Embodied-RAG: General non-parametric Embodied Memory for Retrieval and
  Generation
Authors: name: Quanting Xie, name: So Yeon Min, name: Tianyi Zhang, name: Aarav Bajaj, name: Ruslan Salakhutdinov, name: Matthew Johnson-Roberson, name: Yonatan Bisk

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 workhouse of large-scale
non-parametric knowledge, however existing techniques do not directly transfer
to the embodied domain, which is multimodal, 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 200
explanation and navigation queries across 19 environments, highlighting its
promise for general-purpose non-parametric system for embodied agents.