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
Stefano
Di Cairano
Toshiaki
Koike-Akino
Daniel N.
Nikovski
Arvind
Raghunathan
Ankush
Chakrabarty
Philip V.
Orlik
Christopher R.
Laughman
Mouhacine
Benosman
Karl
Berntorp
Kieran
Parsons
Ye
Wang
Yebin
Wang
Matthew
Brand
Scott A.
Bortoff
Petros T.
Boufounos
Devesh K.
Jha
Hassan
Mansour
Pu
(Perry)
WangJianlin
Guo
Diego
Romeres
Hongbo
Sun
Abraham P.
Vinod
Dehong
Liu
Avishai
Weiss
Vedang M.
Deshpande
Yanting
Ma
Hongtao
Qiao
Saviz
Mowlavi
Gordon
Wichern
William S.
Yerazunis
Jinyun
Zhang
Abraham
Goldsmith
Chungwei
Lin
Bingnan
Wang
Wataru
Tsujita
Jose
Amaya
Anoop
Cherian
Radu
Corcodel
Pedro
Miraldo
Joshua
Rapp
Luigi
Vanfretti
Jing
Liu
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Awards
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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
Research Areas: Artificial Intelligence, Optimization, RoboticsBrief- 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.
- 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.
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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 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 ProcessingBrief- 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.
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News & Events
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NEWS Ankush Chakrabarty served as Co-Chair of ACM BALANCES 2023 Date: November 14, 2023
Where: Istanbul, Turkey
MERL Contact: Ankush Chakrabarty
Research Areas: Control, Data Analytics, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems team at MERL, served as Co-Chair at the 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities (BALANCES'23). The workshop places spotlights on two different IEA EBC Annexes: the Annex 81 - Data-Driven Smart Buildings and Annex 82 - Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems.
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TALK [MERL Seminar Series 2023] Prof. Zac Manchester presents talk titled Composable Optimization for Robotic Simulation, Planning, and Control Date & Time: Wednesday, September 27, 2023; 1:00 PM
Speaker: Zac Manchester, Carnegie Mellon University
MERL Host: Devesh K. Jha
Research Areas: Optimization, RoboticsAbstract- Contact interactions are pervasive in key real-world robotic tasks like manipulation and walking. However, the non-smooth dynamics associated with impacts and friction remain challenging to model, and motion planning and control algorithms that can fluently and efficiently reason about contact remain elusive. In this talk, I will share recent work from my research group that takes an “optimization-first” approach to these challenges: collision detection, physics, motion planning, and control are all posed as constrained optimization problems. We then build a set of algorithmic and numerical tools that allow us to flexibly compose these optimization sub-problems to solve complex robotics problems involving discontinuous, unplanned, and uncertain contact mechanics.
See All News & Events for Optimization -
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Research Highlights
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Internships
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CA1940: Autonomous vehicle planning and contro in uncertain environments
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.
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SA2114: Multilayer broadband metalenses
MERL is seeking a talented researcher to collaborate in the development of design algorithms for metalenses that are freeform, multilayer, and broadband. The ideal applicant will have a strong background in the relevant physics & maths, and has some fluency with the topology optimization and EM simulation tools commonly used in metasurface optics. Also desirable: familiarity with machine learning / AI tools and methods.
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OR2105: Preference-based Multi-Objective Bayesian Optimization
MERL is looking for a self-motivated and qualified candidate to work on Bayesian Optimization algorithms applied to industrial applications. The ideal candidate is a PhD student with experience and peer-reviewed publications in the general field of derivative-free/zeroth-order optimization, preference will be given to candidates who have contributed to theoretical advances or practical application of Bayesian optimization, especially for multi-objective optimization problems. The ideal candidate will have a strong general understanding of numerical optimization and probabilistic machine learning e.g. Gaussian process regression, and is expected to develop, in collaboration with MERL researchers, state of the art algorithms to optimize parameters for industrial processes or control systems. Proficiency in Python is required. An expected outcome of the internship is one or more peer-reviewed publications. The expected duration is 3-4 months, with flexible starting date.
