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，最优拾取动作亦应随之旋转或平移；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"传送网络"的拾取与放置框架虽能捕捉部分对称性，但未能覆盖全部。本文从解析角度系统研究平面机器人拾取与放置任务中的对称性，并提出一种将等变神经网络模型融入传送网络的方法，从而完整捕获所有对称性。新模型（命名为等变传送网络）对拾取与放置对称性均具等变性，可立即将拾取放置知识泛化至不同位姿。通过实证评估，该模型相较非对称版本具有显著更高的样本效率，使得系统在多种模仿学习任务中仅需极少量人类演示即可复现拾取与放置行为。