Chelsea Finn on Meta Learning & Model Based Reinforcement Learning

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

Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSS

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".