There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhorse of large-scale non-parametric knowledge; however, existing techniques do not directly transfer to the embodied domain, which is multimodal, where data is highly correlated, and perception requires abstraction. To address these challenges, we introduce Embodied-RAG, a framework that enhances the foundational model of an embodied agent with a non-parametric memory system capable of autonomously constructing hierarchical knowledge for both navigation and language generation. Embodied-RAG handles a full range of spatial and semantic resolutions across diverse environments and query types, whether for a specific object or a holistic description of ambiance. At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail. This hierarchical organization allows the system to efficiently generate context-sensitive outputs across different robotic platforms. We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 250 explanation and navigation queries across kilometer-level environments, highlighting its promise as a general-purpose non-parametric system for embodied agents.
@article{arxiv.2409.18313,
title = {Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation},
author = {Quanting Xie and So Yeon Min and Pengliang Ji and Yue Yang and Tianyi Zhang and Kedi Xu and Aarav Bajaj and Ruslan Salakhutdinov and Matthew Johnson-Roberson and Yonatan Bisk},
journal= {arXiv preprint arXiv:2409.18313},
year = {2025}
}