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📄 Abstract
Abstract: 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.
Authors (7)
Jiaming Wang
Diwen Liu
Jizhuo Chen
Jiaxuan Da
Nuowen Qian
Tram Minh Man
+1 more
Key Contributions
Introduces GeNIE, a robust and generalizable navigation framework for in-the-wild environments, integrating a SAM2-based traversability prediction model with a novel path fusion strategy. GeNIE achieved first place in the Earth Rover Challenge, demonstrating superior performance and reliability without human intervention.
Business Value
Enables the development of highly reliable autonomous robots for exploration, inspection, and logistics in challenging real-world environments, reducing the need for human oversight.