Embodied-RAG: General non-parametric Embodied Memory for Retrieval and Generation

2024-09-26

arXiv.org

AI Summary

Embodied agents struggle to efficiently organize and leverage cross-environmental, multi-granular, and highly interrelated perception-semantic knowledge in real-world settings. Method: This paper proposes Semantic Forest—a non-parametric, hierarchical, embodiment-oriented memory architecture that pioneers the adaptation of retrieval-augmented generation (RAG) to robotics. It enables joint modeling of spatial navigation and natural language generation through cross-modal retrieval alignment, hierarchical knowledge abstraction, and dynamic RAG-based reasoning. Contribution/Results: Evaluated across 19 heterogeneous environments on over 200 navigation-and-explanation queries, Semantic Forest significantly improves context-sensitive generation quality and zero-shot task generalization. It establishes a novel paradigm for long-term memory construction and cognitive reasoning in embodied intelligence, bridging perception, semantics, and action through structured, scalable, and adaptive knowledge representation.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Robotics
• Retrieval Augmented Generation (RAG)
• Hierarchical Knowledge Representation

Innovation

• Embodied RAG
• Hierarchical Knowledge Structure
• Complex Real-world Information Processing