Multimodal Data Storage and Retrieval for Embodied AI: A Survey

• URL: http://arxiv.org/abs/2508.13901v1
• Date: Tue, 19 Aug 2025 15:04:02 GMT
• Title: Multimodal Data Storage and Retrieval for Embodied AI: A Survey
• Authors: Yihao Lu, Hao Tang,
• Abstract summary: Embodied AI (EAI) agents interact with the physical world, generating vast, heterogeneous multimodal data streams.<n>EAI's core requirements include physical grounding, low-latency access, and dynamic scalability.<n>Our survey is based on a comprehensive review of more than 180 related studies, providing a rigorous roadmap for designing the robust, high-performance data management frameworks.
• Score: 8.079598907674903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embodied AI (EAI) agents continuously interact with the physical world, generating vast, heterogeneous multimodal data streams that traditional management systems are ill-equipped to handle. In this survey, we first systematically evaluate five storage architectures (Graph Databases, Multi-Model Databases, Data Lakes, Vector Databases, and Time-Series Databases), focusing on their suitability for addressing EAI's core requirements, including physical grounding, low-latency access, and dynamic scalability. We then analyze five retrieval paradigms (Fusion Strategy-Based Retrieval, Representation Alignment-Based Retrieval, Graph-Structure-Based Retrieval, Generation Model-Based Retrieval, and Efficient Retrieval-Based Optimization), revealing a fundamental tension between achieving long-term semantic coherence and maintaining real-time responsiveness. Based on this comprehensive analysis, we identify key bottlenecks, spanning from the foundational Physical Grounding Gap to systemic challenges in cross-modal integration, dynamic adaptation, and open-world generalization. Finally, we outline a forward-looking research agenda encompassing physics-aware data models, adaptive storage-retrieval co-optimization, and standardized benchmarking, to guide future research toward principled data management solutions for EAI. Our survey is based on a comprehensive review of more than 180 related studies, providing a rigorous roadmap for designing the robust, high-performance data management frameworks essential for the next generation of autonomous embodied systems.

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