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FIRM-WM: State-factorized factual-interventional recurrent modeling for reward-free visual planning

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Reward-free latent world models can learn from offline videos and solve new image--goal tasks by optimizing actions through predicted latent futures. This setting places two demands on the planning state: its coordinates must be comparable with a goal image. Moreover, its dynamics must retain velocity, motion trend, contact, and other history--dependent information beyond those goal coordinates. Offline training creates a second mismatch: each recorded trajectory reveals one factual future, whereas a sampling--based planner compares many actions that were not taken from the same state. We intr

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