<p>The field of Social Robotics is concerned with the development and enhancement of robots as interactive social companions and tools, aimed at aiding humans in a variety of tasks. Despite the ongoing progress, a primary issue encountered is the discrepancy between human instructions and the robot’s interpretation and execution of these directives. Often, this is attributed to the deterministic nature of pre-defined programming, resulting in poor performance during tasks that deviate from this programming. Prior approaches have explored natural language interfaces for logistics and task planning, however, they rely on predefined task structures and focus on non-social or highly constrained scenarios. This research contributes to this problem and proposes a solution by enhancing the autonomous function and interaction of a Pepper robot through the assessment of Large Language Models (LLMs). By leveraging LLM capabilities, the objective is to create a system allowing the robot to autonomously interpret instructions given in natural language to perform general-purpose tasks. The study involves the comparison of different LLMs proficiency in generating code commands for robotics. The assessment of the quality and efficiency of the produced code will be grounded upon the results of code execution, leveraging diverse strategies and code abstraction tiers. The evaluation methodology combines automated tests with human evaluations. Our principal contribution encompasses the development of a task-processing system that links natural language instructions to robotic operations. Furthermore, our analysis revealed that our best configuration using GPT-4 successfully completed 89 out of 120 algorithmically generated tasks, achieving a success rate of 74.17%.</p>