AlphaGo作者领衔，8个机械臂协同干活0碰撞，DeepMind新作登Science子刊

Core Viewpoint - The article discusses the innovative RoboBallet project, which combines Graph Neural Networks (GNN) with Reinforcement Learning to enhance multi-robot collaboration in complex environments, showcasing significant advancements in robotic motion planning and task allocation [5][9][24]. Summary by Sections Introduction - RoboBallet is a collaborative robotic system that allows multiple robotic arms to work together efficiently in a shared space without collisions [1][2]. Technical Innovation - The project utilizes GNNs for strategy networks and state-action value estimation, enabling the control of up to 8 robotic arms and managing 56 degrees of freedom [6][9]. - It addresses three complex sub-problems: task allocation, task scheduling, and motion planning, which are traditionally challenging for existing algorithms [10][12]. Methodology - The environment is modeled as a graph structure, where nodes represent robots, tasks, and obstacles, and edges denote relationships among them [11][14]. - The GNN processes dynamic graph sizes and generates joint velocity commands for the robotic arms based on observed states [14][15]. Performance Metrics - RoboBallet was tested in a simulated environment with 4 to 8 robots, 40 tasks, and 30 obstacles, demonstrating superior performance compared to traditional methods [18][19]. - The planning speed is remarkable, with each planning step taking approximately 0.3 milliseconds on an NVIDIA A100 GPU, achieving over 300 times real-time planning speed [21]. - The average execution time decreased by about 60% as the number of robots increased from 4 to 8 [22]. Generalization and Applications - The model exhibits zero-shot transfer capabilities, allowing it to adapt to new environments without additional training [24]. - RoboBallet's efficiency can optimize work unit layouts, reduce task execution time by 33%, and enhance fault-tolerant planning [24].