Reducing Human Efforts for Expert Acquisition in Imitation Learning

Machines in Motion Laboratory


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.

Dec 2, 2022 5:00 PM
Machines in Motion Laboratory, New York University
6 MetroTech Center, Brooklyn, New York 11201
Chenhao Li
Chenhao Li
Reinforcement Learning for Robotics

My research interests focus on the general field of robot learning, including reinforcement learning, developmental robotics and legged intelligence.