CityLoop: Sandbox for Tool-Grounded Urban Navigation

Urban navigation requires an embodied agent to connect a user request with global navigation guidance, local visual perception, continuous action, and eventual arrival. Existing research, however, typically studies these components in isolation, making it difficult to evaluate how they work together over complete routes. We introduce CityLoop, a unified request-to-arrival sandbox for tool-grounded urban navigation. CityLoop connects online navigation, 2D Gaussian Splatting (2DGS) simulation, and physical robots through a shared policy interface, while providing controllable localization perturbations and geographically aligned supervision from map and street-view data. Systematic evaluation in CityLoop reveals three limitations of existing models: unreliable execution of navigation instructions even under clean conditions, weak coordination between navigation guidance and egocentric visual evidence, especially when the two disagree, and compounding errors over long-horizon closed-loop execution. To address these limitations, we introduce RouteGround, a capability-structured training framework that develops navigation-context understanding, visual grounding, conflict-aware decision making through counterfactual tool–vision conflicts, and full-route execution. RouteGround improves keypoint decision accuracy over the strongest baseline from 47.6% to 78.4% with accurate localization and from 26.0% to 59.0% under medium localization errors, achieves 46.0% and 32.0% success in complete 2DGS rollouts under clean and medium-error guidance, respectively, and succeeds in 6 of 20 preliminary physical trials after zero-shot transfer.