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 Yuki Shirai appointed as an Associate Editor for IEEE Robotics and Automation Letters (RA-L).
  Date: March 4, 2025
  Where: IEEE Robotics and Automation Society (RAS)
  MERL Contact: Yuki Shirai
  Research Areas: Artificial Intelligence, Optimization, Robotics
  Brief
  - MERL researcher, Yuki Shirai, has been appointed to the editorial board of the IEEE Robotics and Automation Letters (RA-L) as an Associate Editor. IEEE RA-L publishes peer-reviewed articles in the areas of robotics and automation which can also be presented at the annual flagship conferences of IEEE Robotics and Automation Society (RAS), including IEEE International Conference on Robotics and Automation (ICRA) and International Conference on Intelligent Robots and Systems (IROS).
- NEWS MERL Papers and Workshops at AAAI 2025
  Date: February 25, 2025 - March 4, 2025
  Where: The Association for the Advancement of Artificial Intelligence (AAAI)
  MERL Contacts: Ankush Chakrabarty; Toshiaki Koike-Akino; Jing Liu; Kuan-Chuan Peng; Diego Romeres; Ye Wang
  Research Areas: Artificial Intelligence, Machine Learning, Optimization
  Brief
  - MERL researchers presented 2 conference papers, 2 workshop papers, and co-organized 1 workshop at the AAAI 2025 conference, which was held in Philadelphia from Feb. 25 to Mar. 4, 2025. AAAI is one of the most prestigious and competitive international conferences in artificial intelligence (AI). Details of MERL contributions are provided below.
    
    - AAAI Papers in Main Tracks:
    
    1. "Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage" by M.R.U. Rashid, J. Liu, T. Koike-Akino, Y. Wang, and S. Mehnaz. [Oral Presentation]
    
    This work proposes a novel unlearning-based model poisoning method that amplifies privacy breaches during fine-tuning. Extensive empirical studies show the proposed method’s efficacy on both membership inference and data extraction attacks. The attack is stealthy enough to bypass detection based defenses, and differential privacy cannot effectively defend against the attacks without significantly impacting model utility.
    
    Paper: https://www.merl.com/publications/TR2025-017
    
    2. "User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian Optimization with Local Gradient Search" by J.H.S. Ip, A. Chakrabarty, A. Mesbah, and D. Romeres. [Poster Presentation]
    
    This paper introduces a sample-efficient multi-objective Bayesian optimization method that integrates user preferences with gradient-based search to find near-Pareto optimal solutions. The proposed method achieves high utility and reduces distance to Pareto-front solutions across both synthetic and real-world problems, underscoring the importance of minimizing gradient uncertainty during gradient-based optimization. Additionally, the study introduces a novel utility function that respects Pareto dominance and effectively captures diverse user preferences.
    
    Paper: https://www.merl.com/publications/TR2025-018
    
    - AAAI Workshop Papers:
    
    1. "Quantum Diffusion Models for Few-Shot Learning" by R. Wang, Y. Wang, J. Liu, and T. Koike-Akino.
    
    This work presents the quantum diffusion model (QDM) as an approach to overcome the challenges of quantum few-shot learning (QFSL). It introduces three novel algorithms developed from complementary data-driven and algorithmic perspectives to enhance the performance of QFSL tasks. The extensive experiments demonstrate that these algorithms achieve significant performance gains over traditional baselines, underscoring the potential of QDM to advance QFSL by effectively leveraging quantum noise modeling and label guidance.
    
    Paper: https://www.merl.com/publications/TR2025-025
    
    2. "Quantum Implicit Neural Compression", by T. Fujihashi and T., Koike-Akino.
    
    This work introduces a quantum counterpart of implicit neural representation (quINR) which leverages the exponentially rich expressivity of quantum neural networks to improve the classical INR-based signal compression methods. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods.
    
    Paper: https://www.merl.com/publications/TR2025-024
    
    - AAAI Workshops Contributed by MERL:
    
    1. "Scalable and Efficient Artificial Intelligence Systems (SEAS)"
    
    K.-C. Peng co-organized this workshop, which offers a timely forum for experts to share their perspectives in designing and developing robust computer vision (CV), machine learning (ML), and artificial intelligence (AI) algorithms, and translating them into real-world solutions.
    
    Workshop link: https://seasworkshop.github.io/aaai25/index.html
    
    2. "Quantum Computing and Artificial Intelligence"
    
    T. Koike-Akino served a session chair of Quantum Neural Network in this workshop, which focuses on seeking contributions encompassing theoretical and applied advances in quantum AI, quantum computing (QC) to enhance classical AI, and classical AI to tackle various aspects of QC.
    
    Workshop link: https://sites.google.com/view/qcai2025/
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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++
- EA0149: Internship - Electric Motor Design Optimization
  
  MERL is seeking a motivated and qualified individual to conduct research on physics informed neural network-based modeling for electric motor design optimization. Ideal candidates should be Ph.D. students with solid background and proven publication record in one or more of the following research areas: 2D/3D electromagnetic modeling and simulation, analytical modeling methods for electromagnetics and iron losses (e.g. magnetic equivalent circuit), and machine learning-based surrogate modeling. Strong coding skill with ANSYS or open-source FEM software and Python-based learning library is a must and prior experience with running jobs over cluster is a plus. Start date for this internship is flexible and the duration is 3-6 months.
  Required Specific Experience
  - Experience with modeling and simulations for motor design
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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