling (ROM), and partial differential equations (PDEs). Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.
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CA0117: Internship - Feedforward-Feedback Co-Design
MERL is seeking a graduate student to develop a scalable optimization-based framework for feedforward-feedback co-design for nonlinear dynamical systems subject to path constraints. The framework will 1) support modeling and operational uncertainties, and 2) guarantee static and dynamic feasibility in closed-loop. The solution approach will leverage the state-of-the-art in sequential convex programming, contraction analysis, and first-order methods for semidefinite programming. The methods will be evaluated on high-dimensional motion planning problems in robotics. The results of the internship are expected to be published in top-tier conferences and/or journal in robotics, control systems, and optimization.
The internship is expected to start in Spring or Summer 2025 with an expected duration of 3-6 months depending on the agreed scope and intermediate progress.
Required Specific Experience
- Current/Past enrollment in a Ph.D. program in Mechanical, Aerospace, Electrical Engineering, Computer Science, or Applied Mathematics.
- 2+ years of research in at least some of: first-order algorithms for SDPs, contraction analysis, nonconvex trajectory optimization.
- Strong programming skills in Python and/or C/C++.
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CA0107: Internship - Perception-Aware Control and Planning
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of visual perception-aware control. The overall objective is to optimize control policy where the perception uncertainty is affected by the chosen policy. Application areas include mobile robotics, drones, autonomous vehicles, and spacecraft. The ideal candidate is expected to be working towards a PhD with a strong emphasis on stochastic optimal control/planning or visual odometry and to have interest and background in as many as possible among: output-feedback optimal control, visual SLAM, POMDP, information fields, motion planning, and machine learning. The expected start date is in the late Spring/Early Summer 2025, for a duration of 3-6 months.
Required Specific Experience
- Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, or a related field
- 2+ years of research in at least some of: optimal control, motion planning, computer vision, navigation, uncertainty quantification, stochastic planning/control
- Strong programming skills in Python and/or C++
See All Internships for Dynamical Systems -
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Openings
See All Openings at MERL -
Recent Publications
- , "Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2024.3433229, Vol. 32, No. 6, pp. 2492-2499, January 2025.BibTeX TR2024-136 PDF
- @article{Vinod2025jan,
- author = {Vinod, Abraham P. and Safaoui, Sleiman and Summers, Tyler and Yoshikawa, Nobuyuki and Di Cairano, Stefano}},
- title = {Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2025,
- volume = 32,
- number = 6,
- pages = {2492--2499},
- month = jan,
- doi = {10.1109/TCST.2024.3433229},
- url = {https://www.merl.com/publications/TR2024-136}
- }
- , "Memory-Based Learning of Global Control Policies from Local Controllers", 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO'24), November 2024.BibTeX TR2024-158 PDF
- @inproceedings{Nikovski2024nov,
- author = {{Nikovski, Daniel N. and Zhong, Junmin and Yerazunis, William S.}},
- title = {Memory-Based Learning of Global Control Policies from Local Controllers},
- booktitle = {21st International Conference on Informatics in Control, Automation and Robotics (ICINCO'24)},
- year = 2024,
- month = nov,
- url = {https://www.merl.com/publications/TR2024-158}
- }
- , "Single-pixel imaging of spatio-temporal flows using differentiable latent dynamics", IEEE Transactions on Computational Imaging, October 2024.BibTeX TR2024-151 PDF
- @article{Sholokhov2024oct,
- author = {{Sholokhov, Aleksei and Nabi, Saleh and Rapp, Joshua and Brunton, Steven and Kutz, Nathan and Boufounos, Petros T. and Mansour, Hassan}},
- title = {Single-pixel imaging of spatio-temporal flows using differentiable latent dynamics},
- journal = {IEEE Transactions on Computational Imaging},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-151}
- }
- , "Autonomous Horizon-Based Optical Navigation on Near-Planar Cislunar Libration Point Orbits", 4th Space Imaging Workshop, October 2024.BibTeX TR2024-139 PDF
- @inproceedings{Shimane2024oct,
- author = {Shimane, Yuri and Ho, Koki and Weiss, Avishai}},
- title = {Autonomous Horizon-Based Optical Navigation on Near-Planar Cislunar Libration Point Orbits},
- booktitle = {4th Space Imaging Workshop},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-139}
- }
- , "Reinforcement Learning-Based Estimation for Spatio-Temporal Systems", Nature Scientific Reports, DOI: 10.1038/s41598-024-72055-1, Vol. 14, pp. 22464, October 2024.BibTeX TR2024-134 PDF
- @article{Mowlavi2024oct,
- author = {Mowlavi, Saviz and Benosman, Mouhacine}},
- title = {Reinforcement Learning-Based Estimation for Spatio-Temporal Systems},
- journal = {Nature Scientific Reports},
- year = 2024,
- volume = 14,
- pages = 22464,
- month = oct,
- doi = {10.1038/s41598-024-72055-1},
- url = {https://www.merl.com/publications/TR2024-134}
- }
- , "ROSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, September 2024.BibTeX TR2024-132 PDF
- @article{Cao2024sep,
- author = {Cao, Weidong and Gao, Jian and Ma, Tianrui and Ma, Rui and Benosman, Mouhacine and Zhang, Xuan}},
- title = {ROSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning},
- journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-132}
- }
- , "From Convexity to Strong Convexity and Beyond: Bridging The Gap In Convergence Rates", IEEE Conference on Decision and Control (CDC), September 2024.BibTeX TR2024-131 PDF
- @inproceedings{Romero2024sep,
- author = {Romero, Orlando and Benosman, Mouhacine and Pappas, George}},
- title = {From Convexity to Strong Convexity and Beyond: Bridging The Gap In Convergence Rates},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-131}
- }
- , "Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning", IEEE Transactions on Control Systems Technology, September 2024.BibTeX TR2024-123 PDF
- @article{Quirynen2024sep,
- author = {Quirynen, Rien and Safaoui, Sleiman and Di Cairano, Stefano}},
- title = {Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-123}
- }
- , "Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2024.3433229, Vol. 32, No. 6, pp. 2492-2499, January 2025.
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