Training Humans to Teach Robots: Large and Lasting Skill Gains

Abstract

Recent evidence has shown that, contrary to expectations, it is difficult for novices to teach robots tasks through learning from demonstration (LfD). Novices often struggle with understanding the relationship between robot states and actions, leading to suboptimal demonstrations. This paper introduces a framework that leverages machine teaching algorithms to train novices in a controlled, ideal environment where optimal control parameters are predefined. The training enables participants to internalise fundamental control principles, preparing them to adapt to new skills that share similar properties. The study evaluates whether such teaching ability is (i) retained beyond the training period (including a long-term follow-up) and (ii) generalised so that novices teach robots more effectively in environments where control parameters are not predefined. It reports a series of between-subjects studies that demonstrate that trained novice teachers achieve a 75% improvement in teaching ability, with these gains retained even after guidance is removed, and exhibit a 71% enhancement in applying skills beyond the training content.