CausalWAM: Learning the Causes of Actions in World Action Models
Overview
This paper introduces CausalWAM (CWAM), a method designed to address limitations in World Action Models (WAMs) where action-relevant latent variables become entangled with nuisance variables. The authors prove that under a sparsity constraint, the latent variables used for generating actions are an invertible function of the action-relevant ones alone. CWAM incorporates this by adding a sparse routing path to the action head of a pretrained WAM while keeping the world modeling objective unchanged. Experiments on synthetic data and real-world robots demonstrate the theoretical validity and improved efficiency in closed-loop success.
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- arXiv Robotics — research abstractsSignalCausalWAM: Learning the Causes of Actions in World Action Models
This paper introduces CausalWAM (CWAM), a method designed to address limitations in World Action Models (WAMs) where action-relevant latent variables become entangled with nuisance variables. The authors prove that under a sparsity constraint, the latent variables used for generating actions are an invertible function of the action-relevant ones alone. CWAM incorporates this by adding a sparse routing path to the action head of a pretrained WAM while keeping the world modeling objective unchanged. Experiments on synthetic data and real-world robots demonstrate the theoretical validity and improved efficiency in closed-loop success.
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