标题： MonoDream：全景梦境的单目视觉语言导航 显示英文标题

标题： MonoDream: Monocular Vision-Language Navigation with Panoramic Dreaming

摘要： 视觉-语言导航（VLN）任务通常利用全景RGB和深度输入来提供丰富的空间线索以进行动作规划，但这些传感器在现实世界的部署中可能成本较高或难以获取。 基于视觉-语言动作（VLA）模型的最新方法使用单目输入取得了良好的结果，但它们仍然落后于使用全景RGB-D信息的方法。 我们提出了MonoDream，一个轻量级的VLA框架，使单目代理能够学习统一导航表示（UNR）。 这种共享特征表示联合对齐与导航相关的视觉语义（例如，全局布局、深度和未来线索）和语言引导的动作意图，从而实现更可靠的动作预测。 MonoDream进一步引入了潜在全景梦境（LPD）任务来监督UNR，这训练模型仅基于单目输入就预测当前和未来步骤的全景RGB和深度观测的潜在特征。 在多个VLN基准上的实验表明，MonoDream始终提升了单目导航性能，并显著缩小了与基于全景的代理之间的差距。 显示英文摘要

摘要： Vision-Language Navigation (VLN) tasks often leverage panoramic RGB and depth inputs to provide rich spatial cues for action planning, but these sensors can be costly or less accessible in real-world deployments. Recent approaches based on Vision-Language Action (VLA) models achieve strong results with monocular input, yet they still lag behind methods using panoramic RGB-D information. We present MonoDream, a lightweight VLA framework that enables monocular agents to learn a Unified Navigation Representation (UNR). This shared feature representation jointly aligns navigation-relevant visual semantics (e.g., global layout, depth, and future cues) and language-grounded action intent, enabling more reliable action prediction. MonoDream further introduces Latent Panoramic Dreaming (LPD) tasks to supervise the UNR, which train the model to predict latent features of panoramic RGB and depth observations at both current and future steps based on only monocular input. Experiments on multiple VLN benchmarks show that MonoDream consistently improves monocular navigation performance and significantly narrows the gap with panoramic-based agents.