Control
If it moves, we control it.
Our expertise in this area covers multivariable, nonlinear, optimal and model-predictive control theory, nonlinear estimation, nonlinear dynamical systems, and mechanical design. We conduct both fundamental and applied research targeting a wide range of applications including autonomous driving, factory automation and HVAC systems.
Quick Links
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Researchers
Stefano
Di Cairano
Yebin
Wang
Karl
Berntorp
Scott A.
Bortoff
Mouhacine
Benosman
Avishai
Weiss
Ankush
Chakrabarty
Christopher R.
Laughman
Daniel N.
Nikovski
Diego
Romeres
Abraham P.
Vinod
Devesh K.
Jha
Arvind
Raghunathan
Philip V.
Orlik
Abraham
Goldsmith
Jianlin
Guo
Chungwei
Lin
William S.
Yerazunis
Vedang M.
Deshpande
Toshiaki
Koike-Akino
Hongtao
Qiao
Matthew
Brand
Yanting
Ma
Hassan
Mansour
Pedro
Miraldo
Jinyun
Zhang
Petros T.
Boufounos
Siddarth
Jain
Kieran
Parsons
James
Queeney
Hongbo
Sun
Gordon
Wichern
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Awards
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AWARD Arvind Raghunathan receives Roberto Tempo Best CDC Paper Award at 2022 IEEE Conference on Decision & Control (CDC) Date: December 8, 2022
Awarded to: Arvind Raghunathan
MERL Contact: Arvind Raghunathan
Research Areas: Control, OptimizationBrief- Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).
The award is given annually in honor of Roberto Tempo, the 44th President of the IEEE Control Systems Society (CSS). The Tempo Award Committee selects the best paper from the previous year's CDC based on originality, potential impact on any aspect of control theory, technology, or implementation, and for the clarity of writing. This year's award committee was headed by Prof. Patrizio Colaneri, Politecnico di Milano. Arvind's paper was nominated for the award by Prof. Lorenz Biegler, Carnegie Mellon University, with supporting letters from Prof. Andreas Waechter, Northwestern University, and Prof. Victor Zavala, University of Wisconsin-Madison.
- Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).
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AWARD Best Student Paper Award at the IEEE Conference on Control Technology and Applications Date: August 26, 2020
Awarded to: Marcus Greiff, Anders Robertsson, Karl Berntorp
MERL Contact: Karl Berntorp
Research Areas: Control, Signal ProcessingBrief- Marcus Greiff, a former MERL intern from the Department of Automatic Control, Lund University, Sweden, won one of three 2020 CCTA Outstanding Student Paper Awards and the Best Student Paper Award at the 2020 IEEE Conference on Control Technology and Applications. The research leading up to the awarded paper titled 'MSE-Optimal Measurement Dimension Reduction in Gaussian Filtering', concerned how to select a reduced set of measurements in estimation applications while minimally degrading performance, was done in collaboration with Karl Berntorp at MERL.
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AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019 Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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News & Events
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TALK [MERL Seminar Series 2024] Chuchu Fan presents talk titled Neural Certificates and LLMs in Large-Scale Autonomy Design Date & Time: Wednesday, May 29, 2024; 12:00 PM
Speaker: Chuchu Fan, MIT
MERL Host: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Machine LearningAbstractLearning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics. However, this performance often arrives with the trade-off of diminished transparency and the absence of guarantees regarding the safety and stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies — these certificates provide concise, data-driven proofs that guarantee the safety and stability of the learned control system. These methods not only allow the user to verify the safety of a learned controller but also provide supervision during training, allowing safety and stability requirements to influence the training process itself. In this talk, we present two exciting updates on neural certificates. In the first work, we explore the use of graph neural networks to learn collision-avoidance certificates that can generalize to unseen and very crowded environments. The second work presents a novel reinforcement learning approach that can produce certificate functions with the policies while addressing the instability issues in the optimization process. Finally, if time permits, I will also talk about my group's recent work using LLM and domain-specific task and motion planners to allow natural language as input for robot planning.
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NEWS Saviz Mowlavi gave an invited talk at North Carolina State University Date: April 12, 2024
MERL Contact: Saviz Mowlavi
Research Areas: Control, Dynamical Systems, Machine Learning, OptimizationBrief- Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
The talk, entitled "Model-based and data-driven prediction and control of spatio-temporal systems", described the use of temporal smoothness to regularize the training of fast surrogate models for PDEs, user-friendly methods for PDE-constrained optimization, and efficient strategies for learning feedback controllers for PDEs.
- Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
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Internships
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CA2182: Motion Planning and Control for Articulated Vehicles
MERL is seeking a highly skilled and self-motivated intern to work on motion planning of articulated vehicles. The ideal candidate should have solid backgrounds in established path/motion planning algorithms (A*, D*, graph-search) and optimization-based control for ground and articulated vehicles. Excellent coding skills in MATLAB/Simulink and publication records are necessary. Experience with CasADi and dSPACE is a plus. Ph.D. students in robotics, computer science, control, electrical engineering, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 4-6 months.
