Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation

In tightly cooperative manipulation tasks, robotic manipulators must follow collision‐free and coordinated trajectories. Existing multiagent learning frameworks often rely on centralized planners that provide strong coordination but fail to scale with larger teams. Alternatively, decentralized approaches offer better scalability but typically lack communication, which limits their ability to achieve highly cooperative behaviors. To address this gap, this paper proposes a graph neural network (GNN)‐based framework for scalable multiagent reinforcement learning (RL). In our formulation, each manipulator is represented as a node in a GNN, and message‐passing edges provide a communication mechanism that enables agents to share information effectively. This design allows the team to achieve flexible decentralized planning while maintaining strong cooperation. Our results also demonstrate the scalability of the approach, showing that a single control policy can be trained once and successfully applied to tightly cooperative manipulation tasks across teams of varying sizes, without retraining.