summarize this:
\section*{Introduction}
Grass mowing is a tedious and time-consuming task that requires a significant amount of effort and attention. In recent years, advances in artificial intelligence and robotics have made it possible to automate this task. Precisely, in the last year, new models have been launched on the market that include AI technologies, see section \nameref{sec: market analysis}. Nevertheless, none of the current models is providing a natural communication system, nor a system for detecting humans and pets as is intended here. 

This project aims to follow the strategy outlined in \cite{vemprala2023chatgpt}, which involves combining principles of prompt engineering design with a high-level function library to enable a LLM, to adapt to various robotics tasks. Specifically, this project seeks to integrate the reported engineering techniques and dialog strategies for executing a range of actions within the domain of automated lawn mowing. 

In addition, this idea was triggered by the publication of \cite{driess2023palm} in which the authors present an Embodied Large Language Model  that can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments. Therefore, the expected input for the lawnmower is multi-modal sentences that combine visual, continuous state estimation and speech/text encoding, where the multimodal tokens correspond to the robot's observations. In contrast, the LLM will be integrated as a decoder, where the encoded user