# Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2606.09610 · HTML: https://mlsanity.com/paper/2606.09610/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving](https://mlsanity.com/paper/2010.09776/) (2020)
- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers](https://mlsanity.com/paper/2212.11498/) (2022)
- [ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models](https://mlsanity.com/paper/2404.02318/) (2024)
- [End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/1907.01713/) (2019)
- [Self-Healing Distributed Swarm Formation Control Using Image Moments](https://mlsanity.com/paper/2312.07523/) (2023)

## Abstract (arXiv)

Cooperative object transportation is essential in numerous domains, including industrial to domestic services. A popular transportation strategy is to carry objects on top of multi-robot systems. The corresponding task is typically solved by decomposing it into three interconnected subproblems: formation control, cooperative navigation, and collision avoidance. A particular challenge posed by real-world objects is their potentially arbitrary shape and non-uniform mass distribution, necessitating robot formations that securely support the object. In this work, we address the challenge of pattern formation control for transporting such real-world objects by proposing a novel multi-agent reinforcement learning approach. Our approach enables a multi-robot system to autonomously position itself underneath an object to support its weight while avoiding obstacles during the formation process. Our evaluations with diverse environments and varying numbers of robots show that our approach leads to policies that reliably produce balanced formations and generalize to cluttered scenes and objects with complex geometry and non-uniform mass distribution.