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University of Science and Technology of China (USTC)
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Context-gated latent-action conditioning enables VLA models to achieve unprecedented success rates in robot manipulation tasks without relying on separate action-generation modules.
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.
Surgical robots can now perform complex tasks like knot tying and organ dissection with state-of-the-art accuracy, thanks to a new method that directly infers 3D spatial awareness from standard endoscopic images.
By encoding 3D point clouds in spherical coordinates, SoPE overcomes RoPE's limitations and significantly boosts spatial reasoning in 3D LVLMs.
