GeNIE: A Generalizable Navigation System for In-the-Wild Environments
This addresses the problem of robust outdoor robot navigation across diverse terrains and conditions for embodied agents, representing a strong specific advance rather than a foundational breakthrough.
The paper tackles the challenge of reliable navigation in unstructured real-world environments by introducing GeNIE, a robust navigation framework that integrates a generalizable traversability prediction model with a novel path fusion strategy. In the Earth Rover Challenge at ICRA 2025, GeNIE took first place, achieving 79% of the maximum possible score, outperforming the second-best team by 17%, and completed the competition without human intervention.
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.