Improving Human-Robot Teaching by Quantifying and Reducing Mental Model Mismatch

TL;DR

This paper presents the MMM Score, a feedback mechanism using LLMs to quantify and reduce mental model mismatches in human-robot teaching, leading to improved learning outcomes and understanding.

Contribution

It introduces the MMM Score for quantifying mental model mismatch and demonstrates its effectiveness using intention-based feedback in human-robot teaching.

Findings

Intention-based feedback outperforms traditional methods.

Participants better understood robot learning process.

Reduced misconceptions in human-robot interaction.

Abstract

The rapid development of artificial intelligence and robotics has had a significant impact on our lives, with intelligent systems increasingly performing tasks traditionally performed by humans. Efficient knowledge transfer requires matching the mental model of the human teacher with the capabilities of the robot learner. This paper introduces the Mental Model Mismatch (MMM) Score, a feedback mechanism designed to quantify and reduce mismatches by aligning human teaching behavior with robot learning behavior. Using Large Language Models (LLMs), we analyze teacher intentions in natural language to generate adaptive feedback. A study with 150 participants teaching a virtual robot to solve a puzzle game shows that intention-based feedback significantly outperforms traditional performance-based feedback or no feedback. The results suggest that intention-based feedback improves instructional outcomes, improves understanding of the robot's learning process and reduces misconceptions. This research addresses a critical gap in human-robot interaction (HRI) by providing a method to quantify and mitigate discrepancies between human mental models and robot capabilities, with the goal of improving robot learning and human teaching effectiveness.