Reducing Mental Model Mismatch with Intention-Based Feedback in Human-Robot Teaching

Abstract

This paper introduces the Mental Model Mismatch (MMM) Score, a feedback mechanism designed to align human teaching behavior with robot learning by quantifying mismatches between the human teacher’s mental model and the robot’s learning capabilities. Utilizing a LLM, the system analyzes human teacher intentions in natural language to generate adaptive feedback for the human. A study with 150 participants teaching a virtual robot learner demonstrates that intention-based feedback significantly improves the robots learning outcomes compared to traditional performance-based feedback or no feedback. The findings suggest that this approach improves understanding of the robot’s learning process and reduces misconceptions to enhance human-robot interaction.