Robotics
Where hardware, software and machine intelligence come together.
Our research is interdisciplinary and focuses on sensing, planning, reasoning, and control of single and multi-agent systems, including both manipulation and mobile robots. We strive to develop algorithms and methods for factory automation, smart building and transportation applications using machine learning, computer vision, RF/optical sensing, wireless communications, control theory and signal processing. Key research themes include bin picking and object manipulation, sensing and mapping of indoor areas, coordinated control of robot swarms, as well as robot learning and simulation.
Quick Links
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Researchers
Devesh
Jha
Daniel
Nikovski
Diego
Romeres
Mouhacine
Benosman
Stefano
Di Cairano
Arvind
Raghunathan
Yebin
Wang
William
Yerazunis
Karl
Berntorp
Scott
Bortoff
Tim
Marks
Radu
Corcodel
Jeroen
van Baar
Matthew
Brand
Uroš
Kalabić
Bingnan
Wang
Avishai
Weiss
Jianlin
Guo
Siddarth
Jain
Toshiaki
Koike-Akino
Jonathan
Le Roux
Philip
Orlik
Ronald
Perry
Rien
Quirynen
Alan
Sullivan
Koon Hoo
Teo
Varun
Haritsa
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Awards
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AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019 Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- 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.
See All Awards for MERL -
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News & Events
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NEWS Diego Romeres gave an invited talk at the Autonomy Talks at ETH, Zurich. Date: February 15, 2021
Where: Virtual
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- 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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EVENT MERL Virtual Open House 2020 Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
MERL Contacts: Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
Location: 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 & AudioBrief- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
Registration: https://mailchi.mp/merl/merl-virtual-open-house-2020
Schedule: https://www.merl.com/events/voh20
Current internship and employment openings:
https://www.merl.com/internship/openings
https://www.merl.com/employment/employment
Information about working at MERL:
https://www.merl.com/employment
- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
See All News & Events for Robotics -
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Internships
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CV1569: Robot learning from videos of human demonstrations
MERL is looking for a highly motivated and qualified intern to work on developing algorithms for robot learning from videos of human demonstrations. The ideal candidate would be a current Ph.D. student with a strong background in computer vision, deep learning, and robotics. Familiarity with imitation learning, learning from demonstrations (LfD), reinforcement learning, and machine learning for robotics will be valued. Proficiency in Python programming is necessary and experience in working with a physics engine simulator like Mujoco or pyBullet is a plus. A successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. Start date is flexible and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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CA1520: Autonomous Vehicles: Perception, Planning, and Control
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of algorithms for planning and control of autonomous vehicles. The potential subjects include high level decision making using formal methods and set-based control, coordination or perception and control strategies to improve environment knowledge while achieving a goal, and distributed control for multi-vehicle systems. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible among: motion planning, predictive control, perception and object detection optimization, machine learning for vehicle prediction, autonomous vehicles. Good programming skills in MATLAB, Python or C/C++ are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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CA1530: Hybrid Control of Cyberphysical Systems
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of hybrid control algorithms for cyberphysical system. The potential subjects include formal methods for control synthesis, control barrier-functions, stabilizing control for hybrid dynamical systems, and optimal control of hybrid dynamics. The ideal candidate is expected to be working towards a PhD with strong emphasis in control theory, and to have interest and background in as many as possible among: predictive control, Lyapunov stability, formal methods for control, constrained control, optimization, and machine learning. Good programming skills in MATLAB, and/or Python are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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Recent Publications
