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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  • Awards

    •  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, Robotics
      Brief
      • 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.
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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 Processing
      Brief
      • 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.
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  • News & Events

    •  NEWS    MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
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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, Robotics
      Brief
      • 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.
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  • Internships

    • CA0111: Internship - Nonconvex Trajectory Optimization

      MERL is seeking a graduate student to develop an optimization-based framework for nonconvex trajectory generation with emphasis on continuous-time modeling/constraint satisfaction, convergence guarantees, and real-time performance. The framework will support hybrid dynamical systems, spatio-temporal logical specifications, multi-body systems, and contact-rich motion. The methods will be evaluated on real-world robotics applications based on locomotion, manipulation, and motion planning. 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: sequential convex programming, augmented Lagrangian, operator-splitting first-order optimization algorithms, contact-rich motion, multi-body systems, signal temporal logic specifications, direct shooting and collocation methods.
      • Experience in design and simulation tools for robotics such as ROS, Mujoco, Gazebo, Isaac Lab.
      • Strong programming skills in Python and/or C/C++.

    • 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.

    • OR0132: Internship - Motion Planning for Robotics

      MERL is looking for a highly motivated and qualified PhD student in the areas of motion planning, machine learning and control for robotics, to participate in research on advanced algorithms for motion planning and skill learning of robotic systems. Solid background and hands-on experience with classical motion planning and trajectory optimization algorithms for robotic manipulators is expected. Exposure to machine learning for policy optimization and skill learning, understanding of various optimization solvers and control theory is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Solid experimental skill and hands-on experience in coding in Python and ROS are required for the position. A successful internship will result in submission of results to top tier robotics venue in collaboration with MERL researchers. Start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their updated CV and list of publications.

      Required Specific Experience

      • Experience with robotic manipulators or other system like robot quadrupeds is required.
      • Experience with motion planning and trajectory optimization algorithms
      • Strong programming skills in Python and ROS
      • Experience in at least one physics simulator


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  • Openings


    See All Openings at MERL

  • Recent Publications

    •  Vinod, A.P., Safaoui, S., Summers, T., Yoshikawa, N., Di Cairano, S., "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}
      • }
    •  Greiff, M., Di Cairano, S., Berntorp, K., "Bayesian Measurement Masks for GNSS Positioning", IEEE Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-172 PDF
      • @inproceedings{Greiff2024dec,
      • author = {Greiff, Marcus and Di Cairano, Stefano and Berntorp, Karl}},
      • title = {Bayesian Measurement Masks for GNSS Positioning},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-172}
      • }
    •  Ozcan, E.C., Giammarino, V., Queeney, J., Paschalidis, I.C., "A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations", IEEE Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-178 PDF
      • @inproceedings{Ozcan2024dec,
      • author = {Ozcan, Erhan Can and Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.}},
      • title = {A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-178}
      • }
    •  Vinod, A.P., Weiss, A., Di Cairano, S., "Inscribing and separating an ellipsoid and a constrained zonotope: Applications in stochastic control and centering", IEEE Annual Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-173 PDF
      • @inproceedings{Vinod2024dec,
      • author = {Vinod, Abraham P. and Weiss, Avishai and Di Cairano, Stefano}},
      • title = {Inscribing and separating an ellipsoid and a constrained zonotope: Applications in stochastic control and centering},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-173}
      • }
    •  Greiff, M., Berntorp, K., "Asynchronous Variational-Bayes Kalman Filtering", IEEE Annual Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-177 PDF
      • @inproceedings{Greiff2024dec2,
      • author = {Greiff, Marcus and Berntorp, Karl}},
      • title = {Asynchronous Variational-Bayes Kalman Filtering},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-177}
      • }
    •  Lishkova, Y., Vinod, A.P., Di Cairano, S., Weiss, A., "Divert-feasible lunar landing under navigational uncertainty", 2024 Conference on Decision and Control, December 2024.
      BibTeX TR2024-174 PDF
      • @inproceedings{Lishkova2024dec,
      • author = {Lishkova, Yana and Vinod, Abraham P. and Di Cairano, Stefano and Weiss, Avishai}},
      • title = {Divert-feasible lunar landing under navigational uncertainty},
      • booktitle = {2024 Conference on Decision and Control},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-174}
      • }
    •  Yin, J., Tsiotras, P., Berntorp, K., "Chance-Constrained Information-Theoretic Stochastic Model Predictive Control with Safety Shielding", IEEE Annual Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-179 PDF
      • @inproceedings{Yin2024dec,
      • author = {Yin, Ji and Tsiotras, Panagiotis and Berntorp, Karl}},
      • title = {Chance-Constrained Information-Theoretic Stochastic Model Predictive Control with Safety Shielding},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-179}
      • }
    •  Berntorp, K., Greiff, M., "A Framework for Joint Vehicle Localization and Road Mapping Using Onboard Sensors", Control Engineering Practice, November 2024.
      BibTeX TR2024-163 PDF
      • @article{Berntorp2024nov,
      • author = {Berntorp, Karl and Greiff, Marcus}},
      • title = {A Framework for Joint Vehicle Localization and Road Mapping Using Onboard Sensors},
      • journal = {Control Engineering Practice},
      • year = 2024,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2024-163}
      • }
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