SOURCE-LINKED INTELLIGENCE
GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation
Vision-language pre-training and predictive world modeling provide robot policies with rich semantic and dynamic visual features, but their native action and visual-prediction objectives may omit critical physical and task structure while retaining control-irrelevant visual redundancy. We call this mismatch between visual richness and control utility the action-sufficiency gap. We investigate whether this gap can be bridged by guiding intermediate features to preserve three control-relevant structure in robotic manipulation: geometry governing motion feasibility, affordance encoding instructio
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T17:59:03.000Z
First collected: 2026-09-21T04:31:57.454Z. This is not the publication date.