FINE: Future-Informed Navigation Encoding for Data-Efficient Vision-Language Navigation

arXiv:2609.32855 · cs.RO, cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Comments: 8 pages, 3 figures

Project page: https://finevln.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 11× in this paper · explained in 7 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Grounding — 5× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Action Decoder — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Success Rate — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Navigation Error — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Oracle Success Rate — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
• Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks
• StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling
• FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation
• VLN-Zero: Rapid Exploration and Cache-Enabled Neurosymbolic Vision-Language Planning for Zero-Shot Transfer in Robot Navigation
• Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation
• Joint On-and-Off Policy Learning for Vision-and-Language Navigation
• Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation
• Matterport3D: Learning from RGB-D Data in Indoor Environments
• Nano World Models: A Minimalist Implementation of Future Video Prediction

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