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University of Science and Technology of China (USTC)
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Role-aware training can boost video diffusion models' physical consistency by up to 39.4% without sacrificing visual fidelity.
Context-gated latent-action conditioning enables VLA models to achieve unprecedented success rates in robot manipulation tasks without relying on separate action-generation modules.
Humanoid robots can complete laboratory tasks but often fail to meet the precision required for scientific validity, exposing a critical gap in current automation efforts.
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.
Single-view RGB input can revolutionize how robots perceive and manipulate transparent objects, achieving reliable grasping without complex depth sensing.
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.
