SLA: Does 3D Pretraining Improve pi0.5 Robot Manipulation?
Can a robot learn better 3D manipulation without running an additional perception model at deployment? This episode examines how SLA extends π0.5 through spatially supervised continual pretraining and downstream adaptation. We cover three questions: how a frozen OmniVGGT teacher shapes intermediate visual features; why adaptive layer selection and state dropout belong in the training recipe; and what the moving-target experiment measures. The headline result is a rise from 42% to 76% success on a real-world bimanual Piper fruit-manipulation condition requiring grasping, handover, and…
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