Article

CausalWAM: Learning the Causal Sources of Actions in World Action Models

The authors propose CWAM (Causal WAM), which adds sparse routing paths to the action head of a world action model to separate the latent variables that truly drive actions from the nuisance variables that scene prediction depends on, while keeping the world modeling objective unchanged. The paper proves that under a sparsity constraint, the latent variables used to generate actions are an invertible function of the action-relevant latent variables alone, and the true structure is identifiable up to a permutation of the latent variables, without requiring sufficient temporal dependence or assumptions about how unobserved states evolve. Synthetic data and real-robot experiments validate the theory and show improved closed-loop success rate efficiency.

Part of the event: Authors propose Causal World Action Model (CWAM)

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