We consider the problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface visible from an overhead camera. The objective is to efficiently grasp and transport all objects into a bin. Specifically, we explore multi-object push-grasps where multiple objects are pushed together before the grasp can occur. We provide necessary conditions for multi-object push-grasps and apply these to filter inadmissible grasps in a novel multi-object grasp planner. We find that our planner is 19 times faster than a Mujoco simulator baseline. We also propose a picking algorithm that uses both single- and multi-object grasps to pick objects. In physical grasping experiments, compared to a single-object picking baseline, we find that the multi-object grasping system achieves 13.6% higher grasp success and is 59.9% faster. See https://sites.google.com/view/multi-object-grasping for videos, code, and data.
翻译:我们考虑一个问题,即多个硬性锥形多边形物体停留在从高端摄像头可见的平面上随机放置的位置和方向上。目标是有效地捕捉所有物体并将其迁移到垃圾桶中。 具体地说, 我们探索多个物体一起推动的多弹体推式graps, 多弹体推动- graps 将多弹体推动- graps 提供必要的条件, 并将这些条件应用到新颖的多弹体捕捉规划器中过滤不可接受的捕捉。 我们发现我们的规划器比 Mujoco 模拟器基线快19倍。 我们还提出一种选择算法, 使用单项和多项抓抓取器来抓取物体。 在物理抓取实验中, 与单项选择基线相比, 我们发现多弹抓取系统取得了13.6%的捕捉取成功率更高, 并更快59.9% 。 见 https://sites.gogle. com/view/ mulb-object-graph-graphing for view formationsing forview for Vide)