11 citations · 16 across the 4 of their papers we have counts for
4 papers
SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning
Taewook Nam, Junmo Cho, Youngsoo Jang +1
Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human opera…
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
Taewook Nam, Juyong Lee, Jesse Zhang +3
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback. In o…
Skill-based Meta-Reinforcement Learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch +2
While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real…
Meta Dropout: Learning to Perturb Features for Generalization
Hae Beom Lee, Taewook Nam, Eunho Yang +1
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test exa…