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

• URL: http://arxiv.org/abs/2607.10383v3
• Date: Fri, 17 Jul 2026 07:17:10 GMT
• Title: ABot-N1: Toward a General Visual Language Navigation Foundation Model
• Abstract summary: ABot-N1 is a step toward a general Visual Language Navigation foundation model.<n>A slow vision-language reasoner performs explicit Chain-of-Thought reasoning while producing a pixel goal.<n>New benchmarks are released as open source to advance the field of urban-scale navigation.
• Score: 27.085429358326504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer from coordinate drift and poor handling of long-tail semantics. Furthermore, these black-box mappings lack interpretability, hindering the simultaneous achievement of generality, robustness, and transparency. We present ABot-N1, a step toward a general Visual Language Navigation foundation model, that addresses these challenges by decoupling cognition from control via a slow-fast architecture guided by dual visual-language signals. More specifically, a slow vision-language reasoner performs explicit Chain-of-Thought reasoning while producing a pixel goal. This compact set of image-space anchor points serves as a universal interface for diverse tasks, including point-goal, object-goal, poi-goal, instruction-following, and person-following. Subsequently, a fast action expert leverages both the textual cues and the pixel guidance to generate continuous waypoints at the native control frequency. By bridging high-level intents and low-level control through pixel-grounded anchors paired with explicit linguistic traces, our approach ensures robust, generalizable, and interpretable navigation across simulation and real-world benchmarks. ABot-N1 establishes new state-of-the-art records, delivering massive gains specifically in urban-scale navigation: boosting POI arrival by 35.0% (to 77.3%) and achieving 95.4%/92.9% SR in complex indoor and outdoor scenes. It also maintains superior robustness across object-reaching, person-following, and instruction-following tasks. New Point-Goal/POI-Goal benchmarks are released as open source to advance the field of urban-scale navigation.

Related papers

• Multi-Task Visual Perception Network with LLM Conditioning for Autonomous Navigation [4.075320514790725]
  Long-term navigation for service robots faces challenges like the accumulation of odometry drift and sensor error.<n>We propose a user-friendly, interactive framework that eliminates the reliance on globally consistent maps.
  arXiv  Detail & Related papers  (2026-09-13T05:38:52Z)
• Goal-oriented Navigation Instruction Generation with Tour Video Priors [3.3906611506156916]
  Navigation Instruction Generation (NIG) aims to produce step-by-step natural language instructions for navigation guidance.<n>We introduce VideoNIG, a goal-oriented video-grounded NIG task that generates navigation instructions from ego-centric tour videos.<n>We propose a two-stage Curriculum Learning framework that decomposes the learning into foundational motion perception and long-horizon navigation reasoning.
  arXiv  Detail & Related papers  (2026-08-09T09:21:47Z)
• ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models [7.354500239997416]
  We present ViTL, a framework that enables robots to follow natural language commands to complete zero-shot long-horizon tasks.<n>At the navigation level, we introduce directional score, rather than producing a direction-agnostic value across the entire field of view.<n>Experiments on Habitat-Matterport 3D show that the full framework enables zero-shot long-horizon completion of natural language navigation tasks.
  arXiv  Detail & Related papers  (2026-06-29T02:22:31Z)
• WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation [31.539730066120374]
  Visual navigation requires generating smooth and collision-free trajectories under complex geometric and physical constraints.<n>We propose WAM-Nav, a Latent World-Action Model for embodied visual navigation that jointly learns action generation and latent visual foresight.
  arXiv  Detail & Related papers  (2026-06-03T14:05:19Z)
• LookasideVLN: Direction-Aware Aerial Vision-and-Language Navigation [96.09246387639006]
  LookasideVLN exploits directional cues in natural language to achieve both more accurate spatial reasoning and greater computational efficiency.<n>LookasideVLN significantly outperforms the state-of-the-art CityNavAgent, even with a single-level lookahead.
  arXiv  Detail & Related papers  (2026-04-19T01:36:53Z)
• HaltNav: Reactive Visual Halting over Lightweight Topological Priors for Robust Vision-Language Navigation [1.774434289475737]
  Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy.<n>We propose a hierarchical navigation framework HaltNav that couples the robust global planning of osmAG with the local exploration and instruction-grounding capability of VLN.
  arXiv  Detail & Related papers  (2026-03-13T06:22:35Z)
• OpenFrontier: General Navigation with Visual-Language Grounded Frontiers [54.661157616245966]
  Open-world navigation requires robots to make decisions in complex everyday environments.<n>Recent advances in vision--language navigation (VLN) and vision--language--action (VLA) models enable end-to-end policies conditioned on natural language.<n>We propose OpenFrontier, a training-free navigation framework that seamlessly integrates diverse vision--language prior models.
  arXiv  Detail & Related papers  (2026-03-05T17:02:22Z)
• AutoFly: Vision-Language-Action Model for UAV Autonomous Navigation in the Wild [62.47761809929869]
  Vision-language navigation (VLN) requires intelligent agents to navigate environments by interpreting linguistic instructions alongside visual observations.<n>Current VLN research for unmanned aerial vehicles (UAVs) relies on detailed, pre-specified instructions to guide the UAV along predetermined routes.<n>We propose AutoFly, an end-to-end Vision-Language-Action model for autonomous UAV navigation.
  arXiv  Detail & Related papers  (2026-02-10T11:08:07Z)
• GoViG: Goal-Conditioned Visual Navigation Instruction Generation [69.79110149746506]
  We introduce Goal-Conditioned Visual Navigation Instruction Generation (GoViG), a new task that aims to autonomously generate precise and contextually coherent navigation instructions.<n>GoViG exclusively leverages raw egocentric visual data, substantially improving its adaptability to unseen and unstructured environments.
  arXiv  Detail & Related papers  (2025-08-13T07:05:17Z)
• GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation [65.71524410114797]
  GOAT-Bench is a benchmark for the universal navigation task GO to AnyThing (GOAT) In GOAT, the agent is directed to navigate to a sequence of targets specified by the category name, language description, or image. We benchmark monolithic RL and modular methods on the GOAT task, analyzing their performance across modalities.
  arXiv  Detail & Related papers  (2024-04-09T20:40:00Z)
• How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers [94.46825166907831]
  We present a training-free solution to tackle the object goal navigation problem in Embodied AI. Our method builds a structured scene representation based on the classic visual simultaneous localization and mapping (V-SLAM) framework. Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
  arXiv  Detail & Related papers  (2023-05-26T13:38:33Z)
• Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation [41.334731014665316]
  Most existing works in vision-and-language navigation (VLN) focus on either discrete or continuous environments. We propose a predictor to generate a set of candidate waypoints during navigation. We show that agents navigating in continuous environments with predicted waypoints perform significantly better than agents using low-level actions.
  arXiv  Detail & Related papers  (2022-03-05T14:56:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.