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，泛化能力——无论是在分布外场景中的泛化还是从仿真到现实的迁移——始终是一项长期挑战。本文提出NaVid——一种基于视频的大型视觉语言模型（VLM），以弥合此类泛化差距。NaVid首次尝试展示VLM在无需地图、里程计和深度输入的情况下实现最先进导航性能的能力。NaVid仅需搭载于机器人的单目RGB摄像头实时获取视频流，即可根据人类指令输出下一步动作。该设计方案模拟人类导航方式，从根本上消除了里程计噪声以及深度或地图输入带来的仿真到现实差距。此外，基于视频的方法能够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轨迹中采集的55万条导航样本（包含动作规划与指令推理样本）及66.5万条大规模网络数据训练NaVid。大量实验表明，NaVid在仿真环境和真实世界中均达到最先进性能，展现出卓越的跨数据集与仿真到现实迁移能力。我们因此认为，本文提出的VLM方法不仅为导航智能体、也为该研究领域规划了下一步行动。