用意图反馈降低人机教学中的认知错位，提升机器人学习效率
Improving Human-Robot Teaching by Quantifying and Reducing Mental Model Mismatch
- 通过大模型分析教学意图，生成自适应反馈
- 150人实验表明意图反馈显著优于传统反馈
- 适合人机交互、智能教学系统研究者参考
人工智能与机器人快速发展，智能系统越来越多地承担原本由人类完成的任务。高效的知识传递需要匹配人类教师的认知模型与机器人学习能力。本文提出一种心理模型错位（MMM）评分机制，通过分析自然语言中的教学意图，利用大语言模型（LLMs）生成自适应反馈，以量化并减少人机认知差异。在150名参与者教虚拟机器人解谜游戏的实验中，基于意图的反馈显著优于基于性能的反馈或无反馈。结果表明，该方法能提升教学效果、增强对机器人学习过程的理解，并减少误解。本研究填补了人机交互（HRI）中认知不匹配的关键空白，为提升机器人学习和人类教学效能提供可量化的解决方案。
原文摘要 · Abstract (English)
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.
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