ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts
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 dis...
| Authors | Mingxin Wang, Bin Hu, Bin Qian et al. |
|---|---|
| Primary category | cs.RO |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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