- Date & Time: Tuesday, November 2, 2021; 1:00 PM EST
Speaker: Dr. Hsiao-Yu (Fish) Tung, MIT BCS
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Robotics
Abstract 
Current state-of-the-art CNNs can localize and name objects in internet photos, yet, they miss the basic knowledge that a two-year-old toddler has possessed: objects persist over time despite changes in the observer’s viewpoint or during cross-object occlusions; objects have 3D extent; solid objects do not pass through each other. In this talk, I will introduce neural architectures that learn to parse video streams of a static scene into world-centric 3D feature maps by disentangling camera motion from scene appearance. I will show the proposed architectures learn object permanence, can imagine RGB views from novel viewpoints in truly novel scenes, can conduct basic spatial reasoning and planning, can infer affordability in sentences, and can learn geometry-aware 3D concepts that allow pose-aware object recognition to happen with weak/sparse labels. Our experiments suggest that the proposed architectures are essential for the models to generalize across objects and locations, and it overcomes many limitations of 2D CNNs. I will show how we can use the proposed 3D representations to build machine perception and physical understanding more close to humans.
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- Date: September 17, 2021 - October 31, 2021
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Optimization, Robotics
Brief - Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, is serving as an Associate Editor (AE) for the IEEE International Conference on Robotics and Automation (ICRA) 2022.
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- Date: August 12, 2021
MERL Contact: Anthony Vetro
Research Areas: Artificial Intelligence, Computer Vision, Control, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - Anthony Vetro gave a keynote at the inaugural IEEE Conference on Autonomous Systems (ICAS), which was held virtually from August 11-13, 2021. The talk focused on challenges and recent progress in the area of robotic manipulation. The conference is sponsored by IEEE Signal Processing Society (SPS) through the SPS Autonomous Systems Initiative.
Abstract: Human-level manipulation continues to be beyond the capabilities of today’s robotic systems. Not only do current industrial robots require significant time to program a specific task, but they lack the flexibility to generalize to other tasks and be robust to changes in the environment. While collaborative robots help to reduce programming effort and improve the user interface, they still fall short on generalization and robustness. This talk will highlight recent advances in a number of key areas to improve the manipulation capabilities of autonomous robots, including methods to accurately model the dynamics of the robot and contact forces, sensors and signal processing algorithms to provide improved perception, optimization-based decision-making and control techniques, as well as new methods of interactivity to accelerate and enhance robot learning.
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- Date: July 13, 2021
Where: Robotics: Science and Systems
MERL Contacts: Siddarth Jain; Devesh K. Jha; Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - MERL researchers Diego Romeres, Devesh Jha, and Siddarth Jain together with research groups at MIT, NVIDIA, NIST, TUM, Google DeepMind, ETH Zurich, Google AI, and UMASS Lowell organized a workshop at the Robotics: Science and Systems 2021 conference. The workshop was on "Advancing Artificial Intelligence and Manipulation for Robotics: Understanding Gaps, Industry and Academic Perspectives, and Community Building". The workshop had a list of excellent speakers both from academia and industry. Recording of the talks and of the panel discussion can be found in the link below.
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- Date: April 22, 2021
Where: Houston, Texas
Research Areas: Control, Dynamical Systems, Robotics, Signal Processing
Brief - The invited seminar "System Design, Planning, and Control for Autonomous Driving" was part of the Distinguished Seminar series at the Department of Mechanical Engineering at the University of Houston, Houston, Tx. The invited lecture described MERL research related to the different system components involved in autonomous driving, with particular focus on motion-planning and predictive-control methods.
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- Date: April 7, 2021
Where: Online
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - Devesh Jha, a Principal Research Scientist in MERL's Data Analytics group, gave an invited talk at the robotics seminar series at the University of Leeds. The talk presented some of the recent work done at MERL in the areas of robotic manipulation and robot learning.
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- Date: April 6, 2021
Where: Linköping University, Sweden
Research Areas: Control, Dynamical Systems, Robotics
Brief - MERL researcher Karl Berntorp was invited to give a lecture in the ELLIIT PhD course "Motion Planning and Control" at the Division of Vehicular Systems, Department of Electrical Engineering, Linköping University. The course is open for Ph.D. students as well as senior undergraduate students, and covers both fundamental algorithms and state-of-the-art methods for motion planning and control. The invited lecture described MERL research on the use of invariant sets for safe motion planning and control, with application to autonomous vehicles.
