Diego Romeres

Diego Romeres
  • 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.

  • News & Events

    •  NEWS   MERL Researchers Demonstrate Robot Learning Technology at CEATEC'18
      Date: October 15, 2018 - October 19, 2018
      Where: CEATEC'18, Makuhari Messe, Tokyo
      MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar; William 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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    •  NEWS   MERL Researchers Demonstrate New Model-Based AI Learning Technology for Equipment Control
      Date: February 14, 2018
      Where: Tokyo, Japan
      MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; William Yerazunis; Jeroen van Baar; Alan Sullivan
      Research Areas: Optimization, Computer Vision, Artificial Intelligence, Data Analytics, Robotics
      Brief
      • New technology for model-based AI learning for equipment control was demonstrated by MERL researchers at a recent press release event in Tokyo. The AI learning method constructs predictive models of the equipment through repeated trial and error, and then learns control rules based on these models. The new technology is expected to significantly reduce the cost and time needed to develop control programs in the future. Please see the link below for the full text of the Mitsubishi Electric press release.
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  • MERL Publications

    •  van Baar, J., Corcodel, R., Sullivan, A., Jha, D., Romeres, D., Nikovski, D.N., "Simulation to Real Transfer Learning with Robustified Policies for Robot Tasks", arXiv, September 2018.
      BibTeX Download PDFAbout TR2018-144
      • @techreport{MERL_TR2018-144,
      • author = {van Baar, J. and Corcodel, R. and Sullivan, A. and Jha, D. and Romeres, D. and Nikovski, D.N.},
      • title = {Simulation to Real Transfer Learning with Robustified Policies for Robot Tasks},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2018-144},
      • month = sep,
      • year = 2018,
      • url = {http://www.merl.com/publications/TR2018-144/}
      • }
  • Other Publications

    •  Romeres, Diego; Prando, Giulia; Pillonetto, Gianluigi; Chiuso, Alessandro, "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}
      • }
    •  Romeres, Diego; Zorzi, Mattia; Camoriano, Raffaello; Chiuso, Alessandro, "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}
      • }
    •  Romeres, Diego; Zorzi, Mattia; Camoriano, Raffaello; Chiuso, Alessandro, "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}
      • }
    •  Prando, Giulia; Romeres, Diego; Pillonetto, Gianluigi; Chiuso, Alessandro, "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}
      • }
    •  Prando, Giulia; Romeres, Diego; Chiuso, Alessandro, "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}
      • }
    •  Muenz, Ulrich; Romeres, Diego, "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}
      • }
    •  Romeres, Diego; Doerfler, Florian; Bullo, Francesco, "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}
      • }
    •  Bolognani, Saverio; Carron, Andrea; Di Vittorio, Alberto; Romeres, Diego; Schenato, Luca; Zampieri, Sandro, "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}
      • }