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University of Science and Technology
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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.
Recovering static 3D scenes from monocular video with dynamic objects gets a boost: GA-GS leverages diffusion models to inpaint occluded regions, outperforming existing methods, especially in scenarios with large-scale occlusions.
