Reducing Human Efforts for Expert Acquisition in Imitation Learning
Dec 2, 2022·
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0 min read

Chenhao Li

Abstract
We analyze human involvement in generic imitation learning settings, especially in the data collection phase. We also review methods that aim to reduce such human efforts in the training loop, by either inferring learning signals from poor demonstrations, or extracting latent skills from noisy datasets.
Date
Dec 2, 2022 5:00 PM
Event
Location
Machines in Motion Laboratory, New York University
6 MetroTech Center, Brooklyn, New York 11201