Navigating complex urban environments using natural language instructions poses significant challenges for embodied agents, including noisy language instructions, ambiguous spatial references, diverse landmarks, and dynamic street scenes. Current visual navigation methods are typically limited to simulated or off-street environments, and often rely on precise goal formats, such as specific coordinates or images. This limits their effectiveness for autonomous agents like last-mile delivery robots navigating unfamiliar cities. To address these limitations, we introduce UrbanNav, a scalable framework that trains embodied agents to follow free-form language instructions in diverse urban settings. Leveraging web-scale city walking videos, we develop an scalable annotation pipeline that aligns human navigation trajectories with language instructions grounded in real-world landmarks. UrbanNav encompasses over 1,500 hours of navigation data and 3 million instruction-trajectory-landmark triplets, capturing a wide range of urban scenarios. Our model learns robust navigation policies to tackle complex urban scenarios, demonstrating superior spatial reasoning, robustness to noisy instructions, and generalization to unseen urban settings. Experimental results show that UrbanNav significantly outperforms existing methods, highlighting the potential of large-scale web video data to enable language-guided, real-world urban navigation for embodied agents.

Detecting 3D objects from surround-view images focuses on capturing the spatio-temporal positions of the surrounding environment, serving as a pivotal capability for vision-centric autonomous driving and robotics. While existing approaches primarily employ either dense BEV queries or sparse 3D queries, both paradigms have inherent limitations: dense queries suffer from redundant feature interactions and optimization conflicts, while sparse queries rely on high-quality initialization and struggle with error propagation in complex scenarios. To address these challenges, we proposeTAS-DAQ, a novel two-stage framework that synergizes dense and sparse query strategies. In Stage I, we generate geometry-aware coarse queries through the BEV feature providing robust initialization, thereby ensuring robust query initialization with explicit 3D priors. Stage II introduces a learnable Query Bank with temporal fusion to iteratively refine sparse queries by capturing discriminative instance features across views and frames. Moreover, considering the optimization conflicts caused by redundant query interactions in dense paradigms, we introduce adaptive query aggregation in the query bank that dynamically prioritizes high-confidence queries from BEV features, effectively addressing query error propagation while enhancing instance-level representation consistency. Extensive experiments on the nuScenes R50 benchmark demonstrate state-of-the-art performance, achieving56.9 % NDSand46.1% mAP.