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- Date: March 14, 2021 - April 20, 2021
Where: IROS
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Robotics
Brief - Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, is serving as an Associate Editor (AE) for the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021).
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- Date: March 7, 2021
MERL Contact: Stefano Di Cairano
Research Areas: Control, Dynamical Systems, Robotics
Brief - Stefano Di Cairano has joined the Editorial Board of the IEEE Transactions on Intelligent Vehicles (T-IV) as an Associate Editor. The IEEE T-IV publishes peer-reviewed articles in the area of intelligent vehicles in a roadway environment, and in particular in automated vehicles. While primarily led by the IEEE ITS Society, IEEE T-IV is an IEEE multi-society journal.
As Associate Editor Stefano will be responsible for the review process of some of the papers submitted to T-IV and will work with the Editorial Board to monitor the status and continuously strengthen the journal.
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- Date: February 15, 2021
Where: Virtual
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, gave the invited talk "Reinforcement Learning for Robotics" at the Autonomy Talks organized at ETH, Zurich. In the presentation, some directions to apply Model-based Reinforcement Learning algorithms to real-world applications are presented together with a novel MBRL algorithm called MC-PILCO. The link to the presentation is https://www.youtube.com/watch?v=wYgbgMa4j-s.
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- Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
Location: Virtual
MERL Contacts: Elizabeth Phillips; Anthony Vetro
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
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- Date: November 16, 2020
MERL Contacts: Devesh K. Jha; Daniel N. Nikovski; Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - MERL researchers, in collaboration with researchers from MELCO and the Department of Brain and Cognitive Science at MIT, have released simulation software Circular Maze Environment (CME). This system could be used as a new benchmark for evaluating different control and robot learning algorithms. The control objective in this system is to tip and the tilt the maze so as to drive one (or multiple) marble(s) to the innermost ring of the circular maze. Although the system is very intuitive for humans to control, it is very challenging for artificial intelligence agents to learn efficiently. It poses several challenges for both model-based as well as model-free methods, due to its non-smooth dynamics, long planning horizon, and non-linear dynamics. The released Python package provides the simulation environment for the circular maze, where movement of multiple marbles could be simulated simultaneously. The package also provides a trajectory optimization algorithm to design a model-based controller in simulation.
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- Date: October 29, 2020
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Machine Learning, Optimization, Robotics
Brief - MERL Researcher Devesh Jha has been appointed to the editorial board of the IEEE Robotics and Automation Letters (RA-L) as an Associate Editor. IEEE RA-L publishes peer-reviewed articles in the areas of robotics and automation which can also be presented at the annual flagship conferences of RAS like ICRA, IROS and CASE.
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- Date: October 13, 2020
MERL Contact: Siddarth Jain
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Robotics
Brief - Computer vision and robotics researcher, Siddarth Jain, has been appointed to the editorial board of the IEEE Robotics and Automation Letters (RA-L) as an Associate Editor. Siddarth joined MERL in September 2019 after obtaining his Ph.D. in robotics from Northwestern University, where he developed novel robotics systems to help people with motor-impairments in performing activities of daily living tasks.
RA-L publishes peer-reviewed articles in areas of robotics and automation. RA-L also provides a unique feature to the authors with the opportunity to publish a paper in a peer-reviewed journal and present the same paper at the annual flagship robotics conferences of IEEE RAS, including ICRA, IROS, and CASE.
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- Date: October 8, 2020
Where: Linkoping University
Research Areas: Control, Dynamical Systems, Robotics, Signal Processing
Brief - MERL researcher Karl Berntorp was invited to give a lecture in the class "Autonomous vehicles – planning, control, and learning systems" at the Division of Vehicular Systems, Department of Electrical Engineering, Linkoping University. The course is for the engineering-program students at Linkoping University and gives a basic understanding of the available models, methods, and software libraries to work on autonomous vehicles, with particular focus on motion-planning and control methods. The invited lecture described the different system components and design of motion planning and predictive control methods targeted to autonomous driving.
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- Date: August 25, 2020
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - Ankush Chakrabarty co-organized an invited session on “Data-Driven Control For Industrial Applications” at the IEEE Conference on Control Technology and Applications with Shahin Shahrampour (Asst. Prof., Texas A&M). Talks covered topics including reinforcement learning for aerospace systems, constrained reinforcement learning for motors, deep Q learning for traffic systems and participants included speakers from Stanford University, North Carolina State University, Texas A&M, Oklahoma State University, University of Science and Technology at Beijing, and TU Delft.
