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The paper identifies persistent high-norm spatial outliers in World Action Model representations and introduces RegisterWAM, which adds learnable non-spatial registers paired with observation flow-matching loss supervision to reorganize action-relevant information, achieving 81.64% success on LIBERO-Plus and 93.0% on RoboTwin 2.0 without extra embodied pretraining data.
NAVA-WAM 通过从纯观察视频直接预训练动作策略（native action-prior learning），结合流匹配监督与转移结构化联合注意力，避免了间接的表征-控制转移和独立的隐动作模型，仅需少量动作标注即可实现高效的机器人控制与跨域泛化。