Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning
2026-06-08 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors focus on helping multiple robots carry oddly shaped and heavy objects together by learning how to form the right support pattern underneath them. They use a type of machine learning where the robots figure out how to position themselves on their own while avoiding obstacles. Their method works well in different environments and with various numbers of robots, even when the objects have tricky shapes and uneven weight. This helps ensure the robots can carry things safely and efficiently.
multi-robot systemsformation controlcooperative navigationcollision avoidancemulti-agent reinforcement learningobject transportationnon-uniform mass distributionrobot formationsobstacle avoidance
Authors
Mohamed Sayed, Wolfram Burgard, Tanja Katharina Kaiser
Abstract
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.