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.
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Researchers
Stefano
Di Cairano
Yebin
Wang
Avishai
Weiss
Scott A.
Bortoff
Ankush
Chakrabarty
Christopher R.
Laughman
Daniel N.
Nikovski
Abraham P.
Vinod
Diego
Romeres
Devesh K.
Jha
Arvind
Raghunathan
Abraham
Goldsmith
Philip V.
Orlik
William S.
Yerazunis
Vedang M.
Deshpande
Jianlin
Guo
Chungwei
Lin
Hongtao
Qiao
Purnanand
Elango
Toshiaki
Koike-Akino
Matthew
Brand
Dehong
Liu
Yanting
Ma
Pedro
Miraldo
Bingnan
Wang
Petros T.
Boufounos
Hassan
Mansour
Ye
Wang
Gordon
Wichern
Jinyun
Zhang
Siddarth
Jain
Kieran
Parsons
Alexander
Schperberg
Hongbo
Sun
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Awards
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AWARD MERL work receives IEEE Transactions on Automation Science and Engineering Best New Application Paper Award from IEEE Robotics and Automation Society Date: May 19, 2025
Awarded to: Yehan Ma, Yebin Wang, Stefano Di Cairano, Toshiaki Koike-Akino, Jianlin Guo, Philip Orlik, Xinping Guan and Chenyang Lu
MERL Contacts: Stefano Di Cairano; Jianlin Guo; Toshiaki Koike-Akino; Philip V. Orlik; Yebin Wang
Research Areas: Communications, Control, Machine LearningBrief- The paper “Smart Actuation for End-Edge Industrial Control Systems”, co-authored by MERL intern Yehan Ma, MERL researchers Yebin Wang, Stefano Di Cairano, Toshiaki Koike-Akino, Jianlin Guo, and Philip Orlik, and academic collaborators Xinping Guan and Chenyang Lu, was recognized as the Best New Application Paper of the IEEE Transactions on Automation Science and Engineering (T-ASE), for "a new industrial automation solution that ensures safety operation through coordinated co-design of edge model predictive control and local actuation".
The award recognizes the best application paper published in T-ASE over the previous calendar year, for the significance of new applications, technical merit, originality, potential impact on the field, and clarity of presentation.
- The paper “Smart Actuation for End-Edge Industrial Control Systems”, co-authored by MERL intern Yehan Ma, MERL researchers Yebin Wang, Stefano Di Cairano, Toshiaki Koike-Akino, Jianlin Guo, and Philip Orlik, and academic collaborators Xinping Guan and Chenyang Lu, was recognized as the Best New Application Paper of the IEEE Transactions on Automation Science and Engineering (T-ASE), for "a new industrial automation solution that ensures safety operation through coordinated co-design of edge model predictive control and local actuation".
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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 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 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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NEWS MERL contributes to ICRA 2025 Date: May 19, 2025 - May 23, 2025
Where: IEEE ICRA
MERL Contacts: Stefano Di Cairano; Jianlin Guo; Chiori Hori; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Philip V. Orlik; Arvind Raghunathan; Diego Romeres; Yuki Shirai; Abraham P. Vinod; Yebin Wang
Research Areas: Artificial Intelligence, Computer Vision, Control, Dynamical Systems, Machine Learning, Optimization, Robotics, Human-Computer InteractionBrief- MERL made significant contributions to both the organization and the technical program of the International Conference on Robotics and Automation (ICRA) 2025, which was held in Atlanta, Georgia, USA, from May 19th to May 23rd.
MERL was a Bronze sponsor of the conference, and MERL researchers chaired four sessions in the areas of Manipulation Planning, Human-Robot Collaboration, Diffusion Policy, and Learning for Robot Control.
MERL researchers presented four papers in the main conference on the topics of contact-implicit trajectory optimization, proactive robotic assistance in human-robot collaboration, diffusion policy with human preferences, and dynamic and model learning of robotic manipulators. In addition, five more papers were presented in the workshops: “Structured Learning for Efficient, Reliable, and Transparent Robots,” “Safely Leveraging Vision-Language Foundation Models in Robotics: Challenges and Opportunities,” “Long-term Human Motion Prediction,” and “The Future of Intelligent Manufacturing: From Innovation to Implementation.”
MERL researcher Diego Romeres delivered an invited talk titled “Dexterous Robotics: From Multimodal Sensing to Real-World Physical Interactions.”
MERL also collaborated with the University of Padua on one of the conference’s challenges: the “3rd AI Olympics with RealAIGym” (https://ai-olympics.dfki-bremen.de).
During the conference, MERL researchers received the IEEE Transactions on Automation Science and Engineering Best New Application Paper Award for their paper titled “Smart Actuation for End-Edge Industrial Control Systems.”
