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...
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.
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.
DriveVLA-M0 introduces a failure-aware latent memory system for Vision‑Language‑Action models in autonomous driving. It stores past failure cases with scene representations and expert trajectories, and uses a retrieval model to decouple static road structure from dynamic agent interactions. During inference, retrieved cases are injected via a lightweight LoRA‑based test‑time training mechanism, yielding significant performance gains on NAVSIM benchmarks with minimal latency overhead.
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.
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.
arXiv:2608. 07267v1 Announce Type: new Abstract: Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies that map egocentric observations and language instructions directly to navigation actions.
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:2608. 15284v1 Announce Type: cross Abstract: Navigation instruction generation from ego-centric RGB video in continuous environments is an important yet challenging task for human-robot interaction and scalable dataset construction.
arXiv:2604.07279v2 Announce Type: replace Abstract: Streaming 3D perception is well suited to robotics and augmented reality, where long visual streams must be processed efficiently and consistently....
UniMem is a framework that unifies visual and textual memory with low‑level control in a single Vision‑Language‑Action model, using an event classifier, keyframe encoder, and caching to manage memory efficiently. It addresses non‑Markovian tasks that require remembering past scenes, avoiding the memory bottleneck of separate VLMs and the performance drop from fixed‑interval frame sampling. Across five simulation and four hardware tasks, UniMem outperforms fixed‑interval sampling (93.4% vs. 68.2%) and hierarchical VLM‑VLA baselines (80.0% vs. 43.5%) while offering faster inference and a simpler training pipeline.
Building memory is essential for long-horizon planning in zero-shot embodied navigation. Detector-centric scene graphs often compress observations into sparse nodes, discarding fine-grained visual evidence and accumulating noise, while 3D reconstruction-based methods remain computationally prohibitive.