Recently, robots have seen rapidly increasing use in homes and warehouses to declutter by collecting objects from a planar surface and placing them into a container. While current techniques grasp objects individually, Multi-Object Grasping (MOG) can improve efficiency by increasing the average number of objects grasped per trip (OpT). However, grasping multiple objects requires the objects to be aligned and in close proximity. In this work, we propose Push-MOG, an algorithm that computes "fork pushing" actions using a parallel-jaw gripper to create graspable object clusters. In physical decluttering experiments, we find that Push-MOG enables multi-object grasps, increasing the average OpT by 34%. Code and videos will be available at https://sites.google.com/berkeley.edu/push-mog.

翻译：近年来，机器人在家庭和仓库中的使用迅速增长，通过从平面表面收集物体并将其放入容器中来清理杂物。虽然现有技术采用逐物体抓取方式，但多目标抓取（MOG）可通过提高单次抓取行程的平均物体数量（OpT）来提升效率。然而，抓取多个物体需要物体相互对齐且紧密相邻。本文提出推-MOG算法，该算法利用平行夹爪生成"叉式推挤"动作，以形成可抓取的物体簇。在物理环境清理实验中，我们发现推-MOG实现了多目标抓取，使平均OpT提高了34%。相关代码和视频将发布在https://sites.google.com/berkeley.edu/push-mog。