  Vision-and-Language Navigation (VLN) agents navigate to a destination using
natural language instructions and the visual information they observe. Existing
methods for training VLN agents presuppose fixed datasets, leading to a
significant limitation: the introduction of new environments necessitates
retraining with previously encountered environments to preserve their
knowledge. This makes it difficult to train VLN agents that operate in the
ever-changing real world. To address this limitation, we present the Continual
Vision-and-Language Navigation (CVLN) paradigm, designed to evaluate agents
trained through a continual learning process. For the training and evaluation
of CVLN agents, we re-arrange existing VLN datasets to propose two datasets:
CVLN-I, focused on navigation via initial-instruction interpretation, and
CVLN-D, aimed at navigation through dialogue with other agents. Furthermore, we
propose two novel rehearsal-based methods for CVLN, Perplexity Replay (PerpR)
and Episodic Self-Replay (ESR). PerpR prioritizes replaying challenging
episodes based on action perplexity, while ESR replays previously predicted
action logits to preserve learned behaviors. We demonstrate the effectiveness
of the proposed methods on CVLN through extensive experiments.
