  In social robotics, a pivotal focus is enabling robots to engage with humans
in a more natural and seamless manner. The emergence of advanced large language
models (LLMs) such as Generative Pre-trained Transformers (GPTs) and
autoregressive models like Large Language Model Meta AI (Llamas) has driven
significant advancements in integrating natural language understanding
capabilities into social robots. This paper presents a system for speech-guided
sequential planning in autonomous navigation, utilizing Llama3 and the Robot
Operating System~(ROS). The proposed system involves using Llama3 to interpret
voice commands, extracting essential details through parsing, and decoding
these commands into sequential actions for tasks. Such sequential planning is
essential in various domains, particularly in the pickup and delivery of an
object. Once a sequential navigation task is evaluated, we employ DRL-VO, a
learning-based control policy that allows a robot to autonomously navigate
through social spaces with static infrastructure and (crowds of) people. We
demonstrate the effectiveness of the system in simulation experiment using
Turtlebot 2 in ROS1 and Turtlebot 3 in ROS2. We conduct hardware trials using a
Clearpath Robotics Jackal UGV, highlighting its potential for real-world
deployment in scenarios requiring flexible and interactive robotic behaviors.
