Agent Journey Beyond RGB: Unveiling Hybrid Semantic-Spatial Environmental Representations for Vision-and-Language Navigation

2024-12-09

arXiv.org

AI Summary

Vision-language navigation (VLN) faces challenges including a large modality gap between natural language instructions and unseen environments, semantically impoverished RGB representations, and insufficient spatial reasoning. To address these, we propose the Semantic Understanding and Spatial Awareness (SUSA) architecture—the first framework to jointly model instruction-driven real-time scene description generation and depth-enhanced spatial exploration mapping. Methodologically, SUSA integrates a Text Semantic Understanding (TSU) module and a Depth-enhanced Spatial Perception (DSP) module, enabling multimodal alignment, on-the-fly scene description generation, and incremental construction of depth-augmented exploration maps to synergistically ground linguistic intent in geometric structure. Our approach achieves new state-of-the-art performance on three major benchmarks—REVERIE, R2R, and SOON—significantly improving both navigation success rate and path fidelity. The code will be publicly released.

Technology Category

• Computer Vision: Language and Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Navigating unseen environments based on natural language instructions remains difficult for egocentric agents in Vision-and-Language Navigation (VLN). Existing approaches primarily rely on RGB images for environmental representation, underutilizing latent textual semantic and spatial cues and leaving the modality gap between instructions and scarce environmental representations unresolved. Intuitively, humans inherently ground semantic knowledge within spatial layouts during indoor navigation. Inspired by this, we propose a versatile Semantic Understanding and Spatial Awareness (SUSA) architecture to encourage agents to ground environment from diverse perspectives. SUSA includes a Textual Semantic Understanding (TSU) module, which narrows the modality gap between instructions and environments by generating and associating the descriptions of environmental landmarks in agent's immediate surroundings. Additionally, a Depth-enhanced Spatial Perception (DSP) module incrementally constructs a depth exploration map, enabling a more nuanced comprehension of environmental layouts. Experiments demonstrate that SUSA's hybrid semantic-spatial representations effectively enhance navigation performance, setting new state-of-the-art performance across three VLN benchmarks (REVERIE, R2R, and SOON). The source code will be publicly available.

Problem

• Bridging modality gap between instructions and RGB-based environmental representations
• Enhancing agent navigation with hybrid semantic-spatial environmental understanding
• Improving Vision-and-Language Navigation performance via depth-augmented spatial perception

Innovation

• Hybrid semantic-spatial environmental representations
• Textual Semantic Understanding module
• Depth-enhanced Spatial Perception module