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.
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:2610.14541v1 Announce Type: cross Abstract: Emerging strong Vision-Language Models (VLMs) have advanced navigation foundation models. However, these models still have unsatisfactory performance...
arXiv:2609.06476v1 Announce Type: cross Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural la...
Goal2Pixel introduces a pixel‑based approach for vision‑language navigation in continuous environments, replacing low‑level action prediction with navigable pixel grounding. The model predicts a visible pixel on the image plane, which is back‑projected into a 3D waypoint for forward motion, while auxiliary regions handle turning and stopping. Coupled with a visibility‑aware keyframe memory (ViKeyMem) and semantic embeddings, Goal2Pixel achieves state‑of‑the‑art performance on R2R‑CE and RxR‑CE with significantly fewer VLM calls.
arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.
arXiv:2606. 12550v1 Announce Type: cross Abstract: Open-world mapless navigation from sparse language instructions requires resolving underspecified goals and inferring which environmental cues are relevant for reaching the goal.
In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV navigation. Naively applying MLLMs leads to unreliable navigation due to weak directional grounding and the lack of explicit spatial memory.
arXiv:2610.16558v1 Announce Type: new Abstract: World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies beyo...
arXiv:2606. 08992v1 Announce Type: cross Abstract: Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instructions.
arXiv:2608.23354v1 Announce Type: cross Abstract: Autonomous indoor navigation requires both semantic understanding and precise geometric control. We propose OptiSight, a hybrid framework that combin...
GuidedNav is a new framework for vision‑and‑language navigation that explicitly shapes the representations linking instructions to actions. It uses subtask and landmark guidance to focus pre‑action attention on the relevant instruction segment and visual regions, and spatial guidance to align internal representations with geometric features of the environment. The method achieves state‑of‑the‑art success rates on R2R‑CE and RxR‑CE val‑unseen, improving by 8.1% and 13.1% respectively, while also providing faster inference and introducing two new annotation‑enriched datasets, Guided‑R2R and Guided‑RxR.