多智能体强化学习让机器人自动找到最佳承重位置搬运不规则物体
Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning
- 用强化学习让机器人自主规划承重队形
- 在复杂环境和非均匀质量物体下仍能保持平衡
- 适合需要灵活搬运的工业与家用场景
协作搬运在工业到家庭服务中至关重要。常见策略是将物体放置于多机器人系统上方运输，通常分解为形成控制、协同导航和避障三个子问题。现实物体具有任意形状和非均匀质量分布，要求机器人队形能稳定支撑物体。本文提出一种新型多智能体强化学习方法，使多机器人系统在搬运过程中自主定位至物体下方以承重，并避开障碍物。在多种环境和不同数量机器人下的评估表明，该方法生成的策略能可靠形成平衡队形，且可泛化至杂乱场景及几何复杂、质量分布不均的物体。
原文摘要 · Abstract (English)
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.
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