Diego Romeres
- Phone: 617-621-7561
- Email:
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Position:
Research / Technical Staff
Principal Research Scientist -
Education:
Ph.D., University of Padova, 2017 -
Research Areas:
External Links:
Diego's Quick Links
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Biography
Diego's research interests are in machine learning, system identification and robotic applications. At MERL he is currently working on applying nonparametric machine learning techniques for the control of robotic platforms. His Ph.D. thesis is about the combination of nonparametric data-driven models and physics-based models in gaussian processes for robot dynamics learning.
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Recent News & Events
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NEWS Diego Romeres appointed as Associate Editor at ICRA 2022. Date: September 17, 2021 - October 31, 2021
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Optimization, RoboticsBrief- 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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NEWS MERL researchers Diego Romeres, Devesh Jha and Siddarth Jain co-organized a workshop on Robotic Manipulation at the RSS 2021 conference Date: July 13, 2021
Where: Robotics: Science and Systems
MERL Contacts: Siddarth Jain; Devesh K. Jha; Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, RoboticsBrief- 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.
See All News & Events for Diego -
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Internships with Diego
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DA1702: Machine Learning for Robotic Manipulation
MERL is looking for a self-motivated and qualified candidate to work on robotic manipulation projects. The ideal candidate is a PhD student and should have experience and records in one or multiple of the following areas. 1) Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. 2) Knowledge of standard Reinforcement Learning algorithms. 3) Experience in working with robotic systems and familiarity with one physics engine simulator like Mujoco, pyBullet, pyDrake. 4) Optimization-based control for complementarity systems. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex robotic manipulation tasks that will lead to a scientific publication. Typical internship length is 3-4 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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DA1768: Contact Modeling and Optimization
MERL is looking for a self-motivated and qualified candidate to work on modeling for contact phenomenon. Robotic manipulation is heavily affected by external contacts that can be modeled with physical and data driven models. We are interested in researching those models for analysis and control purposes. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas. Contact modeling and robotic manipulation. Physic Engines like Mujoco, Bullet, Drake and sim2real gap problems. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Experience in working with robotic systems. Knowledge in learning from demonstration algorithms and standard Reinforcement Learning algorithms is a plus. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms that will lead to a scientific publication. Typical internship length is 3-4 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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MERL Publications
- , "Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC", IEEE International Conference on Systems, Man, and Cybernetics, October 2021.BibTeX TR2021-126 PDF
- @inproceedings{Chakrabarty2021oct,
- author = {Chakrabarty, Ankush and Quirynen, Rien and Romeres, Diego and Di Cairano, Stefano},
- title = {Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC},
- booktitle = {IEEE International Conference on Systems, Man, and Cybernetics},
- year = 2021,
- month = oct,
- url = {https://www.merl.com/publications/TR2021-126}
- }
- , "Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation", Automatica.it, September 2021.BibTeX TR2021-108 PDF
- @inproceedings{Amadio2021sep,
- author = {Amadio, Fabio and Dalla Libera, Alberto and Carli, Ruggero and Romeres, Diego},
- title = {Model-Based Reinforcement Learning Using Monte Carlo Gradient Estimation},
- booktitle = {Automatica.it},
- year = 2021,
- month = sep,
- url = {https://www.merl.com/publications/TR2021-108}
- }
- , "Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models", European Control Conference (ECC), June 2021.BibTeX TR2021-068 PDF
- @inproceedings{DallaLibera2021jun,
- author = {Dalla Libera, Alberto and Amadio, Fabio and Nikovski, Daniel N. and Carli, Ruggero and Romeres, Diego},
- title = {Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models},
- booktitle = {European Control Conference (ECC)},
- year = 2021,
- month = jun,
- url = {https://www.merl.com/publications/TR2021-068}
- }
- , "PYROBOCOP: Python-based Robotic Control & Optimization Package", arXiv, June 2021.
- , "Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units", IEEE International Conference on Robotics and Automation (ICRA), June 2021.BibTeX TR2021-072 PDF
- @inproceedings{Jin2021jun,
- author = {Jin, Shiyu and Romeres, Diego and Raghunathan, Arvind and Jha, Devesh K. and Tomizuka, Masayoshi},
- title = {Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2021,
- month = jun,
- url = {https://www.merl.com/publications/TR2021-072}
- }
- , "Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC", IEEE International Conference on Systems, Man, and Cybernetics, October 2021.
