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Achieving 97.2% accuracy on N-MNIST while consuming only 0.8 pJ/SOP could redefine energy-efficient neuromorphic computing.
GEAR-VLA achieves a remarkable 90.1% success rate in universal grasping tasks, showcasing its ability to generalize across unseen objects and diverse robot embodiments.
