A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics

TL;DR

This paper compares various prompt engineering techniques for high-level task planning and execution in service robotics, evaluating their effectiveness in simulation across different models and tasks.

Contribution

It provides a comprehensive comparison of prompt engineering methods and their combinations for improving robot task planning and execution performance.

Findings

Prompt engineering techniques significantly impact task success rates.

Certain prompt combinations improve execution efficiency.

Model performance varies with different prompt strategies.

Abstract

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.