EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data

Egocentric human data offers scalable supervision for robot manipulation.

However, behavior cloning entangles transferable content like objects, scenes, and task semantics, with non-transferable factors like human morphology, head motion, and behavioral style.

We study whether World Action Models (WAMs) provide a better training signal by requiring policies to predict not only actions, but also how the scene evolves.

The central question is what world representation best enables human-to-robot transfer.

We hypothesize that an effective world target should abstract appearance, capture agent-invariant physical effects, and separate camera motion from environment change.

We introduce EgoWAM, a controlled human-robot co-training framework that fixes the policy backbone, action head, and data mixture while varying only the world prediction target, comparing Pixel, DINO, and 3D motion flow.

Across three real-world bimanual tasks, WAM co-training scales more effectively with in-the-wild egocentric human data than behavior cloning.

Pixel-based prediction transfers weakly, while DINO and 3D flow yield substantial gains: DINO improves out-of-distribution object and scene generalization by up to 4x, and 3D flow improves in-domain performance by 20-30%.

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