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

arXiv – CS AI|Ling Luo, Qianqian Bai|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose MA-CoNav, a multi-agent collaborative framework for robot navigation that uses a Master-Slave architecture to distribute cognitive tasks among specialized agents. The system outperforms existing Vision-Language Navigation methods by decoupling perception, planning, execution, and memory functions across different AI agents with hierarchical collaboration.

Key Takeaways

→MA-CoNav introduces a Master-Slave multi-agent architecture that distributes navigation tasks among specialized agents to reduce cognitive overload.
→The framework includes four specialized agents: Observation, Planning, Execution, and Memory agents, each handling distinct navigation functions.
→A dual-stage Local-Global reflection mechanism dynamically optimizes the entire navigation pipeline for better performance.
→Real-world testing on indoor datasets showed comprehensive performance improvements over existing VLN methods without scene-specific fine-tuning.
→The approach addresses perceptual distortion and decision drift issues in complex, long-distance robotic navigation tasks.

#multi-agent #robotics #vision-language-navigation #ai-collaboration #embodied-ai #robot-navigation #distributed-cognition #hierarchical-agents

Read Original →via arXiv – CS AI

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