MA-CoNav: A Master-Slave Multi-Agent Framework with Hierarchical Collaboration and Dual-Level Reflection for Long-Horizon Embodied VLN

• URL: http://arxiv.org/abs/2603.03024v1
• Date: Tue, 03 Mar 2026 14:16:02 GMT
• Title: MA-CoNav: A Master-Slave Multi-Agent Framework with Hierarchical Collaboration and Dual-Level Reflection for Long-Horizon Embodied VLN
• Authors: Ling Luo, Qianqian Bai,
• Abstract summary: Vision-Language Navigation (VLN) aims to empower robots with the ability to perform long-horizon navigation in unfamiliar environments.<n>This paper proposes MA-CoNav, a Multi-Agent Collaborative Navigation framework.
• Score: 1.7508558850131373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Navigation (VLN) aims to empower robots with the ability to perform long-horizon navigation in unfamiliar environments based on complex linguistic instructions. Its success critically hinges on establishing an efficient ``language-understanding -- visual-perception -- embodied-execution'' closed loop. Existing methods often suffer from perceptual distortion and decision drift in complex, long-distance tasks due to the cognitive overload of a single agent. Inspired by distributed cognition theory, this paper proposes MA-CoNav, a Multi-Agent Collaborative Navigation framework. This framework adopts a ``Master-Slave'' hierarchical agent collaboration architecture, decoupling and distributing the perception, planning, execution, and memory functions required for navigation tasks to specialized agents. Specifically, the Master Agent is responsible for global orchestration, while the Subordinate Agent group collaborates through a clear division of labor: an Observation Agent generates environment descriptions, a Planning Agent performs task decomposition and dynamic verification, an Execution Agent handles simultaneous mapping and action, and a Memory Agent manages structured experiences. Furthermore, the framework introduces a ``Local-Global'' dual-stage reflection mechanism to dynamically optimize the entire navigation pipeline. Empirical experiments were conducted using a real-world indoor dataset collected by a Limo Pro robot, with no scene-specific fine-tuning performed on the models throughout the process. The results demonstrate that MA-CoNav comprehensively outperforms existing mainstream VLN methods across multiple metrics.

Related papers

• Experience-Driven Multi-Agent Systems Are Training-free Context-aware Earth Observers [27.817039954088315]
  We introduce textbfGeoEvolver, a self-evolving multi-agent system for learning tool-level expertise.<n>We show that GeoEvolver consistently improves end-to-end task success, with an average gain of 12% across multiple backbones.
  arXiv  Detail & Related papers  (2026-01-30T15:11:07Z)
• Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism [61.01709143437043]
  We introduce a novel agent design framework centered on a Hierarchical Task Abstraction Mechanism (HTAM)<n>Specifically, HTAM moves beyond emulating social roles, instead structuring multi-agent systems into a logical hierarchy that mirrors the intrinsic task-dependency graph of a given domain.<n>We instantiate this framework as EarthAgent, a multi-agent system tailored for complex geospatial analysis.
  arXiv  Detail & Related papers  (2025-11-21T12:25:47Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• AgentOrchestra: Orchestrating Hierarchical Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol [22.406849007798858]
  We propose the Tool-Environment-Agent Protocol to integrate environments, agents, and tools into an unified system.<n>We introduce AgentOrchestra, a hierarchical multi-agent framework with a central planning agent that decomposes complex objectives and coordinates specialized agents.
  arXiv  Detail & Related papers  (2025-06-14T13:45:37Z)
• Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System [8.88014241557266]
  Heterogeneous multirobot systems show great potential in complex tasks requiring coordinated hybrid cooperation.<n>Existing methods that rely on static or task-specific models often lack generalizability across diverse tasks and dynamic environments.<n>We propose a hierarchical multimodal framework that integrates a prompted large language model (LLM) with a fine-tuned vision-language model (VLM)
  arXiv  Detail & Related papers  (2025-06-05T13:27:41Z)
• OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis [70.39500621448383]
  Open-world mobile manipulation task remains a challenge due to the need for generalization to open-ended instructions and environments.<n>We propose a novel multi-modal agent architecture that maintains multi-view scene frames and agent states for decision-making and controls the robot by function calling.<n>We highlight our fine-tuned OWMM-VLM as the first dedicated foundation model for mobile manipulators with global scene understanding, robot state tracking, and multi-modal action generation in a unified model.
  arXiv  Detail & Related papers  (2025-06-04T17:57:44Z)
• Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration [63.90193684394165]
  We introduce multi-agent cross-task experiential learning (MAEL), a novel framework that endows LLM-driven agents with explicit cross-task learning and experience accumulation.<n>During the experiential learning phase, we quantify the quality for each step in the task-solving workflow and store the resulting rewards.<n>During inference, agents retrieve high-reward, task-relevant experiences as few-shot examples to enhance the effectiveness of each reasoning step.
  arXiv  Detail & Related papers  (2025-05-29T07:24:37Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [112.04307762405669]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine.<n>G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• AppAgent v2: Advanced Agent for Flexible Mobile Interactions [57.98933460388985]
  This work introduces a novel LLM-based multimodal agent framework for mobile devices.<n>Our agent constructs a flexible action space that enhances adaptability across various applications.<n>Our results demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-05T06:31:39Z)
• Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation [12.753472502707153]
  We design a hierarchical auto-organizing navigation system for multi-agent organization in Minecraft. We also design a series of navigation tasks in the Minecraft environment, which includes searching and exploring. We aim to develop embodied organizations that push the boundaries of embodied AI, moving it towards a more human-like organizational structure.
  arXiv  Detail & Related papers  (2024-03-13T06:22:17Z)
• Breaking Down the Task: A Unit-Grained Hybrid Training Framework for Vision and Language Decision Making [19.87916700767421]
  Vision language decision making (VLDM) is a challenging multimodal task. From an environment perspective, we find that task episodes can be divided into fine-grained textitunits We propose a novel hybrid-training framework that enables active exploration in the environment and reduces the exposure bias.
  arXiv  Detail & Related papers  (2023-07-16T11:54:16Z)

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.