Chelsea Finn on Meta Learning & Model Based Reinforcement Learning

In episode 13 of The Gradient Podcast, we interview Stanford Professor Chelsea Finn

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Chelsea is an Assistant Professor at Stanford University. Her lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with SAIL and the Statistical ML Group. I also spend time at Google as a part of the Google Brain team. Her research deals with the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction.

Links:

* Learning to Learn with Gradients

* Visual Model-Based Reinforcement Learning as a Path towards Generalist Robots

* RoboNet: A Dataset for Large-Scale Multi-Robot Learning

* Greedy Hierarchical Variational Autoencoders for Large-Scale Video

* Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks   

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

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