# MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments

arXiv: https://arxiv.org/abs/2306.06543 · HTML: https://mlsanity.com/paper/2306.06543/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 10 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [Rearrangement: A Challenge for Embodied AI](https://mlsanity.com/paper/2011.01975/) (2020)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [NeRP: Neural Rearrangement Planning for Unknown Objects](https://mlsanity.com/paper/2106.01352/) (2021)
- [Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning](https://mlsanity.com/paper/1904.10348/) (2019)
- [Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly](https://mlsanity.com/paper/2106.02489/) (2021)
- [Multi-Agent Routing Value Iteration Network](https://mlsanity.com/paper/2007.05096/) (2020)
- [Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm](https://mlsanity.com/paper/1703.04159/) (2017)
- [Spatial Intention Maps for Multi-Agent Mobile Manipulation](https://mlsanity.com/paper/2103.12710/) (2021)
- [Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks](https://mlsanity.com/paper/2202.10765/) (2022)
- [Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces](https://mlsanity.com/paper/2110.04089/) (2021)
- [Cooperative Task and Motion Planning for Multi-Arm Assembly Systems](https://mlsanity.com/paper/2203.02475/) (2022)
- [Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement](https://mlsanity.com/paper/2207.08078/) (2022)
- [Hypergraph-based Multi-Robot Task and Motion Planning](https://mlsanity.com/paper/2210.04333/) (2022)
- [Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons](https://mlsanity.com/paper/2208.02312/) (2022)

## Abstract (arXiv)

Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. This paper proposes a comprehensive learning-based framework for multi-agent object rearrangement planning, addressing the challenges of task sequencing and path planning in complex environments. The proposed method iteratively selects objects, determines their relocation regions, and pairs them with available robots under kinematic feasibility and task reachability for execution to achieve the target arrangement. Our experiments on a diverse range of simulated and real-world environments demonstrate the effectiveness and robustness of the proposed framework. Furthermore, results indicate improved performance in terms of traversal time and success rate compared to baseline approaches.