- Date & Time: Wednesday, October 2, 2024; 1:00 PM
Speaker: Zhaojian Li, Mivchigan State University
MERL Host: Yebin Wang
Research Areas: Artificial Intelligence, Computer Vision, Control, Robotics
Abstract 
Harvesting labor is the single largest cost in apple production in the U.S. Surging cost and growing shortage of labor has forced the apple industry to seek automated harvesting solutions. Despite considerable progress in recent years, the existing robotic harvesting systems still fall short of performance expectations, lacking robustness and proving inefficient or overly complex for practical commercial deployment. In this talk, I will present the development and evaluation of a new dual-arm robotic apple harvesting system. This work is a result of a continuous collaboration between Michigan State University and U.S. Department of Agriculture.
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- Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: 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, Robotics
Brief - 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.
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- Date & Time: Wednesday, May 29, 2024; 12:00 PM
Speaker: Chuchu Fan, MIT
MERL Host: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Machine Learning
Abstract 
Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics. However, this performance often arrives with the trade-off of diminished transparency and the absence of guarantees regarding the safety and stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies — these certificates provide concise, data-driven proofs that guarantee the safety and stability of the learned control system. These methods not only allow the user to verify the safety of a learned controller but also provide supervision during training, allowing safety and stability requirements to influence the training process itself. In this talk, we present two exciting updates on neural certificates. In the first work, we explore the use of graph neural networks to learn collision-avoidance certificates that can generalize to unseen and very crowded environments. The second work presents a novel reinforcement learning approach that can produce certificate functions with the policies while addressing the instability issues in the optimization process. Finally, if time permits, I will also talk about my group's recent work using LLM and domain-specific task and motion planners to allow natural language as input for robot planning.
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- Date: April 12, 2024
MERL Contact: Saviz Mowlavi
Research Areas: Control, Dynamical Systems, Machine Learning, Optimization
Brief - Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
The talk, entitled "Model-based and data-driven prediction and control of spatio-temporal systems", described the use of temporal smoothness to regularize the training of fast surrogate models for PDEs, user-friendly methods for PDE-constrained optimization, and efficient strategies for learning feedback controllers for PDEs.
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- Date & Time: Wednesday, April 10, 2024; 12:00 PM
Speaker: Na Li, Harvard University
MERL Host: Yebin Wang
Research Areas: Control, Dynamical Systems, Machine Learning
Abstract 
The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, translating these successes to the domain of dynamical, physical systems remains a significant challenge, hindered by the complex and often unpredictable nature of such environments. Closing the loop from data to actions in these systems faces many difficulties, stemming from the need for sample efficiency and computational feasibility amidst intricate dynamics, along with many other requirements such as verifiability, robustness, and safety. In this talk, we bridge this gap by introducing innovative approaches that harness representation-based methods, domain knowledge, and the physical structures of systems. We present a comprehensive framework that integrates these components to develop reinforcement learning and control strategies that are not only tailored for the complexities of physical systems but also achieve efficiency, safety, and robustness with provable performance.
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- Date: March 20, 2024
Where: Austin, TX
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
Brief - Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.
The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.
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- Date: December 7, 2023
Research Areas: Control, Dynamical Systems
Brief - Karl Berntorp has joined the Editorial Board of the IEEE Transactions on Control Systems Technology (T-CST) as an Associate Editor. The IEEE T-CST publishes peer-reviewed papers on technological advances in the design, realization, and operation of control systems, and bridges the gap between the theory and practice of control engineering.
