用自然语言指导机器人规划路径，让导航更智能。
Path Planning using Instruction-Guided Probabilistic Roadmaps
- 用大模型将指令转为向量，生成符合语义的路径成本图
- 在真实和模拟环境中均实现安全高效的路径规划
- 适合需要灵活指令交互的机器人导航场景
本文提出一种名为指令引导概率路图（IG-PRM）的数据驱动路径规划算法。尽管移动机器人导航技术发展迅速，但其安全高效运行仍需大量工程适配以考虑机器人特性与任务约束。IG-PRM通过自然语言指令（如“选择更宽路径”或“避开小缝隙”）让操作者表达这些约束。核心思想是利用大语言模型（LLMs）将指令转换为嵌入向量，并以此条件从占用图中预测出指令引导的成本图。基于该成本图构建路图后，即可通过标准最短路径搜索获得满足指令的路径。实验结果表明，该方法在合成与真实室内导航环境中均有效。
原文摘要 · Abstract (English)
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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