News & Events

191 MERL Events and MERL Talks found.

Learn about the MERL Seminar Series.

  •  EVENT    MERL Contributes to ICASSP 2023
    Date: Sunday, June 4, 2023 - Saturday, June 10, 2023
    Location: Rhodes Island, Greece
    MERL Contacts: Petros T. Boufounos; Francois Germain; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Suhas Lohit; Yanting Ma; Hassan Mansour; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
    Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Speech & Audio
    • MERL has made numerous contributions to both the organization and technical program of ICASSP 2023, which is being held in Rhodes Island, Greece from June 4-10, 2023.


      Petros Boufounos is serving as General Co-Chair of the conference this year, where he has been involved in all aspects of conference planning and execution.

      Perry Wang is the organizer of a special session on Radar-Assisted Perception (RAP), which will be held on Wednesday, June 7. The session will feature talks on signal processing and deep learning for radar perception, pose estimation, and mutual interference mitigation with speakers from both academia (Carnegie Mellon University, Virginia Tech, University of Illinois Urbana-Champaign) and industry (Mitsubishi Electric, Bosch, Waveye).

      Anthony Vetro is the co-organizer of the Workshop on Signal Processing for Autonomous Systems (SPAS), which will be held on Monday, June 5, and feature invited talks from leaders in both academia and industry on timely topics related to autonomous systems.


      MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, June 8. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.

      MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Rabab Ward, the recipient of the 2023 IEEE Fourier Award for Signal Processing, and Prof. Alexander Waibel, the recipient of the 2023 IEEE James L. Flanagan Speech and Audio Processing Award.

      Technical Program

      MERL is presenting 13 papers in the main conference on a wide range of topics including source separation and speech enhancement, radar imaging, depth estimation, motor fault detection, time series recovery, and point clouds. One workshop paper has also been accepted for presentation on self-supervised music source separation.

      Perry Wang has been invited to give a keynote talk on Wi-Fi sensing and related standards activities at the Workshop on Integrated Sensing and Communications (ISAC), which will be held on Sunday, June 4.

      Additionally, Anthony Vetro will present a Perspective Talk on Physics-Grounded Machine Learning, which is scheduled for Thursday, June 8.

