982 citations · 2.8k across the 47 of their papers we have counts for
6 papers · 1 filter
Instruction-Following Agents with Multimodal Transformer
Hao Liu, Lisa Lee, Kimin Lee +1
Humans are excellent at understanding language and vision to accomplish a wide range of tasks. In contrast, creating general instruction-following embodied agents remains a difficu…
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Younggyo Seo, Kimin Lee, Fangchen Liu +2
Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent vid…
Masked World Models for Visual Control
Younggyo Seo, Danijar Hafner, Hao Liu +4
Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient robot learning from visual observations. Yet the current approaches typically train a si…
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Xinran Liang, Katherine Shu, Kimin Lee +1
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible rewar…
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
Jongjin Park, Younggyo Seo, Jinwoo Shin +3
Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the rewar…
Reinforcement Learning with Action-Free Pre-Training from Videos
Younggyo Seo, Kimin Lee, Stephen James +1
Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks. In this paper,…