MotionWeave: Learning Motion-Centered Future Dynamics for Vision-Language-Action Policies
Vision-Language-Action (VLA) models have recently incorporated world models to provide richer dynamic supervision beyond sparse action labels. However, explicitly predicting future images or videos may include control-irrelevant appearance, while guidance derived from holistic future visual representations and shared global action features may fail to establish timestep-specific correspondence between actions and local visual changes. To address this issue, we propose MotionWeave, a motion-centric future-dynamics framework for action-chunk prediction with two modules: the Action-Induced Motion Grounder (AIMG) and the Horizon Residual Composer (HRC). Specifically, AIMG conditions on action...
| Authors | Jingqiu Wang, Yan Wang |
|---|---|
| Primary category | cs.RO |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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