ZeST：一种面向未知环境的基于 LLM 的零样本可通行性导航
ZeST: an VLM-based Zero-Shot Traversability Navigation for Unknown Environments
AI总结：
针对真实地形数据采集危险且成本高的问题，本文提出 ZeST，利用 LLM 的视觉推理实时进行零样本可通行性建图，并在室内外实验中实现更安全且稳定到达目标的导航。
摘要：
机器人与自主导航系统的发展取决于准确预测地形可通行性的能力。用于生成训练这些预测模型的数据集的传统方法，往往需要将机器人置于潜在危险环境中，给设备和安全带来风险。为解决这一问题，我们提出 ZeST，这是一种利用大型语言模型（LLM）视觉推理能力的新颖方法，可在不让机器人暴露于危险的情况下实时创建可通行性地图。我们的方法不仅执行零样本可通行性预测、降低与真实世界数据收集相关的风险，还加快了先进导航系统的开发，提供了具有成本效益且可扩展的解决方案。为支撑研究发现，我们展示了在受控室内环境和非结构化室外环境中的导航结果。实验表明，与其他最先进方法相比，我们的方法提供了更安全的导航，并能够持续到达最终目标。
英文摘要：
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 that treats repeated VLM outputs as stochastic measurements and fuses them into an uncertainty-aware posterior. 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, ZeST provides safer navigation with 90-100% success rate with up to 4s inference delays when compared to other state-of-the-art methods, constantly reaching the final goal.
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。
