Many complex robotic manipulation tasks can be decomposed as a sequence of
pick and place actions. Training a robotic agent to learn this sequence over
many different starting conditions typically requires many iterations or
demonstrations, especially in 3D environments. In this work, we propose Fourier
Transporter (\ours{}) which leverages the two-fold $\SE(d)\times\SE(d)$
symmetry in the pick-place problem to achieve much higher sample efficiency.
\ours{} is an open-loop behavior cloning method trained using expert
demonstrations to predict pick-place actions on new environments. \ours{} is
constrained to incorporate symmetries of the pick and place actions
independently. Our method utilizes a fiber space Fourier transformation that
allows for memory-efficient construction. We test our proposed network on the
RLbench benchmark and achieve state-of-the-art results across various tasks.

利用傅里叶转换器在复杂机器人操作任务中提高样本效率，通过训练和预测新环境下的拾取 - 放置动作来实现开环行为克隆方法，并独立考虑拾取和放置动作的对称性，取得了在 RLbench 基准测试中的最新成果。