# Multi-Object Grasping in the Plane

arXiv: https://arxiv.org/abs/2206.00229 · HTML: https://mlsanity.com/paper/2206.00229/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Occlusion-Aware Search for Object Retrieval in Clutter](https://mlsanity.com/paper/2011.03334/) (2020)
- [Collision-Aware Target-Driven Object Grasping in Constrained Environments](https://mlsanity.com/paper/2104.00776/) (2021)
- [Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp](https://mlsanity.com/paper/2112.01270/) (2021)
- [GOMP: Grasp-Optimized Motion Planning for Bin Picking](https://mlsanity.com/paper/2003.02401/) (2020)
- [Pushing Fast and Slow: Task-Adaptive Planning for Non-prehensile Manipulation Under Uncertainty](https://mlsanity.com/paper/1805.03005/) (2018)
- [Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty](https://mlsanity.com/paper/1807.09049/) (2018)
- [O2A: One-shot Observational learning with Action vectors](https://mlsanity.com/paper/1810.07483/) (2018)
- [Multi-Object Grasping -- Generating Efficient Robotic Picking and Transferring Policy](https://mlsanity.com/paper/2112.09829/) (2021)
- [Combining Coarse and Fine Physics for Manipulation using Parallel-in-Time Integration](https://mlsanity.com/paper/1903.08470/) (2019)
- [Human-like Planning for Reaching in Cluttered Environments](https://mlsanity.com/paper/2002.12738/) (2020)
- [Robust Physics-Based Manipulation by Interleaving Open and Closed-Loop Execution](https://mlsanity.com/paper/2105.08325/) (2021)
- [Parareal with a Learned Coarse Model for Robotic Manipulation](https://mlsanity.com/paper/1912.05958/) (2019)

## Built on by (most cited first, 5 of 5)

- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Grasp Multiple Objects with One Hand](https://mlsanity.com/paper/2310.15599/) (2023)
- [Learning to Efficiently Plan Robust Frictional Multi-Object Grasps](https://mlsanity.com/paper/2210.07420/) (2022)
- [Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation](https://mlsanity.com/paper/2503.22370/) (2025)
- [Learning to Push, Group, and Grasp: A Diffusion Policy Approach for Multi-Object Delivery](https://mlsanity.com/paper/2502.08452/) (2025)

## Abstract (arXiv)

We consider a novel 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 using multi-object push-grasps, where multiple objects are pushed together to facilitate multi-object grasping. We provide necessary conditions for frictionless 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 comparing performance with a single-object picking baseline, we find that the frictionless multi-object grasping system achieves 13.6% higher grasp success and is 59.9% faster, from 212 PPH to 340 PPH. See urlhttps://sites.google.com/view/multi-object-grasping for videos and code.