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CA2131: Collaborative Legged Robots
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on control and planning algorithms for legged robots for support activities of and collaboration with humans. The ideal candidate is expected to be working towards a PhD with strong emphasis in robotics control and planning and to have interest and background in as many as possible of: motion planning algorithms, control for legged robot locomotions, legged robots, perception and sensing with multiple sensors, SLAM, vision-based control. Good programming skills in Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.
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CA2132: Optimization Algorithms for Motion Planning and Predictive Control
MERL is looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.
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Openings
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Recent Publications
- "Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning", IEEE Transactions on Robotics, DOI: 10.1109/TRO.2024.3387010, Vol. 40, pp. 2529-2542, July 2024.BibTeX TR2024-048 PDF Video
- @article{Safaoui2024jul,
- author = {Safaoui, Sleiman and Vinod, Abraham P. and Chakrabarty, Ankush and Quirynen, Rien and Yoshikawa, Nobuyuki and Di Cairano, Stefano},
- title = {Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning},
- journal = {IEEE Transactions on Robotics},
- year = 2024,
- volume = 40,
- pages = {2529--2542},
- month = jul,
- doi = {10.1109/TRO.2024.3387010},
- url = {https://www.merl.com/publications/TR2024-048}
- }
, - "Leveraging Computational Fluid Dynamics in UAV Motion Planning", American Control Conference (ACC), July 2024.BibTeX TR2024-050 PDF Video
- @inproceedings{Huang2024jul,
- author = {Huang, Yunshen and Greiff, Marcus and Vinod, Abraham P. and Di Cairano, Stefano}},
- title = {Leveraging Computational Fluid Dynamics in UAV Motion Planning},
- booktitle = {American Control Conference (ACC)},
- year = 2024,
- month = jul,
- url = {https://www.merl.com/publications/TR2024-050}
- }
, - "Variational Bayes Kalman Filter for Joint Vehicle Localization and Road Mapping Using Onboard Sensors", European Control Conference (ECC), June 2024.BibTeX TR2024-082 PDF
- @inproceedings{Berntorp2024jun,
- author = {Berntorp, Karl and Greiff, Marcus}},
- title = {Variational Bayes Kalman Filter for Joint Vehicle Localization and Road Mapping Using Onboard Sensors},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-082}
- }
, - "A Robust Invariant Set Planner For Quadrotors", European Control Conference (ECC), June 2024.BibTeX TR2024-081 PDF
- @inproceedings{Greiff2024jun,
- author = {Greiff, Marcus and Weiss, Avishai and Berntorp, Karl and Di Cairano, Stefano}},
- title = {A Robust Invariant Set Planner For Quadrotors},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-081}
- }
, - "Distributed Co-Design of Motors and Motions for Robotic Manipulators", European Control Conference (ECC), June 2024.BibTeX TR2024-083 PDF
- @inproceedings{Lu2024jun,
- author = {Lu, Zehui and Wang, Yebin and Sakamoto, Yusuke and Mou, Shaoshuai}},
- title = {Distributed Co-Design of Motors and Motions for Robotic Manipulators},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-083}
- }
, - "Physics-Informed Road Monitoring and Suspension Control using Crowdsourced Vehicle Data", European Control Conference (ECC), June 2024.BibTeX TR2024-084 PDF
- @inproceedings{Wang2024jun4,
- author = {Wang, Yanbing and Berntorp, Karl and Menner, Marcel}},
- title = {Physics-Informed Road Monitoring and Suspension Control using Crowdsourced Vehicle Data},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-084}
- }
, - "Structural Exploitation for the Homogeneous Reformulation of Model Predictive Control Problems", European Control Conference (ECC), June 2024.BibTeX TR2024-080 PDF
- @inproceedings{Hall2024jun,
- author = {Hall, Jonas F and Raghunathan, Arvind}},
- title = {Structural Exploitation for the Homogeneous Reformulation of Model Predictive Control Problems},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-080}
- }
, - "Simultaneous State Estimation and Contact Detection for Legged Robots by Multiple-Model Kalman Filtering", European Control Conference (ECC), June 2024.BibTeX TR2024-085 PDF
- @inproceedings{Menner2024jun,
- author = {Menner, Marcel and Berntorp, Karl}},
- title = {Simultaneous State Estimation and Contact Detection for Legged Robots by Multiple-Model Kalman Filtering},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-085}
- }
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- "Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning", IEEE Transactions on Robotics, DOI: 10.1109/TRO.2024.3387010, Vol. 40, pp. 2529-2542, July 2024.
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Videos
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Software & Data Downloads