用大模型指导机器人选择推物接触点，提升协作搬运的可扩展性。
ConPoSe: LLM-Guided Contact Point Selection for Scalable Cooperative Object Pushing
- 结合大模型推理与局部搜索，智能选择多机器人推物接触点。
- 支持立方体、圆柱体、T形等多种物体，可扩展至更多机器人和更大尺寸。
- 相比传统解析方法和纯大模型方案，效率更高，适合复杂场景应用。
在杂乱环境中的物体运输是家庭服务和仓库物流等领域的基础任务。协作搬运中，多个机器人需协同移动单个机器人无法处理的大件物体。一种有效策略是推动物体，仅需简单的机器人结构。然而，为沿预设路径推送物体，必须精细选择机器人与物体的接触点。尽管该问题可通过解析方法求解，但解空间随机器人数量和物体尺寸呈组合爆炸式增长，限制了可扩展性。受人类协作搬运时依赖常识推理的启发，我们提出将大语言模型（LLM）的推理能力与局部搜索相结合，用于接触点选择。所提出的ConPoSe方法成功应用于多种形状物体，包括立方体、圆柱体和T形物体。实验表明，与解析方法相比，ConPoSe在机器人数量和物体尺寸增加时表现出更好的可扩展性，并优于纯大模型选择方案。
原文摘要 · Abstract (English)
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.
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