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

arXiv: https://arxiv.org/abs/2306.14021 · HTML: https://mlsanity.com/paper/2306.14021/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Learning to Push, Group, and Grasp: A Diffusion Policy Approach for Multi-Object Delivery](https://mlsanity.com/paper/2502.08452/) (2025)

## Abstract (arXiv)

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.