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2026-01-06 16:22:14

raw text

Transporter Networks: Rearranging the Visual World for Robotic Manipulation Transporter Networks : Rearranging the Visual World for Robotic Manipulation Conference on Robot Learning (CoRL) 2020 Abstract . Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass object(s) or an end effector. In this work, we propose the Transporter Network, a simple model architecture that rearranges deep features to infer spatial displacements from visual input -- which can parameterize robot actions. It makes no assumptions of objectness (e.g. canonical poses, models, or keypoints), it exploits spatial symmetries, and is orders of magnitude more sample efficient than our benchmarked alternatives in learning vision-based manipulation tasks: from stacking a pyramid of blocks, to assembling kits with unseen objects; from manipulating deformable ropes, to pushing piles of small objects with closed-loop feedback. Our method can ...

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