- "Model-based Policy Search for Partially Measurable Systems", Advances in Neural Information Processing Systems (NeurIPS), December 2020.BibTeX TR2020-174 PDF
- @inproceedings{Romeres2020dec2,
- author = {Romeres, Diego and Amadio, Fabio and Dalla Libera, Alberto and Nikovski, Daniel N. and Carli, Ruggero},
- title = {Model-based Policy Search for Partially Measurable Systems},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-174}
- }
, - "Feedback Linearization Robot Control based on Gaussian Process Inverse Dynamics Model", Conferenza Italiana di Robotica e Macchine Intelligenti, December 2020.BibTeX TR2020-173 PDF
- @inproceedings{Romeres2020dec,
- author = {Romeres, Diego and Dalla Libera, Alberto and Amadio, Fabio and Carli, Ruggero},
- title = {Feedback Linearization Robot Control based on Gaussian Process Inverse Dynamics Model},
- booktitle = {Conferenza Italiana di Robotica e Macchine Intelligenti},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-173}
- }
, - "Modelica-Based Control of A Delta Robot", ASME Dynamic Systems and Control Conference, December 2020.BibTeX TR2020-154 PDF
- @inproceedings{Bortoff2020dec,
- author = {Bortoff, Scott A. and Okasha, Ahmed},
- title = {Modelica-Based Control of A Delta Robot},
- booktitle = {ASME Dynamic Systems and Control Conference},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-154}
- }
, - "Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving", IEEE Conference on Decision and Control (CDC), December 2020.BibTeX TR2020-168 PDF
- @inproceedings{Ahn2020dec2,
- author = {Ahn, Heejin and Berntorp, Karl and Di Cairano, Stefano},
- title = {Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-168}
- }
, - "Reachability-based Decision Making for Autonomous Driving: Theory and Experiment", IEEE Transactions on Control Systems Technology, December 2020.BibTeX TR2020-165 PDF
- @article{Ahn2020dec,
- author = {Ahn, Heejin and Berntorp, Karl and Inani, Pranav and Ram, Arjun Jagdish and Di Cairano, Stefano},
- title = {Reachability-based Decision Making for Autonomous Driving: Theory and Experiment},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-165}
- }
, - "Interactive Tactile Perception for Classification of Novel Object Instances", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), DOI: 10.1109/IROS45743.2020.9341795, November 2020, pp. 9861-9868.BibTeX TR2020-143 PDF
- @inproceedings{Corcodel2020nov,
- author = {Corcodel, Radu and Jain, Siddarth and van Baar, Jeroen},
- title = {Interactive Tactile Perception for Classification of Novel Object Instances},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- year = 2020,
- pages = {9861--9868},
- month = nov,
- publisher = {IEEE},
- doi = {10.1109/IROS45743.2020.9341795},
- url = {https://www.merl.com/publications/TR2020-143}
- }
, - "Deep Reactive Planning in Dynamic Environments", Conference on Robot Learning (CoRL), November 2020.BibTeX TR2020-144 PDF
- @inproceedings{Ota2020nov2,
- author = {Ota, Kei and Jha, Devesh and Onishi, Tadashi and Kanezaki, Asako and Yoshiyasu, Yusuke and Mariyama, Toshisada and Nikovski, Daniel N.},
- title = {Deep Reactive Planning in Dynamic Environments},
- booktitle = {Conference on Robot Learning (CoRL)},
- year = 2020,
- month = nov,
- url = {https://www.merl.com/publications/TR2020-144}
- }
, - "CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2020.BibTeX TR2020-140 PDF Software
- @inproceedings{Zhang2020nov,
- author = {Zhang, Wenyu and Seto, Skyler and Jha, Devesh},
- title = {CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- year = 2020,
- month = nov,
- url = {https://www.merl.com/publications/TR2020-140}
- }
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- "Model-based Policy Search for Partially Measurable Systems", Advances in Neural Information Processing Systems (NeurIPS), December 2020.
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Videos
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Modelica-Based Modeling and Control of a Delta Robot
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Towards Human-Level Learning of Complex Physical Puzzles
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Assembly of Belt Drive Units
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Examples of Robotic Manipulation
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Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
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Cooperating Modular Goal Selection and Motion Planning for Autonomous Driving
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Deep Reactive Planning in Dynamic Environments
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Monte Carlo Probabilistic Inference for Learning Control
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Experimental Validation of Reachability-based Decision Making for Autonomous Driving
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Software Downloads