  In recent years, autonomous agents have surged in real-world environments
such as our homes, offices, and public spaces. However, natural human-robot
interaction remains a key challenge. In this paper, we introduce an approach
that synergistically exploits the capabilities of large language models (LLMs)
and multimodal vision-language models (VLMs) to enable humans to interact
naturally with autonomous robots through conversational dialogue. We leveraged
the LLMs to decode the high-level natural language instructions from humans and
abstract them into precise robot actionable commands or queries. Further, we
utilised the VLMs to provide a visual and semantic understanding of the robot's
task environment. Our results with 99.13% command recognition accuracy and
97.96% commands execution success show that our approach can enhance
human-robot interaction in real-world applications. The video demonstrations of
this paper can be found at https://osf.io/wzyf6 and the code is available at
our GitHub repository (https://github.com/LinusNEP/TCC_IRoNL.git).