      About ICASSP

      ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
  •  TALK    [MERL Seminar Series 2023] Prof. Mark Ku presents talk titled A beginner’s guide to quantum sensing illustrated with nitrogen vacancy centers in diamond
    Date & Time: Wednesday, May 17, 2023; 1:00 PM
    Speaker: Mark Ku, The University of Delaware
    MERL Host: Chungwei Lin
    Research Areas: Applied Physics, Computational Sensing
    • Quantum technology holds potential for revolutionizing how information is processed, transmitted, and acquired. While quantum computation and quantum communication have been among the well-known examples of quantum technology, it is increasingly recognized that quantum sensing is the application with the most potential for immediate wide-spread practical utilization. In this talk, I will provide an overview of the field of quantum sensing with nitrogen vacancy (NV) centers in diamond as a specific example. I will introduce the physical system of NV and describe some basic quantum sensing protocols. Then, I will present some state-of-the-art and examples where quantum sensors such as NV can accomplish what traditional sensors cannot. Lastly, I will discuss potential future directions in the area of NV quantum sensing.
  •  TALK    [MERL Seminar Series 2023] Prof. Dan Stowell presents talk titled Fine-grained wildlife sound recognition: Towards the accuracy of a naturalist
    Date & Time: Tuesday, April 25, 2023; 11:00 AM
    Speaker: Dan Stowell, Tilburg University / Naturalis Biodiversity Centre
    MERL Host: Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    • Machine learning can be used to identify animals from their sound. This could be a valuable tool for biodiversity monitoring, and for understanding animal behaviour and communication. But to get there, we need very high accuracy at fine-grained acoustic distinctions across hundreds of categories in diverse conditions. In our group we are studying how to achieve this at continental scale. I will describe aspects of bioacoustic data that challenge even the latest deep learning workflows, and our work to address this. Methods covered include adaptive feature representations, deep embeddings and few-shot learning.
  •  TALK    [MERL Seminar Series 2023] Dr. Michael Muehlebach presents talk titled Learning and Dynamical Systems
    Date & Time: Tuesday, April 11, 2023; 11:00 AM
    Speaker: Michael Muehlebach, Max Planck Institute for Intelligent Systems
    MERL Host: Marcel Menner
    Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • 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.
  •  TALK    [MERL Seminar Series 2023] Prof. Zoltan Nagy presents talk titled Investigating Multi-Agent Reinforcement Learning for Grid-Interactive Smart Communities using CityLearn
    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
    • 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.
  •  TALK    [MERL Seminar Series 2023] Dr. Suraj Srinivas presents talk titled Pitfalls and Opportunities in Interpretable Machine Learning
    Date & Time: Tuesday, March 14, 2023; 1:00 PM
    Speaker: Suraj Srinivas, Harvard University
    MERL Host: Suhas Lohit
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • In this talk, I will discuss our recent research on understanding post-hoc interpretability. I will begin by introducing a characterization of post-hoc interpretability methods as local function approximators, and the implications of this viewpoint, including a no-free-lunch theorem for explanations. Next, we shall challenge the assumption that post-hoc explanations provide information about a model's discriminative capabilities p(y|x) and instead demonstrate that many common methods instead rely on a conditional generative model p(x|y). This observation underscores the importance of being cautious when using such methods in practice. Finally, I will propose to resolve this via regularization of model structure, specifically by training low curvature neural networks, resulting in improved model robustness and stable gradients.
  •  TALK    [MERL Seminar Series 2023] Prof. Shaowu Pan presents talk titled Neural Implicit Flow
    Date & Time: Wednesday, March 1, 2023; 1:00 PM
    Speaker: Shaowu Pan, Rensselaer Polytechnic Institute
    MERL Host: Saviz Mowlavi
    Research Areas: Computational Sensing, Data Analytics, Machine Learning
    • High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.
  •  TALK    Prof. Kevin Lynch presents talk titled Autonomous and Human-Collaborative Robotic Manipulation
    Date & Time: Tuesday, February 28, 2023; 12:00 PM
