对比提示工程方法在服务机器人任务规划中的效果
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
- 在仿真环境中测试多种提示工程组合
- 评估不同模型的任务完成率与执行时间
- 为机器人高阶任务规划提供实用提示策略
大型语言模型（LLM）的最新进展通过利用其广泛的通用知识和能力，在自主机器人控制与人机交互中发挥了关键作用，能够理解并推理各种任务与场景。此前研究探索了多种提示工程技巧以提升LLM的任务表现，也有方法利用LLM根据特定机器人平台的功能进行任务规划与执行。本文结合这两方面，比较提示工程技巧及其组合在服务机器人高层任务规划与执行中的应用效果。我们在仿真环境中定义了一组多样化任务与一组基础功能，测量多个先进模型在任务完成准确率与执行时间上的表现。
原文摘要 · Abstract (English)
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.
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