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- Date: November 14, 2023
Where: Istanbul, Turkey
MERL Contact: Ankush Chakrabarty
Research Areas: Control, Data Analytics, Machine Learning, Multi-Physical Modeling, Optimization
Brief - 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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- Date & Time: Tuesday, November 21, 2023; 11:00 AM
Speaker: Gioele Zardini, ETH Zürich and MIT
Research Areas: Control, Dynamical Systems
Abstract 
When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. Optimally co-designing sociotechnical systems is a complex task for at least two reasons. On one hand, the co-design of interconnected systems (e.g., large networks of cyber-physical systems) involves the simultaneous choice of components arising from heterogeneous natures (e.g., hardware vs. software parts) and fields, while satisfying systemic constraints and accounting for multiple objectives. On the other hand, components are connected via collaborative and conflicting interactions between different stakeholders (e.g., within an intermodal mobility system). In this talk, I will present a framework to co-design complex systems, leveraging a monotone theory of co-design and tools from game theory. The framework will be instantiated in the task of designing future mobility systems, all the way from the policies that a city can design, to the autonomy of vehicles part of an autonomous mobility-on-demand service. Through various case studies, I will show how the proposed approaches allow one to efficiently answer heterogeneous questions, unifying different modeling techniques and promoting interdisciplinarity, modularity, and compositionality. I will then discuss open challenges for compositional systems design optimization, and present my agenda to tackle them.
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- Date & Time: Tuesday, October 10, 2023; 1:00 PM
Speaker: Shaoshuai Mou, Purdue University
MERL Host: Yebin Wang
Research Areas: Control, Dynamical Systems, Robotics
Abstract 
Inverse Optimal Control (IOC) aims to achieve an objective function corresponding to a certain task from an expert robot driven by optimal control, which has become a powerful tool in many applications in robotics. We will present our recent solutions to IOC based on incomplete observations of systems' trajectories, which enables an autonomous system to “sense-and-adapt", i.e., incrementally improving the learning of objective functions as new data arrives. This also leads to a distributed algorithm to solve IOC in multi-agent systems, in which each agent can only access part of the overall trajectory of an optimal control system and cannot solve IOC by itself. This is perhaps the first distributed method to IOC. Applications of IOC into human prediction will also be given.
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- Date: July 9, 2023 - July 14, 2023
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Diego Romeres; Abraham P. Vinod
Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
Brief - MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.
MERL's contributions covered topics including decision-making for autonomous vehicles, statistical and learning-based estimation for GNSS and energy systems, impedance control for delta robots, learning for system identification of rigid body dynamics and time-varying systems, and meta-learning for deep state-space modeling using data from similar systems. The invited session (MERL co-organizer: Ankush Chakrabarty) was on the topic of “Estimation and observer design: theory and applications” and the workshop (MERL co-organizer: Karl Berntorp) was on “Gaussian Process Learning for Systems and Control”.
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- Date: June 8, 2023
Where: Zoom
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Optimization, Robotics
Brief - Abraham Vinod gave an invited talk at the Electrical and Computer Engineering Department, the University of California Santa Cruz, titled "Motion Planning under Constraints and Uncertainty using Data and Reachability". His presentation covered recent work on fast and safe motion planners that can allow for coordination among agents, mitigate uncertainty arising from sensing limitations and simplified models, and tolerate the possibility of failures.
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- Date: June 30, 2023 - June 2, 2023
Where: San Diego, CA
MERL Contact: Ankush Chakrabarty
Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
Brief - Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.
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- Date: June 1, 2023
Where: San Diego, CA
MERL Contact: Abraham P. Vinod
Research Areas: Control, Optimization
Brief - The student networking event provides an opportunity for all interested students attending American Control Conference 2023 to receive career advice from professionals working in industry, academia, and national laboratories during a structured event. The event aims to provide an engaging experience to students that illustrates the benefits of involvement in the control community and encourage their continued participation as the future leaders in the field.
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- Date: May 31, 2023 - June 2, 2023
Where: San Diego, CA
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Devesh K. Jha; Christopher R. Laughman; Arvind Raghunathan; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, Optimization
Brief - MERL will present 10 papers at the American Control Conference (ACC) in San Diego, CA, with topics including autonomous-vehicle decision making and control, physics-informed machine learning, motion planning, control subject to nonconvex chance constraints, and optimal power management. Two talks are part of tutorial sessions.
MERL will also be present at the conference as a sponsor, with a booth for discussing with researchers and students, and hosting a special session at lunch with highlights of MERL research and work philosophy.
