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

arXiv: https://arxiv.org/abs/2407.21512 · HTML: https://mlsanity.com/paper/2407.21512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [MeROS: SysML-based Metamodel for ROS-based Systems](https://mlsanity.com/paper/2303.08254/) (2023)
- [An intent-based approach for creating assistive robots' control systems](https://mlsanity.com/paper/2005.12106/) (2020)

## Abstract (arXiv)

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.