做多机器人协同运输的团队终于有了能处理真实物体形状和质量分布的方案——不用再手工设计队形，机器人能自主适应。做物流、仓储或服务机器人的开发者值得关注。
该研究提出一种基于多智能体强化学习的方法，解决多机器人系统协同运输任意形状、质量分布不均物体时的队形控制问题。传统方法将任务分解为队形控制、协同导航和避障三个子问题，但难以应对真实物体的复杂几何与质量分布。新方法让机器人自主在物体下方定位以支撑重量，同时避开障碍物形成平衡队形。实验表明，该方法在不同环境和机器人数量下均能生成可靠策略，并泛化到复杂场景。
Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning
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.