Navigation-GPT: A Robust and Adaptive Framework Utilizing Large Language Models for Navigation Applications

• URL: http://arxiv.org/abs/2502.16402v1
• Date: Sun, 23 Feb 2025 01:41:58 GMT
• Title: Navigation-GPT: A Robust and Adaptive Framework Utilizing Large Language Models for Navigation Applications
• Authors: Feng Ma, Xiu-min Wang, Chen Chen, Xiao-bin Xu, Xin-ping Yan,
• Abstract summary: Existing navigation decision support systems often perform poorly when handling non-predefined scenarios.<n>This research proposes a dual-core framework for LLM applications to address this issue.
• Score: 6.990141986853289
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Existing navigation decision support systems often perform poorly when handling non-predefined navigation scenarios. Leveraging the generalization capabilities of large language model (LLM) in handling unknown scenarios, this research proposes a dual-core framework for LLM applications to address this issue. Firstly, through ReAct-based prompt engineering, a larger LLM core decomposes intricate navigation tasks into manageable sub-tasks, which autonomously invoke corresponding external tools to gather relevant information, using this feedback to mitigate the risk of LLM hallucinations. Subsequently, a fine-tuned and compact LLM core, acting like a first-mate is designed to process such information and unstructured external data, then to generates context-aware recommendations, ultimately delivering lookout insights and navigation hints that adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) and other rules. Extensive experiments demonstrate the proposed framework not only excels in traditional ship collision avoidance tasks but also adapts effectively to unstructured, non-predefined, and unpredictable scenarios. A comparative analysis with DeepSeek-R1, GPT-4o and other SOTA models highlights the efficacy and rationality of the proposed framework. This research bridges the gap between conventional navigation systems and LLMs, offering a framework to enhance safety and operational efficiency across diverse navigation applications.

