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 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.
- 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, Optimization
  Brief
  - 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.
- AWARD Outstanding Presentation Award at the 28th Conference of Information Processing Society of Japan/Consumer Device & Systems
  Date: October 20, 2020
  Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
  MERL Contacts: Jianlin Guo; Philip V. Orlik
  Research Areas: Communications, Optimization, Signal Processing
  Brief
  - MELCO and MERL researchers have won "Outstanding Presentation Award" at 28th Conference of Information Processing Society of Japan (IPSJ)/Consumer Device & Systems held on September 29-30, 2020. The paper titled "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands" reports IEEE 802.19.3 standard development on coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. MERL and MELCO have been leading this standard development and made major technical contributions, which propose methods to mitigate interference in smart meter systems. The authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
See All Awards for Optimization
News & Events
- NEWS Toshiaki Koike-Akino to give a tutorial talk at ISIT 2025 Quantum Hackathon
  Date: June 22, 2025
  Where: IEEE International Symposium on Information Theory (ISIT)
  MERL Contact: Toshiaki Koike-Akino
  Research Areas: Artificial Intelligence, Communications, Data Analytics, Machine Learning, Optimization, Signal Processing, Human-Computer Interaction, Information Security
  Brief
  - Toshiaki Koike-Akino is invited to present a tutorial talk at IEEE ISIT 2025 Quantum Hackathon, to be held at Ann Arbor, Michigan, USA. The talk, entitled "Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum artificial intelligence (QAI) technologies.
    
    The ISIT 2025 Quantum Hackathon invites participants to explore the intersection of quantum computing and information theory. Participants will work with quantum simulators, available quantum hardware, and state-of-the-art development kits to create innovative solutions that connect quantum advancements with challenges in communication and signal processing.
    
    The IEEE International Symposium on Information Theory (ISIT) is the flagship conference of the IEEE Information Theory Society. The symposium centers around the presentation in all of the areas of information theory, including source and channel coding, communication theory and systems, cryptography and security, detection and estimation, networks, pattern recognition and learning, statistics, stochastic processes and complexity, and signal processing.
- 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 Interaction
  Brief
  - 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.
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Research Highlights
- Edge-Assisted Internet of Vehicles for Smart Mobility
Internships
- EA0069: Internship - PWM inverter switching loss reduction
  
  MERL is looking for a self-motivated intern to work on PWM inverter simulation and design. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics, control, and optimization. Experience in switching loss reduction modulation is desired. The intern is expected to collaborate with MERL researchers to carry out simulations, optimize design, analyze results, and prepare manuscripts for scientific publications. The total duration is 3 months.
  Required Specific Experience
  - Experience with simulation tools for PWM inverter design.
- 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
  - Experience with one or more of Blender, Unreal, Unity, along with their APIs
  - Strong programming skills in one or more of Matlab, Python, and/or C/C++
- OR0115: Internship - Whole-body dexterous manipulation
  
  MERL is looking for a highly motivated individual to work on whole-body dexterous manipulation. The research will develop robot motor skills for whole-body, dexterous manipulation using optimization and/or learning algorithms. The ideal candidate should have experience in either one or multiple of the following topics: Optimization Algorithms for contact systems, Reinforcement Learning, control through contacts, and Behavioral cloning. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision and tactile sensors) is required as results need to be implemented on a physical hardware. Good coding skills in Python ML libraries like PyTorch etc. and/or relevant Optimization packages is required. A successful internship will result in submission of results to a peer-reviewed robotics journal in collaboration with MERL researchers. The expected duration of internship is 4-5 months with start date in May/June 2025. This internship is preferred to be onsite at MERL.
  
