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 referencesMarc 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, 2015Steindór 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, 2018Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc P. Deisenroth, Variational Integrator Networks for Physically Meaningful Embeddings, arXiv:1910.09349
2019-12-14 09:05 — 09:30
NeurIPS Workshop on Robot Learning: Control and Interaction in the Real World