Push-MOG: Efficient Pushing to Consolidate Polygonal Objects for Multi-Object Grasping

TL;DR

Push-MOG is a novel algorithm that uses pushing actions to cluster objects for multi-object grasping, significantly improving efficiency in robotic decluttering tasks.

Contribution

The paper introduces Push-MOG, a new method for creating graspable object clusters through push actions, enhancing multi-object grasping capabilities.

Findings

Increases average OpT by 34% in physical experiments

Enables multi-object grasps through push-based clustering

Uses a parallel-jaw gripper for effective pushing actions

Abstract

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.