Optimization

Efficient solutions to large-scale problems.

Much of MERL's research activity involves formulating scientific and engineering problems as optimizations, which can be solved in an efficient way. We have developed fundamental algorithms to better solve classic problems, such as quadratic programs and minimum-cost paths. Our work also involves developing theoretical bounds to understand performance limits.

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Researchers
Awards
- AWARD Mitsubishi Electric and MERL work recognized with IEEJ Distinguished Paper Award
  Date: June 1, 2025
  Awarded to: Arvind Raghunathan, Daniel Nikovski
  MERL Contacts: Daniel N. Nikovski; Arvind Raghunathan
  Research Areas: Electric Systems, Optimization
  Brief
  - A publication jointly authored by Mitsubishi Electric Corporation's Advanced Technology Center (ATC) and MERL researchers has been recognized with the 2025 IEEJ Distinguished Paper Award by the Institute of Electrical Engineers Japan. The paper titled "Power Band Model Based on Flow Network and Weekly Unit Commitment Problem Considering Reserve Market" published in the IEEJ Transactions on Power and Energy presents a novel Unit Commitment formulation for scheduling the generator operations. Arvind Raghunathan and Daniel Nikovksi were co-authors on this publication.
- AWARD Mitsubishi Electric Team Wins Awards at GalFer Contest
  Date: June 23, 2025
  Awarded to: Bingnan Wang, Tatsuya Yamamoto, Yusuke Sakamoto, Siyuan Sun, Toshiaki Koike-Akino, and Ye Wang
  MERL Contacts: Toshiaki Koike-Akino; Bingnan Wang; Ye Wang
  Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
  Brief
  - The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.
    
    The GalFer (Galileo Ferraris) contest aims to compare the accuracy and efficiency of data-driven methodologies for the multi-physics simulation of traction electric machines. A total of 26 teams worldwide participated in the contest, which consists of three categories. The MELSUR team, including MERL staff Bingnan Wang, Toshiaki Koike-Akino, Ye Wang, MERL intern Siyuan Sun, Mitsubishi Electric researchers Tatsuya Yamamoto and Yusuke Sakamoto, ranked first for the category of "Novelty" and "Interpolation". The results were announced during an award ceremony at the COMPUMAG 2025 conference in Naples, Italy.
- AWARD MERL Researchers Win Best Workshop Poster Award at the 2023 IEEE International Conference on Robotics and Automation (ICRA)
  Date: June 2, 2023
  Awarded to: Yuki Shirai, Devesh Jha, Arvind Raghunathan and Dennis Hong
  MERL Contacts: Devesh K. Jha; Arvind Raghunathan; Yuki Shirai
  Research Areas: Artificial Intelligence, Optimization, Robotics
  Brief
  - MERL's paper titled: "Closed-Loop Tactile Controller for Tool Manipulation" Won the Best Poster Award in the workshop on "Embracing contacts : Making robots physically interact with our world". First author and MERL intern, Yuki Shirai, was presented with the award at a ceremony held at ICRA in London. MERL researchers Devesh Jha, Principal Research Scientist, and Arvind Raghunathan, Senior Principal Research Scientist and Senior Team Leader as well as Prof. Dennis Hong of University of California, Los Angeles are also coauthors.
    
    The paper presents a technique to manipulate an object using a tool in a closed-loop fashion using vision-based tactile sensors. More information about the workshop and the various speakers can be found here https://sites.google.com/view/icra2023embracingcontacts/home.
See All Awards for Optimization
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 Interaction
  Brief
  - 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.
- 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, Robotics
  Brief
  - 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.
See All News & Events for Optimization
Research Highlights
- Edge-Assisted Internet of Vehicles for Smart Mobility
Internships
- 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!
- CA0157: Internship - Spatio-temporal monitoring using mobile robots
  
  MERL is seeking a highly motivated intern to collaborate and develop a framework for spatio-temporal monitoring using heterogeneous mobile robots. The work will involve multi-domain research, including multi-agent planning and control, optimization, adaptive and learning-based control, and computer vision. The methods will be implemented and evaluated using physical experiments on robotic platforms (e.g., Crazyflies,Turtlebots). The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during Fall/Winter 2025 (exact dates are flexible) with an expected duration of 4-6 months.
  Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.
  Required Specific Experience
  - Current enrollment in a PhD program in Mechanical, Electrical Engineering, Computer Science, or related programs, with a focus on Robotics and/or Control Systems
  - Experience in some/all of these topics: multi-agent planning and control, optimization, adaptive and learning-based control, and computer vision
  - Experience with ROS2 and validation of algorithms on robotic platforms
  - Strong programming skills in Python and/or C/C++
  Desired Specific Experience
  - Experience with Crazyflie quadrotors and the Crazyswarm2 library
  - Experience with cvxpy and/or gurobipy
  - Experience in convex optimization and model predictive control
  - Experience with computer vision
- EA0076: Internship - Machine Learning for Electric Motor Design
  
  MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is about 3 months.
See All Internships for Optimization
Openings
See All Openings at MERL
Recent Publications
- Zhang, Q., Avraamidou, S., Paulson, J.A., Thakkar, V., Wang, Z., Chiang, L., Braun, B., Rathi, T., Chakrabarty, A., Sorouifar, F., Tang, W.-T., Guertin, F., Munoz, P., Sampat, A., "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}
  - }
- Cardona, G., Vasile, C.-I., Di Cairano, S., "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}
  - }
- Chavez Armijos, A., Berntorp, K., Di Cairano, S., "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}
  - }
- Pavlasek, N., Di Cairano, S., Weiss, A., "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}
  - }
- Shimane, Y., Di Cairano, S., Ho, K., Weiss, A., "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}
  - }
- Raghunathan, A., Cardonha, C., Bergman, D., Nohra, C.J., "Recursive McCormick Linearization of Multilinear Programs", INFORMS J Computing, June 2025.
  BibTeX TR2025-098 PDF
  - @article{Raghunathan2025jun,
  - author = {Raghunathan, Arvind and Cardonha, Carlos and Bergman, David and Nohra, Carlos J.},
  - title = {{Recursive McCormick Linearization of Multilinear Programs}},
  - journal = {INFORMS J Computing},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-098}
  - }
- Das, G., Wang, B., Lin, C., "Topology Optimization of Electric Motors using Mesh Projection", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.
  BibTeX TR2025-089 PDF
  - @inproceedings{Das2025jun2,
  - author = {Das, Ghanendra and Wang, Bingnan and Lin, Chungwei},
  - title = {{Topology Optimization of Electric Motors using Mesh Projection}},
  - booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-089}
  - }
- Li, X., Wang, Y., Ozbay, K., Jiang, Z.-P., "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}
  - }
See All Publications for Optimization
Videos

Energy-Efficient Motion Planner for Legged Robots

[MERL Seminar Series Summer 2025] Robust Trajectory Planning and Control

FDPP: Fine-tune Diffusion Policy with Human Preference

Motion Planning for Information Acquisition via Continuous-time Successive Convexification

[MERL Seminar Series Spring 2025] Optimizing the Path Towards Plastic-Free Oceans

Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Robot Teams

[MERL Seminar Series Fall 2024] AI-assisted Power Grid Dispatch and Control: Optimization, Safety, and Real-world Demonstrations

Description of the abort-safe spacecraft rendezvous example

Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning

[MERL Seminar Series Fall 2023] Co-Design of Complex Systems: From Autonomy to Future Mobility

[MERL Seminar Series Fall 2023] Composable Optimization for Robotic Simulation, Planning, and Control

[MERL Seminar Series Fall 2023] A Process Systems Engineering Perspective on Carbon Capture: Key Challenges and Opportunities

[TRO 2024] Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning

Simultaneous Trajectory Optimization and Contact Selection for Multi-Modal Manipulation Planning

[MERL Seminar Series Spring 2023] Learning and Dynamical Systems

Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization

Robot Locomotion by Automated Controller Tuning

Real-time Mixed-integer Programming for Vehicle Decision Making and Motion Planning

Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Networks

[MERL Seminar Series Fall 2022] Design, Identification and Simulation of PM Synchronous Machines for Traction

Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics

[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?

[MERL Seminar Series Spring 2022] Extreme optics design as a large-scale optimization problem

[MERL Seminar Series 2021] Integration of Analytics Techniques for Algorithmic Sports Betting

Multiview Sensing with Unknown Permutations: An Optimal Transport Approach

Imaging for inverse scattering in Reflection Tomography

Electric Satellite Station Keeping, Attitude Control, and Momentum Management by MPC

Polar Coding with Chemical Reaction Networks for Molecular Communications

EMI reduction in PWM inverters using adaptive frequency modulated carriers

Five Axis Additive Manufacturing

Fast Pattern Search in Big Data

Globally Optimal Power Flow
Software & Data Downloads