From the 1 of 6 linked papers with an AI index.
6 papers
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Byeongguk Jeon, Seonghyeon Ye, JaeHyeok Doo +4
RoboWorld is an automated pipeline that uses a fast autoregressive video world model and a vision-language scoring system to evaluate generalist robot policies efficiently and reli…
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…
Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
JaeHyeok Doo, Byeongguk Jeon, Seonghyeon Ye +2
There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due to their high expressive capacity. However, effectively leveragin…
Latent Reasoning via Sentence Embedding Prediction
Hyeonbin Hwang, Byeongguk Jeon, Seungone Kim +7
Autoregressive language models (LMs) generate one token at a time, yet human reasoning operates over higher-level abstractions - sentences, propositions, and concepts. This contras…
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…