  In this paper, we extended the method proposed in [17] to enable humans to
interact naturally with autonomous agents through vocal and textual
conversations. Our extended method exploits the inherent capabilities of
pre-trained large language models (LLMs), multimodal visual language models
(VLMs), and speech recognition (SR) models to decode the high-level natural
language conversations and semantic understanding of the robot's task
environment, and abstract them to the robot's actionable commands or queries.
We performed a quantitative evaluation of our framework's natural vocal
conversation understanding with participants from different racial backgrounds
and English language accents. The participants interacted with the robot using
both spoken and textual instructional commands. Based on the logged interaction
data, our framework achieved 87.55% vocal commands decoding accuracy, 86.27%
commands execution success, and an average latency of 0.89 seconds from
receiving the participants' vocal chat commands to initiating the robot's
actual physical action. The video demonstrations of this paper can be found at
https://linusnep.github.io/MTCC-IRoNL/.
