Interpreting and learning voice commands with a Large Language Model for a robot system

TL;DR

This paper explores integrating Large Language Models with robot systems to enhance voice command interpretation and decision-making, aiming to create more intuitive and adaptable human-robot communication interfaces.

Contribution

It introduces a novel approach combining LLMs with databases to improve robot understanding and learning from voice commands in real-time.

Findings

Enhanced robot response accuracy to voice commands

Improved decision-making capabilities in robot systems

Successful integration of LLMs with database systems

Abstract

Robots are increasingly common in industry and daily life, such as in nursing homes where they can assist staff. A key challenge is developing intuitive interfaces for easy communication. The use of Large Language Models (LLMs) like GPT-4 has enhanced robot capabilities, allowing for real-time interaction and decision-making. This integration improves robots' adaptability and functionality. This project focuses on merging LLMs with databases to improve decision-making and enable knowledge acquisition for request interpretation problems.