  Language instruction plays an essential role in the natural language grounded
navigation tasks. However, navigators trained with limited human-annotated
instructions may have difficulties in accurately capturing key information from
the complicated instruction at different timesteps, leading to poor navigation
performance. In this paper, we exploit to train a more robust navigator which
is capable of dynamically extracting crucial factors from the long instruction,
by using an adversarial attacking paradigm. Specifically, we propose a Dynamic
Reinforced Instruction Attacker (DR-Attacker), which learns to mislead the
navigator to move to the wrong target by destroying the most instructive
information in instructions at different timesteps. By formulating the
perturbation generation as a Markov Decision Process, DR-Attacker is optimized
by the reinforcement learning algorithm to generate perturbed instructions
sequentially during the navigation, according to a learnable attack score.
Then, the perturbed instructions, which serve as hard samples, are used for
improving the robustness of the navigator with an effective adversarial
training strategy and an auxiliary self-supervised reasoning task. Experimental
results on both Vision-and-Language Navigation (VLN) and Navigation from Dialog
History (NDH) tasks show the superiority of our proposed method over
state-of-the-art methods. Moreover, the visualization analysis shows the
effectiveness of the proposed DR-Attacker, which can successfully attack
crucial information in the instructions at different timesteps. Code is
available at https://github.com/expectorlin/DR-Attacker.