    Speaker: Prof. Kevin Lynch, Northwestern University
    MERL Host: Diego Romeres
    Research Areas: Machine Learning, Robotics
    • Research at the Center for Robotics and Biosystems at Northwestern University includes bio-inspiration, neuromechanics, human-machine systems, and swarm robotics, among other topics. In this talk I will focus on our work on manipulation, including autonomous in-hand robotic manipulation and safe, intuitive human-collaborative manipulation among one or more humans and a team of mobile manipulators.
  •  TALK    [MERL Seminar Series 2023] Prof. Stefanie Tellex presents talk titled Towards Complex Language in Partially Observed Environments
    Date & Time: Tuesday, February 14, 2023; 12:00 PM
    Speaker: Stefanie Tellex, Brown University
    MERL Host: Daniel N. Nikovski
    Research Area: Robotics
    • Robots can act as a force multiplier for people, whether a robot assisting an astronaut with a repair on the International Space station, a UAV taking flight over our cities, or an autonomous vehicle driving through our streets. Existing approaches use action-based representations that do not capture the goal-based meaning of a language expression and do not generalize to partially observed environments. The aim of my research program is to create autonomous robots that can understand complex goal-based commands and execute those commands in partially observed, dynamic environments. I will describe demonstrations of object-search in a POMDP setting with information about object locations provided by language, and mapping between English and Linear Temporal Logic, enabling a robot to understand complex natural language commands in city-scale environments. These advances represent steps towards robots that interpret complex natural language commands in partially observed environments using a decision theoretic framework.
  •  TALK    [MERL Seminar Series 2023] Dr. Rupert Way presents talk titled Empirically Grounded Technology Forecasts and the Energy Transition
    Date & Time: Tuesday, January 31, 2023; 11:00 AM
    Speaker: Rupert way, University of Oxford
    MERL Host: Ye Wang
    • Rapidly decarbonising the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Historically, most energy-economy models have overestimated the future costs of renewable energy technologies and underestimated their deployment, thereby overestimating total energy transition costs. These issues have driven calls for alternative approaches and more reliable technology forecasting methods. We use an approach based on probabilistic cost forecasting methods to estimate future energy system costs in a variety of scenarios. Our findings suggest that, compared to continuing with a fossil fuel-based system, a rapid green energy transition will likely result in net savings of many trillions of dollars - even without accounting for climate damages or co-benefits of climate policy.
  •  TALK    [MERL Seminar Series 2022] William M. Sisson presents talk titled Sustainability, Innovation and Technology
    Date & Time: Tuesday, December 20, 2022; 1:00 PM
    Speaker: William M. Sisson, WBCSD North America
    MERL Host: Scott A. Bortoff
    • Sustainability today encompasses three interconnected imperatives that all businesses must face and help to address: the increasing impact of climate change, the degradation of natural systems, and the growth of inequality. Business leaders today are increasingly understanding, particularly with the engagement of capital markets, that investors, consumers, and other business stakeholders are setting expectations on how companies are responding to these challenges and preparing for their business impact. More and more companies have shifted from sustainability as a single function in the company to one the is integrated across the firm. This translates directly into how companies are rethinking their product design and innovation efforts for sustainability and the technologies they will require. Some product categories, like heating and air conditioning systems for buildings, are both a part of the problem as well as potentially offering real solutions.
  •  EVENT    Prof. Paris Smaragdis of UIUC to give keynote at MERL's Virtual Open House
    Date & Time: Monday, December 12, 2022; 1:00pm - 5:30pm
    Location: MERL, Virtual
    Speaker: Prof. Paris Smaragdis, University of Illinois at Urbana-Champaign
    • MERL is excited to announce the featured keynote speaker for our Virtual Open House 2022:
      Prof. Paris Smaragdis from University of Illinois at Urbana-Champaign.

