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3D World Simulator is introduced, an action-conditioned multimodal world modeling framework that jointly predicts appearance, geometry, and tactile observations from a unified 3D representation that enables scalable data generation for policy training and evaluation using tactile and multi-view visual observations.
It is shown that a flexible and expressive transformer, PointZero, outperforms prior methods on the same data and demonstrates the utility of the pre-training objective by post-training PointZero for two downstream applications: action-conditioned 3D dynamics prediction and imitation learning.
Agentic Real2Sim is introduced, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states.
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