  Vision-and-Language Navigation (VLN) is a task where agents must decide how
to move through a 3D environment to reach a goal by grounding natural language
instructions to the visual surroundings. One of the problems of the VLN task is
data scarcity since it is difficult to collect enough navigation paths with
human-annotated instructions for interactive environments. In this paper, we
explore the use of counterfactual thinking as a human-inspired data
augmentation method that results in robust models. Counterfactual thinking is a
concept that describes the human propensity to create possible alternatives to
life events that have already occurred. We propose an adversarial-driven
counterfactual reasoning model that can consider effective conditions instead
of low-quality augmented data. In particular, we present a model-agnostic
adversarial path sampler (APS) that learns to sample challenging paths that
force the navigator to improve based on the navigation performance. APS also
serves to do pre-exploration of unseen environments to strengthen the model's
ability to generalize. We evaluate the influence of APS on the performance of
different VLN baseline models using the room-to-room dataset (R2R). The results
show that the adversarial training process with our proposed APS benefits VLN
models under both seen and unseen environments. And the pre-exploration process
can further gain additional improvements under unseen environments.
