Friday, January 25 • 11:30am - 11:50am
Latent Structure in Deep Robotic Learning

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Traditionally, deep reinforcement learning has focused on learning one particular skill in isolation and from scratch. This often leads to repeated efforts of learning the right representation for each skill individually, while it is likely that such representation could be shared between different skills. In contrast, there is some evidence that humans reuse previously learned skills efficiently to learn new ones, e.g. by sequencing or interpolating between them.
In this talk, I will demonstrate how one could discover latent structure when learning multiple skills concurrently. In particular, I will present a first step towards learning robot skill embeddings that enable reusing previously acquired skills. I will show how one can use these ideas for multi-task reinforcement learning, sim-to-real transfer and imitation learning.

avatar for Karol Hausman, Google Brain

Karol Hausman, Google Brain

Research Scientist, Google Brain
Karol Hausman is a Research Scientist at Google Brain in Mountain View, California working on robotics and machine learning. He is interested in enabling robots to autonomously acquire general-purpose skills with minimal supervision in real-world environments. His current research... Read More →

Friday January 25, 2019 11:30am - 11:50am
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA