To Err Is Robotic; to Earn Trust, Divine: Comparing ChatGPT and Knowledge Graphs for HRI

Abstract

The paper discusses two current approaches to conversational AI, using large language models and knowledge graphs, and compares types of errors that occur in human-robot interactions based on these approaches. It provides example dialogues and describes solutions to several error types including false implications, ontological errors, theory of mind errors, and handling of speech recognition errors. The paper addresses issues of particular concern for earning user trust.