Related papers

• 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)
• STRIDER: Navigation via Instruction-Aligned Structural Decision Space Optimization [73.98141357780032]
  VLN-CE task requires agents to navigate 3D environments using natural language instructions, without any scene-specific training.<n>Existing methods often fail to achieve robust navigation due to a lack of structured decision-making and insufficient integration of feedback from previous actions.<n>We propose STRIDER, a novel framework that systematically optimize the agent's decision space by integrating spatial layout priors and dynamic task feedback.<n>Our approach introduces two key innovations: 1) a Structured Waypoint Generator that constrains the action space through spatial structure, and 2) a Task-Alignment Regulator that adjusts behavior based on task progress, ensuring semantic alignment throughout navigation.
  arXiv  Detail & Related papers  (2025-10-27T04:37:21Z)
• DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation [73.80968452950854]
  Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces.<n>Existing VLN-CE approaches typically use a two-stage waypoint planning framework.<n>We propose DAgger Diffusion Navigation (DifNav) as an end-to-end optimized VLN-CE policy.
  arXiv  Detail & Related papers  (2025-08-13T02:51:43Z)
• Grid2Guide: A* Enabled Small Language Model for Indoor Navigation [6.341317643879287]
  This research presents a hybrid navigation framework that combines the A* search algorithm with a Small Language Model (SLM) to generate clear, human-readable route instructions.<n>The results validate the proposed approach as a lightweight, infrastructure-free solution for real-time indoor navigation support.
  arXiv  Detail & Related papers  (2025-08-11T15:39:27Z)
• Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation [12.561993540768729]
  We present LE-Nav, an interpretable and scene-aware navigation framework for service robots.<n>To achieve zero-shot scene understanding, we utilize one-shot exemplars and chain-of-thought prompting strategies.<n>Experiments show that LE-Nav can generate hyperparameters achieving human-level tuning across diverse planners and scenarios.
  arXiv  Detail & Related papers  (2025-07-15T05:37:24Z)
• VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning [77.34267241692706]
  Vision-Language Navigation (VLN) is a core challenge in embodied AI, requiring agents to navigate real-world environments using natural language instructions.<n>We propose VLN-R1, an end-to-end framework that leverages Large Vision-Language Models (LVLM) to directly translate egocentric video streams into continuous navigation actions.
  arXiv  Detail & Related papers  (2025-06-20T17:59:59Z)
• EvolveNav: Self-Improving Embodied Reasoning for LLM-Based Vision-Language Navigation [111.0993686148283]
  We propose a novel sElf-improving embodied reasoning framework for boosting Vision-Language Navigation, dubbed EvolveNav.<n>Our EvolveNav consists of two stages: (1) Formalized CoT Supervised Fine-Tuning, where we train the model with formalized CoT labels to activate the model's navigational reasoning capabilities and increase the reasoning speed; (2) Self-Reflective Post-Training, where the model is iteratively trained with its own reasoning outputs as self-enriched CoT labels to enhance the supervision diversity.
  arXiv  Detail & Related papers  (2025-06-02T11:28:32Z)
• Learning to Reason and Navigate: Parameter Efficient Action Planning with Large Language Models [63.765846080050906]
  This paper proposes a novel parameter-efficient action planner using large language models (PEAP-LLM) to generate a single-step instruction at each location.<n>Experiments show the superiority of our proposed model on REVERIE compared to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2025-05-12T12:38:20Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms. Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer. These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Dynamic Path Navigation for Motion Agents with LLM Reasoning [69.5875073447454]
  Large Language Models (LLMs) have demonstrated strong generalizable reasoning and planning capabilities. We explore the zero-shot navigation and path generation capabilities of LLMs by constructing a dataset and proposing an evaluation protocol. We demonstrate that, when tasks are well-structured in this manner, modern LLMs exhibit substantial planning proficiency in avoiding obstacles while autonomously refining navigation with the generated motion to reach the target.
  arXiv  Detail & Related papers  (2025-03-10T13:39:09Z)
• Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation [35.71602601385161]
  We present a novel vision-language model (VLM)-based navigation framework.<n>Our approach enhances spatial reasoning and decision-making in long-horizon tasks.<n> Experimental results demonstrate that the proposed method surpasses previous state-of-the-art approaches in object navigation tasks.
  arXiv  Detail & Related papers  (2025-02-20T04:41:40Z)
• OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation [96.46961207887722]
  OVER-NAV aims to go over and beyond the current arts of IVLN techniques. To fully exploit the interpreted navigation data, we introduce a structured representation, coded Omnigraph.
  arXiv  Detail & Related papers  (2024-03-26T02:34:48Z)
• TINA: Think, Interaction, and Action Framework for Zero-Shot Vision Language Navigation [11.591176410027224]
  This paper presents a Vision-Language Navigation (VLN) agent based on Large Language Models (LLMs) We propose the Thinking, Interacting, and Action framework to compensate for the shortcomings of LLMs in environmental perception. Our approach also outperformed some supervised learning-based methods, highlighting its efficacy in zero-shot navigation.
  arXiv  Detail & Related papers  (2024-03-13T05:22:39Z)
• NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning [101.56342075720588]
  Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability. This paper introduces a novel strategy called Navigational Chain-of-Thought (NavCoT), where we fulfill parameter-efficient in-domain training to enable self-guided navigational decision.
  arXiv  Detail & Related papers  (2024-03-12T07:27:02Z)
• How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments? [16.45529092831176]
  This paper pioneers the exploration of vulnerabilities in navigation systems based on Large Language Models (LLMs) We introduce a novel Navigational Prompt Suffix (NPS) Attack that manipulates LLM-based navigation models by appending gradient-derived suffixes to the original navigational prompt. Our results highlight the generalizability and transferability of the NPS Attack, emphasizing the need for enhanced security in LLM-based navigation systems.
  arXiv  Detail & Related papers  (2024-02-14T19:45:17Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [87.1164964709168]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)
• NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models [17.495162643127003]
  We introduce the NavGPT to reveal the reasoning capability of GPT models in complex embodied scenes. NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status. We show that NavGPT is capable of generating high-quality navigational instructions from observations and actions along a path.
  arXiv  Detail & Related papers  (2023-05-26T14:41:06Z)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration [83.96729205383501]
  We introduce prompt-based learning to achieve fast adaptation for language embeddings. Our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE.
  arXiv  Detail & Related papers  (2022-03-08T11:01:24Z)

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.