1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.RO2025
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…
cs.AI2024
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
Ruochen Wang, Sohyun An, Minhao Cheng +3
Large Language Models (LLMs) exhibit strong generalization capabilities to novel tasks when prompted with language instructions and in-context demos. Since this ability sensitively…
cs.LG2023★ 1 cited
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…