EgoWAM: 実世界の自己中心視点データを用いたピクセルを超えた世界行動モデル
EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data
自己中心視点の人間データからロボット操作を学習する際、世界の変化を予測する「世界行動モデル」の訓練信号として、ピクセルではなくDINO特徴や3Dモーションフローを用いることで、人間からロボットへの転移性能が大幅に向上することを示した。
著者: Baoyu Li, Xinchen Yin, Mengying Lin, Yixin Zhang, Danfei Xu
分類: cs.RO, cs.AI
原文アブストラクト
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