AMCoNav: Asynchronous Multi-module Collaborative Framework for Embodied Visual Navigation

Embodied visual navigation requires agents with real-time perception, logical reasoning and efficient decision-making capabilities. Existing collaborative modular frameworks struggle to balance accurate decision-making and efficient online execution, leading to error propagation and inference blocking. To tackle this limitation, we propose AMCoNav, an Asynchronous Multi-module Collaborative Framework that unites real-time lightweight backbone networks with on-demand zeroshot large model modules (ZLMM) for robust and real-time embodied navigation. The framework has four tightly coupled components: (1) a Real-Time Multimodal Decision Module (RMDM) for continuous decision making; (2) Zero-shot Large Model Modules (ZLMM) for on-demand asynchronous reasoning; (3) a latency-tolerant Multimodal Shared Context (MSC) for cross-module coordination; and (4) a Bayesian Probabilistic Decision Fusion Module (BPDFM) for robust score fusion and mode switching. Experiments on three representative embodied navigation benchmarks (HM3D-OVON, SG3D, and GOAT-Bench) demonstrate that AMCoNav consistently improves success rates while maintaining real-time execution. The most significant gain appears on HM3DOVON, where AMCoNav achieves an average 8.7% gain over prior stateof-the-art results, with 60.0%/60.0%/53.3% success rate (SR) on Val Seen/Val Seen Synonyms/Val Unseen. Ablation results show that asynchronous guidance from MSC and ZLMM and the BPDFM both contribute to performance improvement, and their combination yields the best overall results. These results show that asynchronous multi-module collaboration can improve navigation capability without sacrificing realtime responsiveness.