Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation (2409.18313v5)

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 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.

• The paper introduces a novel framework that integrates non-parametric memory construction to enhance robotic navigation and language generation.
• The methodology builds a semantic forest and topological map, employing parallelized tree traversals with LLM scoring for context-aware retrieval.
• Results demonstrate significant improvements over benchmarks in implicit and global queries, underscoring the system’s enhanced performance and versatility.

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

The paper "Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation" introduces a significant framework aimed at extending the capabilities of retrieval augmented generation (RAG) into the domain of embodied agents. The authors identify the critical challenge of enabling robots to effectively navigate and communicate in diverse environments, incorporating both spatial and semantic memory.

Framework Overview

Embodied-RAG proposes a non-parametric memory system designed for embodied agents, capable of autonomously creating hierarchical knowledge structures suitable for various tasks, including navigation and language generation. The framework is structured around a semantic forest, which organizes language descriptions at different levels of granularity. This hierarchical memory allows the system to generate context-sensitive outputs across diverse robotic platforms and environments.

Methodology

Key components of the framework include Memory Construction, Retrieval, and Generation:

1. Memory Construction: Embodied-RAG constructs a topological map representing spatial data, complemented by a semantic forest that organizes knowledge hierarchically. This method effectively encapsulates spatial and semantic correlations inherent in embodied experiences.
2. Retrieval: The retrieval mechanism integrates a reasoning component to address perceptual hallucinations. Using parallelized tree traversals scored by an LLM, the system retrieves contextually relevant data for further processing.
3. Generation: By leveraging the retrieved chains from the semantic forest, the system generates actionable insights, including navigational waypoints and language-based explanations, tailored to the specified queries.

Results and Evaluation

The paper presents robust quantitative and qualitative results, showcasing Embodied-RAG's capabilities across various benchmark tasks. Evaluation metrics include success rates for explicit and implicit queries and Likert scale assessments for global queries. Significant improvements over benchmarks like RAG and Semantic Match highlight the utility of the hierarchical structure employed by Embodied-RAG. Notably, the system demonstrates improved performance in implicit and global query tasks, underscoring its ability to provide a nuanced understanding of complex environments.

Implications and Future Work

The introduction of Embodied-RAG suggests a promising direction for future advancements in AI-enabled robotics. By addressing key limitations of current methods, such as restricted memory scopes and lack of holistic reasoning, Embodied-RAG enhances the operational scope of embodied agents across various domains, including drones, locobots, and quadrupeds. The ability to generate multimodal outputs and reason across hierarchical layers sets the stage for more dynamic and responsive robotic systems.

Looking forward, the extension of this framework to more dynamic environments and tasks, such as manipulation and interaction with dynamic objects, presents a rich area for further exploration. Moreover, incorporating multi-view consistency in the semantic forest could enhance robustness in object recognition and spatial determinations.

In conclusion, Embodied-RAG provides a comprehensive approach to bridging non-parametric memory techniques with the challenges of embodied agents, offering a scalable and efficient solution to enhance robot intelligence in real-world scenarios.