SOURCE-LINKED INTELLIGENCE
TrAct: Bridging Robot Control and Visual Prediction with Visual Tracks
Robot actions are inherently embodiment-specific and only weakly aligned with image-space visual changes, limiting their effectiveness as conditioning signals for robot world models. In contrast, visual tracks provide an embodiment-agnostic representation of how task-relevant points move through a scene, offering dense image-space guidance for accurate and spatially precise future video prediction. Building on this observation, we propose TrAct, a world-model-based robot decision-making framework that uses visual tracks as an intermediate interface between control and prediction. TrAct consist
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T06:00:01.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.