GPA-RAM fuses frozen grasp-pose features into an attention-Mamba imitation policy, reporting 87.5% average success on RLBench and 98%/56% on ALOHA cube transfer and bimanual insertion at about 71 FPS.
Rog- sam: A language-driven framework for instance-level robotic grasping detection,
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GPA-RAM: Grasp-Pretraining Augmented Robotic Attention Mamba for Spatial Task Learning
GPA-RAM fuses frozen grasp-pose features into an attention-Mamba imitation policy, reporting 87.5% average success on RLBench and 98%/56% on ALOHA cube transfer and bimanual insertion at about 71 FPS.