EV-WM: Event-Verified World Models for Long-Horizon Robotic Manipulation

• URL: http://arxiv.org/abs/2606.13053v2
• Date: Mon, 15 Jun 2026 03:07:35 GMT
• Title: EV-WM: Event-Verified World Models for Long-Horizon Robotic Manipulation
• Abstract summary: Long-horizon manipulation requires progress signals that are relational, predicate-level, and physically grounded.<n>We introduce textbfEV-WM, a predicate-grounded verification framework for world-model planning.
• Score: 0.815557531820863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained-feature world models provide a useful substrate for robot imagination, but visual or latent prediction alone does not determine whether an imagined future satisfies task-relevant predicates. Long-horizon manipulation requires progress signals that are relational, predicate-level, and physically grounded: whether an object has moved, whether a drawer or contact state has changed, whether a placement predicate is satisfied, and whether a candidate future is reliable enough for execution. We introduce \textbf{EV-WM}, a predicate-grounded verification framework for world-model planning. EV-WM rolls out candidate futures in pretrained visual-feature space, decodes them into structured event states, and scores them using task-progress, semantic-consistency, physical-feasibility, and uncertainty terms. The verifier guides sampling-based planning, gates candidate actions, and, in the contact-sensitive LIBERO wine-rack setting, selects among PPO-generated proposals. Across navigation, deformable-object, wall-constrained, and language-described manipulation studies, EV-WM shows that predicate-grounded verification can make feature-space world-model planning more interpretable and better aligned with task progress.

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