#Vision-language navigation (7)

AGC-VLN: Air-Ground VLN via Shared Bird's-Eye Maps

@heetezition · 59 views · 2026-09-07

Token-Wise Latent Streaming from Slow Reasoners to Fast Planners for Dynamic Vision Language Navigation

Vision-Language Navigation in dynamic, human-centric environments exposes a fundamental tension: linguistic reasoning is slow and deliberative, whereas safe, socially compliant planning should be instant and reactive. The resulting observation staleness is safety-critical: a maneuver chosen during inference can already be unsafe by the time it executes. We observe that, long before a VLM finishes its inference, its intermediate hidden states already encode action-relevant intent. We propose SPARK-VLN, a dual-system framework for dynamic social VLN that streams the slow VLM reasoner's knowledge to a fast flow-matching expert planner throughout token generation, providing fresh and evolving guidance during inference. This design is realized by three modules: a Token-Wise Hidden Streamer that extracts intermediate hidden states along the token generation process, a Sequence-to-Slot Latent Bridge that projects them into fixed-size latent slots, and an Evolving Latent Conditioner that infuses them into the expert planner. We also introduce a human-centric benchmark suite for dynamic social vision-language navigation that keeps pedestrians and the robot active throughout inference and reports navigation success, social compliance, human collisions, and explicit staleness statistics. Across these settings, SPARK-VLN mproves navigation success and social compliance while sustaining inference efficiency. Webpage: https://hutslib.github.io/SPARK-VLN/.

Tianshuai Hu, Yangyi Zhong, Zeying GongJul 18, 2026

Vision-language navigationSocial NavigationDual-SystemJul 18, 2026

1:09

LightNav-0 Deploys Zero-Shot Across Two LimX Robots

@LimX_Dynamics · 247 views · 2026-09-04

LightNav-0VLNNavigation

ABot-N1: Toward a General Visual Language Navigation Foundation Model

ABot-N1 is a general visual-language navigation foundation model built on a slow-fast dual-system architecture: a 4B slow VLM reasoner emits explicit chain-of-thought plus dual pixel goals (Target Pixel + Affordance Pixel), while a 2B fast action expert decodes continuous SE(2) waypoints via QFormer action queries. The unified pixel-goal interface covers five tasks — point-goal, object-goal, POI-goal, instruction-following and person-following — in a single 30M-sample multi-task checkpoint, further aligned by GRPO post-training with format/target/safety rewards. Two closed-loop benchmarks are released (ABotN-PointBench and ABotN-POIBench). ABot-N1 sets new SOTA on all five benchmarks, boosting POI entrance arrival to 77.3% (+35.0 pp) and reaching 92.9%/95.4% outdoor/indoor point-goal SR, with full deployment on the TuTu quadruped running on a Jetson AGX Orin.

Ruiyan Gong, Yingnan Guo, Junjun HuJul 11, 2026

VLNNavigation Foundation ModelSlow-Fast ArchitectureJul 11, 2026

SuperMap: A Spatio-Temporal SLAM System for Visual-Language Navigation

SuperMap is a real-time 4D spatio-temporal mapping framework from CMU that integrates high-frequency geometric SLAM with asynchronous open-vocabulary perception. Its consistency-driven mapping engine maintains stable object identities and prunes outdated content under occlusions and scene changes, producing a queryable 4D scene-graph interface for VLM-based zero-shot reasoning and language-guided navigation.

Shibo Zhao, Guofei Chen, Honghao ZhuJul 1, 2026

SLAMVision-language navigationScene graphsJul 1, 2026

1:00

SuperMap: Spatio-Temporal SLAM for Visual-Language Navigation

@ShiboZhaoSLAM · 93 views · 2026-07-14

SLAMSuperMapvisual-language navigation

AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation

Vision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment. While state-of-the-art methods leverage the reasoning capabilities of Vision-Language Models (VLMs) for end-to-end action prediction, they often lack an explicit and explainable understanding of the relationships between the agent, the instruction, and the scene. Conversely, explicitly building a scene map for heuristic planning is intuitively appealing but relies on additional 3D sensors and hinders large-scale vision-language pre-training. To bridge this gap, we propose AwareVLN, a novel framework that equips the navigation model with a self-aware reasoning mechanism, enabling it to understand the agent's state and task progress in a fully end-to-end and data-driven manner. Our approach features two key innovations: (1) a structural reasoning module that fosters spatial and task-oriented self-awareness, and (2) an automatic data engine with progress division for effective training. Extensive experiments on various datasets in Habitat simulator show our AwareVLN significantly outperforms previous state-of-the-art vision-language navigation methods. Project page: https://gwxuan.github.io/AwareVLN/.

Wenxuan Guo, Xiuwei Xu, Yichen LiuMay 21, 2026

VLMVLNVision-language navigationMay 21, 2026