UrbanNav: 从网络级人类轨迹学习语言引导的城市导航
UrbanNav: Learning Language-Guided Urban Navigation from Web-Scale Human Trajectories
AI总结：
UrbanNav通过大规模网络视频数据训练具身智能体，实现基于语言指令的复杂城市导航，展示了在多样城市场景中的高效导航能力。
摘要：
使用自然语言指令导航复杂城市环境对具身智能体来说是一个重大挑战，包括噪声语言指令、模糊的空间参考、多样化的地标和动态街道场景。当前的视觉导航方法通常局限于模拟或非街道环境，并且通常依赖于精确的目标格式，如特定坐标或图像。这限制了它们在自主代理如最后-mile配送机器人导航陌生城市时的有效性。为了解决这些限制，我们引入了UrbanNav，一个可扩展的框架，该框架训练具身智能体在多样化的城市环境中遵循自由形式的语言指令。利用网络级城市步行视频，我们开发了一个可扩展的注释管道，将人类导航轨迹与基于现实世界地标的语言指令对齐。UrbanNav涵盖了超过1500小时的导航数据和300万个指令-轨迹-地标三元组，捕捉了广泛的城市场景。我们的模型学习了稳健的导航策略，以应对复杂的城市场景，展示了卓越的空间推理能力、对噪声指令的鲁棒性以及对未见过的城市环境的泛化能力。实验结果表明，UrbanNav显著优于现有方法，突显了大规模网络视频数据在为具身智能体实现语言引导的真实世界城市导航中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
