A student-teacher framework with a memory-augmented student policy over SAM 2 detections learns prompt-responsive grasping from clutter in simulation and transfers to a real robot on tabletop tasks.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,
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Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning
A student-teacher framework with a memory-augmented student policy over SAM 2 detections learns prompt-responsive grasping from clutter in simulation and transfers to a real robot on tabletop tasks.