Useful Models for Robot Learning


In robot learning we face challenge of data-efficient learning. In this talk, we will make the case for three types of useful models that become handy in robot learning: probabilistic models, hierarchical models, and models that allow us to incorporate the underlying physics. We will briefly outline strong use cases for these three models in the context of model-based reinforcement learning, meta learning, and system identification.

Key references

  • Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 37, pp. 408–423, 2015
  • Steindór Sæmundsson, Katja Hofmann, Marc P. Deisenroth, Meta Reinforcement Learning with Latent Variable Gaussian Processes, Proceedings of the International the Conference on Uncertainty in Artificial Intelligence, 2018
  • Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc P. Deisenroth, Variational Integrator Networks for Physically Structured Embeddings, arXiv:1910.09349
  • Date
    NeurIPS Workshop on Robot Learning: Control and Interaction in the Real World
    Vancouver, Canada
    Marc Deisenroth
    DeepMind Chair in Artificial Intelligence