用大模型零样本生成地形可通行性地图，让机器人安全导航
ZeST: an LLM-based Zero-Shot Traversability Navigation for Unknown Environments
- 利用大模型视觉推理能力实时生成可通行性地图
- 零样本下在室内外环境均实现安全抵达目标
- 适合需要快速部署、避免实地测试风险的导航系统
机器人自主导航的发展依赖于对地形可通行性的准确预测。传统数据集构建方法常需将机器人置于潜在危险环境中，存在设备与安全风险。为此，我们提出ZeST，一种基于大语言模型（LLM）视觉推理能力的新方法，可在不暴露机器人于危险的情况下实时生成可通行性地图。该方法不仅实现零样本可通行性预测，降低真实世界数据采集风险，还加速先进导航系统的开发，提供低成本且可扩展的解决方案。实验结果表明，在受控室内和非结构化室外环境中，相比现有最先进方法，本方法能更安全地持续抵达目标。
原文摘要 · Abstract (English)
The advancement of robotics and autonomous navigation systems hinges on the ability to accurately predict terrain traversability. Traditional methods for generating datasets to train these prediction models often involve putting robots into potentially hazardous environments, posing risks to equipment and safety. To solve this problem, we present ZeST, a novel approach leveraging visual reasoning capabilities of Large Language Models (LLMs) to create a traversability map in real-time without exposing robots to danger. Our approach not only performs zero-shot traversability and mitigates the risks associated with real-world data collection but also accelerates the development of advanced navigation systems, offering a cost-effective and scalable solution. To support our findings, we present navigation results, in both controlled indoor and unstructured outdoor environments. As shown in the experiments, our method provides safer navigation when compared to other state-of-the-art methods, constantly reaching the final goal.
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