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.
Quick Links
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Researchers
Stefano
Di Cairano
Toshiaki
Koike-Akino
Daniel N.
Nikovski
Arvind
Raghunathan
Ankush
Chakrabarty
Christopher R.
Laughman
Philip V.
Orlik
Mouhacine
Benosman
Karl
Berntorp
Yebin
Wang
Ye
Wang
Kieran
Parsons
Devesh K.
Jha
Matthew
Brand
Scott A.
Bortoff
Petros T.
Boufounos
Hassan
Mansour
Diego
Romeres
Abraham P.
Vinod
Pu
(Perry)
Wang
Jianlin
Guo
Avishai
Weiss
Dehong
Liu
Hongbo
Sun
Vedang M.
Deshpande
Hongtao
Qiao
Yanting
Ma
Saviz
Mowlavi
Yuki
Shirai
Bingnan
Wang
Gordon
Wichern
William S.
Yerazunis
Jinyun
Zhang
Abraham
Goldsmith
Chungwei
Lin
Wataru
Tsujita
Jose
Amaya
Anoop
Cherian
Radu
Corcodel
Pedro
Miraldo
Joshua
Rapp
Na
Li
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 MERL researchers present 9 papers at ACC 2024 Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: Karl Berntorp; Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
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.
In addition, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
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NEWS MERL at the International Conference on Robotics and Automation (ICRA) 2024 Date: May 13, 2024 - May 17, 2024
Where: Yokohama, Japan
MERL Contacts: Anoop Cherian; Radu Corcodel; Stefano Di Cairano; Chiori Hori; Siddarth Jain; Devesh K. Jha; Jonathan Le Roux; Diego Romeres; William S. Yerazunis
Research Areas: Artificial Intelligence, Machine Learning, Optimization, Robotics, Speech & AudioBrief- MERL made significant contributions to both the organization and the technical program of the International Conference on Robotics and Automation (ICRA) 2024, which was held in Yokohama, Japan from May 13th to May 17th.
MERL was a Bronze sponsor of the conference, and exhibited a live robotic demonstration, which attracted a large audience. The demonstration showcased an Autonomous Robotic Assembly technology executed on MELCO's Assista robot arm and was the collaborative effort of the Optimization and Robotics Team together with the Advanced Technology department at Mitsubishi Electric.
MERL researchers from the Optimization and Robotics, Speech & Audio, and Control for Autonomy teams also presented 8 papers and 2 invited talks covering topics on robotic assembly, applications of LLMs to robotics, human robot interaction, safe and robust path planning for autonomous drones, transfer learning, perception and tactile sensing.
- MERL made significant contributions to both the organization and the technical program of the International Conference on Robotics and Automation (ICRA) 2024, which was held in Yokohama, Japan from May 13th to May 17th.
See All News & Events for Optimization -
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Research Highlights
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Internships
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CA2182: Motion Planning and Control for Articulated Vehicles
MERL is seeking a highly skilled and self-motivated intern to work on motion planning of articulated vehicles. The ideal candidate should have solid backgrounds in established path/motion planning algorithms (A*, D*, graph-search) and optimization-based control for ground and articulated vehicles. Excellent coding skills in MATLAB/Simulink and publication records are necessary. Experience with CasADi and dSPACE is a plus. Ph.D. students in robotics, computer science, control, electrical engineering, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 4-6 months.
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ST2083: Deep Learning for Radar Perception
The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open indoor/outdoor radar datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.
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CA2132: Optimization Algorithms for Motion Planning and Predictive Control
MERL is looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.
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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}
- }
- , "Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning", IEEE Transactions on Control Systems Technology, September 2024.BibTeX TR2024-123 PDF
- @article{Quirynen2024sep,
- author = {Quirynen, Rien and Safaoui, Sleiman and Di Cairano, Stefano}},
- title = {Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-123}
- }
- , "MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models", International Conference on Automation Science and Engineering (CASE), August 2024.BibTeX TR2024-115 PDF
- @inproceedings{Yan2024aug,
- author = {Yan, Jiaqi and Chakrabarty, Ankush and Rupenyan, Alisa and Lygeros, John}},
- title = {MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models},
- booktitle = {International Conference on Automation Science and Engineering (CASE)},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-115}
- }
- , "Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics", IEEE Conference on Control Technology and Applications (CCTA) 2024, August 2024.BibTeX TR2024-114 PDF
- @inproceedings{Park2024aug,
- author = {Park, Seho and Wang, Yebin and Qiao, Hongtao and Sakamoto, Yusuke and Wang, Bingnan and Liu, Dehong}},
- title = {Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-114}
- }
- , "Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems", IEEE Conference on Control Technology and Applications (CCTA), August 2024.BibTeX TR2024-110 PDF
- @inproceedings{Sorouifar2024aug,
- author = {Sorouifar, Farshud and Paulson, Joel A. and Wang, Ye and Quirynen, Rien and Laughman, Christopher R. and Chakrabarty, Ankush}},
- title = {Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems},
- booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-110}
- }
- , "Optimization-based Phase-Constrained Station-Keeping Control on Libration Point Orbit", AAS/AIAA Astrodynamics Specialist Conference, August 2024.BibTeX TR2024-109 PDF
- @inproceedings{Shimane2024aug,
- author = {Shimane, Yuri and Ho, Koki and Weiss, Avishai}},
- title = {Optimization-based Phase-Constrained Station-Keeping Control on Libration Point Orbit},
- booktitle = {AAS/AIAA Astrodynamics Specialist Conference},
- year = 2024,
- month = aug,
- url = {https://www.merl.com/publications/TR2024-109}
- }
- , "Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning", IEEE Transactions on Robotics, DOI: 10.1109/TRO.2024.3387010, Vol. 40, pp. 2529-2542, July 2024.BibTeX TR2024-048 PDF Video
- @article{Safaoui2024jul,
- author = {Safaoui, Sleiman and Vinod, Abraham P. and Chakrabarty, Ankush and Quirynen, Rien and Yoshikawa, Nobuyuki and Di Cairano, Stefano},
- title = {Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning},
- journal = {IEEE Transactions on Robotics},
- year = 2024,
- volume = 40,
- pages = {2529--2542},
- month = jul,
- doi = {10.1109/TRO.2024.3387010},
- url = {https://www.merl.com/publications/TR2024-048}
- }
- , "Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming", Nonlinear Analysis: Hybrid Systems, December 2024.
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Videos
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Software & Data Downloads