About ICRA
The IEEE International Conference on Robotics and Automation (ICRA) is the flagship conference of the IEEE Robotics and Automation Society and the world’s largest and most comprehensive technical conference focused on research advances and the latest technological developments in robotics. The event attracts over 7,000 participants, 143 partners and exhibitors, and receives more than 4,000 paper submissions.
- MERL made significant contributions to both the organization and the technical program of the International Conference on Robotics and Automation (ICRA) 2025, which was held in Atlanta, Georgia, USA, from May 19th to May 23rd.
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Internships
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MS0098: Internship - Control and Estimation for Large-Scale Thermofluid Systems
MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.
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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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CV0063: Internship - Visual Simultaneous Localization and Mapping
MERL is looking for a self-motivated graduate student to work on Visual Simultaneous Localization and Mapping (V-SLAM). Based on the candidate’s interests, the intern can work on a variety of topics such as (but not limited to): camera pose estimation, feature detection and matching, visual-LiDAR data fusion, pose-graph optimization, loop closure detection, and image-based camera relocalization. The ideal candidate would be a PhD student with a strong background in 3D computer vision and good programming skills in C/C++ and/or Python. The candidate must have published at least one paper in a top-tier computer vision, machine learning, or robotics venue, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS. The intern will collaborate with MERL researchers to derive and implement new algorithms for V-SLAM, conduct experiments, and report findings. A submission to a top-tier conference is expected. The duration of the internship and start date are flexible.
Required Specific Experience
- Experience with 3D Computer Vision and Simultaneous Localization & Mapping.
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Openings
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EA0042: Research Scientist - Control & Learning
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CA0093: Research Scientist - Control for Autonomous Systems
See All Openings at MERL -
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Recent Publications
- , "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}
- }
- , "A Hierarchical Approach for Tractor-trailer Motion Planning Using Graph Search and Reinforcement Learning", European Control Conference (ECC), June 2025.BibTeX TR2025-093 PDF
- @inproceedings{Ma2025jun,
- author = {Ma, Haitong and Zhang, Tianpeng and Li, Na and {Di Cairano}, Stefano and Wang, Yebin},
- title = {{A Hierarchical Approach for Tractor-trailer Motion Planning Using Graph Search and Reinforcement Learning}},
- booktitle = {European Control Conference (ECC)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-093}
- }
- , "State Representation Learning for Visual Servo Control", European Control Conference (ECC), June 2025.BibTeX TR2025-094 PDF
- @inproceedings{Wang2025jun,
- author = {Wang, Jen-Wei and Nikovski, Daniel N.},
- title = {{State Representation Learning for Visual Servo Control}},
- booktitle = {European Control Conference (ECC)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-094}
- }
- , "Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control", IEEE Intelligent Vehicles Symposium (IV), June 2025.BibTeX TR2025-087 PDF
- @inproceedings{Li2025jun2,
- author = {Li, Xianning and Wang, Yebin and Ozbay, Kaan and Jiang, Zhong-Ping},
- title = {{Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control}},
- booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-087}
- }
- , "pycvxset: A Python package for convex set manipulation", American Control Conference (ACC), June 2025.BibTeX TR2025-086 PDF Software
- @inproceedings{Vinod2025jun,
- author = {Vinod, Abraham P.},
- title = {{pycvxset: A Python package for convex set manipulation}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jun,
- url = {https://www.merl.com/publications/TR2025-086}
- }
- , "Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning", IEEE International Conference on Robotics and Automation (ICRA), May 2025.BibTeX TR2025-065 PDF
- @inproceedings{Giammarino2025may,
- author = {Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.},
- title = {{Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning}},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-065}
- }
- , "Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion", IEEE International Conference on Robotics and Automation (ICRA), May 2025.BibTeX TR2025-063 PDF
- @inproceedings{Zhou2025may,
- author = {Zhou, Ziyi and {Di Cairano}, Stefano and Wang, Yebin and Berntorp, Karl},
- title = {{Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion}},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-063}
- }
- , "A Unified Observer for Smooth Speed-Sensorless Drive Control of Induction Machines at Full Speed Range", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.BibTeX TR2025-060 PDF
- @inproceedings{Wu2025may,
- author = {Wu, Jingjie and Goldsmith, Abraham and Liu, Dehong and Wang, Bingnan and Zhou, Lei and Wang, Yebin},
- title = {{A Unified Observer for Smooth Speed-Sensorless Drive Control of Induction Machines at Full Speed Range}},
- booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-060}
- }
- , "Enhanced Agility and Safety in Mobile Manipulators through Centroidal Momentum-Based Motion Planning", European Control Conference (ECC), June 2025.
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Videos
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