Transporter Networks

A vision-based manipulation architecture that learns pick-and-place policies by predicting dense pixel-wise correspondences between source (pick) and target (place) locations. Transporter Networks use spatial attention to detect where to pick and cross-correlation to determine where to place. They are highly sample-efficient and effective for precise rearrangement tasks.