MemVLN: Episodic and Procedural Memory for Vision-and-Language Navigation

arXiv:2607.23504v1 Announce Type: new Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to maintain long-horizon visual history for trajectory consistency while executing actions with low latency. Existing video-based VLN approaches typically struggle to satisfy both demands simultaneously. To address these challenges, we propose MemVLN, a novel VLN framework that achieves state-of-the-art performance with real-time inference efficiency (14 FPS). MemVLN utilizes a visual encoder to process continuous observations and a Large Language Model (LLM) to interpret instructions and generate actions. Central to our approach is an Episodic Memory management that applies pyramidal resolutions. This mechanism concentrates computation on immediate percepts while retaining compressed long-term history. Complementing to this design, we introduce Procedural Memory for fast action with a compact vocabulary of atomic mid-level actions to bypass auto-regressive decoding latency. Experiments on VLN-CE show that MemVLN-4B surpasses the baseline Qwen3-VL-4B architecture by 5.8% SR in R2R and 9.7% SR in RxR, while achieving a 7imes speedup in inference latency.

Related

• AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents
• ChronoStitch: Training-Free Composition of Visual KV Memories for Long-Horizon Temporal Reasoning
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• WorldMAP: Bootstrapping Vision-Language Navigation Trajectory Prediction with Generative World Models
• MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models

Source: arXiv cs.CV | 2026-07-28