Work

Integrating large language models for intuitive robot navigation

Year: 2025

Type: article

Abstract: Home assistance robots face challenges in natural language interaction, object detection, and navigation, mainly when operating in resource-constrained home environments, which limits their practical deployment. In this study, we propose an AI agent framework based on Large Language Models (LLMs), which includes EnvNet, RoutePlanner, and AIBrain, to explore solutions for these issues. Utilizing quantized LLMs allows the system to operate on resource-limited devices while maintaining robust interaction capabilities. Our proposed method shows promising results in improving natural language understanding and navigation accuracy in home environments, also providing a valuable exploration for deploying home assistance robots.

Source: Frontiers in Robotics and AI

DOI: 10.3389/frobt.2025.1627937

Authors: Ziheng Xue, Arturs Elksnis, Ning Wang

Institutions: University of the West of England, University of Bristol, Bristol Robotics Laboratory, Sheffield Hallam University

Language: English

Topic: Robotics and Automated Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,353

OpenAlex ID: W4413989149

FWCI: 0.766

Cites: 18

Cited by: 2

Related to: 10