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Large language models offer new ways of empowering people to program robotapplications-namely, code generation via prompting. However, the code generatedby LLMs is susceptible to errors. This work reports a preliminary explorationthat empirically characterizes common errors produced by LLMs in robotprogramming. We categorize these errors into two phases: interpretation andexecution. In this work, we focus on errors in execution and observe that theyare caused by LLMs being "forgetful" of key information provided in userprompts. Based on this observation, we propose prompt engineering tacticsdesigned to reduce errors in execution. We then demonstrate the effectivenessof these tactics with three language models: ChatGPT, Bard, and LLaMA-2.Finally, we discuss lessons learned from using LLMs in robot programming andcall for the benchmarking of LLM-powered end-user development of robotapplications.

Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming