Chelsea Finn on Meta Learning & Model Based Reinforcement Learning
Chelsea Finn on Meta Learning & Model Based Reinforcement Learning
An interview Stanford Professor Chelsea Finn, whose research deals with the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction
Appears in this episode
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:
Visual Model-Based Reinforcement Learning as a Path towards Generalist Robots
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".
Chelsea Finn on Meta Learning & Model Based Reinforcement Learning