FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation

• URL: http://arxiv.org/abs/2604.03139v1
• Date: Fri, 03 Apr 2026 16:01:58 GMT
• Title: FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
• Abstract summary: This paper proposes FSUNav: a Cerebrum-Cerebellum architecture for fast, safe, and universal zero-shot goal-oriented navigation.<n>The proposed architecture integrates vision-language models (VLMs) with the proposed architecture.<n>FSUNav achieves state-of-the-art performance on object, instance image, and task navigation, significantly outperforming existing methods.
• Score: 8.950226217725994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current vision-language navigation methods face substantial bottlenecks regarding heterogeneous robot compatibility, real-time performance, and navigation safety. Furthermore, they struggle to support open-vocabulary semantic generalization and multimodal task inputs. To address these challenges, this paper proposes FSUNav: a Cerebrum-Cerebellum architecture for fast, safe, and universal zero-shot goal-oriented navigation, which innovatively integrates vision-language models (VLMs) with the proposed architecture. The cerebellum module, a high-frequency end-to-end module, develops a universal local planner based on deep reinforcement learning, enabling unified navigation across heterogeneous platforms (e.g., humanoid, quadruped, wheeled robots) to improve navigation efficiency while significantly reducing collision risk. The cerebrum module constructs a three-layer reasoning model and leverages VLMs to build an end-to-end detection and verification mechanism, enabling zero-shot open-vocabulary goal navigation without predefined IDs and improving task success rates in both simulation and real-world environments. Additionally, the framework supports multimodal inputs (e.g., text, target descriptions, and images), further enhancing generalization, real-time performance, safety, and robustness. Experimental results on MP3D, HM3D, and OVON benchmarks demonstrate that FSUNav achieves state-of-the-art performance on object, instance image, and task navigation, significantly outperforming existing methods. Real-world deployments on diverse robotic platforms further validate its robustness and practical applicability.

Related papers

• Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation [80.18527639791074]
  World action models have shown great promise for autonomous driving and urban navigation.<n>Existing approaches suffer key limitations.<n>We propose Metis, an end-to-end WAM framework that decouples video generation and action prediction.
  arXiv  Detail & Related papers  (2026-06-14T15:40:49Z)
• A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration [29.871705369272124]
  In vision-language navigation (VLN), existing approaches often face a trade-off between strong reasoning capabilities and efficient deployment on real-world platforms.<n>We present a deployable embodied VLN system that achieves both high efficiency and robust high-level reasoning on real-world robotic platforms.
  arXiv  Detail & Related papers  (2026-04-23T07:27:00Z)
• ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation [66.02142169323521]
  Vision-Language-Action models and world models have recently emerged as promising paradigms for general-purpose robotic intelligence.<n>Existing benchmarks are largely simulator-centric, which provide controllability but fail to capture the reality gap caused by perception noise.<n>We introduce ManipArena, a standardized evaluation framework designed to bridge simulation and real-world execution.
  arXiv  Detail & Related papers  (2026-03-30T15:06:41Z)
• 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)
• 3DGSNav: Enhancing Vision-Language Model Reasoning for Object Navigation via Active 3D Gaussian Splatting [12.057873540714098]
  3DGSNav is a novel framework that embeds 3D Gaussian Splatting (3DGS) as persistent memory for vision-language models (VLMs) to enhance spatial reasoning.<n>3DGSNav incrementally constructs a 3DGS representation of the environment, enabling trajectory-guided free-viewpoint rendering of frontier-aware first-person views.<n>During navigation, a real-time object detector filters potential targets, while VLM-driven active viewpoint switching performs target re-verification.
  arXiv  Detail & Related papers  (2026-02-12T16:41:26Z)
• APEX: A Decoupled Memory-based Explorer for Asynchronous Aerial Object Goal Navigation [26.546610806602803]
  Aerial Object Goal Navigation, a challenging frontier in Embodied AI, requires an Unmanned Aerial Vehicle (UAV) agent to autonomously explore, reason, and identify a specific target using only visual perception and language description.<n>Existing methods struggle with the memorization of complex spatial representations in aerial environments, reliable and interpretable action decision-making, and inefficient exploration and information gathering.<n>We introduce textAPEX, a novel hierarchical agent designed for efficient exploration and target acquisition in complex aerial settings.
  arXiv  Detail & Related papers  (2026-01-31T06:27:57Z)
• CoINS: Counterfactual Interactive Navigation via Skill-Aware VLM [13.352875026471445]
  We propose Counterfactual Interactive Navigation via Skill-aware VLM (CoINS)<n>We fine-tune a VLM, named InterNav-VLM, which incorporates skill affordance and concrete constraint parameters into the input context.<n>To execute the generated high-level plans, we develop a comprehensive skill library through reinforcement learning.
  arXiv  Detail & Related papers  (2026-01-07T14:10:46Z)
• Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems [75.78934957242403]
  Self-driving vehicles and drones require true Spatial Intelligence from multi-modal onboard sensor data.<n>This paper presents a framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal.
  arXiv  Detail & Related papers  (2025-12-30T17:58:01Z)
• RANGER: A Monocular Zero-Shot Semantic Navigation Framework through Contextual Adaptation [9.379574254353352]
  RANGER is a novel zero-shot, open-vocabulary semantic navigation framework that operates using only a monocular camera.<n>By simply observing a short video of a new environment, the system can also significantly improve task efficiency without requiring architectural modifications or fine-tuning.<n> Experiments on the HM3D benchmark and real-world environments demonstrate that RANGER achieves competitive performance in terms of navigation success rate and exploration efficiency.
  arXiv  Detail & Related papers  (2025-12-30T13:25:22Z)
• FOM-Nav: Frontier-Object Maps for Object Goal Navigation [65.76906445210112]
  FOM-Nav is a framework that enhances exploration efficiency through Frontier-Object Maps and vision-language models.<n>To train FOM-Nav, we automatically construct large-scale navigation datasets from real-world scanned environments.<n> FOM-Nav achieves state-of-the-art performance on the MP3D and HM3D benchmarks, particularly in navigation efficiency metric SPL.
  arXiv  Detail & Related papers  (2025-11-30T18:16:09Z)
• DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation [55.888688171010365]
  DORAEMON is a cognitive-inspired framework consisting of Ventral and Dorsal Streams that mimics human navigation capabilities.<n>We evaluate DORAEMON on the HM3D, MP3D and GOAT datasets, where it achieves state-of-the-art performance on both success rate (SR) and success weighted by path length (SPL) metrics.
  arXiv  Detail & Related papers  (2025-05-28T04:46:13Z)
• ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation [75.13546386761153]
  We present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC) ESC transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience. Experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines.
  arXiv  Detail & Related papers  (2023-01-30T18:37:32Z)

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.