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Other Publications
- , "On-line bayesian system identification", Control Conference (ECC), 2016 European, 2016, pp. 1359-1364.BibTeX
- @Inproceedings{romeres2016line,
- author = {Romeres, Diego and Prando, Giulia and Pillonetto, Gianluigi and Chiuso, Alessandro},
- title = {On-line bayesian system identification},
- booktitle = {Control Conference (ECC), 2016 European},
- year = 2016,
- pages = {1359--1364},
- organization = {IEEE}
- }
- , "Online semi-parametric learning for inverse dynamics modeling", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 2945-2950.BibTeX
- @Inproceedings{romeres2016online,
- author = {Romeres, Diego and Zorzi, Mattia and Camoriano, Raffaello and Chiuso, Alessandro},
- title = {Online semi-parametric learning for inverse dynamics modeling},
- booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
- year = 2016,
- pages = {2945--2950},
- organization = {IEEE}
- }
- , "Online semi-parametric learning for inverse dynamics modeling", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 2945-2950.BibTeX
- @Inproceedings{romeres2016onlinesemiparametric,
- author = {Romeres, Diego and Zorzi, Mattia and Camoriano, Raffaello and Chiuso, Alessandro},
- title = {Online semi-parametric learning for inverse dynamics modeling},
- booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
- year = 2016,
- pages = {2945--2950},
- organization = {IEEE}
- }
- , "Classical vs. Bayesian methods for linear system identification: Point estimators and confidence sets", Control Conference (ECC), 2016 European, 2016, pp. 1365-1370.BibTeX
- @Inproceedings{tprando2016classical,
- author = {Prando, Giulia and Romeres, Diego and Pillonetto, Gianluigi and Chiuso, Alessandro},
- title = {Classical vs. Bayesian methods for linear system identification: Point estimators and confidence sets},
- booktitle = {Control Conference (ECC), 2016 European},
- year = 2016,
- pages = {1365--1370},
- organization = {IEEE}
- }
- , "Online identification of time-varying systems: A Bayesian approach", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 3775-3780.BibTeX
- @Inproceedings{tprando2016online,
- author = {Prando, Giulia and Romeres, Diego and Chiuso, Alessandro},
- title = {Online identification of time-varying systems: A Bayesian approach},
- booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
- year = 2016,
- pages = {3775--3780},
- organization = {IEEE}
- }
- , "Region of attraction of power systems", IFAC Proceedings Volumes, Vol. 46, No. 27, pp. 49-54, 2013.BibTeX
- @Article{munz2013region,
- author = {Muenz, Ulrich and Romeres, Diego},
- title = {Region of attraction of power systems},
- journal = {IFAC Proceedings Volumes},
- year = 2013,
- volume = 46,
- number = 27,
- pages = {49--54},
- publisher = {Elsevier}
- }
- , "Novel results on slow coherency in consensus and power networks", Control Conference (ECC), 2013 European, 2013, pp. 742-747.BibTeX
- @Inproceedings{romeres2013novel,
- author = {Romeres, Diego and Doerfler, Florian and Bullo, Francesco},
- title = {Novel results on slow coherency in consensus and power networks},
- booktitle = {Control Conference (ECC), 2013 European},
- year = 2013,
- pages = {742--747},
- organization = {IEEE}
- }
- , "Distributed multi-hop reactive power compensation in smart micro-grids subject to saturation constraints", Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, 2012, pp. 1118-1123.BibTeX
- @Inproceedings{bolognani2012distributed,
- author = {Bolognani, Saverio and Carron, Andrea and Di Vittorio, Alberto and Romeres, Diego and Schenato, Luca and Zampieri, Sandro},
- title = {Distributed multi-hop reactive power compensation in smart micro-grids subject to saturation constraints},
- booktitle = {Decision and Control (CDC), 2012 IEEE 51st Annual Conference on},
- year = 2012,
- pages = {1118--1123},
- organization = {IEEE}
- }
- , "On-line bayesian system identification", Control Conference (ECC), 2016 European, 2016, pp. 1359-1364.
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Software Downloads
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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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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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Monte Carlo Probabilistic Inference for Learning Control
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MERL Issued Patents
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Title: "System and Method for Automatic Error Recovery in Robotic Assembly"
Inventors: Nikovski, Daniel N.; Jha, Devesh; Romeres, Diego
Patent No.: 11,161,244
Issue Date: Nov 2, 2021
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Title: "System and Method for Automatic Error Recovery in Robotic Assembly"
