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R2RDreamer achieves spatial generalization improvements in manipulation tasks by leveraging 3D-aware data augmentation without the pitfalls of complex scene setups or sim-to-real gaps.
Treating raw visual images as action representations revolutionizes embodied control, outperforming traditional methods in accuracy and generalization.
SAMOSA makes SAM-based tracking robust to complex motion and occlusions by explicitly modeling target dynamics and enforcing geometric and semantic consistency across frames.
