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.
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Researchers
Devesh
Jha
Daniel
Nikovski
Diego
Romeres
Mouhacine
Benosman
Yebin
Wang
Tim
Marks
Arvind
Raghunathan
Stefano
Di Cairano
William
Yerazunis
Karl
Berntorp
Scott
Bortoff
Matthew
Brand
Uroš
Kalabić
Jeroen
van Baar
Bingnan
Wang
Avishai
Weiss
Radu
Corcodel
Jianlin
Guo
Toshiaki
Koike-Akino
Jonathan
Le Roux
Philip
Orlik
Ronald
Perry
Rien
Quirynen
Alan
Sullivan
Koon Hoo
Teo
Siddarth
Jain
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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 Stefano Di Cairano Appointed Inaugural Chair of IEEE CSS Technology Conference Editorial Board Date: December 12, 2019
MERL Contact: Stefano Di Cairano
Research Areas: Control, Dynamical Systems, RoboticsBrief- 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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NEWS MERL researcher Diego Romeres gave an invited talk at University of Connecticut on Reinforcement Learning for Robotics Date: November 20, 2019
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, RoboticsBrief- 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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Internships
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CV1425: Contact implicit manipulation
The Computer Vision group at MERL is offering and internship opportunity to a highly skilled PhD student to work on robotic manipulation. Candidates should have a solid understanding of contact mechanics, path planning and dexterous manipulation. The intern will deploy control software on physical robots. Strong programming skills are required, including ROS, C++ and Python. Duration and start dates are flexible.
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CD1402: Predictive control for Performance and Perception Optimization
The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on stochastic planning and control for concurrently achieving control performance while concurrently improving the perception of the environment. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Computer Science or related program, with focus on Control Theory. The ideal candidate will have experience in (one or more of) predictive control or model-based motion planning, stochastic control and estimation, interaction between control and estimation algorithms, control with chance constraints, tube-based control. Good programming skills in Matlab (or alternatively Python) are required, working knowledge of C/C++ is a plus. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.
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DA1396: Machine learning for Contact-rich Robotic Manipulation
MERL is looking for a highly motivated individual to work on contact-rich robotic manipulation applications. The ideal candidate is expected to have expertise both in machine learning techniques, such as Gaussian Process Regression and Deep Neural Networks, as well as in reinforcement learning algorithms. In addition, having experience with robotic systems would be considered a significant plus. The candidate will be expected to develop novel algorithms and possibly implement them on robotic systems. Proficiency in Python programming is necessary, and experience with ROS would be a plus. The candidate will collaborate closely with MERL researchers. Start date for this internship is flexible, and the duration is expected to be 3-6 months.
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Recent Publications
- , "Control Allocation and Quantization of a GEO Satellite with 4DOF Gimbaled Thruster Booms", AAS/AIAA Space Flight Mechanics Meeting, DOI: 10.2514/6.2020-1687, January 2020.BibTeX Download PDFAbout TR2020-008
- @inproceedings{Caverly2020jan,
- author = {Caverly, Ryan and Di Cairano, Stefano and Weiss, Avishai},
- title = {Control Allocation and Quantization of a GEO Satellite with 4DOF Gimbaled Thruster Booms},
- booktitle = {AAS/AIAA Space Flight Mechanics Meeting},
- year = 2020,
- month = jan,
- doi = {10.2514/6.2020-1687},
- url = {https://www.merl.com/publications/TR2020-008}
- }
- , "Control Strategy for Long-Term Station-Keeping on Near-Rectilinear Halo Orbits", AAS/AIAA Space Flight Mechanics Meeting, DOI: 10.2514/6.2020-1459, January 2020.BibTeX Download PDFAbout TR2020-006
- @inproceedings{Muralidharan2020jan,
- author = {Muralidharan, Vivek and Weiss, Avishai and Kalabic, Uros},
- title = {Control Strategy for Long-Term Station-Keeping on Near-Rectilinear Halo Orbits},
- booktitle = {AAS/AIAA Space Flight Mechanics Meeting},
- year = 2020,
- month = jan,
- doi = {10.2514/6.2020-1459},
- url = {https://www.merl.com/publications/TR2020-006}
- }
- , "Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning", NeurIPS Workshop on Safety and Robustness in Decision Making, December 2019.BibTeX Download PDFAbout TR2019-156
- @inproceedings{Jha2019dec2,
- author = {Jha, Devesh and Kolaric, Patrik and Romeres, Diego and Raghunathan, Arvind and Benosman, Mouhacine and Nikovski, Daniel N.},
- title = {Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning},
- booktitle = {NeurIPS Workshop on Safety and Robustness in Decision Making},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-156}
- }
- , "QNTRPO: Including Curvature in TRPO", Optimization Foundations for Reinforcement Learning Workshop at NeurIPS, December 2019.BibTeX Download PDFAbout TR2019-154
- @inproceedings{Jha2019dec,
- author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
- title = {QNTRPO: Including Curvature in TRPO},
- booktitle = {Optimization Foundations for Reinforcement Learning Workshop at NeurIPS},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-154}
- }
- , "Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2019.BibTeX Download PDFAbout TR2019-129
- @inproceedings{Ota2019nov,
- author = {Ota, Kei and Jha, Devesh K. and Oiki, Tomohiro and Miura, Mamoru and Nammoto, Takashi and Nikovski, Daniel and Mariyama, Toshisada},
- title = {Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- year = 2019,
- month = nov,
- url = {https://www.merl.com/publications/TR2019-129}
- }
- , "Quasi-Newton Trust Region Policy Optimization", Conference on Robot Learning (CoRL), October 2019.BibTeX Download PDFAbout TR2019-120
- @inproceedings{Jha2019oct,
- author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
- title = {Quasi-Newton Trust Region Policy Optimization},
- booktitle = {Conference on Robot Learning (CoRL)},
- year = 2019,
- month = oct,
- url = {https://www.merl.com/publications/TR2019-120}
- }
- , "Detection, Tracking and 3D Modeling of Objects with Sparse RGB-D SLAM and Interactive Perception", IEEE-RAS International Conference on Humanoid Robots, October 2019.BibTeX Download PDFAbout TR2019-119
- @inproceedings{Almeida2019oct,
- author = {Almeida, Diogo and Ataer-Cansizoglu, Esra and Corcodel, Radu},
- title = {Detection, Tracking and 3D Modeling of Objects with Sparse RGB-D SLAM and Interactive Perception},
- booktitle = {IEEE-RAS International Conference on Humanoid Robots},
- year = 2019,
- month = oct,
- url = {https://www.merl.com/publications/TR2019-119}
- }
- , "Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks", International Conference on Automated Planning and Scheduling (ICAPS), July 2019, pp. 764-772.BibTeX Download PDFAbout TR2019-072
- @inproceedings{Takahashi2019jul,
- author = {Takahashi, Takeshi and Sun, He and Tian, Dong and Wang, Yebin},
- title = {Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks},
- booktitle = {International Conference on Automated Planning and Scheduling (ICAPS)},
- year = 2019,
- pages = {764--772},
- month = jul,
- publisher = {AAAI},
- url = {https://www.merl.com/publications/TR2019-072}
- }
- , "Control Allocation and Quantization of a GEO Satellite with 4DOF Gimbaled Thruster Booms", AAS/AIAA Space Flight Mechanics Meeting, DOI: 10.2514/6.2020-1687, January 2020.
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