  Vision-Language Navigation (VLN) tasks require an agent to follow human
language instructions to navigate in previously unseen environments. This
challenging field involving problems in natural language processing, computer
vision, robotics, etc., has spawn many excellent works focusing on various VLN
tasks. This paper provides a comprehensive survey and an insightful taxonomy of
these tasks based on the different characteristics of language instructions in
these tasks. Depending on whether the navigation instructions are given for
once or multiple times, this paper divides the tasks into two categories, i.e.,
single-turn and multi-turn tasks. For single-turn tasks, we further subdivide
them into goal-oriented and route-oriented based on whether the instructions
designate a single goal location or specify a sequence of multiple locations.
For multi-turn tasks, we subdivide them into passive and interactive tasks
based on whether the agent is allowed to question the instruction or not. These
tasks require different capabilities of the agent and entail various model
designs. We identify progress made on the tasks and look into the limitations
of existing VLN models and task settings. Finally, we discuss several open
issues of VLN and point out some opportunities in the future, i.e.,
incorporating knowledge with VLN models and implementing them in the real
physical world.