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- Date: May 31, 2023 - June 3, 2023
Where: 2023 SIAM Conference on Optimization
MERL Contacts: Devesh K. Jha; Arvind Raghunathan
Research Areas: Control, Optimization, Robotics
Brief - Arvind Raghunathan, Senior Team Leader and Senior Principal Research Scientist in Optimization & Intelligent Robotics team, will organize two minisymposia at the 2023 SIAM Conference on Optimization to be held in Seattle from May 31 to June 3. The two minisymposia titled "Optimization in Control – Algorithms, Applications, and Software" and "New Algorithmic Techniques for Global Optimization" will feature twelve invited speakers from academia and national labs.
Additionally, Arvind together with Devesh Jha, Principal Research Scientist in Optimization & Intelligent Robotics Team, and collaborators will present five invited talks covering the topics of algorithms for convex programs, multilinear programs, mixed-integer nonlinear programs, and robotics.
See:
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76268
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76269
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76270
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76256
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=75897
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- Date: May 15, 2023 - May 18, 2023
Where: San Francisco, CA
MERL Contacts: Dehong Liu; Bingnan Wang
Research Areas: Applied Physics, Control, Electric Systems, Machine Learning, Optimization, Signal Processing
Brief - MERL researchers Yusuke Sakamoto, Anantaram Varatharajan, and
Bingnan Wang presented four papers at IEMDC 2023 held May 15-18 in San Francisco, CA. The topics of the four oral presentations range from electric machine design optimization, to fault detection and sensorless control. Bingnan Wang organized a special session at the conference entitled: Learning-based Electric Machine Design and Optimization. Bingnan Wang and Yusuke Sakamoto together chaired the special session, as well as a session on: Condition Monitoring, Fault Diagnosis and Prognosis.
The 14th IEEE International Electric Machines and Drives Conference: IEMDC 2023, is one of the major conferences in the area of electric machines and drives. The conference was established in 1997 and has taken place every two years thereafter.
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- Date & Time: Tuesday, April 11, 2023; 11:00 AM
Speaker: Michael Muehlebach, Max Planck Institute for Intelligent Systems
Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
Abstract 
The talk will be divided into two parts. The first part of the talk introduces a class of first-order methods for constrained optimization that are based on an analogy to non-smooth dynamical systems. The key underlying idea is to express constraints in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. This results is a simplified suite of algorithms and an expanded range of possible applications in machine learning. In the second part of my talk, I will present a robot learning algorithm for trajectory tracking. The method incorporates prior knowledge about the system dynamics and by optimizing over feedforward actions, the risk of instability during deployment is mitigated. The algorithm will be evaluated on a ping-pong playing robot that is actuated by soft pneumatic muscles.
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- Date & Time: Wednesday, March 29, 2023; 1:00 PM
Speaker: Zoltan Nagy, The University of Texas at Austin
MERL Host: Ankush Chakrabarty
Research Areas: Control, Machine Learning, Multi-Physical Modeling
Abstract 
The decarbonization of buildings presents new challenges for the reliability of the electrical grid because of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response. Residential demand response programs are hindered by the need for manual intervention by customers. To maximize the energy flexibility potential of residential buildings, an advanced control architecture is needed. Reinforcement learning is well-suited for the control of flexible resources as it can adapt to unique building characteristics compared to expert systems. Yet, factors hindering the adoption of RL in real-world applications include its large data requirements for training, control security and generalizability. This talk will cover some of our recent work addressing these challenges. We proposed the MERLIN framework and developed a digital twin of a real-world 17-building grid-interactive residential community in CityLearn. We show that 1) independent RL-controllers for batteries improve building and district level KPIs compared to a reference RBC by tailoring their policies to individual buildings, 2) despite unique occupant behaviors, transferring the RL policy of any one of the buildings to other buildings provides comparable performance while reducing the cost of training, 3) training RL-controllers on limited temporal data that does not capture full seasonality in occupant behavior has little effect on performance. Although, the zero-net-energy (ZNE) condition of the buildings could be maintained or worsened because of controlled batteries, KPIs that are typically improved by ZNE condition (electricity price and carbon emissions) are further improved when the batteries are managed by an advanced controller.
