arXiv Computer Vision
VerNav: Verifier-First Low-Latency Vision-and-Language Navigation
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CLOSER-VLN: Closed-Loop Self-Verified Retrieval-Augmented Reasoning for Aerial Vision-Language Navigation
arXiv:2606. 28397v1 Announce Type: cross Abstract: Vision-language navigation (VLN) has recently advanced with large language and multimodal models, enabling agents to follow natural-language instructions in unseen environments without training a task-specific navigation policy.
arXiv Computer Vision
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LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory
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PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model
arXiv:2607. 17806v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations.
arXiv AI
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Path-level Hindsight Instructions for Semantic Exploration in Vision-Language Navigation
arXiv:2607. 01754v1 Announce Type: new Abstract: On-policy exploration is a crucial component for training robust Vision-Language Navigation agents, as it exposes the policy to a broader state distribution.
arXiv AI
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CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation
arXiv:2607. 02222v1 Announce Type: cross Abstract: Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decomposition, while the low-level action representation remains comparatively underexplored.
arXiv Machine Learning
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Think Less, Act Early: Reinforced Latent Reasoning with Early Exit in Vision-Language-Action Models
arXiv:2606. 15099v1 Announce Type: cross Abstract: Existing Vision-Language-Action (VLA) models predominantly rely on explicit Chain-of-Thought (CoT) reasoning to bridge perception and action.