SOURCE-LINKED INTELLIGENCE

ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Robotic manipulation faces a fundamental scaling challenge: robust generalization demands broad physical experience, yet action-labeled robot trajectories are expensive to collect and inherently limited in diversity. Egocentric videos offer a far more scalable source of embodied experience, capturing object interactions, contact dynamics, tool use, and long-horizon behaviors across diverse environments. The central challenge is how to convert this abundant but action-free experience into effective robot control. We introduce ZimaBlue, a scalable framework for learning generalizable World Actio

Read original source ↗ Open in workspace

recordType

paper

region

Global