Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning

TL;DR

This paper introduces a novel cross-modal grounding module and an alternating imitation-exploration learning scheme, enhanced by adversarial training, to improve vision-and-language navigation in unseen environments, achieving state-of-the-art results.

Contribution

It proposes a cross-modal grounding module with dual attention mechanisms and a recursive alternating learning scheme combined with adversarial training for better navigation performance.

Findings

Achieves superior success rates on R2R benchmark.

Effectively narrows the training-inference discrepancy.

Demonstrates improved efficiency and effectiveness over prior methods.

Abstract

The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments according to the given language instruction. The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments; second, during the training process, the agent usually imitate the shortest-path to the target location. Due to the discrepancy of action selection between training and inference, the agent solely on the basis of imitation learning does not perform well. Sampling the next action from its predicted probability distribution during the training process allows the agent to explore diverse routes from the environments, yielding higher success rates. Nevertheless, without being presented with the shortest navigation paths during the training process, the agent may arrive at the target location through an unexpected longer route. To overcome these challenges, we design a cross-modal grounding module, which is composed of two complementary attention mechanisms, to equip the agent with a better ability to track the correspondence between the textual and visual modalities. We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference. We further exploit the advantages of both these two learning schemes via adversarial learning. Extensive experimental results on the Room-to-Room (R2R) benchmark dataset demonstrate that the proposed learning scheme is generalized and complementary to prior arts. Our method performs well against state-of-the-art approaches in terms of effectiveness and efficiency.