语言即成本：利用VLM进行机器人导航的主动危险映射
Language as Cost: Proactive Hazard Mapping using VLM for Robot Navigation
AI总结：
提出零样本语言即成本映射框架，利用VLM预判动态危险并分配导航成本，集成几何地图实现主动避障，显著提升导航成功率并减少危险遭遇。
摘要：
在以人为本或危险环境中运行的机器人必须主动预测并减轻超出基本障碍物检测的危险。传统导航系统通常依赖静态地图，难以应对动态风险，例如人从突然打开的门中走出。因此，这些系统在处理动态危险时往往是被动的而非主动的。预训练大语言模型和视觉语言模型（VLM）的最新进展为主动避障创造了新机遇。在本工作中，我们提出了一种零样本语言即成本映射框架，利用VLM解释视觉场景、评估潜在动态风险，并预先分配风险感知的导航成本，使机器人能够在危险显现之前预测危险。通过将该基于语言的成本图与几何障碍物地图集成，机器人不仅能识别现有障碍物，还能预测并主动规划绕开由环境动态引起的潜在危险。在模拟和多样化动态环境中的实验表明，与被动基线规划器相比，所提方法显著提高了导航成功率并减少了危险遭遇。代码和补充材料可在 https://github.com/Taekmino/LaC 获取。
英文摘要：
Robots operating in human-centric or hazardous environments must proactively anticipate and mitigate dangers beyond basic obstacle detection. Traditional navigation systems often depend on static maps, which struggle to account for dynamic risks, such as a person emerging from a suddenly opening door. As a result, these systems tend to be reactive rather than anticipatory when handling dynamic hazards. Recent advancements in pre-trained large language models and vision-language models (VLMs) create new opportunities for proactive hazard avoidance. In this work, we propose a zero-shot language-as-cost mapping framework that leverages VLMs to interpret visual scenes, assess potential dynamic risks, and assign risk-aware navigation costs preemptively, enabling robots to anticipate hazards before they materialize. By integrating this language-based cost map with a geometric obstacle map, the robot not only identifies existing obstacles but also anticipates and proactively plans around potential hazards arising from environmental dynamics. Experiments in simulated and diverse dynamic environments demonstrate that the proposed method significantly improves navigation success rates and reduces hazard encounters, compared to reactive baseline planners. Code and supplementary materials are available at https://github.com/Taekmino/LaC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
