Object transportation in cluttered environments is a fundamental task in various domains, including domestic service and warehouse logistics. In cooperative object transport, multiple robots must coordinate to move objects that are too large for a single robot. One transport strategy is pushing, which only requires simple robots. However, careful selection of robot-object contact points is necessary to push the object along a preplanned path. Although this selection can be solved analytically, the solution space grows combinatorially with the number of robots and object size, limiting scalability. Inspired by how humans rely on common-sense reasoning for cooperative transport, we propose combining the reasoning capabilities of Large Language Models with local search to select suitable contact points. Our LLM-guided local search method for contact point selection, ConPoSe, successfully selects contact points for a variety of shapes, including cuboids, cylinders, and T-shapes. We demonstrate that ConPoSe scales better with the number of robots and object size than the analytical approach, and also outperforms pure LLM-based selection.

翻译：在杂乱环境中进行物体运输是家庭服务和仓储物流等多个领域的一项基本任务。在协同物体运输中，多个机器人必须协调移动单个机器人无法搬运的大型物体。推动是一种运输策略，它只需要结构简单的机器人即可。然而，为了沿着预规划的路径推动物体，必须仔细选择机器人与物体之间的接触点。尽管可以通过解析方法求解此选择问题，但解空间会随着机器人数量和物体尺寸的组合增长而急剧扩大，从而限制了可扩展性。受人类依赖常识推理进行协同运输的启发，我们提出将大语言模型的推理能力与局部搜索相结合，以选择合适的接触点。我们提出的这种基于大语言模型引导的接触点选择局部搜索方法——ConPoSe，能够成功为包括长方体、圆柱体和T形在内的多种形状选择接触点。我们证明，与解析方法相比，ConPoSe在机器人数量和物体尺寸方面的可扩展性更好，并且其性能也优于纯基于大语言模型的选择方法。