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A groundbreaking pipeline has generated over 8,000 hours of action-conditioned video, overcoming the limitations of real-world data collection.
Bridging the action-sufficiency gap in robotic manipulation, GIFT achieves up to 12.6 points improvement over existing models by integrating structured intermediate features.