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

• URL: http://arxiv.org/abs/2607.08436v1
• Date: Wed, 08 Jul 2026 16:11:37 GMT
• Title: EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data
• Abstract summary: EgoWAM is a controlled human-robot co-training framework.<n>It fixes the policy backbone, action head, and data mixture while varying only the world prediction target.<n>It scales more effectively with in-the-wild egocentric human data than behavior cloning.
• Score: 23.843545167506818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%. More details: https://gatech-rl2.github.io/egowam.github.io

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