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V2-STRep is presented, a zero-shot framework that converts generated video motion into reusable robot skills through VLM-grounded structured task representations, and demonstrates improved execution success over baselines, reliable cross-scene transfer of successfully acquired skills, and adaptation to changed deployment instructions.
Robots can now learn to execute complex manipulation tasks in both directions from just one incomplete demonstration, revolutionizing the efficiency of learning from demonstration.