Push-MOG：用于多物体抓取的高效推动以聚合多边形物体
Push-MOG: Efficient Pushing to Consolidate Polygonal Objects for Multi-Object Grasping
AI总结：
提出Push-MOG算法，利用平行夹爪执行叉式推动动作以聚合多边形物体形成可抓取簇，从而实现多物体抓取，在物理清理实验中使每次行程平均抓取物体数提高34%。
摘要：
近年来，机器人在家庭和仓库中越来越多地被用于清理，通过从平面收集物体并放入容器中。虽然现有技术逐个抓取物体，但多物体抓取（MOG）可通过增加每次行程平均抓取物体数（OpT）来提升效率。然而，抓取多个物体要求物体对齐且彼此靠近。在本研究中，我们提出Push-MOG算法，利用平行夹爪计算“叉式推动”动作，以生成可抓取的物体簇。在物理清理实验中，我们发现Push-MOG实现了多物体抓取，使平均OpT提高了34%。代码和视频可在https://sites.google.com/berkeley.edu/push-mog获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
