A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
This work addresses the problem of optimizing LLM-based task planning for service robotics, but it is incremental as it compares existing techniques.
The paper compared prompt engineering techniques for task planning and execution in service robotics, finding variations in task completion accuracy and execution time across state-of-the-art models in simulation.
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.