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- Date: August 27, 2024 - August 30, 2024
Where: Kyoto, Japan
Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
Brief - MERL researcher Rien Quirynen has been appointed as Vice-Chair from Industry of the International Program Committee of the 8th IFAC Conference on Nonlinear Model Predictive Control, which will be held in Kyoto, Japan, in August 2024.
IFAC NMPC is the main symposium focused on model predictive control, theory, methods and applications, includes contributions on control, optimization, and machine learning research, and is held every 3 years.
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- Date: February 16, 2023 - February 17, 2023
Where: Pennsylvania State University
MERL Contact: Christopher R. Laughman
Research Areas: Control, Machine Learning, Multi-Physical Modeling
Brief - On February 16 and 17, Chris Laughman, Senior Team Leader of the Multiphysical Systems Team, presented lectures for the Systems, Robotics, and Controls Seminar Series in the School of Engineering, and for the Distinguished Speaker Series in Architectural Engineering. His talk was titled "Architectural Thermofluid Systems: Next-Generation Challenges and Opportunities," and described characteristics of these systems that require specific attention in model-based system engineering processes, as well as MERL research to address these challenges.
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- Date: December 9, 2022 - December 11, 2022
MERL Contact: Yebin Wang
Research Areas: Communications, Control, Optimization
Brief - Future factory, in the era of industry 4.0, is characterized by autonomy, digital twin, and mass customization. This talk, titled "Future factory automation and cyber-physical system: an industrial perspective," focuses on tackling the challenges arising from mass customization, for example reconfigurable machine controller and material flow.
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- Date: December 2, 2022 - December 8, 2022
MERL Contacts: Matthew Brand; Toshiaki Koike-Akino; Jing Liu; Saviz Mowlavi; Kieran Parsons; Ye Wang
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Signal Processing
Brief - In addition to 5 papers in recent news (https://www.merl.com/news/news-20221129-1450), MERL researchers presented 2 papers at the NeurIPS Conference Workshop, which was held Dec. 2-8. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.
- “Optimal control of PDEs using physics-informed neural networks” by Saviz Mowlavi and Saleh Nabi
Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown state, PINNs can seamlessly blend measurement data with physical constraints. Here, we extend this framework to PDE-constrained optimal control problems, for which the governing PDE is fully known and the goal is to find a control variable that minimizes a desired cost objective. We validate the performance of the PINN framework by comparing it to state-of-the-art adjoint-based optimization, which performs gradient descent on the discretized control variable while satisfying the discretized PDE.
- “Learning with noisy labels using low-dimensional model trajectory” by Vasu Singla, Shuchin Aeron, Toshiaki Koike-Akino, Matthew E. Brand, Kieran Parsons, Ye Wang
Noisy annotations in real-world datasets pose a challenge for training deep neural networks (DNNs), detrimentally impacting generalization performance as incorrect labels may be memorized. In this work, we probe the observations that early stopping and low-dimensional subspace learning can help address this issue. First, we show that a prior method is sensitive to the early stopping hyper-parameter. Second, we investigate the effectiveness of PCA, for approximating the optimization trajectory under noisy label information. We propose to estimate the low-rank subspace through robust and structured variants of PCA, namely Robust PCA, and Sparse PCA. We find that the subspace estimated through these variants can be less sensitive to early stopping, and can outperform PCA to achieve better test error when trained on noisy labels.
- In addition, new MERL researcher, Jing Liu, also presented a paper entitled “CoPur: Certifiably Robust Collaborative Inference via Feature Purification" based on his previous work before joining MERL. His paper was elected as a spotlight paper to be highlighted in lightening talks and featured paper panel.
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- 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.
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- Date: December 6, 2022 - December 9, 2022
Where: Cancún, Mexico
MERL Contacts: Ankush Chakrabarty; Devesh K. Jha; Arvind Raghunathan; Diego Romeres; Yebin Wang
Research Areas: Control, Optimization
Brief - MERL researchers presented six papers at the Conference on Decision and Control that was held in Cancún, Mexico from December 6-9, 2022. The papers covered a broad range of topics in the areas of decision making and control, including Bayesian optimization, quadratic programming, solution of differential equations, distributed Kalman filtering, thermal monitoring of batteries, and closed-loop control optimization.
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