NEWS  |  Diego Romeres gave an invited talk at the Autonomy Talks at ETH, Zurich.

Date released: February 20, 2021


  •  NEWS   Diego Romeres gave an invited talk at the Autonomy Talks at ETH, Zurich.
  • Date:

    February 15, 2021

  • Description:

    Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, gave the invited talk "Reinforcement Learning for Robotics" at the Autonomy Talks organized at ETH, Zurich. In the presentation, some directions to apply Model-based Reinforcement Learning algorithms to real-world applications are presented together with a novel MBRL algorithm called MC-PILCO. The link to the presentation is https://www.youtube.com/watch?v=wYgbgMa4j-s.

  • Where:

    Virtual

  • MERL Contact:
  • External Link:

    https://www.youtube.com/watch?v=wYgbgMa4j-s

  • Research Areas:

    Artificial Intelligence, Machine Learning, Robotics

    •  Romeres, D., Amadio, F., Dalla Libera, A., Nikovski, D.N., Carli, R., "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}
      • }
    •  Romeres, D., Dalla Libera, A., Jha, D.K., Yerazunis, W.S., Nikovski, D.N., "Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements", Robotics and Automation Letters, DOI: 10.1109/​LRA.2020.2977255, Vol. 5, No. 2, pp. 3548-3555, May 2020.
      BibTeX TR2020-063 PDF
      • @article{Romeres2020may,
      • author = {Romeres, Diego and Dalla Libera, Alberto and Jha, Devesh K. and Yerazunis, William S. and Nikovski, Daniel N.},
      • title = {Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements},
      • journal = {Robotics and Automation Letters},
      • year = 2020,
      • volume = 5,
      • number = 2,
      • pages = {3548--3555},
      • month = may,
      • doi = {10.1109/LRA.2020.2977255},
      • issn = {2377-3766},
      • url = {https://www.merl.com/publications/TR2020-063}
      • }