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Gripper-aware models can dramatically improve robotic manipulation by tailoring strategies to specific gripper types, leading to better performance in diverse tasks.
A single test-time adaptation can enable aerial point cloud segmentation across diverse domains and categories without any additional training, achieving up to 70.09% mIoU.