MERL presented research (Chakrabarty, Danielson, Wang) on constraint-enforcing output-tracking with approximate dynamic programming for servomotor systems.
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- Date: August 3, 2020
Where: Cambridge, MA
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Optimization, Robotics
Brief - Mitsubishi Electric Research Laboratories is excited to welcome Abraham P. Vinod as the newest member of its research staff, in the Control for Autonomy Team. Abraham joins MERL from the University of Texas, Austin, where he was a Postdoctoral Research Fellow. He obtained his Ph.D. from the University of New Mexico. His PhD research produced scalable algorithms for providing safety guarantees for stochastic, control-constrained, dynamical systems, with applications to motion planning. In his postdoctoral research, Abraham studied theory and algorithms for on-the-fly, data-driven control of unknown systems under severely limited data. His current research interests lie in the intersection of optimization, control, and learning. Abraham won the Best Student Paper Award at the 2017 ACM Hybrid Systems: Computation and Control Conference, was a finalist for the Best Paper Award in the 2018 ACM Hybrid Systems: Computation and Control Conference, and won the best undergraduate student research project award at the Indian Institute of Technology, Madras.
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- Date: July 12, 2020 - July 18, 2020
Where: Vienna, Austria (virtual this year)
MERL Contacts: Anoop Cherian; Devesh K. Jha; Daniel N. Nikovski
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - MERL researchers are presenting three papers at the International Conference on Machine Learning (ICML 2020), which is virtually held this year from 12-18th July. ICML is one of the top-tier conferences in machine learning with an acceptance rate of 22%. The MERL papers are:
1) "Finite-time convergence in Continuous-Time Optimization" by Orlando Romero and Mouhacine Benosman.
2) "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?" by Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski.
3) "Representation Learning Using Adversarially-Contrastive Optimal Transport" by Anoop Cherian and Shuchin Aeron.
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- Date: June 9, 2020
Where: ICRAxMIT
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Dynamical Systems, Machine Learning, Robotics
Brief - Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, gave an invited talk at the workshop ICRAxMIT organized at MIT. The talk briefly described a derivative-free framework that doesn't take in consideration velocities and accelerations to model and control robotic systems. The proposed approach is validated in two real robotic systems.
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- Date: February 14, 2020
Where: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Robotics
Brief - Diego Romeres, a Research Scientist in MERL's Data Analytics group, will be serving as an Associate Editor (AE) for the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).
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- Date: December 12, 2019
MERL Contact: Stefano Di Cairano
Research Areas: Control, Dynamical Systems, Robotics
Brief - Stefano Di Cairano has been appointed inaugural chair of the IEEE CSS Technology Conference Editorial Board. In this role Stefano will coordinate the creation and maintenance of the Editorial Board, and will coordinate the editorial board activities supporting the IEEE CCTA conference series, including manuscript assignment to associate editors, monitoring of the manuscript assessment, and program finalization with the conference program chairs. Stefano will also work with the other IEEE CSS Editorial Board Chairs and IEEE CSS Leadership to ensure the quality and improve the processes of IEEE CSS publications.
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- Date: November 20, 2019
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Robotics
Brief - Diego Romeres, a Research Scientist in MERL's Data Analytics group, gave a seminar lecture at the Electrical and Computer Engineering Colloquium of the University of Connecticut. The talk described novel reinforcement algorithms based on combining physical models with non-parametric models of robotic systems derived from data.
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- Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
Brief - MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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- Date: April 28, 2019
Where: 3rd IAVSD Workshop on Dynamics of Road Vehicles: Connected and Automated Vehicles
MERL Contact: Stefano Di Cairano
Research Areas: Control, Optimization, Robotics
Brief - Stefano Di Cairano, Distinguished Scientist and Senior Team Leader in the Control and Dynamical Systems Group, will give an invited talk entitled: "Modularity, integration and synergy in architectures for autonomous driving" that covers recent work in the lab concerning building a modular, robust control framework for autonomous driving.
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- Date: October 15, 2018 - October 19, 2018
Where: CEATEC'18, Makuhari Messe, Tokyo
MERL Contacts: Devesh K. Jha; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Robotics
Brief - MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
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