  Interactive robots navigating photo-realistic environments need to be trained
to effectively leverage and handle the dynamic nature of dialogue in addition
to the challenges underlying vision-and-language navigation (VLN). In this
paper, we present VISITRON, a multi-modal Transformer-based navigator better
suited to the interactive regime inherent to Cooperative Vision-and-Dialog
Navigation (CVDN). VISITRON is trained to: i) identify and associate
object-level concepts and semantics between the environment and dialogue
history, ii) identify when to interact vs. navigate via imitation learning of a
binary classification head. We perform extensive pre-training and fine-tuning
ablations with VISITRON to gain empirical insights and improve performance on
CVDN. VISITRON's ability to identify when to interact leads to a natural
generalization of the game-play mode introduced by Roman et al.
(arXiv:2005.00728) for enabling the use of such models in different
environments. VISITRON is competitive with models on the static CVDN
leaderboard and attains state-of-the-art performance on the Success weighted by
Path Length (SPL) metric.