      Our virtual open house will take place on December 12, 2022, 1:00pm - 5:30pm (EST).

      Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Smaragdis' talk is scheduled for 3:15pm - 3:45pm (EST).


      Keynote Title: Dragging Audio Processing Past the 1970s (and the 2010s!)
      Abstract: Audio processing has not changed appreciably in the last 50 years. However, novel tasks, new computational demands, attention to human-centered evaluation, and a strong influence from machine learning, all point towards new ways of thinking about sound. In this talk I will go over multiple examples of how one can modernize standard audio processing in order to serve ambitious project goals. I will specifically talk about the use of meta learning for adaptive filtering, and how we can outperform humans in the game of optimizer design; I will show new ways to represent and process time series based on graph networks that results in highly desirable scaling properties for audio and speech recognition; and I will also talk about how we can move towards unsupervised learning from real-world data in a way that (almost) matches curated data performance and allows highly-distributed learning from audio devices in the wild.

      Speaker Bio:
      Paris Smaragdis is a Professor and an Associate Department Head in the Computer Science department in the University of Illinois at Urbana-Champaign. He completer his graduate studies and postdoc at MIT in 2001. He has been a research scientist at Mitsubishi Electric Research Labs in Cambridge MA, a senior research scientist at Adobe Research, and an Amazon Scholar with AWS. His research lies in the intersection of signal processing and machine learning, where he has contributed multiple widely used methods for source separation and audio analysis throughout his 150+ publications and 60+ US and international patents. His research has been productized many times worldwide, has been widely used in personal computers and commercial systems, and has been used in award winning movies and music releases. He was recognized by the MIT Technology Review as one of the "world's top innovators under 35 years old" in 2006 (TR35 award) and he has received the IEEE Signal Processing Society (SPS) Best Paper Award twice (2017,2020). He was elected an IEEE Fellow (class of 2015), and selected as an IEEE SPS Distinguished Lecturer (2016-2017). Within IEEE SPS he has served as the chair the Machine Learning for Signal Processing Technical Committee, the Audio and Acoustic Signal Processing Technical Committee, and the Data Science Initiative. He has been elected to and served in the IEEE Signal Processing Society Board of Governors, and is currently the Editor in Chief of the ACM/IEEE Transactions on Audio, Speech, and Language Processing.
  •  EVENT    MERL's Virtual Open House 2022
    Date & Time: Monday, December 12, 2022; 1:00pm-5:30pm ET
    Location: Mitsubishi Electric Research Laboratories (MERL)/Virtual
    Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video
    • Join MERL's virtual open house on December 12th, 2022! Featuring a keynote, live sessions, research area booths, and opportunities to interact with our research team. Discover who we are and what we do, and learn about internship and employment opportunities.
  •  TALK    [MERL Seminar Series 2022] Dr Mathew Hampshire-Waugh presents talk titled Climate Change and the road to Net-Zero
    Date & Time: Tuesday, November 29, 2022; 1:00 PM
    Speaker: Mathew Hampshire-Waugh, Net-Zero Consulting Services LTD
    MERL Host: Ye Wang
    • A seminar based upon the Author’s bestselling book, CLIMATE CHANGE and the road to NET-ZERO. The session shall explore how humanity has broken free from the shackles of poverty, suffering, and war and for the first time in human history grown both population and prosperity. It will also delve into how a single species has reconfigured the natural world, repurposed the Earth’s resources, and begun to re-engineer the climate.

