ChatAdp: ChatGPT-Powered Adaptation System for Human-Robot Interaction

Different people have different preferences when it comes to human-robot interaction. Therefore, it is desirable for the robot to adapt its actions to fit users’ preferences. Human feedback is essential to facilitating robot adaptation. However, when the task is complex or the robot action space is large, it requires a large amount of user feedback. ChatGPT is a powerful generative AI tool based on large language models (LLMs), which possesses a significant corpus of information obtained from human society, and exhibits robust proficiency in the comprehension and acquisition of natural language. Therefore, in this paper, we proposed a ChatGPT-powered adaptation system (ChatAdp) for human-robot interaction which requires less user feedback to achieve a good adaptation result. In the proposed ChatAdp, we use ChatGPT as a user simulator to provide feedback. We evaluated ChatAdp in a case study for context-aware conversation adaptation. The results are very promising. Our proposed method can achieve a mean success rate of 92% on the user’s natural language-described preferences after receiving 33 rounds of feedback from a user on average, which is only 2% of the number of states covered by the user preferences and outperforms the two baseline methods.