4 papers · 1 filter
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Byeongguk Jeon, Seonghyeon Ye, JaeHyeok Doo +4
Video world models are emerging as a scalable alternative for evaluating generalist robot policies, bypassing the physical constraints and engineering burdens of real-world deploym…
HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation
Jaehwi Song, Suchae Jeong, Byeongguk Jeon +4
Large-scale demonstration datasets have been central to recent progress in general-purpose robot policies. However, existing datasets are collected in human-absent settings, and po…
SPACE: Enabling Learning from Cross-Robot Data Toward Generalist Policies
Haeone Lee, Byeongguk Jeon, Suchae Jeong +2
In robot learning, scaling training datasets across diverse embodiments and environments has become a dominant paradigm for learning generalizable robot policies. These policies ar…
Latent Action Pretraining from Videos
Seonghyeon Ye, Joel Jang, Byeongguk Jeon +13
We introduce Latent Action Pretraining for general Action models (LAPA), an unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot actio…