Vision-and-Language Navigation (VLN) stands as a key research problem of Embodied AI, aiming at enabling agents to navigate in unseen environments following linguistic instructions. In this field, generalization is a long-standing challenge, either to out-of-distribution scenes or from Sim to Real. In this paper, we propose NaVid, a video-based large vision language model (VLM), to mitigate such a generalization gap. NaVid makes the first endeavour to showcase the capability of VLMs to achieve state-of-the-art level navigation performance without any maps, odometer and depth inputs. Following human instruction, NaVid only requires an on-the-fly video stream from a monocular RGB camera equipped on the robot to output the next-step action. Our formulation mimics how humans navigate and naturally gets rid of the problems introduced by odometer noises, and the Sim2Real gaps from map or depth inputs. Moreover, our video-based approach can effectively encode the historical observations of robots as spatio-temporal contexts for decision-making and instruction following. We train NaVid with 550k navigation samples collected from VLN-CE trajectories, including action-planning and instruction-reasoning samples, along with 665k large-scale web data. Extensive experiments show that NaVid achieves SOTA performance in simulation environments and the real world, demonstrating superior cross-dataset and Sim2Real transfer. We thus believe our proposed VLM approach plans the next step for not only the navigation agents but also this research field.

翻译：视觉语言导航（VLN）是具身智能领域的一项关键研究问题，旨在使智能体能够遵循语言指令在未知环境中导航。在该领域中，泛化能力（无论是针对分布外场景还是从仿真到现实）一直是一项长期挑战。本文提出NaVid，一种基于视频的大型视觉语言模型（VLM），以缩小这类泛化差距。NaVid首次尝试展示VLM在无需地图、里程计或深度输入的情况下，实现最先进导航性能的能力。NaVid仅需机器人搭载的单目RGB摄像头实时采集的视频流，即可根据人类指令输出下一步动作。该设计模仿人类导航方式，自然消除了里程计噪声引入的问题，以及地图或深度输入带来的仿真到现实差距。此外，我们的基于视频方法能有效将机器人历史观测编码为时空上下文，用于决策制定和指令遵循。我们使用从VLN-CE轨迹中收集的55万个导航样本（包括动作规划样本和指令推理样本）以及66.5万个大规模网络数据训练NaVid。广泛实验表明，NaVid在仿真环境和真实世界中均取得最先进性能，展现出卓越的跨数据集和仿真到现实迁移能力。我们相信，所提出的VLM方法不仅为导航智能体，也为该研究领域规划了下一步发展方向。