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

TL;DR

The paper introduces a self-refining data flywheel that iteratively improves training data quality for language-guided navigation, leading to significant performance gains surpassing human benchmarks.

Contribution

It presents a novel self-refining data generation process that enhances training data quality without human annotation, improving navigation performance and generalization.

Findings

Navigator performance improved from 70% to 78% SPL.

Generated data quality surpassed previous methods, with SPICE increasing from 23.5 to 26.2.

Achieved state-of-the-art results across multiple navigation tasks.

Abstract

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.