Memory-Augmented Vision-Language Models for Interactive Autonomous Navigation in Dynamic Environments
Keywords:
autonomous navigation; vision-language models; memory augmentation; dynamic environments; socio-technical systems; governanceAbstract
Vision-language models have substantially expanded the capacity of autonomous agents to interpret visual scenes through natural language supervision, yet their application to interactive navigation in dynamic environments remains constrained by limited persistent memory, weak action grounding, and unresolved operational trade-offs. This paper presents a system-level examination of memory-augmented vision-language models designed for interactive autonomous navigation. Rather than proposing a single algorithmic solution, the analysis draws on architectural patterns from embodied artificial intelligence, differentiable memory, retrieval-augmented generation, and world modeling to characterize how persistent multimodal memory can support situational awareness, instruction following, and exploratory interaction. The discussion addresses structural tensions among representation fidelity, inference latency, scalability, and safety, while connecting these concerns to broader socio-technical dimensions including fairness, accountability, energy consumption, and regulatory governance. We consider multiple deployment contexts, including service robotics, assistive navigation, warehouse automation, and augmented reality interfaces. The paper argues that robust interactive navigation systems require not only improved model architectures but also integrated infrastructure for memory curation, uncertainty estimation, auditability, and human oversight. By synthesizing recent advances and their structural implications, the work offers a research agenda for building navigation systems that are both capable and governable. The analysis highlights that action-aware memory and cross-modal grounding must be treated as sociotechnical design choices rather than purely computational optimizations if autonomous navigation is to become safe, equitable, and sustainable in real-world settings.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