  Required Specific Experience
  - Prior experience working with physical hardware system is required.
  - Prior publication experience in robotics venues like ICRA,RSS, CoRL.
See All Internships for Optimization
Openings
See All Openings at MERL
Recent Publications
- Chen, Y., Jha, D.K., Tomizuka, M., Romeres, D., "FDPP: Fine-tune Diffusion Policy with Human Preference", IEEE International Conference on Robotics and Automation (ICRA), May 2025.
  BibTeX TR2025-053 PDF Video
  - @inproceedings{Chen2025may,
  - author = {Chen, Yuxin and Jha, Devesh K. and Tomizuka, Masayoshi and Romeres, Diego},
  - title = {{FDPP: Fine-tune Diffusion Policy with Human Preference}},
  - booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  - year = 2025,
  - month = may,
  - url = {https://www.merl.com/publications/TR2025-053}
  - }
- Shirai, Y., Raghunathan, A., Jha, D.K., "Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations", IEEE International Conference on Robotics and Automation (ICRA), May 2025.
  BibTeX TR2025-052 PDF
  - @inproceedings{Shirai2025may,
  - author = {Shirai, Yuki and Raghunathan, Arvind and Jha, Devesh K.},
  - title = {{Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations}},
  - booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  - year = 2025,
  - month = may,
  - url = {https://www.merl.com/publications/TR2025-052}
  - }
- Zhang, M., Jha, D.K., Raghunathan, A., Hauser, K., "Simultaneous Trajectory Optimization and Contact Selection for Contact-rich Manipulation with High-Fidelity Geometry", IEEE Transactions on Robotics (T-RO), April 2025.
  BibTeX TR2025-047 PDF
  - @article{Zhang2025apr,
  - author = {Zhang, Mengchao and Jha, Devesh K. and Raghunathan, Arvind and Hauser, Kris},
  - title = {{Simultaneous Trajectory Optimization and Contact Selection for Contact-rich Manipulation with High-Fidelity Geometry}},
  - journal = {IEEE Transactions on Robotics (T-RO)},
  - year = 2025,
  - month = apr,
  - url = {https://www.merl.com/publications/TR2025-047}
  - }
- Chakrabarty, A., Vanfretti, L., Wang, Y., Mineyuki, T., Zhan, S., Tang, W.-T., Paulson, J.A., Deshpande, V.M., Bortoff, S.A., Laughman, C.R., "Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation", Building Simulation, April 2025.
  BibTeX TR2025-043 PDF
  - @article{Chakrabarty2025apr,
  - author = {Chakrabarty, Ankush and Vanfretti, Luigi and Wang, Ye and Mineyuki, Takuma and Zhan, Sicheng and Tang, Wei-Ting and Paulson, Joel A. and Deshpande, Vedang M. and Bortoff, Scott A. and Laughman, Christopher R.},
  - title = {{Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation}},
  - journal = {Building Simulation},
  - year = 2025,
  - month = apr,
  - url = {https://www.merl.com/publications/TR2025-043}
  - }
- Vinod, A.P., Weiss, A., Di Cairano, S., "Projection-free computation of robust controllable sets with constrained zonotopes", Automatica, DOI: 10.1016/j.automatica.2025.112211, Vol. 175, pp. 112211, March 2025.
  BibTeX TR2025-023 PDF Video
  - @article{Vinod2025mar,
  - author = {Vinod, Abraham P. and Weiss, Avishai and Di Cairano, Stefano},
  - title = {{Projection-free computation of robust controllable sets with constrained zonotopes}},
  - journal = {Automatica},
  - year = 2025,
  - volume = 175,
  - pages = 112211,
  - month = mar,
  - doi = {10.1016/j.automatica.2025.112211},
  - issn = {0005-1098},
  - url = {https://www.merl.com/publications/TR2025-023}
  - }
- Shao, K., Chakrabarty, A., Mesbah, A., Romeres, D., "Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine", IEEE Control Systems Letters, February 2025.
  BibTeX TR2025-021 PDF
  - @article{Shao2025feb,
  - author = {Shao, Ketong and Chakrabarty, Ankush and Mesbah, Ali and Romeres, Diego},
  - title = {{Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine}},
  - journal = {IEEE Control Systems Letters},
  - year = 2025,
  - month = feb,
  - url = {https://www.merl.com/publications/TR2025-021}
  - }
- Ip, J.H.S., Chakrabarty, A., Mesbah, A., Romeres, D., "User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization", AAAI Conference on Artificial Intelligence, February 2025.
  BibTeX TR2025-018 PDF
  - @inproceedings{Ip2025feb,
  - author = {{{Ip, Joshua Hang Sai and Chakrabarty, Ankush and Mesbah, Ali and Romeres, Diego}}},
  - title = {{{User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization}}},
  - booktitle = {AAAI Conference on Artificial Intelligence},
  - year = 2025,
  - month = feb,
  - url = {https://www.merl.com/publications/TR2025-018}
  - }
- 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}
  - }
See All Publications for Optimization
Videos

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