Recent advances in LLM have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLM to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models.

翻译：近年来，大型语言模型（LLM）凭借其广泛通用知识以及在多种任务和场景中理解和推理的能力，在自主机器人控制和人机交互领域发挥了关键作用。先前研究探索了多种提示工程技术以提升LLM执行任务的性能，亦有工作提出利用LLM根据给定机器人平台的可用功能进行任务规划与执行的方法。本研究综合考量这两类研究方向，在服务机器人高层级任务规划与执行的应用场景中，系统比较了多种提示工程技术及其组合策略。我们在仿真环境中定义了一组多样化任务和基础功能集，并对多种前沿模型的任务完成准确率和执行时间进行了量化评估。