      Using these conflicting narratives, the talk will explore the science, economics, technology, and politics of climate change. Constructing an argument that demonstrates, under many energy transition pathways, solving global warming requires no trade-off between the economy and environment, present and future generations, or rich and poor. Ultimately concluding that a twenty-year transition to a zero-carbon system provides a win-win solution for all on planet Earth.
  •  TALK    [MERL Seminar Series 2022] Prof. Jiajun Wu presents talk titled Understanding the Visual World Through Naturally Supervised Code
    Date & Time: Tuesday, November 1, 2022; 1:00 PM
    Speaker: Jiajun Wu, Stanford University
    MERL Host: Anoop Cherian
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • The visual world has its inherent structure: scenes are made of multiple identical objects; different objects may have the same color or material, with a regular layout; each object can be symmetric and have repetitive parts. How can we infer, represent, and use such structure from raw data, without hampering the expressiveness of neural networks? In this talk, I will demonstrate that such structure, or code, can be learned from natural supervision. Here, natural supervision can be from pixels, where neuro-symbolic methods automatically discover repetitive parts and objects for scene synthesis. It can also be from objects, where humans during fabrication introduce priors that can be leveraged by machines to infer regular intrinsics such as texture and material. When solving these problems, structured representations and neural nets play complementary roles: it is more data-efficient to learn with structured representations, and they generalize better to new scenarios with robustly captured high-level information; neural nets effectively extract complex, low-level features from cluttered and noisy visual data.
  •  TALK    [MERL Seminar Series 2022] Prof. Ufuk Topcu presents talk titled Autonomous systems in the intersection of formal methods, learning, and control
    Date & Time: Wednesday, October 26, 2022; 1:00 PM
    Speaker: Ufuk Topcu, The University of Texas at Austin
    MERL Host: Abraham P. Vinod
    Research Areas: Control, Dynamical Systems, Optimization
    • Autonomous systems are emerging as a driving technology for countlessly many applications. Numerous disciplines tackle the challenges toward making these systems trustworthy, adaptable, user-friendly, and economical. On the other hand, the existing disciplinary boundaries delay and possibly even obstruct progress. I argue that the nonconventional problems that arise in designing and verifying autonomous systems require hybrid solutions in the intersection of learning, formal methods, and controls. I will present examples of such hybrid solutions in the context of learning in sequential decision-making processes. These results offer novel means for effectively integrating physics-based, contextual, or structural prior knowledge into data-driven learning algorithms. They improve data efficiency by several orders of magnitude and generalizability to environments and tasks that the system had not experienced previously.
  •  TALK    [MERL Seminar Series 2022] Prof. Gianmario Pellegrino presents talk titled Design, Identification and Simulation of PM Synchronous Machines for Traction
    Date & Time: Friday, October 14, 2022; 11:00 AM
    Speaker: Gianmario Pellegrino, Politecnico di Tornio, Italy
    MERL Host: Anantaram Varatharajan
    Research Areas: Electric Systems, Electronic and Photonic Devices, Multi-Physical Modeling, Optimization
    • This seminar presents a comprehensive design and simulation procedure for Permanent Magnet Synchronous Machines (PMSMs) for traction application. The design of heavily saturated traction PMSMs is a multidisciplinary engineering challenge that CAD software suites struggle to grasp, whereas design equations are way too approximated for the purpose. This tutorial will present the design toolchain of SyR-e, where magnetic and structural design equations are fast-FEA corrected for an insightful initial design, later FEA calibrated with free or commercial FEA tools. One e-motor will be designed from zero referring to the specs and size of the Tesla Model 3 rear-axle e-motor. The circuital model of one motor with inverter and discrete-time control will be automatically generated, in Simulink and PLECS, with accessible torque control source code, for simulation of healthy and faulty conditions, ready for real-time implementation (e.g. HiL).
  •  TALK    A Tunable Control/Learning Framework for Autonomous Systems
    Date & Time: Thursday, October 13, 2022; 1:30pm-2:30pm
    Speaker: Prof. Shaoshuai Mou, Purdue University
    MERL Host: Yebin Wang
    Research Areas: Control, Machine Learning, Optimization
    • Modern society has been relying more and more on engineering advance of autonomous systems, ranging from individual systems (such as a robotic arm for manufacturing, a self-driving car, or an autonomous vehicle for planetary exploration) to cooperative systems (such as a human-robot team, swarms of drones, etc). In this talk we will present our most recent progress in developing a fundamental framework for learning and control in autonomous systems. The framework comes from a differentiation of Pontryagin’s Maximum Principle and is able to provide a unified solution to three classes of learning/control tasks, i.e. adaptive autonomy, inverse optimization, and system identification. We will also present applications of this framework into human-autonomy teaming, especially in enabling an autonomous system to take guidance from human operators, which is usually sparse and vague.
  •  EVENT    SANE 2022 - Speech and Audio in the Northeast
    Date: Thursday, October 6, 2022
    Location: Kendall Square, Cambridge, MA
    MERL Contacts: Anoop Cherian; Jonathan Le Roux
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    • SANE 2022, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, was held on Thursday October 6, 2022 in Kendall Square, Cambridge, MA.

