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arXiv · 2610.39528

PRIME W0: Physical Priors from World Models for Embodied Manipulation

Huabin Peng·Fagang Jin·Yancong Wei·Tengfei Ma·Chuhang Liu·Chao Yu·Liqing Chen
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Abstract

Vision-Language-Action (VLA) models excel at instruction following, yet most remain feed-forward: they condition actions only on the current observation and discard both long-horizon temporal context and the hierarchical structure of VLM representations. We present PRIME W0, a hierarchical conditional flow-matching framework that closes this gap through three synergistic designs: (1)per-layer KV injection from a pretrained VLM into an Adaptive MoE action expert; (2)dynamics-aware conditioning via asynchronous world-model subgoal-image prediction and a DINOv2 historical-frame semantic bridge; and (3)Action Dynamic Manifold (ADM) flow matching with covariance-structured noise that respects robot kinematic coupling. PRIME W0 is trained in four stages---VLM pre-training, DAT/NTP visuomotor pre-training, DiT flow-matching decoder assembly with WM injection, and end-to-end SFT---and achieves state-of-the-art results on LIBERO and real-robot benchmarks, with systematic ablations validating each architectural component.

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Huabin Peng, Fagang Jin, Yancong Wei, Tengfei Ma, Chuhang Liu, Chao Yu, Liqing Chen. 2026-10-29. PRIME W0: Physical Priors from World Models for Embodied Manipulation. https://arxiv.org/abs/2610.39528

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