Robotic pick and place tasks are symmetric under translations and rotations of both the object to be picked and the desired place pose. For example, if the pick object is rotated or translated, then the optimal pick action should also rotate or translate. The same is true for the place pose; if the desired place pose changes, then the place action should also transform accordingly. A recently proposed pick and place framework known as Transporter Net captures some of these symmetries, but not all. This paper analytically studies the symmetries present in planar robotic pick and place and proposes a method of incorporating equivariant neural models into Transporter Net in a way that captures all symmetries. The new model, which we call Equivariant Transporter Net, is equivariant to both pick and place symmetries and can immediately generalize pick and place knowledge to different pick and place poses. We evaluate the new model empirically and show that it is much more sample efficient than the non-symmetric version, resulting in a system that can imitate demonstrated pick and place behavior using very few human demonstrations on a variety of imitation learning tasks.

翻译：机器人拾取与放置任务在待拾取物体和期望放置位姿的平移与旋转下具有对称性。例如：若拾取物体发生旋转或平移，最优拾取动作也应随之旋转或平移；同理，若期望放置位姿改变，放置动作也需相应变换。近期提出的基于对齐的拾取与放置框架Transporter Net虽能捕获部分对称性，但未能覆盖全部。本文从解析角度研究了平面机器人拾取与放置任务中存在的对称性，并提出一种将等变神经网络模型融入Transporter Net的方法，使其能完整捕获所有对称性。我们称新模型为等变Transporter Net，该模型对拾取与放置对称性均具有等变性，可立即将拾取与放置知识泛化至不同位姿。通过实验评估，我们证明该模型在样本效率上显著优于非对称版本，从而在多种模仿学习任务中，仅需少量人类示范即可模仿演示的拾取与放置行为。