      It was the 9th edition in the SANE series of workshops, which started in 2012 and was held every year alternately in Boston and New York until 2019. Since the first edition, the audience has grown to a record 200 participants and 45 posters in 2019. After a 2-year hiatus due to the pandemic, SANE returned with an in-person gathering of 140 students and researchers.

      SANE 2022 featured invited talks by seven leading researchers from the Northeast: Rupal Patel (Northeastern/VocaliD), Wei-Ning Hsu (Meta FAIR), Scott Wisdom (Google), Tara Sainath (Google), Shinji Watanabe (CMU), Anoop Cherian (MERL), and Chuang Gan (UMass Amherst/MIT-IBM Watson AI Lab). It also featured a lively poster session with 29 posters.

      SANE 2022 was co-organized by Jonathan Le Roux (MERL), Arnab Ghoshal (Apple), John Hershey (Google), and Shinji Watanabe (CMU). SANE remained a free event thanks to generous sponsorship by Bose, Google, MERL, and Microsoft.

      Slides and videos of the talks will be released on the SANE workshop website.
  •  TALK    [MERL Seminar Series 2022] Prof. Chuang Gan presents talk titled Learning to Perceive Physical Scenes from Multi-Sensory Data
    Date & Time: Tuesday, September 6, 2022; 12:00 PM EDT
    Speaker: Chuang Gan, UMass Amherst & MIT-IBM Watson AI Lab
    MERL Host: Jonathan Le Roux
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    • Human sensory perception of the physical world is rich and multimodal and can flexibly integrate input from all five sensory modalities -- vision, touch, smell, hearing, and taste. However, in AI, attention has primarily focused on visual perception. In this talk, I will introduce my efforts in connecting vision with sound, which will allow machine perception systems to see objects and infer physics from multi-sensory data. In the first part of my talk, I will introduce a. self-supervised approach that could learn to parse images and separate the sound sources by watching and listening to unlabeled videos without requiring additional manual supervision. In the second part of my talk, I will show we may further infer the underlying causal structure in 3D environments through visual and auditory observations. This enables agents to seek the sound source of repeating environmental sound (e.g., alarm) or identify what object has fallen, and where, from an intermittent impact sound.
  •  EVENT    Sonja Glavaski-Radovanovic presents at MERL's Annual Women In Science Event
    Date: Monday, August 8, 2022
    Location: MERL
    MERL Contact: Elizabeth Phillips
    • On August 8th MERL hosted its annual Women in Science event-in person. Our guest speaker Sonja Galvaski-Radovanovic, Chief Energy Digitalization Scientist and Principal Technology Strategy advisor for the Energy & Environment Directorate at PNNL, shared career wisdom and led a lively, interactive discussion with MERL staff and interns about the significant contributions women have made to the scientific society.
  •  EVENT    MERL Celebrates 30th Anniversary
    Date: Wednesday, June 15, 2022
    Location: Cambridge, MA
    MERL Contacts: Philip V. Orlik; Elizabeth Phillips; Anthony Vetro; Richard C. (Dick) Waters
    • On June 15th MERL celebrated 30 years of inspiration, imagination and innovation in Cambridge Massachusetts. We invite you to visit our website to learn more about our history, read a booklet describing our achievements during the last three decades, and watch a brief video highlighting some of the impacts we have had.
  •  TALK    [MERL Seminar Series 2022] Prof. Michael Posa presents talk titled Hybrid robotics and implicit learning
    Date & Time: Tuesday, May 3, 2022; 1:00 PM
    Speaker: Michael Posa, University of Pennsylvania
    MERL Host: Devesh K. Jha
    Research Areas: Control, Optimization, Robotics
    • Machine learning has shown incredible promise in robotics, with some notable recent demonstrations in manipulation and sim2real transfer. These results, however, require either an accurate a priori model (for simulation) or a large amount of data. In contrast, my lab is focused on enabling robots to enter novel environments and then, with minimal time to gather information, accomplish complex tasks. In this talk, I will argue that the hybrid or contact-driven nature of real-world robotics, where a robot must safely and quickly interact with objects, drives this high data requirement. In particular, the inductive biases inherent in standard learning methods fundamentally clash with the non-differentiable physics of contact-rich robotics. Focusing on model learning, or system identification, I will show both empirical and theoretical results which demonstrate that contact stiffness leads to poor training and generalization, leading to some healthy skepticism of simulation experiments trained on artificially soft environments. Fortunately, implicit learning formulations, which embed convex optimization problems, can dramatically reshape the optimization landscape for these stiff problems. By carefully reasoning about the roles of stiffness and discontinuity, and integrating non-smooth structures, we demonstrate dramatically improved learning performance. Within this family of approaches, ContactNets accurately identifies the geometry and dynamics of a six-sided cube bouncing, sliding, and rolling across a surface from only a handful of sample trajectories. Similarly, a piecewise-affine hybrid system with thousands of modes can be identified purely from state transitions. Time permitting, I'll discuss how these learned models can be deployed for control via recent results in real-time, multi-contact MPC.
  •  TALK    [MERL Seminar Series 2022] Prof. Sebastien Gros presents talk titled RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
    Date & Time: Tuesday, April 12, 2022; 11:00 AM EDT
    Speaker: Sebastien Gros, NTNU
    MERL Host: Rien Quirynen
    Research Areas: Control, Dynamical Systems, Optimization
    • Reinforcement Learning (RL), similarly to many AI-based techniques, is currently receiving a very high attention. RL is most commonly supported by classic Machine Learning techniques, i.e. typically Deep Neural Networks (DNNs). While there are good motivations for using DNNs in RL, there are also significant drawbacks. The lack of “explainability” of the resulting control policies, and the difficulty to provide guarantees on their closed-loop behavior (safety, stability) makes DNN-based policies problematic in many applications. In this talk, we will discuss an alternative approach to support RL, via formal optimal control tools based on Model Predictive Control (MPC). This approach alleviates the issues detailed above, but also presents some challenges. In this talk, we will discuss why MPC is a valid tool to support RL, and how MPC can be combined with RL (RLMPC). We will then discuss some recent results regarding this combination, the known challenges, and the kind of control applications where we believe that RLMPC will be a valuable approach.
  •  TALK    [MERL Seminar Series 2022] Albert Benveniste, Benoît Caillaud, and Mathias Malandain present talk titled Exact Structural Analysis of Multimode Modelica Models
    Date & Time: Tuesday, April 5, 2022; 11:00 AM EDT
    Speaker: Albert Benveniste, Benoît Caillaud, and Mathias Malandain, Inria
    MERL Host: Scott A. Bortoff
    Research Areas: Dynamical Systems, Multi-Physical Modeling
    • Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. In our introduction, will briefly explain why and when the approximate structural analysis implemented in current Modelica tools leads to such errors. Then we will present our multimode Pryce Sigma-method for index reduction, in which the mode-dependent Sigma-matrix is represented in a dual form, by attaching, to every valuation of the sigma_ij entry of the Sigma matrix, the predicate characterizing the set of modes in which sigma_ij takes this value. We will illustrate this multimode analysis on example, by using our IsamDAE tool. In a second part, we will complement this multimode DAE structural analysis by a new structural analysis of mode changes (and, more generally, transient modes holding for zero time). Also, mode changes often give raise to impulsive behaviors: we will present a compile-time analysis identifying such behaviors. Our structural analysis of mode changes deeply relies on nonstandard analysis, which is a mathematical framework in which infinitesimals and infinities are first class citizens.