  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.
