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
-
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
Saviz
Mowlavi
Kieran
Parsons
Alexander
Schperberg
Hongbo
Sun
-
Awards
-
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".
-
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).
-
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.
See All Awards for MERL -
-
News & Events
-
NEWS Diego Romeres Delivers Invited Talks at Fraunhofer Italia and the University of Padua Date: July 16, 2025 - July 18, 2025
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics, Human-Computer InteractionBrief- MERL researcher Diego Romeres was invited to present MERL's latest research at two institutions in Italy this July, focusing on human-robot collaboration and LLM-driven assembly systems.
On July 16th, Dr. Romeres delivered a talk titled “Human-Robot Collaborative Assembly” at Fraunhofer Italia – Innovation Engineering Center (EIC) in Bolzano. His presentation showcased research on human-robot collaboration for efficient and flexible assembly processes. Fraunhofer Italia EIC is a non-profit research institute focused on enabling digital and sustainable transformation through applied innovation in close collaboration with both public and private sectors.
Two days later, on July 18th, Dr. Romeres was hosted by the University of Padua, one of Europe’s oldest and most renowned universities. His invited lecture, “Robot Assembly through Human Collaboration & Large Language Models”, explored how artificial intelligence can enhance human-robot synergy in complex assembly tasks.
- MERL researcher Diego Romeres was invited to present MERL's latest research at two institutions in Italy this July, focusing on human-robot collaboration and LLM-driven assembly systems.
-
NEWS MERL researchers present 13 papers at ACC 2025 Date: July 8, 2025 - July 10, 2025
Where: Denver, USA
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Purnanand Elango; Jordan Leung; Saviz Mowlavi; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Electric Systems, Machine Learning, Multi-Physical Modeling, RoboticsBrief- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
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.
- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
See All News & Events for Control -
-
Internships
-
OR0171: Internship - Foundation Models for Robotic Manipulation
MERL is seeking a highly motivated and qualified intern to conduct research on applying foundation models to robotic manipulation. The focus will be on leveraging large-scale pretrained models (e.g., vision-language models, multimodal transformers, diffusion policies) to enable generalist manipulation capabilities across diverse objects, tasks and embodiments including humanoids. Potential research topics include few-shot policy learning, multimodal grounding of multiple sensor modalities to robot actions, and adapting foundation models for precise control and high success rate.
The ideal candidate will be a senior Ph.D. student with a strong background in machine learning for robotics, particularly in areas such as foundation models, imitation learning, reinforcement learning, and multimodal perception. Knowledge on large-scale Vision-Language-Action (VLA) and multimodal foundation models is expected. The internship will involve algorithm design, model fine-tuning, simulation experiments, and deployment on physical robot platforms equipped with cameras, tactile sensors, and force/torque sensors. The successful candidate will collaborate closely with MERL researchers, with the expectation of publishing in top-tier robotics or AI conferences/journals. Interested candidates should apply with an updated CV and relevant publications.
Required Specific Experience
-
Strong background in machine learning for robotics, particularly foundation models (e.g., pi_0, OpenVLA, RT-X, etc.) and imitation learning.
-
Experience with simulation environments such as Mujoco, Isaac Gym, or RLBench.
-
Experience with physical robot platforms and sensors (vision, tactile, force/torque).
-
Proficiency in Python, PyTorch, and modern deep learning frameworks
-
Strong publication record in robotics, machine learning, or AI venues
Internship Details
- Duration: ~3 months
- Start Date: Fall 2025 (flexible based on mutual agreement)
- Goal: Publish research at leading robotics/AI conferences and journals
-
-
CI0169: Internship - Robotic AI Agent
Those who are passionate about pushing the boundaries of embodied AI, join our cutting-edge research team as an intern and contribute to the development of generalist AI agents for humanoid robots. This is a unique opportunity to work on impactful projects aimed at publishing in top-tier AI and robotics venues.
What We’re Looking For
We’re seeking highly motivated individuals with:
- Advanced research experience in robotic AI, edge AI, and agentic AI systems.
- Hands-on expertise in Large Language Models (LLMs), Vision-Language-Action (VLA) models and Foundation Models
- Strong proficiency with Python, PyTorch, deep learning, and robotic agent frameworks
Internship Details
- Duration: ~3 months
- Start Date: Flexible
- Goal: Publish research at leading AI/robotics conferences and journals
If you're excited about shaping the future of humanoid robotics and AI agents, we’d love to hear from you!
-
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.
See All Internships for Control -
-
Openings
-
EA0042: Research Scientist - Control & Learning
-
CA0093: Research Scientist - Control for Autonomous Systems
See All Openings at MERL -
-
Recent Publications
- "Optimal Measurement Projection in GNSS-RTK Factor Graph Optimization", IEEE Conference on Control Technology and Applications (CCTA), August 2025.BibTeX TR2025-118 PDF
- @inproceedings{Hu2025aug,
- author = {Hu, Yingjie and {Di Cairano}, Stefano and Berntorp, Karl},
- title = {{Optimal Measurement Projection in GNSS-RTK Factor Graph Optimization}},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2025,
- month = aug,
- url = {https://www.merl.com/publications/TR2025-118}
- }
, - "Dynamic Sensor Scheduling for Spatio-temporal Monitoring of Water Bodies", IEEE Conference on Control Technology and Applications (CCTA), August 2025.BibTeX TR2025-117 PDF
- @inproceedings{Deshpande2025aug,
- author = {Deshpande, Vedang M. and Vinod, Abraham P.},
- title = {{Dynamic Sensor Scheduling for Spatio-temporal Monitoring of Water Bodies}},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2025,
- month = aug,
- url = {https://www.merl.com/publications/TR2025-117}
- }
, - "Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering", American Control Conference (ACC), July 2025.BibTeX TR2025-106 PDF
- @inproceedings{Zhang2025jul2,
- author = {Zhang, Qi and Avraamidou, Styliani and Paulson, Joel A. and Thakkar, Vyom and Wang, Zhenyu and Chiang, Leo and Braun, Birgit and Rathi, Tushar and Chakrabarty, Ankush and Sorouifar, Farshud and Tang, Wei-Ting and Guertin, France and Munoz, Paola and Sampat, Apoorva},
- title = {{Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-106}
- }
, - "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}
- }
,
- "Optimal Measurement Projection in GNSS-RTK Factor Graph Optimization", IEEE Conference on Control Technology and Applications (CCTA), August 2025.
-
Videos
-
Software & Data Downloads