Exposing the Long-tail in Embodied Urban Navigation via Scalable Learning from In-the-Wild Videos

Abstract:Learning embodied urban navigation policies from real-world data is constrained by the cost of task-specific data collection and the limited coverage of rare yet safety-critical scenarios. To address these challenges, we present a scalable framework for learning point-goal urban navigation from web-scale in-the-wild egocentric videos while systematically exposing its long tail. The framework automatically annotates uncurated web videos with metric trajectories and structured navigation semantics, which are then used to train a vision-language-action policy for interpretable navigation planning. We characterize the long tail based on model performance and the distribution of perception-motion patterns, and employ reflection-based analysis to diagnose recurring failure modes. Experiments on web-video data and real-world urban navigation tasks demonstrate effective knowledge transfer from unconstrained videos and reveal coherent long-tail structures beyond aggregate navigation performance.

Robotics (cs.RO)

arXiv:2608.16476 [cs.RO]

(or arXiv:2608.16476v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.16476 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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