  The vision-language navigation (VLN) task requires an agent to reach a target
with the guidance of natural language instruction. Previous works learn to
navigate step-by-step following an instruction. However, these works may fail
to discriminate the similarities and discrepancies across
instruction-trajectory pairs and ignore the temporal continuity of
sub-instructions. These problems hinder agents from learning distinctive
vision-and-language representations, harming the robustness and
generalizability of the navigation policy. In this paper, we propose a
Contrastive Instruction-Trajectory Learning (CITL) framework that explores
invariance across similar data samples and variance across different ones to
learn distinctive representations for robust navigation. Specifically, we
propose: (1) a coarse-grained contrastive learning objective to enhance
vision-and-language representations by contrasting semantics of full trajectory
observations and instructions, respectively; (2) a fine-grained contrastive
learning objective to perceive instructions by leveraging the temporal
information of the sub-instructions; (3) a pairwise sample-reweighting
mechanism for contrastive learning to mine hard samples and hence mitigate the
influence of data sampling bias in contrastive learning. Our CITL can be easily
integrated with VLN backbones to form a new learning paradigm and achieve
better generalizability in unseen environments. Extensive experiments show that
the model with CITL surpasses the previous state-of-the-art methods on R2R,
R4R, and RxR.
