Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel

Creating high-quality data for training robust language-instructed agents is a longlasting challenge in embodied AI. In this paper, we introduce a Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale navigational instruction-trajectory pairs by iteratively refining the data pool through the collaboration between two models, the instruction generator and the navigator, without any human-in-the-loop annotation. Specifically, SRDF starts with using a base generator to create an initial data pool for training a base navigator, followed by applying the trained navigator to filter the data pool. This leads to higher-fidelity data to train a better generator, which can, in turn, produce higher-quality data for training the next-round navigator. Such a flywheel establishes a data selfrefining process, yielding a continuously improved and highly effective dataset for large-scale language-guided navigation learning. Our experiments demonstrate that after several flywheel rounds, the navigator elevates the performance boundary from 70% to 78% SPL on the classic R2R test set, surpassing human performance (76%) for the first time. Meanwhile, this process results in a superior generator, evidenced by a SPICE increase from 23.5 to 26.2, better than all previous VLN instruction generation methods. Finally, we demonstrate the scalability of our method through increasing environment and instruction diversity, and the generalization ability of our pre-trained navigator across various downstream navigation tasks, surpassing state-of-the-art methods by a large margin in all cases. Figure 1: (a) Our Pipeline: After using the (instruction) generator to label paths for data augmentation in navigator training, we leverage the trained navigator to filter high-quality data to train a better generator, and the improved generator refines the data pool to train a stronger navigator, iteratively running on the flywheel. It also surpasses human performance on R2R and approaches human-level results on RxR-English and CVDN (for other tasks, human performance is not reported in their paper). The R2R result is from the test set, while others are from val unseen. The lack of high-quality data is one of the main bottlenecks in training embodied agents to complete real-world human activities. Unlike many other discriminative or generative learning problems, where the data itself naturally formulates a self-supervised learning objective (Devlin, 2018; He et al., 2022) or the data labeling can be facilitated by existing models (Ros et al., 2016; Tian et al., 2024), training embodied agents usually requires expensive human annotation on complex visionlinguistic contents and physical interactions.