arXiv Computer Vision
MemVLN: Streaming Episodic Memory for Vision-Language Navigation
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MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation
Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-horizon trajectories due to context-length limits...
arXiv AI
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MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation
MemoryVLN introduces a memory-driven navigation system that compresses long navigation histories and reuses past spatial experience. It uses forgetting-curve-inspired temporal sampling for long sequence compression, an ego-aware memory representation that fuses 2D semantic, 3D geometric, and 6D pose tokens, and a hierarchical memory bank for in-episode and context-level memory management. The system retrieves top‑K relevant memories to augment current visual tokens, achieving significant improvements on R2R‑CE and RxR‑CE benchmarks.
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Dual-Anchoring: Addressing State Drift in Vision-Language Navigation
arXiv:2604. 17473v3 Announce Type: replace-cross Abstract: Vision-Language Navigation(VLN) requires an agent to navigate through 3D environments by following natural language instructions.
arXiv Computer Vision
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DriveVLA-M0: Failure-Aware Memory Augmentation for Autonomous Driving
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What Should a Navigator Remember? Spatiotemporal Memory for Zero-Shot Vision-Language Navigation
The paper investigates how an 8‑billion‑parameter vision‑language navigation system should store and organize past observations for zero‑shot navigation. It proposes a structured memory comprising task progress, hierarchically compressed observation history tied to poses, and accumulated geometry, which an observer summarizes and a reasoner uses to select waypoint intents or request new views. Experiments on R2R‑CE and real‑world Unitree Go2 robots show that preserving temporal and spatial memories improves success rates and path efficiency compared to baselines that omit these components.
arXiv Computer Vision
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LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory
LookStep is a new end‑to‑end framework for Vision‑Language Navigation that integrates Language‑Centric Future State Modeling with an Event‑Driven Rolling Memory. It uses language labels to predict coarse navigation progress and future states for candidate actions, and autonomously decides which observations to store in a bounded memory with semantic roles. Empirical results show that LookStep outperforms existing methods on VLN‑CE tasks, achieving a 49.7% success rate on R2R‑CE Val‑Unseen while improving memory efficiency and reducing data requirements.