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A single policy now masters both pushing and grasping, achieving 57.91% success on unseen objects and enabling complex emergent behaviors like table-assisted reorientation during grasping.
A single GPU can now plan complex, contact-rich robotic manipulation trajectories for thousands of parallel worlds, achieving near-80% success on challenging in-hand rotation tasks with unprecedented speed.
A simulation flywheel recursively bootstraps embodied data, generating richer scenes and tasks for robot pretraining and evaluation.
A unified architecture achieves SOTA embodied AI by jointly learning physical reasoning, world generation, and action prediction, outperforming prior models in generalization and long-horizon tasks.