Reliable navigation in unstructured, real-world environments remains a significant challenge for embodied agents, especially when operating across diverse terrains, weather conditions, and sensor configurations. In this paper, we introduce GeNIE (Generalizable Navigation System for In-the-Wild Environments), a robust navigation framework designed for global deployment. GeNIE integrates a generalizable traversability prediction model built on SAM2 with a novel path fusion strategy that enhances planning stability in noisy and ambiguous settings. We deployed GeNIE in the Earth Rover Challenge (ERC) at ICRA 2025, where it was evaluated across six countries spanning three continents. GeNIE took first place and achieved 79% of the maximum possible score, outperforming the second-best team by 17%, and completed the entire competition without a single human intervention. These results set a new benchmark for robust, generalizable outdoor robot navigation. We will release the codebase, pretrained model weights, and newly curated datasets to support future research in real-world navigation.

翻译：在非结构化真实环境中实现可靠导航对于具身智能体而言仍是一项重大挑战，尤其是在跨越不同地形、天气条件和传感器配置下运行时。本文介绍GeNIE（适用于野外环境的通用导航系统），这是一个为全球部署设计的鲁棒导航框架。GeNIE集成了基于SAM2构建的通用可通行性预测模型，以及一种新颖的路径融合策略，该策略增强了在噪声和模糊环境下的规划稳定性。我们在ICRA 2025的地球漫游者挑战赛（ERC）中部署了GeNIE，并在跨越三大洲的六个国家对其进行了评估。GeNIE获得第一名，达到了最高可能得分的79%，以17%的优势超越第二名团队，并在整个比赛过程中未出现任何人工干预。这些结果为鲁棒、通用的户外机器人导航设立了新基准。我们将发布代码库、预训练模型权重和新整理的数据集，以支持未来在真实世界导航领域的研究。