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

arXiv: https://arxiv.org/abs/2506.00075 · HTML: https://mlsanity.com/paper/2506.00075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 12 of 12)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Language to Rewards for Robotic Skill Synthesis](https://mlsanity.com/paper/2306.08647/) (2023)
- [Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://mlsanity.com/paper/2312.08782/) (2023)
- [Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics](https://mlsanity.com/paper/2403.19578/) (2024)
- [GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://mlsanity.com/paper/2308.12488/) (2023)
- [SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge](https://mlsanity.com/paper/2308.12682/) (2023)
- [ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application](https://mlsanity.com/paper/2304.03893/) (2023)
- [Real-time Robotics Situation Awareness for Accident Prevention in Industry](https://mlsanity.com/paper/2409.15305/) (2024)
- [Virtual Reality Platform to Develop and Test Applications on Human-Robot Social Interaction](https://mlsanity.com/paper/2208.06711/) (2022)
- [Advancing Behavior Generation in Mobile Robotics through High-Fidelity Procedural Simulations](https://mlsanity.com/paper/2405.16818/) (2024)

## Built on by (most cited first, 1 of 1)

- [Grid Spatial Understanding: A Dataset for Textual Spatial Reasoning over Grids, Embodied Settings, and Coordinate Structures](https://mlsanity.com/paper/2603.17333/) (2026)

## Abstract (arXiv)

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.