用大模型指导采样，让机器人在障碍物中高效规划移动路径。
NAMO-LLM: Efficient Navigation Among Movable Obstacles with Large Language Model Guidance
- 基于大模型引导的非均匀采样策略，智能选择搜索方向。
- 在复杂环境中的路径规划速度与质量优于现有方法。
- 适合需要动态重构环境的机器人导航任务。
许多规划器可计算避开障碍物到达目标区域的机器人路径，但在所有路径被堵死时失效。此时机器人需推理如何重新配置环境以进入任务相关区域——即移动障碍物导航（NAMO）问题。尽管已有多种解决方案，但难以扩展至高度杂乱环境。为此，我们提出NAMO-LLM，一种基于采样的规划器，通过搜索机器人与障碍物配置空间，生成包含移动哪些障碍物、移动到何处及顺序的可行路径。其关键创新在于由大语言模型（LLM）引导的非均匀采样策略，使树结构构建更偏向可能成功解的方向。我们证明了NAMO-LLM具有概率完备性，并通过实验表明其在复杂环境中高效扩展，运行时间与路径质量均优于相关工作。
原文摘要 · Abstract (English)
Several planners have been proposed to compute robot paths that reach desired goal regions while avoiding obstacles. However, these methods fail when all pathways to the goal are blocked. In such cases, the robot must reason about how to reconfigure the environment to access task-relevant regions - a problem known as Navigation Among Movable Objects (NAMO). While various solutions to this problem have been developed, they often struggle to scale to highly cluttered environments. To address this, we propose NAMO-LLM, a sampling-based planner that searches over robot and obstacle configurations to compute feasible plans specifying which obstacles to move, where, and in what order. Its key novelty is a non-uniform sampling strategy guided by Large Language Models (LLMs) biasing the tree construction toward directions more likely to yield a solution. We show that NAMO-LLM is probabilistically complete and demonstrate through experiments that it efficiently scales to cluttered environments, outperforming related works in both runtime and plan quality.
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