Zero-shot World Models Are Developmentally Efficient Learners
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The paper explores whether inexpensive, procedurally generated synthetic data can provide useful priors for robot learning models before any real-world data is observed. By creating three data generation pipelines that capture mathematical abstractions of robotic control, the authors pretrain a Transformer and evaluate its zero‑shot action prediction on unseen physical robot tasks. The results show that the model achieves non‑trivial performance via in‑context learning, with specialized control priors outperforming generic causal priors, though a compute‑matched real‑robot baseline still excels on its own training datasets.
ZimaBlue is a scalable framework that learns generalizable World Action Models (WAMs) from large-scale egocentric videos. It follows a three-stage curriculum: causal video pre‑training, video‑action mid‑training with a unified action representation, and final specialization to a target robot. The system employs an asynchronous Slow‑Fast architecture to enable real‑time 30 Hz action prediction, achieving a jump in real‑robot zero‑shot success from 36.1% to 77.8% when leveraging over 120,000 hours of embodied video.
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