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
Avishai
Weiss
Yebin
Wang
Abraham P.
Vinod
Scott A.
Bortoff
Christopher R.
Laughman
Saviz
Mowlavi
Ankush
Chakrabarty
Hassan
Mansour
Hongtao
Qiao
Petros T.
Boufounos
Daniel N.
Nikovski
Purnanand
Elango
Chungwei
Lin
Abraham
Goldsmith
Devesh K.
Jha
Yanting
Ma
Diego
Romeres
Pedro
Miraldo
Philip V.
Orlik
Arvind
Raghunathan
Alexander
Schperberg
William S.
Yerazunis
Jianlin
Guo
Kieran
Parsons
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.
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News & Events
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NEWS MERL contributes to 2025 European Control Conference Date: June 24, 2025 - June 27, 2025
Where: Thessaloniki
MERL Contacts: Stefano Di Cairano; Daniel N. Nikovski; Diego Romeres; Yebin Wang
Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers contributed to both the technical program and workshop organization at the 2025 European Control Conference (ECC), held in Thessaloniki, Greece, from June 24 to 27. ECC is one of the premier conferences in the field of control.
In the main conference, MERL researchers presented four papers covering a range of topics, including: Representation learning, Motion planning for tractor-trailers, Motion planning for mobile manipulators, Learning high-dimensional dynamical systems, Model learning for robotics.
Additionally, MERL co-organized a workshop with the University of Padua titled “Reinforcement Learning for Robotic Control: Recent Developments and Open Challenges.” MERL researcher Diego Romeres also delivered an invited talk titled “Human-Robot Collaborative Assembly” in that workshop.
- MERL researchers contributed to both the technical program and workshop organization at the 2025 European Control Conference (ECC), held in Thessaloniki, Greece, from June 24 to 27. ECC is one of the premier conferences in the field of control.
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TALK [MERL Seminar Series 2025] Behçet Açıkmeşe presents talk titled Robust Trajectory Planning and Control Date & Time: Wednesday, June 25, 2025; 12:00 PM
Speaker: Behçet Açıkmeşe, University of Washington
MERL Host: Avishai Weiss
Research Areas: Control, Dynamical Systems, OptimizationAbstract
Next-generation aerospace systems – from asteroid-mining robots and spacecraft swarms to hypersonic vehicles and urban air mobility – demand autonomy that transcends current limits. These missions require spacecraft to operate safely, efficiently, and decisively in unpredictable environments, where every decision must balance performance, resource constraints, and risk. The core challenge lies in solving complex optimal control problems in real time while: i) Exploiting full system capabilities without violating safety limits, ii) Certifying algorithmic reliability for critical Guidance, Navigation, & Control (GN&C) systems, iii) Proving robustness in the presence of uncertainty. Our solution is optimization-based control. By transforming GN&C challenges into structured optimization problems and applying methods of convexification, we achieve provably robust, computationally tractable solutions.
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Internships
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CA0148: Internship - Motion Planning and Control for Autonomous Articulated Vehicles
MERL is seeking an outstanding intern to collaborate in the development of motion planning and control for autonomous articulated vehicles. The ideal candidate is expected to be working towards a PhD in electrical, mechanical, aerospace engineering, robotics, control or related areas, with a strong emphasis on motion planning and control, possibly with applications to ground vehicles, to have experience in at least some of path/motion planning algorithms (A*, D*, graph-search) and optimization-based control (e.g., model predictive control), to have excellent coding skills in MATLAB/Simulink and a strong publication record. The expected start date is the Spring/Early Summer 2025 and the expected duration is 6-9 months, depending on candidate availability and interests.
