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SeededGrasp achieves a remarkable 78% success rate in real-world grasping by decoupling semantic reasoning from geometric execution, enabling efficient multi-embodiment support.
Robots can now learn long-horizon tasks far more effectively by distilling complex histories into a few key visual moments, outperforming standard imitation learning by 70% on real-world tasks.