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Openings
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Recent Publications
- "Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming", Nonlinear Analysis: Hybrid Systems, December 2024.BibTeX TR2024-008 PDF
- @article{Shirai2024dec,
- author = {Shirai, Yuki and Jha, Devesh K. and Raghunathan, Arvind and Romeres, Diego},
- title = {Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming},
- journal = {Nonlinear Analysis: Hybrid Systems},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2024-008}
- }
, - "Control Challenges and Opportunities in Building Automation" in The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, February 2024.BibTeX TR2024-011 PDF
- @incollection{Bortoff2024feb,
- author = {Bortoff, Scott A. and Eisenhower, Bryan and Adetola, Veronica and O'Neil, Zheng},
- title = {Control Challenges and Opportunities in Building Automation},
- booktitle = {The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future},
- year = 2024,
- month = feb,
- url = {https://www.merl.com/publications/TR2024-011}
- }
, - "Lunar Landing with Feasible Divert using Controllable Sets", AIAA SciTech, DOI: 10.2514/6.2024-0324, January 2024, pp. AIAA 2024-0324.BibTeX TR2024-004 PDF
- @inproceedings{Srinivas2024jan,
- author = {Srinivas, Neeraj and Vinod, Abraham P. and Di Cairano, Stefano and Weiss, Avishai},
- title = {Lunar Landing with Feasible Divert using Controllable Sets},
- booktitle = {AIAA SCITECH 2024 Forum},
- year = 2024,
- pages = {AIAA 2024--0324},
- month = jan,
- doi = {10.2514/6.2024-0324},
- url = {https://www.merl.com/publications/TR2024-004}
- }
, - "Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments", Automatica, DOI: 10.1016/j.automatica.2023.111418, December 2023.BibTeX TR2023-147 PDF
- @article{Bonzanini2023dec,
- author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
- title = {Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments},
- journal = {Automatica},
- year = 2023,
- month = dec,
- doi = {10.1016/j.automatica.2023.111418},
- url = {https://www.merl.com/publications/TR2023-147}
- }
, - "Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine", Advances in Neural Information Processing Systems (NeurIPS), December 2023.BibTeX TR2023-146 PDF
- @inproceedings{Shao2023dec,
- author = {Shao, Ketong and Romeres, Diego and Chakrabarty, Ankush and Mesbah, Ali},
- title = {Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2023,
- month = dec,
- url = {https://www.merl.com/publications/TR2023-146}
- }
, - "Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States", IEEE Control Systems Letters, DOI: 10.1109/LCSYS.2023.3334959, November 2023.BibTeX TR2023-138 PDF
- @article{Deshpande2023nov,
- author = {Deshpande, Vedang M. and Chakrabarty, Ankush and Vinod, Abraham P. and Laughman, Christopher R.},
- title = {Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States},
- journal = {IEEE Control Systems Letters},
- year = 2023,
- month = nov,
- doi = {10.1109/LCSYS.2023.3334959},
- url = {https://www.merl.com/publications/TR2023-138}
- }
, - "Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems", IEEE Transactions on Transportation Electrification, DOI: 10.1109/TTE.2023.3331243, November 2023.BibTeX TR2023-137 PDF
- @article{Farakhor2023nov,
- author = {Farakhor, Amir and Wu, Di and Wang, Yebin and Fang, Huazhen},
- title = {Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems},
- journal = {IEEE Transactions on Transportation Electrification},
- year = 2023,
- month = nov,
- doi = {10.1109/TTE.2023.3331243},
- issn = {2332-7782},
- url = {https://www.merl.com/publications/TR2023-137}
- }
, - "Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points", Nature Scientific Reports, October 2023.BibTeX TR2023-136 PDF
- @article{Sholokhov2023oct,
- author = {Sholokhov, Aleksei and Liu, Yuying and Mansour, Hassan and Nabi, Saleh},
- title = {Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points},
- journal = {Nature Scientific Reports},
- year = 2023,
- month = oct,
- url = {https://www.merl.com/publications/TR2023-136}
- }
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- "Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming", Nonlinear Analysis: Hybrid Systems, December 2024.
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