Multi-Grounding Navigator for Self-Supervised Vision-and-Language Navigation

Abstract

Vision-and-language navigation (VLN) based on human-robot interaction has become increasingly attractive in recent years for flexible robot navigation. As key metrics, the success rate and path length are equally important in VLN, where the former represents the accuracy of navigation and the latter indicates the efficiency. However, the most current approaches aim to improve the accuracy of navigation while rarely consider the path efficiency. For this situation, we propose a progress predictor module, where the progress predictor is used to predict the future navigation progress to be attained by taking action. Using the progress predictor, we design a multi-grounding navigator (MGN) to generate the distribution of actions by adding the predicted future progress for improving the accuracy and efficiency simultaneously. After action distribution is given, a path-shortening (PS) method is presented to further improve the path efficiency. On the other hand, by considering an extra progress monitor, we design a self-supervised (SS) module to further improve the navigation performance. Experiments using Room-to-Room (R2R) dataset are conducted to demonstrate the effectiveness of our MGN with SS and/or PS. Experiment results show that our scheme enhances accuracy and efficiency than baselines, especially in unseen environments.