路径规划：基于指令引导的概率路线图
Path Planning using Instruction-Guided Probabilistic Roadmaps
AI总结：
本文提出指令引导概率路线图（IG-PRM），利用大语言模型将自然语言指令转为嵌入向量，预测成本地图并构建路线图，实现移动机器人按指令导航，实验验证其在合成及真实室内环境中的有效性。
摘要：
这项工作提出了一种新颖的数据驱动路径规划算法，名为指令引导概率路线图（IG-PRM）。尽管移动机器人导航技术近期发展迅速且应用广泛，但移动机器人的安全有效行驶仍需大量工程努力来考虑机器人及其任务的约束。通过IG-PRM，我们旨在解决这一问题，允许机器人操作员通过自然语言指令（如“追求更宽的路径”或“注意小间隙”）来指定此类约束。关键思想是利用大语言模型（LLMs）将此类指令转换为嵌入向量，并将这些向量作为条件，从占用地图预测指令引导的成本地图。基于预测成本构建路线图后，我们可以通过标准最短路径搜索找到指令引导的路径。实验结果证明了该方法在合成和真实世界室内导航环境中的有效性。
英文摘要：
This work presents a novel data-driven path planning algorithm named Instruction-Guided Probabilistic Roadmap (IG-PRM). Despite the recent development and widespread use of mobile robot navigation, the safe and effective travels of mobile robots still require significant engineering effort to take into account the constraints of robots and their tasks. With IG-PRM, we aim to address this problem by allowing robot operators to specify such constraints through natural language instructions, such as ``aim for wider paths'' or ``mind small gaps''. The key idea is to convert such instructions into embedding vectors using large-language models (LLMs) and use the vectors as a condition to predict instruction-guided cost maps from occupancy maps. By constructing a roadmap based on the predicted costs, we can find instruction-guided paths via the standard shortest path search. Experimental results demonstrate the effectiveness of our approach on both synthetic and real-world indoor navigation environments.
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