Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities

TL;DR

This paper presents llama_ros, a tool that integrates quantized Large Language Models into robotic systems using ROS 2, enabling resource-efficient natural language processing for improved human-robot interaction and decision-making.

Contribution

The paper introduces llama_ros, a novel framework that allows efficient deployment of quantized LLMs in resource-constrained robotic systems, enhancing natural language capabilities.

Findings

Enables efficient execution of quantized LLMs in robotics

Improves human-robot interaction and decision-making

Provides use cases for planning and explainability

Abstract

Large Language Models (LLMs) have experienced great advancements in the last year resulting in an increase of these models in several fields to face natural language tasks. The integration of these models in robotics can also help to improve several aspects such as human-robot interaction, navigation, planning and decision-making. Therefore, this paper introduces llama\_ros, a tool designed to integrate quantized Large Language Models (LLMs) into robotic systems using ROS 2. Leveraging llama.cpp, a highly optimized runtime engine, llama\_ros enables the efficient execution of quantized LLMs as edge artificial intelligence (AI) in robotics systems with resource-constrained environments, addressing the challenges of computational efficiency and memory limitations. By deploying quantized LLMs, llama\_ros empowers robots to leverage the natural language understanding and generation for enhanced decision-making and interaction which can be paired with prompt engineering, knowledge graphs, ontologies or other tools to improve the capabilities of autonomous robots. Additionally, this paper provides insights into some use cases of using llama\_ros for planning and explainability in robotics.