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导航的任务，在具身人工智能领域日益重要，潜在应用包括自主导航、搜索救援与人机交互。本文针对更具实际挑战性的变体——连续环境中的视觉语言导航（VLN-CE）提出解决方案。为构建鲁棒的VLN-CE智能体，我们提出新导航框架ETPNav，聚焦两个关键能力：1）抽象环境并生成长程导航规划的能力，2）在连续环境中实现避障控制的能力。ETPNav通过沿行进路径自组织预测航点，无需先验环境经验即可在线构建环境的拓扑地图，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采用基于Transformer的跨模态规划器，根据拓扑地图与指令生成导航计划，并通过利用试错启发式的避障控制器执行该计划，防止导航陷入障碍物。实验结果表明所提方法的有效性。在R2R-CE和RxR-CE数据集上，ETPNav相较先前最优方法分别提升超过10%和20%。我们的代码开源在https://github.com/MarSaKi/ETPNav。