Required Specific Experience
- Path/motion planning algorithms (A*, D*, graph-search)
- Nonlinear model predictive control
- Programming in Matlab/Simulink
- Applications to mobile robots or vehicles
Additional Useful Experience
- Nonlinear MPC Design in CasADi
- Code generation tools and dSPACE
- Applications to autonomous vehicles and articulated vehicles
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CA0153: Internship - High-Fidelity Visualization and Simulation for Space Applications
MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
Required Specific Experience
- Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field
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Experience with one or more of Blender, Unreal, Unity, along with their APIs
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Strong programming skills in one or more of Matlab, Python, and/or C/C++
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ST0105: Internship - Surrogate Modeling for Sound Propagation
MERL is seeking a motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. 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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Openings
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Recent Publications
- , "Policy Optimization for PDE Control with a Warm Start", American Control Conference (ACC), July 2025.BibTeX TR2025-105 PDF
- @inproceedings{Zhang2025jul,
- author = {Zhang, Xiangyuan and Mowlavi, Saviz and Benosman, Mouhacine and Basar, Tamer},
- title = {{Policy Optimization for PDE Control with a Warm Start}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-105}
- }
- , "Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints", American Control Conference (ACC), July 2025.BibTeX TR2025-103 PDF
- @inproceedings{Cardona2025jul,
- author = {Cardona, Gustavo and Vasile, Cristian-Ioan and {Di Cairano}, Stefano},
- title = {{Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-103}
- }
- , "Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning", American Control Conference (ACC), July 2025.BibTeX TR2025-104 PDF
- @inproceedings{ChavezArmijos2025jul,
- author = {Chavez Armijos, Andres and Berntorp, Karl and {Di Cairano}, Stefano},
- title = {{Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-104}
- }
- , "GNSS-RTK Factor Graph Optimization with Adaptive Ambiguity Noise", American Control Conference (ACC), July 2025.BibTeX TR2025-102 PDF
- @inproceedings{Hu2025jul,
- author = {Hu, Yingjie and {Di Cairano}, Stefano and Berntorp, Karl},
- title = {{GNSS-RTK Factor Graph Optimization with Adaptive Ambiguity Noise}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-102}
- }
- , "Geostationary Satellite Station Keeping and Collocation under High-Thrust Impulsive Control", American Control Conference (ACC), July 2025.BibTeX TR2025-101 PDF
- @inproceedings{Pavlasek2025jul,
- author = {Pavlasek, Natalia and {Di Cairano}, Stefano and Weiss, Avishai},
- title = {{Geostationary Satellite Station Keeping and Collocation under High-Thrust Impulsive Control}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-101}
- }
- , "Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control", American Control Conference (ACC), July 2025.BibTeX TR2025-100 PDF
- @inproceedings{Shimane2025jul,
- author = {Shimane, Yuri and {Di Cairano}, Stefano and Ho, Koki and Weiss, Avishai},
- title = {{Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-100}
- }
- , "Enhanced Agility and Safety in Mobile Manipulators through Centroidal Momentum-Based Motion Planning", European Control Conference (ECC), June 2025.BibTeX TR2025-092 PDF
- @inproceedings{Dai2025jun,
- author = {Dai, Min and Lu, Zehui and Li, Na and Wang, Yebin},
- title = {{Enhanced Agility and Safety in Mobile Manipulators through Centroidal Momentum-Based Motion Planning}},
- booktitle = {European Control Conference (ECC)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-092}
- }
- , "Learning Positive Definite Inertia Matrices in Black-Box Inverse Dynamics Models via Gaussian Processes: a Constraint Learning Approach", European Control Conference (ECC), June 2025.BibTeX TR2025-090 PDF
- @inproceedings{Giacomuzzo2025jun,
- author = {Giacomuzzo, Giulio and Romeres, Diego and Carli, Ruggero and Dalla Libera, Alberto},
- title = {{Learning Positive Definite Inertia Matrices in Black-Box Inverse Dynamics Models via Gaussian Processes: a Constraint Learning Approach}},
- booktitle = {European Control Conference (ECC)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-090}
- }
- , "Policy Optimization for PDE Control with a Warm Start", American Control Conference (ACC), July 2025.
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