GeNIE让机器人在真实复杂环境中自主导航，跨地域表现卓越。
GeNIE: A Generalizable Navigation System for In-the-Wild Environments
- 基于SAM2构建通用通行性预测模型，结合路径融合策略提升规划稳定性。
- 在六大洲三地赛事中获第一，得分达满分的79%，领先第二名17%。
- 全程无需人工干预，适合追求高鲁棒性的户外机器人研究者使用。
在非结构化、真实世界环境中实现可靠导航仍是具身智能体的重大挑战，尤其在不同地形、天气和传感器配置下。本文提出GeNIE（面向野外环境的可泛化导航系统），一个专为全球部署设计的鲁棒导航框架。GeNIE融合基于SAM2的通用通行性预测模型与新颖的路径融合策略，显著提升在噪声和模糊场景下的规划稳定性。我们在ICRA 2025地球越野挑战赛（ERC）中部署GeNIE，覆盖横跨三大洲的六个不同国家。该系统斩获第一名，取得最高分的79%，较第二名领先17%，且全程未发生任何人工干预。这些成果树立了室外机器人导航的新基准。我们将开源代码库、预训练模型权重及新构建的数据集，以支持未来真实世界导航研究。
原文摘要 · Abstract (English)
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.
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