Robotic manipulation can be formulated as inducing a sequence of spatial
displacements: where the space being moved can encompass an object, part of an
object, or 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 represent complex multi-modal
policy distributions and generalizes to multi-step sequential tasks, as well as
6DoF pick-and-place. Experiments on 10 simulated tasks show that it learns
faster and generalizes better than a variety of end-to-end baselines, including
policies that use ground-truth object poses. We validate our methods with
hardware in the real world. Experiment videos and code are available at
this https URL

本文提出了 Transporter Network，一种简单的模型体系结构，它可以重新排列深层特征以从视觉输入中推断出空间位移，使得机器人的行动具有参数化，是一种可以表示复杂的多模式策略分布并推广到多步顺序任务的方法，并且采样效率比基准方法快数个数量级。