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

Recent advances in Large Language Models (LLMs) 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 pro...

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Bibliographic Details
Main Authors: Bode, Jonas, Patzold, Bastian, Memmesheimer, Raphael, Behnke, Sven
Format: Conference Proceeding
Language:English
Subjects:
Humanoid robots
Instruments
Knowledge engineering
Large language models
Planning
Prompt engineering
Reliability engineering
Service robots
Time measurement
Tuning
Online Access:Request full text
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Summary:Recent advances in Large Language Models (LLMs) 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 LLMs 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. We make our code, including all prompts, available at https://github.com/AIS-Bonn/Prompt_Engineering.
ISSN:2164-0580
DOI:10.1109/Humanoids58906.2024.10769825