NEWS  |  MERL researcher Diego Romeres gave an invited talk at University of Connecticut on Reinforcement Learning for Robotics

Date released: Nov 22, 2019


  •  NEWS   MERL researcher Diego Romeres gave an invited talk at University of Connecticut on Reinforcement Learning for Robotics
  • Date:

    November 20, 2019

  • Description:

    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.

  • MERL Contact:
  • Research Areas:

    Artificial Intelligence, Data Analytics, Machine Learning, Robotics

  • Related Publications:
  •  Jha, D., Raghunathan, A., Romeres, D., "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}
    • }
  •  Romeres, D., Jha, D., Dalla Libera, A., Yerazunis, W.S., Nikovski, D.N., "Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze", IEEE International Conference on Robotics and Automation (ICRA), May 2019.
    BibTeX Download PDFAbout TR2019-028
    • @inproceedings{Romeres2019may,
    • author = {Romeres, Diego and Jha, Devesh and Dalla Libera, Alberto and Yerazunis, William S. and Nikovski, Daniel N.},
    • title = {Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze},
    • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
    • year = 2019,
    • month = may,
    • url = {https://www.merl.com/publications/TR2019-028}
    • }