TR2018-165

Derivative-Free Semiparametric Bayesian Models for Robot Learning


    •  Romeres, D.., Jha, D., Dalla Libera, A., Chiuso, A., Nikovski, D.N., "Derivative-Free Semiparametric Bayesian Models for Robot Learning", Advances in Neural Information Processing Systems (NIPS), December 2018.
      BibTeX TR2018-165 PDF
      • @inproceedings{Romeres2018dec,
      • author = {Romeres, Diego and Jha, Devesh and Dalla Libera, Alberto and Chiuso, Alessandro and Nikovski, Daniel N.},
      • title = {Derivative-Free Semiparametric Bayesian Models for Robot Learning},
      • booktitle = {Advances in Neural Information Processing Systems (NIPS)},
      • year = 2018,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2018-165}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Data Analytics, Dynamical Systems, Machine Learning, Robotics

Model-Based Reinforcement Learning (MBRL) is gaining much interest in the robot learning community; in MBRL, the model serves as a representation which is largely task-invariant, and thus can facilitate transfer of knowledge across multiple tasks in the same domain. Learning reliable models for physical systems, however, remains a challenging problem. This paper summarizes recent semiparametric and derivative-free modelling techniques, and presents some key points for a new methodology to formulate derivative-free semiparametric Bayesian models with applications to robot learning. The modeling technique is demonstrated using a real robotic system, and is shown to consistently perform better than other state-ofthe-art techniques.