Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. It becomes increasingly crucial in the field of embodied AI, with potential applications in autonomous navigation, search and rescue, and human-robot interaction. In this paper, we propose to address a more practical yet challenging counterpart setting - vision-language navigation in continuous environments (VLN-CE). To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav performs online topological mapping of environments by self-organizing predicted waypoints along a traversed path, without prior environmental experience. It privileges the agent to break down the navigation procedure into high-level planning and low-level control. Concurrently, ETPNav utilizes a transformer-based cross-modal planner to generate navigation plans based on topological maps and instructions. The plan is then performed through an obstacle-avoiding controller that leverages a trial-and-error heuristic to prevent navigation from getting stuck in obstacles. Experimental results demonstrate the effectiveness of the proposed method. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets, respectively. Our code is available at https://github.com/MarSaKi/ETPNav.

翻译：视觉语言导航是一项要求智能体根据指令在环境中进行导航的任务。在具身人工智能领域，该任务日益重要，具有自主导航、搜索救援与人机交互等潜在应用。本文旨在解决更具现实挑战性的场景——持续环境中的视觉语言导航（VLN-CE）。为开发鲁棒的VLN-CE智能体，我们提出一种新型导航框架ETPNav，其聚焦于两项关键能力：1）抽象环境并生成远程导航规划的能力；2）在持续环境中实现避障控制的能力。ETPNav通过自组织行走路径上的预测航点执行在线拓扑环境映射，无需先验环境经验，使智能体能够将导航过程分解为高层规划与低层控制。同时，ETPNav利用基于Transformer的跨模态规划器，根据拓扑图与指令生成导航规划，并通过采用试错启发式的避障控制器执行规划，防止导航陷入障碍物困境。实验结果表明了所提方法的有效性。在R2R-CE和RxR-CE数据集上，ETPNav相较于先前最优方法分别实现了超过10%和20%的性能提升。我们的代码开源在https://github.com/MarSaKi/ETPNav。