Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation

TL;DR

This paper presents an architecture integrating ChatGPT with ROS 2 to reduce latency in natural language command processing for robot navigation, enhancing real-time control in industrial robotics.

Contribution

The study introduces a novel architecture that integrates LLMs with ROS 2 without middleware, reducing latency and improving human-robot interaction in simulated environments.

Findings

Latency decreased by 7.01% on average

Improved execution speed and usability

Enhanced real-time robot control

Abstract

The integration of Large Language Models (LLMs), such as GPT, in industrial robotics enhances operational efficiency and human-robot collaboration. However, the computational complexity and size of these models often provide latency problems in request and response times. This study explores the integration of the ChatGPT natural language model with the Robot Operating System 2 (ROS 2) to mitigate interaction latency and improve robotic system control within a simulated Gazebo environment. We present an architecture that integrates these technologies without requiring a middleware transport platform, detailing how a simulated mobile robot responds to text and voice commands. Experimental results demonstrate that this integration improves execution speed, usability, and accessibility of the human-robot interaction by decreasing the communication latency by 7.01\% on average. Such improvements facilitate smoother, real-time robot operations, which are crucial for industrial automation and precision tasks.