  This paper presents an experimental study regarding the use of OpenAI's
ChatGPT for robotics applications. We outline a strategy that combines design
principles for prompt engineering and the creation of a high-level function
library which allows ChatGPT to adapt to different robotics tasks, simulators,
and form factors. We focus our evaluations on the effectiveness of different
prompt engineering techniques and dialog strategies towards the execution of
various types of robotics tasks. We explore ChatGPT's ability to use free-form
dialog, parse XML tags, and to synthesize code, in addition to the use of
task-specific prompting functions and closed-loop reasoning through dialogues.
Our study encompasses a range of tasks within the robotics domain, from basic
logical, geometrical, and mathematical reasoning all the way to complex domains
such as aerial navigation, manipulation, and embodied agents. We show that
ChatGPT can be effective at solving several of such tasks, while allowing users
to interact with it primarily via natural language instructions. In addition to
these studies, we introduce an open-sourced research tool called PromptCraft,
which contains a platform where researchers can collaboratively upload and vote
on examples of good prompting schemes for robotics applications, as well as a
sample robotics simulator with ChatGPT integration, making it easier for users
to get started with using ChatGPT for robotics.
