Multimodal Data Storage and Retrieval for Embodied AI: A Survey

TL;DR

This survey reviews storage and retrieval architectures for multimodal data in Embodied AI, analyzing their suitability, challenges, and future research directions to support robust, real-time autonomous systems.

Contribution

It systematically evaluates five storage architectures and five retrieval paradigms, identifying key bottlenecks and proposing a research agenda for data management in Embodied AI.

Findings

Graph and multi-model databases are suitable for physical grounding.

A trade-off exists between semantic coherence and real-time responsiveness.

Key challenges include cross-modal integration and dynamic adaptation.

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.