The research reveals that large language models exhibit strong capabilities in online context learning and can learn to write robot code from human feedback. Through the Language Model Predictive Control (LMPC) framework, the efficiency of adapting LLMs in writing robot code based on human input was successfully improved. Experiments demonstrate that LMPC enhances the success rate of unseen tasks, providing robust support for adaptive learning in robots. The research team, through the application of the LMPC framework, has successfully paved new paths and improved the ability.