Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation (2202.11742v1)

Abstract: 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.

• The paper presents its main contribution by introducing a dual-scale graph transformer that dynamically fuses global planning with fine-grained local navigation.
• It leverages topological mapping and dynamic fusion to achieve approximately 20% improved success rates on standard vision-and-language navigation benchmarks.
• The study highlights practical implications for real-world autonomous navigation by effectively aligning language instructions with visual scenes.

Overview of "Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation"

The paper "Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation" presents a novel approach to the challenge of autonomous vision-and-language navigation (VLN) in unseen environments. The authors propose a strategy that integrates a dual-scale graph transformer (DUET) to improve the navigation and instruction-following capabilities of embodied agents.

Introduction and Problem Background

VLN tasks require an agent to interpret language instructions and navigate through complex environments to reach target locations, which introduces multiple challenges. These include the need for the agent to understand and ground linguistic cues within visual scenes while managing the exploration of unfamiliar environments. Traditional approaches often relied on fine-grained step-by-step navigation guidance, which although effective for grounding, is impractical for real-world applications due to its rigidness. Conversely, goal-oriented instructions necessitate the agent's ability to infer broader objectives and navigate with flexibility, enhancing the complexity of the task.

Methodology

The proposed methodology leverages a dual-scale graph transformer, referred to as DUET, to enhance the agent's decision-making process by combining coarse-scale and fine-scale reasoning, using graph transformers to offer both global planning capabilities and detailed language grounding.

• Topological Mapping: The method constructs a dynamic topological map using observations obtained by the agent as it navigates. This map helps in visualizing both the areas already visited and the potential navigable paths. The map supports both coarse navigation and the fine-grained representation of the current location's visuals.
• Dual-scale Action Planning: At the core of DUET is the integration of dual-scale action planning utilizing:
  • A coarse-scale graph-aware encoding for modeling global action space, allowing the agent to plan effectively over long distances.
  • A fine-scale cross-modal encoder focusing on precise navigation actions within the agent's immediate vicinity, enhancing object and scene detail resolution for better alignment with instructions.
• Dynamic Fusion: The interplay between coarse and fine-scale data is crucial for dynamic decision making. The model employs a fusion mechanism that balances these two scales, providing an adaptable approach for global action selection.

Experimental Results

The evaluation on established benchmarks, REVERIE, SOON, and R2R, demonstrates significant improvements over prior methods. DUET achieves enhanced success rates on goal-oriented tasks, showing notable gains in environments requiring intricate action planning and fine-grained grounding. The success rates (SR) on challenging datasets reflect improvements of approximately 20% over existing models, underscoring DUET's advanced long-range reasoning and effective language grounding.

Implications and Future Directions

The introduction of a dual-scale reasoning framework presents a significant stride in VLN tasks, marrying large-scale action planning with detailed local observation interpretation. The results suggest that this framework not only aids in navigation accuracy but also in effectively following abstract instructions, an essential capability for autonomous agents in complex real-world settings.

Moving forward, expansion to continuous environments and integration with real-time SLAM for better metric mapping could further enhance embodied agent navigation. Addressing inherent limitations, such as memory constraints and handling more varied instruction sets, remains an area ripe for exploration.

Conclusion

This research represents a meaningful advance in the design of VLN systems, leveraging sophisticated transformer architectures to dynamically optimize navigation decisions. The efficient integration of topological maps and dual-scale reasoning not only enhances the navigation success but also lays the groundwork for more informed and context-aware autonomous systems.