Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation
This work addresses the problem of autonomous embodied agents navigating unseen environments based on language instructions, representing an incremental improvement with strong specific gains.
The paper tackles the challenge of vision-and-language navigation by proposing a dual-scale graph transformer (DUET) that jointly handles long-term action planning and fine-grained cross-modal understanding, significantly outperforming state-of-the-art methods on benchmarks like REVERIE, SOON, and R2R.
Following language instructions to navigate in unseen environments is a challenging problem for autonomous embodied agents. The agent not only needs to ground languages in visual scenes, but also should explore the environment to reach its target. In this work, we propose a dual-scale graph transformer (DUET) for joint long-term action planning and fine-grained cross-modal understanding. We build a topological map on-the-fly to enable efficient exploration in global action space. To balance the complexity of large action space reasoning and fine-grained language grounding, we dynamically combine a fine-scale encoding over local observations and a coarse-scale encoding on a global map via graph transformers. The proposed approach, DUET, significantly outperforms state-of-the-art methods on goal-oriented vision-and-language navigation (VLN) benchmarks REVERIE and SOON. It also improves the success rate on the fine-grained VLN benchmark R2R.