ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts 文章
详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Mingxin Wang, Bin Hu, Bin Qian, Kaitao Jiang, Haoning Wu, Feng Yan, Bowen Jing, Ruiyang Hao, Enyi Wang, Kangning Niu, Yandan Yang, Mu Xu, Yan Wang, Houde Liu, Tianlun Li
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-08-03
摘要
arXiv:2607.28993v1 Announce Type: cross Abstract: World Action Models (WAMs) have emerged as a promising paradigm by jointly modeling robot actions and future visual dynamics. However, their reliance on pixel-generative future supervision can entangle action-relevant state transitions with task-irrelevant visual content, limiting robustness under visual distribution shifts. We identify Training-Distribution Hallucination, a recurring phenomenon in which futures conditioned on visually shifted observations hallucinate training-domain content rather than remain faithful to the current scene. A controlled frame-triplet diagnosis further shows that DINOv3 features remain more stable across visual shifts while better preserving task-state distinctions than Wan-VAE latents.