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
Toshiaki
Koike-Akino
Bingnan
Wang
Avishai
Weiss
Jianlin
Guo
Siddarth
Jain
Jonathan
Le Roux
Philip
Orlik
Ronald
Perry
Rien
Quirynen
Alan
Sullivan
Koon Hoo
Teo
Ye
Wang
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.
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News & Events
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NEWS Invited talk at University of Leeds Date: April 7, 2021
Where: Online
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- 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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NEWS Karl Berntorp gave an invited lecture at the Department of Electrical Engineering at Linköping University Date: April 6, 2021
Where: Linköping University, Sweden
MERL Contact: Karl Berntorp
Research Areas: Control, Dynamical Systems, RoboticsBrief- 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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Internships
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CA1531: Learning-based multi-agent motion planning
MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. 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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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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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
- "Robust Coordinated Hybrid Source Seeking with Obstacle Avoidance in Multi-Vehicle Autonomous Systems", IEEE Transactions on Automatic Control, DOI: 10.1109/TAC.2021.3056365, March 2021.BibTeX TR2021-022 PDF
- @article{Poveda2021mar,
- author = {Poveda, Jorge and Benosman, Mouhacine and Teel, Andrew R. and Sanfelice, Ricardo G.},
- title = {Robust Coordinated Hybrid Source Seeking with Obstacle Avoidance in Multi-Vehicle Autonomous Systems},
- journal = {IEEE Transactions on Automatic Control},
- year = 2021,
- month = mar,
- doi = {10.1109/TAC.2021.3056365},
- url = {https://www.merl.com/publications/TR2021-022}
- }
, - "AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference", IEEE Access, DOI: 10.1109/ACCESS.2021.3064530, Vol. 9, pp. 39955-39972, March 2021.BibTeX TR2021-016 PDF
- @article{Demir2021mar,
- author = {Demir, Andac and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz},
- title = {AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference},
- journal = {IEEE Access},
- year = 2021,
- volume = 9,
- pages = {39955--39972},
- month = mar,
- doi = {10.1109/ACCESS.2021.3064530},
- issn = {2169-3536},
- url = {https://www.merl.com/publications/TR2021-016}
- }
, - "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, DOI: doi.org/10.1115/DSCC2020-3158, 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,
- doi = {doi.org/10.1115/DSCC2020-3158},
- 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), DOI: 10.1109/CDC42340.2020.9304101, 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,
- doi = {10.1109/CDC42340.2020.9304101},
- url = {https://www.merl.com/publications/TR2020-168}
- }
, - "Reachability-based Decision Making for Autonomous Driving: Theory and Experiment", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2020.3022721, 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,
- doi = {10.1109/TCST.2020.3022721},
- 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}
- }
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- "Robust Coordinated Hybrid Source Seeking with Obstacle Avoidance in Multi-Vehicle Autonomous Systems", IEEE Transactions on Automatic Control, DOI: 10.1109/TAC.2021.3056365, March 2021.
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Videos
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Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
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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