arXiv AI
Fine-Grained Instruction-Guided Graph Reasoning for Vision-and-Language Navigation
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arXiv AI
Jun 9
SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
arXiv:2606. 08992v1 Announce Type: cross Abstract: Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instructions.
arXiv AI
Sep 1
CGFM-Nav: Cognitive Graph-Field Memory for Semantic-Guided Lifelong Multimodal Embodied Navigation
arXiv:2608.29114v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) requires agents to reason over accumulated observations while continuously exploring unseen regions. However, ex...
arXiv Computer Vision
Oct 12
Robust Vision-Language Navigation with Awareness of Instruction-Execution Progress
The paper introduces VLN-AIEP, a Vision‑Language Navigation framework that integrates instruction‑execution progress awareness into navigation decisions. By linking the visited trajectory to the instruction, it suppresses embeddings of already executed content and matches candidate locations with pending instruction segments. The method employs a landmark‑anchored trajectory representation to encode semantic labels and geometry, enabling progress estimation without explicit supervision and achieving strong results on R2R, RxR, CVDN, and R2R‑CE benchmarks.
arXiv AI
Jun 2
Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation
arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.
arXiv AI
Jun 12
Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning
arXiv:2606. 12830v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) demonstrate strong multimodal understanding, they remain limited in spatial reasoning tasks that require active evidence acquisition and multi-step visual interaction.
arXiv AI
Jul 14
ABot-N1: Toward a General Visual Language Navigation Foundation Model
arXiv:2607. 10383v1 Announce Type: cross Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks.