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

Fuente: arXiv
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Autori principali: Stankevich, Stanislau, Dudek, Wojciech
Natura: Preprint
Pubblicazione: 2024
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author Stankevich, Stanislau
Dudek, Wojciech
author_facet Stankevich, Stanislau
Dudek, Wojciech
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2407_21512
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Interpreting and learning voice commands with a Large Language Model for a robot system
Stankevich, Stanislau
Dudek, Wojciech
Robotics
Computation and Language
Neural and Evolutionary Computing
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.
title Interpreting and learning voice commands with a Large Language Model for a robot system
topic Robotics
Computation and Language
Neural and Evolutionary Computing
url https://arxiv.org/abs/2407.21512