Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel
This addresses the data bottleneck for embodied AI in language-guided navigation, offering a scalable and automated solution that achieves state-of-the-art performance.
The paper tackles the challenge of generating high-quality data for training language-instructed navigation agents by introducing a Self-Refining Data Flywheel (SRDF) that iteratively refines instruction-trajectory pairs without human annotation, resulting in a navigator achieving 78% SPL on the R2R test set (surpassing human performance) and a generator with SPICE increasing from 23.5 to 26.2.
Creating high-quality data for training robust language-instructed agents is a long-lasting 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 self-refining 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.